From the street, the only indication I\u2019ve found Physical Intelligence\u2019s headquarters in San Francisco is a pi symbol that\u2019s a slightly different color than the rest of the door. When I walk in, I\u2019m immediately confronted with activity. There\u2019s no reception desk, no gleaming logo in fluorescent lights.<\/p>\n
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.<\/p>\n
During my visit, one arm is folding a pair of black pants, or trying to. It\u2019s 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 \u2014 this one seems to have found its calling \u2014 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.<\/p>\n
\u201cThink of it like ChatGPT, but for robots,\u201d 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\u2019s co-founders, has the amiable, bespectacled demeanor of someone who has spent considerable time explaining complex concepts to people who don\u2019t immediately grasp them.\u00a0<\/p>\n What I\u2019m watching, he explains, is the testing phase of a continuous loop: data gets collected on robot stations here and at other locations \u2014 warehouses, homes, wherever the team can set up shop \u2014 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\u2019s 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\u2019s never encountered.<\/p>\n The company<\/a> 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\u2019s a sophisticated espresso machine nearby, and I assume it\u2019s for the staff until Levine clarifies that no, it\u2019s 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.<\/p>\n The hardware itself is deliberately unglamorous. These arms sell for about $3,500, and that\u2019s with what Levine describes as \u201can enormous markup\u201d 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\u2019s the point \u2014 good intelligence compensates for bad hardware.<\/p>\n Techcrunch event<\/p>\n \n\t\t\t\t\t\t\t\t\tBoston, MA<\/span> As Levine excuses himself, I\u2019m 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\u2019s boy wonder, 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).<\/p>\n 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: \u201cAbsolutely not, I\u2019ve got meetings.\u201d Now he has 10 minutes, maybe.<\/p>\n 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\u2019s 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. \u201cIt was just one of those meetings where you walk out and it\u2019s like, This is it.\u201d<\/p>\n 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\u2019d loved as a kid building Lego Mindstorms. As he jokes, he was \u201con vacation much more as an investor.\u201d But investing was just a way to stay active and meet people, not the endgame. \u201cI was looking for five years for the company to go start post-Stripe,\u201d he says. \u201cGood ideas at a good time with a good team \u2014 [that\u2019s] extremely rare. It\u2019s all execution, but you can execute like hell on a bad idea, and it\u2019s still a bad idea.\u201d<\/p>\n The two-year-old company has now raised over $1 billion<\/a>, and when I ask about its runway, he\u2019s quick to clarify it doesn\u2019t 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\u2019d raise more. \u201cThere\u2019s no limit to how much money we can really put to work,\u201d he says. \u201cThere\u2019s always more compute you can throw at the problem.\u201d<\/p>\n What makes this arrangement particularly unusual is what Groom doesn\u2019t give his backers: a timeline for turning Physical Intelligence into a money-making endeavor. \u201cI don\u2019t give investors answers on commercialization,\u201d he says of backers that include Khosla Ventures, Sequoia Capital, and Thrive Capital among others that have valued the company at $5.6 billion. \u201cThat\u2019s sort of a weird thing, that people tolerate that.\u201d But tolerate it they do, and they may not always, which is why it behooves the company to be well-capitalized now.<\/p>\n So what\u2019s the strategy, if not commercialization? Quan Vuong, another co-founder who came from Google DeepMind, explains that it revolves around cross-embodiment learning and diverse data sources. If someone builds a new hardware platform tomorrow, they won\u2019t need to start data collection from scratch \u2014 they can transfer all the knowledge the model already has. \u201cThe marginal cost of onboarding autonomy to a new robot platform, whatever that platform might be, it\u2019s just a lot lower,\u201d he says.<\/p>\n The company is already working with a small number of companies in different verticals \u2014 logistics, grocery, a chocolate maker across the street\u00a0\u2014 to test whether their systems are good enough for real-world automation. Vuong claims that in some cases, they already are. With their \u201cany platform, any task\u201d approach, the surface area for success is large enough to start checking off tasks that are ready for automation today.<\/p>\n Physical Intelligence isn\u2019t alone in chasing this vision. The race to build general-purpose robotic intelligence \u2014 the foundation on which more specialized applications can be built, much like the LLM models that captivated the world three years ago \u2014 is heating up. Pittsburgh-based Skild AI, founded in 2023, just this month raised $1.4 billion at a $14 billion valuation<\/a> and is taking a notably different approach. While Physical Intelligence remains focused on pure research, Skild AI has already deployed its \u201comni-bodied\u201d Skild Brain commercially, saying it generated $30 million in revenue in just a few months last year across security, warehouses, and manufacturing.\u00a0<\/p>\n![\"\"](\"https:\/\/techcrunch.com\/wp-content\/uploads\/2026\/01\/IMG_4029-rotated.jpeg?w=510\")
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