The $10 Trillion Bet on Humanoid Robots | Figure Founder Brett Adcock

The meta problem in robotics is to be able to solve a humanoid robot. If you can solve this, it'll build the biggest business in the world by a large factor. A little under half the world's GDP is human labor.

So, OpenAI led our Series B a couple of years ago. They brought in Satya and Microsoft. It got to a point where our team internally that was designing these models were running circles around OpenAI. We were just way better at this. So, I fired them. I self-funded the whole company up front. You know, we got to a million a month of burn in four months. Getting the robots to do the things we showed you today has almost killed me. We had record production in March and we're going to try to 3x that by May. We want to be able to get to a million units a year.

We'll just build like enormous tens of trillions of revenue. You'll build something massive. I mean, what do most companies trade at? Tech companies trade at 10 or 20 times revenue. Like $10 trillion or, you know, whatever, a hundred billion dollars to a trillion dollars in revs. Like this is going to be a huge business.

Thanks for having me.

Yeah, hot take. Um, I think one thing that we spend a lot of time on is doing things with like fully autonomous and end-to-end. And you asked us a few questions here when we walked in of like, is this like teleoperated? We're not teleoperating this stuff. I think our hot take for robotics is it's like it's kind of really difficult to see what's really happening in the space without coming on site and really seeing stuff. So I hope you had a good experience here today seeing everything we're doing. Um, I think my and I think the hottest take I have is like we just want humanoid robots to work and like they're working now. It's pretty simple. Like we're seeing robots to do everyday things like we saw clean up a living room, do commercial work. Like it's just it's cool to see it. Like it's cool to see that this is going to happen in the next few years. This is a very hotly contested space and it's becoming more and more competitive.

Um, I think like listen, our internal goal is like how do we get these things to do real stuff and get paid for it. And so we think a lot about how do we do autonomous useful work. That's our bar. And we need to do that with like AI models really well and we need to do it on good hardware. It's like, you know, cost-effective. We can make a lot of robots. Um, you know, we think we're probably at this point a few years ahead of everybody globally doing this at this level, which is like we're kind of early in the humanoid book. And hopefully the next step is like how do we get more robots out at scale and we want to be first to be able to do that. How do we get, you know, hundreds and thousands and tens of thousands of robots in the world that run every day? And the space is just early. There's a lot of... we're just like, we're like in the first chapter of humanoids coming into society at scale. And yeah, like what we're pumped about here internally is it's working and it's just chapter one. Chapter two is like get more out the door and get them working at even a bigger scale. Then at some point we want to really be able to generalize to do everything a human can.

We'll make thousands of robots basically as fast as we can this year. So we're ramping bot lines up as fast as we can. We had record production in March and we're going to try to 3x that by May and we'll build thousands of robots. We basically already have the parts in house to do this and we're ramping up production. And then, you know, from there we want to build tens of thousands and hundreds of thousands and we want to be able to get to a million units a year. And then we need commercial progress to also match that. We have like so much commercial demand. It's like it's hard to... we have like just an overwhelming amount of... I think I could put so many robots into commercial customers today if they were all ready. So the big gap here is like getting the robots ready to do at-scale autonomous operations.

It's having enough of them and making them run to a level of human performance at scale. Like what we don't want to do is like we don't want to put like a thousand robots out to market and have like a thousand problems every single hour. That's not good for any of us. So we had a small batch of robots that went out to BMW last year and did work every day. We ran for six months every single day. It was phenomenal. We learned a ton. We refactored our whole approach to how to commercialize the software and AI systems after that. And that kind of led us to Helix 2, which is our second-generation AI model internally that we launched a couple of months ago. And now it's like, you know, how do we put these robots into many different customers that we'll probably announce a lot of this in the next 90 days and put them out into those groups at like decent scale like this year. And assuming that goes well, we'll just keep compounding that. So we had robots there last year that went well. We learned a lot. We'll have a much larger amount of robots going into many different customers this year. And then since that goes well, we'll just keep scaling like crazy.

In my mind, this is not a manufacturing problem. This is an intelligence problem.

Um, yeah, I've been building companies for like about 20 years now. Scaled a software company up pretty fast, sold it, scaled Archer up pretty fast, took it public. And so, you know, every time I'm in this phase, I get to sit back and say, what did I learn from past experiences and how do I do it better? And at Figure we took a very differentiated approach to basically vertically design everything. I don't think there's any group in the world on the robotic side that designs more parts than we do on the robot. We designed the motors, basically every part within there, the rotor, stator, everything, the sensors, the structure, the kinematics, the joints, the batteries that you saw today, the battery packs. That I think has really enabled us to control our destiny. We get to build our own supply chain and without that you're left at the mercy of some vendor. And then if that has an issue, how are you going to go solve it? If it's got a code problem, do you understand it? Can you fix it? Can you patch it? So we understand the whole stack from top to bottom. There was an enormous lift up front to get the right people here that could do that. And then we've now been iterating through, as you can see behind us, to a point where we have decently reliable systems now that run really well. But you know, head on, I self-funded the whole company up front. We got to a million a month of burn in four months. It was like no joke. We got to a 40-person team in four or five months. They were very good. And then just here, you know, 100 hours a week just trying to make it work with the team. And we've made some mistakes. We've learned a lot. We've done some things well and just recursively getting better.

The meta problem in robotics is to be able to solve a humanoid robot. If you can solve this, it'll build the biggest business in the world by a large factor. A little under half the world's GDP is human labor. I wanted to go work on building this holy grail of robotics. At Archer, I led design for every aircraft we have there. And I feel like this is now the decade we're going to bring humanoid robots to the masses. It's probably the most important, maybe one of the most important businesses of our lifetime. And so I basically get to spend time, I get to work now on what I think is probably one of the more important areas of my whole career. At Archer I built the whole team up, led all engineering design for all the aircraft and led the company through a public offering. So we're now in a good spot to certify the aircraft and enter federal airspace. And Figure is also in a good spot here to really scale up physical intelligence in the world.

Yeah. I've started a few other companies actually since then as well. I think the short story is I feel that I've been watching the humanoid space for like a couple of decades. We've been seeing humanoids for a long time. They've just been on the wrong vector. Like we're building the wrong stuff or we're doing it in a hobby. It's great, or you know, the engineering decisions were, I think, not maybe correct. And I just felt there was a need to advance the space much more rapidly. And I'm doing the same with Hark and Cover. I have a couple other companies I'm doing where I also feel a similar situation there, where I feel like if left to the world to go do it, I'm unclear if we would head in the right direction. And I think for humanoids, the best we had four years ago was like, you know, Boston Dynamics had a hydraulic humanoid called Atlas. It dripped oil everywhere, lasted like 20 minutes. It was very big and very unsafe. You could never put it next to humans. Classical controls methodology there. And it was just like, man, you need to... and you know, Boston is really a large heritage around research and not commercialization, or not as much commercialization. So I just felt that there was a need for a group to come in to really send this thing to the masses. And I felt without my intervention in the space, I don't know if we would have gotten there, or maybe we will, we'll find out. But I think Figure has really shown that we've been able to kind of push the timelines left now in this chapter book to get into the real world. And like I said, I think it's a significant business.

The trick is just not sleep.

You just don't sleep. Um, the trick... Okay, listen. I like...

Okay. So, Cover is basically designing detection systems for K-through-12 schools. So, school shootings in the US have gone up 10x in the last 10 years, and just like this is actual, like, a weapon's been fired, and it just basically got fully out of hand. My view is that it's a perception problem. We need to see if people have guns, like kids or students have guns on them when they're entering schools. If they do, let's get them off the students. If they don't, let those students go in. There's a technology that was designed at NASA Jet Propulsion Lab about a decade ago that can detect weapons underneath clothes and in backpacks and bags from like 5, 10, 20 meters away. And they built it for the Iraq and Afghanistan war to find bomb vests and other explosives on folks from a standoff distance. So think about this like a technology similar to the L3 scanning systems at an airport, but you can only do that from a few feet away. But if you can do that 10x further away, you can just basically scan everybody as they're coming into schools. And it's not just a school thing. You can use it at every public venue in the world. And it's been my passion. I've been following the space in here. Like this is the nerdy topic here is called terahertz imaging radar. And that's what we do here at Cover. And so I have a team, most of which are from NASA's Jet Propulsion Lab. So one, I own the IP from NASA Jet Propulsion Lab. I spun it out two years ago and I'm funding this project.

I bought it.

Yeah. So they sold it to me.

Yeah.

And then like Caltech, which is like, you know, NASA basically, JPL, has a small, very small minority interest in the business. And then a lot of the core team that did that is on my team now at Cover. And it's awesome. Like the images we get, it's like a hardware and AI problem. It's like a vision problem that you have to solve with AI. And we have prototypes now that are running. And we will deploy hopefully to our first schools in beta by end of year. And we want to just deploy... there's 130,000 K-through-12 schools. There's like a huge amount we have to manufacture and get out the door. So I've been self-funding that company for two years. We're making incredible progress and I have a team in Pasadena right next to Jet Propulsion Lab in LA and we're funding that. And then I have a company called Hark. It's an AI lab I started about seven, eight months ago. I was trying to design really highly personalized intelligence. So, we're designing next-generation AI models and also the next generation of AI devices to interact with AI. Like right now, we interact with AI through 20-year-old computers like phones and MacBooks and stuff like that. And they're just not the ideal intermediary and interface to AI. So we just came out of stealth like two weeks ago and we have a team of 50 and we're building really awesome magical AI models and we're building really magical hardware.

Yeah. So OpenAI led my Series B a couple of years ago. And as a part of that we did a collaboration agreement to work on next-generation AI models together. And it was great, got to know the team really well over there, Sam and the rest of the group. They led our round. They brought in Satya and Microsoft. They co-led it. And then we spent basically a year collaborating together on how do we get AI models to work on a humanoid or how do we get language models to work on a humanoid. And they were very interested in robotics and we were very interested in understanding better about how do we get language models on robots, like what part do they play or do they play a part in robotics? And so we spent a year working with them. Nice folks. I was working with them almost every day or every week. And it got to a point where our team internally that was designing these models were running circles around OpenAI. We were just way better at this. And we were better about testing on the robots, training the models, all of it. My team had come from robot learning backgrounds for over a decade. And I think there was also some interest as OpenAI was watching us get into robotics. And so I fired them.