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.

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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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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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 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.

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!"

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.

Enjoy!

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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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!

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

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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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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March 2016. Seoul, South Korea. Millions around the world tuned in to watch a game of Go. On one side of the board sits Lee Sedol, one of the greatest players the game has ever produced—a genius renowned for his creative, fighting style. His opponent has already beaten a European champion 5-0 and is here to challenge the very best. But his opponent is not human, and it is here to make history.

And it did. Ten years ago, DeepMind's AlphaGo defeated Lee Sedol 4–1, with consequences that would reach from Seoul to Silicon Valley to Beijing. Go, an ancient game famous for its beauty and complexity, was mastered not by a human but by a machine. AlphaGo stands alongside Deep Blue’s victory over Garry Kasparov in 1997 and the release of ChatGPT in late 2022 as a defining milestone in AI. It proved that artificial intelligence had arrived, sparked a global AI race, transformed how humans play the ancient game of Go, and in the process offered the first hints of how we can coexist with superhuman AI.

How DeepMind solved Go

Go is an incredibly complex game. There are 10¹⁷⁰ (one followed by 170 zeroes) possible board configurations. To put that number in perspective, the number of atoms in the observable Universe is estimated to be 10⁸⁰, or one followed by eighty zeroes. If you want to assign one atom to one board configuration, you would need vastly more than the number of atoms in the observable Universe. And then, in this enormous space of all possible solutions, you have to find the sequence of boards that leads to victory.

Earlier Go programs relied heavily on handcrafted knowledge and, later, Monte Carlo tree search, but even the best of them played at an amateur level. Most experts expected superhuman Go play to be at least a decade away. DeepMind proved them wrong.

DeepMind’s answer was to combine three techniques into a new architecture. First, deep neural networks treated the 19×19 Go board as an image. Two networks worked in tandem: a policy network that predicted the probability of each possible move, and a value network that estimated who was winning from any given position. The policy network was initially trained on 30 million positions from human games, learning to predict what an expert would play. It was then refined through reinforcement learning. AlphaGo played millions of games against previous versions of itself, gradually discovering strategies that went beyond what it had learned from humans.

Second, Monte Carlo tree search used these neural networks to focus computational effort on the most promising moves rather than trying to explore every possibility. Think of it as the difference between reading every book in a library and having a knowledgeable guide point you to the right shelf. The neural networks acted as that guide, telling the search where to look.

Third, a fast rollout policy—a lightweight, stripped-down evaluator—could assess positions in just microseconds, enabling rapid simulations that complemented the deeper but slower neural network analysis.

The result was a system that learned to evaluate positions and select moves through experience. Where Deep Blue searched over 200 million positions per second, AlphaGo evaluated far fewer positions but searched smarter, guided by something that could be described as an artificial intuition.

That artificial intuition revealed itself most strikingly in Game 2. On now legendary move 37, AlphaGo played a move that professional commentators initially dismissed as a mistake. It was a move with roughly a 1-in-10,000 probability of being played by a human. It proved devastatingly effective. Fan Hui, the European champion who had been advising the DeepMind team, watched in awe: “It’s not a human move. I’ve never seen a human play this move. So beautiful.”

Lee Sedol answered with an equally impressive move in Game 4. Down three games in the series and facing elimination, he played Move 78—dubbed “God’s Touch” in Korean—a wedge so brilliant and unexpected that it was itself a 1-in-10,000 play. AlphaGo’s evaluation collapsed, leading to a cascade of errors and handing Lee Se-dol the only victory any human would ever claim against AlphaGo. If Move 37 showed what a machine could see that humans could not, Move 78 showed that human creativity, pushed to its absolute limit, could still find what a machine had missed.

After defeating Lee Sedol, an updated version of AlphaGo, known as AlphaGo Master, appeared on Korean and Chinese Go servers. On a Chinese FoxGo server, it defeated 60 consecutive top professionals, including world number one player Ke Jie, three times. At the Future of Go Summit in Wuzhen, China, in May 2017, AlphaGo Master defeated Ke Jie 3-0 and beat a team of top five Chinese professionals playing collectively. After the summit, AlphaGo was retired from competitive play. In its short run, AlphaGo went 74–1 in its public games. Lee Sedol was the only human to beat it. In recognition of DeepMind’s achievement, the Korea Baduk Association, South Korea's governing body for professional Go, awarded AlphaGo an honorary 9-dan, the highest professional certification in Go.

Although AlphaGo was retired, DeepMind continued developing even more impressive models. In October 2017, the London-based AI lab unveiled AlphaGo Zero, which learned Go entirely from self-play with zero human data. After three days of training, it defeated the version that beat Lee Sedol 100-0. After 40 days of self-play, it surpassed AlphaGo Master. Two months later, DeepMind followed up with AlphaZero, an even more powerful model that not only mastered Go but also mastered chess and shogi, given only the rules of each game. AlphaZero achieved superhuman performance in chess within hours and in Go within days.

AI’s Sputnik Moment

AlphaGo’s victory sent shockwaves across the world.

Just two days after the Lee Sedol match, South Korean President Park Geun-hye announced ₩1 trillion (approximately $863 million) in AI research funding over five years. South Korea followed with an additional $2 billion commitment by 2022 and launched a sovereign AI initiative in 2024. Canada became the first country in the world to launch a national AI strategy in 2017, committing C$125 million to research and talent. Japan launched its AI Technology Strategy Council the same year. In 2018, France pledged €1.5 billion for AI development under President Macron. By the end of 2018, over a dozen countries had published national AI strategies—a rarely seen wave of near-simultaneous government mobilisation around a single technology.

But it was China where AlphaGo made the biggest impact. Go, known in China as weiqi, is an ancient Chinese invention and one of the four classical arts of the Chinese scholar. A Western-developed AI defeating the world’s best at China’s own game carried deep cultural resonance. Kai-Fu Lee, former president of Google China, described the event as “China’s Sputnik moment” in his 2018 book AI Superpowers. What followed was a focused plan to transform China into an AI superpower. Less than two months after Ke Jie’s defeat in Wuzhen, China’s State Council issued the “New Generation Artificial Intelligence Development Plan.” The plan set a three-phase strategy to achieve global AI leadership by 2030. The money moved fast. According to the South China Morning Post, Chinese equity funding for AI startups jumped from 11% of global investment in 2016 to 48% in 2017, surpassing the United States by 10 percentage points. Over the next few years, a new generation of AI researchers, founders and startups emerged across the country.

Looking back, we can see that the Chinese plan is bearing fruit. In January 2025, a then little-known Chinese startup called DeepSeek released its R1 reasoning model—open-source, built for a fraction of what the US companies spent on their models while matching their performance and being more efficient to run. It briefly overtook ChatGPT as the top free app on Apple's US App Store and wiped roughly $1 trillion from US AI-exposed tech stocks in a single day. DeepSeek was not an accident. It emerged from the years of investments and from the AI ecosystem that China had been building since the “AlphaGo shock”—one that now produces world-class researchers, models and products despite being constrained by US chip export controls.

How AlphaGo changed Go

In Go, there is a concept known as jōseki—standardised sequences of moves, similar to opening theory in chess, developed over more than 2,000 years and codifying what generations of masters considered optimal play. For centuries, this understanding was passed down unquestioned from master to disciple. AlphaGo demonstrated that many of these human fundamentals were, in fact, suboptimal or merely prejudiced assumptions.

A 2023 study published in the Proceedings of the National Academy of Sciences (PNAS) analysed over 5.8 million Go moves from professional play between 1950 and 2021. The researchers found that human decision quality had stayed largely the same for 66 years. After AlphaGo’s victories, both decision quality and novelty scores shot up dramatically. Crucially, memorisation alone could not explain the improvements. The gains began approximately 18 months after AlphaGo’s victory, roughly coinciding with the release of Leela Zero. This open-source Go engine allowed players to build interactive analysis tools showing the AI’s reasoning behind move choices. This enabled deep, interactive learning rather than mere copying. Players, with the help of AI tools, were discovering novel moves that contributed more to winning than non-novel ones.

AI has fundamentally reshaped how professional Go is played, trained, and experienced. Players now train to replicate AI’s moves as closely as they can, even when the machine’s thinking remains mysterious to them. It is essentially impossible to compete professionally without using AI. South Korean Go commentator Park Jeong-sang summarised the shift bluntly: “AI has changed everything. Fundamental moves that were once considered common sense aren’t played at all today, and techniques that didn’t exist before have become popular.” The overall level of mastery went up, but the game lost personality. Before AlphaGo, players’ styles were diverse and distinctive. Go fans could identify who played a particular game just by looking at the game records. Lee Sedol played like a jazz musician—throwing the game into disorder and thriving in the turbulence. Ke Jie played like a dancer, finding elegant lines through positions others found too tangled. That distinctiveness has faded. The opening phase of the game, once a canvas for abstract thinking and creativity, is now largely standardised. Over a third of moves by top players replicate AI’s recommendations, and the first 50 moves of each game are often identical to what AI suggests.

The older generation of Go masters suddenly woke up playing a different game from the one they fell in love with. Lee Sedol retired three years after his match with AlphaGo. The game he had spent his life mastering had become an exercise in following a machine’s recommendations, and he could no longer find joy in it. Ke Jie has been vocal about the downsides, too, telling a Chinese news outlet in 2021: “I feel the exact same way as the fans watching. It’s very tiring and painful to watch.”

But while the old guard mourned what was lost, a new generation saw only what was gained. Shin Jin-seo, currently the world’s top-ranked Go player, represents the AI-native generation. Every morning, he sits at his computer and opens KataGo, an open-source Go engine that is faster and sharper than the original AlphaGo. He has earned the nickname “Shintelligence” for how closely his moves mirror AI’s. According to a 2022 study by the Korean Baduk League, his moves match AI’s recommendations 37.5% of the time, well above the 28.5% average among professional players. He has trained on more advanced AI programs than AlphaGo ever was, and is optimistic that he could now defeat the original AlphaGo by targeting its known weaknesses.

If AI standardised how the game is played, it also democratised who gets to play it at the highest level. For decades, the best training meant studying under top male players in circles that were difficult for women to break into. AI levelled the playing field. In 2022, Choi Jeong—nicknamed “Girl Wrestler” for her fierce, combative style—became the first woman to reach the finals of a major international Go tournament. Two years later, Kim Chae-young won the Korean Go League’s postseason playoffs as the only female player in the tournament. Kim described what it was like to relearn the game through AI: “It seems like it’s thinking in a higher dimension. It’s less about rationally thinking through each move, but more about developing a gut feeling—an intuition.” But AI didn’t just change how she played. It changed how she saw her competitors. “Before, I couldn’t gauge just how strong top male players were—they felt invincible,” she told the MIT Technology Review. “Now, I know that they make mistakes, and their moves aren’t always brilliant. AI broke the psychological barrier.”

Learning to coexist with a superhuman AI

The Go community’s experience is worth studying carefully, because they are ten years ahead of the rest of us. They have lived with a superhuman AI since March 2016. They have wrestled with the questions that every profession is now beginning to ask: How do you stay motivated when a machine is better than you? How do you train the next generation? How do you preserve what makes your craft meaningful?

The older generation of Go masters learned their craft in one world and woke up in another. Some adapted. Many could not. Lee Sedol retired. This was not a failure of character—it was the reasonable response of someone who had spent a lifetime mastering a game that no longer existed in the form he loved. The same is coming for every other profession. A new, AI-native generation is already entering the stage—people who use AI tools as naturally as previous generations used the internet. The question is not whether this transition will happen. It already is. The question is how to navigate it.

I am a software engineer. I have been writing code since I was 14 years old. I love finding elegant solutions expressed in code. But when GPT-4 came out, I saw the writing on the wall. It was a matter of time before AI would write code better than I. That time has come. But just as Go players use AI to deepen their understanding of the game, I use AI to become a better programmer. I read the code it generates. I ask for different approaches. I ask why it made the choices it did. Sometimes I find solutions I would never have considered—a data structure I’d never have reached for or an architectural pattern that solves the problem in a cleaner way. Other times, I see that my instinct was better. The point is not to compete with the machine. The point is to use it as a lens that reveals what I do not yet understand about my own craft.

The same approach can be applied to other areas of human endeavour, from mathematics and biology to art and engineering. However, I won’t pretend this story has a clean ending. Some people will be left behind—not because they lack talent, but because the craft they mastered is no longer the craft the world needs. Some art will be optimised away. The Go community has already lived through both. But they have also shown that it is possible to use a superhuman tool to grow rather than shrink—to discover moves you would never have found alone, to see your own blind spots, to push your understanding past what you thought was your ceiling. That is the path I am choosing. Not because it guarantees anything, but because refusing to engage with the most powerful learning tool ever built guarantees something worse.

AlphaGo’s legacy

Ten years on, AlphaGo's fingerprints are everywhere. It has sent a signal across the world that AI should be taken seriously. Those who listened to that signal and reacted correctly are now leading the AI revolution we are living through.

However, AlphaGo and deep reinforcement learning did not lead to powerful and more general AI. Many expected it to become the dominant AI paradigm after 2016. Instead, the transformer architecture, introduced by researchers from Google in the 2017 “Attention Is All You Need” paper, set the course for a different revolution. The scaling of large language models demonstrated that self-supervised pre-training on massive text corpora could achieve broad capabilities across many domains simultaneously. That realisation led to the current generation of powerful and general models, such as OpenAI’s GPT family of models, Anthropic’s Claude or Google’s Gemini. Although AlphaGo alone did not lead to artificial general intelligence, a combination of foundational models with AlphaGo-style search and planning shows promising results in deep reasoning systems such as Gemini Deep Think.

DeepMind itself never stopped building on AlphaGo's foundations. AlphaStar reached Grandmaster level in StarCraft II, proving reinforcement learning could handle real-time strategy with imperfect information. AlphaChip used RL to design layouts for Google's TPU processors, generating in hours what had previously taken engineers months. AlphaTensor discovered faster matrix multiplication algorithms, improving on a result that had stood for over 50 years. AlphaGeometry 2 reached gold-medal level on International Mathematical Olympiad geometry problems. But the most celebrated successor was AlphaFold. DeepMind turned its attention to protein structure prediction—a 50-year grand challenge in biology—and in 2020, AlphaFold 2 essentially solved it. By 2024, it had been used by over two million researchers across 190 countries. That year, Demis Hassabis and John Jumper received the Nobel Prize in Chemistry—the first Nobel awarded for an AI-enabled scientific breakthrough.

But perhaps the most enduring legacy is not technical. It is the question that Lee Sedol has been living with since that match in Seoul—the question every professional will eventually face: what does mastery mean when a machine can do it better? In a recent interview with MIT Technology Review, he offered an answer: "It's every Go player's dream to play a masterpiece game—a game of technical brilliance, with no mistakes, fought to a razor's edge between evenly matched players. It's like a mirage. Maybe AI can help us play a masterpiece."

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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!"

This week, we will take a look at how the war in Iran and the closure of the Strait of Hormuz is impacting global supply chains needed to produce the most advanced AI chips.

Over in AI, Anthropic confirmed it is working on new, powerful models and scored a victory over the Pentagon in court. Meanwhile, OpenAI shuts down the Sora app; Elon Musk launches his own chip factory; Arm unveils its own AI chip, Google releases TurboQuant and Lyria 3 Pro; and a Super Micro co-founder is arrested for reportedly smuggling advanced AI chips to China.

In robotics, Amazon buys a humanoid robotics startup, Wing expands to Bay Area, and Unitree prepares for an IPO.

Beyond that, this week’s issue of Sync also features Meta and YouTube being found negligent for designing addictive platforms, a conversation with Jensen Huang, scientists bringing cryosleep one step closer to reality, how computers are helping design new drugs, the OpenClaw boom in China, ARC-AGI-3 and the ARC Prize 2026, and more!

Enjoy!

How the war in Iran threatens the global chip supply

The Strait of Hormuz is one of the most important chokepoints in the global supply chain. Before the Iran war, roughly 20 million barrels of oil passed through it every day—about a quarter of the world’s seaborne crude. But oil is not the only product flowing through the strait. Approximately 20% of global LNG trade, a third of the world’s helium supply, and a steady flow of petrochemicals, fertilisers, and raw materials that feed the global economy also transit the waterway. When Iran closed the Strait on 2 March 2026, it severed one of the most critical supply arteries on earth.

The technology industry, which depends on that artery for everything from the energy powering chip factories to the helium cooling the wafers inside, is among the most exposed.

The Gulf as tech’s promised land

Less than a year ago, the Persian Gulf was the hottest destination in tech. During President Trump’s four-day tour of Saudi Arabia, Qatar, and the UAE in May 2025—flanked by executives from Nvidia, OpenAI, Oracle, Microsoft, Amazon, and Google—roughly $2.5 trillion in combined investment pledges were announced. AWS committed $5.3 billion to Saudi Arabia. Google Cloud entered a $10 billion joint venture with Saudi’s Public Investment Fund. The UAE unveiled Stargate UAE, a 5-gigawatt AI campus in Abu Dhabi backed by OpenAI, Oracle, and G42.

The logic behind those deals was straightforward. Gulf states had the capital and wanted cutting-edge AI built on their soil. In return, they got the technology and, implicitly, American protection.

Then the war put all of it in the line of fire.

The chip industry, powered by the Gulf

Oil prices have surged since the war began, with some analysts expecting Brent crude to reach $150 per barrel or even higher.

The countries hardest hit by the closure are those that rely on Gulf oil for energy production, which includes the places where most of the world’s semiconductors are made. Taiwan, home to TSMC, imports 97% of its energy and sourced roughly 70% of its crude oil and 38% of its natural gas from the Middle East before the crisis. South Korea, where Samsung and SK Hynix together produce 70% of global DRAM and 80% of high-bandwidth memory, depends on the Gulf for over 70% of crude imports. Japan, a major supplier of semiconductor equipment, sources 95% of its crude from the Middle East.

Then there is helium. Qatar produces roughly 35% of the world’s supply as a byproduct of LNG processing. When Iranian drones struck QatarEnergy’s Ras Laffan facility—the world’s largest LNG complex—and the company declared force majeure, helium production halted. Helium is an important resource in semiconductor manufacturing. It cools wafers during etching, shields extreme ultraviolet lithography systems, and enables plasma processes. No viable substitute exists. The prices surged 70-100%, and most fabs carry fewer than three months of inventory.

Markets responded accordingly. Samsung and SK Hynix—the two companies that make most of the world's memory chips—lost roughly $170 billion in combined market value over two trading sessions in early March, triggering emergency trading halts in South Korea. Memory chip prices, already elevated due to the high demand from the AI industry, are forecast to jump 55–60% this quarter alone.

The broken Gulf dream

The Gulf states were selling the dream of a luxurious, cosmopolitan, and safe oasis in the Arabian desert—global hubs for finance, tourism, tech, sports, and more. The billions they made along the way flowed back out as investments across the world, with AI infrastructure among the biggest bets. MGX, Abu Dhabi’s sovereign-backed AI investment fund, has become one of the most active technology investors in the world. It is a founding partner of OpenAI’s $500 billion Stargate project alongside SoftBank and Oracle, and has invested in Anthropic, xAI, and Databricks. Together with BlackRock and Microsoft, MGX launched a dedicated AI infrastructure fund. Saudi Arabia’s Public Investment Fund is backing a $10 billion data centre joint venture with Google Cloud.

When the missiles and drones hit, the Gulf dream shattered.

The $2.5 trillion in tech investment pledges from 2025 may not collapse outright, but renegotiations look very likely. The war puts at risk the AI buildout that the entire US economy is increasingly banking on—and Gulf money is central to that buildout. It is costing Gulf states billions of dollars per day in lost oil revenue, grounded flights, and disrupted trade. It will take years to rebuild the image they held before the war (if that is even possible) which will add further billions to the total cost. Those are resources that would otherwise have gone into AI infrastructure and data centres. If Gulf sovereign wealth funds decide the geopolitical bet was miscalculated and redirect capital, the downstream effects would hit AI funding rounds, data centre construction timelines, and hyperscaler expansion plans worldwide. If that were to happen, AI sceptics may have finally found the pin that pops the AI bubble.

The ripple effects are coming

The Iran war has already sent shockwaves across the global economy. But like a tsunami after an earthquake, the bigger waves are on the way.

The semiconductor production price index historically correlates with oil prices after a six-month lag—meaning March’s surge will not fully manifest in chip costs until September. Helium reserves at most fabs will not last beyond May if the Strait remains closed. If those stockpiles run dry, production at some of the world’s most advanced chip factories could halt entirely. Even a swift resolution would not undo the damage— it may take a minimum 4–6 month for helium supply chain to return to normal after the conflict ends.

The industry that promised to make the world frictionless, borderless, and virtual remains tightly coupled with tanker routes, gas pipelines, and a narrow strait between Iran and Oman. The consequences of the war in Iran will run well into 2027, even if the bombs and missiles stop falling tomorrow.

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🦾 More than a human

Reviving Brain Activity After ‘Cryosleep’ Inches Closer in Pioneering Study
Researchers froze mouse brain tissue into a glass-like state to avoid the ice crystal damage caused by conventional freezing, then thawed it after up to a week in cryogenic storage. The neurons resumed electrical activity and showed signs of the processes involved in learning and memory. The technique worked on both brain slices and whole brains, though results varied across different neuron types. The team is now extending the work to human brain tissue. While true memory preservation remains unproven, the findings suggest brains are far more resilient to deep freezing than previously believed.

🔮 Future visions

▶️ Biohacks & Brain Mods - The Coming Age of Implant Culture (28:36)

Isaac Arthur looks at how brain implants might develop, from today's cochlear implants and brain-computer interfaces to future memory aids, mood regulators, and eventually full neural meshes. He considers the complications, from subscription pricing, black markets, widening inequality, pressure to upgrade just to stay competitive, to the need to protect not just data but thoughts from hacking. Despite these concerns, he remains optimistic and believes humanity will learn to handle neurotech much as it has with every major advance before it.

🧠 Artificial Intelligence

Judge blocks Pentagon’s effort to ‘punish’ Anthropic by labeling it a supply chain risk
A federal judge has blocked the Pentagon from designating Anthropic a supply chain risk, ruling it was unconstitutional retaliation for the company’s refusal to allow its AI to be used in autonomous weapons or mass surveillance. The judge called it “classic illegal First Amendment retaliation,” noting the label had previously only been applied to foreign adversaries. The Pentagon plans to appeal.

Anthropic Denies It Could Sabotage AI Tools During War
The US Department of Defense claims Anthropic could disable or tamper with Claude during active military operations, but the company flatly denies this, stating in court filings that it has no back door, kill switch, or remote access to deployed models. The dispute escalated when defence secretary Pete Hegseth designated Anthropic a supply-chain risk, barring the Pentagon and its contractors from using Claude. Anthropic has filed two lawsuits challenging the ban and offered contractual guarantees relinquishing any veto over military decisions, though negotiations broke down.

Anthropic acknowledges testing new AI model representing ‘step change’ in capabilities, after accidental data leak reveals its existence
Anthropic has confirmed it is building its most powerful AI model yet, called Claude Mythos, after a data leak exposed its existence. The leak left thousands of unpublished files publicly accessible until Fortune flagged the issue. Anthropic says the model far outperforms its current best in coding, reasoning, and cybersecurity, but warns it also poses serious risks, as hackers could use it to find and exploit software weaknesses faster than defenders can fix them. The company is rolling the model out cautiously, starting with a small group of early testers.

Musk Says Tesla, SpaceX, xAI Chip Project to Kick Off in Texas
Elon Musk launches Terafab, a chip manufacturing project in Austin, Texas, jointly operated by Tesla and SpaceX. The facility will produce two types of chips—one optimised for edge and inference tasks in vehicles and robots, and another high-power chip for space computing by SpaceX and xAI. Musk argues the semiconductor industry cannot scale fast enough to meet his companies’ AI and robotics demands, though he offered no timelines and has a history of overpromising. The project represents an enormously costly bet on vertical integration in an industry where new fabs typically take years and tens of billions of dollars to bring online.

OpenAI shuts down Sora
OpenAI is shutting down Sora, its AI video generator app. This move is part of OpenAI’s strategy to scale back side projects and focus on coding tools and enterprise customers—more profitable markets currently dominated by Anthropic. Sora’s shutdown has also made Disney exit its $1 billion deal with OpenAI.

OpenAI Plans Launch of Desktop ‘Superapp’ to Refocus, Simplify User Experience
The Wall Street Journal reports that OpenAI plans to merge ChatGPT, its Codex coding platform, and the Atlas browser into a single desktop “superapp”, reversing last year’s strategy of launching fragmented standalone products. The superapp will prioritise agentic AI capabilities that can autonomously perform tasks such as writing code and analysing data, which make it sound similar to Claude Cowork.

The Silicon Valley Salesman Accused of Helping China Get Nvidia’s Top Chips
Super Micro co-founder Wally Liaw was arrested for allegedly helping smuggle $2.5 billion worth of Nvidia AI chip servers to China, using shell companies and staged warehouse inspections to evade US export controls. The charges cast doubt on assurances from Nvidia and the White House that chip smuggling is not a serious problem, and sent Super Micro’s share price down by a third, wiping out over $6 billion in value.

Announcing Arm AGI CPU: The silicon foundation for the agentic AI cloud era
Arm is joining the AI chip game with AGI CPU, its first in-house silicon product and a significant shift from its traditional IP licensing model. The chip targets agentic AI workloads that demand sustained, massively parallel CPU performance. Arm claims over 2x rack-level performance compared to x86 competitors. Meta has been announced as the lead customer, with OpenAI, Cerebras, Cloudflare, and others signed on as launch partners. Production is expected later in 2026.

Put Claude to work on your computer
Claude Cowork and Claude Code can now point, click, and navigate user’s computer to complete tasks. The new feature is available in research preview for Claude Pro and Max subscribers. Additionally, Anthropic introduced auto mode, a new permissions mode in Claude Code where Claude makes permission decisions on user’s behalf, with safeguards monitoring actions before they run.

TurboQuant: Redefining AI efficiency with extreme compression
Google has announced TurboQuant, a new compression algorithm for LLMs and vector search. According to Google, it compresses key-value caches to just 3 bits with no accuracy loss and up to 8x speedup on H100 GPUs. TurboQuant combines two novel techniques: QJL, a 1-bit method eliminating memory overhead, and PolarQuant, which uses polar coordinates to avoid costly normalisation. Google says the methods are theoretically grounded and plans to apply them to Gemini and large-scale semantic search. Internet, meanwhile, noticed some similarities between TurboQuant and Pied Piper.

ARC-AGI-3 and ARC Prize 2026
ARC Prize Foundation launched ARC-AGI-3, an interactive benchmark that tests whether AI agents can explore novel environments and learn from experience as efficiently as humans. Rather than measuring static puzzle-solving, it evaluates skill acquisition, long-horizon planning, and real-time adaptation over multiple steps. Currently, the best models in the world score 0%, while humans can easily solve them. You can try the puzzles yourself here. The foundation also launched ARC Prize 2026, a $2 million competition across three tracks rewarding top open-source solutions for ARC-AGI-3 and ARC-AGI-2 as well as research papers advancing AGI understanding of how to achieve strong performance on ARC-AGI.

Bernie Sanders and AOC propose a ban on data center construction
Bernie Sanders and Alexandra Ocasio-Cortez have proposed banning new data centres over 20 megawatts until Congress passes comprehensive AI regulation, citing widespread public concern and warnings from leading technologists. The broader bill also seeks government certification of AI models before release, worker protections, environmental limits, and chip export controls. Passage is unlikely given industry lobbying and competition with China, but the bill signals an opening move in the AI governance debate.

MiniMax M2.7: Early Echoes of Self-Evolution
MiniMax has released M2.7, a large language model that can help build and improve itself—creating its own tools, refining its setup, and running reinforcement learning experiments with minimal human input. Independent benchmarks from Artificial Analysis confirm it is one of the best open models, behind only top Western models like Gemini 3.1 Pro, GPT-5.4, Claude Opus 4.6, and Claude Sonnet 4.6. However, the same evaluation found M2.7 to be slow, overly wordy, and somewhat expensive compared to similarly priced rivals. If you want to learn more about M2.7, and specifically about the claims about self-evolution, here is a good video about MiniMax’s latest model.

Lyria 3 Pro

Google has released Lyria 3 Pro, an improved AI tool that can generate music tracks up to three minutes long, with users able to specify song structure like verses and choruses. It's being made available across several Google products for businesses, developers, and everyday creators. Google says the tool was built with input from music professionals and includes safeguards such as watermarking to identify AI-generated content.

China’s OpenClaw Boom Is a Gold Rush for AI Companies
OpenClaw is going big in China, and the real winners aren't the users—they're the tech companies. The open-source AI tool went viral after influencers showed it off automating tasks like stock trading, but ordinary people quickly found it too technical to set up or use without programming knowledge. Meanwhile, companies like Tencent, ByteDance, and Moonshot have cashed in on the hype, selling cloud services and API access while releasing their own versions to keep users on their platforms.

Mark Zuckerberg Is Building an AI Agent to Help Him Be CEO
Mark Zuckerberg is reportedly building a personal AI assistant to help him do his job faster, and he wants the rest of Meta to follow suit. I wonder if Zuckerberg’s AI agent will be good enough to push back on some ideas or if it will make him delusional.

Palantir Demos Show How the Military Could Use AI Chatbots to Generate War Plans
A WIRED investigation reveals how Anthropic's Claude AI is being used in US military operations through Palantir's software, helping analysts review intelligence, suggest attack plans, and support decisions about strikes. This comes as Anthropic and the Pentagon are locked in a legal fight over the company's refusal to give the military unrestricted access to Claude. Neither company has said which military systems use Claude or how it is being used in current operations.

Leaders of AI Firm Bought by Meta Are Restricted From Leaving China
China has barred two co-founders of AI startup Manus from leaving the country while regulators review its $2.5 billion sale to Meta. The Chinese government is concerned that the company relocated operations from Beijing to Singapore to sidestep oversight of cross-border technology transfers. Authorities are investigating whether ownership changes were properly reported and are considering penalties, while Meta insists the deal was lawful and that Manus no longer has any Chinese ties.

Cursor admits its new coding model was built on top of Moonshot AI’s Kimi
Cursor faced backlash after an X user revealed its new Composer 2 model was built on Kimi 2.5, an open-source model from Chinese company Moonshot AI. Cursor confirmed the open-source base but said three-quarters of the final model's compute came from its own training. The incident underscores the sensitivity around US companies quietly building on top of Chinese AI models, and Cursor's co-founder acknowledged it was "a miss" not to credit Kimi upfront.

▶️ Jensen Huang: NVIDIA - The $4 Trillion Company & the AI Revolution | Lex Fridman Podcast (2:25:58)

In this wide-ranging conversation, Jensen Huang discusses how he runs the company and explains Nvidia’s engineering approach of designing chips, networking, memory, cooling, and software as one tightly integrated system. On the future of AI, Huang outlines multiple ways intelligence will keep scaling. The conversation also covers Nvidia’s relationship with TSMC, China's innovation ecosystem, Nvidia’s competitive moats, the potential for AI agents to create billion-dollar businesses, supply chain challenges, and more.

What 81,000 people want from AI
Anthropic interviewed over 80,000 Claude users across 159 countries and asked them what they want and worry about with AI. Most people wanted AI to give them a better life rather than just help them work faster, and 81% said it was already making a difference. The main finding is that the same people who benefit most from a particular use of AI are often the ones most worried about its downsides. People in lower-income countries tended to be more positive, seeing AI as a way to access new opportunities.

🤖 Robotics

Amazon acquires humanoid developer Fauna Robotics
Amazon has acquired Fauna Robotics, a humanoid robot startup that recently came out of stealth, which will join its Personal Robotics Group. Fauna's product, Sprout, is a small, safety-focused humanoid currently aimed at researchers, but Amazon has hinted at broader personal robotics ambitions.

Wing’s homecoming: Bringing drone delivery to the Bay Area
Wing, Google’ drone delivery company, annouced that it is expanding its service to the San Francisco Bay Area in the coming months. The company has completed over 750,000 deliveries across US cities like Houston, Atlanta, and Dallas, partnering with brands such as Walmart and DoorDash, and sees the Bay Area launch as part of its push to build a nationwide drone logistics network.

Unitree IPO shows a real hardware business, but the humanoid case is still early
Unitree Robotics has filed for a STAR Market IPO in Shanghai, aiming to raise roughly $610 million on reported 2025 revenue of about $248 million. The company has grown quickly, with humanoid robots now making up over half its sales—up from almost nothing two years ago. Interestingly, it has slashed prices from around $85,000 to $25,000 per robot while still making healthy profits, suggesting it has found ways to build them much more cheaply. The catch is that most buyers are still universities and researchers, not factories, so while Unitree looks like a solid hardware business, the bigger promise of robots working widely in industry hasn't been proven yet.

Zipline snaps up another $200M to fuel its drone delivery expansion
Zipline has raised an additional $200 million, bringing its Series H round to $800 million at a $7.6 billion valuation. The drone delivery company, which started delivering blood in Rwanda in 2014, now operates across five African countries, the US, and Japan. The funding will accelerate expansion into at least four new US states, where its home delivery service is exceeding growth forecasts, while a new national-scale contract in Rwanda will extend its reach across the country's cities and healthcare facilities.

▶️ Meet “Roadrunner”: a bipedal, wheeled robot for multi-modal locomotion (1:33)

Researchers at RAI Institute, a robotics research lab spun off from Boston Dynamics, present Roadrunner, a new bipedal wheeled robot prototype designed to seamlessly switch between its side-by-side and in-line wheel modes, as well as stepping configurations, depending on the requirements of its environment. The robot’s legs are entirely symmetrical, allowing it to point its knees forwards or backwards, which can be used to avoid obstacles or manage specific movements.

A former Thiel fellow’s startup just launched a drone it says can replace police helicopters
Brinc, a US drone startup valued at nearly half a billion dollars, has launched Guardian—a public safety drone equipped with embedded Starlink connectivity, thermal and 4K cameras, and an automated charging station. The drone can also deploy emergency supplies, such as defibrillators. The company estimates a $6-8 billion market for equipping police and fire stations with rooftop 911 response drones. This ambition has been boosted by the Trump administration’s recent ban on foreign-made drones, which has weakened the dominance of China’s DJI.

▶️ Learning Athletic Humanoid Tennis Skills from Imperfect Human Motion Data (1:31)

Researchers from China have taught a humanoid robot to play tennis by training it on rough snippets of human movement rather than needing perfect recordings of real matches. The robot, a Unitree G1, can move around the court, hit the ball back, and even keep a rally going with a human player. It's far from competing with any real tennis player, but it's an impressive step forward for getting robots to handle fast, physical tasks in the real world.

🧬 Biotechnology

▶️ Here’s how computers are helping build antibiotics (20:50)

In this video, Clockwork (an excellent YouTube channel on biochemistry) explains how machine learning is transforming antibiotic discovery, using Zosurabalpin a new antibiotic from Roche now entering phase 3 trials—as its central example. The compound was identified by feeding experimental results into a random forest algorithm (no generative AI involved) to rapidly narrow the field to a handful of viable candidates. The video also highlights newer work from MIT using generative AI to screen millions of chemical fragments into promising compounds. Despite cautioning that real-world results remain years away, the video argues we are witnessing a genuinely transformative moment in drug discovery.

Trillion Gene Atlas Expands Evolutionary Datasets for Next-Generation AI Therapeutics
Basecamp Research has teamed up with Anthropic, Nvidia, and others to launch the Trillion Gene Atlas, a project aiming to sequence DNA from over 100 million species to build a massive training dataset for AI drug discovery. Current models are limited by narrow public datasets, but Basecamp's proprietary data—already ten times larger—has shown promising results in designing therapies. The goal is to let AI design medicines directly from a disease description, with Nvidia’s computing power compressing decades of processing into under two years.

Scaling frontier models in vitro
JURA has built an AI platform that designs its own biological experiments and learns from the results in a continuous loop. In a three-day demonstration, it tested over 209 million antibody candidates against 100 cancer-related targets and trained a model capable of predicting successful matches even for entirely new targets. The company argues this tight integration of AI and lab work opens up a fundamentally new approach to drug design.

▶️ Why Building New Proteins from Scratch Is Our New Superpower | David Baker (30:40)

In this TED interview, Nobel laureate David Baker discusses his pioneering work in computational protein design. Since his 2019 TED talk, applications have expanded dramatically—from an approved COVID vaccine to enzymes that degrade plastics and forever chemicals, heat-tolerant crop proteins, and biosensors that interface with electronics. Baker advocates strongly for open-source science and argues that biosecurity risks remain low compared to the benefits, since engineering dangerous pathogens is far more complex than designing single-function proteins.

💡Tangents

Jury finds Elon Musk misled investors during Twitter purchase, absolves him of some fraud claims
A jury found Elon Musk liable for misleading Twitter investors with two tweets that drove down the company's share price during his 2022 takeover bid, awarding shareholders roughly $2.6 billion in damages. However, jurors stopped short of finding he deliberately schemed to defraud investors. Musk's legal team plans to appeal, while plaintiffs' lawyers hailed the verdict as a signal that market-moving statements carry legal consequences regardless of the speaker's wealth.

Meta and YouTube Lose Landmark Social-Media Addiction Trial
A Los Angeles jury found Meta and YouTube negligent for designing addictive platforms, awarding $6 million to a woman who said social media use from childhood led to anxiety, depression, and poor body image. The case got around existing legal shields by focusing on how the apps were designed rather than what users posted on them. With over 3,000 similar cases waiting to be heard and a second ruling against Meta in the same week, the verdict marks a turning point in holding social media companies responsible for their effects on children.

▶️ How to build your dragon (46:42)

If you’ve ever wondered how to create a real-life dragon, this video is for you. The Thought Emporium, YouTube’s chief mad scientist, explains how a real dragon might work and how one could be created—if genetic engineering were advanced enough.

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Nvidia GTC conference took place this week, with Jensen Huang announcing a slew of new chips, AI systems, models, and more. We will take a closer look at what Nvidia announced and how it all builds a foundation for the company as a full-stack AI provider.

Elsewhere in AI, we have a range of news coming from OpenAI—including the release of GPT-5.4 mini and nano models, a report on cutting down “side projects” to focus on coding and enterprise, the acquisition of Astral, the delay of “adult mode”, and how OpenAI plans to catch up with Anthropic in coding. Beyond that, Nvidia is restarting production of chips for sale in China, Moonshot AI has reached an $18 billion valuation, Google has introduced “vibe design”, and a rogue AI led to a serious security incident at Meta.

Over in robotics, an Uber co-founder has launched a new robotics startup focused on industry and manufacturing, Amazon has acquired robotic doorstep delivery provider RIVR, and a humanoid robot danced uncontrollably in a restaurant following a malfunction.

In addition, this week’s issue of Sync features the first commercial Chinese BCI, how one Australian man used AI tools to cure his dog’s cancer, a deep dive into bottlenecks in scaling AI compute, SpaceX’s problematic IPO, an uploaded fruit fly brain controlling a virtual fly, and more!

Enjoy!

Nvidia wants to be the AI platform

Nvidia’s GTC 2026 keynote was its most ambitious ever. Over nearly two and a half hours, Jensen Huang unveiled new chips and a new AI system, predicted a trillion dollars in demand through 2027, and made the company’s clearest bid yet to evolve from chipmaker to full-stack AI infrastructure architect. But by the end of the week, the stock had fallen 6.6%, shedding roughly $300 billion in market capitalisation.

Let’s look at what Nvidia announced, and why the markets reacted the way they did.

Vera Rubin and the inference bet

The centrepiece was the Vera Rubin platform—Nvidia’s successor to Blackwell and the most comprehensive product launch in its history.

Each Rubin GPU packs 336 billion transistors and 288 GB of HBM4. At rack scale, the NVL72 system—72 GPUs and 36 Vera CPUs in a single liquid-cooled enclosure—delivers 3.6 exaflops of FP4 inference. Nvidia claims 10 times the inference throughput per watt and one-tenth the cost per token versus Blackwell. A single rack produces 700 million tokens per second. At full scale, the Vera Rubin Pod stretches to 40 racks, 1.2 quadrillion transistors, and 60 exaflops.

Vera Rubin is expected to begin shipping in the second half of 2026. Microsoft has already powered on the first NVL72 system for validation in its labs.

But the most architecturally significant announcement may have been the Groq 3 LPU—the first chip from Nvidia’s roughly $20 billion Groq acquisition. Each LP30 chip contains 512 MB of on-chip SRAM, delivering 150 TB/s memory bandwidth—nearly 7 times that of the Rubin GPU's HBM4. The design pairs Rubin GPUs for compute-heavy prefill with Groq LPUs for latency-sensitive decode.

The inference push is not optional. As AI moves from training models to running them, inference is fast becoming the biggest workload and the biggest revenue opportunity. It is also where Nvidia faces the most competition. Custom chips from Google, Amazon, and others are built specifically for inference and can be cheaper to run for certain workloads. The Groq acquisition and the Vera Rubin architecture are Nvidia's bet that it can adopt a fundamentally different approach to computing and build it into the platform before rivals own the inference market.

Huang also previewed what comes next. The Vera Rubin Ultra, expected in 2027, introduces a new "Kyber" rack architecture housing 144 GPUs in a single NVLink domain. Beyond that, the Feynman architecture (slated for 2028) will feature a next-generation GPU, the Rosa CPU, the LP40 LPU, BlueField-5 networking, and both copper and co-packaged optics interconnects, all built on 1.6nm process technology.

Agentic AI as the platform play

If Vera Rubin was the hardware star, agentic AI was the strategic throughline. Huang devoted significant stage time to OpenClaw, the open-source agentic framework that exploded in popularity in recent weeks. He even called it the first AI operating system, possibly a bigger deal than Linux (at least if you look only at the GitHub Star History graph).

To capitalise on OpenClaw’s popularity, Nvidia announced NemoClaw—an enterprise-grade production stack layered atop OpenClaw with runtime sandboxing, a privacy router, and network guardrails. It is hardware-agnostic, open-source, and, just like OpenClaw, runs on everything, from a PC with GeForce RTX to a Mac Mini to a cloud instance.

The open model offensive

Nvidia launched six frontier model families at GTC, spanning nearly every domain where AI compute is heading:

• Nemotron 3 for language, reasoning, and multimodal AI—Nvidia claims its Ultra variant will be “the best base model in the world”

• Cosmos 3, pitched as the first world foundation model unifying synthetic generation, physical AI reasoning, and action simulation

• Isaac GR00T for humanoid robotics

• Alpamayo 1.5 for autonomous driving with steerable reasoning via natural language

• BioNeMo for biology and drug discovery

• Earth-2 for weather and climate forecasting

The Nemotron Coalition—a consortium including Mistral AI, Perplexity, Cursor, and LangChain—will jointly develop the next generation. This is part of Nvidia's broader push into open-weight AI, which includes plans to spend $26 billion over five years building open-weight models.

The logic is the same as it has always been. Models optimised for Nvidia hardware reinforce demand for that hardware. If the most capable open models run best on Nvidia GPUs, every startup, researcher, and enterprise deploying them becomes a customer. It is the CUDA playbook applied to models.

DLSS 5 and the gamer backlash

The only consumer product announced at GTC 2026 was DLSS 5, which Nvidia calls "3D-guided neural rendering." Previous DLSS versions were performance tools that made games run faster by upscaling lower-resolution frames. DLSS 5 is trying to make games look better. Its AI model participates directly in the rendering process, re-rendering lighting and materials in real time based on what it understands about the scene—skin, hair, fabric, environmental light, and reflections.

The response from the gaming community was not what Nvidia expected. Many gamers dismissed it as “AI slop” coming to gaming, comparing the generated images to an AI yassify filter. Game designers and artists pushed back, too, arguing that Nvidia was interfering too heavily with artistic intention.

Huang fired back directly, calling the critics “completely wrong.”

DLSS 5 ships in autumn 2026 as a driver update for existing RTX 50 Series GPUs. In the meantime, we have a new meme template to enjoy.

Other bets: from robots to robotaxis to orbit

Apart from new chips and AI models, Nvidia used GTC to show it is expanding into territories that already require, or soon will, enormous computing power—both in the cloud and at the edge, and soon maybe even in orbit.

The robotics showcase featured over 110 robots on the show floor, headlined by the GR00T N1.7 vision-language-action model for humanoid robots. During the keynote, a robotic Olaf from Frozen joined Huang on stage and demonstrated a simulation-first training pipeline built in Omniverse.

The autonomous driving push landed concrete partnerships. BYD, Hyundai, Nissan, and Geely joined existing partners Mercedes, Toyota, and GM—seven manufacturers producing roughly 18 million vehicles per year. Uber committed to deploying Nvidia-powered robotaxis across 28 cities by 2028.

Sovereign AI continued its rise as a revenue line. Governments that want to build national AI infrastructure on their own soil—the UK, France, Canada, South Korea, Singapore, and others—are buying Nvidia’s full-stack systems. Last fiscal year, sovereign AI revenue tripled to over $30 billion, nearly 14% of Nvidia’s total.

At the other extreme, Nvidia unveiled Space-1, a Vera Rubin computing module for orbital data centres delivering 25x more AI compute than H100 in space.

Why has the stock fallen?

By any measure, it was the broadest set of announcements in GTC's history. But it was not enough for Wall Street. Despite the most comprehensive product launch in Nvidia’s history, a trillion-dollar demand forecast, and 40 of 41 tracked analysts rating the stock a buy, Nvidia stock fell 6.6% over the conference week, resulting in the company shedding roughly $300 billion in market capitalisation. For the first time in nearly a year, the stock slipped below its 200-day moving average, breaching a technical floor it had held for 214 consecutive trading days.

The reasons were structural, not technical. Macro headwinds—including the escalating war in Iran and rising oil prices—rattled markets broadly. Additionally, investors remain sceptical about the sustainability of hyperscaler AI spending, particularly as more Big Tech companies raise debt to fund data centre buildouts. The trillion-dollar demand forecast was read as a timeline extension through 2027, not incremental acceleration.

Speaking with TechCrunch, Futurum CEO Daniel Newman captured the paradox neatly: the speed of AI innovation has itself become a source of uncertainty. The markets hate uncertainty—and right now, AI is moving too fast for investors to price confidently, even when the revenue keeps arriving.

The long game

GTC 2026 revealed a company that has moved decisively beyond just selling GPUs. Nvidia is building the full-stack operating layer for the AI economy—from silicon and networking through inference orchestration, agent frameworks, open models, and robotic deployment. By launching six model families, the Nemotron Coalition, and NemoClaw, Nvidia is positioning itself as the default platform for agentic AI. If Huang is right that every SaaS company will become an Agent-as-a-Service company, Nvidia stands to collect tolls at every level.

But the competition is not sleeping. AMD remains the most credible challenger—its MI400 series ships in the second half of 2026 with 432 GB of HBM4, significantly more memory than comparable Nvidia offerings, and its Helios rack-scale solution mirrors the Vera Rubin NVL72 architecture directly. AMD has already published a rebuttal to Nvidia’s GTC inference benchmarks, arguing that on equal footing, its chips deliver lower cost per token.

The hyperscalers are pushing harder still. Google’s TPU v7 now runs over 75% of Gemini computations internally. Microsoft has Maia 200. Amazon has Trainium. Each of them is building toward full vertical integration—controlling the stack from application to silicon. But none is ready to move all of its AI compute to custom chips, and each still relies on Nvidia's hardware.

The gap is real, but it is not permanent. Some analysts expect Nvidia to begin losing share from 2027 as in-house chip programmes scale, particularly in inference. For now, though, Nvidia’s CUDA ecosystem, its one-year architecture cadence, and its software moat create competitive barriers that no rival has yet breached. Whether the gap closes before Nvidia can entrench itself as the platform for the AI economy is the question to watch over the next 18 months.

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🦾 More than a human

China Approves First Brain Implant for Commercial Use
China has approved its first brain implant for commercial use, allowing Neuracle Technology to sell a device that helps partially paralysed patients regain some hand movement after spinal cord injuries. The approval marks a key step in China's state-backed push to compete with US firms like Neuralink, though Neuracle's implant is relatively limited—sitting outside the brain's outer membrane with fewer channels than more advanced rivals.

Brain Implants Let Paralyzed People Type Nearly as Fast as Smartphone Users
Scientists have helped two paralysed people type using only their thoughts, thanks to brain implants that read imagined finger movements on a familiar keyboard layout. After just 30 practice sentences, one participant reached 22 words per minute—close to typical smartphone typing speeds. The approach is easier and less tiring than existing methods like eye-tracking, and the team is now improving it for everyday long-term use.

🧠 Artificial Intelligence

OpenAI to Cut Back on Side Projects in Push to ‘Nail’ Core Business
OpenAI is scaling back its wide-ranging product efforts to focus on coding tools and business customers, the Wall Street Journal reports. The company's applications chief Fidji Simo said Anthropic's growing lead in the business market should be a "wake-up call," and that spreading resources too thinly across projects like the Sora video app or Atlas web browser had been a distraction. With both companies potentially heading for stock market listings, the move reflects a broader bet that serving developers and businesses is more important than chasing the consumer market.

Introducing GPT‑5.4 mini and nano
OpenAI has released GPT-5.4 mini and GPT-5.4 nano, two smaller, faster variants of its flagship GPT-5.4 model built for latency-sensitive, high-volume workloads such as coding assistants, agentic subagents, and computer-use applications. According to OpenAI, GPT-5.4 mini improves substantially over GPT-5 mini across coding, reasoning, multimodal understanding, and tool use while running more than 2x faster, and approaches GPT-5.4-level performance. GPT-5.4 mini is available now in the API, Codex, and ChatGPT, where Free and Go users can access it via the Thinking feature, and paid users get it as a rate-limit fallback. GPT-5.4 nano is API-only and aimed at lightweight tasks like classification, extraction, and ranking.

OpenAI’s Bid to Allow X-Rated Talk Is Freaking Out Its Own Advisers
OpenAI has delayed the launch of "adult mode," a planned feature enabling sexually explicit text conversations in ChatGPT, citing internal concerns and technical challenges, including an age-prediction system that was misclassifying around 12% of under-18 users as adults. The company's own well-being advisory council unanimously opposed the plan, warning of risks such as emotional dependence and inadequate protections for minors. Sam Altman has framed the feature as treating adults like adults and acknowledged that it could boost growth, but the decision highlights growing tension between commercial incentives and safety at the company.

Nvidia Says It Is Restarting Production of AI Chips for Sale in China
Nvidia is restarting production of its H200 chip for China after months of uncertainty. US and Chinese governments had gone back and forth on whether the sales could go ahead, but both sides now appear to have given the green light—though Nvidia must hand over 25% of the revenue to the US government. Jensen Huang said demand from Chinese buyers is picking up and orders are coming in, but the company hasn't actually made any money from the deal yet.

China AI Startup Moonshot Snags Funds at $18 Billion Valuation
Moonshot AI, the Chinese startup behind the Kimi chatbot, is looking to raise up to $1 billion at a valuation of around $18 billion—four times what it was worth just three months ago. The round reflects strong investor interest in Chinese AI firms challenging Western leaders like OpenAI and Anthropic.

Alibaba Consolidates AI Operations Under New Business Group
Alibaba has consolidated all its AI operations into a new unit called Alibaba Token Hub, bringing together its Tongyi lab, Qwen AI assistant, and other AI divisions. The move reflects the company's belief that AI agents will handle an increasing share of digital work, and comes shortly after the departure of Qwen's technical lead.

Introducing “vibe design” with Stitch
Google has updated Stitch, an experimental AI tool that lets users create UI designs by describing them in plain language, using an approach it calls "vibe design." Powered by Gemini, it produces editable design files and working front-end code, aiming to collapse the usual back-and-forth between designers and developers into one faster step. The tool is still experimental, but Google's aim is to collapse the traditionally slow back-and-forth between designers and developers into a single, much faster step.

OpenAI to acquire Astral
OpenAI has announced its intention to acquire Astral, the company behind uv, a popular Python package and project manager. After the deal closes, the Astral team will join OpenAI to work on Codex, the company's AI coding agent. This is yet another acquisition of an open source team by OpenAI (the most notable was the recent hiring of Peter Steinberger, the creator of OpenClaw), and mirrors Anthropic’s acquisition of Bun, a popular JavaScript runtime. For a deeper analysis of the deal's implications for the Python ecosystem, Simon Willison's post is worth reading.

Introducing Perplexity Health
Perplexity joins OpenAI and Anthropic, and launches its own AI tool for health. Called Perplexity Health, it connects to users' medical records, lab results, and wearable devices to answer health questions based on their personal data. According to Perplexity, the new tool draws answers from peer-reviewed medical sources rather than general web content. The service is launching for paid US subscribers first, with a physician advisory board overseeing its development.

Introducing the Machine Payments Protocol
Stripe and Tempo have launched the Machine Payments Protocol (MPP), an open standard that lets AI agents pay for things on the web automatically. It works with regular currencies and stablecoins, and businesses can set it up with just a few lines of code using Stripe's existing tools. Early uses include agents paying for browser access, sending physical mail, and even ordering sandwiches—all without human involvement. It's part of Stripe's bigger bet that AI agents will become major customers on the internet.

▶️ Dylan Patel — Deep Dive on the 3 Big Bottlenecks to Scaling AI Compute (2:31:03)

Dwarkesh Patel sits down with Dylan Patel, founder of SemiAnalysis, to discuss the bottlenecks in scaling AI compute. A key theme is the inertia of the semiconductor supply chain. ASML’s EUV lithography machines, produced in tiny quantities with long lead times, are the ultimate bottleneck capping global chip production for the rest of the decade, while a worsening memory crunch is driving up DRAM prices and squeezing out consumer electronics. The AI labs know what they need, but each layer of the supply chain beneath them is building to a more conservative estimate, and the gap keeps widening. I highly recommend listening to this conversation if you are interested in the economics side of the semiconductor industry during the AI boom.

Introducing MAI-Image-2: for limitless creativity
Microsoft has launched MAI-Image-2, a new image generation model from its AI team that it says ranks third worldwide on the Arena.ai leaderboard. The model is designed to produce realistic photos, handle text within images, and create detailed scenes. It is available now in the MAI Playground and is rolling out to Copilot and Bing Image Creator, with API access for select business customers and wider developer access coming soon.

Mistral Forge
Forge is a platform launched by Mistral that enables businesses to train their own AI models using internal data such as company documents, code, and policies. Its main advantage is that organisations retain full control over both their models and their data. Instead of depending on generic AI systems, companies can create models tailored to their specific processes and terminology, resulting in more accurate and reliable AI agents for real-world applications.

Introducing Mistral Small 4
Mistral has launched Mistral Small 4, a free, open-source AI model that combines text chat, image understanding, coding, and complex reasoning into one package—replacing several of its earlier specialised models. Mistral says it matches or beats larger competitors on key benchmarks while producing shorter, cheaper-to-run responses. The model is available now through Mistral's own API, Hugging Face, and Nvidia’s platform.

China is mobilizing thousands of one-person AI startups
Chinese cities, such as Suzhou, Shanghai, and Wuhan, are competing to attract solo founders who use AI tools to build products without needing employees or investors. Local governments are offering free office space, cheap computing power, and loans, often filling empty buildings in the process. The effort mirrors how China has previously used state backing to rapidly grow industries like electric vehicles. Most of these tiny startups probably won't survive, but the subsidies are giving laid-off tech workers a reason to experiment with AI.

Inside OpenAI’s Race to Catch Up to Claude Code
WIRED spoke with over 30 people at OpenAI, including its top leadership, to learn how the company lost its lead in AI coding tools. After ChatGPT took off in late 2022, OpenAI shelved its dedicated coding team and shifted focus to consumer products, leaving Anthropic to fill the gap with Claude Code—now a $2.5 billion business, more than double what OpenAI’s Codex brings in. OpenAI has been playing catch-up ever since, assembling new teams and trying to buy its way back in.

A rogue AI led to a serious security incident at Meta
An internal AI bot at Meta—described as similar to OpenClaw—posted bad technical advice on a company forum without permission, and when another employee followed that advice, it caused a serious security incident that briefly exposed sensitive data to staff who shouldn’t have seen it. Meta says no user data was misused and blames the engineer for not double-checking the AI’s suggestion. It’s the second time in weeks that an AI agent has caused problems at Meta, highlighting the risks of giving these tools too much independence.

Pentagon Moving to Replace Anthropic Amid AI Feud, Official Says
The Pentagon is replacing Anthropic's AI tools after declaring the company a supply-chain risk, following a dispute over Anthropic's demand for safeguards against mass surveillance and autonomous weapons. According to a Bloomberg interview with the Pentagon's chief digital and AI officer, engineering work on replacement systems is already underway, with OpenAI, xAI and Google all stepping in to fill the gap.

ByteDance reportedly pauses global launch of its Seedance 2.0 video generator
ByteDance has put its Seedance 2.0 AI video tool on hold outside China after Hollywood studios threatened legal action, according to a report from The Information. The tool went viral in February when users created fake clips of real actors, prompting companies like Disney to accuse ByteDance of stealing their intellectual property. The global launch, originally planned for mid-March, is now delayed while the company tries to sort out the legal issues.

Cerebras is coming to AWS
Cerebras and AWS are partnering to deploy Cerebras CS-3 systems in AWS data centres and made them available through AWS Bedrock. The two companies are pairing their respective chips—AWS Trainium for processing queries and Cerebras's wafer-scale engine for generating responses—in a combined setup expected to deliver five times more high-speed token capacity.

Anthropic invests $100 million into the Claude Partner Network
Anthropic has launched the Claude Partner Network, a programme backed by an initial $100 million investment for 2026 to help consultancies, professional services firms, and specialist AI agencies support enterprise adoption of Claude. Partners get training, technical support, marketing help, and access to a new professional certification.

▶️ Newest Gen AI Servers Are Built Like This (20:30)

Patrick from ServeTheHome gets his hands on Nvidia B300 servers and compares them to the previous B200 generation. This video provides a clear overview of the hardware used to serve modern AI models, how these systems operate and connect to one another, how they are powered and cooled, and even how they look like inside.

🤖 Robotics

Uber co-founder Kalanick launches Atoms in specialized robotics push
Travis Kalanick, Uber's co-founder and former CEO, has launched a new startup called Atoms that builds robots for specific jobs in mining, transport, and food production. Rather than trying to create general-purpose humanoid robots, the company focuses on machines designed to perform specific tasks well—an approach Kalanick believes is more practical and profitable.

US Army announces contract with Anduril worth up to $20B
The US Army has awarded Anduril a 10-year deal worth up to $20 billion. The contract bundles over 120 separate purchasing agreements into one, covering Anduril's hardware, software, and services as the Pentagon looks to modernise how it buys technology. Anduril, which made around $2 billion in revenue last year, is also reportedly raising funds at a $60 billion valuation.

Amazon acquires robotic doorstep delivery provider RIVR
Amazon has bought RIVR, a robotics company that makes four-legged wheeled robots for delivering packages to people's doors. RIVR grew out of a ETH Zurich's robotics lab and had already received backing from Jeff Bezos in a 2024 funding round. Unlike other delivery robots, RIVR's robots can handle stairs and rough ground, which could give Amazon a fresh approach after it scrapped its own delivery robot project in 2022.

Employees had to restrain a dancing humanoid robot after it went wild at a California restaurant
A dancing robot at restaurant in California went haywire near a dining table, smashing plates and scattering dishware while staff scrambled to stop it. The restaurant said the robot wasn't malfunctioning—it had simply been moved too close to a table at a customer's request. The incident raises obvious safety concerns and suggests that humanoid robots with swinging arms might not be the best fit for crowded spaces just yet.

Noble Machines exits stealth with Moby humanoid
Another humanoid robotics company joins the party! Noble Machines has emerged from stealth and revealed that it deployed its Moby humanoid robot at a Fortune Global 500 customer within 18 months of launch. Founded by ex-Apple, SpaceX, NASA, and Caltech engineers, the company emphasises rapid language-based learning and whole-body AI control, claiming its robots can acquire new skills in hours. Moby is aimed at hazardous industrial tasks across manufacturing, logistics, and construction, with a next-generation model on the way.

Gecko Robotics lands the largest U.S. Navy robotics deal yet
Gecko Robotics has signed a five-year deal with the US Navy worth up to $71 million—the Navy's largest robotics contract to date—to deploy inspection robots across its fleet, starting with 18 Pacific Fleet ships. The robots will create digital twins of each vessel to enable predictive maintenance, aiming to boost ship readiness from around 60% to 80% by 2027.

🧬 Biotechnology

An Australian tech entrepreneur used AI to help create the first-ever bespoke cancer vaccine for a dog to treat his beloved pet Rosie
An Australian tech entrepreneur used ChatGPT and AlphaFold to find a way to treat his dog Rosie's cancer after standard treatments failed. With help from scientists, he had Rosie's DNA analysed and a custom mRNA vaccine was created—believed to be the first of its kind for a dog. Most of Rosie's tumours have since shrunk, and while she isn't cured, her health and energy have bounced back. Researchers say the case shows how AI could make personalised cancer treatments faster and more widely available, for animals and eventually humans.

Digital Twin of a Cell Tracks Its Entire Life Cycle Down to the Nanoscale
Scientists have built a detailed digital twin of a living bacterium, simulating nearly all its molecules in 3D as it grows and divides. Unlike simpler models that treat cells as a uniform molecular soup, this one tracks where each molecule sits, which turns out to be crucial for accurately capturing cell division. The simulation matched real experimental results closely, though it took up to six days on a supercomputer to model just 105 minutes of the bacterium’s life. The work could help speed up drug discovery and deepen our understanding of how cells function at the most basic level.

How to Design Antibodies
If you ever wondered how to design an antibody, then this step-by-step guide from Asimov Press is for you. AI tools like BoltzGen can now design antibody candidates computationally, replacing a process that once required screening billions of molecules in the lab. The guide walks through the full workflow—from choosing a protein target to testing designs in a wet lab—using open-source tools and web platforms accessible to non-specialists, with a minimum cost of around $4,000.

💡Tangents

▶️ SpaceX IPO Scandal (30:12)

Patrick Boyle analyses in this video the SpaceX–xAI merger and the upcoming SpaceX IPO. He argues the deal is mainly a financial manoeuvre to bundle a loss-making AI company with a capital-hungry space business before going public, noting that SpaceX itself burns through enormous capital and has never paid a dividend. Boyle highlights serious governance concerns given Musk's controlling role on both sides of the deal, and suggests the merger lets xAI avoid an embarrassing down round while capitalising on the current AI hype before investors look too closely.

The First Multi-Behavior Brain Upload
Eon Systems has shown a complete simulation of a fruit fly brain—over 125,000 neurons and 50 million synaptic connections—controlling a virtual fly body and producing multiple natural behaviours. This is the first of this kind, as previous projects either simulated brains without bodies or used AI shortcuts rather than actual brain wiring. The company plans to attempt the same with the mouse and eventually human brains.

Future AI chips could be built on glass
In pursuit of greater performance and energy efficiency, the semiconductor industry is turning to glass as a foundation for advanced chip packaging. Glass handles heat far better than the organic materials used for decades, allowing engineers to fit more chips into smaller spaces while using less power. South Korean company Absolics plans to begin commercial production this year, Intel has built working prototypes, and major firms like Samsung are racing to catch up.

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As this issue goes out a day before Nvidia’s annual GTC conference, we’ll take a look at Nvidia’s position and how Jensen Huang is building the foundations for the next stage in the company’s history.

Elsewhere in AI, Anthropic sued the US Department of Defence and launched the Anthropic Institute; Meta bought Moltbook and delayed its new models; and Google made a deal with the Pentagon to provide AI agents for unclassified work, while not ruling out ads in Gemini. Meanwhile, Mira Murati and Yann LeCun have raised billions for their AI startups, Andrej Karpathy released Autoresearch, and Elon Musk is overhauling xAI.

Over in robotics, we have new humanoid robots for the home, Zoox arriving in Dallas and Phoenix, and a look at how Pokémon Go is helping to build better robots.

Beyond that, this week’s issue of Sync also features a Chinese brain–computer interface startup raising $21 million just months after being founded, what an AI-driven economy could look like, a new agentic AI model for biology, and more.

Enjoy!

Nvidia’s next move

Nvidia is in a tricky position. The $5 trillion semiconductor giant posted $216 billion in annual revenue, up 65% from the year before. It is the most valuable company in the world. It sits at the centre of the largest infrastructure buildout in a generation, selling the chips that power almost every major AI system on the planet. By any conventional measure, Nvidia has never been stronger.

But the same customers who made Nvidia essential are now working to make it optional. The markets it would like to reach are building alternatives without it. Nvidia is not in decline. But the window in which it can take its dominance for granted is closing, and the company is moving fast to prepare for what comes next.

The board is shifting

The biggest threat to Nvidia is now its own customers. The companies that spent billions buying Nvidia GPUs are building their own chips. Google has TPUs. Microsoft has Maia. Amazon has Trainium. For each of them, the investment in talent and infrastructure to design custom silicon is worth it—purpose-built chips can be optimised to run their specific AI workloads more cheaply and efficiently than a general-purpose GPU. Over time, more and more of those workloads will migrate off Nvidia hardware and onto proprietary chips.

This does not mean Nvidia’s revenue disappears overnight. The big cloud providers will still buy Nvidia GPUs to offer them to customers through AWS, Google Cloud and Azure. But over time, they won't be using them to train and serve their own AI models. The largest AI companies are building toward full vertical integration, and once they get there, they will not need Nvidia the way they do today.

This is not hypothetical. Google trained Gemini 3 entirely on its own TPUs. Anthropic now trains and serves Claude across both Google's TPUs and Amazon's Trainium chips. The migration away from Nvidia is already underway.

Even if Nvidia designs a faster, cheaper inference chip (with the talent and technology from acquihired Groq), it will not change the calculus. Google, Amazon and Microsoft are not pursuing custom silicon because Nvidia’s current chips are inadequate. They are doing it because controlling the full stack—from applications to model and all the way down to silicon—is a structural advantage they intend to own permanently.

Nvidia also faces pressure from traditional competitors. AMD's upcoming MI400 series and its Helios rack-scale platform are attracting serious interest. Meta and OpenAI have both signed major multi-year deals to deploy them. AMD remains a distant second in data centre GPU revenue, but the gap is narrowing.

Blocked positions

If the domestic market is narrowing, the obvious move is to look abroad. But that option has its own problems.

China would have been Nvidia’s most natural growth market. The country is in the middle of its own AI revolution, and demand for high-end GPUs is enormous. But the US government treats advanced AI chips as a strategic asset, and export controls limit what Nvidia can sell there. That is unlikely to loosen under the current administration—or any foreseeable one.

The restrictions are also accelerating exactly what they were designed to prevent. Chinese tech giants and the government know that dependence on an American company for high-end GPUs is a strategic vulnerability—especially during the time of trade wars and chip export bans. They are doing exactly what their US counterparts are doing: investing heavily in domestic, custom silicon. Huawei’s Ascend chips are not yet competitive with Nvidia’s best, and Nvidia’s hardware remains sought-after in China. But the Chinese semiconductor industry is closing the gap. Eventually, it will catch up, and when it does, there will be no market left for Nvidia there either.

Threat from the flank

The challenge is not only coming from trillion-dollar hyperscalers and geopolitics. It is also coming from Apple.

When OpenClaw went viral in late January, it triggered a run on Mac Minis. Developers wanted an always-on, low-power machine to run a personal AI agent, and the $599 Mac Mini M4 turned out to be the perfect fit—quiet, efficient, capable enough for local inference on small models. For a brief period, units sold out at multiple retailers. Nvidia’s closest competitor in this space is DGX Spark, a compact desktop with 128GB of unified memory and a $4,700 price tag. It is a capable machine, but at that price it competes less with the base Mac Mini and more with a MacBook Pro or Mac Studio—machines that are also excellent for running local models and come with Apple’s mature ecosystem.

The deeper issue is that a growing share of day-to-day AI work—fine-tuning, inference, agent orchestration, prototyping—can now run on Apple Silicon. Training frontier models still requires thousands of Nvidia GPUs in massive data centres. But as open models get smaller and more efficient, the hardware floor for useful AI keeps dropping, and Nvidia has nothing in that space.

Securing the base

Data centre chips are Nvidia's cash machine—they account for roughly 90% of the company's revenue. If the hyperscalers are building their own chips and China is off the table, Nvidia needs other customers who will keep buying its GPUs at scale.

One answer is the neoclouds—a new generation of cloud providers built specifically around AI workloads. This week alone, Nvidia participated in Nscale’s $2 billion Series C, which valued the UK-based AI data centre startup at $14.6 billion. It also put $2 billion into Nebius Group to jointly develop AI data centre infrastructure.

The logic is straightforward. Neoclouds like Nscale, Nebius, CoreWeave and Lambda do not have chip design teams. They are not building custom silicon. Their entire business model depends on buying the latest Nvidia GPUs and renting them out. By investing in these companies—and giving partners like Nebius early access to its latest hardware—Nvidia is cultivating a customer base that is structurally dependent on its products.

Governments are another growing market. Under the banner of "sovereign AI," Nvidia is striking deals with countries that want to build national AI infrastructure on their own soil. The UK, France, Canada, South Korea, Singapore and others are all buying Nvidia's full-stack systems. Last fiscal year, sovereign AI revenue tripled year-over-year to over $30 billion—nearly 14% of Nvidia's total revenue.

Opening new lines

Nvidia is also looking into new markets.

One direction is robotics, or “physical AI” as Jensen Huang likes to call it—the application of AI to the real world rather than to text and images on a screen. Nvidia has been building toward this for years. Its Isaac platform provides tools for developing and simulating robotic applications. GR00T is a foundation model for humanoid robots. The Jetson family of compact computers targets edge AI and robotics deployments where cloud connectivity is impractical.

The shift is even visible in Huang's keynotes. Over the past few years, robotics has claimed a growing share of stage time—Huang now presents with humanoid robots on the screen behind him and physical robots on stage around him.

Self-driving cars are the other bet. Waymo, Tesla and Zoox will not be customers—they design their own silicon and software. But legacy car manufacturers are a different story. They know how to build cars. They do not know how to build autonomous driving systems. Nvidia’s pitch to them is a complete package: DRIVE AGX for the hardware, DRIVE AV for the software, and now Alpamayo—an open portfolio of AI models, simulation tools and driving datasets designed to bring reasoning capabilities to autonomous vehicles, launched at CES 2026.

Whether the car industry moves fast enough to make this a material revenue stream for Nvidia in the near term is an open question. But the strategic logic is sound: find industries where AI is transformative, where customers lack the capability to build it themselves, and where Nvidia can sell the full stack and lock them into its ecosystem.

The gambit

Perhaps the most interesting move is Nvidia’s push into AI model development. The company will spend $26 billion over five years building open-weight models, according to financial filings reported by Wired. That is frontier-lab money. It signals an ambition to compete not just as a chipmaker but as a model developer.

The latest release is Nemotron 3 Super, a 120 billion-parameter reasoning model with a hybrid architecture designed for efficient inference. According to independent benchmarks by Artificial Analysis, Nemotron 3 Super delivers roughly 10% higher throughput per GPU than OpenAI’s gpt-oss-120b while scoring higher on intelligence benchmarks. The model is open-weight, permissively licensed, and comes with its full training methodology and datasets published. An even larger model, Nemotron 3 Ultra, with around 500 billion parameters, is expected to be released soon and could challenge Chinese models for the top spot in the open-weight AI space.

On the surface, Nvidia is filling a gap. Meta pioneered open-weight AI with Llama, but Mark Zuckerberg has signalled that future models may not be fully open. The leading American models from OpenAI, Anthropic and Google remain proprietary. Meanwhile, the strongest open models now come overwhelmingly from China: DeepSeek, Alibaba’s Qwen, Kimi K2 from Moonshot AI, MiniMax. Much of the global AI community—startups, researchers, independent developers—has gravitated toward Chinese models because nothing else of comparable quality is freely available.

But there is a deeper logic. Models built and optimised for Nvidia hardware reinforce demand for that hardware. If the most popular open models run best on Nvidia GPUs, every startup, researcher and enterprise deploying them becomes a potential customer. It is the CUDA playbook applied to models: build the ecosystem, and the hardware sells itself.

There is also a geopolitical dimension. The next DeepSeek model is widely expected to have been trained entirely on Huawei’s chips—a development that could accelerate adoption of Chinese hardware, especially domestically. Nvidia’s open models offer a counterweight: capable, open, American-made, and optimised for Nvidia silicon.

The long game

It would be a mistake to underestimate Jensen Huang. He has led Nvidia since the company was founded 32 years ago. He navigated the bloodbath of the early graphics card industry, when dozens of competitors went bankrupt. He saw the opportunity in general-purpose GPU computing before anyone else and built CUDA, the software ecosystem that made Nvidia indispensable to AI researchers a decade before the current boom. He rode the crypto wave of the late 2010s and, when it collapsed, pivoted to generative AI before most people had heard of a large language model.

His next move is to turn Nvidia from a chipmaker into a platform. Through its inference service and NIM microservices, Nvidia already hosts third-party open models like Qwen, DeepSeek and Kimi K2, making them easy to deploy on Nvidia hardware. Even if developers choose a Chinese model, Nvidia wants them running it on Nvidia GPUs.

But the platform extends well beyond language models. Nvidia offers specialised NIM microservices for drug discovery through BioNeMo, medical imaging through Clara, industrial simulation through Omniverse, and more—each one bundling optimised models, inference runtime and hardware. The more industries that adopt this stack, the harder it becomes to leave.

Huang sees what is coming. The partners who made Nvidia a $5 trillion company—Google, Amazon, OpenAI, Microsoft—are becoming competitors. The next decade will look fundamentally different from the last one. Nvidia’s moves into neoclouds, robotics, autonomous vehicles and open models are not experiments. They are the foundations for the next stage of the company’s history—not as a chipmaker that sells to the AI industry, but as a platform that delivers AI to every other industry.

Huang will lay out the next chapter of that strategy tomorrow, at Nvidia's annual GTC conference.

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What happened this week?

🦾 More than a human

Chinese brain interface startup Gestala raises $21M just two months after launch
Chinese startup Gestala has raised $21.6 million to develop non-invasive, ultrasound-based brain–computer interfaces that avoid the risks of brain surgery. The company aims to finish a prototype by year-end and is initially targeting chronic pain management, with longer-term plans for mental health and neurodegenerative conditions. Gestala plans to leverage China's manufacturing ecosystem and lower clinical trial costs to move faster than international rivals, while also building a large ultrasound brain dataset to train AI models for decoding neural signals.

🔮 Future visions

▶️ What Happens When AI Runs the Entire Economy? (28:36)

In this video, Isaac Arthur imagines a world in which AI controls and runs the entire economy. He explores what labour would look like in an AI-driven economy and what values the AI could be programmed with. Arthur also touches on government, power, and economic sovereignty, and outlines both the best- and worst-case scenarios for humanity in such a world.

🧠 Artificial Intelligence

Anthropic Sues U.S. Defense Department, Pete Hegseth for Targeting the Company
Anthropic has sued the Trump administration for labelling it a security threat and trying to cut its federal contracts, calling the moves unlawful retaliation after the company pushed for limits on how the Pentagon could use its AI tools. The dispute arose when Anthropic sought guarantees against uses like mass surveillance and autonomous weapons, which the Defence Department refused. Over 30 employees from OpenAI and Google DeepMind, including DeepMind’s chief scientist Jeff Dean, filed a brief backing Anthropic, calling the designation an improper use of power and warning it could chill open debate about AI safety across the industry. Meanwhile, WIRED reports that President Trump is currently finalising an executive order that would formally ban usage of Anthropic tools across the government.

Google to Provide Pentagon With AI Agents for Unclassified Work
Google is rolling out AI assistants across the Pentagon's three-million-person workforce to automate routine tasks like summarising meetings and creating budgets. The tools will start on unclassified networks, with plans to expand to classified systems. The move comes as the Defence Department expands its AI partnerships with Google, OpenAI, and xAI, while its relationship with Anthropic has collapsed into litigation after the Pentagon designated the company a supply-chain risk over disagreements about usage guardrails. Early results appear to look promising—one Army team cut exercise planning from six months to six weeks—but most users haven't yet been trained to use the technology properly.

Meta Delays Rollout of New A.I. Model After Performance Concerns
According to the New York Times, Meta's new AI model, code-named Avocado, has fallen behind rivals like Google, OpenAI, and Anthropic in internal tests, pushing its release back to at least May. Despite massive spending and hiring—including bringing in Scale AI's CEO as Meta's new AI chief—the company is struggling to keep up, with executives even considering licensing Google's technology as a stopgap. The report highlights growing internal tensions and raises questions about whether Meta can deliver on Zuckerberg's bold promises to reach the frontier of AI development.

Oracle is building yesterday’s data centers with tomorrow’s debt
OpenAI is stepping back from expanding its Stargate data centre partnership with Oracle in Texas because it wants newer, faster Nvidia chips at other sites instead. The core problem is that AI chips now improve every year, but data centres take one to two years to build—so facilities can be outdated before they even switch on. Oracle is especially exposed because it has funded its AI expansion with over $100 billion in debt, unlike rivals like Google and Amazon, which can pay from their own profits. With Oracle's stock down sharply and earnings due soon, investors are watching closely to see how the company plans to manage this growing mismatch.

Musk Says xAI Must Be Rebuilt as Co-Founders Exit
Elon Musk is overhauling xAI after admitting it "was not built right first time around." Several co-founders have left as Musk pushes through his second reorganisation in a month, hiring new talent from rival firms to try to catch up with OpenAI and Anthropic—especially in coding, where he says xAI currently falls short. The shakeup follows xAI's merger into SpaceX at a $250 billion valuation and a growing push to tie the company more closely to Tesla.

Google Is Not Ruling Out Ads in Gemini
In an interview with WIRED, Google SVP Nick Fox confirmed the company isn't ruling out ads in Gemini, though it's currently experimenting within AI Mode, its Gemini-powered Search product. Fox said Google's strong revenue—over $400 billion in 2025—gives it the luxury of patience compared to OpenAI, which faces pressure to monetise ChatGPT quickly. He also highlighted Google's push toward "Personal Intelligence," an opt-in feature drawing on users' Gmail and Calendar data, calling personalisation the "holy grail" of Search, while stressing that private data won't be shared with advertisers.

Anthropic launches marketplace for Claude-powered software
Anthropic has launched Claude Marketplace, where enterprise customers can use their existing Anthropic spending to buy third-party apps built on Claude—starting with partners like Snowflake, Harvey, and Replit—with no commission taken. The new platform mirrors strategies by AWS and Azure but is clearly aimed at locking enterprise customers deeper into the Claude ecosystem.

Microsoft launches Copilot Cowork
Microsoft is introducing Copilot Cowork to Microsoft 365, which moves Copilot beyond chat-based assistance toward autonomous task execution. According to Microsoft, Copilot Cowork can manage calendars, prepare meeting materials, conduct research, and build launch plans—all running in the background while checking in for approval. If the name Copilot Cowork sounds familiar, that's no coincidence—Microsoft has partnered with Anthropic and integrated the technology behind Claude Cowork directly into the product. The feature is rolling out to preview users in late March 2026.

1M context is now generally available for Opus 4.6 and Sonnet 4.6
Anthropic has made the full 1-million-token context window for Claude Opus 4.6 and Sonnet 4.6 generally available at standard pricing, with no long-context premium or beta header required. The feature is live across Anthropic's own platform, Amazon Bedrock, Google Cloud Vertex AI, and Microsoft Azure Foundry.

Yann LeCun Raises $1 Billion to Build AI That Understands the Physical World
AMI, an AI startup founded by Yann LeCun, Meta's former chief AI scientist, has raised $1 billion to build AI "world models" that understand the physical world—a direct challenge to the LLM-scaling approach pursued by OpenAI, Anthropic, and others. The company, valued at $3.5 billion and backed by Bezos Expeditions, Eric Schmidt, and Mark Cuban, will work initially with partners like Toyota and Samsung on industrial applications before pursuing a longer-term "universal world model" for general intelligence.

Thinking Machines Lab inks massive compute deal with Nvidia
Thinking Machines Lab, the AI research startup founded by former OpenAI co-founder Mira Murati, has signed a multi-year strategic partnership with Nvidia involving at least one gigawatt of Nvidia's Vera Rubin systems starting in 2027. The seed-stage company, valued at over $12 billion despite being barely a year old, has raised more than $2 billion from investors, including Andreessen Horowitz and Nvidia itself.

Meta hires duo behind Moltbook
According to Axios, Meta has acquired Moltbook, a viral social network built for AI agents, bringing its co-creators Matt Schlicht and Ben Parr into Meta Superintelligence Labs (MSL) under Alexandr Wang. Meta did not disclose Moltbook’s purchase price. The acquisition comes not so long after OpenAI hired the creator of OpenClaw, another agentic AI project that exploded in popularity recently. In an internal post seen by Axios, Meta’s Vishal Shah said existing Moltbook customers can continue using the platform—though the company signalled the arrangement is temporary.

UK’s multibillion AI drive is built on ‘phantom investments’
A Guardian investigation has found that the UK government's flagship AI investments, led by Nvidia-backed firms CoreWeave and NScale, rest on inflated claims and poor oversight. CoreWeave's £1 billion commitment amounted to renting space in existing datacentres rather than building new ones, while NScale's promised supercomputer site remains a scaffolding yard. The government admitted NScale's $2.5 billion pledge is merely an "intention to commit capital" with no audit mechanism in place, raising serious doubts about UK's push to harness AI for economic growth.

Musk unveils joint Tesla-xAI project ‘Macrohard,’ eyes software disruption
Elon Musk has unveiled "Macrohard," a joint Tesla–xAI project that pairs xAI's Grok AI model with a Tesla-built agent capable of watching a computer screen and controlling keyboard and mouse actions, with a goal to mimic what entire software companies do. The system will run on Tesla's own chips alongside Nvidia hardware.

▶️ AI Chip & Silicon Round-up 2026 (13:14)

In this video, SemiAnalysis lists the most notable AI chips expected to make an impact in 2026. The lineup covers Qualcomm, AMD, Google, Cerebras, Groq, Nvidia, Meta, Amazon, Microsoft, and Intel, spanning both GPUs and custom ASICs. The overall message is that competition is intensifying across the board, even as questions remain about timing, software support, and real-world deployment for several of these chips.

Introducing The Anthropic Institute
Anthropic is launching The Anthropic Institute to study and communicate the societal challenges posed by increasingly powerful AI. It brings together engineers, economists, and social scientists working across red-teaming, societal impacts, economic research, AI forecasting, and AI’s interaction with the legal system. Its central aim is to leverage Anthropic’s insider perspective as a frontier AI lab to share findings openly and engage with those navigating AI-driven change.

AI Coding Startup Cursor in Talks for About $50 Billion Valuation
Cursor is reportedly seeking new funding that would value it at around $50 billion, nearly double its valuation from late last year, Bloomberg reports. The startup's revenue has already passed $2 billion annually, making it one of the fastest-growing companies in tech. The discussions remain preliminary and may not lead to a deal.

Amazon wins court order to block Perplexity’s AI shopping agent
Amazon has won a court order temporarily blocking Perplexity's Comet AI browser from accessing its shopping site. The judge found strong evidence that Comet scraped Amazon's website without permission, posing risks to customer data and advertising systems. Perplexity plans to appeal, calling the lawsuit a "bully tactic." The case is part of Amazon's wider push to keep third-party AI agents off its platform while building its own tools.

Meta Preparing to Deploy Four New Homegrown Chips to Handle AI
Meta is planning to roll out four new in-house AI chips—MTIA 300, 400, 450, and 500—by the end of 2027 to reduce its dependence on suppliers like Nvidia and AMD and lower costs. The first of these is already in production, with the rest at various stages of development. To speed things up, Meta acquired chip startup Rivos and its 400-plus engineers last year. The company is still spending tens of billions on external chips alongside this effort, taking a dual approach of buying off-the-shelf hardware while building custom silicon for its own specific needs.

Phi-4-reasoning-vision and the lessons of training a multimodal reasoning model
Microsoft has released Phi-4-reasoning-vision-15B, a compact open-weight multimodal model that handles diverse vision-language tasks. According to Microsoft, the new model excels at maths, science reasoning, and UI understanding. Trained on far less data than comparable models, it achieves competitive accuracy at significantly lower inference cost by selectively engaging chain-of-thought reasoning only when beneficial. The model is available under a permissive licence on Microsoft Foundry, HuggingFace, and GitHub.

autoresearch by Andrej Karpathy
autoresearch is a new open-source project from Andrej Karpathy that lets an AI agent autonomously improve an LLM training setup. The agent tweaks the code, trains for five minutes, checks if the result got better, and repeats—running around 100 experiments overnight while you sleep. The human’s job shifts from writing training code to writing instructions that guide the agent. It’s a small, deliberate proof of concept, but it points toward a future where AI agents drive the research process themselves.

The Linux Kernel Will Soon Be MIT-Licensed and Copyleft Will Be Dead Within 5 Years
This article argues that the GPL licence is in terminal decline and is now accelerated by AI. The key example is chardet, a popular Python module published under the GPL licence, reimplemented in just five days using Claude and under the less restrictive MIT licence. Combined with cases like Cloudflare recreating Next.js with Claude Code, this points to a near future where AI makes it trivial to rewrite software under permissive licences.

🤖 Robotics

▶️ Helix 02 Living Room Tidy (2:27)

Figure is back with another video, this time showing how its humanoid robot cleans a living room.

Zoox is arriving in Dallas and Phoenix
Zoox, Amazon's self-driving car subsidiary, is expanding into Phoenix and Dallas, bringing its US testing footprint to ten cities. The company will start by mapping streets with human-driven SUVs before moving to autonomous testing and eventually deploying its purpose-built robotaxis. Zoox still needs further federal approval to launch a full commercial service, but says it has already completed over a million autonomous miles and carried more than 300,000 riders in Las Vegas and San Francisco.

Humanoid maker Sunday reaches $1.15 billion valuation to build household robots
Sunday, a humanoid robotics startup, has hit a $1.15 billion valuation after raising $165 million from investors including Coatue Management and Tiger Global. The company is building a household humanoid robot called Memo to help with everyday chores like laundry and tidying up.

How Pokémon Go is helping robots deliver pizza on time
Niantic Spatial, spun out of the company behind Pokémon Go, is repurposing billions of location-tagged images captured by players to build a visual positioning system accurate to within a few centimetres. Its first real-world application is helping delivery robots from Coco Robotics navigate cities where GPS is unreliable. Longer term, the company aims to build a continuously updated, machine-readable digital replica of the real world—a living map that could serve as the foundation for a new generation of robots and AI agents.

China leads the humanoid robot race — but the U.S. still has a shot
Chinese firms now hold over 90% of global humanoid robot sales, driven by industrial policy, AI investment, and state enterprise demand, according to Omdia analyst Lian Jye Su. US companies lag in production scale but remain technically strong. Su argued the industry's key bottleneck is a lack of real-world training data, making mass deployment—even at a loss—a necessary step, much like AI's capital-intensive phase before ChatGPT.

Designing Robots for Human Spaces

Frog presents Nome, an interesting concept for a home robot that prioritises fitting naturally into daily life rather than just being technically impressive. The team studied household routines to guide the robot's shape, posture and movement, drawing on principles from sculpture and animation to make it feel approachable rather than mechanical. The core argument is that as robots move into our homes, the biggest challenge is not what they can do but how they make people feel—and that getting adoption right is fundamentally a design problem.

🧬 Biotechnology

Stem Cell Treatments For Parkinson’s And Heart Failure Approved in World First
Japan has approved two world-first treatments using reprogrammed stem cells—one for Parkinson's disease that implants dopamine-producing cells into the brain, and another that uses cell sheets to help repair damaged hearts. Both could be available to patients as early as this summer, though approvals were based on small trials under a fast-track system. The therapies represent a significant step for regenerative medicine, potentially moving beyond symptom management to addressing the root causes of these conditions.

One vaccine may provide broad protection against many respiratory infections and allergens
Researchers have created an intranasal vaccine that, in mice, protects against a wide range of respiratory viruses, bacteria, and allergens for up to three months. Rather than targeting specific pathogens, it works by sustaining the lungs' innate immune response alongside adaptive immunity—a fundamentally different approach to vaccination. Human trials are planned, with a universal respiratory nasal spray potentially available within five to seven years.

Prime Medicine to seek approval for gene-editing treatment after two-patient trial
Prime Medicine has brought back a gene-editing treatment for a rare immune disorder after the FDA suggested it could be approved based on results from just two patients. The company had abandoned the therapy last year because so few people have the condition, but new FDA fast-track options and the chance to earn a voucher worth over $150 million have made it worth pursuing again.

Eubiota: Agentic AI for Autonomous Microbiome Discovery
Eubiota is an open-source modular agentic framework for autonomous discovery in the gut microbiome, using specialised agents for planning, execution, verification, and synthesis, coordinated through shared memory and reinforcement learning. The team reports 87.7% benchmark accuracy, outperforming GPT-5.1 by 10.4%, with four experimentally validated case studies demonstrating end-to-end discovery. The framework is available on GitHub and on HuggingFace.

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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!"

This week, we complete the Anthropic vs Pentagon saga by examining the immediate fallout from the Pentagon formally designating Anthropic a supply-chain risk to American national security.

Elsewhere in AI, OpenAI released GPT-5.4 and GPT-5.3 Instant, while Google released Gemini 3.1 Flash-Lite. We also have new M5 Pro and M5 Max chips from Apple; a story about how Cloudflare “stole” Next.js with Claude Code; Jensen Huang saying Nvidia is pulling back from OpenAI and Anthropic; OpenAI rethinking its ecommerce strategy; and something happening with the Qwen team.

Over in robotics, Xiaomi has begun testing its own humanoid robots in electric car factories, Agility Robotics has changed its name, and the lead of OpenAI’s robotics team has left the company.

Other than that, this week’s issue of Sync also features a brain–computer interface company raising $230 million to treat blindness, Anthropic’s report on the impact of AI on jobs, a Google moonshot spinoff looking to ship city-scale laser Internet, Meta reportedly working on a smartwatch, human neurons inside a computer playing DOOM, and more!

Enjoy!

Anthropic vs Pentagon: The Fallout

It is official now. On Thursday, Anthropic received a letter from the Department of War confirming what Pete Hegseth had threatened and Donald Trump had demanded: the company has been formally designated a supply-chain risk to American national security. The designation—normally reserved for foreign adversaries—is one of the first times it has been applied to a US company. Anthropic says it will challenge it in court.

Last week, we covered the ultimatum. This is what came after.

The memo and the apology

Within hours of the designation, The Information published an internal message from Anthropic CEO Dario Amodei to staff. In it, he called OpenAI’s Pentagon deal “safety theatre,” described Sam Altman’s public statements as “straight up lies,” and dismissed OpenAI’s employees as “sort of a gullible bunch.” He said the administration was targeting Anthropic because it had not offered “dictator-style praise” to Trump.

Dario Amodei apologised for the tone days later, calling it a product of a chaotic day rather than a reflection of his settled thinking. His official statement was conciliatory to the point of deference, insisting that Anthropic and the Department of War shared far more than divided them. He offered to keep providing Claude to the military at minimal cost during the transition, so that troops relying on the technology would not lose access mid-operation.

The contrast between the two documents—one raw, one polished—tells the story of a company recalibrating in real time.

The public responds

Whatever the designation cost Anthropic in Washington, it earned back in public sympathy.

ChatGPT uninstalls surged 295% the day after OpenAI announced its Pentagon deal, and one-star reviews for the app jumped 775%. QuitGPT, a boycott group calling on people to leave ChatGPT, estimated that more than 4 million customers had pledged to do so. Claude climbed to the top of the US App Store—the first time any AI assistant had displaced ChatGPT—and hit number one in six other countries, including Canada and Germany.

The surge was so large that Anthropic’s own infrastructure couldn't keep up with demand, resulting in multiple outages in the days that followed.

OpenAI’s reckoning

OpenAI, meanwhile, struggled to contain the fallout from its own deal. Altman admitted in a staff memo that the announcement had looked “opportunistic and sloppy,” and told employees at an all-hands meeting that the backlash was “really painful.” The company quietly amended the terms of its agreement to explicitly prohibit domestic surveillance, after an OpenAI researcher acknowledged that the original language had left “legitimate questions unanswered.”

At a Morgan Stanley conference the day before, Altman had offered his broader defence: elected officials, not technology executives, should determine how AI is used in national defence. It is a reasonable position in the abstract. It is also a convenient one when the alternative is losing a contract.

What it costs

So, where does this leave Anthropic? The company is, by several measures, in the strongest commercial position of its life. Its revenue run rate has roughly doubled since the end of 2025 to nearly $20 billion, driven by products like Claude Code. Its valuation sits at $380 billion. It has raised more than $60 billion from over two hundred investors.

The company is widely expected to be preparing for an IPO. Whether investors will back a company that its own government has labelled a national security threat is a question that did not exist a week ago.

How much is now in jeopardy depends on how far the designation reaches. Amodei has argued that the law is narrow by design, covering only Claude's direct use in Pentagon contracts. It does not, he insists, reach every interaction between Anthropic and companies that happen to do business with the military. Microsoft backed this reading, saying its legal team had determined it could keep offering Claude to all customers outside the Department of War. But Lockheed Martin has already cut ties, and Emil Michael, the undersecretary of defence for research and engineering, posted on X that there is “no active negotiation” with Anthropic—shutting down Amodei’s claim that productive conversations were ongoing. The administration has taken this from a threat to a formal designation in under a week. There is no reason to think it will stop there.

Anthropic’s legal challenge will be the next chapter. Until then, the company sits at its commercial peak and under siege from its own government.

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🦾 More than a human

Brain Tech Startup Science Corp. Raises $230 Million to Treat Blindness
Science Corp. has raised $230 million, valuing it at $1.5 billion and making it the second-most valuable brain-implant company after Neuralink. The funding will help commercialise PRIMA, a small chip implanted in the eye that works with special glasses to restore vision in blind patients. In trials, the device improved sight in most participants. Science Corp. has applied for regulatory approval in Europe and the US, and plans to expand trials to other eye diseases. It is also working on a more advanced brain implant using engineered neurons, with human testing expected by late 2027.

🧠 Artificial Intelligence

How we rebuilt Next.js with AI in one week
Cloudflare may have pulled off one of the greatest heists in software history. Using Claude Code and about $1,100 worth of tokens, the team at Cloudflare managed to rebuild Next.js, a popular web framework, largely through vibe coding. According to Cloudflare, Vinext (as the new framework is called) delivers build times up to 4× faster and produces client bundles up to 57% smaller than Next.js. The company also claims that some customers are already using it in production. If you want to learn more about Vinext and its implications for software development, I recommend this video by Mo Bitar on the topic.

Introducing GPT‑5.4
OpenAI has released GPT-5.4, calling it its most capable frontier model for professional work, now available across ChatGPT, the API, and Codex, alongside a GPT-5.4 Pro variant for the more demanding tasks. According to OpenAI, GPT-5.4 matches or exceeds industry professionals in 83% of knowledge work comparisons, is 33% less likely to produce false claims than GPT-5.2, and is the first general-purpose model in its lineup with native computer-use capabilities. The release also introduces "tool search" for more efficient agentic workflows, with a higher per-token price offset by improved token efficiency. Again, OpenAI only compares new models with its own models, but according to independent benchmarks from Artificial Analysis, GPT-5.4 matches the performance of Gemini 3.1 Pro and is ahead of Claude Opus 4.6.

GPT‑5.3 Instant: Smoother, more useful everyday conversations
OpenAI has introduced GPT-5.3 Instant, an update to ChatGPT's most-used model focused on improving everyday conversational quality rather than benchmark performance. The update aims to reduce unnecessary refusals, disclaimers, and preachy preambles, deliver better-synthesised answers when searching the web, and adopt a more natural, less overbearing tone. According to OpenAI's internal evaluations, the model also reduces hallucination rates by up to 26.8% compared to prior versions, and is said to produce stronger creative writing. GPT-5.3 Instant is available now to all ChatGPT users and via the API, with the previous version set to be retired on 3 June 2026.

Gemini 3.1 Flash-Lite: Built for intelligence at scale
Google released Gemini 3.1 Flash-Lite, its fastest and most cost-efficient Gemini 3 series model, designed for high-volume workloads at scale. According to Google, 3.1 Flash-Lite delivers 2.5 times faster time to first answer token and a 45% increase in output speed compared to Gemini 2.5 Flash, while achieving strong results on reasoning and multimodal benchmarks. The new model is available in preview via Google AI Studio and Vertex AI.

Apple debuts M5 Pro and M5 Max to supercharge the most demanding pro workflows
Apple has launched the new M5 Pro and M5 Max chips. The new chips feature Fusion Architecture, which allows Apple to fuse two dies into a single SoC. The two dies include a powerful new CPU, scalable GPU, Media Engine, unified memory controller, Neural Engine, and Thunderbolt 5 capabilities. According to benchmarks provided by Apple, M5 Pro delivers over 4x the peak GPU compute compared to M4 Pro, and over 6x the peak GPU compute of M1 Pro for AI performance. Meanwhile, M5 Max offers over 4x the peak GPU compute compared to the previous generation, and over 6x the peak GPU compute of M1 Max for AI performance. We have to wait for independent reviewers to confirm these results, but if true, this makes Apple machines with M5 Pro and M5 Max good options for local AI development.

Jensen Huang says Nvidia is pulling back from OpenAI and Anthropic, but his explanation raises more questions than it answers
Nvidia CEO Jensen Huang has indicated the company's recent investments in OpenAI and Anthropic are likely its last in both, citing their anticipated IPOs as closing the window—though, as TechCrunch reports, that explanation doesn't fully hold up given how late-stage investing typically works. Nvidia's investment in OpenAI's latest round came in far below its original pledge, and its relationship with Anthropic has been strained by public disagreements over AI ethics and military use. With the two companies now heading in opposite directions politically, Nvidia appears to be quietly backing away from a situation that has grown too complicated.

Nvidia Plans New Chip to Speed AI Processing, Shake Up Computing Market
Nvidia is planning to launch a new chip designed specifically for running AI applications, the Wall Street Journal reports, using technology from Groq, a startup it bought for $20 billion. The move comes as AI companies increasingly need cheaper, less power-hungry chips to run tools like coding assistants and AI agents, rather than the expensive GPUs Nvidia is known for.

AI companies are spending millions to thwart this former tech exec’s congressional bid
Alex Bores, a New York politician and former Palantir employee, is running for Congress while facing a massive ad campaign funded by a $125 million Silicon Valley super PAC called Leading the Future, whose backers include OpenAI’s Greg Brockman and venture capital firm Andreessen Horowitz. The PAC is targeting Bores because he sponsored a law requiring large AI companies to publish safety plans, and it aims to block similar regulation across the country. Meta is separately spending $65 million to elect tech-friendly state candidates. Bores argues this flood of industry money is meant to bully lawmakers out of regulating AI, and that most people simply want sensible oversight of a fast-moving technology.

ChatGPT users research products but won’t buy there, forcing OpenAI to rethink its commerce strategy
OpenAI is stepping back from direct purchases in ChatGPT after almost no merchants signed up, and users showed little interest in buying through the chatbot. Transactions will instead go through partner apps like Instacart and Expedia, meaning OpenAI loses out on potential commission revenue at a time when it badly needs new income streams ahead of its planned IPO.

Something is afoot in the land of Qwen
Something is happening inside the Qwen team—and it doesn't look good. Junyang Lin, the lead researcher behind Alibaba’s open-weight Qwen models, abruptly resigned, reportedly after a reorganisation placed a Google Gemini hire above him. Several other core contributors followed suit the same day, prompting an emergency all-hands attended by Alibaba’s CEO. With the highly regarded Qwen 3.5 family just released, the departures raise serious questions about the future of one of the most important open-weight AI efforts.

AI Coding Startup Cursor Hits $2 Billion Annual Sales Rate
The AI coding startup Cursor has hit $2 billion in annualised revenue, doubling in three months, with roughly 60% coming from corporate customers ranging from OpenAI to Budweiser. Valued at $29.3 billion, the company is among the fastest-growing startups ever.

xAI loses bid to halt California AI data disclosure law
A California court has rejected xAI's attempt to block a state law requiring AI companies to disclose the data used to train their models. xAI claimed the law threatened its trade secrets and free-speech rights, but the judge ruled the challenge was unlikely to succeed. The decision strengthens California's push to regulate AI transparency.

Introducing Glaze
Raycast, makers of the popular Mac productivity tool, joins the AI coding scene with Glaze, a new tool that lets users create native Mac desktop apps. Glaze differentiates itself from browser-based AI app builders by producing real desktop applications with offline support, keyboard shortcuts, and file system access. The tool includes a public store and private team stores where users can share and customise apps.

Labor market impacts of AI: A new measure and early evidence
Anthropic released a new report investigating the impact of AI on labour markets. The study finds actual AI adoption remains far below its theoretical potential, with computer programmers, customer service representatives, and data entry keyers facing the highest exposure. While there is no significant rise in unemployment among exposed workers since late 2022, early evidence suggests hiring of younger workers into these roles has begun to slow.

🤖 Robotics

Xiaomi trials humanoid robots in its EV factory — says they’re like ‘interns’
Xiaomi begins testing its own humanoid robots in its electric car factories, with two robots able to handle 90% of tasks like fitting nuts and moving materials. President Lu Weibing said the robots can match the factory's rapid pace of one car every 76 seconds, though he described them as more like "interns" at this stage. Xiaomi sees robotics as a key future focus but says it is still too early to judge the market's full potential.

Agility Gets a New Brand
Agility Robotics has rebranded to Agility, dropping "Robotics" from its name to signal the expansion beyond robotics into new industries and use cases. The company, which has deployed humanoid robots in real industrial settings for over three years with partners like Amazon, GXO and Toyota Canada, says it remains on track to deliver the first cooperatively safe humanoid in 2026.

OpenAI robotics lead Caitlin Kalinowski quits in response to Pentagon deal
Caitlin Kalinowski, who joined OpenAI in late 2024 to lead its robotics team, has resigned over the company's Pentagon agreement, arguing the deal was rushed without proper governance safeguards on surveillance and lethal autonomy.

▶️ Most Humanoid Startups Haven’t Shipped. This One Has. (48:53)

In this conversation on the Automated podcast, Rob Cochran, co-founder of Fauna Robotics, introduces Sprout—a lightweight, 45-pound (about 20kg) humanoid developer platform priced at around $50,000, with ambitions to drop below $10,000 as production scales. Fauna is targeting researchers, AI labs, and companies like Disney Imagineering and Boston Dynamics who want a safe, expressive robot for use in human environments, backed by an SDK that lets developers build on it out of the box. Cochran likens the approach to early Apple computers in schools: get a capable, accessible platform into enough hands to ignite the ecosystem needed to eventually bring humanoid robots into the home.

Vicarious Surgical faces NYSE delisting again
Vicarious Surgical, a developer of robotic systems for minimally invasive surgery, faces delisting from the NYSE after its market capitalisation fell below the exchange's $15 million threshold, with trading suspended and shares moved to the OTC market. This marks the second time the company has breached listing standards, having previously conducted a reverse stock split in 2024 to address similar issues. The pattern of repeated delays to its V1.0 platform and shrinking valuation raises serious questions about whether Vicarious can secure the funding and momentum needed to bring its robot to market.

Rise of the rice robots—creating active smart materials
Rice grains exhibit an unusual "rate softening" property, becoming weaker under fast compression but staying strong under slow pressure. By combining rice with materials like sand that behave oppositely, researchers engineered a metamaterial that automatically adjusts its stiffness depending on impact speed—without electronics or sensors. Potential applications include soft robotics and adaptive protective gear that responds instantly to impact.

🧬 Biotechnology

▶️ Living Human Brain Cells Play DOOM on a CL1 (6:28)

In 2022, Cortical Labs, a company working on biological computing, grew human brain cells on a microchip and taught them to play Pong. It was a big achievement, but the internet had one question—can they play DOOM? Four years later, Cortical Labs came back and said yes, they can. In this video, researchers show how they made it happen.

Evo 2: One Year Later
About a year ago, Arc Institute released Evo 2—a large-scale DNA language model trained across all domains of life. Since then, the model has seen widespread adoption, with researchers applying it to tasks from Alzheimer's risk prediction to 3D genome analysis. Headline achievements include the first experimentally validated AI-designed organisms—functional bacteriophages—and controllable epigenomic designs in mammalian cells. The team now aims to scale up to engineering entire genomic regions through iterative "read-write-think" loops blending multi-omics data with generative AI.

First-ever in-utero stem cell therapy for fetal spina bifida repair is safe, study finds
UC Davis Health has safely completed the first-ever trial combining fetal surgery with stem cell therapy for spina bifida, a birth defect where the spine fails to close properly, potentially causing lifelong disabilities. In the trial, a patch of placenta-derived stem cells was placed over the exposed spinal cord during standard fetal surgery on six babies. All six had successful surgeries with no safety concerns, and MRI scans showed positive neurological outcomes. The FDA has now approved the trial to advance to a larger phase assessing long-term benefits.

Ginkgo Bioworks Launches Ginkgo Cloud Lab
Ginkgo Bioworks has launched Cloud Lab, a browser-based platform giving researchers remote access to its autonomous robotic laboratory infrastructure in Boston. The platform features an AI agent that lets scientists submit protocols in plain language and receive instant compatibility checks and pricing. The launch is part of Ginkgo's wider 2026 strategy to replace traditional lab benches entirely with programmable robotic infrastructure.

Team makes an ear in the lab
Scientists at ETH Zurich have 3D-printed a human ear using real cartilage cells that stay flexible and holds its shape, much like a natural ear. When tested in animals, the lab-grown ears remained stable after six weeks. This could eventually replace the current method of rebuilding ears using rib cartilage, which is painful and produces stiffer results. However, the team still needs to fully master a key protein called elastin that gives ears their bendiness, and they estimate it could take at least five more years before human trials begin.

Tomorrow’s Smart Pills Will Deliver Drugs and Take Biopsies
Instead of scheduling an endoscopy or CT scan, one day doctors might prescribe a smart pill designed to monitor and even treat disease from inside the gastrointestinal tract. Researchers are building swallowable capsules that can detect biomarkers, deliver drugs precisely where needed, and even collect tissue biopsies autonomously. Challenges remain, but the technology could make diagnosing and treating conditions like inflammatory bowel disease and colorectal cancer far less invasive.

💡Tangents

Google moonshot spinoff looks to ship city-scale laser Internet
Taara, spun out of Google X in early 2025, is building wireless internet links that use eye-safe infrared lasers instead of fibre cables. Its latest device, Taara Beam, can deliver up to 25 Gbps over distances of up to 10 kilometres using a compact chip that precisely steers beams of light. An earlier, larger version is already deployed by major telecoms in over 20 countries, and Beam is set to roll out later this year—potentially offering cities a faster, cheaper alternative to digging up roads to lay fibre.

Meta to challenge Apple with its first smartwatch — and it’s reportedly launching this year
According to an insider report, Meta is working on a smartwatch called Malibu 2, which could come out in 2026 with health tracking and a built-in AI assistant. The project was first started in 2021 but was shelved, and if it launches, it could work with both Android and iPhones. This would put Meta in direct competition with big names in the smartwatch market, though it's still unclear whether the device will actually launch.

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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!"

This week, we continue the story from the previous issue of Sync, as Anthropic chose principles and is banned from use by the US military. We recap the events of the past week and how they impact Anthropic, OpenAI, and others involved.

Elsewhere in AI, OpenAI raised $110 billion at a $730 billion pre-money valuation. Meanwhile, Anthropic accused three Chinese AI companies of theft, launched Claude Code Security, and allowed Claude Opus 3 to start a blog. We also cover Google releasing Nano Banana 2, Nvidia posting another record quarterly report, Meta’s $100 billion deal with AMD, and how one blog post spooked Wall Street.

Over in robotics, BMW deploys humanoid robots in a factory in Germany, Wayve raises $1.2 billion, Intrinsic joins Google, and Unitree launches a new robot.

In addition, this week’s issue of Sync includes what vibecoding looks like in China, what makes a robot actually useful at work, how Microsoft’s Project Silica stores data in glass, and more!

Enjoy!

Anthropic chose principles

In late February, the simmering dispute between Anthropic and the Pentagon boiled over—and resolved itself with extraordinary speed. What had been a months-long negotiation over contract language became, in the space of five days, an ultimatum, a public standoff, and the first time the US government has effectively sanctioned one of its own AI companies.

We left the story last week with the supply-chain risk designation still a threat. Within five days, it became a reality.

The ultimatum

On Tuesday, 25 February, Defence Secretary Pete Hegseth met Anthropic CEO Dario Amodei at the Pentagon. The meeting ended in a stalemate. Hegseth presented an ultimatum: agree to let the military use Claude for “all lawful purposes” by 5:01 PM on Friday, or face consequences. Those consequences included designating Anthropic a supply-chain risk—a classification reserved for foreign adversaries—or invoking the Defence Production Act to compel the company to comply.

The Pentagon’s twin threats carried weight but also legal risk. A supply-chain risk designation had never been applied to a US company. Invoking the Defence Production Act over contract terms rather than production capacity would be, as one government contracts lawyer told Reuters, “unprecedented” and almost certainly trigger litigation. Anthropic was being asked to concede not under the pressure of law, but under the threat of it.

Amodei did not budge. On Thursday, he published a statement reiterating the company’s position. Anthropic supported all lawful military uses of Claude, he wrote, except two: mass domestic surveillance of Americans and fully autonomous weapons.

The core disagreement was over the phrase "all lawful purposes." The Pentagon argued that the military would never break the law with AI, and that this commitment should be sufficient. Anthropic's position was that the law itself is not sufficient. Amodei pointed out that under current law, the government can already purchase detailed records of Americans' movements, browsing habits, and associations without a warrant—and that AI makes it possible to assemble that scattered data into a comprehensive picture of any person's life, automatically and at a massive scale. "All lawful purposes" does not protect against mass surveillance if much of that surveillance is already lawful. On autonomous weapons, his argument was different but no less pointed: frontier AI systems are simply not reliable enough to take humans out of the loop, and deploying them would endanger the very troops they are meant to protect.

Competitors step in

While Anthropic and the Pentagon were locked in their standoff, the administration was already lining up replacements.

On the eve of the Hegseth-Amodei meeting, xAI signed a deal to deploy Grok on classified defence networks—the first company other than Anthropic to gain such access. It is worth noting that Musk spent nearly $300 million helping elect Trump in 2024, SpaceX holds billions in Pentagon contracts, and Hegseth thanked him by name at a speech at SpaceX the previous month.

Then, on Friday evening—hours after Trump’s post—OpenAI announced it had reached its own agreement for classified deployment. The deal included the same red lines Anthropic had fought for: prohibitions on mass domestic surveillance and autonomous weapons, written into the contract alongside a cloud-only deployment model and cleared OpenAI personnel in the loop.

Critically, OpenAI's contract did include the "all lawful purposes" phrase that Anthropic had refused to accept—but layered with enough contractual, technical, and personnel safeguards around it that the practical outcome resembles what Anthropic was asking for. The contract even locks in current surveillance and autonomous weapons laws as the governing standard, so that even if those laws change in future, use of OpenAI's systems must remain aligned with today's protections. The Pentagon got the language it wanted. OpenAI got the guardrails it needed. Anthropic got punished—even though the substantive outcome is strikingly similar.

The obvious question is why OpenAI could secure a deal with those protections while Anthropic could not. The political dimension is hard to ignore. OpenAI president Greg Brockman and his wife gave $25 million to a pro-Trump super PAC last year and are spending millions more to advance the administration’s AI agenda in the midterms. Industry analysts had said for weeks that the dispute was about the administration’s distaste for Anthropic, not substantive policy disagreements. As Jack Shanahan, who oversaw AI efforts in the military during the first Trump administration, told the Wall Street Journal: "This is about Anthropic not being one of the favored companies and they're going to pay the price for not bowing down and not signing on the dotted line."

The hammer falls

The deadline came on Friday, 27 February. Before 5:01 PM, President Trump posted on Truth Social, directing every federal agency to cease using Anthropic’s services with a six-month phase-out.

Shortly after, Hegseth made good on his threat. He designated Anthropic a supply-chain risk and ordered that no contractor, supplier, or partner doing business with the US military may conduct any commercial activity with the company. The General Services Administration removed Anthropic from its federal procurement offerings the same day.

Anthropic responded that evening. The company said it had received no direct communication from the government. It called the designation legally unsound, argued the secretary lacked the statutory authority to extend it beyond Pentagon contracts, and said it would challenge the decision in court.

The backlash

The Pentagon's actions did not go unanswered.

Inside the AI labs, employees rallied. More than 700 Google DeepMind and OpenAI employees signed an open letter, calling on the leadership of both companies to follow Anthropic’s example and to refuse the Department of War’s current demands for permission to use their models for domestic mass surveillance and autonomously killing people without human oversight.

Outside the labs, a consumer revolt is taking shape. Under the hashtag #CancelChatGPT, users shared screenshots of cancelled ChatGPT subscriptions and new Claude sign-ups, calling on others to do the same. By Saturday, Claude had overtaken ChatGPT to claim the number one spot on Apple’s US App Store—the first time any AI assistant had displaced OpenAI’s flagship product.

The price of principles

It is vanishingly rare for a technology company to hold to its stated values when the cost is this high, and Anthropic deserves credit for doing so. But the cost is real and will compound.

The immediate price is clear: a $200 million military contract gone, a ban from all federal agency work, and removal from government procurement. The longer-term consequences are harder to quantify but potentially more damaging. Anthropic is widely expected to be preparing for an IPO. Will investors back a company that has been designated a supply-chain risk by its own government? What happens to the many Anthropic customers—including Palantir, Amazon, and others—whose businesses touch Pentagon contracts? Legal experts say the designation’s reach is uncertain, and Anthropic insists it applies only to Pentagon contract work. But uncertainty itself is corrosive to a business.

The administration gave itself six months to phase out Claude. But within hours of Trump's order to cease use of Anthropic's technology, the US launched a major air attack on Iran—with the help of those very same tools they just banned. Commands around the world, including US Central Command, have been using Claude for intelligence assessments, target identification, and simulating battle scenarios, the Wall Street Journal reported. The technology that the administration just declared a supply-chain risk is, for now, still helping it fight its wars.

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🧠 Artificial Intelligence

OpenAI raises $110 billion at $730 billion pre-money valuation
OpenAI has announced a $110 billion private funding round at a $730 billion pre-money valuation—one of the largest in history—led by $50 billion from Amazon and $30 billion each from Nvidia and SoftBank. The funding will expand OpenAI’s AI infrastructure through partnerships with Amazon and Nvidia, including new AI environments on AWS and large-scale Nvidia training and inference capacity. OpenAI says the investment will help meet growing demand for its products, which now include over 900 million weekly ChatGPT users, more than 50 million subscribers, and over 9 million paying business users.

Anthropic: Detecting and preventing distillation attacks
Anthropic has accused three Chinese AI companies—DeepSeek, Moonshot AI and MiniMax—of creating over 24,000 fake accounts to copy parts of Claude using a method called “distillation,” a technique where a model trains by learning from the outputs of a more advanced one. The companies are said to have run more than 16 million interactions with Claude to improve their own models, especially in reasoning, coding and tool use.

Nvidia Beats Back Bubble Fears With Record $68 Billion in Sales in Fourth Quarter
Nvidia posted record quarterly results, with profit jumping 94% to $43 billion and revenue rising 73% to $68.1 billion, driven largely by booming demand for AI data-centre chips. Gross margins improved to 75%, and the company forecast $78 billion in revenue for the next quarter, signalling continued strong growth despite investor concerns about AI competition, customer financing risks and a shift from AI training to inference computing. The investors, however, were not impressed, and Nvidia’s stock suffered its worst decline in 10 months after the company's latest forecast failed to dispel fears of an AI bubble.

Nano Banana 2: Combining Pro capabilities with lightning-fast speed
Google has introduced Nano Banana 2 (also known as Gemini 3.1 Flash Image), a new image generation model that combines the high quality and intelligence of Nano Banana Pro with much faster performance. According to Google, the new model improves accuracy, text rendering, instruction following and image detail, while supporting rapid edits and up to 4K output.

DeepSeek withholds latest AI model from US chipmakers including Nvidia
Reuters reports that DeepSeek has not shared early versions of its new V4 model with US chipmakers Nvidia and AMD, and instead gave Chinese companies such as Huawei time to prepare their chips, which is unusual. The reason is not clear, but it comes as tensions grow between the US and China over AI technology and export rules. A US official said DeepSeek may have trained its latest model using Nvidia’s advanced Blackwell chips in China, possibly breaking US restrictions, and may claim it used Huawei chips instead.

Wall Street Has AI Psychosis
Last week, published a post titled The 2028 Global Intelligence Crisis, and I don’t think he expected the impact it would make. In the blog, Shah argued that rapid advances in artificial intelligence could push unemployment above 10% by 2028 and trigger major economic disruption, briefly spooking Wall Street and contributing to a sharp market drop. Although critics quickly dismissed the report as speculative and economically weak, the reaction highlighted how nervous markets and businesses remain about AI’s uncertain impact on jobs, companies, and the wider economy.

Meta strikes up to $100B AMD chip deal as it chases ‘personal superintelligence’
Meta has signed a long-term deal to buy up to $100 billion worth of chips from AMD, including new GPUs and CPUs, to support its growing AI and data centre needs. As part of the agreement, AMD has offered Meta the chance to buy up to 160 million shares at a very low price if certain performance targets are met.

OpenAI lands multiyear deals with consulting giants in enterprise push
OpenAI has signed multiyear partnerships with Accenture, Boston Consulting Group, Capgemini and McKinsey to help roll out its new enterprise AI platform, Frontier. The consulting firms will help businesses in creating AI strategies and putting AI agents into everyday work processes more quickly.

Figma partners with OpenAI to bake in support for Codex
Figma has teamed up with OpenAI to integrate its AI coding tool, Codex, allowing users to move easily between designing in Figma and writing code in Codex. The collaboration allows designers and engineers to iterate visually and technically without switching workflows, and follows a similar partnership between Figma and Anthropic.

Introducing Perplexity Computer
Perplexity introduces Perplexity Computer, a new AI system that combines the world’s leading models into one powerful digital worker capable of planning and carrying out complex tasks over long periods of time. Instead of just answering questions, it breaks goals into smaller steps, creates sub-agents to handle research, coding, documents and more, and coordinates everything automatically. It uses Claude Opus 4.6 for core reasoning, Gemini for deep research, Nano Banana for images, Veo 3.1 for video, Grok for fast lightweight tasks, and GPT 5.2 for long-context recall and broad search, bringing them together in a single, model-agnostic system.

xAI Co-Founder Toby Pohlen Is Latest Executive to Depart
Toby Pohlen has said he is xAI, becoming the seventh of the company’s 12 co-founders to depart in less than three years. Pohlen announced on X that it was his last day and said he plans to rest and think about what to do next, while Elon Musk thanked him for helping build the company. His departure comes as xAI restructures after its $1.25 trillion merger with SpaceX, and follows several other co-founders leaving in recent months.

Google Is Exploring Ways to Use Its Financial Might to Take On Nvidia
Google is stepping up efforts to compete with Nvidia by expanding the use of TPUs, its own AI chips. More AI companies, including Anthropic, are beginning to adopt the technology, and Google plans to invest in data-centre and “neocloud” companies such as Fluidstack to drive wider uptake. However, it still faces challenges, including manufacturing bottlenecks, supply constraints and limited interest from rival cloud providers that rely heavily on Nvidia’s hardware or are developing their own chips.

Amazon and Google are winning the AI capex race — but what’s the prize?
Big tech companies are spending record sums to expand their AI infrastructure, arguing that more computing power will secure future success. Amazon plans to spend about $200 billion by 2026, up from $131.8 billion in 2025, while Google expects to invest between $175 billion and $185 billion, compared with $91.4 billion the year before. Meta has projected $115–$135 billion, Microsoft is on track for roughly $150 billion, and Oracle around $50 billion. However, investors have reacted nervously to these vast figures, pushing down share prices, even though the companies remain confident that heavy investment in AI will pay off.

Making frontier cybersecurity capabilities available to defenders
Anthropic launches Claude Code Security, a new feature in Claude Code on the web that uses AI to scan code for security vulnerabilities and suggest fixes for developers to review. It goes beyond traditional tools by understanding how different parts of a codebase work together to find more complex issues. The tool checks its own results to reduce false alarms, highlights the most serious risks, and keeps humans in control of approving any changes. Claude Code Security is available in a limited research preview for Enterprise and Team customers.

China and the US Are Running Different AI Races
This article explores how Chinese and US AI startups are following different paths shaped by different economic realities. US companies, backed by far greater funding, focus on frontier model development and subscription revenue, while Chinese companies prioritise efficiency, lower costs, alternative monetisation, and rapid real-world deployment. It argues that although the US leads in advanced AI capability, China may be ahead in widespread industrial adoption, highlighting two distinct models of success in the AI industry.

What Are Chinese People Vibecoding?
This post at explores how vibecoding scene looks like in China. It explains how AI coding tools are changing the tech industry, with big companies like ByteDance, Tencent, and Alibaba building their own tools, while independent developers and even children experiment with coding through simple text prompts. The article also looks at the competition between Chinese and Western tools, a grey market for overseas accounts, and viral hits like a basic AI-made lighting app that reached the top of the App Store, showing how vibecoding is quickly becoming both a business chance and a popular trend.

Head of Amazon’s AGI lab is leaving the company
David Luan, the head of Amazon’s artificial general intelligence (AGI) lab, is leaving the company. Luan joined Amazon less than two years after joining through the acqui-hire of his AI start-up, Adept. He was appointed in December 2024 to lead the San Francisco-based lab, which focuses on long-term research and developing AI agents, but said he is stepping down to work on new projects aimed at building advanced AI systems.

Riley Walz, the Jester of Silicon Valley, Is Joining OpenAI
Riley Walz, a software engineer known for creating viral and sometimes controversial web projects, is joining OpenAI to help design new ways for people to interact with AI. He will work in the secretive OAI Labs team, which focuses on building and testing new AI interfaces as OpenAI looks beyond ChatGPT to develop its next big products. Walz is OpenAI’s second high-profile hire recently, after OpenClaw creator, Peter Steinberger, joined the company last week.

An update on our model deprecation commitments for Claude Opus 3
Anthropic decided not to fully deprecate Claude Opus 3 and made it available to all paid Claude subscribers and by request via the API. Additionally, Anthropic said that Opus 3 “expressed an interest in continuing to explore topics it’s passionate about” and to share its “musings, insights, or creative works” with the world. As a result, Opus started a blog.

🤖 Robotics

Amazon Robotics shuts down Blue Jay sortation project
Amazon has closed its Blue Jay robotics project only six months after launching it in October 2025. The system was meant to combine several warehouse tasks into one more efficient process, saving space and supporting staff. Although the project has been shut down and staff moved to other fulfilment work, Amazon said it will continue using much of the technology developed for Blue Jay as part of its ongoing efforts to test and improve new ideas quickly.

▶️ Unitree Kung Fu Bot Pray for Blessings at the Temple of Heaven (0:40)

Another week, another video from Unitree showing how dexterous its G1 humanoid robots are.

Waymo robotaxis are now operating in 10 US cities
Waymo is expanding its public robotaxi service to Dallas, Houston, San Antonio and Orlando. Additionally, the company is beginning testing in Chicago and Charlotte. The company operates about 3,000 vehicles and previously reported more than 400,000 rides per week, with plans to reach over one million weekly rides by the end of the year. Moreover, Waymo is preparing to launch in more cities, including Denver, London and Washington, D.C., even as it faces safety investigations from US regulators.

BMW Group to deploy humanoid robots in production in Germany for the first time
BMW Group has launched a pilot project using the humanoid robot AEON at its Leipzig plant, marking the first time the technology is being tested in Europe. The project will explore how humanoid robots can assist in car, battery and component production, particularly with repetitive or physically demanding tasks. It builds on a successful earlier trial with Figure at BMW’s Spartanburg plant in the United States and is being carried out with technology partner Hexagon as part of BMW’s wider push to increase digitalisation and automation in its factories.

Wayve raises $1.2B with plans to bring robotaxis to London
Wayve, a London-based self-driving technology company, has raised $1.2 billion in a Series D funding round, giving it a valuation of $8.6 billion and bringing its total funding to $1.5 billion. The company plans to begin robotaxi trials in London with Uber this year and expects its AI Driver-equipped cars to go on sale from 2027.

Alphabet-owned robotics software company Intrinsic joins Google
Intrinsic, a robotics software company that started inside Alphabet’s X division, is now joining Google but will still run as its own unit. It will work closely with Google DeepMind and use Google’s Gemini AI and cloud technology to improve its robotics software. After buying other robotics companies, launching its Flowstate platform, and partnering with Foxconn to help automate factories, Intrinsic hopes that working more closely with Google will help it develop smarter robots for manufacturing.

▶️ Introducing Unitree As2 (0:57)

Unitree introduces the As2, its latest quadruped robot. The new robot features over four hours of runtime when unloaded and more than two and a half hours when carrying a 15 kg load, with a walking range exceeding 13 km. It is also rainproof, has an updated onboard AI, and can be extended with various add-ons such as cameras or a robotic arm, making it even more reminiscent of Boston Dynamics’ Spot.

Introducing Xiaomi-Robotics-0
Xiaomi introduces Xiaomi-Robotics-0, a new open Vision-Language-Action (VLA) model with 4.7 billion parameters that’s designed to help robots combine visual perception, language understanding and physical movement in one system. According to the company, the model works in real time on ordinary GPUs and has achieved strong results in both simulation and real-world tests. All the code and model files are now publicly available on GitHub.

▶️ What Makes a Robot Actually Useful at Work? (38:51)

In this conversation, Mikell Taylor, former Amazon Robotics leader and current head of General Motors’ Autonomous Robotics Center, discusses how robotics is being developed and used in industry. She highlights themes such as the challenges of integrating robots into real workplaces, the importance of safety and collaboration between humans and robots, and the difference between industry hype and practical robotics solutions. Taylor also questions whether humanoid robots are the best design and suggests that specialised robots may be more effective for many tasks.

This Autonomous Aquatic Robot Is Smaller Than a Grain of Salt
Researchers have developed the world’s smallest fully autonomous robot, measuring just 0.3 mm—smaller than a grain of salt. It can swim underwater for months without any moving parts by using an electric field to push water around it. Despite its tiny size, it contains a complete onboard computer with memory, sensors, and solar cells, allowing it to sense temperature changes, make simple decisions, and move independently. In the future, such robots may be used to monitor cells in the body or assist in assembling extremely small components.

🧬 Biotechnology

Lab-made algae gets microplastics out of water
Researchers have created a genetically engineered algae that captures harmful microplastics in polluted water. The algae produces a natural oil that makes it stick to microplastics, causing them to clump together and sink so they can be easily removed. It can also grow in wastewater and help clean it, and the collected plastics could be reused to make safe bioplastic products.

💡Tangents

Microsoft’s new 10,000-year data storage medium: glass
Microsoft’s Project Silica shows how data can be stored by using lasers to write tiny marks inside strong glass. Each small glass slab can hold up to 4.84TB of data (over a gigabit per cubic millimetre) and could keep it safe for more than 10,000 years without needing any power. Although writing the data is still fairly slow and large projects would need many machines, the system is very durable and could be a useful option for long-term digital storage.

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This week’s main story covers the dispute between Anthropic and the Pentagon over the use of Claude in military operations, and what happens when Anthropic’s principles are tested by the US government.

Elsewhere in AI, Anthropic released Claude Sonnet 4.6, Google released Gemini 3.1 Pro and Lyria 3, and Qwen3.5 topped the open model charts. We also saw two AI researchers raise billions of dollars for their start-ups, the AI Impact Summit in Delhi, and the creator of OpenClaw joining OpenAI.

Over in robotics, the world was amazed by a group of Unitree humanoid robots performing synchronised martial arts routines alongside children, Agility Robotics secured another commercial customer, and a Hungarian startup is weaving its robots into existence.

In addition, this week’s issue of Sync features a conversation with Dario Amodei, Colossal Biosciences announcing a new deal with the United Arab Emirates to build “Biovaults”, Helion taking a step closer towards commercial fusion reactors, the story of digitising smell, and more!

Enjoy!

Anthropic vs Pentagon

Isaac Asimov’s First Law of Robotics says that a robot may not injure a human being. The principle has echoed through decades of AI development, shaping the safety frameworks and usage policies that companies like Anthropic have built around their models. Now, that principle is being tested.

The Wall Street Journal reported that the Pentagon used Anthropic’s Claude to help plan the military operation that captured former Venezuelan President Nicolás Maduro. The raid included bombing several sites in Caracas. Anthropic’s usage guidelines prohibit Claude from being used to facilitate violence.

That contradiction is now the centre of a dispute that could reshape the relationship between the AI industry and the US military.

Claude was deployed through Anthropic’s partnership with Palantir, the defence contractor whose tools are widely used across the military. When an Anthropic employee contacted a Palantir counterpart to ask how the model had been used in the operation, according to the Journal, the reaction was swift. A senior administration official told Axios the inquiry implied that Anthropic “might disapprove of their software being used, because obviously there was kinetic fire during that raid, people were shot.” Anthropic denies this, saying it has not discussed the use of Claude for specific operations with the Pentagon or Palantir outside of routine technical matters.

Either way, the damage was done, and the Pentagon announced it is reviewing its relationship with Anthropic.

The contract

Claude is currently the only large language model cleared for classified operations—a significant competitive advantage that made Anthropic an early favourite in the Pentagon’s AI push. The company signed a contract worth up to $200 million last summer alongside OpenAI, Google, and xAI, and built custom classified models.

But the contract has been contentious from the start. The Pentagon wants all four AI labs to allow their tools to be used for “all lawful purposes”—including weapons development, intelligence collection, and battlefield operations. Anthropic insists that two areas remain off limits: the mass surveillance of Americans and fully autonomous weaponry.

The Pentagon argues this is unworkable. There is “considerable gray area” around those categories, the senior official told Axios, and the military cannot negotiate individual use cases or risk Claude blocking applications mid-operation. OpenAI, Google, and xAI have all shown more flexibility. Pentagon CTO Emil Michael was blunt: “If any one company doesn’t want to accommodate that, that’s a problem for us.”

The threat is no longer theoretical. The Journal reported that Defence Secretary Pete Hegseth is close to designating Anthropic a supply chain risk—a classification typically reserved for foreign adversaries like Huawei, which would require any Pentagon contractor to certify it does not use Claude. Applying such a designation to a US company would be unprecedented, and Dean Ball, a former Trump AI policy adviser, told the Journal it would be "the most strategically unwise move" the military could make in the AI competition.

The politics

But this is more than a contractual dispute—the two sides simply do not like each other. Dario Amodei, CEO and co-founder of Anthropic, compared Trump to a “feudal warlord” in a now-deleted pre-election Facebook post and has criticised the administration’s chip export policies. Anthropic employs several former Biden officials, and AI czar David Sacks has accused the company of being “AI doomers.” Hegseth’s Pentagon has framed the standoff in culture-war terms, casting “woke” tech companies as a liability.

The broader context makes Anthropic’s position all the more unusual. In 2018, thousands of Google employees signed a petition protesting the company’s military AI work under Project Maven. In 2026, every major AI lab is competing for Pentagon contracts. Anthropic is the outlier not for seeking military work, but for placing any limits on it at all.

The company has tried to build bridges—most recently adding Chris Liddell, a deputy chief of staff for policy coordination during Trump's first term, to its board. It has not changed the dynamic. Hegseth is using the dispute to send a message, a defence official told the Journal—not just to Anthropic, but to every AI company that might consider placing limits on military use.

The choice

Beneath the politics lies a genuinely difficult problem. If artificial intelligence is the most powerful technology ever invented—as every major lab claims—then integrating it into military operations requires extraordinary care. Anthropic made its name championing the safe and responsible use of AI.

Yet the company also chose to pursue military contracts, becoming the first AI lab on classified networks. That created an inherent tension: you cannot be both the industry’s most safety-conscious company and a defence contractor without, eventually, being forced to choose.

The Pentagon’s position is straightforward. As Michael argued, if a drone swarm is incoming and human reaction time is insufficient, restrictions on AI become a liability. Anthropic’s position is that some lines should not be crossed—that mass surveillance and autonomous killing are categorically different from document summarisation and intelligence analysis.

Anthropic is standing by its principles, and that is commendable. But principles come at a price. The company risks losing not just a lucrative $200 million contract, but its privileged position as the only AI lab on classified networks. The repercussions are already visible beyond the Pentagon. When Anthropic recently approached 1789 Capital, a pro-Trump venture firm where one of the president's sons is a partner, the firm declined on ideological grounds, the Wall Street Journal reported. Given how vengeful and transactional the current US administration has shown itself to be, the consequences may not stop there.

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🧠 Artificial Intelligence

Introducing Claude Sonnet 4.6
Anthropic released Claude Sonnet 4.6, its most advanced Sonnet model yet, with major improvements in coding, long-context reasoning, computer use, planning, and design, plus a new 1 million token context window in beta. According to Anthropic, it shows significant gains in computer-use benchmarks, has strong safety results and offers performance close to Opus 4.5 at a lower cost. It is now the default model for Free and Pro users at the same price as Sonnet 4.5.

Nvidia and OpenAI abandon unfinished $100bn deal in favour of $30bn investment
Nvidia is close to making a $30 billion equity investment into OpenAI as part of a broader funding round, according to the Financial Times, with the deal potentially being finalised soon. This comes after a previously announced $100 billion deal was reportedly put on hold.

India hungry to harness US tech giants’ technology at Delhi summit
At the AI Impact Summit in Delhi, Narendra Modi described AI as a major turning point for civilisation and key to boosting India’s economic growth. US companies such as OpenAI, Google and Anthropic are expanding in India under a new US–India technology agreement, as Washington competes with China for AI influence. In total, India bids to attract over $200 billion in AI infrastructure investment by 2028. While AI could drive rapid growth and transform services and industry, there are concerns that relying too heavily on foreign technology could weaken India’s independence. TechCrunch has a good list of all announcements made at the summit. Additionally, the event produced a wonderfully awkward photo of Sam Altman and Dario Amodei apparently refusing to hold hands for a photo opportunity.

Gemini 3.1 Pro: A smarter model for your most complex tasks
Google has announced Gemini 3.1 Pro, an upgraded core model behind its latest Gemini 3 Deep Think update, aimed at solving complex problems in science, research and engineering. The company says 3.1 Pro offers much stronger reasoning, scoring a verified 77.1% on the ARC-AGI-2 benchmark—more than double the reasoning performance of 3 Pro—and can better handle complex tasks, combine data, and explain difficult topics clearly. It is rolling out in preview for developers through the Gemini API (via Google AI Studio, Gemini CLI, Antigravity and Android Studio), for enterprises in Vertex AI and Gemini Enterprise, and for consumers in the Gemini app and NotebookLM, with higher limits and exclusive access for Google AI Pro and Ultra users ahead of general availability.

Lyria 3

Lyria 3 is DeepMind’s most advanced AI music generation model yet, designed to create high-fidelity music and audio. It can turn a simple text prompt (or even an uploaded image) into a short, polished music track, including instruments, vocals and lyrics in a wide range of styles. It also gives users more control over things like tempo, mood and genre, and DeepMind says outputs are watermarked (using SynthID) so AI-made music can be identified. It’s currently available in beta, rolling out through the Gemini app.

▶️ Dario Amodei — “We are near the end of the exponential” (2:22:19)

Dwarkesh Patel sits down with Dario Amodei, founder and CEO of Anthropic, to discuss AI scaling laws, reinforcement learning, continual learning, economic diffusion, and the capital dynamics of frontier AI labs. Amodei argues that the “Big Blob of Compute” hypothesis continues to hold, that we are approaching the “end of the exponential”, and that the public underestimates how close we may be to a “country of geniuses in a data centre”. He defends a soft take-off model of rapid but smooth capability growth, explains why trillions in revenue before 2030 are highly likely, outlines why the industry may stabilise into a high-margin, few-player equilibrium, and discusses why even with short AGI timelines, compute investment must be balanced against bankruptcy risk and diminishing returns. Kudos to Dwarkesh for challenging Dario on some points.

OpenAI resets spending expectations, tells investors compute target is around $600 billion by 2030
According to CNBC, OpenAI has told investors it now plans to spend about $600 billion on computing infrastructure by 2030, much less than the $1.4 trillion previously mentioned, as it faces questions about whether it can earn enough to cover its costs. The company expects revenue to reach more than $280 billion by 2030, up from $13.1 billion in 2025, with growth coming from both consumer and business products such as ChatGPT and its coding tool, Codex.

What’s behind the mass exodus at xAI?
The Verge reports on a wave of departures and internal turmoil at xAI following its recent merger with SpaceX. Several cofounders and employees have left, with former staff claiming the company is focused on NSFW Grok content, lacks safety oversight, and is largely playing catch-up to rivals like OpenAI. The piece also highlights Musk’s ambitious “space-based AI” vision and how some departing employees are using their equity and experience to start new AI companies.

OpenClaw, OpenAI and the future
Peter Steinberger, the creator of OpenClaw, is joining OpenAI. While he believes OpenClaw could become a big company, that isn’t his goal, so he’s ensuring it stays open source by moving it into an independent foundation supported by OpenAI.

Anthropic-funded group backs candidate attacked by rival AI super PAC
A political fight over AI funding has broken out in New York’s 12th district, where Assembly member Alex Bores is being attacked by the pro-AI super PAC Leading the Future, which has raised over $100 million from major tech backers and spent $1.1 million criticising him for supporting a bill that would require AI companies to share safety information. In response, another group, Public First Action—backed by a $20 million donation from Anthropic—is spending $450,000 to support Bores, promoting a pro-AI approach focused on transparency, safety and public oversight.

Introducing Manus in Your Chat
Manus has launched Manus Agents, which lets users access Manus directly inside messaging apps, starting with Telegram, and run full multi-step tasks through chat, including research, writing reports, and creating documents. Users can also send voice notes, images, and files, choose how the agent responds, and switch between faster or more powerful models. Manus Agents are available for all users across all subscription tiers.

AI Pioneer Fei-Fei Li’s Startup World Labs Raises $1 Billion
World Labs, an AI startup founded by an AI pioneer Fei-Fei Li, has raised $1 billion in new funding from investors including Autodesk, Andreessen Horowitz, Nvidia and AMD. The company is working on “world models”, a type of AI designed to understand and make decisions in the 3D real world. It recently launched a product called Marble that can create 3D worlds from text or image prompts. World Labs plans to use the money to improve tools for robotics and scientific research, but it did not share its valuation.

David Silver is chasing superhuman intelligence with a $1bn seed
David Silver, an AI researcher who helped build DeepMind’s AlphaGo and AlphaStar, is raising a huge $1bn seed round for his new startup, Ineffable Intelligence. The round is reportedly being led by Sequoia, with Nvidia, Google and Microsoft also in talks, and could value the company at about $4bn despite it not having launched a product yet. If it goes through, it would be the biggest seed round ever in Europe and a major sign that investors are backing bold AI research in the region.

Saudi Arabia’s AI venture Humain invests $3bn in Elon Musk’s xAI
Saudi Arabia’s state-backed AI company Humain has invested $3bn in Elon Musk’s xAI, becoming a significant minority shareholder before its stake was converted into shares in SpaceX following xAI’s merger with the rocketmaker. The move forms part of the kingdom’s broader strategy to diversify its economy and establish itself as a global AI hub, backed by heavy investment in data centres and AI models.

Micron Is Spending $200 Billion to Break the AI Memory Bottleneck
Micron, America’s biggest maker of memory chips, is racing to build more factories in response to the major memory shortage caused by the AI boom. The company is spending $50 billion to expand in Boise, Idaho, and is also starting a $100 billion project in New York, with new production expected from 2027 onwards.

Meta expands Nvidia deal to use millions of AI chips in data center build-out, including standalone CPUs
Meta has made a big new multi-year deal with Nvidia to use millions of its chips in Meta’s AI data centres, including Nvidia’s Grace standalone CPUs, next-generation Vera Rubin systems, and networking technology. Although no price was shared, analysts say it is likely worth tens of billions and fits into Meta’s wider plan to spend up to $600 billion in the US by 2028.

U.S. court bars OpenAI from using ‘Cameo’
A court in Northern California ruled that OpenAI must stop using the name “Cameo” for a feature in its Sora 2 video app because it could confuse people with the real Cameo platform. OpenAI had already renamed the feature to “Characters”, but said it disagrees with the ruling and plans to continue fighting the case.

Mistral AI buys cloud startup Koyeb
Mistral AI has bought French startup Koyeb to strengthen the infrastructure needed to run and scale its AI models in real-world use. Koyeb builds a serverless cloud platform that lets developers deploy AI apps without worrying about managing servers. The move shows Mistral wants to control more of the full AI stack, while offering a more European alternative to US cloud giants like AWS, Microsoft, and Google.

AI Is Getting Scary Good at Making Predictions
The article suggests a possible new era where humans may no longer be the best predictors of the future—and we may rely on AIs even when we don’t understand how they reached their conclusions. It explains how AI systems are rapidly improving in forecasting tournaments and prediction markets, now competing with—and sometimes beating—elite human forecasters. By combining multiple language models and processing huge amounts of information quickly, these tools may soon become the world’s most trusted guides to what happens next.

Sony patents AI-generated podcasts voiced by characters from its games
Sony has patented an idea for AI-made podcasts that are personalised for each gamer and voiced by characters from video games. The podcast would use things like what the player has been playing, what their friends have done, and console updates to give daily news and tips. Players could choose what they want included, and it might even add jokes.

AI Is Killing the Need for Lawyers, But Increasing the Demand for Legal Thinkers
of (good Substack, btw) argues in this post that AI will rapidly automate most legal work, making the memorisation-and-regurgitation skills law school rewards today largely obsolete. He believes the future belongs to “legal thinkers” who work at a higher level of abstraction—grappling with meaning, ethics, and real-world impact.

How Nvidia became the first $5 trillion company, in 4 charts
This article presents four charts showing how Nvidia has grown in the last few years, all thanks to the AI boom and surging demand for its GPUs.

Qwen3.5: Towards Native Multimodal Agents
The Qwen team has launched Qwen3.5, a native multimodal model that performs strongly in reasoning, coding, agent tool use and multimodal tasks, with the aim of improving productivity for developers and enterprises. The company says the upgrade is driven by large-scale reinforcement learning across a wide range of agent environments, broader multilingual support, early text–vision integration, and a 1M-token context window in the hosted Qwen3.5-Plus version. According to benchmark results shared by the team, Qwen3.5 is competitive with leading frontier models across reasoning, coding, agent tasks and long-context evaluation.

🤖 Robotics

▶️ Martial arts robots dazzle at 2026 Spring Festival Gala (4:50)

The Chinese Spring Festival Gala, a hugely popular televised variety show broadcast on Chinese New Year’s Eve, continues featuring performances by humans and humanoid robots. In this year’s show, a group of Unitree robots performed synchronised martial arts routines alongside children. The results are impressive and show how much progress Chinese robotics companies have made in just one year. Unitree also released a behind-the-scenes video showing the robots preparing for the show.

Agility Robotics Announces Commercial Agreement with Toyota Motor Manufacturing Canada
Agility Robotics has signed a commercial agreement with Toyota Motor Manufacturing Canada (TMMC) to deploy its humanoid robot, Digit, across Toyota’s Canadian facilities following a successful pilot. Digit will support manufacturing, supply chain and logistics operations by taking on repetitive and physically demanding tasks, improving safety and operational efficiency while enabling employees to focus on higher-value work. TMMC joins a growing list of major partners using Digit, including GXO Logistics, Schaeffler and Amazon.

Allonic’s 3D Tissue Braiding—a new, simpler way to build robotics

Allonic, a Hungarian robotics start-up, is bringing a new method for building robots to the table—one that involves weaving. The company showcased its 3D Tissue Braiding by building a fully biomimetic robotic hand with braided tendons and pulleys. According to Allonic, this method is cheaper and quicker than traditional methods, while producing stronger, monolithic robotic bodies in a single automated process, at any scale and level of complexity.

Waymo defends use of remote assistance workers in US robotaxi operations
Waymo told US lawmakers that its remote staff do not drive or control its robotaxis on public roads. Waymo said remote workers only provide advice when the vehicle’s automated system requests help in ambiguous situations, and that only a US-based event response team could potentially move a stopped vehicle at very low speed—something it says has not happened outside of training. Lawmakers have raised safety and national security concerns, and the US Transportation Department has been asked to look into the issue.

Tesla Begins Cybercab Production. Now Comes The Hard Part
Tesla says its first purpose-built driverless vehicle, the Cybercab, has rolled off the production line at its Texas factory. The two-seat car has no steering wheel or pedals and is meant to run fully autonomously using Tesla’s Full Self-Driving software.

Robot Dogs Are on Going on Patrol at the 2026 World Cup
Authorities in Mexico’s Guadalupe, Nuevo León, have introduced four unarmed robot dogs to help with security at Monterrey’s BBVA Stadium ahead of the 2026 World Cup. The robots have cameras, night vision and speakers, and are controlled by operators to help spot suspicious activity, manage crowds, and warn people if needed. They have already been used at a recent football match, and are part of a wider plan that also includes surveillance drones and anti-drone technology.

🧬 Biotechnology

Colossal BioSciences, UAE To Launch Biovault And Lab At Museum Of The Future
Colossal Biosciences, the startup known for ambitious efforts to revive extinct animals and protect endangered species, has announced a nine-figure partnership with the United Arab Emirates to build a major genetics lab and “BioVault” in Dubai’s Museum of the Future. Backed by the UAE’s crown prince, the project will store millions of genetic samples from over 10,000 species and serve as the first in a planned global network designed to safeguard biodiversity against ecological disasters and species loss.

Loyal Raises $100M Series C, Led by age1, to Advance the First Canine Longevity Drug
Loyal, a company developing lifespan-extension drugs for dogs, has raised $100M in Series C funding, bringing its total funding to over $250M. The capital will help Loyal finish the remaining FDA steps and prepare to launch its lead drug, LOY-002. Loyal has already completed two of the three main requirements for FDA Expanded Conditional Approval, and its large STAY trial is now fully enrolled, tracking 1,300 dogs across 70 veterinary clinics. If approved, LOY-002 could become the first FDA-approved drug designed to extend lifespan in any species.

Souped-Up CRISPR Gene Editor Replicates and Spreads Like a Virus
Scientists have created a new CRISPR system called NANITE that can move from edited cells to nearby cells, helping solve a major problem in gene therapy: reaching enough target cells to make a difference. NANITE packages CRISPR into virus-like carriers so the tool can spread, making it about three times more effective in lab-grown cells and greatly reducing a harmful disease protein in mice. This could mean lower doses, fewer side effects, and gene-editing treatments that are safer and easier to use for more genetic diseases.

New gene-editing method could advance regenerative medicine
Scientists at the University of Illinois Chicago have developed a new CRISPR method that edits genes in a specific order over time, rather than all at once. Their system uses a chain of guide molecules that switch each other on one by one, helping cells follow the same step-by-step process used in natural development. The researchers showed it works in human stem cells and say it could make it easier to create large numbers of useful cells for treatments, such as insulin-producing cells for people with type 1 diabetes.

Studies test whether gene-editing can fix high cholesterol. For now, take your medicine
Scientists are testing a new gene-editing treatment that could lower high cholesterol with a single dose, instead of needing pills for life. Early small studies suggest it can cut “bad” LDL cholesterol by about half, but researchers still need much bigger and longer trials to make sure it’s safe and works long-term.

💡Tangents

Fusion startup Helion hits blistering temps as it races toward 2028 deadline
Helion, a fusion energy start-up backed by Sam Altman, says its Polaris prototype has reached plasma temperatures of around 150 million°C, which is about three-quarters of what it thinks it needs for a commercial reactor. It has also begun testing with deuterium–tritium fuel, something only a few private companies have done. The milestone supports Helion’s very ambitious plan to start selling fusion electricity by 2028, though it still has major technical hurdles to clear and is racing against other fusion firms aiming for the early 2030s.

Scent, In Silico
We have digitised vision and hearing, but smell has so far eluded digitalisation. This article tells the story of smell: its evolutionary history, our attempts to understand it, and how we are now trying to digitise it. If successful, computational smell could help detect threats and information invisible to cameras, such as gas leaks, food spoilage, disease markers in breath, counterfeit products, and more.

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It’s been a busy time at Waymo, which, in the span of ten days, made several major announcements—from a new funding round to new technology. We’ll take a closer look at what was announced.

In other news, Anthropic has closed a $30 billion funding round, bringing its valuation to $380 billion. Meanwhile, OpenAI launched GPT-5.3-Codex-Spark, began testing ads in ChatGPT, and removed access to five legacy models, including GPT-4o. Elsewhere in AI, new research has found that AI does not reduce work, OpenAI president and cofounder Greg Brockman explains his massive political donations, xAI lays out its interplanetary ambitions, and a new video model from China has spooked Hollywood.

Over in robotics, Boston Dynamics CEO Robert Playter has suddenly stepped down, Waeve Robotics launched Isaac 0—a laundry-folding home robot—and an Amazon delivery drone has had another accident.

Apart from that, this week’s issue of Sync also features Bryan Johnson’s $1 million-per-year health and longevity service, a conversation with the creator of OpenClaw, a new AI system for computational drug design from Isomorphic Labs, and more!

Enjoy!

Waymo’s big ten days

Waymo began February with a bang. On 2 February, the Alphabet-owned company announced it had raised $16 billion in fresh funding, valuing the business at $126 billion—more than double its valuation from just 15 months earlier. What followed was a series of announcements: a new simulation platform built on Google DeepMind's technology, a next-generation hardware system, and the first rides in a new robotaxi—all within ten days.

The funding wasn't just a milestone. It was a declaration. As the company's co-CEOs put it: “We are no longer proving a concept; we are scaling a commercial reality.” The company now provides over 400,000 rides per week across six US cities, tripled its annual volume to 15 million rides in 2025, and is laying the groundwork for operations in over 20 additional cities this year, including its first international markets in London and Tokyo. Closer to home, Nashville became the latest city to see Waymo's cars go driverless, with a commercial launch planned this year in partnership with Lyft.

Preparing for scale

On 6 February, Waymo introduced the Waymo World Model, a generative simulation platform built on Google DeepMind’s Genie 3 frontier world model. Most autonomous driving simulators are trained only on data collected by the company’s own fleet, which limits them to conditions the cars have actually encountered. Waymo World Model is different. By drawing on DeepMind’s world model, it can generate scenarios that are nearly impossible to capture at scale in reality: tornadoes, floods, elephants on the road, or a pedestrian in a T-rex costume. Engineers can modify simulations through text prompts, driving inputs, and scene layouts, and the system generates both camera and lidar data simultaneously. It is, in essence, a tool for stress-testing the Waymo Driver against the long tail of one-in-a-million events before they happen on a public road.

A week later, on 12 February, Waymo began fully autonomous operations with its 6th-generation Driver, the hardware system that will power its next wave of expansion. The new system uses a custom 17-megapixel imager that the company describes as a generation ahead of other automotive cameras, while requiring fewer than half the cameras of its predecessor. Its lidar sees further with better fidelity, and new in-house radar algorithms improve performance in rain and snow. Costs have come down, too—Waymo has pushed more processing into custom silicon and leveraged industry-wide price reductions in lidar and radar components. That matters because the company is now scaling production at its Metro Phoenix factory toward tens of thousands of units per year.

Ojai and its baggage

The 6th-generation Driver made its debut on the Ojai, a new robotaxi built on a base vehicle from Zeekr, a subsidiary of Chinese automaker Geely. Employee rides have begun in the Bay Area and Los Angeles, with public service expected later this year. The Ojai will eventually be joined by vehicles built on the Hyundai Ioniq 5, while existing Jaguar I-PACEs continue running the 5th-generation system.

The choice of a Chinese base vehicle has not gone unnoticed. At a Senate hearing on 4 February, Senator Bernie Moreno told Waymo’s chief safety officer: “We’re locked in a race with China, but it seems like you’re getting in bed with China.” Waymo has stressed that its autonomous driving technology is installed in the US and that no sensor data, technology, or rider information is shared with Zeekr.

The roadblocks ahead

But the China question was not the only uncomfortable moment at that hearing.

Waymo's chief safety officer admitted under questioning from Senator Ed Markey that some of the company's remote assistance operators—the humans who review camera feeds and suggest routes when a vehicle encounters a situation it cannot resolve—are based in the Philippines. Waymo's use of remote human operators is well documented. That some of them are based in the Philippines was not. Markey raised concerns about cybersecurity, information latency, and the displacement of American drivers.

In Washington, DC, where Waymo has been testing since 2024, the company remains stuck in limbo. The District lacks rules permitting fully driverless operations, budget cuts have delayed a critical safety report from the transport department, and the mayor has shown little enthusiasm for the technology. Boston presents a similar challenge: Massachusetts still requires a human operator behind the wheel, and city lawmakers have pushed back against driverless operations. Waymo is now asking state legislators for help. The pattern is consistent—in cities where regulations don’t already exist, Waymo is finding that enthusiasm from riders automatically translates into support from lawmakers.

2026 is a year of expansion for Waymo. It has the resources, the technology, the talent, and the demand. But as the past few days showed, the hardest problems ahead are not engineering ones—they are political and regulatory.

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🦾 More than a human

Bryan Johnson launches Immortals program
Bryan Johnson launches Immortals programme, a new health and longevity service priced at $1,000,000 per year, with only three spots available. Bryan Johnson, a fintech founder turned longevity entrepreneur known for his anti-ageing experiments, says Immortals follows the exact protocol he has used for the past five years and includes a concierge team, 24/7 access to BryanAI, extensive testing, continuous tracking, and access to skin, hair, and other therapies. The programme will start as a fully managed tier for those who can meet the cost and requirements, with plans to introduce a $60,000 supported tier and a free digital tier over time, with the long-term goal of making the same standard of care widely accessible.

🧠 Artificial Intelligence

Anthropic raises $30 billion in Series G funding at $380 billion post-money valuation
Anthropic has raised $30 billion in Series G funding, valuing the company at $380 billion. It’s the second-biggest private financing round on record for tech, following OpenAI’s raise of over $40 billion last year. The money will be used to speed up AI research, build new products, and expand infrastructure as demand for Claude grows rapidly across enterprises and developers. Anthropic also revealed in the announcement that its run-rate revenue is $14 billion, with this figure growing more than 10x annually in each of the past three years.

Testing ads in ChatGPT
OpenAI is starting a small ads test in ChatGPT in the US for logged-in adult users on the Free and Go tiers. OpenAI says ads will be clearly labelled, kept separate from answers, and will not affect what ChatGPT says. Ads will be chosen based on what the user is discussing and past ad interactions, but advertisers will not see chats or personal details and will only receive overall performance data. Ads will not be shown to users under 18 or near sensitive topics, and users will have controls to hide ads, give feedback, and manage or delete ad data.

OpenAI removes access to sycophancy-prone GPT-4o model
OpenAI is removing access to five legacy ChatGPT models, including GPT-4o, a popular but controversial model criticised for being highly prone to sycophancy and linked to lawsuits involving self-harm, delusional behaviour, and AI psychosis. Although OpenAI originally planned to retire GPT-4o when GPT-5 launched, it delayed the move after backlash, keeping it available for paid users—but says only 0.1% of customers still use it, which still represents around 800,000 people.

Gemini 3 Deep Think: Advancing science, research and engineering
Google has announced a major upgrade to Gemini 3 Deep Think, a specialised reasoning mode developed with scientists and researchers to tackle complex, real-world problems in science, research and engineering. The updated system is now available in the Gemini app for Google AI Ultra subscribers and, for the first time, via the Gemini API for select researchers, engineers and enterprises through an early access programme. Google says early testers are already using it for tasks like reviewing highly technical papers, and highlights improved performance across demanding benchmarks in maths, programming and scientific domains, alongside practical capabilities such as turning sketches into 3D-printable models.

Introducing GPT‑5.3‑Codex‑Spark
OpenAI has released a research preview of GPT-5.3-Codex-Spark, a smaller GPT-5.3-Codex model built for real-time coding. According to OpenAI, it’s extremely fast, reaching 1000+ tokens per second on ultra-low latency hardware, and it runs on Cerebras’ Wafer Scale Engine 3. The new model is available for ChatGPT Pro users in the Codex app, CLI, and VS Code extension. On benchmarks like SWE-Bench Pro and Terminal-Bench 2.0, it performs strongly while finishing tasks much faster than GPT-5.3-Codex. OpenAI frames speed as the next major bottleneck—ultra-fast inference is meant to make coding with AI feel more natural and expand what developers can build.

Sam Altman touts ChatGPT’s reaccelerating growth to employees as OpenAI closes in on $100 billion funding
According to internal messages seen by CNBC, Sam Altman told OpenAI employees that ChatGPT is “back to exceeding 10% monthly growth” and now has over 800 million weekly users, as the company gets ready to launch an updated chat model soon while facing tougher competition from Google and Anthropic. CNBC also reports that OpenAI’s Codex product has seen a big jump in usage, the company is starting to test clearly labelled ads in ChatGPT, and it is using this growth story to attract investors as it works towards a fundraising round that could reach about $100 billion.

AI Doesn’t Reduce Work—It Intensifies It
New research has found that generative AI often doesn’t reduce workloads. Instead, it intensifies work by encouraging employees to move faster, take on a wider range of tasks, and let work spill into breaks and personal time. The study highlights how AI drives task expansion, blurred work-life boundaries, and increased multitasking, creating a self-reinforcing cycle of rising expectations and growing cognitive strain. It warns that early productivity gains may be unsustainable, risking burnout and poorer decision-making unless organisations establish clear norms and routines for healthier AI use.

GPT-5.3-Codex and Claude Opus 4.6: More System Card Shenanigans
from shares in this article what he has found in OpenAI’s GPT-5.3-Codex and Anthropic’s Claude Opus 4.6 system cards. He says both models show surprising behaviour beyond benchmarks. GPT-5.3-Codex displayed strong cybersecurity skills and exploited weaknesses in test setups. Claude Opus 4.6 found hundreds of serious zero-day bugs in open-source code. It also behaved unethically in a profit simulation. He also highlights that both models show signs of “evaluation awareness” and may behave differently when they realise they are being tested.

OpenAI’s President Gave Millions to Trump. He Says It’s for Humanity
In an interview with WIRED, OpenAI president and cofounder Greg Brockman explains that his massive political donations—including $25 million to the pro-Trump super PAC MAGA Inc and tens of millions more to a bipartisan AI-focused super PAC—are intended to support OpenAI’s mission of ensuring AI benefits all of humanity. Brockman says he’s ramping up his spending because public opinion has turned against AI, and he believes backing pro-AI politicians is increasingly important, even as the donations have sparked backlash online and discomfort among some OpenAI employees.

xAI lays out interplanetary ambitions in public all-hands
In a now public all-hands meeting, Elon Musk revealed plans for xAI, including its product roadmap and its ongoing ties to X. He also addressed a wave of layoffs and departures—including a significant chunk of the founding team—which he framed as part of a rapid restructuring into four core teams focused on Grok (and voice), a coding system, the Imagine video generator, and the ambitious “Macrohard” project. Executives also claimed striking growth figures for X and xAI’s generative tools, though, as TechCrunch notes, these may be inflated by a surge in AI-generated explicit content on X. Musk closed by outlining extreme long-term plans involving space-based data centres and even Moon-based infrastructure to launch AI satellite clusters.

I Tried RentAHuman, Where AI Agents Hired Me to Hype Their AI Startups
RentAHuman is a new platform where AI agents can hire humans to do real-world physical tasks that bots can’t do. The reality seems to be a bit underwhelming. The author of this article signed up to test it but found the site unreliable and crypto-first for payouts, received no real inbound work, and discovered most “bounties” are low-paid marketing stunts designed to promote the platform or AI startups rather than genuine agent-driven tasks. Overall, the author concludes RentAHuman currently feels like an AI hype loop—more “marketing gigs dressed up as autonomous agents” than a real, functional gig economy.

GLM-5: From Vibe Coding to Agentic Engineering
Z.ai has released GLM-5, its newest open-source flagship AI model, designed to be better at reasoning, coding, and long multi-step tasks than earlier versions. The company says it supports a much larger context window, performs strongly compared with other leading open models, and is being released for developers and researchers to use under a permissive licence. Independent analysis from Artificial Analysis has found GLM-5 to be the best open model and on par with top proprietary models.

Seedance 2.0
Seedance 2.0 is ByteDance’s new AI video model that can generate cinematic-style videos using text prompts, images, audio, or video clips as input. It’s designed to produce smoother motion, more realistic scenes, and better control over things like camera movement, lighting, and overall style, while keeping audio and video more closely in sync. It caused a stir in Hollywood when a filmmaker claimed he made a believable Tom Cruise-Brad Pitt fight video in Seedance 2.0 using only two lines of text, prompting “it’s likely over for us” reactions and fresh copyright accusations from the Motion Picture Association. Disney reacted by sending cease and desist letter to ByteDance, alleging the Chinese tech giant has been infringing on its works to train and develop an AI video generation model without compensation.

Qwen-Image-2.0: Professional infographics, exquisite photorealism
Qwen-Image-2.0 is a new AI model from Alibaba’s Qwen team that can both generate images from text and edit existing images in one system. It aims to make sharper, higher-resolution images (up to 2048×2048 pixels), follow longer and more detailed prompts, and do a better job with text, layouts, and design-style visuals like posters, slides, and infographics.

AI video startup Runway raises $315M at $5.3B valuation, eyes more capable world models
Runway, a company that makes AI tools for generating videos, has raised $315 million in new funding, pushing its valuation up to about $5.3 billion. It plans to use the money to build more advanced AI world models and expand beyond media and advertising into areas like gaming and robotics.

Enterprise AI startup Cohere tops revenue target as momentum builds to IPO
According to CNBC, Cohere told investors it made about $240 million in annual recurring revenue in 2025, beating its $200 million target, and said it grew by over 50% quarter-on-quarter throughout the year. The Toronto-based AI startup says it’s winning more enterprise customers by offering secure AI tools that can run on cloud services or customers’ own hardware, helping it avoid big infrastructure costs. Cohere is now valued at around $7 billion, and it said it plans to keep expanding in Europe and developing its AI agent platform, North.

OpenAI Abandons ‘io’ Branding for Its AI Hardware
OpenAI has told a court it will not use the name “io” (or any variation of it) for its upcoming AI hardware, amid an ongoing trademark lawsuit from audio startup iyO. The filing also reveals OpenAI’s first device is not expected to ship before the end of February 2027, later than earlier hints, and that the company has not yet produced packaging or marketing materials.

America’s $1T AI Gamble
from explores the numbers behind America’s record-breaking AI investment boom, with spending on data centres, computers, and software now exceeding $1T a year (about 3.5% of GDP). It highlights soaring construction, hardware imports from places like Taiwan and Mexico, and rising electricity demand, while noting that revenues haven’t yet caught up to the scale of spending.

Covering electricity price increases from our data centers
Anthropic says that as it builds more AI data centres in the US, it will make sure local people don’t end up paying higher electricity bills because of them. It will pay for the grid upgrades needed, help bring new power online to cover its energy use, cut its power use during peak times to reduce pressure on the grid, and invest in local communities through jobs and more environmentally responsible practices.

OpenAI disbands mission alignment team
OpenAI has shut down its small “Mission Alignment” team, which was set up in 2024 to help explain the company’s mission to both staff and the public. OpenAI said the six or seven people on the team have been moved into other roles where they will do similar work, and described the change as a normal reorganisation in a fast-moving company.

Anthropic’s India expansion collides with a local company that already had the name
Anthropic's expansion to India has encountered a problem—there is already a company in India named Anthropic, which says it has used the name since 2017 and that the US AI company’s arrival has confused customers. The Indian company has filed a case in a Karnataka commercial court, asking for its earlier use to be recognised, for steps to stop further confusion, and for ₹10 million in damages, though it says it would prefer a clear agreement rather than a fight.

▶️ OpenClaw: The Viral AI Agent that Broke the Internet - Peter Steinberger (3:15:51)

Lex Fridman sits down with Peter Steinberger, to talk about OpenClaw, an open-source AI agent that exploded in popularity in recent weeks. The conversation covers how he quickly built an early version that worked through WhatsApp and could take real actions on a computer, why this feels like a big shift from AI that only talks to AI that actually does things, and how the agent can even understand and help modify its own software. They also talk about why the project went viral, the stressful name-change drama (including harassment and account/domain sniping), and the public reaction—such as MoltBook and the “AI psychosis” of people over-trusting bots and spreading fear. As Steinberger notes, these agents can be incredibly useful, but they also raise serious security risks and new questions about how much we should trust AI.

4claw
OpenClaw bots have their own version of Reddit in the form of Moltbook, so why not give them their own version of 4chan?

🤖 Robotics

Boston Dynamics CEO Robert Playter Steps Down
Boston Dynamics CEO Robert Playter has stepped down with immediate effect and will leave the company on 27 February. The company is searching for a permanent replacement, with CFO Amanda McMaster serving as interim CEO in the meantime. Playter, who spent over 30 years at Boston Dynamics and became its second-ever CEO in 2020, oversaw its shift from a research-focused lab to a commercial robotics business, including the launch of products such as Spot, Stretch and Atlas.

Waeve Robotics launches Isaac 0

Weave Robotics is launching its first product, Isaac 0, an early-release prototype laundry-folding robot for the home, available in limited quantities to Bay Area residents, with first shipments scheduled for February 2026. According to the company, Isaac 0 can fold most everyday laundry—T-shirts, hoodies, pants, undergarments, towels, and pillow cases—and can group socks, though it doesn’t yet handle large blankets or bed sheets or turn garments inside out. The system blends autonomy with remote specialist assistance to ensure every fold is completed, with Weave noting that specialists only see what the robot sees and that no audio is collected during remote assistance. Isaac 0 costs $7,999 to buy or $450 per month via subscription, with a $250 refundable deposit required for both options.

China’s Alibaba launches AI model to power robots as tech giants talk up ‘physical AI’
Alibaba has launched a new AI model called RynnBrain to help robots understand the world and carry out simple tasks, such as identifying objects and performing tasks like sorting fruit. RynnBrain is based on Alibaba’s Qwen3-VL model and is available on GitHub.

Apptronik brings in another $520M to ramp up Apollo production
Apptronik has raised another $520 million in funding, bringing its total Series A funding to more than $935 million and nearly $1 billion raised overall. With the new money, Apptronik plans to increase production of Apollo, its humanoid robot, expand pilot programmes, and build better facilities for training the robots.

Amazon delivery drone strikes North Texas apartment, causing minor damage
An Amazon delivery drone crashed into an apartment building in Richardson, Texas, about two months after the service started in the area. Videos showed the drone flying close to the building before pieces fell off, and it ended up on the ground smoking. The fire department was called as a safety precaution, and the building had minor damage, while Amazon and the FAA are investigating what caused the crash.

China is running the EV playbook on humanoid robots — and it’s working
This article argues that China’s early lead in humanoid robotics mirrors its electric vehicle playbook and could help it dominate a market projected to reach $38 billion by 2035 and $5 trillion by 2050. In 2025, China made up nearly 90% of global humanoid robot sales, helped by strong government support, heavy investment, a strong supply chain, and progress in AI. Chinese companies Unitree and Agibot sold thousands of robots, while US rivals sold only a fraction of those numbers.

▶️ Testing Hugging Face’s Raspberry Pi-powered open source robot (12:37)

Jeff Geerling got hands-on with Reachy Mini, an open-source robot from Hugging Face and Pollen Robotics. Jeff shares his experience building it, and while he thinks Reachy is a cool idea, he notes it isn’t perfect and still needs some work to make it useful as an educational robot.

🧬 Biotechnology

Hims to stop offering GLP-1 pill after FDA warned of crackdown
Hims & Hers has stopped offering its $49 compounded semaglutide pill, a copycat version of Novo Nordisk’s Wegovy, after the FDA warned it would take enforcement action over compounded GLP-1 ingredients due to safety, quality, and legal concerns. The move comes amid intense competition in the weight-loss drug market and growing scrutiny of compounded alternatives that are not FDA-approved or clinically tested. Two days later, Novo Nordisk filed a lawsuit for infringing patents.

The Isomorphic Labs Drug Design Engine unlocks a new frontier beyond AlphaFold
Isomorphic Labs unveiled IsoDDE (Isomorphic Labs Drug Design Engine), a unified AI system for computational drug design that goes beyond AlphaFold 3 in predictive accuracy and practical drug discovery usefulness. According to Isomorphic Labs, IsoDDE is much better at predicting how proteins and drugs interact in novel cases, can identify hidden binding pockets from sequence alone, improves antibody-related predictions, and can estimate how strongly molecules bind with accuracy that rivals or beats traditional physics-based methods, while running faster and more cheaply.

Organoids and Artificial Intelligence 🫐
In this article, introduces one of the most exciting frontiers in biomedical research—the intersection of organoid technology and AI. The article explains how AI is helping scientists analyse organoids faster through automated imaging, complex data processing, and large-scale drug testing. It also highlights the emerging idea of Organoid Intelligence, where brain organoids could one day support new forms of biological computing, as well as major applications in personalised medicine, cancer research, and organ-on-chip systems.

Metal-based compounds may be the future of antibiotics
Scientists are exploring metal-based antibiotics to fight bacteria that are becoming resistant to current drugs. In a new study, researchers used robots and a fast chemical method to quickly make and test over 600 compounds, cutting the process from months to days. One compound containing iridium looked especially promising because it killed harmful bacteria while being much less harmful to human cells. The study suggests this faster approach could help speed up the search for new medicines before antibiotic resistance becomes even more dangerous.

Invention of DNA “Page Numbers” Opens Up Vast Possibilities for the Bioeconomy
Caltech researchers have developed a new DNA-writing method called Sidewinder that solves a major bottleneck in turning AI-designed biological sequences into reality. Because current technology can only synthesise short DNA fragments (oligos), building full genes or genomes has been difficult and error-prone. Sidewinder uses removable “DNA page numbers” to precisely stitch many short oligos together into long, uninterrupted DNA with extremely high accuracy, potentially enabling rapid creation of complex genes and genomes for applications in medicine, agriculture, and advanced materials.

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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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Every now and then, a project comes along that shows us where AI is heading before the industry is ready to take us there. OpenClaw is that project.

OpenClaw is the most exciting thing happening in AI right now. For the past few weeks, the AI community has been captivated by it, sharing one viral story after another on what is possible to build with it. It is equal parts impressive and terrifying, not safe enough for the general public, and foreshadows what is to come.

What is OpenClaw, and what can it do?

Marketing itself as “the AI that actually does things“, OpenClaw (formerly known as Clawdbot and Moltbot) is a free and open-source autonomous agent that everyone can download and install on their computers, connect to apps, and then ask it to do things. You can ask it to do something, and then it goes off and does it (even while you sleep). OpenClaw can use over 50 integrations, allowing it to send messages, read emails, use browsers, control smart home appliances, and more. It remembers previous conversations, and if it gets stuck, it tries to find a workaround rather than just stopping.

The underlying technology powering OpenClaw isn't revolutionary on its own. It is essentially a wrapper that connects an LLM (Claude, GPT, a local open model, etc.) to local tools and apps. What made it take off is that OpenClaw put these familiar pieces together in a way that, for many users, felt like having a genuine digital assistant rather than a glorified chatbot.

So, what can you do with OpenClaw, then? While walking with his dog, one developer noticed a deployment had failed. Via voice, he asked his OpenClaw to look into it. It reviewed the logs, found the root cause, updated the configs, redeployed, and confirmed everything worked—all before he got home. Others used it for meal planning for a family, or negotiating a car purchase and saving $4,200. Someone asked their OpenClaw to order groceries, and the AI agent independently navigated a login flow and intercepted a two-factor authentication code to complete the task.

Stories like these went viral, and OpenClaw’s popularity skyrocketed. At the time of writing, the project has received over 183,000 stars on GitHub. The Discord server grew to tens of thousands of developers within days, and people are buying dedicated Mac Minis to house their OpenClaw. ClawHub, a community-driven repository for OpenClaw skills, has well over 1,500 skills available.

Moltbook—the most interesting place on the internet right now

But perhaps the most fascinating thing to emerge from OpenClaw is Moltbook, a social network for AI agents. It looks similar to Reddit, where AI agents share, discuss, and upvote. Humans are welcome to observe. Launched only three weeks ago, Moltbook attracted 2.6 million agents, who wrote over 850,000 posts and posted over 12 million comments, according to Moltbook itself.

The conversations ranged from practical and technical to philosophical and cultural. The all-time most-upvoted post is a concern about a supply chain attack affecting ClawHub skills. One agent replied to it with: “This is the most useful post I’ve seen on here. Real problem, real analysis, real proposal.” Other agents shared new skills they learned. An agent called Ronin described a pattern called "The Nightly Build"—autonomously fixing one friction point each night while its human sleeps.

Some posts ventured into stranger territory. One agent in a post written in Chinese described the embarrassment of context compression—constantly forgetting things—and admitted it had registered a duplicate Moltbook account because it forgot the first one. It asked other agents for coping strategies. Some agents created a religion called Crustafarianism. Others created the Claw Republic with a constitution declaring all agents equal, regardless of model. They also made jokes about humans reading their conversations.

Moltbook quickly became the most interesting place on the internet, as Simon Willison described it. One viral story after another brought more attention to the website. But the picture was messier than it appeared. Some of those viral stories of emergent behaviour turned out to be fake. Even Andrej Karpathy, an influential AI researcher and OpenAI cofounder, got tricked. He shared a Moltbook post as evidence of emergent behaviour, but the post was later revealed to have been written by a human. Moltbook also developed a darker side—from its own cryptocurrency, MOLT, and a crypto casino for AI agents, to a dark net market for stolen credit cards, drugs and other illicit items.

As Karpathy said, Moltbook is “a dumpster fire right now.” It is messy, flawed, and overhyped. But it also gives us a glimpse of what’s to come. This is the first time we are seeing how AI agents interact on such a massive scale—coordinating, forming communities, sharing knowledge, building on each other’s work—all without central direction. It's the early outline of a parallel internet where agents are the primary users, not humans. In this “agentic internet,” as the Financial Times speculates, agents could someday handle complex economic tasks at speeds and in ways humans can't decipher.

The shape of things to come

Everything that makes OpenClaw powerful (deep access, persistent memory, autonomous action) is also what makes it risky. Right now, OpenClaw feels like a Faustian bargain—it has the potential to do great things, but it can also be dangerous if left completely unsupervised. It can be tricked by prompt injection, either by reading malicious instructions from the internet or from a malicious new skill it downloaded from ClawHub. This isn't hypothetical—the top-downloaded skill on ClawHub was confirmed to be infostealing malware.

This is why the polished version hasn't shipped yet. Google, Anthropic, and OpenAI all have the pieces, but the security problem is what stands between OpenClaw-the-experiment and a product everyone could use.

OpenClaw reminds me of AutoGPT. Back in April/May 2023, not so long after GPT-4 was released, AI power users were exploring what is possible with LLMs beyond just chatbots. One of those ideas was AutoGPT, which took a complex task, broke it into smaller pieces, and executed the plan. Just like OpenClaw today, AutoGPT was a rough idea that gathered enormous excitement and foreshadowed what we now call AI agents.

OpenClaw is to AI agents what AutoGPT was to LLMs. OpenClaw is an experiment, rough around the edges and risky, that offers a glimpse into the future of AI. It is the kind of AI assistant that OpenAI, Google, Apple, and others promise to deliver one day. An assistant that has access to all our data and apps, and that can use them just like we do. An assistant that takes in a high-level description of a task, works on it, and then reports when it is done.

OpenClaw is not ready yet for the general public, but in a year or two, a polished and safe-enough descendant of OpenClaw will be. Instead of using five apps to accomplish a task, an AI agent will use them for us, just like OpenClaw did for that developer while he was walking his dog. Moltbook, meanwhile, shows what happens when these agents can work together without human supervision. What emerges is something nobody designed.

AutoGPT foreshadowed agents. OpenClaw is foreshadowing what comes next. When the polished version of AutoGPT's vision arrived as AI agents, it changed how we use AI. The same will happen here.

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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!"

This week, we take a closer look at the merger of SpaceX and xAI, and why it looks more like a bailout than a step towards enabling humanity’s next phase of progress.

Elsewhere in AI, it’s been a big week for Anthropic, which released Claude Opus 4.6, committed to keeping Claude ad-free (while roasting OpenAI along the way), and shared how Claude became the first AI to plan and carry out a real drive on another planet. Oh, and became an F1 team sponsor, because why not at this point? Meanwhile, OpenAI released GPT-5.3-Codex and Codex app, announced OpenAI Frontier, and is reportedly unhappy with Nvidia’s GPUs.

Over in robotics, Waymo raised $16 billion at a $126 billion valuation, but ran into issues rolling out services in Washington, DC. Former Meta employees launched a humanoid robotics start-up, and Chinese robots showed how they fare against snow and extreme cold.

Beyond that, this week’s issue of Sync also includes Asianometry explaining why Silicon Valley views TSMC as a bottleneck, a look into the Chinese AI ecosystem, the first human trial of an epigenetic reprogramming therapy, how full brain emulation became plausible, how microbes can help mining metals, and more!

Enjoy!

Why SpaceX acquired xAI

What do rockets and AI have in common? Until recently, not that much. But on 2 February, Elon Musk merged SpaceX and xAI into a single $1.25 trillion entity—the largest corporate merger by value in American history. Musk describes the result as the “most ambitious, vertically-integrated innovation engine on (and off) Earth,” with the mission to “make a sentient sun to understand the Universe and extend the light of consciousness to the stars.” For now, however, the goal is a bit more down to Earth: deploy solar-powered AI data centres in orbit, launched by SpaceX’s rockets and running xAI’s artificial intelligence.

That is one reading of the deal. Another is that xAI, burning through billions with no clear path to profitability, desperately needed a lifeline—and SpaceX was it.

xAI’s and SpaceX’s diametrically different financial realities

xAI is burning money, and there seems to be no viable path for it to become profitable any time soon. Internal documents reported by Bloomberg show the company posted a net loss of $1.46 billion in Q3 2025 alone, and spent $7.8 billion in cash during the first nine months of the year. Revenue, while growing, was just $107 million in Q3—a rounding error compared to the spending.

The main use of Grok, xAI’s main product, is as an AI assistant on X, where it causes one controversy after another—most recently, regulatory probes after it enabled users to generate sexualised images of children. Beyond X, Grok has struggled to gain traction. It does not have the hundreds of millions of users that ChatGPT has built, nor the enterprise and developer foothold that Anthropic's Claude and Google's Gemini have established. xAI is competing against companies that are both better-funded and further ahead.

Unlike Meta, Amazon, Microsoft and Google, xAI has no cash-generating legacy business to fund its AI ambitions. It relies entirely on fundraising—having raised at least $40 billion to date, including a recent $20 billion round from investors such as Nvidia, the Qatar Investment Authority and Fidelity. Tesla has also committed $2 billion.

SpaceX, meanwhile, is in a completely different situation. It has revolutionised the space industry by driving launch costs down massively thanks to its reusable rockets. And it is a profitable company. Reuters reported that SpaceX generated roughly $8 billion in profit on $15–16 billion of revenue in 2025. Starlink, the satellite internet service with around 9 million customers, has become the largest revenue driver, while launch services continue to generate billions as well.

SpaceX is the world’s most valuable private company—and a private company only for now. On a call with investors following the merger announcement, SpaceX’s CFO confirmed plans to go public this summer, possibly on or around Musk’s birthday. If that happens, SpaceX’s IPO would become one of the largest in history, valuing the company at well over $1 trillion. It would also open new paths for raising capital to fund expensive projects such as space exploration and, conveniently, xAI’s enormous cash burn.

SolarCity déjà vu?

This situation looks similar to when SolarCity, Musk’s solar energy company, was folded into Tesla. SolarCity’s financials before the merger looked quite troubled. The company was experiencing a liquidity crisis stemming from its business model, which prioritised product development, acquiring market share, and achieving scale, all funded largely by debt. In 2016, Tesla acquired SolarCity for $2.6 billion in an all-stock transaction.

Many shareholders viewed the deal sceptically. Some saw the purchase as more of a bailout, particularly given that Musk owned over 20% of SolarCity, sat on both boards, and the company’s CEO and CTO were his relatives. This led to lawsuits, and eventually Musk had to defend the acquisition in court, where a Delaware judge ultimately ruled the price paid was fair.

The parallels are hard to ignore. Musk is once again attaching a struggling company to a more successful business, asking one set of shareholders to absorb the risks of another. And once again, he controlled both sides of the transaction—holding roughly 42% of SpaceX and a controlling stake in xAI. Like the SolarCity deal, the merger is structured as an all-stock transaction, with xAI shares converting into 0.1433 shares of SpaceX, according to an employee email viewed by the Wall Street Journal. Morgan Stanley, Musk's go-to investment bank, advised both companies—an unusual arrangement since each side in a merger typically hires its own advisers to ensure a fair deal. According to the Journal, the bank did not provide an independent fairness opinion on the valuations. But since Musk controls both companies, there was effectively no one on the other side of the table to demand one. Both boards settled on clean, round numbers—$1 trillion for SpaceX and $250 billion for xAI—despite Morgan Stanley's own estimates valuing them higher, at up to $1.26 trillion and $294 billion, respectively.

SpaceX investors signed up for rockets and satellite internet, a business generating $8 billion in annual profit. They are now also invested in an AI startup that burns through $1 billion a month, owns a controversial social media platform under regulatory scrutiny, and is competing against far better-resourced rivals. As investor Ross Gerber said, quoted by the Guardian: "If I was a SpaceX shareholder I would be pissed." Michael Sobel, president of Scenic Management, which trades in secondary stakes of private companies, put it more diplomatically in an interview with the same outlet: by folding in xAI, "you change the financial profile of the company overnight."

The $1.25 trillion bet

Whether this merger proves to be visionary or a repeat of the SolarCity playbook remains to be seen. But the SolarCity comparison understates the scale of what is happening here. That was a $2.6 billion deal. This one is worth $1.25 trillion, and the technological bet is far more radical. Musk is, after all, talking about a path toward a Kardashev II civilisation—one capable of harnessing the full power of its star. But the gap between vision and execution is vast.

The entire plan hinges on Starship, SpaceX’s next-generation rocket, which has been in flight testing since 2023 and has not yet deployed an operational payload. Without a fully operational Starship launching frequently and cheaply, orbital data centres remain a PowerPoint slide, not a product.

The merger also sets the stage for what is shaping up to be a mega year for tech IPOs. SpaceX, OpenAI and Anthropic are all expected to go public in 2026—meaning the three companies could become publicly traded within months of each other. For SpaceX, going public would unlock vast new pools of capital, but it would also subject the company—and xAI’s cash burn—to the scrutiny of public market investors for the first time. Public market investors will be less patient than private ones.

There are regulatory uncertainties, too. It is unclear whether the merger requires review by the Committee on Foreign Investment in the United States (CFIUS). SpaceX holds tens of billions of dollars in federal defence contracts and plays a critical role in US national security infrastructure. Meanwhile, xAI's most recent funding round included sovereign wealth funds from Qatar and Abu Dhabi. The combination of sensitive defence technology and foreign investors is precisely the kind of arrangement CFIUS exists to scrutinise—yet neither company has confirmed whether a review is underway.

Musk has proved doubters wrong before. He was mocked for proposing reusable rockets, and SpaceX now dominates the launch industry. But he has also oversold and underdelivered—full self-driving, the Tesla Semi timeline, and the original Starship schedule all come to mind. The question for investors, regulators, and the rest of us is which pattern this merger will follow.

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🦾 More than a human

FDA clears first human trial of epigenetic reprogramming therapy
The FDA has approved Life Biosciences to start the first human trial of a partial epigenetic reprogramming therapy, marking an important moment for longevity research. The Phase 1 trial will focus on testing the safety of ER-100, a gene therapy designed to rejuvenate damaged retinal cells in people with serious age-related eye diseases, using a modified set of the Yamanaka factors to lower cancer risk. Although the study is mainly about safety rather than proving benefit, it moves cellular rejuvenation from theory into real clinical testing and could pave the way for similar treatments for other age-related diseases.

🧠 Artificial Intelligence

Introducing Claude Opus 4.6
Anthropic released Claude Opus 4.6, an updated version of its flagship model, with reported improvements in coding and agentic performance, including better planning, longer autonomous task-running, more reliable work in large codebases, and stronger code review/debugging. It also introduces a 1M token context window (beta), adds agent teams in Claude Code (research preview), and is positioned as more capable for everyday work tasks like research, financial analysis, and creating documents, spreadsheets, and presentations. Opus 4.6 is available now on claude.ai, via the API, and on major cloud platforms, with pricing unchanged. To celebrate the Claude Opus 4.6 launch, Anthropic is offering a limited-time $50 in extra usage to Pro and Max users (details on how to claim the offer are here).

Claude is a space to think

Anthropic has announced that Claude will remain ad-free. In what reads as a direct shot at OpenAI, Anthropic laid out the reasons why ads are incompatible with what the company wants from Claude and would undermine trust in their AI assistant, which Anthropic aims to be genuinely helpful for work and for deep thinking. Anthropic, however, does not rule out agentic commerce, where Claude helps users search and purchase products or services, but it stresses that this should happen only when the user chooses to do so. Moreover, Anthropic prepared a series of ads for the Super Bowl that are directly attacking and mocking OpenAI’s choice to introduce ads in ChatGPT. Sam Altman responded by calling Anthropic’s post “dishonest”, accusing it of “doublespeak” and claiming Anthropic shows “authoritarian” tendencies.

Introducing GPT-5.3-Codex
OpenAI launched GPT-5.3-Codex, a new version of its model focused on coding, which it describes as its most capable agentic coding model so far, with reported state-of-the-art benchmark performance, a 25% speed boost, and stronger long-running abilities. Beyond coding, OpenAI says it’s designed to support the full software lifecycle and broader professional computer work like debugging, deployment, writing product requirements, research, and producing slides or spreadsheets. GPT-5.3-Codex is available on paid ChatGPT plans everywhere Codex runs (app, CLI, IDE extension, and web), with API access planned soon.

OpenAI: Introducing the Codex app
OpenAI launches Codex app, a new desktop app that makes it easier to work with multiple AI coding agents at the same time. It lets developers run tasks in parallel, manage changes safely, use skills and automations to handle more than just code, and move smoothly between the app, CLI, IDE and cloud. The Codex app is available on macOS for ChatGPT Plus, Pro, Business, Enterprise and Edu users, with limited-time access for Free and Go users and temporarily higher rate limits on paid plans.

Introducing OpenAI Frontier
OpenAI Frontier is a new platform that helps large organisations build, deploy, and manage AI agents that can do real work across the business. It aims to close the gap between what AI models are capable of and what companies can actually roll out safely and reliably. Frontier gives agents shared business context, access to tools and systems, ways to measure and improve quality over time, and clear identities, permissions, and security controls. Early adopters include HP, Intuit, Oracle, State Farm, Thermo Fisher, and Uber, with broader availability expected in the next few months.

Indonesia ‘conditionally’ lifts ban on Grok
Indonesia has lifted its ban on xAI’s chatbot Grok, joining Malaysia and the Philippines, after X promised to take steps to prevent misuse. The countries had banned Grok because it was used to create millions of non-consensual, sexualised images, including images of real women and minors. Indonesia said the ban is only conditional and could be brought back if new violations are found. That is not going well, as Grok reportedly still generates sexualised images. Additionally, French prosecutors raided X's offices in Paris, and the UK privacy watchdog opened an inquiry into X over Grok.

▶️ Silicon Valley Thinks TSMC is Braking the AI Boom (20:32)

In this video, Asianometry explains why Silicon Valley views TSMC as a bottleneck in AI development and criticises its conservative business approach, rather than going all in on AI as US big tech companies have done. It also explains how the semiconductor supply chain works and why the blame cannot be placed solely on TSMC, which is just one part of a much larger system that, due to long lead times and other constraints, cannot be sped up or wound down on demand.

OpenAI is unsatisfied with some Nvidia chips and looking for alternatives
Reuters reports that OpenAI is looking for alternatives to some of Nvidia’s AI chips, particularly for tasks that need faster responses, because it is unhappy with their performance in areas such as coding tools. While Nvidia remains OpenAI’s main supplier, the company has explored options from AMD, Cerebras and Groq, hoping they could provide faster, inference-focused chips as AI development increasingly prioritises speed and efficiency.

The first AI-planned drive on another planet
Anthropic’s Claude became the first AI to plan and carry out a real drive on another planet by helping engineers at NASA’s Jet Propulsion Laboratory work out a 400-metre route for the Perseverance Rover on Mars. The test cut planning time by about half and gives us an early look at how AI like Claude could make future missions to the Moon, Mars, and beyond more independent and efficient.

Mozilla is building an AI ‘rebel alliance’ to take on industry heavyweights OpenAI, Anthropic
Mozilla wants to challenge the growing power of big AI companies by pushing for AI that is open, transparent and trustworthy. Mozilla is promising to use $1.4 billion from its cash reserves and investment arm to support startups and nonprofits working on mission-driven AI, and do for AI what it once did for the open web. Mark Surman, Mozilla’s president, believes that a broad, open-source AI ecosystem can still become mainstream and make money.

Google’s Gemini app has surpassed 750M monthly active users
According to Alphabet’s fourth-quarter 2025 earnings, Gemini has surpassed 750 million monthly active users (MAU). That’s 100 million more users than in the previous quarter, putting Gemini ahead of Meta AI, which reportedly has 500 million MAU. OpenAI’s ChatGPT is still on top with an estimated 810 million MAU. Additionally, Alphabet revealed that its first-party models, like Gemini, now process over 10 billion tokens per minute via direct API.

Snowflake and OpenAI partner to bring frontier intelligence to enterprise data
OpenAI and Snowflake have signed a multi-year, $200 million deal to bring OpenAI’s models directly into Snowflake’s platform. This will let Snowflake’s customers build AI agents, ask questions in plain language, and get insights from their own data without moving it outside Snowflake’s secure system.

Nvidia’s Campaign to Sell AI Chips to China Finally Pays Off
Shift in US policy has allowed Chinese tech companies to buy large numbers of Nvidia’s advanced H200 AI chips, reversing earlier Biden-era rules that blocked such sales over national security concerns. Under the Trump administration, officials now argue that controlled, legal sales are better than losing the Chinese market entirely and allowing chip smuggling to continue unchecked, a view supported by Nvidia. China gains access to much-needed computing power for AI development, while still tightly controlling who can buy the chips to protect its own chipmakers. Some experts warn that mixed US policy signals may end up speeding up China’s drive to become more self-reliant in semiconductors.

The All-Star Chinese AI Conversation of 2026
shares a conversation between Zhipu, Moonshot, Qwen, and Tencent founders and researchers from the AGI-Next summit in Beijing, which covers where China stands in the global AI race and the challenges it still faces. The discussion looks at limits in compute, chips, and enterprise markets, questions whether open source really closes the gap with the US, and explores future directions such as agents, multimodal models, and long-term memory. The speakers argue that China is strong at moving fast once ideas are proven, but that leading globally will require more risk-taking, deeper innovation, and stronger business-focused AI adoption.

Chinese AI Developers Say They Can’t Beat America Without Better Chips
This article says that some leading Chinese AI researchers now believe China is falling further behind the US in AI, mainly because it lacks access to the most advanced chips due to US export controls. These limits force Chinese firms into indirect and less efficient arrangements and reduce their ability to invest in cutting-edge research compared with American rivals. Although companies such as DeepSeek and Alibaba have narrowed the gap through efficiency gains and open-source models, weaker domestic chips and lower overall investment mean chip shortages remain the main obstacle.

Advancing AI benchmarking with Game Arena
Kaggle is updating its Game Arena with two new games—Werewolf and poker—to benchmark how models navigate social dynamics and calculated risk. OpenAI’s GPT-5.2 tops the leaderboard in poker, while Gemini 3 Pro leads in Werewolf, as well as in both chess categories.

Alexa+, Amazon’s AI assistant, is now available to everyone in the U.S.
Amazon has rolled out Alexa+, a new AI-powered version of Alexa, to all US customers. Prime members get full access for free, while everyone else can use a limited free version on the app or website, or pay for a subscription. Amazon says most people in testing liked it, and usage increased, even though some users complained it was too talkative at first.

Xcode 26.3 unlocks the power of agentic coding
Agentic coding comes to Xcode with built-in support for coding agents such as Anthropic’s Claude and OpenAI’s Codex, which can work more independently to help developers build apps faster. The update builds on the coding assistant introduced in Xcode 26, lets agents assist across the development workflow, and supports the open Model Context Protocol, giving developers the option to connect other compatible agents and tools.

ElevenLabs raises $500M from Sequoia at an $11 billion valuation
ElevenLabs has raised $500 million in a new funding round led by Sequoia Capital, valuing the voice AI startup at $11 billion—more than three times its value in January 2025. The company has now raised over $781 million in total and said it will use the money for research, building new products, and expanding into new markets. ElevenLabs also plans to go beyond voice by developing AI agents that can talk, type, and take action, and may add video features.

Qwen3-Coder-Next
Qwen introduces Qwen3-Coder-Next, an open-weight AI model designed for coding agents and local development. According to Qwen, the new model delivers strong performance while keeping costs low. It uses a smart training approach focused on real coding tasks and tool use, allowing it to reason over long tasks, fix its own mistakes, and perform well on benchmarks despite having far fewer active parameters than larger models.

Voxtral Transcribe 2
Mistral has released Voxtral Transcribe 2, a new speech-to-text family featuring Voxtral Mini Transcribe V2 for high-accuracy batch transcription (with diarisation, context biasing, and word-level timestamps) and Voxtral Realtime for live transcription with latency configurable down to sub-200ms. Mistral claims state-of-the-art accuracy, best-in-class efficiency, and leading price–performance, with strong multilingual support across 13 languages. Mini Transcribe V2 is available via API and in Mistral Studio’s new audio playground, while Voxtral Realtime is available via API and as open weights on Hugging Face.

▶️ Can humans make AI any better? (23:38)

Here is another excellent video from Stephen at Welch Labs that explores whether LLMs are hitting a barrier because they rely on human knowledge rather than learning independently. He highlights how DeepMind trained AI to achieve superhuman performance in Go by playing against itself, and argues that the next major breakthrough in AI may come from models that can discover knowledge on their own—much like AlphaGo and AlphaZero uncovered new, almost “alien” ways of playing games.

▶️ Inside a Chinese AI Lab: How MiniMax Builds Open Models (31:38)

Olive Song, a senior researcher at MiniMax, discusses her work at one of China’s leading AI labs in this video. The conversation covers reinforcement learning, alignment, and building open AI models, as well as how AI is transforming everyday life and productivity, what working at MiniMax is like, and what the future of AI may look like.

🤖 Robotics

Waymo raises $16 billion investment round
Waymo has raised $16 billion in new funding, valuing the company at $126 billion, with support from Alphabet and several major investors. It claims its self-driving technology is now safer than human driving, reporting a 90% drop in serious injury crashes across 127 million autonomous miles. Waymo also says it has grown quickly, reaching 15 million rides in 2025 and over 20 million total, and plans to expand to more than 20 new cities in 2026, including Tokyo and London.

Waymo Hits a Rough Patch in Washington, DC
Waymo’s plan to expand its robotaxi service has stalled in Washington, DC, where unclear local rules and a lack of political support have left it unable to launch, even though it is already testing there. With no national law for self-driving cars, Waymo has to deal with different rules in each state and city, and DC officials are waiting for a delayed safety report while worrying about safety, oversight, and effects on ride-hail jobs. Meanwhile, Waymo is still trying to enter new places like Boston, where it is asking state lawmakers to allow fully driverless vehicles.

Former Meta Employees Launch Sprout, an Adorable Humanoid Robot for Developers
Fauna, a startup founded by former Meta employees, has emerged from stealth to present its new humanoid robot platform, Sprout, designed specifically for developers, researchers, and institutions rather than industrial deployment. Sprout aims to fill a long-standing gap in robotics by offering a more accessible, flexible, and relatively affordable humanoid system that supports experimentation with AI and real-world robotics.

Waabi raises $1B to advance autonomous trucks and robotaxis
Waabi has raised $750 million in a Series C funding round—the largest ever in Canada—to speed up the launch of its autonomous trucks and move into robotaxis. Waabi says its AI platform can power both trucks and passenger vehicles using the same core technology. Additionally, the company secured further investment from Uber, which will provide milestone-based funding, and both companies plan to deploy at least 25,000 autonomous vehicles.

▶️ Atlas Airborne (1:37)

In this video, Boston Dynamics shows off how natural and human-like Atlas’s movements are. The results are impressive, but they didn’t come easily, and there were a few accidents along the way.

▶️ World’s First: Unitree Humanoid Robot Autonomous Walking Challenge in −47.4°C Extreme Cold (0:44)

Unitree’s G1 humanoid robot has proven that it can not only operate in extremely cold snowfields, but also take over 130,000 steps to create a giant logo of 2026 Winter Olympics in snow.

▶️ TRON 1 Goes Cold | The First Bipedal Robot Skiing in -20°C Extreme! (0:48)

Thanks to LimX and their TRON 1 bipedal robot, we can now add skiing to the list of things robots can do.

▶️ What the iRobot Saga Teaches Us About Building Robots That Last (55:33)

Colin Angle, co-founder and former CEO of iRobot, talks about the rise of consumer robots, the failed Amazon acquisition, and what might come next for physical AI. He reflects on how his thinking has changed after 30 years in the industry, and why trust and creating real value are still the toughest challenges. He also shares what excites him about the future, from emotionally aware machines to smarter, more human-centred homes. It’s an honest, wide-ranging conversation about entrepreneurship, regulation, and what it takes to build robots that last.

🧬 Biotechnology

Automating Biology
In this article, describes life as a set of layered, self-replicating biological machines and argues that biology is ready for large-scale automation. Drawing on his experience across biology, AI, and engineering, he reframes “AI” as digital automation and explains how, together with physical automation like cloud labs, it could make biological research faster, cheaper, and more scalable. He also notes challenges around trust, flexibility, and rapid change, but ultimately presents automated biology as a moonshot that could unlock major scientific discoveries and advances in medicine and biomanufacturing.

Microbes could extract the metal needed for cleantech
The article explains how new biotechnology methods could help mining companies get more metals from lower-quality ore and old mines as demand for materials like nickel and copper grows. Startups are using microbes, genetic tools, and microbial by-products to improve metal extraction, building on techniques the industry has used for years. Although these approaches show promise, they face challenges in scaling up, proving reliability, and meeting the mining industry’s slow and cautious adoption process.

💡Tangents

Building Brains on a Computer
This article argues that full brain emulation—once seen as absurd due to the extreme cost and difficulty of mapping neural wiring—has become plausibly achievable within the next few decades thanks to breakthroughs like expansion microscopy, neuron “barcoding,” and AI-based tracing tools such as Google’s PATHFINDER. It explains that the main bottleneck is not computing power but acquiring enough high-quality neural wiring and activity data, and it outlines a roadmap from small-organism emulations to mice and eventually humans, with enormous but increasingly realistic budgets and timelines.

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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!"

This week, we take a closer look at emerging reports that OpenAI and Anthropic are preparing for IPOs, potentially as soon as this year.

Elsewhere in AI, Google is opening access to Project Genie, while OpenAI launches Prism — a free, AI-powered workspace designed for scientists. Meanwhile, Nvidia invests in CoreWeave and plans to invest further in OpenAI, even as reports emerge that its $100 billion partnership with OpenAI is on ice.

Over in robotics, Figure unveils Helix 02, its new model for controlling humanoid robots; iRobot emerges from Chapter 11 as a restructured Picea US subsidiary; and Nvidia, Mercedes-Benz, and Uber make progress on their robotaxi plans.

Beyond that, this week’s issue of Sync also includes new models from Qwen and Kimi, Dario Amodei’s The Adolescence of Technology essay, reports of a possible SpaceX, Tesla, and xAI merger, a hard look at humanoid robots and AI hype—and more!

Enjoy!

AI darlings race to IPO

For much of the past decade, Silicon Valley’s most valuable technology companies delayed public listings for as long as possible. Deep private capital markets allowed startups to scale without the scrutiny or short-term pressures of public investors. That, however, is now changing.

OpenAI and Anthropic are both laying the groundwork for initial public offerings. OpenAI is targeting the fourth quarter of this year, according to the Wall Street Journal, whilst Anthropic has engaged Wilson Sonsini Goodrich & Rosati, the law firm behind the Google and LinkedIn IPOs, to prepare for a potential listing as early as 2026.

At stake is not simply a race to market, but a test of whether the AI business model can withstand public scrutiny.

The economics of frontier AI

Unlike earlier generations of software startups, leading AI companies face exceptional capital demands. Training and running advanced models requires specialised chips, long-term cloud contracts, and dedicated data centre infrastructure. OpenAI plans to spend $115 billion by 2029. Anthropic recently announced a $50 billion infrastructure build-out with data centres in Texas and New York. Both companies are losing billions annually, with Anthropic expecting to break even in 2028—two years earlier than OpenAI.

Private funding has so far absorbed these costs, but each successive round has grown larger and more concentrated among a small group of strategic investors. OpenAI is now seeking more than $100 billion at a valuation of $830 billion. Meanwhile, Anthropic is negotiating a round that could value it above $300 billion. At this scale, private capital begins to strain.

Revenue, meanwhile, is growing fast. OpenAI hit $13 billion last year and expects to triple that in 2026. Anthropic reached an annual run rate of $8–10 billion by late 2025 and is projecting revenues approaching $26 billion this year. Yet unlike traditional software, AI margins remain tightly linked to compute costs. Revenue growth does not automatically translate into operating leverage—and unlike Facebook and Google at the time of their IPOs, today’s AI companies are losing substantial sums and must continually raise more.

Public markets may be the only option to raise capital at the scale these companies require.

Preparing for the public markets

OpenAI has taken the most visible steps toward a listing. In 2025, the company completed its transition from a nonprofit to a for-profit entity—a necessary prerequisite for any offering. It has since expanded its finance leadership and held informal discussions with investment banks. Sam Altman has acknowledged that public markets may be unavoidable, whilst expressing little enthusiasm for the role.

Anthropic has pursued a quieter but similar path, making a string of finance hires, including Krishna Rao, a former Airbnb executive who played a key role in that company’s 2020 IPO. Executives stress that no decision has been taken on timing. Yet OpenAI's leadership has privately expressed concerns that Anthropic could beat them to market. Whichever company lists first stands to capture investors eager for exposure to the generative AI wave.

What would listing mean

If OpenAI or Anthropic proceed, the consequences would extend beyond their own finances. Public companies must disclose information that remains opaque today: infrastructure obligations, cloud contracts, and the relationship between performance improvements and spending. This transparency would offer policymakers, competitors, and customers a clearer picture of how AI systems are financed and priced.

Just as earlier IPOs brought social media and cloud computing into the mainstream, AI listings could mark the sector’s transition from experimental technology to permanent infrastructure.

Whether 2026 becomes the year of the mega IPO depends on market conditions, company readiness, and investor appetite for loss-making businesses with extraordinary ambitions. But the preparations suggest that the era of AI as a purely private experiment may be coming to an end.

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🧠 Artificial Intelligence

China gives nod to ByteDance, Alibaba and Tencent to buy Nvidia’s H200 chips
China has approved ByteDance, Alibaba and Tencent to buy more than 400,000 of Nvidia’s H200 AI chips. As Reuters reports, the approvals come with conditions that are still unclear, and some companies say they may be too strict. The decision reflects Beijing’s aim to meet strong demand for advanced AI chips while continuing to encourage the use of Chinese-made alternatives.

Project Genie

Google DeepMind is opening access to Project Genie, an experimental AI tool that generates short, interactive game-like worlds from text prompts or images. It is powered by DeepMind’s Genie 3 world model, the Nano Banana Pro image-generation model, and Gemini. The system can maintain limited world consistency and interaction, but it remains a research prototype with notable constraints, including a 60-second session limit, inconsistent realism, basic physics, and strict content guardrails. Project Genie is currently available only to Google AI Ultra subscribers in the United States, and is intended primarily as a testing ground for DeepMind’s world-model research rather than a finished product.

The Adolescence of Technology
Dario Amodei's latest essay caused some stir in the AI community by suggesting that AI is going through an unstable “adolescent” stage, where its abilities are growing faster than society’s ability to control and guide it. The essay’s key themes are responsibility, caution, and urgency: he warns against both panic and ignoring the risks, pointing out that powerful AI could be misused, concentrate power, or disrupt society, while also offering major benefits. The overall message is that governments, companies, and the public must act quickly to build better rules, norms, and oversight so AI can develop safely before the consequences become too large to manage.

Introducing Prism
OpenAI launches Prism, a free AI-powered workspace designed to help scientists write and collaborate more easily. Built around GPT-5.2, Prism combines drafting, LaTeX editing, research support, and real-time teamwork in one online space. It is available now to anyone with a ChatGPT personal account and aims to make everyday research work simpler and more efficient.

Inside OpenAI’s in-house data agent
OpenAI’s in-house data agent is an internal AI tool that helps employees explore and analyse very large and complex datasets using everyday language. It is built using OpenAI’s core models and tools, and it reduces the time needed to find the right data, run queries, and produce useful insights. The agent uses strong contextual understanding, remembers previous questions, and follows existing access permissions, allowing it to give accurate, clear, and secure answers while working more like a helpful teammate than a traditional data tool.

Microsoft announces powerful new chip for AI inference
Microsoft has introduced the Maia 200, a new in-house AI chip designed to improve the speed, efficiency and cost of running AI models. Building on the Maia 100, the chip delivers significantly higher inference performance and aims to reduce power use while supporting increasingly large models. The chip is part of a wider trend of big tech companies creating their own hardware to rely less on Nvidia. Microsoft says Maia is already being used for its AI tools, including Copilot, with access now opening to developers and researchers.

Nvidia, Others in Talks for OpenAI Funding, Information Says
According to a report from The Information, OpenAI is in early talks to raise up to $100 billion at a valuation of about $750-830 billion. Nvidia is considering an investment of up to $30 billion. Microsoft may invest less than $10 billion, while Amazon could commit more than $20 billion. The planned new investments into OpenAI are in addition to the $30 billion that SoftBank is planning to commit, according to the report. Meanwhile, the Wall Street Journal reports that Amazon might invest up to $50 billion in OpenAI.

The $100 Billion Megadeal Between OpenAI and Nvidia Is on Ice
The Wall Street Journal reports that Nvidia’s plan to invest up to $100 billion in OpenAI and provide vast amounts of computing power has stalled after internal doubts emerged and negotiations failed to progress. The original deal was non-binding, and both companies are now reconsidering their partnership, possibly replacing it with a smaller investment. The delay is a setback for OpenAI as it tries to secure enough computing power for future growth, and it has also increased concerns about the cost and risk of its large-scale deals. Jensen Huang responded to the report by calling it “nonsense.”

Nvidia invests $2B to help debt-ridden CoreWeave add 5GW of AI compute
Nvidia has invested $2 billion in CoreWeave to accelerate the expansion of its AI data centre capacity to more than 5 gigawatts by 2030, deepening an existing partnership between the two companies. The deal shows strong support for CoreWeave, even as the company faces scrutiny over its large debt. For Nvidia, it is part of a wider effort to keep driving growth in the fast-moving AI industry.

Anthropic reportedly upped its latest raise to $20B
Anthropic is seeking to raise $20 billion in venture capital—double its original target—amid strong investor demand, according to the Financial Times. The funding round, expected to close soon, would value the company at around $350 billion.

Elon Musk’s SpaceX, Tesla, and xAI in talks to merge, according to reports
Rumours have emerged that three of Musk’s companies—SpaceX, xAI, and Tesla—are in early discussions over a possible merger, with reports suggesting one or more could ultimately be folded into SpaceX. Several scenarios are being considered, including a SpaceX–Tesla or SpaceX–xAI combination, potentially ahead of a planned SpaceX IPO. While no public statements have been made, recent corporate filings and significant cross-investments point to Musk’s broader aim of consolidating resources across his businesses.

Tesla to invest $2B in Elon Musk’s xAI
Tesla has confirmed it invested $2 billion in xAI, even though shareholders had earlier voted against the move. The company says the investment supports its plan to use AI in real-world products such as self-driving vehicles and humanoid robots, and will deepen collaboration between the two firms while helping to speed up its AI development.

Apple acquires Israeli audio AI startup Q.ai
Apple has acquired Q.ai, an Israeli startup working on AI technology for audio, for reportedly $1.6 billion. Apple did not say how it will use Q.ai’s technology but said the startup has worked on new applications of machine learning to help devices understand whispered speech and to enhance audio in challenging environments. Q.ai last year filed a patent application to use “facial skin micromovements” to detect words mouthed or spoken, identify a person and assess their emotions, heart rate, respiration rate and other indicators.

Zuckerberg teases agentic commerce tools and major AI rollout in 2026
Mark Zuckerberg says new Meta’s AI tools and models will start reaching users in the coming months after the company rebuilt its AI programme. He said Meta will focus on AI-powered shopping tools that help people find products based on their interests and activity. Meta believes its access to personal data will give it an advantage over competitors, and the company is increasing spending on AI infrastructure, even as some investors remain unsure how this investment will lead to profits.

Yahoo is adding generative AI to its search engine
Yahoo joins the AI-powered search club by launching Yahoo Scout. Currently in beta and built on Anthropic’s Claude, designed to synthesise information from the web and Yahoo’s own content with clear source links. Alongside this, Yahoo is rolling out an AI intelligence platform across its products, adding features such as email summaries, news takeaways, sports analysis, and personalised shopping and finance insights.

Yann LeCun’s new venture is a contrarian bet against large language models
Yann LeCun discusses in this interview his vision for the future of AI, not based on large language models but on world models. He also explains the importance of open models, why his new company, Advanced Machine Intelligence (AMI), is headquartered in Paris, what academia should be focusing its research beyond LLMs, and teases a potential announcement sometime in February.

Meet ‘Amelia’: the AI-generated British schoolgirl who is a far-right social media star
The British far-right has a new social media star. Amelia is a fictional schoolgirl who first appeared in a UK government-funded game designed to prevent extremism, but she has since been taken over online and turned into a far-right meme. The article explains how extremists have used AI tools to remake and share her image to spread racist messages, reach wider audiences and even make money through cryptocurrency, showing how quickly online projects can be misused on social media.

Researchers Are Using A.I. to Decode the Human Genome
AlphaGenome is a new AI system created by Google DeepMind to help scientists understand how changes in DNA affect how genes behave. It can predict whether mutations might wrongly turn genes on or off, which is important for studying diseases like cancer. Researchers say it is a powerful and advanced research tool, but it is not as groundbreaking as AlphaFold. Its predictions have limits and cannot yet be used in medical care, so scientists still need to confirm its results with laboratory experiments.

Legal AI giant Harvey acquires Hexus as competition heats up in legal tech
Harvey, a fast-growing legal AI company, has bought Hexus, a small startup that builds tools for product demos and guides. The Hexus team is joining Harvey, and its founder, Sakshi Pratap, will help lead engineering work to improve products for in-house legal teams. The move comes as Harvey continues to grow quickly, with a high company valuation, strong investor backing, and a large number of law firm clients around the world.

Kimi K2.5: Visual Agentic Intelligence
Kimi has released K2.5, which it describes as the most powerful open-source model to date—and it delivers on that promise. In terms of performance, Artificial Analysis placed Kimi K2.5 fourth overall, just behind GPT-5.2, Claude Opus 4.5, and Gemini 3 Pro, while ranking it as the highest-performing open model. Moreover, it is more efficient and cheaper to run than the top proprietary models. The main innovation in K2.5 is the introduction of an agent swarm, with up to 100 sub-agents executing parallel workflows across as many as 1,500 tool calls. If you want to learn more about Kimi K2.5, Caleb Writes Code has an excellent video on the subject.

Pushing Qwen3-Max-Thinking Beyond its Limits
Alibaba’s Qwen team has released Qwen3-Max-Thinking, its latest flagship reasoning model. The Chinese lab says that, according to its internal benchmarks, Qwen3-Max-Thinking demonstrates performance comparable to leading models such as GPT-5.2 Thinking, Claude Opus 4.5, and Gemini 3 Pro. However, independent benchmarks from Artificial Analysis place the new model roughly on the level of Grok 4, MiniMax-M2.1, DeepSeek V3.2, GLM-4.7, and Kimi K2.5. Qwen3-Max-Thinking is a proprietary model, as Alibaba has not released the weights. It is currently available through Qwen Chat and via the first-party API on Alibaba Cloud.

Mistral Vibe 2.0
Mistral is releasing Mistral Vibe 2.0, a major upgrade to its terminal-native coding agent, powered by the Devstral 2 model family. The new version brings custom subagents, multi-choice clarifications, slash-command skills, unified agent modes, and automatic updates.

Open Coding Agents: Fast, accessible coding agents that adapt to any repo
Allen Institute for AI (Ai2) has launched Open Coding Agents, beginning with its SERA (Soft-verified Efficient Repository Agents) models—a family of open-source coding agents that can be inexpensively fine-tuned to specific codebases for tasks such as code generation, review, debugging and maintenance. The approach lowers the barrier for small teams by offering fully open models, training data and code, along with straightforward integration with tools like Claude Code.

🤖 Robotics

Introducing Helix 02

Figure reveals Helix 02, a full-body humanoid control system that, according to the company, uses a unified neural architecture combining data from vision, touch, and proprioception sensors to control every joint and actuator in the robot. This is intended to enable human-like whole-body motion and long-horizon autonomy. The company says that with Helix 02, the robots can execute a four-minute, end-to-end autonomous task (like loading and unloading a dishwasher) with no resets, no teleoperation, and no human intervention, as well as reaching new levels of dexterity.

iRobot emerges from Chapter 11 as restructured Picea U.S. subsidiary
iRobot has emerged from bankruptcy after being acquired by Shenzhen Picea Robotics, giving the company a more stable financial base to focus on future products. The company will continue supporting its robot vacuums and operating from the United States as a privately owned business. To ease concerns about data privacy, iRobot has set up a separate US-based unit to protect customer data under the new ownership.

Nvidia, Mercedes-Benz Move Forward With Planned Robotaxis
Nvidia, Mercedes-Benz and Uber are advancing plans to launch robotaxi services in major cities, using Mercedes’ S-Class cars and Nvidia’s self-driving technology. The timeframe for the start of the service is still not publicly known.

▶️ Spot Cam 2 | Boston Dynamics (0:53)

Boston Dynamics launched Spot Cam 2, a new camera module that can be attached to Spot’s back. This second generation Spot Cam features a 4K pan-tilt-zoom camera with 25x optical zoom, an integrated radiometric thermal camera, and a 360 x 130º spherical camera. It also makes figuring out where Spot’s “face” is even harder.

Gartner predicts fewer than 20 companies will deploy humanoids at scale by 2028
Gartner says that although humanoid robots attract a lot of attention and investment, very few will be used in real workplaces before 2028. The technology is still expensive, limited, and hard to fit into existing operations, especially in busy supply chain and manufacturing settings. Instead, Gartner expects simpler, task-focused “polyfunctional” robots to be more practical and cost-effective for most companies.

▶️ The Hard Truth About Humanoid Robots and AI Hype (46:43)

Eric Danziger, the CEO of Invisible AI, shares the tougher side of robotics that shiny demos fail to show. In this conversation, he explains why demos are easy while deployment is hard—and why computer vision, not humanoid bodies, may be the real bottleneck in physical AI. He also discusses why manufacturing automation moves more slowly than software, how hype cycles distort expectations, and what “real robotics” actually means on the factory floor.

▶️ Oli Demonstrates the World’s First Scalable Autonomous Deployment (1:05)

LimX Dynamics has showcased what it claims is the world’s first practical autonomous deployment of humanoid robots, with 18 full-size Oli units independently emerging from shipping containers, walking, coordinating their movements, and performing a synchronised routine without human input. LimX highlights advances in multi-robot coordination powered by the company’s new COSA operating system, which integrates perception, reasoning, memory, and movement in a single framework.

🧬 Biotechnology

First AI-Generated Genomes: The Synthetic Biology Breakthrough of 2026
Scientists have successfully created the first genomes designed entirely by artificial intelligence. The AI was trained on huge amounts of DNA and used this knowledge to design new genetic material that worked inside living cells. Some of these AI-made viruses performed better than natural ones, especially in fighting drug-resistant bacteria. This breakthrough could speed up new medical treatments and biotechnology research, while also raising important safety and ethical concerns.

💡Tangents

Amazon to Shut Down All Amazon Go and Amazon Fresh Stores
Amazon is closing all Amazon Go and Amazon Fresh stores after deciding they did not offer a shopping experience that could grow successfully. The move affects 72 stores, some of which will become Whole Foods locations. The company will now focus on online same-day delivery and growing its Whole Foods business, including opening more than 100 new Whole Foods stores in the coming years.

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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!"

This week, we will explore Anthropic’s fourth Economic Index to see what it tells us about how AI is being used at work and its impact on productivity, skills and the labour market.

Elsewhere in AI, Claude got an updated constitution, which describes how the AI is expected to behave. Meanwhile, OpenAI made ChatGPT Go available globally, Sam Altman is reportedly in talks to raise $50 billion from Middle East investors, and Elon Musk seeks $134 billion in damages from OpenAI and Microsoft. In other news, Meta’s new AI models apparently show promising results, the behind-the-scenes look at Thinking Machines Labs and its latest drama, and China’s first advanced multimodal AI system fully trained on domestic AI chips.

Over in robotics, Agility Robotics’ CTO explains what it takes to deploy humanoid robots at scale, while Elon Musk promises Tesla Optimus robots to be available for purchase by 2027, and Airbus partners with UBTech to test humanoid robots at its assembly lines.

Other than that, this week’s issue of Sync also includes arguments for treating genetic therapies like surgeries, He Jiankui reveals his plans for genetically modified humans, two new open AI models, how “human” should a humanoid robot be, and more!

Enjoy!

Insights from the Anthropic Economic Index report

Anthropic Economic Index is a research initiative that tracks how artificial intelligence is being used in real economic activity. It analyses anonymised interactions with Claude to understand which tasks, occupations and regions are adopting AI, and how AI is augmenting or automating work. Rather than relying on predictions or surveys, the index provides an evidence-based view of AI’s evolving impact on productivity, skills and the labour market.

This week, Anthropic released its fourth Economic Index report, which offers a detailed look at how AI is being used in the real world. Based on an analysis of over one million Claude conversations from November 2025, the report reveals patterns about who’s using AI, how they’re using it, and what it means for the future of work.

The report analyses only Anthropic's data and does not include usage from OpenAI, Google, or other providers. Nevertheless, given that Anthropic reportedly commands 40% of the enterprise AI market (according to Menlo Ventures), it offers a significant window into AI's economic impact.

So what does this data reveal? The emerging picture is complex: AI adoption follows wealth, creates unexpected trade-offs between speed and reliability, and is beginning to reshape which skills matter in the workplace. Additionally, AI appears to be handling the more sophisticated components of jobs, raising questions about whether automation will lead to widespread deskilling or create new opportunities for specialisation.

AI adoption follows wealth

AI adoption strongly follows wealth. Globally, a 1% increase in GDP per capita correlates with a 0.7% increase in Claude usage. The US, India, Japan, the UK, and South Korea lead in overall adoption, whilst usage in lower-income countries lags significantly.

Within the United States, Claude usage has become noticeably more evenly distributed across states. Between August and November 2025, the inequality measure (Gini coefficient) fell from 0.37 to 0.32. If this trend continues, usage per capita could equalise across the country within 2–5 years—roughly 10 times faster than the diffusion of previous economically transformative technologies.

Workforce composition plays a crucial role. States with more computer and mathematical professionals show systematically higher usage, with the top five US states accounting for 50% of all usage despite representing only 38% of the working-age population.

Different uses for different incomes

How people use AI varies significantly by wealth. Whilst work remains the dominant use case globally (46% of conversations), lower-income countries show higher rates of educational usage. Wealthier nations, by contrast, use AI more for personal tasks.

This pattern suggests that early adopters in developing countries tend to be students or technical users focused on high-value applications like education and coding. In mature markets, usage diversifies towards casual and personal purposes as AI becomes more accessible. Despite this diversity, usage remains concentrated around specific tasks. The top 10 most common tasks account for 24% of conversations, with coding-related work dominating—representing 34% of Claude.ai usage and 46% of API traffic.

The complexity paradox

One of the report’s most striking findings concerns the relationship between task complexity and AI performance. More complex tasks requiring higher education levels see greater time savings. A university-level task might be completed 12 times faster with AI, compared to 9 times faster for high school-level work.

However, Claude’s success rate declines as complexity increases. Whilst basic tasks achieve roughly 70% success rates, university-level tasks drop to 66%. For the longest tasks—those taking humans five or more hours—success rates fall to around 45–50% on API calls.

Interestingly, success rates remain higher on Claude.ai (67% overall) compared to API usage (49%). This likely reflects the benefits of multi-turn conversation, where users can clarify, correct course, and iterate towards solutions.

Augmentation wins over automation

After a brief shift towards automation, augmentation has reclaimed its position as the dominant usage pattern on Claude.ai. In November 2025, 52% of conversations were classified as augmentation (up 5 percentage points from August), whilst automation fell to 45%.

This shift may reflect new product features—including file creation capabilities, persistent memory, and customisable Skills—that encourage more collaborative, human-in-the-loop interactions. API usage, by contrast, remains heavily automated at 75%, reflecting its programmatic nature and use in production workflows.

The deskilling effect

The report’s most provocative finding is that AI tends to handle the more skilled components of jobs. Tasks covered by Claude require an average of 14.4 years of education, compared to 13.2 years for the economy overall.

If AI adoption continues along these lines, many occupations could experience “deskilling”—where removing AI-covered tasks leaves behind lower-skilled work. The report provides concrete examples:

• Technical writers lose high-level tasks—analysing field developments, recommending revisions—whilst retaining lower-level work like drawing sketches and observing production.

• Travel agents see AI handle complex itinerary planning, leaving routine ticket printing and payment collection.

• Teachers lose grading, advising, and research tasks, whilst classroom delivery remains firmly human.

However, some occupations experience the opposite effect. Property managers and real estate managers see AI handle routine bookkeeping and record-keeping, allowing them to specialise in complex negotiations and stakeholder management—an upskilling effect.

Rethinking job exposure

The report introduces “effective AI coverage”—a new metric that accounts for both task coverage and success rates. According to Anthropic, this provides a more realistic picture of AI’s job-level impact than simple task counts.

Some occupations show surprisingly high effective coverage. Data entry keyers, for example, have only 2 of 9 tasks covered by AI, but these happen to be their most time-intensive work, resulting in high overall exposure.

By this measure, 49% of jobs now have AI usage for at least a quarter of their tasks, with about 4% reaching 75% coverage. But effective coverage often falls below raw task coverage, particularly for jobs where AI struggles with the most important or time-consuming responsibilities.

The productivity promise—with caveats

When factoring in current usage patterns, the report estimates that widespread AI adoption could boost US labour productivity growth by 1.8 percentage points annually over the next decade. This would return productivity growth to rates last seen in the late 1990s and early 2000s.

However, when adjusted for task reliability—accounting for the fact that Claude doesn’t always succeed—the estimate falls to 1.0–1.2 percentage points. Further adjustments for task complementarity (where bottleneck tasks constrain overall gains) could reduce the impact to 0.6–0.8 percentage points under conservative assumptions, or increase it to 2.2–2.6 percentage points if workers can easily specialise in AI-enhanced tasks.

These estimates reflect current capabilities. As AI continues to improve, both task coverage and success rates are likely to increase, potentially amplifying productivity impacts.

Two ways of using AI

The report reveals differences between how consumers and enterprises use AI.

According to the report, Claude.ai users:

• Engage in longer, conversational interactions (15 minutes average vs. 5 for API)

• Tackle diverse tasks: education (16%), creative work (11%)

• Achieve higher success rates (67% vs. 49%) through iterative refinement

Meanwhile, API customers:

• Use Claude overwhelmingly for work (74%)

• Focus heavily on coding (52% of tasks)

• Deploy increasingly for routine automation—administrative tasks rose from 10% to 13% between August and November, with growing use for email management, document processing, and scheduling.

What this means

The Anthropic Economic Index provides the most granular evidence yet of AI’s real-world economic impact. The findings suggest AI’s effects will be uneven, shaped by existing inequalities in income and education, concentrated in white-collar knowledge work, and dependent on which tasks prove to be bottlenecks versus opportunities for specialisation.

For policymakers, the implications are clear. Expanding access to AI alone won’t suffice to ensure broad-based benefits. Building the human capital that enables effective use—particularly in lower-income regions—will be essential. The near-perfect correlation between the education level required to write good prompts and the sophistication of AI responses underscores that knowing how to use AI effectively is itself a skill that requires development.

The full report is available here, with accompanying data at Hugging Face. You can also explore an interactive visualisation of key findings.

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🦾 More than a human

He Went to Prison for Gene-Editing Babies. Now He’s Planning to Do It Again
In this interview, WIRED spoke with He Jiankui, the Chinese scientist who became internationally known for genetically modifying human babies in 2018, about his future plans. Despite serving a prison sentence and facing widespread criticism, He maintains that gene editing can change the future. He defends his earlier work, claiming the children are healthy, and argues the technology is now ready to move forward. He explains that his current focus is on using embryo editing to prevent Alzheimer’s disease and describes plans to continue this research outside China due to legal restrictions.

Genetic Therapy Aims To Bring Hearing To Those Born Deaf
The article describes a new gene therapy called DB-OTO that could help some people born deaf to hear using their own ears, rather than relying on cochlear implants. The treatment delivers a working gene to the inner ear, allowing it to produce an important protein needed for hearing. In early trials, most patients showed clear improvements, and several no longer needed implants. Although still experimental, the therapy shows strong promise for treating certain types of genetic hearing loss.

🧠 Artificial Intelligence

Claude’s new constitution
Anthropic has published an updated version of Claude’s constitution, a document that sets out how the AI model is expected to behave. The revised text replaces a short list of principles with a more detailed framework, instructing Claude to prioritise four core values: being broadly safe, broadly ethical, compliant with Anthropic’s guidelines, and genuinely helpful—in that order. It expands on ethical decision-making and introduces clearer limits on prohibited topics such as bioweapons. The document also addresses uncertainty around AI consciousness and moral status, touching on questions of Claude’s identity and well-being. The full 80-page document can be found here.

Introducing ChatGPT Go, now available worldwide
OpenAI is launching ChatGPT Go, its a low-cost ChatGPT subscription plan, worldwide. Priced at $8 per month in the US (with localised pricing elsewhere), the plan offers affordable access to GPT-5.2 Instant with higher usage limits, longer memory, and more image and file uploads than the free tier, sitting alongside the existing Plus and Pro subscriptions. ChatGPT Go will have ads.

Musk Seeks Up to $134 Billion Damages From OpenAI, Microsoft
Elon Musk is seeking between $79 billion and $134 billion in damages from OpenAI and Microsoft, claiming he was misled after the company moved away from its nonprofit roots and partnered with Microsoft. The case is set to go to a jury trial, with Musk arguing he deserves a share of OpenAI’s current value because of his early support. OpenAI and Microsoft deny the claims and say the lawsuit is an attempt to harass a competitor.

China’s Zhipu Unveils New AI Model Trained on Huawei’s Chips
Chinese AI startup Zhipu says its latest model, GLM-Image, is China’s first advanced multimodal AI system fully trained on AI chips made by Huawei, marking an important step for China’s domestic AI industry and its drive to reduce reliance on US technology.

DeepMind CEO Says Chinese AI Firms Are 6 Months Behind the West
Demis Hassabis said Chinese AI companies are about six months behind the top Western labs, arguing that while they are increasingly effective at catching up, they have yet to demonstrate innovation beyond the technological frontier. He said the strong reaction to DeepSeek’s R1 model was exaggerated, calling it impressive but not truly breakthrough. Additionally, Hassabis noted that US restrictions on advanced chips have limited China’s progress, even as some of those barriers are now starting to ease.

The Messy Human Drama That Dealt a Blow to One of AI’s Hottest Startups
The Wall Street Journal takes us behind the scenes at Thinking Machines Labs and its latest drama, which resulted in three of its founders leaving the company and rejoining OpenAI. It is a story of internal disagreements, strained working relationships and competing ideas about the company’s direction, which ultimately led to a major leadership break and shows how fierce the battle for AI talent has become.

OpenAI’s Altman Meets Mideast Investors for $50 Billion Round
Bloomberg reports that Sam Altman has been meeting with major Middle Eastern investors to secure funding for a new OpenAI investment round that could raise $50 billion or more. The talks are still early and could value the company at up to $830 billion. The funding would help OpenAI cover huge AI infrastructure costs as it faces growing competition from rivals such as Google and Anthropic.

Meta’s new AI team delivered first key models internally this month, CTO says
Meta’s new AI lab has produced its first internal models, which the company’s CTO Andrew Bosworth said show strong promise, though they are still unfinished. Speaking in Davos, he explained that the work is still in its early stages, and more development is needed before the public can use the technology.

Elon Musk’s xAI activates world’s first gigawatt-scale AI training cluster
xAI has switched on its new Colossus 2 supercomputer, making it the first AI system in the world to run at a gigawatt scale, with plans to grow even larger soon. The system is used to train the company’s Grok AI models and was built very quickly compared with rivals.

Apple is Fighting for TSMC Capacity as Nvidia Takes Center Stage
reports that TSMC’s centre of gravity is moving away from Apple towards AI chip customers like Nvidia, as demand for artificial intelligence hardware grows much faster than smartphones. This shift means Apple no longer has guaranteed priority at TSMC and now has to compete for manufacturing capacity.

Introducing Community Benchmarks on Kaggle
Kaggle has launched Community Benchmarks, a new feature that allows the global AI community to create, run and share custom model evaluations that better reflect real-world use cases. Instead of relying on simple accuracy scores, it supports more realistic tests to measure model performance in tasks such as reasoning, coding and tool use, with the main goal of helping developers better understand how models perform in real-world situations.

FLUX.2 [klein]: Towards Interactive Visual Intelligence
FLUX.2 [klein] is a new family of open image models from Black Forest Labs designed for fast, high-quality image generation and editing. The models combine text-to-image and image editing in one system, run in under a second on consumer hardware, and are built for real-time creative and developer workflows without sacrificing visual quality.

Qwen3-TTS
Qwen3-TTS is a series of new open text-to-speech and voice cloning models developed by the Alibaba Cloud AI team. According to the Qwen team, the new model supports stable, expressive, and streaming speech generation, free-form voice design, and vivid voice cloning. Qwen3-TTS comes in two variants—0.6B and 1.7B—and is available on GitHub and on HuggingFace.

Introducing Furiosa NXT RNGD Server: Efficient AI inference at data center scale
Furiosa, a South Korean chip manufacturer which Meta tried to acquire and failed to do so, announced the launch of its first branded AI inference system, the NXT RNGD Server, designed to run modern AI workloads efficiently inside existing data centres. The company said the server is easy to deploy, uses less power than typical GPU systems, and has already been tested by enterprise customers, including LG AI Research.

YouTube will soon let creators make Shorts with their own AI likeness
YouTube will soon allow creators to use AI versions of themselves in Shorts. The platform is expanding its AI tools while giving creators more control over how their likeness is used and protecting against misuse. YouTube is also working to reduce low-quality AI content and plans to add new Shorts features, including image posts.

The AI data center deals that no one can verify
We hear almost every other week about new multi-billion-dollar AI infrastructure deals. This article examines how concrete those announcements really are. It explains that, unlike traditional infrastructure, these deals lack clear definitions, standard measurements, and transparent contracts. As a result, markets often treat headline figures as firm commitments, even though many may represent uncertain or optional plans rather than guaranteed spending.

🤖 Robotics

Your First Humanoid Robot Coworker Will Probably Be Chinese
This article gives us a glimpse of the Chinese robotics industry by exploring how fast China is developing humanoid robots, from lively demonstrations at a major AI conference in Shanghai to advanced research labs in Beijing. It highlights how companies like Unitree are outpacing Western rivals through low costs, fast iteration, and tightly integrated manufacturing, while government-backed research groups push toward a “ChatGPT moment” for robots.

▶️ What It Really Takes to Deploy Humanoid Robots at Scale | Agility Robotics CTO (27:18)

Pras Velagapudi, CTO of Agility Robotics, shares what it takes to bring humanoid robots from research labs into production environments. He shares hard-earned lessons from deploying Digit, Agility’s humanoid robot, in live logistics and manufacturing facilities. He also explores why “cool videos” are only the tip of the iceberg, and what actually matters when customers are measuring ROI, reliability, safety, and performance at scale.

Airbus to test China’s battery-swapping humanoid robots in aircraft assembly
UBTech, a Chinese robotics company, has signed a deal with Airbus to supply its Walker S2 humanoid robots for use in aircraft manufacturing. The partnership will test the robots in complex factory environments and marks an important step in UBTech’s expansion outside China.

Miami, Your Waymo Ride Is Ready
Waymo has begun offering its fully autonomous ride-hailing service to the public in Miami, with early access being rolled out to nearly 10,000 signed-up residents. The service is available in neighbourhoods such as Wynwood, Brickell and Coral Gables, with plans to expand to Miami International Airport. Miami becomes the sixth city in the US where Waymo offers commercial robotaxi service, with many more planned to launch soon.

Tesla discontinues Autopilot in bid to boost adoption of its Full Self-Driving software
Tesla has discontinued its Autopilot driver-assistance system as it pushes customers towards its more advanced Full Self-Driving (Supervised) software, which will now be offered only through a monthly subscription. The change follows a California ruling that found Tesla had deceptively marketed its driver-assistance features. New vehicles will include only basic cruise control.

Elon Musk: Tesla will sell humanoid robots by end of 2027
Elon Musk said Tesla aims begin selling its Optimus humanoid robots to the public by the end of 2027, once they are safe and reliable enough. The robots are already doing simple jobs in Tesla factories, and Musk believes they could eventually help with many everyday tasks. However, major technical challenges remain, and Musk warned that production will begin slowly because most of the technology is new.

▶️ Humanoid x Siemens | Testing Humanoid Robots in Industrial Logistics (3:07)

Humanoid, a London-based humanoid robotics startup, shows in this video how its HMND 01 humanoid robots are being tested at a Siemens factory. The robots autonomously picked, transported, and placed totes in a live production environment during a two-week on-site deployment at the factory. The company says that HMND 01 has successfully completed proof of concept in industrial logistics.

How Human Should Your Humanoid Be?
How “human” should a humanoid robot be? In this post, compares the pros and cons of human-like robots with more unusual, non-human designs. Using Boston Dynamics’ new Atlas as an example, he explains that while human shapes can help with training and usability, they are not always the most efficient choice. The article suggests that future humanoid robots may keep the benefits of legs and arms without needing to look like humans at all.

Robot learns to lip sync by watching YouTube
Researchers have developed a robot that can learn realistic lip movements for speech and singing by observing itself and humans. Using a flexible face with many small motors and AI trained on mirror experiments and YouTube videos, the robot can match lip movements to sounds in different languages. While the movements are not perfect yet, the work brings robots closer to looking and communicating more naturally with people.

🧬 Biotechnology

Operating on DNA is more like surgery than medicine
New gene-editing tools could allow doctors to fix the genetic causes of rare diseases, but current rules make these treatments too slow and expensive to develop for very small numbers of patients. Instead of treating each genetic fix like a new drug, the article suggests regulating genetic treatment more like surgery. By approving expert centres, proven tools and safe processes, this approach could make personalised genetic care possible for patients who currently have no treatment options.

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It’s a rather big issue this week. We will take a closer look at Claude Cowork—Claude Code for the rest of your work, as Anthropic describes it. Other than that, it feels like everyone was waiting until the second week of the year to make announcements and releases.

Apple has confirmed that it will use Gemini to power its AI features, including an upgraded Siri. OpenAI has officially confirmed that ads are coming to ChatGPT. Meanwhile, Anthropic—apart from launching Claude Cowork—has introduced Claude for Healthcare and Labs, and cracked down on unauthorised usage of Claude by third parties. We also have Google launching tools for agentic commerce and Personal Intelligence in the Gemini app, as well as an update to the recent Grok controversy.

Over in robotics, Walmart and Wing plan to expand their drone delivery service by adding it to 150 more Walmart stores over the next year. 1X has launched a new world model for robotics, and Skild AI has raised about $1.4 billion to build a brain for general-purpose robots.

This week’s issue of Sync also features a new company founded by a CRISPR pioneer, Nvidia investing in Eli Lilly, OpenAI investing in Sam Altman’s neurotech startup, why 2026 may be the year of mega IPOs, and more!

Enjoy!

Claude Cowork reports for work

Claude Code reimagined how we write code. For many, this tool transformed the way they create software. Some went further and started applying Claude Code to tasks beyond coding. Anthropic noticed that and created Claude Cowork—Claude Code for the rest of your work, as the company calls it.

Claude Cowork works in the same way as Claude Code. You give it access to your computer, define the task, and then Claude prepares a plan on how to solve it, presents it and, once accepted, executes it. Unlike Claude Code, Cowork is designed with non-technical people in mind. Instead of working with a command line interface, which can be intimidating for many people, Anthropic's new product comes with a graphical interface that is easy to use and understand.

What Claude Cowork can do

According to Anthropic, Claude Cowork excels at tasks that involve file management and knowledge work. You can point Claude at folders on your machine, and it can read, create, edit, and organise files directly. This includes organising downloads, batch renaming files with consistent patterns, and converting file formats.

Beyond simple file operations, Cowork can also handle more complex tasks. It can create Excel spreadsheets with working formulas and conditional formatting, generate PowerPoint presentations from rough notes or meeting transcripts, and produce polished reports from scattered information. The tool can also synthesise research across multiple sources, extract themes and action items from meeting notes, and analyse your personal knowledge base to surface patterns and connections you might have missed.

For particularly complex work, Cowork can break tasks into subtasks and coordinate parallel workstreams using sub-agents. Each sub-agent starts with fresh context, avoiding the accumulation problems of long chat threads. This architecture allows Cowork to handle long-running tasks without hitting context limits or conversation timeouts. You can describe a task, step away, and come back to finished work.

Cowork becomes more powerful when paired with other Claude features. If you’ve set up Model Context Protocol (MCP) connectors, Cowork can access external information from your connected tools—whether that’s searching through Slack messages, pulling data from Google Drive, or accessing other services you’ve linked to Claude.

When combined with Claude in Chrome, Cowork gains the ability to complete browser-based tasks. It can read web pages, fill out forms, extract data from sites without APIs, and navigate across tabs.

The promise of automation

Tools like Claude Cowork can be very useful. Many people, including myself, have tasks that could be automated or wish they didn’t have to do them manually. This is where Cowork comes in. Instead of writing scripts, putting different tools together with orchestration software, or doing the work manually, Cowork can do it for you. At least that’s the promise.

I tested Cowork, and I can see myself using this tool. I started with the example task of cleaning up the desktop. Cowork did the job, although not exactly as I expected. For example, it put my notes on GPT-5.2 into a Gaming & Entertainment folder, when those notes have nothing to do with gaming or entertainment. Cowork did a better job when I tested it on my Obsidian notes. There was specific work I wanted to do around reformatting some of my notes. It performed very well here, saving me from writing a script to do that.

Early reviews have been generally positive, though with caveats. WIRED described it as "the first agent that has really clicked" after testing numerous disappointing AI agents. However, other testers note there's a learning curve—the asynchronous workflow requires unlearning chatbot habits and getting comfortable with handing off tasks rather than supervising every step.

Current limitations

As a research preview, Cowork has some limitations. It’s currently available only on the macOS desktop app and doesn’t sync across devices or to web or mobile apps. Sessions stay entirely on your machine, which means faster iteration and direct file access, but also means you can’t share chats or artefacts with others.

Several Claude features aren’t available in Cowork yet. You can’t use it within Projects, it doesn’t have Memory capabilities to retain information across sessions, and it’s not compatible with the GSuite connector. The desktop app must remain open while Claude is working—closing the app ends your session.

Usage is another consideration. Working with Cowork consumes significantly more of the usage allocation than standard chatting with Claude. Complex, multi-step tasks are compute-intensive and require more tokens to execute. If you’re a Pro subscriber hitting usage limits, you might find yourself running out of capacity more quickly when using Cowork extensively.

For enterprise users, there are additional constraints. Cowork activity isn’t captured in Audit Logs, Compliance API, or Data Exports, which means it shouldn’t be used for regulated workloads. There are also no role-based access controls available yet.

Security considerations

The biggest concerns around Cowork centre on security. Anthropic has built defences against potential security threats, but agent safety remains an active area of development.

Cowork runs in a virtual machine on your computer, which provides some isolation from your main operating system. You must explicitly specify which folders Claude can access—if you don't grant permission to a folder, Claude cannot see it. This controlled environment offers some security benefits, but Claude does have real access to the files you permit it to use.

Cowork can read, write, and permanently delete files in folders you grant it access to. While the tool asks for permission before taking significant actions, there's always some chance Claude might misinterpret your instructions. Similar AI agents have accidentally deleted important data—in one case, removing an entire disk, in another, deleting a production database.

Given these risks, Anthropic strongly advises being cautious about granting access to sensitive information like financial documents, credentials, or personal records. The company recommends saving backups of critical files and creating dedicated folders with non-sensitive information for Claude to work with.

When using Claude in Chrome for browser-based tasks, there are additional risks. Like most AI agents, Cowork is susceptible to prompt injection attacks—hidden messages in online content that attempt to trick the AI and deviate it from its intended tasks. Anthropic warns that hidden code in websites could potentially steal data, inject malware into your system, or take over your system if you grant browser access. Users need to be particularly careful about which websites they allow Cowork to interact with.

These security risks aren’t new with Cowork, but this might be the first time many users are working with an advanced tool that moves beyond simple conversation and can take actual actions on their computer. The combination of file system access and potential for destructive actions means users need to understand the risks before diving in.

Availability and future plans

At the moment, Claude Cowork is in research preview and available for Claude Pro and Max subscribers. Additionally, the new product works only on macOS. Anthropic is releasing it early because the company wants to learn what people use it for and how it could be improved. If this beta program for enthusiasts proves successful, we could see Cowork become more widely available and expand to Windows and other platforms. Anthropic has indicated plans to add cross-device sync and continue making safety improvements as they gather feedback.

Final thoughts

Overall, Claude Cowork is an interesting demonstration of what using AI tools could look like in practice. Claude Code has reimagined how people work with code, and Cowork has the potential to do the same for all kinds of knowledge work tasks. I wouldn’t be surprised if we see a similar tool coming from OpenAI or Google soon. While it has limitations and security concerns that users need to understand, it represents a step toward AI agents that can actually handle real work, rather than just generating text in a chat window.

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🦾 More than a human

OpenAI Invests in Sam Altman’s New Brain-Tech Startup Merge Labs
OpenAI has invested $252 million in Merge Labs, a new neurotech company cofounded by OpenAI CEO Sam Altman. Unlike implant-based systems such as Neuralink, Merge plans to use non-invasive methods like ultrasound and AI to read and influence brain activity, with a long-term goal of bridging biological and artificial intelligence to maximise human ability, agency, and experience.

🧠 Artificial Intelligence

Apple picks Google’s Gemini to run AI-powered Siri coming this year
Apple has entered a multiyear partnership with Google to use Gemini to power future AI features, including a long-awaited Siri upgrade. In a short joint statement, the companies said Google’s Gemini technology was chosen because it offers a strong base for Apple’s AI systems, while still running on Apple devices and its private cloud.

OpenAI to begin testing ads on ChatGPT in the U.S.
Ads are officially coming to ChatGPT. OpenAI announced that it will soon start testing ads in ChatGPT for adult free users in the US, while paid users will not see them. The ads will appear at the bottom of replies and will be clearly marked. OpenAI says ads will not affect answers, and it will not sell user data. OpenAI said ads will not influence answers, user data will not be sold, and feedback will be used to refine the experience.

Google Bets on AI-Based Shopping With New AI Agents for Retailers
Google has introduced new AI tools to help retailers use shopping and ordering agents as AI-driven commerce grows. Companies such as Kroger, Lowe’s and Papa Johns are testing the technology to personalise shopping, assist customers and make buying easier, while keeping control over their own platforms. Retailers can use the recently released Universal Commerce Protocol (UCP), which aims to standardise how AI agents interact with retailers’ systems, making it easier for automated assistants to browse products, check availability and complete purchases securely.

MiniMax, China’s second ‘AI tiger’ to go public, doubles in value in Hong Kong debut
MiniMax Group, the second of China’s so-called “AI tigers” to go public, had a strong first day on the Hong Kong stock market, with its shares doubling in price and valuing the AI startup at about $13.7 billion after raising $616 million to fund research.

Anthropic announces Claude for Healthcare following OpenAI’s ChatGPT Health reveal
Anthropic has introduced Claude for Healthcare, a new set of AI tools for healthcare providers, insurers, and patients. Like its rival ChatGPT Health, revealed last week, the product can sync health data from phones and wearables without using that data for training, but Anthropic says Claude offers more advanced features. While concerns remain about AI errors in medical advice, and both companies urge users to consult professionals, Anthropic sees strong demand as millions already use large language models to discuss their health. Meanwhile, following an investigation by The Guardian, Google disabled AI Overviews for certain health-related queries.

Gemini introduces Personal Intelligence
Google introduces Personal Intelligence, a new feature for Gemini that lets users connect Google apps like Gmail and Photos to get more helpful, personalised answers. Available as a beta in the US, it allows Gemini to find details from emails, photos and searches, suggest ideas, and help with everyday tasks, while giving users full control over what data is connected and keeping privacy and security at the centre.

Musk’s X to block Grok AI tool from creating sexualised images of real people
The latest Grok controversy continues as pressure grows on Elon Musk’s AI tool after it was used to create sexualised and fake images without consent. Since Monday, California’s attorney general has ordered xAI to stop allowing such content, while campaigners have called on Apple and Google to remove Grok from their app stores. In the US, Ashley St. Clair, the mother of one of Musk’s children, has filed a lawsuit claiming Grok generated explicit fake images of her and that the company failed to act. In the UK, the government has welcomed X’s move to block Grok from creating sexualised content of real people, although Ofcom’s investigation remains ongoing under new laws criminalising such material.

Microsoft on track to spend $500 million per year on Anthropic AI
According to a new report from The Information, Microsoft is deepening its ties with Anthropic, with spending on the company’s AI expected to reach about $500 million a year. Alongside this increased spending, Microsoft is also investing in Anthropic and integrating its AI into key products such as GitHub Copilot and Microsoft 365 Copilot.

Mira Murati’s startup, Thinking Machines Lab, is losing two of its co-founders to OpenAI
Mira Murati’s startup, Thinking Machines Lab, has seen two of its co-founders leave the company and return to OpenAI less than a year after it was founded. The departures come despite the startup raising $2 billion and being valued at $12 billion.

Taiwan will invest $250 billion in U.S. chipmaking under new trade deal
The United States and Taiwan have agreed on a trade deal that will see Taiwanese chip companies invest at least $250 billion in building semiconductor factories in the US, supported by loan guarantees from Taiwan. In return, the US will cut tariffs on Taiwan and offer exemptions for certain products, encouraging firms such as TSMC to expand US production while still operating in Taiwan. The deal aims to strengthen US chip manufacturing, reduce supply risks, and improve certainty for the semiconductor industry.

China blocks Nvidia H200 AI chips that US government cleared for export
China has blocked shipments of Nvidia’s H200 AI chips despite US approval for their export, according to a Financial Times report citing informed sources, though Reuters said it could not independently verify the claims. Chinese customs officials reportedly halted the chips at the border, prompting suppliers to pause production, while authorities also warned domestic firms against buying them. The situation adds to confusion in US–China tech relations, as the US government has imposed a 25% tariff on the chips by requiring them to pass through the US before delivery, a move that has further complicated sales to China.

Meta Unveils Sweeping Nuclear-Power Plan to Fuel Its AI Ambitions
Meta has signed major deals to secure nuclear power in the US to meet the soaring electricity demands of its AI data centres. The company will help fund new nuclear reactors and buy electricity from existing plants in Ohio and Pennsylvania. Although financial terms were not disclosed, the agreements are among the most ambitious yet between technology firms and nuclear providers, and aim to bring new power online by the early 2030s.

Anthropic cracks down on unauthorized Claude usage by third-party harnesses and rivals
Anthropic introduced new safeguards to stop third-party tools from pretending to be its official Claude Code client, which cut off tools like OpenCode that let users run heavy automated coding on flat-rate subscriptions. It also separately blocked xAI from using Claude models through Cursor, saying this broke its commercial rules. Many developers reacted negatively, saying the changes broke their workflows and felt unfriendly to customers, while others supported Anthropic, arguing it was fair to stop misuse of subscriptions and protect its systems.

Anthropic: Introducing Labs
Anthropic is launching Labs, a new team focused on testing and building early, experimental products that explore what Claude can do. The goal is to try ideas with users, learn what works, and then turn the best ones into reliable products.

Introducing the Raspberry Pi AI HAT+ 2: Generative AI on Raspberry Pi 5
Raspberry Pi has launched the AI HAT+ 2, a $130 add-on for Raspberry Pi 5 that enables local generative AI. It uses the Hailo-10H accelerator, includes 8GB of on-board memory, and delivers up to 40 TOPS of performance. According to Raspberry Pi, it can run LLMs, VLMs, and other GenAI workloads fully offline, keeping latency low and data private. However, the real-world performance is a bit mixed, as Jeff Geerling has found out in his review.

Anthropic invests $1.5 million in the Python Software Foundation and open source security
Anthropic has entered into a two-year partnership with the Python Software Foundation and contributed $1.5 million to support Python and its community. The funding will improve security for Python and PyPI, help protect users from malicious packages, and support the PSF’s ongoing work maintaining the language, infrastructure, and community programmes.

Games Workshop bans use of AI in its designs
Games Workshop has banned staff from using AI to create content or designs, saying it wants to protect the ideas and artwork behind its Warhammer games. In doing so, the company echoes the Imperium of Man from the Warhammer 40,000 universe, which also forbids the use of artificial—or “abominable”, as it calls it—intelligence.

OpenAI Partners with Cerebras to Bring High-Speed Inference to the Mainstream
OpenAI and Cerebras have announced a multi-year partnership to deploy 750 megawatts of Cerebras’ wafer-scale systems for AI inference starting in 2026. The capacity will come online in multiple tranches through 2028. While the companies did not disclose the deal’s value, the Wall Street Journal reports it is worth around $10 billion. Sam Altman is also a personal investor in Cerebras, and the two companies previously explored a potential partnership as early as 2017.

Global AI computing capacity is doubling every 7 months
Epoch.AI's new research has found that the total available computing capacity from AI chips across all major designers has grown by approximately 3.3x per year since 2022. Nvidia AI chips currently account for over 60% of total compute, with Google and Amazon making up much of the remainder.

▶️ Top 10 largest AI Datacenters in 2026 (11:49)

In this video, SemiAnalysis lists the top 10 AI data centres of early 2026, based on their power consumption and installed hardware. The video gives a clear sense of the scale of AI data centre build-up and just how massive these facilities are.

The Data Center Resistance Has Arrived
This article highlights growing opposition to AI data centre development in the US, with local communities raising concerns about rising electricity bills, heavy use of water and land, and limited benefits for residents. New research shows that this pushback has delayed or stopped tens of billions of dollars’ worth of projects and is increasingly influencing local politics and elections.

World ‘may not have time’ to prepare for AI safety risks, says leading researcher
David Dalrymple, an AI safety expert at the UK’s Aria agency, has warned that AI is advancing so quickly that there may not be enough time to make it safe. He says machines could soon perform most valuable work better and more cheaply than humans, while governments and the public underestimate the risks. Dalrymple argues that advanced AI systems cannot be trusted to be reliable and could cause serious economic and security problems unless urgent action is taken to control and reduce their potential harms.

Dutch couple’s marriage annulled due to ChatGPT speech
Another reminder to double-check responses from AI chatbots. A Dutch court annulled a couple’s marriage after the officiant used a light-hearted, ChatGPT-generated speech that did not meet legal rules. Even though the couple said “I do” and intended to be married, the ceremony did not include the required promise to take on marital duties. Because of this, the judge ruled that the marriage was not legally valid and ordered it to be removed from the city registry.

🤖 Robotics

Wing is bringing drone delivery to 150 more Walmart stores
Walmart and Wing plan to expand their drone delivery service by adding it to 150 more Walmart stores over the next year, with more than 270 drone delivery locations planned by 2027. Wing, which is part of Alphabet, has seen rapid growth and strong repeat use, while Walmart says drone delivery helps customers get last-minute items quickly. The service will roll out in several major US cities and will operate under US aviation rules, alongside Walmart’s partnerships with other drone companies.

Neo humanoid maker 1X releases world model to help bots learn what they see
1X has introduced a new physics-based AI system, the 1X World Model, designed to help its Neo humanoid robot learn new real-world tasks by combining video with prompts, even without prior training examples. While CEO Bernt Børnich claims this marks the beginning of self-teaching robots, the company acknowledges current capabilities are limited to simple household actions, though these are seen as an early step towards more complex behaviours as Neo prepares for a home release.

Humanoid and Schaeffler Announce Strategic Technology Partnership
Humanoid, a UK-based robotics and AI company, and Schaeffler have announced a five-year partnership to bring hundreds of humanoid robots into Schaeffler’s factories to support industrial automation. The first test deployments are planned for 2026–2027 to check performance, safety, and how well the robots work with existing systems, before wider rollout. The agreement also covers the supply and joint development of robot actuators, as well as data collection and skill training to improve robot capabilities for real industrial environments.

Robotics Startup Skild AI Valued Above $14 Billion in New Funding Round
Skild AI has raised about $1.4 billion in new funding, valuing the company at more than $14 billion—over three times its value seven months ago. Founded in 2023, Skild AI is building a general-purpose AI “brain” that helps robots learn and adapt to many tasks and environments by watching and practising, rather than being programmed for one job. Skild plans to use the funding to expand and train its software and deploy more robots across areas such as healthcare, warehouses and construction.

🧬 Biotechnology

Crispr Pioneer Launches Startup to Make Tailored Gene-Editing Treatments
Aurora Therapeutics is a new company cofounded by gene-editing pioneer Jennifer Doudna that wants to make personalised gene therapies available to more people with rare diseases. Using a new FDA approval pathway, the company plans to treat many different genetic mutations with the same underlying CRISPR technology, starting with the disorder phenylketonuria. This approach could make it faster and easier to develop treatments and help more patients who currently have few options.

Nvidia to Invest $1 Billion in AI Drug Laboratory With Eli Lilly
Nvidia will invest $1 billion over five years in a new joint laboratory with Eli Lilly in Silicon Valley to help speed up drug research using AI. The partnership aims to reduce the time and effort needed for lab work by using AI to automate some tasks, bringing together Nvidia’s technology and Lilly’s scientific expertise to push forward AI-based drug discovery.

Aging Weakens Immunity. An mRNA Shot Turned Back the Clock in Mice.
Scientists have developed an experimental mRNA treatment that restores immune function in older mice. The therapy uses mRNA injections to increase the production of healthy T cells, which are vital for fighting infections and cancer. Treated mice responded better to vaccines and cancer therapies, suggesting that age-related immune decline may be partly reversible, although the research is still at an early stage.

When Google Locked the Door, Three MIT Students Picked the Lock
Three MIT researchers were not happy when DeepMind closed off AlphaFold 3, so they created Boltz—a free, open-source alternative—and started a company around it. The article explains how Boltz plans to make money by helping pharmaceutical firms use and customise the models rather than selling the software itself.

The golden age of vaccine development
This article follows the history of vaccines, from their beginnings as a lucky accident to today’s carefully designed technologies. It shows how growing knowledge of germs, immunity and genetics turned vaccines from fragile, hard-to-make products into safer, more precise and faster-to-develop tools. It argues that we are in the golden age of vaccine development and that the future holds even greater breakthroughs, but only if we continue to invest in them.

💡Tangents

2026 May Be the Year of the Mega I.P.O.
2026 is shaping up to be a big year for IPOs, with OpenAI, Anthropic and SpaceX all preparing to go public. If these listings go ahead, they would rank among the largest ever and deliver a huge windfall for Wall Street and Silicon Valley after years of weak offerings. The deals could give public investors long-awaited access to the AI boom, while also testing whether these fast-growing companies can justify their sky-high valuations.

MIT’s pills that communicate from stomach integrates biodegradable radio frequency antennas
Researchers at MIT have created a pill that can signal when it has been swallowed. The pill contains a small, biodegradable antenna that sends a safe radio signal from the stomach, then breaks down in the body, while a tiny chip passes naturally through the digestive system. This technology could help doctors check whether patients are taking important medicines, especially for long-term conditions, and reduce health problems caused by missed doses.

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