From the street, the only indication of Physical Intelligence’s headquarters in San Francisco is a pi symbol painted a slightly different color than the rest of the door. Inside, the space is a giant concrete box softened by a haphazard sprawl of long blonde-wood tables. Some are clearly meant for lunch, dotted with Girl Scout cookie boxes, jars of Vegemite, and small wire baskets stuffed with condiments. The rest tell a different story entirely. They are laden with monitors, spare robotics parts, tangles of black wire, and fully assembled robotic arms in various states of attempting to master the mundane.
During my visit, one arm is folding a pair of black pants, or trying to. It’s not going well. Another is attempting to turn a shirt inside out with a determination that suggests it will eventually succeed, just not today. A third seems to have found its calling, quickly peeling a zucchini before depositing the shavings into a separate container. The shavings, at least, are going well.
Sergey Levine, an associate professor at UC Berkeley and one of the company’s co-founders, explains the scene. He describes it as being like ChatGPT, but for robots. What I’m watching is the testing phase of a continuous loop. Data gets collected on robot stations here and in other locations like warehouses and homes. That data then trains general-purpose robotic foundation models. When researchers train a new model, it returns to stations like these for evaluation. The pants-folder is someone’s experiment, as is the shirt-turner. The zucchini-peeler might be testing whether the model can generalize across different vegetables, learning the fundamental motions of peeling well enough to handle an apple or a potato it has never encountered.
The company also operates test kitchens in this building and elsewhere, using off-the-shelf hardware to expose the robots to different environments and challenges. There is a sophisticated espresso machine nearby, which Levine clarifies is there for the robots to learn from, not for the staff. Any foamed lattes are data, not a perk.
The hardware itself is deliberately unglamorous. These arms sell for about three thousand five hundred dollars, which includes what Levine describes as an enormous markup from the vendor. If manufactured in-house, the material cost would drop below one thousand dollars. A few years ago, a roboticist would have been shocked these things could do anything at all. But that is the point: good intelligence compensates for bad hardware.
As Levine excuses himself, I am approached by Lachy Groom, a co-founder moving with purpose through the space. At thirty-one, Groom has the fresh-faced quality of Silicon Valley’s boy wonder, a designation he earned early by selling his first company nine months after starting it at age thirteen in his native Australia.
Groom found what he was looking for when he started following the academic work from the labs of Levine and Chelsea Finn, a former Berkeley PhD student of Levine’s who now runs her own lab at Stanford. Their names kept appearing in everything interesting happening in robotics. When he heard rumors they might be starting something, he tracked down Karol Hausman, a Google DeepMind researcher also involved. Groom describes it as one of those meetings where you walk out knowing this is it.
Groom never intended to become a full-time investor, even after leaving Stripe where he was an early employee. He spent roughly five years as an angel investor, making early bets on companies while searching for the right company to start or join himself. His first robotics investment reintroduced him to a field he had loved as a kid. He jokes that he was on vacation much more as an investor, but investing was just a way to stay active and meet people, not the endgame. He was looking for five years for the right company after Stripe.
The two-year-old company has now raised over one billion dollars. When I ask about its runway, Groom is quick to clarify it does not actually burn that much, with most spending going toward compute. A moment later, he acknowledges that under the right terms, with the right partners, he would raise more. He states there is no limit to how much money they can really put to work, as there is always more compute you can throw at the problem.
What makes this arrangement unusual is what Groom does not give his backers: a timeline for turning Physical Intelligence into a money-making endeavor. He says he does not give investors answers on commercialization, noting it is a weird thing that people tolerate that. But tolerate it they do, and they may not always, which is why it benefits the company to be well-capitalized now.
So what is the strategy, if not commercialization? Quan Vuong, another co-founder from Google DeepMind, explains it revolves around cross-embodiment learning and diverse data sources. If someone builds a new hardware platform tomorrow, they will not need to start data collection from scratch; they can transfer all the knowledge the model already has. The marginal cost of onboarding autonomy to a new robot platform is just a lot lower.
The company is already working with a small number of companies in different verticals to test whether their systems are good enough for real-world automation. Vuong claims that in some cases, they already are. With their any platform, any task approach, the surface area for success is large enough to start checking off tasks that are ready for automation today.
Physical Intelligence is not alone in chasing this vision. The race to build general-purpose robotic intelligence is heating up. Pittsburgh-based Skild AI, founded in 2023, recently raised one point four billion dollars at a fourteen billion dollar valuation and is taking a notably different approach. While Physical Intelligence remains focused on pure research, Skild AI has already deployed its omni-bodied Skild Brain commercially, saying it generated thirty million dollars in revenue in just a few months last year across security, warehouses, and manufacturing.
Skild has even taken public shots at competitors, arguing that most robotics foundation models are just vision-language models in disguise that lack true physical common sense because they rely too heavily on internet-scale pretraining rather than physics-based simulation and real robotics data.
It is a sharp philosophical divide. Skild AI is betting that commercial deployment creates a data flywheel that improves the model with each real-world use case. Physical Intelligence is betting that resisting the pull of near-term commercialization will enable it to produce superior general intelligence. Who is more right will take years to resolve.
In the meantime, Physical Intelligence operates with what Groom describes as unusual clarity. He calls it a pure company where a researcher has a need, they go and collect data to support that need, and then they do it. It is not externally driven. The company had a five- to ten-year roadmap of what the team thought would be possible. By month eighteen, they had blown through it.
The company has about eighty employees and plans to grow, though Groom says hopefully as slowly as possible. He states the most challenging aspect is hardware, noting that everything they do is so much harder than a software company. Hardware breaks, arrives slowly, and safety considerations complicate everything.
As Groom springs up to rush to his next commitment, I am left watching the robots continue their practice. The pants are still not quite folded. The shirt remains stubbornly right-side-out. The zucchini shavings are piling up nicely.
There are obvious questions about whether anyone actually wants a robot in their kitchen peeling vegetables, about safety, about dogs going crazy at mechanical intruders, and about whether all the time and money being invested solves big enough problems or creates new ones. Meanwhile, outsiders question the company’s progress, whether its vision is achievable, and if betting on general intelligence rather than specific applications makes sense.
If Groom has any doubts, he does not show it. He is working with people who have been working on this problem for decades and who believe the timing is finally right, which is all he needs to know.
Besides, Silicon Valley has been backing people like Groom and giving them a lot of rope since the beginning of the industry. There is a known chance that even without a clear path to commercialization, even without a timeline, and even without certainty about the future market, they will figure it out. It does not always work out. But when it does, it tends to justify a lot of the times it did not.