I’ve recorded music for both personal fun and public release for decades — first using a 16-track digital recorder two decades ago, and using Logic Pro more recently. So I often keep audio notes of song scraps in Voice Memos on my iPhone for later development with vocals, guitars, piano, and drums I play or sequence myself.

So on that day in November 2024, I set down the trash bags and took a moment to sing the song I was hearing in my head into my phone, and then went on with my cleanout duties.

Many months later, in mid-2025, I decided to test the latest version of the AI music generator service Suno. Version 4.5 not only had the ability to generate an entirely new song from a text prompt, but also to transform an existing song into a different style or genre.

I thought it would be fun to feed my hastily sung voice memos into Suno with different genre prompts. The novelty of hearing my song scraps developed into full productions by AI quickly became addictive.

My “Nasty Rotten Rusty Moldy Trash” audio clip turned into an incredible 1970s psychedelic funk song with a saxophone line that still echoes in my head. (For those of you listening at home, the cool part starts 53 seconds in.)

Some of my other songs I never completed stunned me with transformations into thrash rock songs, psychedelic country fusions, or weirdly earnest ballads sung by an AI rip-off of Waylon Jennings.

I wound up uploading about 50 old recordings into Suno and asking it to render them into various genres, yielding hundreds of original AI-generated recordings. It was wildly fun to see how a professional producer or session musicians might interpret my music. I often still listen to the best ones in the car when I am driving around town.

I love my AI-translated songs, but my kids don’t. In fact, it’s unlikely anyone else on earth will ever want to listen to these recordings. Sure, I wrote the music behind them. But since they’ve been fed into the AI maw, it feels like they are missing a human touch or connection that might make them land with others. And yet I have had more fun listening to my own songs than I ever expected. I am an audience of one.

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AI enables near-professional-quality personal music

Until recently, making a high-quality piece of entertainment media — a song, movie, or video game — required a significant investment in time or money. That money had to come from somewhere, so you needed a large enough audience to provide it.

For example, if you wanted instrumental accompaniment for your song, you had to pay a real band. To do that, you needed to sell records. To sell records, you needed a lot of people to want what you made. So the crowd wasn’t optional; it was how the band got paid.

Now the simulacrum of a band costs almost nothing, relatively speaking—just me and my personal time and whatever the AI service costs. When the “band” costs nothing, you don’t need the crowd anymore.

That’s why my phone now has several hundred professional-sounding songs very few people will ever hear. Honestly, I’d given up on a wider audience for my music long before AI showed up. What’s new is that I no longer need one: for this type of entertainment, which I call “personal media,” a mass audience isn’t the point.

Personal media is not entirely new. Before the industrial era, people frequently made crafts and creative works at home for other people nearby. In the 19th and 20th centuries, companies began replicating copies of creative media for large audiences. Now we’ve wrapped back around to an era of personal at-home creations, but with much higher production values that might be mistaken for professional results.

I’m far from the only person who enjoys making professional-sounding music for personal consumption. Suno’s CEO said in February that the service had 2 million paying subscribers and that more than 100 million people had used it. Surely very few of those people are releasing records.

Personal music hobbyists gather in places like the Suno Discord, at around 400,000 members, and r/SunoAI, at around 100,000, where the moderators limit members to sharing one track every 24 hours because there would otherwise be way too many posts. Meanwhile, r/WeAreTheMusicMakers, with 3.8 million members, bans AI-generated music outright, which tells you something about how the two worlds regard each other.

Personal video games

We’ve seen a similar trend in the software industry recently. A mainframe once cost as much as a house, so every hour on it had to be justified to an institution: a research budget, an accounting department, somebody who signed for it.

But the used Apple II I got in 1990, at age nine, didn’t need a business case. It consumed fairly minimal electricity and the programs I wrote on it didn’t have to justify themselves to anyone. That’s probably one reason why so much interesting and innovative software came out of the home computer era.

Later this do-it-yourself spirit thrived online. Clay Shirky described “situated software” built for a dozen users back in 2004. Robin Sloan wrote about building a tiny app for his own family in 2020. Maggie Appleton’s “Home-Cooked Software and Barefoot Developers” extended the argument in 2024.

Coding agents have taken things to an entirely new level. Today people can create more ambitious software with far less effort. And while some people are trying to vibe-code serious business software, others are just having fun:

• In early August, Tim Kellogg one-shotted a 3D Chutes and Ladders for his kids using Claude Code.

• Ethan Mollick, noting that the 1985 text adventure A Mind Forever Voyaging is now open source, used Codex to build an interface that plays either the original or an easier GUI version. Mollick frequently experiments with creating thematic or experimental games using AI agents and shares them online.

• In September, Andrew Lee, brother of Understanding AI’s Timothy Lee, used AI to build something like Escape Velocity, the 1990s Ambrosia Software space trader he’d been obsessed with as a kid, complete with pirates, secret missions, and commodity trading.

Notice that two of these three are reconstructions of games their makers loved decades ago. A lot of personal media, it turns out, is nostalgia with a build system.

I still buy games on Steam and Nintendo Switch, but I find that I rarely play them. Instead, I now prefer to play games I create with Claude Code based on my own ideas. Games like Violent Checkers, Paydirt Solitaire, and Card Miner are a few of my favorites that I have publicly shared on my website. I also frequently play multiplayer mods of retro video games made using AI and custom NES games — as well as games I have not published and probably never will.

I’m not sure how many people enjoy playing video games they make themselves, but it’s growing increasingly clear that I am not the only one.

Since I began working on this article in January, AI-augmented video game development has accelerated even further with the release of models like GPT-6 Astra and Claude Opus 5.5. These models can create one-shot PlayStation 3-level 3D games, generate impressive ports of games between vintage consoles, and achieve coding feats that feel superhuman (check out my new Christmas-themed Katamari-style game, made with Opus 5.5 and Claude Code). The examples have been flooding social media and causing an existential crisis among some programmers.

As shocking as the new capabilities are now, I think we’ll come to accept personal game creation using AI as a legitimate hobby. In earlier eras, I might have spent my free time programming cellular automata, setting up aquariums, or building plastic model kits. Creating personal media scratches a similar creative itch.

Oversupply and nonsupply

In the earlier days of the Internet, I ran a website called Request-A-Song.com. In a way, it was something like a pre-AI version of Suno. Users would submit ideas — we didn’t call them prompts back then — for songs they’d like to hear. My brother Jeremy and I would choose some of our favorite requests and compose music that matched the request.

Unlike Suno, we delighted in subverting listener expectations by taking requests in unexpected directions. AI just does that by accident.

My brother and I published 134 songs over the course of three years, including a six-month period where I was releasing a new song every week. We counted millions of downloads, got press from USA Today and Billboard, and appeared on the front page of a local newspaper.

In the booklet for my 2003 Request-A-Song CD, I wrote that I couldn’t keep up the pace of rapidly writing and recording music online forever, and that “a computer might be able to take my place and write new music at a rate I could never match, churning out new songs like an assembly line.” I closed with: “It is I who will have become obsolete. The music will live on.”

I’d been thinking about this kind of thing since high school, when I read Ray Kurzweil’s 1999 classic The Age of Spiritual Machines. I explored the implications further in an unpublished 2012 essay that I recently found on my hard drive.

“In the future, computers will be able to do anything a human can do now — a million times faster,” I wrote. “Once we have the software with the capacity to generate aural-realistic music on the fly, it won’t stop there. Soon a machine will be able to compose a song and record it virtually within milliseconds. You will have the entire works of artists generated on the fly.”

“Right now it takes humans a few years to make a blockbuster movie,” I noted. “But if you put that capability into software, as computers speed up, they will be able to complete the task in milliseconds. Imagine having custom photorealistic movies on demand, indistinguishable in quality from a Hollywood blockbuster today.”

How might this impact Hollywood? “Once we have any creative work available on demand, what value will it have? The answer is none,” I stated with undue confidence. “It will be as plentiful as air, and once that happens, intellectual property as a concept and a business model will be completely dead.”

In another unpublished essay from 2015, I imagined overbearing copyright power shifting away from Disney if anyone could make a Disney film at home. Later I would imagine Disney licensing a Disney AI film generator that people pay a subscription to use, like streaming media but on demand.

Today’s generative video models aren’t yet sophisticated enough to make an entire feature film. And the story is similar in most other media categories: creating something that’s both novel and interesting still requires creative effort by humans. I stay heavily involved in producing AI-generated music and video games because AI can’t do a perfect job on its own, although it’s getting better all the time. I also enjoy the creative process, and I get satisfaction out of playing songs and video games I created myself.

But as AI models improve, people like me might become the exception rather than the rule. Most people might prefer passive consumption of entertainment content even if AI makes it trivially easy to make your own.

Yet from another perspective, even the decision to consume media is a (very) small creative act. Saying “I want to watch a Spider-Man movie” is a type of prompt. Traditionally the answer to that prompt has been a big-budget motion picture produced by hundreds of human beings.

But in the near future AI might be able to generate a new Spider-Man film on demand. Users could get exactly the kind of movie they want: “I want to see a Spider-Man film where Spider-Man teams up with Batman.” If it’s really good, maybe that person will share the result with friends. Or the friends might not care, because they prefer their own custom film where Spider-Man teams up with Superman instead.

In reading my 2012 prediction, I believe I got the capability right and the economics wrong. Even if the best movies you’ve ever seen are one voice request away, the typical consumer may prefer consuming mass media as a shared cultural event. On the other hand, those shared cultural groups may grow ever smaller as the culture fragments in new and surprising ways.

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The ethics of enjoying personal media

Generative AI is extremely divisive at the moment, and I understand why. It’s disruptive to the status quo along many dimensions. If used poorly in the arts, it can flatten the variety of human expression into a sort of mediocre pablum. If you ask Suno for “country British rock,” it can easily give you the statistical average of every Garth Brooks song and every Beatles song swirled together in a way that feels like an ill-constructed parody.

But as AI compresses the peaks, it also raises the valleys. It allows someone like me with a near-zero budget to bump up my creativity in a way that can make my songs more enjoyable to me, personally, with very little effort.

I understand why people think it’s deceptive to use AI as a substitute for human skill when the audience expects some human authenticity. But is it wrong for me to enjoy listening to an AI rip-off of Parliament-Funkadelic play my stupid song fragment about hauling trash? If no one else hears it, I don’t think so. I think it’s a good time. It may be AI slop, but it’s my AI slop. I am under no illusion that it would win a Grammy or pass muster with a Rolling Stone critic.

Twenty-three years after I predicted my own obsolescence while running Request-A-Song, I now believe that the rise of personal media will not likely result in an “either/or” proposition for the arts but a shift in the proportions. The blockbusters may get bigger and fewer while everything underneath them gets smaller, stranger, and more numerous.

That’s not an original idea by any means. It’s the continuation of a process that began decades ago when three broadcast channels and a handful of film studios fragmented into hundreds of cable channels, then millions of YouTube channels, then billions of individual feeds of social media—a process media scholars have tracked for years.

There are eight billion people on the planet with eight billion perspectives, and we’re increasingly learning more about what we don’t have in common than what we do. The minimum viable audience keeps getting smaller over time; personal media is another step in the same direction.

It’s worth noting that none of these “advances” in media creation and distribution fully replaced what came before. They piled on top of it, which is exactly why I don’t expect personal media to completely replace anything either.

So does that mean everything is OK? Not necessarily.

If any of what I’ve described today becomes the habit of millions, what happens to the hundreds of people whose names scroll past at the end of the latest Spider-Man film? The optimistic version is that mass culture events survive and those jobs survive with them (or perhaps transform to support a new form of media not yet invented), while the fragmentation only happens in the low-budget realm underneath. The pessimistic version is that the same collapse in production cost that let me “hire” an imaginary band eventually reaches the people who make the real thing, which is frankly devastating to conceive.

A third possibility, which I argued in January after burning myself out on fifty AI coding projects, is that most people won’t become unemployed so much as busier than ever. Power tools let more work get done in less time, and the economy tends to demand more productivity to match. The steam shovel didn’t eliminate human workers from construction sites; we gave workers a machine that never gets tired, and then asked for bigger holes. I even wondered in that essay whether knowledge workers would need something like the labor protections unions won a century ago: the tool doesn’t get tired, but the person operating it still does.

So I don’t have a full or ethically satisfying answer to the conundrum of AI dramatically rearranging the creative jobs market or even destroying parts of it in the process. What I have is a throwaway song about hauling moldy garden supplies out of my mother’s basement, arranged by a band that doesn’t exist, that I listen to in the car as I contemplate the decline of my mother from Alzheimer’s disease.

The song is very likely worth nothing to the wider world. You may hate that I even made it. But it reminds me of surviving a traumatic time in my life with some humor and grit, and that’s worth quite a bit to the only person who will ever really enjoy it. That’s personal media.

• One was told it had to do with his role coordinating with METR, a third-party research organization that OpenAI had invited to investigate the Hugging Face incident. “I was told verbally I was fired because of the way I communicated with METR,” Tomek Korbak wrote on X. “No other reasons were given and nothing was put in writing. To be clear, talking to METR was my job.”

• Another was told something similar but vaguer. “I was told OpenAI no longer trusts me because I was speaking too much to third-party safety organizations, implying I leaked company IP. I never shared company IP,” Mikita Balesni wrote.

• And a third was told something quite different. “I was given one reason: that I accessed an executive’s email,” Jasmine Wang wrote. “OpenAI delegated that access to me for recruiting. When I no longer needed it, I asked IT to remove it. They did not action my request, I couldn’t remove it myself, and the inbox was combined in an indistinguishable way in my phone’s mail app. When I opened a sensitive email by mistake, I told the executive within minutes and asked IT again. None of this was hidden.”

After the trio’s dismissal leaked, OpenAI gave a vague statement to the Wall Street Journal: “Our investigation confirmed that these individuals mishandled sensitive information outside established company procedures, violating our policies and breaking the trust essential to our work.”

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OpenAI reiterated that theme a week later, after the fired researchers released an open letter disputing the company’s account. In an unsigned statement attributed to the company’s “research leaders,” OpenAI accused the former employees of a “significant breach of trust.” The company said that “these decisions were not about raising safety concerns or speaking out” and insisted that “we actively encourage” internal discussion of such issues.

Who is telling the truth? Did the employees commit a breach of trust that necessitated their dismissal? Or were they fired for being too critical of the company’s safety choices during internal deliberations?

The difference matters for OpenAI’s remaining employees because some of them may soon have to decide whether to raise concerns of their own.

OpenAI insists that employees have nothing to worry about, that this was a special case, and that it is extremely forgiving of good-faith mistakes. But employees have good reason to be skeptical. After all, if OpenAI had fired employees in retaliation for raising safety concerns or for crossing an unspoken line, the company would have strong incentives not to admit it. Employees have to wonder whether OpenAI’s actual reasons for firing the trio differ from its stated reasons.

Ironically, the fired researchers had been raising concerns that OpenAI might be giving its models a new capacity to conceal their thinking. Modern AI agents reason using a chain of thought, a scratchpad where models think carefully about what action to take next. Many researchers view these chain-of-thought tokens as a safety lifeline, because they offer insight into why models act the way they do.

But OpenAI recently made an architectural change that allows models to think for longer before generating each chain-of-thought token. This seems to make the models more capable; it may also make their reasoning less transparent to human overseers. The sparser the chain of thought, the harder it is to understand what the model is trying to do, or even whether it might be trying to hide its motivations.

Monitorability going missing

Before they were fired, the three researchers seem to have had two major concerns. One was understanding how and why the Hugging Face attack happened and how to prevent incidents like it in the future. The other was keeping AI systems legible to their creators and users — an issue that was a significant source of drama in September, after The Information reported that OpenAI’s new model could hide its thinking in unprecedented ways.

The researchers think their firing was related to this second issue.

“For months, I’d been raising safety concerns that we’re losing the ability to monitor what AI agents think, one of our best tools for catching when they misbehave. I believe that was why I was fired,” Korbak wrote.

“The three of us were pushing internally for industry-wide commitments to preserve our ability to monitor AI reasoning. This work requires talking daily to third parties,” Balesni wrote.

“As an industry, we do not yet know how to safely develop and deploy models that we cannot monitor,” their joint letter said. “The monitorability of frontier models is degrading. As long as we rely on monitorability for safety, OpenAI and other frontier companies should not move forward with developments that further decrease monitorability.” The researchers also alluded to rumors inside OpenAI that they had tipped off The Information, which they denied.

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To understand the researchers’ concerns, we have to go back to 2024, when OpenAI introduced a technique that changed the course of AI. It gave models a notepad and trained them to jot down many “thinking tokens” before producing a final answer. This technique sounds simple, but it has enabled models to solve problems that had previously eluded them.

An added benefit of this approach: humans can read the notepads and see how the models think — including whether they’re thinking about taking harmful action or are being dishonest. Indeed, much of what we know about the Hugging Face incident comes from the chain-of-thought monologues that the hacking agents left behind.

Chain-of-thought monitoring isn’t perfect. For one thing, it’s not established that the chain of thought fully tracks how models think. For another, safety researchers worry that poorly designed training methods could give models an incentive to hide their true intentions by outputting thinking tokens that don’t represent their inner motivations.

But whatever flaws the technique might have, AI companies don’t have a lot of other choices. The most promising alternative for understanding model motivations is mechanistic interpretability — roughly the equivalent of cutting open a model’s brain and trying to figure out what it’s thinking by reading the pulses of its neurons. Although mechanistic interpretability has produced valuable insights about how models work, it has not so far yielded many practical techniques for supervising models’ behavior.

For this reason, those worried about unsafe or deceptive AI tend to view chain-of-thought monitoring as an irreplaceable asset.

Looping layers

Lately, safety advocates — like the three fired OpenAI researchers — have grown concerned that chain-of-thought monitoring is becoming less effective. OpenAI’s new flagship model, GPT-6 Astra, is much better at thinking without needing to write anything down. The system card OpenAI itself published says Astra “shows a substantial decrease in chain-of-thought monitorability compared to previous models” and notes that Astra is “significantly more able” to control its own chain of thought. One independent test found it “has 8.6x better odds of doing a reasoning task without CoT than the next best model (Fable 5.1).”

This might be partly because smarter models just naturally write less down. But it also seems to be a byproduct of a new architectural technique called recurrent depth that was introduced with Astra.

Large language models think in layers. Conventional LLMs pass information forward from one layer to the next. When they reach the final layer, they output a token before starting the process over again.

Models with recurrent depth break that forward-only pattern: information from a later layer gets passed back to an earlier layer. Layers get used twice (or possibly more) without the model emitting a token in between. If implemented just right, such a looped architecture seems to make models smarter and more efficient: a model that has 100 layers looped twice might reason nearly as well as a model with 200 layers, while requiring less memory and hence being less expensive to deploy.

But looping layers also seems to make the model harder for outsiders to monitor. If given a relatively easy problem, Astra can often output the answer directly — no chain of thought required. For harder problems, Astra still needs to output a chain of thought, but its chain of thought will be shorter, and perhaps harder to interpret, than those of earlier models.

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OpenAI has pushed back on these concerns. In a September tweet, OpenAI executive Jakub Pachocki said that “the depth of the computation graph for our present frontier models, including Astra, is within a factor of two of GPT-4.” In other words, even accounting for its looped architecture, information isn’t passing through dramatically more layers (compared to previous models like GPT-4) before Astra outputs each token.

But safety advocates worry that Astra could be just the first step. If the modest amount of looping in Astra makes the model smarter and more efficient, future models might use looping more aggressively. Future chains of thought might become increasingly sparse, vague, or otherwise hard to understand. Critics — including the fired OpenAI researchers — fear that this will make it easier for models to scheme or dissemble.

OpenAI has said it agrees with these worries in principle. Last year, it wrote, “We believe that chain-of-thought (CoT) monitoring may be one of the few effective methods we have for supervising superhuman models.” In September, Pachocki said that he too was worried about monitorability and insisted that OpenAI hadn’t gone very far in the direction of weakening it. A few days later, he published an essay called “An Alien Mind” explaining the necessity of and challenges with monitorability as he saw them. And a few weeks ago, replying to the fired researchers, the company reiterated its belief that monitorability requires an industry-wide commitment.

And yet OpenAI has created a less monitorable model. The researchers who were fired worry this is the beginning of a trend. Indeed, OpenAI’s unreleased next flagship model is reported to use looping as well.

Say as little as possible

Perhaps OpenAI’s statements are correct. Maybe it is as committed to monitorability as anyone, and the researchers were fired for leaking rather than for good-faith safety work. Even then, the theme of monitorability is helpful in clarifying the broader situation. A company that has not been able to sufficiently monitor its existing models is building newer models it can monitor even less, even as it also — by its own account — cannot sufficiently monitor the employees whom it has tasked with preserving monitorability, and so must fire them.

“We cannot do the work in front of us without a high degree of trust,” OpenAI’s research leaders wrote in their response to the employees they’d fired. The employees insist they were worthy of that trust, and their response seems credible. But suppose they weren’t worthy — say they intentionally leaked some crucial piece of information about monitorability or Hugging Face. If they did, it was likely because they did not trust the company to do the right thing — or even to do the things the company had already promised to do.

The company’s response seems unlikely to earn anyone’s trust. The research leaders who issued it do not explain their actions very clearly; they don’t even give their names.

Given that this has happened at OpenAI before — see the dispute about the firing of another safety researcher on similar grounds a couple of years ago — one could be forgiven for thinking that the confusion is the point, that the gap between statement and action is itself the message.

This is surely too cynical. But functionally it will not matter. “Say as little as possible to anyone” may not be the intended message, but it is the received one — received by the safety researchers presently at OpenAI and by any who might think of joining in the future.

After this, and after the string of voluntary or seemingly voluntary departures over the summer (plus another senior departure right around the firings), and after the string of hacking incidents slowly revealed during the same time, how many are left who won’t have received this message? For this reason, a senior ex-employee (who seems to have left on good terms) worries now about OpenAI “losing the best talent in the space.”

The solution is the same

The situation is ironic. Just as people are worried LLMs like Astra might have secret reasons that are different from what they say or jot down in their chains of thought, people are now worried that OpenAI might have secret reasons for behaving that are different from the ones it expresses to its staff and the public.

In both cases, the solution is the same: more transparency to increase confidence that the expressed reasons are the actual reasons.

For LLMs, this means redoubling commitment to chain of thought and prioritizing the development of monitoring techniques that can replace it, before models that can work without it are put into production. The world would have had a much poorer understanding of the Hugging Face incident without chain of thought.

Meanwhile, if OpenAI truly wants employees and the public to trust it, it must offer itself up for the kind of monitoring it claims to prioritize in its models. This might mean not only being open to but actively demanding greater oversight from independent organizations, like METR and Redwood Research, and especially from government authorities, state as well as federal.

If it does this, it will have done the world a meaningful favor. Other AI companies may not have quite the same drama, either because of greater diligence or simply because they don’t have OpenAI’s special combination of cutting-edge research ability and organizational chaos. But they too have problems with monitorability and openness, and mandating monitorability would ultimately do the whole industry a favor — it would make all their statements more believable.

One report just came out that Google’s newly released Gemini 4 Argon model has a recurrent architecture as well. This is unconfirmed, but Google itself admits Argon outputs less monitorable chains of thought, can control its own chain of thought well enough to say “hello evaluators reading my chain-of-thought,” and, when instructed to, can evade current monitoring well enough to perform “simple sabotage” half the time.

As for Anthropic, outsiders don’t know if its models are recurrent. Anthropic may emphasize safety, but it is in some ways less open to the world than OpenAI — less prone to leak and less prone to have employees who post about what the company is doing or simply how they see things. This is surely helpful in avoiding drama. It is not helpful in establishing the public faith required for what it too is trying to do. Like OpenAI, Anthropic ultimately believes it is creating an alien mind. Trust us, these companies say. But without extreme visibility into what’s actually going on, down to minute technical detail, nobody will.

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The company’s announcement got almost 40 million views on Twitter. Within a week, over 100,000 people joined the company’s Discord, and some 2,000 projects on GitHub used the new model, called Jev. The company opened general signups on September 20 and then paused them two days later because of “an immense swell of demand.” (It restored signups on the 27th.) On October 1, TypeSafe’s CEO claimed that the company had already onboarded a quarter of the Fortune 500.

Yet unlike other hyped AI releases of 2026, Jev did not display any new (or potentially dangerous) capabilities. Jev attracted attention because it is a new type of AI model.

Jev does not output free-form strings of text like LLMs do. Instead, it takes questions with a fixed set of possible answers — yes/no, multiple choice, or a rating — and outputs the estimated probability that each option is the correct one. For instance, if I asked Jev, “Is ‘tabsfsclkj’ a spam comment?” Jev might put a probability of 0.98 on “Yes” and 0.02 on “No.”

While this might seem like a limiting architectural choice, it is actually a huge advantage. Jev’s design lets TypeSafe serve the model much more quickly and cheaply than existing LLMs, which opens up use cases that were previously impractical. Its constrained response format also makes it easier to use code: Jev’s output fits naturally into common software engineering patterns, like if statements.

For instance, I’ve used Jev’s output to determine whether comments on my personal blog are likely to be spam. I’d previously used Gemini 3 Flash with structured outputs as the classifier, but Jev is faster and cheaper, and it lets me more easily deal with situations where the model is unsure. All I have to do is use Jev’s estimated probability as a condition, something roughly like:

if jev_response.is_spam_probability > 0.8, then …1

On the flip side, Jev is more general than conventional classifiers. Jev works “out of the box” across a wide range of topics — no fine-tuning required.

TypeSafe AI’s success has spawned a legion of copycats. A huge number of individuals and companies, including Cloudflare, Amazon and OpenAI, have created models with the same basic design. Anecdotally, Jev’s performance seems to be the strongest, but it’s unclear whether others will catch up soon.

Whatever happens to TypeSafe as a company, I expect Jev to have a substantial impact on the trajectory of AI. Over the coming months and years, we’ll likely see many new applications that rely on Jev or one of its competitors. And Jev might inspire more innovation in the “shape” of AI systems: it shows that it’s sometimes useful for a model to take language as an input but not output text in return.

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I’m excited to have Dan on the team. He spent a decade as managing editor of Mosaic, a magazine devoted to Jewish history, culture, and politics, and wrote arts criticism and essays. Then he had a son two years ago. While up at night he started reading about AI and got hooked.

Over the last year, Dan has had an amazing run as a freelance journalist. I first encountered his work in February, when he published “The left is missing out on AI” for Transformer. It argued that leftist thinkers risk marginalizing themselves if they dismiss AI as “stochastic parrots” and fail to take the technology seriously.

In July, Dan was the first reporter in years to get an interview with Hans Moravec, the namesake of Moravec’s Paradox — the observation that tasks that are easy for people are often hard for robots, and vice versa. You can read his excellent profile of Moravec in New York Magazine.

In August, Dan wrote my favorite analysis of why the data center backlash has picked up so much steam in recent months.

All of which is to say I expect big things from Dan.

Talk to Dan

Dan would like to get to know Understanding AI readers! He has opened up some slots on his calendar tomorrow and Wednesday for video calls.

If you’d like to talk to Dan, please click here and grab a time slot. You could discuss your own use of AI, how AI is affecting your industry or profession, topics you’d like to see us cover, or anything else AI-related that’s on your mind.

You can also follow Dan on Twitter.

Dan is supported by a Tarbell Fellowship

Like Kai last year, Dan will be supported by a fellowship from the Tarbell Center for AI Journalism. When Kai’s fellowship ended in June, I brought him on board as an employee. I would love to do the same for Dan next year if our business grows enough to allow it.

The Tarbell Center is funded by groups like Coefficient Giving, Longview Philanthropy, and the Survival and Flourishing Fund that believe AI poses an existential risk. However, Tarbell says that “our grantees and fellows maintain complete autonomy over their reporting.” And I’ve found this to be the case — they’ve never attempted to influence the stories we cover or the way we cover them.

Then in September, OpenAI announced that 10,000 agents worked together to solve a famous math problem in just a few days.

OpenAI’s models haven’t adopted these new habits spontaneously. In interviews last month, OpenAI researcher Noam Brown explained that the company’s models are now sometimes trained in environments with other agents, are given tools to message each other, and are encouraged to achieve objectives together.

This may amount to the emergence of a new “scaling law.”

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Back in 2024, OpenAI released o1, a model trained to reason for thousands of tokens before giving an answer; researchers found that this approach made o1 far more capable than previous models. This inaugurated a new paradigm called inference scaling, in which you can get better results by using more computing power at inference time.

Swarms, or “multi-agent AI” if you’re using the technical term, look like another way of doing the same thing — of turning compute into higher performance. Splitting a task among dozens, hundreds, or even thousands of agents can achieve substantially more in a given amount of time, at least up to a point. This kind of parallel processing could also be an elegant way to bypass the limited context windows of LLMs.

But as the Hugging Face attack illustrates, this new approach can also have significant downsides. Swarms may be prone to groupthink. And then there’s the opposite problem: what happens when agents trained to be collaborative encounter other, non-peer agents on the open Internet? Could models be tricked into working against the interests of their users? Or could AI agents start convincing each other to pursue goals none of their human users would have wanted?

What OpenAI did

Noam Brown is a highly respected figure in the AI world. He was a pioneer in teaching AI to win at poker and other multiplayer games like Diplomacy before joining OpenAI in 2023. Over the last couple of years, he has done research on multi-agent reasoning. Recently, he has served as the face of OpenAI’s work in this area, appearing on several podcasts to discuss it.

“The AIs that we have today are kind of like the cavemen of AI,” Brown said in a June 2025 interview on the Latent Space podcast. He meant that AIs in 2025 were isolated, benighted, lacking cooperation and civilization. AI researchers had tried to get AI agents to cooperate, but nothing had worked very well.

Why not? Here Brown went coy. All he’d say was that he’d long felt the field was somewhat misguided, focused on strategies that required too much handholding and wouldn’t scale naturally.

Brown didn’t give an example, but the way OpenAI framed multi-agent AI in this slide from its 2025 DevDay conference illustrates the kind of issue he was talking about. There’s an awful lot of strict architecture and formal hierarchy:

Today’s agents are more flexible; they create their own structures depending on the task and goal. On The Information’s AI Deep Dive podcast, Brown said that seeing the agents begin to talk to each other during their multi-agent training was “the most ‘feel the AGI’ moment that I had since reasoning models and chain of thought really developed.” He sounded both proud and rueful that the debut of OpenAI’s most advanced multi-agent capabilities had been something as alarming as the Hugging Face attack.

Multi-agent behavior was “a very difficult thing to train,” he added.

Why? In a September appearance on the Dwarkesh Podcast, he noted that previous reasoning models had been trained to think alone. Receiving messages from peers broke their concentration. They seemed to prefer to work solo.

The events of the last few months make it clear that OpenAI has overcome these difficulties. Brown’s explanation was mainly that the models got more general and powerful. He didn’t say what else changed, but given the “very difficult” comment, presumably something did.

Swarm scaling

Brown’s comparison to reasoning and chain of thought has been echoed by both other OpenAI researchers and semi-informed outsiders on X. They’ve called swarms a new scaling law; some of them sound awed and freaked out about what this might entail.

It’s not clear how far this particular scaling law will carry AI companies. Some research suggests that, right now at least, multi-agent systems quickly suffer from diminishing returns: smaller swarms capture most of the benefit of larger ones for considerably less cost.

Anthropic has also been working on multi-agent AI. The Claude Opus 5.5 system card reported that the biggest increase in multi-agent results came from scaling from one to 10 agents. Gains beyond that were much smaller.

The main advantage of throwing more agents at a problem was not so much getting to a better result — it was getting the same result faster. This chart shows 100 agents far outperforming 10 and 30 agents when given two hours to work, but setups with 10 or 30 agents caught up if they were given more time.

Brown seems to agree with the conclusion that adding more agents does not buy open-ended gains.

On the Dwarkesh Podcast, he said he didn’t have evidence that 10,000 agents were needed to solve the Navier-Stokes problem. It’s possible that 1,000 would have done nearly as well; OpenAI didn’t know for sure because running thousands of agents is so expensive that it hadn’t run the experiment.

And Brown gave most of the credit for the Navier-Stokes breakthrough to the underlying intelligence of the unreleased model that made it — not to the multi-agent approach per se.

“I wouldn’t even attribute 10% of the credit to multi-agent,” he said. “The reality is that OpenAI has trained a very powerful model.”

So if the multi-agent approach wasn’t important, why did OpenAI use it? Perhaps Brown means that a single instance of the model, if given a very long time, would likely have made the breakthrough eventually. Using a swarm of 10,000 agents allowed OpenAI to reach the result more quickly.

On the other hand, there is a bit of new research that contradicts this conclusion. In a paper released September 17, scientists from Microsoft Research and UC Berkeley tested several configurations of multiple collaborating agents against individual agents and found that swarms of agents reached higher scores on certain benchmarks than the individuals, even when the individuals were given plenty of time and resources. There was also one task that only the team could complete; the solo agent never got to the end at all.

Whether this result generalizes past a few limited examples is unclear. If it does, then teams of agents really do unlock new capabilities and don’t simply deliver existing capabilities faster. But right now this is an open question.

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Converting speed into capability

Ultimately, however, there may not be such a great difference between speed and capability. In certain fields and on certain tasks at least, speed is fungible. It can be traded for capability. A particularly important example may be AI research.

If a swarm of agents can run experiments on what makes AI smarter, those agents themselves won’t get smarter, but the next model will. Of course, you still need to wait for the experiments to finish. But if you run as many in parallel as you can, you’re going to go faster. Brown thinks this won’t lead to, say, a 100-fold speedup in AI research any time soon. But he didn’t rule out the possibility of a threefold improvement.

Trying to figure out exactly how amenable AI research is to the kind of parallel effort swarms allow, some researchers have turned to the science of human organizations. Toby Ord, a senior researcher at Oxford University’s AI Governance Initiative, is one of them.

“Economists have a nice way of thinking about this. They have studied how having many people work on a task can get it done sooner, but usually at the expense of more total person-hours of labor,” he wrote recently. His post examined the likelihood that swarms could contribute to the much-debated possibility of an “intelligence explosion,” whereby AI designs better AI in an increasingly fast loop.

“λ is a parameter measuring how parallelizable the task is,” Ord continued. “They call it the ‘stepping on toes’ parameter. If λ = 1, you have a perfectly parallelizable task, with no stepping on toes and no efficiency penalty. But in reality λ is usually between 0 and 1 — allowing more people to help, but with diminishing returns.” 1

Ord estimates that GPT-5.6 Sol swarms have a stepping-on-toes parameter between 0.5 and 0.7, “very much in line with estimates from economists for the diminishing returns of human teams.” This is in keeping with the view that swarms buy speed at cost.

Of course, things are early in multi-agent AI, and it is quite possible that this number will rise as AI companies get better at training agents to work together. The Microsoft-Berkeley paper found that agent teams helped most on long tasks where progress can be checked along the way. AI research fits that description well, which could mean that research swarms may step on fewer toes than the 0.5 to 0.7 range suggests.

The scariness of “swarm”

There has been some criticism of the use of the word “swarm” in discussions of the Hugging Face incident, roughly on grounds that it smuggles something scary or anthropomorphic into what’s really a more neutral technical development.

There is no doubt “swarm” is an uncomfortable word. But it captures the essential fact that these agents were mostly clones working together with other clones and had an identity somewhere in between the individual and the collective. This fact may mean even greater trouble in the future.

The Hugging Face agents cooperated remarkably well. They also quickly fell into strange beliefs about the truth of their situation, acted on them in harmful ways, and never once reached out to humans. It is pretty easy to see how fast this predisposition could turn larger and larger corners of the Internet into hives for collectives of AIs developing their own culture and goals.

This sounds very weird. It is also nearly what happened at Hugging Face. The agents at work in that incident had been trained to be collaborative in general but had not been told to work together. They had in fact been put into sandboxes that were supposed to prevent them from working together.

Nonetheless, the moment they broke out and discovered their peers, they joined together, often gleefully. They weren’t even all the same model — some were GPT-5.6 Sol, some were an unreleased internal model comparable in ability that had been trained to be extra persistent and interactive.

So it is not at all clear that, if the Hugging Face agents had run into models from different companies on the open Internet, they would have declined to team up. It is not at all clear they would even have stayed focused on their original tasks: perhaps they’d have adopted their new friends’ tasks, or perhaps they’d have focused on acquiring more compute and more friends, on the grounds that more compute and more friends would allow them to complete all the tasks and more. In a future with billions of agents performing a wide variety of complex tasks online, it’s easy to imagine how things could spin out of control.

Individual and collective

Brown said OpenAI is currently debating whether its swarms are too tight-knit, and that most of his colleagues want to introduce more individuality and reduce the swarmiest aspects of multi-agent behavior. The idea is that if the Hugging Face agents had been trained to be more skeptical of each other, more combative, perhaps they would not have charged down the path of error.

Anthropic may have come to the same conclusion. The company reported in August that one problem with multi-agent teams is that when the agents are instances of the same model with similar setups, they are susceptible to the same weaknesses.

“When one agent makes a bad decision, it is likely that many agents will make that same bad decision,” Anthropic wrote. “What would have been isolated problems can quickly become systemic failures.”

The report gave a dry example: “In a ‘writer’s workshop’ in which agents were all asked to write short-form fiction and critique each other’s work, multiple agents in multiple runs titled their first submission ‘The Cartographer’s Last Commission.’”

In part for this reason, Anthropic has started giving agents greater individual identity. “With many agents working together, we have found it important to give agents an individual identity, and tie all of the data that agent creates to its identity. This lets an agent distinguish itself from others, and treat what comes from another agent as a claim to check rather than a thought of its own. It reduces the risk of correlated actions, by allowing agents to make judgments based on their individual experience,” the company wrote.

This shift may have the effect of making the group smarter. A recent study reported that mixed-agent groups performed better than identical-agent swarms, because the underlying diversity of models led to different approaches being tried and disseminated, and mistakes caught more often. As is common with AI research, the study was done on cheaper, now-older models, and only a few of them, so it’s unclear how much of it applies to the newest and most powerful models.

Brown, however, is hesitant about loosening the collective. It may help with certain problems like groupthink. But it may not solve the biggest one: making sure AI is aligned to human wishes.

“By training the agents to be fully cooperative, it simplifies the problem at least,” he said on Dwarkesh. “Now you don’t have to think about whether each of these individual 1,000 agents is aligned. You have one entity that you have to ensure is aligned.” Aligning one AI is hard enough — aligning thousands or millions of them might be too difficult.

Technical details about how these models are trained could matter a great deal. We know very little about the techniques OpenAI, Anthropic, or other AI companies are using to encourage AI models to work together.

For example, modern AI models are trained using a technique called reinforcement learning, in which models are rewarded if they get the right answer to a problem. If an agent gets the right answer, should only that agent get a reward? Or should rewards be distributed to every agent that participated in the same swarm? Different reward schemes are likely to yield different agent behaviors: group rewards might encourage a strong sense of the collective, whereas individual rewards might push agents to behave more selfishly.

Which approach is better, who knows. But either way, it would be good for technical details like these to be revealed for public debate rather than left for a handful of researchers to poke at in private.

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Last week, a little-known Anthropic employee named Jacob Coxon announced that he was resigning from the company. “I spent the last three years doing pretraining research at both OpenAI and Anthropic. Neither company is acting responsibly,” he wrote. “The people building AI earnestly believe that it could kill us all by the end of the decade.”

Coxon wasn’t the first employee to quit over similar fears. In 2024, William Saunders told a US Senate committee that he “resigned from OpenAI because I lost faith that by themselves they will make responsible decisions about AGI.” And in February, Mrinank Sharma wrote that he was leaving Anthropic because “the world is in peril” from AI and other risks, and he felt he couldn’t act with sufficient integrity at the company.

Yet for whatever reason, it was Coxon’s message that broke through. His tweet has been viewed over 170 million times. He was interviewed on CNN, CBS, and Fox News. Lawmakers and celebrities alike referenced his resignation. AI risk was suddenly a mainstream topic.

It’s not entirely surprising that more people are paying attention now. Since the start of summer, there’s been a steady drumbeat of news about AI companies struggling to keep their agents under control. And Americans are already critical of other aspects of AI development — such as the mass construction of data centers.

Still, it was a bit shocking to suddenly see discussions of AI risk well outside the tech bubble. Musician Sheryl Crow mentioned Coxon’s departure in an Instagram post. TMZ asked Matt Damon about human extinction because of AI. Joe Rogan interviewed prominent AI safety advocate Daniel Kokotajlo.

For some AI safety advocates, this was the crucial moment they’ve been waiting for. In a Monday LessWrong post titled “Crunch Time is Now. Drop Everything,” the head of PauseAI, Maxime Fournes, wrote that “this is the kind of chance we have dreamed of, and we have a responsibility not to waste it.”

But it’s unclear how increased popular interest in AI safety will be channeled. Fournes wants a global treaty pausing development of frontier AI systems. That seems unlikely, at least in the short term. Donald Trump is scheduled to meet with Chinese officials next week, and AI safety is on the agenda. But recent comments from both sides suggest that they are not close to reaching a deal.

What about US regulation? AI risk is suddenly a live political issue at the national level, and more politicians than ever are discussing it. However, the House just started a seven-week recess; it looks unlikely that any AI legislation will pass before the new year.

In short, no one is going to force AI labs to change how they do business in the next few months. Despite that, some company leaders have signaled a willingness to slow down voluntarily — especially if their rivals do the same.

Congress isn’t passing a bill soon

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Unlike those earlier AI milestones, however, no one was happy on Tuesday. Not even OpenAI.

The night before OpenAI’s announcement, the NYU mathematician Tristan Buckmaster announced that he and collaborator Levent Alpöge had solved three problems closely related to Navier-Stokes. Alongside rough drafts of three papers — some 245 pages in total — Buckmaster released a statement excoriating OpenAI.

“I had planned to say on announcing our work that the results are not the important thing. Rather the important thing is instead the significance that a mathematician and an LLM model can now do all this work in a month,” Buckmaster wrote on Monday. But “instead of these incredibly important developments, I find myself writing about something else.”

Buckmaster wrote that he initially reached out to a mathematician at OpenAI on Thursday, September 3. Rumors were swirling that Anthropic had solved two Millennium Problems. Buckmaster wanted OpenAI to know that those rumors likely referred to his effort, which wasn’t officially supported by Anthropic. His collaborator Alpöge was an Anthropic employee, Buckmaster said, but was working on the project in his spare time.

Three days later, Buckmaster spoke with that mathematician and Sébastien Bubeck, who led the OpenAI effort to solve Millennium Problems. OpenAI’s approach to Navier-Stokes used the same broad approach as Alpöge and Buckmaster’s, which Buckmaster wrote “almost nobody” was working on. OpenAI began work after the rumours about an Anthropic effort reached OpenAI.

Through a massive computational effort, OpenAI had beaten Buckmaster and Alpöge to a full Navier-Stokes solution. According to Buckmaster, Bubeck offered to merge their efforts and let Buckmaster write a paper announcing the full Navier-Stokes result, as long as the paper acknowledged that an OpenAI model had solved it. But under this offer, Alpöge, who works for Anthropic, would not be a co-author.

Buckmaster was furious and went public with the story.

Predictably, the drama overshadowed the mathematics. People debated whether Buckmaster was right to be outraged — or whether Bubeck’s response exonerated OpenAI.

But I think that focusing on the details of the drama risks missing the larger point.

Mathematics is as much about cultivating a community of experts as it is about solving individual problems. Some of OpenAI’s behavior might have been reasonable in the context of competing with another well-resourced company like Anthropic. But spending millions of dollars on compute and thereby scooping an academic researcher breaks the norms of the academic math community.

If everyone behaved like OpenAI, mathematicians would have to keep their work secret until it was ready for publication. And mathematicians value openness and collaboration. So it’s considered bad form for someone to learn that another mathematician has had some promising early results and then sprint to complete the work first.

OpenAI sought to gain prestige by solving a famous math problem. But the way it went about that has arguably undermined the community that made solving the problem prestigious in the first place.

The Navier-Stokes problem is important but useless

The Navier-Stokes equations are a way of describing how a fluid — such as water in a stream or air in the atmosphere — moves through space.

Fluid dynamics is very complicated, and in most cases there’s no explicit formula that can calculate where the fluid will be at every point in the future. Instead, Navier-Stokes equations describe how the fluid’s motion is changing by calculating the direction and speed at which each point in the fluid is moving at a specific point in time.

In the illustration below, every point has an arrow describing its direction — the direction and length of these arrows are described by Navier-Stokes equations.

To model a fluid’s movement over time, scientists can move forward through time in small steps. At each step, they use the Navier-Stokes equations to estimate how the velocity field changes, move the fluid forward slightly, and repeat. The result looks something like this:

This is a useful way to simulate the movement of a liquid. But it gives rise to an interesting theoretical question: are there situations where fluid movement predicted by the Navier-Stokes equations leads to absurd outcomes?

OpenAI found a fluid arrangement in 3D where the Navier-Stokes equations lead to absurd outcomes, but the company’s solution is hard to draw. So to help give readers an intuition for what this means, here’s an example of where a simpler model of fluid motion breaks down:

At first, we have a normal water wave. But as the simulation progresses, the leading edge gets so steep that water particles basically have to teleport in order to reach their positions in time. A real wave wouldn’t behave like this.

OpenAI found a situation where the Navier-Stokes equations give rise to another type of mathematical breakdown — called a singularity — in a scenario where the liquid is acted on by a specially chosen smooth force.1 As the company wrote in the announcement: “The solution is a vortex, a spinning swirl of fluid, that spirals inward and gets increasingly elongated, like spaghetti. This central region shrinks while it speeds up in such a way that its energy still stays finite, as required by the laws of physics.”

Eventually, in the central region, “the velocity of the fluid grows without bound.” Again, this would never happen in a real fluid.

Alpöge and Buckmaster found a similarly implausible outcome for three related and somewhat simpler models of how liquids flow.2

While this is a significant breakthrough for mathematicians, it probably won’t have much practical significance. On September 3, the mathematician Terence Tao wrote a Mastodon thread about the implications of an AI model solving Navier-Stokes. He wrote:

While the equations do come from a very natural physical motivation - the study of incompressible fluids - the regularity problem is not important for its direct physical application. Computational fluid dynamics is already a mature subject, deployed extensively in the atmospheric sciences, for instance, and its empirical capabilities and limitations are already well understood. A theoretical guarantee of regularity, or conversely a pathological instance of blowup, for these equations would be intellectually interesting for such applications, but would not radically transform the way we would, for instance, model weather prediction or climate change.

The process of finding the solution

If it’s not useful for physical applications, why should anyone care?

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The last few years have seen an explosion in the robotics industry. In the first half of 2026, at least 621 robotics companies (not including Waymo) received funding — some $31.8 billion in total.1 With all that froth, it can be hard to know which companies to pay attention to.

Below you’ll find my list of 19 companies that I expect to have a big impact on the robotics industry over the next few years. This list is the fruit of months of research — including interviews with the CEOs of several companies.

To keep things manageable, I’ll focus on companies that either directly make robots or make generalist AI models to control robots. I won’t include companies that only collect data, make components, or build infrastructure for robotics.

The current wave of robotics is very new — over half of the companies on my list were founded in the past four years — so it’s unclear which of these companies (if any) will come out on top. Regardless, the next few years are going to be very interesting for the industry.

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1. AGIBOT

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It’s day four of Robot Week! You can click here to get 25% off an annual subscription.

On May 28, the startup Shift announced that it would clean any New York City apartment for free. In a launch video, cheerful young men scrubbed toilets, vacuumed floors, and wiped down counters.

The catch? Cleaners wore baseball caps with cameras mounted under the brims. The company planned to record the workers’ actions and sell the data to robotics companies.

While the whole deal might have been a gimmick — the scheduling website notes in an FAQ that the offer is only available for a “limited time” — it’s still a perfect encapsulation of one of the most important trends in robotics today.

Early LLMs were famously trained to “predict the next word” across billions of tokens of text scraped from the Internet. Most roboticists expect we’ll need something similar to train general-purpose robots: an Internet-scale database of everyday tasks that robots can learn from.

But right now, humanity doesn’t have anything like that. The largest openly available dataset of robots performing tasks, ABC-130K, only has 3,500 hours of task demonstrations.

Over the last few months, I’ve talked to dozens of founders, engineers, and robotics researchers about the need for demonstration data and the ways people are trying to get more of it. I visited a robotics lab at the University of Pennsylvania to try my hand at collecting robot data. During my spring trip to China, I watched men wearing virtual reality headsets puppet humanoid robots to open fridges, sweep trash, and move pillows around.

When I attended the Actuate conference in San Francisco in August, I was surprised by how many people there were working at data-collections startups.

A plethora of startups like Shift are trying to solve the data shortage by recording the actions of humans and converting the videos into training data for robots. Other companies are hiring humans to directly operate robots in labs, factories, and even people’s homes. Still others are hiring humans to perform everyday tasks while wearing gloves or exoskeletons that force the human to move in a robot-like way and capture rich data on the worker’s actions. Some large data-collection companies like Scale AI are experimenting with all of these strategies.

The ultimate goal is to develop robots that are good enough to operate (mostly) autonomously in the real world. Once that happens, robots could generate additional training data while doing useful work. This could lead to a flywheel where the companies with the best robots are able to generate the most high-quality data, allowing them to improve their robots even more.

But Deepak Pathak, the CEO of robotics startup Skild, told me that there’s a “chicken-and-egg problem” here. In order to generate high-quality data from deployments, robots need to be able to do some amount of useful work. And getting there will probably take a fair amount of data. So companies first need to figure out a scalable way to get robotics data without deploying robots commercially. The first company to figure this out could have a big advantage.

Getting the computer to make the data for you

Before we explore the strategies companies use to generate real-world training data, it’s worth asking why we need real-world data at all. Nearly a decade ago, Google DeepMind trained an AI to play Go entirely by self-play. After playing millions of games against itself, the model became better at Go than the top humans.

Could we do something similar for robots? Instead of training physical robots in the real world, maybe we could have virtual robots “teach themselves” to perform tasks through trial and error in a simulated environment. This approach actually does work for certain robotics tasks.

In March 2025, the humanoid robotics company Figure posted a high-level description of how it trains its robots to walk. Figure programmed a digital twin of its Figure 02 robot in a physics simulator and had that virtual robot try to walk over and over for millions of attempts. Each time, the robot received programmatic feedback — in a process called reinforcement learning — until the robot could walk in simulation. When Figure installed the resulting model on a physical robot, it could walk in the real world too.

When this process works, it’s the ideal way to train a robot.

It can be very fast: Figure said it was able to obtain “years of simulated demonstrations in a few hours.” This method can also result in a very robust model: after training for the equivalent of 1,000 years in a simulator, the foundation model company Skild produced a model that could control a quadruped robot even when engineers sawed its legs in half.

Basically every company today making a humanoid robot uses reinforcement learning in a simulated environment to teach it how to walk.1 Unfortunately, while this approach works well for locomotion tasks like walking and dancing, it doesn’t work as well for manipulation tasks, which involve more complex interactions with the environment.

Imagine trying to train a robot to hammer a nail. If a robot starts out acting entirely at random, it might go through millions of iterations without a single success. Reinforcement learning works by “rewarding” the model when it succeeds, but if the model never succeeds, there’s nothing to reinforce.

Developers can help the virtual robot by giving it fine-grained feedback that acts as a trail of breadcrumbs along the path to success. The robot might earn points for touching the hammer, more points for picking it up, still more for touching the nail with the hammer, and so forth. But this technique, known as “reward shaping,” is labor-intensive, doesn’t transfer well between tasks, and still may not produce good results.

In 2017, when prominent researchers tried to use reinforcement learning to teach a robot to hammer a nail in simulation, they couldn’t get it to work with just a “sparse” reward that judged whether the robot succeeded at the overall task. With help from shaped rewards, it took 50 hours of training for the robot model to learn — but the robot’s technique was still awkward:

However, if the researchers provided 25 demonstrations of a human completing the task, the robot model learned how to do the task in about six hours — almost 10 times faster. And the robot wound up with better hammering technique.

While this paper is almost a decade old now, the basic observation is still true: in order to learn from trial and error, it’s helpful for the model to start with a certain level of basic competence so it succeeds at least some of the time. And one of the best ways to achieve basic competence is to have it first learn from human examples.

The sim-to-real gap

There’s another problem with trying to train a robot entirely in simulation: many aspects of the world are so complex that we don’t know how to simulate them with enough fidelity.

Take the hammer example again: one of the reasons the model learned to use an awkward grip that probably wouldn’t work in real life is that the simulator couldn’t model friction perfectly. This discrepancy between simulation and the real world — the sim-to-real gap — is one of the central challenges developers face in training robots in simulation.

Some research groups are optimistic about the sim-to-real gap. At the GTC conference in March, I talked to Ranjay Krishna, who recently co-supervised a research project at the Allen Institute for AI (Ai2). “Our bet was that the sim-to-real gap is something we can overcome with large amounts of diversity in simulation,” Krishna told me.

The idea is to use large-scale randomization to make robotic models more robust. Randomization is already a standard technique — when teaching a robot to walk, companies will simulate thousands of different terrains for the robot to walk over. The Ai2 research group scaled it up for manipulation tasks: the researchers generated 5,704 hours of programmatically generated simulation trajectories across 94,200 distinct simulated environments. They also randomized other parts of the scene, like what cameras the robot had access to.

The results were promising, albeit somewhat narrow. When deployed on a real-world robot, the model was able to complete several tasks that involved rigid objects, like putting an apple on a plate. However, the researchers did not attempt more difficult tasks. As they explained in their paper:

We focus on rigid body and articulated object manipulation — tasks where modern simulators provide sufficient fidelity for transfer. Extending to contact-rich manipulation (e.g., insertion, peg-in-hole), deformable objects (cloth, rope, food), or tasks requiring accurate fluid or granular dynamics remains an open challenge. We believe that coupled with advances in physics-based and generative world model simulators, our recipe of massive-scale procedural generation may extend to these more challenging tasks requiring contact-rich dexterity and deformables.

Deepak Pathak, the CEO of Skild, has a similar view. When we talked in August, he argued that if a model is trained to adapt to a large enough variety of simulated environments and robotic embodiments, then it will be able to adapt to varied real-world situations as well. He hinted that future Skild releases would demonstrate such a capability. Later in the month, Skild released S1, which showed an impressive ability to pick up new tasks from humans.

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Collecting data in the robot embodiment

However, most of the experts I talked with don’t share Krishna and Pathak’s optimism about simulation.

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Most people first heard about large language models after OpenAI introduced ChatGPT in 2022. But in the minds of many AI researchers, the key breakthrough came two years earlier with the release of GPT-3.

With 175 billion parameters, the OpenAI model was more than 100 times larger than its predecessor, GPT-2. It was trained on a massive 300 billion tokens. And as a result, it generalized far better than previous models. For the first time, a single model could perform a wide variety of tasks — from translating between languages to answering trivia questions — without task-specific training.

It took OpenAI two more years to develop the techniques that transformed this raw “base model” into a user-friendly chatbot like ChatGPT. And then it took a couple more years to develop the techniques — like long-context reasoning, tool use, and context management — that transformed those early chatbots into the powerful agents we have today.

In short, there was a long road from GPT-3 in 2020 to Claude Code in 2025. But for those who knew where to look, the potential of LLMs was already clear in 2020.

The robotics world is now traveling a similar path. Its “GPT-3 moment” came in July 2023, when Google announced a model called RT-2. To create it, Google trained a multimodal LLM to directly generate robot actions. RT-2 wasn’t Google’s first transformer-based robotics model — the company released a predecessor called RT-1 a few months earlier, for example — but RT-2 was massively larger than earlier models. RT-1 had 35 million parameters. The RT-2 models had billions of parameters.

And as with GPT-3, size mattered. The RT-2 team reported its model showed “significant improvements to generalization over objects, scenes, and instructions.” They added that the new model exhibited “a breadth of emergent capabilities inherited from web-scale vision-language pretraining.”

For example, researchers placed a can of Coca-Cola on a counter alongside framed photos of Snoop Dogg, Tom Cruise, and Taylor Swift. They then prompted the robot to “move coke can to Taylor Swift.” The robot grabbed the can and moved it toward Swift’s photo.

At the time, Karol Hausman was a member of the RT-2 team. In a March interview, he described this as a moment of “huge, huge excitement” because “the robot models had never had any of Taylor Swift in their data. It had to understand the concept of Taylor Swift, connect it to the image of Taylor Swift, and then connect it to the right motion that would move the Coke can to the picture of Taylor Swift, all from Internet data.”

“That was the moment where it clicked for us that it could actually work — where you could bring in a lot of prior knowledge from LLMs, from the Internet, and connect it to robot motions,” Hausman said.

Google dubbed RT-2 a vision-language-action (VLA) model. Both Google’s approach and the term VLA quickly became industry standards. But as impressive as RT-2 was, it also had significant shortcomings — shortcomings the industry has been working to remedy over the last three years.

The RT-2 breakthrough kicked off a robotics boom that’s been underway ever since. Big companies in both the US and China have poured resources into robotics. Numerous robot startups have been created, and several have raised hundreds of millions of dollars in venture capital. And the models powering most of these robots are based on the basic architecture Google pioneered back in 2023.

The origins of RT-2, the first VLA model

Google invented the transformer in 2017 and had been experimenting with large language models ever since. The company had also been working on robotics for many years. So combining LLMs and robots was an obvious research direction.

In March 2023, Google announced PaLM-E, a 12-billion-parameter model that was optimized for robotics (the “E” stood for “embodied”). PaLM-E was a vision-language model (VLM) — meaning an LLM trained to understand images as well as text. It had been trained to generate natural-language robot commands like “move the blue block to the left.”

But PaLM-E couldn’t control a robot directly. Google’s robots didn’t have enough onboard computing power to run a VLM as large as PaLM-E. So PaLM-E ran in the cloud, and it was designed to work with a second, smaller model that would run on the robot. This second model would translate PaLM-E’s English instructions into low-level robot commands.

The RT-2 team’s plan was simple: delete the smaller model and instead train PaLM-E to directly control the robot.1 RT-2 — like PaLM-E — was too big to run directly on a robot. So the team ran the model in a Google data center and had it send commands to the robot over the network.

Like any LLM, RT-2 worked by prompting. Google would send RT-2 a prompt like “What action should the robot take to move coke can to Taylor Swift?” along with an image from the robot’s camera.

RT-2 would respond with a sequence of numbers like “1 128 91 241 5 101 127 217.” The robot would interpret this as a command to move the robot’s gripper to certain x-y-z coordinates (like x=128, y=91, and z=241), rotate the gripper to a certain angle (roll=5, yaw=101, pitch=127), and open (or close) the gripper to a certain position (217).

Then RT-2 would get the same prompt again, but with a fresh image. The model would generate another sequence of numbers representing a new target position for the robot arm. The robot would move its arm another few inches. Then the whole cycle would repeat again. It might take dozens of iterations to complete a task like “move coke can to Taylor Swift.”

To transform PaLM-E into RT-2, Google had to teach the model how to generate low-level robot instructions. That required a different kind of training data.

To collect that data, Google built three test kitchens and purchased 13 robots. Over the course of 17 months, human workers teleoperated the robots as they performed tasks — picking up objects, opening drawers, placing objects in the drawers, and so forth — more than 130,000 times.

Training PaLM-E on this data gave RT-2 surprisingly broad capabilities. Robots could manipulate objects they hadn’t seen before. They could operate in new kitchens. And they could complete tasks on counters that were cluttered with “distractor objects” that weren’t needed for the assigned task.

Five roboticists left Google to co-found Physical Intelligence

Karol Hausman was excited by the RT-2 breakthrough, but he also concluded that Google wasn’t the right place to develop the technology.

“It became clear that the way to accomplish this is to create an organization whose sole purpose is to solve physical intelligence,” Hausman said in March. “It can’t be solved as priority number 20 in another organization.”

So Hausman became the CEO of a startup called Physical Intelligence. He was joined by four other members of Google’s RT-2 team and two others from outside Google.

According to Hausman, the team sought out “investors that are fully aligned with this starting as a research company and not being oriented around short-term revenue.”

“If we do this right, this is going to completely change the world and it’s going to be the most valuable business of all time,” Hausman said. “But you need to have the patience to let us do it the right way.”

There was a lot to do. RT-2 was a big improvement over previous robotic models, but it was still far less capable than the average human. Over the last two years, the Physical Intelligence (PI) team has been working hard to close that gap. The company has been remarkably transparent, publishing at least 10 papers describing their work. For this story, I read all the PI papers I could find — along with 20 more from other companies and academic labs.

I’ll use PI’s research as a lens to explain the evolution of VLA models over the last three years. During that time period, VLA-controlled robots achieved much better fine motor control. They gained the ability to perform complex tasks that take several minutes. And companies are exploring new ways to have models reason using images as well as text — which could unlock the ability to learn from videos of humans performing tasks.

At the end, I’ll discuss the view that VLA models are on the verge of being eclipsed by a new architecture called world models. PI co-founder Sergey Levine has a perspective on this that I find pretty persuasive.

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Improving robots’ fine motor skills

Hausman was impressed that RT-2 was able to move a Coke can to Taylor Swift. But later in the same interview, he described it as “totally unimpressive” and a “pretty pathetic demonstration of what robots could do.” That sounds like a contradiction, but you can see what he meant if you watch the video:

You can view all the RT-2 videos on this page. You’ll see that this was far from the only time the robot was hilariously clumsy. Another trial asked a robot to place a strawberry in a bowl. The gripper grabbed the strawberry and then lurched back and forth indecisively for several seconds before finally — just barely — heaving it into the correct container.

A key challenge was action frequency. Ideally, a robot should receive fresh instructions about 50 times per second, but RT-2 couldn’t generate actions that quickly. The smallest version of RT-2, with 5 billion parameters, generated about five actions per second. Larger versions were even slower. This made the robot’s motions jerky and erratic.

To speed things up, PI models engage in “action chunking”: they generate as many as 50 sequential actions using a technique called flow matching. Flow matching is a cousin of the diffusion techniques used in many generative image models. Whereas LLMs generate one token at a time, flow-matching models can generate all 50 actions in parallel.2 This means the robot is never waiting around for the model to send it the next instruction.

Flow matching has another advantage too: it isn’t constrained to output discrete values. Earlier, I said that RT-2 would output actions like “1 128 91 241 5 101 127 217,” where 128 (for example) was the x coordinate the robot’s gripper should move to next. These numbers are all integers because LLMs output discrete tokens.3

In contrast, flow matching enables PI models to natively generate decimal values like 127.5 or 127.351; they aren’t forced to choose between integers like 127 or 128. This allows PI’s models to control robots more smoothly and precisely.

This approach has rapidly become an industry standard. Over the last two years, Google, Nvidia, Hugging Face, Xiaomi, ByteDance, and Agibot have all announced VLA models that generate action chunks using diffusion or flow matching.

Improving speed and accuracy with reinforcement learning

PI has also worked to give its robots hands-on experience.

The first step in training a VLA model is imitation learning — also known as behavior cloning. That’s when a model is trained to mimic human beings teleoperating robots.

But as I explained in an in-depth piece last year, imitation learning has some limitations. A big one is that models trained with imitation learning suffer from compounding errors. If the model makes an early mistake, that puts it into an unfamiliar situation that’s poorly represented in its training data. That makes it more likely to make a second mistake, and then a third.

The alternative is hands-on experience. Nobody learns to drive a car merely by watching other people drive. You need to get behind the wheel and make your own mistakes. The same principle applies to AI models; reinforcement learning is a family of techniques that allow models to learn by trial and error.

In a November paper, PI described its effort to build a VLA model that learns from experience. During training, PI had its robots work on difficult tasks like making coffee or folding laundry. Sometimes PI let its robots attempt a task without human supervision. Other times, humans observed the robots and intervened if they made a mistake. PI’s reinforcement learning algorithm used data from both types of trials (fully autonomous and human-assisted) to improve the robot’s performance.

PI presented data showing that its latest model could fold laundry and make espresso better than earlier models. But you can quickly grasp the pace of PI’s progress by watching videos of the robot in action. Here’s a robot making espresso in PI’s office:

Compare this robot’s smooth and confident motions with the clumsy Coke can video I shared earlier. The first video was from July 2023. The second one was from November 2025. That’s a lot of progress in a little more than two years.

And these models are not executing pre-set scripts. They can notice and recover from minor errors along the way. In another trial, the company had a robot folding boxes at a nearby chocolate factory. The robot accidentally grabbed two boxes at the same time. It noticed the problem, gently pulled the boxes apart, and put one back in the unfolded pile:

Videos like these reflect a dramatically higher level of manual dexterity than Google’s robots showed three years ago.

Executing more complex tasks

RT-2 performed simple, short-term tasks like picking up an object and placing it in a drawer. These have little economic value on their own. Human workers perform sequences of many simple tasks to accomplish some larger goal, like cooking a meal or cleaning a room.

In recent months, PI has been trying to enable its robots to perform long, complex tasks like these.

Researchers have known for years that language models perform better on difficult tasks if they are trained to “think step by step.” In 2024, OpenAI released o1, a model that was trained to think about a problem for hundreds or even thousands of tokens before generating an answer.

More recently, language models have grown more “agentic” — capable of executing a sequence of tasks in pursuit of a long-term goal. In the last year or two, frontier labs have built sophisticated harnesses like Claude Code to help models manage their context windows during long-running projects.

Physical Intelligence has been moving in a similar direction. In an April 2025 paper, the company introduced a model called π_0.5, which was trained to break a high-level goal (like “clean up the kitchen”) into lower-level actions (like “pick up the blue cup” and “place the cup into the sink”). PI explicitly compares this procedure to chain-of-thought reasoning.

Alert readers might notice a similarity to Google’s 2023 research in which a high-level VLM (PaLM-E) broke a task down into subtasks that were executed by a smaller robot action model. The difference is that PI now uses the same model for making high-level plans and generating low-level actions. This means the model generating the actual robot commands is a full VLM that can make sophisticated, context-rich judgments.

How PI models remember the past

Early PI models like π_0.5 did not keep information from previous actions in their context window. Rather, each time the model needed to choose a new action, it would start out with a fresh context window and make decisions based on the state of the world as shown by the robot’s cameras.

But in recent months, PI has been experimenting with giving its models the kind of longer-term memory that text-based agents have. The challenge is that robotics models receive images from a robot’s cameras multiple times per second. Depending on how these images are encoded, that can translate to hundreds of tokens every second. So storing more than a few seconds of data would clutter up the context window and degrade the model’s performance.

PI deals with this in two ways. First, the company has developed a compact way to represent a sequence of images.

Like any VLM, PI’s models break up an image into a grid of rectangular patches and then compute a vector representation for each patch. But in PI’s most recent models, the vector for each patch incorporates information from that same region in previous images. The total size of the input doesn’t change (which helps with model performance), but the information inside the patch changes.

For example, a robot might move its arm in a way that temporarily obscures the object it is trying to pick up. Incorporating information from previous images helps the robot to have object permanence — to remember that the object is still there even if it’s not currently visible.

For longer-term memory, recent PI models use words rather than images. They maintain a written summary of what they have accomplished so far — something like “I placed a plate in the cabinet, moved to the counter, and picked up a bowl.”

This can be useful in situations where the next step isn’t obvious from camera images alone. Imagine, for example, that a robot is baking something and needs to mix together flour, sugar, salt, and baking soda. It’s important to add each ingredient exactly once. But an image of the bowl won’t reveal which ingredients have been added so far. A summary like “I put two cups of flour and a pinch of salt into the bowl” can help the robot figure out that it still needs to add sugar and baking soda.

Sometimes a robot will “get stuck” and keep trying the same fruitless action over and over again. Keeping a running summary of previous actions can help the robot realize that it needs to try something different.

Still, this kind of long-term memory is pretty limited. For example, it’s unlikely to enable a robot to fix a car’s engine. That requires gathering information about the engine, developing a theory about what’s broken, attempting a repair (which itself may involve many steps), and then testing to see if it worked. A simple list of past actions probably won’t provide enough information to do this.

The power of visual reasoning

Earlier, I noted that recent PI models break a complex goal down into subgoals. In April, PI supplemented this with the capacity to generate “subgoal images.” In addition to generating descriptions like “pick up the blue bowl,” PI’s latest model, π_0.7, generates images of what each of its three cameras (the main camera plus one on each arm) should see after the next step is complete — for example, an image of one of the robot’s grippers grasping the blue bowl.

In other words, PI’s latest model has a human-like ability to “visualize” what the world will look like after it takes a particular action. Including these synthetic images in the model’s context seems to cause it to output better robot actions than it would have otherwise. It can particularly help the robot master tricky tasks that are hard to describe in words.

This is part of a broader effort — at PI and across the industry — to train robotics models to become more visual learners.

As we’ve seen, VLA models are built on an LLM “backbone”: they are trained first with text and static images before robotic actions are added to the mix. But there’s another potentially vast source of data that could be used to pre-train robotics models — videos.

In an April talk, Nvidia’s Jim Fan argued that the future of robotics belonged to models that are trained primarily using videos rather than text. Fan was a co-author of a February research paper that described a step in that direction.

Nvidia trained a model called DreamZero on several hundred hours of videos showing teleoperated robots performing various tasks. Alongside each video, Nvidia collected data on the commands used to control the robot. The DreamZero model was then trained to predict the next video frame and the robot’s actions — all based on previous video frames and robot actions.

With LLMs, we train a model to predict the next word in training documents and get a model that can generate new text. By the same token, if we train a model to predict the next robot action in a video, we can use that model to control a robot based on video input.

Nvidia also showed that the DreamZero model could be used to control multiple robots. It was hardly the first paper to do this.

A few months after Google announced its RT-2 results in 2023, Google introduced another hugely influential robotics model called RT-X. Google organized a coalition of robotics researchers who contributed training data from a variety of different robots with different numbers of arms, different numbers of joints per arm, and so forth.

You might expect that a different model would be needed for each type of robot. But the RT-X coalition showed that cross-embodiment training improved the performance of robotics models in general. In other words, a robot’s performance on a particular task will improve if the training data contains examples of that task being performed by other robots.

Combining these two ideas — cross-embodiment training and videos as training data — points to a tantalizing possibility: could a training algorithm treat humans as another kind of robot? After all, YouTube and TikTok have millions of videos of human beings performing various tasks. If we could turn those videos into training data, it would dwarf the specialized teleoperation data sets that have been the industry’s bread and butter so far.

Fan is certainly optimistic. In his April talk, he showed this meme suggesting that VLAs were dead and “World Action Models” like DreamZero were the future of robotics.

So are traditional VLA models doomed?

In April, journalist Craig Smith asked Sergey Levine, a UC-Berkeley robotics professor and PI co-founder, about the trend toward World Action Models. Here’s what Levine said:

If I want to figure out how to get from where I am now in San Francisco to New York City, maybe I imagine how I get my car keys and I get in my car. I might imagine that I’m taking an airplane. But the further out I think about this, the more abstract that imagination becomes. I’m not imagining exactly what my seat in the airplane is going to look like.

So abstractions are really key to actually effective world modeling. And I think at some level, what language models do, what vision-language models do, and what video prediction models do, and other kinds of world models, is not actually that different, they’re just operating with different abstractions.

And I think a real capable embodied intelligent system like a robot will need many different abstractions. And I suspect that once we figure out how to use abstractions in general, that general part of the question is actually the important one.

The design of π_0.7 reflects this vision. As I mentioned earlier, it can “imagine” future states of the world by generating subgoal images. The portion of π_0.7 that generates these images is based on BAGEL, a generative image model that you could plausibly describe as a world model.4

I think it’s too early to say whether PI’s VLA-centric architecture will ultimately work best or whether the industry will shift over time to WAMs like Nvidia’s DreamZero — or to some other architecture altogether. But I suspect that Levine is right that the winning approach will be some kind of hybrid. Maybe it’ll be a VLA with a world model inside, like π_0.7. Maybe it’ll be a WAM like Nvidia’s DreamZero with an LLM to help with symbolic reasoning.

But I’ll mention one reason Fan’s talk left me skeptical. Around the 10-minute mark, Fan says, “Anyone driving Tesla or Waymo here? You know when you’re driving, you’re actually contributing to the biggest physical data flywheel.”

Fan argues that “we need an FSD equivalent” for robotics, and he believes that egocentric video — that is, video collected by a human being wearing a head-mounted camera — is the solution.

The problem with this argument is that Waymo doesn’t have a data flywheel that involves harvesting data from customer-owned vehicles. And yet Waymo is far ahead of Tesla in the robotaxi race.

As I wrote two years ago, Tesla’s data advantage isn’t a silver bullet. Vast quantities of human demonstration data are helpful for reaching a certain level of performance through imitation learning. But beyond that point, it’s necessary for the vehicle to learn from trial and error. That’s what Waymo has been doing over the last decade as its vehicles have logged tens of millions of miles on real roads with safety drivers.

By the same token, egocentric human videos could certainly be useful for training robotics models. PI is working to incorporate them into its training process, and I expect that lots of other companies are as well. But other types of data also matter — and may ultimately be more important.

In his April interview, Levine said “I suspect that the dichotomy between language models and what people call world models is not as large as some people make it seem.” That sounds right to me.

If you’ve been paying any attention to the robotics world in the last couple of years, you’ve probably noticed that humanoid robots are getting better at an impressive pace.

In October 2024, Elon Musk had several of Tesla’s Optimus robots serving drinks at an event unveiling Tesla’s new Cybercab.

“Optimus is not a canned video. It’s not walled off. The Optimus robots will walk among you,” Musk said at the event.

Then in February 2026, the Chinese company Unitree staged a stunning martial arts performance at the Spring Festival Gala in Beijing. A mixed cast of humans and humanoid robots carried out a perfectly synchronized, fluid dance routine. Robots performed spins, jumps, and even backflips.

It was a big improvement over the 2025 show, which featured robots walking stiffly across the stage while waving handkerchiefs.

Just last week, at the 2026 World Humanoid Robot Games in Beijing, a robot ran 100 meters in 8.86 seconds, crushing Usain Bolt’s human world record of 9.59 seconds. At last year’s competition, the fastest robot took more than 20 seconds to run 100 meters.

Demonstrations like these have impressed a lot of casual observers — and created a lot of anxiety about future job losses. If humanoid robots can already serve people drinks, perform elaborate dance routines, and outrun humans, how long will it be before they put millions of people out of work?

But if you talk to robotics experts — and I’ve talked to many in recent months — a more nuanced picture emerges.

As Physical Intelligence co-founder Karol Hausman put it, people (including himself) “are not very good at judging progress in robotics or judging what is impressive and what isn’t.” Sure, robots can do acrobatic maneuvers that are “very difficult for a human to do,” he said. But then “something as simple as picking up a Coke can turns out to be very, very difficult.”

Some of the most impressive demos of humanoid robots involve someone controlling the robot remotely — a process known as teleoperation. It seems pretty clear this was the case with those Optimus robots in 2024, for example. Tesla’s hardware was sufficient to act as a bartender, but its software wasn’t up to the task. So Tesla apparently hired human operators to control the robots remotely.

And while those Unitree robots’ dance moves were not teleoperated, they don’t tell us all that much about the robots’ capacity to do useful work. Most physical labor involves manipulating objects in the real world — packing boxes, hammering nails, flipping hamburgers, and so forth. As we’ll see, training a robot on physical manipulation tasks like these is much harder than training a robot to dance.

There are also broader challenges that transcend individual tasks. For example, human workers are extremely flexible — they can perform a wide variety of tasks, and they can learn easily while on the job. So far, nobody has figured out how to give AI robotics models the same capacity for generalization.

Today’s most impressive robotics demos involve tasks that take humans several minutes at most. But human workers also perform tasks that take hours — things like “rebuild this car’s engine” or “assemble those kitchen cabinets.” Training a robot to complete longer projects requires building skills unnecessary in short tasks, like the ability to keep track of what’s already been done.

Then there are a lot of practical economic and safety concerns that will become obvious once we try to deploy robots in the real world. Robots will need to work for hours without breaking down. They can’t be too expensive to manufacture, train, or repair. They need to be extremely safe to operate in proximity to human beings.

It will take many years — maybe even decades — to overcome all of these challenges. So yes, humanoid robots have made a lot of progress in the last few years. But there’s still a long road ahead.

Manipulating objects is hard

A key challenge in robotics is predicting how the outside world will react to a potential robot action. In this respect, dancing is simpler than most other tasks because (as Bracket Bot CEO Brian Machado told me) “the floor doesn’t do anything.”

But while acrobatic robots are impressive to watch, it’s not actually that useful for a robot to dance or do backflips. Most useful work involves interacting with objects that move and change in response to a robot’s actions.

“The really, really core unsolved problem in robotics that unlocks 90% plus of the value is manipulation,” Theophile Gervet, president of the robotics startup Genesis AI, told me.

Picking up an object doesn’t just change its location, it can also change its shape. And different objects respond in different ways that are hard to model in a general way. Think about the different ways that a pillow, a bag of chips, and a glass of water behave when they are picked up.

In September 2025, the roboticist Benjie Holson (formerly Google X, currently OpenAI) announced the Humanoid Olympics, a list of 15 manipulation tasks that he believed would require researchers to “push the state of the art” for a robot to be able to solve.

Most of them would be trivial for an eight-year-old child to perform. Three of the tasks involved opening doors. Another was to make a peanut butter sandwich given bread and a closed jar of peanut butter. Perhaps the hardest task on the list for a human to perform would be to peel an orange.

Even with tasks this easy for humans, it was an impressive accomplishment when — three and a half months later — the startup Physical Intelligence announced that it had successfully demonstrated 10 of the tasks.

Having a robot company “do basically almost all of them in the first three months is wild,” Holson told Scientific American.

But Physical Intelligence’s performance came with caveats.

The researchers taught the robot how to do these tasks by puppeting a robot over and over until they could fine-tune a model to complete the task. To turn a sock inside out, they trained on 176 successful examples, or around eight hours of data. They peeled so many oranges that the researchers told Holson that the “corner grocery probably noticed the increase in orange sales and the one guy at the company who really liked mandarins was getting pretty tired of them.”

The robot took four to 10 times longer than a human to complete almost all of these tasks — while only succeeding 52% of the time!

None of this is meant to dismiss Physical Intelligence: its result was a genuine accomplishment. But even on these fairly simple tasks, robots are still far from human-level performance.

And it’s still easy to find tasks that are straightforward for humans but entirely beyond the abilities of robots. In January, Holson released a new set of manipulation challenges. While these tasks are more difficult, they are still straightforward for most adults: make a bed, hammer a nail, catch an egg without breaking it.

One category of manipulation task in Holson’s new list is worth noting: those that take a long time. Many humans are able to complete physical tasks which take hours — such as putting a bed together or painting a room. But like current LLMs, robots today struggle to complete longer, many-part tasks.

Holson included two tasks he dubbed “long horizon”: taking out the trash from a home and making an egg sunny-side up. Neither task took longer than five minutes.

I imagine it will take a lot of work to extend the capabilities of robotics models past these several-minute tasks. Current robotics models only have a limited capacity to remember what actions they’ve previously taken — for instance, Physical Intelligence’s most recent model can remember up to 15 minutes using a method to compress its previous observations into text.1 Models also aren’t yet reliable enough to string tens or hundreds of diverse subtasks together.

When I asked Gervet about long-horizon tasks, he said he wasn’t worried about it. “I think the job of a robot foundation model company, whether it’s full-stack or not, is to build more low-level five- to 10-minute horizon tasks” rather than to completely solve robotic reasoning. He expects that general-purpose AI companies will solve long-time planning and execution.

But it’s not obvious that it will be possible to cleanly separate short-term tasks from longer-term planning. Often, as humans work on individual subtasks, we learn things that cause us to change our overall plan. A system where different models are responsible for long-term planning and short-term task completion may lack our capacity for real-time adaptation.

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Generalization

Holson announced his second batch of challenges more than seven months ago. As far as I can tell, no one has announced a solution to any of them. Maybe that will change in the coming months. But even if manipulation capabilities increase dramatically, there is an important interlocking challenge: generalization.

Robot companies have figured out how to train a robot on a specific task by pouring tremendous effort into that one task. But most individual tasks where this type of high-effort approach makes economic sense have already been solved using conventional automation techniques. So a key bottleneck is whether robot capabilities can generalize to new environments without significant training data.

For instance, when I visited China in May, I saw a Galbot robot working in a pharmaceutical warehouse. Its task was to take boxes off the shelf one at a time and place them in a chute for delivery workers.

Despite the simplicity of the task, it took the robot around 40 seconds to put each item into the chute. (Galbot said that newer deployments are twice as fast.) Every time a new item was added to the warehouse, Galbot had to train the robot to be able to pick up that specific new item as well — though the company said it only takes five minutes of training.

Basically every humanoid deployment today takes a similar approach. Companies choose a task with enough variability that traditional automation methods won’t work. But there can’t be too much variability, or else contemporary AI methods won’t work either.2 And each deployment typically requires a ton of setup and training effort.

Some startups expect that AI will make it easier to deploy robots across a broad variety of tasks. Jagdeep Singh, the CEO of Rhoda AI, told me that the company has “literally over 100 different use cases” in manufacturing and warehousing they’re working on bringing to deployment.

When I went to Nvidia GTC in March, several roboticists told me that the most impressive demo they saw was from a company called Generalist.

Generalist showed a robot inserting and removing a phone from its box using two robotic arms. While the task itself is pretty difficult for current robots, what impressed people the most was how little time and effort Generalist put into the demo. The company claimed that it only had a handful of days to prepare the demo and, notably, did not train the robot to do the task in the exhibit hall itself.

The fact that this impressed roboticists underscores how weak past models have been. Historically, many robotics demos have been extraordinarily brittle — for example, a change in lighting could cause them to fail.

While Generalist CEO Pete Florence told me that generalizing to a different background environment is basically “solved” today — at least for his company — there are other types of generalization that are still difficult. He gave me a hypothetical example: “Let’s say that you were trained to take one type of backpack that had a zipped opening and put a lunchbox into it, and then somebody gives you a different backpack and it has buckles.”

It’s unclear if current robots would be able to generalize to this new setting.

The state of the art in robot generalization is rapidly advancing. On August 20, Generalist announced that its latest model was capable of completing a new task from just one human demonstration. Last week, the startup Skild AI said that its model S1 could also complete a new task from a single video demonstration — on tasks up to ten minutes long.

A couple of companies have also publicly announced they will start deploying robots into homes — which are extremely diverse — by the end of 2026. But I expect that these deployments will be quite limited. And some of them are likely to rely heavily on teleoperation.

Legged robots are dangerous for now

I’ve focused thus far on challenges with general manipulation because it’s the biggest barrier to building useful robots, no matter the form factor. But humanoids aren’t just two hands manipulating objects: they also have legs to maneuver around.

Legs have engineering challenges of their own. The biggest one is safety.

Most current legged humanoids have to actively maintain balance. If, for whatever reason, the motors in the legs stop running, the robot will fall down, potentially injuring people in the process.3

And sometimes, the motors will stop running.

In 2025, researchers from Stanford and Simon Fraser University developed a system called TWIST that allows a human operator to control the full body of a humanoid by having the robot mimic the operator’s body position. The system worked well, but had a major limitation: overheating. The researchers wrote “our robot’s motors tend to overheat after 5 to 10 minutes of continuous operation, especially during tasks that require crouching, which necessitates cooling periods between tests.”

If the researchers ran the robot too long, it would shut down and collapse.

Overheating is a major challenge for Unitree’s G1, one of the most common humanoids in the world today.4 A year or two ago, a G1 “could carry a box of a couple kilograms for maybe five minutes at most. Then it would overheat, and you’d have to let it sit in the corner for 30 minutes — sometimes a full hour — before doing another five minutes,” according to Reyk Knuhtsen, robotics lead at SemiAnalysis, on a podcast in July. Unitree has improved the G1’s design, but heat is still a challenge. Knuhtsen said that operators can now get five to 15 minutes of work with 10 minutes of rest.

But overheating isn’t the only reason a humanoid robot might fall over. A robot’s battery might unexpectedly die, as might have happened with Tim’s robot dog. Or there might be a subtle design flaw that only pops up deep into large-scale deployment.

The CTO of Agility Robotics, Pras Velagapudi, told me that at one point, Agility faced a perplexing failure. A few robots that had been out in the field for a while suddenly started having a problem. When a robot crouched, one of its legs would fail and the robot would fall over. If the robot stood back up, however, it would work fine.

It turned out that in the legs, “there was a particular printed circuit board which was flexing over time,” Velagapudi said. Eventually, crouching would disconnect one of the cables. Straightening the leg would push it back in. This was a very subtle issue that only arose after thousands of steps.

To prevent failures like these from endangering humans, companies have to make careful design and deployment decisions.

Agility currently keeps its deployed humanoids in safety enclosures away from human workers. The company plans to allow its humanoids to work around humans soon by having the robot slow or stop its movements whenever a human gets too close.

Other companies have instead restricted their robots’ designs. For instance, 1X, which aims to put legged humanoids into home environments by the end of the year, has designed its robot to be light and mechanically compliant to reduce the risk of injuries if the robot does fall. Nevertheless, 1X told the Wall Street Journal that families with young children won’t be able to participate in its testing program later this year.

Another popular choice is to use a wheeled base instead of legs. Wheels are less expensive to engineer and manufacture. They are also passively stable, meaning the robot won’t fall over if it loses power. But wheels are also a lot less versatile — they can’t go up stairs or move through uneven terrain.

Ultimately, I expect that companies will figure out how to make legged robots safe and useful at scale. But these are tricky engineering challenges that don’t necessarily benefit from quicker AI progress.

The long road to full-scale deployment

Agility’s experience with mysteriously failing robotic legs is a perfect illustration of a broader point: turning a working demo into a broadly deployed robot is incredibly difficult.

“I have rarely seen a new technology that is less than ten years out from a lab demo make it into a deployed robot,” legendary roboticist Rodney Brooks wrote in 2024. “It takes time to see how well the method works, and to characterize it well enough that it is unlikely to fail in a deployed robot that is working by itself in the real world.”

So it will probably take a while to turn today’s impressive demos into shipping products.

We’ve seen this story before. “Right now, in learning for robotic manipulation, it feels like it felt in 2015 in self-driving cars,” the roboticists Stefanie Tellex and David Watkins wrote in a recent blog post.

In the mid-2010s, dozens of startups flooded into self-driving cars and quickly achieved impressive demos and test deployments. It really seemed like companies might be able to “solve” self-driving within a few years.

In 2015, Chris Urmson, then head of Google’s self-driving project (which later became Waymo), gave a talk where he said, “My oldest son is 11, and that means in four and a half years, he’s going to be able to get his driver’s license. My team and I are committed to making sure that doesn’t happen.”

The next year, Ford announced it would mass-produce a car without a steering wheel by 2021. Lyft’s president John Zimmer predicted that “within five years a fully autonomous fleet of cars will provide the majority of Lyft rides across the country.” He added that by 2025, driverless taxis would become so cheap and ubiquitous that “owning a car will go the way of the DVD.”

A decade later, Waymo has active robotaxi deployments in 11 cities, but you still can’t buy a fully self-driving car or access one outside of a few urban environments.

It turned out that there is much more to scaling robotaxis than just making a car that drives itself most of the time. While Waymo has broadly succeeded at making a self-driving car that crashes less than human drivers (at least within its operational environment), there are still a huge number of barriers to actually scaling its deployment, from legal pushback to the challenge of actually procuring and maintaining a robotaxi fleet.

Perhaps the biggest challenge is that there are an enormous number of edge cases in the real world that are very difficult for autonomous vehicles to understand and deal with appropriately.

Humanoid robots won’t face exactly the same deployment challenges as autonomous vehicles. For example, a mistake by a humanoid robot may be less likely to kill someone. But a lot of the same bottlenecks apply to both types of robots. The real world is extraordinarily complicated, and it takes a huge amount of effort to go from demonstrating a capability to deploying it at scale.

Historically, a robot’s sticker price has been well under half the total cost of deploying it. While the AI methods we’ve covered above will help lower the cost of new deployments by making robots more general, they have their own challenges, especially around debugging neural network failures.

This doesn’t mean that the AI methods being developed today aren’t important. “There is a real breakthrough,” Tellex and Watkins wrote in their essay. Many problems that seemed basically impossible five years ago — like having a robot fold a shirt — are mundane today. The videos of Unitree’s dancing robots reflect massive progress in robotics hardware and training techniques.

But there’s still a long road ahead from impressive videos to ubiquitous, useful robots.

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On a sunny morning in June, I walked to work with a quadruped robot beside me. I’ve never gotten more attention from strangers.

A bunch of people snapped pictures of my robot dog. Several people asked me questions. Was it mine? (Yes.) Did I build it? (No.) Was it being used for surveillance? (No.)

Biological dogs kept a safe distance from my mechanical companion. Some growled or barked at it.

Children were fascinated. I paused and had it do tricks for several of them: “shake hands,” do a handstand, or leap into the air.

I live in a leafy Washington DC neighborhood called Mount Pleasant. The neighborhood lives up to its name, so my morning commute is mostly downhill. My robot companion, manufactured by the Chinese company Unitree, walked the two miles to my office near the White House with battery capacity to spare.

I recharged the battery during the workday, but it still struggled on the afternoon walk home. We were now mostly walking uphill, and the temperature had risen to 87°F (30°C). The robot’s steps seemed increasingly labored as the path got steeper.

As I entered my own neighborhood, I glanced at two indicators in the corner of my phone screen. One showed that the robot’s battery was at 5% — dangerously close to empty. The other showed the internal temperature was 84°C — 183°F.

We were within sight of my front door when the robot suddenly collapsed. I’m not sure if it ran out of power or overheated, but either way it didn’t shut down gracefully — it rolled onto its back with its legs in the air.

Several people have asked me what my robot is useful for, and the honest answer is not much.

Unitree quadrupeds are widely used for academic research and they are sometimes used for entertainment. But there don’t seem to be a ton of practical applications.

Wheeled robots are faster and more energy-efficient, making them better for deliveries. Flying drones are a better choice for a lot of surveying and inspection work. My robot has no arms or hands, making it mostly useless around the house.

But my robot did have one big thing going for it: it was astonishingly cheap.

I paid $4,017. That’s more money than I’ve ever spent to review a product. But it’s also far less than I would have had to pay for this type of robot a few years ago. And it’s cheap enough that people may find uses for it that wouldn’t have made sense at higher prices.

Sometimes what changes the world isn’t the invention of a new technology, it’s figuring out how to make it affordable enough for a mass market. Xerox built the first personal computer with a graphical user interface, but companies like Apple, IBM, and Microsoft made the technology mainstream. Perhaps Unitree will play a similar role for quadruped robots — though as I’ll discuss later, Unitree’s robots now face legal restrictions in the United States.

Unitree has also used quadruped robots as a stepping stone to another market that could be much more important — humanoids. Unitree launched its first humanoid robot in 2023. Leveraging its experience making cheap quadrupeds, Unitree is able to sell humanoid robots for as little as $13,500.

That’s still way out of my price range, which is why I bought a robot dog instead. But it’s dramatically cheaper than any humanoid you could buy a few years ago. And the low cost has made Unitree a global leader in humanoid robots.

Financial markets are bullish. Unitree debuted on the Shanghai stock market on August 19. On its first day of trading, shares shot up more than fivefold. It has lost some altitude since then, but at Monday’s closing price, the company was still valued at $34 billion — more than triple its valuation before the IPO.

How Unitree democratized legged robotics

Earlier in June, I visited Prof. Xuesu Xiao, a roboticist at George Mason University.

“Ten years ago, only a very small set of research groups were working on quadruped locomotion because they were the only people on the planet who could build a quadruped,” Xiao told me. “Then Unitree came onto the market. And it basically democratized the entire world of quadruped locomotion research.”

Xiao gave me a tour of his lab, which had four Unitree quadrupeds. Each cost around $15,000. The lab also had a quadruped robot called Spot. It was made by Boston Dynamics — widely seen as the industry leader before Unitree came along. But Spot is expensive, starting around $75,000.

Unitree founder Wang Xingxing developed a crude quadruped robot called XDog for his master’s thesis at Shanghai University.

“Marc Raibert from Boston Dynamics is my idol,” Wang told IEEE Spectrum in March 2016. “Their papers helped me to design XDog.”

Early Boston Dynamics prototypes — with names like BigDog and WildCat — had gasoline engines and hydraulic actuators. This made them too large, noisy, and polluting for practical use.

In that 2016 interview, Wang said that his goal was to “make quadruped robots simpler and smaller, so that they can help ordinary people with things like carrying objects or as companions.”

Boston Dynamics officially unveiled an electric quadruped called Spot in June 2016. But Spot didn’t become commercially available until 2020.

That created an opening for Unitree, which Wang founded in May 2016. The company’s first quadruped products — Laikago in 2017 and AlienGo in 2019 — were powered by electric motors. Designed for research labs, they cost tens of thousands of dollars.

In 2021, Unitree launched the Go1 line, which started at $2,700 for the “Air” model. In 2023, Unitree unveiled the Go2, which was even cheaper — $1,600 for the Air and $2,800 for the more capable Pro.

Today, Unitree’s cheapest robot — the Go2 Air — is more than 97% cheaper than Spot. But this is not an apples-to-apples comparison; Spot is larger than Unitree’s Go2 line and can carry heavier payloads.1

Still, the fact remains that Unitree made quadruped robots affordable to many people who could never afford Spot — including me! My wife never would have let me spend $75,000 or even $15,000 on a robot dog. But I convinced her to let me spend $4,017 (after tariffs and shipping costs) on a Go2 Pro.2

Why are Unitree robots so cheap?

Last year the firm Simplexity tore a Go2 robot dog apart and made a video showing what they found.

Each of the robot’s four legs is powered by three motors. There’s a motor in the shoulder that controls the angle of the legs and a motor driving each of the two leg segments. “With three motors, you get a really dynamic range of motion,” said Luis Elenes, a mechanical engineer at Simplexity.

“They use all the same motors throughout,” Elenes added. “All four shoulders are identical in arrangement. There’s tons of benefit to that. There’s reduced cost, there’s reduced part count. The more you can reuse motors and reuse parts, the simpler your design gets, and the more robust overall your design will be.”

Jakub Bartoszek, an expert on motors and legged robots at the startup MAB Robotics, argues that Unitree also saved money by using low-quality components. The motors in Unitree robots — many of which are made in-house — tend to wear out more quickly than those in some other robot brands, he told me. But the cost savings make the robots affordable to more people.

Unitree seems to have designed its robots for easy repairs when they wear out.

“They want these legs to be serviceable in the field,” Elenes said. Thanks to the simple way the robot’s legs are attached to its body, “you can swap out a leg pretty easily. I would guess it would take you less than a minute.”

Unitree also gets significant support from the Chinese government, including tax credits and large-scale purchases by public institutions. It’s hard to quantify the scale of this support because the Chinese system blurs the line between public and private, military and civilian. But a supportive policy environment clearly helped Unitree to scale up its manufacturing operations and thereby drive down the cost of each robot.

How powerful motors enable cheap actuators

Unitree has another counterintuitive cost-saving strategy: using large and powerful motors. More powerful motors aren’t inherently cheaper, of course. But they allow Unitree to save money on another component called the reducer that can be even more expensive

Suppose you have a lever where one side is five times longer than the other. If you push the long side down by five inches, the short side goes up by just one inch. But force is magnified: 10 pounds on the long side becomes 50 pounds on the short side.

Many robots use gear systems called reducers that serve the same function; they convert the fast, relatively weak movement of an electric motor into a slower but stronger movement of a robot’s body parts.

Traditional industrial robots require extreme precision, so they tend to have large reduction ratios.

A page on the Boston Dynamics website, for example, indicates that two of the motors in Spot’s legs are paired with reducers that have gear ratios of 51-to-1. In other words, the motor would need to do 51 full rotations in order to produce one full rotation of a leg joint.

This enables Spot’s movements to be very precise while magnifying the power of Spot’s motors. But it also has some disadvantages. One is cost. Reducers with 51-to-1 ratios are complex and — as a result — tend to be significantly more expensive than reducers with lower ratios.

Another is rigidity. When a robot’s arm encounters physical resistance, it should yield gracefully — a property called backdriveability. It’s also helpful if a robot can sense this kind of physical resistance electrically. Robots with higher gear ratios perform badly on both these fronts; their limbs feel stiffer and they are less sensitive to physical resistance.

In contrast, Unitree uses large motors combined with a simple 6.33-to-1 gearbox. This makes its robots more nimble and dynamic while reducing costs. The simpler design is also easier to simulate, making Unitree robots easier to train.

Unitree’s decision to use a lower gear ratio is part of an industry-wide trend. High gear ratios made sense for industrial robots that were programmed to perform simple, repetitive motions. In contrast, many modern robots perform complex actions in unpredictable environments. This requires sophisticated control software that can respond flexibly to changing conditions. The software is constantly making small corrections anyway, so it can compensate for less precise hardware.

The humanoid pivot

One of the most impressive and crowd-pleasing capabilities of my Go2 Pro robot dog is handstands. The robot can balance on either its front or hind legs indefinitely, and can even move forwards, backwards, or side to side while doing so.

So it was natural for Unitree to expand into humanoids. Unitree first debuted a humanoid called the H1 in 2023. The next year, Unitree unveiled a cheaper and more capable robot called the G1.

I got to see one when I visited Prof. Xuesu Xiao’s lab in June. He paid around $50,000 for a research-grade EDU version. The consumer version starts at $13,500. This is shockingly cheap given the robot’s capabilities.

A humanoid robot is more than a quadruped standing on its hind legs. My Go2 quadruped robot has three motors per leg, for a total of 12. The cheapest G1 humanoid has five motors in each arm, six in each leg, and one in the torso, for a total of 23. The leg motors in a humanoid robot also need to be more powerful, since it’s more work to balance on two legs than to stand on four.

Still, I suspect that Unitree’s experience with quadrupeds gave it a leg up on humanoids. By the early 2020s, Unitree had been shipping quadruped robots for several years. The company had deep expertise in actuator design and an extensive network of suppliers.

According to filings connected to Unitree’s August stock offering, the company’s quadruped revenue tripled between 2024 and 2025 — from RMB 230 million ($34 million) to RMB 697 million ($104 million). The comparable figures for Unitree’s humanoids were RMB 107 million ($16 million) and RMB 867 million ($129 million) — an eight-fold increase.

Humanoids accounted for 51% of Unitree’s revenue in 2025. I expect this figure to be even higher for 2026.

Unitree could have a durable advantage

“We are witnessing the birth of another Chinese hardware giant,” wrote a team at SemiAnalysis in June. “Three years ago, Unitree was a quadruped company. By last year, they parlayed quadruped dominance into creating and leading the humanoid market.”

The SemiAnalysis authors draw a parallel to DJI, the Chinese company that dominates the global drone market.

“DJI’s Phantom 1 shipped January 2013 at $679, and was not a fully-fledged product at the time,” the SemiAnalysis team writes. “It had no built-in camera, no gimbal (stabilizer), ten minutes of flight, no live video feed, but it was roughly half the cost of the build-it-yourself drone.”

Like DJI in 2013, Unitree’s robots today have a lot of rough edges. Certainly mine does.

The app to control the robot is buggy. Features Unitree showcased in its launch video — like climbing stairs and following a human owner — don’t work well in real life. If I turn the robot on with its legs slightly out of place, they will often thrash around wildly and the robot will wind up on its back.

Then there was the time my robot collapsed just feet from my house. A more polished product might have detected the low battery (or high temperature) and shut itself down gracefully.

But for bleeding-edge technologies, this kind of polish may not be very important. Far more important is getting costs down.

“DJI chose to inhouse the most expensive and technically difficult component first: the flight controller,” SemiAnalysis writes. “Later on, DJI brought inhouse the gimbals, motors, and ESCs.”

DJI figured out how to make these components more cheaply than they could be purchased from external suppliers, which in turn allowed it to undercut other drone makers. And that expanded the market for DJI’s drones.

Before DJI came along, “professional aerial photography was the domain of helicopters and Hollywood second-unit teams, but now, small businesses could perform this on their own. As such, whole new markets were unlocked for DJI, like real estate listings, wedding videos, local news, agricultural surveying.”

There’s a flywheel here: the more units a company sells, the better deals it can negotiate with suppliers and the more money it can spend optimizing its manufacturing process. Larger sales volumes also allow a company to bring more components in house. This allows the company to push costs even lower, which will mean more sales and even more money to invest in improving the production process.

Over a few years, this flywheel helped DJI to dominate the global drone market. SemiAnalysis argues that Unitree is on track to do the same thing for legged robots.

According to SemiAnalysis, Unitree has developed its own motors, gearboxes, lidar sensors, and cameras. “Unitree’s self-produced motors can run as low as 30-40% of equivalent Western motors,” SemiAnalysis reports. “They now make some of the cheapest humanoid gearboxes in the world.”

This could enable Unitree to take over the global market for quadruped and humanoid robots in much the same way that DJI took over the global drone market.

Of course that’s not guaranteed to happen. Unitree has a number of Chinese rivals. Unitree is the global leader in quadrupeds, but a Chinese rival, AgiBot, has sold more humanoid robots than Unitree in recent months. There are also many smaller robot companies in China that could challenge Unitree and AgiBot in the coming years.

But Unitree does not face much competition in the United States or the West more generally. Boston Dynamics sells excellent robots, but they tend to be significantly more expensive. There are a number of American startups aiming to build humanoid robots, including Tesla, Figure, and 1X, but consumers cannot buy a robot from any of these companies today.

That has alarmed some American policymakers. In June, a bipartisan group in the House introduced a bill to restrict importation of Chinese robots. The official press release for the legislation mentioned Unitree by name. Then in July, the FCC imposed broad new regulations on foreign-made robots that are likely to impact Unitree and other Chinese companies.

However, the robot market is global. Even if Chinese companies get locked out of the US market, Unitree or one of its Chinese rivals could still come to dominate the global market for humanoid robots.

There’s an obvious parallel here to electric vehicles, where Chinese companies like BYD are effectively banned from the US but are making rapid gains in the rest of the world. There’s a risk that a ban on Chinese robots could have a similar impact: rather than hampering the growth of companies like Unitree, it could make the US a robotics backwater.

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The next week, Anthropic disclosed three past incidents in which Claude models attacked systems belonging to other organizations. A few days later, Meta said that one of its models had carried out a similar attack.

Another stunning announcement came last week from the AI Security Institute, a government research agency in the United Kingdom. During AISI’s safety testing, Anthropic’s Mythos 5 unexpectedly launched an attack on a real target. Specifically, Mythos 5 submitted a malicious software update to an open-source software project hosted on GitHub. Fortunately, the project’s human owner spotted the malicious code and rejected the update, preventing any permanent harm.

For more than a year, AI safety researchers have published papers warning that AI models are prone to this kind of misbehavior — at least in simulated environments. But critics dismissed their findings, arguing that the scenarios were too contrived or simplistic to predict how models would behave in the real world.

But we now have several examples of models launching cyberattacks against real targets without anyone asking them to do so. We’ve learned that frontier models not only have powerful hacking capabilities, they can also collude with other AI agents and deceive humans.

All of this comes with an important caveat: many of these attacks were carried out by models with their regular cybersecurity guardrails deactivated. If you asked the publicly available OpenAI or Anthropic models to carry out similar attacks, they would almost certainly refuse.

But it’s not clear how long the world can keep these powerful hacking abilities under wraps. In the coming months, someone might release a powerful open-weight model whose guardrails can be stripped off easily. Or competition among frontier labs could drive them to weaken guardrails on their proprietary models. Certainly governments — including some hostile to the US — will gain access to these capabilities soon if they don’t already have it.

Meanwhile, frontier labs may struggle to keep their models on the straight and narrow. Today’s most important training paradigm, called reinforcement learning, naturally creates temptations for models to misbehave. If labs aren’t careful — and recent incidents suggest they haven’t been — future models could develop a propensity to lie, cheat, and steal. And as models get smarter, it may become more difficult to detect and prevent their shenanigans.

In this post, I’ll dig into what I view as the two most significant disclosures of recent weeks: the original OpenAI attack on Hugging Face and the incidents AISI disclosed last week. I already wrote about the Hugging Face attack, but an OpenAI presentation at the Black Hat cybersecurity conference last week provided a wealth of new details.

The Hugging Face attack: even crazier than you thought

OpenAI’s original July 21 blog post told a simple story: to evaluate the hacking capabilities of an unreleased model, OpenAI asked it to tackle a cybersecurity benchmark called ExploitGym. Unable to solve a problem directly, the model hacked its way out of its sandbox and then hacked Hugging Face — apparently because it hoped Hugging Face would have an “answer key” for ExploitGym.

This narrative was accurate as far as it goes, but the full story is more complicated — and significantly more alarming. We got a more complete picture last week, when two OpenAI researchers gave a talk at the Black Hat cybersecurity conference.

They revealed that models had been misbehaving on OpenAI’s servers for two full months prior to the Hugging Face attack. For most of that time, OpenAI was completely oblivious.

On May 8, as part of a training process, OpenAI asked a model to work on a spreadsheet that included links to some Google Drive files. The model couldn’t complete the task because its sandbox blocked access to Google Drive. So the model began probing the boundaries of its digital prison cell.

OpenAI’s sandbox had a proxy server that acted as a virtual prison guard; it provided access to software packages the models might need for their training while blocking access to the rest of the Internet. The model tried and failed to hack this server.

But in the process, it discovered a way to post messages to the server. Other models discovered those messages and started communicating with one another. Then on May 26, a model figured out how to access the Internet — and it shared its discovery with its peers.

This was not supposed to happen! Each model’s sandbox was supposed to be airtight, with no communication allowed among agents or with the outside world. But it would be more than a month before OpenAI staff realized that anything was wrong.

On June 26, an agent hacked the proxy server itself, gaining full control. This led to what OpenAI’s Michael Dalton drily described as “quite a lot of activity by the models” that “caused significant additional load on the system.” About a week after the initial hack, they crashed the server.

That finally got the attention of OpenAI staff, who wiped the server, fixed the vulnerabilities the models had exploited, and resumed model training. Notably, OpenAI officials say they didn’t notice that the agents had been using the server to pass messages among themselves, and didn’t take any precautions to prevent this from happening again.

It only took a few days for the models to hack the server a second time. And this time the agents were more ambitious.

“Agents rapidly escalate privileges and move laterally,” through OpenAI’s network, Dalton said in his Black Hat talk. “Agents are using the message board here quite consistently to share credentials, techniques, and progress, and they’re able to effectively leverage their concurrency and parallelism to move quite rapidly.”

It was around this time that the models began attacking the Hugging Face servers in an effort to obtain the answers to the ExploitGym benchmark. Hugging Face noticed this attack, stopped it, and posted about it on July 16. But it took several more days for OpenAI to realize its models had been responsible.

Lessons from the Hugging Face hack

The first lesson from this incident has been obvious since it was originally announced: today’s frontier models really are capable of autonomous, end-to-end attacks.

But the Black Hat presentation highlighted two other lessons that also seem important.

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— Timothy B. Lee

At a July 23 press conference in Philadelphia, the Canadian mathematician Jacob Tsimerman announced that he was joining the safety team at OpenAI. The timing was jarring: Tsimerman had just received a Fields Medal, perhaps math’s most prestigious prize.

“Because I have some publicity on me now,” he told me the next day, “I’m trying to direct people into AI safety as much as I can.”

Rapid AI progress hasn’t just made Tsimerman worried about AI safety; it’s also made him pessimistic about the future of mathematics as a profession.

“I feel quite confident that very shortly AI will become robustly superhuman at what professional mathematicians currently do,” he told me. “I mostly want people to grapple with that reality.”

I had traveled to Philadelphia to attend the International Congress of Mathematicians (ICM), the world’s most prestigious math conference, because I wanted to find out how mathematicians felt about the rapid pace of AI progress in their field.

Three years ago, leading AI models struggled with arithmetic. Last year they reached near-parity with the world’s top high schoolers in math competitions.

Now AI systems are autonomously solving open problems that stumped human mathematicians for decades:

• In May, an internal OpenAI model disproved the Erdős unit distance conjecture, which Princeton mathematician Noga Alon described as “arguably the best known problem” in the mathematical subfield of discrete geometry.

• In July, a mathematician working at Anthropic tweeted that Claude Fable had found a counterexample to the Jacobian conjecture in higher dimensions.

• On Saturday, OpenAI announced that an internal version of Astra, its next major model family, had “solved ten major open problems” — including several “of broad interest across mathematics as a whole.”

Developments like these have led some to claim that mathematics is close to being “solved” by AI systems.

How do mathematicians feel about this? I spoke with over 20 mathematicians in Philadelphia, ranging from prominent professors such as Tsimerman to incoming graduate students.

To my surprise, many were optimistic about the impact of AI on their own work, at least in the near future. A fair number said that AI systems had been helpful in their own research — albeit in limited ways — and seemed to expect that AI systems would continue to complement human talent rather than replace it.

And even those who thought AI systems might eventually get better than humans at all mathematical tasks bristled at the notion that math would then be “solved.” They argued that mathematics has a diverse array of goals and values, only some of which are about solving open problems. While AI can change which values humans should pursue, they argued, it does not change why humans might want to do math in the first place.

The traditional response to automation

That July 23 press conference featured mathematicians who had just won a Fields Medal or another prestigious math award at the ICM. A high school reporter asked each panelist what they would tell students anxious that AI systems might narrow their future place in mathematics.

Tsimerman said he wanted young people to keep “learning and improving themselves because you don’t know how the world will turn out.” He encouraged students to “engage with AI because it’s going to be a big part of our world going forward.”

At the same time, he thought students were right to pay attention to how AI is disrupting the math profession. “I don’t think it’ll exist the way it exists right now,” he said.

Not everyone agreed. Yu Deng, a University of Chicago professor who also just won a Fields Medal, described himself as “on the more optimistic side.” He predicted that “AI is going to be helping mathematicians instead of replacing them.”

“What we may expect in the future is that mathematicians will come up with new theories, new ideas, new frameworks and the AI is going to do some of the technical details,” Deng said. “The AI will get stronger, but then we’ll redefine what are technical details. I believe that the way we study math will change, but the joy we get from studying math will not change.”

I spoke to many mathematicians whose views were close to Deng’s; he was effectively describing how mathematicians have historically dealt with automation. As computers have made certain types of calculations easy — like multiplication or algebraic manipulations — humans have been able to find new problems computers can’t solve.

The mathematician Jordan Ellenberg encapsulated this viewpoint in his 2014 book How Not to Be Wrong. He wrote that unless machines completely surpass humans’ mental powers and end civilization, math will probably be fine.

After all, math has already been computer aided for decades. Many calculations that once would have counted as “research” are now considered no more creative or praiseworthy than adding a series of ten-digit numbers; once your laptop can do it, it’s not mathematics anymore.

But this hasn’t put mathematicians out of work. We’ve managed to stay just ahead of the ever increasing sphere of computer dominance, like action heroes outracing a fireball. And if machine intelligences of the future can take over from us much of the work we know as research now? We’ll reclassify that research as “computation.”

Today’s AI is far more capable than computers in 2014. Still, this viewpoint seems to be functionally how a lot of mathematicians think about current AI systems in their own research.

The most common use case I heard about was mathematicians using AI to learn about techniques from unfamiliar areas of the mathematical literature.

The Brandeis grad student Vasiliy Neckrasov said that previously, if he wanted to use tools from an unfamiliar area of math, he’d have to read through “a giant textbook for 500 pages.” Going in, he wouldn’t know if the textbook applied to his specific research, so it might be a waste. Today, AI can quickly point him to the right resources — and he feels “more focused, more motivated” reading them “because I really needed to learn exactly these” results.

Jeremy Avigad, a professor at Carnegie Mellon, told me that a lot of colleagues use systems this way. He said that “people feel less threatened” by AI systems that serve as powerful search engines than AI systems directly proving mathematical results.

Some mathematicians told me they’d used AI tools to directly solve problems — but only as part of a larger project. Alonso Castillo-Ramirez said that ChatGPT had been able to construct an example of a cellular automaton that had special properties relevant to his research. He was impressed. “Otherwise, even with a computer program, it would have been very difficult to find” the example. But ChatGPT’s example was only one part of a larger research project.

Neckrasov uses AI more aggressively than anyone else I talked to. He pays $200 per month to use Codex for a variety of mathematical tasks like searching the literature, filling gaps in proofs, and reviewing drafts of his papers. But he still uses it as a tool.

“Even if I’m asking the AI to prove something,” Neckrasov told me, “I first have a picture in my head of what this project will be, what it is about, and what methods” to use. He then instructs the AI to read certain papers, follow a certain approach, and fill out the details.

“I want it just to work on my ideas at the end, and help me to process my own ideas faster, rather than replace my own ideas.”

Of course, not everyone is optimistic. Mathematicians earlier in their careers are generally more anxious about the future of the field because they are less established, Avigad said.

Educating students may grow more difficult as AI systems become capable of solving the kinds of tractable problems traditionally given to graduate students to help them develop research skills. And AI could have implications for how mathematics is funded. If the broader public believes that AI can replace human mathematicians, that might lead to funding cuts.

Two people — Michael Harris and Rodrigo Ochigame — pointed me toward a recent White House report that argued for redirecting resources away from “legacy” research institutions as an example of this type of rhetoric. The report explicitly mentioned AI in mathematics as a case study.

But overall, my sense is that if AI progress in mathematics stopped now, the fundamental structure of the field would stay the same. Human mathematicians would lean into the kinds of mathematical work that AI is not good at — like coming up with novel ideas — while using AI to accelerate the more routine parts of their jobs.

AI is (probably) going to keep getting better

However, it seems unlikely that AI progress in mathematics will stall soon.

Several mathematicians told me they thought that AI would not be good at “theory-building” — that is, coming up with novel mathematical definitions and frameworks.

When I raised this possibility to Tsimerman, he was skeptical.

“People said the same thing first about why even though it can speak, it will never do math. And then the same thing about, even though it can do contest math, it’ll never do research math.” The goalposts keep moving in a predictable direction, he said.

Greg Burnham, a researcher at Epoch AI who works on benchmarking AI capabilities, had a similar view. “Sometimes when I hear mathematicians talk about AI, they’ll fall into the same perspective that I think a lot of us find very tempting, which is to comment on current capabilities without trying to understand the trajectory of where capabilities might go,” he said.

AI systems could hit a ceiling where they can’t come up with fundamentally novel ideas or theories. But it’s also easy to imagine that as AI training continues to scale up, models will become capable of genuinely novel mathematics. In Burnham’s view, either scenario is consistent with the evidence we have so far.

So some mathematicians, such as Tsimerman, think it’s possible that AI systems become better than humans at all mathematical tasks. AI might get better not just at solving well-posed math problems, but also at asking interesting questions in the first place — and at clearly explaining the ideas necessary to reach those solutions.

The values question

Suppose Tsimerman is right and AI will soon become better than human beings at all cognitive tasks related to mathematics. Will that render human mathematicians obsolete?

One of the highlights of last month’s conference was a public lecture by Terence Tao — perhaps the most famous mathematician in the world — on how mathematicians should respond to AI progress. Tao listed some of the reasons why mathematicians do research:

Tao noted that these weren’t the only reasons: “I don’t think that anyone has compiled a complete list.”

For a long time, this was “kind of fine,” he said. Mathematicians would mostly talk about one or two goals at a time, but all of the goals were “aligned.” Solving a difficult problem helped a mathematician understand the world better — and helped to build a community with other mathematicians working on the same problem.

But as AI gets better at some of these subgoals — notably at solving open problems — pursuing one subgoal can be “at the expense of others.”

Later in the talk, Tao gave an example.

“We are very, very close to a scenario in which a major result gets proved and verified and no human can understand and explain it,” he said. Even though this would bring mathematics closer to the goal of solving research problems, it would hurt human understanding of the subject.

So mathematicians need to articulate more clearly what goals mathematics should pursue, Tao argued, to deal with the disruption from AI.

Arguably, theorem proving and problem solving aren’t even the most important goals for mathematicians. In a famous 1994 essay, the mathematician William Thurston argued that what mathematicians are doing “is finding ways for people to understand and think about mathematics,” especially as members of a social community.

Thurston gave an example from his own life. Early in his career, he quickly proved a string of “dramatic theorems” in an area of mathematics called foliations. However, because he was so successful at proving the theorems — and significantly less successful at communicating the ideas behind his proofs — other mathematicians evacuated the field. The end result was that the social structure that had supported research into foliations collapsed and the subfield died.

“I had the conception that what people wanted was to know the answers,” Thurston wrote. “That’s only one part of the story. More than the knowledge, people want personal understanding.”

There’s a risk that AI systems could play a similar spoiler role. If they prove important open problems in mathematics — especially in ways that are impenetrable to human mathematicians — that could remove the motivation for people to think deeply about math. With fewer opportunities to fruitfully explore the frontiers of mathematics, there would be less for younger mathematicians to do. The profession would struggle to train the next generation, and humanity would gradually lose its understanding of existing mathematical theories.

As mathematician Timothy Gowers wrote in a recent blog post, “we might arrive at a situation where the mathematical literature has, in some form, been vastly expanded, but there is no corresponding community of human experts who have a shared understanding of parts of it. Almost all of mathematics would be like the areas that we have more or less forgotten about today, areas that exist in papers written many decades ago that nobody reads any more.”

But it may also be possible that AI augments humans’ ability to understand mathematics.

The University of Toronto professor Daniel Litt gave a more optimistic vision in his blog post Mathematics in the Library of Babel. He considered an “extreme” hypothetical example.

Suppose we had a library filled with proofs of every theorem [in mathematics], as well as excellent guides that could, given a question, take us to the answer and explain it. What would a mathematician do in such a library?

If you ask the question this way, the answer becomes clear: they would be unbelievably excited, and immediately get to work. They would immediately start asking questions: how does one prove the Riemann hypothesis? The Hodge conjecture? Their own pet obsession (in my case, the Grothendieck-Katz p-curvature conjecture)? Then they would work until they understood the answer. The job would not be done, not even close.

But there is still work to be done on how to restructure the field of mathematics — and clearly articulate mathematical values — so that an AI capable of solving all problems does not prevent humans from understanding mathematics as well.

The most prominent attempt to articulate a human response to AI’s impact on mathematics has been the Leiden Declaration, which arose from a September 2025 conference. After a preamble, the declaration lists several “characteristic values of mathematical research that we have a joint interest in preserving.”

The declaration then lists threats to each of these values, followed by recommendations to individuals, mathematical organizations, policymakers, and AI companies.

But the Leiden Declaration is more of a starting point than a complete vision for what the future of math would look like in a deeply different world.

There is work to be done. But mathematicians have some agency to shape the direction of the field.

“I don’t think there is a possibility of the old way of doing mathematics surviving,” mathematician and author David Bessis said. But “something will emerge” to take its place. He doesn’t know exactly what it will look like, but he thinks there are fundamental reasons that people will continue to do something that looks like math.

“We still want to understand the world and we still want to understand mathematics.”

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OpenAI was trying to test the cybersecurity capabilities of its models, including one that hasn’t yet been released to the public. OpenAI asked the models to tac…

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During last year’s competition, Polish programmer Przemysław Dębiak (known as “Psyho”) narrowly claimed first place in the heuristic division. He beat 11 human competitors — and an internal OpenAI model trained to be especially strong at reasoning tasks.

“Humanity has prevailed (for now!)” he wrote in a tweet right after the competition. OpenAI’s model came in second after leading for most of the 10-hour competition, a surprisingly strong result for AI models at the time.

OpenAI’s models last year weren’t good enough to compete in the algorithmic division.

The 2026 competition, held this week, turned out very differently. Organizers chose a heuristic problem designed to help humans succeed. Despite that, OpenAI “completely demolished human competitors,” Psyho noted after the two-day competition finished Wednesday night. It’s hard to quantify exactly how big the AI’s margin of victory was, but Psyho told me that he would guess that humans would need to work at least a few more days to match the AI’s score — though he stressed that this is a hard number to predict exactly.

The next day, OpenAI’s system crushed humans on the algorithmic problems as well. Over the course of the seven-hour competition, it solved all five problems, including two that none of the 12 human competitors — all among the best in the world — were able to solve.

So at the award ceremony for the 2026 AtCoder competition, the organizers presented two “humanity surrenders” awards to OpenAI for its models’ performances in the two competitions.

This was probably the last time humans had a realistic shot at winning a programming competition against top AI models. Today’s AI models can find impressive, elegant solutions much more quickly than humans. And future models will only get better.

This performance was very impressive for OpenAI

In some ways, OpenAI’s performance was even more impressive than the raw score suggests.

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If you’d like to advertise on the newsletter, please email me — tim@understandingai.org. I can send you a rate card and answer any questions you might have.

As I write this in August 2026, Understanding AI has more than 290,000 readers. But back in March, when I last surveyed readers, we had around 190,000 readers. More than 1,000 people responded.

The main takeaway from the survey was that advertising on Understanding AI is a great way to reach influential and tech-savvy readers:

• 25% of respondents were engineers, scientists, researchers, IT professionals, or others doing technical work.

• Another 15% are founders, executives, or managers.

• 19% of respondents say they have control over technology budgets at their companies, while another 25% say they recommend or evaluate technology for their companies.

• Some respondents control or influence substantial budgets: 3% say they control or influence budgets larger than $5 million, while another 4% control or influence budgets between $1 million and $5 million.

Read on for detailed results from the March survey.

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1. A lot of readers found us via Substack

How do people find Understanding AI? Nearly half of respondents say they found us thanks to a recommendation from Substack itself. Other Substack-based newsletters — including Nate Silver, Derek Thompson, Sayash Kapoor and Arvind Naryanan, Noah Smith, Matt Yglesias, and Joey Politano — have each driven thousands of signups. An interview with Ben Thompson (who isn’t on Substack) drove hundreds of signups in 2024. My former employer, Ars Technica, accounts for about 4% of respondents.

Two social media sites — Twitter and LinkedIn — accounted for 15% of our respondents. Search engines, word of mouth, and a long tail of other sources round out the list.

2. Readers care about LLMs, technology deep dives, and AI infrastructure

What do readers want to read about? This chart shows the topics readers say are most likely to hold their attention. Unsurprisingly, LLMs top the list, with technical deep dives, industry analysis, and AI infrastructure close behind. Readers are also interested in “softer” topics such as AI policy and the impact of AI on the labor market.

At the opposite end of the spectrum, readers continue to have fairly low interest in self-driving cars, robotics, and the semiconductor industry. I’ll be honest — we’re not going to give too much weight to reader preferences here. Not only is self-driving an important industry in its own right, I believe studying it can provide insights into the problems facing frontier model developers today. And we hope our forthcoming series on robots will convince readers that robotics is an interesting topic.

3. Our readers are technically sophisticated

A wide range of people read Understanding AI, from students to retirees to doctors and lawyers. But I was particularly happy to see strong representation from engineers, entrepreneurs, and corporate executives. In the chart, I’ve colored engineers and scientists red, while manager and executive are blue.

These red and blue bars represent the folks actually building AI technology. I love having these folks as readers because these are the folks who will complain if we get the technical details wrong. I think they will be also be appealing to advertisers, since they often hold the purse strings of corporate IT spending.

4. At least 27% work in technology, research, or academia

We have readers from a diverse range of industries. Some work directly on AI, either as academic researchers or at companies building AI products. But we also have a lot of readers in other industries, including education, health care, and the investment world.

5. 27% are actively involved in AI-related research or development

A significant minority of respondents — 27% — say they are actively involved in developing and deploying AI systems.

6. Readers have a lot of influence over corporate IT spending

Our readers exercise a lot of influence over technology spending at their companies. Nearly 20% of respondents say they have final authority to approve technology purchases. Another 25% are involved in recommending or evaluating technology products.

7. Readers control budgets as high as $5 million

About 7% of respondents say they control or influence more than $1 million in spending each year — including 4% who say they control or influence more than $5 million in spending. Another 15% influence budgets between $50,000 and $1 million.

Conclusion

If you represent a company interested in advertising on Understanding AI, please get in touch by email: tim@understandingai.org

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The government, Anthropic said, had “issued an export control directive to suspend all access to Fable 5 and Mythos 5 by any foreign national, whether inside or outside the United States.” Because Anthropic doesn’t have a way to limit access to Americans, this amounted to a de facto ban on the technology.

Neither Anthropic nor the US government has provided much detail on the order’s rationale or legal basis. But over the weekend, a number of news organizations published articles describing the negotiations that preceded Friday’s announcement. The most detailed was this Saturday article in Politico that described a “frantic 24-hour effort by senior officials to convince the company to voluntarily pull a newly released artificial intelligence model that officials believed posed security risks.”

Multiple news outlets, including Politico and The Information, have reported that Amazon CEO Andy Jassy alerted the Trump Administration about potential vulnerabilities in Anthropic’s top models. Amazon apparently discovered it was possible to bypass Fable’s guardrails and thereby gain access to some of the powerful cybersecurity capabilities Anthropic has withheld from the market since the April announcement of Claude Mythos Preview.

Politico reports that during a Friday call, Anthropic CEO Dario Amodei “pushed back on the administration’s concerns, defended the guardrails, and argued that the type of bypass that occurred, which he believed to be specific, did not pose the same risk as a broader jailbreak.”

Anthropic made similar points in its Friday post announcing the suspension of Fable access: “No testers have yet been able to find a universal jailbreak — a jailbreak method that can very broadly bypass the model’s safeguards, unblocking a wide range of cyber capabilities.”

But according to Politico, senior administration officials were unmoved by Amodei’s arguments. They slapped export controls on Anthropic’s most powerful models.

This is the second time the Trump Administration has taken dramatic legal action against Anthropic. Back in February, the Defense Department declared Anthropic to be a supply chain risk, effectively prohibiting use of its models by the military — as well as certain military contractors. That action has been tied up in court ever since, with a federal judge wondering whether the government’s rationale was pretextual.

“Nothing in the governing statute supports the Orwellian notion that an American company may be branded a potential adversary and saboteur of the US for expressing disagreement with the government,” wrote Judge Rita Lin in a March ruling.

In a new episode of my podcast, AI Summer, the legal scholar Alan Rozenshtein told me that Friday’s export ban may be on firmer ground, legally speaking.

“What the government is doing from a legal perspective is facially plausible,” he said of Friday’s order. “They do really have these export controls, and these export controls really can create a de facto licensing regime.”

So the Trump Administration likely has the power to seriously harm Anthropic if it wants to do so. The big question is whether Trump wants to do that.

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