Physical Intelligence, Stripe veteran Lachy Groom’s latest bet, is building Silicon Valley’s buzziest robot brains Connie Loizos 4:09 PM PST · January 30, 2026 From the street, the only indication I’ve found Physical Intelligence’s headquarters in San Francisco is a pi symbol that’s a slightly different color than the rest of the door. When I walk in, I’m immediately confronted with activity. There’s no reception desk, no gleaming logo in fluorescent lights.
Inside, the space is a giant concrete box made slightly less austere by a haphazard sprawl of long blonde-wood tables. Some are clearly meant for lunch, dotted with Girl Scout cookie boxes, jars of Vegemite (someone here is Australian), and small wire baskets stuffed with one too many condiments. The rest of the tables tell a different story entirely. Many more of them 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 the kind of determination that suggests it will eventually succeed, just not today. A third – this one seems to have found its calling – is quickly peeling a zucchini, after which it is supposed to deposit the shavings into a separate container. The shavings are going well, at least.
“Think of it like ChatGPT, but for robots,” Sergey Levine tells me, gesturing toward the motorized ballet unfolding across the room. Levine, an associate professor at UC Berkeley and one of Physical Intelligence’s cofounders, has the amiable, bespectacled demeanor of someone who has spent considerable time explaining complex concepts to people who don’t immediately grasp them.
What I’m watching, he explains, is the testing phase of a continuous loop: data gets collected on robot stations here and at other locations — warehouses, homes, wherever the team can set up shop — and that data trains general-purpose robotic foundation models. When researchers train a new model, it comes back to stations like these for evaluation. The pants-folder is someone’s experiment. So 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’s never encountered.
The company also operates a test kitchen in this building and elsewhere using off-the-shelf hardware to expose the robots to different environments and challenges. There’s a sophisticated espresso machine nearby, and I assume it’s for the staff until Levine clarifies that no, it’s there for the robots to learn. Any foamed lattes are data, not a perk for the dozens of engineers on the scene who are mostly peering into their computers or hovering over their mechanized experiments.
The hardware itself is deliberately unglamorous. These arms sell for about $3,500, and that’s with what Levine describes as “an enormous markup” from the vendor. If they manufactured them in-house, the material cost would drop below $1,000. A few years ago, he says, a roboticist would have been shocked these things could do anything at all. But that’s the point – good intelligence compensates for bad hardware.
Techcrunch event TechCrunch Founder Summit 2026: Tickets Live On June 23 in Boston , more than 1,100 founders come together at TechCrunch Founder Summit 2026 for a full day focused on growth, execution, and real-world scaling. Learn from founders and investors who have shaped the industry. Connect with peers navigating similar growth stages. Walk away with tactics you can apply immediately Save up to $300 on your pass or save up to 30% with group tickets for teams of four or more. TechCrunch Founder Summit: Tickets Live On June 23 in Boston , more than 1,100 founders come together at TechCrunch Founder Summit 2026 for a full day focused on growth, execution, and real-world scaling. Learn from founders and investors who have shaped the industry. Connect with peers navigating similar growth stages. Walk away with tactics you can apply immediately Save up to $300 on your pass or save up to 30% with group tickets for teams of four or more. Boston, MA | June 23, 2026 REGISTER NOW As Levine excuses himself, I’m approached by Lachy Groom, moving through the space with the purposefulness of someone who has half a dozen things happening at once. At 31, Groom still has the fresh-faced quality of Silicon Valley’s boy wonders, a designation he earned early, having sold his first company nine months after starting it at age 13 in his native Australia (this explains the Vegemite).
When I first approached him earlier, as he welcomed a small gaggle of sweatshirt-wearing visitors into the building, his response to my request for time with him was immediate: “Absolutely not, I’ve got meetings.” Now he has ten minutes, maybe.
Groom found what he was looking for when he started following the academic work coming out of the labs of Levine and Chelsea Finn, a former Berkeley PhD student of Levine’s who now runs her own lab at Stanford focused on robotic learning. 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 who also taught at Stanford and who Groom had learned was involved. “It was just one of those meetings where you walk out and it’s like, This is it.”
Groom never intended to become a full-time investor, he tells me, even though some might wonder why not given his track record. After leaving Stripe, where he was an early employee, he spent roughly five years as an angel investor, making early bets on companies like Figma, Notion, Ramp, and Lattice while searching for the right company to start or join himself. His first robotics investment, Standard Bots, came in 2021 and reintroduced him to a field he’d loved as a kid building Lego Mindstorms. As he jokes, he was “on vacation much more as an investor.” But investing was just a way to stay active and meet people, not the endgame. “I was looking for five years for the company to go start post-Stripe,” he says. “Good ideas at a good time with a good team – [that’s] extremely rare. It’s all execution, but you can execute like hell on a bad idea, and it’s still a bad idea.”
The two-year-old company has now raised over $1 billion , and when I ask about its runway, he’s quick to clarify it doesn’t actually burn that much. Most of its spending goes toward compute. A moment later, he acknowledges that under the right terms, with the right partners, he’d raise more. “There’s no limit to how much money we can really put to work,” he says. “There’s always more compute you can throw at the problem.”
What makes this arrangement particularly unusual is what Groom doesn’t give his backers: a timeline for turning Physical Intelligence into a money-making endeavor. “I don’t give investors answers on commercialization,” he says of backers that include Khosla Ventures, Sequoia Capital and Thrive Capital among others that have valued the company at $5.6 billion. “That’s sort of a weird thing, that people tolerate that.” But tolerate it they do, and they may not always, which is why it behooves the company to be well-capitalized now. Not because it needs to be, but because it enables the team to make long-term decisions without compromise.
Quan Vuong, another cofounder who came from Google DeepMind, explains that the strategy revolves around cross-embodiment learning and diverse data sources. If someone builds a new hardware platform tomorrow, they won’t 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, whatever that platform might be, it’s just a lot lower,” he says.
The company is already working with a small number of companies in different verticals – logistics, grocery, a chocolate maker across the street – 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.
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