Prediction
Pending
Adcock: Figure will 100x–1,000x in 5 years or go bust, with no flatline
Brett Adcock says his companies are only now hitting an inflection point and the outcome is binary: within about five years humanoid robots either scale and Figure grows 100–1,000x from its current valuation, or it goes very badly.
“So if it works, this will like 100x. 1,000x from here. So most of my energy is like, how do I make sure that works? There is no flatline here. It's either like it goes down or goes up, right? Either like it's binary, either the robots go out of scale or they don't go out of scale. So in like 5 years' time, it's either gonna be a very big thing or very bad.”
Take
Why cars are brutal to manufacture: you can't hold the part in your hand
Adcock argues that mass production isn't Figure's hardest problem; intelligence is. Building humanoids is closer to consumer electronics, where parts fit in your hand, than to cars, whose lines chain hundreds of robots so that any single failure stops production.
“The reason why cars are so hard is that you can't hold the part in your hand. Phones you can just like always hold in your hand and go change or whatever, move and hold. Like cars you can't, you physically can't. So you need robots that like literally pass it to other robots that put things on the chassis. And if any of those break across like thousands or 800 robots, you're dead. It is. Your whole line's down.”
Number
Figure robot sorted packages for 200 hours straight at 2.9 seconds each
Figure's livestreamed logistics demo was a real customer task with a 3-seconds-per-package requirement. The robot ran 200 hours straight at 2.9 seconds a package, which is human speed. Customers buy because of labor shortages, with some sites seeing over 100% annual turnover.
$2.9
Figure robot package-handling speed (200-hour continuous run) · seconds/package
“The task you saw like on the, on the, on the like logistics line that we should livestream was actually a real use case for one of our customers. That needs to be done at 3 seconds a package, and it initially needs to be done 5 hours a day. Like, I think it's like 5 days a week. We did that 200 hours straight at 2.9 seconds a package. So we're already at human speeds.”
Tactic
The scar test: tell who did the work from who only watched
Adcock personally assesses technical hires. His filter is that people who actually did the work can go layer after layer into the details and reverse-engineer their decisions, while people who only watched it fall apart after one layer of questions.
“I need to know like certain guiding principles. Like for instance, I need to know like if, A, if you did the work or if you like watched somebody do the work. If you've done the work, it's like a scar you carry with you. It's like dug into you. Like you know all the details. You can talk about it freely. You don't need to think. You'll understand how to like reverse engineer everything you've done and discuss it. The folks that haven't done it can't do that. They just like, they can't even go like, they get one layer and they just like instantly blow up.”
Steal thisIn interviews, keep drilling one layer deeper into a candidate's claimed project until they either explain it fluently or break.
Take
Why Figure dropped warehouse-first: the home is "super solvable today"
Figure's original plan was to use commercial work (easier, higher-priced, lower-variability) to fund the home robot later. Adcock now believes general home robotics can be solved today, possibly with 100 robots and a 50-person team, and that the first company to do it would be worth a trillion dollars overnight.
“Uh, what I've learned now is that the home is super solvable today. So like, We can like not go work on that problem and just like sit here and work in a warehouse. But me or none of my guys wanna solve that problem. We wanna solve a robot that can go into any environment just through language and do work. We wanna be the first to do that. You can probably do that with 100 robots and a 50-person team so that, that company overnight would be a trillion-dollar market cap.”
Story
48 of 50 startups in Vettery's NYU incubator class went to zero
Adcock says founders sign up for a game where about 95% fail: of roughly 50 companies in Vettery's NYU incubator cohort, only two returned more than $0 five years later. He then went all-in again, funding Figure with falling Archer stock and a second mortgage on his house.
“I remember what Vettery, we started at the NYU incubator. I was so excited. We got in one of the semesters and there was, I think it was like 50 companies that were there. We started in SoHo. It was great. We had a great time. I think if you look back, I think like 5 years later, me and one other guy are the only 2 people that made greater than $0 on our team. 48 companies went to zero. Wow. And I was just like, holy shit, if you're around this game for long enough, everybody dies.”
Framework
Hard things are only 3–4x harder but can pay off 100x more
Adcock's case for hard problems: less competition, bigger markets, the best overachievers want to join, and investors chase binary 100x outcomes. Difficulty grows much more slowly than payoff; humanoids are perhaps 3–4x harder than robot dogs but have a vastly larger market.
“And I think there's a nonlinear curve to scaling here, of the difficulty here, meaning I think a lot of the hard things are not 10 or 100 times harder. I think the hard things sometimes are 2 or 3 or 4 times harder, maybe 5 times harder, but they're not 100 times harder. So you might have 100 times better payoff, but it might be 3 or 4 times harder. I'll give you an example in robotics. I think largely building quadruped robots, like 4-legged dog robots versus humanoids, probably humanoids are probably 3 times harder than that, maybe 4. That's it.”
Steal thisWhen picking what to build, compare how many times harder it is with how many times bigger the payoff is, and choose the problem that's a few times harder for a 100x outcome.