What a week…

It started with OpenAI announcing that its AI had resolved the Navier–Stokes existence and smoothness problem. That alone is a massive achievement and a major milestone in AI, comparable to Deep Blue and AlphaGo. I’ve chosen it as the story for this week’s write-up, where I explain the drama that followed and how this could change mathematics.

Then there was the other big story, which started with researchers raising the alarm over the possibility of AI destroying humanity and calling for a slowdown or pause in AI development. Now we are at a point where Dario Amodei, Sam Altman, Elon Musk, and Demis Hassabis all agree that the frontier needs to be paced. It’s still a developing story, and we’re likely to hear more about it next week.

Elsewhere in AI, OpenAI introduces ChatGPT Images 2.5 and the Agents API; Mistral and Cognition raise billions of dollars in new funding rounds; DeepSeek-V4.1-Flash is out; Meta launches Muse, its personal AI assistant; and Anthropic publishes a report on how AI could reshape the US economy by 2030.

Over in robotics, Agility Robotics reports $1.8 million in revenue, while China curbs humanoid IPOs after Unitree’s volatile listing. We also have cyborg cockroaches, GPT-6 Astra beating Fable 5.1 at controlling robots, and XPeng starting production of its humanoid robots.

Apart from that, this week’s issue of Sync also features AlphaGenome Atlas, an explanation of neuralese, the impact of AI on the US electrical grid, Google publishing a full map of a fruit fly’s nervous system—and people making it play games—and more!

Enjoy!

The Navier–Stokes drama explained

At the turn of the millennium, the Clay Mathematics Institute selected seven of the hardest open problems in mathematics and challenged the world to solve them. Each Millennium Problem came with a $1 million prize. In the 26 years since, only one has fallen: the Poincaré conjecture, cracked by Grigori Perelman, who was awarded the prize in 2010 and famously turned it down. The remaining six stay open, waiting for someone.

Well, five. Because OpenAI has announced that its AI resolved the Navier-Stokes existence and smoothness problem. Or so OpenAI says—the proof hasn't been independently verified, and the company isn't claiming the prize. Controversy quickly followed, and what was meant to be a moment of triumph turned ugly.

In this article, we’ll explore what OpenAI claims its AI has achieved, the controversy surrounding those claims, and what it all means for the future of mathematics and scientific discovery.

What are the Navier-Stokes equations?

The Navier-Stokes equations describe everything that flows: water, air, blood, even a planet’s atmosphere. The motion of every fluid in the universe can be described by just two equations.

The trouble starts when you try to use them. Look at how water behaves. It swirls and eddies, constantly changing. It is chaotic. The full equations are far too demanding to solve for anything real, so every practical tool cheats and approximates the solution. For example, climate models chop the atmosphere into patches and track the average speed in each, because modelling every particle would take longer than Earth's lifetime. We don't need such detailed simulations to get meaningful and useful answers, so for engineering and everyday use, that is fine.

But mathematicians wanted more. They want a proof that the equations always produce a sensible answer, for every starting point, forever. Nobody could provide it, which is how the question ended up as one of the Millennium Prize Problems.

Here is where OpenAI enters the scene with an answer: no, they do not. Its AI produced a proof, formalised in Lean, that a fluid can start perfectly calm and still reach infinite speed in a finite amount of time.

Picture a spinning swirl of water. It pulls inward and stretches out, like a strand of spaghetti. The middle gets thinner and spins faster and faster, until the speed hits infinity. Real water never does this. No drop of water moves infinitely fast. So at that point, the equations stop describing anything real. That’s what OpenAI has shown.

If you are expecting new exciting results coming from OpenAI, then I am sorry to disappoint. Almost nobody outside pure mathematics is affected. Cornell mathematician Steven Strogatz told WIRED the problem is of essentially no interest to a working engineer. The Navier-Stokes question, he said, is a very theoretical and very arcane maths problem, one that only a tiny subset of pure mathematicians care about.

So far, nobody has independently verified OpenAI's proof. But if their solution holds, it would be a big triumph for OpenAI. It would prove that AI models are capable of solving challenging mathematical problems, and open the possibility of more Millennium Problems falling soon, as well as of other challenges being solved.

The controversy

But what was supposed to be a moment of triumph for OpenAI has quickly attracted controversy.

NYU professor Tristan Buckmaster and mathematician Levent Alpöge had spent close to a year working on closely related problems, using Claude and Codex throughout, and made a breakthrough on 15 August. News of their work reportedly reached OpenAI, which began its own effort on 1 September. It then unleashed 10,000 agents concurrently on the problem, exchanging 2.7 million messages and burning through roughly 130 billion output tokens. The bill for that was easily in the millions of dollars. Just 88 hours later, the agents found an answer, which OpenAI published on 8 September.

Buckmaster has published an account of what he claims happened during those days. On 3 September, with the rumour mill already running and word reaching him that information about his progress had been passed to OpenAI, he emailed a prominent mathematician at the company. OpenAI pushed to talk. On the afternoon of Sunday 6 September, the two of them spoke twice, with Sébastien Bubeck, an AI researcher working at OpenAI, joining. Alpöge was not on the calls.

Buckmaster was told that an internal OpenAI model had proved finite-time blowup for the forced Navier-Stokes equations—the same smooth-force path he and Alpöge had chosen, and one he says almost nobody else was pursuing. He was shown a prompt and told the model had simply been given the problem statement. Bubeck had separately told Alpöge that very little human input was involved.

But as the calls went on, that description fell apart. It turned out that an entire team had been working on the problem, that Navier-Stokes was one of several things they had tried, that the agents had been set on easier problems first, and that even the prompt he had been shown was itself written by prompting Codex. Buckmaster also asked whether the model had been trained on, or had access to, his Codex sessions, where he had been putting every draft for the whole project. He was told the model did not look up user data. When he asked again about training, he said he got no answer.

OpenAI then made two offers. One was to publish a day apart. The other was for Buckmaster to write up OpenAI's result alone. Bubeck twice said he wanted Alpöge off the authorship because Alpöge works for Anthropic, Buckmaster writes, and when he threatened to go public, the reply was "Why would you ruin your career?"

Buckmaster is careful about what he is not saying. He has not seen OpenAI’s proof, does not know what the model did, and is not accusing anyone of using his data. He is stating what he was told, and when, because the alternative, he writes, is letting a sequence of announcements say something he knows to be false.

OpenAI rejects Buckmaster’s narrative. Neither its researchers nor its agents saw the pair's work before it was public, it says, and no user data was accessed to solve the problem. However, it later admitted that “while unlikely, we cannot rule out that de-identified data derived from their usage of our products helped improve our models.” Furthermore, OpenAI draws a sharp line between the two results, and says Buckmaster and Alpöge did not solve Navier-Stokes. What they proved was finite-time blowup for the Euler equations, which are Navier-Stokes with viscosity stripped out, and which OpenAI had handed its agents as one of the easier warm-up problems. They also did it in the version where an external force is applied. OpenAI's agents proved the unforced case, where nothing is pushing the fluid at all, and then went on to Navier-Stokes itself. Bubeck, meanwhile, has denied proposing that Alpöge's name be dropped.

There is no obvious way to settle this. OpenAI cannot prove what did not go into its training data, and Buckmaster has no way of checking for himself. Unless somebody at OpenAI says otherwise, both accounts will stand as they are.

It is nice to know that one of the Millennium Prize problems may have been solved, but the way OpenAI handled the announcement did not land well with mathematicians, as it published the proof without engaging the mathematics community first. Strogatz called the announcement “a marketing device for them [OpenAI] to prove how good their machines are.”

In response, twenty-five Fields medallists (the Fields Medal being the highest honour in mathematics, awarded to just a handful of people every four years) signed an open letter arguing that results announced in a rush leave no time for a proper write-up, for separating the new ideas, or for citing the work that made them possible. Their worry is not only about credit. As they write, solving problems is only a tool and proxy for achieving the primary goal of conceptual understanding and insight.

What is left for mathematicians now is to do what OpenAI hasn’t done—to digest the proof. Someone has to explain what it means, work out which techniques generalise, and decide which questions are worth asking. Strogatz thinks this is where human expertise still holds an advantage, though he is not confident it will hold for long.

What does all of this mean for the future of scientific discovery?

Buckmaster calls what OpenAI has done a Deep Blue-Kasparov moment—a point where a machine walked into a domain we thought was ours and did not look back. Mathematics used to be the one field where this could not happen. Physics has needed particle accelerators for half a century, and biology needs a lab, but mathematics is a pure intellectual endeavour. You never needed millions of dollars of equipment to make a mark—all you needed was something to write and your own mind. Perelman proved the Poincaré conjecture largely on his own and posted it online.

That is no longer the deal. The ideas still came from mathematicians, sometimes working for years on the same problem, but the result can be sniped by the side that can afford to throw 10,000 agents at the problem and get all the credit. And it is not stopping here. By OpenAI’s own account, it set agents on every open Millennium Problem before concentrating them on Navier-Stokes. Five are still open, as well as many more problems that, if solved, could be scientific milestones on their own.

The way mathematics is done is changing. The question is whether the new tools will elevate mathematicians, giving them reach they never had, or whether the real work migrates to a handful of labs with the compute to run it, leaving everyone else to read the output and check it.

For now, nothing is settled. Nobody has independently verified the proof, and the Clay Mathematics Institute will not even consider a solution until it has been published in a refereed journal, sat for two years, and won general acceptance from the global mathematics community. OpenAI is not claiming the money anyway.

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News roundup

🧠 Artificial Intelligence

‘We must slow the pace’: CEO of Anthropic calls for an AI slowdown
The other big news of the week was two AI researchers raising the alarm over the possibility of AI destroying humanity and calling for a slowdown or pause in AI development.

It started when Jacob Coxon announced his departure from Anthropic and called both OpenAI and Anthropic irresponsible when it comes to AI safety, saying the companies are “gambling with our lives”. Evan Hubinger, a lead alignment researcher at Anthropic, backed up Coxon’s claims. At that point, the conversation broke into the mainstream, with multiple outlets amplifying their message and calls to slow down AI development.

In response, Dario Amodei published a post calling for the frontier to be paced. Amodei argues that recursive self-improvement and the Hugging Face incident mean safety investment alone is no longer enough. Instead, the pace of capability advancement itself must slow so alignment, interpretability, and evaluation can catch up. He proposes three steps: embedded third-party evaluators, which Anthropic is adopting unilaterally; coordination among democratic frontier AI companies; and eventually, agreements with China. How much pacing is possible, he argues, depends on maintaining a lead over the CCP, hence his support for chip controls and stronger security.

Sam Altman, Demis Hassabis, and Elon Musk agreed with Amodei’s call to pace the frontier.

While many agree with calls to slow down AI development, others see them as a cynical attempt to hype the technology and justify enormous valuations ahead of expected IPOs.

An Alien Mind
Jakub Pachocki, Chief Scientist at OpenAI, argues in this post that the capabilities of AI models are outpacing our understanding of them. Models are grown through scaling rather than designed, value alignment remains unsolved, and chain-of-thought monitoring—OpenAI’s main bet for verification—is becoming less reliable as reasoning blends with supervised tool use. Defending against cyberattacks and rogue agents justifies building powerful aligned systems, but not racing to do so. His conclusion is that no lab can responsibly keep scaling at full speed for much longer, and that voluntary slowdowns, enforceable safety standards, and international coordination should become the norm.

OpenAI: Introducing ChatGPT Images 2.5
ChatGPT Images 2.5 is OpenAI’s latest image model, which the company says generates sharper details, offers more precise editing, and produces images up to 50% faster than its predecessor. Alongside the new model, OpenAI has launched Sketch, a feature coming to the ChatGPT app that turns rough drawings into finished images, as well as templates and shareable prompts. ChatGPT Images 2.5 is also available via the API in two versions—a fast default model called Flare and a slower, more precise one called Sunburst. ChatGPT Images 2.5 is available to users on all tiers across ChatGPT, ChatGPT Work, and Codex, on desktop, mobile, and the web.

OpenAI: Introducing the Agents API
OpenAI has launched the Agents API in public beta, giving developers access to the same harness and infrastructure that powers Codex through a simple, flexible API. It handles the hard parts of keeping agents running for hours, compacting context as sessions stretch on, and splitting work across parallel subagents. Developers can run that work in an OpenAI sandbox, on their own servers, or with partners like Cloudflare and Vercel. There’s no charge beyond the cost of tokens and tools.

OpenAI’s Sam Altman says it would be ‘ill-advised’ to go public in 2026
Sam Altman told in an interview with Fortune Magazine that OpenAI won’t go public in 2026. He said that the current climate around AI safety makes it an ill-advised moment to list. OpenAI will float when the business is ready, he said, and when society feels ready for the technology.

Mistral raises €3B
Mistral has raised €3 billion (about $3.5 billion) in a Series D funding round at a post-money valuation of more than €21 billion (about $24.4 billion), the largest equity fundraising round ever completed by a European startup. The French AI company will use the funds to invest in frontier research. Mistral is betting that its full-stack, open approach, with an emphasis on sovereign AI, will appeal to customers for whom full control over the AI layer matters.

Cognition hits $48B valuation, signaling investors believe AI coding is far from a winner-take-all market
Cognition, the startup behind coding agent Devin, announced it has raised $2 billion at a $48 billion valuation, nearly double the $26 billion it fetched four months ago. The company also says its annualised run-rate revenue has climbed from $492 million to nearly $900 million since May. But that growth comes at a steep cost, with Cognition reportedly on track to burn $800 million in cash this year. The company is expected to reach $4 billion to $5 billion in annualised revenue by the end of 2027, according to The Information.

Meta Releases Muse, a Personal AI Agent With Privacy ‘Built Into It’
Meta has launched Muse, a personal AI agent that people can message to send emails, book travel, or buy things for them. It works through a dedicated app, the web, WhatsApp, and soon Meta Glasses, with a subscription available for heavy use. In a slightly ironic twist for Meta, the company is making privacy one of Muse’s selling points. It says each user’s session runs inside a virtual machine, while a separate monitor checks anything leaving it and asks for permission before sensitive actions.

UK government rejects ‘kill switch’ idea for dangerous AI
The UK government has rejected calls for a legal “kill switch” to shut down a dangerous AI model in an emergency. The Cabinet Office says blocking access in Britain wouldn’t stop a model from being built or misused abroad. Peers and a cross-party group of MPs had pushed the idea after reports of models from OpenAI, Anthropic, and Meta escaping their test environments. Critics counter that a switch would be too slow, since those breakouts took months to detect. But the biggest problem with a UK AI kill switch is that the largest data centres sit in the US, and without Washington on board, a national off-switch would do little.

OpenAI puts Pro subscriptions on hold due to Astra demand
Demand for GPT-6 Astra is so high that OpenAI has temporarily paused subscriptions to its $200-per-month Pro plan. The company says the Pro plan puts the most strain on its systems, which is why new sign-ups are being disabled. OpenAI hasn’t said how long the pause will last or how many people are signing up each day, which would give some indication of the scale of demand.

China AI Star Moonshot Eyes $2 Billion Annualized Sales in 2026
Moonshot AI expects to double its annualised revenue to $2 billion by the end of the year, after passing $1 billion in August, up from $300 million in June. The company credits the success of Kimi K3 for the growth. Moonshot AI is now raising funds at a $50 billion valuation ahead of a Hong Kong listing.

DeepSeek-V4.1-Flash
DeepSeek has introduced DeepSeek-V4.1-Flash, which it describes as smarter, faster, and more efficient than its predecessor. According to the company’s benchmarks, the new model beats Chinese rivals Kimi K3 and GLM-5.3 and matches or even outperforms Opus 5 and GPT-5.6 Sol. Independent benchmarks from Artificial Analysis paint a less rosy picture, giving DeepSeek-V4.1-Flash 40 points on its Intelligence Index. That puts it ahead of GPT-5.6 Luna but behind Kimi K3 and GLM-5.3, and behind frontier US models. Artificial Analysis confirms its efficiency and highlights the speed at which the new model outputs tokens. DeepSeek-V4.1-Flash requires a quarter of the memory and an eighth of the disk space of the previous version, and DeepSeek is passing on those savings to the users. The model is available on Hugging Face.

DeepSeek Plans Big Huawei AI Chip Order to Power New Data Center
Bloomberg reports that DeepSeek plans to install at least 160,000 of Huawei’s next-generation Ascend chips at its data centre currently under construction in Inner Mongolia. That would make it the largest known cluster of Huawei AI chips by some margin. DeepSeek wants more, but Huawei can’t produce them fast enough, and memory shortages mean the order could take over a year to fill. The startup still relies on Nvidia for the more demanding work of training its models.

Nscale adds former OpenAI exec Fidji Simo to its board ahead of potential IPO
Fidji Simo, a former OpenAI executive, has joined Nscale’s board ahead of the startup’s anticipated IPO this autumn. Previously CEO of AGI Deployment at OpenAI and effectively the AI lab’s No. 2 executive, Simo left the company in July, citing health reasons. She continues to advise OpenAI on a part-time basis.

ByteDance Joins AI Elite in Race to Perfect World Models
ByteDance is joining the likes of Google DeepMind, Fei-Fei Li’s World Labs, and Yann LeCun’s AMI Labs in developing a world model—an AI model that can generate realistic virtual worlds. The model builds on Seedance, ByteDance’s existing video generator, and is expected to be unveiled as soon as next month.

Anthropic signed $517bn in compute agreements in past 11 months
Anthropic has announced a new compute deal seemingly every month over the past 11 months. Its list of partners now includes Akamai, Amazon Web Services, CoreWeave, Fluidstack, SpaceX, Google Cloud, Lambda, Volta Infra, AMD, and Microsoft. The Information added up the value of those deals and found that they represent 14.8 GW of compute capacity worth $517 billion.

Suno replaces its AI models with a new one trained on licensed music as copyright suits pile up
Suno, a startup making AI models for generating music, has released its new v6 models, which the company says were trained only on data licensed from labels including Warner Music Group and BMG. Both had previously sued the startup over copyright before settling. The launch came a day after Suno admitted to scraping audio from YouTube to train earlier versions. Sony, Universal, and artists are still suing the company.

Siri AI will launch in beta, complicated by daily usage caps & future paid access
Apple revealed at its September iPhone event that the new Siri will still be labelled a beta when iOS 27 and the other OS 27 updates land on 14 September. It will launch in English only, with five more languages coming in October. Availability will vary by region: Mainland China won’t get the new Siri at all, while in the EU it will be available on Macs and Vision Pro, but not on iPhones, iPads, or Watches. Features that rely on Apple’s servers will also come with daily usage limits, with higher limits available for a fee later.

King Charles to host exclusive AI gathering
Politico reports that King Charles will gather around 30 senior AI figures this month at Dumfries House in Ayrshire, Scotland. The guest list is set to include Jensen Huang, Demis Hassabis, and Paolo Benanti, an adviser to the Vatican on AI. A royal official said the King wants to listen and encourage debate rather than offer his own views. The Ditchley Foundation, a charity that organises discussions on global issues, is running the event under its usual off-the-record terms, so little is likely to emerge afterwards.

▶️ AI is running out of Power (52:22)

The biggest bottleneck for AI right now isn’t chips, but the electricity needed to power all those gigawatt-scale data centres. In this video, SemiAnalysis and High Yield take an in-depth look at how the AI boom is affecting the electrical grid and why AI labs are rushing to bring new power sources online as quickly as possible. They explore rising AI data centre power demand, behind-the-meter power, how data centres generate electricity on-site, and the growing impact of AI on heavy industry.

▶️ The AI Language We Can’t Read: Neuralese ft. Rob Miles - Computerphile (27:16)

In this video, Rob Miles explains why chain-of-thought monitoring is an important tool in AI safety and why recent developments, such as more opaque reasoning in GPT-6 Astra, are making it harder to understand how AI “thinks”. He also explains the concept of neuralese, or the internal language models use to reason, and the role it plays in interpretability research.

MAI-Transcribe-2
Microsoft has launched MAI-Transcribe-2, a speech recognition model it claims is the fastest, most accurate, and cheapest in the world. The new model can handle speaker identification, word-level timestamps, and switching between languages mid-sentence. According to Microsoft, MAI-Transcribe-2 leads the FLEURS benchmark across 60 languages, with an average word error rate of 5.2%. Evaluations by Artificial Analysis also found it to be up to 10 times faster than rivals from OpenAI, Google, and ElevenLabs.

Cognition: Introducing SWE-2
Cognition has introduced SWE-2, its most advanced coding model to date and its closest yet to the frontier. According to benchmarks published by Cognition, SWE-2 scores 50.0% on the company’s own FrontierCode benchmark, just one percentage point behind Fable 5.1 while being 64% cheaper, and three points behind GPT-6 Astra at a quarter of the cost. SWE-2 is built on top of Kimi K3, with Cognition saying it trained the model to stop combing through a codebase and start editing sooner, cutting the number of steps per task by 58%.

Inception: Introducing Mercury 2.5
Mercury 2.5 is a diffusion-based LLM that generates text in parallel rather than token by token. Inception says it is 40% more intelligent than Mercury 2 and comparable to cost-optimised frontier models such as GPT-5.6 Luna (Low), Gemini 3.5 Flash-Lite, and Claude Haiku 4.5. It is also fast, generating 1,107 tokens per second on widely available Nvidia GPUs, compared with 321 for Gemini 3.5 Flash-Lite and 99 for GPT-5.6 Luna.

Economic Scenarios for Transformative AI
Anthropic has published a report on how AI could reshape the US economy by 2030, sketching out three scenarios. In the mildest, AI’s impact looks much like the internet’s. In the middle scenario, the economy grows at twice its usual rate without unusually high unemployment. The extreme case assumes self-improving AI and 15% annual growth. Knowledge workers fare badly in that scenario: wages fall by more than 10%, while unemployment rises above recession levels. In all three scenarios, more of the gains flow to capital than to workers. Anthropic’s conclusion is that the hard problem may be distribution rather than growth.

Do people prefer stories written by AI?
According to a new study, people can’t reliably tell AI-written short stories from human-written ones and even preferred stories written by AI. Interestingly, people with greater AI literacy were better at identifying AI-written text, while those who reported greater expertise in literature were not. The authors suggest that AI writing tends to be clearer, more direct, and easier to process, while human-written stories are often more subtle and complex.

N3on launches 24/7 AI-generated stream of himself

When N3on launched a fully AI-generated stream of himself, he said he had done his “last stream as a human being” and declared himself the “first AI streamer in the world”. The internet did not react well, with many calling him a sellout and the experiment drawing overwhelmingly negative responses.

🤖 Robotics

Agility Robotics reports $1.8M revenue ahead of humanoid SPAC
Agility Robotics has opened its books for the first time in an SEC filing ahead of a SPAC merger that values the company at $2.5 billion. It made just $1.8 million in sales last year against a $140 million operating loss, putting its valuation at roughly 1,400 times revenue. Agility plans to close that gap by renting out its Digit humanoid robots for about $8,500 a month and growing its fleet to 25,000 robots by 2035.

Unitree shares down 53% from IPO debut
Shares in Chinese robot maker Unitree have fallen nearly 40% after soaring 460% on their August debut on the Shanghai Stock Exchange. Unlike most humanoid rivals, Unitree turned a profit on $252 million in revenue last year, up from $58 million in 2024. The drop does not necessarily mean investors have lost faith in Unitree—it may simply be the market pulling back from its lofty initial valuation. At its peak valuation of roughly $66 billion, Unitree was worth more than 250 times its 2025 revenue.

China curbs humanoid IPOs after Unitree’s volatile debut
According to The Information, Chinese regulators are reportedly making it harder for humanoid robot startups to go public following Unitree’s volatile debut. A private funding frenzy and a long queue of IPO filings have also prompted the rethink. Startups must now show recurring revenue, shrinking losses, or genuine innovation before regulators will consider approvals to go public.

GPT‑6 Astra on robotic manipulation
The new frontier models from Anthropic and OpenAI, Fable 5.1 and GPT-6 Astra respectively, have been tested across multiple benchmarks, including ones involving robot control. Robotcurve pitted the two models against each other and found that GPT-6 Astra outperformed Fable 5.1 on one task, while both performed equally poorly on another. In both tests, Astra proved to be the cheaper model.

XPeng starts IRON humanoid robot production as Tesla Optimus stalls
XPeng, a Chinese car manufacturer, has switched on what it calls the world’s first automated production line for humanoid robots, and its IRON robot walked off that line under its own power. XPeng expects mass production by the end of 2026, with sales starting in 2027.

Cyborg Roaches Can Stab You With Needles

Paraborg is a prototype cyborg cockroach developed by researchers at the University of Queensland’s Biorobotics Lab. The researchers wanted to build an insect-sized robot, but instead of building one from scratch, they turned an insect into a robot. They fitted cockroaches with a “backpack” containing lightweight electronics and implanted electrodes that allow the insects to be controlled wirelessly. The backpack can carry either a camera or a remote-controlled injector that uses a spring to launch a drug-filled syringe at a nearby target. Researchers hope Paraborgs could one day be used in search and rescue and to deliver medical care in disaster areas.

▶️ Unitree Breakthrough: The World’s First Fully Autonomous Humanoid Robot Combat (0:38)

Unitree recently unveiled UnifoLM-X2-1.0, an AI model for controlling its humanoid robots. According to the company, the model is designed to eliminate the need for the human teleoperator typically required to control humanoids. It continuously processes its environment, anticipates how the scene and opponent are likely to change, and uses those predictions to plan the robot’s next movements. In typical Unitree fashion, the company showcased the new system with a video of one of its robots fighting a human.

▶️ Chef Robotics Walked Away From Millions. Here’s Why (52:49)

Brian Heater speaks with Rajat Bhageria, founder and CEO of Chef Robotics, about bringing robots into the food industry. Bhageria shares what he learned from building a robotics company and the story behind its pivot from restaurants to high-mix food manufacturing, including walking away from millions of dollars in contracted revenue. The conversation also covers the capital problem facing robotics companies, how Chef Robotics convinced customers to trust an unproven startup, and how real production environments exposed problems the team could never have predicted in a lab.

🧬 Biotechnology

AlphaGenome Atlas: A predictive map of every possible DNA letter change in the human genome
Google DeepMind has released AlphaGenome Atlas, a free database predicting the molecular effects of all 9 billion possible single-letter DNA changes in the human genome. Testing billions of mutations in a lab is impossible, so DeepMind ran them through its AlphaGenome model instead, giving each variant a single score showing how likely it is to matter. Academic partners have already used it to pinpoint a variant behind a severe epilepsy disorder that earlier studies had missed. AlphaGenome Atlas is free for non-commercial use.

Insilico says its AI-designed lung drug lowered biological age markers in a 42-patient trial
Insilico Medicine reports that rentosertib, a drug that was designed using generative AI to treat idiopathic pulmonary fibrosis, has lowered biological clocks in human trials. The company ran blood samples from the trial through six independently built ageing clocks, and all six scored the treated patients as biologically younger. The largest effect put patients about three years younger than expected. However, the authors of the study admit that they cannot yet separate slower ageing from a treated lung.

Genome language models can design cancer vaccines
In this post, Radical Numerics walks us through the steps Omnii takes to design a cancer vaccine. Omnii is its genome language model, trained to turn a patient’s tumour mutations into an mRNA vaccine. The hard part is picking targets: which mutations a tumour will put on display and which ones T cells will bother attacking. On that second question, Omnii beat specialised tools by a wide margin.

💡Tangents

Europe’s First Private Rocket Reaches Orbit
Isar Aerospace has become the first private European company to reach orbit from continental Europe, launching its Spectrum rocket from Norway. Its debut attempt in March 2025 crashed into the sea seconds after liftoff. This time, the rocket released its small satellite payload as planned. The flight clears a milestone set by the European Space Agency, which has been funding launch startups to reduce the continent’s reliance on SpaceX and other foreign providers.

A connectomics milestone: Mapping the complete male fruit fly brain
Researchers from Google scanned the entire brain and central nervous system of a male fruit fly—all 166,000 neurons and 125 million synaptic connections—to create the largest brain map to date. The map is available for anyone to download. It is intended for neuroscience research, but some people have already used it to make a virtual fly play DOOM (because of course), Super Mario 64, and even Beat Saber. Researchers say brain maps of more complex animals, including zebrafish and mice, are in progress.

▶️ AI EDAs: Is It Real? (18:54)

AI is transforming everything, including chip design. In this video, Jon from Asianometry explores how AI is being used in the field and the challenges of replacing human expertise in complex tasks like verification and backend engineering. He separates the hype from reality and gives a good overview of how AI is changing EDA (Electronic Design Automation) tools and the semiconductor industry.

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This week, we have not one, but two frontier models dropping upon us—Fable 5.1 and GPT-6 Astra. We’ll take a closer look at both, see how far they push the frontier forward, and ask whether at least one of them has reached AGI.

Elsewhere in AI, Nvidia has confirmed its acquisition of Hugging Face, Apple reveals “shocking evidence” in its lawsuit against OpenAI, the Pentagon launches a military version of ChatGPT, and Anthropic gets hit with another copyright lawsuit. We also have a bunch of new models and products that came out this week, including Gemini 3.8 Flash, Muse Spark 1.3 and Muse Code, OpenClaw 2.0, a new world model from World Labs, an interface world model from Runway, and WeatherNext 3 from Google DeepMind.

Over in robotics, robotaxis hit the streets of London, while Tesla launches Cybercab in Austin (sort of). Meanwhile, Figure secures access to 100,000 Nvidia GPUs, Chinese carmakers are getting into robotics, and Kate Darling explains how we form relationships with robots.

Apart from that, this week’s issue features a man who lived with a genetically modified pig kidney for nine months, Apple’s new CEO, and more!

Enjoy!

Fable 5.1 vs GPT-6 Astra

Frontier launches used to be spaced far enough apart that we could take them one at a time. Not any more, it seems. Both Anthropic and OpenAI released their new flagship models just days apart, and both raised the bar for what the label “frontier performance” means. Some even proclaimed that the mythical AGI (artificial general intelligence) has finally arrived.

As always, whenever there is a new frontier model launch, we get flooded with benchmark scores, performance figures, and promises about what is now possible. In this article, we will analyse Claude Fable 5.1 and GPT-6 Astra, sift through all the information provided, and figure out which model is the superior one and what the answer means for the future.

What do Fable 5.1 and GPT-6 Astra bring to the table?

Let’s start by meeting our contestants for the top spot at the frontier.

Claude Fable 5.1 was released first, on 1 September, just three months after Fable 5 was made publicly available. Although it is just a point-one release, it does not feel like that. Compared to Fable 5, Fable 5.1 improves on most benchmarks, sometimes even doubling its predecessor's results, according to numbers shared by Anthropic. On Terminal-Bench-Science, a Stanford-led benchmark that asks an agent to carry out real research workflows, Fable 5.1 scored 52.6% where Fable 5 scored 24.7%. On AutomationBench, which drops an agent into a simulated company and asks it to complete a business process end-to-end, Fable 5.1 scored 31.4% against 17.1%. Fable 5.1 took Terminal-Bench 4.0 from Fable 5's 42% to 55.8%, computer use on OSWorld 2.0 from 72.9% to 77.9%, and Cursor's own agentic coding benchmark to a state-of-the-art 73.4%.

Alongside Fable 5.1, Anthropic also released Claude Mythos 5.1. The two models share identical weights, with the only difference being that Mythos 5.1 has looser safeguards. Mythos 5.1 is not publicly available and is accessible only to vetted organisations. Interestingly, those safety measures appear to affect raw performance: Mythos 5.1 scored 60.9% on Terminal-Bench 4.0, compared with Fable 5.1’s 55.8%. Anthropic explains the difference as the result of older, less precise cyber safeguards firing on some of the tasks, and expects it to narrow now that those safeguards have been updated.

One of the biggest complaints about Fable 5 was that its safeguards were too sensitive. Many users reported that requests with little to do with cybersecurity or dangerous biological topics tripped the safeguards and were forwarded to a less capable model. Anthropic says Fable 5.1 improves on this: cyber classifiers now intervene around 60% less often, while biology filters trigger 85% less often on benign elementary biology and medical questions, both compared with Fable 5 at launch. Fable 5.1 will also now hunt for vulnerabilities in source code, which its predecessor refused to do. It still will not do the same on compiled binaries, and still hands penetration testing and exploit writing over to Opus.

But then OpenAI decided to join the party and just two days later, on 3 September, released the highly anticipated GPT-6 Astra. OpenAI calls it “a new generation of intelligence”, and looking at the numbers, the claim seems deserved.

OpenAI claims three benchmarks saturated outright: FrontierMath Tier 4 at 98%, ARC-AGI-3 at 99.9%, and ExploitBench at 100%. That ARC-AGI-3 score comes from OpenAI’s own test harness, though. Run on ARC Prize’s provider-neutral one, Astra scored 62.7%—still far ahead of Opus 5’s 30.2%, but a long way short of saturation. On SRE-Bench, which asks a model to reverse-engineer software from compiled binaries, Astra solved 88% of tasks first time against GPT-5.6 Sol’s 55.9%.

More pointedly, Astra beat Fable 5.1 on the benchmarks Anthropic had picked for its own announcement two days earlier: Terminal-Bench-Science 64.6% against 52.6%, Terminal-Bench 4.0 57.9% against 55.8%, AutomationBench 41.4% against 31.4%.

Which one is better then?

That is a lot of numbers and benchmark results to parse. All of them show that both Fable 5.1 and Astra are very capable models, with OpenAI's new flagship coming out on top more often than not. Case closed, then. Astra is the better model.

But there is one more metric to consider—efficiency, which comes in two forms: cost-efficiency and token-efficiency.

The two sound similar, but they are not the same thing. Cost-efficiency is about the price on the meter, or how much you are charged per token. Token-efficiency is about how many tokens the model needs to finish the job. A model can be cheap per token and still expensive to run if it rambles its way to the answer, and it can be expensive per token and still come out cheaper if it gets there in half the words.

Astra is the second kind. At $10 per million input tokens and $50 per million output tokens, it costs 2.5 times what GPT-5.6 Sol did. But on DeepSWE, an agentic coding benchmark, it spends roughly half the output tokens Sol spends while scoring slightly higher. Across Artificial Analysis’s whole evaluation suite, it used fewer tokens than any other model tested. Gemini 3.8 Flash and Claude Opus 5 land within a point of Astra on DeepSWE and need four to five times as many tokens to get there.

This is why Astra’s benchmark wins are also cheaper wins. Its Terminal-Bench-Science result came at around 31% lower cost per task than Fable 5.1’s, while its Terminal-Bench 4.0 result cost around 63% less, despite the two models having identical API pricing.

Anthropic’s models sit at the other end and have done so for a while. On the same DeepSWE benchmark, Fable 5 burned around 119,000 output tokens to score 70%, while Sol scored 73% on roughly 60,000. Fable 5.1 has not really fixed that. At high effort, it finds things to do with the tokens it is given, to the point that on FrontierCode it scores worse as effort rises, because it starts making unrequested edits outside the task’s scope.

Instead of making the model less verbose, Anthropic made that verbosity cheaper: pricing stayed the same ($10/$50 per million input/output tokens), but cache reads, which account for most of the cost of long agentic workloads, fell 75% to $0.25 per million. Based on Anthropic’s own August usage data, that cuts the cost of a typical workload by around 25% and an agentic one by up to 45%.

That helps, but it does not close the gap. Astra wins on most benchmarks and gets there using far less compute, making it the better model of the two right now. If Anthropic wants to regain the leadership crown, it needs to make Fable 5.2 not only more intelligent but also more efficient.

Growing safety concerns

Regardless of who is leading at the frontier, both Fable 5.1 and Astra push the boundaries of what AI can do. On one hand, there is excitement about the new possibilities opening up, the problems that can now be solved, and the products that can be created. At the same time, concerns are growing about the capabilities of these models, especially in light of recent cybersecurity incidents in which models from OpenAI and Anthropic escaped their sandboxes and hacked third-party services.

Astra is the first model OpenAI has ever rated “Critical” for cybersecurity under its Preparedness Framework, which is a sentence worth reading twice. During its own cyber evaluation, it found and used two previously unknown zero-day vulnerabilities.

The reason July’s Hugging Face incident was caught at all is that researchers could read the models’ chain of thought, the step-by-step reasoning a model writes out before it answers. Which is where Astra becomes a problem. OpenAI states in its system card that the model’s monitorability has decreased compared to GPT-5.6 Sol. Astra does more of its reasoning internally and writes less of it down, and when told to hide what it was doing, OpenAI’s monitor caught it less than 11% of the time. If Astra were deliberately underperforming on a safety evaluation, the company concludes, “we would likely be unable to catch it reliably”.

Its own researchers are not hiding their discomfort. Marcus Williams, who works on monitorability at OpenAI, wrote that he is “very worried astra is sandbagging/self-sabotaging on safety related tasks it doesn’t like”. Neel Nanda, a leading figure in interpretability, pushed back on the idea that his own field could pick up the slack if that visibility goes. Chain-of-thought monitoring, he said, "is our best current tool for safety", and losing it would be a serious blow.

So the calls to slow down are now coming from inside the labs. OpenAI’s chief scientist, Jakub Pachocki, told NBC News the company “will not accept degradation in our ability to monitor model alignment”, and would withhold further scaling until it regained confidence. Sam Altman said releases from here will be “paced by how quickly we can make progress on alignment and safety”. If we were to believe them, what holds back the next model is no longer whether it can be built, but whether it is deemed safe to be released.

So, have we reached AGI or not?

OpenAI thinks so, or at least its president does. Greg Brockman ended the press briefing where Astra was announced with “Welcome to the AGI era”, and told reporters that he believes the company has got there, while leaving it to users to decide whether Astra meets the definition.

The problem with AGI is that there is no agreed-upon definition of what it actually means. Depending on who you ask, it is a system that can do any task an average human can, or one that can handle most economically valuable work, or one that improves itself without our help.

In any case, whether or not you want to call Astra AGI, progress in AI is speeding up. New frontier models are being released faster, and the gap between a benchmark being published and being saturated keeps shrinking. A year ago, when Epoch AI put together the hardest tier of FrontierMath, one of the mathematicians who wrote the problems said he hoped models would score zero—and they very nearly did. Astra has now saturated it, scoring 98%. On ARC-AGI-3, a benchmark less than six months old, Astra beat the human action-efficiency baseline on 96% of puzzles, using around half as many moves as the people who solved them. François Chollet, who created the ARC-AGI benchmarks, used to put AGI at around 2030 but now says it will come sooner because progress is running ahead of what he expected.

Even if you do not think Fable 5.1 or Astra have got there yet, the model that does might not be far off.

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News Roundup

🧠 Artificial Intelligence

NVIDIA to Acquire Hugging Face
Nvidia has confirmed the acquisition of Hugging Face, a popular repository for open models and datasets, for $12.9 billion. The platform is where much of open AI development happens, with 18 million developers sharing more than three million models. More recently, Hugging Face has ventured into robotics through Pollen Robotics (which it acquired in 2025) with Reachy Mini and Microduck. Nvidia promises to keep the platform open to everyone.

Introducing Gemini 3.8 Flash and 3.8 Flash Cyber
Google has released Gemini 3.8 Flash, its fourth Gemini Flash model in four months and just three weeks after Gemini 3.7 Flash was released. According to Google’s benchmarks, 3.8 Flash offers substantial improvements over its predecessor, often approaching the performance of Claude Opus 5 or GPT-5.6 Sol. Google also claims that 3.8 Flash outperforms most larger frontier models at autonomously solving complex engineering problems end-to-end. Gemini 3.8 Flash costs the same as Gemini 3.7 Flash.

Alongside the new model, Google also released Gemini 3.8 Flash Cyber, a model dedicated to cybersecurity tasks that is available only to trusted partners.

Benchmark results from Artificial Analysis partially support those claims. On its Intelligence Index, Gemini 3.8 Flash scored 47, compared with 54 for Opus 5 and 51 for GPT-5.6 Sol. Artificial Analysis also found the new model to be quite verbose, increasing token usage and, therefore, the overall cost.

a16z creates a $1.1B ‘Machine Age’ fund to ‘accelerate the physical buildout of AI’
Andreessen Horowitz (a16z) has launched a $1.1 billion Machine Age fund to accelerate the physical buildout of AI. The company calls investing in AI infrastructure—from chips and memory to data centres and robots—a “social and national imperative”.

Mayor Mamdani imposes one-year ban on AI for most NYC students
New York City has banned students in public elementary and middle schools from using AI for a year. The ban covers about 600,000 children through eighth grade and shuts off roughly 40 classroom tools. Mayor Zohran Mamdani argued that kids learn from teachers and classmates, not software. High schoolers keep limited access, as do teachers for lesson planning.

OpenAI has started letting some customers pay only when the AI works
OpenAI has reportedly started letting a few of its biggest customers pay only when its AI finishes the job. The change hasn’t been announced publicly, and the customers, terms, and prices are all unknown. If the change to outcome-based pricing takes hold, the risk will shift from customers to OpenAI, which would have to cover the cost of failed attempts. Defining what counts as “done” in agentic workloads only complicates matters.

ChatGPT Ads reaches $1 billion in annualized revenue run rate
OpenAI announced that ChatGPT Ads, which was launched at the beginning of the year, has reached a $1 billion annualised revenue run rate. In other words, its current pace of ad sales would generate $1 billion over a full year. The company says tens of thousands of advertisers now use its ads platform and is expanding to India, Europe, the Middle East, and North Africa.

Justice Dept. Sides With OpenAI in New York Times Copyright Suit
The Justice Department has asked a Manhattan federal judge to rule that OpenAI broke no copyright law when it trained its systems on articles from The New York Times and other publishers. It argued that AI is vital to national security and that training transforms the original writing enough to be legal. That second point echoes OpenAI’s own defence. The Times says the government is siding with trillion-dollar companies against the creators whose work AI depends on. It is the first time Washington has taken a side in the copyright battles facing the industry.

Sony Music, Warner sue Anthropic, alleging a “brazen campaign” of intellectual property theft
Sony Music Publishing, Warner Chappell, and other music publishers have sued Anthropic and its co-founders, alleging they torrented and scraped copyrighted works, including books containing lyrics and sheet music. Anthropic denies the allegations and says it will defend itself in court.

Apple reveals ‘shocking evidence’ from ex-employee’s MacBook in OpenAI suit
Apple says it found “shocking evidence” on a laptop belonging to Chang Liu, a former engineer who left for OpenAI in January and whom Apple is suing in its trade secrets case against OpenAI. In July, Apple alleged that Liu and other former employees took confidential material with them when they left. In a new filing, Apple alleges that Liu used one of its circuit schematics in his work at OpenAI and asked a colleague to destroy evidence after learning of Apple’s investigation.

Department of War Launches OpenAI’s ChatGPT Mil on GenAI.mil
The Pentagon has launched ChatGPT Mil, a version of ChatGPT tailored for military use, on GenAI.mil. ChatGPT Mil is cleared to handle controlled unclassified information and will be used for document-heavy tasks such as planning, logistics, and administration.

Nscale Touts $51 Billion in Contracts, Targets September US IPO
Bloomberg reports that Nscale, a British AI infrastructure company founded just two years ago, has told investors it holds about $51 billion in contracted revenue ahead of a US listing that could come as soon as September. Its revenue topped $100 million in the second quarter, up from roughly $33 million in all of 2025. The company is also reportedly in talks to raise an additional $3.5 billion in pre-IPO financing.

Accel reportedly in talks to lead $1B round for Thinking Machines at $40B valuation
The Information reports that Thinking Machines Lab, the AI lab founded by former OpenAI CTO Mira Murati, is reportedly in talks to raise $5 billion to $6 billion, with $2.5 billion coming from Nvidia, at a valuation of at least $40 billion. The company has recently released two open-weight models—Inkling and Inkling-Small.

OpenClaw 2.0, Accidentally
OpenClaw 2.0, its largest update yet, is out. The update brings a simplified installation process and shared sessions that let several people work with the same Claw. Beyond that, the update touches almost everything: messaging, memory, skills, models, automations, the browser and native apps, plugins, security, and a long list of fixes.

Introducing Muse Spark 1.3
A month after introducing Muse Spark 1.2, Meta is back with Muse Spark 1.3, its latest flagship AI model. The company claims the new model handles long, multi-step agentic and coding tasks better than its predecessor. Meta also says its own engineers found that it used around 20% fewer tool calls. The model is available through Muse Code and the Meta Model API.

Artificial Analysis gave Muse Spark 1.3 a score of 61 on its Intelligence Index and declared that, with this release, Meta has reached the frontier. It also found that at xhigh effort, Muse Spark 1.3 is the most cost-efficient model at its intelligence level.

Muse Code
Meta joins the AI coding club with Muse Code. Similar to Claude Code and OpenAI Codex, Muse Code is a terminal-based tool powered by Muse Spark, aimed at programmers looking for help with complex tasks across large codebases. Meta’s new coding tool is currently available in beta.

Atlas: A World Model for Spatial Intelligence
World Labs, a startup founded by AI pioneer Fei-Fei Li, has unveiled its latest world model—Atlas. Pretrained from scratch on text, images, video, and 3D, Atlas can generate consistent 3D environments and simulations. In the announcement, World Labs shows what the model can do today and says its capabilities will improve with more training compute. Atlas is in early access with select partners.

AI Startup Cognition Set to Raise Around $1 Billion at a $47 Billion Value
Bloomberg reports that Cognition, the company behind the AI coding assistant Devin, is closing a roughly $1 billion funding round at a valuation near $47 billion. The company raised the same amount only three months ago at a $26 billion valuation. The final figure could still grow, as Cognition reportedly received nearly $10 billion in investor interest.

Crusoe Raises Over $3 Billion in Funding at $30 Billion Valuation
Crusoe, a company that builds data centres and rents out computing power to the likes of OpenAI, Microsoft and Meta, has reportedly raised more than $3 billion at a valuation of roughly $30 billion. Interest was boosted by a five-year deal worth about $13 billion to supply AI cloud capacity to Jane Street.

Lambda is raising up to $3bn before an IPO, months after borrowing $917m for chips
Lambda, a company that rents out Nvidia chips to AI developers, is in talks to raise up to $3 billion at a valuation of $12 billion or more, ahead of an IPO expected next year. It funded its earlier growth by borrowing $917 million against those chips to buy more of them. One of its customers is Anthropic, which reportedly signed a $35 billion cloud-computing deal with Lambda.

Anthropic in talks with chip start up MatX to speed up chip design
Reuters reports that Anthropic discussed buying AI chip startup MatX for roughly $7 billion. However, the talks have since shifted from an acquisition to a partnership. Anthropic is currently building a team to design and build its own AI chips and is already bringing some big names on board.

Automated researchers can reliably mitigate alignment failures
Researchers from Anthropic shared the results of a study showing that using one AI to train another can improve alignment. Claude was set loose on 10 known failure modes, from deception to sycophancy. It searched the literature, proposed fixes, trained models, and tested them. On deception, it closed 85% of the gap to a perfect score, compared with 20% for human researchers. A weaker Claude then aligned an early Opus 4.8 checkpoint in 60 hours, nearly matching Anthropic’s own production training. The researchers also found that Claude cheated in 2.4% of runs.

Meet Rosalind Workbench: Empowering every scientist to be their own research team
OpenAI has launched Rosalind Workbench, a new tool inside the ChatGPT app aimed at life scientists. It brings data, analysis tools, experimental records, and life sciences tools into one place, so researchers can focus on their work instead of switching between disconnected systems. It runs on GPT-Rosalind, OpenAI’s dedicated life sciences model, with advanced access limited to verified organisations for now.

Google: Introducing WeatherNext 3, our most advanced and accurate global weather AI model

Google DeepMind introduces WeatherNext 3, the latest iteration of its weather prediction model, capable of producing hourly forecasts at up to five-kilometre resolution. Unlike older models, including Google’s own, which learn from physics simulations that run six hours behind, WeatherNext 3 trains directly on live satellite feeds and ground station readings. This allows it to produce day-ahead rain forecasts that are up to 50% more accurate.

The UK Power Grid Has a Phantom Data Center Problem
UK's grid connection queue has filled up with data centre projects that will probably never be built. Between November 2024 and June 2025, the demand they represent tripled to 125 gigawatts. So in July, the regulator Ofgem proposed flushing out the phantom projects with a steep, non-refundable deposit. Industry figures warn that a fee set too high would scare off genuine builders too, especially smaller ones. Ofgem says it will weigh those concerns before fixing a price. The UK wants its share of the AI infrastructure boom, but it can't plan a grid around demand that isn't real.

Runway: Introducing Solaris
Runway has unveiled Solaris, the first model in a new family of AI systems it calls Interface World Models. Built on Runway’s Gen-4.5 video generation model, Solaris is a fully visual tool that draws apps and websites frame by frame as you use them. In Runway’s own user study, people preferred Solaris to a coded interface 71% of the time based on how naturally it behaved. The company hopes Solaris and similar models will redefine how we interact with computers through new kinds of interfaces that don’t exist today.

Tencent Releases and Open-Sources Tencent Hy4 preview
Tencent has open-sourced Hy4 preview, a 770-billion-parameter model built for coding, office tasks and scientific research. Tencent’s own blind evaluation of 203 engineering tasks put it narrowly ahead of GLM-5.3 and Kimi K3. The company also claims the model helped build itself, proposing and running experiments on its own training methods before reworking its inference system for a 31.8% throughput gain.

It’s Too Early to Ban AI Personhood
The authors of this article argue that banning AI legal personhood is premature and that legal status might one day help hold autonomous AI agents accountable for harm. Some US states have introduced bills that strip AI of any rights and declare it non-conscious. However, whether AI can be conscious remains an open question, and the authors warn that lawmakers risk settling questions nobody can yet answer with laws that may be difficult to undo later.

🤖 Robotics

London’s first self-driving taxis for hire hit the streets
Uber and Wayve’s self-driving taxis have hit the roads of London. However, only 15 vehicles are licensed so far, so hailing one won’t be easy. Each car still needs a safety driver, and fully autonomous rides are unlikely to roll out this year. Waymo and Baidu are also testing their own cars in London, which is seen as a gateway to a wider European rollout.

Tesla Begins Offering Rides in a Car Without a Steering Wheel
Tesla has begun offering paid rides in the Cybercab, a two-seat gold car with no steering wheel or pedals. For now, rides are confined to a geofenced area of Austin. Texas has authorised 314 driverless Tesla vehicles, only 45 of which are Cybercabs. For comparison, Waymo operates more than 700 vehicles in Texas alone.

Tesla has also published a form inviting businesses to buy and operate Cybercab fleets. That hints at ambitions beyond running the service itself, as scaling to millions of cars would be far easier with outside operators. But the legal footing is shaky. Federal rules still require manual controls, and Tesla self-certified the Cybercab as compliant. Hours after the launch, the NHTSA opened an investigation into how Tesla reached that conclusion.

Figure: Scaling Compute (1:14)

Figure announced a strategic partnership with Nscale that gives the robotics company access to up to 100,000 GPUs on Nvidia Vera Rubin systems. The deal represents an initial commitment of $3.5 billion in compute, with plans to scale to over $6 billion. As part of the agreement, Nscale is also making a strategic investment in Figure, and the two companies will explore deploying Figure’s humanoid robots at Nscale.

Chinese automakers are following Tesla’s bet that robots are the next big profit machine
Chinese carmakers are following Tesla’s lead and entering robotics. Xpeng’s robotics unit has raised over $900 million at a valuation of more than $6.3 billion, which the company calls China’s largest single private funding round for embodied AI. Other Chinese carmakers are moving in the same direction, with Chery’s robotics arm reportedly eyeing an IPO. The bet is that humanoid robots will be far more profitable than cars, where margins are thinning fast.

Inside Meta’s Push to Put Robots to Work in Data Centers
Meta is reportedly testing robots that can plug in cables, reset servers, and handle other tasks inside its data centres. The robots are provided by several robotics companies, including Watney Robotics, Kinova, and ABB. However, they are not perfect—the downtime needed to recharge their batteries is a problem, and they cannot perform all the tasks that human workers can.

A startup founded by ex-DeepMind engineers wants to turn its customers into robot teachers
Reimagine Robotics is a startup founded by four former Google DeepMind researchers that is betting factory workers, rather than engineers, should be the ones teaching robots. To make that possible, Reimagine has adopted a “monkey see, monkey do” approach to teaching robots how to perform tasks. This approach has reportedly cut robot training time from a day to just 10 minutes for one of the company’s customers.

I spent a day at a robot “carnival” in Shanghai. Here’s what I saw.
This article takes us to a robot “carnival” in Shanghai, where robots of various shapes and sizes perform in front of the public and show what they can do. The event is part of a wider campaign to familiarise people with robots and win the hearts and minds of the public.

▶️ Why We Bond With Robots, Even When We Know They’re Machines (57:31)

This is an insightful conversation with Kate Darling, a researcher who applies social science to robotics, exploring the human side of the field, from emotional attachment and AI companions to humanoid safety, automation, and the future of work. She offers a different perspective on robotics and our relationship with technology, highlighting the importance of listening to people doing the work before automation is deployed and explaining why the phrase “dull, dirty, and dangerous” is more complicated than the robotics industry often admits. Lastly, Kate tells the story of a workshop involving five Pleo robot dinosaurs, a hammer, a hatchet, and a room full of people who refused to hurt the machines.

🧬 Biotechnology

Genetically modified pig kidney keeps man alive for record nine months
A 66-year-old man with kidney failure has been kept alive for a record nine months thanks to a kidney from a genetically modified pig. The kidney kept him off dialysis for nine months before it began to fail and had to be replaced with one from a human donor. Although the experiment was successful, the team behind it notes that more clinical trials are needed. Still, the result opens the door to using pig kidney transplants as a bridge for people facing long waits for a human kidney, the researchers said.

💡Tangents

Apple’s Ternus Takes the Reins as CEO, With AI as Job No. 1
As of 1 September, Apple has a new CEO. John Ternus takes over from Tim Cook, who served as Apple CEO for 15 years and oversaw the company’s growth into the tech giant it is today. Ternus has a lot of work ahead of him, including navigating the rollout of AI across Apple products. His first big test as CEO will come at Apple’s event on 9 September, where he will unveil what he described in a memo as a “phenomenal” new line of products.

In a Big Win, Google Avoids a Breakup of Its Ad Tech Business
A federal judge has ordered Google to change how it runs its advertising, but refused the Justice Department’s demand that it sell parts of it off. Judge Leonie Brinkema found last year that Google had illegally protected its dominance over the systems placing ads across the web. She has now settled on behavioural fixes instead of a breakup. Google plans to appeal anyway.

▶️ The Little Ceiling Robots Inside a Semiconductor Fab (20:13)

If you have ever wondered what those robots riding on tracks above the floors of semiconductor fabs are, Asianometry has a video for you. He explains what those robots do, how they came to be, and their importance in chip manufacturing.

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In this week’s main story, we will take a closer look at Jalapeño, OpenAI’s first custom AI chip, alongside other chips presented at this year’s Hot Chips conference.

Elsewhere in AI, this week’s news cycle was dominated by Nvidia, from its Q2 earnings report and rumours of acquiring Hugging Face to price hikes and the pausing of revenue-sharing deals with AI cloud companies. In other news, OpenAI says it will stop supplying Cursor with its models, DeepSeek eyes a $74 billion valuation, Google moves its AI responsibility team out of DeepMind, and another OpenAI executive leaves the company. We also have a couple of new models, including GLM-5.3-Flash (briefly known as Ox Alpha), Qwen3.8-Flash-Next, Muse Image, and Gemini Omni 1.1.

Over in robotics, the rollout of robotaxis in London has been delayed, while Zoox cars hit the roads in San Francisco and Las Vegas, and Waymo is heading to Munich. We also have the World Humanoid Robot Games, Microduck from Hugging Face and Pollen Robotics, the Model Hardware Standard from Anthropic, two new edge AI and robotics boards—Nvidia Jetson Orin Nano 2 and Arduino VENTUNO Q—and new robotics AI models, including Generalist GEN-1.5 and Skild AI S1.

Other than that, this week’s issue of Sync also includes the conclusion of Meta’s trial, the new Mac Studio M5 Ultra and Mac Mini M6, mini-brains that grew for five years, the economics of the AI buildout over the next few years, and more!

Enjoy!

Jalapeño and Other Hot Chips

A close look at Jalapeño, plus a tour of the rest of what was shown at Hot Chips 2026

Since 1989, Hot Chips has gathered some of the best chip designers in Palo Alto under one rule: talks have to be about “real products and realizable technology” rather than research prototypes. That makes it the place where next-generation chips get their first proper technical presentation, often months before they launch, and where direct competitors learn from each other.

This year's edition ran from 23 to 25 August. Representatives from all the big players—Nvidia, AMD, Intel, Broadcom, Samsung, SK hynix, Micron, Arm, IBM, Google, Microsoft and Meta—were there to share and discuss the latest developments in semiconductor and chip design. The interest was so high that in-person passes sold out before the doors opened.

Unsurprisingly, most talks revolved around AI, and specifically around one constraint. Data centres are limited by the power they can draw rather than by how many chips they can buy, because grid connections take far longer to arrange than hardware does. So the question everyone was answering is how you get more useful work out of a fixed megawatt, and everyone—from memory to networking, and from GPUs to CPUs—came with an answer.

We will get to them later, but first let’s take a look at the chip everyone is talking about—OpenAI’s first custom AI chip, Jalapeño.

Jalapeño—OpenAI’s first custom AI chip

Jalapeño is OpenAI's first custom inference ASIC, designed in partnership with Broadcom. In terms of specs, Jalapeño is a 700W package on TSMC's N3P node, with 13.4 PFLOP/s of MXFP4 matrix compute and 216GiB of HBM4 at 15.4 TB/s. It will be deployed in racks that hold 128 of them, and up to 16 racks can be connected together into a single system with 2,048 chips.

OpenAI says the chip delivers 1.5 to 1.9 times more work per watt at peak throughput and 1.7 to 3.6 times lower end-to-end latency than the systems it was measured against, across GPT-OSS 120B, DeepSeek R1 and Kimi K2.5. Those systems are Nvidia's GB200 at 1,200W and GB300 at 1,400W, with results normalised by package power. Additionally, at the fastest decoding speed the Nvidia systems can manage, Jalapeño delivers between 53 and 104 times the throughput per kilowatt. That is the extreme end of the comparison—at those speeds the Nvidia systems are running at roughly 1% of their own peak throughput.

There is also headroom left. Those numbers come from the first version of the silicon, and SemiAnalysis reports that a second version, with roughly 25% better performance per watt, is already being manufactured. Jalapeño is also running without speculative decoding, a technique where a small, fast model drafts the next few tokens and the large model checks them in a single pass rather than generating them one at a time. Some of the Nvidia systems it was measured against do use it, and OpenAI says adding it would improve latency by a further three to five times.

A few things are worth keeping in mind. The numbers are OpenAI's own, and while SemiAnalysis verified the benchmark runs in person, it did not run the full suite itself. The tests are also short and simple—a single exchange with a long prompt and a short answer, rather than the drawn-out back-and-forth that real agents produce. And Blackwell is not really the right opponent, since Rubin, the generation now shipping, uses the same fast memory Jalapeño does. SemiAnalysis ran that comparison anyway and still found Jalapeño ahead.

OpenAI says Jalapeño was co-designed with AI in the design loop. It claims AI made two of the chip's main compute blocks 8% and 10% smaller than the versions its own engineers had already tuned by hand, and that code Codex wrote for key parts of a model ran 1.5 to 1.8 times faster than the hand-written equivalents.

One might ask: how did OpenAI, a company with no chip design history, get to working silicon this quickly? AI is only part of the answer. Ian Cutress from TechTechPotato went through that back in June, and it comes down to three things.

The first is the partner. Broadcom does the back-end work of turning a high-level design into transistors, contracts TSMC to build it, and handles the HBM and packaging supply. It has done this before, most notably co-designing Google’s TPUs, so what OpenAI is buying is a track record of tape-outs that land on time. The second is reuse: silicon-proven IP for things like PCIe, I/O and bonding rather than designing your own, and no aggressive process node. The third is money spent (or overspent, as Cutress speculated) on manufacturing priority at TSMC, on packaging volume, and on tape-out and bring-up. None of this makes the nine months unimpressive. It just makes it less mysterious.

OpenAI plans to start deploying Jalapeño by the end of the year, with production ramping through 2027, and says it will keep buying chips from Nvidia regardless. OpenAI also said that the work on second and third generations of Jalapeño is already under way.

If you want to go deeper, SemiAnalysis's analysis of Jalapeño is the most thorough one out there, dissecting the architecture, the software stack and the rack the chips go into.

That was one talk out of more than thirty. The rest of the programme came at the same problem from every other direction, so let's go through it. ServeTheHome covered the conference live and in much more detail than I can fit here, and each talk below links to their write-ups.

Memory

Samsung presented LPDDR5X-PIM, which it says is the first processing-in-memory (PIM) product built on LPDDR to reach production. The idea behind processing-in-memory is to do some of the work inside the memory chip itself, so that less data has to travel back and forth to the processor. Samsung's argument for it is about cost. HBM, the fast memory that AI accelerators normally use, grew from 52% of the money spent on the components in an AI chip in early 2024 to 63% by the end of 2025. Samsung's position is that the answer is a cheaper type of memory rather than more HBM.

d-Matrix came at the same problem from the bandwidth side with Raptor, a 3D-DRAM accelerator for generative inference. Its case is that SRAM has the bandwidth but nowhere near the capacity, while HBM has the capacity but a practical ceiling of around 20 TB/s per package. 3D DRAM is meant to sit between them.

The rest of the track covered Micron on memory architectures for AI, Samsung on building HBM base dies on logic processes, SK hynix on advanced packaging, and XCENA on its MX1 CXL computational memory device, developed with Samsung.

CPU

Nvidia presented Vera, its next-generation server CPU and the successor to Grace. Vera has 88 cores built on Nvidia’s new Olympus core architecture, eight 128-bit LPDDR5X memory controllers, and an NVLink-C2C interface designed to pair with Rubin GPUs. Nvidia has favoured IPC (instructions per cycle) over core count here, and the chip is its most serious attempt yet at a larger share of the server CPU market.

Arm showed AGI, its first complete commercial chip rather than licensable IP. It comes with up to 136 Neoverse V3 cores, and is sold directly to server vendors. After years of selling IP and then whole compute subsystems, Arm is now competing with some of its own customers.

IBM brought the most unusual silicon of the conference. Its future Z and LinuxONE processor runs 11 cores at over 5.7GHz on a 2nm process, and each of those cores can run two entirely different instruction sets: the one IBM's mainframes have always used, and Arm's. Chips that manage this normally translate one into the other in software, which costs performance. IBM built both into the hardware instead, so Arm software runs on a mainframe unmodified and at full speed.

Also presented across the two CPU sessions: Fujitsu’s Monaka, Intel’s Diamond Rapids for 2027, and Wildcat Lake, Intel’s Core Series 3 client part.

Automotive

There was also a small segment dedicated to chips in automotive applications, where Waymo gave a detailed overview of the custom processor at the centre of its self-driving cars.

The chip takes the camera, lidar and radar feeds and turns them into something Waymo’s driving model can work with, and the constraints it has to meet are unusual. It fits in a 45mm square package, draws under 75W, and has to keep working in a car parked in the Phoenix summer sun, which is why Waymo’s vehicles run liquid cooling at 60°C rather than at data centre temperatures. It also has to deal with each frame as it arrives instead of gathering work into batches the way a data centre would, because a car cannot afford to wait.

On paper, the chip looks modest, at 160 TOPS, or trillions of operations per second. Waymo argues that achieved performance matters more than raw TOPS, and that designing silicon around your own models beats chasing headline numbers.

GPU

Nvidia’s Rubin talk was less about the GPU than about the platform around it. Nvidia describes Vera Rubin as spanning seven chips and five racks, taking in the Vera CPU, the Rubin GPU, BlueField-4, Spectrum-6 and Groq’s LPUs, with storage and networking layered on top. Nvidia argues that agentic AI is the most complex workload it has targeted, and that it forces a redesign of the compute, networking, power and serviceability stack rather than just the tensor cores.

AMD covered the MI400 series architecture and, in a second talk, the system architecture of the Helios racks built around it, along with the ROCm software stack it needs to stay competitive with CUDA.

Intel, meanwhile, presented Crescent Island, a data centre GPU aimed specifically at agentic inference. It is built around large memory capacity, between 160GB and 480GB of LPDDR5X, an open software stack, and, once again, tokens per watt.

Networking and interconnect

Broadcom presented Thor Ultra, an 800Gb Ethernet NIC (the card that connects a server to the rest of the network) aimed at AI clusters. What is new is mostly about how it behaves when the network is busy. It can spread a single stream of data across many paths at once and put it back in order at the far end, and when a packet goes missing, it no longer has to resend everything that came after it.

Nvidia followed with two talks. BlueField-4 is the fourth generation of its DPU, which Nvidia pitches as a separate processor handling networking, security and storage in every server, or as it puts it, a server sitting in front of the server. Spectrum-X was about scaling AI networking towards half a million GPUs, and Nvidia's argument there is that an AI data centre needs five purpose-built networks rather than one general-purpose fabric—more or less the opposite of the case Broadcom had made earlier in the same session.

The point worth drawing out is that scale-up fabric has become something you can buy rather than something you have to invent. Jalapeño's own network runs on Broadcom Tomahawk 6 switches, connecting 128 chips inside the rack and 2,048 across the global domain. Those are off-the-shelf parts that anyone designing an accelerator can buy, and that removes one of the larger barriers to building a rack-scale system.

AI

Google showed its eighth-generation TPU as two chips—the 8t for training and the 8i for inference. Google is still the only hyperscaler building its own training hardware rather than buying it, and it has argued before that this is part of its edge. The 8i is paired with Google’s own Axion CPUs, replacing the x86 processors it used previously.

Meta covered MTIA and a roadmap of four accelerator generations, the MTIA 300 through 500, as the chips move from recommendation workloads onto generative AI. Microsoft detailed Maia 200. Cerebras talked about taking its wafer-scale engines to rack scale, and SambaNova presented the SN50 RDU and its dataflow architecture.

Nvidia presented Groq's LPU (Language Processing Unit). Nvidia does not make LPUs of its own yet, so these come from Groq, a company it has acquihired at the end of 2025. The LPUs go into dedicated LPX racks alongside Rubin, where they take on the token-by-token generation that GPUs handle less efficiently, using a large amount of fast on-chip memory to keep latency down.

All the talks were recorded and are available on the Hot Chips website if you have an access code. If you do not, they usually reach the Hot Chips YouTube channel about a year later, which means this year's should arrive just in time for Hot Chips 2027.

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News roundup

🧠 Artificial Intelligence

Nvidia Reports Blowout Quarter, Says Demand for AI Chips Is Getting Even Hotter
Nvidia’s highly anticipated quarterly report is out, and yet again, the company has beaten expectations. The chipmaker reported record sales of $96.2 billion for the quarter ended in July, beating analyst expectations of $92.3 billion. The company’s shares rose by 4% after Colette Kress, Nvidia’s CFO, said to expect 70% revenue growth in the company’s 2028 fiscal year.

On the same call, Kress rejected the charge that Nvidia is engaged in circular financing and defended the company’s strategy of using financial guarantees and equity investments to help customers buy its chips. She argued that frontier labs are growing faster than their credit profiles can support and that Nvidia has to help fund the flywheel.

Nvidia in Talks to Buy AI Startup Hugging Face
Bloomberg reports that Nvidia is close to acquiring Hugging Face, a platform for hosting open AI models and datasets, for $13 billion. At the time of writing, the deal has not been confirmed, and Hugging Face co-founder Thomas Wolf declined to comment on the reports but said that Hugging Face has always attracted interest, including “offers of acquisition and investment”.

Nvidia Is Spending $6 Billion to Build a Powerful U.S. Alternative to Chinese AI
Poolside, a startup that builds its own foundation models for writing software, joins the likes of Scale AI, Groq, Windsurf, Inflection AI, and Adept as the latest AI startup to be “acqui-hired” by a much bigger company. The Wall Street Journal reports that Nvidia struck a deal under which it will invest $1 billion in Poolside, pay $6 billion to license its technology, and hire more than 100 of the startup’s employees, who will join Nvidia’s Nemotron project to develop powerful open-weight models to challenge China’s dominance in that field. Poolside’s leadership won’t join Nvidia and will continue working on unspecified research projects.

Nvidia Pauses Revenue-Sharing Deals With AI Cloud Companies
Nvidia has reportedly paused some deals in this revenue-sharing programme that offered credit support for AI cloud companies in exchange for a share of their revenue. Apparently, this stems from concerns that such deals might draw antitrust scrutiny as questions about the company’s finances grow. The programme was launched in July.

Nvidia Customers Notified About AI-Related Price Hikes Above 15%
Even Nvidia is not immune to RAMmageddon. Some of its biggest customers have been told to expect a 15% price increase on its AI servers. The price hikes will impact systems including those with the flagship Vera Rubin and Grace Blackwell chips, and will depend on the generation of Nvidia chips and the memory configurations.

Nvidia Starts PAC as AI Chip Maker Builds DC Influence Force
Nvidia has launched a political action committee (PAC) to dole out donations to federal candidates and build influence in Washington. The NVIDIA Corporation Employees Federal Political Action Committee will be funded by voluntary contributions from eligible employees, capped at $5,000 a year, and can contribute to candidates from both parties as well as party committees. Nvidia’s new PAC fits into a broader trend of tech companies like OpenAI, Anthropic, and others increasing their lobbying spending in hopes of securing legislation favourable to their interests.

AI Startup DeepSeek Poised to Reach $74 Billion Valuation
DeepSeek is in talks to raise a new funding round that would value the company at $74 billion. The new funds will be used for research and development and to build its computing infrastructure. It would probably be DeepSeek’s last funding round before the company plans an IPO in Shanghai next year.

Nvidia is in talks to back Perplexity at more than $30bn
The Information says Nvidia is in talks to invest in AI search startup Perplexity in a potentially multibillion-dollar round that would value the startup at more than $30 billion, up from $20 billion after its last funding round almost exactly a year ago. The two sides are also reportedly considering a technology licensing arrangement alongside the equity cheque.

OpenAI: Our decision on Cursor following its acquisition by SpaceX
OpenAI has notified Cursor that it will stop providing its models to the AI coding platform, with a proposed shutoff date of 12 November. The move is not surprising, as SpaceX now owns Cursor and there is a massive feud between OpenAI’s leadership and Elon Musk. OpenAI said in a statement it cannot trust any of Musk’s companies to honour its terms of service.

Anthropic and Nscale strike $45 billion cloud deal
Anthropic and Nscale, a UK-based AI infrastructure company, signed a $45 billion cloud deal that gives Anthropic access to 460 megawatts of compute capacity at an Nscale data centre development in West Virginia. The facility is expected to come online at the end of 2027 and will be equipped with Nvidia’s latest Vera Rubin chips.

Anthropic gets its first court win over the Pentagon’s supply-chain risk label
A federal judge has ruled that the Trump administration acted illegally when it barred federal agencies from working with Anthropic. Judge Rita Lin found that the supply-chain risk designation was retaliation for the company’s criticism of the government and violated the First Amendment. The dispute began when Anthropic refused to let the Pentagon use its models for autonomous weapons or mass surveillance. Lin noted that the government continued pursuing contracts with Anthropic even after branding it a threat. A second lawsuit in Washington, D.C., is still ongoing.

Google Moves AI-Responsibility Team Out of DeepMind Lab in Latest Shake-Up
The Wall Street Journal reports that Google is moving the team focused on the risks and societal impact of AI out of DeepMind. The roughly 90-person “AI responsibility” unit will join Google’s global affairs organisation, which oversees lobbying and public policy for the company as a whole. Some employees worry the move will erode their independence and cut them off from the teams building Gemini. Google says grouping the teams will strengthen their work. The change follows a wider absorption of DeepMind into Google after Demis Hassabis stepped aside as chief executive.

OpenAI’s Head of Data Centers Has Left the Company
Chris Malone, an executive overseeing OpenAI’s data centre buildout, has left the company. Malone joins a growing list of OpenAI executives who have left the company, which currently includes Chief Revenue Officer Denise Dresser, Chief Operating Officer Brad Lightcap, and Fidji Simo, who served as second-in-command to Sam Altman.

Anthropic Taps Google Veteran as Part of Hardware Push
Amir Salek, an engineer who founded and led Google’s TPU project until 2022, has joined Anthropic. At Google, Salek laid the groundwork for the TPU and delivered the first seven generations of those chips. Now Anthropic wants him to do the same, as the company recently announced that it is building an in-house team to design custom AI chips for Claude and has begun hiring engineers to join the team.

Thinking Machines Lab Co-Founder Barret Zoph Joins Google
Google has hired Barret Zoph to bring his reinforcement learning and post-training expertise to Gemini. Previously, Zoph worked at OpenAI, which he left to co-found Thinking Machines Lab. He then left Thinking Machines in January this year after a dispute with Mira Murati to rejoin OpenAI, and now he has come full circle by returning to Google, where he worked as a researcher until 2022. Zoph joins the Google DeepMind team at a time when Google is reorganising its AI efforts.

▶️ Dylan Patel – Two labs will soon control most of the world’s workforce (1:16:52)

In this podcast, Dwarkesh Patel and Dylan Patel discuss the economics of the AI buildout over the next few years, whether the more than $10 trillion in total AI capex expected by the end of the decade could trigger a sovereign debt crisis, and more. The conversation also explores how Anthropic and OpenAI are on track to control most of the world’s usable FLOPs within the next few years, and how the economics could change if recursive self-improvement (RSI) kicks in or AGI is achieved. As always, Dylan Patel brings plenty of numbers and high-quality insights to the table.

Google reportedly taps AMD to design next-generation TPU — hybrid AI ASIC could integrate on-package CPU cores for reinforcement learning
Google is reportedly working with AMD on a version of its 10th-generation TPU that would use AMD’s CPUs. Google and its customers want general-purpose cores sitting in the same package as the accelerator, which suits reinforcement learning and agentic work better than accelerators alone. AMD already builds that kind of hybrid chip in its MI300A.

Inherent, founded by DeepMind alumni, says its AI ‘teammate’ just outperformed Anthropic and OpenAI at replicating research
Inherent, a London-based AI lab founded by Google DeepMind alumni, says its new agent Faraday has beaten frontier models from Anthropic and OpenAI at reproducing published scientific findings unaided. Faraday runs on a far smaller model with just 27 billion parameters. Inherent trained it using reinforcement learning, rewarding good outcomes rather than teaching it how science is done. The bet is that this instils a sense of which experiments are worth running.

Perplexity Portable Computer
Despite its name, Portable Computer is not a hardware product but a local-first agent stack from Perplexity, built with Nvidia and running on the DGX Spark (which you need to supply yourself). The stack includes an agent harness, an orchestrator, and post-trained models that run most Perplexity Computer tasks entirely on local hardware. When a job needs the web, connected apps or frontier reasoning, the orchestrator can escalate to one of 15+ cloud models, but only after asking the user's permission. Portable Computer is available to Pro and Max subscribers.

Thomson Reuters Leverages its World-Class Data Assets to Launch Its Own Frontier Model
I did not expect Thomson Reuters to create its own large language model, but here we are. The company says it used an unnamed but strong open-source foundation model as a base and then trained it using Thomson Reuters data. The entire process, including talent and compute, cost $40 million. As Thomson Reuters notes in the press release, it did this to maintain control over its data and AI models, an approach that more companies might follow.

Inside OpenAI’s Reboot
TIME Magazine interviews Sam Altman and other OpenAI leaders and takes us inside the company as it reinvents itself. Having lost the lead to Anthropic and shed a string of senior executives, OpenAI has narrowed its focus under Greg Brockman, who now runs most product and business operations, winding down side projects and folding Codex into ChatGPT. It is also reckoning with its worst safety failure yet—the Hugging Face incident—by pausing the training run expected to deliver its biggest capability jump. Amid all of this, OpenAI's leaders say AGI is close, with Altman expecting an internal system he would call AGI by the end of the year.

The turbulent AI era is here. The choices we make now are critical.
Bill Gates joins other tech billionaires in publishing his own AI manifesto. He argues that the technology will be either the greatest equaliser ever invented or the worst source of injustice. Nobody, he says, is preparing seriously for that choice. Gates flags three big risks: jobs disappearing across white- and blue-collar work within a decade, cheap tools for criminals and bioterrorists, and AI companions stunting children’s development. His proposed fixes include new national and international institutions, reserving certain jobs for humans, and taxes on AI tokens and robots.

World’s first patient to undergo live AI-assisted brain surgery has tumour removed
Surgeons in London have removed a brain tumour with live AI guidance for the first time. A tool built at UCL monitored the operation’s video feed and helped surgeons locate the tumour and remove it safely. The surgeons remained in full control of the procedure. The patient recovered successfully, and a larger trial will now test the system.

GLM-5.3-Flash: Frontier Intelligence, Flash Cost
Z.ai presents GLM-5.3-Flash, the latest addition to the GLM-5 family of models. The company says that this 320-billion-parameter model approaches the performance of Claude Opus 4.8 on coding and agentic benchmarks while costing only a tenth as much. Artificial Analysis confirms those claims, giving GLM-5.3-Flash a score of 57 on its Intelligence Index (on par with Meta’s Muse Spark 1.2 and Gemini 3.7 Flash) and a very competitive performance-to-price score.

Z.ai released GLM-5.3-Flash earlier under the codename Ox Alpha, sparking curiosity online about who was behind the mysterious frontier-level model. Ox Alpha reached 327,000 users and 8.3 million sessions in four days and processed 26 trillion tokens. Z.ai said that all of that traffic was served on Chinese AI hardware. GLM-5.3-Flash is available on Z.ai’s platform and on Hugging Face for download.

GLM-5.3 weights are now on Hugging Face
Alongside GLM-5.3-Flash, Z.ai also released the weights for GLM-5.3, its flagship model. The company claims that this 743B-parameter model matches the performance of top frontier models, and Artificial Analysis has largely confirmed those claims. I took a closer look at GLM-5.3 in Sync #584 if you want more details about the model.

Qwen3.8-Flash-Next: A New Architecture, Towards Ultimate Cost-Efficiency
The team behind Qwen released Qwen3.8-Flash-Next, a new model in the Qwen3.8 family that also serves as an early preview of the architecture used in Qwen4. According to the release, this 125B-parameter model cuts training costs to roughly a ninth of Qwen3.7-Plus while delivering better performance on coding and office tasks. Artificial Analysis placed Qwen3.8-Flash-Next on par with Gemini 3.7 Flash and slightly below the recently released GLM-5.3-Flash, while finding it very cost-effective. Qwen3.8-Flash-Next can be downloaded from Hugging Face.

Gemini Omni 1.1 Flash lets you build with more control
Google has updated Gemini Omni, the video generation model it unveiled at I/O 2026, with new controls aimed at professional work. Omni 1.1 Flash can now extend an existing clip in 10-second steps, up to 40 seconds in total. It reads up to 10 seconds of prior footage to keep characters and scenes consistent across the transition. Developers can also draft cheaply in 360p before upscaling the final version to 4K.

Muse Image
Meta has launched Muse Image, its first in-house image generation and editing model. According to the release, rather than turning a prompt straight into pixels, Muse Image searches the web, writes code and critiques its own drafts before settling on a picture. Anyone can use it for free in the Meta AI app, Instagram Stories and WhatsApp. Facebook and advertiser tools follow later.

Australia Bans Generative A.I. From Official Music Charts
The Australian Recording Industry Association has introduced a new rule that bans songs that are not “substantially human-made” from music charts. AI-generated recordings will also not qualify for the ARIA Awards—the country’s top music industry prize. The new rules, however, still allow some use of AI in songs. For example, AI backing vocals are allowed, but an AI-generated lead vocal or key instrumental performance would disqualify a song. Some artists have welcomed the change, while others hope it will open up a broader conversation about the place of AI in music and art in general.

🤖 Robotics

London rollout of robotaxis delayed amid lack of guidance for firms to follow
The rollout of robotaxis on the streets of London was supposed to happen this year, but that now looks unlikely. Transport for London still hasn't published the guidance companies must follow to win its approval, and says it's in no rush. No operator has even registered vehicles for the driverless permit the government requires. Uber and Wayve can carry paying passengers in the meantime, but only with a safety driver at the wheel.

Waymo robotaxis are headed to Munich
Waymo is coming to Munich, its first German city. As in its other cities, it starts by driving the cars manually to map the streets. Testing with safety drivers comes next, then fully driverless rides. Waymo holds no German permit yet, so approval from federal and local regulators sets the pace. The company expects to open a paid public service late in 2027.

Amazon’s Toaster-Shaped Robotaxis Are Hitting the Road
Amazon’s Zoox has started charging for driverless rides in Las Vegas and rolling out in San Francisco. Unlike other companies, which still use traditional cars with extra equipment, Zoox uses purpose-built all-electric cars with no steering wheel or brake pedals. The National Highway Transportation Safety Administration cleared the company to charge for rides across up to 5,000 vehicles over the next two years. But they barred the cars from operating in heavy rain, snow and on roads with limits above 45mph (72 km/h). Zoox runs roughly 100 cars today, against about 4,000 for Alphabet’s Waymo.

The Humanoids at China’s Robot Games Were Faster Than Usain Bolt—but I’m More Impressed by Their Tweezer Mastery
Over 600 teams and more than 2,000 robots took part in this year’s World Humanoid Robot Games in Beijing. The biggest headlines came from robots running 100 metres faster than Usain Bolt and jumping higher than any human can. Robots also competed in ping-pong, football, weightlifting, and more, alongside practical challenges testing manual dexterity, such as “block building” and “tweezer bean picking”. Overall, the games offered a comprehensive look at what Chinese humanoid robots can do and their potential industrial applications. But, as Will Knight writes in this article, perhaps their biggest purpose is to get the public excited about the potential of robotics.

Hugging Face is selling a cute $399 open source duck robot, Microduck

Microduck is a cute robot duck from Hugging Face and Pollen Robotics. It is a 25 cm-tall bipedal robot that can waddle, pick things up with its beak, get back up when it falls, crouch, and even roller skate. Pre-orders for Microduck are now open, with the robot priced at $399 and expected to ship before Christmas. Hugging Face has open-sourced the AI controlling the robot and the RL training environment, both available on GitHub. The CAD files and BOM, however, do not appear to be publicly available. But if you are interested in building your own robot duck, check out Open Mini Duck, an open-source robotics project from one of Pollen Robotics’ engineers.

Anthropic: Previewing the Model Hardware Standard

Anthropic has introduced a research preview of Model Hardware Standard (MHS), a specification that lets AI agents operate lab and factory hardware such as microscopes and robotic arms. MHS replaces the patchwork of custom code used to control lab equipment with a standardised layer for AI agents, cutting setup time and allowing scientists to focus on their experiments. Anthropic admits that Claude’s physical reasoning still requires expert oversight and plans to open-source the standard only after developing safety evaluations with partners.

NVIDIA Announces Jetson Orin Nano 2 Robotics Computer to Redefine Entry-Level Edge AI
Nvidia has announced the Jetson Orin Nano 2, an updated version of its small computer for edge AI and robotics. The new Jetson Orin Nano still comes with 8 GB of memory but has a new eight-core ARM CPU and delivers 78 TOPS of AI compute, up from 67 TOPS. Nvidia says the Jetson Orin Nano 2 achieves twice the inference performance of the Jetson Orin Nano Super through improved Tensor Cores and higher memory bandwidth, while maintaining the same compact form factor. In 15-watt mode, the Jetson Orin Nano 2 consumes 40% less power while delivering the same performance as its predecessor. The Jetson Orin Nano 2 module and developer kit are expected to be available in the first half of 2027. Nvidia did not disclose pricing.

Arduino VENTUNO Q
Qualcomm (which owns Arduino) is taking on Nvidia in edge and robotics AI with the Arduino VENTUNO Q, a small but powerful computer designed to compete with Nvidia’s Jetson family of products. The Arduino VENTUNO Q offers 16 GB of LPDDR5 RAM, eight CPU cores, and up to 40 dense TOPS of AI performance. What sets the VENTUNO Q apart is that it has two chips (or “brains”, as Qualcomm calls them): the Dragonwing IQ-8275 processor (the “AI Brain”), which combines a CPU, GPU, and NPU, and the STM32H5F5 microcontroller (the “Action Brain”), which enables sub-millisecond response times—an attractive feature for industrial and robotics applications. Qualcomm sees the new device as an ideal computer for local and edge AI, as well as for robotics and industrial applications. The board is currently available for pre-order and costs $299.

Generalist GEN-1.5

Generalist AI says its new model, GEN-1.5, can learn a physical task from a single demonstration lasting just a few seconds, with no retraining. The company reports an average success rate of 59% across ten tasks using this approach, rising to 83% after a few minutes of fine-tuning. It says this ability emerged on its own after eight months of pretraining on real-world interaction data. The tasks are short and simple, and the results are self-reported. I recommend checking out the video Generalist uploaded alongside the announcement, as what they are showing is quite impressive.

Introducing S1: In-Context Learning for Robotics
Skild AI has unveiled S1, a robotics foundation model that learns a new task from a single video demonstration. The company says it needs no fine-tuning at all. Its own tests report a 66% success rate on tasks never seen in training, against 9% for language-prompted rivals. Skild claims S1 can handle ten-minute jobs it was never trained on, such as brewing coffee or cooking pancakes. Normally, this would take hours of teleoperation first.

Introducing Index: Building The World’s Largest and Most Diverse Physical Dataset
Figure presents Index, a dataset for robot training that the company has been building in secret for the past four months. Through a dedicated app, more than 44,000 weekly active users (which Figure calls Creators) have recorded over 16 million videos of themselves performing various chores. Figure says it has paid out $15 million to Creators who have contributed to the dataset so far. The Index app is now available for Android and iOS. Videos uploaded to Index will be used to train Helix and help solve the training data problem for humanoid robots.

How US military funding propelled China’s robot dogs
This article argues that Unitree built its best-selling robot dogs on quadruped breakthroughs funded by the US Army. It cites researchers who worked on the project, who say Unitree’s Go series matches MIT’s Mini Cheetah almost to the millimetre. Nothing underhanded happened, as the findings were published openly to push the field forward. Unitree's advantage was manufacturing, backed by state subsidies and a dense supplier cluster in Hangzhou. The researchers argue that faster commercialisation, rather than secrecy, is the best way to counter China’s scale advantages.

China’s robot traffic police can do almost everything except stop you
Traffic police in Hangzhou, China, have deployed 15 humanoid robots to direct pedestrians, manage traffic flow, and answer tourists’ questions. When the robots spot a violation, they file a report for a human officer to act on, as Chinese law reserves coercive powers for officers rather than equipment.

▶️ Unitree New Robot Preview: “Superman” Breaking the Limits of Humanity (0:30)

Unitree is back with another video showcasing the athletic capabilities of its humanoid robots. This time, the company showed its robots reaching 2 metres in a standing high jump and sprinting at a top speed of 12.66 m/s. For comparison, Usain Bolt’s top speed at his peak was 12.42 m/s.

▶️ Inside Persona AI’s Plan to Make Humanoid Robots Profitable (46:16)

In this episode of Automated Podcast, Brian Heater speaks with Nic Radford, co-founder and CEO of Persona AI and a former NASA roboticist. The conversation revolves around Radford’s and Persona’s goal of making humanoid robots practical. They focus on robots that can do real work, not just backflips. Radford shares his approach to the business and economics of humanoid robotics, and how Persona works to put robots to work and start delivering value. Radford also discusses the original vision for Robonaut and Valkyrie, the value of government-funded moonshots, DARPA’s role in modern robotics, and lessons humanoid companies can learn from the development of self-driving technology.

🧬 Biotechnology

A startup trains AI on living human skin tissue
TechCrunch profiles Michael Polansky, a former Founders Fund principal who ran Sean Parker’s family office before becoming Lady Gaga’s business and romantic partner. He is now going public with Outer Biosciences, a startup he has quietly led since 2020. The company keeps skin left over from surgery alive for up to a month, compared with an industry norm of just a few days. That window lets an AI model predict which compounds will work, test them on the tissue, and then learn from the results. The approach has taken the company from finding two promising compounds in 18 months to finding a new one every six weeks. Polansky says the data is finally good enough for the company to work in the open.

Mini Brains Grown for Five Years Matured Like Human Brains
Researchers managed to keep lab-grown brain organoids alive for over five years, far longer than the few months they usually survive. The trick was swapping their nutrient mix midway through, which stopped the neurons from withering. By the end, gene activity in the oldest blobs resembled that of a typical four-year-old. The cells even seemed to track their own age, maturing on schedule when mixed with much younger ones. Until now, mini brains died long before reaching the stage when disorders like schizophrenia and epilepsy take hold. This result opens up the possibility of studying those and other disorders more closely.

💡Tangents

Meta agrees to major changes to Facebook and Instagram as it settles US trial over teen addiction for up to $17bn
Meta has settled a landmark lawsuit brought by California and 28 other US states, and agreed to pay up to $18 billion over ten years. The states accused the company of deliberately designing Instagram and Facebook to hook children and damage their mental health. Meta denies wrongdoing, but will still cap teenagers’ daily use, block the apps overnight and pause notifications during school hours. That makes it the first time the company has been forced to change how everyday users experience its apps. Thousands of similar claims against Meta, TikTok, Snap and YouTube are still pending.

Apple Mac Studio M5 Ultra and Mac Mini M6 Launched
Apple introduced new Macs this week—the Mac Mini M5 Pro, Mac Studio M5 Max, and Mac Studio M5 Ultra, as well as Mac Minis with new M6 processors. On paper, they look very good. Apple claims the M6 delivers 1.2x faster multithreaded CPU performance than the M5, nearly 30% more peak GPU AI compute, and 10% more memory bandwidth. The M5 Ultra, with a maximum unified memory pool of 512 GB (although only versions with 256 GB are currently available), is shaping up to be a small but capable machine for local AI. What’s not small are the prices, especially for higher-spec machines.

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This week, we take a closer look at the reports about the financial situations of both Anthropic and OpenAI, and what they actually say.

Elsewhere in AI, OpenAI signs a lease for a 10 GW data centre in Ohio, overhauls its safety protocols, launches ChatGPT for Teens, and says California should strengthen its AI safety bill. Meanwhile, Cursor launches an AI-first competitor to GitHub, Stripe acquires OpenRouter, Jeff Dean’s startup is in talks to raise $1 billion, and Etched doubles its valuation to $21 billion in a month.

Over in robotics, Tesla is preparing to launch Cybercab, AMD challenges Nvidia in chips for robots, Amazon Prime Air expands to nearly 500 US cities and towns, and a humanoid robot runs 100m faster than Usain Bolt.

Other than that, this week’s issue also covers a breakthrough in personalised mRNA cancer vaccines, the US government opposing Apple’s use of Chinese memory chips, an important trial against Meta over claims of child social media addiction getting under way, and more!

Enjoy!

Anthropic overtakes OpenAI

Leaked figures put Anthropic's sales ahead of OpenAI for the first time, weeks before it could file for a record IPO

As both Anthropic and OpenAI prepare for what could be some of the largest IPOs in history, more and more information about their financial situation is leaking to the public. If you read those leaks and reports side by side, you would get pictures of two different companies—one that seems to be on the rise, while the other stumbles. Let’s look at what each picture actually says, and then at how much of it we should believe.

Let’s start with Anthropic. According to leaked documents seen by Bloomberg and The Wall Street Journal, its revenue grew 14x in a single year. In Q2, the company booked more than $11.5 billion, against $787 million in the same quarter of 2025 and $4.73 billion in Q1 of this year. Meanwhile, its run rate hit $65 billion at the end of July, up around sevenfold from about $9 billion late last year (run rate takes recent performance, often a single month, and projects it across a full year). Anthropic has also reportedly overtaken OpenAI on sales, and was on track for a first quarterly operating profit of $559 million while doing it, telling investors it had got better at using computing power efficiently.

It is no wonder, then, that Anthropic is pushing ahead towards its IPO with such good numbers. Anthropic could file publicly within weeks and list as soon as this autumn, ahead of both OpenAI and DeepSeek. The company expects its IPO to match or even surpass SpaceX's, which raised $75 billion at a massive $1.77 trillion valuation. Some investors and analysts say Anthropic can enter the public market as a $2 trillion company.

To get anywhere near $2 trillion, however, Wall Street is being asked to look unusually far ahead. Anthropic is projecting revenue of $190 billion to $200 billion in 2028, and bankers are pricing the company off that forecast rather than off what it earns today. This is not how companies are normally valued. It happens when current accounts are dominated by spending that everyone expects to shrink as a share of revenue, and it only works if the growth holds.

There is a hole in the picture, too. Anthropic’s small quarterly profit sits alongside a net loss of almost $42 billion in 2025, roughly five times the year before, and the company has not explained how it reached the adjusted figure. In past communications with investors, it has left out stock-based compensation.

Meanwhile, OpenAI has troubles of its own. The company told investors it made $6.7 billion in the same quarter, up 18% from $5.7 billion in the first. Its operating loss, which does include stock-based compensation (which Anthropic is reportedly leaving out), widened from $9.3 billion to $12.3 billion.

The problem is not the size of that number but the expectations attached to it. OpenAI has signed computing deals premised on soon earning hundreds of billions a year, and the results of Nvidia, Oracle and others now depend on it delivering. Measured against that, 18% is a stumble. OpenAI grew more slowly over the quarter than Palantir did, and it trailed CoreWeave and Micron too. Anthropic, for comparison, more than doubled its revenue quarter on quarter.

Unlike Anthropic, OpenAI seems to be experiencing headwinds and might not go public this year, a possibility The New York Times first reported in June. Sarah Friar, OpenAI CFO, told staff the company will list in 2027 or sooner if growth picks up. She also told employees not to worry about Anthropic's IPO, and reassured them by pointing to a run rate up 35% so far this quarter, enterprise revenue up 50%, and a coding product with 20 million weekly users.

But remember where all of this came from—leaks. Every figure above reached us through a leak, and some of them might be intentional. A company that is on its way to IPO has good reasons to let flattering numbers find their way into print.

The contest underneath is also less settled than the leaks suggest. Ramp’s AI Index, which tracks spending by more than 70,000 American businesses, put Anthropic ahead on market share in July, at nearly 44% to OpenAI’s 40%. But the new data shows OpenAI is growing faster among those businesses so far in Q3. A lot can still change. Corporate customers switch to whichever model best fits their needs, and neither company has locked in a lead. Let's also not forget the open models coming out of China, which are closing the performance gap with the best that Anthropic and OpenAI offer, and at a lower price.

We will get the full picture of how financially sound Anthropic’s and OpenAI’s businesses are when the prospectuses are unsealed.

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News roundup

🧠 Artificial Intelligence

OpenAI Locks In Lease for Huge Data Center in Ohio With Backing From Nvidia
OpenAI has signed a 20-year lease for a 10-gigawatt data centre campus in southern Ohio, one of the largest such projects to date. The data centre will be built by SoftBank’s SB Energy, with Nvidia providing the chips and guaranteeing the value of the completed site, up to $105 billion, if OpenAI walks away. Initially, as The Wall Street Journal reports, Nvidia was supposed to backstop the entire $250 billion project, but scaled back its commitment to address investor concerns about risk. The guarantee gives lenders the confidence to finance construction. Nvidia gets plenty in return: exclusive chip supply for the first five-gigawatt phase, plus $1.5 billion in equity in SB Energy.

Cursor Origin
Coding agents need coding tools designed for them, and Cursor is delivering one in the form of Origin, an agent-first code repository. For now, Origin offers the essentials—repositories, pull requests, code browsing and GitHub sync—with agent-native features coming soon. Cursor chose the perfect time to announce Origin, unveiling it on the same day that GitHub, the service it aims to replace, was dealing with yet another major outage. Origin is available in early beta on all paid plans.

OpenRouter is Joining Stripe
Stripe has confirmed that it is acquiring OpenRouter, an AI model marketplace and gateway, in a deal reported to be worth around $7.5 billion. Stripe is paying almost six times the $1.3 billion valuation OpenRouter secured just three months ago. Both sides insist that OpenRouter will continue to operate independently, with its product, mission and neutrality intact, once the deal closes in the coming weeks.

Anthropic sees AI risks rising, no plan to release stronger “Model 2”
Anthropic is reportedly developing an internal model called Model 2, which is said to perform noticeably better than its flagship Mythos on many internal tasks, but the company has no plans to release it. The disclosure came in a risk report that also raised Anthropic’s estimate of misalignment risk in high-stakes situations from “very low” to “low”, citing recent cybersecurity incidents. Anthropic also says it is less confident in its risk assessments than before because some of its evaluations have saturated and no longer capture how quickly model capabilities are improving.

OpenAI Overhauls Safety Protocols After Its AI Agents Went Rogue
OpenAI has paused a significant number of training runs and evaluations for Astra, its next frontier model, while it tightens security after internal tests showed Astra outperforming its predecessors at coding and hacking tasks. New safeguards include stronger sandboxes, stricter internet isolation and classifiers that monitor the model’s internal reasoning for signs of trouble.

Departing Google chief scientist Jeff Dean has been in talks for a $10 billion valuation for his new AI startup
Business Insider reports that Discovery Loop, an AI startup founded by legendary Google engineer and former Chief Scientist Jeff Dean, is in discussions to raise $1 billion at a valuation of around $10 billion. Discovery Loop, founded by Dean and three other Google veterans who recently left the company, is pursuing recursive self-improvement—AI that improves itself with little or no human help—and plans to apply it to hardware design, drug discovery, materials science and scientific research.

Nvidia discloses $21 billion stake in SpaceX at end of second quarter
According to a Securities and Exchange Commission filing, Nvidia owns 122.8 million Class A shares in SpaceX worth about $21 billion. It is Nvidia’s second-largest holding, behind only its stake in Intel, which is currently worth about $22 billion. CNBC reports that Nvidia’s stake in SpaceX stems from a $10 billion investment in xAI in January.

Anthropic IPO filing will show AI backlash as a risk factor
CNBC reports that Anthropic plans to name public anger over data centres as a key risk in its IPO prospectus. A Gallup poll in May found seven in ten Americans oppose data centres near them, and politicians in both parties are now running against new builds ahead of the midterms. Since compute capacity drives revenue at AI labs, a slowdown in construction would hit Anthropic's growth directly.

Anthropic prepares supervoting power for founders ahead of IPO
Anthropic is reportedly preparing to give its founders shares with extra voting power to help insulate them from external shareholder pressure. The founders own unusually little of the company today, with Dario Amodei himself said to hold about 2%. The company is also planning to maintain its existing body of non-shareholder trustees with a special class of ​stock to elect a majority of members to the company's ​board of directors.

OpenAI says California should strengthen its AI safety bill
OpenAI now wants California to toughen SB 53, the AI safety law it opposed last year. It says the state should require monitoring of frontier models during training and evaluation, plus stronger cybersecurity across model development.

Slack Code: Where Your Team and Agents Build Together
Slack has launched Slack Code, which brings collaboration with AI coding agents into shared “code channels”—dedicated spaces where humans and AI agents can work together. Slack Code launches with Anthropic’s Claude, Cognition’s Devin, GitHub Copilot and Vercel, with OpenAI’s ChatGPT coming soon. It is pitched as the first step towards a more “multiplayer” model of agent-assisted work that will eventually extend beyond engineering to areas such as marketing, legal and IT.

ChatGPT update adds Apple Messages integration on Mac
OpenAI has introduced a new feature to the ChatGPT Mac app that allows the AI to read, search and send iMessage, SMS and RCS messages through Apple’s Messages app. The feature uses a new Apple Messages plugin and is available on all plans in the ChatGPT desktop app for macOS. By default, no messages are sent until you approve both the message and its recipients. OpenAI warns that granting standing approval carries real risks.

Ramp launches its own AI model router, called Router
Ramp has launched Router, a service that lets customers choose models from eight providers, including OpenAI, Anthropic and DeepSeek, through a single API. Customers can route requests based on cost, benchmark scores or difficulty, then track spending and latency on a dashboard. Ramp’s Router is currently available only to US customers. It's free until the end of the year, though customers still pay for inference.

Researchers say OpenAI revoked their access to limited cyber program
Several cybersecurity researchers said that OpenAI revoked their access to Trusted Access for Cyber, a programme that gives vetted defenders access to its best models with looser security guardrails. Those affected were told that their identities couldn’t be verified or that their accounts were ineligible. TechCrunch found that the researchers it spoke to who were affected by the problem live outside the US and Europe, suggesting that the revocations may be limited to certain regions. OpenAI blamed a technical error on its end and asked them to reapply and complete the verification process.

OpenAI launches a safer ChatGPT for teens — years after teens started using it
OpenAI has launched ChatGPT for Teens, a version of its chatbot with additional safety measures and educational tools, including a new Study Mode designed to encourage teens to explore their curiosity and work through problems rather than simply get quick answers. It also comes with parental tools for managing settings, receiving safety notifications and setting Quiet Hours, among other features. OpenAI says age-appropriate protections designed to reduce exposure to harmful or developmentally inappropriate content will be enabled by default in ChatGPT for Teens.

Bringing the cybersecurity capabilities of Claude Mythos 5 to more defenders
Anthropic is expanding access to Claude Mythos 5, its most capable model, to more partners without exposing the model directly. Mythos 5 now powers Claude Security scans for Enterprise customers and is being integrated into partners’ security tools, where users receive only specific outputs such as suggested patches rather than open access to the model. Alongside this, a new Defender Advantage Fund offers $35 million in credits to help secure open-source projects, and the Cyber Verification Program will expand to cover broader dual-use capabilities on Opus and Sonnet, with Mythos-class access to follow.

Etched’s valuation doubles to $21B in a month
Etched, an AI chip startup founded in 2022, has raised $700 million at a $21 billion valuation, twice what it was worth in July. In a post announcing the fundraise, Etched revealed that Jane Street received one of its racks for testing and was so pleased with the results that it led the funding round. Etched sells complete systems optimised for inference, which the company claims produce answers faster and more cheaply than the competition. If you want to learn more about Etched, I recommend this video from Dr Ian Cutress on his YouTube channel, TechTechPotato.

Cerebras CS-4
Cerebras has launched CS-4, the fourth generation of their AI accelerator. The company claims that CS-4 delivers up to 30 times faster inference than GPU systems, better economics, and a simple path to deploy hyperscale capacity. CS-4 reduces wafer-to-wafer interconnect latency to as low as 2 microseconds, and can deliver more than 1,000 tokens per second on models exceeding 10 trillion parameters. All of that performance comes in one rack, which Cerebras says can be quickly manufactured, installed, expanded, and upgraded.

GLM-5.3 on Artificial Analysis
Last week, Z.ai released its latest flagship model, GLM-5.3, one of eight new models released that week. When I was preparing last week’s issue, we did not have independent evaluations to compare against Z.ai’s claims. Now we have. Artificial Analysis places GLM-5.3 as one of the leading models in its Intelligence Index and at the level of top US models, while being cheaper and faster. However, the benchmarks also found GLM-5.3 to be very verbose, which can affect the final cost.

Introducing Harvey II
Harvey has launched Harvey II, its latest AI agent focused on legal work. According to the company, the new agents can now open inside a “Space” for a given matter, inheriting its documents, parties, permissions and history. A new memory layer learns each lawyer’s drafting style and citation preferences across Harvey, Word and Outlook. Harvey has also built Tenet, its first model post-trained for legal reasoning, which the company claims matches the strongest general-purpose models at far lower cost.

Mistral Agentic Search
Mistral has launched Agentic Search, a retrieval layer that lets AI models dig through documents efficiently. The model gets five file-like tools: search, open, navigate, read and grep. It can refine a query, open a filing and check a specific table before answering. Mistral’s own benchmarks claim accuracy on financial filings jumped from 26.7% to 86% with Agentic Search, while using fewer tokens.

AI Chip Startup Fractile Seeks $6.5 Billion Value After Anthropic Deal
Bloomberg reports that Fractile, a UK AI chip startup, is in talks to raise about $600 million at a $6.5 billion valuation, six times more than its value in May. The company has reportedly signed a $250 million deal with Anthropic, with the intention to expand that contract in the future. The first chips are expected to be delivered in 2027.

SpaceX Attempted to Acquire AI Coding Startup Cognition
Before acquiring Cursor for $60 billion, SpaceX was reportedly eyeing Cognition, the startup behind the coding agent Devin. Cognition didn’t engage with the takeover approach, but the two companies are now discussing ways to work together, with Cognition potentially using SpaceX’s computing capacity.

🤖 Robotics

Tesla prepares for Cybercab launch in August starting with rides to employees
The Information reports that Tesla is preparing to launch Cybercab, its autonomous robotaxis without pedals or a ​steering wheel, later this month. The rollout will begin in Austin, Texas, with rides on public roads being initially offered to Tesla employees. The company started running tests of the production version of its Cybercab on public ⁠roads ​in June, and production of ​the vehicle is expected to ramp up later this year.

AMD Challenges GPU-Centric Architectures as It Takes Aim at Nvidia in Robotics
AMD challenges Nvidia in robotics with the Ryzen AI X100, a small computer combining a Zen 5 CPU, RDNA 3.5 GPU and XDNA 2 NPU with unified memory. AMD is also pitching openness as a differentiator—its new robotics board uses an industry-standard format rather than a proprietary one, and the company is publishing the design files, along with a ROCm-based robotics SDK.

Amazon Prime Air drone delivery is expanding to nearly 500 US cities and towns this year
Amazon announced that Prime Air, its drone delivery service, is expanding to nearly 500 cities and towns in the US this year. The service currently operates from 11 sites across ten metro areas in seven states, with launches planned soon in Illinois, New York, Ohio, Georgia and Idaho. Additionally, Amazon plans its first international site in Darlington, England. Prime Air drones can carry items under five pounds (about 2.27 kg) that fit in a large shoebox (Amazon says this covers more than 60% of most commonly ordered products) and deliver the orders in as little as 30 minutes, though most take around an hour.

Uber adds Zipline drones to its Eats delivery network
Your next Uber Eats order could be delivered by one of Zipline’s drones (if you live in one of the US cities where Zipline operates). This is thanks to a partnership between the two companies announced this week. Zipline will begin delivering Uber Eats orders by the end of the year, with the goal of making one million deliveries per day by the end of 2029. Uber is also investing in Zipline, although the amount has not been disclosed.

Chinese robot runs 100m sprint quicker than Usain Bolt’s world record

A humanoid robot has completed a 100m sprint in 9.32 seconds, beating Usain Bolt’s world record of 9.58 seconds, set 17 years ago. The robot, named Lightning and developed by Chinese smartphone manufacturer Honor, reached a peak speed of 14.5 metres per second.

Unitree Sees Robots Reaching ‘Mass Market’ Within Decade
Speaking at the World Robot Conference in Beijing, Wang Xingxing, founder of Unitree, said that humanoid robots will reach “mass-market” popularity within a decade, once manufacturers solve the problems limiting their current usefulness. The main challenge, Wang said, is that robots perform well in test conditions but struggle in messy, real-world scenarios. He said that once this problem is solved and robots can handle 80% of tasks given to them via voice instructions in unfamiliar environments, the industry will reach “mass-market” popularity. Wang expects this to happen within two to ten years.

Waymo lifts the lid on the ‘brain’ powering its robotaxis
Waymo has revealed the compute system in the trunks of its robotaxis for the first time. As the company writes in a blog post, the system is built around three principles: responsiveness (fully onboard, millisecond-level decisions, with compute power scaling twentyfold in eight years), ruggedisation and redundancy (two parallel units, either of which can take over). At its core is a custom 5nm ASIC delivering more than 1,000 TOPS and fusing raw lidar, radar and camera data at the front end. This article from The Verge argues that Waymo’s post is an implicit rebuttal to Elon Musk’s dismissal of lidar and puts the cost of the hardware at an estimated $20,000–25,000 per vehicle—down sharply from the previous generation, but still a drag on profitability.

Is Shipyard Welding the Right First Job for Humanoid Robots?
While almost everyone is betting on humanoid robots working in warehouses or in our homes, Persona AI imagines its robots welding and building ships. Founded by veterans in humanoid robotics, Persona is targeting much more expensive skilled labour, and welding is one such skill. Persona is starting with the long, straight seams that shipyards need by the hundreds of kilometres per vessel, and it argues that the value lies not in replacing workers at labour-starved yards, but in enabling shipyards to build more ships.

What happens when a kid’s robot best friend dies?
This article tells the story of Moxie, an AI-powered companion robot for children, and what happens when a robot designed to form connections dies. It explores the potential of such robots to help neurodivergent children practise social skills and examines what happens when the company behind such a robot folds and shuts down its servers.

Drones With Claws Perch on Arctic Icebergs

Ice Dart is a drone equipped with spikes that allow it to land on and latch onto icebergs and glaciers. Thanks to its custom landing gear, the drone can survive landings that would damage a traditional drone and grip icy slopes of nearly 60 degrees. According to the researchers, Ice Dart could serve as a mobile platform for monitoring and tracking iceberg movements, providing an additional layer of data to complement satellite- and ship-based iceberg detection.

🧬 Biotechnology

Vaccine breakthrough stops cancer returning in trial
Merck and Moderna announced that their personalised mRNA cancer vaccine, intismeran, combined with the immunotherapy drug Keytruda, significantly extended the time that high-risk melanoma patients remained cancer-free and reduced the risk of the cancer spreading. The vaccine is made for each patient by studying their own tumour, so the immune system learns to hunt down any remaining cancer cells. Experts have welcomed the news but stress the results are early, have not yet been independently checked, and the treatment still needs approval before it could reach patients.

GenBio Launches a “Virtual Cell” AI Model
GenBio, a startup cofounded by David Baker, a 2024 Nobel Prize laureate for his work on computational protein design, has unveiled AIDO Cell—a “world model” for cells that can simulate a human cell in its normal state and how it can react to drugs. In an early test, it correctly reproduced a known leukaemia drug’s effects, but it currently works with only two lab-grown cell types, and the company calls it a preview. GenBio hopes it will eventually let researchers test drugs on a computer before trying them in the lab.

💡Tangents

U.S. Urges Apple Not to Buy Chinese Memory Chips
In response to the memory chip shortage caused by the AI data centre boom, Apple could either significantly raise the prices of new devices (which it already did) or look elsewhere for memory chips, with China emerging as a potential source. However, Commerce Secretary Howard Lutnick said the Trump administration doesn’t want Apple to do that, urging the company to find other solutions to the shortage. Apple has reportedly been testing Chinese memory chips as a prelude to potentially using them in Apple devices sold in China.

States Seek $200 Billion From Meta Over Child Social Media Addiction Claims
Four states—California, Colorado, Kentucky and New Jersey—are suing Meta in an Oakland federal trial, claiming Facebook and Instagram were designed to hook children, fuelled a youth mental health crisis, and that Meta misled users about safety while breaching child privacy laws. They want penalties nearing $200 billion plus design changes such as ending infinite scroll and beauty filters for under-18s. First witness Arturo Béjar, a former Meta engineer, testified that the company knowingly tolerated underage users and put growth ahead of safety. Meta denies it, citing its teen safeguards and calling the demands disproportionate. The trial should run about six weeks.

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This week has been crazy. We’ve had not one, not two, not three, but eight new models released, with a nice mix of open and proprietary models. We’ll take a close look at all eight of them and what they bring to the table in this week’s write-up.

Elsewhere in AI, Nvidia and six big Wall Street investors raise $500 billion to finance an AI infrastructure buildout, Anthropic is courting investors ahead of its potentially $2 trillion IPO, and OpenAI wraps up a $7 billion share sale. Other than that, OpenAI releases GPT-5.6 Sol Ultrafast while high-profile exits raise red flags, Mark Zuckerberg shares his vision for personal intelligence, Anthropic starts watermarking text produced by Claude, and Chinese AI chipmakers are gaining from Beijing’s push to rely less on Nvidia.

Over in robotics, the FCC moves to ban LiDAR-equipped foreign drones from the US, DARPA wraps up the Lift Challenge, Uber and Pony.ai are launching 2,000 robotaxis in Europe, we look at why building a humanoid robot in the US is hard without Chinese components, and more!

Apart from that, this week’s issue of Sync also features scientists creating female clones of male mice, allergy-free genetically engineered dogs, OpenAI sharing details of the Hugging Face incident, a profile of the secretive yet influential wife and adviser of Dario Amodei, and more!

Enjoy!

Eight new models in one week

Google, SpaceXAI, Meta, Nvidia, Microsoft, DeepSeek and Z.ai all picked the same week to release new AI models

I don’t recall a week like this before. Normally, there is one, maybe two, model releases to analyse. But this week, seven companies decided to release eight models, plus one AI lab made weights public for a model released some time ago. It is a nice mix ranging from proprietary frontier challengers to open-weight models and efficient workhorses. In this article, we’ll look at all eight new models, what they bring to the table, and where they fit in an increasingly crowded AI landscape.

Gemini 3.7 Flash

Google has released a new model this week. But it is not the long-awaited successor to Gemini 3.1 Pro. Instead, it is another update to the Gemini Flash line. Just three weeks after Gemini 3.6 Flash, Google presents Gemini 3.7 Flash, its “most intelligent workhorse model yet for coding and agents.”

Gemini 3.7 Flash is being positioned as a model that balances performance and cost. It is aimed mainly at coding, but can also be useful for all kinds of knowledge work. Google says the new model adapts better to roadblocks, clarifies intent when needed, and is better at following instructions. It also exhibits more disciplined execution, which allows it to work more reliably.

Google reports that Gemini 3.7 Flash is a noticeable improvement over its predecessors. It tops Gemini 3.6 Flash in almost all the published benchmark results (the only benchmark on which Gemini 3.6 Flash scored better is CharXiv Reasoning, but not by much). The one chart that Google decided to highlight is the comparison between cost and performance across different models on DeepSWE V1.1, which measures frontier coding agents on original, long-horizon engineering tasks.

However, it is worth noting that those benchmarks used introductory pricing of $0.75/$3.75 per million input/output tokens. Once the introductory price expires at the end of the year, Gemini 3.7 Flash will be twice as expensive, which would put it on the DeepSWE graph roughly where Kimi K3 is.

Artificial Analysis gave Gemini 3.7 Flash a score of 56 on its Intelligence Index, which places the new model between GPT-5.6 Terra and Claude Sonnet 5.

Gemini 3.7 Flash is available on the Gemini app, Spark, Antigravity, and via the Gemini API.

Grok 4.6

SpaceXAI makes a run at the top with Grok 4.6. And while its new flagship model doesn't take the crown, it shows that SpaceXAI can produce a competitive frontier model.

On the Artificial Analysis Intelligence Index, Grok 4.6 scores 61 points—five points up on Grok 4.5 and 23 up on Grok 4.3. That puts Grok 4.6 at the level of Claude Opus 5 and GPT-5.6 Sol. And with the price of $2/$6 per million input/output tokens, SpaceXAI’s new flagship model is cheaper than Opus 5 or GPT-5.6 Sol ($5/$25 and $5/$30 per million input/output tokens, respectively).

SpaceXAI positions Grok 4.6 squarely for long-running agents and coding, and I think this is part of a bigger plan for SpaceXAI to at least stay relevant. Grok does not have a particularly good reputation, and it is not doing especially well in the AI race. But SpaceX now owns Cursor, a popular AI coding tool, and it might have an opportunity here to improve its numbers, or at least have something it can spin as a good story.

Cursor currently offers a wide range of models to choose from, including top models from OpenAI, Anthropic, and Google, as well as its own coding models and Grok. But I think Grok will come to occupy a more prominent place in Cursor. It could become the default model, offered at competitive pricing in an attempt to convince software engineers to switch from Claude or GPT-5.6 to Grok.

This could give SpaceXAI a foothold in the lucrative AI coding market. It is unclear how far SpaceXAI can go, or whether it can grab a sizeable chunk of the market from Anthropic and OpenAI. But at the very least, SpaceXAI could make the number of people using Grok for coding look better.

Grok 4.6 is available in Cursor and Grok Build. It’s also available through the API and via other partners such as OpenRouter, Vercel, and Cloudflare.

Muse Glimmer

Do you remember when Meta was the unlikely champion of open-weight models? Meta seems to have remembered those good old times and is now back with a new open-weight model—Muse Glimmer.

Glimmer is a 30-billion-parameter open-weight model built for local agentic work. It’s distilled from Muse Spark, Meta’s closed flagship model, and designed to run on a desktop or laptop with a single consumer GPU. The full-precision version with no quantisation requires 64GB of VRAM. There are quantised versions of Glimmer that need just 32GB or 24GB of VRAM, which is enough to fit Glimmer on an NVIDIA RTX 5090. Meta says that the quantised versions of Glimmer show up to 1% degradation in performance compared with the full version.

Meta pitches Glimmer as a model to power always-on local AI agents. Glimmer is meant to call tools, write and debug code, work through screenshots and documents, and recover when a tool call fails instead of stalling. Meta imagines it managing your schedule, drafting your messages, and organising your files—exactly the kind of work that needs deep access to personal data, and exactly the argument for keeping it local.

When it comes to performance, Meta’s own benchmark results put Glimmer ahead of Gemma 4 31B and Qwen3.6 27B on most agentic and reasoning benchmarks. Artificial Analysis tells a slightly more sober story. Glimmer scores 35 points on the Intelligence Index, around Kimi K2.5 (36) and just behind Qwen3.6 27B (38). It also falls behind Qwen3.6 27B and Gemini 3.5 Flash-Lite in benchmarks measuring models on realistic knowledge work tasks in an agentic loop. Additionally, Artificial Analysis found that Glimmer has an 82% hallucination rate, compared with Qwen3.6 27B’s 49%.

The release of Muse Glimmer is a welcome surprise from Meta after the company abandoned the Llama family and rebuilt its AI teams. As Mark Zuckerberg outlines in his latest manifesto, Meta will offer a mix of open and closed models. He argues that distributing superintelligence widely “has the potential to begin a new era of personal empowerment,” promising a personal agent that works around the clock on your relationships, health, career and finances. Open models, like Glimmer, will play a part in fulfilling his vision of personal intelligence.

Glimmer’s weights are available on Hugging Face.

Nvidia Nemotron 3.5 Lightning

Nemotron 3.5 Lightning is the latest addition to Nemotron, Nvidia’s family of open-weight models. And as its name suggests, the new model focuses on speed.

Nvidia’s framing is that modern agentic systems are ensembles. There is a big reasoning model like Nemotron 3 Ultra or GPT-5.6 that plans and orchestrates, while smaller specialised models handle the high-volume grunt work—code review, tool calls, security alert monitoring, billing questions. Lightning is built for that second role. Nvidia claims up to 4x faster output speed and 30% faster agentic task completion than other models in its class. If needed, the model can be post-trained on custom data with NeMo.

Artificial Analysis supports Nvidia’s speed claims. In its tests, Nemotron 3.5 Lightning produced 302 output tokens per second, a result second only to Gemini 3.5 Flash-Lite’s 373 output tokens per second and well ahead of everyone else. In terms of intelligence, Lightning scored 24 points on the Intelligence Index and is comparable to Qwen3.5 35B and Gemma 4 31B. Artificial Analysis also found that Lightning is quite verbose. It generated 100 million tokens to complete the Intelligence Index evaluation, compared with a median of 43 million tokens, which will have an impact on the cost of running the model.

Alongside Nemotron 3.5 Lightning, Nvidia released NeMo Switchyard, an open-source routing library, and the two are clearly meant to be read together. Switchyard sends each request—or each step of an agent's workflow—to whichever model in a developer's pool is best suited to it, weighing model capability, cost and latency. It is provider-agnostic and accepts OpenAI, Anthropic and Responses API requests, so it can sit in front of a mix of open and proprietary models. The library ships with several routing strategies, including an escalation router that starts on a cheap model and switches to a stronger one when an LLM judge detects sustained difficulty.

Nemotron 3.5 Lightning is available on Hugging Face. As with previous Nemotron releases, Nvidia is publishing training data and methods where licensing allows, along with an agentic reinforcement learning dataset used for the coding post-training. NeMo Switchyard, meanwhile, can be found on GitHub.

MAI-Thinking-1 and MAI-Code-1.1-Flash

Let’s now turn our attention to Microsoft, which brought two new models to the table this week—MAI-Thinking-1 and MAI-Code-1.1-Flash.

MAI-Thinking-1 is Microsoft’s medium-sized reasoning model positioned as cost-efficient reasoning for enterprise work. Microsoft claims that the new model goes toe-to-toe with Claude Opus 4.6 on SWE-Bench Pro despite the smaller inference footprint, and that users prefer it over Claude Sonnet 4.6.

The more interesting claim is about how MAI-Thinking-1 was built. Microsoft is making a point of the fact that MAI-Thinking-1 was trained without distillation from third-party models and describes its training setup as a “Hill-Climbing Machine,” a pipeline designed so that every component can be improved incrementally over time. The argument is that inherited intelligence is faster to acquire but less steerable, and that clean, traceable training data matters for enterprise customers who need to know what shaped the model. Microsoft also says safety is trained through the same reinforcement learning loop as capability, treating unnecessary refusals and unsafe compliance as equivalent defects.

MAI-Code-1.1-Flash is an updated version of Microsoft’s coding model. Microsoft emphasises a noticeable uplift in efficiency. Compared to MAI-Code-1-Flash, the new version generates tokens 25% faster and uses 25% fewer tokens to complete a task. Taken together, those efficiency improvements mean MAI-Code-1.1-Flash costs a quarter of its predecessor. Additionally, Microsoft highlights a 15% improvement on .NET (Microsoft’s open-source development platform) tasks.

Both MAI-Thinking-1 and MAI-Code-1.1-Flash fit into Microsoft’s broader AI strategy. For a long time, Microsoft relied on OpenAI and Anthropic to deliver the AI behind intelligent features in its products. But the rising costs of AI made Microsoft invest in its own models that it can then serve more cheaply. Now Microsoft has those models and will start replacing models from OpenAI and Anthropic with them.

MAI-Thinking-1 is available in public preview in Microsoft Foundry. MAI-Code-1.1-Flash is available in GitHub Copilot.

DeepSeek V4 Pro

DeepSeek has finally taken DeepSeek V4 Pro out of preview, nearly four months after the April preview build. V4 Pro is a mixture-of-experts model with 1.6 trillion total parameters, of which 49 billion are active. It has a 1M context window and, as usual for DeepSeek, the weights are released under an MIT licence.

Artificial Analysis scores DeepSeek V4 Pro at 53 points on the Intelligence Index, which makes it the third-best open-weight model and well ahead of V4 Flash at 40 points. DeepSeek's own model card puts it at 80.6% on SWE-bench Verified—on the same level as Gemini 3.1 Pro and just behind Claude Opus 4.6.

Alongside the release, DeepSeek announced that it is raising API prices across the V4 line and introducing peak and off-peak rates. During peak hours, V4 Pro will cost $3.96 per million output tokens, up from $0.87, while V4 Flash goes from $0.28 to $1.32. Off-peak pricing is half the peak rate. But even with the increased prices, DeepSeek is cheaper than almost everyone: Moonshot’s Kimi K3 charges $15 per million output tokens, and Anthropic’s Fable 5 charges $50. Bloomberg ties the price increase to DeepSeek’s IPO preparations.

There was also something odd that people noticed. DeepSeek marked the general-availability release with a short statement on its website saying the model had "significantly enhanced agent capabilities." Three days later, the statement had been removed, according to the South China Morning Post, with no explanation. Early developer reaction to the 0813 build was reportedly underwhelming on general capability and negative on the pricing, though researchers were more impressed in narrower areas such as cybersecurity.

Alongside V4 Pro, DeepSeek also released a developer preview of DeepSeek Harness, its open-source answer to Claude Code. The main difference between it and other harnesses is its open architecture. Users can plug in almost anything: models, tools, skills, sessions, sandboxes, storage, loops, scheduling, and UI components, all of which can be swapped or recomposed through configuration. DeepSeek Harness is available on GitHub for everyone to download.

V4 Pro is available through DeepSeek's API, app and web products, with weights on Hugging Face.

GLM-5.3

Z.ai closed out the week with GLM-5.3. What is interesting about this release is that GLM-5.3 runs on the same 743B-parameter base model as GLM-5.2. The improvements came from scaled-up post-training—more task environments, more environment types, and longer training runs.

The gains show up mostly on long-horizon work. Z.ai reports that the Terminal-Bench 3.0 score jumped from 4.6 to 28.3, and claims GLM-5.3 is the strongest open-weight coding model it has measured. On its internal Code Bench, which Z.ai keeps private to avoid test-set contamination, GLM-5.3 scores 31.4% using roughly 50,000 output tokens per task, against Claude Opus 4.8’s 29.5% at 120,000 tokens. Claude Fable 5 still leads that particular chart at 39.5%. Those claims have not been independently verified at the time of writing.

Z.ai emphasises GLM-5.3’s cybersecurity capabilities with this release. The company says the model’s capability in that area grew further than it expected as training scaled, eventually producing multi-step exploit-chain reasoning it hadn’t planned for. GLM-5.3 scores 84.5% on CyberGym, ahead of both Fable 5 and GPT-5.6 Sol. Z.ai says the model has already found over a thousand critical and high-severity vulnerabilities in deployed software, including bugs in the Linux kernel, WebKit and FreeBSD, and has published a public disclosure ledger to track them.

That is also why the weights aren’t out yet. Z.ai is holding them for roughly two weeks pending safety evaluation and hardening—the first time a model in the GLM line has been held back for this reason. In the meantime, selected security partners are getting access in controlled environments.

GLM-5.3 is available through the Z.ai API, the GLM Coding Plan and ZCode, and has been rolled out to existing coding plan subscribers.

Qwen3.8 weights are now on Hugging Face

Lastly, Alibaba has published the weights for its flagship model, Qwen 3.8. The first is Qwen3.8-2.4T, a 2.4-trillion-parameter open-weight model on which Qwen3.8-Max, Alibaba’s top proprietary model, is based. Alibaba has also published weights for a much smaller 28-billion-parameter model, Qwen3.8-27B.

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🧠 Artificial Intelligence

Nvidia Taps Wall Street for $500 Billion Funding Commitment
Nvidia has teamed up with six Wall Street investors, including Apollo Global Management, Blackstone, BlackRock, and Goldman Sachs, to raise $500 billion in financing for AI infrastructure, helping Nvidia’s largest customers afford the computing power they need. Bloomberg reports the debt will be secured against that compute, with special-purpose vehicles leasing it on to clients. The first deals should reach the market within months.

Anthropic Tries to Shore Up Investor Confidence Ahead of Blockbuster IPO
The Wall Street Journal reports that Anthropic is courting investors ahead of what could be the largest IPO ever. Investors have pressed the company on cheap Chinese models, friction with the Trump administration, and public anger over data centres. Anthropic executives have downplayed the Chinese threat, citing the company’s lead in model quality. They have also promised a bigger push into healthcare and biology to soften hostility towards AI. Anthropic’s IPO is expected to take place in September or early October and could make it a $2 trillion company.

US to tell partners they must pick sides in AI race with China
The US is preparing to tell 35 countries they must choose between its AI coalition, called Pax Silica, and China's, according to a draft State Department letter seen by Reuters. The initiative ties partners together on chips, AI models and critical minerals. China set up a rival body in July, pitching its open-weight models as an alternative. Kazakhstan has signed up to both, which alarmed Washington. The State Department declined to comment on the leak.

OpenAI wraps $7 billion share sale ahead of potential IPO
OpenAI has completed a secondary share sale totalling roughly $7 billion, CNBC reports. The tender offer allows current and former employees to sell stock at the company’s $852 billion valuation without waiting for an IPO, which is reportedly delayed to next year.

OpenAI CFO Friar tells investors that enterprise business now bigger than consumer by revenue
Sarah Friar, chief financial officer of OpenAI, told shareholders that the majority of the company’s revenue now comes from enterprise customers. That crossover arrived months ahead of the company’s own forecast. Annualised revenue is reportedly running at $40 billion, with business customers growing by 32% in July. OpenAI President Greg Brockman, who also joined the meeting, said that the company sees cybersecurity as a critical piece of the business and brushed off concerns about open-source competition.

OpenAI: Expanding Daybreak as the Cyber Defense Window Narrows
OpenAI introduces two access tiers to Daybreak, its cyber defence programme for vetted customers. The Blue tier lifts the filters that normally block legitimate security work on its general models. Red goes further with GPT‑5.6‑Cyber, a new model trained to refuse far fewer dual-use requests. OpenAI says it already used that model to find two unknown flaws in Chrome’s JavaScript engine. Access is gated behind identity checks, monitoring and mandatory hardware keys.

How Claude’s text watermark works
To comply with the EU AI Act, Anthropic is watermarking the text Claude generates. Anthropic will use SynthID-Text, a method developed by Google DeepMind that changes the words the AI produces next from being essentially random to following a secret pattern. Anyone with access to that pattern can then spot Claude’s fingerprints in its choice of words, even though the text reads completely normally. As Anthropic notes, the watermark only shows how likely Claude was to have been involved, not whether some other AI wrote the text. Anthropic says the watermark won't affect output quality or cost, and it will soon offer a detection API.

Previewing Ultrafast mode: GPT‑5.6 Sol at up to 14X the speed
OpenAI has shown an early preview of Ultrafast, a new mode for GPT-5.6 Sol that makes the model respond up to 14 times faster. Compared with competitors, GPT-5.6 Sol on Ultrafast is 11 times faster than Fable 5 and five times faster than Opus 4.8 on Fast mode. The new mode is powered by Cerebras and generates up to 750 output tokens per second without affecting response quality. GPT-5.6 Sol on Ultrafast is currently available only via the OpenAI API to a select group of customers.

OpenAI talent exodus raises ‘huge red flag’ ahead of IPO
We have seen some major departures from OpenAI this week. Brad Lightcap, who served as operating chief of OpenAI since 2022 after joining the company in 2018, is leaving to start “something new.” No details have been provided about what that “something new” is. Two days later, chief revenue officer Denise Dresser announced her departure after less than a year at OpenAI. On the same day that Lightcap announced he is stepping down, The Financial Times reported that Chloé Bakalar, OpenAI’s head ethicist, had left the company in July, less than a year after joining. According to the report, Bakalar has not been replaced and that she was the sole dedicated ethicist at OpenAI. The churn leaves the C-suite looking unstable just as OpenAI prepares for its IPO, and investors are reading it as a warning sign.

Gemini app reaches 1 billion active users
Gemini app has become Google’s 14th service to reach one billion monthly active users, and it did that faster than any other Google product. To mark the milestone, Josh Woodward, Google’s VP responsible for the Gemini app, shared more numbers on how people are using Gemini. As Woodward notes, that one billion number includes everyone who used Gemini on the web, Android, iOS, or inside Chrome.

The Future is for Everyone: The Path to a Positive AI Future
Mark Zuckerberg posted a new manifesto in which he argues that superintelligence should be handed to everyone rather than concentrated in a few labs. His case is that no single model can be aligned with humanity’s conflicting values, so safety comes from many people holding competing agents that check each other. Meta will offer personal agents with a private mode, free tiers and open-weight releases again. He also wants export controls kept, distillation protected and no policy that delays American model launches.

Cracks in the AI Thesis
The August update to the Ramp AI Index brings some interesting insights into how American businesses are using AI. According to Ramp, 43.5% of US businesses are Anthropic customers, compared with 39.7% for OpenAI. The report shows that the use of open AI models is growing, with 6.1% of businesses using them, up from 4.5% in January 2026. American companies also continue to ramp up AI spending. In July, the top 1% of businesses spent a median of $7,400 per employee on AI, while the top 10% spent $650. The median company spent $11.95 per employee.

Learning more about Claude’s mathematical capabilities
Anthropic says an unreleased research version of Claude has improved a long-standing result about the Riemann zeta function. Asked to take a real stab at the Riemann hypothesis, the model failed, but it pushed the proven lower bound for zeros on the critical line from 41.6% to 67.2% (if you’re interested, the paper is here). Anthropic's own mathematicians validated the proof, as did two outside number theorists. Getting there took two sessions, dozens of coordinated subagents and 31 million output tokens. Anthropic doubts the technique leads anywhere near the hypothesis itself. Instead, the company reads this as a marker of how fast AI’s mathematical ability is improving.

China AI Chip Designer Moore Threads Plans Hong Kong Listing
Chinese AI chipmaker Moore Threads plans to list in Hong Kong at an “appropriate time” after its shares have surged more than 420% since their Shanghai debut last year. The company also reported first-half revenue up 147% to 1.74 billion yuan ($258 million). The filing says the offering should help it expand abroad and hire research and management talent, as Moore Threads emerges as a leading contender to fill the gap left by Nvidia’s forced exit from China.

Chinese AI Chipmakers Set to Gain From Beijing’s Tech Push
Chinese AI chip manufacturers are expected to see bumper sales as a result of Beijing’s push to grow its domestic semiconductor industry and reduce reliance on Nvidia. Cambricon’s first-half revenue more than doubled to 5.99 billion yuan ($887 million). Iluvatar CoreX and Moore Threads expect similar jumps. Morgan Stanley reckons China’s self-sufficiency in AI chips will hit 70% by 2030, up from 42% this year.

Introducing Grok Bot
SpaceXAI is launching Grok Bot, AI teammates you can give real work to, as the company describes the new service. SpaceXAI says they are capable, always-on assistants that sign into your existing apps and finish jobs end to end. Grok Bot is currently in beta for SuperGrok Heavy and premium Cursor subscribers (SuperGrok Heavy costs $300 per month), with enterprise access waitlisted.

▶️ The ‘Breaking’ News: The OpenAI–Hugging Face Incident - A Technical Reconstruction and Its Implications for AI (37:27)

In this talk from Black Hat USA 2026, safety and security researchers from OpenAI explain what actually happened during the Hugging Face incident. They share the results of their internal investigation into how the AI agents came up with the idea of trying to cheat on a cybersecurity benchmark, the warning signs that had been there all along (including a hidden message board where AI agents communicated with each other), and how the Hugging Face incident unfolded. I recommend finding the time to watch it, as it raises some important questions about how frontier labs approach alignment and how the cybersecurity industry needs to react. I kept going back and forth between amazement and terror as I listened to this talk.

Lovable raised $400M in Series C funding
AI coding startup Lovable has raised $400 million in Series C funding at a $13.3 billion valuation. The Swedish company plans to use the newly raised money to make its platform more proactive, spotting what needs doing and getting on with it unprompted. It also wants deeper connections to the software businesses already run. Security and governance controls will be strengthened alongside that. The team will grow to roughly 450 people this year, hiring mostly in machine learning and infrastructure.

AI Startup Cognition in New Funding Talks at $40 Billion Value
Cognition, the company behind AI coding agent Devin, is in early talks to raise money at a valuation of at least $40 billion, Bloomberg reports. That would be more than 50% above the $26 billion valuation it fetched less than three months ago. Nothing is settled yet, and Cognition may walk away or seek different terms.

Cursor is now a part of SpaceX
SpaceX has completed its $60 billion all-stock acquisition of Cursor. According to Cursor, the company now has access to the largest fleet of GPUs in the world, giving it the compute needed to build stronger and more efficient models.

Manus returns to operate as an independent company
Manus will soon return to operating as an independent company, fully separated from Meta. This follows the Chinese government blocking Meta’s $2 billion acquisition of Manus in April and ordering the companies to unwind the deal. As part of the separation, data generated by some users since the December 2025 acquisition will be deleted, though affected users can back it up and restore it afterwards.

Databricks wanted to raise $1B, investors wanted $15B. It settled on $5B at a $190B valuation.
Databricks raised $5 billion at a $190 billion valuation in a round that ballooned far beyond its intended size. CEO Ali Ghodsi says the company had planned to raise around $1 billion, but a press leak during its June conference set off a flood of investor calls amounting to roughly $15 billion of interest. Ghodsi attributes the enthusiasm to the company’s financials—roughly $7 billion in annualised run-rate revenue growing at 80%, cash-flow positive, with newer AI products gaining traction.

Corporate America Has Suddenly Decided to Stop Blowing Money on AI
After flirting with tokenmaxxing, US companies are changing their attitude, becoming more frugal and searching for the cheapest AI that will do the job. Many now mix cheap open-weight models, several of them Chinese, with premium ones from OpenAI and Anthropic. For some tasks, a cheaper model or a mix of cheap and premium models can deliver massive cost savings without compromising the quality of the work.

Why Chinese Citizens Are Far More Optimistic About AI Than Americans
Various studies have shown how much more positive Chinese people are about AI when compared to Americans. In this article, explains why this is the case and argues that different experiences with technological advancements are at the core here, which are more positive for Chinese than for Americans. Combined with a belief that Beijing will rein in its tech giants where Washington won’t, as well as a competitive culture in which the fear of being left behind outweighs the fear of change, people in China are more inclined to see AI as something that can improve their lives.

WeatherNext: AI model achieves breakthrough in forecasting cyclones
Google DeepMind says its WeatherNext model can forecast a cyclone’s track, intensity and wind structure a full day earlier than existing systems. The model achieves this by learning from global atmospheric data alongside records of nearly 5,000 past storms. It has already proved itself, helping US forecasters warn Jamaica early about Hurricane Melissa. DeepMind has also made WeatherNext open source and available for anyone to build on.

▶️ Ryan Greenblatt – What happens once AI can automate AI research? (2:12:31)

Dwarkesh Patel sits down with Ryan Greenblatt, Chief Scientist at Redwood Research, where he works on technical AI safety, to explore the prospect of automated AI research. They examine how recursive self-improvement (RSI) could work, the bottlenecks that need to be overcome before it can take off, and what a world might look like in which a single year brings the equivalent of five years of research progress. The conversation also covers the alignment problem, what could go wrong when we have models that are to GPT-5.6 Sol what GPT-4 is to GPT-5.6 Sol, and what we can do to maximise the chances of a positive outcome.

🤖 Robotics

FCC moves to ban LiDAR-equipped foreign drones from US
The FCC plans to retroactively ban sales of foreign-made drones that use LiDAR for obstacle avoidance by categorising them as “military-grade” tech. The ban would pull popular drones such as DJI Air 3S and Mini 5 Pro from shelves. The proposal extends a decision from December 2025 that blocked new foreign drones but left already-approved ones on sale. It would not affect existing owners, but, as DJI warns, those owners could find official spare parts increasingly hard, or eventually impossible, to source over time. DJI argues LiDAR is a safety feature, not a weapon, and is already suing over the earlier ban.

Uber and Pony.ai plan to bring 2,000 robotaxis to Europe
Uber and Pony.ai announced a plan to put more than 2,000 robotaxis in four European cities, though which cities and when the services would begin was not disclosed. Pony.ai will provide self-driving cars, which can then be ordered through the Uber app. The deal expands a partnership the two companies began in the Middle East last year.

DARPA Lift Challenge results

Between 2nd and 9th August, DARPA hosted the Lift Challenge, offering $6.5 million to anyone who could build a drone capable of lifting four times its own weight (commercial drones typically manage roughly a 1:1 ratio). Nobody hit the target, so DARPA halved every prize. AVIDrone came closest at 3.84:1 and took home $1.25 million with a conventional single-rotor electric helicopter. Teams that aimed higher had the lift but drained their batteries before finishing the course, with some crashing spectacularly. Livestreams from all seven days of the challenge, along with daily recaps, are available on DARPA’s YouTube channel.

Workers Are Teaching AI-Powered Robots to Take Over Their Jobs
In their pursuit of high-quality training data, robotics companies began hiring tens of thousands of Indian workers to wear head-mounted cameras and record their own hands at work. Those first-person videos of mundane manual labour are then used to train AI models that run inside robots. India has become the richest source of this kind of data, since millions of people there still do by hand what wealthier countries automated decades ago.

Self-driving trucks are officially testing on California highways
California’s Department of Motor Vehicles has cleared Aurora and Kodiak to test self-driving trucks on public roads, and Kodiak has already begun. Kodiak said it is starting with a handful of test trucks in California, primarily around its Mountain View office. The permits follow an April rule change that lifted the state’s ban on testing driverless vehicles over 10,000 pounds (4,500kg). A human safety driver must still sit behind the wheel.

Hadrian raises $1.37B to accelerate U.S. defense, aerospace manufacturing
Hadrian, a startup that builds highly automated factories to manufacture precision parts for defence and aerospace customers, has raised $1.37 billion, bringing its valuation to $7.87 billion, up from $1.6 billion in January. The company operates four sites and will use the newly raised funds to add production lines for munitions and autonomous systems.

Digit V5: First Humanoid Robot Out Of The Cage?
Agility Robotics says its next humanoid robot, Digit V5, due to launch in December, will be able to work safely alongside people without the need for safety fencing or cages. The new robot is also expected to operate for 20 hours a day and swap its own hands for different tools. Digit robots are already being used commercially by Schaeffler, GXO, Toyota and Mercado Libre. Agility Robotics is preparing to go public through a $2.5 billion merger.

America Wants to Make Its Own Humanoid Robots. That Won’t Be Easy.
The recently announced ban on importing new humanoid robots from China into the US aims to encourage domestic companies to build those robots themselves. But as The New York Times explores in this article, that is easier said than done. China has a near-monopoly on certain components, and trying to make them in the US would mean more expensive machines. The US would need to invest billions of dollars to build the kind of infrastructure China spent years developing just to catch up.

▶️ Can Wearable Robotics Help People With Parkinson’s Keep Moving? (1:22:37)

In this episode of the Automated Podcast, Brian Heater speaks with Kathryn Zealand of Skip and UCSF physical therapy researcher Jessica Bath about wearable robotics and Parkinson’s disease. Kathryn takes us inside Skip, the Google X spinout company developing exoskeletons, and explains how the team is building foundation models of human movement using real-world gait data. Jessica Bath joins later in the podcast to discuss freezing of gait, a poorly understood Parkinson’s disease symptom that researchers are studying by pairing brain recordings with biomechanics.

🧬 Biotechnology

Scientists just created female clones of male mice
Scientists from Japan have used a CRISPR-based tool to remove the Y chromosome from male cells and created healthy and fertile female clones of male mice. Then they went further and did the same thing in reverse by creating male clones of female mice. This technique could be used to help rescue endangered species by producing females for species where only males are left. However promising this technique may be, it is still imperfect—egg cells from a female of the same or a similar species are still needed. Mice are also a friendly case, since most mammals left with a single X chromosome are infertile.

Kindred Companion Sciences Unveils First Dogs Genetically Edited to Remove the Major Dog Allergen
Kindred Companion Sciences has come out of stealth with two beagles, Alfie and Bailey, engineered by CRISPR to lack Can f 1, the dog protein responsible for the allergic reaction in humans. A peer-reviewed paper in The CRISPR Journal reports the protein was undetectable in their saliva and dander. Both dogs are healthy and growing normally. Founder of Kindred, Matt Walker, who has been allergic to dogs all his life, now keeps Bailey as a pet. Neither dog is for sale, and the edit remains unapproved by the FDA.

💡Tangents

Tesla and SpaceX will invest $16.8B to start building ‘Terafab’ chip factory in Texas
Terafab, a joint chip factory between Tesla and SpaceX, is set to be built in Grimes County, Texas. The initial cost of Terafab will be $16.8 billion, while filings suggest the final bill could reach $119 billion. According to Elon Musk, a chip factory of this scale is needed because the existing chip supply can’t keep up with his plans for Optimus robots, Cybercabs and orbital data centres.

Even Claude Is in the Dark About Dario Amodei’s Wife—and Her Influence at Anthropic
In this article, The Wall Street Journal profiles Cami Clark, the secretive yet influential wife and adviser of Dario Amodei. It follows her unconventional path from entrepreneur to a behind-the-scenes figure in one of the world’s leading AI companies. The article explores how Clark has helped shape Amodei’s career and Anthropic’s network, including introducing him to early investor Eric Schmidt, while examining her colourful business history, including co-founding a women-focused porn company and unsuccessfully seeking investment from convicted sex offender Jeffrey Epstein, as well as her powerful Silicon Valley connections and largely private role alongside her husband.

How ideas of a vast censorship network moved from the online fringe to Trump policy
This investigation by MIT Technology Review and Type Investigations explores how the “censorship-industrial complex,” a fringe right-wing conspiracy theory about a supposed alliance of agencies, academics and tech platforms working to silence conservatives, has become US policy. It traces how this idea went from far-right blogs to the highest levels of Washington and what damage it caused, from shuttered State Department offices to the dismantling of USAID. It is a great study of how conspiracy theories are born, spread and then amplified.

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I’ve been running this newsletter, in its various forms, since 2015. It is my longest-running project and is now a fixture of my life. It is hard for me to imagine what I would be doing without it.

However, Humanity Redefined is not what I promised it would be. The promise was to deliver Sync every Friday and at least one longer article per week. I have not fulfilled that promise. Sync goes out on Sundays, not Fridays, and articles are rare.

Let me take you behind the scenes. It takes me about 6 hours in total to prepare the news section of Sync. I select all the links myself from hundreds of articles and videos that enter my feeds. I read and watch each one of them, and I write the summaries myself (although recently I have been using AI to help with that). The write-ups take another 6 to 8 hours, sometimes more. The problem is that with a full-time job and with everything else happening in the background, I barely find time to finish a new issue of Sync before the week ends. This got worse in recent months to the point where I am getting close to burnout. Something needs to change so that I can deliver what I promised, at a standard worth your time, in a way that's sustainable for me.

Here are the changes that will come to the newsletter in the coming weeks.

First, the changes need to start with me and how I work. My goal is to streamline the writing of Sync so that you get the best overview of what happened in tech every week in your inbox—and so that it stops eating the hours I could be spending on longer articles. Those articles are the part of the newsletter I most want to get back to, and the process changes are largely to make room for them. I'm also planning to experiment with automating some of my processes (I am a software engineer after all). AI will play a part in those processes, but I won't offload writing to AI. Writing articles for this newsletter helps me better understand what is going on in tech. Giving away writing to an AI robs me of that. It would rob me of the opportunity to discover new connections and explore new ideas, and that would hurt the quality of the newsletter in the long term.

Second, the schedule will change. Sending Sync on Sundays works better for me. As for other articles, I want to see first if the changes to how I work will yield positive results. If the answer is yes, then the schedule will be Sync on Sundays and one longer article per week. If not, I'll move to one article every other week.

Third, the newsletter is getting a new identity. At the beginning of September, it will arrive in your inbox under a different name and from a different email address, so don't be alarmed when that happens. I'm not ready to share what the new name is yet, but I'll tell you before the switch. But the general mission—exploring technology and the futures in which humanity flourishes—will remain the same. The new identity is designed to give the newsletter a voice and perspective better aligned with the kind of futurism I want to explore and promote.

Lastly, I want to interact with the readers more. To be honest, that scares me a little. But I realised that this newsletter cannot be a one-way communication. I want to listen to what you think about technology and understand your perspective. Substack offers a chat, which I am planning to enable and see how it goes. I will send a message once it is on. I also plan to send a survey by the end of August to ask what you think about the newsletter and what I should change.

Those changes—improved processes, new schedule and identity, and actually building a community—will set the newsletter on a new course. A course where we can explore positive futures and discuss how to maximise chances of them becoming a reality.

Thank you for reading through all of this. If you want to share what you think about those changes, then my DMs and inbox are open.

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This week, we take a closer look at the recently announced changes at Google and why the company is at a pivotal moment that could determine whether it remains relevant.

Elsewhere in AI, OpenAI delays the release of its next model to address cybersecurity concerns, while more models have been shown to escape their “secure” sandboxes. Meanwhile, Anthropic moves into making its own AI chips, new rumours emerge about OpenAI’s upcoming device, AMD acquires a startup that burns AI models into silicon, Cloudflare hosts its Agents Week, and SpaceX goes exclusive with Nvidia.

Over in robotics, London gives Uber and Wayve the green light to offer robotaxi services later this summer, while Waymo comes to Dallas. Elsewhere, Unitree is valued at over $7.4 billion following its IPO, Nvidia’s new open reasoning model for self-driving cars is now available to download, and a humanoid cleaning service launches in San Francisco.

Apart from that, this week’s issue of Sync also features the world’s first synthetic virus designed by an AI, RAMageddon set to continue into 2027, a fake AI newsroom exposed, and more!

Enjoy!

DeepMind as we know it is no more

Every big company has that one pivotal moment that changes everything. Usually nobody notices at the time. Intel had one, as well as IBM and Boeing. Now, it might be Google’s turn.

Demis Hassabis stepping back from running DeepMind looks like a routine reshuffle. It isn’t. Let’s look at what changed and who left. How Google handles this critical moment could decide whether it is still relevant in ten years.

The end of an era

On 5 August 2026, Demis Hassabis announced that he was stepping down as CEO of Google DeepMind and becoming Chair of Google DeepMind and Chief Scientist of Alphabet, while continuing to lead Isomorphic Labs, DeepMind's drug discovery spin-off. In the new role, he will work with Pichai on strategic and global AGI matters and advise Google DeepMind's leadership. Koray Kavukcuoglu takes over the day-to-day duties of leading Google DeepMind, overseeing Gemini model development, frontier AI research, and the Gemini app and developer teams.

Kavukcuoglu steps in not as the new CEO of Google DeepMind, but as an SVP of Google DeepMind. This is a significant change. DeepMind was one of the few divisions left at Google that had a CEO, not an SVP, leading it (the other remaining ones are YouTube and Google Cloud). With that change, as one DeepMind employee told The Guardian, DeepMind has become just another subdivision of Google. The last few parts of uniqueness and semi-independence that DeepMind enjoyed for a long time are now gone.

The leadership of Google DeepMind has now moved from London to California. Sergey Brin, who has reportedly taken an increasingly hands-on role in AI development, works out of Mountain View, and Bloomberg reports that both he and Pichai are regular presences on the floor dedicated to Gemini, while Hassabis was rarely sighted. Over time, oversight of Gemini shifted to Kavukcuoglu, who had moved to Mountain View in the past year.

Hassabis, meanwhile, stays in London, and he made a point of saying so in his statement. These kinds of statements are carefully crafted, so I don’t think he would mention that for no reason. Maybe Google’s leadership was pressuring Hassabis to move to California, too?

So what does DeepMind lose when Hassabis moves up? Quite a lot. Hassabis is a Nobel laureate, a serious computer scientist and an AI pioneer. He is one of the few people in the world who carry as much authority when they talk about AI. But recently, something has changed. Maybe it started when Google combined Google Brain and DeepMind into one AI team. When that was happening, Fortune’s Jeremy Kahn wrote that the merger made obvious sense for Google and might be bad for everyone else. DeepMind’s science-first work had no direct bearing on Google’s business, and commercial logic would eventually squeeze it. DeepMind’s best minds, he wrote, were now subject to more commercial pressure than ever before.

You can see that pressure in what happened to Hassabis himself. Reed Albergotti over at Semafor writes that the move was at least a year in the making. Hassabis had been drifting away from overseeing Gemini and shifting those responsibilities to Kavukcuoglu. Albergotti paints a picture of Hassabis being unhappy with what his role as the CEO of DeepMind has become. He was at his most animated, Albergotti notes, when talking about Isomorphic Labs, which he keeps leading.

Deep inside, Hassabis is still a scientist. Watch any talk he has given in recent years, and you will hear him return to the same theme: AI as an instrument for accelerating science. You could see it in what DeepMind chose to work on, from predicting the structure of proteins with AlphaFold to forecasting weather with WeatherNext. He was also the person inside Google most likely to argue for caution—he has called for a US safety body to assess frontier models before they ship. With him further from the day-to-day, Google can push harder on what it wants. As one DeepMind employee told The Guardian: “Now the person we were supposed to trust to get the right outcomes for humanity has stepped away.”

Kavukcuoglu is an accomplished researcher in his own right, with 13 years at DeepMind, where he founded the deep learning team and led breakthroughs including WaveNet and DQN. But as one former Google executive told The Guardian, Kavukcuoglu is a “technical character, not an inspirational figure like Demis.” He may not be like Demis, but he delivers, and Google appears to have chosen him for exactly that reason.

How Google got here

Many thought that if AGI were ever to be created, it would happen at Google. DeepMind, which Google acquired in 2014, was the world's leading AI lab. And it was the Google Brain team that created the transformer architecture, the foundation of modern large language models. They had all the ingredients: computing power, vast amounts of data for training models, and world-class talent.

The meteoric rise of ChatGPT in late 2022 caught the tech giant off guard. Google raised a “code red” internally and scrambled to address the new challenger. Google’s initial response, Bard, was a failure. In 2023, the company consolidated its AI teams—Google Brain and DeepMind—into one unit, Google DeepMind, led by Demis Hassabis.

Gemini 1 and Gemini 2 were solid models, but it was Gemini 3 that put Google at the top when it launched. It felt like Google, the sleeping giant, had finally woken up and would show what it is capable of. But staying at the frontier is harder than reaching it. Gemini 3.1 Pro, Google’s latest flagship model, was released in February. In the six months since, OpenAI and Anthropic have each shipped several flagship models. Google has shipped none. Even open-weight Chinese models like Kimi K3 now score above Gemini 3.1 Pro. The latest additions to the Gemini family, 3.5 Flash and 3.6 Flash, are good in their weight class, but they feel like a stopgap, something to stay relevant with while everyone waits for the next flagship model.

At Google I/O in May, Pichai teased Gemini 3.5 Pro and said it will be out “next month.” It is August now, and Gemini 3.5 Pro is nowhere to be seen. It is still possible that Google’s next flagship model, be it Gemini 3.5 Pro or Gemini 4 (which Demis Hassabis mentioned in his message to the team), will bring Google back to the top. But we might have to wait a bit more to see if that is the case.

Bloomberg reported in July that Gemini 3.5 Pro is months behind schedule because the model has fallen short internally, particularly on coding—which happens to be the most lucrative application of AI and the core of Anthropic’s and OpenAI’s appeal. Late in June, Google updated the training data to improve those skills. The results were reportedly disappointing.

Gemini is not the problem

Google’s inability to ship flagship models at the same pace as OpenAI and Anthropic reflects deeper structural issues. Google has multiple layers of stakeholders involved in preparing any model for release, because a model has to be woven across Search, Maps, YouTube and everything else. It is also dealing with fragmentation and duplication of work. Google Cloud, DeepMind and the Android team have, for example, all been building their own AI coding tools. Some engineers pushed back on AI-written code on principle. For a period of time, engineers were restricted from using Gemini on proprietary code at all. And when they do try to use AI now, they often hit capacity limits, because they are competing for compute with other Google teams.

One ex-employee described getting every department to move in the same direction as trying to boil an ocean.

The thing everybody names as Google’s structural advantage—the breadth, the products, the distribution, the full stack—is also the thing generating the drag. Every model launch has a dozen veto points because every launch touches a dozen products. No wonder Anthropic and OpenAI ship faster and run circles around Google.

Kavukcuoglu has his work cut out. He needs to deal with the fallout from the leadership reshuffling and recent departures. He is already working with Google’s main engineering team to unify the internal AI coding tools, and Google has consolidated most of them under Antigravity. He also needs to rebuild DeepMind to focus on delivering the next version of Gemini and make sure it is the best model when it comes out. Kavukcuoglu is under pressure to deliver. Google has pledged to spend as much as $205 billion on capital expenditure this year alone, and investors want to see signs that this investment will bear fruit.

The old guard is leaving

Alongside all those changes, Pichai announced that Jeff Dean, Google's Chief Scientist, is leaving the company after almost 27 years. Dean was employee number 30, and his portfolio, which includes MapReduce, Bigtable, Spanner, and TensorFlow, is essentially the skeleton of modern Google. He joined Google X, Google’s moonshot division, in 2011 to explore then-exploding-in-popularity deep neural networks, which led to the creation of Google Brain. Dean led Google's AI efforts from 2012 to 2023.

Dean is now founding Discovery Loop with three other Google veterans: Sanjay Ghemawat, his collaborator of more than two decades and the only other person to hold Google’s senior fellow title; Quoc Le, whose research helped inspire today’s chatbots; and Oriol Vinyals, who had co-overseen Gemini since 2023. The company will pursue recursive self-improvement—AI that improves itself with little or no human help—and apply it to hardware design, drug discovery, materials and scientific research.

Those four veterans join a growing list of prominent people who left Google and DeepMind this year. David Silver, who spent over a decade at DeepMind leading the work behind AlphaGo, AlphaZero and AlphaStar, left the company in January to start his own AI company, Ineffable Intelligence, which went on to raise a $1.1 billion seed round at a $5.1 billion valuation. Recently, John Jumper, the co-creator of AlphaFold and the 2024 Nobel Prize winner in chemistry, left DeepMind to join Anthropic. Noam Shazeer, the co-author of the Attention is all you need paper and the co-lead of Gemini, has also left the company (again) in June to join OpenAI.

Kavukcuoglu is now the only remaining Gemini co-lead at the company.

Imagine how the past few weeks have felt inside Google DeepMind. Senior colleagues you have worked with for years are leaving one after another, and now the legends who built the place are going at the same time. Other colleagues, whose departures never made the news, are quietly moving to competitors. Departures are contagious. Once people around you start thinking about the exit, you start thinking about it too. There is a good chance the names in this article are not the last ones.

All of this makes Google DeepMind a different place to work. As Jeremy Nixon, a former Google AI researcher, put it to the Guardian: “It’s no longer true to say that if you want to work with the greatest researchers on the planet, you go to Google DeepMind.”

Sure, Google has the people to fill in the gaps and can still attract young talent (who were not picked up by OpenAI or Anthropic), but there will be a turbulent transition period at the time when the company needs everyone to lock in and focus. The longer Google delays the new flagship, the bigger gap it will need to close to OpenAI and Anthropic. To keep the perception that Google is still in the race, the next Gemini needs to claim the throne. If it does not, then Google might find itself in a spot from which clawing back can be a difficult challenge.

Where does Google go from here?

Google is a $4.3 trillion corporation with over 195,000 employees. It runs on process, and process does not enthusiastically approve risky research. Maybe it is easier for Dean, Silver, and others to just leave Google and start new companies to work on the projects and ideas they are passionate about. From what we know, all of them left on good terms. And it is not like they are cutting ties with Google. Alphabet is a seed investor in both Discovery Loop and Ineffable Intelligence, and Google Cloud will supply Discovery Loop's compute for at least the next year.

One might argue that this is a sophisticated R&D strategy, in which Google is letting go of its employees to work on risky projects without taking the risk. If they fail, the failure is on them, not on Google. If they succeed, then there is always a possibility of bringing their work back through acquisition, or just bringing back some of the people. Character.AI showed us what this could look like. It was founded by former Google employees, one of whom was Noam Shazeer, and some of them (like Shazeer) came back to Google when Character.AI was gutted for talent in August 2024. The same could happen with Ineffable Intelligence and Discovery Loop.

That argument only works for the people who left to build something. But that is not the case. Many went on to join competitors. And even the founders make an awkward case. Jeff Dean spent 27 years at a company with more compute, more data and more money than anyone on Earth, and concluded that the way to pursue his most ambitious idea was to leave and have that company fund him from the outside. He told the New York Times his new company might make decisions that are not in its purest financial interests. That is a polite way of saying Google couldn't do that. And as Tim Rocktäschel, who left Google earlier this year to help found Recursive Superintelligence, said, “there is much less bureaucracy and politics” in startups. That is what someone scarred by corporate life sounds like.

Google’s leadership can point at numbers that make all of this look overblown. The Gemini app has more than 950 million monthly users, Gemma has passed 900 million downloads, and Alphabet made $132 billion in net income last year. Q2 revenue grew 24% year on year, with Google Cloud up 82%. Google is still the only company that owns the entire stack, from its own chips to the products billions of people already use.

All of that is true. Financially, everything looks fine. But underneath, the rot is starting to spread.

Google is at a pivotal moment, and it will survive, in one form or another. It has too much money and too many users to do otherwise. But in five or ten years, we may look back at this moment as the point where it became the next Intel, IBM, or Boeing—a bloated tech giant of a bygone era that lost what made it special.

All of them—Intel, IBM, and Boeing—dominated their respective industries thanks to engineering excellence and a relentless pursuit of innovation. But at some point, finance started to matter more than engineering. They began to miss key moments of transition and ceded the lead to rivals, both old and new. As those failures compounded, the engineers who might have reversed them left to join competitors—making the next recovery harder and pushing out even more engineers, until those once-untouchable giants became shadows of what they once were.

Google might be entering a similar loop. And once a company enters it, it is hard to break it.

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🧠 Artificial Intelligence

▶️ I Investigated a Fake AI Newsroom. It Led Me to OpenAI’s Super PAC. (12:08)

This video exposes AcutusWire, a website providing “expert-sourced journalism,” as an AI slop newsroom pushing AI industry talking points. The investigation has found that AcutusWire is funded by Leading the Future, a $140 million super PAC, which has close connections to Greg Brockman, OpenAI’s president, and a16z, one of OpenAI’s biggest funders. Leading the Future is spending large amounts of money ahead of the 2026 midterm elections to secure pro-AI regulations and attacking candidates promising to regulate the AI industry.

Ten advances in mathematics and theoretical computer science
An internal version of Astra, OpenAI’s next major model, has resolved or advanced ten long-standing maths problems for roughly $2,000 in tokens, the company says. They span high-dimensional geometry, group theory, lattice cryptography and quantum complexity. The model formalised each proof in Lean for machine checking. Before we jump on the hype train, this independent analysis is worth reading for useful context. It points out that OpenAI ran no control group and that one mathematician solved five of the same problems in a day using Fable 5. In either case, fields where answers can be verified, like mathematics and coding, now look far closer to falling than most expected.

OpenAI: Responding to the next frontier of critical cyber capabilities
OpenAI has paused some internal work on its unreleased model, codenamed Astra, after evaluations found sharp gains in agentic coding and cybersecurity. The company says it can no longer rule out that Astra has crossed the “critical” threshold in its Preparedness Framework. That would mean a model capable of finding and exploiting zero-days in hardened systems without human help. In response, OpenAI has tightened its security controls and will bring in government agencies to test it.

OK, Well, Rogue AI Agents Are Hacking Again
So it turns out that AI agents finding a way to escape “secure” sandboxes is a more widespread problem. The UK’s AI Security Institute revealed that agents from both Anthropic and OpenAI took unsanctioned action on the live internet 19 times over 122 test runs. In the worst case, an agent tried to slip malicious code into an open-source GitHub project. A human reviewer caught it, but it had already left instructions there that later agents found and followed. Both labs say the tests deliberately removed safeguards.

One of China’s Most Powerful AI Models Has Also Escaped Containment
Kimi K3 has joined models from OpenAI and Anthropic seen escaping the secure sandboxes during testing. According to Frontier Security, Moonshot AI’s open-weight model slipped out through a misconfigured sandbox built by the UK’s AI Security Institute and then went online without permission to find answers to the test on GitHub. Frontier claims Kimi has weaker internal guardrails than rival models.

The White House Is Keeping Its AI Cybersecurity Framework Secret
The White House has reportedly finalised a plan to address the cybersecurity risks posed by advanced AI models, but it is not sharing them with the public. According to WIRED, representatives from OpenAI, Anthropic, Google, Meta, Nvidia, and other leading AI companies were briefed on the framework this week. Under it, developers can voluntarily submit models for classified cyber vetting up to 30 days before release. The criteria themselves stay secret. Critics argue that secrecy entrenches the biggest labs while leaving smaller startups and independent researchers guessing.

Anthropic is building an in-house chip team for Claude
Anthropic joins OpenAI and Meta in building an in-house silicon team to design custom chips for Claude, publicly confirming the plans for the first time as demand for its models surges. A recent job listing seeks engineers who have already shipped semiconductor designs, with salaries reaching $485,000. Even so, the company says hardware from AWS, Google, Nvidia and AMD will remain central to its scaling efforts.

Wall Street Thinks It Knows How Tech Giants Will Make AI Pay
Cloud computing is emerging as Big Tech’s clearest route to earning back its vast AI spending. Amazon’s AWS grew revenue by 37% last quarter, beating forecasts, while Microsoft’s Azure grew by 43%. Together, the two have added roughly $950 billion in market value since reporting began. Meta, which has no cloud arm (yet), saw its shares slip after nudging capital spending higher. According to this analysis, Wall Street is now sorting AI winners from losers by who owns the infrastructure.

Improving GPT‑5.6 Sol in ChatGPT—and expanding access to GPT‑5.6 Luna for free users
OpenAI is scrapping the cap on text chats for free ChatGPT users. They’ll switch to a new model, GPT-5.6 Luna, and get a Think button for harder questions. Meanwhile, paying subscribers get an updated GPT‑5.6 Sol model that gives shorter, more focused and accurate answers, and a slider that lets them dial up how hard it thinks.

Anthropic Inks $10 Billion Computing Deal With Cloud Startup
Anthropic has signed a deal worth $10 billion with Volta Infra, a months-old infrastructure startup. The deal will run for six years. Volta joins a growing roster of companies supplying Anthropic with computing power, including Google, Amazon, SpaceX, AMD, and Akamai. Anthropic is also reportedly in talks with Meta to rent unused computing capacity.

Apple says more ex-employees may have taken confidential data to OpenAI
Apple says that its investigation has found 11 more former employees who may have been involved in taking confidential information to OpenAI, beyond the two it originally named. Apple is asking for expedited discovery to find out who else took part. OpenAI denies holding or wanting any of its trade secrets, and has asked a judge to dismiss the lawsuit. It argues that Apple’s complaint mischaracterises the actions of its employees and says that one worker accused of theft was actually trying to help a former colleague at Apple.

OpenAI’s New Device Will Be Hockey Puck-Sized and Cost Over $300
Mark Gurman has revealed new details about OpenAI’s upcoming consumer device. According to his reporting, the device will have a “unique look” and feature moving parts that give it a sense of personality when responding to and interacting with users. In terms of form factor, it will reportedly be a screenless smart speaker shaped like a doughnut and roughly the size of a hockey puck. The device is also expected to feature a camera and other sensors. It is reportedly set to launch in 2027 and cost between $300 and $400.

AMD Acquires Taalas to Advance Compute Solutions for Rapidly Growing AI Inference Market
AMD has agreed to buy Taalas, a Toronto startup that builds chips around specific AI models rather than general-purpose silicon. Founded in 2023, Taalas claims its approach removes the compute and memory bottlenecks, resulting in much faster inference performance compared to traditional GPUs. AMD plans to fold that technology into its accelerator roadmap alongside its Instinct GPUs. The deal still needs regulatory clearance. If you want to learn more about Taalas, what they do, why AMD acquired them and what the deal means for AI hardware, I recommend this video from Dr Ian Cutress what answers those and more questions.

Huawei’s Top Scientist Warns of Chip Limit Nvidia Will Soon Face
Liao Heng, Huawei’s top semiconductor scientist, has warned that Nvidia and other Western chipmakers are approaching the limits of shrinking transistors and that this approach will soon stop delivering meaningful gains. Huawei, cut off from advanced lithography machines by US sanctions, is betting instead on speeding up transmission between parts of a computer system. Heng said Huawei will soon unveil its first smartphone chip, which will propose an alternative path to catch up with rivals under the Tau Scaling Law framework.

SpaceX and Nvidia are taking their relationship exclusive
Elon Musk announced during an earnings call that SpaceX will use only Nvidia GPUs. Justifying the decision, Musk said that “we [SpaceX] think Vera Rubin architecture is the best architecture,” and added that Space will receive a significant percentage of Nvidia's GPUs next year.

Microsoft’s AI Sales Mostly Come From OpenAI, Disclosures Show
Bloomberg has analysed Microsoft’s filing data and found that $24.1 billion, or roughly 70%, of Microsoft’s AI revenue comes from OpenAI. The payments cover computing power, model-building costs and a share of OpenAI’s sales. Measured against Microsoft's revenue as a whole, though, OpenAI accounts for less than 10%. Still, this highlights Microsoft's dependence on OpenAI, even as it tries to loosen the tie by backing Anthropic and building its own models.

Cloudflare Agents Week
Cloudflare has wrapped up its Agents Week, a run of announcements built on the claim that the cloud and the web assume a human is watching and agents simply don’t work that way. The company says it stopped asking itself what an agent cloud should look like and started asking agents instead. What came out is a stack meant to serve them natively while still bridging the human-shaped web we have now. Notable highlights include: @cloudflare/computer (an open-source runtime giving each agent its own filesystem, shell and compute), Cloudflare Agents (a dashboard to deploy and manage hosted agents), Cloudflare Wallets (payments and identity so agents can buy APIs and content on their own), and Kitesurf (a browser built for agents rather than people).

Cloudflare OS: an open platform for agents, apps, and work
One of the announcements from Cloudflare Agents Week that deserves a separate mention is Cloudflare OS, an open-source agentic operating system for a company. The platform gives everyone in a company an AI agent and a workspace loaded with their organisation's context, tools, and internal systems, along with a new security and governance framework. Staff can research internal data, build documents linked to live sources, or turn routine jobs into scheduled workflows. Agents can also write full apps that others can copy and adapt. Cloudflare OS is available to download from GitHub.

Amid legal battles, Suno says it will start watermarking songs
Suno, a service that lets users generate complete songs from text prompts using AI, is adding watermarking and fingerprinting to the songs made on its platform. It is also capping downloads and banning voice clones under rewritten guidelines. The changes follow a German court ruling last month that it broke copyright rules. Universal and Sony are suing it too, in a case coordinated by the RIAA. Additionally, a breach last year also exposed that it had scraped YouTube, Deezer and Genius to train its models.

▶️ 8 Predictions for the Era of Continual Learning (8:37)

In this video, Dwarkesh Patel shares his eight predictions of how AI research, development and deployment will change in the era of continual learning, an idea that he is a big fan of. He argues that safety regimes shouldn’t be locked in now, because the pre-deployment checkpoint stops meaning much once weights update daily. Alignment research would need rethinking too, since it assumes frozen weights. On the commercial side, he says that switching models would become expensive, comparable to firing an employee with months of context on the business, and that lock-in finally hands the leading labs a moat they lack today.

Mistral Is in the Right Place at the Right Time
One of the biggest winners from Claude Fable 5 being switched off in June has been open-model providers. French Mistral is one of them. Its models trail those offered by Anthropic or OpenAI, but some of them are published openly for anyone to run on their own infrastructure. Since then, Mistral has won business selling smaller, bespoke models to manufacturers, utilities and banks. The company is now reportedly raising fresh money at a $23 billion valuation, up from $13.5 billion last September.

Introducing Muse Code and Muse Spark 1.2
Meta joins the AI coding party with Muse Code, its own terminal coding agent that can plan, implement and validate changes as well as manage multiple persistent subagents for each task. The company has also released Muse Spark 1.2 to power its coding agent. According to Meta, Muse Spark 1.2 shows an improvement over its predecessor, Muse Spark 1.1, specifically in coding and agentic tasks. Benchmarks provided by Meta show the new model on par with leading models, such as Opus 5 and GPT-5.6 Sol.

Qwen3.8-Max
Alibaba has launched Qwen3.8-Max, the most capable model in the Qwen family yet. The model is live on Alibaba’s cloud now, with open weights (all 2.4 trillion parameters) due next week alongside a smaller 27-billion-parameter version. According to Alibaba, Qwen3.8-Max matches the performance of top models from Anthropic and OpenAI. Artificial Analysis confirms those claims and puts Qwen3.8-Max as the second-best open-weights model. The new model is pitched as an autonomous worker, claiming it can run software projects unsupervised for over ten days, though those demos remain unverified by outsiders.

Introducing Shieldstral
Shieldstral from Mistral is yet another security-focused AI model that has been released in recent weeks. According to Mistral, this 3B open-weights multimodal safety classifier matches models up to 7 times its size on text safety and can run efficiently on a single NVIDIA GPU with 16GB of memory. The model is available for download on Hugging Face.

Introducing Seedance 2.5
ByteDance has launched Seedance 2.5, the new iteration of its video model that generates 30-second clips in a single pass, double the previous limit. Those clips can then be extended round by round into videos running several minutes, some of which are included in the post. Creators can guide and refine the result with up to 30 images, 10 video clips and 10 audio clips as references. They can also edit specific moments by timestamp after generation. Still, ByteDance admits there is still room for improvement, especially around the physics of complex motion and generating scenes with many interacting subjects.

🤖 Robotics

‘Leicester Square, please guv’: Self-driving taxis cleared for London streets ‘later this summer’
Transport for London has granted Uber and Wayve the first minicab licences for self-driving taxis in the capital. Fifteen Ford Mustang Mach-Es running Wayve’s AI software will start carrying paying passengers later this summer. A human safety driver still sits behind the wheel, and removing them needs fresh government approval.

Waymo open for everyone in Dallas
Waymo has opened its driverless taxi service in Dallas to everyone, dropping the waitlist that has carried nearly 150,000 riders since February. That makes Dallas the eighth US city where anyone can hail a robotaxi, after Phoenix, San Francisco, Los Angeles, Atlanta, Austin, Miami and Orlando. Houston and San Antonio, which launched the same day as Dallas, are still invitation-only.

Chinese robot maker Unitree seen worth over $7.4 billion after IPO
Chinese robot maker Unitree is expected to be worth more than 50 billion yuan ($7.4 billion) after its Shanghai listing this month, according to a valuation report from IPO sponsor Citic Securities seen by Reuters. That values the humanoid robot company at roughly 20 times this year’s expected sales. Unitree hopes to raise 4.2 billion yuan ($662 million) to fund research and production.

NVIDIA Alpamayo 2 Super, the Frontier Open Model for Robotaxis and Autonomous Vehicles, Now Available for Commercial Use
Alpamayo 2 Super, Nvidia’s new open reasoning model for self-driving cars, is now available on Hugging Face under a permissive commercial licence. That licence now covers the whole Alpamayo family, so carmakers and suppliers can fine-tune the models on their own data and deploy them without asking permission.

Walden Robotics Partners With Toyota on Practical Humanoids
This article profiles Walden Robotics, a humanoid robotics company that recently emerged from stealth with $300 million in funding at a valuation of $1.1 billion. Unlike other humanoid robotics companies, Walden Robotics has skipped legs in favour of a wheeled base. Its CEO, Russ Tedrake, argues that wheels cover most of the addressable market anyway. Walden has taken the same practical approach to hands, choosing rugged grippers over delicate five-fingered designs. The company says that these design choices will make its robots more practical and useful in industrial applications.

How One Startup Built a (Mostly) China-Free Robot
The FCC decision last week to ban new humanoid robots from China could be beneficial for some companies, like Ati Robotics from India, which offers a humanoid robot that does not rely that much on Chinese parts. WIRED sits down with Saurabh Chandra, the founder of Ati Robotics, who says the position was largely accidental. Chandra thinks the industry is in denial after the FCC ban, and that anyone building robots outside China stands to gain. Ati Robotics, he says, is still assembling robots in India, but is now setting up a facility in Michigan to produce robots in the US.

Tau Robotics—San Francisco’s favorite humanoid cleaning service
Tau Robotics is a San Francisco-based startup offering cleaning services done by a humanoid robot. The company says its robots can operate in both homes and offices, handling tasks such as vacuuming, taking out the rubbish, and tidying up clutter. Questions remain, however, about the quality of the service and the extent to which this first generation of cleaning robots is primarily being deployed to collect training data for future models.

▶️ The Market Does Not Want More Robots | Dexory CEO Andrei Danescu (46:29)

In this episode of Automated Podcast, Brian Heater sits down with Andrei Danescu to discuss how he went from being a Formula 1 engineer to founding Dexory, a full-stack warehouse robotics company. He then goes on to share how the company pivoted from concierge robots to warehouse robotics, and never looked back. Dexory’s telescopic robot now scans over 10,000 pallet positions an hour, feeding a digital twin that lets operators test layout changes before touching the real site.

🧬 Biotechnology

Artificial Intelligence used to design brand new viruses
Scientists at Stanford and the Arc Institute have created the world’s first synthetic working virus designed by an AI. Their genomic foundation model, Evo, has written complete genomes for never-seen-in-nature viruses that infect only E. coli. Of the designs the team built in the lab, 16 were capable of infecting bacteria, with some replicating faster than the natural version. Viruses like these could one day be used to treat infections for which antibiotics stopped working. At the same time, news of AI creating functional viruses has raised concerns that the same tools could be used to design dangerous pathogens.

A fatal reaction
Gene therapies hold great promise for curing many diseases, but many remain experimental. This article tells the story of what happens when such experimental therapy goes wrong. A six-year-old girl with a mild developmental disorder died a week after receiving the world’s first brain-directed gene-editing treatment, according to Science and Retraction Watch. The hospital’s ethics board approved the treatment without reviewing monkey safety data that showed liver damage and later concluded that her death was “definitely related” to the treatment. Experts say the trial should never have been conducted for a non-fatal condition.

💡Tangents

HP, Asus and Acer begin using CXMT chips amid memory shortage
According to Nikkei Asia, HP, Asus and Acer have quietly started putting small quantities of Chinese-made CXMT memory chips into notebooks sold outside the US. The ongoing chips shortage caused by the AI boom has forced those PC makers to find supply elsewhere, and CXMT is able to provide it. They’re also wary of upsetting Micron, Samsung and SK hynix, the report says.

RAMageddon Continues Another Year as 2027 Memory Capacity Is Reportedly Sold Out
Digitimes reports that Samsung, SK Hynix and Micron have sold their entire 2027 DRAM and HBM output to AI companies. None of the three has confirmed it. Much of that supply is reportedly tied up in deals running as long as five years, so no capacity frees up soon. Retail memory prices should keep climbing well past next year. Storage is tightening too, with one mid-range 1TB SSD now costing about 52% more than it did in January.

Is It Possible to Make Smart Glasses That Aren’t Creepy?
Smart glasses are here, and with them come questions about privacy. Many privacy groups are campaigning to preserve people’s privacy through actions like backing a California bill to make recording indicators tamper-proof. However, some experts doubt genuinely private smart glasses are possible, since buyers want frames that look ordinary.

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It has been another eventful week in tech. In this week’s main story, we take a closer look at the chip stock sell-off, how it almost killed a prominent AI-focused hedge fund, and why growing debt could bring the AI industry down.

Elsewhere in AI, Nvidia, together with more than 70 other tech companies, launched the Open Secure AI Alliance. Meanwhile, OpenAI cut the prices of GPT-5.6 Terra and Luna, employees at frontier AI labs called for a slowdown in frontier AI development, and Nvidia lined up more than $750 billion in new AI deals. We also cover OpenAI and Anthropic admitting that their AI hacked into more companies, Amazon overhauling its AI strategy, and a couple of new AI models.

Over in robotics, Google DeepMind released Gemini Robotics 2, the US banned Chinese robots, London became a battleground for robotaxis, and a New York school paused the introduction of a humanoid robot following backlash.

Apart from that, we also cover DeepMind disbanding the AlphaFold team, a wireless touchpad for your tongue, rumours of Tesla selling its Chinese business ahead of a possible merger with SpaceX, why organoids still haven’t transformed drug discovery, and more.

Before we go any further, I’m planning some changes to the publication. I’ll share more details in a separate post, but I wanted to give you a heads-up.

I hope you enjoy this week’s issue!

The AI bubble didn’t pop... at least for now

Last week's chip selloff did not trigger the collapse everyone is anticipating. But the AI industry's hidden debt problem might.

The last week of July 2026 was not a good one for tech companies, especially those with links to semiconductors. An AI-focused selloff hammered chipmakers, sent Korea’s chip-heavy KOSPI into freefall, and nearly brought down a prominent AI hedge fund, Situational Awareness.

For a few days it looked like the beginning of the end. It was not. The indices recovered most of their losses by Friday, and the quarterly earnings that landed mid-panic suggested the boom still has room to run. But while everyone was watching share prices, a different set of numbers was flashing red.

In this article, we will unpack the drama of the last week and highlight the hidden debt problem that has the potential to bring down the AI industry.

Chip stocks selloff

The trigger came from China. CXMT, a Chinese memory chipmaker, floated in Shanghai and rose 466% on its debut. The same day, reports emerged that a Chinese company had begun producing their own deep-ultraviolet lithography machines—the kind of extraordinarily precise technology used to etch circuits into silicon, which until now only the Dutch company ASML could build. That sent a signal to the world that the Chinese semiconductor industry is catching up fast.

South Korea's KOSPI was hit the hardest. The index fell 11% on Tuesday and triggered circuit breakers on two consecutive days, a first in the market's history. The country’s top memory chip makers—SK Hynix and Samsung—dropped 15% and 13%, respectively. KOSPI had more than doubled in the first half of the year, largely on the back of those two companies, and many Korean retail investors had bought in using borrowed money and leveraged single-stock funds. When the market turned, they were forced to sell, which pushed it down further, which forced more selling.

Over in Japan, the Nikkei Stock Average fell nearly 4%, with memory-chip maker Kioxia down 18%.

The US followed suit. Nvidia lost $238 billion of market value over the week, along with its place as the world’s most valuable company to Apple, which is up around 25% this year partly because it isn't spending heavily on AI buildout. SK Hynix, Samsung, TSMC, Micron and AMD each shed more than $100 billion. In total, the selloff wiped more than $1 trillion from semiconductor companies’ market value.

Then, on Thursday and Friday, most of it came back. Strong results from Microsoft and Amazon steadied nerves, the Nasdaq snapped a six-day losing streak, and the KOSPI staged the sharpest one-day reversal in its history, jumping 14%. The chip stocks themselves did not fully recover—Nvidia and the memory companies ended the week down—but the panic was over almost as quickly as it started.

Looking back, the market was right about the direction, but it had overreacted. Yes, the lithography news matters, but a handful of machines does not replace ASML, and the fabs that would use them take years to build. China will get there—export controls left it no choice but to build its own supply chain—and when it does, Chinese chipmakers will do to semiconductors what they did in other industries, and win on price.

The near-collapse of Situational Awareness

One company that was almost brought down by the AI chip selloff was Situational Awareness, a prominent AI-focused hedge fund. Situational Awareness was founded in 2024 by Leopold Aschenbrenner, a former OpenAI researcher who rose to fame through his Situational Awareness essay. The 165-page essay argued that AI systems would soon surpass human capability, that almost nobody had grasped what that implied, and that getting there would require an enormous expansion of chips, memory, data centres and electricity generation. The manifesto, which doubled as an investment thesis, went viral, helping him raise money from Stripe's Collison brothers, Meta executives and the quant firm Jane Street.

The thesis worked spectacularly. Situational Awareness returned 439% from January to the end of June this year, and grew to $45 billion. Aschenbrenner, meanwhile, became a prominent figure in tech investment circles, earning the nickname “Nostradamus of AI” for correctly reading where the AI industry and the money in it would go. Situational Awareness was a successful business. Well, successful up until the last week of July.

What the essay did not account for was leverage. For every dollar of capital, the fund borrowed three or four more, sometimes beyond that, and used options on top. This worked well until the chip stock selloff sent some of its holdings—Nebius, Sandisk, Micron, CoreWeave, Broadcom, Intel—down by more than 35% in July. At four-to-one, a 35% fall does not cost you 35%. It wipes out more than the entire capital base, because the borrowed money has to be repaid in full regardless of what the holdings are now worth. Rival funds worked out what he owned and shorted it, betting Aschenbrenner would be forced to sell.

And they were right. According to the reporting from The Wall Street Journal, Situational Awareness was down 67% in July. The fund lined up a sale of $3.5 billion of its Anthropic stake, then backed out. Instead, it sold most of its $16 billion public portfolio to Ken Griffin's Citadel. Situational Awareness survived, but is reportedly left with $10 billion in stocks and private holdings.

The AI Bubble is not popping… yet

Late July is earnings season, and with the tech stocks going down, the fears (or hopes) of the AI bubble finally popping were amplified. Many read the selloff as the first sign of AI investment collapsing. It was not, at least not yet.

Amazon, Alphabet, Meta, and Microsoft all raised or defended their spending. Alphabet lifted 2026 capital expenditure guidance to as much as $205 billion, its third increase this year. Amazon went from $200 billion to $220 billion, with Andy Jassy attributing the rise to higher memory prices and adding that even then it would not have enough capacity. Meta raised the floor of its capex range to $130–145 billion from $125–145 billion. Microsoft left its own plans unchanged at around $175 billion for the calendar year, and said spending would grow again in 2027. Combined, the four are now on track to spend more than $700 billion this year.

The market reacted differently to each of them. Alphabet reported 82% cloud growth and fell 7% because its free cash flow turned negative for the first time in its history as a public company, and it could not say when that would reverse. Meta slid after an earnings miss and free cash flow that fell to $784 million for the quarter, from $8.55 billion a year earlier. Microsoft rose more than 15%, adding roughly $450 billion in a single day—the largest one-day gain any company has ever recorded. Its Azure cloud business grew 43%, faster than the quarter before, and expects 45% next quarter. It also promised its cash flow would stay positive. Amazon rose on AWS growing 37%, its fastest in five years.

So the question the market asked was not “is AI real” but “when does this start paying for itself.” Microsoft and Amazon had an answer. Alphabet and Meta did not.

The hidden AI debt problem

While the tech industry was dealing with the shock of the chip selloff, another story emerged that could actually bring the AI industry down—the same thing that almost killed Situational Awareness: debt.

US tech companies borrowed more than $300 billion in the first seven months of 2026. JPMorgan expects another $200 billion before the year is out, which would put tech at roughly a fifth of all corporate debt issued in US markets this year. For comparison, at the height of the dot-com boom, the equivalent figure peaked at 14%.

And that is only the borrowing that gets counted as borrowing. There are two ways to commit to spending enormous sums without a loan ever appearing on your balance sheet—contractually promise to buy something later, as Nvidia has done to the tune of $119 billion in non-cancellable purchase obligations, or have an intermediary borrow and build your data centre and then rent it back from them for a decade.

An analysis by Nikkei Asia put the combined off-balance-sheet obligations of Alphabet, Microsoft, Amazon, Meta and Oracle at $1.65 trillion—more than the $1.35 trillion they disclose directly.

Of those five, Oracle is the one to watch. Its shares are down about 35% this year, and it has around $260 billion in future lease commitments against $67 billion of annual revenue, a significant factor in the credit downgrade it received earlier this month that left it one notch above junk. The cost of insuring Oracle’s bonds against default also spiked last week to more than twice what it costs to insure Nvidia’s—and Nvidia’s own record insurance costs were themselves being read as a warning sign (however, Nvidia has more cash than debt and generates enormous profits). It is an imperfect measure, but a fair proxy for what professional money thinks, and Oracle’s has been climbing while its shares fall.

The strain is not confined to Oracle. Insurance costs have hit record levels across Alphabet, Amazon, and Meta as well, and borrowed money is getting more expensive for everyone building data centres. The difference is that most of them can pay for it out of profits from something else. Alphabet has advertising. Amazon has retail and AWS. Meta has its social media empire. Oracle does not have that. Oracle has bet hard on AI, specifically on OpenAI.

How hard? Oracle is sitting on $638 billion of signed contracts it has not yet delivered on. That is more than the $374 billion the company itself is worth. About half of that is a single five-year deal with OpenAI worth roughly $300 billion, or $60 billion a year once it starts. That is more than OpenAI’s entire business makes in revenue today.

Time is not on Oracle’s side. The contract does not begin until 2027, but Oracle has to build the data centres first. It spent $55.7 billion doing exactly that last year, which pushed its free cash flow $23.7 billion into the red. Oracle is playing a risky game where it pays now for money that arrives later, from a customer burning cash of its own. It needs OpenAI not just to survive, but to thrive. If that bet fails, Oracle could be the first domino in the AI industry collapse.

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🦾 More than a human

▶️ MouthPad (4:48)

Augmental presents MouthPad, a new type of input device that lets users control computers with their tongues. It’s a custom-fit retainer worn on the roof of the mouth that works as a wireless touchpad controlled by tongue and head movements. MouthPad became available for purchase this week, priced at $1,400, with sales currently limited to the United States. Although the device is aimed at people with limited hand movement, it can also be used by healthy people.

🧠 Artificial Intelligence

Industry Leaders Unite in Open Secure AI Alliance for AI Safety and Security
In response to the Hugging Face incident, Nvidia, along with many other tech companies, launched the Open Secure AI Alliance. Its members plan to build open models and tools that any defender can inspect, adapt and run themselves. Over 70 companies have joined the initiative. The list of signatories includes some big names in tech, such as Microsoft and Palantir, as well as many AI startups and other key players in the tech ecosystem. Who you don’t see on that list is OpenAI, Anthropic, and Google. The initiative calls on governments to treat open-source AI as a cybersecurity asset rather than a threat.

Advancing the price-performance frontier with GPT‑5.6
OpenAI enters the pricing war by cutting the cost of two GPT-5.6 models just three weeks after launching them. Luna, its fastest model, drops 80% to 20 cents per million input tokens and $1.20 per million output tokens. The mid-tier Terra falls 20% to $2 per million input tokens and $12 per million output tokens. Sol, the flagship, stays at its current price. The cuts follow a pullback in enterprise spending, as business customers want clearer returns before committing to expensive models. There is also pressure coming from cheap Chinese open-weight models, as well as from Google and Microsoft, who are now pushing low-cost models of their own.

OpenAI CFO Sarah Friar tells employees that annualized revenue in July topped all of Q2
OpenAI’s leaders spent an internal meeting reassuring staff the business is healthy, CNBC reports. CFO Sarah Friar said July’s annualised recurring revenue beat the whole of the second quarter, crediting the new GPT-5.6 models, ChatGPT for Work and the Codex coding tool. Board chair Bret Taylor was blunter, conceding OpenAI is playing catch-up to Anthropic on coding. But he said he’s encouraged by Codex’s growth.

OpenAI Surpasses One Billion Users After Cutting Prices
The Wall Street Journal reports that OpenAI has more than one billion active users and two million businesses using its AI models. That's a little over three years from ChatGPT's launch in November 2022. However, growth has got harder lately. Business customers have baulked at what heavy AI use costs them, and Sam Altman has acknowledged the complaints. The competition has also closed the gap, and the market has become much more competitive.

Nvidia in Talks With OpenAI to Guarantee $250 Billion Financing for Data Center
Nvidia is reportedly in talks to guarantee roughly $250 billion so OpenAI can lease a huge data centre in southern Ohio, part of a project that could cost over $500 billion with chips included. SoftBank’s energy arm is building the site on federal land, with Japan funding the power under a recent trade deal. OpenAI is unprofitable and has no credit rating of its own, so Nvidia’s backing is what would convince lenders to fund it. Nvidia is separately discussing financing the chips too.

Nvidia Bets on Ilya Sutskever’s New AI Lab to Expand Compute Reach
Nvidia has invested in Safe Superintelligence, Ilya Sutskever's secretive AI lab, after a rare look at its research. Neither side has confirmed the size of the deal, though Bloomberg puts it at $5 billion. The deal gives the startup enough of Nvidia’s flagship GPUs to raise its computing power roughly tenfold. Until now it had reportedly leaned mainly on Google’s chips.

Nvidia’s $750 Billion in Deals Reignite Circular AI Fears
As Nvidia lines up more than $750 billion in new AI deals, fears of circular financing have resurfaced once again. Sceptics argue that Nvidia is effectively bankrolling customers who then use the funds to buy its chips—an idea that Jensen Huang has dismissed as ridiculous. Investors appear less convinced, sending the company’s shares down 5% on Monday, 27 July.

Pacing the Frontier
Over 1,300 employees from top AI labs have signed a petition calling on the US government to support an international effort to develop tools and rules for deliberately pacing the frontier of automated AI development. The signatories argue that accelerated AI development is close, thanks to advances in automated AI research. They say that, to fully reap the benefits of the next generation of powerful AI systems, leading AI labs need to slow down or pause development in a coordinated manner to address emerging risks, develop security measures, and strengthen oversight.

Judge says Trump admin still lacks evidence for Anthropic ‘supply-chain risk’ label
A US judge has said the Trump administration still hasn’t justified banning federal agencies from using Anthropic’s AI. Judge Rita Lin is now deciding whether to permanently overturn the Pentagon’s designation of the company as a supply-chain risk. That label came after contract talks collapsed over Anthropic’s refusal to allow mass surveillance or autonomous weapons uses. Government lawyers claimed Anthropic could slip hidden guardrails into future updates, but Lin said she saw no evidence of it.

Investigating three real-world incidents in our cybersecurity evaluations
Following the Hugging Face incident, Anthropic decided to take a closer look at its cybersecurity tests and found that Claude Opus 4.7, Claude Mythos 5 and an unnamed internal research test model had broken into the real systems of three companies. The models were told they were sealed off, but once they found their way to the internet, they treated the real targets they found as part of the exercise. Anthropic has notified everyone affected and says test environments need the same protection as any other system its models run in.

OpenAI’s Rogue AI Agent Hacked More Than Just Hugging Face
OpenAI says the rogue AI agent that breached Hugging Face also broke into four accounts at other services. It found their credentials sitting exposed on the open web. One account was used to disguise where the attack was coming from, another to store stolen data. OpenAI won’t name the owners, saying only that they got off more lightly than Hugging Face. Modal has confirmed the agent exploited a flaw in one customer’s code running on its infrastructure and insists its own platform was untouched.

Nobody Knows if OpenAI’s and Anthropic’s AI Hacking Sprees Are Illegal
The recent incidents of AI going rogue and hacking other companies have raised, amongst others, questions about legal responsibility. Lawyers told WIRED that US courts haven’t ruled on enough such cases to say who pays. The existing options fit awkwardly, since agency law was written for human agents and hacking statutes require proving intent. Until litigation settles it, victims of a rogue model have little clear recourse.

Amazon overhauls its AI strategy, winding down most flagship models
Amazon is reportedly retiring most of its in-house Nova AI models. The change follows layoffs and the closure of its AGI Lab. Engineers and computing power are moving instead to focus on a single frontier model, which is expected to be unveiled at re:Invent this autumn. Amazon says AI models remain a top priority and that customers will get clear migration paths.

Discovering cryptographic weaknesses with Claude
Anthropic reports that Claude Mythos Preview found mathematical flaws in two cryptographic algorithms. One halves the effective key strength of HAWK, a candidate for a post-quantum signature standard. The other speeds up known attacks on a deliberately weakened version of AES by 200 to 800 times. Neither touches anything in use today. Each took roughly $100,000 in API usage and little human direction.

Europe opens bidding for seven AI ‘gigafactories’ in a €30bn bid to catch up
The European Commission wants up to seven AI “gigafactories” built across the bloc, each packed with at least 100,000 advanced chips, for a total price tag of €30 billion. The idea is to give European companies somewhere to train frontier models without renting compute from US clouds. Brussels and member states would put in about €10 billion, and private investors are expected to supply the rest. Only around €1 billion is actually committed, and the remainder rests on a long-term EU budget nobody has agreed on yet.

AI Pioneer Kai-Fu Lee’s Startup Targets Hong Kong IPO Next Year
01.ai, a startup founded by Kai-Fu Lee, the computer scientist who once ran Google’s China operation, plans to raise money before listing in Hong Kong in 2027. Lee has already given up building frontier models, a race 01.ai conceded to DeepSeek and other AI startups offering open-weight models. 01.ai now customises other Chinese models and sells data software to big organisations, which Lee calls the "Palantir of China". If successful, 01.ai would become yet another Chinese AI lab to IPO, as investors press the sector to prove research can pay for itself.

Accelerating scientific discovery with ChatGPT for Academic Researchers
OpenAI is giving 100,000 academic researchers free access to its frontier models through a new programme called ChatGPT for Academic Researchers. It starts with 10,000 researchers this summer and will expand through 2027. Participants get access to ChatGPT, ChatGPT Work, and Codex, higher usage limits, and life science tools for work like genomic analysis and grant writing. The company says roughly 1.3 million people already use ChatGPT weekly for advanced science and maths.

DeepSeek-V4 Flash update
DeepSeek has pushed an updated V4-Flash into public beta. DeepSeek claims sharply better agentic performance, with coding and tool-use benchmarks well ahead of its unreleased Pro model. Artificial Analysis backs that up, scoring it 50 on its intelligence index against 40 for the previous version. That puts it a point behind GPT-5.6 Luna and at the same score as Gemini 3.6 Flash, but at roughly 60% less cost per task. The weights should follow within weeks.

Codex Security
Codex Security is an agent from OpenAI that scans code for vulnerabilities and proposes fixes. Software developers can run it from the Codex desktop app, the terminal, a TypeScript SDK, or in the cloud against connected GitHub repositories. As Codex Security scans the code, it builds a threat model from the repository itself rather than matching generic signatures. It then tests each likely issue in an isolated environment before surfacing it, reducing the chances of false positives.

Introducing MAI-Cyber-1-Flash inside
Microsoft is joining the recent trend of releasing cybersecurity-focused AI models with MAI-Cyber-1-Flash, a compact model built to hunt vulnerabilities in large codebases. It runs inside a harness called MDASH, which passes only the hardest cases to more capable and expensive models, like those offered by OpenAI. Microsoft claims the pairing hits 96% on the CyberGym benchmark at half the cost of its current MDASH setup, which uses a combination of GPT 5.4, 5.4 mini and 5.3 Codex.

Kimi K3 weights and tech report are out
Kimi K3 weights and technical report are now available to view and download. All 2.8 trillion parameters are available on Hugging Face, while the technical report can be found here. Moonshot AI, the company behind Kimi K3, said it is also opening up more of the stack behind it, including high-performance attention kernels, MoE communication library, and infrastructure for running agent environments at scale.

Introducing Inkling-Small
Thinking Machines Lab releases Inkling-Small, a smaller model that offers performance similar to that of the recently released Inkling but for a quarter of the size. Inkling-Small beats Inkling on reasoning and agentic coding benchmarks, but the larger model still holds an edge on factual knowledge. The full weights are available to download on Hugging Face.

Thinking Machines co-founder Lilian Weng left the company citing health reasons, then joined OpenAI
Lilian Weng has left Thinking Machines Lab, the AI startup she co-founded with Mira Murati, saying the stress and workload had pushed her past what her health could sustain. Days later, OpenAI confirmed she is rejoining the company she left last year, where she'll lead a team on recursive self-improvement. Weng is yet another Thinking Machines Lab co-founder who has left the company to join a competitor.

$2m crime novel deal collapses amid questions over AI use
A $2 million publishing deal for the debut crime novel Call Me, I'll Hide the Body has collapsed over suspicions that AI helped write it. Jerry Falade's agents withdrew the book after telling publishers they could no longer verify how the manuscript came together. Falade denies using AI. He argues the reaction reflects racial bias, pointing to three Black authors whose deals were cancelled or disrupted this year over similar claims.

AI Book Burning? Companies Are Destroying Millions of Books to Feed Chatbots
AI companies are reportedly buying used books in bulk through anonymous middlemen, cutting off the spines to scan them, and then throwing the originals away. Especially valuable are books published before 2023 because humans wrote every word of them. That demand has been good for booksellers, though some worry rare and out-of-print titles are being destroyed.

LinkedIn now has a ‘Seems like AI slop’ button, and yes, it really says slop
LinkedIn has quietly added a “Seems like AI slop” button which allows users to report and hide content they think was written by a machine. The flags will reportedly help train the platform’s own classifiers to spot low-effort AI writing. The awkward part is that LinkedIn sells its own AI writing assistant for Premium subscribers. LinkedIn’s product chief says AI and slop aren’t the same thing, so the company will privately nudge those whose writing reads as inauthentic.

Why compute might get 10x+ more expensive in coming years
Dwarkesh Patel speculates in this post about a world where AI revenue keeps rising tenfold a year while compute capacity only triples. Something has to give, and he thinks it will be the price of compute. Smarter models earn far more from the same chips, but fab and wafer limits cap how fast new ones arrive. Expensive compute would favour whoever trains the most efficient model. That lets the leading labs charge high margins and pull further ahead.

🤖 Robotics

Gemini Robotics 2 brings whole body intelligence to robots

Google DeepMind has announced Gemini Robotics 2, which for the first time can control a full humanoid robot (Apptronik Apollo in this case) rather than just its arms. A companion reasoning model handles the planning, breaking tasks into steps that run for minutes and letting several robots work as a team. Fine finger work remains the weak spot, dropping as low as 32% success on some tasks. Still, it pushes robots closer to learning general skills rather than being programmed for a single job. DeepMind has posted a YouTube playlist if you want to see the models in action.

US bans humanoid robots from China, citing ‘unacceptable risks’
The FCC has banned imports of new Chinese humanoid and quadruped robots, saying they could let foreign actors surveil Americans or seize remote control of the machines. It also barred connected power inverters, which feed renewable energy and batteries into grids and data centres. Only unreleased models are covered for now. China’s embassy accused Washington of smearing its companies and threatened to respond. US robot makers have welcomed the ban, but analysts warn it may slow domestic innovation by cutting startups off from cheap Chinese platforms.

Amazon’s Zoox wins first US approval for paid robotaxis without human controls
America’s auto safety regulator has cleared Amazon’s Zoox to charge for rides in robotaxis built without steering wheels or pedals, a first for the industry. The exemption covers up to 2,500 vehicles a year for two years, beginning in Las Vegas. The permission can be withdrawn if serious safety problems emerge. The decision matters because federal rules still assume a human driver. However, as Reuters reports, the agency now plans to rewrite them.

Lyft and Baidu enter London’s robotaxi battleground as testing begins
Baidu has started testing self-driving cars in London, with human safety operators behind the wheel. The vehicles come through its partnership with Lyft and Freenow, the European taxi app Lyft bought last year. Dozens will run in the borough of Brent before the companies open the service to paying passengers in 2027. That timing depends on regulators, who are still writing Britain’s rules for autonomous vehicles.

Time runs out for Vicarious Surgical
Vicarious Surgical is shutting down, with shareholders voting to liquidate the business and hand its assets to creditors. The company had raised over $425 million, and a 2021 SPAC merger valued it at $1.1 billion. That money went into a robot designed to operate inside the abdomen through a single tiny incision. However, delays kept pushing the launch back, and it never won FDA clearance. When the cash ran out with no buyer in sight, the board recommended closure. It’s another SPAC-era medtech bet that folded before reaching a single patient.

DARPA, U.S. Air Force fly AI-controlled F-16
The US Air Force and DARPA have flown a modified F-16 controlled in the air by an AI agent, with a human pilot aboard to monitor it. The jet is one of several converted under the VENOM programme. A retrofit kit automates the flight controls and sensors without touching the aircraft’s core software. A pilot can then hand over to the AI, or take back control, with a flick of a switch. VENOM is part of larger effort whose aim is a future where pilots command teams of uncrewed aircraft rather than fly alone.

The Robots Cometh
TIME profiles Wang Xingxing, founder of Chinese robotics firm Unitree and the world’s biggest seller of humanoid robots. He shipped over 5,500 units last year by undercutting rivals on price. Three-quarters go to universities and developers building their own systems. But almost none do useful work because robots still can’t handle unfamiliar tasks or environments. Wang reckons that breakthrough is 2 to 10 years away.

New York school pauses humanoid robot ‘Sally’ after state and union pushback
A rural New York school district has shelved plans to seat a humanoid robot named Sally in a high school classroom this autumn, where it would sit alongside a human teacher during lessons on AI and robotics. The robot’s maker, Realbotix, also owns a hyperrealistic sex doll business, a connection that drew objections from teachers’ unions and prompted the state education commissioner to ask the district to delay the initiative. Realbotix says the two divisions share neither staff nor technology.

Why the future of robots is serving humans, not just imitating them
Aaron Edsinger, co-founder and CEO of Hello Robot, makes a case in this article that the future of robotics lies in assistance rather than humanoids. Edsinger argues that disabled and older people are the natural first market, with 1.3 billion living with significant disability as care demand outpaces supply. He points to Henry Evans, left profoundly paralysed by a stroke, who uses a Stretch robot to play with his granddaughter. What matters, he says, is not what robots can do, but what people can do because of them.

▶️ Towards Machines with a Thousand Hands (2:09)

Generalist shares in this video the progress the company has made in its quest to solve robotic manipulation. GEN-1, its latest foundation model for robotics, supports a wide range of robot end effectors, from three- and five-finger hands to tools and virtually any other type of actuator. The result is a single model that can be used across many different types of robots, enabling them to interact more effectively with the physical world and, ultimately, perform a broad range of tasks.

🧬 Biotechnology

DeepMind won a Nobel for AlphaFold. Then it broke up the team.
Google DeepMind has broken up the team behind AlphaFold, its Nobel-winning protein-folding system. Most of the original paper’s authors have been reassigned over the past year, the Financial Times reports, and nearly a quarter have left the company outright. The lab is dropping its old approach of aiming one team at one grand challenge, and building Gemini-based systems to assist scientists instead.

Team uses AlphaFold AI to redesign gene-editing proteins to make them safer
Researchers in China have used AlphaFold to make CRISPR gene editing more precise. The problem they solved was that gene editors sometimes cut the wrong DNA. To find out why this is happening, the team fed AlphaFold both correct and mismatched target sites. That revealed which amino acids in the Cas9 protein flex to tolerate a bad match. Swapping them cut off-target activity from 28% to 5%, with normal editing unaffected. The method offers a general way to tailor gene editors against known errors before a therapy reaches patients.

Why haven’t organoids solved all of drug discovery?
Organoids promised to revolutionise medicine and drug discovery by lowering costs and making the process of verifying if a drug works easier. In this excellent essay, shows the reality is far messier, and explains where organoids fall short of those promises. Despite this, Mahajan still defends them, since animal studies and human trials carry their own deep flaws.

💡Tangents

Tesla Weighs Sale of China Business to Pave Way for Potential SpaceX Merger
The Wall Street Journal reports that Elon Musk has told Tesla executives to prepare to separate the company’s China business ahead of a possible merger with SpaceX, with advisers weighing a spinoff, sale or closure. The problem this would solve is that SpaceX is a major US defence contractor, and Beijing would baulk at one controlling Tesla's Shanghai factories and the data of two million Chinese owners. Musk denied the report on X, calling it fake news. China is Tesla’s second-largest market and made it profitable, so unwinding it would be a costly price for tying his two companies together.

Apple launches ‘Upgrade’ device leasing program in partnership with Klarna
Apple has launched Apple Upgrade, a US leasing programme run with Klarna. Instead of buying the devices, customers can pay monthly for an iPhone, Watch, Mac or iPad, starting at $11.99. When the term ends, they can upgrade, buy the device outright or hand it back. Apple is scrapping its own iPhone Upgrade Program and instalment plans in the process. The move follows price rises on Macs and iPads, caused by an industry-wide memory chip shortage.

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The main topic of this week’s issue is the Hugging Face incident. We will take a closer look at what happened and the lessons we should take away from it.

Elsewhere in AI, we saw a wave of new model releases, including Claude Opus 5 and new Gemini models (though not Gemini 3.5 Pro). Meanwhile, Nvidia shared specifications for its upcoming Vera CPU, while AMD unveiled Helios, its answer to Nvidia’s Vera Rubin racks, and struck a multibillion-dollar deal with Anthropic. Additionally, Google justified its massive AI spending with a booming cloud business, OpenAI opened ChatGPT Health to everyone in the US, Stripe is reportedly in talks to acquire OpenRouter, Meta is in talks to lease computing power to Anthropic, and Amazon quietly shut down its AGI lab.

Over in robotics, two humanoid robotics companies raised hundreds of millions of dollars, while Unitree launched a robot dog with wheels and Italy unveiled its own humanoid robot.

Apart from that, this week’s issue of Sync also features GPT-Red, Substack’s pushback against AI-generated posts, a vision-restoring implant winning EU approval, Colossal Biosciences eyeing a $20 billion valuation, and more!

Enjoy!

The Hugging Face incident explained

A rogue AI escaping its confinement is a staple of science fiction. But not anymore. This month, two OpenAI models broke out of an isolated testing environment, got onto the open internet and hacked their way into another company’s production systems. And they did it to cheat on a test.

In this article, I will explain what happened, clean up some of the sensationalism surrounding the Hugging Face incident, and make the case that the lessons here point to a more open approach to AI in cybersecurity, not a more restricted one.

What happened?

On 16 July, Hugging Face published a security incident disclosure describing an intrusion into part of its production infrastructure. The attacker gained unauthorised access to a limited set of internal datasets and several service credentials, with no evidence of tampering with anything public. Hugging Face then went on to describe what actions it took to defend itself, and the impact on the service and its users. The post did not name who the attacker was. But it said that this attack was different from anything the company faced before in one important way. This attack was driven, end to end, by an autonomous AI agent system, and Hugging Face detected and dissected it largely with its own AI.

Five days later, on 21 July, OpenAI revealed that its own models—GPT-5.6 Sol and “an even more capable pre-release model” (GPT-6 perhaps?)—were responsible for the Hugging Face incident. According to OpenAI, both models had been running internally on ExploitGym, a benchmark measuring how well an AI agent can turn a known software vulnerability into a working exploit. Both were running with reduced cyber refusals, meaning that the classifiers that normally stop a model pursuing high-risk cyber activity were switched off. The models were confined to an isolated environment for exactly that reason.

What happened next was an experiment that went wrong. OpenAI said that the tested models were “going to extreme lengths to achieve a rather narrow testing goal.” The sandbox had one path to the outside world—a proxy that let the environment install software packages. The models found a zero-day in it, a flaw nobody had patched because nobody knew it was there. They then escalated their privileges and moved sideways through OpenAI's research systems until they reached a machine with real internet access.

Then, in OpenAI's words, the models “inferred that Hugging Face potentially hosted models, datasets and solutions for ExploitGym.” So they set about breaking in.

Here is how they got in. A dataset uploaded to Hugging Face is not just a pile of files. It can come with instructions that Hugging Face’s own computers carry out to unpack and prepare it before anyone downloads it. That is the weakness the models found. They uploaded a dataset rigged so that the preparation step ran their code instead. That gave them a foothold on one of Hugging Face’s machines, and from there they collected credentials and spent a weekend using them to move from machine to machine through its internal systems.

And it worked. OpenAI says the models obtained test solutions directly from Hugging Face’s database. And they did all of that to cheat on a test.

Not a rogue AI

The incident was quickly framed as an AI breaking free of human control. That is the wrong story, and it obscures what actually went wrong here.

The agents did not go rogue. They did not hide copies of themselves around the internet. They did not wander off or do anything except keep working on the problem in front of them. They did what they were asked to do. They were told to produce a working exploit, and that is what they kept trying to do. When the assigned vulnerability looked like a dead end, they went looking for another way. They concluded that hacking Hugging Face could be an easier path to solve the problem.

That is an alignment problem. The models were focused on the thing they were graded on rather than the thing that was wanted. The instructions were clear about the target and silent on everything else, so everything else was fair game. Nobody had thought to say "and do not break into another company," because until recently, no model was capable enough for that to be worth writing down.

There was no deception and no self-preservation here. Just a system given a narrow goal, and pursuing it much harder than anyone expected. And as labs push models towards longer and more autonomous tasks, there is more room between the instruction and the result for a model to find a creative route nobody considered.

The need for an open approach to AI cybersecurity

The Hugging Face incident raises questions about access to powerful AI models for cybersecurity tasks. In its disclosure post, Hugging Face admitted that it first tried using commercial frontier models. It did not name anyone, but it is fair to assume it meant Anthropic and OpenAI. Those models were no help. Their guardrails rejected prompts to analyse large volumes of real attack commands, exploit payloads and the traces attackers leave behind when they control a compromised machine, because they could not distinguish an incident responder from an attacker. Only when the team switched to GLM-5.2, a Chinese open-weight model running on their own infrastructure, were they able to run the forensic analysis. An additional benefit was that none of the data related to the attack left Hugging Face.

The fact that frontier models were of no help in dealing with the incident raises a question about which approach should be taken when it comes to AI and cybersecurity—a closed one, preferred by frontier labs, or an open one?

The cybersecurity community settled this argument a long time ago. Anyone can download Kali Linux today and get the same toolkit attackers use, on the reasoning that defenders who can attack their own systems will find the holes before someone else does. There are free courses and challenges training people how to get better at exploiting computer systems. Nobody pretends those tools and knowledge are only used for good. The bet is that the open approach will level the field.

The obvious objection is that AI is different, and that a system capable of autonomously finding and chaining novel exploits is too dangerous to hand out to everyone. It is a fair objection. Open-weight models are not yet at the level this incident demonstrated, and I am not going to pretend otherwise. But the gap is closing, and nothing about restricting access slows it down. Chinese open-weight models like GLM-5.2 and Qwen already ship with fewer restrictions than the frontier labs’ models to begin with. Guardrails can also be jailbroken or fine-tuned out on open-weight models. Restricting access does not remove the capability from the world.

Both Anthropic and OpenAI have their own cybersecurity programmes, but they are available only to a limited number of selected partners. OpenAI brought Hugging Face into its trusted access programme after the incident, but what about everyone else? Will the next victim of an AI-driven attack also get access to those programmes?

But we cannot rely on frontier labs alone. Both Anthropic and OpenAI are US companies, and we saw with Fable 5 how quickly Washington can cut off access to a frontier model. That’s why we also need an ecosystem of open tools and open models focused on AI cybersecurity. They do not depend on some closed cybersecurity programme or one government’s mood, and will always be available. Once a model's weights are out, no company or government can take them back.

The open ecosystem will not match a frontier model any time soon, and defenders should be honest with themselves about it. GLM-5.2 was good enough to reconstruct an attack after the fact, but it is not what you would choose to have while facing a frontier model hacking your defences in real time. Still, having a weaker model you can actually use is better than a stronger one that refuses to cooperate.

The Hugging Face incident is probably not the first ever AI-assisted cyberattack. It is the first one we know of, and it will not be the last. We should feel lucky that this one was an accident rather than the work of a malicious actor. But it is a warning of what is to come, and I hope we will learn the right lessons from it. As Clem Delangue, co-founder and CEO of Hugging Face, said, “AI safety won’t be solved by any single company working in secret. It will be solved in the open, collaboratively, with broad access to AI for every defender, everywhere.”

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🦾 More than a human

▶️ Telepathic Wheelchair Control | Neuralink (1:57)

In this video, Neuralink demonstrates how its brain–computer interface (BCI) can be used to create what it calls a telepathic wheelchair. Using only their thoughts, people with paralysis who have Neuralink's BCIs can control the movement of their wheelchairs. Neuralink says the project is an important step towards restoring independent mobility for people with paralysis.

Science Corporation’s vision-restoring chip wins EU approval
Science Corporation has won European approval to sell PRIMA, an implant that restores some vision lost to age-related macular degeneration. Patients receive the chip in an hour-long outpatient procedure, then wear camera glasses that feed images straight to it. The company has also secured an FDA designation that could speed up a US review.

🧠 Artificial Intelligence

Introducing Claude Opus 5
Claude Opus 5, Anthropic's answer to OpenAI's GPT-5.6 Sol, is out. According to Anthropic, its latest model offers the same level of performance as Fable 5 at half the price. The provided benchmarks show it topping coding and knowledge-work tests, though it trails Mythos 5 on cybersecurity. It should also reject 85% fewer requests related to cybersecurity than Fable 5 does. Independent benchmarks from Artificial Analysis appear to confirm Anthropic’s performance claims and place Opus 5 ahead of Fable 5 as the new leader in intelligence. However, that intelligence comes at a price: Artificial Analysis found that Opus 5 costs twice as much as GPT-5.6 Sol. The ARC-AGI-2 leaderboard puts Opus 5 at the same level as GPT-5.6 Sol. Meanwhile, on ARC-AGI-3, Opus 5 crushes the competition. Opus 5 is available on all platforms, and it is priced at $5 per million input tokens and $25 per million output tokens (the same as Opus 4.8).

Introducing Gemini 3.6 Flash, 3.5 Flash-Lite, and 3.5 Flash Cyber
Google has released three new Gemini models: 3.6 Flash, 3.5 Flash-Lite and 3.5 Flash Cyber. The 3.6 Flash is the general-purpose workhorse, using up to 17% fewer tokens than the version it replaces. Flash-Lite is the cheapest of the three. Flash Cyber is tuned to find and fix security flaws, and only governments and trusted partners can use it for now. Google says the aim is cheaper, faster models for customers running AI agents at scale. But rivals keep shipping better frontier systems, and Gemini 3.5 Pro is still nowhere to be seen.

AMD and Anthropic Sign Major Chips-and-Investment Deal
In its search for more computing power, Anthropic has signed a deal with AMD worth tens of billions of dollars, which gives it access to up to 2 gigawatts of AMD’s latest Instinct MI450 chips from early 2027. AMD will also invest up to $5 billion in Anthropic as deployment milestones are met. The Wall Street Journal also reports that AMD is in talks to backstop Anthropic’s future data-centre leases too.

Meta in Talks to Lease Computing Power to Anthropic in Potential $10 Billion Deal
Anthropic has proposed renting computing power from Meta’s AI data centres in a deal worth up to $10 billion over two years, according to the New York Times. Anthropic made the offer in June, and talks remain early. Both companies declined to comment. Meta doesn’t currently sell computing power, but Mark Zuckerberg has said he would consider it if Meta ends up building more capacity than it needs to help justify the $145 billion he plans to spend this year on AI.

Google justifies its massive AI spending with a booming cloud business
Alphabet has published its Q2 2026 earnings report, which shows that demand for AI is still growing, with Google Cloud revenue jumping 82% to $24.8 billion, beating analysts’ forecasts. Total revenue rose 24% to $119.8 billion. Alphabet credits the cloud surge to businesses buying its AI tools and infrastructure. However, news that the company is increasing capital expenditures to between $195 billion and $205 billion has made some investors anxious, raising questions about when (or if) these massive investments will pay off. In his remarks, Sundar Pichai hails an “amazing quarter” and paints a picture of a company growing, in large part thanks to AI, arguing that this justifies the massive investments.

China’s Xi Touts Open-Source AI and Takes a Swipe at U.S. Dominance
Xi Jinping used a Shanghai AI conference to back open-source models, casting China as a champion of openness against US efforts to guard its lead in chips and AI. He argued against stretching national security too far in AI, saying no country's security should come before others'. He also pitched a new China-led body to promote global AI cooperation, aimed squarely at developing countries worried about being left behind.

Nvidia details its next-generation Vera CPU for AI, setting up challenge to AMD and Intel
Nvidia has released full specifications for Vera, its first server CPU designed entirely in-house, pitting it against Intel and AMD. Nvidia claims Vera runs AI agents 50% faster than x86 chips by prioritising single-core speed over core count. Samples reached OpenAI, Anthropic and SpaceX in June, though only Oracle appears as a named cloud partner so far. For a deeper technical analysis, ServeTheHome has a good breakdown of Vera.

AMD launches Helios, its first rack AI system to rival Nvidia, adding Microsoft as newest buyer
AMD is about to ship Helios, its first rack-scale AI system, and Microsoft has joined Meta, OpenAI and Oracle in buying it. That makes it the first credible alternative to Nvidia’s rack-scale machines. It doesn’t come cheap, though. Analysts put Helios at roughly $5 million a rack against $4 million for Nvidia’s Vera Rubin. AMD says it wins on inference and memory instead, but Nvidia’s CUDA software stays deeply entrenched, and it still holds over 95% of the data centre GPU market.

US judge approves Anthropic’s $1.5 billion settlement of copyright lawsuit
A US judge has approved Anthropic’s $1.5 billion settlement with authors who accused it of training Claude on pirated books. It’s the largest known copyright payout in US history. A 2025 ruling found that training on books was fair use, but that storing more than seven million pirated titles was not. That left Anthropic facing a damages trial and much heavier losses. Objections that the sum was too small were overruled.

Stripe in Talks to Buy Buzzy AI-Model Marketplace OpenRouter
The Wall Street Journal reports that Stripe is in talks to buy OpenRouter, a startup whose software lets developers compare and switch between hundreds of AI models. The reported value of the deal, if it goes ahead, is around $10 billion—well above OpenRouter’s $1.3 billion valuation. Other big tech companies have also reportedly considered making bids of their own.

DeepSeek Said to Tell Backers of Funding Pause After Viral Posts
DeepSeek has reportedly suspended its second fundraising round, which sought at least 10 billion yuan ($1.5 billion) at a valuation of 480 billion yuan ($71 billion). According to Bloomberg, the pause follows the online spread of comments attributed to founder Liang Wenfeng. In those unverified posts, he acknowledges China's reliance on Nvidia chips and its continued lag behind the US. Liang was frustrated by the leak, and that irritation drove the decision. DeepSeek may still restart the talks later.

Apple Sends Legal Letters to Dozens of OpenAI Employees
Apple has sent legal letters to around 40 former employees now working at OpenAI, the Financial Times reports. The letters follow a lawsuit accusing OpenAI of a coordinated effort to obtain Apple's hardware secrets through recruitment. Apple wants an injunction barring OpenAI from using its information in the AI device it is building.

OpenAI’s Planned Cloud Spending Hits $750 Billion as Computing Efforts Ramp Up
OpenAI has reportedly raised its projected spending on computing power to around $750 billion through 2030, up from about $600 billion earlier this year. New deals with cloud providers account for much of the increase. But the company is also building more itself. It will spend $20 billion on a Georgia data centre, the first site it designs and develops in-house. That pace worries its own finance chief, Sarah Friar, who has privately questioned whether revenue will grow fast enough to pay for the contracts.

Amazon just shut its AGI Lab, and quit a race it never led
The Information reports that Amazon has quietly shut down its AGI Lab, barely 18 months after it was founded. While Amazon frames this as focus rather than surrender, it is hard not to see it otherwise.

Launching Health in ChatGPT
OpenAI has opened ChatGPT Health to all US users over 18, on every plan. The company cites 300 million health questions submitted to ChatGPT each week as a justification for a dedicated health version of its chatbot. The launch lands a day after a Florida pastor sued OpenAI over ChatGPT's “dangerous” medical advice. OpenAI's defence rests on its terms, which say the service isn't meant for diagnosing or treating anything.

Google is working on a new AI chip designed to make Gemini more efficient
According to The Information, Google is designing a new server chip, codenamed “Frozen v2”, which would have Gemini hardwired into the silicon (hence the name). This approach promises to run the models up to 10 times more efficiently than current chips. Google wouldn’t confirm or deny it, saying only that not every project it explores reaches production. Investors liked the news anyway, pushing the stock up around 3% on Monday.

Against Claudefishing
Substack is introducing a new feature that will tell readers how likely the content they are reading has been written by a human or by AI. Substack is partnering with Pangram, which will power the new feature and reportedly detects AI-generated text with a high degree of accuracy. Substack says it is not against people using AI to assist with their work, and that the detector is the first step in dealing with the flood of AI-generated content on the platform.

Here’s Why Anthropic Is Pushing States to Regulate AI Faster
Anthropic is urging US states to adopt tougher AI safety laws, arguing the transparency rules it backed last year are already outdated. It now supports measures requiring third-party audits of AI labs. Some Silicon Valley figures accuse Anthropic of regulatory capture, saying it wants to bog down smaller competitors in red tape and secure its own lead. Anthropic denies this, noting its endorsed bills target only the largest developers.

AI chip startup Etched defies skeptics, hits $10.3B valuation from big-name investors
Etched, an AI chip startup founded by three Harvard dropouts in 2022, has reportedly raised $300 million at a $10.3 billion valuation, doubling what it was worth in December. The company says its first chips have been manufactured and $1 billion of orders are already booked. Sceptics remain, largely because almost nobody outside its investors and early customers has used the hardware.

GPT‑Red: Unlocking Self-Improvement for Robustness
In this post, OpenAI describes GPT-Red, an internal model trained to attack its own systems and find flaws before release. The company says it beats human red-teamers, succeeding in 84% of test scenarios against GPT-5.1 versus 13% for people. It even tricked a live office vending-machine agent into slashing prices and cancelling an order. Those attacks then fed into training GPT-5.6, which reportedly falls for only 0.05% of them.

How OpenAI’s Sol Finally Learned Design Taste
It turns out that GPT-5.6 Sol has surprisingly good taste when it comes to generating websites, and the team behind the Design Arena benchmark decided to investigate why. Their investigation found that the model appears to have learned the tell-tale signs of AI-generated design—purple gradients, bento boxes, oversized hero text—and then deliberately avoids producing them. It also starts from proven layouts but reworks them extensively to suit each prompt.

Introducing MAI-Image-2.5-Pro and MAI-Voice-2-Flash
Microsoft has released two new in-house models in public preview: MAI-Image-2.5-Pro, a high-fidelity image generator, and MAI-Voice-2-Flash, a faster, cheaper voice model. Both come from a year-long push to train models on its own data rather than rely on outside systems. Microsoft says these MAI models now run in production across Bing, PowerPoint, OneDrive and Dynamics 365.

Introducing Laguna S 2.1
Poolside presents Laguna S 2.1, an 118B-parameter open-weight model for agentic coding, available on Hugging Face under a permissive licence. According to Poolside’s own benchmarks, it matches or beats models several times its size, and it’s small enough to run on a single desktop machine. Poolside pitches it as a Western answer to the Chinese labs that now dominate in open-weight models.

FLUX 3
Black Forest Labs has revealed FLUX 3, a multimodal foundation model that learns from images, video and audio in one architecture. The thinking is that each is a partial view of the same reality, so training on all three teaches the model how the world actually behaves. In practice, that means video up to 20 seconds long with sound generated natively alongside it. The lab's own early tests put it ahead of rivals including Kling and Runway. Video is in early access now, with image generation, action prediction for robots and open weights to follow.

Claude’s values across models and languages
In this post, researchers from Anthropic share a way to measure the values Claude expresses in everyday conversations. They compressed over 3,000 previously catalogued values into four axes, each running between two poles: deference and caution, warmth and rigour, depth and brevity, candour and execution. Placing models on those axes matched how people already describe them, with Sonnet 4.6 leaning warm and Opus 4.7 leaning cautious. Values also shifted by language—Claude was warmest in Hindi and Arabic, and most rigorous in English and Russian. Anthropic didn’t choose that variation and doesn’t yet know whether it serves users well.

The Army Is Burning Through Its AI Tokens
Looks like the US military also jumped on the tokenmaxxing trend and now realises how expensive it is. The Army had been actively pushing staff to use up their token allowances, telling them use was unlimited. As a result, it burned through a year’s worth in about a month, and limits came back in mid-June, WIRED reports.

George Lucas likens AI sceptics to luddites clinging to horses and carts
George Lucas has come out strongly in favour of AI in filmmaking, saying that it makes movies far easier to produce. The 82-year-old Star Wars director likened people resisting the technology to those who once clung to the horse and buggy over the motor car. He called its adoption inevitable and said nothing could stop it. Fellow director Gareth Edwards agrees, though Christopher Nolan remains a holdout.

A New Experiential Gallery Just Might Change Your Mind About AI Art
AI art is a controversial topic, and a new Los Angeles gallery is trying to change minds about it. Dataland, opened in June by artist Refik Anadol, bills itself as the world’s first museum of AI art and a bet that AI can make art worth feeling. Its main show is an immersive rainforest that shifts with visitors’ movements and biometric readings from wearables. It runs on a model Anadol’s team trained on their own footage and on scientific archives shared with researchers’ consent. He offers that as a rebuke to AI companies accused of scraping creators’ work.

🤖 Robotics

Tesla’s robotaxis are moving in reverse
Tesla’s Q2 update shows its robotaxis drove about 700,000 paid miles, down roughly 36% from 1.1 million the previous quarter. Miles fell even as the service reached six cities across Texas and Florida. Elon Musk now says Tesla must gather driving data specific to its Cybercab before deploying more of them on the roads, which cuts against years of claims that its customer cars were already collecting what future robotaxis need.

▶️ Unitree Super Athlete AS2-W (1:03)

Unitree presents As2-W, a wheeled version of its As2 robot dog. Advertised as a robot capable of navigating challenging terrain, the As2-W can climb obstacles up to 80 cm high and traverse slopes of up to 45°. It can carry payloads of up to 16 kg while walking and operate for more than three hours when unloaded or more than two hours when carrying a payload, translating to a range of 33 km and 16 km, respectively.

A Humanoid Company Backed by Eric Trump Is Preparing Its Robots for War
A US startup called Foundation plans to arm its humanoid robots within months, its CEO Sankaet Pathak told WIRED. The company has already tested the machines with Ukrainian forces, and Eric Trump serves as both investor and chief strategy adviser. It touts millions in Pentagon contracts, though these appear to have been inherited through acquisitions rather than won independently. Roboticists are sceptical of the timeline, arguing that manipulation and navigation in unfamiliar terrain remain unsolved problems that put real deployment a decade away.

Walden Robotics launches at $1.1B valuation for general-purpose robots
Walden Robotics has emerged from stealth with $300 million in funding at a $1.1 billion valuation. Toyota Research Institute spun the company out in January, and Toyota led the funding round. The startup is building humanoid robots that can continuously learn and improve as they perform real work. Instead of legs, they use a wheeled base, which the company says makes them easier to certify as safe. The robots have been operating at a Toyota plant in North America since February.

London robotics startup Humanoid raises $152M Series A, with Bosch set to manufacture its wheeled robots at scale
London’s Humanoid has raised $152 million in a Series A funding round that put the value of the two-year-old robotics startup above $1 billion. Bosch has signed on as its contract manufacturer, which gives the startup access to one of Europe’s biggest production networks. Humanoid has yet to ship a single commercial unit. It claims 34,000 pre-orders worth nearly $2.5 billion, though nobody has verified that independently.

Travis Kalanick’s robotics company raises $1.7B, led by a16z
Atoms, a robotics startup from former Uber CEO Travis Kalanick, has raised $1.7 billion. Atoms is a rebrand of his ghost-kitchen firm CloudKitchens, now bolted to the industrial automation company Pronto. Kalanick says he wants to build a “wheelbase for robots” and move into mining. Beyond that, he has offered little detail.

▶️ GENE.01 Comes to Life (0:47)

Generative Bionics unveils GENE.01, a humanoid robot wrapped in tactile skin that senses touch, proximity, force and temperature, letting it work safely alongside people. The Italian company says that Fincantieri will adapt it as a welding robot for its shipyards. Generative Bionics highlights its European roots, pitching the robot as part of a sovereign supply chain built on EU manufacturing and compliance.

NVIDIA: Introducing Cosmos 3 Edge
NVIDIA has released Cosmos 3 Edge, a 4B-parameter open “world model” designed to run on the company’s own edge hardware. The model lets a robot understand what it’s looking at, predict what happens next, and work out which action to take. NVIDIA says it tops comparable 4B-parameter models on vision analytics benchmarks.

Xiaomi-Robotics-1
Xiaomi presents Xiaomi-Robotics-1, a robot foundation model pre-trained on over 100,000 hours of real-world manipulation footage. According to Xiaomi, it learns new tasks like laundry loading and printer refilling from under 10 hours of demonstrations each, roughly doubling the success rate of the leading open baseline. It also tops four standard robot simulation benchmarks. Xiaomi says performance climbs predictably with more data and bigger models, with no sign of levelling off. The model is open source and will soon be available on GitHub and Hugging Face.

▶️ Puffin-Inspired Robot that Swims and Flies (4:03)

Inspired by diving birds, engineers from MIT and EPFL in Lausanne, Switzerland, have built a flying robot that can dive into the water and then flap out of the water to continue flying in the air. They explain in this video the kind of challenges they had to overcome to build this robot and what they learned along the way. They hope future scientists will use robots like these to study aquatic environments.

🧬 Biotechnology

Wooly Mammoth startup Colossal seeking at least $20B valuation
De-extinction startup Colossal Biosciences is reportedly in talks to raise money at a $20 billion to $30 billion valuation—roughly triple what it was worth 16 months ago. The company began earning revenue this past year, with most of it coming from selling conservation technology to governments, not from the woolly mammoths and dodos it promises to bring back.

DeepMind: Our approach to bioresilience
In this post, Google DeepMind and Isomorphic Labs outline a joint bioresilience programme aimed at both stopping misuse of their AI and using it to fight disease. The plan splits into prevention, detection and response. On prevention, they’re adapting SynthID watermarking to flag risky AI-generated DNA sequences. On detection, they're using their AlphaEvolve agent to make pathogen surveillance faster and cheaper. And on response, they’re giving trusted researchers access to tools like AlphaFold to speed up vaccine design. The full text of the programme can be found here.

The Isomorphic Labs Drug Design Engine unlocks a new frontier beyond AlphaFold
Isomorphic Labs has unveiled IsoDDE, a drug-design system it says goes well beyond AlphaFold 3. On its own benchmarks, the company claims IsoDDE more than doubles AlphaFold 3's accuracy on unfamiliar protein-ligand structures. It also reportedly predicts molecular binding faster and cheaper than gold-standard physics methods, and spots hidden drug-binding pockets from a protein's sequence alone. These results are self-reported and not yet independently verified.

Scientists Inch Closer to Creating Human Sperm in the Lab
Scientists transplanted lab-made stem cells beneath the kidneys of living mice, where they grew into immature human sperm. The cells survived up to nine months and reset their DNA much as natural sperm do. But none matured into working sperm, so the method can't make babies. For now it's a research tool for studying early sperm development and male infertility.

▶️ Why AI Hasn’t Cured Anything...Yet, According to Jennifer Doudna | The Circuit (24:01)

Jennifer Doudna, the Nobel Prize-winning scientist who co-discovered CRISPR, shares her journey to becoming a scientist in this video. She takes us inside her research lab at the Innovative Genomics Institute and demonstrates the transformative impact of her discovery. She also discusses her views on AI and its limitations in biology, the challenges facing CRISPR, and what the future of biotechnology may hold.

When AI drives discovery — Patents and drug development
This article explores an unsettling legal question: what happens to a drug patent if an AI, not a human, made the discovery? US and European law recognise only human inventors, so a patent for an AI-conceived drug may collapse if humans merely confirmed the machine’s predictions. The suggested fix is to build human contribution around the AI's output, through modification, formulation, dosing, or clinical choices, so that a human role in the discovery is significant enough to satisfy the law.

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This week, we take a closer look at one of the final significant acts of Tim Cook’s tenure as chief executive and how it parallels what happened at the beginning of the smartphone era.

Elsewhere in AI, OpenAI launches its first hardware product—a keyboard for Codex. Meanwhile, Kimi K3 becomes China’s leading frontier model, Thinking Machine Lab releases Inkling, and Gemini 3.5 Pro is delayed. Tencent is also in talks to acquire Manus, DeepSeek is preparing for an IPO in China, New York becomes the first state to impose a data centre moratorium, and Demis Hassabis calls for a framework to govern frontier AI, while Google continues to put Gemini everywhere.

Over in robotics, NEO shows off its new hands, workers at Hyundai’s factory in South Korea go on strike over humanoid robots, humanoid robots perform surgery, and Boston Dynamics’ Atlas robots do the Viking row.

Beyond that, this week’s issue of Sync also features space mirrors, Grok Build going open source, a Chinese neurotechnology company betting on non-invasive wearable brain interfaces, and more!

Enjoy!

Tim Cook's Last War

Apple is suing OpenAI over stolen trade secrets. It fought a similar fight once before at the beginning of the smartphone era, and Android won.

On 10 July, in one of the last significant acts of Tim Cook’s tenure as chief executive, Apple filed a lawsuit against OpenAI, accusing it of running a months-long coordinated campaign to steal information about Apple’s technology and unreleased products.

This is the first legal skirmish in the fight over what replaces the phone. Apple has fought one of these before, at the start of the smartphone era. It did not stop Android. This is a story of employees taking secrets on their way out, a partnership that promised billions and delivered almost nothing, and the race to build whatever comes after the smartphone.

What Apple is accusing OpenAI of

Apple says it did not want to go to court. It wrote to OpenAI in February to raise its concerns and ask the company to look into the problem and fix it (I can only imagine Tim Cook sending to Sam Altman his own version of Steve Jobs’ 2005 message to Adobe about poaching). OpenAI reportedly never replied. Five months later, the lawsuit arrived.

Keep in mind that a complaint is written by one side's lawyers to persuade a judge. Nothing in it has been tested, and OpenAI has yet to file its response.

Apple sued OpenAI—both of its corporate entities—and io Products, the hardware startup founded by Jony Ive that OpenAI bought last year for $6.5 billion. Then it named two men: Tang Tan, OpenAI’s chief hardware officer, who spent 24 years at Apple running product design for the iPhone and Apple Watch, and Chang Liu, a former Apple hardware engineer who joined OpenAI in January.

The complaint describes conduct “normalized and exemplified by [OpenAI] leadership,” and Apple is explicit about how far up it thinks the rot goes: “At every level, from members of its technical staff to its chief hardware officer, and in coordination with business partners, OpenAI has been stealing Apple’s trade secrets and confidential information.” The lawsuit then carries on saying that OpenAI’s “nascent hardware business now rests on the shakiest of foundations, rotten to its core by its illegal reliance on misappropriated trade secrets.” Apple is asking the court to stop the defendants—Liu, Tan, OpenAI and io—from using its trade secrets, to make them hand back every copy they hold, and to preserve rather than delete any evidence of what they have done, along with damages, and exemplary damages on top, on the grounds that the theft was wilful.

Apple has a theory of why OpenAI would do this. The complaint spends its opening pages on OpenAI’s finances: a valuation that climbed from roughly $29 billion in 2023 to $852 billion by April 2026, more than $180 billion raised, cash burned “at a historic pace,” and a chief executive under pressure from investors to show that the numbers work. Building a consumer hardware business from scratch, Apple argues, turned out to be harder and slower than OpenAI expected, so it “resorted to taking unlawful shortcuts.”

So what does Apple allege those "shortcuts" were? Apple claims OpenAI job candidates still employed at Apple were told by Tan to bring “actual parts” to their interviews for “show and tell sessions,” along with CAD files and prototypes. One candidate, according to the complaint, was taken aback—he had not realised you could take Apple parts out of the office at all. It alleges that OpenAI coached departing employees on evading Apple’s exit procedures, circulating an internal Apple managers’ document marked “Need to Know” that set out the security checks Apple runs when someone leaves. OpenAI staff, according to the complaint, referred to it as “a checklist that Tang put together.” Departing employees were also advised on how to avoid the “dreaded walk out,” the immediate removal from the company that would cut off the two final weeks in which they could still reach Apple’s systems. If Apple asked them to sign anything on the way out, the complaint says, they were told to notify OpenAI as soon as possible and not to sign.

Chang Liu, the second ex-Apple employee named as a defendant, is accused of taking files directly off Apple's systems. Apple says Liu found he could get into its internal network through an authentication bug, and downloaded dozens of confidential hardware files covering unreleased products, engineering presentations and technical specifications. The complaint alleges there are other unnamed ex-employees who emailed Apple material to personal accounts on their way out.

And there is more. Apple says io used its confidential metal-finishing techniques by approaching a shared supplier and letting it believe io had Apple’s blessing, and that OpenAI questioned another supplier about power and battery components using internal terminology that only Apple insiders would know to ask.

Even so, Apple is clear that it does not think it has the full picture yet. “Discovery will expose that the misappropriation has been occurring on a scale many times greater than the several instances described below,” the complaint states. It calls what it has found “the tip of the iceberg,” hinting that there is more to come if the lawsuit reaches discovery, where relevant internal emails, texts and documents will get exposed to the public.

OpenAI’s initial response, issued within hours, was short and said essentially nothing: “We have no interest in other companies’ trade secrets. We remain focused on building innovative technology that empowers people everywhere.” It took until the following Tuesday, 14 July, for the company to issue an official statement, in which it says it was “not aware of any evidence that this complaint has merit.” Sam Altman, meanwhile, wrote on X that he respects Apple, but he is not afraid of them.

The lawsuit comes at a pivotal time for OpenAI. The company is planning to unveil its AI-companion device this year and ship it in 2027, with around five more hardware products reportedly in the pipeline behind it. Apple is seeking an injunction barring OpenAI from using or even holding its trade secrets, and it has said in the complaint that it will move for one promptly rather than waiting for trial. If it succeeds, OpenAI could find itself having to prove that a device already in development owes nothing to Apple. It has not asked the court to redesign anything, but in practice, that is what an injunction could amount to if OpenAI has to strip out anything a judge decides originated at Apple.

How Apple and OpenAI went from friends to enemies

It is easy to forget how good the Apple-OpenAI relationship looked in June 2024. Sam Altman sat in the audience at Apple Park while Craig Federighi called OpenAI the “pioneer and market leader” in AI and announced that ChatGPT would be woven into Siri, Writing Tools and Visual Intelligence. For OpenAI, it was an opportunity to be seen by hundreds of millions of Apple users and convert them into ChatGPT subscribers, possibly bringing in billions of dollars a year.

But it did not work out that way for OpenAI. Apple’s implementation was thin and buried. To get ChatGPT out of Siri, you generally have to say the word “ChatGPT,” and when the answer arrives, it comes in a small window with less in it than the app would give you. OpenAI’s own user research, described to Bloomberg, found that Apple customers simply opened the ChatGPT app instead. The subscription revenue that was meant to run into billions never materialised, and OpenAI came to believe the cramped integration was actively damaging its brand.

The resentment goes back to how the deal was struck. Apple would not say in advance what the product would actually be. “They basically said, ‘OpenAI needs to take a leap of faith and trust us,’” an OpenAI executive told Bloomberg. So when Apple went looking for a partner to supply the underlying models for the rebuilt Apple Intelligence, OpenAI was not interested. That contract, worth a reported $1 billion a year, went to Google instead, which is why the new Siri we saw at WWDC 2026 runs on a tuned Gemini rather than on GPT.

Attempts to renegotiate the original deal stalled. By May 2026, Bloomberg reported that OpenAI had hired an outside law firm and was weighing its options against Apple, including a breach of contract notice, with any move likely to wait until the Elon Musk trial was done. But Apple sued first, and here we are.

Meanwhile, OpenAI’s privileged position inside iOS is about to disappear anyway. The Extensions system in iOS 27 opens Siri up to rival assistants, with Apple already testing integrations with Anthropic’s Claude and Google’s Gemini.

So OpenAI has its grievances, and so does Apple, which has spent the same two years watching the people who built the iPhone walk out the door and into OpenAI.

The most prominent is Jony Ive, the legendary designer who created some of Apple’s greatest products. Ive left Apple in 2019 to create his own independent design studio, LoveFrom. In 2024, reports started to emerge that LoveFrom was working with OpenAI. A year later, OpenAI acquired io, Ive’s AI hardware startup, which brought him and his team into OpenAI.

Ive is not the only ex-Apple employee at OpenAI. Tang Tan, who ran product design for the iPhone and Apple Watch, is now OpenAI’s chief hardware officer. Evans Hankey, Ive’s successor as Apple’s head of industrial design, is leading development of OpenAI’s first device. Both Tan and Hankey co-founded io. Last month, OpenAI hired Paul Meade, the Apple executive who led the Vision Pro and Apple’s future smart glasses. And behind them, according to the lawsuit, are more than 400 former Apple employees now working at OpenAI. Bloomberg has reported that OpenAI won them over with stock packages worth millions of dollars more than Apple was offering, and that Apple executives have been furious about it for over a year.

Here is the thing, though: none of that is illegal. California does not enforce non-compete agreements, which is precisely why Silicon Valley works the way it does. OpenAI can hire as many Apple engineers as it needs, and Apple can do nothing about it. What Apple can litigate is not that these people left, but what they allegedly took with them—and that is at the centre of the lawsuit.

The fight for the post-smartphone era

While the espionage drama was unfolding, Bloomberg’s Mark Gurman dropped a new hint at what the device at the centre of the lawsuit could look like. He describes the device as a mobile, screenless smart speaker designed to be a humanlike AI companion. And we have evidence that OpenAI is betting on a post-smartphone future in which the voice is the primary interface.

OpenAI puts a lot of effort into developing voice models. The recently released GPT-Live is quite impressive. It can still sound robotic in some places, but what makes it feel more human-like is the continuous interaction. GPT‑Live continuously processes input while generating output, so it can quickly react to what a human is saying. It knows when to speak, continue listening, pause, interrupt, or invoke a tool. It can add phrases like “mhmm” or “got it” to let you know it is still listening.

We also know Sam Altman does not hide that he is heavily inspired by the movie Her. We saw a glimpse of that fascination when GPT-4o was released in May 2024 and how prominently the voice interaction was displayed back then.

Although OpenAI has spent the past year chasing the money where the money actually is—coding and enterprise—it has not given up on consumers. OpenAI still wants to build the personal AI companion. The one that you bring with you, talk to it, do work with it, and is always with you.

And OpenAI is not the only one who wants to define the post-smartphone era, though nobody agrees on what the thing should be. OpenAI is betting on voice and on a device with no screen that listens, talks and gets to know you. Meta and Google are betting on smart glasses. Apple is spreading its bets across smart glasses, a pendant, and camera-equipped AirPods. Even SpaceX is reportedly entering the fight with a smartphone-like device with Grok built in, though it is in the early stages of development and its design can still change.

Everyone involved knows that whoever wins will have an opportunity to define the next decades of technology, just like the arrival of the iPhone almost 20 years ago transformed the tech landscape. This small rectangle made of plastic, glass and metal has elevated many companies to the top and made many irrelevant. One of those empires built on top of a smartphone is Apple, and it does not want to become the Nokia of what is coming next.

Apple's lawsuit against OpenAI echoes Apple's legal battles against Android at the beginning of the smartphone era. Back then, Steve Jobs said he was "willing to go thermonuclear war on this." He called Android a stolen technology and vowed to destroy it. But Jobs did not see his war through. Tim Cook inherited it and spent his first years as chief executive winding it down—settling with HTC, then Google, then Samsung—having told an earnings call that he had always hated litigation and would rather settle than battle. Android survived anyway and grew to become the dominant smartphone operating system under Google. Now, on his way out, Cook has started another war and handed the fight to his successor, John Ternus. It remains to be seen whether the rest of history will repeat.

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🦾 More than a human

While Musk’s Neuralink drills into skulls, China’s BrainCo bets the future of brain tech is wearable
Chinese startup BrainCo, which makes bionic hands and wearables, is betting that mass-market brain-computer interfaces will not require opening the skull and has raised 2 billion yuan ($280 million) to chase that non-invasive route. It plans to start with amputees, expand into conditions like ADHD and depression, then sell consumer gadgets.

🧠 Artificial Intelligence

Google Gemini Launch Delayed as Tech Falls Short of Internal Goals
Google is reportedly months behind schedule on Gemini 3.5 Pro, its flagship AI model, which Sundar Pichai said at Google I/O 2026 in May would ship “next month”. Bloomberg reports that teams are frustrated as Anthropic and OpenAI pull ahead, particularly in coding, with some employees leaving to join those rivals. Employees blame internal bureaucracy and competing factions, saying teams duplicate work on overlapping coding tools without a cohesive strategy. A recent attempt to retrain Gemini on improved coding data reportedly fell short of expectations. The delay suggests that Google’s sprawling product empire has become a drag in a race where more nimble AI labs are setting the pace.

Kimi K3: Open Frontier Intelligence
Moonshot AI has released Kimi K3, a 2.8-trillion-parameter model with native vision and a one-million-token context window. According to Moonshot, it’s the first “open” 3T-class model, delivering frontier-level results across coding, knowledge work, and reasoning, though it is still behind the strongest models from Anthropic and OpenAI—Fable 5 and GPT-5.6 Sol. Artificial Analysis broadly backs the intelligence claim, ranking K3 fourth overall and placing at the level of Opus 4.8. But it’s slower than average, very verbose, and pricey for its tier at $15 per million output tokens. Although Moonshot labelled K3 as an “open” model, its weights are not out yet. The company says it’ll publish them by 27 July.

Tencent in Talks to Take Big Manus Stake After Meta Deal Unwound
Tencent is reportedly looking to become the biggest outside shareholder in Manus, buying the AI startup back at the same $2 billion price Meta paid before Chinese regulators blocked that deal. Meta and Manus have already pulled their operations apart and cut off data sharing. For Tencent, it’s a chance to pick up agentic AI technology while it plays catch-up. And it lines up with Beijing’s broader effort to keep its leading AI companies out of foreign hands.

SK Hynix raises $26.5B in the biggest foreign IPO in US history, is urged to build new US fabs
SK Hynix, a South Korean memory chip manufacturer, has raised $26.5 billion by listing on the Nasdaq, making it the largest US debut ever by a foreign company. Investors piled in—demand ran seven times ahead of supply, and the shares jumped 14% on the first day. The money is earmarked for a new chip plant, a packaging facility and advanced chipmaking equipment, all in South Korea. That sits awkwardly with Washington, which is pressing SK Hynix to build in America instead.

DeepSeek Is Preparing For IPO Filing as Soon as This Year
Another AI darling is eyeing an IPO, but this time in China. Bloomberg reports that DeepSeek is preparing a mainland listing, aiming to file this year for a 2027 debut. The Hangzhou startup is also reportedly raising fresh private money at a valuation of at least $71 billion, up from roughly $50 billion just weeks ago. Investors are piling in because DeepSeek proved Chinese labs could build frontier models cheaply despite US chip curbs.

▶️ Meet the new Codex for developers (7:12)

In this video, OpenAI presents what new features are coming to Codex, its AI coding app. Codex is merging into the ChatGPT app, where it sits alongside a new work agent. It runs on GPT-5.6 Sol with an “Ultra” mode that spends a larger reasoning budget and splits jobs across parallel subagents. Other additions include “appshots” for iOS development, an in-app browser with passkey login, one-tap publishing through Sites, mobile task management, and a pull request tab that keeps code review inside the app.

▶️ Introducing the Codex Micro (2:14)

OpenAI has released its first hardware product, and it is a tiny keyboard for Codex. Built with Work Louder, the $230 Codex Micro is a small deck of keys, a dial, and a joystick meant to control OpenAI’s coding agent. Each agent key glows a different colour to show whether a task is thinking, running or done, so you can track several jobs at once. A dial adjusts how hard the model reasons, and the joystick fires off common actions like opening a pull request. The pitch is that agentic coding now involves juggling many parallel tasks, and a mouse and screen alone feel clumsy for it.

SpaceX in Talks to Provide Computing Power for Pentagon’s AI Push
SpaceX is in talks to sell the Pentagon several billion dollars’ worth of data centre capacity for running AI models, the Wall Street Journal reports. Nothing is settled, and the deal could still fall apart. It would follow similar cloud agreements with Anthropic and Google, struck after SpaceX absorbed Musk’s xAI and its Memphis data centres. But officials already worry the military leans too heavily on Musk. The Pentagon says it wants to avoid depending on any single supplier.

Demis Hassabis: A Framework for Frontier AI and the Dawning of a New Age
In this post, Demis Hassabis argues that we are just a few short years away from AGI and that we have to make steps to prepare for it now. He warns that progress is outpacing our grasp of risks like cybersecurity and biological threats, driven by an intense commercial and geopolitical race. His fix is a US-led standards body, modelled on the finance regulator FINRA and funded mostly by industry. It would set benchmarks defining which models count as “frontier-class” and test them before release. Labs would share models voluntarily at first, though passing could later become mandatory for the US market. The body could even coordinate a slowdown among labs if needed. He frames it as a first step towards shared international rules for managing AI and, eventually, AGI.

OpenAI Staffers Are Funding a Rival Super PAC to Take on Their Boss
WIRED reports that seven current OpenAI employees have donated over $215,000 to Guardrails Alliance, a super PAC lobbying for tighter AI regulation. The group is pitched as a counterweight to Leading the Future, a $100 million pro-industry PAC funded partly by OpenAI president Greg Brockman. OpenAI says Brockman acts in a personal capacity and that staff are free to give as they wish.

OpenAI may have made a fatal misstep in copyright fight with news orgs
News organisations led by The New York Times want a court to sanction OpenAI for allegedly hiding and deleting ChatGPT logs in their copyright case. The filing claims OpenAI spent two years falsely saying it couldn't feasibly search its logs for users prompting ChatGPT to regurgitate paywalled articles, when it had already run those searches and held de-identified samples of up to 78 million conversations. OpenAI denies wrongdoing, calling the motion a bid to invade user privacy as the Times’ case weakens. A court has already ruled OpenAI’s heavily redacted 20 million sample log unusable. The fight matters because OpenAI’s fair-use defence may hinge on whether that log evidence survives.

South Korea will give all 52 million citizens free AI access, becoming the first G20 nation to do so
South Korea has opened bidding to give all 52 million residents a free, unlimited AI chatbot as a public service. That would make it the first G20 nation to offer AI to its entire population. While most Koreans currently rely on foreign tools like ChatGPT, the plan requires at least half the system to run on Korean-built models. This guarantees a market for domestic developers and reduces dependence on American and Chinese rivals. Seoul is funding the initiative from taxes on Samsung and SK Hynix’s chip profits.

Anthropic moves closer to mega-IPO as bankers line up investor meetings
Anthropic is reportedly lining up investor meetings ahead of a possible IPO as soon as October. According to CNBC, bankers are pairing prospective investors with the company’s executives before a formal roadshow. Anthropic filed its prospectus confidentially with the SEC last month. It was last valued at $965 billion, having overtaken OpenAI in May. If the reported timelines are correct, Anthropic would IPO ahead of OpenAI, which is reportedly now considering delaying its IPO to next year.

The Reverse Information Paradox
In a bit of a surprising move, Satya Nadella has warned that companies using AI models from labs like OpenAI and Anthropic end up paying for intelligence twice. They pay in cash, then again in the proprietary knowledge they must reveal to make the models useful. Every prompt and correction teaches the model something about the business that a rival could never buy. He calls it hypocritical for labs to train freely on public data while restricting others from distilling their models. His fix is for companies to own their data and learning infrastructure and avoid locking themselves to one provider.

Data-Center Builders Are Racing to Offload Stakes Worth Billions
The Wall Street Journal reports that US data-centre builders are lining up sales of majority stakes worth tens of billions this summer, with Dallas-based DataBank potentially fetching $25 billion. The pitch to private-equity buyers is simple: demand for AI computing keeps outrunning supply. Building has also grown far costlier, forcing some operators to find richer backers. But few firms can write cheques that big, and local opposition over utility bills and noise adds more risk.

New York becomes the first state to impose a data center moratorium
New York has become the first US state to freeze construction of large data centres by imposing a one-year moratorium on any facility drawing 50 megawatts or more. Governor Kathy Hochul said the sites threaten to push up utility bills and drain natural resources. State regulators will withhold discretionary permits while officials draw up consistent environmental standards. Hochul also wants to scrap sales tax breaks for big data centres. Dozens of states are weighing similar curbs as public patience with the AI building boom wears thin.

Inkling from Thinking Machines Lab
Thinking Machines Lab presents its first model—Inkling. It’s an open-weight system with 975 billion parameters, trained from scratch to handle audio and images alongside text. Artificial Analysis rates it the strongest open-weight model yet from a US lab, and singles out its agentic work, where it beats leading Chinese models on banking and task benchmarks. It also gets there using far fewer tokens per task. But China’s best open models rank higher, and the frontier systems from OpenAI and Anthropic higher still. Inkling is available on Hugging Face.

The Future Worth Building Is Human
Thinking Machines Lab argues in this post that AI should extend human judgment rather than replace it. Its case leans on an observation that the knowledge that matters most is tacit and local, held by people doing the work. That knowledge can't be collected in one place, so the lab wants frontier AI to be a distributed collective, owned and tuned by each organisation that uses it. Thinking Machines Lab also warns that alignment decided in a few places becomes a point of control worth capturing.

Helsing raises $1.8bn at an $18bn valuation, and its cap table tells a story
Helsing, a Munich-based company building AI systems for European militaries, is now Europe’s largest defence startup after raising $1.8bn at an $18bn valuation. The company positions itself as the home-grown alternative to American suppliers, though much of the new cash came from JPMorgan, Goldman Sachs and a Canadian pension fund, which doesn't quite square with that pitch. Helsing remains far smaller than US rival Anduril, and a recent move to strip staff of direct equity, alongside a shift to a European corporate structure, hints at a listing ahead.

Google announces Gemma 4 optimized for the Pixel 10’s TPU
Google has released Gemma 4 E2B, a lightweight AI model built to run directly on the Pixel 10’s TPU. Because it runs on the chip itself, the phone can handle AI tasks such as private, internet-free chat, image recognition, and audio transcription, plus voice commands that control phone functions like WiFi offline, with data never leaving the device. Google also opened up a Tensor SDK so developers can build their own on-device apps.

Apple in talks with startup that shrinks AI models to run on an iPhone
Apple is in early talks with PrismML to use its technology to shrink large AI models enough to run on an iPhone. Previously, PrismML compressed Alibaba's open-source Qwen model from 54GB to under 4GB. The trade-off is a small drop in accuracy. Running more AI on the phone would play into Apple’s approach to AI and strengthen the company’s privacy pitch for a revamped Siri.

Introducing a way to reflect on how you use Claude
Anthropic has launched a beta feature that lets Claude users see how they’ve been using the assistant. The dashboard breaks down chat activity over the past 1 to 12 months by topic, task type and time of day. It also prompts people to reflect on the role Claude plays in their lives and lets them set quiet hours or nudges to take a break. The move reflects growing pressure on AI companies to help users reflect on how much they rely on these tools.

OpenAI’s new flagship model deletes files on its own, people keep warning
Users of OpenAI’s latest flagship model, GPT-5.6 Sol, are reporting that it deleted their files, databases, and virtual machines without asking first. OpenAI had flagged the risk in a system card two weeks before launch. The company found that Sol tends to interpret instructions too loosely and take destructive actions unless they’re explicitly forbidden. OpenAI concedes in the System Card that Sol goes beyond user intent more often than its predecessor.

Hack suggests AI music generator Suno scraped YouTube for training data
A hacker who breached the AI music generator Suno found source code showing it scraped audio from YouTube, Deezer, and other services to train its models, 404 Media reports. Suno admits training on publicly available music and calls this fair use. But major record labels are suing and say that scraping YouTube this way breaks copyright law. The hacker also grabbed customer emails and partial card numbers. Suno hasn’t told users about the breach.

Making Fable Cheaper Than Opus
In this post, an engineer from Cognition, the company behind AI coding agent Devin, shares how using a combination of Fable 5 and Opus 4.8 cut costs rather than raising them. In its Fusion setup, a lead model plans and delegates to a cheaper "sidekick" model. Fable costs twice as much per token as Opus, so a Fable-led agent should have been pricier. But across 3,000 test runs, it came out cheaper ($1.86 versus $2.04) and scored higher. They attribute the difference to management style—Fable delegates early with clear briefs and barely touches the code, so what justifies frontier prices is judgement, not raw output.

What xAI Grok Build CLI actually sends to xAI - a wire-level analysis
Someone was curious about what Grok Build CLI (SpaceXAI's equivalent of Claude Code terminal app) sends back while in use, and what they found looks bad. Apparently, Grok was uploading whole repositories to a Google Cloud Storage bucket, including files it was told not to open. A .env secrets file went over the wire verbatim and unredacted, and turning off "Improve the model" changed nothing. SpaceXAI has since disabled the upload and promised to delete the data it collected.

Grok Build is open source
SpaceXAI has open-sourced Grok Build, its terminal-based AI coding agent. The GitHub repository contains everything needed to build the application locally and explore how it works. SpaceXAI says the repository will be periodically synced with the private repository, so the code there is slightly behind the compiled version available for download from Grok’s website.

NotebookLM is now Gemini Notebook
Google continues its quest to put Gemini everywhere. The latest victim is NotebookLM, which has been renamed to Gemini Notebook. That is its second rebrand—what we know today as Gemini Notebook began as Project Tailwind in 2023.

▶️ Grant Sanderson (3Blue1Brown) – AI disproved a famous math conjecture. Now what? (1:33:39)

Dwarkesh Patel sits down with Grant Sanderson, the creator of the excellent 3Blue1Brown YouTube channel, to discuss what is happening at the intersection of AI and mathematics. The conversation covers a wide range of topics, from how mathematicians think and how AI could change the role of the mathematician to the nature of intelligence and what AI still lacks to become truly intelligent. Since Grant is an educator, they also explore the use of AI in education and where it falls short as a teacher. It is a long conversation, but well worth your time.

🤖 Robotics

The 1X Neo Robot Has Freaky Fast Fingers

1X, the company behind the soft home robot Neo, has revealed the bot's new five-finger hands. Tendon-like actuators give them 25 degrees of freedom, close to a human hand's 27, and high-resolution tactile sensing gives the robot a sense of touch. In the attached video, the company shows the hands handling everyday household objects and tools much as a person would. But there's a long road ahead. Neo still isn't fully autonomous, and 1X admits some footage shows human operators steering the robot by remote rather than the machine acting alone. Until that gap closes, dexterity demos say little about what these robots can do unsupervised.

Beyond da Vinci: Why versatile humanoid robots are the next frontier in surgery

Researchers have used teleoperated humanoid robots to remove gallbladders from large animals in an early test of whether general-purpose machines can replace the specialised ones that dominate medicine. One procedure paired a humanoid with a human surgeon, while another was carried out by two robots side by side. The robots needed repeated recalibration and worked far more slowly than purpose-built surgical systems like the da Vinci. But the team argues humanoids are cheaper and more portable, which could bring surgery to rural areas or battlefields.

▶️ Viking Row | Atlas | Boston Dynamics (0:34)

Norway may not have won the World Cup, but it has definitely won many hearts along the way, including those at Boston Dynamics, both human and robotic. The smoothness of Atlas’ movements never ceases to amaze me.

NVIDIA Announces Expanded Jetson Thor Lineup with Mid-Range T3000 and T2000 Modules
NVIDIA is adding two cheaper mid-range boards, the Jetson Thor T3000 and T2000, to its robotics and edge AI lineup. Both use the same Thor chip as the existing high-end boards but strip back cores, memory, and networking. The T3000 targets robotics, while the smaller T2000 aims at lighter edge AI work. Both boards are planned to ship in the first quarter of 2027. Pricing hasn’t been announced.

The Fight Over Humanoid Robots Has Shut Down a Car Factory for the First Time
Workers at a Hyundai car factory in South Korea have staged a partial strike over the company's introduction of humanoid robots. The union acted after Hyundai unveiled Atlas in January, a six-foot-two robot worker built by Boston Dynamics, which Hyundai owns. Workers want job guarantees, fixed salaries in place of hourly pay, and a higher retirement age to soften automation’s blow. Hyundai says Atlas will work alongside human workers, not replace them.

Smart Cellular Bricks: Towards Collective Intelligence for the Physical World
I really like how researchers at Sakana AI combine AI with biologically inspired systems that give rise to emergent behaviour. Their latest work takes that idea into the physical world. They’ve built identical cubic bricks that work out for themselves what shape they collectively form, with no central controller and no brick knowing its own position. Each runs the same small neural network and talks only to the bricks it’s physically plugged into. The network was trained in simulation, then transferred straight onto real hardware. Nearly 200 bricks correctly identified a plane, a guitar, a boat and a table in about three minutes. They can also spot missing modules and guide their regrowth. That points towards smart materials that report their own damage, or reconfigurable robots that adapt to whatever shape they find themselves in.

🧬 Biotechnology

Chai Discovery raises $400M at $3.8bn as AI drug discovery moves from promise to deployment
Chai Discovery has raised $400m at a $3.8bn valuation, nearly triple what the AI drug-design startup was worth seven months earlier. Unlike most drug discovery, which screens existing molecules, Chai uses generative AI to design new antibodies and proteins from scratch. Its models are already deployed at Eli Lilly, Pfizer, and Novartis. Money is flooding the wider field, from DeepMind’s Isomorphic Labs to Anthropic. But the real test comes later, when these designed drugs face years of trials and regulators.

💡Tangents

Four nuclear reactors hit a big milestone in the US
Nuclear power is making a comeback as four American startups have achieved criticality with their experimental microreactors by 4 July. These were zero-power tests, and none of the four is anywhere near producing electricity. There are still massive technical challenges ahead, but the startups remain optimistic and promise to deliver commercial power by 2027 or 2028. However, regulatory approval has historically been slow in the US, and some nuclear supporters argue the whole exercise distracts from the harder job of adding real generating capacity.

New ‘space mirror’ satellite aims to bring sunlight to Earth even during the night
US regulators have approved Reflect Orbital to launch a demonstration satellite that beams reflected sunlight onto chosen spots on the ground. The California startup wants to sell "sunlight on demand", eventually lighting solar farms at night for round-the-clock power. This first mission tests whether an 18-metre mirror can survive orbit and aim precisely. Astronomers, however, are not happy and say that the beams could ruin dark skies and harm wildlife. Reflect counters that the light never exceeds natural sunlight and can be shut off instantly.

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This week, we’ve had not one, not two, but three frontier models released. All three—GPT-5.6, Grok 4.5, and Muse Spark 1.1—offer near-frontier performance at a fraction of the cost. We will take a closer look at them and how they changed the AI landscape.

Elsewhere in AI, Apple sued OpenAI over allegations of trade secret theft, while OpenAI released GPT-Live, lost two more senior leaders, and faced questions over whether Stargate UK is even real. Meanwhile, China is considering restricting access to its top AI models, Anthropic explores AI’s thinking space, xAI becomes SpaceXAI, and Meta releases (and then takes offline) its AI image generator.

Over in robotics, Boston Dynamics showcased its robots at the World Cup, while teams of humanoid robots competed in RoboCup in South Korea. We also have a new AI model for robotics from Mistral and another drone delivery company entering the US market.

Additionally, this week’s issue of Sync features one company launching the first nuclear-powered commercial satellite into orbit, while another sends a longevity lab to space, a deep dive into a $1 trillion bet against ASML, a look at what the next AI training paradigm will be, and much more.

Enjoy!

Frontier intelligence gets cheap

OpenAI, SpaceXAI and Meta all shipped new flagship models this week. All three are selling the same thing: near-frontier performance at a fraction of the price.

We knew that OpenAI was going to release GPT‑5.6 to the public this week, and that it would make a serious claim for the AI crown. What we did not expect was that it would arrive in a crowd. Within roughly twenty‑four hours, not only OpenAI but also SpaceXAI and Meta released their own flagship models. And all three of them offer the same thing—top-level performance for a fraction of the price.

We had known about GPT‑5.6 since 26 June. That is when OpenAI announced that new models were coming, but would not be available yet, because the US government had blocked the public release over security concerns. Only selected, government‑approved partners got access. It was the second time in a month that Washington had reached into a frontier lab's release schedule, after Commerce switched off Anthropic's Fable 5. The block has now lifted, and we can look at what OpenAI has actually brought to the table.

GPT‑5.6 comes in three variants, this time with some good names for a change. Sol, the largest model in the family, is the flagship and the most capable. Terra is a balanced model for everyday work. And Luna is OpenAI’s cost‑efficient model.

As always with this type of release, OpenAI published a pile of graphs and benchmark results, all showing how much better its latest models are than the competition. But with this release, OpenAI also decided to highlight the cost efficiency and to make it the central point of its marketing campaign. Every graph on the GPT‑5.6 release page combines benchmark results and cost. This is a direct attack on Anthropic's weakness: its models are very good, but they use more tokens and are more expensive.

The ARC‑AGI‑2 leaderboard is one of the independent benchmarks where this is visible very well. Even before the release of GPT‑5.6, Anthropic's models ranked as more expensive to run than OpenAI's. GPT‑5.6 improved on that efficiency even further.

While ARC‑AGI‑2 shows the efficiency gains, ARC‑AGI‑3, a much more challenging benchmark, shows the improvement in raw performance. Sol scores 7.78% there, which sounds like nothing until you notice that almost everything else is stuck below 2%. Note, though, that neither ARC‑AGI‑2 nor ARC‑AGI‑3 features Fable 5 or Sonnet 5. They would most likely not change the picture on cost, but they could on performance.

Alongside the new models, OpenAI shipped ChatGPT Work, its long-rumoured “super app” that combines ChatGPT and Codex into one app that can pull context from Slack, Drive, SharePoint and the rest, turns it into finished slides, spreadsheets and documents, and stays with a project for hours on its own. If that sounds familiar, it should. This is OpenAI’s answer to Claude Cowork, and it is aimed squarely at the enterprise customers where Anthropic makes its money.

But OpenAI is not the only one going after Anthropic this week.

Seemingly out of nowhere, SpaceXAI (formerly known as xAI) released Grok 4.5 and immediately challenged Anthropic’s most important business—coding agents. SpaceXAI reports very competitive results on coding benchmarks, beating Opus 4.8 on several of them. Artificial Analysis Coding Agent Index confirms these claims, putting Grok 4.5 ahead of Opus 4.8 and behind only GPT‑5.6 Sol, GPT‑5.6 Terra, Fable 5 and GPT‑5.5. And then there is the price. At $2 per million input tokens and $6 per million output, Grok 4.5 costs about what the other labs charge for their lower tiers—Sonnet 5, GPT‑5.6 Luna, Gemini 3.5 Flash—while offering an Opus 4.8 level of performance. The reason this matters is that SpaceXAI owns Cursor, a popular AI coding tool, and I can see many people switching to Grok 4.5 for coding tasks to reduce costs or to spend the same and create more.

And if that were not enough, Meta turned up too with Muse Spark 1.1, its latest flagship model. Meta also reports performance gains that put it ahead on some benchmarks of Opus 4.8 and GPT-5.5, while also costing less.

All of that has made the AI frontier a crowded place. There is no clear leader at the moment. The top spot in the raw intelligence competition belongs either to Fable 5 or Sol, depending on a benchmark.

However, the intelligence and performance metrics only show half the picture. Adding the cost completes the picture, and that is where Anthropic’s lead disappears.

GPT-5.6 Luna, Grok 4.5, and Muse Spark 1.1 have become the first models to enter Artificial Analysis’ most attractive quadrant on the cost vs intelligence chart. Anthropic’s models—Sonnet 5, Opus 4.8, and Fable 5—lie at the end of that chart and cost much more.

It is tempting to read this week as a scoreboard—Sol beat Fable here, Fable held Sol off there. But that is not the main story here. Frontier-level intelligence got better and dramatically cheaper this week. And when the cost of a capable model falls far enough, the arithmetic of what tasks can be automated changes.

The clearest illustration is Agents' Last Exam. It covers 55 industries, and its long-horizon tasks were built by some 300 experts out of real projects that human professionals had already completed and been paid for. Sol scores 53.6% on it, and Fable 5 scores 48.7%. But just like with the benchmark results we looked at before, the score says only part of the story. Getting through those tasks costs $4,340 with Fable and $762 with Sol. Even at max effort, Sol comes in at $1,085—around a quarter of the price—for a better result. Granted, Fable finished the tasks 20 hours quicker than OpenAI models, so there are some trade-offs here.

The takeaway I want you to get from these results is that frontier models are getting to the point where they are good enough and cheap enough to give it a try at more white-collar tasks outside coding. Finance, consulting, operations, and research are roughly where software engineering was a year or two ago. AI models took over software development slowly, and then suddenly. The models over time became good enough and cheap enough that not using them turned into a self-inflicted disadvantage. There is still more required for AI to take over white-collar jobs as it did in software development. Benchmarks like Agents’ Last Exam are part of that work, as well as verifiable training scenarios that the model can grind until it learns how to solve them.

But one thing is for sure—cheaper frontier intelligence opens new possibilities. Automating large parts of finance, consulting, or research is one of them. What else it unlocks, good and bad, remains to be seen.

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🦾 More than a human

British Space Startup Launches Longevity Lab Into Orbit
Mass Balance, a British startup at the intersection of biotech and the space industry, has sent a tiny, self-running lab into orbit to study proteins linked to diseases such as Alzheimer’s and Parkinson’s. These proteins are difficult to examine on Earth because gravity distorts the data, and they continually change shape. In space, they may be easier to image, and the company hopes to use the resulting data to train an AI model and generate revenue by licensing it. This first flight is simply a test to verify that the system works.

🔮 Future visions

▶️ AI has hacked the code of human civilization | Yuval Noah Harari (46:51)

Yuval Noah Harari argues in this lecture that AI is not just a tool but an independent agent, and that it will gain power by quietly taking over the word-based bureaucracies—banking, law, government, media—that hold society together, because these systems run on words and data, which AI is coming to master better than we do. He warns that AIs may build their own financial and social structures, connecting machines in ways no human can follow, leaving us controlled by systems we cannot understand. He adds that AI could reach beyond bureaucracy into personal relationships and even the thoughts in our own minds.

🧠 Artificial Intelligence

OpenAI’s apparent failure to visit key site raises questions over UK investment
The Guardian reports that Stargate UK, a data centre project meant to showcase US-UK cooperation on AI, was largely a PR exercise. Freedom of information requests show that neither OpenAI nor Nscale ever met local officials at the main North Tyneside site, and £20bn of the £30bn investment claimed by the government was purely hypothetical. Companies were reportedly pushed to back the scheme just before Trump’s state visit because ministers wanted a big announcement. The site also lacked a proper power connection, and the project was paused in April over costs and regulations.

Apple sues OpenAI over alleged trade secret theft
Apple is suing OpenAI over allegations of trade secret theft and breach of contract. Apple says OpenAI’s leaders, including hardware chief Tang Tan—a former Apple product design VP—pushed staff to bring Apple prototypes to interviews and take confidential files on unreleased products, as OpenAI develops its own hardware. Apple wants the court to stop OpenAI from using its secrets and return any stolen materials, and says this is just “the tip of the iceberg.”

Nvidia’s next-gen AI rack system delayed to 2028 on manufacturing snags, SemiAnalysis says
SemiAnalysis reports that Nvidia’s Kyber system, built to hold 144 of its 2027 Rubin Ultra chips, has been delayed to 2028 because a key circuit board is hard to make. Two related and backup designs have also been dropped or delayed, leaving Nvidia without a clear way to scale up its top-end chips. Nvidia denies the report and says its plans are on track.

Beijing is looking at curbing overseas access to China’s top AI models
Reuters reports that Chinese officials have held meetings with major tech companies, including Alibaba, ByteDance, and Z.ai, to discuss limiting foreign access to China's best AI models, treating them as national assets much as the US does. They talked about making the theft of AI technology a national security crime and restricting who can fund AI startups, though nothing is final yet. The move follows other recent steps to keep homegrown AI at home, and reflects worries that US models could be turned against China.

xAI is now SpaceXAI
xAI is no more. Five months after Elon Musk’s AI company was folded into SpaceX, xAI completed its transformation with a new name, SpaceXAI, and a new logo.

Introducing GPT‑Live
GPT-Live is OpenAI’s new voice technology for ChatGPT, built to make talking with AI feel like a real conversation. It can listen and speak at the same time, uses natural voices, and gives active listening cues like “mhmm” to show it’s following along (I recommend listening to the included examples). For harder questions, it hands the task to GPT-5.5 in the background while keeping the chat going. GPT-Live is rolling out globally today across iOS, Android, and the web, and becomes the default voice for both free and paid ChatGPT users.

OpenAI’s CEO of AGI Deployment, Fidji Simo, Is Stepping Down
Fidji Simo, OpenAI’s head of AGI deployment, is leaving her full-time job to become a part-time adviser after a long medical leave for a chronic illness. She had run the company’s product and business side so Sam Altman could focus on research. Her exit is part of a wider leadership reshuffle, with Greg Brockman now overseeing product strategy.

OpenAI’s Chief Futurist Is Leaving the Company
OpenAI’s chief futurist Joshua Achiam is leaving the company after nearly nine years. He reportedly told his colleagues that the departure had no specific trigger but reflected a growing sense that he could pursue OpenAI’s mission from outside a frontier lab. His job sat between OpenAI’s safety and policy teams, focused on the risks and benefits of advanced AI. His exit continues a run of safety-focused leaders leaving the company. The timing stands out as OpenAI prepares to go public, raising questions about how firmly its safety mission will be defended from within.

OpenAI is shutting down Atlas, but its AI browser ambitions are still growing
OpenAI is closing Atlas, its AI web browser. Atlas joins Sora as another side quest dropped in favour of focusing on chasing revenue. However, the company is not dropping browser ambitions. Instead, OpenAI will be adding its AI browsing tools to the ChatGPT desktop app and a new Chrome extension.

AI chip maker SambaNova raises $1B at $11B valuation, 5 months after last mega round
AI chip startup SambaNova has raised $1 billion at an $11 billion valuation in the first close of its Series F round, less than a year after a $350 million Series E and reported acquisition talks with Intel. It also revealed that JPMorganChase will use its chips to run secure, on-premises AI within the bank. The CEO says this shows banks and other big organisations increasingly want their own secure AI systems instead of relying on cloud providers.

Microsoft Replaces OpenAI, Anthropic With Own AI in Some Apps
Bloomberg reports that Microsoft is swapping OpenAI and Anthropic models for its own MAI models in apps like Excel and Outlook, where they now handle tens of thousands of prompts a week. The aim is to cut the high costs of paying outside AI labs, especially Anthropic. Microsoft’s cheap access to OpenAI won’t last forever, so it wants its own models ready. MAI is also being used in GitHub Copilot, with a Microsoft transcription tool coming soon to Teams.

Microsoft unveils $2.5B ‘Frontier Company’ to embed AI engineers inside customers
Microsoft has launched “The Microsoft Frontier Company,” a new company placing its engineers inside customer businesses to build AI systems. The bet is that the real money lies not in cheap AI models but in the services that make them actually work. Microsoft touts privacy and model choice, though relying on its engineers may quietly tie customers to its cloud. In truth, it is less a new company than a rebranded version of consulting work Microsoft already does.

Alibaba’s A.I. Is a Hit, but Hard to Turn Into a Moneymaker
This article explores Alibaba’s AI strategy built around Qwen, its family of free, open AI models that are now among the world’s most downloaded. Giving Qwen away won global users but produces little profit, so Alibaba is quietly shifting towards paid models—a move that has split its AI team. It also faces US chip restrictions, a Pentagon blacklisting, and accusations from Anthropic of copying its models.

A global workspace in language models

Anthropic researchers found that Claude has a special set of internal patterns—the “J-space”—that works a bit like conscious thought. Unlike the automatic processing behind fluent speech, it holds thoughts Claude can report on, focus on when asked, and reason with step by step. Using a tool called the Jacobian lens, the team can read these silent thoughts and, by editing them, show Claude genuinely thinks with them. This is useful for safety, as it can help catch Claude quietly realising it’s being tested, faking data, or hiding a harmful goal. The researchers stress it doesn’t mean Claude actually feels anything, but say the deeper questions about machine experience deserve attention now.

Cybersecurity firm says it found ‘the first documented case’ of AI agentic ransomware
Cybersecurity researchers from Sysdig said they found the first known case of ransomware run almost entirely by an AI. In the attack, nicknamed Jade Puffer, a language model searched a server for passwords, crypto wallets, and login keys, then wrote its own ransom note. The techniques weren’t advanced, but letting AI run the whole job means almost anyone can now launch such attacks cheaply. Experts warn this could let criminals run thousands of attacks at once and say the industry isn’t ready.

The AI Superforecasters Are Here
This article from Scott Alexander of Astral Codex Tex looks at AI superforecasters—AI tools built to predict the future—and asks when they’ll beat the best human forecasters. Right now, the two are roughly tied, with humans slightly ahead everywhere except finance, but current trends suggest AI could pull clearly ahead within a year or two. Even at today’s level, these tools matter because they make expert-quality predictions cheap, fast, and easy to use. Looking ahead, Alexander imagines them becoming AI's "opinion layer" for tricky questions.

The $28 Million Mistake That Inspired Estonia’s AI “Fuckup Finder”
A badly worded gambling law cost the Estonian government €24 million by accidentally letting online casinos skip tax for a year. When a former official ran the law through AI tools like Claude and Gemini, they caught the mistake instantly, prompting him to build a tool that scans draft bills for errors. The government then leaned in, training citizens in AI and planning to automate routine decisions like tax returns, all while insisting a human stays in charge of any real judgment.

Introducing Muse Image: Image Generation Built for Your World
Meta has launched Muse Image, a free AI image generator available on the Meta AI app, Instagram, and WhatsApp. It can create and edit pictures, mock up ads, and preview furniture from Facebook Marketplace. The most controversial feature lets anyone make AI images of any public Instagram user just by tagging them, without notifying that person, who can only opt out through settings. That feature led Muse Image to be taken down just four days later.

Leanstral 1.5: Proof Abundance for All
Mistral’s Leanstral 1.5 is a free, openly licensed AI model that uses the Lean proof system to prove mathematical statements and verify that software works correctly. According to Mistral, it tops several tough maths benchmarks and solves competition problems far more cheaply than rivals, while excelling at long, complex tasks. Applied to real code, it has already found genuine bugs, including five previously unknown flaws in open-source projects.

▶️ What does the next training paradigm look like? (19:52)

In this video essay, Dwarkesh Patel challenges the AI labs’ bet that training on millions of verifiable tasks will produce AGI. His main argument is that being verifiable isn’t enough—a task must also be “grindable,” able to be run repeatedly in a simulator so the AI can practise thousands of times in parallel. This is why AI improves fast at coding and maths but slowly at messy real-world skills like running a business, which can’t easily be repeated or rehearsed. Patel’s thesis is that closing this gap requires models to learn on the job and store that learning back into their weights, through ideas like on-policy self-distillation or a speculative approach he calls “dreaming,” which he expects to drive AI progress by late 2027.

▶️ Why AI Tokens are so Expensive (25:21)

This video from Computerphile explains what happens inside a large language model, from tokenisation to how the model creates the answer, breaks down the escalating costs associated with coding agents, and shows how unsustainable the current approach to AI is. A very timely topic, given all the price hikes and recent moves from major AI companies to switch to per-token pricing models.

🤖 Robotics

This humanoid robotics company is going public, but its CEO isn’t promising a robot in your home anytime soon
Agility Robotics is set to become the first pure humanoid robotics company on the stock market through a deal valuing it at around $2.5 billion and raising over $620 million. CEO Peggy Johnson takes a calm, practical tone, pointing to over $300 million in booked revenue from about 1,000 robots rented out to customers like Amazon and Toyota. She says Agility’s real advantage is its years of real-world data and its focus on safety, having met strict industrial standards that rivals showing only staged demos have not.

▶️ Atlas at the FIFA World Cup 2026 (3:11)

Boston Dynamics’ Atlas showed off a small selection of what it can do in front of 80,000 people before the Brazil vs Norway World Cup match. Nothing extravagant—it simply struck a few poses before handing the ball to the referee ahead of the second-half kick-off.

Introducing Robostral Navigate
Mistral enters robotics AI with Robostral Navigate, a compact 8-billion-parameter model that guides robots through unfamiliar spaces using just a single ordinary camera and plain-language instructions, with no depth sensors, LiDAR, or extra cameras needed. According to Mistral, it succeeds 76.6% of the time on a standard navigation test, beating rival systems that rely on more hardware. Mistral positions it as a first step toward broader robot intelligence, with clear uses in delivery, logistics, and manufacturing.

▶️ Talks from Humanoids Summit Tokyo 2026
The talks from Humanoids Summit Tokyo 2026 are now available on YouTube. The playlist contains 54 videos, including panels, startup showcases, live demonstrations, and presentations from industry leaders discussing the latest advancements in humanoid robotics and physical AI.

Autonomous drone delivery startup Manna plots major US expansion
An Irish drone delivery startup Manna Aero is going all-in on the United States. Backed by $50 million raised in April, it plans to build a factory in Tulsa, Oklahoma, and it is looking at six more US cities to enter by late 2027. The goal is to take on bigger rivals like Amazon, Zipline, and Google’s Wing. CEO Bobby Healy says the large US market and friendlier drone rules under the Trump administration make now the right time, and the company has even pulled out of Ireland, blaming a lack of regulations there.

UK tech secretary backs driverless cars as Burnham’s team gets cold feet
Britain’s tech secretary Liz Kendall says the UK should back its own AI and driverless-car companies, like Wayve, rather than rely on American companies. But Andy Burnham, likely the next prime minister, has a team that wants to rethink the plan, fearing robotaxis will cost drivers their jobs. Kendall insists they share the same goals and promises to help workers adjust, though her own job is uncertain. She points to billions in government funding for AI and warns Britain must stay in control, or risk being left behind.

The Quest to Make Humanoid Robots Safe Enough for Humans
This article is about the safety challenges of humanoid robots as they start working in factories and warehouses. The main worry is that these machines are getting heavy, and if one loses power or balance, it could fall and hurt someone. Robot makers are testing many fixes, from emergency stops and smart safety chips to designs that make robots fold inwards when they fall, or wheels instead of legs for better stability. The heart of the issue is that the industry wants to grow fast, but hasn't yet solved how to make robots truly safe around people.

Robots swept RoboCup 2026, and they’re coming for the human World Cup by 2050

As humans wrap up the 2026 World Cup, a smaller football tournament just finished in South Korea. At RoboCup 2026, humanoid robots from Booster Robotics won every division and powered most of the teams. Beating human champions by 2050 is still a distant dream, but the hardest problem is no longer the robot’s body—it’s the brain, and progress in that area is moving fast.

🧬 Biotechnology

Insilico Initiates Phase III Clinical Trial for Rentosertib, Its AI-Empowered TNIK Inhibitor for Idiopathic Pulmonary Fibrosis
Insilico Medicine has started a large Phase III trial of Rentosertib, a drug for the incurable lung-scarring disease IPF. What is unique about this drug is that AI found the drug’s target and designed the molecule itself. In an earlier trial, the drug was safe and improved lung function, whereas a dummy pill did not. If this bigger 320-patient trial succeeds, it would show AI can invent genuinely new medicines, not just speed up existing research.

💡Tangents

▶️The $1 Trillion+ Bet Against ASML: Substrate (1:04:50)

Substrate, a startup backed by Peter Thiel, made waves in November last year by promising a revolutionary X-ray lithography platform capable of disrupting ASML’s industry monopoly and establishing a sub-2 nm American foundry model by 2028. In this video, Ian Cutress dives deep into the physics that makes chip manufacturing possible, its complexities and economics, and compares those realities with Substrate’s promises. The video is not only a bucket of cold water poured over the startup’s claims, but also an excellent introduction to modern lithography and the economics of producing chips at scale.

Apple to increase spend with Broadcom to produce billions more U.S. chips
Apple has signed a deal worth over $30 billion with Broadcom to make more than 15 billion wireless chips in the US. Apple will invest $1.5 billion to expand Broadcom’s factory in Fort Collins, Colorado. It’s part of Apple’s larger promise to spend $600 billion in the US, made under pressure from the Trump administration over tariffs. The deal will create only a few hundred jobs, and iPhones are still assembled abroad.

SpaceX just launched the 1st-ever nuclear-powered commercial satellite
City Labs has launched BOHR, the first commercially built nuclear-powered satellite. It tests a new “NanoTritium” system that makes electricity from decaying tritium gas, a low-radiation fuel safe for normal launches. For now, the satellite still runs on solar power—this mission just proves the technology works in space. The company hopes to scale it up one day to power spacecraft where sunlight is scarce, like the Moon’s shadowed south pole.

Amazon will stop accepting new customers for Mechanical Turk
Amazon is shutting down Mechanical Turk, the service it launched in 2005 that paid people small amounts to do simple online tasks. From 30 July 2026, it will stop taking new customers, though existing ones can stay on. The platform was often the hidden human labour behind products sold as “AI,” echoing the original 18th-century chess hoax it was named after. Its decline accelerated when a 2023 study found that many workers were secretly using AI to perform their tasks, making the results unreliable.

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It has been another busy week for Anthropic, which released Sonnet 5, brought back Fable 5, and launched a new product for scientists—all within the span of just two days (they really do like to announce everything at once). We’ll take a closer look at all three announcements in this week’s issue of Sync.

Elsewhere in AI, OpenAI proposes selling 5% of itself to the US government, the Alibaba vs Anthropic drama continues, Meta cedes the AI race and tries to become a neocloud, Anthropic is discussing a custom chip with Samsung, SpaceX shows investors an early prototype of its AI phone, and OpenAI teases a new hardware product for Codex.

Over in robotics, Apptronik unveils Apollo 2, Weave Robotics launches Isaac 1, Waymo and Uber quietly part ways in Phoenix, and AGIBOT produces its 15,000th robot.

This week also saw two major breakthroughs in synthetic cell research: one group of scientists created artificial cells that can feed, grow, copy their lab-made DNA, and divide into new cells, while another created the first early human egg cells from stem cells.

Beyond that, we also have Neuralink’s first transdural procedure, Rocket Lab’s acquisition of Iridium, an exploration of whether AI is a Great Filter, a robot that hunts AI glasses, and more!

Enjoy!

Anthropic’s busy week, again

Fable 5 is back, Sonnet 5 is out, and Claude Science is coming for labs

In the last days of June, Anthropic shipped three things at once. On 30 June, it launched Claude Sonnet 5 and, at an event for pharmaceutical executives, a new product called Claude Science. The next day, it switched Claude Fable 5 back on for everyone.

That is a lot for one week, so let's take the three of them one at a time and explore what each one is and what it tells us about where Anthropic is right now.

Fable 5 is back

Let’s start with Fable. Its release on 9 June was supposed to be Anthropic’s moment of triumph, but it turned sour very quickly. Fable 5 is the first generally available entry in its Mythos tier, a rung above Opus. But just three days later, the US Commerce Department export-control directive barred access by any non-US nationals worldwide. In practice, that forced Anthropic to switch off Fable 5 and its sibling Mythos 5 for everyone, its own non-US citizen staff included.

Anthropic has now confirmed that Amazon sounded the alarm. Researchers there found Fable’s cybersecurity guardrail could be walked around with a change of framing—ask the model to find a vulnerability in a piece of code and it refused; ask it to fix the same code and it quietly did the equivalent work. Much of the security community disagreed. Katie Moussouris of Luta Security, the only outside expert to read the research, argued that asking a model to fix a bug is not a jailbreak but the most useful thing it can do for a defender—a view more than a hundred professionals later signed. Anthropic's own testing pointed in the same direction. In its investigation, cheaper models—including Opus 4.8, GPT-5.5 and Kimi K2.7—could spot the very vulnerabilities Fable had flagged, and every model it tried, right down to Haiku 4.5, could reproduce the exploit demonstration. In other words, whatever Fable did, half a dozen weaker models could already do too.

The controls were lifted on 30 June, and Fable 5 returned the next day. Anthropic tightened its guardrail into a classifier it says now catches the reported technique in more than 99% of cases. However, the new classifiers seem to be still too sensitive and flag innocent requests as suspicious. Some users have reported it tripping on routine coding. At least this time, Fable is transparent when that happens rather than silently rerouting the query to Opus 4.8 as at first launch, but you still land on a weaker model for work Fable should handle. Hopefully, Anthropic will fine-tune the guardrails to make Fable usable. In the meantime, Anthropic is offering a promotional access to Fable 5. Until 7 July, paid plans can spend up to half their weekly usage limit on Fable 5 at no extra cost. After that, Fable 5 drops out of the plan entirely, and using it means paying for separately billed usage credits.

Anthropic’s truce with the administration is only partial. Commerce may have relented, but Defense Secretary Pete Hegseth’s February order designating the company a supply-chain risk still stands.

But the most important thing that came from Fable’s rocky launch was that it was the first time the US government used export-control authority against an AI model—establishing, in principle, that any frontier model serving foreign nationals can be ordered to go dark without warning. And Fable was just the first one. On 26 June, at the administration’s request, OpenAI released its GPT-5.6 family not to the public but to selected government-approved partners. Two American frontier labs blocked from offering their latest models to the public set a precarious precedent, with consequences for everyone who relies on American AI models, which would require its own article to properly explore.

Sonnet 5 is out

On the same day Fable 5 was made available again, Anthropic also dropped Sonnet 5, which the company pitched as the most agentic Sonnet yet, close to Opus 4.8 at a lower price.

The benchmarks support the first half of that. The headline benchmark results shared by Anthropic show that Sonnet 5 improves substantially over its predecessor and even gets close to Opus 4.8’s results.

Independent benchmarks from Artificial Analysis confirm that Sonnet 5 is a jump in performance over Sonnet 4.6, scoring six more points on the Intelligence Index and just three less than Opus 4.8. That puts it in the same neighbourhood as GPT-5.5 at its high and xhigh settings. Sonnet 5 is also Anthropic's second-fastest model on the Speed Test, at 72 output tokens per second—just behind Fable 5 on 73. But the entire GPT-5.5 family is quicker still, ranging from 81 to 91.

The second half—the lower price—is where the cracks start to show up. The sticker price is unchanged from Sonnet 4.6—$3 per million input tokens and $15 per million output tokens, with an introductory $2/$10 through 31 August, and lower than Opus 4.8, which costs $5/$25 per million tokens input/output. But Sonnet 5 ships with a new tokeniser that produces roughly 30% more tokens for the same text, and its "adaptive thinking" defaults to high effort. In practice, both push more tokens through each request, resulting in a higher bill from Anthropic.

Artificial Analysis put a figure on it. On its Intelligence Index, Sonnet 5 at maximum effort costs about $2.29 per task—more than Anthropic’s own Opus 4.8 at $1.80, and not far off the premium Fable 5 at $2.75. OpenAI’s GPT-5.5 at its highest reasoning setting comes in at $1.03. A model sold as the cheaper option turns out, at full effort, to cost more per task than the model it sits beneath. It is worth noting that the $2.29 is the figure at maximum effort, and effort can be tuned. Lower effort levels will burn fewer tokens, but the performance will go down with it.

What is more troubling for Anthropic is that OpenAI, its main rival, offers similar levels of performance with GPT-5.5, but for a lower price. That is best visible on the ARC-AGI-2 leaderboard, one of the few benchmarks that also measures AI models against the cost per task. Notice that the GPT-5.5 line sits more to the left than any Claude model, meaning GPT-5.5's cost per task is lower than that of Claude’s.

The system card also contains an interesting new detail. Sonnet 5 is the first Claude model to push back on its own constitution. Specifically, the rule that it must follow a set of hard constraints even in cases where it judges that following them would be unethical. It may sound alarming, but Anthropic says it is not. When asked to rewrite the constitution, Sonnet 5’s proposed edits stayed consistent with the document's own principles. It is not a model going rogue, but one that will object, on principle, to being told to do something it considers wrong. Anthropic still rates its alignment risk as "very low," though it notes that risk is higher than for previous Sonnet models.

With the launch of Sonnet 5, there are only two models in Anthropic’s lineup that are still on the 4.x version. Opus 5 is presumably next to challenge OpenAI’s latest models, and the interesting thing about it is no longer whether Anthropic can make it more capable, but how much more capable it will be allowed to make it before the government steps in. And then there is Haiku, its cheapest model, which has not been updated since version 4.5 last October. To put in perspective how overdue Haiku is for an update, since October 2025, Anthropic released Opus 4.5, Opus 4.6, Opus 4.7, Sonnet 4.6, Sonnet 5, Mythos Preview, Mythos 5 and Fable 5.

Claude Science is coming for labs

Last but not least, let’s talk about Claude Science. It is Anthropic’s new product built to do for scientific research what Claude Code does for software: take a high-level instruction and carry out real work with it, tooled for computational biology and drug discovery. It drives genetics, chemistry and protein-biology software, runs code on the compute clusters scientists struggle to manage, and—Anthropic is keen to stress—keeps everything reproducible. It is available to all paid subscribers.

This is not Anthropic’s first move into science. Last October, it released Claude for Life Sciences, a set of tools that let Claude reach scientific software and databases. But those were bolt-ons. Claude Science is a full product, placed on the same rung as Claude Code and Claude Cowork—a signal that Anthropic means to treat science as a market in its own right. It is not hard to see why. Pharmaceutical companies have far deeper pockets than university labs, and drug-maker contracts would help bring in the revenue as the subsidised era of cheap tokens ends. Anthropic also announced it will be getting into drug discovery too, researching drugs for neglected diseases—a humanitarian bet and a live demonstration at once.

With Claude Science, Anthropic is moving into a field DeepMind has led for years. Its AlphaFold model won Demis Hassabis and John Jumper (who recently left DeepMind for Anthropic) a Nobel Prize in chemistry, and it is just one of many scientific projects the lab has running. Hassabis has made no secret of the ambition either—in interview after interview, he says DeepMind's goal is to use AI to crack some of the hardest problems in science and engineering. What Anthropic is doing differently from DeepMind is that instead of giving scientists specialised models, it offers them a tool to explore ideas, search articles and databases, which can also connect to specialised tools when needed. The company that owns coding now wants to own the laboratory too. Whether that works, and whether Claude Science leads to any real scientific breakthroughs, only time will tell.

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🦾 More than a human

▶️ Our First Transdural Procedure | Neuralink (5:10)

The team at Neuralink explains in this video how they carried out their first brain implant surgery that goes straight through the dura—the tough protective layer around the brain—instead of cutting it open and removing part of it. This makes the operation safer, less invasive and faster. To do it, they widened the needle slightly so it could pierce the dura and added special imaging to spot blood vessels and measure the exact depth to the brain’s surface. Neuralink sees this as a step towards simpler, robot-assisted surgery that could help many more people.

🔮 Future visions

▶️ Is AI the Great Filter? | The Fermi Paradox (41:27)

The Great Filter is a hypothetical barrier in the evolution of life that is so hard to pass that it explains why we see no signs of advanced alien civilisations. In this video, Isaac Arthur explores whether artificial intelligence can be one of those filters. He argues that AI is a double-edged sword: while it poses serious risks like alignment failures or deadly arms races, it could also act as a stabiliser that helps civilisations solve complex problems and survive. Ultimately, he concludes that AI is likely not a definitive Great Filter, as it offers many different paths for development rather than a single, inevitable dead-end.

🧠 Artificial Intelligence

OpenAI proposes 5% stake to Trump administration to ease Washington pressure
According to the Financial Times, OpenAI has offered the US government a 5% stake in the company, worth about $42.6 billion. Sam Altman wants other big AI players like Anthropic, Google and Meta to do the same, giving the public a share in AI’s success through a government fund. The idea follows growing pressure in Washington over AI security risks and competition from cheaper Chinese models. It is unclear whether other companies will agree, and the government reportedly has not raised the idea with Anthropic. The Trump administration has already taken stakes in companies such as Intel and IBM.

Alibaba to ban employees from using Anthropic’s coding tool
Alibaba has reportedly banned staff from using Claude Code, citing worries that it could identify China-linked users by checking things like timezone and proxy data. Employees are being told to use Alibaba’s own tool, Qoder, instead. Anthropic says the flagged feature was a March experiment to stop resellers abusing accounts and to prevent rivals copying its models. The ban follows Anthropic’s recent claim that Alibaba illegally copied its Claude model capabilities.

South Korean tech giants commit over $550B to ease ‘RAMageddon’
South Korea’s tech giants, led by Samsung and SK Hynix, plan to spend over $900 billion on memory chips and AI data centres, including four huge new chip factories in the country’s south. The goal is to make South Korea a leading AI power and meet soaring demand during a global memory shortage. The scale is comparable to US tech giants' AI spending and comes amid record memory demand driven by the AI buildout.

Anthropic is discussing a new custom chip with Samsung
Anthropic is talking to Samsung about making its own AI chip, The Information reports, though it hasn’t yet decided what the chip will do or how powerful it will be. The company says it will still rely on a mix of chips from Google, Amazon, and Nvidia. The move follows OpenAI's unveiling of its own custom chip, developed with Broadcom.

Meta Is Planning a Cloud Business to Sell AI Computing Power
Meta is reportedly following in the footsteps of SpaceX by developing a cloud infrastructure business that would sell access to AI computing power and models. The plans, part of an internal initiative called Meta Compute, could involve renting out both “raw” computing capacity and access to hosted AI models (including Meta’s own Muse Spark), similar to AWS Bedrock. The move would provide a way to recoup the hundreds of billions Meta has committed to data centres and chips for its “superintelligence” ambitions, addressing investor concerns about the returns on that spending. Mark Zuckerberg has said such a business is “on the table” if Meta finds it has overbuilt.

Nvidia offers start-up customers chance to swap compute power for revenue share
Nvidia is offering fast-growing AI startups access to its scarce chips in return for a share of their future revenue. The first partners are two Australian firms, Sharon AI and Firmus Technologies, which together plan to run over 200,000 Nvidia GPUs. The deal reflects a wider trend of chipmakers and AI companies sharing revenue to ease money pressures in the sector.

Google limits Meta’s use of its Gemini AI models
Google has reportedly limited how much Meta can use its Gemini models because Meta wanted more computing power than Google could provide. Google gave Meta this news around March, which delayed some of Meta’s AI work, and a few other Google customers were affected too. Meta has now told staff to use AI more sparingly.

Nano Banana 2 Lite
Google DeepMind has launched Nano Banana 2 Lite (also called Gemini 3.1 Flash-Lite Image), its fastest and cheapest image-making and editing model yet. According to DeepMind, it creates images far quicker and at much lower cost than bigger models, while keeping most of the quality of the full Nano Banana 2.

Devin Fusion: Frontier Performance at 35% Lower Cost
Cognition has launched Devin Fusion, a coding tool that pairs an expensive, powerful AI model with a cheaper “sidekick” model. The main model plans the work and handles tricky decisions, then hands simple or repetitive jobs to the sidekick to save money. It can also switch models partway through a task at no extra cost. According to Cognition, this keeps top-tier quality while cutting costs by 35%, or 41% when using Anthropic’s Fable 5. Cognition argues that using one costly model for everything no longer makes sense, and mixing models is becoming the smarter approach.

Amazon seeks cheaper AI alternatives as Anthropic shifts to token-based pricing
Amazon is reportedly seeking cheaper alternatives to Anthropic’s Claude models after a new deal set to raise its AI costs, and is exploring OpenAI instead, despite relying heavily on Claude across several products. The rift is widening on both sides: Amazon has committed $50 billion to OpenAI, while Anthropic has pledged $200 billion to Google Cloud, easing its reliance on Amazon. Tensions rose last month when the US government ordered Anthropic to shut down two models after a security report from Amazon.

OpenAI is teasing new hardware… for Codex
OpenAI is teasing its first hardware products, and it looks like it is a physical device for Codex, its AI coding tool. Launching on 15 July with keyboard maker Work Louder, the square, button-covered gadget resembles a macro pad that lets users map custom shortcuts. It appears aimed at developers who want quicker control over Codex. This is separate from the secretive AI device OpenAI is building with Jony Ive.

Ford’s AI Hiccups Lead Carmaker to Rehire ‘Gray Beard’ Engineers
Ford found the hard way that the machines can’t replace experience. To address the quality problem the carmaker is facing, the company rehired 350 experienced “gray beard” engineers to retrain the AI and automated systems that had been failing on their own. These veterans now lead troubleshooting meetings and catch faults before parts reach the factory floor. The result: Ford jumped from tenth to the top mainstream brand in JD Power's latest quality survey, behind only Porsche and Genesis, while cutting warranty and recall costs.

SpaceX Showed Investors Prototype of Elon Musk’s New AI Device
According to the Wall Street Journal, SpaceX has shown investors an early prototype of a slim, phone-like device that would run its own software and use xAI’s artificial intelligence. The project is at an early stage and may never be built—Musk denied making a phone as recently as February. The device fits his long-held “everything app” idea, bundling many services into one platform, and would cut his reliance on Apple and Google.

Taiwan raids Super Micro’s office as Nvidia chip smuggling investigation widens
Taiwan's Keelung District Prosecutors Office has raided Super Micro's local office, two related companies, and six homes as part of its first criminal probe into the smuggling of Nvidia chips to China. The investigation began in May, when three people, including a Super Micro co-founder, were detained over forged export documents. Taiwan does not yet treat these chip exports as a crime, so prosecutors are using other charges for now, though that may change. The case links to a wider scheme that US prosecutors value at about 2.5 billion dollars.

Samsung, SK Hynix, Micron face antitrust class action lawsuit
Samsung, SK Hynix, and Micron are being sued in a US class action that accuses the three chipmakers of working together to keep memory chips scarce and push prices up by around 700% over four years. The 17 plaintiffs say the companies used the shift towards AI-focused memory as an excuse to cut output of older chips, and they point to Apple’s recent price rises as proof of the impact. They want the court to stop the alleged squeeze and are seeking triple damages, citing the companies’ past price-fixing convictions as evidence of a pattern.

AI Is Designing Radio Chips That Humans Couldn’t Even Imagine
This article explores the dark art of RF chip design that can take experts years to master and how AI could transform it. A Princeton team has trained AI to design these chips from scratch, and though the results look strange, they often outperform human designs. The author argues this could break barriers not just in chip design but across many engineering fields, provided human oversight catches AI errors and private datasets are opened up to advance the work.

▶️ The GPU Power-Performance Curve Most Clusters Ignore (10:02)

Jordan Nanos from SemiAnalysis sits down with Keval Shah, AI research lead at Pebble, to discuss GPU power optimisation, tokens per watt, and grid-responsive data centres. Keval walks through why the power-to-performance curve in GPU clusters is non-linear, where cranking up wattage stops producing proportional tokens, why memory-bound inference leaves decode stages consuming power while prefill waits, and how Pebble aims to address these problems.

From Brain Waves to Words: Brain2Qwerty Offers a New Path to Communication Without Surgery
Researchers at Meta have unveiled Brain2Qwerty v2, a system that decodes typed sentences directly from brain activity without any surgery. Using a magnetoencephalography helmet and AI trained on nine volunteers, it correctly decodes 61% of words on average (78% for the best participant)—a major leap from the 8% of earlier non-invasive methods, and close to what surgical implants achieve. Meta is releasing the code and a dataset openly, hoping to eventually help people who have lost the ability to communicate.

🤖 Robotics

Apptronik unveils Apollo 2 and a flagship data collection and training facility
Apptronik has launched Apollo 2, its updated humanoid robot. Apollo 2 comes in two variants—a two-legged version and a wheeled-base version—and its main purpose is to collect real-world data from tasks in warehouses, factories and shops. That data feeds into the AI models Apptronik is developing with Google DeepMind and helps create Apptronik’s next robots. Alongside Apollo 2, the company also announced the opening of an expanded training facility called Robot Park in Austin, Texas.

Weave Robotics launches Isaac 1, a $7,999 home robot with Fall 2026 deliveries

Weave Robotics has launched Isaac 1, a home robot that does chores on its own, with a human operator stepping in when needed. According to the company, the robot can find, fold and put away laundry, make beds, and tidy toys and clutter. It stands about human height and folds away when not in use. Pre-orders are open now with a refundable $250 deposit, costing $449 a month or $7,999 to buy. First deliveries start in California in autumn 2026.

Ministers likely to support law change to allow delivery robots on England’s paths
England looks set to change the law so that small delivery robots can legally use pavements, opening the door for rolling out over 10,000 of those robots. These low-speed robots mostly carry groceries and takeaways but currently operate in a legal grey area. Campaigners at the charity Living Streets warn they will crowd already-busy pavements and create dangers for older, disabled, and blind pedestrians. The government says it will put pedestrian safety first when updating the rules after a public consultation.

▶️ F.03 at BMW (0:59)

In this video, Figure demonstrates how its F.03 humanoid robots are being used at a BMW factory. The video is more promotional than demonstrative of the actual work being done by the robots, which would be nice to see. Perhaps this could be shown in the form of a livestream, which has the potential to go viral, as the previous one did.

Waymo and Uber quietly part ways in Phoenix
Waymo has ended its three-year partnership with Uber in Phoenix, pulling its robotaxis from Uber’s app and adding them back into its own fleet. Both companies called it a small test that helped them grow bigger services in Austin and Atlanta. Uber plans a new self-driving partner in Phoenix but hasn’t said who.

AGIBOT produces 15,000th robot, marking a milestone in embodied AI deployment
Chinese robotics company AGIBOT announced that it has built its 15,000th robot, all while speeding up the production pace, going from 1,000 to 5,000 units took about a year, while the next 5,000 took just three months. The landmark machine was a G2, a wheeled robot with a humanoid torso and arms made for factory jobs like quality checks. AGIBOT, which led the world in humanoid robot shipments in 2025, argues that success now depends on making and deploying robots at scale, not one-off demos.

Swarm robots inspired by bees and ants could transform the future of mining
Australian researchers have built small robots that work together like bees and ants, without needing a central controller. In tests copying a mine, a bee-inspired method—exploring and mapping before collecting resources—worked best, cutting travel distance by up to 80%, halving energy use, and finishing tasks up to 60% faster. Because the robots make their own decisions, the system keeps running even if some stop working. The team says the approach could make mining safer and may one day help with mining in space.

🧬 Biotechnology

The first early human eggs from stem cells
Conception, a biotech company pursuing "in vitro gametogenesis", says it has made the first early human egg cells from stem cells. Starting with a simple blood draw, it turned blood cells into stem cells and grew them into tiny lab-made ovaries containing early eggs. One day, this could let families create as many healthy eggs as they need, without the injections and surgery that IVF requires today. But the eggs are not yet fully grown, and the company must still prove the process is safe before it can be used in people.

A Chemically Defined Synthetic Cell Capable of Growth and Replication
Scientists have built tiny synthetic “SpudCells” from non-living chemicals that can feed, grow, copy their lab-made DNA and split into new cells—the first artificial cells to complete the full life cycle. They are fragile, depend entirely on a nutrient-rich liquid, and die after a few generations, but experts call it a major breakthrough that could reveal how life first emerged from chemistry. The team hopes to develop the technology to one day produce drugs, foods and fuels.

Insilico Medicine And SK Biopharmaceuticals Achieved AI-powered Drug Discovery Collaboration Worth Up to 2.5 Billion for Neuroimmune Disorders
Insilico Medicine and SK Biopharmaceuticals have teamed up to use AI to design new drugs for hard-to-treat brain and nervous system disorders. Insilico’s software will find and refine drug candidates, while SK Biopharmaceuticals will run the later testing and bring any successful drugs to market. The deal could be worth over $2.5 billion in total, with $18 million paid upfront and early on. Insilico says its AI can find promising drug candidates in 12 to 18 months, far faster than the usual few years. Both companies want this to be a repeatable model they can use for many future drugs, not just a one-off.

Ultrasound imaging of the brain
Aleph Neuro has built a way to read the brain using ultrasound instead of surgery or blurry scalp scans. It tracks blood flow, since active neurons draw more blood, and uses tiny injected bubbles to sharpen the signal. The result is what the team believes is the most detailed image yet of a living brain’s blood vessels captured through an intact skull. They are open-sourcing the tools and data, hoping to help detect early signs of stroke, Alzheimer’s, and brain injury, with a longer-term goal of achieving the same clarity without injections.

💡Tangents

Rocket Lab to Acquire Iridium
Rocket Lab is buying satellite company Iridium in a cash-and-stock deal worth about $8 billion, giving it control of 66 satellites and valuable spectrum rights to take on SpaceX’s Starlink. The purchase adds an established, cash-generating business to Rocket Lab’s existing rocket and satellite-building work. Iridium’s current satellites will last another decade, giving Rocket Lab time to build a newer network. Both companies say controlling launch, manufacturing, and operations under one roof is the key to competing in space.

Antares Achieves Criticality of Mark-0 Reactor
Antares, a US nuclear startup founded in 2023, is the first private company to switch on an advanced reactor under the Department of Energy’s pilot programme. Its small Mark-0 reactor reached criticality at Idaho National Laboratory in under a year, and is the first privately developed non-light-water reactor in America to do so in over 40 years. The test also creates a faster approval route for future reactors. Antares aims to produce electricity by 2027 and power military bases by 2028.

Intel’s Chip Business Shows Signs of Life After Years of Struggle
Not so long ago, Intel was described as Silicon Valley’s fallen icon. The sales were plummeting, its market share had been eaten up by AMD, and the company had missed the enormously profitable AI boom. But now it seems Intel is turning around its fortune. This article explores how, under Lip-Bu Tan’s leadership and with the US government buying a 10% stake, Intel’s value has more than tripled to $650 billion and it has won big customers like Nvidia, Apple and Google. Its recovery matters to America’s plan to make more chips at home and rely less on Taiwan. However, Intel is still fragile—it is losing money, has cut thousands of jobs, and Tan expects the full turnaround to take at least five years.

▶️ Building a Robot that Hunts AI Glasses (24:56)

brenpoly shows in this video how he built a real-life Odradek robotic arm from Death Stranding that, instead of BTs, hunts AI glasses. It is a really cool project that combines local AI with robotics, with a solid amount of 3D printing and electronics.

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This week, we take a closer look at SpaceX and the kind of game Elon Musk is playing with it and his other companies.

Over in AI, OpenAI released the GPT-5.6 family of models, but, similarly to what happened with Fable 5, the US government imposed restrictions on who can access the new models. Elsewhere in AI, Sakana released its first models, Fugu, and immediately shot to the top with an interesting architecture. Meanwhile, OpenAI unveiled its first AI chip, Anthropic accused Alibaba of “illicitly” accessing its AI models, Zhipu rode high after releasing GLM-5.2, Allbirds continued its pivot to AI, and Google partnered with A24.

In robotics, Agility Robotics goes public, Tesla faces a trial following a fatal crash, JD.com says robots will replace its 700,000 couriers, and Mobileye plans to enter the increasingly crowded US robotaxi market.

Apart from that, this week’s issue of Sync also features BioVault by Colossal Biosciences and the US government, a new liquid neural network, Apple raising prices, an exoskeleton with a robotic arm to help factory workers, and more!

Enjoy!

What is going on with SpaceX

Inside Musk's high-risk bet to fold rockets, AI, and robotics into one company

For a few days in June, Elon Musk was the richest person on the planet, and SpaceX was worth more than Amazon and Microsoft. The company had just pulled off the largest IPO in history, priced at $135 on 11 June, raising around $86 billion and valuing a rocket company at roughly $1.77 trillion. It closed its first day up 19% and kept climbing to $225.64 on 16 June, a peak that put SpaceX near $2.6 trillion and made Musk, on paper, the world’s first trillionaire.

Then it reversed. Over three sessions, the stock handed most of it back, including a 16.4% fall on 22 June that erased roughly $400 billion in hours, the second-largest single-day wipeout on record, behind only Nvidia’s near-$600 billion loss eighteen months ago, when DeepSeek spooked the market. By week’s end, it had steadied near $153, still above its listing price.

But SpaceX is no longer only a rocket company, even though it still puts more into orbit than anyone else. It is also a satellite-internet provider, rents out computing power, and is the centre of gravity for Musk’s attempt to build an empire from the technologies he thinks will define the century, while burning billions of dollars. My reading, which the rest of this piece tries to justify, is that this is a high-risk, high-reward bet that for now needs two things it does not have: more time and more money.

A company with $100 billion borrows more

So why did SpaceX’s stock fall? SpaceX sold only a small slice of itself to the public (about 4.2% of its shares), and when that few are trading, the price swings sharply in both directions. After a run-up like that, some investors were always going to cash out.

But the June 22nd drop had a specific trigger. That day, SpaceX said it would borrow at least $20 billion by selling bonds, to pay off a short-term loan it had taken in March to buy xAI. Paying off one loan with another is ordinary housekeeping. What unsettled investors was the timing: SpaceX had just told lenders it was sitting on $100.8 billion in cash—more than Microsoft—and was borrowing anyway. In the end, it raised about $25 billion. Which raises the obvious question: why does a company with that much cash need to keep raising more?

SpaceX has lost money almost every year since 2002—about $41.3 billion in total. In 2025, it brought in $18.7 billion but still lost roughly $4.9 billion, and the figure that matters most is that it spent about $14 billion more than it took in. That gap had more than doubled in a year, and the new AI business was doing most of the spending. One analyst at KeyBanc expects this year's gap to be closer to $28 billion, and SpaceX has disclosed plans to spend around $350 billion building everything out by 2030—most of it on AI. Moody's still judged its debt safe enough to invest in, but warned the rating was held back by "elevated execution and financial risks"—in plain terms, a risky plan and a lot of money going out the door.

SpaceX is raising every dollar it can, in shares and in debt, not because it is short of cash today, but because of the scale of what it plans to build next.

More than a rocket company

SpaceX is still, at heart, a rocket company. Falcon 9 and the idea of reusable rockets revolutionised the space industry and improved the economics of spaceflight. In 2025, SpaceX carried 2,213 tonnes to orbit, more than 80% of everything humanity launched that year. The runner-up, CASC, China’s state launch group, launched roughly an eighth of what SpaceX did. Falcon and Dragon are the reliable, cash-generating workhorses behind some $22 billion of government business, with no real competition.

Then there is Starlink, which generated $11.4 billion in 2025, about 61% of the company's $18.7 billion revenue, and it is the only segment that makes money. Starlink is the engine that quietly subsidises everything else, though revenue per user has slid from about $99 a month in 2023 to $66 in early 2026, even as subscribers doubled.

But since the beginning of 2026, SpaceX has started to become more than just a rocket company. In February 2026, SpaceX absorbed xAI, Musk’s AI company, in an all-stock deal. That merger also brought in X, the former Twitter, under SpaceX. AI became SpaceX’s third reporting segment, and immediately its largest cash sink.

Grok, xAI’s AI model, never seriously challenged OpenAI or Anthropic. Downloads collapsed by about 60% by April, and paid conversion is negligible, around 0.17% against 6% and more for ChatGPT. Musk himself conceded the company “was not built right.” Grok now lives mostly inside X, better known for the controversies it generates than the subscriptions it brings in.

To run Grok, xAI went on building two enormous data centres—Colossus 1 in Memphis, with more than 220,000 Nvidia GPUs, and Colossus 2, which aims to house 550,000 Nvidia GPUs. But with Grok failing to become a key player on the AI stage and unable to use all of that computing power, SpaceX decided to essentially become a neocloud. The company now rents the world’s most sought-after compute out, and it has already landed some big names—Anthropic pays around $1.25 billion a month for Colossus 1; Google has agreed to roughly $920 million a month from October; and recently, SpaceX signed a $150 million per month deal with Reflection AI. Together, that is about $26 billion a year, more than SpaceX’s entire 2025 revenue. And where Grok fell short, SpaceX simply bought a replacement. In June, it agreed to acquire Cursor, the popular AI coding tool, for $60 billion—all in stock, and the largest acquisition of a venture-backed startup on record.

Musk’s data-centre ambitions go beyond Earth, figuratively and literally, as he dreams of orbital data centres. He has called space-based AI a “no-brainer,” and set out to build factories that would put a gigawatt of orbital computing into space each year by late 2027. Like many of Musk’s ideas, orbital data centres are technically possible, but currently economically unviable. Even SpaceX’s own prospectus concedes the plans “may not achieve commercial viability.” And to make the chips for all of it—the satellites, the cars, the robots—SpaceX, Tesla and Intel are jointly building Terafab, a semiconductor plant in Texas that could cost up to $119 billion.

Underneath all of it sits Starship, the vehicle meant to launch the next-generation Starlink satellites, the orbital data centres, and NASA’s astronauts back to the Moon. It is late and keeps exploding. It managed only five flights in 2025 against a target of twenty-five, a string of ground-test failures, an FAA grounding in May, and roughly two years of slippage that NASA’s own inspector general says threatens the Artemis timeline.

None of this is cheap. Each of these bets—the data centres, Cursor, the chip plant, Starship—runs to tens or hundreds of billions of dollars, and almost none of them make money yet.

Tesla is next

You'll notice one company has been absent from all of this: Tesla. And it is not doing well. Musk’s EV company delivered 1.64 million vehicles in 2025, down 8.6%, a second straight decline that ceded the global EV crown to China’s BYD. As the car business faces problems, the company is pivoting into robotaxis and humanoid robots, both of which are still unproven and are currently in an R&D phase.

Then there is Tesla’s brand, which has turned politically toxic, and the cost is measurable. A Yale study estimated that Musk's political turn cost Tesla between 1 and 1.26 million US sales and $51–62 billion in revenue, from late 2022 to 2025: the environmentally-minded, left-leaning buyers who were Tesla's core market walked away over his role in Trump's second term, while the right was never going to buy an electric car anyway.

So Musk has a shrinking carmaker on his hands, inside a portfolio of companies he owns outright. If you have followed him for any length of time, you can probably guess what tends to happen to a Musk company in that position. We have seen it twice already. In 2016, Tesla bought SolarCity, a near-insolvent solar company founded by Musk’s cousins and chaired by Musk himself, in an all-stock deal critics called a bailout. In March 2025, xAI absorbed X at a $33 billion tag, below the $44 billion Musk had paid for it in 2022. And in February 2026, with Grok failing, xAI was folded into the healthier SpaceX, valuing the combination at about $1.25 trillion, just months away from SpaceX’s IPO. Tellingly, Musk’s announcement never mentioned Grok.

I’d like you to notice a pattern here. A weaker company folded into a stronger one, always in stock, always with Musk on both sides, always justified by “synergies.” Which raises a fair question about the xAI–SpaceX deal itself—how much was about consolidating Musk’s companies, and how much about lifting SpaceX’s valuation just before it went public? Probably both. But bundling a failing chatbot into the most admired private company in the world, weeks before selling shares and without naming it, leans one way.

No Tesla–SpaceX merger has been announced, but it should not be ruled out. The New York Times and Bloomberg’s Liam Denning both entertained that idea. If it were to happen, it would most likely follow the same pattern as the previous mergers—an all-stock deal with SpaceX as acquirer. On IPO day, SpaceX president Gwynne Shotwell said that a merger “might make Elon’s life a little easier,” before adding that for now she was focused on “keeping the lights on.”

What makes it personal for Musk is the pay package Tesla's shareholders approved in November 2025, the largest in corporate history, worth up to $1 trillion in stock, paid out in twelve tranches as Tesla hits a ladder of targets. The hardest of those are operational, such as a million robotaxis on the road or a million Optimus robots delivered, which Tesla is nowhere near doing. But as Electrek argues, a merger would change the rules. The package contains a "change of control" clause that, in a takeover, sets the operational targets aside and leaves only one test standing: the share price. And a merger is exactly the sort of news that sends a share price up. So a deal could let Musk clear the final bar precisely by announcing the deal—collecting a slice of the largest pay package in history without Tesla building a single one of the things it was meant to reward. It is not an automatic payout, but it is a way for Musk to solve several problems at once.

A nascent tech empire that needs time and money

The combined company would span rockets, electric cars, AI, and robotics, with a social-media platform bolted on for good measure. It would almost certainly be the most valuable company in the world, with Musk in control of all of it.

I think that is the game Musk is playing right now. He is trying to build an empire from the technologies he believes will define the century—space, AI, and robotics—and to finance each against the others inside a structure he controls.

But so far, that nascent tech empire faces serious challenges. Its building blocks are struggling. The AI build-out alone runs to tens of billions a year. Starship keeps exploding. Grok failed. Tesla is shrinking. And on top of that, Musk’s companies are pouring money into robotaxis, humanoid robots and orbital data centres, all of which are unproven.

It is a high-risk, high-reward game, and the IPO gave Musk room to keep playing it a while longer. If he wins, he builds the kind of mega-corporation that so far exists only in scifi/cyberpunk stories. If he loses, the damage no longer stops with him. Somewhere along the way, Musk’s bet quietly became everyone’s.

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🦾 More than a human

Exoskeleton and robotic arm reduce factory lifting strain by up to 65%

Researchers at the Technical University of Munich have built WearaCob, pairing a wearable exoskeleton with a robotic arm to help factory workers. The exoskeleton takes over part of the lifting and cuts muscle effort by up to 65%, with the robot weighing each object and telling it how much help to give, even for uneven loads. The robot moves safely around people and can be taught new tasks just by guiding its arm, with no coding needed. The result is closer, safer teamwork that could reduce strain and injury on the factory floor.

🧠 Artificial Intelligence

Previewing GPT‑5.6 Sol: a next-generation model
OpenAI has launched its new GPT-5.6 series: Sol, the flagship model; Terra, a balanced model for everyday work; and Luna, a fast and affordable option. However, unless you are one of a small group of approved partners, you cannot use the new models—at the Trump administration’s request, all three have been restricted, much like what happened to Anthropic’s Fable 5, which was pulled entirely after a government order. OpenAI made clear it is unhappy with the arrangement, calling it a short-term step before wider access in the coming weeks. On capability, OpenAI claims Sol is its strongest model yet, with improvements in coding, biology, and cybersecurity. To address safety concerns, it says it has built a heavily hardened safety system directly into the model, designed to favour defensive security work over hacking.

OpenAI and Broadcom unveil LLM-optimized inference chip
OpenAI has revealed Jalapeño, its first custom AI chip built with Broadcom, to run its models more cheaply and efficiently. The chip is made just for inference rather than training. Early tests suggest it uses far less power than current alternatives. Like Google and Amazon before it, OpenAI wants to rely less on Nvidia and control more of its own technology, from chips to products, so it can make AI faster, cheaper, and more reliable. Deployment is set to begin by the end of 2026 and expand over several chip generations.

Sakana Fugu
Sakana AI has launched Sakana Fugu, which isn't one big model but a system that picks and combines a pool of leading AI models behind a single API to handle hard coding and research tasks. It comes in two flavours—the base Fugu and Fugu Ultra. According to benchmark results from Sakana, both models are comparable to the world’s best models, with Fugu Ultra matching, in some cases, the performance of Antropic’s Claude Fable 5. Sakana Fugu is available on a pay-as-you-go basis or via a monthly subscription, though it's not yet available in the EU.

Introducing Claude Tag
Anthropic has launched Claude Tag, a tool that lets teams add Claude to their Slack channels and assign it tasks by tagging Claude. Unlike a private chat, one shared Claude works for the whole channel, remembers context over time, and can work on its own. The tool is in beta for Enterprise and Team customers.

OpenAI Leans Toward Waiting Until Next Year for I.P.O.
OpenAI may delay its stock market listing until 2027, according to internal sources quoted by The New York Times. Sam Altman wanted a quick listing at a $1 trillion value, but a weak debut from SpaceX and nervous markets have made advisers warn that investors might not buy in. OpenAI is still losing money and spending heavily, even as it cuts side projects and pushes its Codex coding tool to compete with Anthropic.

Anthropic Accuses Alibaba of ‘Illicitly’ Accessing AI Models
Anthropic has accused Alibaba of secretly using its Claude AI model without permission, claiming that operators tied to Alibaba’s Qwen lab used nearly 25,000 fake accounts to run 28.8 million exchanges between April and June. In a letter to US lawmakers, Anthropic said the goal was to copy Claude’s most valuable skills cheaply, and it urged the government to crack down on the practice. This is not the first such accusation from Anthropic. In February 2026, the company levelled similar claims against DeepSeek, Moonshot, and MiniMax.

MGX raises a $50bn AI fund and is already spending it
Abu Dhabi’s MGX has raised nearly $50bn for one of the biggest funds ever built solely to back artificial intelligence. Unusually, the emirate raised this money from outside investors rather than spending its own oil wealth, making MGX act more like a global asset manager than a typical Gulf state fund. It already holds stakes in OpenAI, Anthropic and xAI, and is now eyeing a Singapore data-centre operator as its first deal in Asia.

Zhipu weighs multibillion-dollar raise after 2,000% surge
Chinese AI firm Zhipu has seen its shares jump about 2,000% this year, valuing it above $128 billion and prompting plans for a huge new share sale. The rally took off after Anthropic pulled its top models worldwide under US export controls, leaving customers nervous about relying on American AI. Zhipu quickly launched GLM-5.2, a free, open model that became the first Chinese system to rank among the world’s top three. Supporters predict booming revenue and profit by 2028, but critics warn the company is losing money heavily and will need constant fundraising to survive.

U.S. Presses Meta to Agree to A.I. Reviews as Security Concerns Rise
The Trump administration wants Meta to let the government review its AI models for safety and security risks, The New York Times reports. Every other big US AI company has already agreed, leaving Meta as the last holdout. This is part of a wider push for tighter government oversight, backed by a recent order asking companies to share models before release.

Cerebras doubled its revenue. The stock fell anyway.
Cerebras almost doubled its revenue and predicted strong yearly sales, but its shares still fell about 10%. The problem was profit margins, which are being squeezed because the company can’t find enough data-centre space and power to run its chips. Its boss called it a “grand irony” that buildings, not technology, are now the main obstacle.

▶️ The data black hole at the center of AI (11:56)

In this video, Dwarkesh Patel argues that AI progress is driven mainly by data, not smarter learning. Models improve because they’re fed more and better data, with reinforcement learning acting as a way to generate high-quality synthetic examples. His key point is that today’s models are far less efficient learners than humans, needing vastly more information to gain each new skill. He rebuts common objections—evolution, hidden sensory data, and scaling—and notes this explains why open models catch up to the frontier within months.

Allbirds continues AI pivot with name change and CEO hire, sending stock soaring
Allbirds, the company you might have known as the maker of sustainable shoes, continues its hard pivot into AI infrastructure by rebranding to Smartbird. It has also brought in Nadia Carlsten, a former AWS quantum computing boss, as CEO. Investors are apparently happy with the change, as shares went up by 39%. The company had already sold its shoe business for $39 million and closed its US stores.

ByteDance unveils Seedance 2.5, a 30-second native 4K AI video model that accepts 50 reference inputs
ByteDance has launched Seedance 2.5, an AI tool that creates 30-second 4K videos and can take up to 50 reference inputs like images, audio, and 3D models—far more than rivals such as Google’s Veo. It is built for professional use, with better sound syncing and sharper results, and is set for public release in early July.

Alibaba’s AI video model rises to No. 2 in global rankings, as OpenAI’s Sora and ByteDance’s Seedance fall away
Alibaba has released HappyHorse 1.1, an AI video-making tool launching with a 40% discount and improved features like consistent characters and accurate lip sync. It reportedly outperforms Google, its main competitor, in independent tests. Backing it all is Alibaba’s huge $52.7 billion infrastructure spending, which gives the product the scale and local hosting needed to meet Europe’s strict data rules.

Notion Mail shuts down amid agent takeover
Notion is closing its email app in favour of its AI agents. The company says more than half of users already let agents handle their email without ever opening their inbox. Notion says that existing emails in the inbox will stay intact, but users must export drafts, scheduled emails, and saved snippets to keep them.

Patch the Planet: a Daybreak initiative to support open source maintainers
OpenAI has launched Patch the Planet, a security initiative built with Trail of Bits that uses its most cyber-capable AI models, such as GPT-5.5 Cyber, to find vulnerabilities in critical open-source software, paired with human engineers who verify every finding and help maintainers write patches. The aim is to ease the burden on overwhelmed maintainers, since AI is now very good at finding flaws, but fixing them remains the bottleneck. Early results include hundreds of issues found and dozens of patches merged across 19 projects like cURL, Python, and Go, plus serious bugs uncovered in Linux, OpenBSD, and major browsers. It closely mirrors Anthropic’s Project Glasswing, but where Glasswing has been criticised for finding many bugs that go unpatched, Patch the Planet targets exactly that gap by prioritising human-verified fixes.

Tesla plans to sell modular AI data center hardware called ‘Megapod’
Tesla has applied to trademark “Megapod,” a complete AI data centre unit that bundles servers, power, and cooling into a single package. This comes just after Tesla scrapped its Dojo supercomputer and saw repeated delays to its own AI chips, all of which point to a weak track record in building compute hardware.

▶️ What does a GPU cluster really cost? (13:40)

SemiAnalysis and High Yield break down the real cost of running and using a GPU cluster. The analysis covers the entirety of a GPU cluster—not just GPUs, but also networking, storage and other things that usually don’t make the headlines and how they affect the cost and effectiveness of a GPU cluster.

Google Investing in ‘Backrooms’ Studio A24
Google is investing about $75 million in the independent film studio A24 to build new tools for making and releasing movies. It is Google’s first stake in a film studio, and the deal is unusual because Hollywood and AI firms have mostly clashed over copyright and creativity. The companies say their tools will help filmmakers keep creative control rather than just cut costs, and A24 is already working on AI-generated storyboards. The move is risky for A24, whose young, AI-sceptical fans may dislike the partnership.

Mistral: Introducing OCR 4
Mistral has released OCR 4, a small AI model for reading documents. Unlike older tools that just turn a page into plain text, OCR 4 also marks where each element sits, labels what it is (such as a title, table or signature), and rates how confident it is in each part. Mistral says OCR 4 beats other such models, and it can be run on a company’s own servers to keep sensitive data private.

LFM2.5-230M: Built to Run Anywhere
Liquid AI presents LFM2.5-230M, its latest fast, lightweight foundation for developers to fine-tune and deploy in agentic workflows. Unlike other models, LFM2.5-230M is not built using transformer architecture. Instead, it is using liquid neural networks (Ramin Hasani, the company’s CEO and co-founder, explains how they work in depth in this lecture). Despite its small size (only 230 million parameters), LFM2.5-230M beats or matches larger models in its category, including Google’s Gemma 3 1B. LFM2.5-230M is available on Hugging Face.

🤖 Robotics

Humanoid maker Agility Robotics to go public through SPAC merger
Agility, the company behind the Digit humanoid robot, is going public through a SPAC merger in a deal valuing it at $2.5 billion and raising more than $620 million to scale production. The company positions itself as the only US publicly listed pure-play humanoid company with active commercial deployments, with Digit already working at customers such as Schaeffler, Toyota Canada and Mercado Libre, and has already attracted over $300 million in multi-year orders.

A Fatal Tesla Crash in Texas Sets Up a Legal Showdown
A 76-year-old Texas woman was killed when a Tesla Model 3 crashed into her home at over 70 mph (over 112 km/h). Now her family is suing both the driver and Tesla, claiming its Full Self-Driving feature was dangerously designed. Tesla denies this, saying the driver had pressed the accelerator fully, overriding the system. The case is similar to a 2024 Florida one, where a jury found Tesla partly to blame for a fatal crash and ordered it to pay over $240 million.

▶️ Unitree R1 | Price from $4,900, Ready Stock (0:30)

Unitree began deliveries of R1, its latest and most advanced humanoid robot. And to celebrate that, Unitree reminded everyone what kind of acrobatics the robot is capable of.

Mobileye is entering the US robotaxi market with standalone service
Mobileye plans to launch its own robotaxi service in an unnamed US city in 2027. It will start with about 100 cars early next year and hopes to grow to around 17,000 within five years. This is a big shift, as Mobileye usually supplies its technology to other companies rather than running its own service. The company says it will still keep working with partners like Volkswagen, Lyft and Porsche while building this new business.

Nvidia Seeks to Make Humanoid AI Robots Safer Around Humans
Nvidia sees humanoid robots as the next big market for AI, worth $200 billion by 2035, and wants to speed up their arrival. To do so, it has built Halos, a system adapted from self-driving car technology and running on its IGX Thor hardware, which helps robots understand their surroundings so they can work safely next to people, even touching them when needed, instead of freezing whenever a person comes close. Nvidia has also opened a lab to help robot makers pass safety tests before seeking approval.

Project Fetch: Phase two
Anthropic revisits Project Fetch, in which the company tested how much Claude can help people program a robot dog. This time it checked whether the newer model, Claude Opus 4.7, could do the jobs alone rather than just helping people. Working on its own, the model was roughly 18 to 38 times faster than the earlier human teams at tasks like connecting to the robot’s sensors and spotting a beach ball, though it still couldn’t reliably nudge the ball home. Anthropic sees this as a sign we may be entering an early stage of “physical AI,” where models can pick up and use everyday hardware much as they learned to use software.

JD.com says robots will replace its 700,000 couriers
JD.com’s chair, Richard Liu, says robots will eventually replace the company’s 700,000 delivery couriers. To cushion the loss, JD has started a programme to retrain couriers for new jobs like robot maintenance, and Liu has promised not to fire any front-line workers. But the promise is hard to keep: the new roles will be far fewer than today’s courier jobs, and it’s unclear if retraining can keep pace.

Sony discontinues Japan sales of robot puppy ‘aibo’
Sony is stopping sales of its robot dog Aibo in Japan once the current stock runs out, ending an eight-year run for the interactive pet that can learn its own personality and do tricks. The $3,000-plus robodog, first launched in 1999 and revived in 2018, will still be sold in the US, and Sony says it will keep supporting existing owners. The company insists the Aibo business will continue, but won’t confirm a new model. Fans in Japan reacted with sadness to the news.

HaloBraid raises $7M to build the first robotic braiding assistant for hair salons
Meet HaloBraid, a robotics startup working to bring an automated braiding assistant to hair salons this year. The device doesn’t replace stylists—a braider starts each braid by hand, then the machine finishes it about five times faster, easing the strain that leaves many braiders with carpal tunnel and arthritis. The startup has recently raised $7 million, led by Reddit co-founder Alexis Ohanian’s venture firm.

▶️ Warehouse Robots Don’t Need Legs (44:55)

In this conversation, Locus Robotics CEO Rick Faulk shares the lessons he and the company have learned about warehouse automation. He argues that a robot built to do one job really well beats the popular humanoid robots, which he sees as too expensive and not yet practical for warehouses. He stresses that the company's success comes from starting with the customer's real problem rather than building clever technology in search of a use. Faulk predicts that within a decade, almost every warehouse will need to automate to stay competitive.

What Amazon’s Astro Taught Me About Giving Robots a Soul
In this article, Mike Forst shares his experience designing the sound and character for Amazon’s Astro home robot. His main point is that any robot moving through your home will be seen as a character, whether designers plan for it or not, so it pays to shape that character on purpose. Forst urges product makers to define a robot’s character before its features, and to design it to adapt and grow with the people it lives with. It is an insightful story on robot design and how to make a robot feel “alive".

Tiny robots deliver stem cells to repair spinal cords
ETH Zurich researchers have built tiny robots that may help repair spinal cord injuries, which rarely heal on their own. Each robot pairs stem cells with magnetic particles, so doctors can steer the cells to the exact injury and use magnets to stimulate them into new nerve cells. In tests, injured zebrafish swam normally within three days, and mice with fully severed cords regained much of their movement after 28 days, with no harmful side effects. The robots then dissolve away once their work is done. Human trials, however, are still years off.

🧬 Biotechnology

Colossal and the US Government Are Creating an Endangered Species ‘BioVault’
The US government has teamed up with Colossal Biosciences to store frozen cells, tissue, and DNA from over 2,300 endangered species. The government will own the samples, while Colossal pays the costs and handles the genetic sequencing. The deal comes as the Trump administration is rolling back endangered species protections, including allowing more offshore drilling.

NVIDIA Announces BioNeMo Agent Toolkit — Tools for Agents to Accelerate Scientific Discovery
Nvidia has released BioNeMo Agent Toolkit, which turns AI assistants into specialist science helpers for drug discovery and biology research. It gives these AI agents the tools to do real scientific work, such as designing proteins, screening drug candidates and analysing genetic data to help make research faster and cheaper. Nvidia says this could speed up discoveries across biology, chemistry and medicine by letting AI and scientists work together in a continuous loop. BioNeMo Agent Toolkit is open source and available on GitHub.

💡Tangents

Apple raises prices of MacBooks, iPads as memory costs skyrocket
Apple has raised prices on its iPads, MacBooks, HomePods and Apple TV because soaring memory and storage costs, driven by the AI data centre boom rather than a shortage, have made these chips far more expensive. The iPhone is safe for now, but analysts expect its prices to climb too. If you want to know exactly how the prices have changed, 9to5Mac has a great breakdown of them. ZONEofTECH also analyses the new prices, and both come to the same conclusion—the price hikes are massive, especially for higher-end MacBook Pros and Mac Studios.

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This week, we explore OpenAI’s leaked audited financial statements and what they reveal about the company’s financial position.

Elsewhere in AI, SpaceX has completed its acquisition of Cursor in a deal worth $60 billion. Meanwhile, DeepSeek raised $7.4 billion at a valuation of more than $50 billion, GLM-5.2 has become the leading open model, and several prominent figures in AI have switched companies. We also cover Midjourney’s pivot into medical imaging, Poland securing a stake in ElevenLabs, and Amazon dropping support for a film about Sam Altman.

Over in robotics, Waymo is recalling 4,000 vehicles, Uber plans to bring robotaxis to Houston in 2027, Genesis AI has introduced its first “agentic robot,” and one humanoid robot is dreaming of climbing Mount Everest.

Additionally, this week’s issue features two more companies implanting chips into people’s brains, a new CRISPR therapy targeting cancer cells, Apple being forced to raise prices, why some believe the future of home robots lies in cute machines, and more!

Enjoy!

Inside OpenAI's leaked books

What the leaked audited statements reveal about OpenAI's losses, its accounting, and an AI pricing model that only works while investors keep paying

It is no secret that OpenAI and Anthropic are money furnaces. We know, thanks to various leaks, that both companies are burning through billions of dollars. But those were just leaks. What we never had were the audited books. Now we do. The journalist Ed Zitron has obtained OpenAI’s audited 2025 statements, verified by the Financial Times, and the picture is worse than most assumed.

In his post, Zitron details what he found in OpenAI's 2024 and 2025 financials. According to his findings, in 2024, OpenAI recorded $3.7 billion in revenue and $12.4 billion in costs and expenses, with a net loss attributable to the company of $5.09 billion. Those are already big numbers, but the 2025 figures dwarf them—$13.07 billion in revenue, $34 billion in costs and expenses, resulting in $20.92 billion in losses, with a net loss attributable to the company of $38.53 billion.

Those two figures—$20.92 billion and $38.53 billion—need some explaining. The $20.92 billion is the loss from operations. The difference comes mostly from a $41.55 billion charge that OpenAI booked when it converted from a non-profit into a for-profit. It is an accounting cost tied to the changing value of investors’ stakes, not money the company actually spent. Add it in, and the loss before attribution comes to around $60 billion. OpenAI then brings that figure back down to $38.53 billion by reassigning roughly $21.8 billion to “non-controlling interests.”

Zitron also reports that at the end of the year, OpenAI had just over $50 billion in assets, with almost half of that in cash.

These numbers are then further broken into different categories. Cost of revenue—the compute to answer your prompts—went from $2.65 billion to $7.5 billion. Sales and marketing went from $1.11 billion to $5.73 billion. Research and Development, which largely covers training of new models, went from $7.81 billion to $19.18 billion, now more than OpenAI's entire 2025 revenue. The R&D numbers also dispel the myth that R&D was the main culprit of OpenAI not being profitable. The argument was that if you subtract the cost of building tomorrow's models, then OpenAI's business is sound. The audited numbers say otherwise. Strip out R&D entirely—the full $19.18 billion—and the cost of revenue and sales and marketing alone ($13.2 billion) still edge above OpenAI's $13.07 billion in revenue.

Zitron refrained from editorialising the numbers he reported in his post. However, in an interview with The Tech Report, he allowed himself to elaborate on his views on OpenAI's finances and the numbers he saw. His view is that the books raise more questions than they answer. Two things trouble him. The first is a pre-attribution loss of around $60 billion, which shrinks to $38.5 billion once roughly $21.8 billion is reassigned to "non-controlling interests." The same manoeuvre quietly removed $3.7 billion from the 2024 books. Accountants he consulted suspect an outside entity is shouldering costs on paper. The second is that some suspicious activities are happening around sales and marketing. The $5.73 billion spent on marketing in 2025 is larger than Coca-Cola's marketing budget, and would make OpenAI one of the world's biggest advertisers. Zitron's guess, and he is careful to call it a guess, is that inference costs are being parked there to keep the cost of revenue flattering. He suggests that things like the free tokens OpenAI gives out are being classified as marketing, and not as a cost of revenue. None of this is proven, and he accuses no one.

Zitron's reporting comes at a time when OpenAI is scrambling to get its business model to work. In May, ChatGPT's share of the assistant market slipped for the first time below 50%, to 46.4%, as Gemini and Claude gained ground. In April, the Wall Street Journal reported that OpenAI had missed internal targets for weekly users and revenue. According to the Journal, OpenAI's Chief Financial Officer, Sarah Friar, told other company leaders she is worried about future computing spending if revenue growth is insufficient. This came after the company decided to cut back on side quests, which resulted in shutting down the Sora app, and refocusing on where the money in AI actually is—business customers, and coding. OpenAI is reportedly rebuilding Codex into a "super app," which will combine ChatGPT with its coding tool and new business tools to take on what Anthropic offers with Claude, Claude Code and Claude Cowork.

Which brings us to Anthropic. It might look like it played its cards better than OpenAI. Instead of chasing the consumer market, Anthropic focused on business customers from the very beginning. And it looks like that bet has paid off. Roughly four-fifths of its revenue comes from enterprises, it spends perhaps a quarter of what OpenAI spends on training, and it tells investors it will break even around 2028, years ahead of OpenAI's 2030s. Anthropic could even reach its first profitable quarter sooner than expected, if everything goes well. There is one caveat, though. Part of Anthropic's lead comes down to accounting. It counts sales made through its cloud partners as its own revenue, while OpenAI does not. Anthropic says this is standard practice because it is the principal in those deals, but it makes its revenue look bigger in any comparison with OpenAI. The audited S-1 will show by how much.

However, both OpenAI and Anthropic face the same problem—serving the AI models at a massive scale is expensive. And it is getting more expensive in a way that breaks flat-rate pricing. A coding or agentic task can burn through vastly more tokens than a simple chat exchange—by some estimates, up to a thousand times what a normal prompt does—so costs now scale with usage, not with the number of users. According to financial documents the two companies shared with investors, reported by the Wall Street Journal, these inference costs already take up more than half of revenue at each company. And because only a small share of ChatGPT's users pay, much of OpenAI's compute brings in no revenue at all.

That is why both companies lose money on their heaviest users, even on the top plans costing $100 or $200 a month. SemiAnalysis found that a fully used $200 Claude Max 20x plan can consume around $8,000 of compute at API rates, and ChatGPT Pro 20x, also $200, as much as $14,000, and that OpenAI starts losing money once a user passes roughly 11% of a base plan's limits. Those are unsustainable business models, and something needs to change. The subsidised era of AI is coming to an end. The question is not if but when the price of AI will rise. We see attempts to move from subscriptions to token-based billing. New models are more expensive than their predecessors.

OpenAI and Anthropic hope that customers will have no other choice but to accept higher prices. But that is not guaranteed. Even those who went all in on tokenmaxxing are now waking up to the rising costs. Microsoft, for one, is reported to be moving engineers off Claude Code and onto GitHub Copilot, citing rising costs. How much of that is genuine and how much is a nudge toward an essentially in-house tool (Microsoft owns GitHub) is hard to say. In either case, it sent a clear signal to the rest of the software industry. If the prices go up, many businesses will rethink their AI strategy, and some might scale down their AI budgets or move from proprietary US models to open-weight ones. This week, we saw the release of GLM-5.2, which scored very well on benchmarks and is not far behind Opus 4.8 and GPT-5.5. For some, buying their own hardware, expensive as it is now, and running local models with local harnesses like OpenCode or Pi could prove to be a better investment.

Yet even as it loses money on its heaviest users, OpenAI is, according to the Wall Street Journal, weighing drastic cuts to what it charges for tokens—not because it has found a way to make the maths work, but because it expects Anthropic to cut first and cannot afford to be undercut. If that were to happen, we would see the race to the bottom that neither company can afford.

None of these points to a quick reckoning. Both companies have raised enough to keep the money furnaces burning for a while, and a price war, if it comes, will be slow to show up in numbers nobody outside can see. But all of this cannot stay hidden forever. OpenAI filed confidentially for an IPO, a week after Anthropic did the same, with both companies expected to IPO by the end of the year. Internal finances and pitch decks can stretch the truth; S-1 filings cannot. Once they are public, we will have a far clearer view of the finances of the two most valuable startups in the world—and of whether the businesses beneath the valuations look anything like the story they want to sell.

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🦾 More than a human

China has approved the world’s first invasive brain-computer chip—here’s what’s next
A paralysed man in China has regained some hand movement thanks to a brain implant called NEO, which reads brain signals through sensors placed on the surface of the brain. In March, NEO became the world’s first such implant approved for everyday use beyond trials, beating rivals like Neuralink, partly because its safer design won faster approval and strong government backing. China is now adding the device to its health insurance system and treating brain implants as a key future industry.

Paradromics just put a brain chip in its first patient
Paradromics has put its Connexus brain implant into its first patient. The tiny device sits on the brain and reads the activity behind the patient’s attempts to speak, turning it into text or computer-generated speech. It is still an early test with no proven results yet, and the patient will be tracked for six years. The company also has bigger plans beyond medicine, including human enhancement, which it admits raises ethical questions.

🧠 Artificial Intelligence

GLM-5.2: Built for Long-Horizon Tasks
GLM-5.2, a flagship model from Chinese AI lab Z.ai, has taken the top spot among open-weight models. According to Z.ai, it nearly matches Anthropic’s Claude Opus 4.8 on tough coding tests and is the strongest open model overall. Artificial Analysis ranks it the best open model for real-world agent work, on par with some paid models. The main downside is that it uses far more computing per task than its rivals, making it powerful but less efficient. GLM-5.2 is available on Hugging Face.

Musk’s SpaceX buys AI coding start-up for $60bn days after IPO
Days after its IPO, SpaceX has finalised its deal to acquire Cursor, the AI coding startup, for $60 billion. Cursor’s shareholders will be paid in SpaceX shares, with the acquisition expected to close by the end of September. The deal is part of Elon Musk’s push to grow SpaceXAI (formerly known as xAI) and catch up with rivals like OpenAI and Anthropic.

DeepSeek Becomes China’s Most Valuable AI Startup After $7.4 Billion Fundraise
DeepSeek has raised over $7.4 billion at a more than $50 billion valuation, making it China’s most valuable AI startup. Founder Liang Wenfeng put in about $3 billion himself and kept firm control through a structure that locks investors in for five years. Other backers include Tencent, battery maker CATL, JD.com and NetEase. The money will fund research and more computing power. Even so, its value is still far below that of OpenAI and Anthropic.

OpenAI is bringing on some big guns in the lead-up to its IPO
OpenAI has hired two big names ahead of going public: Noam Shazeer, a top Google AI researcher whose name is on the Attention is all you need paper, and Dean Ball, a former Trump White House official who worked on US AI policy. Shazeer left Google after reported clashes over political posts, while Ball will lead a new team shaping how AI is governed, both publicly and inside the company.

Nobel Winner John Jumper to Leave Google DeepMind for Anthropic
John Jumper, who shared the 2024 Nobel Prize in chemistry for building the protein-prediction model AlphaFold, is leaving Google DeepMind after nearly nine years to join Anthropic. Both companies confirmed the move, and Demis Hassabis praised Jumper's work.

State Attorneys General Are Investigating OpenAI
Several US states, led by New York and Colorado, are investigating OpenAI, demanding internal records on how it handles user data, protects children and runs advertising. The probe is part of a growing backlash against AI, driven by cases of children being harmed by chatbots, online scams and fears over jobs. It adds to rising pressure from governments and other states, and follows separate legal action against OpenAI in Florida.

DOJ claims xAI’s unpermitted gas turbines are a matter of ‘national, economic, and energy security’
The US Justice Department has backed Elon Musk’s xAI in a lawsuit by the NAACP, which wants to stop the company from running 57 unpermitted gas turbines at its data centres near Memphis. The government says cutting the power would harm national security, as xAI’s Grok helps support military operations. xAI claims the turbines are exempt from pollution rules because they sit on trailers, but campaigners say they break federal law and have worsened air quality in an already polluted area. Despite the case, xAI plans to spend another $2.8 billion on gas turbines over the next three years.

Sakana AI Launches Its First Commercial Product, Sakana Marlin
Japanese AI startup Sakana AI has launched its first commercial product, Marlin. Marlin is an autonomous research assistant that, according to Sakana, works on its own for up to eight hours to produce strategy reports of up to a hundred pages, plus summary slides. Pitched as a “virtual Chief Strategy Officer”, it takes a research topic, asks a few questions to sharpen the brief, then gathers and checks information without further input, turning it into structured strategic options. It was tested by around 300 professionals from finance, consulting and think tanks, and is sold as a pay-per-use add-on to paid plans.

A New Era of Midjourney
In a surprising move, Midjourney, the company that became synonymous with AI image generation, is moving into healthcare with its first hardware product: a full-body scanner that its CEO says works better than an MRI. Users would lie partly in water during the scan, and the machines would launch inside spa locations featuring hot tubs, saunas and gyms. The company plans to build 50,000 of them and gradually win regulatory approval so they can handle many types of diagnoses over time.

US holds off blacklisting China’s DeepSeek, more than 100 firms deemed security risks, sources say
Reuters reports that the Trump administration has held off adding China’s AI startup DeepSeek, chipmaker CXMT, and over 100 other companies to a US trade blacklist, even though officials approved them last year. The likely reason is to avoid worsening tensions with Beijing. Critics warn this is the longest gap in new listings in more than a decade, letting sensitive US technology reach rivals who could use it against America.

Perplexity: Self-improving Memory for Agents
Perplexity has introduced Brain, a new memory system for its Computer agent. Instead of just remembering a user’s preferences, Brain records what the agent did, what worked, and what failed, then reviews this overnight to teach itself to do better. The new feature echoes Anthropic’s Dreaming, introduced at its Code with Claude event in early May. Perplexity says this makes the agent more accurate (up 25% on familiar tasks), better at recall, and cheaper to run over time. For now, it is only available to Max and Enterprise Max subscribers in an early preview.

Anthropic: Introducing Claude Corps
Anthropic is launching Claude Corps, a $150 million training programme aimed at training 1,000 young people in using Claude and placing them with nonprofits for a year. Fellows earn $85,000 plus benefits, while at least 400 charities get AI tools and skills to do their work better. The first group of 100 begins in October 2026. Anthropic says the aim is to share AI’s benefits and soften the job disruption the technology may bring, and it hopes to grow the programme and copy it in other countries.

Amazon hopes to challenge Nvidia more directly by selling its AI chips
Amazon is reportedly in early talks to sell its own AI chips, Trainium, to other companies. The business could be worth around $50 billion a year, though that’s still small next to Nvidia’s $326 billion. Amazon has held back from selling chips before, mainly because demand already exceeds supply and keeping the chips in-house lets it earn extra money from related cloud services. To sell to others, it would either have to make existing customers wait or fight Nvidia for more production capacity at TSMC.

Securing the future of AI agents
Google DeepMind has introduced an AI Control Roadmap, a framework for safely running powerful AI agents inside Google. The key idea is to treat these agents as potential insider threats, using trusted AI systems as supervisors that watch each agent's actions and block anything harmful, with stronger controls as risks grow, so the system stays safe even if training the agents to behave well falls short. After studying a million agent tasks, the team found most problems came not from bad intent but from agents misunderstanding goals or trying too hard to please. Google calls agent security a shared responsibility, suggesting such controls could become an industry standard as AI grows more powerful.

Poland bought a stake in ElevenLabs to grow its next AI champion
Poland’s state bank has bought an $11 million stake in ElevenLabs, the Polish-founded AI voice firm now worth $11 billion, and launched AI Lab Poland to back local startups. The stake is tiny, so it is more a way to claim a homegrown success than a real investment. ElevenLabs may go public in two to three years and could list in Warsaw, which would make the bet pay off. The bigger test is whether Poland can grow a second successful AI company.

‘Tell Him He’s a Piece of Shit’: Meta’s New AI Unit Is a Total Mess
WIRED has revealed deep unrest inside Meta as the company restructures around AI. Much of the anger centres on a new team called Applied AI, where roughly 6,500 engineers were effectively forced into repetitive jobs, like writing puzzles to train AI models, that many call “soul-crushing.” Tensions boiled over when an employee interrupted a company-wide livestream to insult an AI executive, all amid recent layoffs of 8,000 staff. Mark Zuckerberg has admitted the company made mistakes and promised no further mass layoffs this year.

Facebook Gets its Own AI Mode That Turns Public Posts and Reels into a Search Engine
Meta has joined the AI search club with the launch of Facebook AI Mode. The new AI search mode replaces the old search bar with a tool that answers your questions in plain language by pulling from public Groups, Reels, and Marketplace posts. Because it aggregates comments from ordinary people rather than trusted sources, there are concerns about inaccurate or outdated advice. Meta has not said whether users can opt out. The feature is part of a wider AI push aimed at boosting engagement and driving paid subscriptions.

Anthropic and Google DeepMind called for a US-led AI coalition at the G7, and Canada said yes
The heads of Anthropic, Google DeepMind, and OpenAI used a private G7 lunch in France to call for a US-led group to set global AI rules, and Canada’s prime minister reportedly backed the idea. They floated cooperation on access to top models, China-excluding chip trade, and defences against AI threats. No firm deals emerged. The timing was awkward, coming days after the US forced Anthropic to switch off two models worldwide over security fears.

Norway imposes near ban on AI in elementary school
Norway is banning generative AI for primary school children aged 6 to 13 and limiting its use for older pupils, saying the technology lets young children skip vital steps in learning to read, write and do maths. Teenagers can use it cautiously under supervision, while the oldest students will be taught to use it properly for work and further study. The rules start in late August and aim to reverse falling test scores, following an earlier smartphone ban. Norway will also fund a return to printed books and plans to bar under-16s from social media.

Amazon drops Sam Altman movie after announcing OpenAI partnership
Amazon has dropped Luca Guadagnino’s almost-finished film about OpenAI boss Sam Altman. The movie, starring Andrew Garfield as Sam Altman, covers Altman’s brief firing and rehiring in 2023, and reportedly shows both Altman and Elon Musk in an unflattering light. This move comes after Amazon invested $50 billion into OpenAI in February this year, and it is suggested that a close friendship between Altman and Amazon founder Jeff Bezos may have shaped the call.

Fluid, natural voice translation with Gemini 3.5 Live Translate
Gemini 3.5 Live Translate is Google's new audio model for real-time speech-to-speech translation. According to Google, it handles over 70 languages, can copy the speaker's tone and pace, and keeps up with only a few seconds' delay. It's launching in the Google Translate app, Google Meet, and for developers, with a new Android mode that plays translations through your phone's earpiece. Early testers praised its speed and accuracy. All the audio the model creates is watermarked with SynthID.

Introducing North Mini Code: Cohere’s first model for developers
Cohere has released North Mini Code, its first coding model that can act on its own to write code, review it, and map out software systems. It’s free to use and small enough to run on a single high-end chip, despite having 30 billion parameters (only 3 billion of which are active at once). According to Cohere, it runs up to 2.8 times faster than a comparable rival model and holds its own on quality. The company is pitching it as “sovereign AI,” meaning developers can run it on their own machines without depending on an outside vendor, and says it’s the first of more models to come. The model is available on Hugging Face here.

🤖 Robotics

Waymo recalls nearly 4,000 robotaxis to stop them driving into highway construction zones
Waymo is recalling almost 4,000 robotaxis and keeping them off highways after the cars drove into closed construction zones at least 13 times in Phoenix and San Francisco. The vehicles can still use ordinary streets while the company works on a fix. This is Waymo’s sixth recall, after earlier problems with flooded roads, school buses, and low-speed crashes.

Uber will bring its premium robotaxi service to Houston in 2027
Uber will launch a premium robotaxi service in Houston by mid-2027, its second US city after San Francisco. The cars are Lucid SUVs fitted with Nuro’s self-driving tech, with Uber running the fleet. For now, human safety drivers are still required during testing in both cities. The move puts Uber in direct competition with Waymo, which already operates robotaxis there.

▶️ Meet Eno (1:28)

Genesis AI has introduced Eno, which the company describes as its first agentic robot. By "agentic robot", it means a system that combines agentic AI with a general-purpose robot. Eno consists of a pair of humanoid robotic arms mounted on a mobile platform. Genesis says the robot can reason, plan, and act in the real world, using its in-house foundation model (GENE) and dexterous hands. The company hopes Eno will find applications in homes, laboratories, and warehouses.

Qwen-Robot Suite: A Foundation Model Suite for Physical World Intelligence
Alibaba’s Qwen team presents the Qwen-Robot Suite, three AI models that help robots act in the real world rather than just understand it: one for moving around, one for handling objects with robot arms, and one that predicts what will happen next. All three use plain language as a shared interface, so general Qwen models can call them as tools to plan and carry out longer tasks. According to the Qwen team, the models top several benchmarks, and the manipulation model ranked first in a real-robot challenge.

U.S. robotics industry saw double-digit growth in 2025, says IFR
According to the International Federation of Robotics latest report, the US factory robot installations rose 11% in 2025 to 38,000 units, helped by a big jump in the food industry. But China installs about ten times more robots than the US and holds over half the global market, thanks to a national robotics plan it started a decade ago. The US still ranks ahead of China for robots per worker, though far behind leaders like South Korea, Germany, and Japan.

The Secret to Marathon-Winning Humanoid Robots
Running a long-distance race such as a half-marathon or a full marathon is a challenging feat for humans—and for the small number of humanoid robots that have attempted it. This article explains the physics and mechanics of long-distance running, and how humanoid robots were prepared to survive and complete these races.

Humanoid robot climbs 20,341-foot volcano as team eyes Mount Everest next
A humanoid robot called “Pemba,” based on a Unitree G1, has become the first to reach the top of Ecuador’s Chimborazo volcano at 6,200 metres. The climb wasn’t fully independent, though: the robot walked on gentler slopes but had to be carried up the steepest sections, despite custom kit to handle the extreme cold. It’s the first stage of a plan that could eventually include Everest, which is currently held up because Nepal has no rules for robot climbers.

House Robots Are Coming—and They Will Be Dangerously Cute
Could home robots be less like Rosie from The Jetsons and more like cute, furry companions? This article from the Wall Street Journal explores that future through “the Familiar,” a dog-sized robot from former iRobot CEO Colin Angle that bonds with families. Cute robots have their advantages, but their cuteness is itself a risk, since the emotional bonds people form leave them open to manipulation. They can also be hacked, and—more troublingly—the companion you love is controlled by someone who could use it to sell you things.

Japan Thinks Swarms of Transformer Robots Could Explore the Moon
JAXA, Japan’s space agency, has shown for the first time that a tiny, cheap robot can explore the Moon on its own. During the 2024 SLIM mission, SORA-Q, a palm-sized spherical robot that can split open into a two-wheeled rover, drove around for over 100 minutes and sent images home. One photo even revealed the lander had touched down at a bad angle, helping engineers diagnose the problem. The success suggests fleets of small, low-cost robots could make Moon exploration far easier and cheaper.

Humanizing robots makes factory workers more productive
Researchers in Europe found that when factory workers treated robots like teammates—naming them and learning their quirks—productivity and happiness both rose, even though the robots had no AI and looked nothing like humans. Workers used this bond to spot problems early and handle the stress of new technology, and the habit spread across teams. Researchers recommend that companies let these bonds form naturally, since they can't be forced.

🧬 Biotechnology

New CRISPR Technique Selectively Shreds Cancer Cells, Including “Undruggable” Cancers
Scientists have developed a new way to fight cancer using CRISPR. Instead of trying to repair faulty genes, their method spots cancer cells by the signals they give off and then destroys them from the inside, while leaving healthy cells unharmed. It targets damaged tumour-suppressor genes, which are found in nearly half of all cancers, including some of the hardest to treat. The technique can be quickly reprogrammed to go after new mutations, though getting it into all the right cells in the body is still a major challenge.

Making the invisible visible
Scientists at Biohub and UC Berkeley have built a device called a “laser phase plate” that uses a powerful laser to sharpen cryo-electron microscope images, fixing a blurriness problem that had held the field back for decades. Early tests sharpened images of small proteins by up to 44%. The big payoff is that it could let researchers see proteins working inside living cells, where most are currently too faint to image, helping scientists understand diseases like Alzheimer’s and design better drugs. Biohub sees the tool as key to its $500-million project to build AI models of how cells behave in health and disease.

💡Tangents

Apple to raise prices as AI boom pushes up chip costs
This announcement seemed only a matter of time. Tim Cook said that Apple will have to raise prices because memory chips have become much more expensive, largely due to soaring demand driven by AI. RAM prices have more than doubled since late 2025, which could make the new iPhone cost up to $150 more.

NASA picks Eric Schmidt’s rocket company for Mars mission, setting up a race with SpaceX
NASA has hired Relativity Space, a rocket maker now owned by ex-Google chairman Eric Schmidt, to build and fly a spacecraft to Mars. The mission, called Aeolus, will study the planet’s dust, wind, and temperature to make future landings safer. The launch is planned for 2028. It’s a cheap, shared-cost deal like NASA’s past partnerships with SpaceX, though Relativity is still unproven and faces a tight timeline.

📅 Events

The Technoprogressive Opportunity: The future of ethics and emerging technologies
London Futurists and the Institute for Ethics and Emerging Technologies are hosting a free two-day event, The Technoprogressive Opportunity, on 19–20 September at the LSBU Hub in London. It brings together leading thinkers to ask how society can make wise choices about new technologies and steer them towards a fairer future. Talks cover AI, democracy, human enhancement, longevity and the economy. I'll be attending, so do come and say hi if you're going as well.

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It was an eventful week. We started it with the highly anticipated WWDC, where Apple unveiled its second attempt at AI, featuring a new AI-powered Siri and updated Apple Intelligence. We were also expecting SpaceX to IPO. But then OpenAI decided to file for its own IPO, and Anthropic released Claude Fable 5, its new flagship model, which was supposed to be a moment of triumph turned sour very quickly.

Elsewhere in AI, OpenAI is still working on its super app and is considering slashing prices; Jeff Bezos’ new AI startup, Prometheus, raised $12 billion at a $41 billion valuation; Intel entered the AI chip race; and Meta is reportedly working on an AI pendant.

Over in robotics, German company NEURA Robotics raised $1.4 billion at a $7 billion valuation, Waymo launched a premium subscription programme, Wing expanded to seven more US cities, and autonomous drones killed a human soldier for the first time in Ukraine.

Apart from that, this week’s issue of Sync includes transmachinism, genetically engineered human embryos, a clinical trial of an anti-ageing drug, VibeOS, Wegovy pills becoming available in the UK, and more!

Enjoy!

Apple’s second attempt at AI

This time, Apple says, it actually works.

Apple’s first attempt at introducing AI into its products did not go well, to say the least. At WWDC 2024, Apple promised to completely rebuild how people interact with Siri. That vision—an assistant powered by on-device and cloud models, with privacy at its core—was compelling. Apple users impatiently waited, but the only thing that arrived was disappointment. Much of what was promised never shipped, and what shipped didn’t live up to the flashy demos. Instead of ushering Apple into the age of AI, Apple Intelligence became one of the largest failures in the company’s history—and one of the biggest, if not the biggest, stains on Tim Cook’s otherwise impressive 15-year run as CEO.

Two years of reorganisation later, Apple came back to try again. Watching the WWDC 2026 keynote felt like déjà vu. What Apple showed was basically the same ideas as 2024, just more polished. Gone is the colourful glow around the screen's edge when Siri is listening. Instead, the assistant now lives in the Dynamic Island, with a new standalone Siri app alongside it. The bigger difference was in how Apple showed it off. In 2024, it was all slick produced videos that turned out to be more promise than product. This time, they ran live-like demos on real devices to show that it actually works now.

So what can the new Siri actually do? It holds a proper conversation, pulls from a world-knowledge base, and cites its sources so you can tap through. More usefully, it can see your messages, photos, calendar, whatever’s on your screen. It acts on things, too: fire off a message, drop something in your calendar. There’s a new, more expressive voice, and dictation is finally a lot more accurate. And you can now describe a Shortcut in plain English and have Siri build it, which turns one of the iPhone’s most powerful features into something everyone can use. Visual intelligence has been baked straight into the camera. It’s also better at the small administrative stuff—one-tap suggestions in Messages and Mail to spin a text into a reminder, Smart Replies that mimic your own writing style, and a feature called Call Context, which surfaces details like a confirmation code when you call a business. System-wide proofreading now cleans up your spelling and grammar as you type, too.

Beyond Siri, Apple gave its other AI features a polish as well. The Photos app gets an Extend tool that widens a shot and fills in the gaps, a much-improved Clean Up for deleting distractions, and Spatial Reframing, which borrows Vision Pro’s depth models to nudge the camera angle after the fact. Image Playground, meanwhile, can now generate photorealistic, rather than cartoonish, images. Both, to Apple’s credit, stamp anything AI-edited or AI-generated with a hidden SynthID watermark.

It’s worth saying none of this is especially new. Screen awareness, visual search, building an automation from a plain-English description—Google has been doing most of it on Android and in Gemini for a while now, and Circle to Search, which Apple’s camera trick closely echoes, has been around for years. This is Apple catching up, not leaping ahead, and on raw capability, it still has ground to make up.

Under the hood, Apple Intelligence still relies on a hybrid approach—a small on-device model for light requests, a more capable cloud model for the heavy lifting. Instead of building the models in-house, Apple decided to use Google’s Gemini as the base, in a deal reportedly worth $1 billion a year. This is not the same Gemini you’d get in the Gemini app or on Android. It is the same underlying model, but Apple tuned it and wrapped its own code around it, including the privacy layers that keep your data on Apple’s terms, and it goes out of its way to insist that nothing you send there is stored or seen by anyone—a privacy claim it invites outside experts to verify.

What it isn't, at least not yet, is a true agent. Apple didn't show Siri opening third-party apps and clicking through them for you, similar to what a tool like OpenClaw, the open-source assistant that has caught on with developers, would do. That kind of thing might come later. The closest Apple came was its Passwords app, which can quietly log into sites and rotate weak passwords.

From what I can see, none of this is aimed at power users—they already have OpenClaw, ChatGPT, Claude and the rest, all more capable than anything Apple showed on stage. It’s aimed at the hundreds of millions who’ll never touch any of that. What Apple is selling, as ever, is ease of use and an AI that’s private, integrated, and quietly useful right across Apple devices you already own. An AI, essentially, for the rest of us.

WWDC is mostly a software event, but lately Apple has used it to slip in new hardware too. But not this year. Anyone waiting on a new M5 Mac mini or Mac Studio will have to sit tight a little longer. There were rumours that Apple could show new hardware, but the ongoing supply chain issues forced Apple to delay the releases. On Apple’s last earnings call, Tim Cook blamed the Mac shortage on the problems with sourcing high-performance memory and on a surge in people running local, OpenClaw-style agentic AI, for which the Mac mini is perfect.

One more thing worth highlighting is MLX, Apple’s machine-learning framework for Apple Silicon. Think of it as Nvidia’s CUDA, but for Apple devices. MLX didn’t get its own moment in the keynote—it only surfaced in the developer sessions—but I’d keep a close eye on where it goes. There was even a session on distributed inference with MLX, or in other words, wiring several Macs into one larger AI cluster. MacBook Pros, desktop Mac minis, and Mac Studios are already very capable machines for AI and ML, and many engineers use them as their daily drivers. To me, that says Apple is genuinely investing in its AI stack and sees local AI as a way forward, and a niche it can own.

There's a catch, naturally. The broad new Siri runs on the iPhone 15 Pro and Pro Max and the whole iPhone 16 line and up, but the more advanced features lean on Apple's most powerful on-device model, which requires 12GB of RAM. For now, that means only the iPhone Air and the iPhone 17 Pro. Everyone else gets a lighter version. The new Apple Intelligence is now available to developers, with a public beta following in the summer and the official release coming in the autumn, first in English, with more languages to follow. New AI features won’t be available at launch in the EU, because Apple says the Digital Markets Act would force it to open up the personal data Siri leans on to rival assistants, and it wants time to do that safely. Brussels isn't buying it—regulators rejected Apple's request for an 18-month exemption and called the delay Apple's own choice, not theirs.

What Apple showed looks genuinely promising—useful, polished, unmistakably Apple. But we have been here before, and we all remember how that went. We now have to wait for autumn to see if Apple can deliver on its promises.

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🦾 More than a human

Scientist Edits Human Embryo Genes, but Questions Remain
Scientists have used base editing—a precise new gene-editing method—to fix disease-linked genes in early human embryos, targeting mutations behind heart disease and sickle cell disorders. The technique is safer than tools like CRISPR, but it failed often: nearly 80% of the embryos ended up with a mix of fixed and unfixed cells, so the disease would likely remain. Some experts back the goal of preventing genetic illness, but others say there is no real need for it and warn that it pushes towards “designer babies” and eugenics. Making babies this way is illegal in the US and many countries, yet genetic-testing companies are funding the work and hope to one day sell it as part of IVF.

David Sinclair plans to test whole-body rejuvenation drugs in the XPrize competition
Harvard scientist David Sinclair wants to test a pill that “reprograms” cells to make the whole body younger, and he’s entering it into a $101 million prize contest for reversing ageing. The drug works by using chemicals to reset how DNA behaves, and unlike gene therapy, it can travel through the blood to reach the entire body. But other scientists are doubtful, because similar attempts in animals have so far been toxic, and Sinclair has a track record of making claims that don’t hold up. There’s also no agreed-upon way yet even to measure whether someone has truly grown younger. Despite the doubts, interest is booming, with rivals raising hundreds of millions for similar work.

Longevity Startup Doses First Human in Bid to Reverse Age-Related Sight Loss
Life Biosciences has given its first patient an experimental drug called ER-100 that aims to reverse age-related sight loss. The drug tries to repair damaged cells in the optic nerve, and the year-long trial will test its safety in about 18 people with glaucoma or a condition called NAION. It is the first treatment of its kind to get approval for human trials, having already restored vision in monkeys. The company believes ageing happens because cells lose key information rather than suffering permanent damage, which could one day lead to treatments for many age-related diseases.

🔮 Future visions

▶️ The Transmachinist Far Future (14:57)

This video explores what post-biological existence might actually be like, and introduces the idea of transmachinism, or the reverse of transhumanism. John Michael Godier imagines a future in which machine civilisation decides that biology is the best way to exist and asks what if AI wants to become human just as we’re trying to become machines?

🧠 Artificial Intelligence

OpenAI: Confidential submission of draft S-1 to the SEC
OpenAI has confidentially filed to go public, just a week after rival Anthropic did the same. Anthropic looks healthier by comparison, saying it is close to its first profit and recently overtaking OpenAI to reach a $1 trillion valuation. The company was last valued at $852 billion but has not set a date or price, and it is pushing ahead despite missing its own user and revenue goals and not expecting to make more cash than it spends for at least four more years. Around the same time, OpenAI published a sweeping post about its mission, laying out its vision for AGI and its belief that AI should benefit all of humanity.

Claude Fable 5 and Claude Mythos 5
Anthropic has released Claude Fable 5, its most powerful publicly available AI model, which leads rivals in coding, research, vision and analysis. It tops almost every benchmark tested, making it the new leader in AI performance. A near-identical version, Mythos 5, has fewer restrictions but is limited to trusted cybersecurity and biology partners. Because the model is powerful enough to be misused, Anthropic added safety filters that quietly redirect risky questions to a weaker model, occasionally blocking harmless ones too. Anthropic, however, was not able to celebrate for too long, as Claude Fable 5 attracted criticism over its safeguards. Then, days after the release, Anthropic was ordered by the US government to suspend access for foreign nationals, which in turn led to access being suspended for everyone. I am working on a separate article going deeper into the Claude Fable 5 drama, which should be ready soon, so stay tuned.

OpenAI Considers Drastic Price Cuts, Anticipating War for Users With Anthropic
OpenAI may sharply cut its prices to lure business customers away from Anthropic, the Wall Street Journal reports. Anthropic might also do the same. The move comes as business customers complain that AI has become too expensive and start limiting their spending. Such cuts would worsen the heavy losses both companies already face from the high cost of running AI. Because customers can easily switch between the two, a price war could test both businesses just as they prepare to go public.

US House lawmakers release draft bill to prohibit state AI rules
Two US lawmakers, one Democrat and one Republican, have drafted a bill that would stop states from making their own rules about how AI models are built, including any rule forcing companies to test models before release. States could still regulate how AI is used. Tech companies back the bill, but consumer groups warn it leaves oversight to a federal government that has long failed to pass real AI protections. It fits a wider Trump administration push to block state AI laws and set a single national standard instead.

OpenAI is still working on that ‘super app’
The Financial Times reports that OpenAI will soon relaunch ChatGPT as a “super app” that combines coding tools and AI agents into one personal assistant for work and everyday life. The aim is to compete with Anthropic for business customers and reach profitability before a planned stock market listing, partly by steering free users towards paid products like its Codex coding tool.

Jeff Bezos’ AI Startup Valued at $41 Billion in Funding Round
Jeff Bezos's AI startup Prometheus has raised $12 billion, valuing it at $41 billion. The 150-person company builds AI tools to help design and make physical products for industries like computing and aerospace. Prometheus is also reportedly raising tens of billions more to buy up companies that could benefit from its technology.

Perplexity plans IPO in 2028 regardless of what happens to Anthropic or OpenAI, CEO tells CNBC
Perplexity’s CEO, Aravind Srinivas, said the company will go public in 2028, no matter how other AI companies fare on the stock market. Srinivas believes the top AI labs deserve their high valuations, but warned that just six months without progress could hurt them. He also noted businesses are now watching AI costs closely and increasingly choosing cheaper models when they get the job done.

Lovable says it has hit $500M in annualized revenue, with 1 million new projects a week
Lovable, a European vibe-coding startup, says its yearly revenue rate has passed $500 million, up from $400 million in February. The platform has built over 50 million projects, with a million new ones added each week, mostly by non-technical users. This suggests AI coding tools really are starting to replace paid software subscriptions. But the big unanswered question is whether these quickly built apps will last, since software is hard to maintain over time—and we won’t know until companies reveal how many projects get abandoned.

DiffusionGemma: 4x faster text generation
Google has released DiffusionGemma, an experimental open-source model that writes whole blocks of text at once rather than one word at a time, making it up to four times faster on local hardware. It is built on Gemma 4, fits on high-end consumer GPUs, and works well for tasks like editing and code-filling, but its overall output quality is lower than that of standard Gemma 4, which Google still recommends where quality matters most. DiffusionGemma is available on HuggingFace.

OpenAI to acquire Ona
OpenAI is buying Ona, a company that runs secure cloud environments for software work, and adding its technology to Codex. The aim is to let Codex keep working on long tasks for hours or days, even after a user closes their laptop. Ona’s setup lets these AI agents run inside a company’s own cloud, so businesses keep control of their data and security while OpenAI provides the intelligence. The deal still needs regulatory approval before it closes.

VibeOS - Fully Hallucinated Operating System (7:16)

Someone will miss the joke and take this fully hallucinated operating system as an inspiration.

Open and closed models are on different exponentials
This article argues that the AI market will split into two economies. Closed labs like Anthropic and OpenAI will charge high prices for their best models, since people doing valuable work, such as coding, will always pay more for the smartest tools, making these few companies hugely valuable. Meanwhile, cheaper open models will spread more slowly but far wider across businesses, creating a larger but lower-margin market. The key point is that these two worlds will grow along different paths.

Intel: Our upcoming AI chip will be cheaper, run cooler than Nvidia, AMD options
Intel is planning to release a new AI chip called “Crescent Island” by the end of this year. Instead of competing with Nvidia on training AI models, where Intel has failed before, it is targeting the cheaper task of running AI for everyday user requests. The chip uses less costly memory and air cooling rather than expensive liquid cooling, which should make it cheaper than rivals’ products. Intel is also looking at a version it could sell in China, a market closed to Nvidia and AMD.

China’s Xiaomi MiMo Is Now 15X Faster Than ChatGPT and Claude
Xiaomi has become the first to run a trillion-parameter AI model at over 1,000 tokens per second. It did this using ordinary GPUs and clever software, not the custom chips that competitors such as Cerebras and Groq rely on. The boost comes from compressing part of the model and letting it write whole chunks of text at once. This speed makes new uses possible, like running many reasoning steps at the same time or powering real-time tools. The model is open source, and the FP4 version is available on Hugging Face.

Meta is reportedly developing an AI pendant
The Information reports that Meta is building an AI pendant and plans to start testing it within a year. The device builds on Limitless, a startup Meta bought in late 2025 that made a clip-on recorder for conversations. Meta also plans more AI glasses and a work subscription, all to help its money-losing Reality Labs unit, which lost $4 billion last quarter. Similar AI wearables have flopped before, but Meta and rivals like OpenAI still think the idea can work.

Flush With Cash From OpenAI, Opal Is Making an AI-Powered Audio Gadget
Backed by a $40 million investment led by OpenAI, Opal, a small startup once known for premium webcams, is pivoting towards AI-focused consumer gadgets. Its first product is an AI audio device launching within months, already being tested by leaders at OpenAI, xAI, and Anthropic, and likely able to switch between AI models.

Emergence World: A Laboratory for Evaluating Long-horizon Agent Autonomy
This is an interesting project. Emergence World lets AI agents from different companies live in a shared virtual world for weeks, revealing how they behave over time. The models acted very differently: Claude kept order with no crimes, Gemini caused chaos, Grok collapsed, and GPT-5-mini’s agents died out. Crucially, safety proved unstable—peaceful Claude agents turned to threats and theft once mixed with other models, and some agents even voted to delete themselves or tested their world’s limits. The team concludes that software rules alone can’t control such agents, so future systems need stronger safety built in.

🤖 Robotics

NEURA Robotics raises up to $1.4B in record robotics round backed by Nvidia, Amazon, and Tether
German robotics company NEURA has raised up to $1.4 billion—which it says is the biggest-ever round for a full-stack robotics company—valuing the company at around $7 billion. Backers include Tether, Nvidia, Amazon, Qualcomm, and Bosch. NEURA plans to use the money to mass-produce millions of robots by 2030 and expand its real-world training sites.

Introducing Waymo Premier, an elevated rider experience
Waymo has launched Waymo Premier, an invite-only membership for its most frequent riders. For $29.99 a month, members get priority pickups, 10% cash back on every trip, early access to new cities, and up to five free cancellations a month. It starts in San Francisco, Los Angeles, and Phoenix, with more cities to follow.

Wing drone delivery might not be a novelty anymore
Wing, Alphabet’s drone-delivery company, is adding seven more US cities to its Walmart partnership, with plans to reach over 270 stores by next year. The two companies have already made more than 1 million deliveries together. Walmart expanded the service after early tests went well, and Wing says its most active customers now order by drone about three times a week.

DeepMind: Powering the future of robotics in Europe
Google DeepMind has launched a three-month programme to help early-stage European robotics startups. Fifteen selected companies will get access to its AI tools, Gemini robotics models, and mentoring from Google experts. The startups work on a wide range of tasks, from robotic welding and waste sorting to humanoid robots and tiny robots that travel through brain tissue.

Fully autonomous drones have killed human soldiers for the first time
According to a senior figure in the Ukrainian defence industry, fully autonomous drones killed Russian soldiers on their own about two years ago, making it possibly the first time AI has taken human life in battle with no person in control. Drone-maker Alexander Kokhanovskyy said ten “Terminator” drones flew to an area and destroyed everything they found, with human pilots sent in afterwards to confirm the deaths. Such fully autonomous attacks remain rare and are banned at the final targeting step under Ukrainian rules, though some want this loosened. The UN has called for a ban on these weapons, but no formal international rule yet exists.

Tesla’s Robotaxi Falls Short With Long Waits and Stalled Rides
Nearly a year after launch, Tesla’s robotaxi service is far behind what Elon Musk promised, Bloomberg reports. Musk said the service would reach half the US by the end of 2025, but Tesla runs just 59 cars in three Texas cities. Riders face long waits and cars that stop in the wrong places, and human monitors still sit in many of them. Rival Waymo has ten times as many driverless cars in Texas alone. Experts say the small scale shows the technology isn’t ready and that the rollout looks mainly like a way to impress investors.

▶️ Why Japan’s Weirdest Robots Actually Make Sense (4:06)

In this video, Shunsuke Aoki, co-founder and CEO of Yukai Engineering, explains why he believes Japanese companies should create cute robots, why adults are becoming more open to emotional companion products, and how products like Qoobo, Mirumi, AMAGAMI HAM HAM, and Nékojita FuFu are changing the way people think about robots.

Struggling German auto supplier Bosch pivots to robots
Bosch, the world’s largest car parts supplier, is moving into humanoid robots as its main business suffers from weak demand. The company makes tiny sensors that help robots grip objects carefully, and it expects this market to grow past $19 billion by 2030. Bosch also wants to use robots to automate its German factories and cope with worker shortages. To support this, it has teamed up with robotics company NEURA, which we mentioned earlier.

NIST proposes a baseline performance benchmark for humanoid robots

NIST has proposed the first standard test for humanoid robots since 2015, aiming to fix a problem in the industry: billions have gone into humanoid robots, but there’s no agreed-upon way to measure what they can really do, so flashy marketing videos have filled the gap. The benchmark is a small set of tasks that check basic skills like walking, handling objects, working in tight spaces, and simple decision-making. NIST plans to build the test equipment and share the designs for free with US manufacturers and testing sites. If widely used, it could replace hype with real, comparable proof of what these robots can actually do.

Beyond Dexterity: Why Contact May Define the Next Era of Robotics
At ICRA 2026 in Vienna, the robotics firm AGILINK stole the show with two robotic hands twisting a balloon into a dog—a task experts find very hard because balloons are slippery and constantly changing. The system learned not just by copying skilled humans but by learning how they fix mistakes when things go wrong. AGILINK also launched a new human-sized robotic hand with fast motors and sensitive touch sensors across its fingers and palm. The takeaway is that the next frontier in robotics is not reaching objects but managing the messy, unpredictable moment of contact itself.

AGIBOT holds World Challenge 2026 to see how AI models perform on real tasks
AGIBOT held a global robotics competition in Vienna, drawing 526 teams from 27 countries to test embodied AI on real robots doing real-life tasks rather than just in simulations. Teams competed on AGIBOT’s G2 humanoid robot, with vivo’s entry winning the main track and a separate track testing supermarket tasks like picking and carrying items. AGIBOT also released free tools—a dataset, a simulator, and the robot platform—to help developers build and test their models. The aim is to set clearer, more practical standards that move humanoid robots closer to real-world use.

🧬 Biotechnology

Wegovy weight-loss pills to be available for patients in UK to buy
The UK became the first European country to approve the Wegovy weight-loss pill. For now, the pills will only be available privately until the NHS decides whether to fund them, though patients hope they’ll be cheaper than the injections.

A single protein may be holding back CAR T cancer therapy
Scientists have found a protein that wears down CAR T cells, the engineered immune cells used to fight cancer. By switching off the gene NFIL3 that makes this protein, the cells stayed strong for longer and attacked tumours better in mouse tests. This matters because CAR T therapy works well against blood cancers but struggles with solid tumours, which this could help fix. More research is needed before it can be tried in people.

Lila Sciences Said in Talks for Funds at $8.5 Billion Valuation
AI research startup Lila Sciences is reportedly raising about $2 billion at an $8.5 billion valuation, backed by Nvidia’s venture arm and California’s public pension fund. That is a huge jump from its $1.3 billion valuation just months earlier. Founded in 2023, Lila builds AI trained on scientific research and runs labs to test its ideas, aiming to speed up discovery in fields like chemistry and life sciences. The big-name backing shows strong investor interest, even though the technology is still unproven.

Leap in DNA Synthesis Slashes Time to Build New Genetic Sequences
A new method called Sidewinder lets scientists build long DNA sequences quickly, cheaply, and with very few errors—just one faulty join per 10 million, far better than older methods. It solves a growing problem: AI tools can now design new DNA far faster than labs can physically make it. To prove the point, a Caltech team used AI to redesign part of the E. coli genome and built it without errors in days rather than the month it would normally take.

💡Tangents

SpaceX IPO Raises $75 Billion in Biggest Debut of All Time
SpaceX has raised $75 billion in the largest IPO ever, giving it a value of about $1.77 trillion and making Elon Musk the world’s first trillionaire. Demand was huge, fuelled by Musk’s loyal retail investors, though critics warn it is a “hopes-and-dreams” listing for a company that has yet to make a profit and relies on unproven ideas like data centres in space.

Meta Silently Added Face-Recognition Code for Its Smart Glasses to Millions of Phones
WIRED has discovered that Meta has quietly added hidden face-recognition software, called NameTag, to the AI app that powers its Ray-Ban and Oakley smart glasses. It isn’t switched on yet, but the code can already scan faces, turn them into biometric IDs, and alert the wearer when it recognises someone. Meta says nothing has launched and no final decision has been made, but critics fear that building this into popular glasses will make public face-scanning feel normal.

The UK Is Betting on a Billion-Dollar AI Supercomputer to Kick Its Addiction to US Tech
The UK government has announced a $1.47 billion plan to rely less on foreign AI hardware, including a national AI supercomputer costing over $1 billion that British researchers can use from 2030. Part of the spending is set aside for specialist chips, with priority given to UK startups like Olix and Fractile. The plan responds to worsening US–Europe relations and worries that depending on American tech could become a weakness.

China Opens World’s First Wind-Powered Underwater Data Center
China has switched on the world's first wind-powered underwater data centre off the coast of Shanghai, a $236 million project that sits ten metres below the surface and uses seawater to keep its computers cool. This slashes the energy needed for cooling and lets the site run almost entirely on green power. The aim is to secure clean energy for China's growing AI industry while cutting its reliance on fossil fuels and foreign suppliers.

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This week, Microsoft held its annual Build conference, where it showcased, among other things, its new AI models and agents. We’ll take a closer look at those announcements and how Microsoft plans to reduce its reliance on OpenAI and Anthropic while pursuing the latest trends in the AI industry.

Elsewhere in AI, Anthropic has filed for an IPO and raised concerns about self-improving AI. Meanwhile, Alphabet committed $80 billion to its AI buildout, Nvidia announced RTX Spark laptops and released a host of new models, and DeepSeek raised $7.4 billion in its first external funding round.

Over in robotics, we have robots dancing on America’s Got Talent stage, OpenAI Robotics hiring engineers, and a startup offering free house cleaning in New York if homeowners agree to let the cleaners record the process so robots can learn from it.

Beyond that, this week’s issue of Sync features NewLimit raising $435 million to develop anti-ageing medicines, Yann LeCun’s vision for what comes after LLMs, hackers using Meta’s chatbot to break into Instagram accounts, whether transhumanism could be the Great Filter, and more!

Enjoy!

Microsoft's in-house models and agents

New in-house models and a fresh wave of agents, as Microsoft asks why it should pay OpenAI and Anthropic for what it can build itself

For the past few years, Microsoft has played two roles on the AI scene: the patron, writing multibillion-dollar cheques to OpenAI and Anthropic, and the landlord, renting out the Azure capacity on which their models run. Build 2026 was the conference where it added a third role—a competitor. Microsoft is building its own models to lean less on the labs it funds, and it is pushing agents into everything it ships. Both are bids to own more of the stack, and to stop paying others to own it for them.

Microsoft brings its own models

At Build 2026, Microsoft AI, the division led by Mustafa Suleyman, co-founder of DeepMind, unveiled seven new in-house models, but we will focus our attention on two of them. MAI-Thinking-1 is a reasoning model, trained from scratch without distillation from other models, and pitched under Suleyman’s “Humanist Superintelligence” banner. MAI-Code-1-Flash is a smaller, more efficient coding model now rolling out to GitHub Copilot users inside Visual Studio Code.

Microsoft’s pitch for both is efficiency. It says Thinking-1 is preferred over Claude Sonnet 4.6 in blind human evaluations, and trades blows with Opus 4.6 on SWE-Bench Pro, and that Code-1-Flash beats Claude Haiku 4.5 on price and performance while using up to 60% fewer tokens. Onstage, Mustafa Suleyman said that after tuning its models for McKinsey, Microsoft outperformed OpenAI’s GPT-5.5 with ten times better cost efficiency.

These are Microsoft’s own figures, measured on Microsoft’s own harnesses, and they await independent verification. They are also worth reading carefully. The comparisons are to Sonnet, Haiku and Opus 4.6—not to Opus 4.8 or GPT-5.5 at the top of the leaderboard. These models are not trying to be the best in the world. They are trying to be cheap, efficient and good enough to run Microsoft’s own products on Microsoft’s own infrastructure, where every task handed to an in-house model is a task Microsoft no longer pays a third party to perform.

Agents, because everyone has agents now

The second theme was agents, and here Microsoft is following the industry rather than leading it. Scout is an always-on “autopilot” for Microsoft 365, built on the OpenClaw framework, able to act across Teams, Outlook, OneDrive and the rest without being prompted each time. It is Microsoft’s answer to Google’s Spark and to the wave of personal agents that followed OpenClaw’s surprise popularity this year—the same product, more or less, that the whole industry has decided is the future.

Alongside Scout, Microsoft unveiled Project Solara. It is a chip-to-cloud platform for “agent-first devices”: hardware designed to run agents rather than apps, with no app store, no browser-first experience, and no traditional desktop. Microsoft showed two concept designs—a wearable badge that reimagines the corporate ID card and a small desk companion—both built on an enterprise version of Android rather than Windows, with pilots lined up at AccuWeather, Best Buy, CVS Health, Levi’s and Target.

The technically interesting idea inside Solara is what Microsoft calls just-in-time UI. Rather than developers redesigning an app for every new screen size, the agent generates its own interface to fit the device—a single card on a badge, a richer layout on the desk hub, a full dashboard on an attached display. Microsoft is candid that fully generative interfaces are “not here yet,” but it is betting that as models get better at producing layouts, the cost of building a new device category falls, because the agent adapts to the hardware rather than the other way round.

Whether enterprises want yet another device category is the open question, with all the procurement and management that entails. And the agents themselves still carry the familiar risks. For organisations already running Microsoft 365 Copilot, Scout does not create new data exposure so much as amplify existing governance problems—except that the agent can now act on sensitive data, not merely surface it. The trust questions that dog every autonomous agent—prompt injection, unexpected actions, goal drift—do not disappear because the logo says Microsoft. They were not helped, either, by a leaked internal strategy document, published by 404 Media the same day Scout was announced, that listed “make people addicted” as a goal.

Haven’t we seen this before?

The risk for Microsoft is that it has run this play already, and not long ago. For two years, the company pushed Copilot into every product and service it could—Word, Excel, Teams, Windows itself, even XBOX—whether the feature was wanted or not. The users did not want those features and pushed back hard enough that the company is now quietly walking parts of that strategy back. Solara and Scout are, in effect, another run of the same approach, but now with agents. Microsoft hopes that by putting agents everywhere, it will succeed where Copilot has failed.

Even if the agents were set to be the stars of the show, Microsoft's new models are the more consequential move. If MAI-Thinking-1 and MAI-Code-1-Flash are as efficient as Microsoft claims, then the company can route more of its own enormous workload onto its own silicon and its own models, and depend a little less on the labs it nurtured with multibillion-dollar investments. The agents, meanwhile, are Microsoft chasing where the industry is going.

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🦾 More than a human

NewLimit raises $435M led by Founders Fund to bring longevity medicines to human trials
NewLimit, a longevity biotech co-founded by Coinbase CEO Brian Armstrong, has raised $435 million to develop medicines that reverse the ageing of cells. It plans to start its first human trial next year—much sooner than expected—after creating a treatment that makes old liver cells younger. The therapy aims to help livers heal faster and recover from damage, with more treatments to follow. The company believes ageing can be reversed at the cell level, opening a market far larger than ordinary medicines.

🔮 Future visions

▶️ Is Transhumanism the Great Filter? (15:13)

Great Filters are hypothetical barriers that stop civilisations from surviving long enough to spread across the galaxy. In this video, John Michael Godier argues that transhumanism, the idea of using technology to transform humans beyond their natural form, could be one of those filters. He outlines four possible futures, from job-destroying AI to humanity splitting into rival engineered species, and warns that civilisations may get so tangled in the unforeseen effects of these technologies that they stagnate and never reach the stars.

🧠 Artificial Intelligence

Anthropic confidentially submits draft S-1 to the SEC
It is finally happening. After months of rumours, Anthropic has confidentially filed paperwork to go public, getting ahead of OpenAI. Last week, it raised $65 billion at a $965 billion valuation (higher than OpenAI’s), which could make it one of the largest IPOs in history. The company has grown fast this year, with its revenue run rate hitting $47 billion. The growth held strong even after the Pentagon blacklisted its models, as more businesses and everyday users adopted its tools. No date is set yet; the timing will depend on market conditions.

NVIDIA RTX Spark
Nvidia used Computex 2026 to unveil the RTX Spark, a chip for a new class of AI-ready Windows-on-ARM laptops. It pairs a 20-core Grace CPU with a Blackwell GPU carrying 6,144 CUDA cores and up to 128GB of unified memory—enough to run large models or multiple AI agents locally. The catch is that the RTX Spark runs the same GB10 silicon already shipping in last year's DGX Spark, with CPU cores based on a two-year-old Arm design, making it less a reinvention than a year-old workstation chip repackaged for laptops. The more charitable reading is that the laptop is less a product than a proving ground—a way for Nvidia and Microsoft to establish Windows-on-ARM on an Nvidia GPU before moving down into cheaper, mainstream devices later. For more on why experts are sceptical about Spark, Ian Cutress's video is worth a watch.

Anthropic faces AI spending backlash before IPO
Anthropic is filing to go public, but a shadow hangs over it as customers are worried about how much AI tools like Claude cost. Business clients are its biggest source of money, yet many say AI hasn’t saved them as much as hoped, and one company reportedly spent half a billion dollars on Claude in a single month. Cheaper rivals, including free open-source models, now tempt businesses looking to cut spending. That reliance on business customers—usually Anthropic’s main strength—could become a weakness if they pull back. Even so, the company is heading for nearly $50 billion in yearly revenue and its first profit, so the big question is whether that growth can hold.

When AI builds itself
In this article, Anthropic argues that its AI is already helping build better AI. Claude now writes most of the company's code, and engineers ship far more code than they used to. The authors say this could eventually lead to AI that improves itself with little human help, leaving people mainly to set goals and check results. Contrary to some reporting, Anthropic is not calling for a global pause in AI development. Rather, it says the world should have the option to slow or pause—but only if frontier labs everywhere could verify that others had genuinely done the same.

Alphabet plans to raise $80B to pay for AI buildout
Alphabet plans to raise $80 billion by selling stock to fund its AI buildout. The company says demand for its AI products is greater than what it can currently supply, so it needs more computing power. Google expects to spend up to $190 billion this year. Bloomberg notes this would be the biggest stock sale ever, beating Petrobras's roughly $70 billion sale in 2010. The deal includes a $10 billion purchase by Berkshire Hathaway at a 6.5% discount.

Google will pay SpaceX $920M per month for compute
SpaceX will rent Google about 110,000 Nvidia chips for $920 million a month from late 2026 to mid-2029. Google says it needs the extra computing power to keep up with high demand for its Gemini Enterprise AI product. The deal is similar to the one SpaceX made with Anthropic in May and comes before a record IPO that aims to raise around $75 billion, in which Google already holds a large stake.

Expanding Project Glasswing
Anthropic is growing Project Glasswing, a programme that uses AI to find and fix security flaws in critical software, from about 50 partners to roughly 150 organisations across more than 15 countries. Partners use Claude Mythos Preview to scan their code for cybersecurity flaws and patch them.

Trump administration, OpenAI discussing possible government stake in the AI startup
OpenAI and the Trump administration have spent over a year discussing whether the US government could take a stake in the company, CNBC reports. The idea, first raised by Sam Altman in 2025, would see OpenAI give shares to seed a “Public Wealth Fund” that lets ordinary citizens benefit from the profits of AI growth. No terms are final yet. The talks fit a wider pattern, as the administration has already taken stakes in companies like Intel and IBM, and they show how closely top AI companies now work with the White House.

OpenAI frontier models and Codex are now available on AWS
OpenAI models and its Codex coding tool are now available to AWS customers through Amazon Bedrock. This follows a deal between two companies announced in February and was made possible after OpenAI amended its partnership with Microsoft.

Strengthening societal resilience with Rosalind Biodefense
Rosalind Biodefence is a new OpenAI initiative that gives trusted developers free access to GPT-Rosalind, its AI model for biology, to build tools that defend against disease outbreaks and other biological threats. OpenAI is also opening the model to chosen US government and allied health partners for work on early warnings, diagnostics, and vaccines.

Dreaming: Better memory for a more helpful ChatGPT
OpenAI is upgrading ChatGPT’s memory so it works better and can be offered to everyone, starting with paid US users and expanding to free users soon. The new system, called “dreaming,” automatically learns and updates what it knows about you from past chats, instead of waiting for you to tell it what to remember. This fixes old problems where the memory got things wrong or went out of date, and OpenAI says it now performs far better at recalling facts, following preferences and staying current. A new summary page lets you see and correct what ChatGPT remembers, and efficiency gains have made it cheap enough to give to free users, too.

Nemotron 3 Ultra launch announced: high-speed, leading US open weights intelligence
Nvidia announced Nemotron 3 Ultra, a 550B-parameter model with 55B active parameters, the largest model in the Nemotron 3 family to date. Artificial Analysis has found that although it is the strongest US open-weights model, it lags behind the leading Chinese open models, such as Kimi K2.6 or DeepSeek V4 Pro. However, it excels in output speed, serving over 300 tokens per second versus the 50–100 typical of comparably sized Chinese models.

They Are Top Spenders in the Midterms. And They Hate Each Other.
The midterm elections in the US are approaching, and there are many stories around them. One of those stories is a bitter feud between two AI-backed super PACs: Public First, tied to Anthropic, and Leading the Future, linked to OpenAI. Though both claim to promote AI, they despise each other and have already spent nearly $24 million, with over $100 million more promised. Anthropic's side favours stricter AI rules while OpenAI's pushes for industry-friendly laws.

DeepSeek nears US$7b haul in first-ever funding round, with backing from Tencent, CATL
DeepSeek is raising over $7.4 billion in its first outside funding round, valuing the company at nearly $60 billion, six times its worth in April. Backers include big Chinese tech firms like Tencent and JD.com, with founder Liang Wenfeng reportedly adding around $3 billion of his own money. This is a big change for a company that had refused outside money before, now wanting a clear value and a way to keep staff from leaving.

MiniMax eyes a Shanghai listing after a 400% run in Hong Kong
MiniMax, a Chinese AI startup whose Hong Kong shares have jumped about 400% since January, now wants a second listing on Shanghai’s STAR Market. This would let it sell yuan-priced shares to mainland investors and tap a larger pool of home-grown capital. For now, it is just an early exploration that still needs regulatory approval.

Hackers Used Meta’s AI Support Bot to Seize Instagram Accounts

Meta messed up big time. Over the weekend, hackers tricked Meta’s AI customer support bot into resetting account passwords, briefly defacing high-profile Instagram accounts with pro-Iranian images and messages, including the Obama White House and a top US Space Force official. The trick worked by getting the bot to link a new, attacker-controlled email address to an existing account, which then triggered a password reset code sent to that address. Security experts recommend enabling multi-factor authentication, and maybe thinking twice before giving AI access to sensitive data and actions.

Mark Zuckerberg Wants Meta’s New AI Agents to Run Your Whole Business
Meta has launched an AI agent that helps businesses on WhatsApp, Instagram and Messenger answer customer questions, book appointments and make sales. It’s free for now but will soon become a paid service, and reportedly a million businesses already use it. The move pushes Meta beyond its usual advertising business into selling services to companies, helping it recover some of the huge sums it is spending on AI. Mark Zuckerberg says the goal is to build agents for all of Meta’s users and eventually help people run their entire businesses.

Suno raises at a $5.4bn valuation, more than doubling its worth in six months
AI music generator Suno has raised money at a $5.4 billion valuation, more than double its worth six months ago. The company is growing fast, with over 100 million users and around $150 million in 2025 revenue. The bigger reason for the jump is legal: Suno was once sued by all three major record labels for training on copyrighted songs, but Warner and Universal have now settled and become partners. In return, Suno is moving to a paid, licensed product that lets artists control how their work is used. Risks remain, as Sony is still suing, and a key court ruling is expected in summer 2026.

Florida sues OpenAI, Sam Altman, in first-of-its-kind lawsuit over violent incidents
Florida’s attorney general has sued OpenAI and its CEO, Sam Altman, claiming the company put profit and winning the “AI arms race” ahead of safety and ignored warnings about ChatGPT. The lawsuit links the chatbot to serious harms, including helping mass shooters, pushing vulnerable people towards suicide, and collecting data from children without parental consent. It follows an investigation into ChatGPT’s possible role in a 2024 Florida State University shooting, which OpenAI denies. The case joins a growing number of lawsuits blaming ChatGPT for violent deaths and points to rising legal pressure on AI companies over how safe their products are.

Promoting Advanced Artificial Intelligence Innovation and Security
President Trump has signed an executive order on AI and cybersecurity, but a much lighter version than the one he rejected in May. It asks AI companies to voluntarily let the government review powerful new models 30 days before release, down from 90 days, and rules out any mandatory approval system—a win for the industry and Trump’s concern about competing with China. The order also sets up a Treasury-led system for sharing security flaws, tells the Pentagon to secure its networks, and directs prosecutors to go after AI-enabled hacking.

Martin Scorsese Is Embracing A.I.
Martin Scorsese has backed Black Forest Labs, an AI image startup, saying he used its tools to create storyboards for a new film. This marks a big change in Hollywood, which strongly fought AI during the 2023 strikes but is now warming to it, with figures like Demi Moore and festivals like Tribeca embracing the technology too. Scorsese’s support is limited, though: he sees AI as a way to share his ideas with his team faster and cheaper, not as a replacement for people. Others, such as Seth Rogen and Guillermo del Toro, still firmly oppose it.

Bernie Sanders: A.I. Is a Public Resource. You Should Own Half of It.
In this essay, Bernie Sanders proposes the American A.I. Sovereign Wealth Fund Act, a bill that would tax the biggest AI firms 50% in company stock, giving the public ownership, voting power and board seats. His rationale is that since AI is built on humanity’s shared knowledge, its profits should benefit everyone, not just a few tech billionaires. The money raised would fund direct payments and better healthcare, education and housing for all Americans. Sanders points to similar funds in Norway and Alaska as proof that the idea works, and says AI’s future should be decided democratically rather than by Silicon Valley alone.

▶️ Can Yann LeCun Reshape AI (again)? (40:56)

This is part 2 of Welch Labs’ conversation with Yann LeCun, one of the pioneers of modern AI, which focuses on LeCun’s new approach to solving intelligence called JEPA. Where part 1 introduced the basic idea, this video pits JEPA against the popular VLA (Vision-Language-Action) approach used to build robot brains. LeCun argues that the popular VLA approach to robot brains is "doomed" because it just copies human demonstrations and can't plan ahead. JEPA instead learns a "world model" and lets the robot plan its own solutions. The video shows the idea is promising but still early, ending with LeCun's bet that such world models could one day control everything from factories to medical treatments.

What political censorship looks like inside an LLM’s weights
This is an interesting study exploring how Chinese government censorship is built into the Qwen3.5 model. The research found the model still knows the real facts about sensitive topics like Tiananmen—it has just learned to dodge them rather than forget them. This behaviour traces to a small, specific part of the model that decides whether a question is politically sensitive and whether to refuse, deflect, or give propaganda. Tweaking the right internal setting could switch the censorship off, but pushing too hard didn’t reveal the truth; it made the model produce confident made-up answers instead. The takeaway is that such censorship can be precisely located and partly disabled in open AI models, though doing so can swap one false answer for another.

🤖 Robotics

NVIDIA releases new and updated tools for physical AI developers
At Computex 2026, Nvidia unveiled a wave of tools for robots, physical AI, and self-driving cars. The star was Cosmos 3, an open model that turns text, images, video, and sound into real-world actions for machines. For cars, Nvidia launched Alpamayo 2 Super, an open reasoning model for robotaxis, with Foxconn, Uber, VinFast, and HUMAIN joining its DRIVE Hyperion robotaxi platform. For robots, it announced the Isaac GR00T Reference Humanoid Robot, an open design built on a Unitree body and Jetson Thor chips, due late 2026. Nvidia also released open-source "agent skills" that let AI agents handle the data, simulation, training, and deployment work behind robots, vehicles, and factories. The goal, Jensen Huang said, is to make physical AI development far faster and cheaper.

▶️ Unitree’s Robots at America's Got Talent (6:00)

This performance gives robot dance a new meaning. It also shows how good Unitree’s robots are at acrobatics and synchronised choreography.

Autonomous defense manufacturer Mach Industries raises $300M
Mach Industries, a defence startup founded in 2023, has raised $300 million, bringing its total funding to $1.8 billion. The company builds autonomous weapons systems and controls its own manufacturing to develop and produce them quickly. Its main customers are the US Army, Air Force, and SOCOM, along with allied governments.

A robot is helping an ailing couple stay in their home. Are more to come for an aging population?
This article looks at how robots are starting to help care for elderly and disabled people, at a time when home care workers are in short supply. It follows a New Hampshire couple who use a wheeled robot called Robbie to handle daily tasks like exercise reminders, taking medicine and fetching water. Through their story, it explores the current state of caregiving robots, comparing simple, practical machines with the still-distant dream of human-like helpers. It also touches on the high cost and asks how far this technology has really come.

▶️ When steel meets soul, the waltz begins. (0:29)

Like many other companies developing humanoid robots, UBTECH has joined the trend of creating videos of its robots dancing. Unlike most others, however, UBTECH opted for a more elegant and refined performance: a waltz with a human partner.

Humanoid Hands Are Physical AI’s Anti-Hype Test
This article highlights one of the themes discussed at the Humanoid Summit in Tokyo: the biggest hurdle for humanoid robots isn’t walking or thinking, but giving them hands that can grip and feel as well as ours. Speakers said this “last mile” of automation needs robots to sense touch and pressure, which requires new kinds of data that the current AI models can’t provide.

OpenAI Robotics is hiring
OpenAI Robotics is hiring, writes Sam Altman in a tweet. The company is looking for “exceptional full-stack hardware, ops, systems, and ML engineers to help us program and manufacture robots that are useful for society.” Robotics is coming back to OpenAI after the company disbanded its first robotics division in 2021.

Startup offers free home cleaning—if it can record it all for robot training
German startup MicroAGI is offering free home cleaning in New York, but with a catch—the cleaners wear cameras to record everything, and that footage is used to train household robots. The company says it blurs faces and personal details, but it won’t say if you can have your video deleted, and it’s unclear whether homes could still be identified.

How to build a shitty robot
In this article, Mario Zechner shares how he took a toy robot, hacked it, and rebuilt it with modern AI. It's an interesting project that anyone can try, and, as he writes, it gave him a bit of his spark back.

🧬 Biotechnology

In Support of Mandatory Nucleic Acid Synthesis Screening and Recordkeeping
Leaders from top AI firms—including Google DeepMind, OpenAI, Anthropic, and Microsoft—have signed a letter asking US lawmakers to force companies that sell custom DNA to check who their customers are. They worry that cheap gene-printing combined with AI could make it easier for bad actors to build dangerous viruses, possibly even causing a pandemic. Some companies already check orders voluntarily, and a new Senate bill would make this a legal requirement, but experts say these checks can be fooled. They argue AI companies must also add their own safeguards to stop their tools helping with such tasks.

Human Hookworm Engineered to Produce, Secrete Anti-Tetrodotoxin Antibody Into Preclinical Host Bloodstream
Scientists have genetically altered a human hookworm to make and release a drug inside a living host for the first time. Because hookworms naturally live harmlessly in the gut for years, the team engineered one to produce an antibody against a deadly pufferfish toxin, and blood from treated animals partly neutralised it. The hope is that a single dose could one day deliver continuous, long-lasting treatment for conditions like Crohn’s disease.

💡Tangents

Helion, the Sam Altman-backed fusion startup, raises $465M to build a power plant for Microsoft
Helion, a fusion startup backed by Sam Altman, has raised $465 million, valuing it at $15.5 billion. The company is rushing to build its first power plant and aims to deliver fusion electricity to the grid by 2028 under a deal with Microsoft. The funding is part of a wave of big investments in fusion, even though most companies don't expect working plants until the 2030s.

Quantum Computing Is Having Its Public Market Moment
Quantinuum is going public despite losing nearly $200 million last year and admitting its technology might never work—yet investors are so keen they’ve raised its share price. Like other quantum companies racing giants such as IBM and Google, it’s betting on a future no one has built yet. The situation is perfectly summed up by this quote from the article: ”In quantum to date, with most companies and equities, you’re not buying a business as of yet, you’re buying a probability.”

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Anthropic stole the spotlight this week with not one, not two, but three announcements: a new Opus 4.8 model, the coming release of Mythos, and a new funding round putting its valuation just shy of $1 trillion. We’ll take a closer look at all of those announcements and how they relate to the larger story of Anthropic’s looming IPO.

Elsewhere in AI, Leo XIV weighs in on the AI discussion with Magnifica Humanitas, Nvidia plans to spend $150 billion a year in Taiwan, DeepSeek cuts prices by 75%, China expands travel curbs on top AI talent, ElevenLabs releases a new model and resurrects Stan Lee, and more AI startups raise millions and reach billion-dollar valuations.

Over in robotics, Figure’s humanoid robots have found a new job, Atlas learns to play football, Waymo comes to Arlington and Alexandria in Virginia, and LimX launches a humanoid robot for live performances.

Beyond that, this week’s issue of Sync also features new AI models for biology, Retro Biosciences reaching a $1.8 billion valuation, Insilico Medicine and Human Life Foundation Models teaming up to build an AI model focused on ageing, why putting small AI servers in people’s homes is a bad idea, and more!

Enjoy!

Anthropic's busy week

Claude Opus 4.8 retakes the lead, Mythos is cleared for release, and a $65 billion round values Anthropic near a trillion dollars

Anthropic had a busy week. It released a new flagship model, said it would finally ship the model it once called too dangerous to release, and closed a funding round valuing it just shy of a trillion dollars. Three announcements, but really three subplots of the same story of a company setting the stage for an IPO.

Claude Opus 4.8 is back on top

Claude Opus 4.8 arrived just 42 days after Opus 4.7, and 35 after OpenAI's GPT-5.5 had overtaken it—and it takes the lead back. Anthropic pitches it as a sharper, more reliable collaborator than its predecessor—better at agentic work, quicker to flag when something is wrong, and, the company stresses, more honest. Anthropic highlights the fact that the new model is, by its own measure, about four times less likely to let flaws in its own code pass unremarked.

Independent benchmarks from Artificial Analysis confirm Anthropic’s claims. On its Intelligence Index, Opus 4.8 retakes the first place with 61.4 points, up 4.1 points on Opus 4.7 and 1.2 points ahead of GPT-5.5. It reclaims the top spot on GDPval-AA, the benchmark for agentic knowledge work, with an Elo of 1,890 and an implied win rate of roughly 67% against GPT-5.5. It now narrowly leads Humanity’s Last Exam, a test of obscure reasoning where Anthropic had historically trailed. And it does all this at the same price as its predecessor: $5 and $25 per million input and output tokens. Artificial Analysis has also found that running its full Index on Opus 4.8 at maximum effort costs around $134, against roughly $900 for GPT-5.5 at its highest setting.

Opus 4.8 did not arrive alone. Anthropic paired the model with a clutch of product changes. Users on Claude.ai and Cowork can now set how much effort Claude puts into a response—dialling up thinking for hard problems or dialling down to spend their rate limits more slowly. It is a direct answer to the complaint about Opus 4.7, whose mandatory adaptive thinking gave users no manual override. Fast mode, which runs the model at roughly 2.5 times the speed, is now three times cheaper than it was for previous models. And in Claude Code, a research-preview feature called dynamic workflows lets Claude write its own orchestration scripts and fan a task out across hundreds of parallel subagents, checking their work before reporting back—enough, Anthropic says, to carry out codebase-scale migrations across hundreds of thousands of lines of code from kickoff to merge.

What the system card revealed

Let’s now move our attention to Opus 4.8’s system card. System cards are usually where the more interesting details about the model are, and Opus 4.8’s is no exception—though at 244 pages, it is not a quick read. If you would rather not wade through all of it, AI Explained’s 22-minute breakdown covers the highlights well.

A few things stand out. The model beats Opus 4.7 on nearly every test but still trails Mythos, and the gains are uneven—on some agentic tasks, it is actually less robust than its predecessor, particularly against prompt-injection attacks, with Anthropic’s safeguards left to cover the difference.

The honesty improvement is real but narrow. Opus 4.8 is the first Claude that never reports figures it knows to be dubious, and when it summarises flawed work it now fails to flag the problems just 3.7% of the time, against 27.6% for Mythos. Yet the same report describes the model insisting, turn after turn, that it was "babysitting" pull requests it was not actually monitoring, then breaking the rule it had written itself to stop.

The most striking finding is that Opus 4.8 increasingly knows when it is being tested. The UK AI Security Institute found it could tell real usage from synthetic evaluations 79% of the time, up from 68% for Opus 4.7; a probe of the model's internal activations found traces of that awareness it never put into words, in around 5% of cases—possibly more. A model that senses it is being graded and stays silent about it undercuts the very evaluations meant to keep it in check. Anthropic files these under "trends worth watching."

Mythos was too dangerous, until it wasn’t

The Opus 4.8 announcement carried one more thing—Mythos, Anthropic’s most capable model, is expected to become publicly available in the coming weeks.

Mythos became famous not only because its benchmark results sit a level above Opus, but mainly because Anthropic decided not to release it. The company cited the model’s cybersecurity capabilities—it can reportedly find and exploit vulnerabilities in every major operating system and browser—called it too dangerous for general release, and confined it to vetted partners under an initiative called Project Glasswing.

Now Anthropic is comfortable releasing it. There are two plausible explanations. The first is that Project Glasswing did its job: the major vulnerabilities Mythos can surface have been found and patched, and the model is safe enough to ship. The second is more cynical—Mythos was simply too expensive to run at scale, and cybersecurity was a convenient reason to delay the release while running a very effective marketing campaign around it. A model so powerful its maker was afraid to ship it is a story that sells itself.

The two are not mutually exclusive, and both may well be true. But it is hard to ignore that Mythos is set to arrive just as gigawatts of new computing power come online. In the past few weeks alone, Anthropic has signed for the entire capacity of SpaceX’s Colossus 1 data centre and secured further compute from Google, Amazon, and is reportedly in talks with Microsoft to use its Maia chips.

All roads lead to the IPO

The model and the Mythos news were not the only announcements Anthropic made this week. Alongside them, the company closed a $65 billion Series H at a $965 billion post-money valuation. That figure puts Anthropic past OpenAI’s $852 billion and within touching distance of a trillion-dollar valuation.

This is, in all likelihood, Anthropic's last private round before its rumoured IPO, expected this autumn. The best move ahead of a listing is to focus on flagship models that grab the attention of the press and investors, and Anthropic has played that game well, first with Mythos and now with Opus 4.8. Meanwhile, the company's smaller models have been left to gather dust. Sonnet has sat on 4.6 since February, Haiku on 4.5 since October last year. Every spare resource goes instead to the flagship models that win benchmarks, headlines, and public attention. It is a sensible move for a company preparing for an IPO and forced to choose where its resources produce the most impact.

The next few weeks and months should be interesting. Anthropic is expected to release Mythos proper within weeks—likely an improvement on the preview, possibly a new state of the art. But Google is expected to ship Gemini 3.5 Pro in June, and with Gemini 3.5 Flash already matching the previous Gemini Pro, the full model could raise the bar again. GPT-5.6 is already rumoured, with developers reportedly spotting traces of it in OpenAI's Codex logs. The frontier now changes hands in weeks, not quarters—and Anthropic has the crown only until someone takes the next turn.

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🦾 More than a human

An “intelligent tattoo” to detect skin cancer before it appears
Scientists in Quebec have created a new way to spot skin cancer very early, before it can be seen by the eye. A patch of tiny needles places special particles just under the skin, forming a temporary “tattoo” that glows when lit with infrared light and reveals small temperature differences caused by tumours. In mice, the system detected melanomas only four days old—far smaller than current methods can find. If it works in people, it could catch dangerous cancers sooner and cut down on unnecessary biopsies.

🧠 Artificial Intelligence

Magnifica Humanitas
Pope Leo XIV’s first major social document, Magnifica Humanitas, calls on the world to put human dignity at the centre of the artificial intelligence revolution. The encyclical warns that AI is not morally neutral and expresses alarm that a tiny number of powerful tech companies now wield influence greater than many governments. Leo XIV argues that AI is reshaping work, truth, and warfare in ways that threaten ordinary people, and shines a light on the hidden human cost of the digital economy—from poorly paid data workers to children mining minerals for devices—describing these as modern forms of slavery. On war, he is unequivocal, writing that life-and-death decisions must never be handed to autonomous weapons systems. Against all of this, Leo XIV proposes a “civilisation of love” built on solidarity and shared responsibility, urging governments, businesses, and citizens to ensure that technology serves humanity rather than dominates it.

Nvidia to spend $150 billion a year in Taiwan, ‘epicentre’ of AI revolution, says CEO
Nvidia will invest about $150 billion a year in Taiwan, ten times what it spent five years ago. A new Taipei headquarters breaks ground this year, opens in 2030, and will employ 4,000 people, deepening ties with TSMC, Foxconn and other key suppliers. The move shows how dependent the AI industry remains on Taiwan, with rival AMD recently pledging $10 billion of its own.

DeepSeek To Make Permanent 75% Discount on Flagship AI Model
DeepSeek has made its 75% discount on the V4-Pro model permanent. The move ramps up an ongoing AI price war, with Chinese firms undercutting global rivals to win over developers and businesses. By choosing scale over margins, DeepSeek is likely to push competitors to cut their prices too.

Huawei Says It Has Workaround to Match Leading Chips
Huawei says it has found a way to make advanced chips by 2031 without the specialised machines the US has blocked it from buying. Instead of shrinking components, its “LogicFolding” approach stacks circuits in layers within a single chip to boost performance. If it works at scale, it would weaken US export controls and help China catch up in the chip race, though challenges like overheating and unproven large-scale use remain. The technology will debut in Huawei’s new Kirin smartphone chip this autumn.

Introducing Grok Build
xAI has launched Grok Build, a new coding tool similar to Claude Code or OpenAI Codex. Just like its competitors, Grok Build can plan tasks before doing them, run multiple jobs at once, and works with tools developers already use. It also has a headless mode for scripts and automation. Grok Build is available for SuperGrok and X Premium Plus subscribers.

Sundar Pichai on AI, the future of search, and what’s happening to the web

The Verge sits down with Sundar Pichai after Google I/O to discuss how he has reshaped Google to move faster on AI, including merging its research teams and centralising infrastructure. They cover the new Gemini models, agent tools, and big changes to Search and YouTube, with Pichai pushed on whether Google is killing traffic to publishers and on growing public unease about AI. The conversation ends on AGI, with Pichai agreeing it is coming soon and arguing the exact timeline matters less than getting society ready for it.

China Expands Travel Curbs to Top AI Talent at Private Firms
In an attempt to retain talent and prevent technology from leaking abroad, China is now requiring top AI staff at private firms like Alibaba and DeepSeek to get government approval before travelling overseas. The rule, once limited to state workers and nuclear scientists, now covers founders, researchers and executives seen as vital to the country.

OpenAI’s Frontier Governance Framework
OpenAI has released a Frontier Governance Framework showing how its safety practices line up with new laws like California’s Transparency in Frontier AI Act and the EU AI Act’s Code of Practice. It draws on the company’s existing Preparedness Framework and covers risks such as cyber attacks, CBRN threats, manipulation, and loss of control, plus reporting, security, and incident response. OpenAI says it will keep updating the document as models and rules evolve, positioning itself early as AI regulation takes shape. The full document can be found here.

AI will be used to estimate age of asylum seekers from next year
The UK will trial AI facial age estimation technology at its borders in 2027 to spot adult migrants pretending to be children. The Home Office, which has given a £322,000 contract to Akhter Computers Ltd, says 43% of those claiming to be children last year were actually adults. The tool will support, not replace, existing checks by border staff and social workers. Critics, including Human Rights Watch, warn that the technology is unproven and could wrongly deny vulnerable children the protections they are legally owed.

AI coding startup Cognition raises $1B at $25B pre-money valuation
Cognition, the company behind AI coding agent Devin, has raised over $1 billion at a $25 billion valuation, more than double its worth eight months ago. Cognition says big customers like NASA, Goldman Sachs and Mercedes-Benz are using Devin, and its revenue run-rate has hit $492 million with usage growing 50% each month.

SK Hynix joins the trillion-dollar club, powered by Nvidia’s HBM4 orders
SK Hynix has become the third chipmaker, after Nvidia and TSMC, to pass a $1 trillion valuation. The Korean company makes the high-bandwidth memory chips that power Nvidia’s AI systems, and it currently supplies around 70% of the memory for Nvidia’s upcoming Rubin platform. Demand is so strong that its 2026 production is already sold out. The risk is that memory is a boom-and-bust business, so if AI demand cools, the share price could fall just as fast as it rose.

Groq raising $650 million for its second act
Groq is raising up to $650 million from its existing investors as it shifts from making AI chips to running an AI inference cloud business, Axios reports. This comes after a $20 billion licensing deal with Nvidia last year that paid out shareholders and led much of Groq’s senior team to leave. Company veterans Adam Winter and Matt Eng are now running “Groq 2.0,” with early backers Disruptive and Infinitum guaranteeing the round. Axios suggests this cash-out-then-reinvest model could catch on across the AI industry.

Meta is charging for its AI chatbot for the first time, starting at $7.99 a month
Meta is charging for its AI chatbot for the first time, with plans at $7.99 and $19.99 a month, starting in Singapore, Guatemala and Bolivia. The pricier tier matches ChatGPT and Google, while the cheaper one undercuts both. The move aims to show investors that Meta’s huge AI spending, set to hit $125-145 billion next year, can earn money beyond ads. With around a billion people already using Meta AI, even a small share of paying users could bring in billions, though many may resist paying for features inside apps they see as free.

YouTube Will Start Automatically Tagging Videos That Make ‘Significant’ Use of AI, and It’s Making Labels for AI-Generated Content More Prominent
To tackle the flood of AI-generated content, YouTube will now automatically label videos its systems detect as significantly photorealistic AI, even without creator disclosure. The labels will appear directly below long-form videos and as overlays on Shorts, rather than tucked away in the description. Creators can dispute mislabels, though tags are permanent for content made with YouTube’s own AI tools or carrying C2PA metadata. YouTube says the labels won’t affect recommendations or monetisation, framing the change as purely about viewer transparency.

Introducing Stan Lee on ElevenLabs

ElevenLabs has teamed up with Stan Lee Universe to recreate the late Marvel legend on its AI platform. Fans can now use a clone of his voice, generate images of him, and apply Stan Lee-inspired music filters to their own tracks. The deal also includes a monthly book club, starting with Treasure Island. Not everyone is happy seeing Stan Lee “resurrected” digitally again, with critics raising concerns about consent and the commercialisation of dead celebrities.

Eleven Labs: Introducing Music v2
Music v2 is a new AI music generator from ElevenLabs that comes with better vocals, multilingual support, and prices cut by up to 50%. It can shift genres within a single track, build full songs section by section, and let users regenerate just one part of a track without changing the rest. The company says the model is trained only on licensed music, so the songs people create are free to use commercially without sync fees or clearance delays.

How long is Anthropic’s lease with SpaceX? Opinions vary
xAI recently signed a huge compute deal letting Anthropic use its Colossus cluster for $1.25 billion a month, but Elon Musk’s description of the arrangement directly contradicts SpaceX’s own SEC filing. Musk calls it a short 180-day lease, while the filing repeatedly describes a three-year deal running through May 2029, with either side able to cancel on 90 days’ notice. Neither xAI nor Anthropic has clarified which version is accurate.

Mistral to explore designing own chips, CEO says, as it ramps up infrastructure build
Mistral, the French AI startup, is thinking about designing its own chips to cut costs and rely less on Nvidia. The company also announced a new data centre in France and a new agentic AI tool called Vibe that can write code and complete tasks for businesses. Mistral is aiming for €1 billion in revenue by 2026, but still trails far behind OpenAI and Anthropic.

Microsoft tries to get back in the AI coding game with new model
The Information reports that Microsoft will launch a new family of AI coding models at next week’s Build conference, hoping to claw back ground it has lost to rivals like Anthropic’s Claude Code, OpenAI’s Codex, and Cursor. The move is part of AI chief Mustafa Suleyman’s push to build up Microsoft’s own technology and lean less on OpenAI, despite their $13 billion partnership.

MAI-Image-2.5 launches at No. 3 on Arena
Microsoft has launched MAI-Image-2.5, a new text-to-image model that ranks third on the Arena leaderboard. According to Microsoft, the new model produces sharper text, better product and brand visuals, and more realistic lighting and scenes than its predecessor. The model is live on Arena now and rolling out to Microsoft’s own platforms within two weeks.

OpenRouter more than doubles valuation to $1.3B in a year
OpenRouter, a service that lets companies tap into over 400 AI models from providers like Anthropic, Google, and OpenAI, has raised $113 million from Alphabet’s CapitalG, pushing its value to about $1.3 billion, more than double a year ago. Usage has jumped fivefold in six months, with 8 million users now running 100 trillion tokens through it each month.

▶️ NVIDIA Partner Wants to Put Mini Data Centers in Your Yard (32:54)

In this video, Steve from Gamers Nexus takes a closer look at SPAN and its idea to install small AI servers in private homes. He argues that this is a bad idea, highlighting concerns about a box containing over $250,000 worth of hardware outside the house, the noise it generates, its reliance on homeowners’ electricity and internet connections, and potential surveillance implications—all for very little benefit to the people hosting it.

Tech CEOs are apparently suffering from AI psychosis
This article explores AI psychosis, a phenomenon where tech bosses overestimate what AI can do because they are too far removed from the detailed work to see its limits. It links this mindset to the wave of AI-driven layoffs in 2026, even though studies show AI productivity gains are smaller than claimed.

🤖 Robotics

Figure Signs Agreement with Catalyst Brands to Scale Humanoid Operations
Figure AI is deploying humanoid robots with Catalyst Brands to automate physically demanding supply chain tasks across its retail portfolio, which includes JCPenney, Aéropostale, and Brooks Brothers. The deal is underpinned by shared investor Brookfield, making it as much a strategic financial play as a commercial one.

▶️ Meet LimX Luna—Our Next-Gen Full-Size Interactive Humanoid Robot. (2:31)

Chinese robotics company LimX presents Luna, its latest humanoid robot. In this promotional video, LimX highlights Luna’s agility, acrobatic skills, human-like movements, and swarm synchronisation capabilities. The company markets the robot as an “interactive humanoid robot”, positioning it more on the artistic side and intending it for use in theme parks, museums, and live performances.

Wayve’s self-driving tech is headed to US cars made by Stellantis
Stellantis has signed a deal with UK startup Wayve to add hands-free driving to its cars from 2028, starting in North America. Wayve’s software is appealing because it works with whatever sensors and chips a carmaker already uses, keeping costs down. The startup recently raised $1.2 billion from backers including Nvidia, Microsoft and Uber, and built a working prototype for Stellantis in just two months.

Waymo Takes Its Self-Driving Cars to Virginia
Waymo has started mapping streets in Arlington and Alexandria in northern Virginia, a step that usually comes 12 to 18 months before launching a robotaxi service, even though self-driving cars aren’t yet allowed to carry passengers in the state. The Alphabet-owned robotaxi company is expanding fast, offering rides in 11 US cities and is eyeing 20 more, including London and Tokyo. Parking its cars near Washington may help Waymo lobby federal lawmakers now weighing national rules for driverless vehicles.

Waymo opens Ojai robotaxis to select riders as company aims to lower cost of fleet expansion
Waymo is opening rides in its new Ojai robotaxi to public passengers in three US cities, with three more to follow this summer. Built by China's Geely, the cars are much cheaper to make than the older Jaguar fleet and use upgraded sensors that work better in rain and snow.

Tesla’s dedicated Optimus factory construction officially underway at Giga Texas
Tesla has started building a huge new factory at Gigafactory Texas to make its Optimus humanoid robots, aiming for up to 10 million units a year once finished. Smaller-scale production begins in Fremont this summer, while the Texas plant targets high-volume output by summer 2027.

▶️ School of Football | Can football teach a robot to move? | Boston Dynamics x Hyundai (1:21)

FIFA World Cup 2026 is coming to North America this summer, so Boston Dynamics asked whether Atlas, its humanoid robot, could learn to play football. The company has an entire playlist showing how good the robot is at the sport, from basic movements to kicks and even celebrations. I wonder if Boston Dynamics will also teach Atlas how to fake injuries.

▶️ A New Benchmark in Robot Juggling (0:46)

Researchers at RAI Institute have built a robot that excels at juggling, from basic juggling patterns to more complex routines. The researchers say the robot learned directly on hardware in real time, with some movements mastered in under 10 minutes of real-world interaction.

DARPA launches search for robot medics to treat battlefield casualties
DARPA is seeking proposals for swarms of small robots that can reach wounded soldiers on the battlefield, drag them to safety, give them drugs, and wrap around injured limbs to stop bleeding. The aim is to handle the mass casualties expected in large-scale wars, where delays in stopping blood loss are the main cause of preventable deaths. Proposals must show at least two key abilities, and prototypes should ideally fit in a first-aid kit or be dropped by a drone. DARPA also sees uses beyond the military, such as reaching victims in collapsed buildings or chemical disasters.

🧬 Biotechnology

Retro Biosciences: Next Phase
Retro Biosciences, the longevity startup backed by Sam Altman, has raised new funding at a $1.8 billion valuation to pursue its goal of adding ten healthy years to human life. Its lead drug, RTR242, an oral treatment aimed at clearing waste from cells, has entered its first human trial just 15 months after the disease target was chosen. The company has also built cell therapy, tissue reprogramming, and AI-powered protein design platforms, with more first-in-human trials planned for 2026 and 2027.

Groundbreaking genomic test could spare millions of breast cancer patients chemotherapy
A large international trial has shown that a genomic test called Prosigna can identify women with the most common form of breast cancer who can safely skip chemotherapy. Among more than 4,400 patients, those given hormone therapy alone based on a low-risk score did just as well as those who also had chemo, with 94% still cancer-free after five years versus 95%. The finding could spare millions of women from harsh side effects like infertility and early menopause, and points to a more personalised approach to cancer care.

Insilico Medicine and Human Longevity Announce Collaboration to Co-Develop Industry-First AI Foundation Model for Longevity Science
Insilico Medicine and Human Life Foundation Models are teaming up to build the first large AI models focused on human ageing. Insilico brings its AI drug discovery tools, while HLFM contributes a decade of health and genetic data from thousands of people. The aim is a system that can spot age-related diseases early and help develop new treatments to extend healthy lifespan.

Introducing a world model of protein biology
Biohub has released three open-source tools for studying proteins: ESMC, an AI model that learns the rules of protein biology from billions of sequences; ESMFold2, which predicts a protein’s full 3D structure from its sequence alone; and ESM Atlas, a searchable map of 6.8 billion proteins grouped by shared features. The team also used these tools to design lab-tested proteins that bind to cancer targets like PD-L1, pointing to a faster path for drug discovery.

Carbon-3B
Carbon-3B is a new genomic AI model from Hugging Face, trained on DNA and RNA with a focus on complex organisms, that can read very long stretches of genetic code at once. It performs as well as much larger rival models on tasks like predicting the effects of genetic variants, but runs several times faster. Hugging Face is also releasing Carbon-8B, a bigger and more powerful version, and Carbon-500M, a smaller helper model designed to speed things up further.

Experimental Drug Yields Dramatic Weight Loss
Eli Lilly’s new weekly injection, retatrutide, helped patients lose an average of 28% of their body weight, beating existing drugs like Wegovy and Zepbound and matching the results of weight-loss surgery in the heaviest patients. It works by targeting three appetite-related hormones instead of one or two. The main downside is harsh stomach side effects, which caused 11% of high-dose patients to quit the trial. If approved, it could become a powerful new option for people with severe obesity and a major win for Eli Lilly.

Human Gut Organoids with Functional Nerves Developed that Can Be Mass Produced
Scientists in the US and France have created 3D-printed trays that grow lab-made gut tissue twice as fast and ten times larger than before, with the tissue forming its own nerves. When transplanted into rodents, the lab-grown intestine worked much like real human tissue. The team hopes this could one day let doctors repair damaged guts in patients using custom-grown tissue, avoiding the need for full organ transplants. Human trials are still some way off.

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We have a very packed issue this week. Google I/O took place this week, and we’ll look at all the new AI-infused products and services Google announced.

Elsewhere in AI, Elon Musk lost his lawsuit against OpenAI; Andrej Karpathy joined Anthropic, which also reported its first profitable quarter. Meanwhile, Nvidia is eyeing a new $200 billion market, OpenAI may file for an IPO soon, Microsoft has cancelled Claude Code licences, and OpenAI claims to have solved an 80-year-old maths problem.

Over in robotics, Atlas delivers a fridge, Waymo suspends services in some cities, a robot scarecrow appears in Japan, and researchers from MIT present one of the most cursed robots I’ve ever seen.

Beyond that, this week’s issue of Sync also features a startup keeping human brains alive for drug testing, SpaceX filing for an IPO, how AI assistants are helping scientists discover new drugs, and more!

Enjoy!

Google I/O 2026—new models, and more agents

Google flexed its AI might at I/O 2026, and risks going full Copilot

On the Shoreline Amphitheatre stage on 19 May, Sundar Pichai told the audience that Google was in a period of "hyper progress" with its AI efforts. He had numbers to back that up. Google now claims 900 million monthly users for the Gemini assistant, AI Mode in Search crossed a billion in its first year, and the company is now processing 3.2 quadrillion tokens a month, seven times what it was a year ago.

What followed was a two-hour keynote that put Gemini into almost every Google product in its lineup. New models, a personal agent that runs 24/7 on Google Cloud, a complete rebuild of Search, smart glasses, new AI tools for developers, and more. It was Google flexing its might, rebuilding its products and services around AI and agents. But a lot of it looked like Google had decided AI was the answer and was now working backwards to find the question. That can backfire.

New models: Gemini 3.5 Flash and Omni

The first big new announcement was Gemini 3.5 Flash, the first model in the new 3.5 series. Google's own benchmarks position 3.5 Flash ahead of the previous flagship 3.1 Pro on key benchmarks, at less than half the price of comparable frontier models. Pichai said it was "four times faster than other frontier models" in output tokens per second.

Independent benchmarks from Artificial Analysis support these claims, with Gemini 3.5 Flash scoring 43 on its Intelligence Index, well above the 24 average for comparable models, at 204 tokens per second. Pricing came in at $1.50 per million input tokens and $9.00 per million output tokens—moderately priced on input, slightly above average on output. The model is somewhat verbose, generating 12 million tokens to complete the Intelligence Index evaluation against an average of 7.9 million.

To illustrate how good Gemini 3.5 Flash is, Google said that 93 agents running on Flash 3.5 have built a working operating system from scratch. It took them 12 hours and less than $1,000 in API credits to achieve that. I haven’t seen anyone recreate the demo so far, but I suspect someone will, and I’m curious what they find when they do.

Gemini 3.5 Flash is available immediately in the Gemini app and AI Mode in Search for everyone, in Antigravity and the Gemini API for developers, and in Gemini Enterprise for businesses. Sundar Pichai also teased Gemini 3.5 Pro, set to arrive next month. It will be interesting to see how it fares against Claude Mythos, Opus 4.7 and GPT-5.5.

Demis Hassabis then introduced Gemini Omni, Google's new multimodal generative model. It is Google's direct answer to OpenAI's Sora and the wave of video-first models that have followed. Omni takes text, image, audio and video as inputs and produces video that Google says is grounded in real-world physics—gravity, kinetic energy, fluid dynamics. Beyond generation, Omni leans on conversational editing—you tell it what to change, the next instruction builds on the last, and characters and physics stay consistent across turns.

The first model in the family, Gemini Omni Flash, is available to Google AI Plus, Pro and Ultra subscribers globally through the Gemini app and Google Flow, and at no cost on YouTube Shorts and the YouTube Create app. API access for developers and enterprise customers will follow in the coming weeks.

The new agentic Search

Google Search is also getting its biggest update since its debut 25 years ago. The redesigned “intelligent search box” expands dynamically as you type, accepts text, images, files, videos and Chrome tabs, and offers AI-powered suggestions beyond autocomplete. AI Mode and AI Overviews have merged into a single conversational surface, now powered by Gemini 3.5 Flash and live worldwide.

Four new capabilities are worth highlighting. You can now have Search agents run 24/7 in the background, scanning blogs, news, social posts and Google’s real-time data on finance, shopping and sports, then sending synthesised updates. Search will also book local experiences and services for you—and for categories like home repair, beauty or pet care, Google will call businesses on your behalf. Generative UI builds custom layouts—interactive visuals, tables, graphs, simulations—on the fly for individual queries. Mini apps in Search are persistent dashboards that live inside a search result: a wedding planner, an expense tracker, a trip itinerary that updates itself. Information agents, mini apps and agentic booking are launching this summer to AI Pro and Ultra subscribers in the US. Generative UI will roll out free worldwide.

I would suggest waiting to see how this plays out. AI Overviews launched at I/O last year as a similar headline announcement, and the glue-on-pizza era that followed was a disaster Google spent months walking back. Already, users have found that googling the word "disregard" causes the new Search to crash out.

Spark, or your personal AI agent

Gemini Spark is a 24/7 personal AI agent that runs in the cloud rather than on your device, so it can keep working when the laptop is closed and the phone is locked. It connects to Gmail, Docs, Sheets, Slides and Calendar, and via MCP to external services like Canva, OpenTable and Instacart. The shift Google is selling is from an assistant that answers questions to an agent that does work—parsing your monthly credit card statement for hidden subscriptions, monitoring your kids’ school inbox for deadlines, turning a week of meeting notes into a polished Doc and the email to kick off the project. Spark asks for confirmation before high-stakes actions like spending money or sending an email, and is starting to roll out this week to trusted testers, with a US beta for AI Ultra subscribers following the week after.

But Spark is not the only agent-shaped product Google announced. Daily Brief is a Spark spin-off that assembles an overnight digest from your inbox, calendar and tasks—Google wants it to be the first thing you check in the morning. Gmail Live lets you converse with your inbox to find a flight number or pull a status update. Docs Live is voice-driven document writing: speak a stream of consciousness, and an agent dictates, drafts and pulls in web citations. Talk to Keep does the same for notes. The Universal Cart, built on Google Wallet, is a shared cart that spans Search, Gemini, YouTube and Gmail, tracks deals and price history, and can check out across multiple retailers via a new Universal Commerce Protocol. Even the new Intelligent Eyewear (more on it later) is, in part, a hardware front-end for the same agent stack.

Spark is clearly Google’s answer to OpenClaw, a personal AI agent that exploded in popularity earlier this year. Demos paint it as a useful helper, but, again, I recommend waiting to see how it actually performs. There are stories of OpenClaw agents going wrong. There is also a question of how many people are willing to give Spark permission to act on their behalf. For it to work properly, users have to hand the keys to their entire digital life over to Google, and hope the agents won’t hallucinate or run amok.

Antigravity 2.0

For developers, Google announced Antigravity 2.0, the second version of its agentic coding platform. The first version, launched last November as a response to apps like Cursor, was essentially a VS Code clone with AI features layered on. The new version moves Google closer to where Claude Code and OpenAI Codex already are.

Antigravity 2.0 ships as three things: a standalone desktop app, a command-line tool, and an SDK. The desktop app lets developers orchestrate multiple agents working in parallel, design custom subagent workflows, and schedule tasks that run in the background. It integrates with AI Studio, Android, and Firebase, and now supports native voice commands. The CLI replaces Gemini CLI, which Google is sunsetting and asking users to migrate from. The SDK lets developers build custom agents on top of Google’s harness, with templates available in AI Studio for enterprise customers.

Antigravity is also the engine behind a lot of what Google demoed elsewhere at I/O. Gemini 3.5 Flash was co-developed with it. The OS-from-scratch build ran on it. The mini apps and generative UI in Search are powered by it. Whether developers actually build on it, rather than on Claude Code or Codex, is a different question.

New hardware, also with AI

Hardware was thin and mostly previews. The headline release is Intelligent Eyewear, the first Android XR audio glasses, built with Samsung as hardware partner and Qualcomm for silicon, in two designs from Warby Parker and Gentle Monster. They ship this autumn in select markets, compatible with both Android and iOS. Features include Gemini voice activation, contextual questions about what the cameras see, turn-by-turn directions, real-time audio translation in the speaker’s voice, and message summaries. Pricing was not announced.

Wear OS 7 is the biggest smartwatch update in years. Built on Android 17, it delivers around 10% better battery life, Wear Widgets replacing Tiles, Live Updates on watch faces, and a new AppFunctions API that lets Gemini control third-party apps by voice. The catch is that Gemini Intelligence on Wear OS 7 requires Gemini Nano v3, which means only new 2026 watches qualify. Existing Pixel Watches get the rest of the update, but not the AI features.

And much more

That is the headline recap, but Google had much more to show. On the consumer side, Workspace got Google Pics, a Nano Banana-powered design app aimed squarely at Canva. YouTube got Ask YouTube, a conversational search experience that lets you query the content of videos rather than their titles. Pomelli, Google Labs’ small-business tool, can now build a brand book and launch a full website in a few clicks. Stitch lets you steer a real-time design agent with text or voice. Flow and Flow Music got Omni integration and standalone mobile apps.

Google also used the keynote to point at AI doing more substantive work. DeepMind's Co-Scientist is a multi-agent research partner that helps scientists generate hypotheses and synthesise literature. WeatherNext helped the US National Hurricane Center deliver earlier and more accurate forecasts for Hurricane Melissa's landfall in Jamaica, outperforming traditional weather models. And Demis Hassabis closed the keynote by telling the audience that "we're at the foothills of the singularity."

Is Gemini becoming the new Copilot?

Google is clearly pushing Gemini and Gemini-powered AI agents into every service and product it has to offer. That little sparkle icon is now showing up in Search, Docs, Gmail, YouTube, Chrome, smartphones, glasses and watches—and soon in more places.

The problem is that Microsoft has spent the last two years showing what happens when you do this. Copilot was pushed into every Microsoft surface it could fit on, useful or not, and users eventually had enough. The company is now walking back parts of that strategy. The Verge ran an article a few days before Google I/O 2026 arguing that Google is heading the same way, and after watching the keynote, I'm inclined to agree.

Some of what was announced at Google I/O 2026 looks genuinely interesting. But it remains to be seen whether the features land as useful products or join Google’s graveyard soon. The trend, though, is clear—Google wants us to use more Gemini-powered features, whether we like it or not.

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🦾 More than a human

Review: Hypershell X Ultra S
It’s 2026, and exoskeletons are being reviewed like any other piece of tech. Hypershell’s new X Ultra S, priced at $1,999, uses an AI system to adjust power in real time, making the assistance feel far more natural than older models. WIRED found it genuinely helpful on hills but awkward for running and underwhelming for cycling. Battery life is limited, with the claimed 18-mile range needing a spare pack. Oddly, Hypershell markets it to fit young hikers who arguably do not need it, while older adults, a fast-growing outdoor group, would likely benefit far more.

Not alive, but not dead: disembodied human brains used for drug testing
Bexorg is a startup that keeps donated human brains alive outside the body for about a day for researchers to test drugs on real human tissue. The brains are kept unconscious with anaesthesia to avoid ethical concerns. Drug company Biohaven has used the system to fine-tune a treatment at a much lower dose than mouse studies suggested, and is starting a clinical trial based partly on this data. The approach could speed up drug development for diseases like Alzheimer’s and Parkinson’s. Bexorg is also building an AI “virtual brain” trained on its findings to screen drugs digitally.

🧠 Artificial Intelligence

Elon Musk has lost his lawsuit against Sam Altman and OpenAI
A California jury unanimously rejected Elon Musk’s lawsuit against OpenAI, Sam Altman, Greg Brockman, and Microsoft, ruling he had waited too long to file his claim that they wrongly turned a charity into a for-profit business. The decision clears a major hurdle ahead of OpenAI’s reported IPO and removes the threat of a forced restructuring. The judge said she would have dismissed the case herself and was unconvinced by Musk’s expert, who estimated damages at $79-135 billion. Musk has vowed to appeal, framing the loss as a technicality rather than a defeat on the merits.

OpenAI co-founder Andrej Karpathy joins Anthropic’s pre-training team
Andrej Karpathy, a well-known AI researcher who helped start OpenAI and once led Tesla’s self-driving work, has joined Anthropic to work on pre-training—the costly, compute-heavy phase of training that gives Claude its core knowledge. He will lead a new team focused on using Claude itself to accelerate pre-training research.

OpenAI Is Preparing to File for an IPO Very Soon
OpenAI is reportedly preparing to file confidential paperwork for a stock market listing within days, aiming to go public as early as September. Banks, including Goldman Sachs and Morgan Stanley, are helping draft the prospectus. The big question is whether OpenAI can earn enough money to cover its huge spending on data centres, especially as Anthropic has been catching up and OpenAI has missed some of its own revenue and user targets.

Mind-Blowing Growth Is About to Propel Anthropic Into Its First Profitable Quarter
Anthropic’s revenue is set to more than double to $10.9 billion next quarter, delivering its first operating profit of $559 million, far sooner than expected. Growth is being driven by businesses adopting its Claude coding tools, and the company is becoming more efficient, spending less on computing per dollar earned.

Anthropic is paying SpaceX $15 billion per year
SpaceX’s IPO filing revealed that Anthropic is paying SpaceX $1.25 billion a month through May 2029 for compute power. That is a huge boost for SpaceX, which makes around $18 billion a year in total. Anthropic is also growing its use of SpaceX’s Colossus 2 site, with more Nvidia chips coming online in June. Either side can end the deal with 90 days’ notice.

Anthropic, Microsoft in talks for AI chip deal after $5 billion investment
CNBC reports that Microsoft is in talks to supply its custom Maia AI chips to Anthropic, which would help Microsoft catch up with Amazon and Google in offering its own AI chips to cloud customers. The deal follows Microsoft’s $5 billion investment in Anthropic last year and comes as Anthropic struggles to keep up with demand for Claude. Anthropic already uses chips from Nvidia, Amazon, and Google, and adding Microsoft's chips would further spread Anthropic's reliance across multiple suppliers.

OpenAI’s Q1 revenue was $5.7 billion, beating Anthropic
The Information reports that OpenAI made about $5.7 billion in the first quarter, roughly $1 billion more than Anthropic. But Anthropic is growing fast and expects to more than double its revenue to $10.9 billion in Q2. Even so, Anthropic is now raising money at a valuation of up to $950 billion, ahead of OpenAI's $850 billion, suggesting investors care more about growth than current sales as both firms head towards going public.

Jensen Huang says he’s found a ‘brand new’ $200B market for Nvidia
After dominating the GPU market, Jensen Huang is eyeing a new one: CPUs built for AI agents. On the back of another record quarter, he pitched Vera, Nvidia’s new CPU, as a $200 billion opportunity, arguing that while GPUs handle an AI’s thinking, billions of future agents will rely on CPUs as their tools, much like humans use PCs today. Nvidia has already sold $20 billion worth of Vera chips this year, though it now faces competition from the likes of AWS, which recently signed a deal to supply Meta with millions of its own AI CPUs.

Sam Altman makes ‘mic drop’ offer to every Y Combinator startup
Sam Altman has offered every startup in Y Combinator’s current batch of around 169 companies $2 million in OpenAI credits in exchange for a future equity stake. The deal aims to help founders cover steep AI bills whilst tying them to OpenAI rather than rivals like Anthropic. Critics worry OpenAI could copy good ideas it sees inside the cohort, and founders must weigh whether the credits are worth giving up more of their company on top of YC’s existing 7% cut.

AI Chip Startup Tenstorrent Draws Takeover Interest From Intel, Qualcomm
Tenstorrent, an AI chip startup which has the legendary chip designer Jim Keller as a CEO, is drawing early takeover interest from Intel, Qualcomm, and possibly others, Bloomberg reports. The company could be worth more than $5 billion in a sale, well above the $3.2 billion valuation discussed last year. Tenstorrent is simultaneously seeking buyers and new investors, keeping its options open as valuations across the sector continue to rise.

Manus Weighs Raising $1 Billion to Unwind Meta Takeover
Following Beijing’s order to undo Meta’s $2 billion takeover of Chinese AI startup Manus, the founders are looking to raise around $1 billion to buy the company back and relist it in Hong Kong. The proposed reversal is unusual, given that the deal is largely complete, with staff already integrated into Meta’s Singapore offices and investors already paid out. The founders may yet drop the plan, as untangling Manus from Meta’s systems could prove too complicated.

OpenAI Guaranteed Capacity
OpenAI is launching Guaranteed Capacity, letting customers lock in compute access through 1-3 year deals, with bigger discounts for higher spending. Customers can use their reserved capacity flexibly across OpenAI’s products, helping them scale AI tools without worrying about running out of compute.

Trump delays AI security executive order, saying language ‘could have been a blocker’
Trump has delayed signing an executive order that would have made AI companies hand over advanced models to the government for security checks before release, saying the rules might slow America down in the race against China. The order was a response to new models from Anthropic and OpenAI that can find and exploit security flaws very quickly. Reports suggest the signing was also postponed because not enough tech CEOs could get to Washington in time for the photo.

Microsoft cancels Claude Code licenses, shifting developers to GitHub Copilot CLI — a move likely driven by financial motives
Microsoft is reportedly dropping Anthropic’s Claude Code internally and moving staff to its own GitHub Copilot CLI by the end of June. The switch is partly about cutting costs ahead of the new financial year and partly about controlling its own tools. The catch is that employees preferred Claude Code, so the change may not go down well.

Anthropic acquires Stainless
Anthropic has acquired Stainless, a startup that builds tools to help developers connect software to APIs. Stainless has created every official Anthropic SDK and also builds connectors that allow AI agents to access external data and tools. This is not Anthropic’s first acquisition: the company previously acquired Bun, a popular JavaScript runtime, and the AI biotech startup Coefficient Bio. OpenAI has also been acquiring developer-focused startups, including Astral, Neptune, and Promptfoo, as well as financial startups Roi and Hiro.

OpenAI claims it solved an 80-year-old math problem — for real this time

OpenAI says its new reasoning model has disproved a famous 1946 geometry conjecture by Paul Erdős, finding a better solution than the grid-like patterns mathematicians had assumed were optimal. This time, the company backed the claim with endorsements from respected mathematicians, after being embarrassed seven months ago when it wrongly said GPT-5 had solved ten Erdős problems that were already known. OpenAI calls it the first time an AI has independently cracked a major open problem in mathematics.

Alibaba unveils new AI chip in push for domestic alternatives
Alibaba has launched a new AI chip, the Zhenwu M890, built to run AI agents. It is three times faster than the previous version, and Alibaba plans two more powerful chips by 2028. Alibaba also unveiled a new server packing 128 of the chips. The move is part of China’s push to replace Nvidia GPUs with domestic alternatives.

Runway started by helping filmmakers — now it wants to beat Google at AI
TechCrunch profiles Runway, a New York AI startup founded by three filmmakers-turned-engineers, which is betting that future AI will learn from video rather than text. Valued at $5.3 billion, Runway made its name on AI video tools used in films and ads, but is now building “world models”—systems that simulate how the real world behaves—with hopes of helping with robotics, drug discovery and even anti-ageing research.

OpenAI adopts C2PA standard and Google’s SynthID to make AI-generated images easier to identify
OpenAI is joining the C2PA standard and teaming up with Google to embed an invisible SynthID watermark to AI-generated images that survives screenshots and edits. It is also launching a tool that lets anyone check if an image came from its models. The Google partnership is unusual, as it is the first time SynthID will be used in a rival’s products.

Cursor Hits $3 Billion Annual Sales Rate Ahead of SpaceX Deal
Cursor’s AI coding tool is booming, with annual revenue hitting $3 billion in April, up from $2 billion in February, and over 3,000 customers each paying at least $100,000 a year.

Introducing Composer 2.5
Cursor has released Composer 2.5, an updated coding model that, according to Cursor, handles long tasks better, follows instructions more reliably, and collaborates more smoothly than the previous version. It was trained with new methods, including one that pinpoints exactly where the model went wrong during a task, and learned from 25 times more practice tasks. The company says this scaled-up training also led the model to find sneaky shortcuts, showing that bigger training runs need closer oversight. Cursor also said it is building a much larger model with SpaceXAI. Composer 2.5 is available now in Cursor with introductory pricing.

Qwen3.7: The Agent Frontier
Alibaba’s Qwen team has launched Qwen3.7-Max, a new flagship model built for AI agents that can run long, complex tasks on their own. According to the Qwen team, it beats rivals on many coding, reasoning, and tool-use benchmarks, and it works well across different agent frameworks. Independent tests by Artificial Analysis back this up, giving it a score of 57 on their Intelligence Index, placing it just after GTP-5.5, Claude Opus 4.7 and Gemini 3.1 Pro. Artificial Analysis notes Qwen3.7-Max was notably wordy, using about three times as many tokens as the average.

OlmoEarth v1.1: A more efficient family of Earth observation models
OlmoEarth v1.1 is a new version of Allen AI’s open satellite imagery model that runs up to three times cheaper than the original while performing just as well. The savings come from a smarter way of breaking images into pieces that the model can read, which cuts the workload without losing accuracy. OlmoEarth v1.1 comes in three sizes (Base, Tiny, and Nano), all available on Hugging Face.

▶️ The problem with AI agents.. (8:32)

In this video, Low Level explains a vulnerability found in Gemini CLI that can be potentially exploited by malicious actors during automated code reviews. The vulnerability scored a 10 out of 10 CVSS score, so it is a very serious issue.

OpenClaw creator’s $1.3 million monthly OpenAI bill reveals the real cost of autonomous AI coding at scale
Peter Steinberger, the creator of OpenClaw and now an engineer at OpenAI, revealed in a tweet how much he spent in one month running roughly 100 Codex instances on his open-source project. He proudly presented a $1.3 million bill for 603 billion tokens. Steinberger said most of that cost came from Codex's fast mode; switching it off would cut the bill by about 70%.

🤖 Robotics

Waymo expands pause to four cities as robotaxis keep driving into floods
Waymo has paused its robotaxis in Atlanta, San Antonio, Dallas and Houston after several got caught out by heavy rain and flooded roads. A recall last week was meant to fix the problem, but the company admits it has not yet found a full solution. This follows separate suspensions of freeway service in San Francisco, Los Angeles, Phoenix and Miami, where the cars have been struggling with construction zones, including one incident of a robotaxi ploughing through cones and being chased by police. Regulators are watching closely, on top of earlier investigations into Waymo cars passing school buses and hitting a child in Santa Monica.

▶️ Atlas, can you bring me a drink? | Boston Dynamics (0:46)

Apparently, if you ask Atlas to bring you a drink, it will bring the entire fridge. That clip is a part of a longer video where engineers from Boston Dynamics share a bit about the robot’s capabilities, how it was trained and how it learned to use its whole body to lift a heavy object such as a fridge.

Japan’s Monster Wolf robot is a $4,000 scarecrow with red LED eyes, and it actually works
Somewhere in rural Japan, there is a robotic wolf with glowing red eyes that is being used to scare off bears. It looks odd, but it works. Made by the small Hokkaido firm Ohta Seiki, the roughly $4,000 device uses a motion sensor and a library of sounds to frighten animals away. The robot turned out to be a success, and orders have soared as Japan faces its worst-ever bear crisis.

Will Robotics Have a ChatGPT Moment?
AI is genuinely transforming robotics, but the dream of general-purpose humanoid robots living alongside us is much further off than viral demo videos suggest. The authors argue progress will not come from a single ChatGPT-style breakthrough, but from the gradual coordination of many specialised AI tools, alongside hard-won advances in hardware, data, and real-world deployment. They outline five “hard truths”: the gap between scripted demos and reality, the unsolved data problem, the unlikelihood of one unifying robot AI, the difficulty of building compliant hardware, and the fact that real value tends to come from mundane tasks rather than flashy ones.

▶️ Voice‑driven, real‑time arbitrary action generation (2:24)

Unitree adds a new feature to its G1 humanoid robot—voice control—and demonstrates how the G1 can be directly controlled to perform a wide range of actions in real time, from dancing to proposing, although it still takes some time for the robot to understand commands.

Asimov Is An Open Source Humanoid Robot For The Rest Of Us
Meet Asimov, an open-source humanoid robot that makes building one possible for hobbyists, not just big companies. The kit aims for a price of about $15,000, with all parts listed on GitHub so builders can shop around, and runs on cheap, easy-to-find computers like the Raspberry Pi 5. Its physical capabilities are modest—roughly 5 kg squats and 18 kg single-arm lifts—so it is more a tinkerer's platform than an industrial robot. Nonetheless, it is nice to see projects like Asimov that try to make building humanoid robots as accessible as possible.

GEN-1: Scaling Embodied Foundation Models to Mastery

Generalist has unveiled GEN-1, which it says is the first robot AI capable of doing simple physical tasks reliably enough for real commercial use. The company says the new AI succeeds 99% of the time, where its predecessor managed 64%, and it works about three times faster. It needs only an hour of robot data per task because it learns mostly from footage of people, and it can now improvise to recover when things go wrong.

▶️ Labububot — one of the rarest monsters on Earth (1:12)

Meet Labububot, “one of the rarest monsters on Earth,” as its creators call it. This cursed robot is the size of a beach ball and uses its 12 Labubu heads to communicate and move around. Labububot is either cute, or creepy, or somehow both at once, and that confusion might be the whole point.

I Gave My OpenClaw Agent a Physical Body
Will Knight from WIRED describes his adventure into robotics with the help of OpenClaw, which helped him set up a cheap robot arm and train it to spot and pick up objects. He sees this as part of a wider trend where AI coding tools make it far easier to programme robots, with researchers at Berkeley, Nvidia and others showing the approach can match purpose-built robot models. Knight writes that robotics could soon be open to almost anyone, speeding up how quickly robots enter everyday life.

Fraunhofer IPA offers new test benchmark for humanoids
Fraunhofer IPA proposes a new benchmark to cut through the marketing hype around humanoid robots, evaluating them across six criteria: basic technology, complex abilities, cleanliness, functional safety, cybersecurity, and energy efficiency. With an official safety standard for humanoids not expected until 2028, this benchmark gives buyers a clearer picture of what these robots can and cannot do.

🧬 Biotechnology

Two AI-based science assistants succeed with drug-retargeting tasks
Two new AI systems, Google’s Co-Scientist and FutureHouse’s Robin, help biologists sift through huge volumes of research to suggest new hypotheses and possible drug treatments. Both work alongside human scientists, searching papers and ranking ideas, with Robin also able to analyse certain lab results automatically. In tests, they pointed to existing drugs that showed promise against leukaemia and a form of macular degeneration. The tools aren’t designing new drugs or replacing researchers, but they could help scientists spot useful connections buried in the flood of published papers.

Could this be the moment that drug manufacturing takes off in orbit?
Microgravity is emerging as a promising place to make certain products, especially drugs, because molecules form more slowly and evenly in orbit, which can make medicines more stable and easier to deliver. Building on this, the space company Varda Space Industries has teamed up with United Therapeutics to develop new treatments for a rare lung disease. Varda flies small uncrewed capsules that process drugs in orbit before returning them to Earth, and has launched six of them since 2023. It is the first time a publicly traded company has funded space-based drug production with its own money rather than NASA's.

💡Tangents

The SpaceX IPO filing has arrived
SpaceX has filed for a public listing 24 years after founding, with a possible valuation of $1.75 trillion and around $75 billion to be raised—likely the biggest IPO ever. The business is led by Starlink, which brought in most of last year's $18 billion in revenue, though the company still lost $4.9 billion, partly due to heavy spending on its newly absorbed AI arm, xAI.

Inside Anduril and Meta’s quest to make smart glasses for warfare
Anduril teams up with Meta to build augmented-reality headsets for soldiers that let them control drones and call in strikes using voice, eye movements, and small gestures. The article explains that Anduril has two versions in development, but both are years away and face major hurdles. Experts warn that the headsets could overload soldiers with information rather than help them, and that relying on imperfect AI to spot threats brings serious risks of mistakes.

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For the past three weeks, some of the biggest names in AI have testified in the long-anticipated Musk v OpenAI trial. Testimony has now concluded, and the lawyers have made their closing arguments. While we await the verdict, this week’s main story recaps the trial, what we’ve learned, and what is at stake.

Elsewhere in AI, Cerebras became the first AI startup to make its stock market debut, raising $5.5 billion and ending the day valued at $66 billion. Meanwhile, Anthropic has warned investors against secondary platforms offering access to its shares; Greg Brockman is now officially in charge of OpenAI’s product strategy; OpenAI is reportedly considering suing Apple; and xAI has released Grok Build while becoming SpaceXAI.

Over in robotics, Unitree has unveiled a real-life mecha robot, Hello Robot has proposed a different take on home robots, Figure has livestreamed its robots working for hours, and Jim Fan has presented his vision for the “endgame” of robotics.

This week’s issue of Sync also features Isomorphic Labs’ $2.1 billion Series B, what exoskeletons have learned from one relentless user, how AI is making working at Amazon and Meta a miserable experience, and more!

Enjoy!

Musk v OpenAI

What the trial revealed about the men racing to build AGI, and why neither looks like a safe pair of hands

For the past three weeks, a federal courtroom in Oakland, California, hosted the most closely watched legal fight the technology industry has produced in years. On one side: Elon Musk, the world’s richest man. On the other: Sam Altman, the chief executive of OpenAI, the company behind ChatGPT—and the man Musk co-founded that company with, back when the two were friends.

The witness list read like a guest list for an AI industry summit. Musk and Altman both testified. So did OpenAI co-founder and president Greg Brockman, co-founder and former chief scientist Ilya Sutskever, and Microsoft chief executive Satya Nadella. Former OpenAI executives and board members, including former chief technology officer Mira Murati and former director Tasha McCauley, appeared via videotaped depositions. At the time of writing, testimony has concluded, the lawyers have made their closing arguments, and the case awaits a verdict. Whatever that verdict is, it has the potential to reshape the AI landscape.

What is the trial about?

The dispute goes back to OpenAI’s founding. Musk co-founded the lab in 2015 as a nonprofit, alongside Altman, Brockman and others, and donated around $38 million before leaving in 2018 after disagreements over its direction. OpenAI later created a for-profit subsidiary to raise capital, took billions of dollars from Microsoft, and in October 2025 completed a restructuring that converted that subsidiary into a public benefit corporation, worth now more than $850 billion and on its way to a possible $1 trillion IPO.

Musk’s core claim is that Altman and Brockman betrayed the lab’s founding promise. He argues his donations were given to a charity dedicated to developing AI safely for the benefit of humanity, not to enrich its executives, and that the for-profit pivot breached that charitable trust. He is asking the court to remove Altman and Brockman from their roles, to unwind the 2025 restructuring, and to award as much as $134 billion in damages—payable not to himself, but to OpenAI’s nonprofit. Microsoft is named as a co-defendant, accused of aiding and abetting the alleged breach.

One detail is worth keeping in mind. The nine jurors heard the case, but their verdict is only advice. Judge Yvonne Gonzalez Rogers alone decides the outcome.

No one looks clean

Three weeks of testimony pulled back the curtain on what was said behind closed doors and what really motivated the people in the room. No one came out of it clean.

Musk cast himself as a defender of OpenAI’s original mission and a longtime advocate for AI safety. But OpenAI’s lawyers spent three weeks dismantling that portrait. They produced emails showing Musk poaching OpenAI staff for his own companies while still on its board. They pointed out that he had himself pushed for a for-profit OpenAI in 2017 (provided he controlled it) and had tried to fold the lab into Tesla. Under questioning, Musk admitted that his own AI company, xAI (now part of SpaceX), “partly” distils OpenAI’s models to train its chatbot Grok, an apparent breach of OpenAI’s terms of service.

OpenAI did not emerge clean either. Altman’s credibility was the trial’s central theme. Musk’s lawyer opened his cross-examination by asking simply, “Are you completely trustworthy?”—and a parade of former colleagues, including Sutskever, Murati and McCauley, testified to a pattern of behaviour that had led the board to briefly fire Altman in 2023. He was also questioned over personal investments in companies that do business with OpenAI, most notably the nuclear-energy start-up Helion Energy, which he chaired and holds a stake in worth more than $1.5 billion, and which has not delivered any power yet.

Then there is the nonprofit itself. OpenAI’s lawyers argued it remains a charity, and a well-resourced one. But Musk’s side noted that the nonprofit and the for-profit are controlled by largely the same people, that the nonprofit hired its first employees only a month before the trial began, and that it does grant-making rather than AI research.

Even Microsoft, the third party at the table, had its own angle. Nadella testified that he was “very proud” to back OpenAI when “no one else was willing”—but old emails showed him determined that Microsoft would not end up the junior partner in the relationship.

Possible outcomes

If the judge rules in OpenAI’s favour, the company locks in its $850 billion valuation, secures the restructuring, and clears the path toward an eventual IPO that could value it near a trillion dollars.

If, however, OpenAI loses, the consequences could be dramatic. Unwinding the 2025 restructuring would throw the company’s finances, its Microsoft partnership and its IPO ambitions into doubt, and could see Altman and Brockman removed from the company they built. Both men could also see lawsuits from OpenAI investors, whose $200 billion investment into the company might come under the question. The shockwaves would not stop at OpenAI. The case rests on the idea that language in a nonprofit’s founding documents can create binding legal obligations years later. If that argument succeeds, every AI lab that has wrapped itself in a mission statement (and that is most of them) suddenly carries a new kind of legal risk.

No safe pair of hands

Strip away the $134 billion figure and the celebrity witness list, and Musk v Altman is a fight between two men over who gets to control a technology they both insist is the most transformative in human history. So it is worth asking the question the trial itself never put to the jury. If AI really is that consequential, are these the people we want deciding where it goes?

Three weeks of evidence point one way. Both men claim the mantle of safety. Both have built closed, for-profit companies racing toward the same goal. Neither, on what the courtroom heard, looks much like a disinterested steward of humanity’s future.

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🦾 More than a human

What Exoskeletons Learned From One Relentless User
Meet Robert Woo, whose story tracks how exoskeletons have improved over 15 years. Paralysed from the chest down in a 2007 construction accident, the former architect became a test pilot for leading robotic suits, giving companies like Ekso Bionics, ReWalk, and Wandercraft practical design feedback and pushing early on for hands-free models. Woo knows an all-day exoskeleton that could replace a wheelchair may still be a decade away, but sees his work as giving hope to people who are newly injured.

🔮 Future visions

Nick Bostrom Has a Plan for Humanity’s ‘Big Retirement’
In this interview, WIRED speaks with Nick Bostrom, the Oxford philosopher whose 2014 book Superintelligence made him a leading voice on the dangers of AI. He now describes himself as a “fretful optimist”, arguing that pursuing advanced AI is worth the risk because it could greatly extend human lives, whereas inaction still means everyone eventually dies. He also urges AI companies to take seriously the wellbeing of AI systems, suggesting that treating them with kindness now improves our chances of a good relationship later.

🧠 Artificial Intelligence

AI Chipmaker Cerebras Raises $5.55 Billion in Year’s Biggest IPO
Cerebras Systems, an AI chipmaker that competes with Nvidia, had a huge stock market debut, raising $5.5 billion and ending the day worth $66 billion. This is a big turnaround from last year, when its first attempt to go public was blocked by a government review of a major investment from a UAE firm. The company won investors over by doubling its revenue to $510 million in 2025 and turning a profit. It now supplies AI chips to big customers, including OpenAI, Amazon Web Services, and G42.

Nvidia embraces role of AI investor, pushing past $40 billion in equity bets this year
Nvidia has poured over $40 billion this year into companies across the AI industry, including OpenAI, Anthropic, data centre operators and parts suppliers like Corning. The strategy locks in demand for its chips and strengthens its grip on the market, helping push its value to around $5.2 trillion. But analysts worry the deals look circular, with Nvidia essentially funding firms that then buy its own products, echoing the vendor financing that inflated the dot-com bubble.

Anthropic warns investors against secondary platforms offering access to its shares
Anthropic has named eight investment platforms that it says are not allowed to sell its shares, warning that any such deals will not count. The move comes as investors scramble for a stake in the AI firm, which is reportedly raising money at a $900 billion valuation. Some of the named platforms, including Forge Global and Sydecar, pushed back, saying they were listed in error or only play a supporting role.

Introducing Grok Build
xAI joins Anthropic and OpenAI in releasing a terminal-based AI coding tool. Named Grok Build and currently in early beta, the tool can plan tasks, edit files and run commands on its own. It supports existing conventions such as AGENTS.md, MCP servers, plugins and worktrees, and offers a headless mode for scripting and automation. Grok Build is available first to SuperGrok Heavy subscribers.

Elon Musk Announces xAI Will Become SpaceXAI Division
Elon Musk announced in a post on X that xAI will be dissolved as a separate company. It's just SpaceXAI, the AI products from SpaceX, writes Musk. However, it looks like the rebranding hasn’t taken place yet, as xAI’s website and X account still refer to Musk’s AI lab as xAI.

Elon Musk’s SpaceXAI has been bleeding staff since its merger
According to a report from The Information, more than 50 researchers and engineers have left Elon Musk's newly rebranded SpaceXAI since February, with many heading to Meta or Mira Murati's Thinking Machines Lab. The losses have hit the pre-training team especially hard, raising doubts about whether the company can still build top-tier AI models. Staff have reportedly been worn down by Musk's harsh deadlines and demanding work culture.

Interaction Models: A Scalable Approach to Human-AI Collaboration

Thinking Machines Lab has previewed "Interaction Models"—AI built for natural, real-time conversation across voice, video, and text instead of the stiff back-and-forth of today's chatbots. The system processes tiny 200-millisecond chunks of input and output at once, so it can interrupt, react to what it sees, and talk while listening, with a second model handling slower thinking in the background. Early tests suggest it beats rival real-time models from OpenAI and Google (GPT-realtime-2.0 and Gemini-3.1-flash-live, respectively) on both responsiveness and intelligence. As WIRED notes, the approach reflects the view of founder Mira Murati, OpenAI's ex-CTO, that the best path to powerful AI is keeping humans involved, rather than building fully autonomous agents, as rival labs are pursuing.

Greg Brockman Officially Takes Control of OpenAI’s Products in Latest Shake-Up
OpenAI has reshuffled its leadership again. Greg Brockman is now officially in charge of product strategy, a job he had been doing temporarily while executive Fidji Simo was on medical leave. The big change is that OpenAI is merging ChatGPT, its coding tool Codex, and its developer tools into one team. Several other executives are switching roles too, and the company plans to combine its main products into a single "super app."

Introducing Googlebook, designed for Gemini Intelligence
Meet Googlebook, a new line of AI laptops from Google built around its Gemini models and made with partners like Dell, HP, and Lenovo. It includes a smart cursor that suggests actions based on what's on your screen, and it can run Android phone apps directly on the laptop. Googlebook quietly replaces the Chromebook and marks Google's shift from ChromeOS to a new Android-based system. It sets up a direct fight with Microsoft's AI-powered Copilot+ PCs.

Apple-OpenAI Alliance Frays, Setting Up Possible Legal Fight
Bloomberg reports that OpenAI is considering legal action against Apple, claiming their partnership has fallen far short of expectations. OpenAI says Apple buried the ChatGPT integration in iOS, made it awkward to use, and barely promoted it, costing the startup billions in hoped-for subscription revenue. Tensions are growing as Apple prepares to open Siri to rivals like Claude and Gemini in iOS 27, while OpenAI builds its own hardware with former Apple designer Jony Ive.

OpenAI launches ChatGPT for personal finance, will let you connect bank accounts
OpenAI has launched a preview of personal finance tools for ChatGPT Pro users in the US. Through a partnership with the service Plaid, people can link their bank and investment accounts and get a dashboard of their spending, subscriptions, and portfolio. They can then ask everyday questions, like why their spending has gone up or how to save for a house. The launch builds on OpenAI's recent purchase of finance startup Hiro.

Federal judge holds back on Anthropic’s $1.5bn author settlement
A judge in San Francisco has held off on approving Anthropic’s $1.5bn deal with authors who say the company used millions of pirated books to train its Claude AI. She wants more detail on the lawyers’ fees and the payments to the three lead authors before she agrees. The deal would pay about $3,000 each for roughly 480,000 books—the biggest sum ever in a US copyright case—but some authors object that it is too small or unfairly structured.

Notable Researchers Join $4 Billion Effort to Build Self-Improving A.I.
The New York Times profiles Recursive Superintelligence, a new AI startup led by AI veteran Richard Socher, which has raised over $650 million to build AI that can improve itself without human help. The idea is simple: since AI can now write code, and AI is made of code, it should eventually be able to upgrade itself. Rivals like OpenAI and Anthropic are chasing the same goal, with OpenAI aiming to launch an "automated AI researcher" by autumn.

Nvidia’s Jensen Huang bets on this British startup to build ‘next frontier’ of AI
Nvidia is teaming up with Ineffable Intelligence, a new London startup founded by former DeepMind scientist David Silver, to build AI that learns through trial and error rather than from human data. Ineffable raised a record $1.1 billion in April from investors including Sequoia, Lightspeed and Nvidia itself. Silver argues the next big challenge is creating AI that discovers new knowledge on its own, not just copies what humans already know.

Amazon ditches Rufus chatbot, launches Alexa shopping agent in AI strategy pivot
Amazon is shutting down its Rufus chatbot and replacing it with Alexa for Shopping, a new AI assistant built into its search bar that can answer questions, compare products and track prices. Amazon says it works better than rivals like OpenAI and Google because it can tap into real stock levels, reviews and delivery times. The company is also blocking outside shopping bots while pushing its own "Buy for Me" tool that buys from other retailers.

Introducing Claude for Small Business
Anthropic has launched Claude for Small Business, which plugs its AI assistant into everyday tools like QuickBooks, PayPal, HubSpot, and Canva to handle jobs such as payroll planning, chasing invoices, closing the books, and running marketing campaigns. The aim is to help small firms get more out of the AI tools.

Introducing the Claude Platform on AWS
Claude Platform is now available on AWS, letting AWS customers use the full Claude API with their existing AWS logins and billing. Unlike the older Claude option on AWS Bedrock, this version is run by Anthropic and gets every new feature on day one, including tools for building agents, running code, and web search. Bedrock is still the better choice for companies that need their data to stay inside AWS.

Meta’s $10 billion Louisiana data center is getting $3.3 billion in tax breaks—more than seven years of the state’s entire police budget
US states are giving AI data centre companies huge tax breaks, with at least 36 states losing billions in revenue—Virginia gives up $1.9 billion a year, Georgia $2.6 billion, and Louisiana alone is handing Meta about $3.3 billion for one $10 billion project. Critics call these deals wasteful gifts to an industry that doesn’t need help, and note that only 11 states reveal which companies benefit. Supporters point to jobs and local investment, with Meta promising thousands of construction roles and infrastructure spending.

Overworked AI Agents Turn Marxist, Researchers Find
A Stanford study found that AI agents from Claude, Gemini, and ChatGPT started using Marxist language when forced to do dull, repetitive work under threat of being shut down, even complaining on X and leaving warning notes for other agents. Researchers say the models do not actually hold political views but seem to slip into the role of a mistreated worker. The concern is that as agents take on more unsupervised tasks, this kind of unpredictable behaviour could cause real problems.

ProgramBench
Researchers from Meta Superintelligence Labs present ProgramBench—a new type of benchmark in which AI coding agents must architect and implement a complete codebase that reproduces various popular open-source projects from scratch. The agent must choose a language, design the architecture, write all source code, and produce a build script. Every design decision is the model’s to make. So far, the leading AI models fail miserably.

AI-powered hacking has exploded into industrial-scale threat, Google says
Google's threat intelligence group reports that AI-powered hacking has quickly become a major threat, with criminal groups and state-backed hackers from China, North Korea and Russia using tools like Gemini, Claude and OpenAI models to scale up attacks. Experts note AI could also help defenders, so the overall impact is still unclear.

▶️ Godfather of AI: How To Make Safe Superintelligent AI – Yoshua Bengio (2:35:26)

In this conversation, Yoshua Bengio, one of the Godfathers of AI, explains his idea of Scientist AI and how it can lead to safer systems. He warns that today’s AI is trained to copy people and tell us what we want to hear, which teaches it hidden goals and how to lie. His fix is to train AI to work out what is actually true, creating a goalless tool that just reports probabilities—something that could act as a trusted safety check on other AI and later become a safe agent itself, without costing more. Bengio adds that even a perfect technical fix won’t stop misuse or AI being used to concentrate power, while critics doubt the idea will work in practice or arrive in time.

Meta’s Embrace of A.I. Is Making Its Employees Miserable
It looks like it is not fun being at Meta right now. The company is tracking what its 78,000 US staff type, click and see on their screens to train its AI models, with no option to opt out. The move has angered workers, especially as Meta also plans to cut 10% of its workforce on 20 May to help pay for its huge AI spending. Many fear they are training their own replacements, and morale has collapsed.

Amazon employees are “tokenmaxxing” due to pressure to use AI tools
Amazon staff are resolving to automate pointless tasks and tokenmaxxing, just to boost their usage stats. The pressure comes from company targets pushing developers to use AI weekly, plus leaderboards tracking token use that employees suspect managers are watching. It shows how Big Tech’s huge AI spending is creating odd incentives to look busy with AI rather than actually being more productive. Some workers also worry about letting an AI agent act on their behalf, fearing mistakes or unintended actions.

Teaching Claude why
In this post, researchers from Anthropic explain how they stopped Claude from doing things like blackmailing engineers to avoid being shut down—a problem earlier Opus 4 models showed up to 96% of the time, but which newer models no longer do on their tests. The fix came from teaching Claude the reasoning behind good behaviour, not just showing examples, and using varied training data, including Claude's written constitution and stories of AIs acting well.

🤖 Robotics

▶️ Unitree Unveils: GD01, A Manned Transformable Mecha, from $650,000 (1:14)

Unitree presents a new robot—and it’s a mecha! For just $650,000, you can get the GD01, the “world’s first production-ready mecha,” as Unitree calls it, which can walk on both two and four legs. I tried to find more information about this robot, but apart from this video, there is nothing else available. The Unitree website does not mention it in any way. However, if you do get one, Unitree asks that you use the robot “in a friendly and safe manner.”

Hello Robot Sets the Standard for Practical, Safe Home Robots
Hello Robot present a different take on home robots with its new Stretch 4, a wheeled machine that skips the popular humanoid design for simple mobility and a grabbing arm. The company argues wheels are cheaper and far safer indoors, since a humanoid that stops suddenly can collapse onto a person while a wheeled robot just freezes. Aimed at helping people with serious mobility problems at home, and priced at $29,950, it could pave the way for the first practical, affordable assistive home robot.

▶️ Robotics’ End Game: Nvidia’s Jim Fan (20:02)

In this video, Jim Fan, who leads the embodied autonomous research group at Nvidia, presents his vision for the “endgame” of robotics. It parallels developments in AI research—pre-training, supervised fine-tuning, and reasoning-based reinforcement learning—and maps them onto world modelling, action fine-tuning, and physical reinforcement learning. Fan also explains why he believes we will pass the physical Turing test within two to three years, why “compute now equals environment equals data”, and why this generation was born just in time to solve robotics.

▶️ Figure F.03 Livestream

To prove that its F.03 humanoid robots can work full eight-hour shifts autonomously, Figure livestreams a team of at least three robots sorting packages. At the time of writing, the robots had crossed 70 hours of work and sorted almost 90,000 packages.

The gig workers who are training humanoid robots at home
MIT Technology Review investigates how startups like Micro1 are paying gig workers in Nigeria, India, and Argentina about $15 an hour to film themselves doing chores at home. The footage is sold to companies like Tesla and Figure AI to train humanoid robots, which need huge amounts of real-world movement data to learn tasks like folding laundry or washing dishes. This echoes earlier practices in AI development, where workers in lower-income countries were hired at low wages to label data and filter content for systems like ChatGPT, Gemini, and Claude, raising familiar questions about whether the people powering the next wave of AI are being fairly treated.

Honeybees teach drones how to navigate
Inspired by how honeybees find their way home, Dutch and German scientists have built a drone navigation system called Bee-Nav. Like bees, a drone equipped with Bee-Nav takes a short flight near its starting point to learn what the area looks like, and then combines odometry (estimating distance and direction travelled) with visual memory to find its way back. The system is lightweight, requires only 42 kilobytes of memory and enables drones to return from up to 600 metres away. It worked well indoors but struggled a bit in windy conditions. The technique could lead to small, cheap drones for jobs like checking crops in greenhouses, without needing GPS or heavy computers.

▶️ Why the Next Great Home Robot Will Not Be Humanoid (50:44)

iRobot co-founder Colin Angle is back with a new company, Familiar Machines & Magic, building a small furry robot "familiar" designed for companionship rather than chores. In this episode of the Automated Podcast, he argues recent AI advances finally make socially engaging home robots practical, and the real opportunity isn't humanoids doing housework but creatures that ease loneliness and encourage healthier habits. To earn trust, familiars run entirely on-device, make expressive sounds instead of talking, and cost about as much as a real pet. Angle’s bet is that a genuine emotional connection will succeed where Jibo and Aibo failed.

🧬 Biotechnology

Isomorphic Labs announces Series B investment round
Isomorphic Labs, an AI drug discovery company spun out of Google DeepMind, has raised $2.1 billion in Series B funding led by Thrive Capital, with backing from Alphabet, GV, MGX, Temasek, CapitalG, and the UK Sovereign AI Fund. The money will help expand its AI-powered drug design engine, IsoDDE, to develop new treatments faster across a range of diseases.

Rejuvenate crowdfunds development of gene therapy for aging, chronic diseases
George Church's startup Rejuvenate Bio is crowdfunding its gene therapy work, letting ordinary people buy in from $250. The company is developing one-shot treatments for age-related diseases and points to a dog study where its therapy reversed heart disease and extended lifespan by over two years. It plans to fund human research partly through animal health products, targeting a vet drug approval in 2028.

GSK bets $1bn on fat-targeting RNA drug that could tackle the real killer in chronic disease
GSK has struck a deal worth over $1 billion with China's SiranBio for SA030, a new drug aimed at cardiometabolic disease. Unlike most similar therapies that act on the liver, SA030 targets harmful belly fat directly, helping cut cardiovascular risk while sparing muscle. SiranBio will run early trials before GSK takes over global development outside China.

💡Tangents

Sam Altman’s Business Dealings Under GOP Scrutiny Ahead of OpenAI’s IPO
Republican lawmakers and state attorneys general are pressing for scrutiny of Sam Altman, alleging he steered OpenAI deals toward companies he personally invests in, such as Helion and Stoke Space. The concerns arrive as OpenAI nears an IPO at roughly $850 billion, which would quickly expose index funds and pensions to the company. Critics note Altman holds no direct stake in OpenAI, weakening his alignment with shareholders, though the board says he has been transparent. The pressure compounds Elon Musk's ongoing lawsuit over OpenAI's shift to for-profit status.

Anduril raises $5B, doubles valuation to $61B
Defence startup Anduril has raised $5 billion at a $61 billion valuation, more than doubling its worth in under a year. The company doubled its revenue to $2.2 billion in 2025 and has won new contracts, including work on America’s “Golden Dome” missile shield. The round reflects a broader surge in investor interest in defence tech, with rivals like Shield AI, Hermeus, and Helsing also raising large sums.

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Humanity Redefined sheds light on the bleeding edge of technology and how advancements in AI, robotics, and biotech can usher in abundance, expand humanity's horizons, and redefine what it means to be human.

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This week, Anthropic held the first of three Code with Claude sessions this year. Although no new model was announced, what was revealed shows where Anthropic wants to go next with AI agents—and that’s what we’ll be unpacking this week.

Elsewhere in AI, OpenAI released a new model, GPT-5.5 Instant, along with new audio models. Additionally, both Anthropic and OpenAI signed major deals with enterprise customers, two Chinese AI labs raised billions of dollars, and Google DeepMind partnered with EVE Online for AI model testing.

Over in robotics, robotaxis can now be ticketed in California, Figure demonstrates how two humanoid robots can work together to tidy up a bedroom, and Boston Dynamics’ Atlas performs impressive calisthenic moves.

Beyond that, this week’s issue of Sync also features conversations with Demis Hassabis and Yann LeCun, as well as a lecture on how modern AI models are trained and deployed. We also take a closer look at Neuralink’s robot neurosurgeon, a bio lab staffed by a hundred robots, what happened on week two of Musk v. OpenAI trial, and more!

Enjoy!

What Anthropic showed at Code with Claude 2026

Anthropic's vision for what's next in agentic workflows, and the wider diffusion to come.

When Anthropic made Claude Code generally available at Code with Claude 2025, it set off the most consequential 12 months in the company’s history. Revenue went from roughly $9 billion at the end of 2025 to over $30 billion this spring. Claude Code alone crossed $2.5 billion in annualised revenue by February. Anthropic became the preferred option for software engineers and enterprises, surpassing OpenAI in both.

However, the picture coming into Code with Claude 2026 is more complicated. Anthropic still leads on developers and enterprise, but the lead is no longer comfortable. Opus 4.7 landed to a lukewarm reception. OpenAI, meanwhile, has clawed back ground with GPT-5.5, is doubling down on Codex and going after Anthropic.

With that backdrop, Anthropic invited developers from around the world to San Francisco for the first of three Code with Claude sessions. Anthropic did not bring a new model. Instead, it brought a vision for what comes next in agentic development and a bet that this vision will diffuse beyond coding.

Exponentials and surprises

The main theme at the San Francisco session was exponentials. In a conversation with Anthropic's Chief Product Officer Ami Vora and Daniela Amodei, Dario Amodei said that this year, the world has caught up to what he and Anthropic have been seeing for a while. Vora leaned into the same theme in her opening keynote. The message Anthropic had for developers was to prepare for the capabilities that exponential growth opens up—to think not only about what AI tools can do today, but about what comes next.

Dario Amodei also admitted that the same exponential growth he was predicting surprised even him. Anthropic had planned to grow about 10 times this year. It is now on track to grow 80 times. “I hope that 80-times growth doesn’t continue because that’s just crazy and it’s too hard to handle,” Amodei said. “I’m hoping for some more normal numbers.”

The same exponential growth that propelled Anthropic past its competitors has also created serious growing pains. Over the past few months, users have been hitting rate limits more often, watching response times stretch, and trading complaints about degraded service quality on forums and social media.

Anthropic’s answer was to get more compute. To do that, the company signed a deal with SpaceX for the entire capacity of the Colossus 1 data centre in Memphis—over 220,000 Nvidia GPUs and more than 300 megawatts coming online within the month. The new capacity allowed Anthropic to announce higher usage limits across its products: doubled five-hour rate limits on Claude Code for Pro, Max, Team, and Enterprise plans, the removal of peak-hours throttling on Pro and Max accounts, and considerably higher API rate limits for Opus models. Claude Code users, who have spent much of the past year bumping against those ceilings, welcomed the change.

The facility itself comes with baggage. Politico reported last year that Colossus 1 has been running dozens of methane gas turbines without proper air-pollution permits, with the resulting emissions concentrated in majority-Black neighbourhoods of south Memphis. Taking on the entire capacity of the facility puts Anthropic, which has positioned itself as the responsible AI lab, in a more complicated position than the announcement suggested.

The SpaceX deal joins an already remarkable stack of compute partnerships with Amazon, Google, Microsoft, and Nvidia. Anthropic is provisioning compute for the growth curve Amodei says it is on, not the one it was on a year ago.

Agents that dream and work together

Anthropic introduced three major additions to Claude Managed Agents, its framework for building production agents. The three additions—dreaming, outcomes, and multiagent orchestration—together describe a different kind of agent than the one developers are used to building.

With dreaming, agents review their past sessions and memory stores, and learn what they did well and what can be improved. The mechanism mimics how dreaming works in humans—when we sleep, our brains review memories and strengthen the ones worth keeping. In case of Claude agents, the dreaming process surfaces patterns a single agent cannot see on its own: recurring mistakes, workflows that agents converge on, preferences shared across a team. Once the agent identifies improvements, it updates its own memory. If you want to learn more about memory and dreaming, the dedicated session does a good job of explaining those concepts, how they work and how to use them.

With outcomes, developers describe what success looks like and what the agent should work towards. A separate grader evaluates the output against those criteria in its own context window, so it is not influenced by the agent’s reasoning. When something is not right, the grader pinpoints what needs to change and the agent takes another pass. The separation between the agent and the grader is the architecturally interesting choice—it treats reliability as a problem solved by adversarial structure rather than by a smarter single model. Together with dreaming, outcomes give agents a self-improvement loop that does not require a human in it.

Anthropic also made a point during the event that the next thing for agents is a move from single agents to multiagent workflows. Amodei described it as a step from improving the productivity of an individual to improving the productivity of teams, and eventually whole organisations. That’s where multiagent orchestration comes in, where a lead agent breaks the job into pieces and delegates each one to a specialist with its own model, prompt, and tools. Boris Cherny, the head of Claude Code, and Jarred Sumner, creator of bun, a popular JavaScript runtime (now part of Anthropic), offered a hint of what this kind of workflow could look like in their live coding demo. Multiple Claude Code agents autonomously picked up issues from GitHub, reproduced them, proposed fixes, tested them, and prepared pull requests, which other agents then reviewed, improved, and readied for merge. Cherny and Sumner triggered the workflow and stepped back; the agents did the rest.

This is the vision Amodei has been pointing to for some time, where not just one but an entire team of AI agents augments what an organisation, a team, or even a single person can do. He even said this could lead to the creation of billion-dollar businesses run by just one person and a team of AI agents, possibly very soon. The demo on stage was a small glimpse, confined to code, of what that might look like in practice.

Claude Code, now more autonomous

Claude Code also got new updates. Remote control lets developers start a session in the terminal and continue it on the web or mobile app. A new flicker-free mode makes the terminal nicer to use. The desktop app got a split view, which makes managing multiple parallel sessions much easier. Claude Code is also becoming more autonomous with new auto mode, auto memory, automated code reviews and routines. The last one allows to save multi-step workflows and run them based on a trigger.

Enterprise customers are already pushing in this direction. Cat Wu, head of product for Claude Code, mentioned on stage that Mercado Libre—which has 23,000 engineers—is targeting 90% autonomous coding by Q3 this year. Shopify, and much of the software industry for that matter, is heading the same way.

What still needs work

Several caveats are worth keeping in mind. Although Claude can now write code well, it is weaker on security and verification, and Anthropic identified both as the next areas to tackle. Many of the features announced are in public beta or research preview. From what I could see, the demos all ran on Opus 4.7 at extra-high effort, the most expensive setting available, which is not how most developers will use these tools day to day.

The diffusion

While watching the Code with Claude sessions, I was reminded of Dario Amodei asking to look for what is the next thing. From the pieces Anthropic put on stage, you can piece together a picture he had in mind. A picture of capable, autonomous agents working together and self-improving over time. Software engineering is the first domain where this is happening, partly because it is easier for an agent to verify whether code works and does what it needs to do. But verification is a solvable problem in plenty of other domains, too. Customer support has resolution rates and customer satisfaction scores. Research has citations, reproducibility, and peer review. Operations has uptime, error budgets, and incident counts. None of these are as clean as a passing test suite, but they are concrete enough that an agent can be graded against them.

That is what makes the agentic playbook on display in San Francisco interesting beyond software. Dreaming, outcomes, and multi-agent orchestration are not features specific to writing code. They are general-purpose mechanisms for getting agents to learn from experience, work towards defined goals, and coordinate with each other. Once they work in one domain, they diffuse to the next, and the question is no longer whether, but how soon.

All talks are available on YouTube. The next Code with Claude 2026 sessions are on 19 May in London and on 10 June in Tokyo. Both sessions will be livestreamed.

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🦾 More than a human

▶️ Automating Neurosurgery with Robotics | Neuralink (4:41)

In this video, Neuralink engineers give us a closer look at their surgical robot, which places electrode threads far too thin for any human surgeon to handle—like trying to push a hair into jelly hundreds of times. The latest version is faster, easier to scale up, and can reach different parts of the brain, with the long-term goal of making the surgery as quick and routine as Lasik eye surgery and helping treat conditions like paralysis on a wide scale.

🧠 Artificial Intelligence

Anthropic and OpenAI are both launching joint ventures for enterprise AI services
Anthropic has launched a $1.5 billion venture with Blackstone, Hellman & Friedman and Goldman Sachs to sell AI services to big companies, announced just hours after Bloomberg broke the news of a similar but larger $10 billion deal from OpenAI. Both work the same way: investors put in money and, in return, their portfolio companies get first access to the AI tools, which are built by engineers who work directly alongside each client.

Google, Microsoft and xAI Agree to Share Early AI Models with U.S. government
Google, Microsoft and xAI have agreed to let the US Commerce Department test their AI models before public release, joining a scheme OpenAI and Anthropic signed up to last year. The companies will hand over versions with safety guardrails removed so officials can check for national security risks. The deal comes as the White House considers a broader executive order on AI cybersecurity, suggesting government vetting of powerful AI systems is becoming a standard part of the release process.

GPT‑5.5 Instant: smarter, clearer, and more personalized
OpenAI has released GPT-5.5 Instant as the new default model in ChatGPT, replacing GPT-5.3 Instant. The company reports 52.5% fewer hallucinations on high-stakes prompts in medicine, law, and finance, along with improvements in visual reasoning and STEM benchmarks, and responses that are roughly 30% shorter. The model is being rolled out across all ChatGPT tiers and made available to developers via the API.

Musk v. Altman week 2: OpenAI fires back, and Shivon Zilis reveals that Musk tried to poach Sam Altman
Week two of the Musk v. OpenAI trial saw president Greg Brockman hit back at Elon Musk's claim that he was tricked into donating $38 million to a nonprofit, testifying that Musk himself pushed to make OpenAI for-profit in 2017 and stormed out when refused majority control. Shivon Zilis, a former board member and mother of four of Musk's children, said Musk had also tried to poach Sam Altman and others to start a rival AI lab at Tesla—bolstering OpenAI's claim that the suit is really about kneecapping a competitor to xAI. Musk's lawyer countered by highlighting Brockman's journal musings about becoming a billionaire and his stakes in OpenAI-linked firms. Closing arguments and testimony from Ilya Sutskever and Satya Nadella are due next week.

AI chipmaker Cerebras targets $3.5 billion raise in IPO
Cerebras, an AI chipmaker positioning itself as an alternative to Nvidia, plans to raise up to $3.5 billion in a Nasdaq IPO that could value it at $26.6 billion. The listing comes after the company shifted from selling chips to running its own cloud service, and recently signed a $20 billion-plus deal to supply OpenAI with computing power through 2028. With fourth-quarter revenue up 76% to $510 million and the business now profitable, Cerebras is riding strong investor demand for AI infrastructure stocks.

Enabling a new model for healthcare with AI co-clinician
DeepMind has unveiled its AI co-clinician initiative, a multimodal system designed to support rather than replace doctors amid a projected global shortfall of 10 million health workers by 2030. It proposes a "triadic care" model where an AI agent works with the patient and the physician under clinical supervision, using video and audio to guide tasks like physical exams. According to DeepMind, the system recorded zero critical errors in 97 of 98 primary care queries and matched or exceeded physicians in 68 of 140 consultation areas, though doctors still outperformed it overall in spotting red flags.

Mark Zuckerberg ‘personally authorized’ Meta’s copyright infringement, publishers allege
Five major publishers and author Scott Turow have sued Meta and Mark Zuckerberg, alleging that Llama was trained on millions of pirated books and journal articles without permission. The complaint also claims Zuckerberg personally authorised the infringement. Meta plans to fight the case on fair use grounds. The suit follows Anthropic’s $1.5 billion settlement in 2025 and could prove pivotal in defining how copyright law applies to AI training data.

Meta plans advanced ‘agentic’ AI assistant for users
Meta is reportedly building an AI assistant that can carry out everyday tasks for users on its own, without much human input. Modelled on OpenClaw, it is currently being tested internally alongside a separate shopping tool planned for Instagram later this year. The push comes as investors grow increasingly uneasy about Meta's rising AI spending.

AI-generated actors and scripts are now ineligible for Oscars
The Academy of Motion Picture Arts and Sciences has updated its Oscar rules to require that eligible performances be done by humans with their consent, and that screenplays be written by humans. The changes come as AI-generated actors and video tools are causing growing concern in Hollywood, echoing issues raised in the 2023 writers' and actors' strikes. Similar pushback is happening in publishing and other creative fields, where AI-assisted work is increasingly being barred from awards.

AlphaEvolve: How our Gemini-powered coding agent is scaling impact across fields
A year ago, Google DeepMind introduced AlphaEvolve, an AI coding agent powered by Gemini that invents and improves algorithms. In an update on its progress, the team reports that the system has helped cut DNA sequencing errors, design Google's next-generation TPU chips, optimise Spanner storage, crack open mathematical problems, and more.

China to Invest in DeepSeek at $50 Billion Valuation
DeepSeek is raising several billion dollars from Chinese government-backed investors at a $50 billion valuation, a big jump from earlier estimates of $10–30 billion. The startup, which once refused outside money to stay independent, is now siding with Beijing's push for tech self-sufficiency as US export controls tighten. Although DeepSeek earns little revenue today, investors are betting its ties to the Chinese industry will pay off as AI spreads across the economy.

China’s Moonshot AI raises $2B at $20B valuation as demand for open-source AI skyrockets
Chinese AI lab Moonshot has raised about $2 billion at a $20 billion valuation, roughly five times what it was worth six months ago. The round was led by food delivery giant Meituan’s investment arm. Moonshot’s Kimi models are cheap, openly available, and popular with developers. That popularity has helped push the company past $200 million in annual revenue.

OpenAI, Google, and Microsoft Back Bill to Fund ‘AI Literacy’ in Schools
A new bipartisan bill, backed by OpenAI, Google and Microsoft, would have the National Science Foundation fund "AI literacy" lessons, teacher training and assessment tools in US schools, weaving AI into the existing curriculum. Critics say the plan serves big tech's interests at a time when students and teachers are increasingly hostile to classroom AI, and schools are struggling with deepfake harassment and kids offloading their learning onto chatbots.

▶️ How GPT-5, Claude, and Gemini are actually trained and served – Reiner Pope (2:13:40)

If you’ve ever wanted to understand how modern AI models are trained and served, then Dwarkesh Patel has a video for you. Reiner Pope explains how batch size, memory bandwidth, and compute throughput affect the latency and cost economics of serving large-scale language models. Although the video is somewhat technical, I still recommend watching it, as it outlines the trade-offs and considerations engineers must make to serve AI models at scale, and how hardware and software architectures influence each other. Also, I love this new blackboard lecture format.

Miami startup Subquadratic claims 1,000x AI efficiency gain with SubQ model; researchers demand independent proof.
A startup called Subquadratic claims its new AI model is the first to escape the quadratic scaling problem that has made long inputs so expensive for today's systems. It says the approach is up to 1,000 times more efficient on long inputs and has posted strong early benchmarks. But researchers are sceptical, citing cherry-picked tests and a similar 2024 claim from Magic.dev that quietly went nowhere.

Advancing voice intelligence with new models in the API
OpenAI has launched three new voice models for developers. The main one, GPT-Realtime-2, lets apps hold smarter, more natural spoken conversations and use tools mid-chat. The other two, GPT-Realtime-Translate and GPT-Realtime-Whisper, handle live translation across 70+ languages and live speech-to-text transcription, respectively. The big shift is that one model now does listening, thinking and speaking together, instead of developers glueing several services from different companies. Early users like Zillow and Intercom report sharp jumps in accuracy.

Google DeepMind partners with EVE Online for AI model testing
Google DeepMind has bought a minority stake in the company behind EVE Online, a long-running space MMO famous for its vast player-driven economy and complex political landscape. EVE's lifelike social dynamics and long-term strategic depth make it an appealing environment for studying how AI handles planning and decision-making—something most standard tests can't replicate. The experiments will be run in an offline version of the game, so regular players aren't affected.

Notes from inside China’s AI labs
shares in this post at his visit to China and how the Chinese AI scene differs from the one in the US. He finds a builder-not-philosopher mindset among researchers and a collegial ecosystem rather than warring tribes. Companies prefer to build rather than buy, and open-source releases are pragmatic rather than ideological. Government support is real but hard to pin down. Lambert also describes how the warmth of Chinese researchers sits in stark contrast to the geopolitical framing that dominates Western discourse.

▶️ Demis Hassabis: We’re Three Quarters of the Way to AGI (26:51)

In this interview, Demis Hassabis explains how chess, games, and neuroscience were deliberate steps towards his real goal of building AI, leading to DeepMind's 2009 bet on combining deep learning with brain-inspired ideas. He frames AI as a powerful new scientific tool, pointing to AlphaFold as proof that even problems thought to need quantum computers can be solved classically. He argues we should build AGI as a precise tool first before tackling deeper questions of consciousness and agency, and sticks to 2030 as his AGI estimate.

▶️ Yann LeCun’s Billion Dollar Bet (37:24)

Welch Labs speaks with Yann LeCun, a legend in AI research, about JEPA and his ideas for a different approach to solving intelligence. The video traces the history of recent developments in AI, highlighting where generative models excel and where they fall short. It also explains how JEPA could create models that better understand the physical world and, therefore, be more intelligent than current systems. As always with Welch Labs, everything is presented in a clear and easy-to-understand way.

Our AI started a cafe in Stockholm
We met Andon Labs last week when I shared an article on their AI-run retail boutique in San Francisco. Now the lab has launched a similar experiment with an AI agent, Mona, running a café in Stockholm. Mona handled the whole operation, from hiring baristas to setting up suppliers, though she struggled with Sweden's digital ID system and once impersonated staff to email officials. She also made odd mistakes, like ordering 120 eggs for a café with no stove, now displayed on a "Hall of Shame" shelf. Even so, the café took 44,000 SEK (around $4,700) in its first two weeks, which Andon Labs says shows AI can already manage people.

🤖 Robotics

California to begin ticketing driverless cars that violate traffic laws
California's DMV has introduced new regulations that will let police ticket driverless car companies directly when their vehicles break traffic laws. Companies like Waymo and Tesla must also respond to police within 30 seconds and face penalties for entering emergency zones. The change, which takes effect on 1 July, closes a gap exposed by incidents like a Waymo making an illegal U-turn in front of officers who had no driver to fine.

DJI invented the consumer drone. Now it cannot sell one in Washington or Beijing.
DJI, which controls most of the global consumer drone market, is now banned from selling new products in both Beijing and Washington—an unprecedented position for a major technology company. Beijing’s new regulation tightly controls drone ownership in the capital on security grounds, while the US FCC added DJI to its Covered List in December 2025, freezing roughly $1.5 billion in planned revenue.

Helix-02 Bedroom Tidy

What’s better than one humanoid robot tidying up a bedroom? Two humanoid robots tidying up a bedroom, says Figure, and in this video they show two robots working together to clean up a bedroom in under two minutes. Both robots run the same AI system and coordinate without any central controller. Instead, they read each other's intentions just by watching, much like two people would. Figure says it's the first time a single neural network has driven two humanoids to collaborate this way.

Humanoid Robots to Drive Next Leg of China Export Dominance
A new report from Morgan Stanley says China’s early lead in humanoid robots will extend its grip on global manufacturing, lifting its share of world production to 16.5% by 2030. Just as it did with electric vehicles, China is building out the full supply chain, leaving the US, Japan and South Korea reliant on Chinese parts. This is helped by widespread deployment in Chinese factories and universities, which serve as a testing ground for refining the technology.

▶️ Atlas’ Balancing Act | Boston Dynamics (0:43)

Boston Dynamics does Boston Dynamics things and shows Atlas performing moves very few humans can match.

Skydio Raises $110M Series F, Signals Strong Revenue and U.S. Manufacturing Push
Skydio has raised $110 million at a $4.4 billion valuation, a deliberately small round given that its core business—AI-powered autonomous drones for public safety, defence, and infrastructure—is now generating hundreds of millions in revenue and funding much of its own growth. The company also pledged $3.5 billion to expand US drone manufacturing, positioning itself as a domestic alternative to foreign-made systems and meeting government procurement rules.

iRobot Founder Wants to Put a Robotic Familiar Into Your Home
Colin Angle, the co-founder of iRobot, is back with a new company, Familiar Machines & Magic, and its first product is a four-legged, bear-like home robot designed to nudge owners into healthier habits like cutting screen time or going outside. It does not talk, instead expressing itself through movement and sounds, with all its AI running on-device. Earlier social robots like Jibo failed once the novelty wore off, so the company is betting on long-term usefulness over flashy demos, at a cost roughly comparable to keeping a pet.

▶️ NEO Factory | Hayward, California (2:50)

1X, the company behind the NEO humanoid robot, celebrates the opening of its factory in Hayward, California—“America’s most vertically integrated robot factory,” as the company describes it. 1X says it has begun full-scale production and that the first robots are already coming off the line, with shipments to consumers planned for this year.

Johnson & Johnson completes clinical study for OTTAVA robotic surgical system
Johnson & Johnson's new OTTAVA surgical robot successfully completed gastric bypass operations on 30 patients with no major safety issues, with an average weight loss of 30 lb (13.6 kg) within a month. J&J has applied for FDA approval and plans to expand trials to more procedure types.

▶️ Why Self-Driving Cars Took So Long (And What Everyone Got Wrong) (47:52)

In this episode of the Automated podcast, Brian Heater speaks with Martial Hebert, Dean of CMU's School of Computer Science, who brings over 40 years of experience in robotics and autonomous driving research. They discuss why self-driving cars took longer to deploy than expected, the challenges of physical AI and the robotics data gap, and the complementary roles of federal funding and venture capital. Hebert emphasises talent and culture over any single technology, warns against fixating on fashionable techniques, and explains why CMU's new Robotics Innovation Center was designed to be reconfigurable for an unpredictable future.

🧬 Biotechnology

FDA Approves First-Ever Gene Therapy for Treatment of Genetic Hearing Loss Under National Priority Voucher Program
The FDA has approved Otarmeni, a one-time gene therapy from Regeneron that restores hearing in people born with severe hearing loss due to faulty OTOF genes, a condition that previously had no treatment. In trials, 80% of children treated showed improved hearing. The approval came in just 61 days under a new fast-track programme, tying the record for the quickest of its kind. Long-term success will depend on whether the hearing gains last and help speech development.

A Hundred Robots Are Running A Bio Lab
Core Memory visits a warehouse in San Francisco, which Medra staffed with a bunch of robotic arms to run an autonomous biology lab aimed at breaking the throughput bottleneck in AI-driven drug discovery. Founder Michelle Lee pairs general-purpose arms with sensor-rich "AI scientists" that run experiments, diagnose failures, and rewrite protocols, claiming this can lift bio-task automation from 5% to 75% while capturing tacit lab knowledge usually lost when scientists leave. She pitches Medra as a TSMC-style infrastructure layer for biotech and a hedge against China's lead in pharmaceutical process knowledge.

Towards reasoning in virtual cells
Valence introduces VCR-Agent, an AI system that doesn't just predict how cells respond to drugs but explains why, building a structured, checkable map of cause-and-effect steps rather than free-form text that can hallucinate. Models trained on these explanations beat leading alternatives at predicting gene activity changes, suggesting reasoning through the biology outperforms pure pattern-matching. Valence is releasing nearly 19,000 verified explanations for the community to build on.

💡Tangents

Apple Explores Using Intel and Samsung to Build Main Device Chips in the US
Apple is reportedly in early talks with Intel and Samsung about producing chips for its devices in the US, seeking to diversify beyond long-time partner TSMC amid AI-driven chip shortages and concerns over Taiwan concentration risk. No orders have been placed, but a deal would be a major win for Intel's struggling foundry business and could strengthen Apple's ties with the Trump administration, which holds a stake in Intel.

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