A Humanoid Robot in Every Home? It's Closer Than You Think w/ Brett Adcock (at A360 2025) | EP #156
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Archer was amazing. Then you jump into arguably what could be described as one of the most difficult businesses to get into. Why'd you start FIGR? 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 FIGR. 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 filed the C-Corp, we had the robot walking in under 12 months. I think you're going to see it in the coming years being put into homes just through speech, be able to do very long horizon hours of work without any problems. It was like an iPhone moment happening with humanoids. It's going to happen right now. Now that's the moonshot, ladies and gentlemen.

I 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 worthwhile 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 dmangus.com slash subscribe. That's dmangus.com slash subscribe. All right, let's go back to the episode. Thank you for being here. Yeah, thanks for having me. I know with three young kids and a robot factory and production team,

And incredible team of engineers, you're really busy. And I don't take it for granted that you joined us here. My only request is next time I want a figure robot with you. Yeah, loud and clear. I begged him. And BMW has been taking the lion's share of them. Yep, we do have a lot. We actually have them running every day now. So they're there today running in their largest plant. Why'd you start Figure?

I mean, you had this incredible, you have a few incredible successes and Archer was amazing. And then you jump into arguably what could be described as one of the most difficult businesses to get into. Yeah. 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 most like 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 the cloud.

than all of human, like everybody. And ultimately, if it wants to do something in the physical world, it'll have to ask or boss a human to do it. And the humanoid robot is like the ultimate deployment vector for AGI. 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. And 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 really good for a neural net. So we basically can build one single neural net foundation model that can power the whole robot to do everything end-to-end.

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. And we're going to be seeing some of the robots in back here.

When I visited you up north, you showed me around. We did a podcast together, and you showed me figure one, and here's figure two, and here's the designs for 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 in 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 than Archer was like building an electric aircraft. So 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 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't 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. By the way, that's pretty amazing just to hear that, right? Every 12 to 18 months, a brand new iteration. I mean, yeah, we had a figure one walking. 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 out of necessity. There's no supply chain for humanoid robots. There's no motor vendors, actuator vendors, sensors, battery systems, structures, kinematics.

All the software, which is like pretty vast. It's like firmware embedded systems, operating systems, middleware controls. So 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 how. Yeah. I mean, you've got, you've got component building, testing, integration. Yeah. So we, so we clean, clean sheet design everything from basically the ground up. Like all the hardware is like clean, clean sheet design. We look at like,

ultimately what does the 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. So 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 like roughly half of GDP as human labor. So it's like the largest market in the world. Yeah. $110 to $120 trillion, the global GDP.

Your TAM is like 50 to 60 trillion dollars. That's pretty good. Yeah, it's like it's going to build the biggest business in the world by a long shot like this in our lifetime. Like the space. Yeah, so we have basically we so we're looking at like the end markets where the robot needs to go. We do all the hardware design, which is like kinematic design, joints, motors, battery systems, sensors, etc.

We 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, we'll show that. And 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. And this is one of the largest logistics 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. Our robots 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. No days off, yeah. 24-7. Totally. I mean, it's an interesting thing, right? Think 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 $20,000 to $30,000. Do you still hold that? Yeah, we've done a lot of work on the bill of materials. If you start breaking this down to the bare, you've basically got to –

line item by line item of what it really looks like and what basically what it looks like in the like high rate manufacturing. There's really nothing in the system right now that would show that this product should be very, like extremely expensive. The calculation I do is if I, if I was going to lease a $30,000 car, it's about 300 bucks a month, which is by the way, $10 a day and 40 cents an hour. So here's my question. How many of these humanoid robots would you own?

At 300 bucks a month. Operating 24-7. No complaints. No fights with their girlfriends or boyfriends. I mean, the number could well be multiple per human. Yeah, you're going to want one. They're going to see, like, I woke up.

I wake up every morning and help unload the dishwasher and pick up kids' toys. I never want to do any of that ever again. It's just not something I need to be doing when I get home or I'm at the house. We really haven't had a lot of innovation in the home for almost 50, 70 years. We have the same appliances, same stuff. We had old robots. We call them dishwashers now. Yeah, they've been around for a long time. Us humans are having to work with it, right? 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 it do it. It'll be on a schedule. Any moment you can just call it, text it, talk to it, and just ask it to do stuff and it'll just go do it. It'll know you better than it'll know you just like yourself. I remember a couple of years ago, I'm very proud, Bold is an early investor in figure and I brought teamwork to meet you.

And I said, listen, the thing, first of all, Brett's an incredible operator, multiple successes. What's 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 pulled together.

Can you talk about that? Cause it's, I think a lot of people in the audience here are focused on their moonshots. This is very much as a moonshot. Yeah. Um, you, you exit Archer. How did you capitalize? What did you start? How do you pull your team together? You described that early moment. Yeah. Like, um, 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'd I get right? Trying to make things better. Um,

And fundamentally, the things that I spend a lot of my time on is just like building. You basically in order to build like one of the world's greatest products, you need like one of the world's greatest teams. And then you need you need to align that team with like what the shared vision is. And everybody needs to be accountable for that and understand it.

And then you've got to figure out how to hit the gas pedal really hard. So the entire culture at Figure, even at Archer when I built initial team, was very deliberate. And even at Figure, if you go to the website now, we have the culture deck. We have the master plan. We have things laid out that are really unique. We're in Silicon Valley, but almost like the anti-Silicon Valley. You have to work every day in the office. We work five to seven days a week. We work really hard.

And not a lot of people want to do that. And that's fine. It's just not the right people for us. We've assembled now hundreds of the best engineers in AI robotics in the world. There's just nobody even close to what we've done. Seriously, incredible. Yeah, it's unbelievable. My whole business team has been with me at Vetteri, Archer, now Figure. We've spent 15 years together. There's unbelievable operators. They give me the ability to spend basically all my time on product engineering projects.

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 is just absolutely like dialed in. Like everybody knows what they should be doing. I 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 human stuff is like, it's like maybe one of the most complex things I could have worked on. And you just, you have to have that fundamentally or there's literally zero shot. This is going to work.

You know, we're going to hear from Travis Klanick 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 when it starts 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? What's that? To raise capital or recruit? No, no, no. To get those employees on board. Oh, the pitch in 2022 was I'm going to fund this whole thing for many years. You know, we, we, and it was expensive. Like we got to a million a month of burn in six months. So it wasn't like a, but I was like full pedal to the metal from day one. I just like knew exactly what to do.

I mean, Archer is kind of like a flying robot in a lot of ways. So I knew how to build teams. I know how to like we knew the product, what to do. I knew the technical understanding of like the power train and control systems and embedded software and sensors. So it's like, you know, we just like went really quickly out of there. The pitch was like, hey, I'm going to fund it. So there's like 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. It's like an iPhone moment happening with humanoids. Like it's going to be this is going to happen right now.

And what did you tell them the probability of success was? Pretty low. Like the thing, the thing that we had to do was like we had to do, 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 like sub, like, you know, sub five years or you fail for sure. You have to build like incredible hardware for human noise. That's like extremely complex. It can never fail. It's always got to work and it's got to work at human speeds with human range of motion.

Nobody's ever done that before. Like most robots that walk around can't even walk right. Like they fall all over the time. It's very complex. Like maybe like rocket turbofan level complexity in terms of hardware systems. The second is you need to be able, 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 like human-like data in the robot through a neural net. And it's got to be able to then 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 arm on a table, which most of none of those have AI. And then the third thing you have to do is you have to figure out how to generalize. You have to do something that's the holy grail of robotics. You have to figure out how to look at something you've never seen before through speech, tell the robot how to do it, and then be able to execute that task fully end to end just with one neural net.

So, the, you know, and I wrote about this in the master plan in 2022. It's like, we need to solve those. If you can solve those, you're in the right decade. You're going to go build the iPhone moment for this whole space. And we're in full liftoff. But like, but those look pretty dire at the time in 2022. There was just nothing out there. I mean, you had Boston Dynamics that was like leaping around and doing backflips and parkour and stuff. But like nowhere near the level of like manipulation and dexterity you needed for humanoid robots to enter the home.

So I think we can confidently say now we've like we have solved or we're making substantial progress on all of those. Amazing. So which is great. So like I think like, yes, there was a pivotal moment late last year where you said OpenAI was a large investor and you were baselining OpenAI's AI systems. And you made a critical decision to say, nope, we have to build our own AI internally. Helix.

Can 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 a large scale AI internally. It's like a basically a large scale like vision language action model.

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 for the robot is they'd never have seen any of the groceries before in training. We 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 like 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 located differently and things are changing throughout the day. So you really have to solve this like, I call it semantic intelligence, but like it's like a semantic grounding that's needed from a human world to robot world. And Helix is...

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 plumb. And so I think this is like the first signs of life. I think I will go even more of a bolder claim. I think this is probably the most important AI update for robotics in 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. It's 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? Yeah, I think that's a good idea to put it up there. Yeah, actually it's...

I mean, is that a created, you know, like they're about to look at each other here as he passes it over. Listen, a part of this was like, that's funny. Part of this was like emerging 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 or the item. Other robot needs to grab it. So it doesn't lose like basically like hold of the item. It doesn't fall down.

So what happened emerging 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. The other stuff of like robots looking at each other and move around, like 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 like interaction design with humans. So you don't want like, you know, you don't want to walk in a room and have a robot just like not move.

and like not look at you like humans look and like do nods and gestures like all of this is extremely important to learn like we need to learn these expressions of humans uh just like we need to learn how to grab items um it's going to be super important as we at scale integrate robots into the entire world that this happens i have a thousand questions for you let me hit a few rapid style here yeah let's do it so figure three when do i get to see i saw the designs when does figure three get shown

Yeah. You keep asking this. You like this one. I do. It was a beauty. It was a, I mean, you know, degree of beauty was increasing. Yeah. I don't think people understand this, how like incredible, well, they don't cause we haven't showed it, but we, um, so we like, we're on, this is like the ones robots you saw here on the videos on stage or figure two. So second generation robot, um,

You can kind of, I guess, figure one's online a little bit, but it's a little bit more gnarly. It's got 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 real use case work. But figure two was like a feature complete robot that was supposed to be able to do almost anything a human can or the vast majority of it. We haven't talked about this publicly a lot, but we're done now with figure three design.

I think we'll probably show an update next week. Just a quick minor update, not anything material as it relates to what we're going about for that process. Figure 3 is like, you look at figure 1 to figure 2 and it's like a huge step up. You're like, wow, so from a college dorm room project to a real 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 like, you know, figure two is probably the best humanoid around the market. Maybe, you know, probably not by a ton, but like, I think it's the best.

10%, 20%. Figure 3 is just like next level design. Like we've spent, it's definitely like the most, like for me, like the most proud moment I've had in engineering in my career, like looking at that robot. And so we're going into production manufacturing with that this year. We'll have some more updates on that soon. That's the robot that we want to send everywhere into the world. We want to make it a low cost, very high rate.

It's even better just on like so many dimensions. Tell me about production rates over the next three, four years and when I'm going to see it in the home. Yeah. So we have like two tracks. We have 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 almost on repeat. The home is like the Wild West. It's like extremely hard. We have a huge safety area of like not falling on like any human or hurting people. There's a semantic in safety of like not knocking over the candle and burning the house down. There's like, the home is just like vastly harder. Like maybe in self-driving, it's like driving on the highway is like workforce for us and driving to the city is like the home. It's just like unbelievably difficult.

Between our two first commercial customers, which are very large businesses, we have demand. Like if we had 100,000 robots today that all worked, they would take 100,000 robots today.

And so, and then we have like 50 customers I could sign by the weekend that are all Fortune 100 companies that we've like 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? All sound great. Like we're just like bombarded with the amount of demand here. You're thinking about like the workforce, you have like a certain number of supply of humans. It's literally going down. Demographically, baby boomers are retiring. So you have less humans in the workforce. There's labor pains everywhere, right?

And, you know, like there's a lot of job shortages. Like we can, so anyway, we see like just unbounded demand. I think we could ship a million robots to this month if we like had them all working and they're ready to go. And I think one thing that we're going to maybe add before you, sorry, I know you want to rocket fire, but you guys saw BMW and you saw our second commercial customer. It took us a year to do BMW fully end to end at high speeds. Like last summer, if you look at figure one, it was four minutes. Now we got down like 40 seconds and just a lot of great engineering work into it.

We started working on Helix. It was just completely transformative, like completely. And then we said, okay, well, what if we use Helix for this next use case for the next second customer? And we did that whole thing end-to-end in under 30 days from scratch. Had nothing. And I think if we had to do it all over again, we could maybe do it in less than 48 hours. And so the robots are going to learn how to do something in like the matter of hours here. Not like 10 years from now, like this year. And I think that has pushed our timeline left multiple years for the home.

Like the hardest thing, like the long pole in the tent for the home is like semantic intelligence. Like can I understand what the hell's going on anywhere it goes? Under over on the home is what?

We'll start alpha testing in the home this year, which means we'll be doing internal work on the home, like my home or engineers' homes. You want to get rid of that dishwashing duty? Dude, I can't do it anymore. What am I doing? It's just not something I want to do. I want to spend time with the family and kids and wife. It's no bueno. So yeah, we've got to fix that. At this point, we just feel data-bound in the home.

Like, we think if we just, like, increase the data set that we trade Helix with by, like, a couple orders of magnitude, it would probably, like, right now, Helix, we put it in, like, we put a little note on the website about Helix. And one of the things we put in is just drop, like, small household objects in front of it. They can pick up almost every object we put in front of it. Like, we put up this, like, weird cactus, like, toy, like, from one of the kids' rooms. And it was, like, seen. And we're, like, pick up the desert item. And it's got to, like, it's got to relate, like, a cactus to a desert item.

Like, you know, plant. And it was a toy and it was singing. It was moving and it picked it up. So like all of that is like in the weights and it has like a very large, like L and backbone to it. So it really understands the world semantic grounding.

So we think just like, we just need more data now, like basically data bound for it. So I guess there's a lot of confidence that you're seeing a sign of life now that you haven't seen in history that a robot, intelligent robot in the world can be built. And the question is, we just got to keep extrapolating that on like the curve far enough to where it's entering. And I think it's like this decade. I think you're going to see it in the coming years being put into homes just through speech, be able to do like very long horizon hours of work without any problems with any fix. Everybody, thanks for listening to Moonshots.

You know, this is the content I love sharing with the world. Every week I put out two blogs, a lot of it from the content here, but these are my personal journals, the things that I'm learning, the conversations I'm having about AI, about longevity, about the important technology transforming all of our worlds. If you're interested, again, please join me and subscribe at dmadness.com slash subscribe. That's dmadness.com slash subscribe. See you next week on Moonshots.

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