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

Episode Date: March 17, 2025

In this episode, recorded at the 2025 Abundance360 summit, Brett Adcock, founder of Figure, shares how his robots are already working in BMW factories, why robotics is about to have its "iPhone moment," and how AI is making general-purpose robots shockingly capable and affordable.  Recorded on March 11th, 2025 Views are my own thoughts; not Financial, Medical, or Legal Advice. ​Brett Adcock is an American technology entrepreneur and the founder of Figure, an AI robotics company developing general-purpose humanoid robots designed to perform human-like tasks in both industrial and home settings. In 2023, he also founded Cover, an AI security company focused on building weapon detection systems for schools. Previously, Brett founded Archer Aviation, an urban air mobility company that went public at a valuation of $2.7 billion, and Vettery, a machine learning-based talent marketplace acquired for $110 million.  Learn about Figure: https://www.figure.ai/  Figure’s Announcement: https://x.com/adcock_brett/status/1900923308411154450?s=46  Learn more about Abundance360: https://bit.ly/ABUNDANCE360  ____________ I send weekly emails with the latest insights and trends on today’s and tomorrow’s exponential technologies. Stay ahead of the curve, and sign up now:  Blog _____________ Connect With Peter: Twitter Instagram Youtube Moonshots

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 Figur? 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 Figur. 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 it for the C Corp, we have the robot walking in under 12 months.

I think you're going to see it in the coming years. You can put it in homes just through speech, be able to do like very long horizon hours of work without any problems. It was like an iPhone moment happening with humanoids. Like it's going to be, this is going to happen right now. Now that's a 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. That's what I pay attention to. Every week I put out two blogs, one on AI and exponential tech and one on longevity, if this is of interest to you. It's available totally for free. Please join me. Subscribe at diamandis.com slash subscribe. That's diamandis.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 and incredible team of engineers, you're really busy. And I don't take it for granted that you joined us here. Yeah. My only request is next time I want a figure robot. Yeah, loud and clear.

I begged him. And BMW has been taking the lion's share of them. Yeah, we do have a lot. We actually have them running every day now. So like they're there today running and their largest plant. Why'd you start FIGUR? I mean, you had this incredible, you have a few incredible successes, and Archer was amazing, and then you jumped 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 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, like foundation model, that can power the whole robot to do everything end to end. I mean, massive congrats. You went from a cold start in 31 months to shipping your first robot, which is extraordinary. I mean, a lot of companies get their PowerPoint decks ready and raise their first capital months to shipping your first robot, 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 1 and here's figure two and here's the designs of 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. Um, 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, first time you make something like you're never going to get it right. In hardware, you have to do that. Like, um, 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 pretty vast. It's firmware embedded systems, operating systems, middleware, controls, AI. So walk me through your factory. You walked me through it before, but what are the different segments of what's going on there? Yeah, in terms of design for how we're operating. Yeah, you've got component building, testing, integration, all those things.

So we clean sheet design everything from basically the ground up. Like all the hardware is 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, $110-120 trillion, the global GDP, your TAM is like $50 to $60 trillion. That's pretty good. Yeah, it's like the, 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, joins, motors, battery systems, sensors. We do all the hardware design, which is like kinematic design, joins, motors, battery systems, sensors. 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 like 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 doing 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... 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 bear, like you basically gotta 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 extremely expensive. The calculation I do is if I was gonna 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 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 not like 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 like almost 50, 70 years. We have like same appliances, same stuff. Like we need to- We had old robots, we call them dishwashers 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 it do it. It'll be on a schedule. Any moment you can just call it, text it, 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.

I remember a couple of years ago, I'm very proud, Bold is an early investor in figure and I brought Timor 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 pull together. Can you talk about that? Because

it's, I think a lot of people in the audience here are focused on their moonshots. This very much is a moonshot. You exit Archer. How did you capitalize? What did you start? How do you pull your team together? You described that early moment. Yeah. I haven't found a lot of companies in my lifetime. I get to go back every time and 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 time on is just building.

Basically in order to build one of the world's greatest products, you need one of the world's greatest teams. And then you need to align that team with what the shared vision is, and everybody needs to be accountable for that and understand it. And then you got to figure out how to hit the gas pedal really hard. So the entire culture at Figure, even at Archer when I built the 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, we're 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, they're 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 a 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 to basically build the best product possible. And they help scale the business, which is great. Hiring, just recruiting, HR, legal, just finance across the board. They're great. So yeah, the team's insane. But what's even better is the culture's just absolutely dialed in. 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 humanoid stuff is like, it's like maybe one of the most complex things I could have worked on. And you just have, you have to have that fundamentally or those literally zero shot. This is going to work. You know, we're going to hear from Travis Glanick tomorrow, who's going to say very much the same thing that your, what we call your massive transformative purpose, that clear mission vision, and then aligning your team and culture around that, which starts

with you. So you made a commitment of your own capital to get it going, and then you started calling people at other companies and what was your pitch? To raise capital. What's that? To raise capital or recruitment? No, no, no, to get those employees on board. Oh, the pitch in 2022 was,

I'm gonna fund this whole thing for many years. You know, 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 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 knew the product, what to do. I knew the technical understanding of the powertrain and control systems and embedded software and sensors. So it was like, we just went really quickly out of there. The pitch was like, hey, I'm gonna fund it. So there's like no funding risk, at least in the near term, like next couple of years. There was a good chance for us to build the next,

it was like an iPhone moment happening with Humanoids. It's gonna be, this is gonna 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, And what did you tell them the probability of success was? Pretty low. The thing that we had to do was we needed to prove 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 humanoids that's extremely complex, that 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, robots that walk around can't even walk right, like they fall over 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 gotta be able to then imitate what the humans do. So you have to solve that, which has never been solved on a humanoid system. It's like a high dimensionality system, not like a robot arm on a table, which 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, do 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. 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 those looked pretty dire at the time in 2022. There was just nothing out there. I mean, you had Boston Dynamics that was leaping around and doing backflips and parkour and stuff, but nowhere near the level of manipulation and dexterity you needed for humanoid robots to enter the home. So I think we can confidently say now,

we have solved or we're making substantial progress on all of those. Amazing. Which which is great. So like I think like yeah. Yes, very good. 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, 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 large-scale AI internally. It's basically a large-scale 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, the tricky part for the the robot is they 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.

We all have different toaster ovens. We have different appliances. We have different spatulas and silverware. It's located differently, and things are changing throughout the day. So you really have to solve this, I call it semantic intelligence, but it's like a semantic grounding

that's needed from a human world to robot world. And 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 plop. And so I think this is like the first science of life. I think I will go even more with 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... They're about to look at each other here as he passes it over. Listen, a part of this was like... That's funny. A 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 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 emergent from training is the robots actually look at each other as the clearing way signal for like for we should be releasing the item into each other's hands, which is really interesting.

The other stuff of robots looking at each other and moving around, I think it's just overall important. There's 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 not move and not look at you. Humans look and do nods and gestures.

All of this is extremely important to learn. 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. I have a thousand questions for you. Let me hit a few rapid style here, okay? 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 beautiful, 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 because we haven't showed it, but we, so we like, we're on, this is like the ones robots you saw here on the videos on stage were Figure 2. So a second generation robot. You can like kind of, I guess Figure 1's like online a little bit, but it's like, it's a little bit more gnarly.

It's like 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 2 was like a feature complete robot that was supposed to be able is able to do almost anything a human can or vast majority of it. 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 like figure one and figure two and it's like a huge step up. You're like, wow, so from a college dorm room project to a real like pretty decent robot and you like the magnitude of the stuff that was pretty material, that same magnitude happened again on figure three.

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, its 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 human way to run the market. Maybe you know, probably not by a ton, but like I think it's the best, 10%, 20%. Figure three is just like the 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. I'll have some more updates on that in the soon. That's the robot that we wanna send everywhere into the world. We wanna make it at low cost, very high rate.

It's even better just on so many dimensions. Tell me about production rates over the next three or four years and when I'm going to see it in the home. Yeah. So we have two tracks. We have a workforce track, which is like, and then we have the home track. What most people don't get is the workforce is the big business. It's half of GDP. We can charge meaningfully more per robot than the house.

And it's also easier. The things that the robot does is just the same things. I'm also going to repeat. The home is like the Wild West. It's extremely hard. We have a huge safety area of not falling on any human or hurting people. There's a semantic in safety of not knocking over the candle

and burning the house down. The home is just vastly harder. Maybe in self-driving, it's like driving on the highway is like work for us 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 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. And 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 the workforce, you have a certain number of supply of humans, it's literally going down. Demographically, the baby boomers are retiring, so you have less humans in the workforce. There's

labor pains everywhere. And there's a lot of job shortages. So anyway, we see just unbounded demand. I think we could ship a million robots this month if we had them all working and they're ready to go. And I think one thing that we're going to maybe add before we go, sorry, I knew you wanted 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. Last summer, if you look at figure one, it was four minutes. Now we got down to 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 new 90 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 gonna 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 like we'll be doing internal work on the home, like my home, our engineers' homes. You want to get rid of that dishwashing duty? Dude, I can't do it anymore.

It's just like, what am I doing? It's just like not something I want to do. Like I want to spend time with my family and kids and wife. You know, it's like just no bueno. So yeah, we got to fix that. I feel, I mean, 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 a that we train Helix with by a couple orders of magnitude, it would probably... Right now, Helix, we put a little note on the website about Helix, and one of the things we put in is just drop small household objects in front of it. It can pick up almost every object we put in front of it. We put up this weird cactus toy from one of the kids' rooms, and it was singing, and we're like, pick up the desert item. And it's got to relate a cactus to a desert plant.

And there was a toy, and it was singing, it was moving. And it picked it up. So all of that is in the weights. And it has a very large LLM backbone to it, so it really understands the world's semantic grounding. So we think we just need more data now. We're 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 diamagnus.com slash subscribe. That's diamagnus.com slash scribe. See you next week on Moonshots. you