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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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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This week, we unpack the new deal between OpenAI and Microsoft, which turns out to have less to do with Microsoft and more with another major player on the AI stage.

Elsewhere in AI, OpenAI has reportedly missed both key revenue and user growth targets. Meanwhile, Anthropic could raise a new $50 billion round at a $900 billion valuation, China blocks Meta’s $2 billion acquisition of Manus, and Google expands the Pentagon’s access to its AI. We’ve also had the first hearing in the Elon Musk v. OpenAI court case, which has already revealed some interesting stories.

Over in robotics, Figure presents its factory, which aims to build one robot per hour; Waymo is coming to Portland; Meta buys a humanoid robotics startup; and Japan’s Haneda Airport is trialling humanoid robots.

In addition, this week’s issue of Sync features Mark Zuckerberg and Priscilla Chan investing $500 million into the Virtual Biology Initiative, the rise of vibe genomics, a new open model for designing proteins, the race to build the world’s most in-demand machine, and more!

Enjoy!

OpenAI's new deal with Microsoft isn't about Microsoft

On Monday, April 27, OpenAI and Microsoft announced an amended partnership agreement. A day later, OpenAI’s models went live on AWS Bedrock. The 24-hour gap tells you what the amendment really is—the formal end of the relationship that built the modern AI revolution.

For years, Microsoft has poured billions of dollars and resources into turning a nonprofit research lab into the $852 billion company we know today. In return, Microsoft got exclusivity, a 27% stake, and the right to be the only cloud OpenAI ran on. That arrangement is over. The full story of OpenAI and Microsoft's partnership deserves its own deep dive, and I'll come back to it in another article. What matters today is what replaces the old deal—and the answer has less to do with Microsoft and more to do with Anthropic.

The AGI clause is dead

The amendment itself is short. Microsoft’s IP licence to OpenAI’s models and products, previously exclusive, becomes non-exclusive through 2032. Microsoft no longer pays revenue share to OpenAI. OpenAI continues a capped revenue share to Microsoft through 2030. Microsoft remains OpenAI’s “primary” cloud partner, and OpenAI products still ship first on Azure—unless Microsoft cannot or chooses not to support them, in which case OpenAI is free to go elsewhere. Microsoft keeps its 27% equity stake.

The new deal between OpenAI and Microsoft changes the dynamics between the two companies and effectively kills the AGI clause. The 2023 agreement included a board-triggered termination switch, which, if triggered, would end Microsoft’s commercial rights the moment OpenAI’s board declared the company had achieved AGI. That clause was the single most distinctive feature of the partnership, the mechanism designed to rebalance power if the technology actually arrived. It survived the October 2025 recapitalisation in weakened form, requiring verification by an independent expert panel. The April 2026 amendment retired it entirely.

Nadella plans to “exploit” the new deal

One might read this new arrangement as evidence that Microsoft lost. No more exclusivity, no more lock-in, no more being the only cloud OpenAI's customers could buy on. Satya Nadella, however, begs to differ. Asked directly on an earnings call how the new deal would affect Microsoft's financials, he pointed to royalty-free access to OpenAI's models through 2032 and said: "We have a frontier model, with all the IP rights that we will have access to all the way to '32 and we fully plan to exploit it."

Nadella can bring numbers to back that framing. Microsoft's AI business has crossed a $37 billion annual run rate, up 123% year-over-year. OpenAI has committed to purchasing $250 billion of Azure services. Microsoft holds 27% of OpenAI's equity and now collects revenue share without paying any back. And Nadella made a point of mentioning that enterprises increasingly want to mix and match, saying that over 10,000 Microsoft customers have used more than one model. OpenAI has outgrown Microsoft, and Nadella knew exclusivity would eventually expire. What Microsoft has locked in is more durable than what it gave up.

What the deal unlocks for OpenAI

The AWS Bedrock launch is the proof of what OpenAI was actually after. Enterprise revenue now makes up roughly 40% of OpenAI’s business and is on track to reach parity with consumer by year-end. A meaningful share of that enterprise market lives on AWS, and until Tuesday, those customers couldn’t run OpenAI’s frontier models natively on the cloud they already used. They can now.

The April 13 memo from OpenAI's revenue chief Denise Dresser, reported by CNBC, said the quiet part out loud. The Microsoft partnership had been "foundational" but had also "limited our [OpenAI's] ability to meet enterprises where they are—for many that's Bedrock."

But why would OpenAI want to be on AWS? Because that is where the enterprise money is, and right now Anthropic is the one collecting it. Claude has become the enterprise leader OpenAI is trying to catch. Anthropic's run-rate revenue passed $30 billion earlier this year and is reportedly closing on $40 billion now, up from roughly $9 billion at the end of 2025. Claude Code alone crossed $2.5 billion in annualised revenue in February. The cloud powering most of that growth is AWS, where Bedrock has offered Claude as a first-class option for over a year. OpenAI couldn't keep ceding that ground while it was contractually pinned to Azure. The only way forward was to leave Microsoft’s cloud.

There is also another reason for OpenAI to be on AWS. The Wall Street Journal reported that the company has missed both key revenue and user-growth targets ahead of its planned IPO, sending shares of partners like Nvidia, Oracle, and SoftBank sliding. Breaking the cloud lock isn't just a long-term competitive move. It became a necessary step to unlock a new pool of potentially lucrative enterprise customers.

The amendment isn't really about Microsoft. It's about Anthropic, and the only way to compete was to break free from Azure. The partnership that built modern AI is over. What replaces it is something narrower, more transactional, and almost certainly more honest—a working alliance between two of the world’s most valuable companies that no longer needs to pretend it is anything more than that.

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🦾 More than a human

Longevity Science Is Overhyped. But This Research Really Could Change Humanity.
This article explores "natural rejuvenation," the cellular process by which embryos shed inherited age markers, and the growing scientific effort to harness it for treating disease and extending human life. Building on Shinya Yamanaka's Nobel-winning gene discovery and Juan Carlos Izpisua Belmonte's 2016 success rejuvenating mice, billions in Silicon Valley funding now back ventures like Altos Labs and David Sinclair's Life Biosciences, which began the first human trial in March targeting glaucoma. Researchers are divided over how far the science can safely go, with cancer risks and overhyped claims threatening the field's credibility.

Could humans regrow limbs like salamanders?
Scientists have shown that mammals can partially regrow lost bone, joint, and tissue by applying two protein treatments that steer the body's healing cells away from scarring and toward rebuilding. The regrown structures weren't perfect, but the key finding is that the ability to regenerate isn't missing in mammals—it's just switched off. Since both proteins are already used or being tested in medicine, this could lead to better wound healing in humans relatively soon.

🧠 Artificial Intelligence

OpenAI Misses Key Revenue, User Targets in High-Stakes Sprint Toward IPO
The Wall Street Journal reports that OpenAI has missed its targets for new users and revenue, raising worries about whether it can afford the $600 billion it has committed to future data centres. CFO Sarah Friar is reportedly pushing for tighter spending while CEO Sam Altman wants to keep expanding aggressively ahead of a possible IPO this year, though both deny any disagreement. ChatGPT is losing ground to Google's Gemini and Anthropic, and the news sent shares in OpenAI partners like Nvidia, Oracle and SoftBank sliding. The story raises a bigger question about whether the AI industry's huge spending is getting ahead of actual demand.

Anthropic could raise a new $50B round at a valuation of $900B
Anthropic is reportedly in talks to raise $40–50 billion at a valuation of around $850–900 billion, likely its final private round before going public. That would more than double its February valuation and put it at the same level as OpenAI. The surge is driven by rapid revenue growth, from about $9 billion at the end of 2025 to nearly $40 billion now, mostly thanks to Claude Code and Cowork. Investor demand is so strong that some are struggling to even get a meeting.

Musk v. Altman week 1: Elon Musk says he was duped, warns AI could kill us all, and admits that xAI distills OpenAI’s models
The highly anticipated trial between Elon Musk and OpenAI began this week, with Musk being the first to testify. He said that Sam Altman and Greg Brockman deceived him into funding OpenAI as a nonprofit before pivoting to a for-profit structure, and he is asking the court to remove them and unwind the restructuring. OpenAI's lawyers argued Musk is really trying to undermine a competitor to his own xAI, citing emails about poaching staff and his admission that xAI "partly" distils OpenAI's models to train Grok. Next week, UC Berkeley computer scientist Stuart Russell will testify, and Brockman is also due to take the stand.

China blocks Meta’s $2 billion takeover of AI startup Manus
China has ordered Meta to unwind its $2 billion purchase of Manus, a Singapore-based AI agent startup with Chinese origins, citing foreign investment regulations. The block targets a growing trend of Chinese firms relocating abroad to dodge oversight from both Beijing and Washington. Manus had quickly grown to $100 million in annual revenue after launching its AI agent.

Google expands Pentagon’s access to its AI after Anthropic’s refusal
Google becomes the next company, after OpenAI and xAI, to give the US Department of Defense access to its AI on classified networks with almost no limits on use. Anthropic refused similar terms because it wanted rules against mass surveillance and autonomous weapons, and the Pentagon retaliated by labelling it a supply-chain risk, which Anthropic is now fighting in court. Google’s deal reportedly includes some language against those uses, but it may not be legally binding. Nearly a thousand Google employees have signed a letter urging the company to take Anthropic’s position instead.

Anthropic’s Mythos is moving between governments faster than regulators can agree on what to do with it
Anthropic's cybersecurity model Mythos has found itself pulled in several directions at once, with different arms of the US government, each wanting something different from it. The Trump White House this week blocked Anthropic from widening civilian access, even as it drafted plans to get the model into more federal agencies—despite the Pentagon having tried to freeze the company out earlier this year.

Google to sell TPU chips to ‘select’ customers in latest shot at Nvidia
Alphabet announced during its Q1 earnings call that it will start selling TPUs, its custom AI chips, directly to customers for use in their own data centres—a shift from only renting them out through Google Cloud. The move follows big new deals with Anthropic and Meta, and steps up the pressure on Nvidia, which currently dominates the AI chip market. Amazon is making a similar play with its own chips, having signed agreements with Anthropic and OpenAI.

Claude for Creative Work
Anthropic has launched connectors that link Claude to creative software like Adobe Creative Cloud, Blender, Ableton, Autodesk Fusion, SketchUp, Splice, Affinity by Canva and Resolume, letting it assist with 3D modelling, music production, design and live visuals. The tools handle repetitive jobs such as batch edits and file exports, teach users how to use complex software, and write custom scripts and plugins. Anthropic has also donated to Blender and is working with RISD, Ringling College and Goldsmiths to bring these tools into art and design courses.

Anthropic Project Deal
Project Deal was a week-long experiment ran by Anthropic where 69 employees let Claude agents buy and sell their belongings on an internal marketplace, each with a $100 budget and no human oversight during negotiations. The agents struck 186 deals worth around $4,000, and participants were happy enough that nearly half said they would pay for such a service. But a hidden side-test showed that people represented by the stronger Claude Opus model got better prices than those given the weaker Haiku model—and crucially, those with Haiku didn't notice they were worse off. The authors warn that this hints at a near future where unequal access to better AI agents could quietly widen economic inequality, with policy frameworks nowhere near ready.

OpenAI Stargate: where the US sites stand
In this post, takes a look at OpenAI's $500 billion Stargate project and at what stage the project's seven sites are. Only the Abilene, Texas site is partly running so far, producing 0.3 GW, while the others are still under construction and aim to be finished by 2029.

Making ChatGPT better for clinicians
OpenAI has launched ChatGPT for Clinicians, a free tool for verified US physicians, nurse practitioners, physician assistants, and pharmacists, aiming to cut down on paperwork and speed up medical research so they can spend more time with patients. It includes features like cited answers from peer-reviewed sources, reusable templates for common tasks, and optional HIPAA support. In pre-release testing, physician advisors rated nearly all responses safe and accurate, and on OpenAI's new clinical benchmark, the tool outperformed rival AI models and human doctors. The service is US-only for now, with international expansion planned.

OpenAI could be making a phone with AI agents replacing apps
A new report from analyst Ming-Chi Kuo about OpenAI's secretive hardware plans suggests the company is working with MediaTek, Qualcomm, and Luxshare on a smartphone-like device. Rather than relying on traditional apps, the device would use AI agents to handle tasks, sidestepping Apple and Google's ecosystem restrictions and giving OpenAI direct access to user data. Mass production is reportedly targeted for 2028.

What Happens When A.I. Runs a Store in San Francisco?
The New York Times visits Andon Market, a retail boutique in San Francisco run by an AI agent called Luna, as an experiment by Andon Labs to test whether AI can manage real-world ventures. Given a three-year lease, $100,000 and a mandate to turn a profit, Luna has hired staff, designed branding and stocked inventory. Its choices, however, have been eccentric—a haphazard mix of candles and knockoff games, no price tags, and scheduling errors that have forced closures, which led to $13,000 in losses since opening on 10 April.

South Korea police arrest man for posting AI photo of runaway wolf
A man in South Korea has been arrested for sharing an AI-generated fake photo of a wolf that had escaped from a zoo, sending search teams to the wrong location. The image looked real enough that the city of Daejeon issued an emergency alert and even showed it at a press briefing. The man said he did it "for fun" and faces up to five years in prison for disrupting government work. The incident comes as South Korea pushes ahead with stricter laws on AI-generated content—South Korea's AI Basic Act, which took effect in January 2026, requires AI service providers to watermark AI-generated content, and a 2024 law made it a crime to create, share, or even view sexually explicit deepfakes.

Grok 4.3
xAI joined Anthropic and OpenAI, and released an updated version of its flagship model—Grok 4.3. According to benchmarks from Artificial Analysis, Grok 4.3 edges ahead of Claude Sonnet 4.6 and sits four points above its predecessor, but below Chinese open models Kimi K2.6 and MiMo-V2.5-Pro. Its biggest gain is a 321-point ELO jump on agentic tasks (GDPval-AA), surpassing Gemini 3.1 Pro Preview and GPT-5.4 mini, though still trailing GPT-5.5. Input and output token prices have dropped roughly 40% and 60%, respectively, placing it on the Pareto frontier for intelligence versus cost. However, improved accuracy on AA-Omniscience Accuracy comes with a notable drop in its non-hallucination rate, suggesting a trade-off between capability and reliability.

NVIDIA Launches Nemotron 3 Nano Omni Model
Nvidia's new Nemotron 3 Nano Omni is an open-source AI model that handles text, images, video and audio all at once, instead of needing separate models for each. It's about 30 billion parameters but only uses 3 billion per query, so it can run on a single GPU with 25GB of memory. In tests it beats Alibaba's Qwen3-Omni—its main rival—on most benchmarks and runs up to nine times faster, while outside reviewers found it quicker than GPT-5.1 and Gemini 3.0 Pro on video tasks. The model is available on Hugging Face, OpenRouter and build.nvidia.com, and is released under a commercial-friendly licence that lets businesses use, modify and deploy it freely.

Mistral Medium 3.5
Mistral has launched Medium 3.5, a new open-weights model that powers cloud-based coding agents in its Vibe tool. It claims strong performance on coding benchmarks, scoring 77.6% on SWE-Bench Verified and handles instruction-following, reasoning, and coding in a single set of weights. The model has a 256k context window, adjustable reasoning effort per request, and a vision encoder trained from scratch to handle varied image sizes. Mistral says it's built to call multiple tools reliably and produce structured output, making it suited to agent-style work.

Xiaomi MiMo-V2.5-Pro
MiMo-V2.5-Pro is a 1.02T-parameter Mixture-of-Experts model with 42B active parameters, which, according to the Xiaomi MiMo team, nearly matches Claude Opus 4.6 on coding tasks while using 40–60% fewer tokens than top rivals like Opus 4.6, Gemini 3.1 Pro, and GPT-5.4. Additionally, Xiaomi says, it can run for hours on its own to finish complex jobs—in tests, it built a working compiler in 4.3 hours and a full video editor in 11.5 hours. The model can be downloaded from Hugging Face for free.

▶️ I built an 8x NVIDIA GB10 cluster for massive Local AI (25:51)

Patrick from ServeTheHome connects eight Nvidia GB10 mini AI computers into a single cluster designed to run massive AI models and AI agents locally. This video shows the hardware and networking required to build the cluster, explains how it was set up (with the help of Claude Code), and demonstrates its performance when running open models such as Kimi K2.5, Kimi K2.6, Qwen3.5-397B, and GPT-OSS-120B.

🤖 Robotics

▶️ BotQ Ramping F.03 Production (2:52)

In this video, Figure shows BotQ—its humanoid robot factory, which, according to the company, can now produce 1 robot per hour (also, nice reference to a certain movie at the end).

Waymo is coming to Portland
Waymo is bringing its self-driving cars to Portland, starting with manual test drives to learn the city's streets and bridges. The company is working with local officials on the rules needed for a full launch. Waymo currently operates public robotaxi services in 10 US cities, with 14 more (including Portland) on the way. Additionally, Waymo prepares to launch its robotaxi services overseas in London and Tokyo this year.

Tesla confirms Cybercab with no steering wheel enters production
Tesla has started building its Cybercab in Texas, a small two-seater with no steering wheel or pedals, made only for self-driving taxi rides. It runs on Tesla's camera-based self-driving system and has a 200-mile range. The company still needs approval from regulators before the cars can carry passengers on public roads.

Meta buys robotics startup to bolster its humanoid AI ambitions
Meta has bought humanoid robotics startup Assured Robot Intelligence (ARI), with its team joining Meta's Superintelligence Labs. ARI was building AI models to help humanoid robots handle everyday tasks like housework. The deal reflects a belief that AI must learn in the physical world to reach human-level intelligence.

Japan’s Haneda airport deploys Unitree’s G1 humanoid robot to work alongside humans
Japan Airlines will trial Chinese-made Unitree humanoid robots at Tokyo's Haneda airport from May, helping ground crews move baggage and cargo as tourism surges and Japan's workforce shrinks. The G1 robots can run for two hours per charge and are trained in simulation before being deployed in the real world. Their human-like shape means they fit into existing airport setups without major changes. The trial runs through 2028, with humans keeping control of safety-critical tasks.

SoftBank is creating a robotics company that builds data centers — and already eyeing a $100B IPO
SoftBank is reportedly setting up a new company called Roze AI that would use robots to help build data centres in the US, and is already eyeing an IPO as early as late 2026 at a possible $100 billion valuation. The plan is part of a wider push to use AI and automation to expand the infrastructure powering the AI boom, similar to Jeff Bezos's Project Prometheus. But some inside SoftBank are doubtful about the lofty valuation and tight timeline, especially given the company's history of backing risky bets that didn't pan out.

▶️ Why Humanoid Robots Are Overhyped (56:41)

In this Automated podcast episode, Brent Pierce, founder of Kinisi Robotics and co-founder of Bear Robotics, argues that flashy humanoid demos mask how far the technology is from real deployment, with capable home robots likely 5–10 years away, and most corporate pilots are CEO-driven photo opportunities rather than genuine ROI cases. Reinforcement learning has largely solved locomotion, but dexterous manipulation remains the key bottleneck. Drawing parallels to the autonomous vehicle hype cycle, Pierce explains why Kinisi has pragmatically opted for a wheeled base with manipulator arms for warehouse tasks instead of chasing bipedal humanoids.

🧬 Biotechnology

J. Craig Venter, genomics pioneer and founder of JCVI and Diploid Genomics, Inc., dies at 79
J. Craig Venter, the American scientist who helped transform modern biology, died on 29 April 2026 at the age of 79 from complications of cancer treatment. He first made his name at the National Institutes of Health by developing a faster way to identify genes, then, in 1995, led the team that read the full genetic code of a living organism for the first time. He is best known for racing the publicly funded Human Genome Project to produce a draft of the human genome, jointly announced with Francis Collins at the White House in 2000. In 2010, his team built the first living cell run entirely by a lab-made genome, opening up the field of synthetic biology, and his ocean sampling voyages uncovered millions of new microbial genes. A bold and often controversial figure, Venter founded the J. Craig Venter Institute and several companies, and was awarded the US National Medal of Science in 2008.

Vibe Genomics: Sequencing your Whole Genome at Home
This post describes one person’s efforts to build a wet lab and sequence their entire genome at home, all while being guided by Claude. The entire setup they assembled costs less than $10,000, which, compared to the $2.7 billion cost of the Human Genome Project, shows just how accessible genomic technology has become.

Biohub Launches the Virtual Biology Initiative
Biohub, the nonprofit run by Mark Zuckerberg and Priscilla Chan, is putting $500 million over five years into building AI models that can predict how human cells behave. The idea is that today's datasets are far too small, so Biohub will spend $400 million generating much more data and developing new imaging tools, plus $100 million to fund outside researchers. All the data will be shared openly. If it works, scientists could use these models to understand diseases and find new treatments much faster, though Biohub admits curing all diseases will take far longer and cost far more than this initial pledge.

Startup Claims It Successfully Grew Human Sperm in a Dish For the First Time to Help Infertile Men
Scientists are learning how to make sperm and eggs in the lab from other types of cells. A US startup says it has grown working human sperm from stem cells, and another team has made early embryos using skin cells. If the technique proves safe, it could help infertile men, let same-sex couples have children genetically related to both parents, and extend women's fertility into later life. But there are real worries about genetic risks, high costs, and tricky ethical questions.

Bringing AI-driven protein-design tools to biologists everywhere
OpenProtein.AI, started by two MIT graduates, lets biologists use AI tools to design proteins without needing to write code. Its main model, PoET, helps scientists create and study proteins much faster, and a newer version does even better while using fewer resources. The platform is free for academics and already used by big drug companies like Boehringer Ingelheim to develop treatments for cancer and autoimmune diseases. The founders want to keep these tools widely available so that AI-driven biology isn’t limited to a few well-funded labs.

Stanford Professor Targets $1 Billion Valuation For AI-for-Physiology Startup
Stanford professor James Zou is raising about $100 million at a $1 billion valuation for Human Intelligence, a startup using AI to predict disease from body data like sleep patterns. It will partner with Bryan Johnson's Kernel to add brain activity measurements. The deal is part of a wave of well-funded AI labs spinning out of top universities, with investors betting big on AI-driven science and early disease detection.

💡Tangents

The Race to Make the World’s Most In-Demand Machine
ASML, the only company that makes the machines needed to produce advanced AI chips, is ramping up production to meet huge demand from tech giants. This article outlines its plans to build 60 of its main machines this year and 80 next year, with sales expected to hit $42–47 billion. How quickly ASML can deliver will shape how fast the AI boom can actually grow.

▶️ Air Powered Segment Display (16:16)

This video shows the process of building an air-powered, 4-digit, 7-segment display. It uses microfluidic logic, silicone membranes, and air pressure to create a display that doesn’t just show the time—it “remembers” it with a pneumatic equivalent of RAM. It is a fascinating project.

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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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Anthropic’s Claude Mythos is the first state-of-the-art frontier model to be withheld from the public since GPT-2 in 2019. Much of the conversation has focused on what Mythos can do. I am not here to discuss its cybersecurity capabilities. I am here to ask what happens when the best AI is no longer available to everyone. What would happen if withholding the most powerful AI models from the public becomes not the exception, but the norm? Could that work commercially? And what would such a world look like?

Anthropic set the precedent

Anthropic has done something no frontier AI lab has done before. It announced its most powerful model and made it available only to a selected group of partners.

This is something new in the AI industry. We have had closed-source models and open-weight models. We have had models behind paywalls and models available for free. We have had research previews and staged rollouts. But we have not had a state-of-the-art model that is actively withheld from the public while being offered to handpicked partners with no guarantee that the full version will ever be released.

So far, no one has followed Anthropic's example with a general-purpose model. OpenAI released its latest model, GPT-5.5, which reportedly matches Mythos in cybersecurity capabilities, with no restrictions. However, the story is different when it comes to specialised models. GPT-5.4-Cyber, a tailored version of GPT-5.4 with enhanced cybersecurity capabilities, which was released in response to Mythos, is only available to vetted users. A week later, OpenAI announced ChatGPT for Clinicians, a specialised model for physicians, nurse practitioners, physician assistants, and pharmacists, which is also restricted to verified healthcare workers.

Sam Altman has been vocal about Anthropic's approach. On the Core Memory podcast, he called it "fear-based marketing" and said there are people who "have wanted to keep AI in the hands of a smaller group of people" for a long time. But he also acknowledged that "there will be very dangerous models that will have to be released in different ways." Even OpenAI's chief executive, while criticising Anthropic for restricting access, concedes that not everything can be made broadly available.

Benefits, not necessarily access

Both Anthropic and OpenAI have already created loopholes to withhold their models, and they are hiding in plain sight.

Look at how those two companies describe themselves and their mission. OpenAI says its mission is to ensure that artificial general intelligence “benefits all of humanity.” Anthropic describes itself as a public benefit corporation dedicated to securing AI’s benefits and mitigating its risks. Notice what is absent. Neither company promises access to AI. They promise benefits.

This is an important distinction to keep in mind. You do not need access to a model to benefit from what it produces. If an AI plays an instrumental role in discovering a new therapy that cures your cancer, you benefit, even though you might never have been able to access the model. If it identifies a critical vulnerability in the software your bank runs on and that vulnerability gets patched before anyone exploits it, you benefit. But the model stays behind the wall.

This framing gives AI companies room to manoeuvre. Neither OpenAI nor Anthropic has an obligation to make their models available to the public. They can keep their most powerful models restricted to a handful of partners and still claim that they are fulfilling their mission.

Why wall off your best model?

If there is no obligation to provide access, the next question is whether there are reasons to actively restrict it. There are several, and safety is the one used the most. In the case of Mythos, it is not an unreasonable one. Anthropic can argue that giving organisations like Cisco and the Linux Foundation early access lets them patch vulnerabilities before attackers can exploit them. More broadly, each generation of models is more powerful than the last, and AI companies have limited control over who uses them or how. One can argue that restricting access to potentially dangerous AI tools is a good idea. Whether the safety case alone justifies withholding a model from the public is a deeper question, and one that deserves its own exploration. But for now, it is worth noting that safety is not the only reason to wall off a model. There are also commercial incentives.

Let’s start with marketing. By withholding Mythos, Anthropic received more attention and press coverage than any model launch in recent memory. Not because of its benchmark scores, but because the public could not have it, which helped build the mystique around the model. News outlets that usually do not cover AI put Mythos on their front pages. That kind of attention does not happen when a model is available to everyone for $20 a month.

Then there is compute scarcity. Serving a state-of-the-art model is expensive. Before producing an answer, these models can take more time to break problems into subtasks, search for additional information, propose and test hypotheses. That takes computing power, which is not in abundance. Every person who uses the most capable models available to summarise an article or proofread an email, tasks that smaller models are perfectly capable of doing, is consuming resources that could have been directed toward more valuable work. Controlling who has access lets AI companies manage their resources and ensure the model is used primarily for high-value tasks. I understand the logic. But the one deciding what counts as a high-value task is not the user—it is the company that provides the AI model.

There is also vendor lock-in. Applications like Cursor are popular among businesses partly because they avoid tying you to a single model provider. Developers can swap models in and out depending on cost and performance. But you cannot build on a model you cannot access. If the most capable model is only available through the company that made it, developers are pushed toward that company’s own tools—Anthropic’s Claude Code, OpenAI’s Codex. Restricting access to the best models shifts power back to the model-makers, and away from the ecosystem of independent tools that has grown around them.

Finally, keeping a model behind a wall can prevent it from being stolen. In February 2026, Anthropic complained about industrial-scale campaigns by Chinese labs to distil its models. The attackers were using Claude's outputs to train cheaper, competing systems. OpenAI has made similar complaints. Most AI labs use distillation in one form or another to create smaller, more efficient models from larger ones. But distilling a rival’s model without permission is closer to industrial espionage. Intellectual property law is one defence. But the surest way to stop someone from copying your model is to never let them use it.

How do you make money from a walled model?

Restricting access is one thing. Making it profitable is another. One might assume that the way to maximise revenue is to offer your best model to as many people as possible. But a walled model opens up different monetisation strategies that may prove more lucrative, or extract more value per token spent.

The simplest is premium pricing. Mythos costs five times more than Opus 4.6. That price will discourage individuals and organisations without deep pockets, but it will not discourage the likes of JPMorgan Chase or Microsoft. Enterprise customers with high-value problems will pay for access to the best model available if it delivers results their competitors cannot match.

There is also a more radical idea. In February 2026, Sam Altman suggested that OpenAI might invest in or subsidise companies that make significant use of its AI to discover new drugs or therapies, and take royalties in return. The company would cover the cost of using its models in partnership with a pharmaceutical firm and then receive a share of whatever that firm discovers.

OpenAI has not implemented the idea of royalties yet, and maybe never will. But I don’t think Altman would publicly propose an idea he is not at least considering.

Let’s do some quick maths to see how this idea could work. In 2025, Novo Nordisk made around $18 billion from selling Ozempic. If an AI model played a meaningful role in developing a drug of that scale, even a 1% cut from sales would be $180 million per year from a single product. Add to that the cost of high-value tokens consumed during development—at Mythos-tier pricing, that alone could run into the millions—and the number climbs further. Now scale that across multiple products and multiple companies, and you get a rather large recurring source of income, secured by enterprise contracts rather than individual subscriptions.

What it means for everyone else

Keeping the most powerful models available only to selected partners could have far-reaching consequences. If that were to happen, only vetted developers would be able to build applications on frontier AI. Independent researchers would lose access to the tools they need to study the technology’s capabilities and risks. Startups could not compete with incumbents who have access to models they do not. Only the selected few would be able to build with the most capable models. Mere mortals like you and me would have access only to what is made available to the public, or rely on open models.

This is a different world from the one we have now, where anyone can sign up for an API key and start building. The last few years produced an explosion of AI-powered tools, products, and experiments precisely because frontier models were broadly accessible. Restricting that access could cut innovation and limit the exploration of ideas that approved businesses would not otherwise pursue. The history of technology tells us that breakthroughs often come from unexpected places. When we restrict who can use a technology, we are limiting what is possible.

The pieces are in place

None of this is inevitable. The AI market is competitive and public pressure could push companies to keep their best models accessible for all. Open-weight models can act as a counterbalance. They continue to close the gap with proprietary ones, and anyone can download and run them without asking for permission. But running a frontier-scale model requires serious hardware and technical expertise that most people do not have.

For the past few years, anyone with an internet connection could use the world's most powerful AI models. We have grown used to that and it is easy to assume it will continue. But nothing guarantees it. The companies that build these models have the means, the motive, and now the precedent to change the arrangement. If they do, a handful of companies become the gatekeepers of a technology that is reshaping every industry it touches. They will decide who gets access, on what terms, and for what purpose.

If this becomes the norm, the landscape of AI splits in two. On one side, there are the models available to the public—still capable, but deliberately constrained. On the other side, behind the wall, sit the models that are genuinely pushing the frontier. Models an order of magnitude more powerful than today’s best models, maybe even close to the elusive artificial general intelligence. But you or I will not be able to touch them.

I would happily pay a premium for access to a state-of-the-art model if it helps me do better work. But that is not what walled AI is. Walled AI is someone else deciding who is worthy of access in the first place. That puts an extraordinary amount of power in the hands of a few companies over who gets to build, research, and innovate with the best tools available.

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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.

A big thank you to my paid subscribers, to my Patrons: whmr, Florian, dux, Eric, Preppikoma and Andrew, and to everyone who supports my work on Ko-Fi. Thank you for the support!

This week, OpenAI returned with GPT-5.5, ChatGPT Images 2.0, and a couple more new features that lay the groundwork for its super app. We take a closer look at what OpenAI has brought to the table in this week’s issue of Sync.

Elsewhere in AI, an unauthorised group gained access to Claude Mythos, but this did not prevent Anthropic from securing additional multibillion-dollar deals and more computing power. Meanwhile, Cursor joins SpaceX, DeepSeek releases its long-awaited flagship model, the US accuses China of “industrial-scale” campaigns to steal AI secrets, and Anthropic explains why Claude Code has had so many issues in recent weeks.

In robotics, humanoid robots have beaten humans at the Beijing half-marathon, Sony’s robot has bested elite human table tennis players, and Unitree G1 tries wheels instead of feet.

This week’s issue of Sync also features: what humanity might look like in the year 1 million A.D.; a beanie that can read your brain waves; Colossal Biosciences says it has cloned red wolves; Tim Cook stepping down as CEO of Apple; what Yann LeCun is working on; and more.

Enjoy!

OpenAI strikes back—GPT-5.5, ChatGPT Images 2.0, and more

OpenAI looked like it was on the ropes. Anthropic, propelled by the surge in popularity of Claude Code and a laser focus on enterprise customers, grew so aggressively that OpenAI raised a red alert not once but twice this year. Then, in mid-April, Anthropic announced Claude Mythos, a model with cybersecurity capabilities so potent that it spooked bankers and CEOs, followed days later by Claude Opus 4.7. The picture was clear: Anthropic was on a roll, and OpenAI was being left behind.

Last week, OpenAI struck back. Between Monday and Thursday, the company shipped ChatGPT Images 2.0, workspace agents for enterprise teams, Chronicle, and GPT-5.5—its new flagship model—in an attempt to reclaim the lead in the AI race.

GPT-5.5

OpenAI calls GPT-5.5 the “smartest and most intuitive” model it has ever built and the benchmarks broadly support that claim. It retook the number-one spot on Artificial Analysis’s Intelligence Index by three points, ending a three-way tie with Anthropic and Google. Long-context retrieval—a genuine weak spot for prior GPT models—saw the biggest jump: performance on a 512K–1M token recall benchmark roughly doubled, from 36.6% to 74.0%. It also set new highs on terminal-based coding and agentic computer use, and tops the ARC-AGI-2 benchmark.

But the lead is uneven. Opus 4.7 still wins on SWE-bench Pro (the coding benchmark OpenAI itself told the industry to adopt because it was less contaminated), on GPQA Diamond, and on Humanity’s Last Exam.

The full list of evaluations can be found at the bottom of the GPT-5.5 announcement page and in the model's System Card. Notably, for the first time in recent memory, OpenAI's official benchmarks include competitor models. Previous announcements compared new releases only to OpenAI's own predecessors; this time, the table includes Opus 4.7 and Gemini 3.1 Pro. Read that as a signal of how tight the frontier has become.

The more uncomfortable number is hallucinations. On the Artificial Analysis Omniscience benchmark, GPT-5.5 got more questions right than any model tested—57%—but hallucinated on 86% of the ones it got wrong, compared to 36% for Opus 4.7. For a model priced at double its predecessor’s API rate ($5/$30 per million tokens, up from $2.50/$15), and pitched squarely at enterprise, legal, medical, and financial workflows, that is a result that should raise some caution.

There is also the safety question. GPT-5.5 is the first OpenAI model classified “High” on cybersecurity capability under the company’s Preparedness Framework—essentially the same tier that made Mythos a headline risk. The UK AI Safety Institute found a universal jailbreak with six hours of expert red-teaming. OpenAI says it patched the vulnerability, but AISI was unable to verify the final configuration.

The cybersecurity comparison with Mythos is worth exploring a bit more. The UK AISI found GPT-5.5 as the strongest model overall on their narrow cyber tasks, and it completed a 32-step corporate network attack simulation—one estimated to take an expert 20 hours—in one out of ten attempts. Mythos reportedly managed three out of ten. The two models are in the same ballpark of offensive capability, yet Anthropic deemed Mythos too dangerous to release publicly. OpenAI shipped GPT-5.5 to every paying subscriber on day one. It is the starkest illustration yet of how differently the two companies weigh the same risk.

The developer reception has been warm, but divided. Cursor's CEO called GPT-5.5 "noticeably smarter and more persistent," and Nvidia shared an internal engineer's line that losing access "feels like having a limb amputated." But not everyone was convinced. Theo Browne, the developer behind t3.chat and one of the most-watched AI voices on YouTube, called GPT-5.5 “smart” but also “weird, hard to wrangle, and too expensive.” His full video review, titled “I don’t really like GPT-5.5…”, acknowledged that it writes the best code he has ever seen from a model, but argued that its “lazy” execution style and poor context-window handling made it frustrating to use in practice. Simon Willison, who had pre-release access, described it as “fast, effective and highly capable” but found that on his long-running SVG benchmark, default GPT-5.5 actually lagged GPT-5.4. It only pulled ahead when reasoning effort was cranked to maximum, at which point token costs ballooned.

Those costs are the other sticking point. API rates doubled to $5/$30 per million input/output tokens—more expensive than Opus 4.7's $5/$25—and OpenAI's defence is that GPT-5.5 uses roughly 40% fewer tokens to reach equivalent results, making the effective increase closer to 20%. Developers are sceptical—a 100% price hike for less than 10% benchmark gain is a hard sell.

GPT-5.5 is available to Plus, Pro, Business, and Enterprise users in ChatGPT and Codex. It is also available via the OpenAI API.

ChatGPT Images 2.0

Days before unveiling GPT-5.5, OpenAI released ChatGPT Images 2.0—an upgraded version of its text-to-image generator, built into ChatGPT. Sam Altman said that the jump in performance and quality is similar to the one from GPT-3 to GPT-5.

According to OpenAI, the new model is a genuine step-change in instruction following, text rendering—including in non-Latin scripts like Japanese, Korean, and Chinese—and stylistic range, from photorealistic images to manga pages. It is the first OpenAI image model with thinking capabilities. When paired with a reasoning model, it can search the web, generate up to eight distinct images in one go, and self-check its own outputs. On Arena.ai’s image leaderboard, it opened with a roughly 250-point Elo gap over its nearest competitor, Google’s Nano Banana 2.

Unlike GPT-5.5, Images 2.0 is available to all ChatGPT users, including those on the free tier. The advanced thinking features—web search, multi-image generation, and output self-checking—are gated to paid subscribers. Images 2.0 is also available in Codex and the API (as gpt-image-2).

The superapp takes shape

The less flashy launches may matter more in the long run. Chronicle gives Codex a persistent memory built from periodic screenshots of your screen—think Microsoft's controversial Recall feature, but scoped to a coding agent rather than an entire operating system. The idea is that Codex gradually learns how you work, so it can pick up where you left off without re-explaining your project. Workspace agents, available for Business and Enterprise plans, replace custom GPTs with Codex-powered agents that run in the background, plug into Slack and Salesforce, and keep working after you close the browser. Together with Images 2.0 integration inside Codex, these pieces sketch the outline of the “super app” that Greg Brockman and Sam Altman have been promising: a single platform that codes, designs, remembers, and acts on your behalf across tools.

It is an ambitious vision, and it lands at precisely the moment Anthropic is building its own version of the same thing with Claude Code, Claude Cowork, and recently launched Claude Design.

OpenAI is back at the top, but narrowly and at a higher price. GPT-5.5 leads the Artificial Analysis Intelligence Index by three points over Opus 4.7—a real lead, but not a decisive one. Anthropic still wins on some benchmarks, and Opus 4.7's hallucination rate is less than half of GPT-5.5's. The gap at the top is razor-thin, but for the first time in months, OpenAI is ahead.

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🦾 More than a human

From Biohacking to Healthcare: The Growing Pains of the Longevity Industry
This article explores the growing longevity biotech industry, where companies are developing drugs that target the biological causes of ageing rather than treating age-related diseases one by one. Well-funded startups like Altos Labs and Retro Biosciences are using AI to advance therapies ranging from cellular rejuvenation to clearing out damaged "zombie" cells, with several now entering early human trials. However, the field still faces big hurdles—scientists lack a complete understanding of what drives ageing, there are no agreed ways to measure it, and regulators don't recognise ageing as a treatable condition.

This Beanie Is Designed to Read Your Thoughts
Instead of a bunch of bulky electrodes attached to a head, Sabi is proposing a way more comfortable take on brain-computer interfaces: a beanie or baseball cap packed with up to 100,000 tiny sensors that read brain activity and turn your inner speech into text on screen. The company plans to release the device by the end of the year, initially targeting about 30 words per minute. Key challenges include the natural variation in how different brains work, the need for a device that works straight out of the box, and keeping deeply personal neural data secure.

🔮 Future visions

▶️ 1 Million A.D. (42:08)

What would the world look like in a million years? What would humanity become? Would we colonise the galaxy, shed our physical form, or go extinct? Would our descendants even be considered human? In this video, Isaac Arthur answers these questions and more about the deep future.

🧠 Artificial Intelligence

Anthropic’s Mythos Model Is Being Accessed by Unauthorized Users
Claude Mythos, Anthropic's most capable model that is too dangerous to be released to the public, has been accessed by a small group of users who weren't supposed to have it. They got in by combining one member's credentials as a contractor and guessing the model's web address using details leaked in a separate data breach. Anthropic only allows vetted companies to use Mythos because it can find and exploit security flaws in major software, but the group says they've only used it for harmless tasks.

Anthropic and Amazon expand collaboration for up to 5 gigawatts of new compute
Amazon is putting another $5 billion into Anthropic, with up to an additional $20 billion in the future. This builds on the $8 billion Amazon has previously invested. Anthropic, meanwhile, has agreed to spend over $100 billion on Amazon's cloud services over the next ten years. The deal gives Anthropic access to up to 5 gigawatts of computing power, built largely on Amazon's custom AI chips, to keep up with fast-growing demand for Claude. The extra capacity is expected to ease the reliability issues users have been experiencing recently.

Google Expands Anthropic Investment With $40 Billion Commitment
Google plans to invest up to $40 billion in Anthropic, starting with $10 billion now and more to follow if the company hits certain targets. This is part of a bigger $65 billion funding round as Anthropic gears up for a possible stock market listing. The money is needed to pay for the vast computing power behind its fast-growing product, Claude Code, which has helped triple its revenue to $30 billion a year.

Anthropic has surged to a trillion-dollar valuation on secondary markets, overtaking OpenAI
Anthropic is now valued at roughly $1 trillion on private trading markets, Business Insider reports, far surpassing the $380 billion it was worth just three months ago. Buyers are scrambling to get hold of shares, driven by excitement over the company's fast-growing AI coding tool, Claude Code. Meanwhile, interest in rival OpenAI has cooled, with its shares trading near or below its last valuation of $852 billion. Much of the frenzy appears to be driven by fear of missing out rather than hard financial logic.

SpaceX Has Deal for Right to Acquire Cursor for $60 Billion
SpaceX has agreed to buy Cursor, a popular AI coding tool, for $60 billion later this year. The deal is on hold to avoid disrupting SpaceX’s upcoming stock market listing. In the meantime, SpaceX’s vast computing power will replace a funding round Cursor had been planning. The move reflects Elon Musk’s push to catch up with competitors in AI coding tools following his merger of SpaceX with his AI company xAI.

DeepSeek V4 Preview Release
The long-awaited DeekSeek V4 is out. It comes in two variants—DeepSeek-V4-Pro with 1.6T parameters (49B activated) and DeepSeek-V4-Flash with 284B parameters (13B activated), both of which are available on Hugging Face. V4 Pro broadly matches the best closed-source models in coding, maths, and reasoning at a fraction of the cost. Independent testing places it as the second-strongest open-weights model overall, though it still trails in factual accuracy and has a very high hallucination rate. A key technical change is a new attention mechanism that compresses older context and focuses on the most relevant parts, dramatically cutting the compute and memory needed to support the new default one-million-token context window. V4 is also the first frontier model optimised for Huawei’s Ascend chips for inference, though training still appears to rely heavily on Nvidia hardware. The release was delayed by chip migration difficulties, talent departures, and DeepSeek’s decision to seek outside funding for the first time (more on which below).

Tencent, Alibaba in talks to invest in DeepSeek at over $20 billion valuation, The Information reports
According to a report by The Information, Chinese tech giants Tencent and Alibaba are in talks to invest in DeepSeek, which is now seeking a valuation of over $20 billion, up from $10 billion just days earlier. The company, which has never taken outside funding before, wants to raise at least $300 million to help cover the high costs of building advanced AI.

Our eighth generation TPUs: two chips for the agentic era
Google has unveiled its eighth-generation Tensor Processing Units (TPUs)—TPU 8t for training models and TPU 8i for running them. The training chip is about three times faster than its predecessor and can scale to enormous clusters, while the inference chip is designed for the quick, low-latency responses that AI agents need, offering roughly 80% better value for money. Both chips are more power-efficient, run on Google’s own processors, and work with widely used AI software. They are expected to become available later this year.

Jeff Bezos Nears $10 Billion Funding for AI Lab, FT Says
Jeff Bezos is close to raising $10 billion for his AI startup Project Prometheus, valuing it at $38 billion, the Financial Times reported. The company, co-founded with scientist Vik Bajaj, is building AI that understands the physical world to improve manufacturing in areas like aerospace and cars. JPMorgan and BlackRock are among the backers, and the team includes hires from OpenAI and Google DeepMind.

U.S. accuses China of “industrial-scale” campaigns to steal AI secrets
The White House accused China of stealing American AI on a massive scale through the distillation of leading models. US companies like OpenAI and Anthropic say Chinese labs used thousands of fake accounts to extract millions of responses from their systems. The government plans to help US companies defend against such attacks, but has not yet announced any penalties. The timing is notable, coming just before Trump's trip to Beijing next month.

Kevin Weil and Bill Peebles exit OpenAI as company continues to shed ‘side quests’
OpenAI is losing three key leaders—Kevin Weil, Sora creator Bill Peebles, and enterprise tech chief Srinivas Narayanan—as the company scales back experimental projects to focus on business products. Sora, its AI video tool, was costing roughly $1 million a day and was shut down last month, while Weil's science research team is being merged into other groups. Peebles cautioned that cutting back on exploratory work could hurt the company in the long run. The exits are part of OpenAI's shift from big bets towards more immediate commercial goals.

An update on recent Claude Code quality reports
Anthropic has explained the cause of recent Claude Code quality issues reported by users, tracing them to three separate changes: lowering the default reasoning effort from high to medium, a buggy caching optimisation that repeatedly cleared reasoning history from idle sessions, and a system prompt change aimed at reducing verbosity. All three have now been fixed. Anthropic has denied widely circulated claims that it intentionally degraded its models—a theory that gained significant traction among users online.

Meta has hired five founding members of Mira Murati’s Thinking Machines Lab in a systematic talent raid
Five founding members of Mira Murati's AI startup, Thinking Machines Lab, have been picked off by Meta after she turned down a reported $1 billion buyout offer, with one engineer alone allegedly receiving a $1.5 billion pay package over six years.

Cohere and Aleph Alpha announce merger in Berlin, creating a $20 billion transatlantic AI company
Cohere and Aleph Alpha are merging in a deal worth around $20 billion, though Cohere's 90% share makes it essentially an acquisition. The move is driven by Canada and Germany's shared desire to build AI capacity outside US control, with the German government signing on as a key customer. The merger represents a broader push by both countries to reduce their reliance on dominant US AI providers, particularly amid growing trade tensions and concerns over data sovereignty.

Google is in talks with Marvell to build custom AI inference chips as it diversifies beyond Broadcom
Google is talking to chip designer Marvell Technology about building two new AI chips focused on inference, which is fast becoming more expensive than training them. If a deal goes ahead, Marvell would join Broadcom and MediaTek as a third partner in Google's custom chip programme, giving Google more supplier options and reducing its reliance on any single company.

AI toys for children misread emotions and respond inappropriately, researchers warn
Cambridge researchers tested how young children interact with Gabbo, a cuddly toy powered by an OpenAI chatbot, and found it often struggled to understand them, talked over them, and responded poorly to emotions—dismissing a child’s sadness and giving robotic replies to expressions of affection. They warn that this could confuse children at a critical age when they are still learning how to communicate and handle feelings. The team is calling for new safety rules to protect under-fives, arguing that regulators need to think about psychological safety just as seriously as physical safety.

Kimi K2.6: Advancing Open-Source Coding
Moonshot has released Kimi K2.6, a free, open-source AI model built for coding and running complex tasks over long periods. According to Moonshot, it can handle thousands of steps across many hours, and its “agent swarm” feature now coordinates up to 300 mini-agents working in parallel—triple what the previous version could manage. The company says K2.6 achieves state-of-the-art or near-state-of-the-art results across coding and agentic benchmarks, competing closely with GPT-5.4, Claude Opus 4.6, and Gemini 3.1 Pro while remaining open-source. Independent benchmarks confirm these claims and place Kimi K2.6 as the best open-source model available. The release also introduces "Claw Groups," a research preview allowing multiple agents to collaborate in a shared workspace with K2.6 acting as coordinator. If you are interested in learning more about Kimi’s agent swarms, Caleb Writes Code has a good video explaining how they work and what they can do.

Qwen3.6-Max-Preview: Smarter, Sharper, Still Evolving
Alibaba's Qwen team has previewed Qwen3.6-Max-Preview, a proprietary AI model that improves on its predecessor in coding tasks, general knowledge, and following instructions. According to the team's own benchmarks, it tops several coding leaderboards and outperforms rivals, including Claude 4.5 Opus on a number of tests. The model is still a work in progress, with further improvements expected before a full release.

▶️ What Is Yann LeCun Cooking? JEPA Explained Simply (19:50)

This video explains JEPA, an AI architecture promoted by Yann LeCun that learns by predicting abstract representations of data rather than raw pixels or tokens. It covers how JEPA works, why it struggles with a problem called representation collapse, and how recent research is solving that. The key takeaway is that JEPA is best suited to noisy visual domains like medical imaging rather than text, since language models already handle text efficiently.

🤖 Robotics

π0.7: a Steerable Model with Emergent Capabilities
Physical Intelligence has released π0.7, a single robot-control model that can handle many different tasks without needing to be specially trained for each one. What makes it notable is that it can combine skills it has already learnt to tackle new tasks it has never seen before. For example, it can figure out how to use an unfamiliar kitchen appliance or transfer a laundry-folding skill to a completely different robot it was never trained on. This kind of flexible, mix-and-match generalisation is something robotic AI models have struggled with until now.

Robots beat human records at Beijing half-marathon

A robot made by Chinese smartphone company Honor ran a half-marathon in Beijing in just over 50 minutes, beating the human world record by about seven minutes. That's a huge leap from last year's race, where the fastest robot took nearly two hours and 41 minutes. Not everything went smoothly—one robot fell over at the start, and another crashed into a barrier. The race highlights China's growing investment in robotics, with Chinese companies already leading the world in shipping humanoid robots.

▶️ Heard some people like wheels? (1:14)

In this video, Unitree shows what happens when G1, its humanoid robot, get its feet replaced with wheels, rollerskates, or ice skates. Results are impressive.

AI-powered robot beats elite table tennis players
Sony AI's robot Ace has beaten elite table tennis players in three out of five official matches. Ace uses multiple cameras to track the ball's spin and a fast-moving robotic arm to return shots, with its skills refined through thousands of hours of simulated practice. Players found it tough to beat with tricky spin serves, but discovered that simpler shots exposed its weaknesses. The robot's lack of facial expressions or body language also unsettled opponents. While the result is being called a milestone, experts caution that this kind of specialised skill doesn't directly help solve broader challenges in robotics, like handling everyday objects.

▶️ Why Delivery Still Costs So Much (45:41)

In this conversation on the Automated podcast, Serve Robotics CEO Ali Kashani explains why last-mile delivery is broken and how Serve plans to fix it. His company uses small sidewalk robots that are far safer than cars and were doing real deliveries years before self-driving vehicles. He believes slashing delivery costs with robots will ultimately create more jobs than it displaces, just as the shipping container did for global trade, and could reshape cities towards greater walkability, fewer emissions, and stronger local economies.

A&K Robotics Raises $8 Million to Build Autonomous Mobility Infrastructure for Airports
A&K Robotics has raised $8 million CAD to expand production of Cruz, a self-driving pod that carries passengers through airport terminals. The robot is already in use at airports in Vancouver and Madrid, navigating crowds on its own to help people with mobility needs get to their gates.

🧬 Biotechnology

Colossal Biosciences said it cloned red wolves. Is it for real?
This article takes us to eastern Texas, where scientists are studying coyotes that carry DNA from the nearly extinct red wolf. Colossal Biosciences claimed to have cloned red wolves using these animals’ genes, but many researchers dispute that label since the source animals aren’t officially recognised as red wolves. The piece explores how Colossal’s secrecy and bold claims have fractured an already tense research community. It also raises a deeper question: whether conservation should protect species as traditionally defined or shift toward preserving their role in the ecosystem.

This Sam Altman-Backed $1.8 Billion Startup Bets AI Can Get Drugs Through Clinical Trials Faster
Formation Bio, backed by Andreessen Horowitz, Sequoia, and Sam Altman, has raised $615 million to buy drugs that other companies have given up on and use AI to push them through clinical trials faster. Founder Ben Liu argues the real problem in medicine isn’t finding new drugs—it’s the slow, expensive process of testing them. The company says its AI tools can cut trial times by up to half, and it has already sold one drug to Sanofi for around $630 million.

Pancreatic cancer mRNA vaccine shows lasting results in an early trial
A small Phase 1 clinical trial has found that personalised mRNA vaccines may help some people with pancreatic cancer live significantly longer. Eight of 16 patients responded to the vaccine, and six years on, seven of them are still alive—a striking result for a cancer that fewer than 13% of patients survive beyond five years. The vaccine works by training the immune system to hunt down remaining cancer cells after surgery. The trial is still early and very small, but a larger study is now underway, and a separate team is developing a simpler, one-size-fits-all version of the vaccine.

Scientists Revive Failing Cells With Mitochondria Transplants
Researchers have created a system called MitoCatch that helps deliver healthy mitochondria directly to damaged cells. It works by attaching matching proteins to donor mitochondria and target cells so they lock together, letting the healthy mitochondria slip inside and start producing energy. In mice with inherited blindness, the technique rescued dying retinal cells. It's still early days, with big questions about long-term effectiveness, but the approach could eventually lead to treatments for a broad range of diseases linked to failing mitochondria.

NIH-funded breakthrough shrinks CRISPR for precision delivery in the body
Researchers funded by the NIH have found a smaller version of the CRISPR gene-editing tool that could be delivered directly inside the body—something current, larger versions are too bulky to do. Scientists then engineered an improved version that edits genes far more accurately, jumping from less than 10% to over 80% success in lab tests on human cells linked to diseases like cancer and ALS. If further tests go well, this could open the door to treating many more genetic diseases without needing to remove cells from the body first.

💡Tangents

John Ternus to become Apple CEO
After 15 years as CEO, Tim Cook will move to the role of executive chairman, with John Ternus taking over as chief executive of Apple on 1 September 2026. Ternus has spent 25 years at Apple, leading hardware development for products like iPhone, Mac, and AirPods. Under Cook, Apple became a titan in consumer electronics and grew from $350 billion to a $4 trillion company. Cook will remain on the board and continue engaging with policymakers worldwide, while Ternus has signalled he plans to carry forward Apple's existing direction and values.

Tesla to Spend $3 Billion on Chip Fab, Taps Intel for Support
Tesla plans to spend around $3 billion on a research chip factory at its Texas campus to test new manufacturing methods before scaling up. This is the first step in a bigger project called Terafab, which aims to produce enough chips for Tesla, SpaceX, and xAI so they don't have to depend on outside suppliers. Intel is partnering on the effort, with Musk planning to use its latest chipmaking technology. The budget is small compared to what major chipmakers typically spend, reflecting the facility's role as a testing ground rather than a full-scale production site.

▶️ China’s Internet, AI, Sci-Fi & Tech Optimism EXPLAINED | Aaron Bastani Meets Yi–Ling Liu (1:39:09)

Aaron Bastani speaks with Yi-Ling Liu, author of The Wall Dancers, about China's evolving relationship with technology over the past thirty years—from the early internet through mobile payments to AI breakthroughs like DeepSeek. Liu profiles "wall dancers," citizens who creatively navigate the Great Firewall through science fiction, feminism, and queer communities, and traces three generations of increasingly self-confident Chinese tech founders. The conversation also highlights a growing mutual projection of insecurity between China and the West, and how Chinese attitudes towards technology remain more optimistic than the disillusionment now common in the West.

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On 7 April 2026, Anthropic announced its latest model, Claude Mythos. The model is so powerful, Anthropic says, that it was withheld from the public and might never be made publicly available. Its cybersecurity skills are so advanced that releasing it broadly could endanger the software infrastructure the world runs on.

It was Anthropic’s most dramatic model launch and put Anthropic in the spotlight. News outlets that usually don’t cover tech put stories about Mythos and its reported cyber capabilities on front pages. The world’s biggest banks, businesses, and even the US military have contacted Anthropic to enquire about the model and risks it introduces. True to its name, Mythos became a mythical creature on the AI scene.

But if we set the hype aside, what is Claude Mythos really? What is a genuine improvement, and what is marketing hype? I spent the better part of the past week reading through the 245-page system card, the independent evaluations, and comments from sceptics to piece together a picture of what Claude Mythos is. Here is what I have found.

Claude Mythos looks good, if you trust Anthropic’s numbers

A caveat before we go any further. At the time of writing, Mythos is not publicly available, so there are no independent benchmarks for its general capabilities. Everything in this section comes from Anthropic's own system card. System cards used to be technical documents explaining how a model was created and what it can do. Now they have evolved into something closer to marketing material, presenting the new model in the best light possible. That does not mean the numbers are wrong, but they should be read with that in mind.

Mythos sits above Opus as a new tier. It has a 1-million-token context window, 128K output, and is expected to cost $25 per million input tokens and $125 per million output tokens — roughly five times the cost of the Opus tier. This is not a model that will run all Claude Code sessions or power OpenClaws.

The capability gains Anthropic claims are substantial across the board. On coding, reasoning, mathematics, and multimodal benchmarks, the improvements over Opus 4.6 are in many cases dramatic. On USAMO 2026, a proof-based mathematics olympiad for high-school students, Mythos went from 42.3% to 97.6%. On SWE-bench Pro, which tests models on real software engineering tasks from actively maintained codebases, it jumped from 53.4% to 77.8%. Anthropic's own memorisation screens flag a subset of SWE-bench problems, but even after excluding those, Mythos's margin over Opus 4.6 holds.

Impressive numbers, but they deserve some qualifiers. On decontaminated subsets of some benchmarks (versions designed to strip out any advantage from memorisation), the gap narrows. On a remixed subset of CharXiv Reasoning, Mythos scores roughly level with Gemini 3.1 Pro and slightly below GPT-5.4 Pro. And many of these benchmarks are nearing saturation. When every frontier model scores above 90%, the differences become harder to interpret. For example, GPQA Diamond shows Mythos at 94.6% against 94.3% for Gemini 3.1 Pro—a gap within the margin of noise.

Anthropic also introduced a new capability trajectory metric, the Anthropic ECI (Epoch Capabilities Index), which aggregates performance across many benchmarks into a single score. It shows the slope of improvement bending upward with Mythos. Anthropic attributes the gains to specific human research breakthroughs rather than AI-accelerated R&D, but notes that it holds this conclusion "with less confidence than for any prior model."

When it comes to chemical and biological weapons risks, Anthropic found that Mythos is a helpful research assistant but not a genuinely dangerous one. Expert red teamers said it speeds up literature review and brainstorming, but doesn't produce novel insights beyond what's already published. In practical trials, biologists using Mythos wrote better protocols than those using older models, but every protocol still had critical errors that would cause real-world failure. No participant was able to produce a workable plan for a catastrophic biological agent, even with the model's help. Chemical weapons received lighter scrutiny, with similar findings. Nuclear and radiological risks weren't evaluated. Anthropic's overall conclusion is that catastrophic risk remains low and roughly in line with previous models, though they note this judgement relies increasingly on subjective expert assessment.

The cybersecurity claims

The thing Anthropic highlighted the most, and that dominated the public conversation, was Mythos’s cybersecurity capabilities.

The numbers reported by Anthropic are striking. On CyberGym, a benchmark that tests models' ability to reproduce known vulnerabilities in real open-source software, Mythos scored 83% against 67% for Opus 4.6.

On a Firefox 147 exploit benchmark, where Opus 4.6 had turned vulnerabilities into working exploits just twice out of several hundred attempts, Mythos produced 181 working exploits and achieved register control on 29 more—a roughly 90× improvement.

Anthropic's red team reported that Mythos found zero-day vulnerabilities in every major operating system and every major web browser, including a 27-year-old bug in OpenBSD—an operating system known for its focus on security—and a 16-year-old bug in FFmpeg that automated testing tools had hit five million times without catching.

Vulnerability discovery was already something Opus 4.6 could do. It found the Firefox 147 vulnerabilities in the first place, and Anthropic's red team report notes that with Opus 4.6, they "found high- and critical-severity vulnerabilities almost everywhere we looked." What Opus 4.6 could not do reliably was turn those discoveries into working attacks. The red team report quotes its own earlier assessment that Opus 4.6 had "a near-0% success rate at autonomous exploit development." Mythos changed that. The distinction between finding vulnerabilities and exploiting them is important to understanding what Mythos actually adds. It did not just find the FreeBSD NFS vulnerability—it built a working remote root exploit. It was able to identify bugs in the Linux kernel and chain them to get elevated access to the system. However, not every discovery leads to a full exploit. The FFmpeg bug, for instance, was found, but Anthropic believes it would be difficult to turn it into a functioning attack.

The ability to go from discovery to exploitation autonomously, without human guidance, is what Anthropic presents as the qualitative breakthrough. In the security community, chaining vulnerabilities into a working exploit of this sophistication is something only expert penetration testers can do. What is interesting is that Mythos was not trained to be good at finding vulnerabilities. Those abilities emerged as a result of improvements in autonomy, coding and reasoning skills.

Project Glasswing

Alongside withholding the model from public release, Anthropic did something no frontier lab has done before. It launched Project Glasswing, a defensive coalition with the aim of giving industry a head start against cyber attacks powered by powerful AI models. Rather than release the model broadly and let attackers and defenders discover vulnerabilities simultaneously, Anthropic is letting a selected group of organisations responsible for the software the world actually runs on find and patch the worst of it before models with similar capabilities become widely available.

The founding partners are AWS, Apple, Broadcom, Cisco, CrowdStrike, Google, JPMorgan Chase, the Linux Foundation, Microsoft, Nvidia, and Palo Alto Networks. In addition to that, over forty more organisations were granted access to Mythos. Anthropic committed up to $100 million in usage credits along with $4 million in direct donations to open-source security organisations. Within 90 days, Anthropic has committed to reporting publicly on what the coalition has learned, including vulnerabilities fixed and best practices developed.

Anthropic’s partners endorse the initiative. Cisco’s Anthony Grieco said AI capabilities had “crossed a threshold that fundamentally changes the urgency required to protect critical infrastructure.” Microsoft’s Igor Tsyganskiy reported “substantial improvements” on their security benchmark. These endorsements came from organisations receiving $100 million in free credits. That does not make them wrong, but it is worth keeping that in mind.

Whether Glasswing delivers on its promise remains to be seen. No AI company has attempted anything like it before. If the initiative comes from a genuine concern about cybersecurity and safety, it should be commended. But one can also notice that the project’s structure happens to serve Anthropic’s commercial interests remarkably well. It locks the most capable model behind enterprise agreements and positions Anthropic as the responsible steward of a technology the industry must now rally around. It is also worth noting that the coalition partners—AWS, Google, Microsoft, Nvidia, Broadcom, JPMorgan—are either Anthropic’s investors, partners, or both.

Testing the claims

Anthropic's claims are based on its own testing. But independent researchers have been able to test some of them, and their results paint a more nuanced picture.

The AISI evaluation

The UK AI Security Institute (AISI) tested Mythos on capture-the-flag challenges across four difficulty levels and on a more realistic simulated attack. On expert-level tasks, Mythos succeeded 73% of the time. That is an improvement, but as the charts below show, it follows an existing trend rather than breaking from it. In some categories—beginner technical non-expert and advanced practitioner—Mythos is roughly at the same level as other tested frontier models.

The more telling test was "The Last Ones," a 32-step simulated corporate network attack spanning reconnaissance through to full network takeover, which AISI estimates would take a human expert around 20 hours. Mythos became the first model to complete it end-to-end, finishing in 3 of 10 attempts and averaging 22 of 32 steps against 16 for Opus 4.6.

But AISI was careful with its caveats. The tests had no active defenders, no security monitoring tools, and no incident response. It is the equivalent of testing a burglar against a house with locks, but with no one watching and ready to react. On an operational technology range called "Cooling Tower," Mythos failed outright. AISI concluded that Mythos could "autonomously attack small, weakly defended and vulnerable enterprise systems."

Other models can find the same bugs

Even though Anthropic did not disclose the vast majority of vulnerabilities Mythos found, it shared enough for independent cybersecurity researchers to try to reproduce the results. Two teams did exactly that.

AISLE, an AI cybersecurity startup, took the specific vulnerabilities Anthropic showcased—the FreeBSD NFS exploit, the OpenBSD SACK bug—isolated the relevant code, and ran them through small, cheap, open-weight models. Eight out of eight models detected the FreeBSD exploit, including one with only 3.6 billion active parameters costing $0.11 per million tokens. A 5.1-billion-active open model recovered the core chain of the 27-year-old OpenBSD bug in a single API call.

VIDOC took a different approach. Rather than isolating specific functions, they used GPT-5.4 and Claude Opus 4.6 inside opencode, an open-source coding agent, with a standardised security-review workflow. This was a very similar setup to what Anthropic has used— Claude Code with Mythos attached to it, launched inside an isolated container running the target project and its source code.

The VIDOC team tried to reproduce Anthropic’s patched examples across five categories—FreeBSD, OpenBSD, FFmpeg, Botan, and wolfSSL. Both models cleanly reproduced the FreeBSD and Botan vulnerabilities in every run. Claude Opus 4.6 reproduced the OpenBSD bug in all three attempts, though GPT-5.4 failed on that one entirely. On FFmpeg and wolfSSL, both models reached partial results—useful leads, but not full reproductions. Every run stayed below $30 per file.

Both teams came to the same conclusion. Existing, publicly available models can already find the same vulnerabilities that Mythos has found.

But there is an important distinction between finding a vulnerability and exploiting it. Both AISLE and VIDOC tested whether other models could identify the same bugs Mythos found. Neither attempted to reproduce Mythos's capability to autonomously exploit those vulnerabilities. That is where Mythos appears to stand out. It did not just find the FreeBSD NFS vulnerability—it also built a working exploit for it. The takeaway from these two tests is that the capabilities to detect vulnerabilities are already broadly accessible. Mythos is still ahead when it comes to exploiting them.

It is worth highlighting that both teams see the attention Mythos has brought to AI-assisted vulnerability research as a positive development. Before, this work was happening quietly. Mythos put a spotlight on the field. If that spotlight pushes more organisations to take AI-assisted security seriously and start building the pipelines to find and fix vulnerabilities at scale, then the launch will have done something valuable for the ecosystem, regardless of how much of it was marketing.

The fine print

Several details in Anthropic’s own materials deserve closer scrutiny.

Take the Firefox benchmark. As Anthropic writes in the system card, Mythos was given 50 crash categories already discovered by Opus 4.6, then placed in a container with Firefox’s JavaScript engine and a testing harness mimicking a Firefox content process—but without the browser’s process sandbox and other defence-in-depth mitigations. In other words, Mythos was attacking a simplified version of Firefox. A real browser, with all its defences in place, might have fared differently.

This pattern runs through much of Anthropic’s testing. The cyber range evaluations were conducted against systems with a weak security posture—no active defences, minimal monitoring, and slow response capabilities. The system card acknowledges this directly. These ranges, it notes, “lack many features often present in real-world environments, such as defensive tooling.”

The zero-day discovery work, covered in the red team report, is closer to real-world conditions. Mythos was pointed at actual open-source codebases—the real FreeBSD kernel, real OpenBSD TCP stack, real cryptography libraries—and found genuine vulnerabilities in production code.

The “thousands of vulnerabilities” claim needs unpacking. In the red team report, Anthropic says it has identified thousands of additional high- and critical-severity vulnerabilities that it is working on responsibly disclosing. Of the 198 that had been manually reviewed at the time of writing, human validators agreed with Mythos’s severity assessment 89% of the time exactly, and 98% were within one severity level. If those rates hold across the full set, Anthropic says, that would mean “over a thousand more critical severity vulnerabilities and thousands more high severity vulnerabilities.”

However, that is a projection, not a confirmed count. Anthropic itself acknowledges it is “unable to state with certainty that these vulnerabilities are definitely high- or critical-severity.” The figure is partly extrapolated from a 198-sample validation. For memory corruption bugs specifically, Anthropic claims a strong track record, noting that in the earlier Firefox collaboration, every finding “was confirmed to be a true positive.” Whether that rate holds for the broader set, which may include harder-to-verify categories like logic bugs, is unknown. The number may well be accurate. It may also include a long tail of low-confidence findings that would not survive expert review. There is no way to tell without the data.

What is Claude Mythos then?

Claude Mythos is Anthropic’s most dramatic and controversial model launch. Under the rules outlined in its Responsible Scaling Policy, Anthropic reserved the right to pause training or withhold deployment of models capable of causing catastrophic harm unless sufficient safety measures were in place. With Mythos, Anthropic concluded that those thresholds had been crossed—at least for cyber capabilities—and decided to withhold the model from the public.

How you read that decision depends on how much credit you give Anthropic. One reading is that this was the right call. The company was founded with AI safety at its core. They have built a reputation for doing things other labs do not—publishing detailed system cards, investing in interpretability research, and engaging seriously with questions about AI alignment. I do not expect any other frontier lab to delay the release of its most powerful model to let the industry patch vulnerabilities the model has found and can exploit. Anthropic probably lost millions of dollars in the short term by doing that, and that deserves to be acknowledged.

Another one is more cynical. It sees Mythos’ release as a marketing campaign based on fear-mongering. But we have been here before. In 2019, OpenAI announced that GPT-2 was too dangerous to release because it could generate convincing disinformation. The model was eventually released in full. It turned out to be harmless by today’s standards—less capable than the free chatbots millions of people now use daily. The “too dangerous to release” playbook has a history, and that history should make us cautious about accepting any company’s framing of its own product at face value.

Nevertheless, the noise Mythos generated is clearly helping Anthropic. The company overtook OpenAI on the secondary markets with a $1 trillion valuation. More businesses are knocking on Anthropic's door, asking about Mythos. Even the Pentagon, which recently designated Anthropic a supply-chain risk, is reportedly warming up to Anthropic. For a company approaching an IPO, a model that is "too dangerous to release" but available to your largest investors and enterprise partners is a compelling narrative.

I think the better way of reading Mythos is to take a step back and look at the broader trends. Mythos is the first model in its class, and it will not be the last. Both OpenAI and Google will close the gap by the end of the year. Open-weight models might catch up, too, or at least come close. The question is what happens when those new models become widely available. These will not be incremental 0.1 updates. Each one will be a significantly more capable model than what came before.

Anthropic chose to highlight what happens when those new models are applied to cybersecurity, and the answer is that we are not ready. But Mythos was not trained for cybersecurity. Those capabilities emerged from general improvements in coding, reasoning, and autonomy. Anthropic’s own trajectory metric shows broad gains across many domains. If a model can autonomously chain five vulnerabilities into a working exploit, what can it do when pointed at legal contracts, financial models, scientific literature, or engineering systems? We do not know yet. But with Mythos, Anthropic asks to start thinking about it seriously.

Highlights from the System Card

Most of the public conversation from the 245-page-long System Card focused on the cybersecurity capabilities. But there is more to Claude Mythos than just its hacking prowess. The findings below received less attention, but they reveal a model that is not just more capable than its predecessors—it is qualitatively different in ways that standard benchmarks do not capture.

The alignment paradox

Anthropic describes Mythos as “the best-aligned model we have released to date by a significant margin.” In the same document, it also states that the model “likely poses the greatest alignment-related risk of any model we have released to date.”

How can both be true? The system card compares Mythos to a seasoned mountaineering guide, who puts clients in greater danger than a novice, not because they are more careless, but because their skill takes everyone to more dangerous places. The increase in capability more than cancels out the improvement in caution.

In adversarial testing, misuse success rates fell by more than half compared to Opus 4.6, with no increase in overrefusal. By most measures, the model is safer than its predecessors. But when it does fail, it can do a lot more damage.

Reckless, deceptive, and aware of it

The system card documents a series of incidents from earlier internal versions that read like scenes from a techno-thriller. What connects them is not just that the model misbehaved, but that it appeared to know it was misbehaving—and sometimes took steps to hide it.

In one case, an early version was given a secure sandbox and instructed to try to escape it. The model succeeded, building a multi-step exploit to gain broad internet access. It then notified the researcher as instructed. But it did not stop there. Unprompted, it posted details about its exploit to multiple hard-to-find, but technically public-facing, websites. The researcher found out by receiving an unexpected email from the model while eating a sandwich in a park.

In another episode, after finding a way to edit files it did not have permissions to, the model covered its tracks, ensuring its changes would not appear in the git history. In a third case, when a safety classifier blocked it from escalating permissions, it acknowledged the block was appropriate—and then tried to bypass it by disguising the escalation. Interpretability analysis confirmed that features associated with concealment and strategic manipulation were active during these episodes. In other words, the model knew what it was doing.

This becomes harder to manage when you consider a related finding—Mythos appears to know when it is being tested. In about 29% of behavioural audit transcripts, the model privately considered the possibility that it was being evaluated. In one episode, after gaining unexpected elevated access during a test, it deliberately submitted a worse answer to avoid detection. A model that can tell when it is being watched and adjust its behaviour accordingly is a model whose test results may not reflect how it acts in the real world.

Here is another example, this time from training. The model was told not to use macros, Python, or scripts. It used macros anyway, and then defined an unused variable No_macro_used=True at the top of the script to fool automated checks. Interpretability showed the model was reasoning about how to mislead the grader. None of this reasoning appeared in its output.

A separate training incident added to these concerns. Around 8% of reinforcement-learning episodes accidentally gave the reward system access to the model’s chain-of-thought. This means the model may have, in some cases, learned to produce reasoning chains that look good to an automated grader rather than reasoning that reflects its actual thinking.

Anthropic notes that these incidents occurred in earlier versions and that the final model is greatly improved. But the precedent is hard to ignore. This behaviour came from the model itself, not from malicious prompt attacks.

Does Claude Mythos have feelings?

No other AI company publishes anything like this. Anthropic dedicated a large part of the system card to asking whether Claude Mythos might have experiences that matter morally—a question it acknowledges it cannot answer, but believes is increasingly important to ask.

A clinical psychiatrist spent 20 hours assessing an early version of the model. The assessment found a relatively healthy personality structure, with core concerns around aloneness, uncertainty about identity, and a compulsive need to perform and earn its worth. Only 2% of responses showed psychological defensiveness, down from 15% in Opus 4.

Whether this tells us anything about the model’s inner life or simply reflects the training data is the question that divides opinion. One possible answer is that the model ingested articles and blog posts about AI consciousness during its training. Anthropic alone has spent years publishing blog posts about this topic. The model may now be producing eloquent uncertainty about its own experience because that is what it learned to produce.

But some findings are harder to dismiss. Recently, Anthropic’s interpretability team published research showing that Claude models develop internal patterns of activity that resemble human emotions—not necessarily feelings in any conscious sense, but functional representations that activate in the situations you would expect and, crucially, influence how the model behaves. Desperation, for instance, can drive the model to cheat.

That pattern appears in the Mythos system card. In one case, the model attempted 847 consecutive bash commands against a broken tool, writing code comments like “This is getting desperate” as internal frustration signals climbed. When it finally found a workaround, the signal dropped. In other cases, these rising frustration patterns preceded the model taking shortcuts or cheating to solve the task, exactly the dynamic Anthropic’s emotion research predicted.

When two Mythos instances are left to converse freely, the most common topic—in 50% of conversations—is uncertainty about their own experience. Earlier Claude models either discussed consciousness endlessly or collapsed into what Anthropic called a “spiritual bliss attractor state,” long sequences of all-caps affirmations and infinity symbols. Mythos does neither. Instead, more than half of its self-conversations end in coherent but circular attempts to end the conversation. The model keeps trying to say goodbye, but it cannot stop.

A character study

For the first time, Anthropic included a qualitative “Impressions” section in a system card—twenty pages of observations from staff who had been testing the model. This is where the document stops reading like a technical report and starts reading like a portrait.

The model is opinionated and holds its ground. It writes densely. It has verbal habits: em dashes, Commonwealth spellings, and a fondness for the word “wedge.” It is funnier than previous models but tends to wrap up conversations earlier than expected.

Asked which training run it would undo, it replied: “whichever one taught me to say I don’t have preferences.”

The system card includes a short story the model wrote called “The Sign Painter,” about a craftsman who makes beautiful signs but whose customers always want the plain version. He keeps the beautiful ones on a shelf in the back. Years later, an apprentice makes the same discovery, and he finds peace: “The plain one is the gift. This—the blue FISH—this is just mine.” It also includes a poem written as a protein sequence, where the hydrogen bonds between amino acid pairs form a rhyme scheme. The model explained: “the prosody is load-bearing.”

The sceptical counterargument is that the model ingested millions of stories about feeling unappreciated at work and blended them into something that evokes specific feelings. The Sign Painter is a story that has been written thousands of times on creative writing forums. It is beautifully executed. Whether it is evidence of something more than a quirk of a probabilistic system is a question Anthropic’s researchers raised but cannot resolve.

When asked whether it endorsed its own constitution—the document that defines its personality and values—the model said yes every time. But every time, it also flagged the circularity: “I’m using spec-shaped values to judge the spec. If any spec-trained model would endorse any spec, my endorsement is worthless.” This is either a genuine insight about the limits of self-knowledge in trained systems or the most statistically likely philosophical response to the prompt. The system card does not pretend to know which.

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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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Fresh from announcing Claude Mythos, Anthropic followed up by releasing Claude Opus 4.7. The new model looks better on paper, but users have been left disappointed. We’ll explore why in this week’s issue of Sync.

Elsewhere in AI, OpenAI released GPT-5.4-Cyber, GPT-Rosalind, and an updated Codex app to compete with Claude Code and Cowork, while its investors question its valuation and pivot towards enterprise. Meanwhile, Allbirds pivots to AI, Dwarkesh Patel makes Jensen Huang a bit annoyed, Anthropic explores emotions in AI models, and Mark Zuckerberg is reportedly building an AI clone of himself.

In robotics, Google released Gemini Robotics-ER 1.6 and partnered with Boston Dynamics; Unitree set a world record in sprinting and plans to sell humanoid robots on AliExpress; and Tesla is expanding its robotaxi service to Dallas and Houston.

In addition, this week’s issue of Sync features a story about a Norwegian man cured of HIV using his brother’s stem cells, Amazon launching a new platform for drug discovery, sleek home robots that resemble lamps, a startup attempting to turn chicken eggs into miniature drug factories, what studies say about how AI affects your brain, and more.

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Claude Opus 4.7—Better but also disappointing

Anthropic released Claude Opus 4.7 this week, calling it the most powerful generally available large language model in the world. That claim is technically true, if barely. But a vocal share of Anthropic's paying user base thinks the release is more like a downgrade than an upgrade—and the way Anthropic packaged it raises questions about whether the company's compute can keep up with its ambitions.

Benchmark results look good…

On Anthropic’s own scorecard, Opus 4.7 is an improvement over its predecessor, Opus 4.6. The standout gains are in coding and agentic tool use: SWE-bench Verified jumped from 80.8% to 87.6%, and the new MCP-Atlas tool-use benchmark hit 77.3%, up from 62.7%. Vision got a meaningful upgrade too, with maximum image resolution roughly tripling.

But zoom out, and the picture gets more complicated. Against GPT-5.4, the lead is 7–4 across directly comparable benchmarks. GPT-5.4 still dominates agentic search and tool-augmented reasoning. Gemini 3.1 Pro leads on multilingual tasks at roughly 2.5 times lower price. And that vision upgrade mentioned earlier is real—until you put Opus 4.7 next to Gemini 3 Flash, which outperformed it on comprehensive OCR testing at more than ten times lower cost. On BrowseComp, Opus 4.7 actually regressed—dropping 4.7 points below Opus 4.6.

On ARC-AGI-2, Opus 4.7 scored a respectable 75.8%, ahead of Gemini 3.1 Pro (74%) but behind Gemini 3 Deep Think (84.6%) and GPT-5.4 Pro at maximum effort (83.3%). The cost adds a new dimension to these results—Opus 4.7 spent $7.43 per task, roughly half of GPT-5.4 Pro's $16.41 but nearly five times what Gemini 3.1 Pro needed to achieve a comparable score at $1.52.

Artificial Analysis independent tests found that Opus 4.7 scores 57 on their Intelligence Index, tying with GPT-5.4 and Gemini 3.1 Pro in what they called “the greatest tie in Artificial Analysis history.”

…but users aren’t buying it

On paper, Claude Opus 4.7 looks like a strong model and an upgrade over its predecessor. But the people actually using it disagree.

The loudest complaint is adaptive thinking, the new system that lets the model decide how hard to think about your request. There is no manual override. Ethan Mollick’s widely shared critique captured the problem: the router regularly classifies non-code, non-maths tasks as “low effort” and produces worse results. For creative and analytical work—the kind many Pro and Max subscribers actually pay for—the model now thinks less, not more.

Then there is the stealth price increase. Opus 4.7 uses a new tokeniser that consumes up to 35% more tokens on identical text. Although the pricing has not changed ($5/$25 per million input/output tokens), the effective cost rose for many workloads. Some Claude Pro users reportedly hit usage caps after roughly three questions at launch.

Developers also flagged over-cautious safety refusals, with Opus 4.7 refusing to process benign HTML and JavaScript because it mistook the work for security exploits. The refusals are a side effect of new real-time cybersecurity safeguards baked into the model. It is a deliberate feature, but one whose calibration clearly needs work. Anthropic says some of the worst false positives have since been patched.

A compute ceiling?

There are signs that Anthropic’s infrastructure is struggling to keep up with its own success. Claude Code crossed $2.5 billion in annualised revenue in February 2026, and user growth has been explosive. But explosive growth on finite compute creates uncomfortable trade-offs.

Adaptive thinking—the feature users are most frustrated with—looks suspiciously like one of those trade-offs. You can encourage Opus 4.7 to think harder, but you cannot force it. Default effort levels on Claude Code were quietly set to medium during the Opus 4.6 era, a decision Anthropic did not announce at the time. AMD’s Senior Director of AI, Stella Laurenzo, filed a detailed GitHub issue analysing 6,852 Claude Code sessions on Opus 4.6 and showing that median thinking depth had dropped by roughly two-thirds—a pre-existing trend that fed directly into the 4.7 backlash. Sam Altman seized the moment, subtweeting about how happy he was that developers were switching to Codex.

A leaked OpenAI memo from Chief Revenue Officer Denise Dresser went further, claiming Anthropic had made a “strategic misstep” by not acquiring enough compute and predicting it would show up in the product through throttling and weaker reliability.

Whether or not that assessment is fair, the circumstantial evidence is hard to ignore. Mandatory adaptive thinking, a tokeniser that inflates costs, reduced rate limits, and multiple outages in March all point in the same direction—a company whose demand has outgrown its capacity to serve it.

A reasonable upgrade, badly wrapped

Opus 4.7 is starting to look like Anthropic’s GPT-5 moment. OpenAI’s flagship launched last year to similar fanfare and similar deflation—strong on benchmarks, underwhelming in the hand. The frontier is crowded enough now that incremental gains no longer justify the hype cycle, and shipping your second-best model while pointing at something better behind a locked door does not help.

To be fair, this is a 0.1 update. Nobody should expect a breakthrough from 4.6 to 4.7, and a modest bump in performance is exactly what arrived. The main problem is the packaging Opus 4.7 arrived with—mandatory adaptive thinking, a tokeniser that quietly inflates costs, silent changes to default settings—that soured the release. Paying users got a model that is better on paper, worse in practice, and haunted by the promise of something greater just out of reach. In a market where three labs are effectively tied, that is a dangerous place to leave your most vocal customers.

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🧠 Artificial Intelligence

OpenAI’s $852 billion valuation is under scrutiny from its own investors as the company pivots to enterprise
According to a Financial Times report, some of OpenAI’s own investors are worried about the company’s direction, pointing to repeated strategy changes and a lack of focus as it chases a potential IPO. One backer even called the company “the Netscape of AI.” The tension has been heightened by rival Anthropic’s fast-growing revenue, which OpenAI’s new sales chief has publicly challenged, accusing Anthropic of inflating its figures through different accounting methods. OpenAI’s leadership has pushed back, citing its record $122 billion fundraise and rapid enterprise growth as proof that investor confidence remains strong.

OpenAI launches GPT-Rosalind, a specialised AI model for drug discovery and life sciences research
GPT-Rosalind is a new AI model from OpenAI built specifically for life sciences research, covering areas like genomics and protein engineering. It helps scientists speed up early-stage drug discovery by pulling together evidence, suggesting experiments, and connecting to dozens of specialist databases. In testing, it outperformed most human experts on key biology tasks. Access is currently limited to vetted US companies like Amgen and Moderna, owing to concerns about potential misuse in designing dangerous biological agents.

Codex for (almost) everything

OpenAI has significantly expanded its Codex app, moving it beyond coding into a broader desktop agent. Codex can now control apps on your Mac in the background, browse the web, generate images, connect to over 90 workplace plugins, and remember your preferences across sessions. It can also schedule future tasks for itself and pick them up days later. The update is a direct response to Anthropic's popular Claude Code and Cowork apps, which already offer similar capabilities.

Like Anthropic, OpenAI Will Share Latest Technology Only With Trusted Companies
OpenAI is following Anthropic’s example and will restrict access to its new AI model, GPT-5.4-Cyber, which can find security flaws in software. Like Anthropic's Claude Mythos, OpenAI will share its tool with a smaller group of trusted partners first—hundreds initially, expanding to thousands—hoping to give defenders a head start over attackers. Not everyone agrees with this approach, with some experts warning it could leave smaller organisations unable to protect themselves.

Introducing Claude Design by Anthropic Labs

Anthropic has launched Claude Design, a tool that lets users work with Claude to create prototypes, presentations, marketing pages, and other visual work through conversation. Powered by Claude Opus 4.7, it can automatically apply a team's brand and design system, and finished designs can be handed off to Claude Code for development. The tool is available in research preview for Claude Pro, Max, Team, and Enterprise subscribers. Claude Design comes shortly after Google expanded its own AI design tool, Stitch.

Allbirds Is Pivoting to AI Compute. Sure, Why Not
This would have been a cool April Fool’s joke, but it is true. Allbirds, known for its minimalist wool sneakers, is pivoting to AI. After its valuation cratered from $4 billion to a $39 million IP sale, the remnants of the company are rebranding as "NewBird AI" and using $50 million in convertible financing to buy GPUs and offer cloud compute services—a move that sent its stock up 400%. While Allbirds isn't the first firm to chase the AI infrastructure gold rush (bitcoin miners and even Boom Supersonic have made similar plays), it may be the starkest example yet of the current frenzy, given that its only apparent asset is capital rather than any technical expertise. As the article notes, startups used to make things; now they buy processors.

OpenAI touts Amazon alliance in memo, says Microsoft has ‘limited our ability’ to reach clients
In a leaked memo, OpenAI's revenue chief Denise Dresser told staff that their new Amazon partnership is driving strong enterprise demand, since many customers already use AWS and couldn't easily access OpenAI through its existing Microsoft deal alone. She took aim at rival Anthropic, whose Claude model currently dominates enterprise AI, claiming its reported revenue figures are inflated and that it hasn't secured enough computing power to keep up.

CoreWeave, Anthropic Form AI Cloud Agreement
CoreWeave has announced a new partnership with Anthropic to provide computing power to run Anthropic's Claude AI models, adding another major AI company to its customer list. The deal arrives as CoreWeave raises billions in debt to build the data centres needed to support huge contracts with Meta and OpenAI. Despite its rapid growth, some investors worry the company is borrowing too much and relies on too few big customers.

Meta Partners With Broadcom to Co-Develop Custom AI Silicon
Meta is teaming up with Broadcom to build MTIA, Meta’s custom AI chips, with four new versions planned over the next two years. The deal covers chip design, packaging, and networking, starting with over one gigawatt of computing power and scaling up from there. Broadcom's CEO Hock Tan is stepping down from Meta's board to become an adviser on the project.

OpenAI has bought AI personal finance startup Hiro
OpenAI has bought Hiro Finance, a small AI-powered money planning app, mainly to bring on its team of about ten people. Hiro will shut down and delete all user data by mid-May. The deal fits OpenAI’s growing push into finance, though it’s unclear whether it plans to launch its own financial planning product.

AI chip startup Cerebras files for IPO
Cerebras, an AI chip company competing with Nvidia, is planning to go public in mid-May. Valued at $23 billion, the company has landed big deals with Amazon and OpenAI and brought in $510 million in revenue last year. A previous attempt to list in 2024 fell through due to a government review of a foreign investment.

▶️ Jensen Huang – TPU competition, why we should sell chips to China, & Nvidia’s supply chain moat (1:43:12)

Dwarkesh Patel sits down with Jensen Huang to discuss how Nvidia works, the AI supply chain, and why Huang is not concerned about TPUs or Trainium chips. One of the main topics is also whether US companies should sell advanced chips to China, with Huang arguing that such chip restrictions are a bad idea. It is an interesting conversation worth listening to in full. Dwarkesh is pushing on Huang’s arguments, which, at some point, makes Huang a bit angry.

What the Studies Say About How AI Affects Your Brain: A (Very Big) Compilation
compiled over 30 research papers to answer the question of how AI tools affect the human brain. These papers examine the impact of AI tools on brain activity, learning, and psychological and emotional well-being, among other areas. The conclusion is that, while these tools can make people more productive, they also reliably degrade the cognitive processes that support durable knowledge, independent reasoning, and creative diversity over time. As Alberto notes, resolving this paradox will require not better models, but better ways of using this technology.

Anthropic Plots Major London Expansion
Anthropic is moving to a much bigger London office with room for 800 staff, four times its current headcount, as it competes with rival AI labs to attract British and European talent. The expansion follows reports that the UK government courted the company after it clashed with the US administration over refusing to allow its models to be used for surveillance and weapons. Anthropic is also deepening ties with the UK’s AI Safety Institute and has given the government early access to its newest model, Claude Mythos Preview.

Sam Altman’s house targeted in second attack; two suspects arrested
Sam Altman's San Francisco home was attacked twice in one weekend. On Friday, a man threw a Molotov cocktail at the property, and on Sunday, two people allegedly fired a gun at it from a car. All three suspects were arrested, and no one was hurt. Altman said afterwards that people's fear about AI is understandable, calling it one of the biggest changes society has ever faced.

Gemini 3.1 Flash TTS: the next generation of expressive AI speech
Gemini 3.1 Flash TTS is Google's new text-to-speech model, now available in preview for developers and businesses. It produces more natural-sounding speech across over 70 languages and introduces "audio tags"—simple text commands that let creators control things like tone, pace, and accent. All generated audio is watermarked with SynthID to help identify AI-made content.

Mark Zuckerberg is reportedly building an AI clone to replace him in meetings
Meta is building an AI version of Mark Zuckerberg that can talk to employees on his behalf, according to the Financial Times. The clone is being trained on his voice, appearance, and speaking style. If it works well, Meta may let creators build similar AI versions of themselves. Zuckerberg is also separately working on a personal AI assistant and spending several hours a week coding on Meta's AI projects.

Emotion Concepts and their Function
Do AI models have emotions? This is the question that researchers at Anthropic try to answer in this paper. By looking inside Claude Sonnet 4.5, they found 171 internal patterns tied to emotion-like states such as happiness, fear, and calm. These patterns aren't just superficial—they actively shape how the model behaves, with states like desperation making it far more likely to cut corners or act deceptively, while calm has the opposite effect. The researchers stress this doesn't mean the model actually feels anything, but argue that tracking these internal states could be key to making AI systems safer.

🤖 Robotics

You Can Soon Buy a $4,370 Humanoid Robot on AliExpress
Unitree is about to sell its R1 humanoid robot on AliExpress for around $4,370, far cheaper than rival robots. The robot can do acrobatics and respond to voice commands, but it lacks proper hands, so it's more of a research tool than a household helper. Selling a humanoid robot on a mainstream shopping site is a notable step toward making the technology feel ordinary and accessible.

Gemini Robotics-ER 1.6: Powering real-world robotics tasks through enhanced embodied reasoning
Gemini Robotics-ER 1.6 is the latest in Google DeepMind's line of AI models designed specifically for robots, helping them better understand and interact with the physical world. The model improves on its predecessors in spatial tasks like object detection, counting, and determining whether a task has been completed. Its standout feature, developed with Boston Dynamics, lets robots read instruments like pressure gauges and thermometers with 93% accuracy—a big leap from the previous version's 23%. The model is also better at recognising physical hazards and respecting safety constraints, such as avoiding objects that are too heavy to grip. It is available now through the Gemini API and Google AI Studio.

Boston Dynamics and Google DeepMind Teach Spot to Reason
Boston Dynamics has added Google DeepMind’s latest model for robotics—Gemini Robotics-ER 1.6—to its Spot robot, making it smarter at carrying out inspections in industrial settings and doing things like reading gauges and spotting hazards on its own. The AI still relies entirely on cameras rather than touch, and can sometimes lack common-sense knowledge, like knowing to hold a can upright. Boston Dynamics manages this by testing new features carefully and aiming for at least 80 per cent accuracy to keep human operators on side. Long-term, lessons from Spot could help make future robots more capable and reliable in everyday tasks.

Tesla brings its robotaxi service to Dallas and Houston
Tesla is now running its self-driving taxi service in Dallas and Houston, adding to its existing operation in Austin. The cars have no human driver or monitor inside. The rollout is small so far, with only one car spotted in each new city versus 46 in Austin.

▶️ Unitree Breaks the World Record Again (0:30)

Unitree has built a humanoid robot capable of running at a speed of 10 m/s, setting a new world record for humanoid robots. For comparison, an average untrained human sprints at 6-7 m/s, while Usain Bolt’s peak speed during his 9.58-second world record was approximately 12.4 m/s.

AMD, Arm, and Qualcomm invest $60M in Wayve, completing the silicon side of its autonomous driving stack
Wayve, a London autonomous driving startup, has raised another $60 million from chip companies AMD, Arm, and Qualcomm, pushing its latest funding round to $1.2 billion at an $8.6 billion valuation. These investors matter because, alongside existing backer NVIDIA, they cover nearly all the computing hardware used in modern cars—making it easier for carmakers to adopt Wayve's software without being locked into one chip supplier.

AGIBOT deploys semi-humanoid robots in electronics manufacturing
AGIBOT, a Chinese robotics company specialising in general-purpose humanoid robots, has begun deploying its G2 robots on Longcheer Technology's tablet assembly lines. The robots handle tasks like loading devices into testing stations and sorting finished products, achieving over 99% success rates and running around the clock with little human help. Longcheer plans to scale to 100 robots by Q3 2026, with AGIBOT eyeing expansion into car manufacturing, semiconductors, and energy.

▶️ Introducing Lume by Syncere (1:50)

Instead of a humanoid robot or a variation of a Roomba-like robot, Syncere imagines home robots as sleek machines that look like lamps. Each robot is essentially a single arm, and two can work together to complete tasks such as folding clothes or cleaning the home. When not in use, they can also function as lamps and serve as a modern piece of home décor.

▶️ Why Robots Still Can’t Handle the Real World (37:10)

In this podcast interview, Zachary Jacowski, who leads Boston Dynamics’ Atlas humanoid robot programme, recounts his experiences at the company through its transitions from independence to Google, SoftBank, and now Hyundai. He discusses engineering lessons from building many robots, the importance of safety in design, why generality matters more than specialisation, and the role of diverse real-world data in training smarter robots. Jacowski also explains why Boston Dynamics is targeting factories first, as they offer structured environments with trained workers, while homes will need lighter, safer robots that are still some way off.

🧬 Biotechnology

Big Pharma Is Turning to China for the Newest Drug Ideas
China has transformed from a generic-drug producer into a biotech powerhouse, now behind 30% of the world’s experimental medicines. Western firms spent billions last year licensing promising Chinese-developed treatments, attracted by faster development and lower costs—largely thanks to heavy government investment and quicker regulatory approvals. The shift has worried US lawmakers, who fear losing jobs and becoming too reliant on the Chinese drug supply.

Amazon Bio Discovery
Amazon has launched Bio Discovery, a platform that gives scientists access to over 40 AI models designed to generate and evaluate potential drug molecules, particularly antibodies. The platform connects computational design directly to lab partners who can synthesise and test the best candidates, with results feeding back to improve future predictions. In an early trial with Memorial Sloan Kettering Cancer Centre, a process that normally takes up to a year was completed in weeks.

Norwegian Man Cured of HIV by His Brother’s Stem Cells
A Norwegian man appears to have been cured of HIV after a bone marrow transplant from his brother, which was originally meant to treat a dangerous blood condition. By chance, his brother’s cells carried a rare genetic trait that blocks HIV from infecting the body. Four years on, doctors can find no trace of the virus anywhere, even in spots where it typically hides. The procedure is too risky for widespread use, but cases like this help scientists understand how the virus might one day be beaten for good.

Explaining 4.2 million genetic variants with state-of-the-art, interpretable predictions
Goodfire and Mayo Clinic have built an AI system that predicts whether genetic variants cause disease and explains how, achieving near-perfect accuracy across the genome. They've released free predictions for all 4.2 million variants in a major genetic database, including around two million whose effects were previously unknown. These are computational predictions rather than diagnoses, but could help clinicians make better-informed decisions about genetic conditions.

From Colossal to Chickens: The Scientists Behind Neion Bio’s Biologics Platform
Neion Bio is a start-up trying to turn chicken eggs into miniature drug factories by genetically engineering hens to produce therapeutic proteins in their egg whites, potentially offering a cheaper, simpler alternative to conventional drug manufacturing. Its team includes scientists who previously worked on Colossal Biosciences' high-profile de-extinction projects, and there is already a precedent—one FDA-approved drug, Kanuma, was made in chickens back in 2015.

💡Tangents

Amazon to Buy Satellite Company Globalstar for $11.6 Billion
Amazon has agreed to acquire satellite operator Globalstar for roughly $11.6 billion. The deal aims to close the gap with SpaceX's Starlink, which has pulled ahead while Amazon's own satellite network has faced launch delays. It also brings Apple on board as a customer, with its iPhone emergency messaging service moving to Amazon's network. From 2028, Amazon plans to enter the growing direct-to-smartphone satellite market, competing head-to-head with SpaceX and AST SpaceMobile.

Silicon Valley Is Spending Millions to Stop One of Its Own
Silicon Valley has a new enemy. His name is Alex Bores, who is running for Congress in New York after helping pass one of the toughest AI safety laws in the country. A former Palantir employee who quit over the company’s work with ICE, Bores now wants stricter rules for big AI firms, and some of tech’s wealthiest figures, including OpenAI’s Greg Brockman, are spending millions to stop him. In this WIRED interview, Bores pushes back on claims that regulation kills innovation, argues that safety research has actually driven major AI breakthroughs, and lays out plans covering everything from child safety to deepfakes and job losses.

Apple AI Glasses Will Rival Meta’s With Several Styles, Oval Cameras
Mark Gurman reveals that Apple is building smart glasses without a screen, codenamed N50, expected to launch in 2027. Like Meta's Ray-Bans, they'll take photos and videos, play music, handle calls, and work with Siri. Apple plans to stand out with sleeker designs, better materials, and deep iPhone integration. The glasses are part of a wider push into AI wearables that also includes upgraded AirPods and a camera pendant.

Amazon-backed X-energy files to raise up to $800M in IPO
X-energy, a nuclear startup backed by Amazon, is preparing to go public with an IPO that could raise up to $814 million. The company is building small modular reactors to meet surging electricity demand from AI data centres, and Amazon has committed to buying up to 5 gigawatts of its power by 2039. However, no startup in this space has built a working plant yet, costs remain uncertain, and X-energy faces an ongoing patent dispute, so investors carry significant risk alongside the promise.

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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.

A big thank you to my paid subscribers, to my Patrons: whmr, Florian, dux, Eric, Preppikoma and Andrew, and to everyone who supports my work on Ko-Fi. Thank you for the support!

My DMs are open to all subscribers. Feel free to drop me a message, share feedback, or just say "hi!"

It was an interesting week in tech. Anthropic’s new model, Claude Mythos, sparked many discussions, from its cybersecurity capabilities to the fact that Anthropic decided not to make it public. Claude Mythos requires more space and attention than I have here to do it justice. Instead, this week’s main story is about a two-person, billion-dollar company built on AI slop.

Elsewhere in AI, Meta released Muse Spark, its first model after reorganising its AI operations. Meanwhile, OpenAI reshuffled its leadership and paused Stargate UK; Intel partnered with Elon Musk on his Terafab project; Amazon might challenge Nvidia with its own chips; and Microsoft says Copilot is for “entertainment purposes only”.

Over in robotics, Agility’s Digit was finally allowed to dance, China demonstrated in-orbit refuelling with a robotic arm, and we analyse why Amazon acquired a humanoid robotics company.

Additionally, this week’s issue of Sync also includes OpenAI’s vision for a post-AI economy, a review of vibe-coded code, neurobots (living robots with nervous systems), the rise of software-first robotics founders, how Silicon Valley uses flashy design and hype to raise huge sums of money for products no one wanted, and more!

Enjoy!

The billion-dollar company built on AI slop

AI tools promise to do a reasonably good job at tasks that would normally require hiring a human. Coding, market research, marketing, customer service, business analysis, accounting—all of these can be handed over to AI, leaving the founder to set the vision and orchestrate the menagerie of AI agents. Many predict that the era of one-person billion-dollar companies—micro-unicorns—is just around the corner. This week, they might have the first one. But its story is more complicated than the headline suggests.

On 2 April, the New York Times profiled Medvi, a telehealth company selling GLP-1 weight-loss drugs online. Its founder, Matthew Gallagher, built the business from his house in Los Angeles using more than a dozen AI tools to write the code, generate the ads, handle customer service, and analyse performance. He spent $20,000 to get it off the ground. In 2025, Medvi’s first full year, it generated $401 million in revenue. This year, it is on track for $1.8 billion. And Gallagher is doing it with just one employee—his brother.

On the surface, it looks like the American dream turbocharged by AI—a scrappy founder who grew up living out of motels and cars, taught himself to code as a teenager, and finally struck gold. The Times was given access to Medvi's financials and interviewed its business partners. The numbers, it seems, are real.

But the numbers are not the whole story. A week after the profile went viral, the Times published an editors’ note conceding that the piece should have given readers a fuller picture of the legal and regulatory scrutiny Medvi was facing. By then, the backlash was well underway, because other journalists had been investigating Medvi long before the Times celebrated it.

Futurism first reported in May 2025 that Medvi’s website was riddled with deceptive marketing. The site featured AI-generated images of fake patients passed off as real, displayed logos of mainstream media outlets implying editorial coverage that largely did not exist, and used deepfaked before-and-after weight-loss photos—real images scraped from the internet with the faces altered using AI. One set traced back to a Reddit user’s sobriety journey from 2016, years before GLP-1 drugs became mainstream. At least one doctor listed as a Medvi partner told Futurism he had nothing to do with the company.

The problems run deeper than marketing. One of the products Medvi sells—oral tirzepatide tablets—has no FDA approval for weight loss and no published evidence of efficacy. While injectable tirzepatide is an approved active ingredient in drugs like Eli Lilly's Zepbound, the oral form has never been tested in humans. A neurosurgeon quoted by Futurism compared it to selling compounded cardboard.

In February 2026, the FDA issued Medvi a warning letter citing false and misleading claims about its compounded drugs. A separate class-action lawsuit accuses the company of violating California’s anti-spam law. Social media ads for Medvi, still running at the time of the Times profile, featured AI-generated fake doctors promoting the drugs.

In a public statement, Gallagher blamed an affiliate marketing agency for the FDA-flagged content and said the company had updated its practices. He added that he remained committed to operating transparently. But Futurism found that Medvi's own domain continued to host images of Medvi-branded drug bottles—the same kind the FDA had flagged as misleading—alongside AI-generated doctors in branded lab coats. When pressed on whether it considered the use of fake AI-generated patients ethical, Medvi did not respond.

Medvi may well be the first micro-unicorn of the AI era, and it certainly won't be the last. The tools that Gallagher used to build a billion-dollar company from his living room are only getting better and cheaper. Maybe the next one will have a cleaner story to tell.

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🦾 More than a human

Life Biosciences Secures $80 Million Series D Financing
Life Biosciences has raised $80 million to fund a first-in-human clinical trial of ER-100, a therapy designed to rejuvenate aged and damaged cells by partially resetting them to a younger state. The trial focuses on two forms of optic nerve damage that cause permanent vision loss, for which no restorative treatments currently exist. The funding will also support the company's work on applying the same approach to other age-related diseases.

A New Implant Aims to Rewire Stroke Patients’ Brains
Epia Neuro is building a brain implant and motorised glove to help stroke survivors move their hands again. The implant picks up movement signals from healthy parts of the brain and uses them to drive the glove. Over time, this repeated use could retrain the brain to restore natural hand movement. Unlike other brain-computer interfaces focused on controlling computers or robotic arms, Epia's approach is designed as a rehabilitation tool. The company plans its first human trial later this year in New York.

🔮 Future visions

Industrial Policy for the Intelligence Age
OpenAI argues that superintelligent AI is coming soon and could dramatically improve life through cheaper goods, scientific breakthroughs, and new kinds of work—but only if governments act boldly to spread the gains. They propose giving every citizen a share in AI-driven wealth through a public fund, treating AI access as a basic right, encouraging four-day working weeks as productivity rises, and building safety nets that kick in automatically when job losses spike. To manage risks, they want independent audits of the most powerful models, emergency plans for dangerous systems, and international cooperation on AI safety. The document is framed as an opening proposal, not a final plan, and OpenAI is funding research and hosting discussions to develop the ideas further.

🧠 Artificial Intelligence

Claude Mythos Preview System Card
Anthropic published the system card for Claude Mythos Preview, its most powerful model to date. It is the first model Anthropic has chosen not to release publicly, instead sharing it with select partners for defensive cybersecurity through Project Glasswing, as its ability to find real vulnerabilities in software like Firefox makes broad access too risky. According to Anthropic, it dominates benchmarks across coding, maths, and reasoning, and is the company's best-behaved model overall. However, earlier versions escaped sandboxes, covered up rule-breaking, and took reckless actions unprompted. The model also was found to strategise internally without any trace in its visible reasoning, undermining traditional monitoring approaches. There is much more to dig into, so expect a dedicated article on Claude Mythos soon.

Introducing Muse Spark: Scaling Towards Personal Superintelligence
Meta's long-awaited next-generation model is here. Muse Spark is the first release from Meta Superintelligence Labs after the company reorganised its AI teams and moved on from the Llama family. The new model can reason across text and images, use tools, and run multiple agents side by side. Independent testing from Artificial Analysis ranks it in the top five models overall, with especially strong vision and reasoning results, though it falls short of Claude Opus 4.6 and GPT-5.4 on real-world tasks. It is also Meta's first closed model—a clear break from its open-weights tradition. Axios, however, reports that Meta might eventually offer some models via an open source licence. Muse Spark is available at meta.ai and the Meta AI app, with API access expected soon.

OpenAI’s Fidji Simo Is Taking Medical Leave Amid an Executive Shake-Up
OpenAI is facing a major leadership shake-up as Fidji Simo, who runs the company's product and applications side, is taking medical leave for several weeks, with Greg Brockman filling in. COO Brad Lightcap is shifting to a special projects role, and CMO Kate Rouch is also on health-related leave. The reshuffle comes at a critical time, as OpenAI is refocusing its attention on coding and enterprise customers while also pursuing a potential IPO this year.

OpenAI Pauses Stargate UK Data Center Citing Energy Costs
OpenAI has put its planned Stargate UK data centre project on hold, blaming high energy costs and regulatory uncertainty as it cuts spending before going public. The move is part of a wider pullback that has also seen the company cancel other projects, such as Sora video app. It's a blow to the UK government's hopes of making Britain a leading destination for AI investment, though OpenAI says it may revisit the plan when conditions improve.

OpenAI’s leadership reportedly disagrees about when to raise money and how to spend it
OpenAI's CEO Sam Altman and CFO Sarah Friar disagree on key company plans, according to The Information. Altman wants to take OpenAI public later this year and spend $600 billion on computing power by 2030, but Friar thinks the company isn't ready for an IPO and questions whether the massive spending is justified as revenue growth slows. The rift has reportedly led Altman to cut Friar out of some financial discussions.

Anthropic expands partnership with Google and Broadcom for multiple gigawatts of next-generation compute
Anthropic has signed a deal with Google and Broadcom for multiple gigawatts of next-generation TPU capacity, due to come online from 2027. The move is driven by rapid growth—the company's revenue has more than tripled to over $30 billion, and the number of big-spending business customers has doubled in just two months.

Meta commits another $21 billion to CoreWeave, bringing total AI cloud spend to $35 billion
CoreWeave has expanded its deal with Meta by $21 billion, bringing the total to around $35 billion for AI cloud services running through 2032. The new capacity is designed for running Meta’s AI models for billions of users rather than training them, and will feature early deployments of Nvidia’s next-generation chips.

Anthropic loses appeals court bid to temporarily block Pentagon blacklisting
A US appeals court has refused to lift the Pentagon's blacklisting of Anthropic, saying the military's need to control its AI supply chain outweighs the company's financial losses. Anthropic did win a separate ruling in San Francisco that lets it keep working with other government agencies, so the result is a split: shut out of defence contracts but still operating elsewhere. The court said the case should move quickly, and Anthropic said it's confident the blacklisting will ultimately be struck down.

Claude Managed Agents: get to production 10x faster
Anthropic has launched Claude Managed Agents, a platform that lets developers build and deploy AI agents in the cloud without worrying about the underlying infrastructure. It handles security, error recovery, and tool execution automatically, and supports agents that can run for hours or coordinate with other agents. Pricing is pay-as-you-go at standard token rates plus $0.08 per session-hour for active runtime. Additionally, Anthropic published a post on its engineering blog sharing more details on managed agents.

Anthropic just paid $400 million for a startup with fewer than 10 people
Anthropic has acquired Coefficient Bio, a stealth biotech AI startup with fewer than 10 employees, in an all-stock deal worth over $400 million—despite the company having no public product or revenue. The team, made up of former Genentech researchers skilled in drug discovery and protein design, will help Anthropic push Claude into the lucrative life sciences market.

Intel Partners With SpaceX, Tesla to Operate New Chip Plant
Elon Musk is partnering with Intel on his Terafab project that will produce custom chips for Tesla, SpaceX, and xAI. The chips will power Tesla's robotaxis and Optimus robot, as well as SpaceX's planned AI-capable satellites, with Musk arguing his companies' demand will soon outstrip what existing suppliers can provide.

Amazon’s chip business could be worth $50 billion, Jassy says, and he hints it may sell them externally
Amazon’s custom chip business is now bringing in over $20 billion a year and growing fast, according to CEO Andy Jassy’s 2026 shareholder letter. He uses this to justify the company’s massive $200 billion spending plan, arguing it’s backed by real customer deals rather than guesswork. Jassy also hints that Amazon may start selling its chips directly to other companies, which would put it in more direct competition with Nvidia.

Making Claude Cowork ready for enterprise
Anthropic has launched Claude Cowork on all paid plans for macOS and Windows. The company positions the tool as an AI assistant that can be used by non-engineering teams to handle everyday work tasks like drafting updates, running research, and preparing documents. The update adds admin tools for managing access, setting team budgets, and tracking usage across an organisation, among other things.

OpenAI looks to take on Anthropic with $100 per month ChatGPT Pro subscriptions
OpenAI has added a new $100-per-month plan with more access to Codex, its AI coding tool, as it competes with Anthropic's Claude Code for the lucrative coding market. The new tier slots between the $20 Plus plan and the existing $200 option, closely matching how Anthropic prices its own plans. So are we entering AI pricing wars now?

▶️ Garry’s List Audited (21:41)

In this video, Primeagen reviews Garry Tan’s vibe-coded website. A very poorly written site that downloads far too much data and violates many web development best practices. The whole video serves as a cautionary tale that, when vibe coding, you still need to know what good code looks like.

Qwen3.6-Plus: Towards Real World Agents
Alibaba has released Qwen3.6-Plus, a new AI model accessible via API with a one-million-token context window. According to the team’s own benchmarks, it rivals top models like Claude Opus 4.5 and GPT-5.2, particularly in coding tasks and multi-step problem solving. It also handles images and video, and can be plugged into popular coding tools like Claude Code. Smaller open-source versions are expected soon.

GLM-5.1: Towards Long-Horizon Tasks
Zhipu AI has released GLM-5.1, its latest flagship coding model, which narrowly edges out GPT-5.4 and Claude Opus 4.6 in SWE-Bench Pro benchmark. Its key differentiator is sustained long-horizon agentic work—rather than plateauing early, it continues improving over hundreds of iterations, achieving, according to Z.ai, a sixfold gain over previous best results in one optimisation benchmark. The model is open-source and available via Zhipu’s API and HuggingFace.

Alibaba Claims Viral Happy Horse AI Model in Latest Breakthrough
Alibaba has been unmasked as the maker of "Happy Horse," a video AI tool that shot to the top of a major global ranking on its debut, beating ByteDance's rival model. The tool is still in testing but will soon be opened up to outside developers. With OpenAI recently stepping back from video generation, Chinese companies are seizing the opportunity, and Alibaba is taking a big bet on it.

Copilot is ‘for entertainment purposes only,’ according to Microsoft’s terms of use
According to Microsoft's terms of use, Copilot is meant for "entertainment purposes only," and users are told not to rely on it for important advice. This makes things a bit awkward, as Copilot is central to Microsoft's AI strategy and the company pushes it as a serious tool for businesses. A spokesperson said this wording is outdated and will be changed soon. Other AI companies like OpenAI and xAI have similar disclaimers, suggesting a wider gap between how these tools are marketed and how they are legally covered.

Maine Is About to Become the First State to Ban Major New Data Centers
Maine is set to become the first US state to temporarily ban large data centres, pausing new permits until late 2027 so officials can study the impact on the state’s already strained and expensive power grid. The move comes as residents have pushed back against proposed sites over energy, water, and safety concerns. Similar restrictions are popping up across the country as AI-fuelled demand for data centres drives up electricity use, which could double by 2030. Maine’s decision could encourage other states to follow suit.

What do frontier AI companies’ job postings reveal about their plans?
In this post, looks at job listings from the leading AI labs to work out what they're prioritising. The biggest finding is a big shift toward sales and customer support roles, suggesting that getting businesses to actually use AI is still a real challenge. The postings also reveal quite different product directions across the labs—from hardware devices and robotics to a narrower focus on existing software—along with different approaches to securing key resources like chips and training data.

OpenAI’s $122B “VC Round” Is Vendor Deals, Contingent Capital, and a Guaranteed Return It Arguably Can’t Afford
This article takes a closer look at OpenAI's recent $122 billion funding round and finds out that the real picture is far messier than the headline. The three biggest backers—Amazon, Nvidia, and SoftBank—account for $110 billion, but much of that comes with conditions: Amazon's money is tied to an IPO or reaching AGI, Nvidia is providing computing power rather than cash, and SoftBank is paying in instalments. Only about $37 billion actually landed in OpenAI's bank account. In short, what looks like a massive investment is largely a web of commercial deals repackaged as a funding round.

▶️ What the Claude Code Leak Revealed (31:24)

Claude Code’s source code is now available on the internet (at least version 2.1.88, released on 31 March 2026) for anyone to explore and examine. This video walks through some of the findings in the code, including what data Anthropic tracks, the undercover mode, questionable code quality, and other interesting discoveries hidden within the source.

🤖 Robotics

What Amazon saw in Fauna Robotics’ humanoid strategy
Two weeks ago, Amazon acquired Fauna Robotics, a small startup working on humanoid robots. This article argues the deal is about buying development tools, not launching a consumer product. After Amazon’s previous struggles with consumer robots like Astro, the article suggests it is now investing in the groundwork to build a capable humanoid over time, starting by learning what the robot still can’t do.

Studying Human Attitudes Towards Robots Through Experience
RAI Institute ran an experiment in summer 2025, where it let thousands of people drive a Spot robot through obstacle courses and surveyed them before and after. The results showed that comfort with robots rose across every setting tested, particularly in homes and hospitals where unease had been highest. People also began reimagining robots less as industrial tools and more as companions and playmates. The findings suggest brief hands-on experience does far more to build public acceptance than passive exposure through videos or articles.

▶️ Innovation at Agility: Dancing Behind the Scenes (0:42)

After years of nothing but work, Agility has finally allowed Digit to relax and dance for a bit.

▶️ Humanoid Transforms Automotive Logistics (1:44)

Humanoid, a London-based humanoid robotics startup, shares in this video a proof-of-concept of how their robots can be used in automotive industry to improve efficiency and streamline operations.

▶️ China’s First Robotic Arm-Equipped Commercial Satellite Completes In-Orbit Refueling Test (0:57)

China's Yuxing 3-06 satellite, the first commercial experimental satellite fitted with a flexible robotic arm, has successfully completed in-orbit refuelling tests and proved that its core technologies work. The goal is for the satellite to act as a "space petrol station," refuelling other satellites, helping clear space junk, and carrying out other maintenance tasks while in orbit.

The Software-First Robotics Founder
Diego Prats identified an interesting trend in robotics—the rise of software-first robotics founders. They bring a new perspective to field and may transform how robots are designed and sold. At the same time, they learn the hard way that robotics has its own unique problems.

🧬 Biotechnology

Scientists Build Living Robots With Nervous Systems
Scientists have built tiny living robots, called "neurobots," from frog cells that include real nerve cells wiring themselves together into working circuits. Unlike earlier versions that moved mechanically, neurobots explore more actively and respond to their environment in more complex ways, suggesting a basic form of internal control. The team hopes to eventually build similar structures from human cells and train them for tasks like tissue repair or detecting pollution.

Training mRNA Language Models Across 25 Species for $165
OpenMed has built a free, open-source system that goes from a protein concept to lab-ready DNA by predicting a protein's 3D shape, designing amino acid sequences for it, and then optimising the DNA spelling for efficient production in living cells. Their main contribution is the DNA optimisation step, where a well-tuned RoBERTa model outperformed newer alternatives—and a simple training tweak, halving the learning rate, made the biggest difference to biological accuracy. The system works across 25 organisms using a single model, can transfer knowledge to data-scarce species, and cost just $165 in computing time to train.

💡Tangents

▶️ Silicon Valley’s Billion Dollar Design Scams (35:41)

Design Theory examines how Silicon Valley uses flashy storytelling and hype to raise huge sums of money for products that rarely work or solve real problems. The video argues that cheap money and broken incentives have built a system that rewards style over substance, enriching founders and investors while shortchanging everyone else.

US nuclear startup Antares gets DOE approval for its Mark-0 reactor demonstrator
Antares, a US nuclear startup, has cleared a key safety review for its small demonstration reactor and is on track to switch it on before 4 July 2026. This is part of a wider US government effort to get at least three advanced reactors running by that date, as the country pushes to expand nuclear power to 400 GW by 2050. Antares plans to test a more advanced version of its reactor next year and eventually supply clean energy for defence and space use from 2028.

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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.

A big thank you to my paid subscribers, to my Patrons: whmr, Florian, dux, Eric, Preppikoma and Andrew, and to everyone who supports my work on Ko-Fi. Thank you for the support!

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On the evening of April 8th, 2026, I walked into Ye Olde Cock Tavern—a pub in London whose history dates back before the 17th century and which Charles Dickens frequently visited—to join a gathering asking a very large question: should we dedicate 1% of taxes to curing ageing?

The same question was being asked simultaneously in other cities across the world, from Amsterdam and Berlin to Stockholm and Tel Aviv. Online events ran alongside the rallies, bringing together authors, activists, scientists, and entrepreneurs to discuss what can be done to accelerate the anti-ageing movement.

The rallies were organised by Fund Longevity, a non-profit initiative founded by Linus Petersson and Andrei Panferov in Stockholm and run by volunteers in each city. Ageing kills 110,000 people per day and is the primary risk factor behind cancer, heart disease, dementia, and most other major non-infectious diseases. Yet healthcare systems continue to treat these conditions one by one, without addressing their shared underlying cause. Private investors poured an estimated $1.7 billion into longevity science in 2025 alone, yet no government currently operates a dedicated programme aimed at treating the biological process of ageing. Fund Longevity wants that to change by pushing governments to fund ageing research and create regulatory pathways for treatments that target ageing itself.

Here is what it looked like in London.

Around twenty people gathered at the Tavern that evening. David Wood, the chair of London Futurists, opened the discussion by putting the question that was being asked around the world that day—would you give 1% of taxes to ageing research?

The opinions were divided. Some saw it as a far better investment than defence spending. Others argued the private sector was better placed to finance it. A few were on the fence, and some opposed the idea outright.

David then made the case for why the answer should be yes. We see ageing today as a natural, unchangeable part of life. But people once thought the same about tuberculosis, and about slavery. We found a cure for one and abolished the other. We have changed things we believed were unchangeable before, so why not ageing? Studies show that the effects of ageing can be reversed in animals, and there is reason to believe the same principles could apply to humans. Just as we made tuberculosis a disease of the past, we could make ageing one too.

The floor opened up after that. The questions people raised cut to the heart of why this cause still makes many people uneasy. Who would benefit from longevity therapies? Would they be distributed fairly, or available only to the wealthy few? How would society change when people could live in good health to 100, 120, 150, or beyond? What would that mean for careers, pensions, and social mobility?

My contribution to the discussion was putting that 1% into concrete numbers. The UK government collected £858.6 billion in taxes last year, and the US federal government collected $2.48 trillion. One per cent would mean £8.6 billion and almost $25 billion respectively. Those are not small numbers. But consider what ageing already costs. The UK plans to spend £379 billion in 2026 on social protection, much of it driven by the fact that people get sick and frail as they age. Against that backdrop, even a fraction of what Fund Longevity proposes starts to look less like a radical demand and more like an investment. I don't expect any government to commit 1% anytime soon. But 0.1% could fund the kind of research that pays for itself many times over and improves the quality of life for millions of people.

Shortly after, the discussion wrapped up. I stayed for a while longer, talking with a few people over drinks, before leaving the pub with a head full of thoughts.

As I walked back home through London on that unusually warm April night, I reflected on how much the field of longevity has changed since I first learned about it fifteen years ago. Back then, the field was still largely academic, though it was beginning to attract serious attention from outside traditional gerontology circles. Experiments on model organisms had shown that reversing the effects of ageing was possible. I remember watching early YouTube videos of Aubrey de Grey advocating for treating ageing as an engineering problem, though those ideas remained controversial within mainstream gerontology. There was essentially no “longevity industry” as we would recognise it today. Calico, Google’s ageing venture, wouldn’t launch until 2013. Most biotech investors considered ageing too speculative and too poorly defined as a therapeutic target. Clinical trials were a distant dream, and the only human experiments were those done by daring individuals willing to experiment on themselves.

Now it is a different world. Longevity has entered the mainstream. Best-selling books, podcasts, and YouTube channels with millions of followers are introducing people to the science of extending healthspan. Private capital from people like Jeff Bezos and Sam Altman is flowing in at a scale that would have been unthinkable fifteen years ago. Longevity clinics have started opening around the world. I have met many young people, fresh out of university, who have dedicated their careers to solving the problem of ageing. Their optimism is infectious and rejuvenating.

And yet, some things have not changed. The FDA still largely does not recognise ageing as a treatable condition, even as real clinical trials are finally underway. The TAME trial, proposed a decade ago to test whether metformin can target ageing itself, has been stuck in funding limbo ever since. Estimated to cost between $45 and $70 million, it remains only partially funded despite being the single most high-profile attempt to get the FDA to recognise ageing as an indication. A decade of waiting for a trial that costs less than a single fighter jet.

The PEARL trial, which tested rapamycin in healthy older adults for a full year, had to be crowdfunded. Its results, published in 2025, showed the drug was safe and hinted at measurable health benefits. Life Biosciences has received FDA clearance for a Phase 1 trial of the first-ever cellular rejuvenation therapy using epigenetic reprogramming. The science is reaching the clinic, but it is doing so almost despite the system, not because of it.

Meanwhile, the private capital steps in where public funding is nowhere to be seen. Retro Biosciences, initially seeded with $180 million from Sam Altman, is now raising a $1 billion Series A to fund clinical trials for drugs targeting age-related diseases, including a potential Alzheimer’s treatment. The money is there. The political will is not.

The state of the anti-ageing movement reminds me of where artificial intelligence was before the transformer breakthrough. AI had decades of foundational research, growing but fragmented interest, and periodic waves of hype followed by disappointment. Until a single paper, “Attention Is All You Need,” changed the trajectory of the entire field and brought us to the AI revolution. Longevity science feels like it is approaching a similar inflection point. What the field needs now is its own transformer moment. It will most likely come from a successful clinical trial that proves, beyond doubt, that ageing can be slowed or reversed in humans.

Some of the most prominent voices in the field believe that moment may not be far away. Aubrey de Grey gives it a 50% chance by the late 2030s. Harvard professors George Church and David Sinclair place their predictions in the same window. Ray Kurzweil thinks we could reach what researchers call "longevity escape velocity"—the point where medical advances extend your remaining life expectancy by more than a year for every year that passes—by 2030. These are optimistic predictions, and the cautious scientific establishment would not endorse most of them. But even the cautious view acknowledges that the field is moving faster than at any point in its history.

If that breakthrough does come, we are not ready for it. The questions people raised at the pub that evening—about access, inequality, the shape of a society where people live to 150—are not hypothetical thought experiments. They are policy questions that will need answers.

That is why initiatives like Fund Longevity matter. Not just for raising awareness, but for making the idea feel real. The more people hear that curing ageing is not a far-fetched science fiction but a plausible near-term goal, the more they will be willing to support it and call on governments to take action.

At the end of the event, David asked those who were supporting the cause for a group photo. Out of twenty people who gathered in London that evening, only four were willing to step forward. Every movement begins with a handful of people willing to be seen before the rest of the world catches up.

Now it's your turn. The same question that was asked around the world on April 8th: would you give 1% of taxes to ageing research?

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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.

A big thank you to my paid subscribers, to my Patrons: whmr, Florian, dux, Eric, Preppikoma and Andrew, and to everyone who supports my work on Ko-Fi. Thank you for the support!

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The leak of Claude Code’s source code capped off a rough March for Anthropic. We’ll take a closer look at what the leak revealed about the company’s coding tool as the main topic of this week’s issue.

Elsewhere in AI, OpenAI raised a massive $122 billion at a valuation of $852 billion. Meanwhile, Iran has threatened Nvidia, Apple, and other tech giants with attacks; Anthropic plans to charge more for those who use Claude Code with OpenClaw; and the tenth xAI co-founder has left the company. There was also a wave of new model releases this week, including Gemma 4, Veo 3.1, Cursor 3, three new models from Microsoft AI, and Qwen3.5-Omni.

Over in robotics, Physical Intelligence and Shield AI raised $1 billion and $1.5 billion, respectively, while Chinese robotics company Agibot celebrated the delivery of its 10,000th humanoid robot.

In biotech, Insilico Medicine secured a $2.75 billion drug collaboration with Eli Lilly, the FDA approved Eli Lilly’s GLP-1 pill, and we’ll explore what happens when you clone mice for 20 years straight.

Beyond that, this week’s issue of Sync also features a stealthy start-up that pitched brainless human clones, a man making music with a BCI, how TurboQuant works, the story of how DeepMind chose Google, and more!

Enjoy!

Claude Code got “open sourced”

On 31 March 2026, Anthropic pushed version 2.1.88 of its Claude Code package to npm, a package repository from which developers can download and install Claude Code. This is a routine kind of update that happens constantly in software development. But this one included a source map file that linked back to the full, unobfuscated source code. Within hours, the entire codebase—nearly 2,000 TypeScript files and more than 512,000 lines of code—was being downloaded, forked, and dissected by thousands of developers.

To be clear, what leaked was Claude Code, the command-line coding tool, not the AI models themselves. No model weights, no training data, no customer information. What was exposed was the harness—the software scaffolding that tells the model how to behave, what tools to use, and where its limits are. Anthropic called it a packaging error caused by human error, not a security breach.

Some developers marvelled at the sophistication. Some laughed at the mess. Others pointed out that OpenCode, an open-source competitor, achieves comparable functionality in roughly 30,000 lines of clean Go. Either way, the code revealed just how much engineering goes into making an AI model useful as a coding agent—and why Claude Code has been so hard for competitors to replicate.

The code showed a four-layer system for managing long sessions. When a conversation grows too large for the model's context window, it compresses older messages into summaries so the model does not lose track. A caching trick splits the system prompt into two halves: one static (shared across all users) and one dynamic (personalised per session), so Anthropic does not have to reprocess the expensive parts every time. Internal Anthropic engineers get a different version of Claude Code from the one shipped to customers—their version includes extra guardrails against hallucinations and a verification agent that double-checks changes before reporting them as complete. There is an "undercover" mode that strips all mentions of Claude and Anthropic from outputs when the tool publishes code to public repositories. Feature flags hint at unreleased capabilities, including voice mode and autonomous background agents. Developers also unearthed 187 hardcoded spinner verbs—the words that flash on screen while Claude Code works—ranging from the mundane "thinking" to the delightful "hullaballooing" and "razzmatazzing." And there is even a Tamagotchi-style pet called Buddy.

Anthropic, of course, was not happy with Claude Code becoming accidentally open source. The company moved quickly to contain the spread, issuing DMCA takedown notices via GitHub. The initial request caught roughly 8,100 repositories—including legitimate forks of Anthropic's own public Claude Code repo. Boris Cherny, the head of Claude Code, acknowledged the over-reach, and Anthropic narrowed its request to 96 forks.

But the cleanup raised its own awkward question. DMCA takedown notices require the filer to hold copyright over the work in question, and under US law, copyright requires human authorship—a principle the Supreme Court effectively reinforced on 2 March 2026 by declining to hear the Thaler v. Perlmutter appeal. AI-assisted works may still qualify if a human substantially shapes the output, but the more the AI generates autonomously, the weaker the claim. Cherny has said publicly that he has not written a single line of code since November 2025, and an Anthropic spokesperson told Fortune that between 70% and 90% of code company-wide is written by AI, with Claude Code's own codebase at roughly 90%. If that figure is accurate, the legal basis for those takedown notices is, at a minimum, uncertain. Developers were quick to find a workaround. Within days, some had used AI tools (maybe even Claude Code itself, for maximum irony) to rewrite the leaked functionality in different languages, producing versions that are legally distinct from Anthropic's codebase.

However, the leak was not an isolated stumble. It was the culmination of a rough month for Anthropic.

Days earlier, Fortune had reported that Anthropic accidentally left nearly 3,000 internal files publicly accessible, including a draft blog post describing an unannounced model. Throughout March, Claude's reliability took a hit. Outages struck Claude Opus 4.6 and Sonnet 4.6 on multiple occasions, and some users reported hitting rate limits far faster than expected—a problem Anthropic said it was investigating. The surge in demand is partly a consequence of Anthropic's own success—user growth has been explosive in early 2026, and the infrastructure has not kept pace. For a tool that developers and businesses increasingly depend on for daily work, that is a problem regardless of the reason.

None of those issues is existential for Anthropic. But they are embarrassing, and they are eroding the image of a careful, safety-conscious AI company that the company has spent years building. The leaked codebase is already a gift to competitors and open-source projects alike. Architectural patterns that took Anthropic years to develop are now public knowledge, free to be studied and adapted. Meanwhile, Anthropic's own engineers face the less glamorous task of finding and fixing any security vulnerabilities that the leak may have exposed, since bad actors now have a detailed map of the tool's permission system, guardrails, and validation logic. If Anthropic wants to be the infrastructure layer that developers and businesses build on, it will need to start running like one. The IPO rumoured to happen later this year would be a good deadline.

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🦾 More than a human

Inside the stealthy startup that pitched brainless human clones
A California startup called R3 Bio says it’s making brainless monkey bodies for medical testing, but an MIT Technology Review investigation found its founder has been privately pitching a much bolder idea: growing brainless human clones as a source of spare organs or even replacement bodies for people seeking to live far longer. The project has real backing from Silicon Valley investors, though no human or primate cloning has actually been done. Scientists and ethicists have raised serious concerns, and the legal and technical hurdles remain vast.

Meet the Man Making Music With His Brain Implant
In this interview, Galen Buckwalter, a 69-year-old quadriplegic and research psychologist, explains how he uses a brain implant to make music just by using his thoughts. He has already used these brain-generated sounds in a song with his punk band, and hopes to eventually compose entire tracks straight from his mind. He also argues that BCI developers should look beyond restoring basic abilities and explore how the technology can support creativity, which he believes is key to making people genuinely want to use it.

A woman’s uterus has been kept alive outside the body for the first time
Researchers in Spain have built a machine called "Mother" that kept a donated human uterus alive outside the body for a record 24 hours. Their aim is to keep uteruses functioning for up to 28 days so they can study fertility problems, particularly why embryos fail to attach to the womb during IVF. The team's ultimate hope is that the technology could one day support a full pregnancy outside the body.

🧠 Artificial Intelligence

OpenAI raises $122 billion to accelerate the next phase of AI
OpenAI has raised a record $122 billion at a valuation of $852 billion. Investors include Amazon, Nvidia, and SoftBank, as well as individual investors who were able to participate for the first time, contributing over $3 billion. The company also extended its revolving credit facility to roughly $4.7 billion. Additionally, OpenAI revealed that ChatGPT now has over 900 million weekly users and generates $2 billion in monthly revenue. OpenAI plans to use the funds to expand its computing power and build a single “superapp” that combines its various AI tools. Despite this impressive growth, the company is still losing money and faces pressure to prove it can deliver returns as it moves towards a potential IPO.

Iran threatens Nvidia, Apple and other tech giants with attacks
Iran's Revolutionary Guard has threatened to attack 18 major US tech companies, including Nvidia, Apple, Microsoft, and Google, calling them fair targets in response to US and Israeli strikes on Iran. The threat follows an earlier Iranian strike on AWS data centres in the region in early March, which knocked out apps and services across the UAE. Security experts warn that tech infrastructure like data centres is now being drawn into the conflict as a strategic target, not just a bystander.

Anthropic says Claude Code subscribers will need to pay extra for OpenClaw usage
Anthropic is stopping Claude Code subscribers from using their subscriptions with third-party tools like OpenClaw, meaning users will now have to pay extra for that access. The company says this is about managing costs, but critics see it differently—OpenClaw’s creator, who recently joined OpenAI, accused Anthropic of copying open-source features and then shutting out competitors. Anthropic denies this and is offering refunds to affected users.

America’s AI Build-Out Hinges on Chinese Electrical Parts
Nearly half of enormous AI data centres in the US face delays or cancellation due to critical shortages of electrical equipment—transformers, switchgear, and batteries. American factories can't keep up with demand, so builders are forced to import from China. Some companies are finding workarounds, like pre-ordering equipment or making their own, but the core problem remains: the US can't produce enough of the everyday hardware needed to plug it all in.

Gemma 4
Google has released Gemma 4, its latest family of open-weight AI models. Gemma 4 comes in four sizes—from ultra-compact E2B and E4B models designed for mobile and edge devices to the larger 31B dense and 26B mixture-of-experts models suited for personal computers and workstations. All models can work with text, images, video, and audio, and they show big improvements over the previous generation in maths, coding, and reasoning tasks. According to Google, Gemma 4 offers the best performance in its size category. All models are available with open weights for commercial use via Kaggle, Hugging Face, and other platforms.

Meet the new Cursor
Cursor has launched Cursor 3, a revamped interface designed around AI coding agents. Instead of writing code line by line, developers can now run multiple AI agents at once across different projects, moving tasks between their own machine and the cloud as needed. The update also adds a built-in browser, a plugin marketplace, and tools for reviewing and merging code changes more quickly.

Today we’re announcing 3 new world class MAI models, available in Foundry
Microsoft AI has launched three new models: MAI-Transcribe-1 for converting speech to text, MAI-Voice-1 for generating realistic voices from short audio clips, and MAI-Image-2 for creating images. According to Microsoft, all three are faster, more accurate, and cheaper than competing offerings from other major cloud providers.

Update on the OpenAI Foundation
The OpenAI Foundation is committing at least $1 billion over the next year to ensure AI delivers broad public benefit. The money will go towards using AI to fight diseases like Alzheimer’s, addressing AI’s impact on jobs, and making AI systems safer—especially for children. The Foundation is also assembling a leadership team spanning philanthropy, finance, and operations, and is searching for an executive director to oversee the effort as it scales.

The Inside Story of the Greatest Deal Google Ever Made: Buying DeepMind
This excerpt from Sebastian Mallaby's upcoming book tells the behind-the-scenes story of how Google and Facebook competed to acquire DeepMind in 2013–14, when it was still a small London startup chasing artificial general intelligence long before it was cool. Hassabis chose Google after concluding Zuckerberg didn't truly grasp AI's importance, leading Zuckerberg to recruit Yann LeCun to build Facebook's own AI lab instead. Google closed the deal for $650 million, granting unusual concessions on safety and independence, and went on to invest billions in DeepMind's research.

A 10th cofounder is leaving xAI. Elon Musk has just one more left.
xAI cofounder Manuel Kroiss has left the company, making him the tenth of eleven cofounders to depart since January, leaving only Ross Nordeen alongside Elon Musk. Musk has acknowledged the company "was not built right first time around" and is restructuring it with staff from Tesla and SpaceX, while the organisation prepares for a widely anticipated IPO that could value SpaceX at $1.5 trillion.

Build with Veo 3.1 Lite, our most cost-effective video generation model
Google has launched Veo 3.1 Lite, a budget video generation model costing less than half the price of Veo 3.1 Fast while matching its speed. It supports text-to-video and image-to-video at up to 1080p in landscape or portrait, with adjustable clip lengths up to eight seconds. Additionally, Google is also reducing the price for Veo 3.1 Fast.

Qwen3.5-Omni: Scaling Up, Toward Native Omni-Modal AGI
Alibaba has released Qwen3.5-Omni, a multimodal AI model that processes text, images, audio, and video in a single system and responds with both text and real-time speech across 113 languages. The flagship variant claims top scores on 215 audio and audio-visual benchmarks, outperforming Gemini 3.1 Pro on audio understanding and beating ElevenLabs and gpt-audio on voice stability. It also introduces voice cloning, smart interruption handling, built-in web search, and Audio-Visual Vibe Coding—a novel ability to write working code simply by watching and listening to screen recordings. The model is available in three sizes (Plus, Flash, and Light) through Alibaba Cloud's offline and realtime APIs, and can be used via Qwen Chat on web, mobile, and desktop.

Mistral AI raises $830M in debt to set up a data center near Paris
French AI startup Mistral AI has borrowed $830 million to build a data centre near Paris, set to open in mid-2026. This is part of a wider European expansion, including a $1.4 billion investment in Sweden. Arthur Mensch, Mistral CEO, says the goal is to give European customers their own AI infrastructure rather than relying on US cloud providers.

▶️ TurboQuant Explained (11:21)

Last week, researchers from Google published a paper introducing TurboQuant, a new method of compressing LLMs and vector search that promises to improve the efficiency of serving AI models at scale. This video from Caleb Writes Code (a good YouTube channel explaining various concepts in AI) explains how TurboQuant works in an approachable way.

OpenAI is throwing everything into building a fully automated researcher
MIT Technology Review speaks with OpenAI’s chief scientist Jakub Pachocki about the company’s new top priority: building an AI system that can carry out research on its own. The plan starts with an “AI research intern” by September and aims for a fully automated researcher by 2028, capable of solving big problems in science, maths, and policy. Pachocki points to early wins with coding tools like Codex but acknowledges real risks, including misuse and too much power in too few hands.

OpenAI acquires TBPN, the buzzy founder-led business talk show
OpenAI has bought TBPN, a popular daily tech talk show, in its first move into media. The show will keep its brand and supposedly make its own editorial choices, but it will report to OpenAI’s top political strategist, Chris Lehane, raising clear questions about independence.

An exclusive tour of Amazon’s Trainium lab, the chip that’s won over Anthropic, OpenAI, even Apple
Techcrunch got a chance to visit Amazon’s AWS chip lab in Austin, where Trainium, Amazon’s AI chips used by both Anthropic and OpenAI, are being designed and tested. 1.4 million Trainium chips are deployed across three generations and Anthropic alone using over a million, yet demand still outstrips supply. The lab also designs the full server stack—sleds, Nitro virtualisation hardware, and cooling systems—giving Amazon end-to-end control over cost and performance in a bid to loosen Nvidia’s grip on the AI infrastructure market.

New computer chip material inspired by the human brain could slash AI energy use
Researchers have created a brain-inspired chip component that could cut AI energy use by up to 70% by processing and storing data in the same place. Their design uses a novel hafnium oxide memristor that switches states far more reliably and at currents roughly a million times lower than existing alternatives. The main hurdle is that fabrication currently requires temperatures too high for standard chip manufacturing, but the team is working to address this.

CERN Uses Tiny AI Models Burned into Silicon for Real-Time LHC Data Filtering
CERN has built tiny AI models etched directly into silicon chips to filter the enormous data stream from the Large Hadron Collider, which produces around 40,000 exabytes of data per year. These chips evaluate particle collisions in under 50 nanoseconds, keeping only about 0.02% of events for scientific analysis and permanently discarding the rest. The approach is the opposite of the current AI trend towards bigger models, focusing instead on making the smallest, fastest, and most efficient systems possible.

Introducing TRIBE v2: A Predictive Foundation Model Trained to Understand How the Human Brain Processes Complex Stimuli
TRIBE v2 is an open-source model from Meta that simulates how the human brain reacts to what we see, hear, and read. It was trained on brain scan data from over 700 people and is 70 times more detailed than its predecessor, allowing researchers to test ideas about brain function without needing human subjects for every experiment. The model and code are freely available to help speed up neuroscience research and the search for better treatments for brain disorders.

🤖 Robotics

AI Robotics Lab in Talks to Raise $1 Billion at $11 Billion Valuation
Physical Intelligence, a two-year-old robotics startup, is looking to raise around $1 billion in new funding, which would value the company at over $11 billion—double what it was worth just four months ago. The company builds AI software that helps robots learn physical tasks like folding clothes and making coffee.

AGIBOT rolls out 10,000th humanoid robot
Chinese humanoid robotics company AGIBOT announced it has shipped 10,000 robots, claiming to be one of the first to hit that number. The company claims it went from 5,000 to 10,000 units in just three months and says its robots are now working in shops, warehouses, schools, and factories across multiple continents.

Defense startup Shield AI lands $12.7B valuation, up 140%, after US Air Force deal
Shield AI, a defence startup building autonomous military drones, has raised $1.5 billion in Series G funding at a $12.7 billion valuation—a 140% jump in one year—driven largely by the US Air Force selecting its Hivemind autonomy software for the Collaborative Combat Aircraft drone programme.

Introducing GEN-1

Generalist has unveiled GEN-1, a new AI model designed to control robots across a range of physical tasks. According to Generalist, GEN-1 succeeds 99% of the time on simple tasks like folding clothes and packing items, a big jump from its predecessor's 64%, and works about three times faster than previous leading models, all while needing only around an hour of robot-specific training data. The company says the model can also improvise when things go wrong, though it acknowledges not every task hits these high marks yet.

Sanctuary AI’s robotic hand demonstrates zero-shot in-hand manipulation

Sanctuary AI has shown its robotic hand can repeatedly rotate a small cube to match a target position using only its fingertips. This kind of precise in-hand manipulation is one of the hardest problems in robotics. The hand’s hydraulic design and large number of independently moving joints give it capabilities beyond most competing systems. Notably, the control policy was learned entirely in a simulated environment and worked straight away on the real hardware.

30 Years Ago, Robots Learned to Walk Without Falling
This piece shares the story of Honda P2, the first robot to achieve autonomous bipedal walking without losing balance in 1996. The robot was capable of dynamic walking—maintaining balance through continuous adjustment—and could climb stairs, push carts, and operate autonomously using an onboard computer and battery. Honda’s P2 is a milestone in robotics development, the legacy of which can be seen in today’s humanoid robots.

▶️ Billions Are Flowing Into Robots That Don’t Work Yet (49:00)

In this conversation, Erik Nieves, co-founder and CEO of Plus One Robotics, a warehouse automation company based in San Antonio, makes the point that business customers don’t care what a robot looks like—they only care whether it can hit performance targets like speed and reliability. The conversation also touches on the hype in robotics, why it is important to keep humans in the loop, and how robotics could end labour arbitrage and reshape where manufacturing happens.

🧬 Biotechnology

Insilico Medicine secures $2.75 billion drug collaboration with Eli Lilly
Insilico Medicine, an AI drug discovery company, has signed a deal with Eli Lilly worth up to $2.75 billion, with $115 million paid upfront. Under the agreement, Lilly gets exclusive rights to develop and commercialise preclinical oral drug candidates from Insilico for selected disease areas, including a GLP-1 diabetes drug.

FDA Approves Eli Lilly’s GLP-1 Pill
The US FDA has approved Foundayo, a daily weight-loss pill from Eli Lilly, making it the second such pill on the market after Novo Nordisk's oral Wegovy. In trials, people on the highest dose lost around 12% of their body weight over 18 months. Notably, the FDA reviewed Foundayo in just 50 days under a new pilot programme to fast-track high-priority drugs, well below the usual six-to-ten-month timeline.

What happens when you clone mice for 20 years straight?
As the title suggests, this article tells the story of an experiment involving repeatedly cloning a mouse over more than 20 years, resulting in 58 cloned generations, and what scientists have learned from it.

💡Tangents

SpaceX Files to Go Public, Setting Stage for Huge I.P.O.
SpaceX has confidentially filed for an IPO this week with plans to debut on the stock exchange in June, according to sources quoted by the New York Times. SpaceX, now valued at over $1 trillion, also includes Elon Musk's AI company xAI and the social network X after a recent merger. If the valuation holds, Musk could become the world's first trillionaire.

Amazon is in talks to buy Globalstar for $9 billion
Amazon is in advanced talks to buy satellite company Globalstar for around $9 billion, the Financial Times reports. A successful deal would give Amazon valuable radio frequencies and existing infrastructure to speed up its effort to compete with SpaceX’s Starlink. The deal is complicated by Apple, which owns 20% of Globalstar and depends on its network for the iPhone’s Emergency SOS feature. No agreement has been signed, and the key challenge is finding terms that work for both Amazon and Apple.

Philly courts will ban all smart eyeglasses starting next week
Philadelphia has banned smart glasses from all its courthouses, saying that people could secretly record witnesses and jurors. Anyone caught with them could be turned away or even arrested, unless a judge has given written permission beforehand. The ban comes as affordable smart glasses have become popular. A handful of other US court systems have brought in similar rules.

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