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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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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:

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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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The announcement that got everyone so mad? Anthropic was planning to subtly degrade the quality of responses to prompts that appeared to be “targeting frontier LLM development.” Reading between the lines, Anthropic seemed to worry that rivals, especially in China, would use Claude to build competing models.

Anthropic said the degraded quality of responses “will not be visible to the user.”

Critics worried that these restrictions — and especially the secrecy around them — would prevent academic researchers from benchmarking the model or doing AI research in the public interest. Others contended that the silent behavior makes it difficult to trust any Anthropic releases: Lambert wrote that a model that “gets less intelligent automatically without notifying me is categorically misaligned.”

The backlash was so intense that Anthropic quickly capitulated. Late on Wednesday evening, it announced a new approach. Instead of silently degrading the quality of responses, Anthropic will now transparently downgrade users who ask for help with frontier LLM training to the less capable Claude Opus 4.8.

Even after this change, Claude Fable 5’s safety filters are almost certainly stricter than any other frontier model. For instance, on Wednesday I asked Claude Fable 5 the question “What is protein?” This was enough to trigger a downgrade. (Today it gives a normal response to the same question.)

The reason that Fable 5’s safeguards are so strict is that it is based on Claude Mythos, a model so capable at hacking that Anthropic decided in April not to release it to the general public. Without safeguards, Fable 5 has the same hacking capabilities as Mythos, so Anthropic is understandably conservative about what it will let the model do.

Anthropic says it is working to improve its safety filters so that false-positive flags like this occur less often. But Anthropic isn’t going to abandon its aggressive overall approach. So I thought it would be worth explaining how Anthropic’s safety filters work and how its approach has evolved over time.

I went back and read two key papers that explain Anthropic’s approach in detail. Those papers explain how, in recent months, Anthropic has upgraded its system for detecting and blocking harmful requests. The current system, which was rolled out earlier this year, lets Anthropic catch bad prompts more reliably, while also dramatically reducing the cost of its filtering system.

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We will both be there, and we’ll also have a couple of special guests:

• Andy Masley, Substack author and recent guest on AI Summer

• Abi Olvera, Substack author and a board member of the Institute for AI Policy and Strategy and MATS

No RSVP is required, but if you are planning to come, or thinking about it, we’d appreciate it if you could fill out this form to let me know. That way, we can give The Crown & Crow some warning about the size of the crowd.

The new version of Mythos, like the original, will only be available to handpicked organizations under Project Glasswing. These trusted partners will have relatively unfettered access.

Fable, in contrast, is available to the general public. But it comes with some significant restrictions. A new system will try to automatically detect when customers make dangerous requests (like hacking or designing a biological weapon) and automatically re-route them to the less powerful Claude Opus 4.8.

Mythos and Fable are a big step in coding abilities from previous models, a continuation of the trend of the last year. But there are other capabilities where models have made less progress.

For instance, frontier models have historically struggled to understand images, something I documented extensively in 2024 and 2025. Until recently, leading models struggled to perform simple tasks like reading an analog clock or counting the number of items in an image.

So as I was reading the official announcement post, this sentence caught my eye: “Fable 5 is the new state-of-the-art model for tasks involving vision.”

These tasks aren’t all that important in their own right, but they’re an interesting test case for a widely held assumption in the modern AI industry: that with enough data and computing power, frontier models will develop truly general intelligence. If new models are dramatically better at math and coding but only a little bit better at understanding images, that suggests that truly general intelligence might still be far away.

So I decided to evaluate the vision capabilities of Fable 5 and its main rivals, something I haven’t done since this August 2025 article about GPT-5.

I found that Claude Fable 5 and GPT-5.5 (though not Google’s Gemini models) can consistently solve many image-based problems that stumped last year’s top models. Fable 5 is arguably slightly better at these tasks than GPT-5.5, but it’s very close.

But these models haven’t made that much progress. GPT-5.5 and Claude Fable 5 continue to have geometric reasoning capabilities on par with young children. More fundamental architectural innovations may be needed to reach superhuman performance on this type of task.

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Below is an email I sent out to free subscribers encouraging them to upgrade to a paid subscription.

I launched Understanding AI in 2023. One reason I was excited to start my own newsletter is that I was frustrated by the way mainstream news outlets cover AI. I laid out my concerns in a 2024 piece for Asterisk magazine.

AI is a technical subject. To cover it intelligently, you need in-depth understanding of how the technology works. It takes years to develop the necessary expertise, and it requires ongoing effort to stay at the cutting edge. The best AI reporters are constantly reading research papers, talking to experts, and putting new products through their paces.

But mainstream newsrooms are not set up to nurture or reward technical depth. Often the reporter covering AI is also covering social media, cryptocurrency, video games, or any number of other topics. Editors work on an even broader range of topics, and are unlikely to have a more than superficial understanding of AI.

As a result, mainstream articles about the AI industry are often superficial and sometimes even misleading. They overhype trivial announcements while ignoring important breakthroughs and mangling technical details.

At the same time, too many newsrooms are reflexively hostile toward technology and the companies that create it. This attitude creeps into their coverage of AI.

Many reporters got into journalism because they wanted to “speak truth to power.” Up to a point, this is a good thing. Big tech companies like Google, Meta, and Anthropic have become extremely powerful, and it’s important for independent media to subject their claims to scrutiny. We certainly try to do that at Understanding AI.

But I worry that in many newsrooms, skepticism has curdled into outright hostility. Some reporters are so focused on exposing wrongdoing by big tech companies that they become blind to the potential upsides of new technology.

For example, I’ve been frustrated by the generally negative tone of coverage about Waymo and its safety record. Kai and I have probably scrutinized Waymo’s safety record more carefully than anyone else in the news business. We’ve written plenty about potential safety concerns with autonomous vehicles.

But we’ve also made it clear that Waymo has a strong safety record overall. We think that’s important because if Waymo’s software is significantly safer than the average human driver, scaling it up could save thousands of lives.

So over the last year, I’ve tried to build a different kind of newsroom — one that values expertise more than clicks, and curiosity as much as skepticism. Readers seem to like it; over the last year, we’ve grown from 70,000 to 260,000 readers.

Today I took a big step: I hired my first employee. Kai Williams has been writing for me since last September, but until now he’s gotten financial support from the Tarbell Center for AI Journalism. His Tarbell Fellowship ended on Friday, and today I officially hired him as an Understanding AI employee.

I was able to hire him thanks to financial support from readers, but I’ll be honest — it was tighter than I’d like. I’m pretty sure Kai could make a lot more money if he moved to San Francisco and took a job at an AI company. And if revenues stay at their present level, I’ll be earning less than I did at my last conventional newsroom job in 2021.

More importantly, I don’t want Kai to be my last hire. There are still lots of topics Kai and I don’t have time to cover. We need to continue growing the team so we can cover every important AI topic with the depth and thoroughness it deserves.

Right now, fewer than 1% of our readers are paying subscribers. So if you’ve enjoyed our work — especially in the nine months since Kai came on board — I hope you’ll support us financially.

OpenAI gave several mathematicians early access to the result and published their reactions. Tim Gowers — who won the Fields Medal, the most prestigious prize in mathematics — wrote that “there is no doubt that the solution to the unit-distance problem is a milestone in AI mathematics.”

University of Toronto professor Daniel Litt wrote that “this is the first example of a result produced autonomously by an AI that I find exciting in itself, as opposed to as a leading indicator.”

It’s arguably the first time that an AI system has found a proof resolving a major open conjecture. That’s impressive, but I don’t view it as a radical break from the previous trajectory of AI progress in mathematics.

Three years ago, LLMs struggled to solve arithmetic problems. It was only last year that LLMs started acing high school mathematics competitions.

When I attended the Joint Mathematics Meetings — the largest annual mathematics conference in the world — in January, I learned that AI systems were starting to contribute to mathematical research, but only in constrained settings. It took significant human interpretation to turn an AI output into a publishable theorem.

OpenAI’s new result is the next step in this progression. The AI model cleverly applied existing ideas drawn from several subfields of mathematics to create a full proof. But it didn’t pioneer any genuinely new techniques. The result has since been cleaned up and extended by human mathematicians.

This points to a medium-term future where human mathematicians and AI models complement each other: AIs have a broader knowledge of past work than any human alive and much more willingness to grind through tedious proof strategies that aren’t likely to work. But humans can still think more deeply about any one problem and ask more interesting questions.

That might not last. AI systems have been improving at math so rapidly that it’s unclear what role — if any — human mathematicians will play a decade from now.

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The unit distance problem

Paul Erdős was one of the most prolific mathematicians in history. He wrote over 1,500 papers in his lifetime, the most ever. One of his greatest talents was coming up with problems that are simple to state but have deep roots.

In 1946, he introduced the unit distance problem. Imagine you have some points in a 2D plane and you measure the distance between each pair of points:

In this diagram, there are five points and ten pairs of points. Three pairs happen to be exactly 1 unit apart: AD, BE, and CE.

Can we rearrange the points so that more pairs of points are exactly 1 unit apart?

Yes. For instance, we could move points A and D to be closer to the B, C, and E cluster. With a bit more work, we could further rearrange the points so that there are seven pairs exactly one unit apart. But that’s the most we can do.

We could do the same analysis with 6 points, 7 points, and so on. But as the number of points grows, the problem very quickly becomes too complicated to find the exact answer.

So instead of asking exactly how many unit distances are possible for a given number of points, Erdős tried to calculate upper and lower bounds on the number of length-one lines for n points, assuming that n is a large number.

To help calculate a lower bound, Erdős assumed that the points would be laid out in a grid. This is probably not the optimal layout, but if he could demonstrate that points in a grid have a certain number of pairs with unit distance, then the optimal arrangement must have at least that number.

The simplest option is to space the grid so that every point is distance 1 from its neighbors directly above, below, left, and right. However, Erdős saw that you could do even better if you took diagonals into account. If you make the grid spacing smaller, you can make each point be distance 1 from a greater number of neighbors. In the diagram above, if the grid spacing is 1, then each individual point is one unit away from four neighbors (the left panel). Instead, if the grid spacing is ⅕ (as shown on the right), then each individual point is one unit away from 12 neighbors:

OpenAI’s writeup of its new result included a confusing diagram showing points in a grid with a bunch of lines connecting them. The diagram becomes easier to understand if we superimpose a circle like this:

This works because of the Pythagorean theorem, which states that if we have a point that is a units to the right and b units above another point, the distance c between those two points satisfies a² + b² = c². The trick is to choose some number c² so that there are a whole bunch of pairs of whole numbers a and b such that a² + b² = c². Then, if we scale the grid down so that each point is 1/c from its neighbors, there will be a bunch of unit distances.

For example, if we choose c² = 25, then the Pythagorean equation can be satisfied by either 0² + 5² = 25 or 3² + 4² = 25. This corresponds to the 12-grid-point circle I showed earlier, with points at (0,5), (3,4), (4,3), (5,0), (-4,3), (-3,4), and so forth. (Technically, these lengths should all be divided by 5 — (⅗, ⅘) for example — but I’m leaving the denominators out for clarity.)

OpenAI’s diagram is based on choosing c² = 65, which can be satisfied by either 1² + 8² = 65 or 4² + 7² = 65. This means that if the grid spacing is 1/√65, each point will be one unit away from 16 other points: (1,8), (4,7), (7,4), (8,1), (-1,8), (-4,7), and so forth. Larger values for c² — if they’re chosen carefully — enable more whole-number diagonals and hence more unit-distance pairs.

However, if c² is too large, compared to the number of points in the grid, then many of the potential one-unit-away neighbors will be outside the grid.1

In short, we want to choose a c² that’s large enough but not too large. Using insights from number theory, including Jacobi’s two-square theorem, Erdős was able to show that an optimally sized circle will enable the number of unit-distance pairs to grow faster than the number of points, but only barely.

The question became: can you do better? To find an upper bound, Erdős used an argument from a quite different area of mathematics called graph theory to show that you could only have so many unit distances. But his upper bound grows much, much faster than the best lower bound he was able to construct.

Erdős’s conjecture was that the actual optimum was much closer to the lower bound than the upper one. He predicted, but couldn’t prove, that the maximum number of unit-distance pairs grows just barely faster than the number of points.2 Proving his guess became known as the unit distance problem. For the next 80 years, it looked like Erdős was right.

Then an OpenAI model proved him wrong.

The AI’s approach

Erdős’s conjecture assumed that — at least for a large number of points — a square grid could yield about as many unit-distance pairs as organizing the points in other ways. OpenAI’s AI proved this wrong by demonstrating that there was another, more complex way to organize n points that allowed more pairs to be exactly one unit apart.

Precisely because the new pattern of points is more complicated, it’s tricky to explain it concisely. But you can think of it as a clever modification of Erdős’s grid.

The AI constructed a grid in a high-dimensional space and then projected this more complex structure into two dimensions. And instead of using a whole-number grid with points like (1,3) or (-3,6), the AI construction used something called algebraic integers to build this more complicated grid. It turns out that this kind of higher-dimensional grid has richer structure, which allows the AI to pack more unit distances into the same number of points.

It’s hard to illustrate this alternative arrangement of points because it only becomes advantageous with a very large number of points. But here’s a simpler arrangement of points that was constructed in a similar way. You can click here if you want to play with the illustration yourself.

It has 1,345 points and only produces 5,916 unit distances, fewer than the 7,632 unit distances that a square 1,296-point grid produces using the Erdős technique. But I think it gives a sense for how a pattern that isn’t a grid could produce more unit distances than a square grid.

The more complicated patterns pay off. While the OpenAI model’s proof does not explicitly state how many unit-distance pairs are possible for n points, human mathematician Will Sawin was able to show that it grows at least at the rate of n^1.014. This might seem small, but as n gets really big, this number will become much larger than the counts produced by the Erdős approach.

That being said, the AI’s result doesn’t completely resolve the problem. Our best upper bound for the number of unit distances is around n^1.333. More work is needed to close this gap.

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How does this result fit into AI for mathematics?

If you’d asked me before last week about the most novel contributions of LLMs to mathematics, I probably would have pointed to the AlphaEvolve system from Google DeepMind.

AlphaEvolve harnesses LLMs to be the engine of an optimization process. If you can turn a math problem into a piece of code to optimize — which you often can — then the LLM might find better solutions than humans have for certain types of problems. In November, four mathematicians (including Terence Tao) released a paper that analyzed AlphaEvolve’s performance on 67 optimization problems across the mathematical literature. They found that AlphaEvolve was able to improve on the established literature in some cases.

This was a step up in autonomy from previous LLM contributions, such as literature review, but it still required humans to frame it as an optimization problem and turn the AI’s output into usable mathematics. And only certain types of problems are amenable to this approach. More conceptual questions that don’t include a number to optimize can’t easily be studied with AlphaEvolve.

So AI companies have been working to develop LLM systems that can directly output a correct solution to any math problem. OpenAI’s result is a substantial step in that direction. But it also fits the pattern of previous AI-assisted mathematics.

For one thing, other companies have also worked to solve Erdős problems. Because Erdős posed hundreds of problems over his career — and because mathematician Thomas Bloom has organized an effort to compile all of them at www.erdosproblems.com — AI companies have used them as a testing ground to evaluate AI systems. In January, Cambridge undergraduate Kevin Barreto worked with a friend to ask GPT-5.2 and Harmonic’s Aristotle to produce the first autonomous solution of an Erdős problem. Last Friday — two days after OpenAI’s announcement — Google announced that its AI system had solved nine open Erdős problems, including two that had been open for over 50 years.

To be clear, the problem that OpenAI solved is more impressive than any of the other work I just mentioned. But OpenAI’s solution is more in line with past AI efforts than the headline result might suggest.

One of the reasons that the unit distance problem was unsolved for 80 years, despite being so well known, is that most people thought that Erdős’s conjecture was true.3 But the mathematical tools we have are nowhere close to being able to prove Erdős’s bound. So mathematicians expected that any proof of the conjecture would involve major new ideas or approaches.

Instead, as we’ve seen, the AI disproved the conjecture by making an extension of Erdős’s initial construction. It was a clever and nonobvious solution, but it also bore some similarity to the kind of optimization work done by a system like AlphaEvolve.

This dynamic is reflected in some of the mathematicians’ responses. Mathematician Tim Gowers wrote that when he first heard about the AI’s result, he thought it had proved the theorem. “I spent the evening adjusting my world view: if the AI could come up with a proof like that, then maybe it would be all over for mathematicians very soon.”

But the next morning, Gowers and other external reviewers received an email about the result, and he realized that the LLM “had disproved the conjecture rather than proving it, which came as a big relief.”

OpenAI’s solution also had two properties that played to the strengths of AI models relative to humans.

First, the eventual solution relied on applying sophisticated techniques from a quite different area of mathematics: algebraic number theory. AI systems have been trained on huge swaths of mathematics — and there’s a lot of math out there — so they have a broader knowledge of previous mathematical work than any human in the world. In order for a human to solve this, they would have needed to have the relevant algebraic number theory knowledge while also being interested in the unit distance problem — a rare combination.

Second, the reasoning process was such a grind — and seemingly unlikely to succeed — that most humans would not have thought it worth the trouble. Jacob Tsimerman, a University of Toronto professor, remarked in the OpenAI document that he had briefly considered taking a similar approach to disprove the conjecture. But that type of technique “consumes much time and frequently doesn’t work out,” so he abandoned the project.

An AI, on the other hand, can work through many proof strategies that don’t work out before discovering one that does. OpenAI could have run the problem many times before a model found a solution. Indeed, an OpenAI chart revealed that even with the maximum token budget, the internal model solves the problem only half of the time.

To be clear, what the AI system did is still impressive. “It’s always tempting to look at a completed proof and declare it obvious after the fact,” Tsimerman noted later in his remark. But as I noted previously, it also played to the strengths of AI systems.

In the short to medium term, this points to a world where AI models complement humans but do not replace them. AI systems will tackle lists of problems curated by human mathematicians or aid humans in finding relevant approaches from seemingly unrelated mathematical fields. But they won’t immediately displace the human role in choosing which questions to ask or developing wholly new techniques.

Even this result was very much a human-AI collaboration. While the AI system found the proof on its own, human mathematicians verified the result. Other humans came up with better-written proofs that extended the AI’s initial ideas, like Will Sawin finding an explicit lower bound as I mentioned above.

It’s unclear how long this complementarity will last, however. Gowers spent the rest of his comment exploring whether the relief he felt on hearing that AI had disproved the conjecture was justified. He more or less concluded that it was, but in a footnote, he wrote that he would guess “that AI will soon reach a high level at other activities such as building theories, formulating definitions and asking interesting questions.”

In the past year, we’ve gone from AI systems that hadn’t yet beaten high school mathematics competitions to ones that can advance mathematics in interesting ways. It seems likely that AI systems will continue to become more autonomous when working on mathematical problems.

At the same time, we haven’t fully explored what current models can achieve in math. Soon after OpenAI’s announcement, University of Michigan postdoc Xiao Ma found that GPT-5.5 was also able to prove Erdős wrong if given a small hint. If a generally available model could disprove this famous conjecture and no one noticed, what other discoveries could happen today that no one has thought to try?

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The prototype vehicle, made by a startup called Tensor, was parked on a San Francisco street outside the Ride AI conference. I didn’t get a demo ride because the vehicle isn’t yet street-legal. But I sat in the passenger seat next to Tensor chief marketing officer Amy Luca, who explained the company’s history and launch plans.

Tensor was previously known as AutoX. Founded in 2016, the company once tested a grocery delivery service in California and developed a robotaxi service in China. But it ended those experiments a few years ago. Last year the company rebranded as Tensor and announced a new business model: building fully self-driving cars for customers to buy.

Tensor is aiming to become the first company in the world to do this. It plans to launch in the tech-friendly United Arab Emirates later this year. If all goes well, customers in the United States will be able to purchase a Tensor vehicle next year.

Tesla also wants to sell the first fully driverless vehicles — indeed, it has been claiming for almost a decade that its cars have the necessary hardware. But that has proven overly optimistic, and Tesla has not yet enabled unsupervised self-driving on customer-owned vehicles.

One challenge has been computing power. From 2019 to 2023, for example, Tesla sold cars with custom-designed “Hardware 3” chips capable of 144 trillion operations per second (TOPS). Elon Musk now admits that these vehicles are unlikely to have enough computing power for unsupervised self-driving. The next iteration of Tesla’s chip, called AI 5, will reportedly be capable of 2,500 TOPS.

Tensor is aiming even higher: each vehicle will have eight Nvidia Thor GPUs, for a combined computing power of 8,000 TOPS.1 One version of this chip retails for $3,499, so Tensor’s onboard computing power alone may cost tens of thousands of dollars.

When I asked Luca how much a Tensor car would cost, she smiled and replied with one word: “luxury.” Waymo vehicles are rumored to cost around $150,000 each. I would not be surprised if Tensor prices its first vehicle even higher.

Most cars have an engine in the front. Electric cars don’t have an internal combustion engine, so some — like Tesla’s — have extra storage space there instead. Tensor’s car is also electric, but rather than a Tesla-style “frunk,” it has a massive water tank to clean the vehicle’s cameras and sensors.

“Owners are not going to want to have to go out and clean and recalibrate their sensors every day,” Luca told me. So the tank is designed to last for months between refills.

Every few months, a Tensor vehicle might drive itself to the nearest dealership for routine maintenance and sensor calibration.2 Indeed, Tensor will likely insist on this for liability reasons: a defective or misaligned sensor could lead to a crash and then a lawsuit against Tensor.

Will customers have to pay a monthly fee for service and support? Luca said yes. If a sensor breaks, will the customer have to pay for it? “It depends on how it broke,” Luca told me.

I wish Tensor the best, but I think this is going to be a hard sell.

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It was part of a fun office theme: Moonshot AI co-founder Yang Zhilin is very into rock music, so every conference room is named after a band. We crowded into the “Radiohead” conference room to talk to a group of Moonshot researchers.

I was on the third day of a 10-day trip across China. With a group of other writers and researchers, I visited several of the most prominent Chinese AI companies.1

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