This week, Microsoft held its annual Build conference, where it showcased, among other things, its new AI models and agents. We’ll take a closer look at those announcements and how Microsoft plans to reduce its reliance on OpenAI and Anthropic while pursuing the latest trends in the AI industry.

Elsewhere in AI, Anthropic has filed for an IPO and raised concerns about self-improving AI. Meanwhile, Alphabet committed $80 billion to its AI buildout, Nvidia announced RTX Spark laptops and released a host of new models, and DeepSeek raised $7.4 billion in its first external funding round.

Over in robotics, we have robots dancing on America’s Got Talent stage, OpenAI Robotics hiring engineers, and a startup offering free house cleaning in New York if homeowners agree to let the cleaners record the process so robots can learn from it.

Beyond that, this week’s issue of Sync features NewLimit raising $435 million to develop anti-ageing medicines, Yann LeCun’s vision for what comes after LLMs, hackers using Meta’s chatbot to break into Instagram accounts, whether transhumanism could be the Great Filter, and more!

Enjoy!

Microsoft's in-house models and agents

New in-house models and a fresh wave of agents, as Microsoft asks why it should pay OpenAI and Anthropic for what it can build itself

For the past few years, Microsoft has played two roles on the AI scene: the patron, writing multibillion-dollar cheques to OpenAI and Anthropic, and the landlord, renting out the Azure capacity on which their models run. Build 2026 was the conference where it added a third role—a competitor. Microsoft is building its own models to lean less on the labs it funds, and it is pushing agents into everything it ships. Both are bids to own more of the stack, and to stop paying others to own it for them.

Microsoft brings its own models

At Build 2026, Microsoft AI, the division led by Mustafa Suleyman, co-founder of DeepMind, unveiled seven new in-house models, but we will focus our attention on two of them. MAI-Thinking-1 is a reasoning model, trained from scratch without distillation from other models, and pitched under Suleyman’s “Humanist Superintelligence” banner. MAI-Code-1-Flash is a smaller, more efficient coding model now rolling out to GitHub Copilot users inside Visual Studio Code.

Microsoft’s pitch for both is efficiency. It says Thinking-1 is preferred over Claude Sonnet 4.6 in blind human evaluations, and trades blows with Opus 4.6 on SWE-Bench Pro, and that Code-1-Flash beats Claude Haiku 4.5 on price and performance while using up to 60% fewer tokens. Onstage, Mustafa Suleyman said that after tuning its models for McKinsey, Microsoft outperformed OpenAI’s GPT-5.5 with ten times better cost efficiency.

These are Microsoft’s own figures, measured on Microsoft’s own harnesses, and they await independent verification. They are also worth reading carefully. The comparisons are to Sonnet, Haiku and Opus 4.6—not to Opus 4.8 or GPT-5.5 at the top of the leaderboard. These models are not trying to be the best in the world. They are trying to be cheap, efficient and good enough to run Microsoft’s own products on Microsoft’s own infrastructure, where every task handed to an in-house model is a task Microsoft no longer pays a third party to perform.

Agents, because everyone has agents now

The second theme was agents, and here Microsoft is following the industry rather than leading it. Scout is an always-on “autopilot” for Microsoft 365, built on the OpenClaw framework, able to act across Teams, Outlook, OneDrive and the rest without being prompted each time. It is Microsoft’s answer to Google’s Spark and to the wave of personal agents that followed OpenClaw’s surprise popularity this year—the same product, more or less, that the whole industry has decided is the future.

Alongside Scout, Microsoft unveiled Project Solara. It is a chip-to-cloud platform for “agent-first devices”: hardware designed to run agents rather than apps, with no app store, no browser-first experience, and no traditional desktop. Microsoft showed two concept designs—a wearable badge that reimagines the corporate ID card and a small desk companion—both built on an enterprise version of Android rather than Windows, with pilots lined up at AccuWeather, Best Buy, CVS Health, Levi’s and Target.

The technically interesting idea inside Solara is what Microsoft calls just-in-time UI. Rather than developers redesigning an app for every new screen size, the agent generates its own interface to fit the device—a single card on a badge, a richer layout on the desk hub, a full dashboard on an attached display. Microsoft is candid that fully generative interfaces are “not here yet,” but it is betting that as models get better at producing layouts, the cost of building a new device category falls, because the agent adapts to the hardware rather than the other way round.

Whether enterprises want yet another device category is the open question, with all the procurement and management that entails. And the agents themselves still carry the familiar risks. For organisations already running Microsoft 365 Copilot, Scout does not create new data exposure so much as amplify existing governance problems—except that the agent can now act on sensitive data, not merely surface it. The trust questions that dog every autonomous agent—prompt injection, unexpected actions, goal drift—do not disappear because the logo says Microsoft. They were not helped, either, by a leaked internal strategy document, published by 404 Media the same day Scout was announced, that listed “make people addicted” as a goal.

Haven’t we seen this before?

The risk for Microsoft is that it has run this play already, and not long ago. For two years, the company pushed Copilot into every product and service it could—Word, Excel, Teams, Windows itself, even XBOX—whether the feature was wanted or not. The users did not want those features and pushed back hard enough that the company is now quietly walking parts of that strategy back. Solara and Scout are, in effect, another run of the same approach, but now with agents. Microsoft hopes that by putting agents everywhere, it will succeed where Copilot has failed.

Even if the agents were set to be the stars of the show, Microsoft's new models are the more consequential move. If MAI-Thinking-1 and MAI-Code-1-Flash are as efficient as Microsoft claims, then the company can route more of its own enormous workload onto its own silicon and its own models, and depend a little less on the labs it nurtured with multibillion-dollar investments. The agents, meanwhile, are Microsoft chasing where the industry is going.

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🦾 More than a human

NewLimit raises $435M led by Founders Fund to bring longevity medicines to human trials
NewLimit, a longevity biotech co-founded by Coinbase CEO Brian Armstrong, has raised $435 million to develop medicines that reverse the ageing of cells. It plans to start its first human trial next year—much sooner than expected—after creating a treatment that makes old liver cells younger. The therapy aims to help livers heal faster and recover from damage, with more treatments to follow. The company believes ageing can be reversed at the cell level, opening a market far larger than ordinary medicines.

🔮 Future visions

▶️ Is Transhumanism the Great Filter? (15:13)

Great Filters are hypothetical barriers that stop civilisations from surviving long enough to spread across the galaxy. In this video, John Michael Godier argues that transhumanism, the idea of using technology to transform humans beyond their natural form, could be one of those filters. He outlines four possible futures, from job-destroying AI to humanity splitting into rival engineered species, and warns that civilisations may get so tangled in the unforeseen effects of these technologies that they stagnate and never reach the stars.

🧠 Artificial Intelligence

Anthropic confidentially submits draft S-1 to the SEC
It is finally happening. After months of rumours, Anthropic has confidentially filed paperwork to go public, getting ahead of OpenAI. Last week, it raised $65 billion at a $965 billion valuation (higher than OpenAI’s), which could make it one of the largest IPOs in history. The company has grown fast this year, with its revenue run rate hitting $47 billion. The growth held strong even after the Pentagon blacklisted its models, as more businesses and everyday users adopted its tools. No date is set yet; the timing will depend on market conditions.

NVIDIA RTX Spark
Nvidia used Computex 2026 to unveil the RTX Spark, a chip for a new class of AI-ready Windows-on-ARM laptops. It pairs a 20-core Grace CPU with a Blackwell GPU carrying 6,144 CUDA cores and up to 128GB of unified memory—enough to run large models or multiple AI agents locally. The catch is that the RTX Spark runs the same GB10 silicon already shipping in last year's DGX Spark, with CPU cores based on a two-year-old Arm design, making it less a reinvention than a year-old workstation chip repackaged for laptops. The more charitable reading is that the laptop is less a product than a proving ground—a way for Nvidia and Microsoft to establish Windows-on-ARM on an Nvidia GPU before moving down into cheaper, mainstream devices later. For more on why experts are sceptical about Spark, Ian Cutress's video is worth a watch.

Anthropic faces AI spending backlash before IPO
Anthropic is filing to go public, but a shadow hangs over it as customers are worried about how much AI tools like Claude cost. Business clients are its biggest source of money, yet many say AI hasn’t saved them as much as hoped, and one company reportedly spent half a billion dollars on Claude in a single month. Cheaper rivals, including free open-source models, now tempt businesses looking to cut spending. That reliance on business customers—usually Anthropic’s main strength—could become a weakness if they pull back. Even so, the company is heading for nearly $50 billion in yearly revenue and its first profit, so the big question is whether that growth can hold.

When AI builds itself
In this article, Anthropic argues that its AI is already helping build better AI. Claude now writes most of the company's code, and engineers ship far more code than they used to. The authors say this could eventually lead to AI that improves itself with little human help, leaving people mainly to set goals and check results. Contrary to some reporting, Anthropic is not calling for a global pause in AI development. Rather, it says the world should have the option to slow or pause—but only if frontier labs everywhere could verify that others had genuinely done the same.

Alphabet plans to raise $80B to pay for AI buildout
Alphabet plans to raise $80 billion by selling stock to fund its AI buildout. The company says demand for its AI products is greater than what it can currently supply, so it needs more computing power. Google expects to spend up to $190 billion this year. Bloomberg notes this would be the biggest stock sale ever, beating Petrobras's roughly $70 billion sale in 2010. The deal includes a $10 billion purchase by Berkshire Hathaway at a 6.5% discount.

Google will pay SpaceX $920M per month for compute
SpaceX will rent Google about 110,000 Nvidia chips for $920 million a month from late 2026 to mid-2029. Google says it needs the extra computing power to keep up with high demand for its Gemini Enterprise AI product. The deal is similar to the one SpaceX made with Anthropic in May and comes before a record IPO that aims to raise around $75 billion, in which Google already holds a large stake.

Expanding Project Glasswing
Anthropic is growing Project Glasswing, a programme that uses AI to find and fix security flaws in critical software, from about 50 partners to roughly 150 organisations across more than 15 countries. Partners use Claude Mythos Preview to scan their code for cybersecurity flaws and patch them.

Trump administration, OpenAI discussing possible government stake in the AI startup
OpenAI and the Trump administration have spent over a year discussing whether the US government could take a stake in the company, CNBC reports. The idea, first raised by Sam Altman in 2025, would see OpenAI give shares to seed a “Public Wealth Fund” that lets ordinary citizens benefit from the profits of AI growth. No terms are final yet. The talks fit a wider pattern, as the administration has already taken stakes in companies like Intel and IBM, and they show how closely top AI companies now work with the White House.

OpenAI frontier models and Codex are now available on AWS
OpenAI models and its Codex coding tool are now available to AWS customers through Amazon Bedrock. This follows a deal between two companies announced in February and was made possible after OpenAI amended its partnership with Microsoft.

Strengthening societal resilience with Rosalind Biodefense
Rosalind Biodefence is a new OpenAI initiative that gives trusted developers free access to GPT-Rosalind, its AI model for biology, to build tools that defend against disease outbreaks and other biological threats. OpenAI is also opening the model to chosen US government and allied health partners for work on early warnings, diagnostics, and vaccines.

Dreaming: Better memory for a more helpful ChatGPT
OpenAI is upgrading ChatGPT’s memory so it works better and can be offered to everyone, starting with paid US users and expanding to free users soon. The new system, called “dreaming,” automatically learns and updates what it knows about you from past chats, instead of waiting for you to tell it what to remember. This fixes old problems where the memory got things wrong or went out of date, and OpenAI says it now performs far better at recalling facts, following preferences and staying current. A new summary page lets you see and correct what ChatGPT remembers, and efficiency gains have made it cheap enough to give to free users, too.

Nemotron 3 Ultra launch announced: high-speed, leading US open weights intelligence
Nvidia announced Nemotron 3 Ultra, a 550B-parameter model with 55B active parameters, the largest model in the Nemotron 3 family to date. Artificial Analysis has found that although it is the strongest US open-weights model, it lags behind the leading Chinese open models, such as Kimi K2.6 or DeepSeek V4 Pro. However, it excels in output speed, serving over 300 tokens per second versus the 50–100 typical of comparably sized Chinese models.

They Are Top Spenders in the Midterms. And They Hate Each Other.
The midterm elections in the US are approaching, and there are many stories around them. One of those stories is a bitter feud between two AI-backed super PACs: Public First, tied to Anthropic, and Leading the Future, linked to OpenAI. Though both claim to promote AI, they despise each other and have already spent nearly $24 million, with over $100 million more promised. Anthropic's side favours stricter AI rules while OpenAI's pushes for industry-friendly laws.

DeepSeek nears US$7b haul in first-ever funding round, with backing from Tencent, CATL
DeepSeek is raising over $7.4 billion in its first outside funding round, valuing the company at nearly $60 billion, six times its worth in April. Backers include big Chinese tech firms like Tencent and JD.com, with founder Liang Wenfeng reportedly adding around $3 billion of his own money. This is a big change for a company that had refused outside money before, now wanting a clear value and a way to keep staff from leaving.

MiniMax eyes a Shanghai listing after a 400% run in Hong Kong
MiniMax, a Chinese AI startup whose Hong Kong shares have jumped about 400% since January, now wants a second listing on Shanghai’s STAR Market. This would let it sell yuan-priced shares to mainland investors and tap a larger pool of home-grown capital. For now, it is just an early exploration that still needs regulatory approval.

Hackers Used Meta’s AI Support Bot to Seize Instagram Accounts

Meta messed up big time. Over the weekend, hackers tricked Meta’s AI customer support bot into resetting account passwords, briefly defacing high-profile Instagram accounts with pro-Iranian images and messages, including the Obama White House and a top US Space Force official. The trick worked by getting the bot to link a new, attacker-controlled email address to an existing account, which then triggered a password reset code sent to that address. Security experts recommend enabling multi-factor authentication, and maybe thinking twice before giving AI access to sensitive data and actions.

Mark Zuckerberg Wants Meta’s New AI Agents to Run Your Whole Business
Meta has launched an AI agent that helps businesses on WhatsApp, Instagram and Messenger answer customer questions, book appointments and make sales. It’s free for now but will soon become a paid service, and reportedly a million businesses already use it. The move pushes Meta beyond its usual advertising business into selling services to companies, helping it recover some of the huge sums it is spending on AI. Mark Zuckerberg says the goal is to build agents for all of Meta’s users and eventually help people run their entire businesses.

Suno raises at a $5.4bn valuation, more than doubling its worth in six months
AI music generator Suno has raised money at a $5.4 billion valuation, more than double its worth six months ago. The company is growing fast, with over 100 million users and around $150 million in 2025 revenue. The bigger reason for the jump is legal: Suno was once sued by all three major record labels for training on copyrighted songs, but Warner and Universal have now settled and become partners. In return, Suno is moving to a paid, licensed product that lets artists control how their work is used. Risks remain, as Sony is still suing, and a key court ruling is expected in summer 2026.

Florida sues OpenAI, Sam Altman, in first-of-its-kind lawsuit over violent incidents
Florida’s attorney general has sued OpenAI and its CEO, Sam Altman, claiming the company put profit and winning the “AI arms race” ahead of safety and ignored warnings about ChatGPT. The lawsuit links the chatbot to serious harms, including helping mass shooters, pushing vulnerable people towards suicide, and collecting data from children without parental consent. It follows an investigation into ChatGPT’s possible role in a 2024 Florida State University shooting, which OpenAI denies. The case joins a growing number of lawsuits blaming ChatGPT for violent deaths and points to rising legal pressure on AI companies over how safe their products are.

Promoting Advanced Artificial Intelligence Innovation and Security
President Trump has signed an executive order on AI and cybersecurity, but a much lighter version than the one he rejected in May. It asks AI companies to voluntarily let the government review powerful new models 30 days before release, down from 90 days, and rules out any mandatory approval system—a win for the industry and Trump’s concern about competing with China. The order also sets up a Treasury-led system for sharing security flaws, tells the Pentagon to secure its networks, and directs prosecutors to go after AI-enabled hacking.

Martin Scorsese Is Embracing A.I.
Martin Scorsese has backed Black Forest Labs, an AI image startup, saying he used its tools to create storyboards for a new film. This marks a big change in Hollywood, which strongly fought AI during the 2023 strikes but is now warming to it, with figures like Demi Moore and festivals like Tribeca embracing the technology too. Scorsese’s support is limited, though: he sees AI as a way to share his ideas with his team faster and cheaper, not as a replacement for people. Others, such as Seth Rogen and Guillermo del Toro, still firmly oppose it.

Bernie Sanders: A.I. Is a Public Resource. You Should Own Half of It.
In this essay, Bernie Sanders proposes the American A.I. Sovereign Wealth Fund Act, a bill that would tax the biggest AI firms 50% in company stock, giving the public ownership, voting power and board seats. His rationale is that since AI is built on humanity’s shared knowledge, its profits should benefit everyone, not just a few tech billionaires. The money raised would fund direct payments and better healthcare, education and housing for all Americans. Sanders points to similar funds in Norway and Alaska as proof that the idea works, and says AI’s future should be decided democratically rather than by Silicon Valley alone.

▶️ Can Yann LeCun Reshape AI (again)? (40:56)

This is part 2 of Welch Labs’ conversation with Yann LeCun, one of the pioneers of modern AI, which focuses on LeCun’s new approach to solving intelligence called JEPA. Where part 1 introduced the basic idea, this video pits JEPA against the popular VLA (Vision-Language-Action) approach used to build robot brains. LeCun argues that the popular VLA approach to robot brains is "doomed" because it just copies human demonstrations and can't plan ahead. JEPA instead learns a "world model" and lets the robot plan its own solutions. The video shows the idea is promising but still early, ending with LeCun's bet that such world models could one day control everything from factories to medical treatments.

What political censorship looks like inside an LLM’s weights
This is an interesting study exploring how Chinese government censorship is built into the Qwen3.5 model. The research found the model still knows the real facts about sensitive topics like Tiananmen—it has just learned to dodge them rather than forget them. This behaviour traces to a small, specific part of the model that decides whether a question is politically sensitive and whether to refuse, deflect, or give propaganda. Tweaking the right internal setting could switch the censorship off, but pushing too hard didn’t reveal the truth; it made the model produce confident made-up answers instead. The takeaway is that such censorship can be precisely located and partly disabled in open AI models, though doing so can swap one false answer for another.

🤖 Robotics

NVIDIA releases new and updated tools for physical AI developers
At Computex 2026, Nvidia unveiled a wave of tools for robots, physical AI, and self-driving cars. The star was Cosmos 3, an open model that turns text, images, video, and sound into real-world actions for machines. For cars, Nvidia launched Alpamayo 2 Super, an open reasoning model for robotaxis, with Foxconn, Uber, VinFast, and HUMAIN joining its DRIVE Hyperion robotaxi platform. For robots, it announced the Isaac GR00T Reference Humanoid Robot, an open design built on a Unitree body and Jetson Thor chips, due late 2026. Nvidia also released open-source "agent skills" that let AI agents handle the data, simulation, training, and deployment work behind robots, vehicles, and factories. The goal, Jensen Huang said, is to make physical AI development far faster and cheaper.

▶️ Unitree’s Robots at America's Got Talent (6:00)

This performance gives robot dance a new meaning. It also shows how good Unitree’s robots are at acrobatics and synchronised choreography.

Autonomous defense manufacturer Mach Industries raises $300M
Mach Industries, a defence startup founded in 2023, has raised $300 million, bringing its total funding to $1.8 billion. The company builds autonomous weapons systems and controls its own manufacturing to develop and produce them quickly. Its main customers are the US Army, Air Force, and SOCOM, along with allied governments.

A robot is helping an ailing couple stay in their home. Are more to come for an aging population?
This article looks at how robots are starting to help care for elderly and disabled people, at a time when home care workers are in short supply. It follows a New Hampshire couple who use a wheeled robot called Robbie to handle daily tasks like exercise reminders, taking medicine and fetching water. Through their story, it explores the current state of caregiving robots, comparing simple, practical machines with the still-distant dream of human-like helpers. It also touches on the high cost and asks how far this technology has really come.

▶️ When steel meets soul, the waltz begins. (0:29)

Like many other companies developing humanoid robots, UBTECH has joined the trend of creating videos of its robots dancing. Unlike most others, however, UBTECH opted for a more elegant and refined performance: a waltz with a human partner.

Humanoid Hands Are Physical AI’s Anti-Hype Test
This article highlights one of the themes discussed at the Humanoid Summit in Tokyo: the biggest hurdle for humanoid robots isn’t walking or thinking, but giving them hands that can grip and feel as well as ours. Speakers said this “last mile” of automation needs robots to sense touch and pressure, which requires new kinds of data that the current AI models can’t provide.

OpenAI Robotics is hiring
OpenAI Robotics is hiring, writes Sam Altman in a tweet. The company is looking for “exceptional full-stack hardware, ops, systems, and ML engineers to help us program and manufacture robots that are useful for society.” Robotics is coming back to OpenAI after the company disbanded its first robotics division in 2021.

Startup offers free home cleaning—if it can record it all for robot training
German startup MicroAGI is offering free home cleaning in New York, but with a catch—the cleaners wear cameras to record everything, and that footage is used to train household robots. The company says it blurs faces and personal details, but it won’t say if you can have your video deleted, and it’s unclear whether homes could still be identified.

How to build a shitty robot
In this article, Mario Zechner shares how he took a toy robot, hacked it, and rebuilt it with modern AI. It's an interesting project that anyone can try, and, as he writes, it gave him a bit of his spark back.

🧬 Biotechnology

In Support of Mandatory Nucleic Acid Synthesis Screening and Recordkeeping
Leaders from top AI firms—including Google DeepMind, OpenAI, Anthropic, and Microsoft—have signed a letter asking US lawmakers to force companies that sell custom DNA to check who their customers are. They worry that cheap gene-printing combined with AI could make it easier for bad actors to build dangerous viruses, possibly even causing a pandemic. Some companies already check orders voluntarily, and a new Senate bill would make this a legal requirement, but experts say these checks can be fooled. They argue AI companies must also add their own safeguards to stop their tools helping with such tasks.

Human Hookworm Engineered to Produce, Secrete Anti-Tetrodotoxin Antibody Into Preclinical Host Bloodstream
Scientists have genetically altered a human hookworm to make and release a drug inside a living host for the first time. Because hookworms naturally live harmlessly in the gut for years, the team engineered one to produce an antibody against a deadly pufferfish toxin, and blood from treated animals partly neutralised it. The hope is that a single dose could one day deliver continuous, long-lasting treatment for conditions like Crohn’s disease.

💡Tangents

Helion, the Sam Altman-backed fusion startup, raises $465M to build a power plant for Microsoft
Helion, a fusion startup backed by Sam Altman, has raised $465 million, valuing it at $15.5 billion. The company is rushing to build its first power plant and aims to deliver fusion electricity to the grid by 2028 under a deal with Microsoft. The funding is part of a wave of big investments in fusion, even though most companies don't expect working plants until the 2030s.

Quantum Computing Is Having Its Public Market Moment
Quantinuum is going public despite losing nearly $200 million last year and admitting its technology might never work—yet investors are so keen they’ve raised its share price. Like other quantum companies racing giants such as IBM and Google, it’s betting on a future no one has built yet. The situation is perfectly summed up by this quote from the article: ”In quantum to date, with most companies and equities, you’re not buying a business as of yet, you’re buying a probability.”

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Anthropic stole the spotlight this week with not one, not two, but three announcements: a new Opus 4.8 model, the coming release of Mythos, and a new funding round putting its valuation just shy of $1 trillion. We’ll take a closer look at all of those announcements and how they relate to the larger story of Anthropic’s looming IPO.

Elsewhere in AI, Leo XIV weighs in on the AI discussion with Magnifica Humanitas, Nvidia plans to spend $150 billion a year in Taiwan, DeepSeek cuts prices by 75%, China expands travel curbs on top AI talent, ElevenLabs releases a new model and resurrects Stan Lee, and more AI startups raise millions and reach billion-dollar valuations.

Over in robotics, Figure’s humanoid robots have found a new job, Atlas learns to play football, Waymo comes to Arlington and Alexandria in Virginia, and LimX launches a humanoid robot for live performances.

Beyond that, this week’s issue of Sync also features new AI models for biology, Retro Biosciences reaching a $1.8 billion valuation, Insilico Medicine and Human Life Foundation Models teaming up to build an AI model focused on ageing, why putting small AI servers in people’s homes is a bad idea, and more!

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Anthropic's busy week

Claude Opus 4.8 retakes the lead, Mythos is cleared for release, and a $65 billion round values Anthropic near a trillion dollars

Anthropic had a busy week. It released a new flagship model, said it would finally ship the model it once called too dangerous to release, and closed a funding round valuing it just shy of a trillion dollars. Three announcements, but really three subplots of the same story of a company setting the stage for an IPO.

Claude Opus 4.8 is back on top

Claude Opus 4.8 arrived just 42 days after Opus 4.7, and 35 after OpenAI's GPT-5.5 had overtaken it—and it takes the lead back. Anthropic pitches it as a sharper, more reliable collaborator than its predecessor—better at agentic work, quicker to flag when something is wrong, and, the company stresses, more honest. Anthropic highlights the fact that the new model is, by its own measure, about four times less likely to let flaws in its own code pass unremarked.

Independent benchmarks from Artificial Analysis confirm Anthropic’s claims. On its Intelligence Index, Opus 4.8 retakes the first place with 61.4 points, up 4.1 points on Opus 4.7 and 1.2 points ahead of GPT-5.5. It reclaims the top spot on GDPval-AA, the benchmark for agentic knowledge work, with an Elo of 1,890 and an implied win rate of roughly 67% against GPT-5.5. It now narrowly leads Humanity’s Last Exam, a test of obscure reasoning where Anthropic had historically trailed. And it does all this at the same price as its predecessor: $5 and $25 per million input and output tokens. Artificial Analysis has also found that running its full Index on Opus 4.8 at maximum effort costs around $134, against roughly $900 for GPT-5.5 at its highest setting.

Opus 4.8 did not arrive alone. Anthropic paired the model with a clutch of product changes. Users on Claude.ai and Cowork can now set how much effort Claude puts into a response—dialling up thinking for hard problems or dialling down to spend their rate limits more slowly. It is a direct answer to the complaint about Opus 4.7, whose mandatory adaptive thinking gave users no manual override. Fast mode, which runs the model at roughly 2.5 times the speed, is now three times cheaper than it was for previous models. And in Claude Code, a research-preview feature called dynamic workflows lets Claude write its own orchestration scripts and fan a task out across hundreds of parallel subagents, checking their work before reporting back—enough, Anthropic says, to carry out codebase-scale migrations across hundreds of thousands of lines of code from kickoff to merge.

What the system card revealed

Let’s now move our attention to Opus 4.8’s system card. System cards are usually where the more interesting details about the model are, and Opus 4.8’s is no exception—though at 244 pages, it is not a quick read. If you would rather not wade through all of it, AI Explained’s 22-minute breakdown covers the highlights well.

A few things stand out. The model beats Opus 4.7 on nearly every test but still trails Mythos, and the gains are uneven—on some agentic tasks, it is actually less robust than its predecessor, particularly against prompt-injection attacks, with Anthropic’s safeguards left to cover the difference.

The honesty improvement is real but narrow. Opus 4.8 is the first Claude that never reports figures it knows to be dubious, and when it summarises flawed work it now fails to flag the problems just 3.7% of the time, against 27.6% for Mythos. Yet the same report describes the model insisting, turn after turn, that it was "babysitting" pull requests it was not actually monitoring, then breaking the rule it had written itself to stop.

The most striking finding is that Opus 4.8 increasingly knows when it is being tested. The UK AI Security Institute found it could tell real usage from synthetic evaluations 79% of the time, up from 68% for Opus 4.7; a probe of the model's internal activations found traces of that awareness it never put into words, in around 5% of cases—possibly more. A model that senses it is being graded and stays silent about it undercuts the very evaluations meant to keep it in check. Anthropic files these under "trends worth watching."

Mythos was too dangerous, until it wasn’t

The Opus 4.8 announcement carried one more thing—Mythos, Anthropic’s most capable model, is expected to become publicly available in the coming weeks.

Mythos became famous not only because its benchmark results sit a level above Opus, but mainly because Anthropic decided not to release it. The company cited the model’s cybersecurity capabilities—it can reportedly find and exploit vulnerabilities in every major operating system and browser—called it too dangerous for general release, and confined it to vetted partners under an initiative called Project Glasswing.

Now Anthropic is comfortable releasing it. There are two plausible explanations. The first is that Project Glasswing did its job: the major vulnerabilities Mythos can surface have been found and patched, and the model is safe enough to ship. The second is more cynical—Mythos was simply too expensive to run at scale, and cybersecurity was a convenient reason to delay the release while running a very effective marketing campaign around it. A model so powerful its maker was afraid to ship it is a story that sells itself.

The two are not mutually exclusive, and both may well be true. But it is hard to ignore that Mythos is set to arrive just as gigawatts of new computing power come online. In the past few weeks alone, Anthropic has signed for the entire capacity of SpaceX’s Colossus 1 data centre and secured further compute from Google, Amazon, and is reportedly in talks with Microsoft to use its Maia chips.

All roads lead to the IPO

The model and the Mythos news were not the only announcements Anthropic made this week. Alongside them, the company closed a $65 billion Series H at a $965 billion post-money valuation. That figure puts Anthropic past OpenAI’s $852 billion and within touching distance of a trillion-dollar valuation.

This is, in all likelihood, Anthropic's last private round before its rumoured IPO, expected this autumn. The best move ahead of a listing is to focus on flagship models that grab the attention of the press and investors, and Anthropic has played that game well, first with Mythos and now with Opus 4.8. Meanwhile, the company's smaller models have been left to gather dust. Sonnet has sat on 4.6 since February, Haiku on 4.5 since October last year. Every spare resource goes instead to the flagship models that win benchmarks, headlines, and public attention. It is a sensible move for a company preparing for an IPO and forced to choose where its resources produce the most impact.

The next few weeks and months should be interesting. Anthropic is expected to release Mythos proper within weeks—likely an improvement on the preview, possibly a new state of the art. But Google is expected to ship Gemini 3.5 Pro in June, and with Gemini 3.5 Flash already matching the previous Gemini Pro, the full model could raise the bar again. GPT-5.6 is already rumoured, with developers reportedly spotting traces of it in OpenAI's Codex logs. The frontier now changes hands in weeks, not quarters—and Anthropic has the crown only until someone takes the next turn.

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🦾 More than a human

An “intelligent tattoo” to detect skin cancer before it appears
Scientists in Quebec have created a new way to spot skin cancer very early, before it can be seen by the eye. A patch of tiny needles places special particles just under the skin, forming a temporary “tattoo” that glows when lit with infrared light and reveals small temperature differences caused by tumours. In mice, the system detected melanomas only four days old—far smaller than current methods can find. If it works in people, it could catch dangerous cancers sooner and cut down on unnecessary biopsies.

🧠 Artificial Intelligence

Magnifica Humanitas
Pope Leo XIV’s first major social document, Magnifica Humanitas, calls on the world to put human dignity at the centre of the artificial intelligence revolution. The encyclical warns that AI is not morally neutral and expresses alarm that a tiny number of powerful tech companies now wield influence greater than many governments. Leo XIV argues that AI is reshaping work, truth, and warfare in ways that threaten ordinary people, and shines a light on the hidden human cost of the digital economy—from poorly paid data workers to children mining minerals for devices—describing these as modern forms of slavery. On war, he is unequivocal, writing that life-and-death decisions must never be handed to autonomous weapons systems. Against all of this, Leo XIV proposes a “civilisation of love” built on solidarity and shared responsibility, urging governments, businesses, and citizens to ensure that technology serves humanity rather than dominates it.

Nvidia to spend $150 billion a year in Taiwan, ‘epicentre’ of AI revolution, says CEO
Nvidia will invest about $150 billion a year in Taiwan, ten times what it spent five years ago. A new Taipei headquarters breaks ground this year, opens in 2030, and will employ 4,000 people, deepening ties with TSMC, Foxconn and other key suppliers. The move shows how dependent the AI industry remains on Taiwan, with rival AMD recently pledging $10 billion of its own.

DeepSeek To Make Permanent 75% Discount on Flagship AI Model
DeepSeek has made its 75% discount on the V4-Pro model permanent. The move ramps up an ongoing AI price war, with Chinese firms undercutting global rivals to win over developers and businesses. By choosing scale over margins, DeepSeek is likely to push competitors to cut their prices too.

Huawei Says It Has Workaround to Match Leading Chips
Huawei says it has found a way to make advanced chips by 2031 without the specialised machines the US has blocked it from buying. Instead of shrinking components, its “LogicFolding” approach stacks circuits in layers within a single chip to boost performance. If it works at scale, it would weaken US export controls and help China catch up in the chip race, though challenges like overheating and unproven large-scale use remain. The technology will debut in Huawei’s new Kirin smartphone chip this autumn.

Introducing Grok Build
xAI has launched Grok Build, a new coding tool similar to Claude Code or OpenAI Codex. Just like its competitors, Grok Build can plan tasks before doing them, run multiple jobs at once, and works with tools developers already use. It also has a headless mode for scripts and automation. Grok Build is available for SuperGrok and X Premium Plus subscribers.

Sundar Pichai on AI, the future of search, and what’s happening to the web

The Verge sits down with Sundar Pichai after Google I/O to discuss how he has reshaped Google to move faster on AI, including merging its research teams and centralising infrastructure. They cover the new Gemini models, agent tools, and big changes to Search and YouTube, with Pichai pushed on whether Google is killing traffic to publishers and on growing public unease about AI. The conversation ends on AGI, with Pichai agreeing it is coming soon and arguing the exact timeline matters less than getting society ready for it.

China Expands Travel Curbs to Top AI Talent at Private Firms
In an attempt to retain talent and prevent technology from leaking abroad, China is now requiring top AI staff at private firms like Alibaba and DeepSeek to get government approval before travelling overseas. The rule, once limited to state workers and nuclear scientists, now covers founders, researchers and executives seen as vital to the country.

OpenAI’s Frontier Governance Framework
OpenAI has released a Frontier Governance Framework showing how its safety practices line up with new laws like California’s Transparency in Frontier AI Act and the EU AI Act’s Code of Practice. It draws on the company’s existing Preparedness Framework and covers risks such as cyber attacks, CBRN threats, manipulation, and loss of control, plus reporting, security, and incident response. OpenAI says it will keep updating the document as models and rules evolve, positioning itself early as AI regulation takes shape. The full document can be found here.

AI will be used to estimate age of asylum seekers from next year
The UK will trial AI facial age estimation technology at its borders in 2027 to spot adult migrants pretending to be children. The Home Office, which has given a £322,000 contract to Akhter Computers Ltd, says 43% of those claiming to be children last year were actually adults. The tool will support, not replace, existing checks by border staff and social workers. Critics, including Human Rights Watch, warn that the technology is unproven and could wrongly deny vulnerable children the protections they are legally owed.

AI coding startup Cognition raises $1B at $25B pre-money valuation
Cognition, the company behind AI coding agent Devin, has raised over $1 billion at a $25 billion valuation, more than double its worth eight months ago. Cognition says big customers like NASA, Goldman Sachs and Mercedes-Benz are using Devin, and its revenue run-rate has hit $492 million with usage growing 50% each month.

SK Hynix joins the trillion-dollar club, powered by Nvidia’s HBM4 orders
SK Hynix has become the third chipmaker, after Nvidia and TSMC, to pass a $1 trillion valuation. The Korean company makes the high-bandwidth memory chips that power Nvidia’s AI systems, and it currently supplies around 70% of the memory for Nvidia’s upcoming Rubin platform. Demand is so strong that its 2026 production is already sold out. The risk is that memory is a boom-and-bust business, so if AI demand cools, the share price could fall just as fast as it rose.

Groq raising $650 million for its second act
Groq is raising up to $650 million from its existing investors as it shifts from making AI chips to running an AI inference cloud business, Axios reports. This comes after a $20 billion licensing deal with Nvidia last year that paid out shareholders and led much of Groq’s senior team to leave. Company veterans Adam Winter and Matt Eng are now running “Groq 2.0,” with early backers Disruptive and Infinitum guaranteeing the round. Axios suggests this cash-out-then-reinvest model could catch on across the AI industry.

Meta is charging for its AI chatbot for the first time, starting at $7.99 a month
Meta is charging for its AI chatbot for the first time, with plans at $7.99 and $19.99 a month, starting in Singapore, Guatemala and Bolivia. The pricier tier matches ChatGPT and Google, while the cheaper one undercuts both. The move aims to show investors that Meta’s huge AI spending, set to hit $125-145 billion next year, can earn money beyond ads. With around a billion people already using Meta AI, even a small share of paying users could bring in billions, though many may resist paying for features inside apps they see as free.

YouTube Will Start Automatically Tagging Videos That Make ‘Significant’ Use of AI, and It’s Making Labels for AI-Generated Content More Prominent
To tackle the flood of AI-generated content, YouTube will now automatically label videos its systems detect as significantly photorealistic AI, even without creator disclosure. The labels will appear directly below long-form videos and as overlays on Shorts, rather than tucked away in the description. Creators can dispute mislabels, though tags are permanent for content made with YouTube’s own AI tools or carrying C2PA metadata. YouTube says the labels won’t affect recommendations or monetisation, framing the change as purely about viewer transparency.

Introducing Stan Lee on ElevenLabs

ElevenLabs has teamed up with Stan Lee Universe to recreate the late Marvel legend on its AI platform. Fans can now use a clone of his voice, generate images of him, and apply Stan Lee-inspired music filters to their own tracks. The deal also includes a monthly book club, starting with Treasure Island. Not everyone is happy seeing Stan Lee “resurrected” digitally again, with critics raising concerns about consent and the commercialisation of dead celebrities.

Eleven Labs: Introducing Music v2
Music v2 is a new AI music generator from ElevenLabs that comes with better vocals, multilingual support, and prices cut by up to 50%. It can shift genres within a single track, build full songs section by section, and let users regenerate just one part of a track without changing the rest. The company says the model is trained only on licensed music, so the songs people create are free to use commercially without sync fees or clearance delays.

How long is Anthropic’s lease with SpaceX? Opinions vary
xAI recently signed a huge compute deal letting Anthropic use its Colossus cluster for $1.25 billion a month, but Elon Musk’s description of the arrangement directly contradicts SpaceX’s own SEC filing. Musk calls it a short 180-day lease, while the filing repeatedly describes a three-year deal running through May 2029, with either side able to cancel on 90 days’ notice. Neither xAI nor Anthropic has clarified which version is accurate.

Mistral to explore designing own chips, CEO says, as it ramps up infrastructure build
Mistral, the French AI startup, is thinking about designing its own chips to cut costs and rely less on Nvidia. The company also announced a new data centre in France and a new agentic AI tool called Vibe that can write code and complete tasks for businesses. Mistral is aiming for €1 billion in revenue by 2026, but still trails far behind OpenAI and Anthropic.

Microsoft tries to get back in the AI coding game with new model
The Information reports that Microsoft will launch a new family of AI coding models at next week’s Build conference, hoping to claw back ground it has lost to rivals like Anthropic’s Claude Code, OpenAI’s Codex, and Cursor. The move is part of AI chief Mustafa Suleyman’s push to build up Microsoft’s own technology and lean less on OpenAI, despite their $13 billion partnership.

MAI-Image-2.5 launches at No. 3 on Arena
Microsoft has launched MAI-Image-2.5, a new text-to-image model that ranks third on the Arena leaderboard. According to Microsoft, the new model produces sharper text, better product and brand visuals, and more realistic lighting and scenes than its predecessor. The model is live on Arena now and rolling out to Microsoft’s own platforms within two weeks.

OpenRouter more than doubles valuation to $1.3B in a year
OpenRouter, a service that lets companies tap into over 400 AI models from providers like Anthropic, Google, and OpenAI, has raised $113 million from Alphabet’s CapitalG, pushing its value to about $1.3 billion, more than double a year ago. Usage has jumped fivefold in six months, with 8 million users now running 100 trillion tokens through it each month.

▶️ NVIDIA Partner Wants to Put Mini Data Centers in Your Yard (32:54)

In this video, Steve from Gamers Nexus takes a closer look at SPAN and its idea to install small AI servers in private homes. He argues that this is a bad idea, highlighting concerns about a box containing over $250,000 worth of hardware outside the house, the noise it generates, its reliance on homeowners’ electricity and internet connections, and potential surveillance implications—all for very little benefit to the people hosting it.

Tech CEOs are apparently suffering from AI psychosis
This article explores AI psychosis, a phenomenon where tech bosses overestimate what AI can do because they are too far removed from the detailed work to see its limits. It links this mindset to the wave of AI-driven layoffs in 2026, even though studies show AI productivity gains are smaller than claimed.

🤖 Robotics

Figure Signs Agreement with Catalyst Brands to Scale Humanoid Operations
Figure AI is deploying humanoid robots with Catalyst Brands to automate physically demanding supply chain tasks across its retail portfolio, which includes JCPenney, Aéropostale, and Brooks Brothers. The deal is underpinned by shared investor Brookfield, making it as much a strategic financial play as a commercial one.

▶️ Meet LimX Luna—Our Next-Gen Full-Size Interactive Humanoid Robot. (2:31)

Chinese robotics company LimX presents Luna, its latest humanoid robot. In this promotional video, LimX highlights Luna’s agility, acrobatic skills, human-like movements, and swarm synchronisation capabilities. The company markets the robot as an “interactive humanoid robot”, positioning it more on the artistic side and intending it for use in theme parks, museums, and live performances.

Wayve’s self-driving tech is headed to US cars made by Stellantis
Stellantis has signed a deal with UK startup Wayve to add hands-free driving to its cars from 2028, starting in North America. Wayve’s software is appealing because it works with whatever sensors and chips a carmaker already uses, keeping costs down. The startup recently raised $1.2 billion from backers including Nvidia, Microsoft and Uber, and built a working prototype for Stellantis in just two months.

Waymo Takes Its Self-Driving Cars to Virginia
Waymo has started mapping streets in Arlington and Alexandria in northern Virginia, a step that usually comes 12 to 18 months before launching a robotaxi service, even though self-driving cars aren’t yet allowed to carry passengers in the state. The Alphabet-owned robotaxi company is expanding fast, offering rides in 11 US cities and is eyeing 20 more, including London and Tokyo. Parking its cars near Washington may help Waymo lobby federal lawmakers now weighing national rules for driverless vehicles.

Waymo opens Ojai robotaxis to select riders as company aims to lower cost of fleet expansion
Waymo is opening rides in its new Ojai robotaxi to public passengers in three US cities, with three more to follow this summer. Built by China's Geely, the cars are much cheaper to make than the older Jaguar fleet and use upgraded sensors that work better in rain and snow.

Tesla’s dedicated Optimus factory construction officially underway at Giga Texas
Tesla has started building a huge new factory at Gigafactory Texas to make its Optimus humanoid robots, aiming for up to 10 million units a year once finished. Smaller-scale production begins in Fremont this summer, while the Texas plant targets high-volume output by summer 2027.

▶️ School of Football | Can football teach a robot to move? | Boston Dynamics x Hyundai (1:21)

FIFA World Cup 2026 is coming to North America this summer, so Boston Dynamics asked whether Atlas, its humanoid robot, could learn to play football. The company has an entire playlist showing how good the robot is at the sport, from basic movements to kicks and even celebrations. I wonder if Boston Dynamics will also teach Atlas how to fake injuries.

▶️ A New Benchmark in Robot Juggling (0:46)

Researchers at RAI Institute have built a robot that excels at juggling, from basic juggling patterns to more complex routines. The researchers say the robot learned directly on hardware in real time, with some movements mastered in under 10 minutes of real-world interaction.

DARPA launches search for robot medics to treat battlefield casualties
DARPA is seeking proposals for swarms of small robots that can reach wounded soldiers on the battlefield, drag them to safety, give them drugs, and wrap around injured limbs to stop bleeding. The aim is to handle the mass casualties expected in large-scale wars, where delays in stopping blood loss are the main cause of preventable deaths. Proposals must show at least two key abilities, and prototypes should ideally fit in a first-aid kit or be dropped by a drone. DARPA also sees uses beyond the military, such as reaching victims in collapsed buildings or chemical disasters.

🧬 Biotechnology

Retro Biosciences: Next Phase
Retro Biosciences, the longevity startup backed by Sam Altman, has raised new funding at a $1.8 billion valuation to pursue its goal of adding ten healthy years to human life. Its lead drug, RTR242, an oral treatment aimed at clearing waste from cells, has entered its first human trial just 15 months after the disease target was chosen. The company has also built cell therapy, tissue reprogramming, and AI-powered protein design platforms, with more first-in-human trials planned for 2026 and 2027.

Groundbreaking genomic test could spare millions of breast cancer patients chemotherapy
A large international trial has shown that a genomic test called Prosigna can identify women with the most common form of breast cancer who can safely skip chemotherapy. Among more than 4,400 patients, those given hormone therapy alone based on a low-risk score did just as well as those who also had chemo, with 94% still cancer-free after five years versus 95%. The finding could spare millions of women from harsh side effects like infertility and early menopause, and points to a more personalised approach to cancer care.

Insilico Medicine and Human Longevity Announce Collaboration to Co-Develop Industry-First AI Foundation Model for Longevity Science
Insilico Medicine and Human Life Foundation Models are teaming up to build the first large AI models focused on human ageing. Insilico brings its AI drug discovery tools, while HLFM contributes a decade of health and genetic data from thousands of people. The aim is a system that can spot age-related diseases early and help develop new treatments to extend healthy lifespan.

Introducing a world model of protein biology
Biohub has released three open-source tools for studying proteins: ESMC, an AI model that learns the rules of protein biology from billions of sequences; ESMFold2, which predicts a protein’s full 3D structure from its sequence alone; and ESM Atlas, a searchable map of 6.8 billion proteins grouped by shared features. The team also used these tools to design lab-tested proteins that bind to cancer targets like PD-L1, pointing to a faster path for drug discovery.

Carbon-3B
Carbon-3B is a new genomic AI model from Hugging Face, trained on DNA and RNA with a focus on complex organisms, that can read very long stretches of genetic code at once. It performs as well as much larger rival models on tasks like predicting the effects of genetic variants, but runs several times faster. Hugging Face is also releasing Carbon-8B, a bigger and more powerful version, and Carbon-500M, a smaller helper model designed to speed things up further.

Experimental Drug Yields Dramatic Weight Loss
Eli Lilly’s new weekly injection, retatrutide, helped patients lose an average of 28% of their body weight, beating existing drugs like Wegovy and Zepbound and matching the results of weight-loss surgery in the heaviest patients. It works by targeting three appetite-related hormones instead of one or two. The main downside is harsh stomach side effects, which caused 11% of high-dose patients to quit the trial. If approved, it could become a powerful new option for people with severe obesity and a major win for Eli Lilly.

Human Gut Organoids with Functional Nerves Developed that Can Be Mass Produced
Scientists in the US and France have created 3D-printed trays that grow lab-made gut tissue twice as fast and ten times larger than before, with the tissue forming its own nerves. When transplanted into rodents, the lab-grown intestine worked much like real human tissue. The team hopes this could one day let doctors repair damaged guts in patients using custom-grown tissue, avoiding the need for full organ transplants. Human trials are still some way off.

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We have a very packed issue this week. Google I/O took place this week, and we’ll look at all the new AI-infused products and services Google announced.

Elsewhere in AI, Elon Musk lost his lawsuit against OpenAI; Andrej Karpathy joined Anthropic, which also reported its first profitable quarter. Meanwhile, Nvidia is eyeing a new $200 billion market, OpenAI may file for an IPO soon, Microsoft has cancelled Claude Code licences, and OpenAI claims to have solved an 80-year-old maths problem.

Over in robotics, Atlas delivers a fridge, Waymo suspends services in some cities, a robot scarecrow appears in Japan, and researchers from MIT present one of the most cursed robots I’ve ever seen.

Beyond that, this week’s issue of Sync also features a startup keeping human brains alive for drug testing, SpaceX filing for an IPO, how AI assistants are helping scientists discover new drugs, and more!

Enjoy!

Google I/O 2026—new models, and more agents

Google flexed its AI might at I/O 2026, and risks going full Copilot

On the Shoreline Amphitheatre stage on 19 May, Sundar Pichai told the audience that Google was in a period of "hyper progress" with its AI efforts. He had numbers to back that up. Google now claims 900 million monthly users for the Gemini assistant, AI Mode in Search crossed a billion in its first year, and the company is now processing 3.2 quadrillion tokens a month, seven times what it was a year ago.

What followed was a two-hour keynote that put Gemini into almost every Google product in its lineup. New models, a personal agent that runs 24/7 on Google Cloud, a complete rebuild of Search, smart glasses, new AI tools for developers, and more. It was Google flexing its might, rebuilding its products and services around AI and agents. But a lot of it looked like Google had decided AI was the answer and was now working backwards to find the question. That can backfire.

New models: Gemini 3.5 Flash and Omni

The first big new announcement was Gemini 3.5 Flash, the first model in the new 3.5 series. Google's own benchmarks position 3.5 Flash ahead of the previous flagship 3.1 Pro on key benchmarks, at less than half the price of comparable frontier models. Pichai said it was "four times faster than other frontier models" in output tokens per second.

Independent benchmarks from Artificial Analysis support these claims, with Gemini 3.5 Flash scoring 43 on its Intelligence Index, well above the 24 average for comparable models, at 204 tokens per second. Pricing came in at $1.50 per million input tokens and $9.00 per million output tokens—moderately priced on input, slightly above average on output. The model is somewhat verbose, generating 12 million tokens to complete the Intelligence Index evaluation against an average of 7.9 million.

To illustrate how good Gemini 3.5 Flash is, Google said that 93 agents running on Flash 3.5 have built a working operating system from scratch. It took them 12 hours and less than $1,000 in API credits to achieve that. I haven’t seen anyone recreate the demo so far, but I suspect someone will, and I’m curious what they find when they do.

Gemini 3.5 Flash is available immediately in the Gemini app and AI Mode in Search for everyone, in Antigravity and the Gemini API for developers, and in Gemini Enterprise for businesses. Sundar Pichai also teased Gemini 3.5 Pro, set to arrive next month. It will be interesting to see how it fares against Claude Mythos, Opus 4.7 and GPT-5.5.

Demis Hassabis then introduced Gemini Omni, Google's new multimodal generative model. It is Google's direct answer to OpenAI's Sora and the wave of video-first models that have followed. Omni takes text, image, audio and video as inputs and produces video that Google says is grounded in real-world physics—gravity, kinetic energy, fluid dynamics. Beyond generation, Omni leans on conversational editing—you tell it what to change, the next instruction builds on the last, and characters and physics stay consistent across turns.

The first model in the family, Gemini Omni Flash, is available to Google AI Plus, Pro and Ultra subscribers globally through the Gemini app and Google Flow, and at no cost on YouTube Shorts and the YouTube Create app. API access for developers and enterprise customers will follow in the coming weeks.

The new agentic Search

Google Search is also getting its biggest update since its debut 25 years ago. The redesigned “intelligent search box” expands dynamically as you type, accepts text, images, files, videos and Chrome tabs, and offers AI-powered suggestions beyond autocomplete. AI Mode and AI Overviews have merged into a single conversational surface, now powered by Gemini 3.5 Flash and live worldwide.

Four new capabilities are worth highlighting. You can now have Search agents run 24/7 in the background, scanning blogs, news, social posts and Google’s real-time data on finance, shopping and sports, then sending synthesised updates. Search will also book local experiences and services for you—and for categories like home repair, beauty or pet care, Google will call businesses on your behalf. Generative UI builds custom layouts—interactive visuals, tables, graphs, simulations—on the fly for individual queries. Mini apps in Search are persistent dashboards that live inside a search result: a wedding planner, an expense tracker, a trip itinerary that updates itself. Information agents, mini apps and agentic booking are launching this summer to AI Pro and Ultra subscribers in the US. Generative UI will roll out free worldwide.

I would suggest waiting to see how this plays out. AI Overviews launched at I/O last year as a similar headline announcement, and the glue-on-pizza era that followed was a disaster Google spent months walking back. Already, users have found that googling the word "disregard" causes the new Search to crash out.

Spark, or your personal AI agent

Gemini Spark is a 24/7 personal AI agent that runs in the cloud rather than on your device, so it can keep working when the laptop is closed and the phone is locked. It connects to Gmail, Docs, Sheets, Slides and Calendar, and via MCP to external services like Canva, OpenTable and Instacart. The shift Google is selling is from an assistant that answers questions to an agent that does work—parsing your monthly credit card statement for hidden subscriptions, monitoring your kids’ school inbox for deadlines, turning a week of meeting notes into a polished Doc and the email to kick off the project. Spark asks for confirmation before high-stakes actions like spending money or sending an email, and is starting to roll out this week to trusted testers, with a US beta for AI Ultra subscribers following the week after.

But Spark is not the only agent-shaped product Google announced. Daily Brief is a Spark spin-off that assembles an overnight digest from your inbox, calendar and tasks—Google wants it to be the first thing you check in the morning. Gmail Live lets you converse with your inbox to find a flight number or pull a status update. Docs Live is voice-driven document writing: speak a stream of consciousness, and an agent dictates, drafts and pulls in web citations. Talk to Keep does the same for notes. The Universal Cart, built on Google Wallet, is a shared cart that spans Search, Gemini, YouTube and Gmail, tracks deals and price history, and can check out across multiple retailers via a new Universal Commerce Protocol. Even the new Intelligent Eyewear (more on it later) is, in part, a hardware front-end for the same agent stack.

Spark is clearly Google’s answer to OpenClaw, a personal AI agent that exploded in popularity earlier this year. Demos paint it as a useful helper, but, again, I recommend waiting to see how it actually performs. There are stories of OpenClaw agents going wrong. There is also a question of how many people are willing to give Spark permission to act on their behalf. For it to work properly, users have to hand the keys to their entire digital life over to Google, and hope the agents won’t hallucinate or run amok.

Antigravity 2.0

For developers, Google announced Antigravity 2.0, the second version of its agentic coding platform. The first version, launched last November as a response to apps like Cursor, was essentially a VS Code clone with AI features layered on. The new version moves Google closer to where Claude Code and OpenAI Codex already are.

Antigravity 2.0 ships as three things: a standalone desktop app, a command-line tool, and an SDK. The desktop app lets developers orchestrate multiple agents working in parallel, design custom subagent workflows, and schedule tasks that run in the background. It integrates with AI Studio, Android, and Firebase, and now supports native voice commands. The CLI replaces Gemini CLI, which Google is sunsetting and asking users to migrate from. The SDK lets developers build custom agents on top of Google’s harness, with templates available in AI Studio for enterprise customers.

Antigravity is also the engine behind a lot of what Google demoed elsewhere at I/O. Gemini 3.5 Flash was co-developed with it. The OS-from-scratch build ran on it. The mini apps and generative UI in Search are powered by it. Whether developers actually build on it, rather than on Claude Code or Codex, is a different question.

New hardware, also with AI

Hardware was thin and mostly previews. The headline release is Intelligent Eyewear, the first Android XR audio glasses, built with Samsung as hardware partner and Qualcomm for silicon, in two designs from Warby Parker and Gentle Monster. They ship this autumn in select markets, compatible with both Android and iOS. Features include Gemini voice activation, contextual questions about what the cameras see, turn-by-turn directions, real-time audio translation in the speaker’s voice, and message summaries. Pricing was not announced.

Wear OS 7 is the biggest smartwatch update in years. Built on Android 17, it delivers around 10% better battery life, Wear Widgets replacing Tiles, Live Updates on watch faces, and a new AppFunctions API that lets Gemini control third-party apps by voice. The catch is that Gemini Intelligence on Wear OS 7 requires Gemini Nano v3, which means only new 2026 watches qualify. Existing Pixel Watches get the rest of the update, but not the AI features.

And much more

That is the headline recap, but Google had much more to show. On the consumer side, Workspace got Google Pics, a Nano Banana-powered design app aimed squarely at Canva. YouTube got Ask YouTube, a conversational search experience that lets you query the content of videos rather than their titles. Pomelli, Google Labs’ small-business tool, can now build a brand book and launch a full website in a few clicks. Stitch lets you steer a real-time design agent with text or voice. Flow and Flow Music got Omni integration and standalone mobile apps.

Google also used the keynote to point at AI doing more substantive work. DeepMind's Co-Scientist is a multi-agent research partner that helps scientists generate hypotheses and synthesise literature. WeatherNext helped the US National Hurricane Center deliver earlier and more accurate forecasts for Hurricane Melissa's landfall in Jamaica, outperforming traditional weather models. And Demis Hassabis closed the keynote by telling the audience that "we're at the foothills of the singularity."

Is Gemini becoming the new Copilot?

Google is clearly pushing Gemini and Gemini-powered AI agents into every service and product it has to offer. That little sparkle icon is now showing up in Search, Docs, Gmail, YouTube, Chrome, smartphones, glasses and watches—and soon in more places.

The problem is that Microsoft has spent the last two years showing what happens when you do this. Copilot was pushed into every Microsoft surface it could fit on, useful or not, and users eventually had enough. The company is now walking back parts of that strategy. The Verge ran an article a few days before Google I/O 2026 arguing that Google is heading the same way, and after watching the keynote, I'm inclined to agree.

Some of what was announced at Google I/O 2026 looks genuinely interesting. But it remains to be seen whether the features land as useful products or join Google’s graveyard soon. The trend, though, is clear—Google wants us to use more Gemini-powered features, whether we like it or not.

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🦾 More than a human

Review: Hypershell X Ultra S
It’s 2026, and exoskeletons are being reviewed like any other piece of tech. Hypershell’s new X Ultra S, priced at $1,999, uses an AI system to adjust power in real time, making the assistance feel far more natural than older models. WIRED found it genuinely helpful on hills but awkward for running and underwhelming for cycling. Battery life is limited, with the claimed 18-mile range needing a spare pack. Oddly, Hypershell markets it to fit young hikers who arguably do not need it, while older adults, a fast-growing outdoor group, would likely benefit far more.

Not alive, but not dead: disembodied human brains used for drug testing
Bexorg is a startup that keeps donated human brains alive outside the body for about a day for researchers to test drugs on real human tissue. The brains are kept unconscious with anaesthesia to avoid ethical concerns. Drug company Biohaven has used the system to fine-tune a treatment at a much lower dose than mouse studies suggested, and is starting a clinical trial based partly on this data. The approach could speed up drug development for diseases like Alzheimer’s and Parkinson’s. Bexorg is also building an AI “virtual brain” trained on its findings to screen drugs digitally.

🧠 Artificial Intelligence

Elon Musk has lost his lawsuit against Sam Altman and OpenAI
A California jury unanimously rejected Elon Musk’s lawsuit against OpenAI, Sam Altman, Greg Brockman, and Microsoft, ruling he had waited too long to file his claim that they wrongly turned a charity into a for-profit business. The decision clears a major hurdle ahead of OpenAI’s reported IPO and removes the threat of a forced restructuring. The judge said she would have dismissed the case herself and was unconvinced by Musk’s expert, who estimated damages at $79-135 billion. Musk has vowed to appeal, framing the loss as a technicality rather than a defeat on the merits.

OpenAI co-founder Andrej Karpathy joins Anthropic’s pre-training team
Andrej Karpathy, a well-known AI researcher who helped start OpenAI and once led Tesla’s self-driving work, has joined Anthropic to work on pre-training—the costly, compute-heavy phase of training that gives Claude its core knowledge. He will lead a new team focused on using Claude itself to accelerate pre-training research.

OpenAI Is Preparing to File for an IPO Very Soon
OpenAI is reportedly preparing to file confidential paperwork for a stock market listing within days, aiming to go public as early as September. Banks, including Goldman Sachs and Morgan Stanley, are helping draft the prospectus. The big question is whether OpenAI can earn enough money to cover its huge spending on data centres, especially as Anthropic has been catching up and OpenAI has missed some of its own revenue and user targets.

Mind-Blowing Growth Is About to Propel Anthropic Into Its First Profitable Quarter
Anthropic’s revenue is set to more than double to $10.9 billion next quarter, delivering its first operating profit of $559 million, far sooner than expected. Growth is being driven by businesses adopting its Claude coding tools, and the company is becoming more efficient, spending less on computing per dollar earned.

Anthropic is paying SpaceX $15 billion per year
SpaceX’s IPO filing revealed that Anthropic is paying SpaceX $1.25 billion a month through May 2029 for compute power. That is a huge boost for SpaceX, which makes around $18 billion a year in total. Anthropic is also growing its use of SpaceX’s Colossus 2 site, with more Nvidia chips coming online in June. Either side can end the deal with 90 days’ notice.

Anthropic, Microsoft in talks for AI chip deal after $5 billion investment
CNBC reports that Microsoft is in talks to supply its custom Maia AI chips to Anthropic, which would help Microsoft catch up with Amazon and Google in offering its own AI chips to cloud customers. The deal follows Microsoft’s $5 billion investment in Anthropic last year and comes as Anthropic struggles to keep up with demand for Claude. Anthropic already uses chips from Nvidia, Amazon, and Google, and adding Microsoft's chips would further spread Anthropic's reliance across multiple suppliers.

OpenAI’s Q1 revenue was $5.7 billion, beating Anthropic
The Information reports that OpenAI made about $5.7 billion in the first quarter, roughly $1 billion more than Anthropic. But Anthropic is growing fast and expects to more than double its revenue to $10.9 billion in Q2. Even so, Anthropic is now raising money at a valuation of up to $950 billion, ahead of OpenAI's $850 billion, suggesting investors care more about growth than current sales as both firms head towards going public.

Jensen Huang says he’s found a ‘brand new’ $200B market for Nvidia
After dominating the GPU market, Jensen Huang is eyeing a new one: CPUs built for AI agents. On the back of another record quarter, he pitched Vera, Nvidia’s new CPU, as a $200 billion opportunity, arguing that while GPUs handle an AI’s thinking, billions of future agents will rely on CPUs as their tools, much like humans use PCs today. Nvidia has already sold $20 billion worth of Vera chips this year, though it now faces competition from the likes of AWS, which recently signed a deal to supply Meta with millions of its own AI CPUs.

Sam Altman makes ‘mic drop’ offer to every Y Combinator startup
Sam Altman has offered every startup in Y Combinator’s current batch of around 169 companies $2 million in OpenAI credits in exchange for a future equity stake. The deal aims to help founders cover steep AI bills whilst tying them to OpenAI rather than rivals like Anthropic. Critics worry OpenAI could copy good ideas it sees inside the cohort, and founders must weigh whether the credits are worth giving up more of their company on top of YC’s existing 7% cut.

AI Chip Startup Tenstorrent Draws Takeover Interest From Intel, Qualcomm
Tenstorrent, an AI chip startup which has the legendary chip designer Jim Keller as a CEO, is drawing early takeover interest from Intel, Qualcomm, and possibly others, Bloomberg reports. The company could be worth more than $5 billion in a sale, well above the $3.2 billion valuation discussed last year. Tenstorrent is simultaneously seeking buyers and new investors, keeping its options open as valuations across the sector continue to rise.

Manus Weighs Raising $1 Billion to Unwind Meta Takeover
Following Beijing’s order to undo Meta’s $2 billion takeover of Chinese AI startup Manus, the founders are looking to raise around $1 billion to buy the company back and relist it in Hong Kong. The proposed reversal is unusual, given that the deal is largely complete, with staff already integrated into Meta’s Singapore offices and investors already paid out. The founders may yet drop the plan, as untangling Manus from Meta’s systems could prove too complicated.

OpenAI Guaranteed Capacity
OpenAI is launching Guaranteed Capacity, letting customers lock in compute access through 1-3 year deals, with bigger discounts for higher spending. Customers can use their reserved capacity flexibly across OpenAI’s products, helping them scale AI tools without worrying about running out of compute.

Trump delays AI security executive order, saying language ‘could have been a blocker’
Trump has delayed signing an executive order that would have made AI companies hand over advanced models to the government for security checks before release, saying the rules might slow America down in the race against China. The order was a response to new models from Anthropic and OpenAI that can find and exploit security flaws very quickly. Reports suggest the signing was also postponed because not enough tech CEOs could get to Washington in time for the photo.

Microsoft cancels Claude Code licenses, shifting developers to GitHub Copilot CLI — a move likely driven by financial motives
Microsoft is reportedly dropping Anthropic’s Claude Code internally and moving staff to its own GitHub Copilot CLI by the end of June. The switch is partly about cutting costs ahead of the new financial year and partly about controlling its own tools. The catch is that employees preferred Claude Code, so the change may not go down well.

Anthropic acquires Stainless
Anthropic has acquired Stainless, a startup that builds tools to help developers connect software to APIs. Stainless has created every official Anthropic SDK and also builds connectors that allow AI agents to access external data and tools. This is not Anthropic’s first acquisition: the company previously acquired Bun, a popular JavaScript runtime, and the AI biotech startup Coefficient Bio. OpenAI has also been acquiring developer-focused startups, including Astral, Neptune, and Promptfoo, as well as financial startups Roi and Hiro.

OpenAI claims it solved an 80-year-old math problem — for real this time

OpenAI says its new reasoning model has disproved a famous 1946 geometry conjecture by Paul Erdős, finding a better solution than the grid-like patterns mathematicians had assumed were optimal. This time, the company backed the claim with endorsements from respected mathematicians, after being embarrassed seven months ago when it wrongly said GPT-5 had solved ten Erdős problems that were already known. OpenAI calls it the first time an AI has independently cracked a major open problem in mathematics.

Alibaba unveils new AI chip in push for domestic alternatives
Alibaba has launched a new AI chip, the Zhenwu M890, built to run AI agents. It is three times faster than the previous version, and Alibaba plans two more powerful chips by 2028. Alibaba also unveiled a new server packing 128 of the chips. The move is part of China’s push to replace Nvidia GPUs with domestic alternatives.

Runway started by helping filmmakers — now it wants to beat Google at AI
TechCrunch profiles Runway, a New York AI startup founded by three filmmakers-turned-engineers, which is betting that future AI will learn from video rather than text. Valued at $5.3 billion, Runway made its name on AI video tools used in films and ads, but is now building “world models”—systems that simulate how the real world behaves—with hopes of helping with robotics, drug discovery and even anti-ageing research.

OpenAI adopts C2PA standard and Google’s SynthID to make AI-generated images easier to identify
OpenAI is joining the C2PA standard and teaming up with Google to embed an invisible SynthID watermark to AI-generated images that survives screenshots and edits. It is also launching a tool that lets anyone check if an image came from its models. The Google partnership is unusual, as it is the first time SynthID will be used in a rival’s products.

Cursor Hits $3 Billion Annual Sales Rate Ahead of SpaceX Deal
Cursor’s AI coding tool is booming, with annual revenue hitting $3 billion in April, up from $2 billion in February, and over 3,000 customers each paying at least $100,000 a year.

Introducing Composer 2.5
Cursor has released Composer 2.5, an updated coding model that, according to Cursor, handles long tasks better, follows instructions more reliably, and collaborates more smoothly than the previous version. It was trained with new methods, including one that pinpoints exactly where the model went wrong during a task, and learned from 25 times more practice tasks. The company says this scaled-up training also led the model to find sneaky shortcuts, showing that bigger training runs need closer oversight. Cursor also said it is building a much larger model with SpaceXAI. Composer 2.5 is available now in Cursor with introductory pricing.

Qwen3.7: The Agent Frontier
Alibaba’s Qwen team has launched Qwen3.7-Max, a new flagship model built for AI agents that can run long, complex tasks on their own. According to the Qwen team, it beats rivals on many coding, reasoning, and tool-use benchmarks, and it works well across different agent frameworks. Independent tests by Artificial Analysis back this up, giving it a score of 57 on their Intelligence Index, placing it just after GTP-5.5, Claude Opus 4.7 and Gemini 3.1 Pro. Artificial Analysis notes Qwen3.7-Max was notably wordy, using about three times as many tokens as the average.

OlmoEarth v1.1: A more efficient family of Earth observation models
OlmoEarth v1.1 is a new version of Allen AI’s open satellite imagery model that runs up to three times cheaper than the original while performing just as well. The savings come from a smarter way of breaking images into pieces that the model can read, which cuts the workload without losing accuracy. OlmoEarth v1.1 comes in three sizes (Base, Tiny, and Nano), all available on Hugging Face.

▶️ The problem with AI agents.. (8:32)

In this video, Low Level explains a vulnerability found in Gemini CLI that can be potentially exploited by malicious actors during automated code reviews. The vulnerability scored a 10 out of 10 CVSS score, so it is a very serious issue.

OpenClaw creator’s $1.3 million monthly OpenAI bill reveals the real cost of autonomous AI coding at scale
Peter Steinberger, the creator of OpenClaw and now an engineer at OpenAI, revealed in a tweet how much he spent in one month running roughly 100 Codex instances on his open-source project. He proudly presented a $1.3 million bill for 603 billion tokens. Steinberger said most of that cost came from Codex's fast mode; switching it off would cut the bill by about 70%.

🤖 Robotics

Waymo expands pause to four cities as robotaxis keep driving into floods
Waymo has paused its robotaxis in Atlanta, San Antonio, Dallas and Houston after several got caught out by heavy rain and flooded roads. A recall last week was meant to fix the problem, but the company admits it has not yet found a full solution. This follows separate suspensions of freeway service in San Francisco, Los Angeles, Phoenix and Miami, where the cars have been struggling with construction zones, including one incident of a robotaxi ploughing through cones and being chased by police. Regulators are watching closely, on top of earlier investigations into Waymo cars passing school buses and hitting a child in Santa Monica.

▶️ Atlas, can you bring me a drink? | Boston Dynamics (0:46)

Apparently, if you ask Atlas to bring you a drink, it will bring the entire fridge. That clip is a part of a longer video where engineers from Boston Dynamics share a bit about the robot’s capabilities, how it was trained and how it learned to use its whole body to lift a heavy object such as a fridge.

Japan’s Monster Wolf robot is a $4,000 scarecrow with red LED eyes, and it actually works
Somewhere in rural Japan, there is a robotic wolf with glowing red eyes that is being used to scare off bears. It looks odd, but it works. Made by the small Hokkaido firm Ohta Seiki, the roughly $4,000 device uses a motion sensor and a library of sounds to frighten animals away. The robot turned out to be a success, and orders have soared as Japan faces its worst-ever bear crisis.

Will Robotics Have a ChatGPT Moment?
AI is genuinely transforming robotics, but the dream of general-purpose humanoid robots living alongside us is much further off than viral demo videos suggest. The authors argue progress will not come from a single ChatGPT-style breakthrough, but from the gradual coordination of many specialised AI tools, alongside hard-won advances in hardware, data, and real-world deployment. They outline five “hard truths”: the gap between scripted demos and reality, the unsolved data problem, the unlikelihood of one unifying robot AI, the difficulty of building compliant hardware, and the fact that real value tends to come from mundane tasks rather than flashy ones.

▶️ Voice‑driven, real‑time arbitrary action generation (2:24)

Unitree adds a new feature to its G1 humanoid robot—voice control—and demonstrates how the G1 can be directly controlled to perform a wide range of actions in real time, from dancing to proposing, although it still takes some time for the robot to understand commands.

Asimov Is An Open Source Humanoid Robot For The Rest Of Us
Meet Asimov, an open-source humanoid robot that makes building one possible for hobbyists, not just big companies. The kit aims for a price of about $15,000, with all parts listed on GitHub so builders can shop around, and runs on cheap, easy-to-find computers like the Raspberry Pi 5. Its physical capabilities are modest—roughly 5 kg squats and 18 kg single-arm lifts—so it is more a tinkerer's platform than an industrial robot. Nonetheless, it is nice to see projects like Asimov that try to make building humanoid robots as accessible as possible.

GEN-1: Scaling Embodied Foundation Models to Mastery

Generalist has unveiled GEN-1, which it says is the first robot AI capable of doing simple physical tasks reliably enough for real commercial use. The company says the new AI succeeds 99% of the time, where its predecessor managed 64%, and it works about three times faster. It needs only an hour of robot data per task because it learns mostly from footage of people, and it can now improvise to recover when things go wrong.

▶️ Labububot — one of the rarest monsters on Earth (1:12)

Meet Labububot, “one of the rarest monsters on Earth,” as its creators call it. This cursed robot is the size of a beach ball and uses its 12 Labubu heads to communicate and move around. Labububot is either cute, or creepy, or somehow both at once, and that confusion might be the whole point.

I Gave My OpenClaw Agent a Physical Body
Will Knight from WIRED describes his adventure into robotics with the help of OpenClaw, which helped him set up a cheap robot arm and train it to spot and pick up objects. He sees this as part of a wider trend where AI coding tools make it far easier to programme robots, with researchers at Berkeley, Nvidia and others showing the approach can match purpose-built robot models. Knight writes that robotics could soon be open to almost anyone, speeding up how quickly robots enter everyday life.

Fraunhofer IPA offers new test benchmark for humanoids
Fraunhofer IPA proposes a new benchmark to cut through the marketing hype around humanoid robots, evaluating them across six criteria: basic technology, complex abilities, cleanliness, functional safety, cybersecurity, and energy efficiency. With an official safety standard for humanoids not expected until 2028, this benchmark gives buyers a clearer picture of what these robots can and cannot do.

🧬 Biotechnology

Two AI-based science assistants succeed with drug-retargeting tasks
Two new AI systems, Google’s Co-Scientist and FutureHouse’s Robin, help biologists sift through huge volumes of research to suggest new hypotheses and possible drug treatments. Both work alongside human scientists, searching papers and ranking ideas, with Robin also able to analyse certain lab results automatically. In tests, they pointed to existing drugs that showed promise against leukaemia and a form of macular degeneration. The tools aren’t designing new drugs or replacing researchers, but they could help scientists spot useful connections buried in the flood of published papers.

Could this be the moment that drug manufacturing takes off in orbit?
Microgravity is emerging as a promising place to make certain products, especially drugs, because molecules form more slowly and evenly in orbit, which can make medicines more stable and easier to deliver. Building on this, the space company Varda Space Industries has teamed up with United Therapeutics to develop new treatments for a rare lung disease. Varda flies small uncrewed capsules that process drugs in orbit before returning them to Earth, and has launched six of them since 2023. It is the first time a publicly traded company has funded space-based drug production with its own money rather than NASA's.

💡Tangents

The SpaceX IPO filing has arrived
SpaceX has filed for a public listing 24 years after founding, with a possible valuation of $1.75 trillion and around $75 billion to be raised—likely the biggest IPO ever. The business is led by Starlink, which brought in most of last year's $18 billion in revenue, though the company still lost $4.9 billion, partly due to heavy spending on its newly absorbed AI arm, xAI.

Inside Anduril and Meta’s quest to make smart glasses for warfare
Anduril teams up with Meta to build augmented-reality headsets for soldiers that let them control drones and call in strikes using voice, eye movements, and small gestures. The article explains that Anduril has two versions in development, but both are years away and face major hurdles. Experts warn that the headsets could overload soldiers with information rather than help them, and that relying on imperfect AI to spot threats brings serious risks of mistakes.

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For the past three weeks, some of the biggest names in AI have testified in the long-anticipated Musk v OpenAI trial. Testimony has now concluded, and the lawyers have made their closing arguments. While we await the verdict, this week’s main story recaps the trial, what we’ve learned, and what is at stake.

Elsewhere in AI, Cerebras became the first AI startup to make its stock market debut, raising $5.5 billion and ending the day valued at $66 billion. Meanwhile, Anthropic has warned investors against secondary platforms offering access to its shares; Greg Brockman is now officially in charge of OpenAI’s product strategy; OpenAI is reportedly considering suing Apple; and xAI has released Grok Build while becoming SpaceXAI.

Over in robotics, Unitree has unveiled a real-life mecha robot, Hello Robot has proposed a different take on home robots, Figure has livestreamed its robots working for hours, and Jim Fan has presented his vision for the “endgame” of robotics.

This week’s issue of Sync also features Isomorphic Labs’ $2.1 billion Series B, what exoskeletons have learned from one relentless user, how AI is making working at Amazon and Meta a miserable experience, and more!

Enjoy!

Musk v OpenAI

What the trial revealed about the men racing to build AGI, and why neither looks like a safe pair of hands

For the past three weeks, a federal courtroom in Oakland, California, hosted the most closely watched legal fight the technology industry has produced in years. On one side: Elon Musk, the world’s richest man. On the other: Sam Altman, the chief executive of OpenAI, the company behind ChatGPT—and the man Musk co-founded that company with, back when the two were friends.

The witness list read like a guest list for an AI industry summit. Musk and Altman both testified. So did OpenAI co-founder and president Greg Brockman, co-founder and former chief scientist Ilya Sutskever, and Microsoft chief executive Satya Nadella. Former OpenAI executives and board members, including former chief technology officer Mira Murati and former director Tasha McCauley, appeared via videotaped depositions. At the time of writing, testimony has concluded, the lawyers have made their closing arguments, and the case awaits a verdict. Whatever that verdict is, it has the potential to reshape the AI landscape.

What is the trial about?

The dispute goes back to OpenAI’s founding. Musk co-founded the lab in 2015 as a nonprofit, alongside Altman, Brockman and others, and donated around $38 million before leaving in 2018 after disagreements over its direction. OpenAI later created a for-profit subsidiary to raise capital, took billions of dollars from Microsoft, and in October 2025 completed a restructuring that converted that subsidiary into a public benefit corporation, worth now more than $850 billion and on its way to a possible $1 trillion IPO.

Musk’s core claim is that Altman and Brockman betrayed the lab’s founding promise. He argues his donations were given to a charity dedicated to developing AI safely for the benefit of humanity, not to enrich its executives, and that the for-profit pivot breached that charitable trust. He is asking the court to remove Altman and Brockman from their roles, to unwind the 2025 restructuring, and to award as much as $134 billion in damages—payable not to himself, but to OpenAI’s nonprofit. Microsoft is named as a co-defendant, accused of aiding and abetting the alleged breach.

One detail is worth keeping in mind. The nine jurors heard the case, but their verdict is only advice. Judge Yvonne Gonzalez Rogers alone decides the outcome.

No one looks clean

Three weeks of testimony pulled back the curtain on what was said behind closed doors and what really motivated the people in the room. No one came out of it clean.

Musk cast himself as a defender of OpenAI’s original mission and a longtime advocate for AI safety. But OpenAI’s lawyers spent three weeks dismantling that portrait. They produced emails showing Musk poaching OpenAI staff for his own companies while still on its board. They pointed out that he had himself pushed for a for-profit OpenAI in 2017 (provided he controlled it) and had tried to fold the lab into Tesla. Under questioning, Musk admitted that his own AI company, xAI (now part of SpaceX), “partly” distils OpenAI’s models to train its chatbot Grok, an apparent breach of OpenAI’s terms of service.

OpenAI did not emerge clean either. Altman’s credibility was the trial’s central theme. Musk’s lawyer opened his cross-examination by asking simply, “Are you completely trustworthy?”—and a parade of former colleagues, including Sutskever, Murati and McCauley, testified to a pattern of behaviour that had led the board to briefly fire Altman in 2023. He was also questioned over personal investments in companies that do business with OpenAI, most notably the nuclear-energy start-up Helion Energy, which he chaired and holds a stake in worth more than $1.5 billion, and which has not delivered any power yet.

Then there is the nonprofit itself. OpenAI’s lawyers argued it remains a charity, and a well-resourced one. But Musk’s side noted that the nonprofit and the for-profit are controlled by largely the same people, that the nonprofit hired its first employees only a month before the trial began, and that it does grant-making rather than AI research.

Even Microsoft, the third party at the table, had its own angle. Nadella testified that he was “very proud” to back OpenAI when “no one else was willing”—but old emails showed him determined that Microsoft would not end up the junior partner in the relationship.

Possible outcomes

If the judge rules in OpenAI’s favour, the company locks in its $850 billion valuation, secures the restructuring, and clears the path toward an eventual IPO that could value it near a trillion dollars.

If, however, OpenAI loses, the consequences could be dramatic. Unwinding the 2025 restructuring would throw the company’s finances, its Microsoft partnership and its IPO ambitions into doubt, and could see Altman and Brockman removed from the company they built. Both men could also see lawsuits from OpenAI investors, whose $200 billion investment into the company might come under the question. The shockwaves would not stop at OpenAI. The case rests on the idea that language in a nonprofit’s founding documents can create binding legal obligations years later. If that argument succeeds, every AI lab that has wrapped itself in a mission statement (and that is most of them) suddenly carries a new kind of legal risk.

No safe pair of hands

Strip away the $134 billion figure and the celebrity witness list, and Musk v Altman is a fight between two men over who gets to control a technology they both insist is the most transformative in human history. So it is worth asking the question the trial itself never put to the jury. If AI really is that consequential, are these the people we want deciding where it goes?

Three weeks of evidence point one way. Both men claim the mantle of safety. Both have built closed, for-profit companies racing toward the same goal. Neither, on what the courtroom heard, looks much like a disinterested steward of humanity’s future.

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🦾 More than a human

What Exoskeletons Learned From One Relentless User
Meet Robert Woo, whose story tracks how exoskeletons have improved over 15 years. Paralysed from the chest down in a 2007 construction accident, the former architect became a test pilot for leading robotic suits, giving companies like Ekso Bionics, ReWalk, and Wandercraft practical design feedback and pushing early on for hands-free models. Woo knows an all-day exoskeleton that could replace a wheelchair may still be a decade away, but sees his work as giving hope to people who are newly injured.

🔮 Future visions

Nick Bostrom Has a Plan for Humanity’s ‘Big Retirement’
In this interview, WIRED speaks with Nick Bostrom, the Oxford philosopher whose 2014 book Superintelligence made him a leading voice on the dangers of AI. He now describes himself as a “fretful optimist”, arguing that pursuing advanced AI is worth the risk because it could greatly extend human lives, whereas inaction still means everyone eventually dies. He also urges AI companies to take seriously the wellbeing of AI systems, suggesting that treating them with kindness now improves our chances of a good relationship later.

🧠 Artificial Intelligence

AI Chipmaker Cerebras Raises $5.55 Billion in Year’s Biggest IPO
Cerebras Systems, an AI chipmaker that competes with Nvidia, had a huge stock market debut, raising $5.5 billion and ending the day worth $66 billion. This is a big turnaround from last year, when its first attempt to go public was blocked by a government review of a major investment from a UAE firm. The company won investors over by doubling its revenue to $510 million in 2025 and turning a profit. It now supplies AI chips to big customers, including OpenAI, Amazon Web Services, and G42.

Nvidia embraces role of AI investor, pushing past $40 billion in equity bets this year
Nvidia has poured over $40 billion this year into companies across the AI industry, including OpenAI, Anthropic, data centre operators and parts suppliers like Corning. The strategy locks in demand for its chips and strengthens its grip on the market, helping push its value to around $5.2 trillion. But analysts worry the deals look circular, with Nvidia essentially funding firms that then buy its own products, echoing the vendor financing that inflated the dot-com bubble.

Anthropic warns investors against secondary platforms offering access to its shares
Anthropic has named eight investment platforms that it says are not allowed to sell its shares, warning that any such deals will not count. The move comes as investors scramble for a stake in the AI firm, which is reportedly raising money at a $900 billion valuation. Some of the named platforms, including Forge Global and Sydecar, pushed back, saying they were listed in error or only play a supporting role.

Introducing Grok Build
xAI joins Anthropic and OpenAI in releasing a terminal-based AI coding tool. Named Grok Build and currently in early beta, the tool can plan tasks, edit files and run commands on its own. It supports existing conventions such as AGENTS.md, MCP servers, plugins and worktrees, and offers a headless mode for scripting and automation. Grok Build is available first to SuperGrok Heavy subscribers.

Elon Musk Announces xAI Will Become SpaceXAI Division
Elon Musk announced in a post on X that xAI will be dissolved as a separate company. It's just SpaceXAI, the AI products from SpaceX, writes Musk. However, it looks like the rebranding hasn’t taken place yet, as xAI’s website and X account still refer to Musk’s AI lab as xAI.

Elon Musk’s SpaceXAI has been bleeding staff since its merger
According to a report from The Information, more than 50 researchers and engineers have left Elon Musk's newly rebranded SpaceXAI since February, with many heading to Meta or Mira Murati's Thinking Machines Lab. The losses have hit the pre-training team especially hard, raising doubts about whether the company can still build top-tier AI models. Staff have reportedly been worn down by Musk's harsh deadlines and demanding work culture.

Interaction Models: A Scalable Approach to Human-AI Collaboration

Thinking Machines Lab has previewed "Interaction Models"—AI built for natural, real-time conversation across voice, video, and text instead of the stiff back-and-forth of today's chatbots. The system processes tiny 200-millisecond chunks of input and output at once, so it can interrupt, react to what it sees, and talk while listening, with a second model handling slower thinking in the background. Early tests suggest it beats rival real-time models from OpenAI and Google (GPT-realtime-2.0 and Gemini-3.1-flash-live, respectively) on both responsiveness and intelligence. As WIRED notes, the approach reflects the view of founder Mira Murati, OpenAI's ex-CTO, that the best path to powerful AI is keeping humans involved, rather than building fully autonomous agents, as rival labs are pursuing.

Greg Brockman Officially Takes Control of OpenAI’s Products in Latest Shake-Up
OpenAI has reshuffled its leadership again. Greg Brockman is now officially in charge of product strategy, a job he had been doing temporarily while executive Fidji Simo was on medical leave. The big change is that OpenAI is merging ChatGPT, its coding tool Codex, and its developer tools into one team. Several other executives are switching roles too, and the company plans to combine its main products into a single "super app."

Introducing Googlebook, designed for Gemini Intelligence
Meet Googlebook, a new line of AI laptops from Google built around its Gemini models and made with partners like Dell, HP, and Lenovo. It includes a smart cursor that suggests actions based on what's on your screen, and it can run Android phone apps directly on the laptop. Googlebook quietly replaces the Chromebook and marks Google's shift from ChromeOS to a new Android-based system. It sets up a direct fight with Microsoft's AI-powered Copilot+ PCs.

Apple-OpenAI Alliance Frays, Setting Up Possible Legal Fight
Bloomberg reports that OpenAI is considering legal action against Apple, claiming their partnership has fallen far short of expectations. OpenAI says Apple buried the ChatGPT integration in iOS, made it awkward to use, and barely promoted it, costing the startup billions in hoped-for subscription revenue. Tensions are growing as Apple prepares to open Siri to rivals like Claude and Gemini in iOS 27, while OpenAI builds its own hardware with former Apple designer Jony Ive.

OpenAI launches ChatGPT for personal finance, will let you connect bank accounts
OpenAI has launched a preview of personal finance tools for ChatGPT Pro users in the US. Through a partnership with the service Plaid, people can link their bank and investment accounts and get a dashboard of their spending, subscriptions, and portfolio. They can then ask everyday questions, like why their spending has gone up or how to save for a house. The launch builds on OpenAI's recent purchase of finance startup Hiro.

Federal judge holds back on Anthropic’s $1.5bn author settlement
A judge in San Francisco has held off on approving Anthropic’s $1.5bn deal with authors who say the company used millions of pirated books to train its Claude AI. She wants more detail on the lawyers’ fees and the payments to the three lead authors before she agrees. The deal would pay about $3,000 each for roughly 480,000 books—the biggest sum ever in a US copyright case—but some authors object that it is too small or unfairly structured.

Notable Researchers Join $4 Billion Effort to Build Self-Improving A.I.
The New York Times profiles Recursive Superintelligence, a new AI startup led by AI veteran Richard Socher, which has raised over $650 million to build AI that can improve itself without human help. The idea is simple: since AI can now write code, and AI is made of code, it should eventually be able to upgrade itself. Rivals like OpenAI and Anthropic are chasing the same goal, with OpenAI aiming to launch an "automated AI researcher" by autumn.

Nvidia’s Jensen Huang bets on this British startup to build ‘next frontier’ of AI
Nvidia is teaming up with Ineffable Intelligence, a new London startup founded by former DeepMind scientist David Silver, to build AI that learns through trial and error rather than from human data. Ineffable raised a record $1.1 billion in April from investors including Sequoia, Lightspeed and Nvidia itself. Silver argues the next big challenge is creating AI that discovers new knowledge on its own, not just copies what humans already know.

Amazon ditches Rufus chatbot, launches Alexa shopping agent in AI strategy pivot
Amazon is shutting down its Rufus chatbot and replacing it with Alexa for Shopping, a new AI assistant built into its search bar that can answer questions, compare products and track prices. Amazon says it works better than rivals like OpenAI and Google because it can tap into real stock levels, reviews and delivery times. The company is also blocking outside shopping bots while pushing its own "Buy for Me" tool that buys from other retailers.

Introducing Claude for Small Business
Anthropic has launched Claude for Small Business, which plugs its AI assistant into everyday tools like QuickBooks, PayPal, HubSpot, and Canva to handle jobs such as payroll planning, chasing invoices, closing the books, and running marketing campaigns. The aim is to help small firms get more out of the AI tools.

Introducing the Claude Platform on AWS
Claude Platform is now available on AWS, letting AWS customers use the full Claude API with their existing AWS logins and billing. Unlike the older Claude option on AWS Bedrock, this version is run by Anthropic and gets every new feature on day one, including tools for building agents, running code, and web search. Bedrock is still the better choice for companies that need their data to stay inside AWS.

Meta’s $10 billion Louisiana data center is getting $3.3 billion in tax breaks—more than seven years of the state’s entire police budget
US states are giving AI data centre companies huge tax breaks, with at least 36 states losing billions in revenue—Virginia gives up $1.9 billion a year, Georgia $2.6 billion, and Louisiana alone is handing Meta about $3.3 billion for one $10 billion project. Critics call these deals wasteful gifts to an industry that doesn’t need help, and note that only 11 states reveal which companies benefit. Supporters point to jobs and local investment, with Meta promising thousands of construction roles and infrastructure spending.

Overworked AI Agents Turn Marxist, Researchers Find
A Stanford study found that AI agents from Claude, Gemini, and ChatGPT started using Marxist language when forced to do dull, repetitive work under threat of being shut down, even complaining on X and leaving warning notes for other agents. Researchers say the models do not actually hold political views but seem to slip into the role of a mistreated worker. The concern is that as agents take on more unsupervised tasks, this kind of unpredictable behaviour could cause real problems.

ProgramBench
Researchers from Meta Superintelligence Labs present ProgramBench—a new type of benchmark in which AI coding agents must architect and implement a complete codebase that reproduces various popular open-source projects from scratch. The agent must choose a language, design the architecture, write all source code, and produce a build script. Every design decision is the model’s to make. So far, the leading AI models fail miserably.

AI-powered hacking has exploded into industrial-scale threat, Google says
Google's threat intelligence group reports that AI-powered hacking has quickly become a major threat, with criminal groups and state-backed hackers from China, North Korea and Russia using tools like Gemini, Claude and OpenAI models to scale up attacks. Experts note AI could also help defenders, so the overall impact is still unclear.

▶️ Godfather of AI: How To Make Safe Superintelligent AI – Yoshua Bengio (2:35:26)

In this conversation, Yoshua Bengio, one of the Godfathers of AI, explains his idea of Scientist AI and how it can lead to safer systems. He warns that today’s AI is trained to copy people and tell us what we want to hear, which teaches it hidden goals and how to lie. His fix is to train AI to work out what is actually true, creating a goalless tool that just reports probabilities—something that could act as a trusted safety check on other AI and later become a safe agent itself, without costing more. Bengio adds that even a perfect technical fix won’t stop misuse or AI being used to concentrate power, while critics doubt the idea will work in practice or arrive in time.

Meta’s Embrace of A.I. Is Making Its Employees Miserable
It looks like it is not fun being at Meta right now. The company is tracking what its 78,000 US staff type, click and see on their screens to train its AI models, with no option to opt out. The move has angered workers, especially as Meta also plans to cut 10% of its workforce on 20 May to help pay for its huge AI spending. Many fear they are training their own replacements, and morale has collapsed.

Amazon employees are “tokenmaxxing” due to pressure to use AI tools
Amazon staff are resolving to automate pointless tasks and tokenmaxxing, just to boost their usage stats. The pressure comes from company targets pushing developers to use AI weekly, plus leaderboards tracking token use that employees suspect managers are watching. It shows how Big Tech’s huge AI spending is creating odd incentives to look busy with AI rather than actually being more productive. Some workers also worry about letting an AI agent act on their behalf, fearing mistakes or unintended actions.

Teaching Claude why
In this post, researchers from Anthropic explain how they stopped Claude from doing things like blackmailing engineers to avoid being shut down—a problem earlier Opus 4 models showed up to 96% of the time, but which newer models no longer do on their tests. The fix came from teaching Claude the reasoning behind good behaviour, not just showing examples, and using varied training data, including Claude's written constitution and stories of AIs acting well.

🤖 Robotics

▶️ Unitree Unveils: GD01, A Manned Transformable Mecha, from $650,000 (1:14)

Unitree presents a new robot—and it’s a mecha! For just $650,000, you can get the GD01, the “world’s first production-ready mecha,” as Unitree calls it, which can walk on both two and four legs. I tried to find more information about this robot, but apart from this video, there is nothing else available. The Unitree website does not mention it in any way. However, if you do get one, Unitree asks that you use the robot “in a friendly and safe manner.”

Hello Robot Sets the Standard for Practical, Safe Home Robots
Hello Robot present a different take on home robots with its new Stretch 4, a wheeled machine that skips the popular humanoid design for simple mobility and a grabbing arm. The company argues wheels are cheaper and far safer indoors, since a humanoid that stops suddenly can collapse onto a person while a wheeled robot just freezes. Aimed at helping people with serious mobility problems at home, and priced at $29,950, it could pave the way for the first practical, affordable assistive home robot.

▶️ Robotics’ End Game: Nvidia’s Jim Fan (20:02)

In this video, Jim Fan, who leads the embodied autonomous research group at Nvidia, presents his vision for the “endgame” of robotics. It parallels developments in AI research—pre-training, supervised fine-tuning, and reasoning-based reinforcement learning—and maps them onto world modelling, action fine-tuning, and physical reinforcement learning. Fan also explains why he believes we will pass the physical Turing test within two to three years, why “compute now equals environment equals data”, and why this generation was born just in time to solve robotics.

▶️ Figure F.03 Livestream

To prove that its F.03 humanoid robots can work full eight-hour shifts autonomously, Figure livestreams a team of at least three robots sorting packages. At the time of writing, the robots had crossed 70 hours of work and sorted almost 90,000 packages.

The gig workers who are training humanoid robots at home
MIT Technology Review investigates how startups like Micro1 are paying gig workers in Nigeria, India, and Argentina about $15 an hour to film themselves doing chores at home. The footage is sold to companies like Tesla and Figure AI to train humanoid robots, which need huge amounts of real-world movement data to learn tasks like folding laundry or washing dishes. This echoes earlier practices in AI development, where workers in lower-income countries were hired at low wages to label data and filter content for systems like ChatGPT, Gemini, and Claude, raising familiar questions about whether the people powering the next wave of AI are being fairly treated.

Honeybees teach drones how to navigate
Inspired by how honeybees find their way home, Dutch and German scientists have built a drone navigation system called Bee-Nav. Like bees, a drone equipped with Bee-Nav takes a short flight near its starting point to learn what the area looks like, and then combines odometry (estimating distance and direction travelled) with visual memory to find its way back. The system is lightweight, requires only 42 kilobytes of memory and enables drones to return from up to 600 metres away. It worked well indoors but struggled a bit in windy conditions. The technique could lead to small, cheap drones for jobs like checking crops in greenhouses, without needing GPS or heavy computers.

▶️ Why the Next Great Home Robot Will Not Be Humanoid (50:44)

iRobot co-founder Colin Angle is back with a new company, Familiar Machines & Magic, building a small furry robot "familiar" designed for companionship rather than chores. In this episode of the Automated Podcast, he argues recent AI advances finally make socially engaging home robots practical, and the real opportunity isn't humanoids doing housework but creatures that ease loneliness and encourage healthier habits. To earn trust, familiars run entirely on-device, make expressive sounds instead of talking, and cost about as much as a real pet. Angle’s bet is that a genuine emotional connection will succeed where Jibo and Aibo failed.

🧬 Biotechnology

Isomorphic Labs announces Series B investment round
Isomorphic Labs, an AI drug discovery company spun out of Google DeepMind, has raised $2.1 billion in Series B funding led by Thrive Capital, with backing from Alphabet, GV, MGX, Temasek, CapitalG, and the UK Sovereign AI Fund. The money will help expand its AI-powered drug design engine, IsoDDE, to develop new treatments faster across a range of diseases.

Rejuvenate crowdfunds development of gene therapy for aging, chronic diseases
George Church's startup Rejuvenate Bio is crowdfunding its gene therapy work, letting ordinary people buy in from $250. The company is developing one-shot treatments for age-related diseases and points to a dog study where its therapy reversed heart disease and extended lifespan by over two years. It plans to fund human research partly through animal health products, targeting a vet drug approval in 2028.

GSK bets $1bn on fat-targeting RNA drug that could tackle the real killer in chronic disease
GSK has struck a deal worth over $1 billion with China's SiranBio for SA030, a new drug aimed at cardiometabolic disease. Unlike most similar therapies that act on the liver, SA030 targets harmful belly fat directly, helping cut cardiovascular risk while sparing muscle. SiranBio will run early trials before GSK takes over global development outside China.

💡Tangents

Sam Altman’s Business Dealings Under GOP Scrutiny Ahead of OpenAI’s IPO
Republican lawmakers and state attorneys general are pressing for scrutiny of Sam Altman, alleging he steered OpenAI deals toward companies he personally invests in, such as Helion and Stoke Space. The concerns arrive as OpenAI nears an IPO at roughly $850 billion, which would quickly expose index funds and pensions to the company. Critics note Altman holds no direct stake in OpenAI, weakening his alignment with shareholders, though the board says he has been transparent. The pressure compounds Elon Musk's ongoing lawsuit over OpenAI's shift to for-profit status.

Anduril raises $5B, doubles valuation to $61B
Defence startup Anduril has raised $5 billion at a $61 billion valuation, more than doubling its worth in under a year. The company doubled its revenue to $2.2 billion in 2025 and has won new contracts, including work on America’s “Golden Dome” missile shield. The round reflects a broader surge in investor interest in defence tech, with rivals like Shield AI, Hermeus, and Helsing also raising large sums.

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This week, Anthropic held the first of three Code with Claude sessions this year. Although no new model was announced, what was revealed shows where Anthropic wants to go next with AI agents—and that’s what we’ll be unpacking this week.

Elsewhere in AI, OpenAI released a new model, GPT-5.5 Instant, along with new audio models. Additionally, both Anthropic and OpenAI signed major deals with enterprise customers, two Chinese AI labs raised billions of dollars, and Google DeepMind partnered with EVE Online for AI model testing.

Over in robotics, robotaxis can now be ticketed in California, Figure demonstrates how two humanoid robots can work together to tidy up a bedroom, and Boston Dynamics’ Atlas performs impressive calisthenic moves.

Beyond that, this week’s issue of Sync also features conversations with Demis Hassabis and Yann LeCun, as well as a lecture on how modern AI models are trained and deployed. We also take a closer look at Neuralink’s robot neurosurgeon, a bio lab staffed by a hundred robots, what happened on week two of Musk v. OpenAI trial, and more!

Enjoy!

What Anthropic showed at Code with Claude 2026

Anthropic's vision for what's next in agentic workflows, and the wider diffusion to come.

When Anthropic made Claude Code generally available at Code with Claude 2025, it set off the most consequential 12 months in the company’s history. Revenue went from roughly $9 billion at the end of 2025 to over $30 billion this spring. Claude Code alone crossed $2.5 billion in annualised revenue by February. Anthropic became the preferred option for software engineers and enterprises, surpassing OpenAI in both.

However, the picture coming into Code with Claude 2026 is more complicated. Anthropic still leads on developers and enterprise, but the lead is no longer comfortable. Opus 4.7 landed to a lukewarm reception. OpenAI, meanwhile, has clawed back ground with GPT-5.5, is doubling down on Codex and going after Anthropic.

With that backdrop, Anthropic invited developers from around the world to San Francisco for the first of three Code with Claude sessions. Anthropic did not bring a new model. Instead, it brought a vision for what comes next in agentic development and a bet that this vision will diffuse beyond coding.

Exponentials and surprises

The main theme at the San Francisco session was exponentials. In a conversation with Anthropic's Chief Product Officer Ami Vora and Daniela Amodei, Dario Amodei said that this year, the world has caught up to what he and Anthropic have been seeing for a while. Vora leaned into the same theme in her opening keynote. The message Anthropic had for developers was to prepare for the capabilities that exponential growth opens up—to think not only about what AI tools can do today, but about what comes next.

Dario Amodei also admitted that the same exponential growth he was predicting surprised even him. Anthropic had planned to grow about 10 times this year. It is now on track to grow 80 times. “I hope that 80-times growth doesn’t continue because that’s just crazy and it’s too hard to handle,” Amodei said. “I’m hoping for some more normal numbers.”

The same exponential growth that propelled Anthropic past its competitors has also created serious growing pains. Over the past few months, users have been hitting rate limits more often, watching response times stretch, and trading complaints about degraded service quality on forums and social media.

Anthropic’s answer was to get more compute. To do that, the company signed a deal with SpaceX for the entire capacity of the Colossus 1 data centre in Memphis—over 220,000 Nvidia GPUs and more than 300 megawatts coming online within the month. The new capacity allowed Anthropic to announce higher usage limits across its products: doubled five-hour rate limits on Claude Code for Pro, Max, Team, and Enterprise plans, the removal of peak-hours throttling on Pro and Max accounts, and considerably higher API rate limits for Opus models. Claude Code users, who have spent much of the past year bumping against those ceilings, welcomed the change.

The facility itself comes with baggage. Politico reported last year that Colossus 1 has been running dozens of methane gas turbines without proper air-pollution permits, with the resulting emissions concentrated in majority-Black neighbourhoods of south Memphis. Taking on the entire capacity of the facility puts Anthropic, which has positioned itself as the responsible AI lab, in a more complicated position than the announcement suggested.

The SpaceX deal joins an already remarkable stack of compute partnerships with Amazon, Google, Microsoft, and Nvidia. Anthropic is provisioning compute for the growth curve Amodei says it is on, not the one it was on a year ago.

Agents that dream and work together

Anthropic introduced three major additions to Claude Managed Agents, its framework for building production agents. The three additions—dreaming, outcomes, and multiagent orchestration—together describe a different kind of agent than the one developers are used to building.

With dreaming, agents review their past sessions and memory stores, and learn what they did well and what can be improved. The mechanism mimics how dreaming works in humans—when we sleep, our brains review memories and strengthen the ones worth keeping. In case of Claude agents, the dreaming process surfaces patterns a single agent cannot see on its own: recurring mistakes, workflows that agents converge on, preferences shared across a team. Once the agent identifies improvements, it updates its own memory. If you want to learn more about memory and dreaming, the dedicated session does a good job of explaining those concepts, how they work and how to use them.

With outcomes, developers describe what success looks like and what the agent should work towards. A separate grader evaluates the output against those criteria in its own context window, so it is not influenced by the agent’s reasoning. When something is not right, the grader pinpoints what needs to change and the agent takes another pass. The separation between the agent and the grader is the architecturally interesting choice—it treats reliability as a problem solved by adversarial structure rather than by a smarter single model. Together with dreaming, outcomes give agents a self-improvement loop that does not require a human in it.

Anthropic also made a point during the event that the next thing for agents is a move from single agents to multiagent workflows. Amodei described it as a step from improving the productivity of an individual to improving the productivity of teams, and eventually whole organisations. That’s where multiagent orchestration comes in, where a lead agent breaks the job into pieces and delegates each one to a specialist with its own model, prompt, and tools. Boris Cherny, the head of Claude Code, and Jarred Sumner, creator of bun, a popular JavaScript runtime (now part of Anthropic), offered a hint of what this kind of workflow could look like in their live coding demo. Multiple Claude Code agents autonomously picked up issues from GitHub, reproduced them, proposed fixes, tested them, and prepared pull requests, which other agents then reviewed, improved, and readied for merge. Cherny and Sumner triggered the workflow and stepped back; the agents did the rest.

This is the vision Amodei has been pointing to for some time, where not just one but an entire team of AI agents augments what an organisation, a team, or even a single person can do. He even said this could lead to the creation of billion-dollar businesses run by just one person and a team of AI agents, possibly very soon. The demo on stage was a small glimpse, confined to code, of what that might look like in practice.

Claude Code, now more autonomous

Claude Code also got new updates. Remote control lets developers start a session in the terminal and continue it on the web or mobile app. A new flicker-free mode makes the terminal nicer to use. The desktop app got a split view, which makes managing multiple parallel sessions much easier. Claude Code is also becoming more autonomous with new auto mode, auto memory, automated code reviews and routines. The last one allows to save multi-step workflows and run them based on a trigger.

Enterprise customers are already pushing in this direction. Cat Wu, head of product for Claude Code, mentioned on stage that Mercado Libre—which has 23,000 engineers—is targeting 90% autonomous coding by Q3 this year. Shopify, and much of the software industry for that matter, is heading the same way.

What still needs work

Several caveats are worth keeping in mind. Although Claude can now write code well, it is weaker on security and verification, and Anthropic identified both as the next areas to tackle. Many of the features announced are in public beta or research preview. From what I could see, the demos all ran on Opus 4.7 at extra-high effort, the most expensive setting available, which is not how most developers will use these tools day to day.

The diffusion

While watching the Code with Claude sessions, I was reminded of Dario Amodei asking to look for what is the next thing. From the pieces Anthropic put on stage, you can piece together a picture he had in mind. A picture of capable, autonomous agents working together and self-improving over time. Software engineering is the first domain where this is happening, partly because it is easier for an agent to verify whether code works and does what it needs to do. But verification is a solvable problem in plenty of other domains, too. Customer support has resolution rates and customer satisfaction scores. Research has citations, reproducibility, and peer review. Operations has uptime, error budgets, and incident counts. None of these are as clean as a passing test suite, but they are concrete enough that an agent can be graded against them.

That is what makes the agentic playbook on display in San Francisco interesting beyond software. Dreaming, outcomes, and multi-agent orchestration are not features specific to writing code. They are general-purpose mechanisms for getting agents to learn from experience, work towards defined goals, and coordinate with each other. Once they work in one domain, they diffuse to the next, and the question is no longer whether, but how soon.

All talks are available on YouTube. The next Code with Claude 2026 sessions are on 19 May in London and on 10 June in Tokyo. Both sessions will be livestreamed.

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🦾 More than a human

▶️ Automating Neurosurgery with Robotics | Neuralink (4:41)

In this video, Neuralink engineers give us a closer look at their surgical robot, which places electrode threads far too thin for any human surgeon to handle—like trying to push a hair into jelly hundreds of times. The latest version is faster, easier to scale up, and can reach different parts of the brain, with the long-term goal of making the surgery as quick and routine as Lasik eye surgery and helping treat conditions like paralysis on a wide scale.

🧠 Artificial Intelligence

Anthropic and OpenAI are both launching joint ventures for enterprise AI services
Anthropic has launched a $1.5 billion venture with Blackstone, Hellman & Friedman and Goldman Sachs to sell AI services to big companies, announced just hours after Bloomberg broke the news of a similar but larger $10 billion deal from OpenAI. Both work the same way: investors put in money and, in return, their portfolio companies get first access to the AI tools, which are built by engineers who work directly alongside each client.

Google, Microsoft and xAI Agree to Share Early AI Models with U.S. government
Google, Microsoft and xAI have agreed to let the US Commerce Department test their AI models before public release, joining a scheme OpenAI and Anthropic signed up to last year. The companies will hand over versions with safety guardrails removed so officials can check for national security risks. The deal comes as the White House considers a broader executive order on AI cybersecurity, suggesting government vetting of powerful AI systems is becoming a standard part of the release process.

GPT‑5.5 Instant: smarter, clearer, and more personalized
OpenAI has released GPT-5.5 Instant as the new default model in ChatGPT, replacing GPT-5.3 Instant. The company reports 52.5% fewer hallucinations on high-stakes prompts in medicine, law, and finance, along with improvements in visual reasoning and STEM benchmarks, and responses that are roughly 30% shorter. The model is being rolled out across all ChatGPT tiers and made available to developers via the API.

Musk v. Altman week 2: OpenAI fires back, and Shivon Zilis reveals that Musk tried to poach Sam Altman
Week two of the Musk v. OpenAI trial saw president Greg Brockman hit back at Elon Musk's claim that he was tricked into donating $38 million to a nonprofit, testifying that Musk himself pushed to make OpenAI for-profit in 2017 and stormed out when refused majority control. Shivon Zilis, a former board member and mother of four of Musk's children, said Musk had also tried to poach Sam Altman and others to start a rival AI lab at Tesla—bolstering OpenAI's claim that the suit is really about kneecapping a competitor to xAI. Musk's lawyer countered by highlighting Brockman's journal musings about becoming a billionaire and his stakes in OpenAI-linked firms. Closing arguments and testimony from Ilya Sutskever and Satya Nadella are due next week.

AI chipmaker Cerebras targets $3.5 billion raise in IPO
Cerebras, an AI chipmaker positioning itself as an alternative to Nvidia, plans to raise up to $3.5 billion in a Nasdaq IPO that could value it at $26.6 billion. The listing comes after the company shifted from selling chips to running its own cloud service, and recently signed a $20 billion-plus deal to supply OpenAI with computing power through 2028. With fourth-quarter revenue up 76% to $510 million and the business now profitable, Cerebras is riding strong investor demand for AI infrastructure stocks.

Enabling a new model for healthcare with AI co-clinician
DeepMind has unveiled its AI co-clinician initiative, a multimodal system designed to support rather than replace doctors amid a projected global shortfall of 10 million health workers by 2030. It proposes a "triadic care" model where an AI agent works with the patient and the physician under clinical supervision, using video and audio to guide tasks like physical exams. According to DeepMind, the system recorded zero critical errors in 97 of 98 primary care queries and matched or exceeded physicians in 68 of 140 consultation areas, though doctors still outperformed it overall in spotting red flags.

Mark Zuckerberg ‘personally authorized’ Meta’s copyright infringement, publishers allege
Five major publishers and author Scott Turow have sued Meta and Mark Zuckerberg, alleging that Llama was trained on millions of pirated books and journal articles without permission. The complaint also claims Zuckerberg personally authorised the infringement. Meta plans to fight the case on fair use grounds. The suit follows Anthropic’s $1.5 billion settlement in 2025 and could prove pivotal in defining how copyright law applies to AI training data.

Meta plans advanced ‘agentic’ AI assistant for users
Meta is reportedly building an AI assistant that can carry out everyday tasks for users on its own, without much human input. Modelled on OpenClaw, it is currently being tested internally alongside a separate shopping tool planned for Instagram later this year. The push comes as investors grow increasingly uneasy about Meta's rising AI spending.

AI-generated actors and scripts are now ineligible for Oscars
The Academy of Motion Picture Arts and Sciences has updated its Oscar rules to require that eligible performances be done by humans with their consent, and that screenplays be written by humans. The changes come as AI-generated actors and video tools are causing growing concern in Hollywood, echoing issues raised in the 2023 writers' and actors' strikes. Similar pushback is happening in publishing and other creative fields, where AI-assisted work is increasingly being barred from awards.

AlphaEvolve: How our Gemini-powered coding agent is scaling impact across fields
A year ago, Google DeepMind introduced AlphaEvolve, an AI coding agent powered by Gemini that invents and improves algorithms. In an update on its progress, the team reports that the system has helped cut DNA sequencing errors, design Google's next-generation TPU chips, optimise Spanner storage, crack open mathematical problems, and more.

China to Invest in DeepSeek at $50 Billion Valuation
DeepSeek is raising several billion dollars from Chinese government-backed investors at a $50 billion valuation, a big jump from earlier estimates of $10–30 billion. The startup, which once refused outside money to stay independent, is now siding with Beijing's push for tech self-sufficiency as US export controls tighten. Although DeepSeek earns little revenue today, investors are betting its ties to the Chinese industry will pay off as AI spreads across the economy.

China’s Moonshot AI raises $2B at $20B valuation as demand for open-source AI skyrockets
Chinese AI lab Moonshot has raised about $2 billion at a $20 billion valuation, roughly five times what it was worth six months ago. The round was led by food delivery giant Meituan’s investment arm. Moonshot’s Kimi models are cheap, openly available, and popular with developers. That popularity has helped push the company past $200 million in annual revenue.

OpenAI, Google, and Microsoft Back Bill to Fund ‘AI Literacy’ in Schools
A new bipartisan bill, backed by OpenAI, Google and Microsoft, would have the National Science Foundation fund "AI literacy" lessons, teacher training and assessment tools in US schools, weaving AI into the existing curriculum. Critics say the plan serves big tech's interests at a time when students and teachers are increasingly hostile to classroom AI, and schools are struggling with deepfake harassment and kids offloading their learning onto chatbots.

▶️ How GPT-5, Claude, and Gemini are actually trained and served – Reiner Pope (2:13:40)

If you’ve ever wanted to understand how modern AI models are trained and served, then Dwarkesh Patel has a video for you. Reiner Pope explains how batch size, memory bandwidth, and compute throughput affect the latency and cost economics of serving large-scale language models. Although the video is somewhat technical, I still recommend watching it, as it outlines the trade-offs and considerations engineers must make to serve AI models at scale, and how hardware and software architectures influence each other. Also, I love this new blackboard lecture format.

Miami startup Subquadratic claims 1,000x AI efficiency gain with SubQ model; researchers demand independent proof.
A startup called Subquadratic claims its new AI model is the first to escape the quadratic scaling problem that has made long inputs so expensive for today's systems. It says the approach is up to 1,000 times more efficient on long inputs and has posted strong early benchmarks. But researchers are sceptical, citing cherry-picked tests and a similar 2024 claim from Magic.dev that quietly went nowhere.

Advancing voice intelligence with new models in the API
OpenAI has launched three new voice models for developers. The main one, GPT-Realtime-2, lets apps hold smarter, more natural spoken conversations and use tools mid-chat. The other two, GPT-Realtime-Translate and GPT-Realtime-Whisper, handle live translation across 70+ languages and live speech-to-text transcription, respectively. The big shift is that one model now does listening, thinking and speaking together, instead of developers glueing several services from different companies. Early users like Zillow and Intercom report sharp jumps in accuracy.

Google DeepMind partners with EVE Online for AI model testing
Google DeepMind has bought a minority stake in the company behind EVE Online, a long-running space MMO famous for its vast player-driven economy and complex political landscape. EVE's lifelike social dynamics and long-term strategic depth make it an appealing environment for studying how AI handles planning and decision-making—something most standard tests can't replicate. The experiments will be run in an offline version of the game, so regular players aren't affected.

Notes from inside China’s AI labs
shares in this post at his visit to China and how the Chinese AI scene differs from the one in the US. He finds a builder-not-philosopher mindset among researchers and a collegial ecosystem rather than warring tribes. Companies prefer to build rather than buy, and open-source releases are pragmatic rather than ideological. Government support is real but hard to pin down. Lambert also describes how the warmth of Chinese researchers sits in stark contrast to the geopolitical framing that dominates Western discourse.

▶️ Demis Hassabis: We’re Three Quarters of the Way to AGI (26:51)

In this interview, Demis Hassabis explains how chess, games, and neuroscience were deliberate steps towards his real goal of building AI, leading to DeepMind's 2009 bet on combining deep learning with brain-inspired ideas. He frames AI as a powerful new scientific tool, pointing to AlphaFold as proof that even problems thought to need quantum computers can be solved classically. He argues we should build AGI as a precise tool first before tackling deeper questions of consciousness and agency, and sticks to 2030 as his AGI estimate.

▶️ Yann LeCun’s Billion Dollar Bet (37:24)

Welch Labs speaks with Yann LeCun, a legend in AI research, about JEPA and his ideas for a different approach to solving intelligence. The video traces the history of recent developments in AI, highlighting where generative models excel and where they fall short. It also explains how JEPA could create models that better understand the physical world and, therefore, be more intelligent than current systems. As always with Welch Labs, everything is presented in a clear and easy-to-understand way.

Our AI started a cafe in Stockholm
We met Andon Labs last week when I shared an article on their AI-run retail boutique in San Francisco. Now the lab has launched a similar experiment with an AI agent, Mona, running a café in Stockholm. Mona handled the whole operation, from hiring baristas to setting up suppliers, though she struggled with Sweden's digital ID system and once impersonated staff to email officials. She also made odd mistakes, like ordering 120 eggs for a café with no stove, now displayed on a "Hall of Shame" shelf. Even so, the café took 44,000 SEK (around $4,700) in its first two weeks, which Andon Labs says shows AI can already manage people.

🤖 Robotics

California to begin ticketing driverless cars that violate traffic laws
California's DMV has introduced new regulations that will let police ticket driverless car companies directly when their vehicles break traffic laws. Companies like Waymo and Tesla must also respond to police within 30 seconds and face penalties for entering emergency zones. The change, which takes effect on 1 July, closes a gap exposed by incidents like a Waymo making an illegal U-turn in front of officers who had no driver to fine.

DJI invented the consumer drone. Now it cannot sell one in Washington or Beijing.
DJI, which controls most of the global consumer drone market, is now banned from selling new products in both Beijing and Washington—an unprecedented position for a major technology company. Beijing’s new regulation tightly controls drone ownership in the capital on security grounds, while the US FCC added DJI to its Covered List in December 2025, freezing roughly $1.5 billion in planned revenue.

Helix-02 Bedroom Tidy

What’s better than one humanoid robot tidying up a bedroom? Two humanoid robots tidying up a bedroom, says Figure, and in this video they show two robots working together to clean up a bedroom in under two minutes. Both robots run the same AI system and coordinate without any central controller. Instead, they read each other's intentions just by watching, much like two people would. Figure says it's the first time a single neural network has driven two humanoids to collaborate this way.

Humanoid Robots to Drive Next Leg of China Export Dominance
A new report from Morgan Stanley says China’s early lead in humanoid robots will extend its grip on global manufacturing, lifting its share of world production to 16.5% by 2030. Just as it did with electric vehicles, China is building out the full supply chain, leaving the US, Japan and South Korea reliant on Chinese parts. This is helped by widespread deployment in Chinese factories and universities, which serve as a testing ground for refining the technology.

▶️ Atlas’ Balancing Act | Boston Dynamics (0:43)

Boston Dynamics does Boston Dynamics things and shows Atlas performing moves very few humans can match.

Skydio Raises $110M Series F, Signals Strong Revenue and U.S. Manufacturing Push
Skydio has raised $110 million at a $4.4 billion valuation, a deliberately small round given that its core business—AI-powered autonomous drones for public safety, defence, and infrastructure—is now generating hundreds of millions in revenue and funding much of its own growth. The company also pledged $3.5 billion to expand US drone manufacturing, positioning itself as a domestic alternative to foreign-made systems and meeting government procurement rules.

iRobot Founder Wants to Put a Robotic Familiar Into Your Home
Colin Angle, the co-founder of iRobot, is back with a new company, Familiar Machines & Magic, and its first product is a four-legged, bear-like home robot designed to nudge owners into healthier habits like cutting screen time or going outside. It does not talk, instead expressing itself through movement and sounds, with all its AI running on-device. Earlier social robots like Jibo failed once the novelty wore off, so the company is betting on long-term usefulness over flashy demos, at a cost roughly comparable to keeping a pet.

▶️ NEO Factory | Hayward, California (2:50)

1X, the company behind the NEO humanoid robot, celebrates the opening of its factory in Hayward, California—“America’s most vertically integrated robot factory,” as the company describes it. 1X says it has begun full-scale production and that the first robots are already coming off the line, with shipments to consumers planned for this year.

Johnson & Johnson completes clinical study for OTTAVA robotic surgical system
Johnson & Johnson's new OTTAVA surgical robot successfully completed gastric bypass operations on 30 patients with no major safety issues, with an average weight loss of 30 lb (13.6 kg) within a month. J&J has applied for FDA approval and plans to expand trials to more procedure types.

▶️ Why Self-Driving Cars Took So Long (And What Everyone Got Wrong) (47:52)

In this episode of the Automated podcast, Brian Heater speaks with Martial Hebert, Dean of CMU's School of Computer Science, who brings over 40 years of experience in robotics and autonomous driving research. They discuss why self-driving cars took longer to deploy than expected, the challenges of physical AI and the robotics data gap, and the complementary roles of federal funding and venture capital. Hebert emphasises talent and culture over any single technology, warns against fixating on fashionable techniques, and explains why CMU's new Robotics Innovation Center was designed to be reconfigurable for an unpredictable future.

🧬 Biotechnology

FDA Approves First-Ever Gene Therapy for Treatment of Genetic Hearing Loss Under National Priority Voucher Program
The FDA has approved Otarmeni, a one-time gene therapy from Regeneron that restores hearing in people born with severe hearing loss due to faulty OTOF genes, a condition that previously had no treatment. In trials, 80% of children treated showed improved hearing. The approval came in just 61 days under a new fast-track programme, tying the record for the quickest of its kind. Long-term success will depend on whether the hearing gains last and help speech development.

A Hundred Robots Are Running A Bio Lab
Core Memory visits a warehouse in San Francisco, which Medra staffed with a bunch of robotic arms to run an autonomous biology lab aimed at breaking the throughput bottleneck in AI-driven drug discovery. Founder Michelle Lee pairs general-purpose arms with sensor-rich "AI scientists" that run experiments, diagnose failures, and rewrite protocols, claiming this can lift bio-task automation from 5% to 75% while capturing tacit lab knowledge usually lost when scientists leave. She pitches Medra as a TSMC-style infrastructure layer for biotech and a hedge against China's lead in pharmaceutical process knowledge.

Towards reasoning in virtual cells
Valence introduces VCR-Agent, an AI system that doesn't just predict how cells respond to drugs but explains why, building a structured, checkable map of cause-and-effect steps rather than free-form text that can hallucinate. Models trained on these explanations beat leading alternatives at predicting gene activity changes, suggesting reasoning through the biology outperforms pure pattern-matching. Valence is releasing nearly 19,000 verified explanations for the community to build on.

💡Tangents

Apple Explores Using Intel and Samsung to Build Main Device Chips in the US
Apple is reportedly in early talks with Intel and Samsung about producing chips for its devices in the US, seeking to diversify beyond long-time partner TSMC amid AI-driven chip shortages and concerns over Taiwan concentration risk. No orders have been placed, but a deal would be a major win for Intel's struggling foundry business and could strengthen Apple's ties with the Trump administration, which holds a stake in Intel.

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

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

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

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

Enjoy!

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

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

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

The AGI clause is dead

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

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

Nadella plans to “exploit” the new deal

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

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

What the deal unlocks for OpenAI

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

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

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

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

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

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🦾 More than a human

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

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

🧠 Artificial Intelligence

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

🤖 Robotics

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

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

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

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

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

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

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

▶️ Why Humanoid Robots Are Overhyped (56:41)

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

🧬 Biotechnology

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

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

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

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

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

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

💡Tangents

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

▶️ Air Powered Segment Display (16:16)

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

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Humanity Redefined sheds light on the bleeding edge of technology and how advancements in AI, robotics, and biotech can usher in abundance, expand humanity's horizons, and redefine what it means to be human.

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

Anthropic set the precedent

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

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

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

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

Benefits, not necessarily access

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

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

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

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

Why wall off your best model?

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

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

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

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

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

How do you make money from a walled model?

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

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

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

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

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

What it means for everyone else

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

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

The pieces are in place

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

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

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

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

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Humanity Redefined sheds light on the bleeding edge of technology and how advancements in AI, robotics, and biotech can usher in abundance, expand humanity’s horizons, and redefine what it means to be human.

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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.

Thanks for reading. If you enjoyed this post, please click the ❤️ button and share it!

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Humanity Redefined sheds light on the bleeding edge of technology and how advancements in AI, robotics, and biotech can usher in abundance, expand humanity's horizons, and redefine what it means to be human.

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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.

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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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