The Man Taking on Tesla in the Race for Humanoid Robots w/ Brett Adcock | EP #116

I am curious how you think about Optimus and Tesla and Elon.

So inspired by what Elon's done the last like 20, 30 years, it's been just unbelievable. We need more like really real players. I think Tesla's a really real player. Like, I mean, you know, highly capitalized, great engineering team, and I think they're directly heading in the right direction.

I'm tracking the robot companies coming out of China.

I spend a lot of time touring kind of what high rate manufacturing processes look like out there. And it was just, it was shocking.

I mean, listen, you're amazing, but the star of the show here is Figure two. So do you mind if maybe we open up this podcast with a quick look at Figure two?

Yeah, sure, yeah. Yes, I guess. Welcome to Figure.

Hey, everybody. Peter Diamandis here. Welcome to Moonshots. On today's episode, we're going to do a deep dive with Brett Adcock, the CEO of Figure Robotics, Figure AI. He's going to be giving us a tour of his Shop and Figure 2, his new robot release. If you're watching us on YouTube, we're going to talk about what he thinks about Elon and Optimus. Talk about the robots in your home, when to expect them, and his projection of 10 billion robots on the planet by 2040. He just did a monster round with 700 million or so from OpenAI, Microsoft. Jeff Bezos, Nvidia and we'll talk about how he's integrating OpenAI's software into his figure two robot. An extraordinary conversation, one of my favorites. All right, if you enjoy conversations like this, please subscribe. Let's jump in over to Brett and Figure AI. Hey, Brett, good to see you, my friend.

Yeah, Peter, thanks for having me.

Yeah, I know for sure it was super fun to come and visit your office. I don't know, it was like two months ago. It was before you rolled out Figure two. And I have to say I said it to you there, the speed at which you're iterating designs is pretty amazing. And I didn't feel that kind of an energy since the early days of me seeing SpaceX back at Falcon 1 days. So congrats on that.

Yeah, thanks.

I think everybody, really, I mean, listen, you're amazing, but the star of the show here is Figure two. So do you mind if maybe we open up this podcast with a quick look at figure 2? If you don't mind, can we go take a quick sneak peek on the shop floor?

Yeah, sure. Yeah. Let's make sure it's not being used. But I Can give you guys a quick maybe tour of the office. Yeah. So I guess. Welcome. Welcome to Figure.

Thank you.

So we're well over 100 engineers now. We're based here in Northern California in the San Francisco Bay area. We just unveiled last week Figure two, which is our second generation humanoid robot. And we have, right now we're manufacturing about one a week in our facility here in California. And we have several of them here now on the floor. So here's a quick look at figure 2 robot that we're about to start doing some tests on right now.

Amazing. I know your team is about to activate it. If you're going to say the principal differences between figure one and figure two, I mean, just for folks, the company's like two years old or less. Right. You've gone from zero to infinity super fast. What's the difference between. What upgrades did you make in figure two here?

Figure? Yeah, I mean, there's, it's, there's several.

So your top five.

Yeah. So I think first is we tripled amount of CPU and GPU on board just for more overall compute and inference. The second is we almost doubled the battery to about 2.3 kilowatt hours. It's all on board the system in the middle of the torso here. Next to basically the compute and gpu, we had all the wires, all internal, so there's no external wires. Cabling, electronics. That's really for reliability and for overall packaging. We also have exoskeleton structure. So all the outer shells of the robot actually take loads, which is opposed to kind of how we did the first generation robots. This would be more akin to how you do it, like, say, aviation. My last company, Archer, we, you know, the skins on the aircraft take the loads of the vehicle. So I think that's pretty unique here for a system like this. We also have six onboard cameras, so we have more perception, more ability to see our surroundings.

Where are the cameras on the robot? Here?

We have them in the head, in the back, and in the lower torso.

And I assume that the exoskeleton helps you reduce overall weight of the robot.

Yeah, it's basically like, overall, the parts will get a little bit stiffer as they get a little bit wider. We found that having one structure for both crass loads and stiffness is the right ideal mass trade. And figure one had kind of like both structure and outer shell for loads, and that's just really not ideal where you, you know, like the structure is really sized by crash loads. So then you end up having basically double Mass in a lot of ways.

And the hands, have you made improvements on the hands on the. Yeah.

So these are.

Show us. Hold his hand and show us what it looks like.

These are our fourth generation hands now, and we've made quite a lot of improvements over the previous generations. Better sensors, better packaging, better for mass, better strength, better speeds of the fingers overall, just better dexterity and control of like fine grain manipulation that we're doing on board the robot. We need to do like, human, like applications. So the more here that we can do human like tasks and grab human like objects, the better for generalization of the robot.

Amazing. And it's standing what about five, six, five, seven behind you? Five, six, yeah. Amazing. All right, thank you for the quick glance. You want to hop back into your conference room? I love the beams up there for carrying heavy weights. So how many figure robots are up and operational right now inside the facility?

Yeah, we have a little under 10 in our facility here. And then we're in process of building as of right now. Basically one a week.

Yeah, amazing. Brett, one of the things that I saw when I was there, you showed me figure one and then figure two, which hadn't been released yet, and then drawings for Figure 3, which I won't talk about, which look beautiful. But can you talk about your rapid iteration strategy here? I mean, how do you think about generations and redesigning and rebuilding? Because a lot of companies get to something and then fix it and then sell it for a long time. Doesn't sound like your strategy is that.

Yeah, I think the rule of thumb here is that roughly you need probably minimum three hardware versions to get to a point where the hardware is relatively commercial reliable, like bug free. And our goal is to, in the limit, make this basically a software limiting issue for us, which means we need really capable hardware that's really reliable, it's safe, it's low mass, and it's like low cost and we can manufacture it really well. That's like a light a lot to bite off in like the first version hardware to get that right. It's just too difficult. I mean, it's like getting the iPhone one right and getting everything okay. And I had the iPhone one and it was just not the greatest phone in the world. But like iPhone 3 and 4 were like certainly the greatest phones in the world. You know, same for cars, right? Like the Tesla Roadster, probably not the greatest car in the world. And I have three Teslas now. They're incredible. Probably the best car I've ever had. So we basically want to Be on this continuum of like rapid, like hardware iterations where we're basically looking at different heuristics of things that we need to mature over those hardware continuum and making all necessary improvements so that the hardware is at some point very mature. And I think our first generation hardware, figure one was mostly trying to get roughly the architecture trades right. So all the details of the engineering system for a, as a batteries example, what is the energy? Is it going to be hydraulic, it's battery powered, what type of battery cell chemistry from there, what type of pat like type of cell? Is it cylindrical, is it prasmatic, pouch? How are we going to pack those? Thermal propagation, all those different trades you have, that's just the battery alone. Then you have the rest of the whole system. So there becomes an order of like 100 or 200 decisions you have to make to go out and build a robot. And you don't want to have to be in a situation where you have to get all those right. And I think we got most of those right on figure one. We did even better job on some of those decisions on Figure 2. Figure 2 was really about getting to a feature complete robot. It has all those systems on it. Whether we're going to build or buy it on the robot that are working, we built most of it. So that's software, whether it's firmware, embedded systems, control software, all the hardware systems on board the actuators, electronics, wiring, battery systems, cameras, sensors. So we think we got roughly to feature complete hardware complete on Figure 2. And so we're really excited there. And then future generation for us will be how do we get the cost down by well over order of magnitude from where we're at now? And how do we get the ability to manufacture unprecedented scale that we've never seen before in robotics?

I know that one of the quotes I heard you say is everyone will own a humanoid and labor will be optional. And those are pretty provocative and I think actually true for everyone to own a humanoid. And I want to get into this for a quick moment. You said reducing the cost 10x. You know, Elon's been aggressive on a price tag. He's not always been right on price or schedule. But I think, do you think of these, these humanoid robots as a ultimately converging on a price per kg of total weight? And what do you think, you know, 10 years out, 20 years out, these things will actually cost.

So I spent the last like I spent all year now looking at how cheap we can make these robots. And the answer really lies in a bottoms up analysis of the entire bill of materials, bom cost, where we take basically a list of, call it roughly 1000 parts and we start itemizing that down and then we start understanding at real scale how we're going to procure those parts, whether we build or buy and what contractual, you know, volume estimates we can get with price prices there.

I feel it's super volume. It's super volume dependent on pricing, I imagine.

Yeah. Every consumer device or car that we know of has been basically like follows a very high correlation with manufacturing volumes. So the only real way to get consumer electronics prices down is by high volumes. That's the only way we know of. So you really want to make a lot of the product to get the cost down. So yeah, I think like over a long enough period with enough high volumes, I think you're getting these costs down like sub $20,000 a unit, like really cheap.

And that's amazing because if I were going to, you know, lease a twenty thousand dollar car, you know, that's costing me like one hundred bucks a month at most. And so, you know, why not get.

Two.

You think at the end.

Yeah, I was saying especially if it makes you money, like if it can go out and do work and things like, you know, it could like actually be in the workforce or it can do things that you would be spending time on during the day. If it can actually be like a real utility here, I think. Yeah. How many? I guess, yeah. How many would you want that could, if it could make you money real quick?

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If these robots can do everything a human can, I have to think that we'd be able to put 3 to 5 billion in the workforce. And I don't see any reason why every human wouldn't want to have a humanoid like you do a car or phone. Perhaps even more important than a car or phone where it can just do all the work that you just don't want to do all day, whether it's walking the dog, getting coffee, doing errands, doing laundry, chores. I mean, every day I go home, I'm cleaning up kids toys, right? So there's just like, I can have a robot, just clean up kids toys two, three hours a day, every day, no problem. It's like endless work every day.

Yeah, I can imagine that. There was something else you said that really hit me philosophically. You said it's a moral imperative to have these kinds of humanoid robots because as we get to AGI and digital superintelligence. Correct me if I'm wrong here, but you said if we don't have those humanoid robots, the AIs are going to be having us do what they say. And it's a lot better for the human soul if the robots are doing what the AI said. Correct me where I'm wrong there, but that was an interesting point of view I hadn't heard before.

I think a pretty depressing future would be one that we solve AGI and that lives in a box like not in the physical world. And in order for that AGI to do anything in the real world, it would have to ask or force a human through wages or whatever to do that action. And I don't know about you, but that seems like a really kind of downer future.

Please plug me into a higher to more kilowatt hours.

Yeah. The collective consciousness of humanity's intelligence is sitting there wanting to do things in the physical world and having to pay humans through wages to do that or through force. That just seems like, I don't know, just a terrible future.

I was interested in the mission statement you wrote. I just recently saw some documents that you'd put out. The mission of figure is expand human capabilities through advanced AI. And that's. I'm curious, it didn't say about humanoid robotics. It was expand human capabilities through advanced AI. How did you end up there? Do you see yourself as an AI company?

Ultimately we do see ourselves as an AI company. The limit that happens to be robotics in the limit. All the challenges that we face to do what we set out in our mission are ultimately going to majority be AI problems and hurdles. And I think there's this dream that we're all having Here now at figure where one day we have these robots out in the world doing really important work. It's really needed for humanity helping to lower goods and services prices to hopefully bring a world of abundance. And I happen to think that that'll free up a lot of the time me and you have. We all have to do things like we really, really love.

Amplified by robots and AI to do far more per unit time than ever before.

Yeah, exactly. If we could just spend time on what we really wanted to do, what would all humans do with their time?

I think that's one of the most important questions is what I recently I've been working on my next book which is called Age of Abundance and I heard you in a previous interview talk about robots enabling an age of abundance. How do you describe that? What do you think that looks like? What did you mean by an age of abundance?

Well, I think one of the interesting things about humanoids is we can put these robots, the goal is to put these robots into the physical world with no additional infrastructure needed for them to operate. So you can put robots into the workforce. And we don't need to go build new systems and new electronics and everything else for the robot to work within. It can just like a special purpose machine. We go in there, we got to try to architect everything and make new space and build a new machine and roll it out, integrate it like a humanoid just integrates right into the world. You can just do human like things the next day. And if we have robots, ultimately we'll build robots themselves from a manufacturing perspective.

I was going to ask you that. That iterates very quickly down to.

Yeah, I mean you can basically bring most manufacturing today. It's just like got a bunch of machines and you have humans. That's basically it. And if we can do human level manufacturing, you're basically at a point where you could theoretically have robots building robots. The price here just collapses to nothing. And those robots can be put into the world to do work. So what is that cost of the work? It's the cost of you renting the robot out and it's the cost of that land. And if you have renewable energy on that facility like that, you know, this, this work area, then that cost will be very small and the output will be really high. So you can basically create a world where goods and services prices are like, you know, trend to zero in the.

Limit and, and GDP spikes to infinity.

Yeah, I mean like, yeah, you basically can request anything you'd want would be relatively affordable for everybody in the World, Yeah.

It's interesting when you look at GDP of countries, they scale with population and access to energy, and population and energy is work. It feels like this will become a mandatory part of any nation that wants to survive and thrive in the decades ahead.

I think it'd be super important to figure this out.

In your mission statement, you said, hence the goal of figure is to develop general purpose humanoids that make positive impact on humanity and create a better life for future generations. These robots can eliminate the need for unsafe and undesirable jobs, ultimately allowing us to live happier, more fulfilling lives. And I buy that. I love the idea of robots doing the jobs that are dull, dangerous and dirty. Cleaning the toilets, cleaning the rooms, because most people, I think, do work. Most people in the world do work not because they love that work, but because they have to do it to get food or insurance, whatever the case might be. But the question ultimately is, I also think these robots will. I've got a niece who's a plastic surgeon and I'm like, don't go into that. Robots are going to become our ultimate surgeons. Is there any job you think that robots aren't going to be able to take on if we want them to?

It certainly seems that over time, both digital and physical, intelligent robots will do more and more things a human can really well. And I think we're really just like. We've been seeing that, though, with technology over the last several centuries, but I think we're seeing that very much accelerate this. The slope of that curve is accelerating and it's accelerating in really interesting places with large language models. And it's almost accelerating in the wrong, different direction than we probably have thought 10 years ago. I happen to think that over a long enough period of time, we'll have automation fit with our physical or digital automation, that we'll be able to do probably the majority of things that humans can do today.

Just to note, I checked this morning and there are 8.2 million job openings in the US. It's not like there's no need for. There's no jobs available. I think the big news and congrats on this is the financing you just did, which is extraordinary, just for those who don't know, you raised $2.6 billion from OpenAI Microsoft. Jeff Bezos, Nvidia I know my own venture fund. Full disclosure, bold as an investor, very proud of that. That's a lot of money.

Yeah. We raised $675 million.

Yes. At a 2.6 billion valuation.

Yeah, we're super proud. We Brought a lot of new investors, including OpenAI, Microsoft, Nvidia and yeah, great to have your support. It was good. It's giving us the ammo now to really take the next step in our mission of rolling these robots out commercially and making really viable. And that's really where we're at right now. It's like, how do we take. We're putting out cool videos and that's great, but the next big step is how do we get those in the workforce working every single day? And we're, you know, we just showed that we just got back from BMW and, you know, and basically close to two weeks, basically doing a full trial there that went really well. BMW actually just put out a press release about that. We'll be going back here in the near term and the goal is like to go back and do useful work continuously. And I couldn't be more excited. It was both hard because we were like outside of our comfort zone in the office also just really energizing. When we got back, we're like, we can do this. This can be done.

Reinforcing. Yeah.

Everybody here is fired up that we get a chance to try to go do this over the next few years. And I didn't mean post financing here. What's holding us back now? We have more cash than we need at the stage of the company we're in. We have great partners like OpenAI helping us with models. We have great companies like Microsoft helping us out with training, Nvidia on GPU hardware, other simulation work. We have the world's best AI robotics team ever put together. We have figure two now, which is, I would say, probably top humanoid hardware in the world. And we're doing some of the best AI learning work in the world. And we see this small light in the tunnel which is like, this is what robots can really do. So we're all just working really hard at that. At this point.

Would you say that what you're doing now is only possible because of the state of AI? I mean, is artificial intelligence and a mass amount of compute the thing that made this possible now, because, you know, I mean, we've been talking about robots for God knows, you know, 50 plus years. I built robots in high school and college. I didn't call them robot. I mean, you call them robots, but they were nothing in computer comparison. But is it AI that made you say, now I'm going to commit? Because you put like $100 million of your own money into the kick this thing off. Right. Which is a significant step for an Entrepreneur.

I think there's a few different things. One is the whole AI ecosystem. It's not just the models, it's the, the overall infrastructure for training, for inference and deployment, deep learning algorithms that can support large scale imitation learning and reinforcement learning. So it was several building blocks there on the AI side that are all maturing to a point where you can deploy these policies, embodied policies in the world, and they work, which is pretty unbelievable. I just got back from taking awaymo last month in the city and it was just very special. And it's just like this, pretty clear. It can just drive like a human can with enough data. And same with our robot. When you see our robot in the facility doing kind of the new generation work that we're working on now, it just feels magical. I think a separate thing is the whole hardware system. It's really hard to know if it was like 10 years ago this was really possible with torque density of actuators, batteries, battery systems, energy density there. I would happen to think that the best humanoid robots 10 years ago were all hydraulic systems. Those are like 3,000 psi systems leaking.

Hydraulic fluid all over the place.

Yeah, like leaking oil everywhere, like intractable to put next to a human. You could kill a human with those systems next to it. So certainly that was the wrong architecture decision. 10 years ago. It'd been unclear for me if 10 years ago that would have been possible, assuming you even had AI that you could build electromechanical system that would work at the levels we have now. Ten years ago, I would probably think not.

I do think it's convergence of a whole bunch of things. I mean, the theme for my abundance Summit next March is convergence. And thank you for joining because I think what you're building is the exact sort of principle example of converging technologies, making new systems and new business models possible. How did you, how did you connect with OpenAI in the first place? I mean that's a big step, getting them involved.

I got introduced to Sam a few years back and we got to know each other a lot better and spent a lot of time together in 2023 and ultimately they wanted to get back into robotics and specifically around AI for, you know, embodied systems. And you know, now here we are like working on like next generation AI models for our robots to make that work and they're supporting us on that. That's been, I would say so far, 10 for 10 system. We happen to think they're the best, they're the best vision language models in the world. They're the best implementators of those models in the world. And we're trying to push the boundaries now on how to push that work as hard and as far as possible in the robotic space, which we've just been starting last several months, I imagine.

There'S a lot of benefits for them as well. You mentioned embodied AI. There's some theories that say, listen, we're not going to get to AGI unless we can embody AIs to understand. Sort of the embodiment gives them an understanding of the universe and allows them to explore. And then there's the other idea that we're going to hit a data wall to get to AGI. And humanoid robots are a means for collecting a lot of data to help shape future models. Can you speak to that a little bit?

I think it's becoming more and more clear that some level of output actions in planning is important to take our next step in intelligence. What we're trying to do here is we're trying to help complete that last leg of actions and reasoning that we're seeing from some of the best world models that we're helping to work on here internally. At the end of the day, if you can talk to a robot and it can output actions like useful actions in the real world, that would just be an incredible technology for the world that we're trying to work on here. Whether you want to call that advanced AI, AGI, whatever, that's just such an important focal point for us to try to get to is how do we output intelligent actions into the world and do useful things. So most of our focus on the AI side is on that topic and how to make that as scalable as possible and generalizable.

I mean, I have to imagine GPT4O or the multimodal versions of GPT4 are key for you having figure C and be able to understand. Was that a relatively. When you started, that didn't exist. The multimodal models weren't there. Was that the conversation you had going on with Sam? Was he sort of like, you know, sort of. Was he part of your inner conversation of what's going to be possible in the future?

I mean, like, one of the biggest breakthroughs we have is we have like with LLMs and, you know, VLMs, more specifically, we've had this semantic grounding that's occurred in robotics. We have the world's knowledge in some way analogy in a zip file for the robot to access and understand. And that bridge from robot to human was never really here before. If you want to talk to Autonomous car and say, drop me off on the curb over there on the right. There was no real semantic bridge for that in the world. And arguably we have that now. We have that semantic bridge has been built in the world. And what we're really lacking is this, I guess, reasoning and planning and perhaps actions from that system for us to provide useful work in a robot. So I would say you've had this unbelievable technology that's been opened up and we're seeing some really cool stuff right now in the world with different technologies all over the world in AI. But one of the things people are not talking a lot about is what does this mean for robotics? This means a robot knows everything you're saying and knows what you mean. And we have all this grounded in human level data, meaning we have all this semantic world's knowledge is written by humans for humans, which is an incredible ability for a humanoid robot that looks like a human to really have really high efficiency transfer rates. So the way humans open up jars is pretty similar to robots or humanoid robots to open a jar. So the affordances are roughly really high affordance levels as it relates to humans work. So this unlocks an ability for humanoid robots to really tackle general robotics. How do we talk to robots and how do we output actions that everything a human can do. And there seems to be a point in time where we can really try to see if we can crack that.

So you mean that we're going to see the normal course of interactions with robots be like, you speak to a human. It's like, can you please go grab that for me? And it says, what do you want me to grab? And you say that thing over there and you point and it looks and it understands the stapler or the bottle of water. And so it has got contextual knowledge and geometric and what do you call it? Positional knowledge. How far is that?

Yes, this is all realistic today. It's beyond even all those things, like in the neural net weights lives the material of the plastic bottle, roughly mass characteristics and friction characteristics and how it will feel to grab. And all of this is in the weights.

I work on longevity. Cause I want to see as much of this stuff as I possibly can. What kind of hours do you work? Because I know your passion, Brett, I know your dedication to this. And you are a kid in a candy store and you have to balance family as well. What's your work week like?

Yeah, I would say I work almost like basically almost seven days a week. There's really no time. I'm like really not working except with home, like with the, you know, wife and my kids.

How old are your kids? Do they. Do they get what you're doing at.

We just had a family day at figure. So, you know, my daughter's five and like was like. And my son is two. So we had the robots walking around. All the kids were so excited. It was really cool. Yeah, they get it. They talk about dad building robots all the time. And I think, yeah, it's like, yeah, I think we actually had probably 50 kids here last week. Watching all of them see the robots and touch them and yeah, it was pretty special.

I think about that. I have two boys who are 13, and I can't wait to have them see what you're building here. And I think about the fact that their future and your kid's future are going to be. If your numbers are correct, and I think that they are, they will be more prevalent than cars are out there. There's like a million cars, like a million other vehicles and we could see five to 10, I'm sorry, a billion cars out there. A billion other vehicles. And so it's going to be a very different future for them. I asked a friend of mine, what's it going to be like when you're seeing humanoid robots walking around the street all the time in five years? And his answer was interesting. He said it's going to seem normal.

Yeah. Can I tell you something?

Please.

Yeah, a bunch we have. A lot of folks here have been around robots for a long time that are going to be like, as soon as we do something, everybody's going to be in awe and then nobody's going to care anymore. It's funny, sometimes that happens. We're walking figure two now around the office quite frequently. And the first few times everybody's just stopped doing work. They're like fist pumping from the conference room or whatever, taking photos. We have this, like, we have this really crazy video where everybody is falling around the robot. The first time I walked in the office with like their phones out, you know, and we do it now pretty regularly and nobody cares. Like, nobody's just like, oh, the robot's close to me. Like, you know, like that's it. Like, it's pretty unbelievable how, like, used to all the stuff we get right.

We adapt so quickly. It's like becomes boring. I remember the first time I got like one of the first Model X's and the door wings come up and everybody's snapping photos and looking at it and then it's a nuisance after that.

Yeah, yeah, we get used to things really quick. I don't know, it's even like using large language models and stuff today, like ChatGPT. It's just like, I use it pretty frequently during the week and it's just, it's totally normal, part of my workflow.

Yeah, it is. It becomes just an extension. GPT 5. Have you had conversations about integrating that and its derivatives or the mythical strawberry as it's being unveiled? Do you get any early views of OpenAI's content for a figure?

No comment, Peter.

No comment. Okay.

Okay.

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I.

All right, let's go back to our episode. I am curious how you think about Optimus and Tesla and Elon. I know you have great respect for him as an engineer and entrepreneur. What are your thoughts there?

I just have to be.

You know.

So inspired by what Elon's done in the last, like 20, 30 years. It's been just unbelievable. I think they're doing a great job at Optimus and I think they have a really good engineering team. I think they're making really good progress and I think they're directly heading in the right direction. Like the vector, like the vector for where we need to go as a society to integrate and build humanoid robots. I think they're following a very good direction. So I think they're going to be a really real competitor with us. I think the world needs them out there doing this and yeah, I hope they do really well. I think we're at a point in time where the window has just opened now for humanoids to be possible. I don't think it was really possible 10 years ago. So it's going to be this, a little bit of a race here to get volumes out the door in terms of manufacturing and get the AI training sets built and deployed on the embodied systems. And yeah, it couldn't be a more important time to make this really work. So, yeah, I think overall just like very. I think they'll do very well and I'm very glad they're tackling it.

Yeah, I think when he walks into a new industry like that, he credentials it in a big way. Do they serve as sort of a stalking horse for you to keep the team? Do you guys like look at and go, oh my God, how'd they do that? Or ours is better? Does it provide a little bit of that kind of gamified motivation for the team or you guys just open loop otherwise?

I think we try to make all of our decisions from first order reasoning. What was the right decision to make if. And that's super important for every company to really do is like, I think we have our own mission vision, values, what we care about it's very different than any other company and we want to head a certain direction like vector space like that we think is the right way to go longer term. So I think that's like where we really ground ourselves in like the decisions we make. I think there'll be things that we do a lot differently than other groups, you know, longer term because we're just making these decisions like in a vacuum with knowledge that we have and going through that. Yeah, I think they're a really good competitor. So for sure we want to be winning and we want to compete. You know, overall, yeah, I think, you know, our goal is to build the best humanoid robotics company in the world over here.

I figure and I think since the marketplace is arguably near infinite, there's plenty of room for two or three significant players on the planet.

I think the way I look at it is we need more really real players. I think Tesla's a really real player, highly capitalized, great engineering team, heading the right direction longer term, moving fast through those high rate iteration cycles and milestones that are needed for engineering teams that really prove out the product is getting commercially viable. And what we're lacking in humanoid space is that we have a lot of groups that have been around for a long time around the hoop. And it does not look like there will be a lot of players that all win. It looks like there'll be very few that make it through this chasm, crossing the chasm here that's needed for getting this market. And I think many will die is trying to, you know, trying to bridge this. I hope that we live at figure but like we have like 14 peaks to go climb now. We have like a very hard road ahead to go from where at now, which is like two year old company, to like a commercially viable, like a real business. And that's what we need to go do. Right. Like we have like years, senior single digit years to go prove that and get into market. So like that's we're charging forward as hard as we can to ship our product into customers and make it useful.

Yeah, I think most people probably don't realize is you're only two years old and what you put together in terms of team, I think this last round of financing, if I looked at what if you had said to the average person or average technologist or venture capitalist, what's the advantage that Elon has for Optimus? He has a manufacturing, he has a car company there that could use it. He's got compute, he's got capital. I think this Last round gave you parity, if not in some places.

It's just such a stupid argument though, because you could say that about every company and why they can't be disrupted. You could have said that those are all Tesla's weaknesses 20 years ago when they didn't have any of those, none of them. There's already groups making electric cars and somehow they won. So like that's not really what happens in real life. Like that's not the, that's not. Those attributes do not set the winner.

I'm not saying I said the winner, I'm just saying that you, those attributes were advantageous and now you've got the capital, you've got to compute with OpenAI and you've got BMW and so forth. So.

No, no, I think like all the building blocks that we need to do to build a healthy company are all like starting to show up. And that's good because those are requirements that are needed in our long term roadmap. And we just got to put all those pieces together very intelligently and then not die into market and make a product work.

Not dying is a great part of the business plan. Can we talk about China? Because I find that absolutely fascinating. China succeeded on the backs of low cost labor and that's going away. We hit, we hit Covid and no one wants to manufacture. And the shipping costs. I'm tracking the robot companies coming out of China because I think that for so many reasons, like their aging population, their one child per family policy, all of that, and trying to actually maintain a manufacturing base all requires robotics. I see Unitree and a few others. What do you think of the Chinese robots? Are there any that seem like good competition out there?

I just got back from China a couple months ago and it was one of the best visits I've done in my career, to be honest. Went to a bunch of kind of like manufacturing focused companies in mainland China and it was just, it was crazy. Like I, we were touring one facility and I was like, what's that written on the wall over there? And they're like, oh, that's just our motto for this building. I'm like, what does it say? They're like, if you're having a bad day, just work harder. And I was like, holy cow. These guys are, they're animals over here. They're just like, they're just trying to build and ship. The work ethic was really high. The, the sheer will of the country to try to be a good, like be, like compete and win is really high. And I Was floored. I was just, I was like, man.

You have, have you been there many times before?

I've not been there like I would call it like a lot and specifically was like spend a lot of time touring kind of what high rate manufacturing processes look like out there. And it was shocking. I think we have in the humanoid space, we have figure and an optimist here outside of China. And I do think China is the next group of folks that are going to be really competitive long term in the humanoid space.

I think they have to be. I used to go to China every year and bring a group of abundance members with me and we'd go and visit all the top tech companies. And I remember the Motto there was996, a great lifestyle was 9am to 9pm Six days a week. That's, that was the work ethic. And China had the reputation as being copycats. But sure they copied a lot of things, but they also did a lot of authentic new development work there. Is that your experience as well?

My experience has been that those folks out there want to win, they want to do it at the lowest cost, they want to do it at the highest rate and they'll stop at nothing to try to be number one. And that's like think about what startups are. It boils down to those key principles you start a company with that are successful. You have nothing and it's just sheer willpower to get there and to go in. And they have that in spades in China and they do not have the resources we do here in the States. Like we said before, does that really matter? That you have all these things is the ultimate crutch. I think there are going to be some unbelievable robotics companies come out of China just because of the sheer number of projects being worked on and with the will that I saw out there is second to none. Yeah.

I mean I think in the same way that Israel developed an amazing defense industry because they had to to survive, I think China and Japan and South Korea with dwindling populations and aging populations are going to need to develop an incredible robotics industry to survive and thrive and maintain a gdp.

Yeah.

So you, you take Archer public, which was an incredible success and congratulations on that. And after starting that running that for a number of years, you break away to found figure. And if I'm correct, you made a commitment, you put a significant amount of capital on the table to start the company which you were able to do because of your previous exits from Vettery and from Archer. But I think when I first Heard your presentation and I brought it to my venture fund. What I was so impressed by, which clinched the deal in my mind, was the team you built. It was an extraordinary group of engineers from the top AI and robotics companies or tech companies out there. Can you word of advice for founders who are starting a company. I mean, you're a technical founder as well, which is important, right? But how did you recruit your team?

Yeah, so my belief is that in order to ship a really good, high quality product, you need the world's best team there to go do that. Especially against the difficulty level. We have a figure of succeeding. The odds of success are always pretty low. So you just need, you need to give it everything you got. You need the best team here. You know, I'm on site every day, you know, work hard, you know, you need to have like a high functioning workaholism bit. So I, so I spent the first year basically trying to map out what the organization needs to look like in terms of skills and like what does the ultimate org chart need to do to support like a really high functioning team to build a product. And I spent the first year just basically head hunting all that whole team by hand. And I was cold emailing and calling, drafted the offer letter, would give the offer letter, go do dinners, try to close onboard, 30, 60, 90 day onboarding, bring them in, lead the engineering decisions and direction and work on those projects with those teams. I think, you know, it's been a huge payoff because we've gotten like now this point where I think we have like one of the better teams ever built in the world for this. And it's like snowballing. We're able to attract like really high quality talent over all of our disciplines.

But in the early days, in the early days, Brett, I mean, how did you get that first dozen? Was it your conviction? Was it, you know, your capital commitment to it? I mean, because in one way you had Tesla bot, I don't know if it was called Optimus back then, and sort of. Elon tends to suck the oxygen out of a room on the stuff that he does, convincing people to jump where they were and join you. How did you do that? Because that Jiu Jitsu is really important.

Yeah, I mean the pitch for early figure was we're working on humanoids. We have this big mission to advance human capabilities with AI. We believe in AI first in the market. We believe a vertically integrated approach to hardware design is important. We're living in the largest TAM or we're working in the largest TAM in the world it's under half of gdp is human labor. I'll fund the first several years. There's no funding risk in the near term. And it's my second time going around building hardware, building hardware teams. So having done I think decently well at Archer, we're getting back and doing it from scratch. Did it even better this time in terms of setting the right direction and mission vision values of the team. Then I just spent a lot of time with those folks saying come here, let's go out and build this commercial. Let's build the best organization we possibly can. From early days, the early folks got all founding member stock which also was helpful and beneficial. Got good salaries. I was funding myself so it was being a part of from scratch startup but almost cushioned by my ability to self fund it for multiple years. Good equity. By the time we had five or six people on the team, they were all superstars. And then that was more de risking for the next five or six folks that were joining. So I was able to find folks that really believed in this space and believed in me. A lot of folks that used to work with me my last two companies, a lot of folks that were new as well. My first two people were folks that I've worked with that were my first employee at Vettery, my early employees at Archer that I worked with. So it was three of us, day one basically that have all worked together. Added some more folks on my old team at Archer, added some more folks from Boston Dynamics and other organizations that were really great. Soon enough we had a dozen people that were superstars in their discipline and then we walked our robot within 12 months of incorporating the company. So we worked really hard and got out of the gates pretty fast. And now it looks like, okay, everything looks great then. But I was in a wework phone booth for nine months making cold calls, trying to convince and talking to wives and husbands and tone to convince their spouse to join. And it was hard.

Yeah, not gonna lie, I bet. What did you give the chances of success back then in the early days or to get to the point where you are now? I mean if you had to give a think back then.

Yeah, I would not have ever assumed that we would be out of place now. We've really. I think there's the like I look at everything as like how well is the product doing as it relates to the roadmap into commercialization. We're still, you know, we're not like, we're not successful. Like we're not even like shipping but like, you know, we've been around for, I don't know, like 20, a little over two years or something like that. Unbelievable. Like, the amount of the hardware that's doing the AI systems and working, executing those policies. Like, look at the humanoids now, versus, like 10 years. It's unbelievable. And we don't ever walk into the office and things go backwards. They always go forwards. We're not. Sometimes we have a couple days where a couple robots break and we don't make progress because we're fixing them or whatever else, or a lot of meaningful progress. But most weeks we're making pretty decent. If folks are gone for a week, they come back and be like, this is a whole new company. It feels like. So every week goes by, we're making pretty substantial impact. So I would say, like, we far exceeded my early impressions of where we'd be at two years from now. Yeah, two years ago.

I'm curious, what did you think would be really hard that turned out to be easy? Nothing easy, but was easier than you expected?

Well, I thought maybe the opposite. I'll do. I thought we'd be able to procure a lot of supply chain to go build the robot. Like electronics, boards, motors, actuator systems, battery pack systems, cameras, lights, speakers, speakers on. Figure 2. I thought maybe go to Alibaba or go to Amazon and buy speakers, screens, screens, speakers, lights. These things have to be bought off the shelf, right? You have to go buy those things and they come in from Amazon and you put them on a prototype. Like that's like, that's like. That should be that easy. It's still not that easy. Like, we're making custom speakers. Like, how is it possible that we're making custom speakers and we're writing custom firmware for some of those areas. Like, how is that even. It doesn't make any sense that that's even possible. So going into it, a little naive of like these sensors for torque cells or whatever, like, would be procured and would come in. I wouldn't have to do it. We make our torque cells, like four cells here, which need like a flexure the board. They need to be gauged, they need to be calibrated, tested, integrated. There's software and firmware on those on there. That's got to work then at really high rates. It just still boggles my mind. There's no mature supply chain across all this stuff. It's just unbelievable.

And I'm assuming that that's not your first choice that you'd rather buy from a reliable supplier. Versus vertically integrate everything.

Everybody would always choose to buy something if it's easy to procure and not the only vendor in town. Everybody would always choose to buy. Nobody in their right mind would ever want to build in that case. It's just enormous effort and burden to maintain it and to QA it and to fix bugs and to pay human salary and manage humans. It's just a hard. It's like a hard thing to do.

Damn humans. You'll get through the human phase pretty soon. What percentage of the robot is manufacturing house, do you think? Like, rough order? Is it like 75%, 90% by whatever metric you want, weight or cost.

I mean, we probably look at how much of this like we're designing ourselves. I don't know the exact number off top of the head, but I have to think it's probably like 70, 80% of things we're designing at this point. For the robot, there is an advantage.

Of controlling quality and pulling out overhead or margins out of the vendors. I remember I was visiting Elon Shop in the early days and he was in this conversation by some supplier because a lot of these suppliers are all defense, aerospace, and they're just extraordinarily expensive. And he was like, screw them. We'll just make it ourselves. Was the attitude.

I don't know if that's what we. We've been around for two years. So most of the engineering decisions around the bill of materials and the supply chain would have been, let's get speed by getting parts in the robot, get robots to AI, you know, AI engineers, controls engineers, let's get work done. So we would have made those decisions quite quickly to outsource that work to get robots up and running faster. So, and I think longer term, we would have worked the supply chain to own it better to have less risk, to reduce, like the margin profile across all the, you know, supply chain. But there's just like, yeah, I think we thought that was easy. It became like a bloody hell.

So what's the flip side of this? The thing that you thought was going to be hard and turned out to be easy. Would it maybe be the AI integration? When you started, did you imagine you're going to have to build out your own AI models?

We still do a lot of our own AI models here, which is like, maybe not super well known, but like, but we do. We have a whole AI team. We do most of the lot of work ourselves. I would say across this, we leverage some group like OpenAI really work with on new models and stuff and use their VLM for some things. So I think and that's been really helpful. I think AI systems is probably the one that's been like wow, this is really working. And you think about this split of what percentage of the things are you going to have to hard code in code to go do, which is more classical controls and heuristics. What percentage of things can we do through neural networks? I would have thought going into this, I guess my thoughts two years ago is a large percentage of the stack would be written in heuristics and code. Call it 95, 90%. And then as we are able to write neural nets for those that are AD equivalent performance, we will just take that level of code or C and remove it with a neural net and it's kind of been flipped. All the neural nets is working for a small team with we don't have a ton of tooling and infrastructure and things almost right off the bat with everything like using SLAM or perception systems and object detectors and planning and speechless piece reasoning, everything. The stack like the high and low levels have been working incredibly well. And so yeah, I think it's pretty magical. It would take us such a long time to code all this stuff.

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Yeah, we're like literally talking to a robot to go do something and it does it wonderful.

So what's like the most interesting thing you've had figure do for you?

Um, I mean, we can literally talk to our robot and it can like do tasks right now, which is like, unbelievable. I mean, like, the end state for this is you really want the default UI to be speech. You just want to talk to the robot. I want to. When I'm like working on.

Yeah.

Another. Another way to think about it is like, I'm on the robot. I'm like, you know, one way to do it is like I pull my phone out or my laptop out and I'm like, you know, opening a terminal and trying to get a command to the robot to go do something. And the robot's standing right there listening. I'm just like, it just wants to be talked to. You just want to just put all this stuff away and just be like, figure two, go do this. We're doing that now. And it's just magical. It's really clear that the default UI for humanoid robot is going to be speech. You really just want speech to speech reasoning to get really high and you ultimately, at the end state, want to talk to the robot and have it be able to learn as it's in the environment through vision and sensors. And you want it to get better over time. And it certainly seems like they were heading in that direction.

Yeah, I remember Rethink Robotics used to move the hand for them and say, do this again. But yeah, I mean, speaking to it and showing it what you want is if it doesn't understand, can we talk about robotic safety? I mean.

How.

Where are you on safety? And how do you make sure there's no errant firmware upgrade that turns robots into overpowered slayers? I mean, there's enough dystopian Hollywood movies. I don't want to bring them forward. But how do you think about safety? How important is that? Where does Asimov's Laws come into your mind and practice?

There's like many ways to think of those. We have this system safety engineering side of making the robot actually really safe. When it walks around the facility and does things where it's next to humans, there's a whole architecture, it's actually designed from top the bottoms up to make that a safe system, that ultimately we can put a safe hardware system into the world and it will basically react like we would want it to in all these different various conditions. And I think that's just one piece. We needed safe hardware around humans. There's another piece of cybersecurity and other things of we don't want anybody to have root access to the robot and be able to take command of robots and to do potentially bad little malicious things with them. And then you have other things like what happens on the onset of AGI, how does that really impact the safety of the robot? So I think there's many things we're doing here as it relates to the low level read only firmware on the robot, as it relates to what is the lowest level code sitting at the robot that can't be overwritten, say more akin to the, you know, three laws.

I mean, it's pretty cool. You get to think about, you know, the laws of robotics you want to instill in your robots.

Yeah, I think you got, I mean a lot of this stuff is like, if we don't think about this now, we have to do like full architecture rebuilds which affect the system quite a lot. So we kind of have to start thinking about all this stuff now to design.

So help me understand this. So right now, can a robot mechanically have enough velocity and torque to harm you? Because a lot of the sort of automotive robots, you know, they put cages around them and so forth. And there have been designs where it's like, okay, I'm just going to make the robot so it can't build enough velocity, it can't outrun you, it can't, you know, sort of, you put that kind of a constriction on it.

I mean, the robot's like 150 pounds, you know, it certainly has enough like gravitational potential energy, no matter what, to hurt you. So if it fell off a bunch of stairs and was falling on you, it certainly could hurt you. So no matter what I think whether even has torque sensing applied so it can't hurt you next to you, there's certainly a scenario where it could be harmful no matter what. So I think yes, this is for sure a system that can be harmful to humans if not designed properly or it has a certain episode or fault on the robot that somehow is next to a human and hurts them. So this is something that has to be done very thoughtfully from the beginning as system safety architecture. And then we have to gradually prove out performance of that safety system over time through incremental approach. So our first robots in the work cells in the workforce will be isolated from humans. If humans enter those work cells, we will shut the robot off. Over time we will go from there to fully next to humans collaboratively. And that will be like a gradual time frame.

Best guess is that two years, five years.

I actually don't. We're mostly concerned on trying to get the performance up of the system in the first few years in the reliability. That's probably the hardest thing we have to do. Do you want to go and see a robot working full. Whether it's, let's say you have a light curtain, an artificial cage or other robot. I think solving a collaborative robot next to all humans is super solvable. I think the hardest hill we have now to climb, which is harder than that, is getting the robot to do end to end work every day without failure. It makes local failures. Sometimes I miss an object, but I go and regrab it. So we can fail locally but not globally. We have certain performance outputs. Let's say we go into a warehouse, they're going to have a certain amount of output per day of packages or whatever they're going to do of SKUs they'll need to go hit. If it's a bunch of robots doing that, they'll still have those same performance goals. So we know if we miss a package, we just got to deliver it the right way on time. So I guess what I'm trying to say is yes, I think we'll solve this decade humanoids around humans and interacting with them closely. But before you ever see that, you'll see it's like the analogy of Waymo, right? You saw in Waymo in certain permitting areas of San Francisco before you saw in other cities.

Sure. You show it with the practice drivers in the seat watching everything.

Yeah, you've seen it with everything, even like Autopilot, right. Like you've seen it on highways doing it well, as they're beta testing the new software updates for other types of concept operations. So I think for us, we're just, we're at this period now where we need to prove that it can work and do useful work, even on a confined perspective. And then over time, like, yeah, we got to build in the right system safety certification almost to like, make sure it's ubiquitous, it can walk around humans, it can like give items to humans. It can, you know, and we'll have certain safety precautions we do next to humans to make sure we're not operating at full torques and full speeds. Next to humans.

Do you think Asimov's Laws would, should actually be incorporated? You know, they're pretty fundamental. You know, don't harm a human or do something that causes a human to be harmed or by, you know, not do something that by, by not taking action causes a human to be harmed. I mean, this sounds pretty fundamental, but it sounds like that's the AI layer.

Yeah, there's certainly a select number of really important, do not override read only instructions that need to live on the robot that can never be altered. That's like for sure.

And then do you imagine figure. I mean, so one of the biggest advantages of having a robotic workforce is that they can all learn. When one robot learns a task, they all know the task. And that requires sort of a, sort of central control. Is that going to be, you know, is that like one central control for a BMW plant or is that figure on a global level where there's a sort of mission control that is watching all robots and learning from them?

Yeah, you sorry for us, we would want robots as a fleet doing continuous learning and continuous training on that data set. So it'll be a situation where we have millions, if not billions of robots on the planet, hopefully someday that are all continuously learning as a group. We're doing offline training on that. And then the robots are basically getting smarter collectively as a collective intelligence, that's for sure. What's happening in the direction we're heading towards. What's so powerful about this is humans. My kids, they learn how to walk, they learn how to walk. They're mostly failing. Learning what not to do in some ways and learn new things. That takes a lot of time. But once we know things really well, we really don't forget. Rarely do we forget how to walk or certain things. And that's happened throughout history. But most of the time we really don't forget. So for robots, one of the biggest advantages we have is once one robot Learns a certain task. Every robot in the fleet will know this. So it'll be very exponential in terms of the amount of new. Almost like the Matrix. Robots will learn through human demonstrations and through reasoning how to do something. And once it's been able to demonstrate that several times and we've been able to close the loop on that, okay, that's worked really well. And there's a reward system, every robot in the fleet will know this.

That's why I think future surgeons will best be done. Surgery will best be done by robots, where one robot is seen millions of different surgeries and can be the best and reliable surgeon out there. Can we wrap up a conversation, just talk a little about jobs? Because I have to imagine that people are still fearful about robots taking their jobs.

We have really good feedback so far. Our goal is to really be able to do a lot of the jobs that are not desirable by humans. I mean, you mentioned earlier in this podcast, we had, like, 8 million U.S. jobs that people just don't want to do. We want to do those jobs, and we want to do the things right now that are potentially harmful and dangerous for humans to do. And a lot of those jobs have very high unemployment rates, very high, very low retention, really high, low retention rates. And we want to start trying to do those and trying to help automate. And we've been doing that as a world for several centuries. My family was farmers. At some point, we had half the world was farmers.

I grew up and I was like, 80% of the world was farming.

Farmers. Yeah. Like, everybody was farming. And now, you know, like, if you want, you know, not very many. So, like, you know, I grew up on a farm. Like, that's like, you know, and nobody's mad that we're all not farming. Like, you know what I mean? Like, nobody. Like, I'm like, you know, I'm not out there being, like, 80 of everybody should be farming right now. Like, that wouldn't be great, right? For all, like, plowing fields and harvesting corn and soybeans. Like, that's not. I don't think that would have been a productive state for humanity in 2024.

Do you remember when the Fukushima nuclear reaction reactor and then DARPA had the DARPA Robotics Challenge? Have you ever seen those videos where the robot was just laughably incapable of opening a door or climbing steps? I mean, I think that's the. Is the Defense Department and the government a client or near client? How do you feel about plugging into that world?

Yeah, we won't do anything defense Related at all. It's like in our manifesto online we think the civilian market is just orders of magnitude bigger than defense. And it's not in our interest to build a war machine of any kind either. Non kinetic war machine. So we won't even have conversations, we won't take phone calls with anybody in this area.

Fascinating. How do you feel about civil police and security right now?

No, we're not touching any of this stuff. We don't want to give any requirements that are needed to have the robot produce harm.

I respect that.

You know, to like any humans. Like our goal is, our goal is to do work. Like we want to do work. We think that frees, we think it significantly helps the economy and helps lower goods and service prices and do good work for the world. And I think it's much needed. Like, I think so. Like we're putting all of our eggs in that basket right now.

What industry do you see next?

I mean you basically have, I mean.

It'S every industry but where do you think you want to play? I mean you're probably getting a ton of solicitations, right? Yeah, we have taking it a step at a time.

I mean these companies are big. I mean these companies we're talking to like are massive. We could ship thousands of robots into these groups. So for us we'd rather work with only a couple of groups right now and do that really well than like open it up to hundreds of groups at the moment. But at some point in the coming years we'll sell to anybody. And we're starting our production line next year. So we'll start with a few. As we have engineering really close with those customers and making sure that the product really works well. There'll be a bunch of bugs and process improvements we need to make to make this a well oiled machine for the market, which we want to do with those customers now. And as we get to some level of product maturity, we'll branch out to more and more customers. And something that we're going to spend more and more time on is also robots in the home.

When can I own a robot in my home? Because I'd like to put my order.

Now every six months that goes by. I go to a couple folks here and I say the timeline for us getting home is accelerating. And one of the things that'll be really helpful for us getting into the workforce. It'll really help us get system reliability up, safety up and the cost down and volumes up for manufacturing. It'll really help us in the home because you really want, you'll probably have an order of magnitude pricing collapse going into the home from the workforce. And you really need economies of scale to get there and you really need a safety certified system system in the home. So you need a really safe product in the home. So we are using the workforce in a lot of ways to bootstrap that vision for us. But I think from a performance perspective, I think we'll start doing early work in the home. And then I think the home for us is an area where I think it's accelerating in my mind every. Every six months that we.

Okay, but give me a guess. Is it three years, five years, eight years?

I would say within the next three years we'll definitely have robots piloting homes. Yeah.

Nice. Okay, I want to volunteer. Early on I'll pay.

Probably start with some rock homes here in our facilities and start getting the bugs worked out, understanding how the system architectures all work. But I'm interested in seeing what problems we face that we're not prepared for that are limiting our ability to get in the home long term as well.

Amazing. So last question for you, Brett. Manufacturing. You're scaling up. You're building a manufacturing plant for figure?

Yeah, we're actually starting our production line next year.

Where's it going to go? Where are you going to do that?

We're going to do that here in California, close to engineering. So we really work out the kinks on more of a traditional pilot manufacturing line or production line. And then from there we'll start announcing our intentions for where high rate manufacturing will look. But our production line is being. We're starting to design it as we speak. We'll have robots coming off the production line next year in 2025.

And you're scaling it for what kind of volume you think.

We'll start with hundreds of robots and then thousands. We really want to get the. What's more important for us than just like having like you know, here's 3,005, 4,000 robots out. Is getting the process really dialed in for how to do that and then also making sure those robots that we, that we're producing are working really well. There's a situation where anybody easily can get into where we have too many robots that don't work well and you're basically in this recursive loop fixing them, always having them down. Volume doesn't really help, so you really need the system reliability to be at a certain point and the performance of those robots then into the use cases to be pretty high. I do not feel like building thousands of robots is. It seems super tractable to go do. Now, building like hundreds of thousands of robots is a different story than building millions is a different story. But building like 2,000, 5,000, 10,000 robots is just. At this point, we're building one a week, so it might seem. And then we'll start building one a day, and then we'll start building multiples a day here next 12 months. So I think. But it seems like pretty straightforward path. I mean, we make cell phones almost by hand in the world, Make a few billion a year. This is more complex than a cell phone, but far less complex than a car. So, yeah, I feel like the path to making thousands in the near term is just not super difficult. What's the difficult part is making those thousands to a point where they're really useful and really work. That's the name of the game for me.

Brett Adcock, I'm a huge fan of what you built and excited for figure 2's roll out into the world. And actually what I saw with figure three is absolutely gorgeous. So excited for that as well. Thank you for all the hard work you're doing. And, you know, I do think this is a way of uplifting humanity and creating an age of abundance. So much appreciate it.

Let's do it, Peter. See ya.

See you, pal.