From the road, the one indication I’ve discovered Bodily Intelligence’s headquarters in San Francisco is a pi image that’s a barely totally different colour than the remainder of the door. Once I stroll in, I’m instantly confronted with exercise. There’s no reception desk, no gleaming brand in fluorescent lights.
Inside, the house is a huge concrete field made barely much less austere by a haphazard sprawl of lengthy blonde-wood tables. Some are clearly meant for lunch, dotted with Lady Scout cookie containers, jars of Vegemite (somebody right here is Australian), and small wire baskets filled with one too many condiments. The remainder of the tables inform a unique story totally. Many extra of them are laden with displays, spare robotics components, tangles of black wire, and totally assembled robotic arms in numerous states of making an attempt to grasp the mundane.
Throughout my go to, one arm is folding a pair of black pants, or making an attempt to. It’s not going nicely. One other is making an attempt to show a shirt inside out with the sort of willpower that implies it would finally succeed, simply not at present. A 3rd – this one appears to have discovered its calling – is rapidly peeling a zucchini, after which it’s alleged to deposit the shavings right into a separate container. The shavings are going nicely, at the very least.
“Consider it like ChatGPT, however for robots,” Sergey Levine tells me, gesturing towards the motorized ballet unfolding throughout the room. Levine, an affiliate professor at UC Berkeley and one in all Bodily Intelligence’s cofounders, has the amiable, bespectacled demeanor of somebody who has spent appreciable time explaining complicated ideas to individuals who don’t instantly grasp them.
What I’m watching, he explains, is the testing section of a steady loop: information will get collected on robotic stations right here and at different places — warehouses, properties, wherever the staff can arrange store — and that information trains general-purpose robotic basis fashions. When researchers practice a brand new mannequin, it comes again to stations like these for analysis. The pants-folder is somebody’s experiment. So is the shirt-turner. The zucchini-peeler is likely to be testing whether or not the mannequin can generalize throughout totally different greens, studying the elemental motions of peeling nicely sufficient to deal with an apple or a potato it’s by no means encountered.
The corporate operates take a look at kitchens on this constructing and elsewhere, together with individuals’s properties, Levine says, utilizing off-the-shelf {hardware} to reveal the robots to totally different environments and challenges. There’s a classy espresso machine close by, and I assume it’s for the workers till Levine clarifies that no, it’s there for the robots to study. Any foamed lattes are information, not a perk for the handfuls of engineers on the scene who’re largely peering into their computer systems or hovering over their mechanized experiments.
The {hardware} itself is intentionally unglamorous. These arms promote for about $3,500, and that’s with what Levine describes as “an infinite markup” from the seller. In the event that they manufactured them in-house, the fabric value would drop beneath $1,000. Just a few years in the past, he says, a roboticist would have been shocked this stuff might do something in any respect. However that’s the purpose – good intelligence compensates for dangerous {hardware}.
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June 23, 2026
As Levine excuses himself, I’m approached by Lachy Groom, shifting by the house with the purposefulness of somebody who has half a dozen issues occurring directly. At 31, Groom nonetheless has the fresh-faced high quality of Silicon Valley’s boy wonders, a designation he earned early, having bought his first firm 9 months after beginning it at age 13 in his native Australia (this explains the Vegemite).
Once I first approached him earlier, as he welcomed a small gaggle of sweatshirt-wearing guests into the constructing, his response to my request for time with him was instant: “Completely not, I’ve obtained conferences.” Now he has ten minutes, possibly.
He discovered what he was on the lookout for when he began following the educational work popping out of the labs of Levine and Chelsea Finn, a former Berkeley PhD scholar of Levine’s who now runs her personal lab at Stanford centered on robotic studying. Their names stored showing in every thing attention-grabbing occurring in robotics. When he heard rumors they is likely to be beginning one thing, he tracked down Karol Hausman, a Google DeepMind researcher who additionally taught at Stanford and who Groom had discovered was concerned. “It was simply a type of conferences the place you stroll out and it’s like, That is it.”
Groom by no means meant to turn out to be a full-time investor, he tells me, regardless that some may surprise why not given his monitor report. After leaving Stripe, the place he was an early worker, he spent roughly 5 years as an angel investor, making early bets on firms like Figma, Notion, Ramp, and Lattice whereas looking for the correct firm to start out or be a part of himself. His first robotics funding, Normal Bots, got here in 2021 and reintroduced him to a subject he’d cherished as a child constructing Lego Mindstorms. As he jokes, he was “on trip way more as an investor.” However investing was only a solution to keep energetic and meet individuals, not the endgame. “I used to be on the lookout for 5 years for the corporate to go begin post-Stripe,” he says. “Good concepts at time with staff – [that’s] extraordinarily uncommon. It’s all execution, however you possibly can execute like hell on a foul concept, and it’s nonetheless a foul concept.”
The 2-year-old firm has now raised over $1 billion, and after I ask about its runway, he’s fast to make clear it doesn’t really burn that a lot. Most of its spending goes towards compute. A second later, he acknowledges that beneath the correct phrases, with the correct companions, he’d elevate extra. “There’s no restrict to how a lot cash we will actually put to work,” he says. “There’s at all times extra compute you possibly can throw on the drawback.”
What makes this association significantly uncommon is what Groom doesn’t give his backers: a timeline for turning Bodily Intelligence right into a money-making endeavor. “I don’t give buyers solutions on commercialization,” he says of backers that embody Khosla Ventures, Sequoia Capital and Thrive Capital amongst others which have valued the corporate at $5.6 billion. “That’s kind of a bizarre factor, that individuals tolerate that.” However tolerate it they do, and so they might not at all times, which is why it behooves the corporate to be well-capitalized now. Not as a result of it must be, however as a result of it allows the staff to make long-term choices with out compromise.
Quan Vuong, one other cofounder who got here from Google DeepMind, explains that the technique revolves round cross-embodiment studying and numerous information sources. If somebody builds a brand new {hardware} platform tomorrow, they received’t want to start out information assortment from scratch – they’ll switch all of the information the mannequin already has. “The marginal value of onboarding autonomy to a brand new robotic platform, no matter that platform is likely to be, it’s only a lot decrease,” he says.
The corporate is already working with a small variety of firms in several verticals – logistics, grocery, a chocolate maker throughout the road – to check whether or not their programs are ok for real-world automation. Vuong claims that in some instances, they already are. With their “any platform, any job” method, the floor space for achievement is massive sufficient to start out checking off duties which might be prepared for automation at present.
Bodily Intelligence isn’t alone in chasing this imaginative and prescient. The race to construct general-purpose robotic intelligence – the inspiration on which extra specialised functions might be constructed, very similar to the LLM fashions that captivated the world three years in the past – is heating up. Pittsburgh-based Skild AI, based in 2023, simply this month raised $1.4 billion at a $14 billion valuation and is taking a notably totally different method. Whereas Bodily Intelligence stays centered on pure analysis, Skild AI has already deployed its “omni-bodied” Skild Mind commercially, saying it generated $30 million in income in only a few months final yr throughout safety, warehouses, and manufacturing.
Skild has even taken public pictures at rivals, arguing on its weblog that almost all “robotics basis fashions” are simply vision-language fashions “in disguise” that lack “true bodily widespread sense” as a result of they rely too closely on internet-scale pretraining slightly than physics-based simulation and actual robotics information.
It’s a fairly sharp philosophical divide. Skild AI is betting that industrial deployment creates an information flywheel that improves the mannequin with every real-world use case. Bodily Intelligence is betting that resisting the pull of near-term commercialization will allow it to provide superior normal intelligence. Who’s ‘extra proper’ will take years to resolve.
Within the meantime, Bodily Intelligence operates with what Groom describes as uncommon readability. “It’s such a pure firm. A researcher has a necessity, we go and acquire information to assist that want – or new {hardware} or no matter it’s – after which we do it. It’s not externally pushed.” The corporate had a 5-to-10-year roadmap of what the staff thought can be potential. By month 18, they’d blown by it, he says.
The corporate has about 80 workers and plans to develop, although Groom says hopefully “as slowly as potential.” What’s probably the most difficult, he says, is {hardware}. “{Hardware} is simply actually onerous. The whole lot we do is a lot more durable than a software program firm.” {Hardware} breaks. It arrives slowly, delaying checks. Security concerns complicate every thing.
As Groom springs as much as rush to his subsequent dedication, I’m left watching the robots proceed their follow. The pants are nonetheless not fairly folded. The shirt stays stubbornly right-side-out. The zucchini shavings are piling up properly.
There are apparent questions, together with my very own, about whether or not anybody really needs a robotic of their kitchen peeling greens, about security, about canines going loopy at mechanical intruders of their properties, about whether or not the entire money and time being invested right here solves sufficiently big issues or creates new ones. In the meantime, outsiders query the corporate’s progress, whether or not its imaginative and prescient is achievable, and if betting on normal intelligence slightly than particular functions is sensible.
If Groom has any doubts, he doesn’t present it. He’s working with individuals who’ve been engaged on this drawback for many years and who consider the timing is lastly proper, which is all he must know.
In addition to, Silicon Valley has been backing individuals like Groom and giving them numerous rope because the starting of the business, figuring out there’s likelihood that even and not using a clear path to commercialization, even and not using a timeline, even with out certainty about what the market will seem like after they get there, they’ll determine it out. It doesn’t at all times work out, however when it does, it tends to justify numerous the instances it didn’t.
