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

In this episode, Brett and Peter discuss Figure’s 2nd generation Humanoid Robot, Figure’s collaboration with OpenAI, how Humanoid Robots will impact jobs, and more. Recorded on Aug 13th, 2024 Views are my own thoughts; not Financial, Medical, or Legal Advice. 02:16 | Company Unveils New Humanoid Robot 36:26 | The Future of Robots and Work-Life Balance 48:24 | China's Robotic Revolution is Coming Brett Adcock is an American technology entrepreneur and the founder of Figure, an AI robotics company building a general-purpose humanoid robot. Previously, Brett founded Archer Aviation, an urban air mobility company that IPO’d at $2.7 billion. He also founded Vettery, a machine learning-based talent marketplace that was acquired for $110 million. Learn more about Figure: https://www.figure.ai/ Learn more about Brett: https://www.brettadcock.com/ Follow him on X: https://x.com/adcock_brett ____________ I only endorse products and services I personally use. To see what they are, please support this podcast by checking out our sponsors: Get started with Fountain Life and become the CEO of your health: https://fountainlife.com/peter/ AI-powered precision diagnosis you NEED for a healthy gut: https://www.viome.com/peter Reverse the age of your skin with Oneskin; 30% here: http://oneskin.co/PETER _____________ Get my new Longevity Practices 2024 book: https://bit.ly/48Hv1j6 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: Tech Blog _____________ Connect With Peter: Twitter Instagram Youtube Moonshots Learn more about your ad choices. Visit megaphone.fm/adchoices

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Peter Diamandis?

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

• 0:05Speaker 01

  So inspired by what Elon's done in 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 directionally heading in the right direction.

• 0:18Peter Diamandis?

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

• 0:21Speaker 01

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

• 0:30Peter Diamandis?

  I mean, listen, you're amazing, but the star of the show here is Figure 2. So do you mind if maybe we open up this podcast with a, a quick look at Figure 2?

• 0:39Speaker 01

  Yeah, sure. Um, yeah, yes, well, I guess welcome, welcome to Figure.

• 0:46Peter Diamandis?

  Everybody, Peter Diamandis here. Welcome to Moonshot. 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, uh, 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, uh, 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 2 robot.

• 1:32Peter Diamandis?

  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.

• 1:44Speaker 01

  Yeah, Peter, thanks for having me.

• 1:46Peter Diamandis?

  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 2. And I have to say, you know, I said it to you there, uh, the speed at which you're iterating designs is pretty amazing. Uh, and I didn't feel that kind of an energy, uh, since the early days of me seeing, you know, SpaceX back at Falcon 1 days. So congrats on that.

• 2:14Speaker 01

  Yeah, thanks.

• 2:15Peter Diamandis?

  You know, I think everybody really, I mean, listen, you're amazing, but the star of the show here is Figure 2. 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?

• 2:32Speaker 01

  Yeah, sure. Yeah, let's make sure it's not being used, but let's give you guys a quick tour of the office. Yeah, so welcome, welcome to Figure.

• 2:44Peter Diamandis?

  Thank you.

• 2:45Speaker 01

  So we're, yeah, 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 2, 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, you know, several of them here now on the floor. So here's, you know, a quick look at Figure 2 robot that we're, I've already started doing some tests on right now.

• 3:22Peter Diamandis?

  Amazing. I know your team is about to activate it. If you're going to say that the principal differences between Figure 1 and Figure 2, I mean, just for folks, you know, the company's like two years old or less, right? You've gone from like zero to infinity, super fast. What's the, what's the difference between, what upgrades did you make in Figure 2 here?

• 3:40Speaker 01

  Yeah, I mean, there's, it's, there's several. So

• 3:46Peter Diamandis?

  Your top five?

• 3:47Speaker 01

  Yeah, so I think first is we tripled the amount of CPU and GPU onboard, just for more overall compute and inference. The second is we almost doubled the battery to about 2.3 kilowatt hours. It's all onboard 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 like 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

• 4:23Speaker 01

  Take loads, which is opposed to kind of how we do the first generation robots. This would be like more akin to how you do it like at aviation. My last company Archer, we, you know, the skins on the aircraft take the loads of the vehicle. So I think it'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.

• 4:47Peter Diamandis?

  Where are the cameras on the robot here?

• 4:50Speaker 01

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

• 4:54Peter Diamandis?

  That's good. And I assume that the extra skeleton helps you reduce overall weight of the robot?

• 5:00Speaker 01

  Yeah, it's basically like a, like overall, like the parts will get a little bit stiffer as they get a little bit wider. We found that having one structure for both crash loads and stiffness is the right ideal mass trade. 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.

• 5:30Peter Diamandis?

  And the hands, have you made improvements on the, on the hands on the?

• 5:34Speaker 01

  Yeah.

• 5:36Peter Diamandis?

  Hold his hand and show us what it looks like to do it.

• 5:38Speaker 01

  So our, um, uh, fourth generation hands now. And we've made like quite a lot of improvements over the previous generations. Better sensors, better packaging, better for mass, better strength, better speeds of the fingers.

• 5:56Speaker 01

  Overall, just better dexterity and control of like fine grain manipulation that we're doing onboard 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.

• 6:13Peter Diamandis?

  Amazing. And it's standing, what, about five, six, five, seven behind you?

• 6:18Speaker 01

  Five, six.

• 6:19Peter Diamandis?

  Yeah. Amazing. All right. Thank you for the quick glass. You want to pop back into your conference room?

• 6:26Peter Diamandis?

  I love the beams up there for carrying heavy weights. So how many, how many, how many figure robots are up and operational right now inside the facility?

• 6:36Speaker 01

  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.

• 6:47Peter Diamandis?

  Amazing. You know, Brett, one of the things that I saw when I was there, you know, you showed me Figure 1, and then Figure 2, which hadn't been released yet, and then drawings for Figure 3, which I won't talk about, which looked 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 like get to something and then fix it and then sell it for a long time. Doesn't sound like your strategy is that.

• 7:24Speaker 01

  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 lot to bite off in like the first version hardware and get that right.

• 8:06Speaker 01

  It's just too difficult. I mean, it's like getting the iPhone 1 right and getting everything okay. And I had the iPhone 1 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 Rotes are 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 hardware iterations where we're basically looking at different heuristics of things that we need to mature over those hardware continuum, and making all like necessary improvements so that the hardware is at some point very mature. And I think our first generation hardware

• 8:50Speaker 01

  Figure one was mostly trying to get roughly the architecture trades right. So all the details of like the engineering system for, you know, 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 pack, like what type of cell? Is it cylindrical? Is it presmatic pouch? You know, how are we going to, how are we going to pack those? Thermal propagation, like all those different trades you have. That's just the battery alone. Then you have like the rest of the whole system. So that, you know, there becomes like an order of like a hundred or 200 decisions you have to make to go out and build a robot. And, you know, you don't want to have to be in a situation where you have to get all those right.

• 9:36Speaker 01

  And I think we got most of those right on figure one. We did even better job on some of those decisions on figure two. Figure two 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 with those firmware embedded systems, control software, all the hardware systems on board, you know, the actuators, electronics, wiring, battery systems, cameras, sensors. So, and then, you know, so we think we got roughly to like feature complete, hardware complete on figure two. And so we're really excited there. And then, you know, future generation for us will be,

• 10:18Speaker 01

  how do we get the costs down by well over order of magnitude from where we're at now? And how do we get the ability to manufacture an unprecedented scale that we've never seen before in robotics?

• 10:28Peter Diamandis?

  I know that, you know, 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. You know, for everyone to own a humanoid, and I want to get into this for a quick moment.

• 10:48Peter Diamandis?

  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 humanoid robots as a ultimately converging on a price per kilogram of total weight?

• 11:09Peter Diamandis?

  And what do you think, you know, 10 years out, 20 years out, these things will actually cost?

• 11:15Speaker 01

  It's been the last, like, it's been all year now, looking at how cheap we can make these robots. And the answer really lies in like a bottoms up analysis of the entire build materials bomb cost. Where we take basically a list of, call it roughly a thousand parts, and we like start itemizing that down, and then we start understanding at real scale, how are we going to procure those parts, whether we build or buy, and what contractual, you know, volume estimates we can get with prices there. I feel-

• 11:54Peter Diamandis?

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

• 12:00Speaker 01

  Yeah. Every consumer device or car that we know of has been, is basically like follows a very high correlation with manufacturing volumes. So the only real way to get, you know, like 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.

• 12:24Speaker 01

  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.

• 12:35Peter Diamandis?

  That's amazing, because if I were going to, you know, lease a $20,000 car, you know, that's costing me like 100 bucks a month at most, and so, you know, why not get two?

• 12:50Peter Diamandis?

  You think at the end, yeah.

• 12:53Speaker 01

  I was saying, especially if it makes you money, like if it can go out and do work. And things like, you know, like it 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 like be like a real utility here, I think, yeah, why, how many? I guess, yeah, how many would you want that could, if it can make you money?

• 13:11Peter Diamandis?

  Real quick, I've been getting the most unusual compliments lately on my skin. Truth is, I use a lotion every morning and every night religiously called OneSkin. It was developed by four PhD women who determined a 10 amino acid sequence that is a senolytic that kills senile cells in your skin. And this literally reverses the age of your skin, and I think it's one of the most incredible products. I use it all the time. If you're interested, check out the show notes. I've asked my team to link to it below. All right, let's get back to the episode. Yeah, I mean, when we first spoke,

• 13:48Peter Diamandis?

  I remember you said something that was kind of shocking, but in retrospect makes sense.

• 13:55Peter Diamandis?

  Correct me if I'm wrong, but I think you said your estimate, there would be a market by 2040 for as many as 10 billion humanoid robots. Do you still hold to that?

• 14:07Speaker 01

  They're like, if these robots can do everything a human can, I have to think that we'd be able to put

• 14:16Speaker 01

  three to five 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, you know, walking the dog, getting coffee, doing errands, doing laundry chores, like, I mean, every day I go home and clean up toys, right? So like, there's just like, like I could have a robot just, just clean up kids' toys two, three hours a day, every day, no problem, like, it's like endless work every day.

• 14:53Peter Diamandis?

  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.

• 15:26Speaker 01

  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, you know, 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.

• 15:58Peter Diamandis?

  And please, please plug me into a higher, more kilowatt hours.

• 16:04Speaker 01

  Yeah, like 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.

• 16:23Peter Diamandis?

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

• 16:52Speaker 01

  We do see ourselves as an AI company in a 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

• 17:15Speaker 01

  Dream that we're all having here now, a figure where one day we have these robots out in the world, um, doing like 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.

• 17:40Peter Diamandis?

  And amplified by robots and AI, you know, to do far more per, per unit time than ever before.

• 17:52Speaker 01

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

• 17:59Peter Diamandis?

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

• 18:21Speaker 01

  Well, I think one of the interesting things about humanoids is, um, we can put these robots in, like 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. We don't need to go build like, um, new systems and new electronics and everything else for the robot to, to work within. It can just, you know, um, like a special purpose machine. We go in there, we gotta 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, uh, the next day. Um, and, and if we have robots, ultimately we'll build robots themselves from a manufacturing perspective.

• 19:01Peter Diamandis?

  I was gonna ask you that. That, that iterates very quickly down to, uh,

• 19:05Speaker 01

  Yeah. I mean, you can basically bring, I mean, 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 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,

• 19:49Speaker 01

  trend to zero in the limit and

• 19:51Peter Diamandis?

  And GDP spikes to infinity.

• 19:54Speaker 01

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

• 20:03Peter Diamandis?

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

• 20:24Speaker 01

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

• 20:28Peter Diamandis?

  In your, 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, and I buy that, you know, I, I love the idea of robots doing the jobs that are dull, dangerous and dirty, cleaning the toilets, you know, cleaning the rooms, you know, 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.

• 21:09Peter Diamandis?

  And, um, but the question ultimately is, I also think these robots will, you know, I'm, I've got a niece who's, who's a plastic surgeon and I'm like, don't go into that, you know, robots are going to become our ultimate surgeons.

• 21:24Peter Diamandis?

  Is there any job you think that, that robots aren't going to be able to take on if we want them to?

• 21:33Speaker 01

  It certainly seems that over time.

• 21:38Speaker 01

  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 in, you know, we've been seeing that though with technology over the last several centuries. But I think we're seeing that very like much accelerate this slope of that curve is accelerating. And it's accelerating in really interesting places with, you know, with large language models. And it's almost accelerating in the wrong, a different direction than we probably have thought like, you know, 10 years ago.

• 22:10Speaker 01

  So I, yeah, I happen to think that over a long enough period of time, we'll have automation fit with our physical or digital automation. That will be able to do is probably the majority of things that humans can do today.

• 22:22Peter Diamandis?

  Yeah, I just to note, I checked this morning and there are 8.2 million job openings in the U.S. Right? So it's not like there's no need for, there's no jobs available.

• 22:39Peter Diamandis?

  Yeah, 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 is an investor, very proud of that. That's a lot of money.

• 23:02Speaker 01

  Yeah, we raised 675 million.

• 23:05Peter Diamandis?

  Yes, at a 2.6 billion valuation.

• 23:06Speaker 01

  Yeah. Yeah.

• 23:09Speaker 01

  Yeah, we're super proud. We have like, we brought a lot of new investors, including OpenAI, Microsoft, Nvidia.

• 23:17Speaker 01

  And yeah, great to have your support.

• 23:20Speaker 01

  It was, it was, it's good. It's going to give, it's giving us the ammo now to really take the next step of our, in our mission of rolling these robots out commercially and making them really viable. And that's, that's really where we're at right now. It's like, how do we take? You know, we're putting out cool videos and that's great, but like the next big step is like, 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 like 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.

• 24:04Speaker 01

  And we couldn't be more excited. It was, it was both hard because we were like outside of our comfort zone in the office. Also just like really energizing when we got back, we're like, we can do this. Like this can be done.

• 24:15Peter Diamandis?

  It was reinforcing, yeah.

• 24:18Speaker 01

  Yeah, we're like, we're all, everybody here is fired up that like, we get a chance to try to go do this over the next few years. And I didn't mean like post financing here, like what's holding us back now? We have, 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 like GPU hardware, other simulation work. We have like the world's best AI robotics team ever put together. We have Figure 2 now, which is, I would say the probably top humanoid hardware in the world. And we're doing some of the best AI learning work in the world. You know, and we see this like small light in the tunnel, which is like, this is what robots can really do.

• 25:02Speaker 01

  So we're all just working really hard at that at this point.

• 25:05Peter Diamandis?

  Would you say that what you're doing now is only possible because of the state of AI? I mean, is artificial intelligence and, 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, I built robots in high school and college. I didn't call them robot, I mean, call them robots, but they were nothing in comparison. But is it now, is it AI that made you say, now I'm going to, you know, commit? Because you put, you put like a hundred million dollars of your own money into the kick this thing off, right? Which, which is a significant step for an entrepreneur.

• 25:47Speaker 01

  I think there's like a few different things. I mean, one is like the whole AI ecosystem. It's not just like, it's not just the models. It it's, you know, the the it's the overall infrastructure for for training, for inference and deployment. Like deep learning algorithms that can like support like, you know, a large scale imitation learning and reinforcement learning. So I think there's just like a it was several billion blocks there on the AI side that are all like maturing to a point where you can deploy these policies like embedded, you know, embodied policies in the in the world and they work, which is pretty unbelievable. Like, I just got back from taking a Waymo like last month in the city.

• 26:31Speaker 01

  It was just like very special. And it's just like, you know, this pretty clear it can just drive like a human can with enough data. And the same with our robot. When you see our robot in the facility doing, you know, kind of the new generation work that we're working on now, it just feels magical. I think a separate thing is like, the whole like hardware system. It's really hard to know if it was like 10 years ago, this was really possible with like torque density of actuators.

• 26:59Peter Diamandis?

  Batteries.

• 27:00Speaker 01

  Battery systems, energy density there. I I would happen to think that, you know, like the best humanoid robots 10 years ago were all hydraulic systems. Those are like 3,000 PSI systems.

• 27:12Peter Diamandis?

  Leaking hydraulic fluid all over the place.

• 27:14Speaker 01

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

• 27:43Peter Diamandis?

  And then I do think it's I do think it's a convergence of a whole bunch of things. I mean, the theme for my abundance summit next March is is convergence. And thank you for for joining because I think I think what you're building is the is the exact, you know, sort of principle example of converging technologies, making new systems and new business models possible.

• 28:09Peter Diamandis?

  How did you, how did you connect with OpenAI in the first place? I mean, that's, that's a big, it's a big step.

• 28:16Speaker 01

  Yeah. I got introduced to SAM a few years back and we got to know each other a lot better and spent, spent a lot of time together in 2023 and, you know, ultimately they wanted to get back into robotics and specifically around like AI for, you know, embodied, 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, they're supporting us on that. That's been, I would say so far 10 for 10 system. We happen to think of the, the best, they're the best, you know, vision language models in the world.

• 29:01Speaker 01

  They're the best implementators of those models in the world. And, you know, we're trying to push the boundaries now on how to push that work as hard and as far as possible in the robotics space, which we're just, you know, we've just been starting in the last like several months.

• 29:19Peter Diamandis?

  I, I imagine there's a lot of benefits for them as well. I mean, in bodies, mentioned embodied AI, there's a lot, there's some theories that say, listen, we're not going to get to AGI unless we can embody AIs to understand, you know, 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?

• 29:52Speaker 01

  I think, I think it's becoming more and more clear that

• 29:58Speaker 01

  some of a, some level of like output actions in planning better is, is important to take their kind of our next step in intelligence.

• 30:08Speaker 01

  What we're trying to do here is we're trying to help complete that.

• 30:15Speaker 01

  Last leg of like actions and reasoning that that we're seeing from kind of some of the best kind of world models that we're helping to work on here internally. At the end of the day, if you can like talk to a robot and it can output actions like useful actions in the real world.

• 30:35Speaker 01

  That would just be an incredible technology for the world that we're trying to work on here, whether you want to call that, you know, advanced AI, AGI, whatever, like that's just such an important

• 30:48Speaker 01

  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.

• 31:03Peter Diamandis?

  I mean, I have to imagine, you know, GPT four O and or you know, the multimodal versions of GPT four are key for you, right? Having 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 was that the conversation you had going on with Sam? Was he sort of like,

• 31:30Peter Diamandis?

  you know, sort of, was he part of your inner conversation of what's going to be possible in the future?

• 31:35Speaker 01

  I think, I mean, like, one of the biggest breakthroughs we have is we have like,

• 31:41Speaker 01

  with LLMs and, you know, VLMs, more specifically, we've had like this like semantic grounding that's

• 31:50Speaker 01

  has occurred in robotics. We have like the world's knowledge, like in a, in some way, like a, you know, analogy, like in a zip file for the robot to access and understand. And that bridge from like robot to human was never really here before. If you want to talk to, you know, autonomous car and say, drop me off in the, you know, on the curb over there on the right, there was no, no real semantic bridge for that in the world. And, you know, arguably, we have that now. We have like the semantic bridge has been built in the world. And what we're really lacking is this like, like, I guess, reasoning and planning and perhaps actions from that system to for us to provide useful work in a robot. And

• 32:34Speaker 01

  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, like, you know, with different technologies all over the world in AI, but like, one of the things people are not talking a lot about is like, what does this mean for robotics? This means like a robot knows everything you're saying and knows, knows what you mean. And we have all this grounded in human level data, meaning we have all this like 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 we like, the way humans open up jars is pretty similar to robots or humanoid robots to open a jar.

• 33:19Speaker 01

  So like the affordances are roughly, you know, really high affordance levels as it relates to humans work. So this, this is like unlocks like an ability for humanoid robots to really tackle like general robotics, like how do we talk to robots and how do we output actions that everything a human can do? And there's, there seems to be at a point in time where we can really try to see if we can crack that.

• 33:45Peter Diamandis?

  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, 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's got contextual knowledge and, and geometric and what do you call positional knowledge.

• 34:14Peter Diamandis?

  How far is that?

• 34:15Speaker 01

  Yes, this is all realistic today. It's not, 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, you know, characteristics and how it will feel to grab and.

• 34:35Speaker 01

  All of this is in the weights.

• 34:37Peter Diamandis?

  I work on longevity because I want to see as much of this stuff as I possibly can. Yeah, how, 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. What's your, what's your, and you have to balance family as well, what's your work week like?

• 34:58Speaker 01

  Yeah, I would say I work almost like basically almost seven days a week.

• 35:03Speaker 01

  There's really no time I'm like really not working.

• 35:07Speaker 01

  Except with home, like with the, you know, wife, wife and my kids.

• 35:13Peter Diamandis?

  How old are your kids? Do they, do they get what you're doing then?

• 35:17Speaker 01

  We just had a family day at, 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, they get it, they talk about dad building robots all the time and.

• 35:34Speaker 01

  You know, I think like.

• 35:37Speaker 01

  Yeah, it's, it's like.

• 35:40Speaker 01

  Yeah, I, I think.

• 35:45Speaker 01

  We actually had probably fifty kids here like last week. Like watching all of them like see the robots and touch them and. Yeah, we, like, it was pretty special.

• 35:58Peter Diamandis?

  You know, I, I think about that, my boys are, I have two boys who are eleven, thirteen. And I can't wait to have them see what you're building here.

• 36:08Peter Diamandis?

  And I, I think about the fact that their future and your kids' future are going to be. You know, if your numbers are correct, and I think that they are. They will be more prevalent than cars are out there. Right, there's like a million cars, like a million other vehicles, and we could see. You know, five to ten, 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, in five years, and his answer was interesting, he said, it's going to seem normal.

• 36:47Speaker 01

  Yeah, can I tell you something?

• 36:51Speaker 01

  Yeah, a bunch, you know, we have a lot of folks here have been around robots for a long time. They're like, um, yeah, as soon as we do something, everybody's gonna be in awe and then nobody's gonna care anymore.

• 36:58Peter Diamandis?

  Yeah.

• 36:59Speaker 01

  It's funny, sometimes that happens. We, we're walking, you know, figure two now around the office quite, quite frequently. And the first few times, like, everybody's just like stopped doing work. They're like fist pumping from the conference room or whatever, uh, taking photos. We have this like, we have this really crazy video where like, everybody is following 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 no, nobody cares. Like, nobody, it's just like, uh, oh, the robot's close to me. Like, uh, you know, like, it's it, like, uh, it's, it's pretty unbelievable how like, um, used to all the stuff we get, right?

• 37:33Peter Diamandis?

  Yeah, we adapt so quickly. It's like, it becomes boring. I remember the first time I, I got like a, one of the first Model Xs and, you know, the, the door wings come up and everybody's snapping photos and looking at it. And then it's a nuisance after that.

• 37:45Speaker 01

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

• 38:01Peter Diamandis?

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

• 38:23Speaker 01

  Uh, no comment, Peter.

• 38:24Peter Diamandis?

  No comment. Okay, okay. Can't hurt to ask. Everybody, I want to take a short break from our episode to talk about a company that's very important to me and could actually save your life or the life of someone that you love. Company's called Fountain Life and it's a company I started years ago with Tony Robbins and a group of very talented physicians. You know, most of us don't actually know what's going on inside our body. We're all optimists. Until that day when you have a pain in your side, you go to the physician in the emergency room and they say, listen, I'm sorry to tell you this, but you have this stage three or four going on. And, you know, it didn't start that morning. It probably was a problem that's been going on for some time, but because we never look, we don't

• 39:08Peter Diamandis?

  find out. So what we built at Fountain Life was the world's most advanced diagnostic centers. We have four across the US today. And we're building 20 around the world. These centers give you a full body MRI, a brain, a brain vasculature, an AI-enabled coronary CT looking for soft plaque, DEXA scan, a GRAIL blood cancer test, a full executive blood workup. It's the most advanced workup you'll ever receive. 150 gigabytes of data that then go to our AIs and our physicians to find any disease at the very beginning when it's solvable. You're going to find out eventually. Might as well find out when you can take action. Fountain Life also has an entire side of therapeutics.

• 39:53Peter Diamandis?

  We look around the world for the most advanced therapeutics that can add 10, 20 healthy years to your life. And we provide them to you at our centers. So if this is of interest to you, please go and check it out. Go to fountainlife.com/peter. When Tony and I wrote our New York Times bestseller, Life Force, we had 30,000 people reached out to us for Fountain Life memberships. If you go to fountainlife.com/peter, we'll put you to the top of the list. Really, it's something that is, for me, one of the most important things I offer my entire family, the CEOs of my companies, my friends. It's a chance to really add decades on to our healthy lifespans.

• 40:39Peter Diamandis?

  Go to fountainlife.com/peter. It's one of the most important things I can offer to you as one of my listeners. All right, let's go back to our episode. I am curious what you, how you think about,

• 40:53Peter Diamandis?

  about Optimus and Tesla and Elon. I know you have great respect for him as an engineer and entrepreneur.

• 41:03Peter Diamandis?

  What are your thoughts there?

• 41:07Speaker 01

  I just like have to be.

• 41:12Speaker 01

  You know, so inspired by what Elon's done in the last like 20, 30 years. It's been just unbelievable.

• 41:19Speaker 01

  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 directionally heading in the right direction.

• 41:30Speaker 01

  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.

• 41:40Speaker 01

  So I think they're going to be, you know, a really real competitor with us. I think the world needs them out there doing this. And yeah, I hope they do really well. Like, I think we're at a point in time where the the window has just opened now for humanoids to be possible. I don't think it was really possible 10 years ago. So, you know, it's it's going to be this, a little bit of a race here to get volumes out the door in terms of manufacturing and you get the AI training sets built and deployed on the embodied systems. And yeah, it could be like a more important time to make this really work. So, yeah, I think overall, just like very,

• 42:26Speaker 01

  I think they'll do very well and I'm very glad they're tackling it.

• 42:31Peter Diamandis?

  Yeah, I think when he walks into a new industry like that, he credentials it in a big way. Did 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, or, you know, ours is better. Is it, is it provide a little bit of that kind of like gamified motivation for the team? Or are you guys just open loop otherwise?

• 42:51Speaker 01

  I think we try to like make, we try to make all of our decisions from like a first order reasoning. Like what was the right decision to make? If, that's like super important for every company to really do is like, I think we have our own mission, vision, values, what we care about, 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.

• 43:13Speaker 01

  So I think that's like where we really ground ourselves and like the decisions we make. I think there'll be things that we

• 43:20Speaker 01

  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,

• 43:36Speaker 01

  we want to be winning and we want to compete, you know, overall, we. So yeah, I think, you know, our goal is to build the best humanoid robotics company in the world over here, I figure.

• 43:49Peter Diamandis?

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

• 44:00Speaker 01

  I think there'll be like, I think the, the way I look at it is, we need more like really real players. And I think Tesla's a really real player, like, I mean, you know, highly capitalized, great engineering team, heading in the right direction, like longer term, moving fast through those high rate iteration cycles and milestones that are needed for engineering teams to really prove out the product is getting commercially viable. And what we're lacking in humanoid space is that, like, we have a lot of groups that have been around for a long time around the hoop. And, you know, 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 like chasm, crossing the chasm here that's needed for, you know, this market.

• 44:44Speaker 01

  And I think Mini will die is trying to, you know, trying to, to bridge this. I hope that we live it, figure, but like, we have like 14 peaks to go climb now. We have like a, a very hard road ahead to go from where we're 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? We have like years, single-digit years to go prove that and get into markets. Like that's, we're charging forward as hard as we can to ship our product into customers and make it useful.

• 45:13Peter Diamandis?

  Yeah, I, I think, you know, the thing most people probably don't realize is you're only two years old, right? And what you put together in terms of team. And I think this last round of financing, if I looked at what, you know, if you had said to the average person or average technologist or venture capitalist, what's the advantage that Elon has for, for Optimus? It's, 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. Parody, if not in some places.

• 45:47Speaker 01

  It's like, it's such a, it's just like such a stupid argument, though, because like you could say that about every company that's 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. Those are already making a lot of 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.

• 46:09Peter Diamandis?

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

• 46:20Speaker 01

  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 we need, like, those are like requirements that are needed in our long-term roadmap and we just got to put all those pieces together very intelligently. And then not, not die in the market and make a product work.

• 46:38Peter Diamandis?

  Not, not dying is a great part of the business plan. Can we talk about China? Because I find that absolutely fascinating. So 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 another shipping costs. And so I'm, 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. You know, 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, of the Chinese robots? Are there any that seem like good competition out there?

• 47:22Speaker 01

  I just got back from China a couple of 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. Uh, they're just like, they're just trying to build and ship. Um, the work ethic was really high. Um, the, the sheer will

• 48:03Speaker 01

  of the country to try to be a good, be like, compete and win is really high. And, um, I was floored. I was just, I was like, man, you have, you have like-

• 48:14Peter Diamandis?

  Have you been there many times before?

• 48:16Speaker 01

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

• 48:50Peter Diamandis?

  I think they have to be. Um, you know, I used to, when I, I used to go to China every year and bring a, a group of abundance members with me and we'd go and, and visit all the top tech companies. And I remember the motto there was nine nine six, like a great lifestyle was nine AM to nine PM, six days a week. That was the work ethic. And, you know, everybody, China had the, had the reputation, uh, as being copycats, but, you know, 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?

• 49:24Speaker 01

  My experience, uh, has been that those folks out there wanna win. They wanna do it at the lowest cost. They wanna do it at the highest rate. Um, and they'll stop at nothing to try to, try to, try to be number one. And that's, uh-

• 49:41Speaker 01

  That's like, you know, think about like what startups are. It's like it boils down to like those key principles you start a company with that are successful. You 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. Is that, you know, like we said before, does that really matter that you have like all these things? Or is it, you know, is that is that is the ultimate crutch? And so I think I think there are going to be some unbelievable robotics companies come out of China. Just for the just because of the sheer number of products being worked on and with the will that I saw out there is second to none.

• 50:25Peter Diamandis?

  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.

• 50:45Speaker 01

  Yep.

• 50:45Peter Diamandis?

  So you you take Archer Public, which was an incredible success and congratulations on that. And after starting that and running that for a number of years,

• 50:59Peter Diamandis?

  you break away to found Figure. And if I'm correct, you know, 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.

• 51:19Peter Diamandis?

  But I think when I 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.

• 51:31Peter Diamandis?

  I mean, it was an extraordinary group of engineers from the top AI and robotics companies or tech companies out there.

• 51:42Peter Diamandis?

  Can you word of advice for for founders who are starting a company? I mean, you're you're a technical founder as well, which is important, right? But how did you recruit your team?

• 51:54Speaker 01

  Yeah.

• 51:57Speaker 01

  So my belief is that in order to ship like a really good, high-quality product, you need the world's best team there to go do that. Especially against like, like the difficulty level, we have a figure of like succeeding, like the odds of success are always like pretty low. So you need like, you need to give it everything you got. You need the best team here, you know, 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 charting to do

• 52:39Speaker 01

  to support like a really high-functioning team to build a product? And I spent the first year just basically headhunting all that whole team by hand. And like, you know, I was cold emailing and calling, drafted the offer letter, would give the offer letter, go do dinners, try to close onboard, you know, 30, 60, 90-day onboarding, bring them in, lead the engineering decisions in direction and, you know, work on those projects with those teams. Like, 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.

• 53:20Peter Diamandis?

  But in the early days, in the early days, Brad, 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, in one way, you had, you had Tesla Bot, I don't know if it was called Optimus back then, you know, and sort of Elon tends to suck the oxygen out of a room on the stuff that he does. You know, convincing people to jump where they were and join you. Was it, how did you do that? Because that jujitsu is really important.

• 53:54Speaker 01

  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 working in the largest TAM in the world. It's, you know, all under half a GDP is human labor.

• 54:15Peter Diamandis?

  Yeah.

• 54:16Speaker 01

  I'll fund the first several years. There's no funding risk in the near term. And

• 54:22Speaker 01

  You know, it's my second time going around building hardware and building hardware teams. So, you know, having done it, I think, decently well at Archer. You know, we're getting back in the unit from scratch, did even better this time in terms of like setting the right direction and mission, vision, values of the team. And then I just spent a lot of time with those folks saying, you know, come here. Let's go out and build this commercial. Let's, let's build the best organization we possibly can from early days. The early folks got all like founding, you know, founding member stock, which also was helpful and beneficial, got good salaries. I was, you know, funding myself. So it was like, you know, it was being a part of like a from scratch startup, but like almost like cushioned by my ability to self-fund it for multiple years, good equity. You know, by the time we had five or six people in the team, they were all superstars.

• 55:05Speaker 01

  And then that was like more to be risking for the next, you know, five or six folks that were joining. So was able to find folks that really believed in the space and believed in me. A lot of folks that used to work with me in my last two companies, a lot of folks that were new as well. I had a, you know, I had my, my first two people were folks that I've worked with that, you know, were like my first employee at Vettery, my early employees at Archer that I worked with, they were like, you know, so it was, you know, three of us, day one, basically, that have all worked together. Added some more folks from my old team at Archer, added some more folks from Boston Dynamics and other, other organizations that were really great. Soon enough, we had a dozen people that were superstars and their discipline. And then, you know, we walked our robot within 12 months of incorporating the company.

• 55:50Speaker 01

  So we worked really hard and got out of the gates pretty fast. And, you know, now it looks like, okay, you know, it looks, everything looks great then, but I was in a WeWork phone booth for nine months, making cold calls, trying to convince and talking to, you know, wives and husbands and telling them to convince their, you know, spouse to, to join. And it was, it was hard. Yeah, not gonna lie.

• 56:14Peter Diamandis?

  I, I, I bet. What, what did you give the, the, the chances of success back then, the early days, where to get to the point where you are now? I mean, if you had to give a thing back then, would you be like?

• 56:28Speaker 01

  Yeah, I would not have ever assumed that we would be out of place now. We, we've really. You know, we, I think there's the like, you know, I look at everything as like, how well is the product doing as it relates to like the roadmap into commercialization? We're still, you know, we're not like, we're not successful, like, we're not even like shipping.

• 56:44Peter Diamandis?

  Yeah.

• 56:44Speaker 01

  But like, I think that, but like, you know, we've been around for, I don't know, like 20, uh, you know, a little over, like a little over two years or something like that.

• 56:54Speaker 01

  Unbelievable, like the amount of the hardware that's doing the AI systems and working at Excalibur's policies. Like the, the, the, look at the humanoids now versus like 10 years, it's unbelievable. And we don't ever walk into the office and like things go backwards, like they always go forwards, like we're not like, you know, sometimes we have like a couple of days where like, you know, a couple of 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 like making pretty decent, like if, if folks are gone for a week, they come back and be like, this is a whole new company, it feels like. So like, you know, every, every week goes by, we're making pretty substantial impact. So I would say like, we far exceeded my,

• 57:37Speaker 01

  my early impressions of where we'd be at two years from now. Yeah, two years ago.

• 57:42Peter Diamandis?

  I'm curious, what did you think would be really hard that turned out to be easy?

• 57:49Peter Diamandis?

  Nothing easy, but was easier than you expected?

• 57:53Speaker 01

  Well, I thought, I maybe like the opposite will do. I thought like we'd be able to procure a lot of supply chain to go build a robot, like, electronics boards, motors, actuator systems, battery pack systems, cameras, like lights, like speakers, like we like speakers on figure two. I thought maybe go to Alibaba or go to Amazon and buy speakers, screens, like basically screens, speakers, lights.

• 58:21Speaker 01

  These things have to be about off the shelf, right? Like 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, why that should be that easy. It's it's still not that easy. Like, we're making custom speakers. Like, how is it possible that we're making custom speakers and 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. Like, we, we like, we make our torque cells, like, force cells here, which need, like, a flexure, the board, they need to be gauged, they need to be calibrated.

• 59:03Peter Diamandis?

  They be tested.

• 59:04Speaker 01

  Integrated, there's like, there's software and firmware on those on there, like, like, like, and, like, that's gotta work then at really high, high rates, like, um, it just still boggles my mind, there's no, like, mature supply chain across all this stuff. It's just unbelievable.

• 59:17Peter Diamandis?

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

• 59:26Speaker 01

  Everybody would always choose to buy something if it's, like, easy to procure and, um, not the only vendor in town. Everybody would always choose to buy. Uh, 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 like, it's just a hard, it's like a hard thing to do and, uh...

• 59:51Peter Diamandis?

  Damn humans. You'll, you'll get, you'll get through the human phase pretty soon.

• 59:55Speaker 01

  Yeah.

• 59:56Peter Diamandis?

  What, what percentage of the robot is manufacturing house, do you think? Like, rough order? Is it like, 75%, 90%? By whatever, but whatever metric you want, weight or cost?

• 1:00:07Speaker 01

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

• 1:00:22Peter Diamandis?

  I mean, there isn't, there isn't advantage of, of controlling quality and, and pulling out, um... Overhead or margins out of the vendors. I remember I was, I was visiting Elon's shop in the early days. And he was in this conversation by some supplier because it's, 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, was the attitude.

• 1:00:51Speaker 01

  I don't know if that's what we, like, we've been around for two years, so most of the engineering decisions around the build materials and the supply chain would have been, let's get speed by getting parts in the robot, get robots to AIs, 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, up and running faster. So,

• 1:01:20Speaker 01

  and I think longer term, we would have worked the supply chain to own it better, to have less risk, to reduce like the, the margin profile across all the, you know, the supply chain.

• 1:01:31Speaker 01

  But there's just like, um, yeah, I think we thought that was easy. It became like a bloody hell.

• 1:01:37Peter Diamandis?

  So what's, what's, what's the flip side of this? The thing that you thought was going to be hard in turn up to be easy? Would it, would it maybe be the AI integration? Did, I mean, when you started, did you imagine you're going to have to build out your own AI models?

• 1:01:51Speaker 01

  We still do like a lot of our own AI models here, which is like, maybe not super well-known, but like,

• 1:02:02Speaker 01

  but we do, we have a whole AI team. We do most of the, a lot of work ourselves. I would say across this, we leverage some group, like, you know, OpenAI, we like work with on new models and stuff and use their like VLM for some things. So I think, uh, and that's been really helpful. I think AI Systems is probably the one that's been like, wow, this is really working. And it's, you know, you think about like the split of like, What percentage of the things are you going to have to hard code in code to go do, which is like, you know, more classical controls and heuristics? What percentage of things can we do through neural networks? And I, you know, I would, I would have thought going into this, my, I guess my thought two years ago was like, you know, a large percentage of the stack would be written in, in heuristics

• 1:02:46Speaker 01

  and code, um, call it like 95, 90%. And then as we are able to, you know, write neural nets for those that are at equivalent performance, we will just take those, uh, that level of like code or C++ and remove it with a neural net. And it's kind of been flipped.

• 1:03:04Peter Diamandis?

  Yeah.

• 1:03:05Speaker 01

  Um, all the news is working, uh, for like a small team with like, you know, we don't have like a ton of tooling and infrastructure and things, almost like right off the bat, uh, with like everything, like just like, like using SLAM or perception systems and object detectors and, um, uh, planning and, uh, speech to speech reasoning, everything, everything in the stack, like the high and low bit, low, low levels have been working incredibly well. And, um, so I, yeah, I think it's pretty magical. I just like, it would take us such a long time to code all this stuff.

• 1:03:36Peter Diamandis?

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• 1:04:20Peter Diamandis?

  health guidance, like what foods you should eat, what foods you shouldn't eat, as well as your supplements and probiotics, your biological age and other deep health insights. And the results of the recommendations are nothing short of stellar. You know, as reported in the American Journal of Lifestyle Medicine, after just six months of following Viome's recommendations, members reported the following, a 36% reduction in depression. A 40% reduction in anxiety, a 30% reduction in diabetes, and a 48% reduction in IBS. Listen, I've been using Viome for three years. I know that my oral and gut health is one of my highest priorities. Best of all, Viome is affordable, which is part of my mission to democratize health.

• 1:05:05Peter Diamandis?

  If you want to join me on this journey, go to viome.com/peter. I've asked Naveen Jain, a friend of mine, who's the founder and CEO of Viome, to give my listeners a special discount. You'll find it at viome.com/peter. I remember you talking, describing to me the time when Figure One put the Keurig cup and made you a cup of coffee. And you were like, we're trying to code this. And then we just had them watch somebody do it like a dozen times and they were able to model it. I'm curious, is the way you're interacting with Figure Two, like, hey, watch me pick this thing up and put it over here, and now you do it? Is that conversational and the visual models? Is that where the state of the practice is?

• 1:05:51Speaker 01

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

• 1:05:55Peter Diamandis?

  What's like the most interesting thing you've had Figure do for you?

• 1:06:03Speaker 01

  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 in-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 a robot.

• 1:06:19Speaker 01

  Another way to think about it is like, I'm on the robot. I'm like, you know, one way to do is I pull my phone out or my laptop out. And I'm like, you know, opening the terminal and like trying to get a command to the robot to go do something. And the robot's like standing right there listening. And I'm just like, it just like wants to be talked to. Like you just want to just be like, put all this stuff away and just be like, Figure Two, you know, go do this. And we're doing that now and it's just magical. It's really clear that the default UI for human or 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 a robot and have it be able to

• 1:07:03Speaker 01

  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.

• 1:07:12Peter Diamandis?

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

• 1:07:29Peter Diamandis?

  how, you know, where are you on safety and how do you make sure there's no like

• 1:07:36Peter Diamandis?

  errant firmware upgrade turns robots into, you know, overpowered slayers? You know, I mean, there's enough, there's enough dystopian Hollywood movies. I don't want to, you know,

• 1:07:48Peter Diamandis?

  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?

• 1:07:56Speaker 01

  There's like many ways to think of this. We have like the like this system safety engineering side of like making the robot like actually really safe when it walks around the facility and does things where it's next to humans. There's a whole like architecture, it's actually designed from top, like the bottoms up to make that a safe, safe system that ultimately we can like 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.

• 1:08:26Speaker 01

  And I think that's just one piece, like we need this like safe hardware around humans. There's another piece of like cybersecurity and other things of like, we don't want anybody to have like root access to the robot and be able to take command of robots and to do potentially bad malicious things with them.

• 1:08:43Speaker 01

  And then you have other things like what happens on the onset of AGI? How does that really impact the safety of the robot?

• 1:08:50Speaker 01

  So I think there's like many things we're doing here as it relates to like the low level like read only like firmware on the robot as relates to like what it like, what is like the lowest level code sitting at the robot that can't that can't be overwritten, say, is it more akin to like the, you know, three laws?

• 1:09:08Peter Diamandis?

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

• 1:09:15Speaker 01

  Yeah, I think you got, I mean, like 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.

• 1:09:27Peter Diamandis?

  So help me understand this. So right now, can a robot mechanically have enough velocity and torque to harm you? Cause 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, so you put that kind of a constriction on it.

• 1:09:52Speaker 01

  Um, I mean, the robot's like 150 pounds. Uh, 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, like it certainly could hurt you. So like, no matter what, I think whether does it have, even has like, you know, torque sensing applied, so it can't hurt you next to you. There's certainly a scenario where it could be harmful, uh, no matter what. So I think, um, yes, this is for sure a system that can be harmful to humans if not designed properly. Or it has like a certain episod, or um, like fault on the robot that like somehow like, um, is next to human and hurts them. So uh, this is something that has to be done, uh,

• 1:10:36Speaker 01

  very thoughtfully from the beginning as like system safety architecture. And then we have to gradually prove out performance of that system over time through an incremental approach. So like our first robots.

• 1:10:50Speaker 01

  In our, in the work cells in the workforce will be isolated from humans. Um, if humans enter those work cells, we will shut the robot off. Uh, over time, we will go from there to like fully, um, next to humans, um, you know, collaboratively. And that will be like a gradual.

• 1:11:08Peter Diamandis?

  Timeframe, best guess, is that two years, five years?

• 1:11:11Speaker 01

  Um, I actually don't like, we're mostly concerned on trying to get the performance up of the system in the first few years and the reliability. That's probably the hardest thing we have to do, like getting, you, you want to go and see a robot like working full, like whether it's like, you know, like, so you have like a light curtain, like a, like an artificial cage around the robot.

• 1:11:31Speaker 01

  I think solving like a collaborative robot next to all humans longer is like 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 like failure. Like, you know, it makes like local failures. Like sometimes I miss, like I miss an object, but I go and regrab it. So we can like, we can fail locally, but not globally. We need to like, we have certain performance outputs. Like, 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, we know if we miss a package, we just got to deliver it the, the right way, uh, on time. So what I guess what I'm trying to say is, um,

• 1:12:14Speaker 01

  yes, there's, I think, I think we'll solve this decade, humanoids around humans and interacting with them closely. But it'll, but before you ever see that, you'll see, it's like the analogy of like Waymo, right? You saw in Waymo in certain permitting areas of San Francisco before you saw in other cities.

• 1:12:32Peter Diamandis?

  Sure.

• 1:12:35Peter Diamandis?

  You show up with the practice drivers in the seat, you know, watching everything.

• 1:12:38Speaker 01

  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 like, I think for us, we're just, um, you know, we're, 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, uh, 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.

• 1:13:16Peter Diamandis?

  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,

• 1:13:33Peter Diamandis?

  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.

• 1:13:40Speaker 01

  Yeah, there's certainly a select number of really important do not override, read only

• 1:13:51Speaker 01

  instructions that need to live on the robot that can never be altered. That's like for sure.

• 1:13:56Peter Diamandis?

  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, you know, 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?

• 1:14:28Speaker 01

  Yeah, you, um, sorry, for us,

• 1:14:34Speaker 01

  we, 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.

• 1:14:52Speaker 01

  We're doing, we're doing offline training on that and then the robots are basically getting smarter collectively. It's like as a collective intelligence. That's for sure what's happening in the direction we're heading to, towards. You want, what's so powerful about this is like humans. You know, like my kids, like they learn how to walk. They learn how to walk. They're mostly failing, like learning not, like what not to do in some ways. They learn new things. That takes a lot of time. Um, but once we know things like really well, we really don't forget. It rarely do we forget, you know, how to walk or like a certain things. And it's happened throughout history, but most of the time we really don't forget.

• 1:15:30Speaker 01

  Um, so, uh, so for robots, one of the biggest advantages we have is once one robot learns a certain task, uh, every robot in the fleet will know this.

• 1:15:40Peter Diamandis?

  Yeah.

• 1:15:41Speaker 01

  And so it'll be very exponential in terms of like the amount of new, you know, almost like, almost like the matrix, like we will plug, like what will teach, the robots will learn. Through like human demonstrations and through reasoning, like, like how to do something. And once it's been able to demonstrate that several times and we've been able to, um, uh, you know, like close a loop on that, like, okay, that's worked really well. There's a reward system. Uh, every robot in the fleet will know this.

• 1:16:08Peter Diamandis?

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

• 1:16:34Speaker 01

  We have like really good feedback, um, so far. Um,

• 1:16:42Speaker 01

  you know, our goal is to really, um, be able to do a lot of the jobs that are not desirable by, by humans. I mean, you mentioned earlier in this podcast, we have like eight million US jobs that people just don't want to do. Like, we want to do those jobs. And we want to do the things right now that like, that are potentially harmful and dangerous for humans to do. And a lot of those jobs have like very high unemployment rates. Uh, very high, like, like very low, like retention, like, um, like really high, like low retention rates. Um, And we want to start trying to do those and trying to help automate. And, you know, like, we've been doing that as a world for like several centuries. Like, my, my family was farmers, right?

• 1:17:27Speaker 01

  At some point, we had half the world was farmers.

• 1:17:29Peter Diamandis?

  No, it's like ninety, like eighty percent of the world was farmers.

• 1:17:32Speaker 01

  Yeah, like everybody was farming. And now, you know, like well below one percent, 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, I'm like, you know, I'm not out there being like eighty percent 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.

• 1:18:02Peter Diamandis?

  Do you remember when at the Fukushima nuclear reaction reactor? And then DARPA had the DARPA Robotics Challenge?

• 1:18:11Peter Diamandis?

  Have you ever seen those videos where the robot was just, you know, laughably incapable of opening a door or climbing steps?

• 1:18:19Speaker 01

  Yeah.

• 1:18:21Peter Diamandis?

  I mean, I think that's the...

• 1:18:24Peter Diamandis?

  Is it, is the Defense Department and the government a client or near client? How do you feel about, about plugging into that world?

• 1:18:34Speaker 01

  Yeah, we won't do anything defense related at all.

• 1:18:39Speaker 01

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

• 1:19:01Peter Diamandis?

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

• 1:19:06Speaker 01

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

• 1:19:18Peter Diamandis?

  I respect that.

• 1:19:19Speaker 01

  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... Like we think it significantly helps the economy, helps lower goods and service prices and do good work for the world. And I think it's much needed. Like I think, um, so like that's we're putting all of our eggs in that basket right now.

• 1:19:40Peter Diamandis?

  What industries do you see next?

• 1:19:43Speaker 01

  I mean, you basically have.

• 1:19:45Peter Diamandis?

  I mean, it's every industry, but where do you think you want to play? I mean, we're, you're probably getting a ton of solicitations, right?

• 1:19:51Speaker 01

  Yeah.

• 1:19:52Peter Diamandis?

  And you're taking it a step at a time?

• 1:19:53Speaker 01

  I mean, these companies are big. I mean, we like these companies we're talking to like are massive. We could ship thousands of robots in these groups. So, you know, for us, we'd rather work with only a couple of groups right now and do that really well than like, uh, opening up the, you know, hundreds of groups at the moment. Um, but at some point in the coming years, we'll, we'll, we'll sell to anybody. And, uh, and we're, you know, we're starting our, uh, we're starting our production line next year. So, um, we'll start with a few as we, um, you know, have like engineering really close with those customers and making sure that the product really works well. There'll be like a bunch of bugs and process improvements we need to make to make those like a well-oiled machine for the, uh, for the market. We want

• 1:20:35Speaker 01

  to do those with those customers now. And, you know, as we get to some level of product maturity, we'll, we'll branch out to more and more customers. And something that we're going to spend more and more time on is also like robots in the home. And.

• 1:20:48Peter Diamandis?

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

• 1:20:52Speaker 01

  Every six months that goes by, I go to a couple of folks here and I say, the timeline for us getting in home is accelerating. And it, 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. Cause you really want, uh, like you'll probably have like an order of magnitude, uh, pricing collapse going into the home from the workforce.

• 1:21:23Peter Diamandis?

  Sure.

• 1:21:23Speaker 01

  And you really need economies of scale to get there. And you really need a safety certified system, uh, in the home. So like you need a really safe product in the home. So, um, we, we are using the workforce in a lot of ways to bootstrap that vision for us. And, but I think from a performance perspective, I think, like.

• 1:21:45Speaker 01

  I think we'll start doing early work in the home and you know and then like you know. I I think the the home for us is an area where I yeah, I think it's accelerating in my mind every every six months that we.

• 1:21:56Peter Diamandis?

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

• 1:22:00Speaker 01

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

• 1:22:04Peter Diamandis?

  Nice, okay, I want to volunteer early on.

• 1:22:06Speaker 01

  Yeah.

• 1:22:06Peter Diamandis?

  I'll pay.

• 1:22:08Speaker 01

  Probably start with some mock homes here in our facilities and and start, you know, getting the bugs worked out, understanding how to, the system architectures all work, but

• 1:22:17Speaker 01

  I 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.

• 1:22:24Peter Diamandis?

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

• 1:22:32Speaker 01

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

• 1:22:36Peter Diamandis?

  Where's it gonna go? Where are you gonna do that?

• 1:22:38Speaker 01

  Uh, we're gonna do it here in California, like close to engineering, so we really work out the kinks. On like more of a traditional, like kind of pilot manufacturing line or production line. And um, and then from there, we'll, we'll start announcing our intentions for like we're like, like, uh, high rate manufacturing will look, but our production line is being, uh, we're starting to design it as we speak. Uh, we'll have robots coming off the production line next year in 2025.

• 1:23:01Peter Diamandis?

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

• 1:23:06Speaker 01

  Uh, we'll start with hundreds of robots and then thousands like us, uh, we really want to get the pro- What's more important for us than just like having like, you know, here's three thousand, five, four thousand robots out is getting the process really dialed in for how to do that. And then also making sure those robots that we're producing are, are working really well. There's a situation where anybody easily can get into where we have like too many robots that don't work well and you're basically in this like recursive loop fixing them. Uh, always having them down, like, volume doesn't really help. So you really need the system reliability to be at a certain point. And the production, like the performance of those robots then into the, into the use cases to be pretty high. I do not feel like building thousands of robots is.

• 1:23:51Speaker 01

  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, you know, building, we're building one a week, so it might seem, you know, and then we'll start building like one a day and then we'll start building multiples a day here next 12 months. So like, I think, but it seems like a pretty straightforward path. I mean, we make cell phones almost by hand in the world, make a few billion a year, like, this is more complex than a cellphone, but far less complex than a car. So, yeah, I feel like the path to making thousands in the, you know, in the near term is just not super difficult. What's the difficult part is making those thousands to a point

• 1:24:34Speaker 01

  where they're really useful and really work. That's like, that's the name of the game for me.

• 1:24:38Peter Diamandis?

  Brett Adcock, I'm a huge fan of what you built and excited for, for, for figure twos 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.

• 1:25:02Speaker 01

  Let's do it, Peter. See ya.

• 1:25:04Peter Diamandis?

  See ya, pal.