A Humanoid Robot in Every Home? It's Closer Than You Think w/ Brett Adcock (at A360 2025) | EP #156

0:00Arture was amazing. Then you jump into arguably what could be described as one of the most difficult businesses to get into. Why you start figure? The humanoid robot is like the ultimate deployment vector for AGI. It is truly my honor and pleasure to introduce to you Brett Adcock, founder and CEO of FIGURE. You went from a cold start in 31 months to shipping your first robot. We are designing a new hardware platform every 12 to 18 months. By the time I file the C -Corp, we had the robot walking in under 12 months. I think you're going to see it in the coming years. You can put it in a homes, just through speech, be able to do very long horizon hours of work without any problems.

0:39It was like an iPhone moment happening with humanoids. It's going to happen right now. Now that's the moonshot, ladies and gentlemen.

0:50I think most of you know that the news media is delivering negative news to us all the time. because we pay 10 times more attention to negative news than positive news. For me, the only news worth while that's true and impacting humanity is the news of science and technology. And that's what I pay attention to. And every week I put out two blogs, one on AI and exponential tech and one on longevity. If this is of interest to you and it's available totally for free, please join me. subscribe at dmandis .com slash subscribe. That's dmandis .com slash subscribe. All right, let's go back to the episode.

1:30Thank you for getting here. Yeah, thanks. I know with three young kids and a robot factory and production and incredible team engineers, you're really busy and I don't take it for granted that you you joined us here. Yeah. My only request is next time I want a figure robot with you. Yeah, I begged him. And BMW has been taking the lion's share of them. We do have a lot. We actually have them running every day now. So they're there today running. And they're largest plant. Why do you start figure? I mean, you had this incredible few incredible successes. And Arture was amazing. and then you jump into arguably what could be described as one of the most difficult businesses to get into.

2:22Yeah, I think we really need to figure out a way to give like AGI a body here. I think it's like a really negative or like a most dystopian future if we figure out how to solve AGI and it lives in a server somewhere And it's like, you know, more intelligent than all of human, like, like everybody. And ultimately, if it wants to do some of the physical world, it'll have to ask or boss so human to do it. And the humanoid robot is like the ultimate deployment vector for AGI. It's, you can't solve this with anything else besides a human, like a mechanical human. You need something that is a single platform that with no hardware changes can do everything a human can.

3:10And you need something that can also be good for the neural nets. Like the neural net here in a humanoid can basically learn from transfer learning. It can basically multitask across a variety of different applications, which is like really good for a neural net. So we basically can build like one single neural net, like foundation model that can power the whole robot to do everything in and. I mean, you know, massive congrats. You went from a cold start in 31 months to shipping your first robot, which is, which is extraordinary. I mean, a lot of companies get their PowerPoint decks ready and raise their first capital in that period of time.

3:50And we're going to be seeing some of the robots in back here. When I visited you up north, you know, you showed be around. We did a podcast together and you showed me figure one and here's figure two and here's the designs are figure three. One of the things I truly find amazing is the speed of your iteration. Can you speak to that and how important rapid iteration and hardware is? Because hardware is hard. Yeah, this is a hard problem. We have to figure out how to do something that's never been done before and it's like a very complex system. Like definitely more complex from an engineering perspective the nurture was, like building an electric aircraft.

4:32So, yeah, my rule of thumb is like the first or second generation hardware is always going to suck. You know, like the first iPhone was not great, like the first first time you make something like you're never going to get it right. In hardware you have to do that, like you have to see like five years in the future. You have to know exactly what the product does and then you have to clean sheet design it for that exact thing day one. And if you mess up any of those, you can go back and fix it through the design process. You have like long lead time supply chain everything else, So we are designing a new hardware platform every 12 to 18 months.

5:00By the way, that's pretty amazing just to hear that right every 12 to 18 months of brand new iteration. I mean, yeah, we had a figure one walking by it by the time I filed the C Corp We had the robot walking in under 12 months Another thing you've done is you've completely vertically integrated Yeah, that was not a necessity like there was there's no supply chain for human under robots like there's no like motor vendors, actually, vendors, sensors, battery systems, structures, like kinematics, like all the software, which is like pretty vast, it's like firmware embedded systems, operating systems, middleware, controls.

5:35So walk me through your factory, you walked me through it before, but like what are the different segments of what's going on there? Yeah, in terms of like design for however. Yeah, I mean, you've got component building, testing, integration. Yeah, so we clean, clean sheet design, everything from basically the ground up. All the hardware is like, clean sheet design. We look at ultimately what does a product need to do. The product needs to, you basically want to talk to a robot and you want it to just do things without any human intervention. You just want it to go out and do stuff in the world.

6:07So we're designing it for a capable robot that can go out and do everything from putting robots in a home to walk your dog, make coffee, do the laundry, and then the commercial workforce, which is roughly half of GDP as human labor. So it's like the largest market in the world. Yeah, 110, $120 trillion dollars, the global GDP, your tam is like 50 to 60 trillion dollars. That's pretty good. Yeah, it's going to build the biggest business in the world by a long shot in our lifetime. Like the space. Yeah, so basically we look at the end markets where the robot needs to go, we do all the hardware design, which is like Ken and Matt design, joins motors, battery systems, sensors.

6:47We do all the software, firmware, embedded systems, controls, all the AI work end to end. And then we do all the testing and manufacturing and integration and fleet operations and deliver those to the clients. So we have robots now, we have two commercial customers. The first is BMW, we have robots there that are operating every single day. They're in Spartanburg, South Carolina. They're helping to build cars. We've got some video, I think, from the BMW plant, if we can roll in background or repeat that video. Yeah, I wish I that. We have a second customer, we just signed. And then within 30 days of starting the work, we were doing the work all end -to -end with neural nets.

7:27And this is a little like one of the largest such as companies in the world. And then we're also pushing really hard on the home. So yeah, here's a quick update for BMW. So we have just robots here that are basically doing, like basically putting sheet metal on fixtures. This is a job that every major manufacturing company in the world does. I wrote about to have been that fully autonomously. At the speeds we need to basically hit high performance with no human intervention, no faults, no failures. And no drug testing, no sick days, no days off. No days off, 24 -7, totally. I mean, it's an interesting thing, right?

8:07Think about this. Let me jump into one thing in volume. In the future, I believe I heard you say, you'll see these at a price point of 20 to $30 ,000. You still hold that? Yeah, we look done a lot of work on the bill materials. Like if you start breaking this down to the bear, like you got to basically, like I'd line in by light on them, and what it really looks like, and basically what it looks like into like high -remain manufacturing. There's really nothing in the system right now that would show that this product should be very, extremely expensive. The calculation I do is if I, if I was going to lease a $30 ,000 car, it's about $300 a month, which is by the way $10 a day and $0 .40 an hour.

8:48So here's my question, how many of these humanoid robots would you own at $300 a month? Operating 24 -7, no complaints, no fights for the girlfriend or boyfriends. I mean, the number could well be multiple per human. Yeah, you're gonna want one. They're gonna see, like I woke up, like I wake up every morning and help unload the dishwasher and pick up kids toys. Like I never wanna do any of that, ever again. Like, you know, it's just like, nah, like something I need to be doing when I get home or am I the house. We really haven't had a lot of innovation in the home for like almost 50, 70 years.

9:25You're like same appliances, same stuff. Like we need old robots. We call them dishwasher now. Yeah, they're just like been around for a long time. Yeah, and us humans are having to like work with it Right like we have to work with that machine every day And it's just like not something you'll do anymore in the future You'll just like talk to the robot and have a do it. It'll be on a schedule Any moment you can just call it texted talk to it and it's asking to do stuff and it'll just go do it It'll know you better than it'll know you just like yourself Remember a couple of years ago. I'm very proud.

9:53Uh bold as an early investor in In figure and I I brought teamwork to to meet you And I said, listen, the thing, first of all, Brett's incredible operator, multiple successes. Once one of the best predictors of the future, it's what a person's done in their past, right? It is very much one of the best predictors. But what I found amazing that sold me instantly beyond your charm is the team you pull together. Can you talk about that? because I think a lot of people know what into your focus on their moonshots. This is very much as a moonshot.

10:37You exit Archer. How did you capitalize? What did you start? How do you pull your team together? You described that early moment. Yeah, like, you know, I haven't found a lot of companies in my lifetime. I get to like go back every time and like, what did I mess up on? What did I get right? Trying to make things better. fundamentally the things that I spend a lot of my time on is just building. You basically in order to build one of the world's greatest products, you need one of the world's greatest teams. Then you need to line that team with what the shared vision is, and everybody needs to be accountable for that and understand it.

11:13Then you've got to figure out how to hit the gas pedal, like really hard. The entire culture at figure, even in archery, when I built initial team was very deliberate. And even at figure if you go to the website now we have like the culture deck we have the master plan We have like things laid out that are like really unique We're in Silicon Valley almost like the anti -silicon value have to work every day in the office We work five to seven days a week we work really hard And now I people want to do that and that's fine. It's just not the right people for us We've assembled now a couple like hundreds of like the best engineers in air robotics in the world There's just like no nobody even close to what we've done my seriously like incredible.

11:52Yeah, like it's unbelievable. My whole business team's been with me at a veterinary archer now figure. They're just, I mean, we've spent 15 years together. There's unbelievable operators. They give me the ability to like spend basically all my time on product engineering to basically build the best product possible and they help scale the business, which is great. Hiring, just recruiting HR like legal just like finance across the board. They're great. So yeah, the team's insane. but what's even better is like the culture's just absolutely like dialed in. Like everybody knows what they should be doing.

12:23I don't do one -on -ones, things like that. We have a shared vision of what to do and we work really hard to go get there. And the dopamine that we all get is the same. Like we want to ship product. That's what we're aligned to. Like that's what everybody like basically, yeah, gets their dopamine, which is really great. So it's like this shared fuel that we have to ship product. And this is such a hard thing, like this humanoid stuff is like, that's like maybe one of the most complex things I could have worked on. And you just have to have that fundamentally or those little easier shot. It's going to work.

12:56You know, we're going to hear from Travis Glanick tomorrow who's going to say very much the same thing that you're what we call your massive transformative purpose that clear mission vision and then aligning your team and culture around that. that would start with you. So you made a commitment of your own capital to get it going. And then you start calling people at other companies and what was your pitch to raise capital? Which end? To raise capital or recovery? No, no, no, to get those employees on board.

13:34The pitch in 2022 was I'm going to fund this whole thing for many years.

13:42It was expensive. We got to a million a month of burn in six months. But I was full pedal to the metal from day one. I was just like knew exactly what to do. Archers kind of like a flying robot in a lot of ways. So I knew how to build teams. I knew how to, we knew the product what to do. I knew the technical understanding of the power train and control systems and about software and sensors. So it was like, we just went really quickly out of there. The pitch was like, hey, I'm going to fund it. So there's no funding risk, at least in the near term. Like next couple of years, there's a good chance for us to build like the next, there's like an iPhone moment happening with humanoid.

14:17So it's going to happen right now. And what did you tell them the probability of success was? Pretty low. The thing that we had to do was like, we needed to prove like three things that have never been done before that you had to go get all three of those right in the next sub five years, or you fail for sure. You have to build incredible hardware for humanoid. It's extremely complex. It can never fail. It's always got to work. It's got to work at human speeds with human range and motion. Nobody's ever done that before. Rose robots that walk around can't even walk right. They fall over the time.

14:49It's very complex. Maybe like rocket, turbofan, level complexity, in terms of hardware systems. The second is you need to be, this is a neural net problem, not a control problem. You can't write code your way out of this. You can't hire a PhD with a robot and solve every problem. You have to basically ingest human -like data in the robot through a neural net, and it's got to be able to imitate what the humans do. So you have to solve that, which has never been solved on a humanoid system of like, you know, it's like a high -dimensionality system. Not like a robot on a table, which most of the none of those have AI.

15:20And then the third thing you have to do is you have to figure out how to generalize. You have to do something that's a holy grail of robotics. You have to figure out how to look at something you've never seen before. through speech, tell robot how to do it, and then be able to execute that task fully into end, just with one neural net. So the, you know, and I wrote about this in the master plan in 2022. So if we need to solve those, if you can solve those, you're in the right decade, you're gonna go build the iPhone moment for this whole space and we're in full lift off. But like, but those look pretty dire at the time in 2022.

15:48There was just nothing out there. I mean, you had Boston and Amics, it was like leaping around and doing back flips and parkour and stuff, but like nowhere near the level of manipulation and dexterity you needed for human -udrobots to enter the home. So I think we can confidently say now we've like, we have solved over making substantial progress on all of those. Amazing. So which is great. So like I think like, yes, you're correct. You're correct. There was a pivotal moment late last year where you said, open AI was a large investor and you were baselining open AI's AI systems. And you made a critical decision, say, Nope, we have to build our own AI internally, Helix.

16:31Can you speak to that moment? And I'd like to show the video of figure at home along that lines. Yeah, that'd be great. Okay, so what you're seeing is Helix. This is our like, this is our like, large scale AI internally. It's like a basically a large scale like vision language action model. And this is public, it's on our YouTube. So the prompt here that Corey gave, he leads the Helix team, was putting groceries on the table and the prompt was just put the groceries away. Not telling you where they go, not telling you where they are, just put them away. And the trick here, like the tricky part of the robot, they never have seen any of the groceries before in training.

17:10We purposely withheld all of these items. So it's like the first time the robot has ever seen these in its life with its own cameras and sensors. And so you basically have to solve the generalization problem in a home. Every home is different. Like you know, we all have different like toaster ovens, we have different appliances, we have different like spatulas and silverware. And it's a located differently and things are changing throughout the day. So you really have to solve this, like I call like semantic intelligence, but like it's like it's semantic grounding that's needed from a human world to robot world.

17:43And Helix, we can talk about why I was able to do that, is able to communicate on a single neural net on each robot and collectively together able to put these all away with just a single English plot. And so I think this is like the first signs of life. I think I will go even more move of a bolder claim. I think this is probably the most important AI update for robotics and human history Everything in the future that moves will be a robot and it will be powered by AI agents like this This was trained on also very little data Like 500 hours of data trained in this. I love the way. They're like looking at each other to confirm Like yes, I get it like oh, where are you putting that thing?

18:29Yeah, I think that's a good Good idea to put it up there. Yeah, actually this I mean, is that a created, you know, like they're about to look at each other here as you pass these it over? Like, listen, a part of this was like, that's funny. Part of this was like, emergent from training. So when the robots are doing handovers, they actually look at each other. There's actually a very split second where like one robot needs to release the package with the item. Other robot needs to grab it. So it doesn't lose like, basically like hold of the item and doesn't fall down. So what happened to emergent from training is the robots actually look at each other as the clearing way, signal, for like we should be releasing the item into each other's hands, which is like really interesting.

19:09The other stuff of robots looking at each other and moving around, I think it's just overall important. There's like a certain level of communication that needs to happen from a robot in terms of interaction design with humans. So you don't want to walk in a room and have a robot just like not move and not look at you. Humans look and do nods and gestures. All of this is extremely important to learn. Like we need to learn these expressions of humans Just like we need to learn how to grab items. It's gonna be super important as we at scale Integrate robots into the entire world that this happens.

19:40I have a thousand questions for you Let me hit a few rapid style here. Yeah, so figure three when do I get to see I saw the designs when does figure three get shown? Yeah, you're asking this you like this one. I do it. It was a beauty. I was a be I mean, you know So degree of beauty was increasing. Yeah, I don't think people understand this how incredible, well, they don't, because we haven't showed it. But we, so we, like, we're on, this is like, the ones Robachi saw here on the videos on stage were figure two, so second generation robot. You can like kind of, I guess, figure one's like online a little bit, but it's like, it's a little bit more gnarly.

20:14It's like a wires outside of it, and it's a little bit more fast. And it was a much more quicker design cycle to get this to our engineers to start doing really use case work. The figure two was like a feature complete robot that was supposed to be able to do almost anything of human can or vast majority of it. You know, we haven't talked about this publicly a lot, but we were done now with figure three design. I think we'll probably show an update next week. It's a quick minor update, not anything material as it relates to what we're going about for that process. Figure three is like, you look at figure one and figure two, and it's like a huge step up.

20:49You're like, wow, so we're going to call to dorm room projects to a real like pretty decent robot. And the magnitude of the stuff that was pretty material, that same magnitude happened again on Figure 3. So if you were to see it, it's just unbelievable. We spent like 18 months designing it from scratch. The high level, it's just like 90 % cheaper. It's smaller, it's less mass. It's got better sensors. It's hands -head and feet were designed for neural nets. It's a completely, I would say, figure 2's probably the best humanoider on the market. Maybe, you know, probably not by a ton, but like, I think it's the best.

21:2310 % 20%. Figure three is just like the next level design. Like we've spent, um, it's definitely like the most like for me, like the most proud moment I've had in engineering and my career, like looking at that robot. And so, uh, we're going into production, uh, manufacturing with that this year. Uh, well, some more of this in that in soon. Um, that's the robot we want to send everywhere into the world. Uh, we want to make it a low cost, very high rate. Um, it's even better just on almost like so many dimensions. What can we about production rates over the next three, four years and when I'm going to see it in the home?

21:56Yeah, so we have like two tracks. We have a like workforce track, which is like, and then we have the home track. Like the, what most people don't get is like the workforce is the big business. Like it's half of GDP. We can charge meaningfully more per robot in the house. And it's also easier. The things that the robot does is just like the same things is almost on a repeat. The home is like the wild west. It's like extremely hard. We have a huge safety area of not falling on any human or hurting people. There's a semantic and safety of not knocking over the candle and burning the house down. There's like, the home is just vastly harder.

22:30Maybe in self -driving, it's driving on the highways, like workforce for us and driving to the city is like the home. It's just unbelievably difficult. Between our two first commercial customers, which are very large businesses, we have demand. If we had a hundred thousand robots today that all worked, they would take a hundred thousand robots today. And then we have 50 customers that I could sign by the weekend that are all Fortune 100 companies. That we've literally visited, we know them. We just like, we can't, I've done a bunch of meetings today at lunch. Everybody's like, what do you think about helping out here in healthcare?

23:03All sound great. We're just bombarded with the amount of demand here. You're thinking about the workforce, you have a certain number of supply of humans. It's literally going down. So you have less humans in the workforce. There's labor pains everywhere. And there's a lot of job shortages. We can, anyway, we see just unbounded demand. I think we could ship a million robots this month if we had them all working in the right to go. And I think one thing that we're going to maybe add before you go to site, I knew you wanted rocket fire. But you guys saw BMW and you saw our second commercial customer.