Brett Adcock: Humanoid Run on Neural Net, Autonomous Manufacturing, $50T Market #229

I am blown away by how far you've come. The things that you can do in your own nets now does a completely blow my mind. Every year to year, the whole business looks completely different. (upbeat music) It's amazing to me how you accumulate data and the data becomes this incredible barrier to entry. There's incredible asset. The one thing that's important here is that once one robot learns how to do a task, every robot that's free to know. And humans don't operate like this. When do we start seeing robots building robots? We will put robots on our Baki lines this year. Listen, this is like gonna be the largest economy in the world. It's gonna be super impactful business. It'll lead to like a ubiquitous business services for anybody and age of abundance. And it's gonna be a super fun business too. It's like gonna build a sci-fi future we all want. What you're seeing is every major group in the world will get in this space. You have to. You have like no choice. - When are we gonna see the first figure in the customers' home? - My biggest guess is I think. - Now that's a moonshot, ladies and gentlemen. So Dave and I are in San Jose at figure headquarters. We just did a podcast with our friend, Brett Adcock, extraordinary end. Check it out. Check it out. So, I'll be here. - Yeah, figure one. - This is the original. - Yeah. - Still somewhat functional. - Yeah. - It ran the first large language model, the first neural net. - They built it in under a year. Brett actually was screwing these things together himself. And it was all about gathering telemetric data so they could build this. I guess figure two, much more beautiful, much more functional running neural nets across the board, dumping all the C++. Can you do the belonging prosper? - Yeah. - But I have to buy it. And here we go with figure three is the workhorse right now. We just did a tour. I mean, probably saw a hundred of these walking through the hallways, on test stands, cleaning dishes. - Brett would just thought as they added a flexible toe too so they can go down like this. And before it had this clunky foot here. And figure three has the palm camera. - Palm camera? - Yeah. - They cut about 30 pounds off the weight and 90% of the cost-exering costs. - Wow. - Crazy. - Yeah. Amazing. - Yeah, it's the perfect height between the two of us. - Welcome to Moonshot, everybody. I'm here at Figure Headquarters with Brett Adcock and DB2. Brett, it's been about 18 months since we did a podcast on Moonshot's together. And I am blown away by how far you've come. - 18 months in AI time, that's like a decade. - Welcome to Figure Headquarters, what do you think? - Yeah, it's extraordinary. - Just to describe, we just went on a tour. You've got about 300,000 square feet, 400,000 square feet under development here. I mean, there are figure three robots walking down the halls. There's fully autonomous robots, I guess, running Helix 2. You just released Helix 2 today. - Today? - I got it while I was flying up here. We have these robots doing everything from kitchen tasks to packages, to manufacturing of different type. How many robots do you think we saw? - Seriously. - I wasn't counting as we hundreds, maybe not 1,000. - Yeah, hundreds. - At least a hundred or so. - Yeah, well, there's a lot of partial robots out there. - Yeah, that's hard to. - I'm picking up Figure Head. - What do you mean? - What do you mean? - There's more, many more hands in the room. - The hand line, the head line, the torso line. - Actually, picking up the head was the most surreal. - This is where the pelvis is made, for sure. - Yes. - Pretty amazing. I still remember during my first visit with you, full disclosure, my venture fund is invested in two of your earlier rounds, super proud of the progress that's made that you've made. I still remember your Figure One putting a curing cup in a coffee maker, and that was a big deal 'cause it was done with neural nets and not C++. - I mean, that honestly was like, I think it was a big inflection point for us. I feel like the, I think a few things we need to really run down is can you build electric humanoid, it's like low cost, it's capable like a human, just the hardware side of things. The second thing is can you figure out a way to not code your way out of this problem? You gotta be using neural nets to learn those like human type representations and the new tasks. And when we were doing the curing tasks, it was a basic bi-manual neural net running on the robot, which is now like evolved into helix. And I was able to basically do the whole kind of like, you know, it was a smaller task as a few minutes longer of like picking up the curing cup, like opening the coffee, putting it in, running it. And it was the first time we saw like true instance of kind of like neural nets really working on a, you know, a bi-manual humanoid robot. And that was when we were like, okay, we got to just go all in on neural nets, the whole stack was in neural nets to make this work. And that started like, and that was basically two years ago now. And then you guys all helix two today, which is like the, basically like the best release we've ever had. - So we'll run a clip of helix two while we're describing it because what we saw was figure three running helix two in full autonomy, going into the dishwasher, picking stuff up, putting it away, not pre-programmed. And I loved the human elements of it. Like using its hip to close something and it's put to raise the dishwasher. - That's the neural net difference though. You get unexpected behavior, you know, both good and bad, but things you could never code up. - Like your career went, the software company, VTOL company now, this gotta be the first neural net platform. - Yeah, the like, we wouldn't, like the things that you can do with neural nets now does like completely blow my mind, versus code. Like we could have never done a quarter of the stuff that you saw today with the whole body, with manipulation, with things that you know, there's only so far you can really push like, code is heuristics of new humanon robot. It's just a dead end. - Yeah. - It's just not gonna work. - Yeah. - Yeah, it's amazing to me how you, accumulate data and the data becomes this incredible barrier to entry, this incredible asset. If you were writing all this and C code, that C code would be, you'd have millions, hundreds of millions of dollars invested. You would not wanna mess it up. With the neural net, you can say, look, hey guys, retrain it from scratch. Right off the line, it's just a completely different approach. And it's, and that's why people are way under predicting how important, or how quickly this is gonna evolve. 'Cause it's a completely different paradigm. - Well, we've like lived through it. I mean, like we, you know, I think a baby year or two ago, we had like few, like several hundred thousand lines of C++ code. - Several hundred thousand. - Yeah, hey, hey, hey, hey, hey, code. - Yeah, and then-- - Probably a hundred bucks aligned to write it. - Yeah, very expensive, very hard to like test and like get out reliable. - You. - And like also hard to like model all the different behaviors that we would need to test the, like the, like the, the, the. - Yeah. - And then, you know, we removed a majority of all that in the Helix one, where we still had a lot of like lower body control, being run and basically the control stack in C++. - Yeah. - And then we removed the remaining hundred nine thousand lines of C++. - So there's all neural nets. - All neural nets today. - That's a full body. And that took it from like being able to do really good tabletop manipulation. Like you saw the, the curated coffee, the work were due to logistics, like all that's to be done in neural nets. We've been showing like amazing progress there. But getting the whole body to get out of there and move dynamically to a scene while manipulating and planning is just a whole other, like, we basically spent like a greater part of a year refactoring the Helix architecture to be able to enable us to work. You're talking about now like moving through space, like a human. - Yeah. - I have control of the full body. - I hand foot leg coordination. Everything is-- - Re-sensor data in cameras, tactile, we have camera palm cameras. Basically doing inference onboard the robot fully embedded and then be able to output torques into the motors and do that, you know, a few hundred hertz, you know, in terms of like, you know, that planning and control and do that reliably on very difficult tasks. Like these are biomeenial tasks where it's grabbing and holding things, planning, moving the body, getting things out of the way, making like, airs and replaning and fixing this all done with the neural net now and to end over like a, pretty long, like for us it's like, you know, it's kind of like a room scale autonomy. So we can like now like finish the whole room. Which is important and next we're going to graduate to like be see the full house. This is one of things really obvious when you're walking around looking at what everybody's doing and working on you. You visualize a robot company having lots of people working on microcode or actuators or batteries or whatever, but there's just a huge number of people out there at work stations, they must be working on the neural nets. As you know, it's just gotta be such a dominant part of what makes the thing actually look and feel human and the motions are so smooth and, you know, everybody when they think about the history of robotics, they kind of chart these line charts, but it's not, it's not like that. It's a disruptive change from dropping that last 100,000, 105,000 lines of secode, to moving to an all self organizing neural approach. Yeah, completely different future. It is like, we make these like technology like progress steps and I think it's been very apparent here, like every year to year, the whole business looks completely different. Yeah. In large part of trying to get the hardware, hands, like all this stuff in a good spot. And then, you know, be able to be, basically have like more range of motion and speed and torques human. And then be able to get like, you know, we're all in on neural nets. So it's be like, you know, what is the right data set for that for pre-training and post-training? Do you have the right, you know, training cluster? Do you have the right models? And then deploying those really well on the same human or hardware? That's like a full loop. We've actually designed figure three to, if you say like, what is the guiding principle of figure three more than anything else? It was just designing for Helix. I don't even decide this to run all the way. like he looks on. - So we give him a lot. - So counterintuitive. - Everything, just the-- - Just to build around the neural net. - We built the, we're like, look at the neural net and we said how do we like fit like this into a humanoid robot and what are the best sensors? How should it run? What does the operating system look like? Middleware, firmware, embedded software, like all of it, isn't captured in this like view that we need to go all in on neural nets and do human like work? - About to release the 2026 version of my humanoid to metatrend report. It's a deep dive looking at a hundred different robots in development right now, a deep dive into 10 of them, including figure, 150 pages. You can check it out at Substack for my paid subscribers. Anyway, super pumped. This is a field that's moving at exponential, high-prexvenential speeds. - So in the beginning, you had partnered actually with OpenAI on software and you made a departure from OpenAI and I mean, I guess art. - Is it quite accurate but like-- - Okay, well, you can, you can, you know-- - I mean, I think, you know, I met Sam and OpenAI team and they were just really interested in getting robotics. And it was like in their early master plan to get into like, basically shipping home robots. And they really wanted to kind of like, you know, basically work on a very intimate relationship. They ended up leading our co-lead in our series B, along with Microsoft and we started working on basically like a collaboration agreement to help work on next generation models for humanoids. And, you know, we were like super big then. And we still are on like, how do we like the language condition, the whole stack? How do we use like, no limit in a lot of ways to just like this like world model. I really understand like in the weights, like basically like what things are, what it should do, has like a good semantic understanding. - Yeah. - We're trying to like, how do we tap that for the humanoid? How do we learn from this? As scale and some of those representations. And it just like, the partnership just didn't work. Like my or our team just ran circles around them. - Yeah. - For basically better part of a year. And it just gave me the point where like, it just made sense to just which we were, we were just doing all the work ourselves internally. We had a whole team here, a lot from like some of the best like labs in the world. And we were putting out like work after work. The cured coffee stuff has done my ass. All this stuff was done internally. And at some point just didn't make sense to train other folks on how we basically build AI models internally for better systems like a humanoid. - Did it turn out that LLM matters at all in physical? - Like you could start with an open source LLM and two. - Where's it like a VLA, like a vision language action model? - Yeah, basically like, I think the LLM is definitely a certain piece of this. We basically wanna like take it like the semantic grounding that I could VLM. - Yeah, like the common sense, you know. - Yeah, I could be like, understand from this. So, you know, which we have in, you know, he looks at a super critical. But like, getting to a point where we can understand physics in the robot and have it like really be able to plan and reason at fast endemic speeds was something that nobody in the world's ever really done before. And I think that's the work that we, I think it's been excelling out and we love. It's just like, how do we get it's like, on our same physics? - I think most of our audience probably knows this, but just to rewind the tape, you know, the LLM's GBG2, GBG3, build entirely on text data, scrape it off the internet. And then they supplemented that with a ton of other data, also in text form. - Yeah. - And that creates this machine that has tremendous amounts of common sense. And if you ask it, hey, do you know how to play soccer? It says, yeah, of course I do. But then you try and install it in an actual physical moving machine and has no idea what it's actually doing. - Yeah, I mean, like, maybe you seem to like touch everything in the world. - Yeah. - And we have this really high dimensional robot that has like, you know, 40 plus degrees of freedom. So like, and it's still like, you know, on the surface area, just the math around this is like, like the, the missionality is basically high. So you have like 40 motors. They all can spin 360 degrees. - Yeah. - So the amount of states the robot can be in like positions is like 360 to the power of 40. So there's more states of the humanoid than atoms in the universe. - That's a lot. - Yeah. - So we're not gonna stimulate those one by one. - Yeah, exactly. So like, so the question is like, they don't need to like, understand these fine contact dynamics of like, I need to grab this water bottle. Like, where do I position my elbow, pelvis, like torso, head, like fingertips. How I plan the, you know, to grab this is, you know, like, and then how do I put pressures on there? And we understand those representations really well. You know, from observations now into actions that I'm doing a test time. And this is not in that one. - Yeah. - The element knows none of this. The element knows this is a water bottle. And they probably knows like, I need to grab it from the side. And then, but like all this like, like, you know, like all this implied physics that we need to do here, just we have to go train models to go do that. - It's actually kind of weird, because it thinks it knows how to do it too. You know, the LLLs feel like they can do things. You know, intuitively, and then they completely fail. - I mean, you can, we've done this. You can, I've done this. You can zero shot the LLLs inside a robot. We do it. We still do it. - That would really actively. They just can't do any. - Just for fun, just to watch them fall. - Yeah, I mean, you know, I'm kind of interested, like probably what I've been doing is like, can we just like, the other day I was like, can I zero shot? Like, I'm working on this new AI lab that I founded, we still in called Hark. And we have this new AI model here that is like, it's just completely-- - Wait, wait, wait, you founded a new AI lab? Well, rewind the tape here, what? - Yeah, it's a new AI lab. - I sent you this. Did you? - No, I did, I sent. - Yeah. - I'll send you again, working on it. - And we have some new AI models, and we actually put one of them into the figure robot, like this month. And I was like, okay, let's just zero shot. Let's give the LLL, you know, let's give them the model. This is like a multimodal model. Let's give this access to just like the basic commands, like the basic X, Y core. Like basically, can we give it like acceleration in the next Y coordinates for navigation? Like basically a joystick, can we give it a digital joystick? - Yeah. - And I asked it to like find the exit sign and just like go, go to that, get it out of the building. And it just, and unfortunately was going the right direction and ran into like a clear glass wall. (laughing) - Well kids do that too. - Yeah, that was my face. - So we've like, we've stressed this. It just doesn't work. Like you're missing so much, like world understanding of like what's really happening. How do we move my body? Like we're thinking like, you know, it's pretty simple to grab an object maybe with a stationary robot with a robot, we have for humans are moving. The pelvis and head and torso and hands and arms. Like when you reach out to grab some over table, your pelvis is going to be backwards. Like it's like very, very difficult to command like very high physical. - Robotic physiology. You know, out of China this year, some of the government employees said we've got a robot bubble. I have 100, I don't know if you saw that, that article came out. We have 150 plus robot companies in China. And I mean, there's a lot going on there. You know, in the US, I would say maybe there's 10 serious players. I mean, two or three who are extremely serious, including figure. There's, but there's a lot of potential humanoid robot companies. I was just at CES and saw, you know, I mean, it was a humanoid robot explosion. And then as many or more hand companies, which is interesting. So I go back to sort of the early 1900s when there were like 250 car companies and like two or 300 tire companies. And then this massive consolidation occurs in GM and Chrysler and Ford sort of buy and consolidate. What, what do you think is going to happen with all the robot companies today? - I think it happens in every industry like this, especially in DTEC, this will all consolidate down to a few groups. Global. - Do you have a guess? Is it a triopoly? That's the right description. Is it, you know, more than 10, less than 10? - Far less than 10. - Far less than 10. Globally. - Globally. - It always seems in the US anyway to settle down to two, three or four. But the borders are not up. Like with cars, cars, cars, cars, cars, right? And actually you had cars and trucks and those were kind of separate for a while. - Well, you also have different designs. Like I want the plush interior. - Right. - I want the sportster. I mean, and that, I mean, I wonder is it going to be, are robots going to be differentiated by their vertical application? They're pretty exactly. - Exactly. - There's so much more variety possible in robotics. - Yeah, I think everybody's just like taking for granted how difficult this is. Like this is like, you have to go out and build like basically like pretty novel, very, very difficult hardware. It needs to be relatively cheap. Then you got to figure out how to make neural nets work on it. And then you got to make neural nets work on it scale. And then you got to manufacture ass scale. Then you got to do these products out reliably that all work every day without any human intervention. And I think we talked a lot about this like how we're doing like cake up coffee work. Like I haven't seen any single human in the world do that or able to do that today globally. And that's been two years. - Yeah. I mean, by the way, a lot of the video we see is actually tele operations. I think I wonder if people will realize that. A lot of the robot companies are teleoperated versus folio-tons. So what we saw just walking around here was a four minute long folio autonomous operation on HelixTube, right? - I've never, I've built a lot of businesses in my day. I've never seen so many companies with a human in the back, commanding the robot and putting out updates in my life. I've just never seen it. I've like, when I started thinking it was stuff was coming out but now it's like every week as somebody just teleoperated a robot and putting out a video. It's just, it'd be the equivalent of like I have a self driving car company and there's a guy in Tennessee driving it. And we're like marketing as like there's no humans in it. - Any self driving. - We're putting out teasers. We're in a lot of cases now that it's company selling the service. Like, so I think like, I mean, if you wanna do this right, you gotta believe in your own. That's all the way down the stack. You gotta basically build for general purpose. [BLANK_AUDIO] So the parameters that make it's going to define this successful top two three four the neural nets Manufacturing. Okay, I would say like I would say what's impressive today is not manufacturing you probably solve You know, we're pushing on manufacturing hard, but you can probably solve general robotics with a hundred robots You what's impressive is like a full end-to-end robot that is generalizing to an unseen place like you drop it into an Airbnb and be able to do long highs and work with neural nets Any long highs and work in unseen places. What are you to find as long horizon hours days? I would like to see days of work. Yeah, yeah full-time as days of work and at least at the very least and We're like so far from that you have like robots out there doing like karate and jumping which is like these are like pre-programmed open loop behaviors They're not impressive. We do that. We've done that stuff here You know to be done like the like the open loop behaviors. It's just there's just like, you know any college kid in a dorm room can do this with with a robot and Yeah, so I think like That plus telly operation telly operation is not impressive You could build a shitty hardware and still tell you operate it and put out videos. That is not hard What's hard is to do full end-to-end neural network and unseen places are generalized to this and then if you can solve that Then the next step is like how do you get that a solid scale? But we are still in the like who can solve Generobotics phase of the humanoid phase and it's just not impressive. I think I can build 100,000 robots right now that like need telly operation or can just only open the replay I think it's not like not cool like we we if we like write your only job is about a hundred thousand robots right now Yeah, we have the capital to do it and we can do it But like do what we really want to solve is like I can I can give you ten robots and they can go into n scene places and do real useful work Like that's what's going to differentiate so iterate that until it's right and then mass produce Yeah, you basically want to bring bring up mass production in parallel because like building like high rate Manufacturing for humanoids is going to be super hard and you're gonna like I have to go through like a lot of iterative Design process. So that's what we're doing now We're bringing up higher volume manufacturing as we're like learning how to build true general purposeness So but my view is like if you think about these like these like level bosses that happen that will like that will hurt like that You need to graduate to You need to graduate to doing like you know first very short periods of like neural network Which we haven't seen a lot of in the world yeah today I don't think there's anything over a minute long in the world that's doing neural nets continuously today and human It's amazing everything's caught all the films are caught or teleoperated. Yeah, yeah, it's pretty crazy So I'm really glad you're telling us that yeah I mean like you watch any video you want to see it uncut you want to see it in neural nets like not teleoperated like And then you want to see stuff we showed you here in person today They're like that are running for hours and hours and just like now We run these robots with no I mean the kung fu videos whether they're teleoperated or fully autonomous are are actually Fascinating and scary when you see them Technology around there's not great. I mean you're basically putting somebody in a mokehapsu You're having some guy like do credit chops are walking around right and then you're running that open loop You mean you're running that blind you're just hitting a replay button right and you can do that with a very simple like RL neural net like you can basically do deep mimic on this and it's it's super simple Yeah, there's all like opens worse code for this you can do with like basically one GPU on your desktop And you can do with any robot and every robot has a very tiny amount of computer These are like these are at single million parameter models are very small You know me lot of memory and they're very simple to execute what you really want is good close loop control We're just reasoning like at over like two hundred hertz or two hundred times a second sure and it's dynamically responding to the scene Yeah, and that is literally a million time a hundred thousand times harder than doing open loop really human Sort of cycle time is I mean it hurts. Oh, yeah, much lower than that. Yeah, I would imagine well One thing we've seen about our robot is we can like balance on one leg like better than a human Yeah, we just have like much better like faster like dynamics But the speed of development here so 2025 I'm just trying to imagine and you put out this beautiful You know post every week on on X about the progress in the robotics field and what and what's going on here at figure and It's just constantly you know local motion was a big a big step forward excuse the pun for for figure just seeing it walk and then run very naturally What else was was significant in 2025 for you? I mean we launched helix in 2025 about this time last year about a year and now I think it was like highly significant Like we basically figured out how to run like basically like longer like over long periods of time Neural networks on a robot. How do we get the data for it? How do we train models? How do we deploy to test time? How do we get this like you do you so you watch like pack will just like I saw yeah It's like it's been running for days now and it's just like It's a neural net all the way down the stack. It's learning how to grab packages kind of like you know individualisms Find the barcode position it down It'll even pat the package down so the barcode reader below can see it and scan it and assume that I'm very high like Soon that high like accuracy and it's in that high speed high speed though. That's important. Yeah, no, it's It's crazy. Well because a lot of what you see in robotics As fast as a human would yeah, like our last like we see air now like last when we did we did like we had one air We're 67 hours continuous over 67 hours. The thing is is crazy Yeah, it's doing an operation every second or two so 67 consecutive hours of that is If you get to guess at so it's a helix Yeah, it's a big one and then figure three So yeah, huge steps in for us in hardware if I could what do you see then going in 2026 here? We got the you know next 11 and a half months. Yeah, what are you excited about? We will build like and our entire roadmap around helix to now We will basically now he looks to can like go from like doing the logistics use case stationary Yeah, so walking and moving and basically do like long horizon full body control So that means the road and then we basically have now integrated all the sensors tactile camera palm into the stack and we're seeing like Improvements overall in the policy layer. So we're getting we're getting better and faster about like BC like taking data and BC Running it on on board the robot now. So I wanted to ask you like what defines helix to because you're probably incrementally improving the neural net every day Yeah, so what is the couple big steps one is we BC have integrated BC a fully learned What we call like system zero, which is our controller into the robot so the robot has a full body reinforcement learning controller in it Okay, so basically now we have like we have like literally no code run on that robot So we can like it can be seen move the whole body itself using a full like Basically like learn controller inside of inside of helix we call it S zero Is there no different than that before that's gotta be um There are reinforcement learned controllers out there like a lot of the karate stuff you see and things like that or that But nobody's only integrated that in the whole body for learned manipulation and perception I mean, nobody's showed that actually working with like moving around and doing things that we saw today Yeah, I actually don't even know if anybody's showed it stationary Standing and doing learn policies actually probably not in the world. So like getting integrated into a stack now that we actually use Going forward. I think one of the things we learned that like we were in vmw last year and we were there for like We did six months like redeployed it to our figure two robots every single day. Yeah The biggest thing we learned there is like the stack we had I think about 80% of the things we got right and 20% of the things we got wrong Meaning like the things that we got wrong on we didn't want to scale it was working the robot ran every single Every single work day and we it worked Um, but let me learn like okay, I don't want to ship 100,000 robots in this like architecture stack It's just like too hard to scale. Yep. I'd be like too brute force. Yep, and so we basically worked on basically for almost basically a year now On like okay, what is the idea of architecture where we can go out and accumulate large sets of pre-training data Yep, put in the robot and it can just like do this do this work and we emerge generalization from this And that's what you're seeing today. Yeah, it's one had the C code in it still Yeah, yeah, it looks he looks well and had a lower body controller That was still written in C++. Yeah, and everything else full upper body was full neural nets Okay, and so we basically completing out the full body Okay, and then in doing so we also can like did some work on our system level system one level where we integrated all the sensor modalities now from the hands In the rest of robot into the stack so like for example, we now have tactile sensors in every fingertip We're using on figure three as well as palm cameras To understand how we're like we're sometimes included and sometimes we won't want to better basically better understand how we're grasping items So we put a bunch of stuff about we're picking pills and stuff out of pill Pill cartridges that you like literally are clueded from from the hand your hands like little in front of the head camera But we were so really want to understand where we're going Yeah, so I think So now with the your with helix - we basically have a full stack in-dem with neural nets and we feel We can we we feel confident in scaling the pre-training data set uh into helix and I'll even go as far as like we've designed helix - for the pre-training data set And then we've designed and then we designed the robot for for helix - so we've like we've designed everything around data Yeah, and how do we get data? If you're in the neural net game, it's like a data play right? It's like how how like high quality and diverse such experience It's just gathered once built in all kinds of circumstances like we're doing in that I know everybody knows this already, but it's accumulating and it never goes away. It's it's an incredible unique data or progress Well, yeah, you teach somebody how to screw the dive or how to play piano And they have that knowledge they live then they die then you have to teach somebody else. This is completely accumulating The reason why he should be like a very few human like groups is like the one thing that's important here is that Once one robot learns how to do a task Every robot or the human still operate like this. Yeah, I wish we did I want my kids like kids like learn how to do stuff and they just don't listen to what I wish we mel We did so 2026 predictions what's your what's your boldest predictions for figure what is your goals for this year? What do you imagine? Yeah, I mean we basically want we're spinning up Bacchew like production enormously right now for figure three. So you said something like a robot every 30 minutes you expect. We're trying to get there in the near term right now. Would you guys solve? We walk through Bacchew today. Would you guys think of Bacchew? Yeah, a lot of humans. I wish everyone could see that. I guess it's all secret. You can't you can't camera through there. We have in this little IP there because like you see exposed boards and actually there's a stuff. I guess people would have used it. It's cool, right? If we get some promotion video, maybe we can mix it in here. But so whenever there's a lot of humans, when do we start seeing robots building robots? We will put robots on our Bacchew lines this year. And then facing like facing humans out of there will be a combination of getting more robots there and doing more high volume like automation over in Bacchew. Okay, so that's the first 2026 objective. We want to scale up robots Bacchew for sure. The second thing is we want to scale out robots in the industrial commercial workforce. So we have like multiple clients that we've signed. They are like buying or leasing robots from us. And we were going to get those out at scale in 2026. We have we know exactly what like where we're going geography wise what use cases are going to be deployment schedules. We want those to be figure three. So we've just retired in the last year, figure two's. And now we're building the arsenal of figure three's outs. You know what's where it's going to be manufacturing to get them out to the world and run every day. We like the commercial workforce because it really helps harden our ability to run robots every day. Like we're here to do is like we're here to build robots and run them in the in the world and they run 24/7. Your ideal customers who I know a lot of people would love them. We have to be frank like so much demand to customers. We have like we've talked to 50 or 100 customers or so in last like six to 12 months. We really want to be like kind of all in with a smaller group of customers and really spend time with them. I integrate well into their facilities and you know do well. We're still at this like we're still early right. We don't have like thousands of robots right at these places. We want to as fast as we possibly can. But like once we get to certain like I mean we could probably ship like I think I think we could ship an enormous amount of robots into the curve customer's we have now. So we see like you know we're kind of good now for the next like two or three years in terms of like we have so much demand like we like they're kind of waiting for us like ship at scale leasing over the scale. Yeah we have like service. We have like a you know we really like the leasing model. Humans are leased. So you know we're not opposed. I think what really matters is trying to figure out how to find the right distribution to get robots out of scale. Yeah. Like it'll really help us get really good at what we do. Like it's one thing to like show a demo or whatever else but like you know when we had robots at you know in our commercial customer last year at BW like it was just taught us a ton about like running every day fleet operations safety like repair and maintenance. There's a lot of other things that need to come or like come come through on the ecosystem that we need to get right. So I say second thing is like getting robots out of scale commercial customers. And then the last thing which is arguably the most important for us is we want to solve general robotics. Yeah. We want to basically like the analogies like we want to build a human in a body suit that you can just talk to that has like common sense reasoning you can communicate with that has like they can be seen almost like you know like almost perfect memory what's really happening or what's going on in your life. Like maybe talk to you almost be your companion. I mean and then go often do things that you would like like you would an everyday human would want to do and I would expect them to get up to speed on those tasks at or faster than human can. Is there two different models than driving it the the VLM model for the body and the physics and the embodiment versus LLM for conversation and memory. We believe we believe this all comes down to like one model at end of the day that is when omnimodel that is trained early in pre training that helps fuse all this together. But yeah you could think of it like we need to have speech we need to have like language condition policies we need to understand physics really well. We need to remember things and be able to recall that easily. We need to have some sort of personality on the robot. I think one thing that you're going to see more and more is we really want to make this robot something you can spend time with. Yeah. And we've been really focusing on getting the core building blocks built. But like over the next year or two I think you'll see us. I think I just want to robot in my home I can talk to you. Sure. I can remember things. I can get my kids come home like sad from school or something. I went through about to understand that. I had the EQ like self-awareness to see that. Talk to them. I think all of this is like something we want to spend more time on now internally. Is it already a big MOE model where it will have different like depending on the task you're doing it'll run different parts of the neural net or does it? We have like one neural net now that's like that's basically there's no like libraries of neural net that we pull down. So there's no like dishes neural net or like logistic neural net that you saw here. Yeah because it's you know at scale like if you teach the thing every physical motion. Yeah. It's massive number of combinations. The storage is actually dirt cheap. Yeah. The processing is very expensive. Yeah even better we've basically seen that we've seen positive transfer now with all this data. Yeah. Like coming in the robot can generalize better with more information. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Like more knowledge is better. Yeah. Yeah. It does cross like it. Like playing piano makes you a slightly better soccer player. Yeah. But also you don't want to run the whole parameter set for piano playing when you're playing soccer. It's an interesting little hybrid problem. Yeah. You don't you don't want to do it. Yeah. Yeah. Um yeah. I mean that's where we try to build best in the world models here and build a great team that can ultimately deploy robots that are useful. I think showing like you know like this type of usefulness like either it's like well as if you saw today in a diversity that is super important for human body to be able to do everything human can which is like yeah. And you know the distribution curve it's like you know we probably do like billions or trillion of unique very unique things in the world. Yeah. What I want to talk about is a bed on on our tour that totally tells me you're on the right track is that you're using normal GPUs for the training like everybody but the Inverstein compute is on super super fast dedicated non H 100 non you know GB 300 hardware. Yeah. Which has got to be you know at least a factor of 10 or a 100 cheaper and faster. Yeah. So so so running fully on board. And it's really fully on board. Yeah. So you can basically like do like very fast inference and policy deployment. Yeah. And it's also not sucking down the entire power of the robot. Yeah. Yeah. I mean you also have an issue where like you know we've also run models off board the robot but if we lose communications or have some sort of one you go there. Yeah. And you know what I mean like if you like lose internet it's like hard to do work and it's like you know sleep and sleep. Yeah. Supply chain batteries and comms. So on the comms side do you imagine we're going to be you're going to be running like a 6G network on there besides Wi-Fi what's going on in batteries these days. Yeah. Yeah. We have so from a network perspective or you know it comes like you can expect the robot we have Wi-Fi on board we have a 5G in SIM card e-SIM on board so we can the robot you can text the robot. You know we can have James. Yeah. We can have a network outside of like a Wi-Fi condition and then we also have Bluetooth on board so almost like a walking phone or something like that you would think of. So we want that they mean you really want like connection at all times but you also want I mean ideally you want to connect you all the time but you also want the robot to be able to perform work without a connection. Yeah. So you really want like a lot of on board intelligence you know that we basically in case you lose internet the robot's like not bricked. I mean humans for the most part can do work without yourself. Not teenagers. Yeah. That's going away. So batteries I mean they've been improving what's battery life right now. I love the charging mechanism by the way for those you don't know your charging basically through your feet. Through your feet. Yeah. No connector. You just stand. Yeah. Yeah. Yeah. Yeah. It's really cool. What kind of battery life are you getting? What do you expect in two, three years to get for battery life? So today it's what? Yeah. We weren't basically around like four to five hours per like full charge in the battery. And we're starting to have full battery life here. And then through full depth of discharge and then we can like charge wirelessly about two kilowatts through the feet inductively. So it's about we have about two kilowatt-hour battery packs. It's about an hour or so for a full charge on the robot. So we can do like four or five hours on an hour off. That's great. Yeah. That's great. I think like I think folks are over indexing too much on how long the robot can run on a single charge. Yeah. I don't expect it. I don't expect it. That may be a task. It's a few like a few hours and you're not like you know you go take a little break like this. I just thought so it's like I think there's um you know ample time to do opportunistic charging. Maybe send another robot in. We also can charge it. Basically you can put like this old thin mat anywhere in the world like it could be like a conveyor system or wherever else could be at home or for the kitchen. You can just charge there while I'm doing work. Yeah. But um it's really cool. Yeah. Um so you don't have like having wires or things like that. You're pulling from that. Well I think one of the greatest value ads you're doing right now is people are over indexing on all kinds of weird things because they're you know they're physical beings and they're watching the robot do physical things and they're saying oh my god. You can you believe it in sprint now? Oh my god. I can do a backflip now. Oh my god. I can do and you're like well it depends whether you program that in C or you tell our operator it or did it actually learn this. Yeah I think those are open loop. They're just like replay buttons. Yeah exactly and it's it's just so hard. So when people say well how long does it run with one charge on a battery you're kind of relating it to your cell phone. Yeah. But it's not it's not relevant in the inflection we're going to do. Yeah I think you just got to like the summer here is just like I need to see like real open like uh I just see like real closed loop control of a robot moving around touching and moving things. like a human wood. And that's where the, that's where the hardest problems all sit. And that's where we've seen, we've seen this huge wave of like human-orged explosion, like you said, out of China, things like this. But we've seen this like very steep drop off from getting to that point next, which is even like, show me a minute of the robot doing Kurek or something like that, uncut, closed loop. Yeah, real time. Yeah, like, and I just like, you just haven't seen that. And I think, I think you will. And I think there's like, there's then there's like a lot more levels to go from there. And that, you know, that took us two years to go from like a few minutes of tabletop manipulation with neural nets to a point where we can do like kitchen work, like, you know, like room, like room-wide, room scale autonomy. And that was two years of working seven years a week. We're here like a lot of nights getting there. So it just gives you a little sense of like, you're not going to do that in six months from there. So I think like that there was a lot of both hardware, low-level software firmware embedded system, sensor, and then like neural net, and then data. All of that came together to build this. Like we couldn't have done this work on, we couldn't do the same work today on a robot that we could go back today. I'm going to show you vertically integrated. I made the choice to vertically integrate. But supply chain, how much supply chain ties back to China? I think the next, I think by summer, we'll have almost none of our supply chain in China anymore. And do you buy into the US versus China sort of AI and robot competition? How do you think about that? I know. I just like, I spend a decent amount of time in China. I love it. China is great. Like I go there and it's, you know, you're watching TV here in the US and just like this massive conflict and battle and everything. And then you go to China and everybody's just trying to help and win and try to work and collaborate and it feels like a startup incubator. And it's just like a one-way competition. It just feels like everybody's in team human. Team humanity to go win. And it's so great. When you come back here, you're like poisoned with all this stuff online and like articles and television. And it's just like, it's not like that. When you're like on boots on the ground and going to do this. It's like, let's go as one and go win. And I just love that spirit of like trying to like just progress this technology as a giant lever arm for humanity to bring like, you know, to bring abundance, basically for everybody and just make it like a sci-fi future. We all want to live it. It's like, oh my god. It is, that is, we want to speedrun Star Trek is what we talk about. It's exactly. It's like, yeah. You see, figure on the moon, figure in orbit, figure on the ocean floor. 100%. So the equivalent, like you guys make your own actuators, motors here. And part of that is because you want the exponential growth effect. But part of that also is a supply chain just doesn't exist to give you the parts here. Yeah. In China, I mean, you're talking about the, you were just now talking about this, the improvements made between figure two and figure three. Yeah. Because you have all of the ability to iterate terms of speed and cost. I mean, the figure, the numbers that you share in on cost, I was like 90% reduction. Yeah, reduce costs like crazy on figure three. I think we listen, we vertical, any degrees we have to. It would be great if we can go off and buy like motors and we can pop them in the robot. It doesn't work like that. It'd be great if we can go by hands and just like, like, you know, screw them on to the end. It just literally doesn't work. Yeah. If you go through the engineering work to basically understand how we do comms and power and sensors and failure cases and thermals and, you know, low level firmware and embedded software. Like it just like, there's like one of the costs, something breaks and the reliability, something breaks in that equation. And you're like left with like hopefully the vendor fixes area die. It doesn't work. And then the stuff like they took technology readiness of these things are really low. We would love to have gone out and like bottle of stuff in the early days. We tried and it would be easy to just failed at all of it. So we have like, okay, we need to design ourselves. And then now we manufacture. Like we do all final simply and everything here. Yeah. And we do that, you know, in some case, because like nobody knows how to do that well. We do that a little bit for IP. We really want to control that here and understand like what we have like people have access to. And then we also want to get good at making a lot of robots. Like what we need to get good at long term is like, probably a few things, getting data at scale that can run their own nets. And then, you know, basically doing he looks really well and then making a lot of robots. And then getting those things out at the road at scale. Like a pretty simple equation in a day. So it feels like the journey of getting figure up and running must have been so much harder than it would have been in China. But then once you have everything built in house, all the actuators, you know, training the neural net and everything in house, then you have a massive advantage versus anything going on in China. Because if you'd been locked into a supply chain, it's only a certain model. It's like, it's like, it's like, even if we used like an existing supply chain fellow stuff, the robot wouldn't be able to do what you saw today. Yeah. Just can't do it. If you go out and buy like a robot, human robot, the shelf today, we can't get it to do this. Yeah. We've like, we've, we've bought robots all the shelf. We've looked at them like you just like you can't get them to do this work. They don't have the right sensors or have compute around thermals, they don't have the right, the hardware, the hands, the head, like all of these are built around our neural net. Yeah. Well, that's a new thing too. The neural net is is incredibly integrated with this specific hardware. If you watch folks that are trying to buy these robots off the shelf, like see from China, they'll come, they'll end up retrofitting them themselves with giant backpacks. They'll have like power, they'll have compute there, they'll have thermals, they'll have a wire hanging out, they're going to have to put it into the back, they probably have their own local battery, they'll hook it into the back of the robot. They have to like take it and they have to like overclock it. And it's just like, it's just like a, it's just the wrong way of doing this. Yeah. It's like a hard thing. It's like a, it's like buying our, it's like doing rockets and like buying a rocket. You know, here's from post stage two on the side or something like that. It just doesn't really work that scale. It works for like a, in the early days, like hobby grade, like demonstrations and things like this. But if you really want to do robotics at scale, you're going to have to go decide yourself. Yeah. Looking at the companies coming out of China, Unitry Engine AI and so what do you have any, which are the ones that you're most interested in excited as friendly competition if you would? Yeah. I think one thing that's great about China is we're just seeing this like, as you mentioned earlier, like this explosion of like really great talent and robots coming out the door. And great, great entrepreneurial work ethic there, right? It's awesome. Like it's great. And I think, um, I think it's just good for humanity and like this needs to happen. I think the thing that we, like, I think we've not seen is we've not seen any like closed loop like AI, like control from these systems at all. Yeah. We've seen a huge lack thereof of that stuff. I mean, usually it's like, here's here's a robots will sell them and the doing a ton of like, like basically open loop, like looking at hand controllers. Yeah. So I think like doing that is very, it's almost like it's a orthogonal work from design, like the system, the right way for autonomy. But there's, I think, you know, if we think about like, who figure really competes with as our main competition, it's certainly China. So like as a whole. Um, and, you know, um, for manufacturing, I mean, for human, human, request labor. I think just for humanoid, like we really don't see anybody else besides China as a real competitive threat today. Uh, do you rumors about Apple getting into the business? They cut down their car, you know, their car project and rumors are that they're heading towards humanoid. Have you heard that? We've heard this. We've had, um, every major, we've been in competition with every major tech company in the world. Last 12 months. Um, and then Nvidia and Google and even Sam. I mean, yeah, every is making noises about. And, uh, Amazon. Yeah. Yeah. Listen, this is like going to be the largest economy in the world. It's like half a gd, roughly a little under half a gdp is human labor. 50 trillion dollars. Yeah, this is like the next great place to be. And I think it's going to be super impactful business. And, um, it's going to be a super fun business too. And so I can build a sci-fi future we all want. Yeah. It's going to feel like it's going to feel like it's going to feel like 20, 80 up in here. Yeah. Um, so what you're seeing is every major group in the world will be, we'll get in this space. Um, you have to, you have like no choice. You have to have a major group being Apple, Microsoft, Google, every, I think every major player that wants to do this. I think the thing that, I think it's going to be hard is like we're doing like rocket type difficulty and design here. So it's like, you know, meta is doing like, you're building rockets. You'd be like, that'd be crazy. Yeah. And there's, I would think maybe the humanoid is probably up there with like rocket design. It's certainly harder from an engineering perspective than when I built Archer, you know, built an electric aircraft. And that was hard. Yeah. That was a very, these are six thousand pound aircraft, 12 motor, 16 independent battery systems. We built our own control stack and embedded systems like, um, so we did all the structural design ourselves, things like this. Uh, so I think, um, I think it's probably up there with like some of the hardest hardware on the planet. And you, you just have got to be all right. Well, let me just keep up that because we were talking backstage at a bunch of 360 last year. And you had a, the basic tech stack had six layers of competency. You could probably rattle them off top of your head actually. But yeah, this is for Archer. It does just for, for robotics prior to neural nets. I guess, so it applied to Archer and figure. Sure. But what were they again? I mean, like, Archer was basically like a flying aircraft. Yeah. So, you know, I basically build electric vertical tick off a landing aircraft. Right. Um, that is basically like, I'm sorry, it's like a flying robot. It's what it means. Yeah. Um, that has, um, you know, basically has battery systems on board. You know, as electric motors. Yeah. Electric motors just be see a, uh, a stator rotor gearbox. Yeah, pretty simple. We have a little bit more sensors and our actuators than that. But like, uh, for the most part, like, and there's like, you know, those encoders and stuff in there, and things like that. You have a, like basically like a control software. Like how do we like control this thing and make it move around? Yep. And in the case of Archer and figure, it's very overactually the system. So, Archer has like 24 degrees of freedom. We have like propellers like, you know, tilting, we have, we have pitch on the, on the blades. Like, you have, um, flaps on both the tail and the wing, tail, um, in the wing. So, um, and then, you know, figure we have over four. 40 or so on the system. You have embedded software on board and sensors. So how do you get the compute sensors and embedded software I'll talk to each other? Then you have structures. So there's kind of the core ingredients of a robot or some physically moving to the world. - Yeah, so the question is, traditionally, the employee base would be experts in 1, 2, 3, 4, 5 and 6. And they'd be really, really good. So then you come in and overlay this with Helix and you've got this massive neural network thing. Is that a seventh competency or is that something that permeates the other, or did you take all of your microcontroller experts and start training them on neural networks? - Yeah, like the next thing archer is like, how are you gonna plan? And you do a few pilot, or my aircraft midnight is a piloted for passenger aircraft. So like who's doing the planning? And basically a higher level, like we see higher level behaviors in the stack of the lower level control and code what to do. And here at figure, it's been changing over time and now it's entirely neural nets with Helix too. So it's like who's gonna, what is the highest level behavior telling the rest of the stack, what to go do? Where's that, you can come from a human, I can come from a joystick, I can come from an open new behavior, which we see we talked about before, or we can come from a neural net. That's like doing the plan in reasoning. So the kitchen demonstration you guys all today and we released, what's telling the robot what to go do next and what to pull the rack out of the dishes and to go grab the cups and not the coffee cups with the water cups. That's a neural net making that plan in. In the case of my aircraft archer, it's a pilot determining when to take off, when to hover and when to transition into full flight. And then how to start with the set, what's the different type of neural net? It's a human biological neural net, yeah. - This episode is brought to you by Blitzie, autonomous software development with infinite code context. Blitzie uses thousands of specialized AI agents that think for hours to understand enterprise scale code bases with millions of lines of code. Engineers start every development sprint with the Blitzie platform, bringing in their development requirements. The Blitzie platform provides a plan, then generates and pre-compiles code for each task. Blitzie delivers 80% or more of the development work autonomously while providing a guide for the final 20% of human development work required to complete the sprint. Enterprises are achieving a 5X engineering velocity increased when incorporating Blitzie as their pre-IDE development tool, pairing it with their coding co-pilot of choice to bring an AI native SDLC into their org. Ready to 5X your engineering velocity? Visit Blitzie.com to schedule a demo and start building with Blitzie today. (upbeat music) - Let's talk into application layers. So we're seeing your movement into the home besides the industrial base and such. And healthcare is gonna be a big part of this. Eldercare, helping people stay healthy at home. By the way, you just came through fountain. - Got, through fountain. - Yeah, I did. - How was the experience for you? - Thanks for referring me. - Yeah. - It was great. I went down to a clinic a couple weeks ago. - Which one in Orlando? - I went to the ortho-garlanda. - Yeah, yeah, I had quadrars. I didn't know what to expect. I've done full body MRIs and CT scans and blood work before, but got there was basically a full stack. I mean, you know this, but like it's a full stack. - Everything measurable about you. - Yeah, exactly. - Like a 200 gigabytes of data. - Exactly. It's been like five hours there left. Got the download like last week. It was just unbelievable. Like what was it great about it? It was a basic comprehensive understanding of like my body, what's happening. But also somebody they were reporting out and I talked to me through how we understand it. - What to do, Nax. - And a plan. - And basically a plan from there. It was great. Like I actually purchased it from like I purchased it as well for my parents and things like this. I think it's just a great gift. - Oh, yeah. - Oh, yeah. - Dave, we need to get you there too. - Why Orlando and why not? Somewhere else like Walewie? - I was on the East Coast. So I popped down to Orlando and so it was just easy for me. - Yeah, we got New York Orlando and Naples, Dallas, Houston's opening, Miami and LA. Now anyway, back to the conversation here. I can imagine this is gonna up the value of health in the home a lot, right? So one of my visions of the future is your constantly monetary for your blood biochemistry, what your protein levels, your vitamin levels and so forth. And that's being uploaded to figure in the kitchen, cooking your meals, ideally suited for what you need in that moment. And then the whole elder care side. - Yeah, so I grew up on a farm in Midwest. And then my parents got into independent and civil living like 15 years ago. So I kind of grew up around senior care a little bit. - We got into that business. - Yeah, my parents own and operate senior housing facilities in the West. - So wait, there's still an Illinois? - Yeah, still in Midwest. - Yeah, Wikipedia says your hometown has 2,000 people in it. - I grew up in like a week well in Illinois, man. I think it was like 1,800 people when I grew up in Illinois. Like middle and nowhere. We had like no traffic lights, like no fast food. It was a dry town. It was just like a whole different world. - Oh man. - Did it? - Did it? - Did it? - Did it? - It's for you when you go back home. - Man, it's just like, (laughing) - We're going by for it. - Going through this. - Yeah, it's gonna be hard. - So you understand the value of a fully autonomous human life. - Yeah, we gotta put like, I'm like really passionate about figuring out how to let like be able to ship robots into in a senior care and letting people age place, Asian place of life. - Yes, yes. - Like even like, you know, it's like, you know, it's hard to get people to move into a citizen and pay the living facilities. - Well, how's that work? - So you sold out, you know, three years into the future. You can't make them fast enough to keep up with the demand. And then you've got BMW, you've got a bunch of industrial use cases, but then you've got this in-home and you've got like a case inside. - Yeah, we like the case. - Yeah, I mean, I'll like give you my, I'll like, you know, level with you on how I think about things. We've been spending the last like three and a half, we're about three and a half years old, trying to figure out how like what the right recipe is in the first instance of like what a general purpose like architecture would look like for human rights. We believe we found it internally, and we understand what that is. And we believe we know how to make robots now and put them out. And we're gonna run them really hard this year. We're gonna run them through. - You showed us, would you quote the grid? - Yeah, grid. Yeah. - Can you describe what we saw in the grid? - The grid is like my favorite place here. It's like a, it's one of, we have like four buildings on campus, it's one of our buildings here, and we have the facility outfitted that we're gonna expand like hundreds of robots in two that will run 24/7. And we have this like a little mission command post, like that's like a second story, like kind of like a 007 like situation room. And you can see every robot there. And it's gonna be doing both home and commercial workforce. We're spinning up like right now, the facility just got open like this week. You guys saw like squeaky clean. And we'll start shipping figure threes into it like this month. - So model homes, model factories, model operations. - Well, so within mission control, you think of like watching the robots, but the robots also have their own vision, which transmits back. So it's more like, you know, in the combat movies, we're back at the home base, they're watching the invasion or whatever, you're seeing through the eyes of the soldiers. You've got all that data coming back in a mission control too. So if the robot, you know, it's too dark and how many in there at any given time? - It's got a lot of 250, 350. - 250, 300 robots here. - Building a house or doing, and all that video and telemetry comes back into mission control as they do it. - Do you believe that AGI requires embodiment? There's a lot of conversation that's been put forward on that note. - I think my definition, like I think like, so I get the chance right now to spend a lot of time on both like the physical AI and also digital AI at Hark. It's kind of both, like both a bit. And I think when I like talk to AGI today, or use it, I just feel like it's so dumb. It just feels like you're starting like a new chat. You're basically asking if like knowledge or trevil, it's like a advanced Google search engine. You know, what I view is like, I don't think about like, we want to build like the future. We want to be like Jarvis, or we want to build like, Jessens. - I want to talk to you. - I want to talk to me, I want to reason. I want to have like perfect memory. I want it to be able to touch the world both digitally and physically. I want to build be general purpose, be able to do things like for me, think about reasoning through things. We have Hark now designing CAD from scratch. It's going out and finding, you ask it to go build a CAD thing. I ask it to build a basic monster truck for my son and CAD. And it's going out. It's like finding a CAD package, it's installing it. It's opening it up. It's like learning how to basically build CAD in the parameters like it needs to look at for building monster trucks. And it was often do it, does it. And we can do that in an hour now. - And just clean sheet. Clean sheet for my single problem. And it's using tools and computers like a human can. And we're going to give it all the same tools. Like we're going to give it all the tools that figure uses for CAD, for FEA, all this different stuff. And it's going to learn all this. And that was at the inspiration for Hark. The fact that there's a lot of LLMs out there doing a lot of things, but none of them are really connected to CAD. And you have so much experience from your-- - My inspiration for Hark is I feel like we're chasing, like all the big frontier labs are chasing this like very abstract version of like reasoning. - Well, specifically, Anthropic wants to dominate coding and code self-improvement. And then OpenAI wants to dominate-- - I want to dominate like a sci-fi AI future. - I want to like, I have a purpose. Everyone knows Jarvis. - Oh, it's a Jarvis. Like I want like the smartest person in the world with everybody. - Yeah. - We have like-- - That's a lot of the fun. The idea that these things go out into the solar system and then ultimately out in the galaxy and start making the system. - No one's out of raw material. - Nobody's doing this. Everybody's copying the other frontier lab that's copying their frontier lab. Like nobody's building true multimodal systems that really can reason and understand and have persistent memory. And that can go out and touch the world and do things. That's my version AGI is like, I can do what humans can do and humans are not sitting there giving me Google search answers. - All right, great. - Which is what we have now. It's terrible. And one aspect, it's great, 'cause like this new alien technology dropped on the planet in 2022. And we're like trying to figure out what to do with it. And but the other aspect is that there's so much the models can do now. There's such an overhang in the product capabilities. And we're understanding that better now at Hark. We're understanding that better now at figure. And I think we're just, we're like abstractly getting to a place where we're building like synthetic humans at scale. And these humans can be both digitally, like we're calling computer use tools. They can physically be there. But they'll be able to like reason with you talk, have memory understand you, and they'll be able to go off and do anything a human can. - Have you been tracking Cloudbot now, Maltbot? - Yeah, I've been tracking Cloudbots, really cool. Yeah, they renamed it to Maltbot. - I think it just like shows you how complacent a lot of the front to your labs have been. - Yeah. - Where you have like such incredible capabilities that can be with very simple harness and very simple like markdown files and very simple tools. You can give it on the back of a focus or whatever you're gonna use. Can you like magical things for the world? And we've had that for like for a long time now. Like not like it just it wasn't like they went out and built a new AM model for this. They basically just put some harnessing in some, you know, MCP and APIs around this. And it like basically went out and can like basically be your executive assistant. It was really awesome. And there's a huge area here to get that to every person in the world and make it easy. And we're doing some model development now at heart that is like I think truly say to the art. And I'm excited about that. And we're also doing some of that now in the physical world. We're a figure. So we have this like digital versus like physical thing that I'm seeing on both. And I'm just like so excited about this future even next like 12 to 18 months. The next 12 to 18 months, I think will be like the largest AI transformation we've ever seen. - Yeah. - And beginning back to your point about like what do we do with healthcare and robots? We're gonna make a shit ton of robots. Like we're spinning up resources right now both at Bacchew or CNU and future Bacchew to basically be able to make like millions of robots. - How long ago for these robots are your physician, your surgeon able to actually support all the complexity of a medical procedure? - I think from a hardware perspective, in 2026 we'll be able to do like from a hardware work, what surgeons can do. - Yeah. - And I think I see you know, giving more route with our roadmap and things like that with figure, I see you know, we can do that. - It's pretty fast. - Yeah, it's pretty fast. I feel pretty confident by the end of this year, you'll have a hardware system that you know, you could basically, if you could like tell you operate or something like that, you could like basically be able to do like real surgery. It's been so tight, but I think like most things are done. - And then the AI system is just layering on top of that. - Yeah, then you gotta get the brain to work really well at these things and like you know, this has got to work at the highest level of like performance. - I'm asking. - So if it's learning gives you an incredible amount of - I think we're like, I think we're very close to this work. I think we've already shown if we can get the right data and the hardware, if the hardware can do it, like if the simple hack is if you can tell you how to do it, we can learn it. - Yeah, I mean, it's a one point, few believe you understand. If you can tell you operate the robot and mechanical systems, the motors, the fidelity can be done. - Yeah, we're just like, and then we're like dumping on tell you out, but like tell you how it's got one good, a couple good things, we're like, it's a really good testing tool, it proves out and it proves it's a hardware. And if you can't tell you operate it, you're not going to learn it. You know, if there's restrictions in the range of motion or payloads, you pick up something heavy, the robot can't do it, their entire operation, it's not going to be able to do it when they learn policy. So I think if you can tell you how you can learn it from a harder perspective, I think we'll be there in terms of like more dexterous type things we talked about here. And then I think what we've already shown is if we can get the right data for it, we can get the hardware to basically do anything that's capable of. - And then you can add infrared ultraviolet, you can add all kinds of additional sensors into the system. - Yeah, for sure. I mean, we have it now, we have it with talk to, like with like the palm cameras, a good example, like humans on palm cameras. And we've been now seeing a boost in performance. - They do. - A lot of cool things, we're reaching in the cabinet now. - They know the camera. - Yeah, as soon as they, on the tour, as soon as I heard it, like, totally makes sense. - It's great, yeah. - I mean, how many times a day are you like reaching? - Yes, thank you. - We're like, you know, blinds are putting your phone down and they're gonna get the camera to look at it. - Yeah. - I'm sure we would have evolved and I right here if we were physically possible. - It is an interesting question for you. You've got the cameras in the head, again, mirroring a human in the hands. Why aren't there cameras rear facing or 360 degree facing? - I just thought, I just thought of a amazing drone, the anti-gravity drone, if you see it, it's the VR headset and it's got 360 above, 360 below, backwards, forwards, and it's extraordinary. So, what, I do think we do, we have a little robot. - You do, we have a lot of that for facing cameras. - Okay, I have to ask this question for our, if you just like, no, for some people over, look behind them, the cameras are like, okay. I'm seeing you rotate here, I'm here. So, one of our moonshot waves, to leave this now, you might know me is there, when a co-founder is ray at sea layer universities, like, why in the world are there only two hands? Why don't we see robots with like four hands or six hands? - To put that to bed, what? - Yeah, personally. - Yeah, we could ask this a lot. It's like, when I'd like superhuman and all these different things, which is a lot of the questions. I think, my summary to this is like, our goal is to be able to do a human scan. And then you wanna do it the cheapest and like lightest possible way you can. Like the lighter, the better for safety, the cheapest is obviously very important. All of those will affect manufacturability in scale. When you start building things that are better than human in a lot of ways, like if it can, you know, run a three minute mile, if you can do backflip, it was like, like, you know, a bunch of arms. It's gonna make them about really heavy. It's gonna make it really costly. It's gonna be really hard to manufacture. And then your question is like, okay, when I look at like the logistics use case, I don't think you actually have four arms or six arms and move any faster. The line is like relatively, it's like, you know, maybe a meter or so in depth. You gotta kind of get a package. The package needs to be roughly in the center of the conveyor system. So the scanner below it can scan it and put a label on. So, you know, in that case, we basically have another 3 to 5x in terms of speed and the actuators that we can run that software's not enabling, 'cause it doesn't know how to do it yet. So we can run like three to five times faster than what you saw today. - Wow. - 'Cause we get the whole body to run. - Yeah, we can run them robots that, like, we look at it in terms of like radians a second. Maybe we're virtually looking at RPMs. We look at radians a second here. We have another three to five times headroom and the actuators that you're seeing now. - I would love to see a real problem. - The cost of a mistake, like, you know, when you're unloading the dishwasher at the current rate of speed, the cost of a mistake is relatively low. You start running three to five x faster. - It's just like, it's just like, and that plate is moving fast. - I just don't know if it's really needed. Like, you're gonna get a really expensive robot and it's gonna be like less safe. It's gonna be a harder manufacturer. And then you're gonna have like a, you know, over time, you're gonna get the robot down to $10,000, $20,000. So you're gonna have a $10,000, $20,000 robot there. And then you're gonna have a really expensive robot. It's called $15,000. - Yeah. - And like, and costs is really a function of manufacturing volumes. So you really wanna build like the car. - Well, that's why going after the industrial use case is such a, you know, - Or just giving it a whole, - Like, the home and he's like, every, like, this for this. - Well, the home is the home is huge in the end, but if you're running three to five times faster than what we're seeing right now in the home and you, you know, you kick the cat or something like that, that's not great. In the industrial use case, everything is kind of taped off, you know, and it's, - I remember I was interviewing you for my next book, which comes out in April. Here it is, we are as guys. - Oh wow. - I talked about this, but I'm super excited about, and of course you and, and figure our prominent in the book, because this is Godlike. I mean, it's extraordinary, giving life to new systems. I was interviewing you about how many and what the price point is. - Yeah. - And I wanna just double down on that because the numbers are pretty staggering, and they make sense. So if you're actually getting a price down to $20,000, a robot, I haven't heard 10,000 a robot, but 20,000 a robot, you're leasing a robot for like $300 a month, $10 a day, 40 cents an hour. And then you ask the question, okay, if it's really $10 a day, how many would you own, or would you have? - You know. - You end up with a lot of robots. So what's your estimate on the number of robots on planet earth, 2035, 2040? Where do you think that's going? - I mean, I think it's relatively straightforward to think that every human should have a humanoid to do all your work. And then we should have maybe in order of like five to seven, maybe 10 billion in a commercial workforce. So I think if it all goes well, I think you could basically build tens of billions of human rights on the planet. I mean, you're basically building like a replica of a human, that's really cheap, that works 24/7. And so there's really no, and then we will be at a point, hope in 24 months for all the robots to build all the robots. - Well, that's where I wanted to ask about skill, 'cause you said, you know, we're gonna ramp up to millions a year, like well, one per person on the planet is eight billion. So millions per year really isn't that much. So then you're like, okay, the self-improvement loop is gonna be incredible. - Yeah, you also need like, we talked about this, but you also need like tons of working capital. If you would have billions of robots, I'll explain it. Even if they're, let's call it $20,000 a piece, it's on 20 trillion. dollars of working capital. I mean, you're not that silly. There's a billion cars on the planet right now. It's not like more than that. But if you tried to build them in five years, it took 80 years to accumulate those cars. Some of those cars are 34 years old. A couple of them cars on the planet, but we have like, we make a billion or more cellphones a year. So like, yeah. And I think it's more cell phone like we're some personal. Like, I don't even go back and forth on like, if you're robot brakes, do you want like a brand new refurbish robot? And you want the old robot used to have because you've known it and you understand it. It's got personality. It's like, I think it's going to be with you. It's going to know everything about you. Why wouldn't you just have a personality transfer? You could. But I think there's like some inner workings of like, I like, it's got like all the, you know, scratches on it. You know, it's like, it's your thing and it's got like a little bit of feeling. But yeah, they're like for sure. Like, I think that'll be fine. But let me ask the geeky finance question though, just before we lose the topic here. Sure. If you have an all neural network based system, it can learn at an incredible rate. The technology is advancing remarkably. You look 24 months in the future. The demand is on the order of billions, not millions. Like you said, to build that out in one iteration, you'll use the cell phone as an analogy. But Apple had 15 years to profitably ramp up production to a billion years a year. And so the demand is there to do it in one year. But you would need a trillion dollars, some insane amount of capital. But that's no longer insane amount of capital. I mean, we're seeing, I think you can. So what do you do? You leave the world starved asking for the robot for five years or do you rate the trillion dollars? You look at like critical receivables or currently seeing these are trillion dollar markets in terms of financing. And so I think the financing market's there for this. What do you do? I think like one is you got to solve the neural network. You have to be able to scale with neural nets and you have to solve pre-training and you have to solve a generalization. So you have to solve for a general purpose robot. That is like tables. You have to you have to solve this. That's what we're so obsessed with like trying to solve it here. If you don't solve that, none of this matters. The second step is you have to have robots in the loop like building other robots. So those two things have to be solved. And you have to design the robot in order to make sure it's it can hopefully design itself at the end of the day. So like we like there's a bunch of stuff we're pretty in place in terms of like manufacturing execution software, the lines, all the design of it. So we can at scale have humanoid go in and build another humanoid and get them off the line. And so I think like I don't think I think this is like a and it took us a while to kind of you know, I think these adoption curves are shortening and shortening. And I do think if we could solve a general purpose humanoid robot today that could do everything you wanted, I think we could ship a bill today. Yeah. We could take it. I think we should have a bill in today. Yeah, I totally agree. So so basically it comes like can you get the neural nets to work at scale? Can you get the models good enough to generalize to this scale? The original purpose call a general purpose robot like a human ensue. Yeah. And then can you get robots and the looped another one? So the other thing is that's really compelling is like the neural net is the only IP you need to protect. So as long as you have the federated learning coming back to the mothership and all the training is happening centrally, like you know that the Star Trek Genesis project, right? You get a little capsule. It has basically the germ of like the you could ship literally a box to Kenya that's like here's the figure box. It opens up and it starts making a figure manufacturing plant right out of thin air in the middle of Kenya. And if there's capital there to bring the resources to it, then that's how you get influence the calve. It's a lot of that. The intermost loop is energy and and AI intelligence and you know like local mining for the materials or whatever, but it's completely self contained. But the key is that you just unlocked that capital that wanted to build something productive. Yeah. The IP is still flowing back. 100x in the GD to train the neural net centrally. Yeah. 100x to GDP of that jurisdiction. There's latent capital all over the world. So we talk about, you know, there's a lot of fear out there in the world about losing jobs to AI and to robots. And the reality is the conversation that shifted now to well, no, this is going to create massive abundance and universal high income. And that happens if in fact the rather than the company hiring a robot to replace me, if I hire a robot to go out and do my work for me, and in fact, it's able to triple my salary because it's working three shifts. And it's doing that for me. And then it earns enough to get a second robot working for me. Yeah. And so the question becomes where is that capital captured and is it inside the hyperscalers is inside of the individual. So that's going to be the interesting conversation coming up. How do you think about that, Brad? I mean, we're going to sell robots at scale. We're going to be able to deploy as many robots as you want to whatever you want to do. Yeah. It will do whatever you want. Like no instruction manual. What do you want it to do? It'll learn it. It'll research the internet. It'll use digital tools if it needs to. It'll talk to you at a reason. If you're just going to be safe to privacy. Let's talk about safety in the home and privacy in the home. You know, they would lawsuits over the last years with Google and Amazon. It's listening to you in your bedroom and so forth. How do you address safety and privacy? Or is it just, is it going to happen? It's just too early because we're not. I think there's a really hard questions answer like in one go because like there's a bunch of different safety implications here that are like just, uh, it just safes like probably the number one thing to tackle to get robots into the home of scale. Yeah. There's like a semantic understanding of safety. Like if there's a, you know, a candle lit and I knock it over back. Or if there's like a, you know, boiling pot of water if I hit it like just understanding how to be safe in an environment where humans are at. And there's actually the intrinsic safety of like, can the robot be with humans and animals and pets and be safe? Yeah. Like those, like that has to be solved. We can talk about like outlinked about like how we're going to solve those problems. And then you have the whole privacy, cybersecurity, other aspects of this that need to be like, like, uh, with good intention. Like, like how do we solve those problems? Um, we are working on all of those now. They are very difficult things to go get right. Um, I do see a path where we can build intrinsically like really safe robots around people and pets. Yeah. Um, we have a plan for how we're going to do that. I mean, they could be safer than humans by a large margin, just like autonomous cars are safer than humans into the day. Yeah. Like super human perception, we can see basically all around us at all times. We're always on. We're always computing like what to go do. We're, um, you know, so I think, you know, assuming nobody's trying to be like, like, you know, mean to the robot, just like that, I think we should be extremely safe around. They're doing it. And then I was on privacy, like, you know, these are going to be in your home. So, um, being upfront about what, what data we're collecting and where that data is going and how we're, um, keeping that data private and encrypting that data. It's like, all this is like super important. We have a, we've entire, uh, team on cybersecurity here and how some of the, uh, both of the product and commercial side, a corporate side that are working through, like, how do we think about this at scale? Yeah. Right now they're great. They're from like the big, uh, the big companies have been doing this for a long time. And we think about the corporate side as well as the product side on the robot side as well. Yeah. You're a facility here, uh, which is your sort of prototype manufacturing facility. Uh, 50,000 robots a year, you imagine. That facility can support about four lines, each line can do about 12,000 units a year. So, little under 50,000 units a year, uh, what's your next step up, do you think? I mean, we're building like thousands of robots right now. Um, so, like, that's the big push we're doing, right? I mean, you just saw it today. Like, that's the, the figure sees that we're doing off the lines today. Um, you know, and then there we want to go to tens of thousands and then hundreds of thousands and millions. I think we need to take those like steps as a company to go do that. Uh, this facility will top out 50,000 a year, a little under 50,000 units a year at full, full capacity. So, kind of think about a long term like our, you probably be a low volume. We look back in five or 10 years and be like, yeah, I think you might franchise out the neural net and the circuitry around it, you know, because all these other people are saying, oh, like, I'm building a robot that cleans industrial pipes and building a robot, you know, all these different form factors. And now, now just, I think it's super unsafe. I think we see these robots out there like this. I think like, uh, they're around humans. We don't have like, we don't own the hardware. We don't know what they're doing. It's like our neural net in it. Like, I think it's, um, interesting. Yeah. I think it's like a, it's similar to archer when we're doing archer, like a building archer, like I think it's like a safety critical system. Yeah. Especially like archer since there, like licensing out there, folks and stuff like that is like a very problematic. Yeah. I think here, it's like the same thing. Like, he wants even a right. Like we have a future due to your civilization to build like really safe human robots at scale. Yeah. And, like just giving this AI system or even hardware to anybody that would want like this is like, uh, not something we will entertain. So then when do you branch out into other form factors like, uh, you know, I don't think we're under water. I don't think, I think the, the amount of, I think in the future, everything that'll move will be a robot. Mm-hmm. And besides humans. Yeah. Um, and within that, I think humanoid will dominate the plurality of all robots. It'll just be so bigger percentage of them. Like the other robots will be like niche and expensive and done like like the super duty trucks that you have like outlining. Uh, they'll just be like, uh, made for specific areas, maybe underwater as you say or some of that. Like hard surgery, you've got, or brain surgery. You've got these very, very fine. Uh, it's like a robot controlling a robot. I think you're left with like very expensive equipment. That's very siloed. Like you really want to build a general purpose machine that can, uh, they can learn across a variety of different tasks and have that transfer learning. I think that's extremely important here and that needs a very high variety of rich data. This is only going to help the robot system get smarter and better. So my view is, I think it'll be like human order robots on human order robots everywhere in the planet. And there will be other robots there, but it'll just be like a niche businesses. When I was climbing up here, I posted your video that you released on, and he looks to you today, and they asked the community for questions and this blew up a whole bunch of amazing questions. So one of the questions is, do you have a blooper reel and can can folks see it? And then what's the weirdest task someone on your team has tried to teach it to do? And it absolutely did not work. And that's from Ben Casper here. Ben Casper, nice. The weirdest task. It did not work. Well, like weirdest task. Listen, every jogging was interesting. Well, okay, jogging was fun. jogging was cool because we like really had a steerable jogger. And a lot of this work in like running has been like again, open loop, but we had a steerable RL controller we could do. Another one, oh, actually I have a gift for you. It kind of goes into two figure dead mouse hats. We basically, we opened at Red Rock late last year at a dead mouse concert and had a little about this stage. So we generally don't venture out into weird stuff. There you go. Nice. And we actually did, we had dead mouse at our last two holiday parties. I think you're just like fun. And we would generally are pretty much like how do we design something really useful, but we've had some pockets of time to do fun stuff like this. So I think like having robots on stage, dead mouse and Red Rocks was just I flew in for it was just it was unbelievable. And you had a month stage. We had a several figure twos on stage. Jamming. We had them all synced so that synced to the music as it danced, which is really cool. So what it heard, it was like moving towards which was on stage last year at the abundance summit, but figure wasn't with you. So you need to get you back there with figure in the loop. Totally. Yeah. For sure. So when are we going to see the first figure in customers home? The next question. Yeah. We want to we want to I want to ship robots when they're really ready. I don't want to ship slot. Guys, guys. Young earliest latest window. We we probably I think last year I said in you know, in this year in 2020, in 2005, we in 2006, we launched. We launched a robot to do like end-to-end homework like an alpha test scene like in my home to do like full like mopping cleaning, full scale like you know long horizon work, figure you and your daughter. Yeah, putting stuff into we've done like pockets of work really well. Like we've done like dishes and laundry and all this and we can like you're seeing something that's getting tied together now, but like I want to do it across like days and weeks of work. And I want to be able to drop it into somebody's home is ever seen and also make that really work well. And I want to be able to talk to it. And I want to be able to understand me and be able to remember things and be able to show us stuff. I'll be able to walk through a room and show it like almost like a visitor you have at your house for a week. I'm like understand what to go do. Wait 27, 28, 29. My my best guess is I think you know I think like well I'll tell you we're we're working until midnight every night to solve this problem. It's like it's like it's like a rear-here every weekend every night to try to figure out how to solve this. This is this question we got on a one-shot general robotics. This is kind of where we went ahead. I think by end of year we'll we'll be able to put a robot into an unseen home and be able to do fairly long horizon work. And then you want to measure how many like human interventions you have is it every it's once an hour or once a day, once a week, once a month. And I think we'll do that. I think that would be a huge accomplishment for us. I think we'd be on the path to solving general robotics. And then I think next year you'd be on a path where you could ship them into users homes and start like making sure they work well. So I think anybody that tells you like hey we're going to ship them or tell you up them in the home or we're going to ship them in at scale in a year like there's you've got a ship in a small quantity and they got to work well. And then you got to work out the problems and you got to then ship again you have to have an iterative design roadmap which we have here and we need to learn. So it's going to work well at one. It's going to work well at 10 homes. It's going to work well 100. It's going to work well at 1000. It's going to be 10,000. It's going to be 100,000. It's going to be a 10 million. So I think it's going to be like super-central growth curve. Exponential growth curve. So I think there's anything to worry about there in terms of time to market is because you know the industrial use like I said you're sold out for years to come anyway. It's competition going to come in and grab market before. Yeah we we feel like your kids are something. The work we show today and the work we showed two years ago has never been done in our mind. Whether any other human like company in history. Yeah. And so that's the marker. It's whenever somebody can do the curing test for a couple minutes with uncut film and I can like watch it close loop do it. Yeah. With buy minimum even not even just standing. That's your two years away from a rep. Okay. So I think we'll see like we're trying to push and continue to pull ahead but I think hopefully by next year. By next year. We can basically really show like real general purpose in Suther Robot. Maybe as soon as this year like me listen it could happen in a couple months. We are we are we have the right stack now. We are we are we are building data sets at scale like so quickly. We are spending so much time and money on this internally. We just launched our new B200 cluster within like a video helped with Jensen helped that went live like this year. How many have I GPUs in here? We we're you're going lot we have we have 3000 B200s that would like better go in live and we have another set much larger GPUs that we plan to put out here and training or we just use it for pre training. Yeah. We do it here physically here. No we do not do it physically. A lot of power. Yeah. A lot of power. So Jay create asks a question to the science fiction geek amongst us. So what's beyond three as mobs laws for you? Have you thought about that? Have you thought about sort of fundamental laws to program in your robots? I think you really want to put these rules down into the kind of non-violent tile memory on board the robot at the chip level. Yeah. So true level. Yeah. I mean you must have thought about that. We've been thinking about this quite a lot. And you know it's in one hand we still want to solve like general purposeness. In the other hand we don't we also want to figure out like once once we're like close there how do we also get all the supporting things ready to go? And this is one of those it's like it's like safety needs to be there like privacy needs to be there fleet operations like the reliability of the robot the like maintenance plan for like how we're going to service this and everything in the business model all of it and financing all of it need to be packaged ready to go. So we're working through all these now. I don't know it's funny it's like it's like you know as much I've got a lot a lot of things right and I feel like the lot of the three like you know like these foundational rules for how do we treat human is like you know we have our own spin on this that we I won't like publicly tell today but like that but like you know the goal is like to do good work and and is it something everyone learns internally and corporate training and memorizes and all that? It's something that we want to put we put and we're going to continue to put on all the robots. So you have a new born. I do what? You have a new born child. Oh yeah. So the question here from KK says when would you trust figure to hold your new born? Yeah that's an interesting one. So the new the figure three is soft. It looks like it's designed for the home but it's still that hard to think about. I like this question a lot because I at Archer I always say like until I put my me and my kids in family on the board it's not safe enough to fly anybody. Yeah. And like I wouldn't do that today at Archer and I hope soon I could do that. I figure here I think it's the same question as like when I feel safe enough to have a robot in my home. You had in your home but like you know I've been there we've had folks there and you know we monitor it. Yeah. I think we're like truly safe and we're not there now and I think that's a good bar for us to hit. When I can put a robot in my home fully ton of sea end to end around all my kids. I think that's a point where I would trust it. I think that's a point I would say like this is ready for everybody and it's a good it's a good like heuristic for us to really try to hit and that's our goal here is to be able to put it like you know like free-rained in my home to go do and now we like you know we're there with it we babysit it and like we watch it and it works good. I've I've showed videos of the robot I've been kids like with the robot like there but like you know I think we're doing it in a safe way. Yeah. And the robots have been totally safe which is great but like the one is we need to build like a system safety architecture that's really really fall tolerant redundant in real time and we're we've done that and we're doing a better job of that in the future and two is you you just have to build a safety tracker for this. There's nothing better than like actually proving this thing is going to be safe. Well it's a nice barrier to entry too if you you know kind of take the apple road to it's got to be a great out of the box experience well that means not stepping on the cat. So it's really not dropping the baby. Yeah. And then the cybersecurity side of it too not transmitting everything back and having it posted on the internet. Yeah. But if you get that reputation which it sounds like if all companies I've met you're perfectly positioned to get that reputation. Yeah. Don't make a mistake along the way. Yeah. And then everybody just says you know what? I'm going to choose a figure robot because I just feel it's the same way people feel about the apple brand with cybersecurity. Yeah. So I think I hope people walk away from this knowing that like general purpose robots are coming. It feels very close. And then there's a lot of other things around there like like that you have to get right to build this scale. You're making a decision you want to get across here to be watching. I think the main message we feel every day if people are excited about like AI and robotics is that this is going to happen really soon. Yeah. And it's happening. I mean people don't have I don't think people will have a clue of how fast this transition time is good. - I mean, just go to our YouTube and watch our videos for the last two years. They're like, in like watching side by side, it's dramatic, a change every single year. I mean, you saw it today in person, and our robots now have been in like, you know, customer sites and things. Like it's been out and we're gonna continue to show more, but like, it is hard to feel 'cause you don't see it every day. But at some point, you're gonna walk out, probably in San Francisco would be the first, and you'll see more humanoidism humans. - Yeah. - And I think that'll be an amazing day. - And right now I'm driving in Santa Monica. Just by the way, we just did a podcast, early this morning, happy wood, who sends her best. - Oh cool. - She's a huge fan of yours. - I'm the invested in me at both our churn figure, and she's great. - Yeah. - She feels the same way. - Yeah, she's very proud to be an investor in figure. And it was time, you know, when I'm out in my kids right now in San Monica, we do something like counting the number of waymoves that we see. - It's crazy. - We'll see like 10 waymoves. And then the cocoa robots, little ground robots, like the starship bots and such. I mean, they're all over the place. - Crazy. And it's interesting, right? Because you, first time you see it, you're playing or falling, you're taking a photo. It's really cool. And then you take it for granted. And then it's in your way. - Yeah. - Right? So my wife and I, in 10 way, we had like last weekend with Danite, and took away my downtown. And it was just so unbelievable. And the experience feels like, you just like as an engineer, working on these hard projects, I feel like the amount of engineering work they had to go do to put up together safely. - Google didn't decide. - You know, you control the control, they have the music and the lights and the environment. Like if you take a New York City cab and you get in the back, and it's like this smoky hell, and then you get into a waymo and you use the app and you turn it into your little paradise. - It's like such a nice job taking the product, right? I mean, Larry Page saw the product when the DARPA Grand Challenge back in 2005. And committed to it and brought the team. - I mean, I think it's been like 16, 17 years. - Yeah, and just they stuck with it. An astroteller at X basically built it out. And then waymo's an amazing, amazing product. - They've been like undeterred for like 16, 17 years. Like don't worry about it, we're just gonna make it and they did it. And it's unbelievable. - Yeah, no, amazing. - It's very inspirational. - Can we ask you, my geeky sci-fi meets geopolitics question, Dijure. So I just got back from Davos on Friday, today's Tuesday, so nine times on his way. And the big topic at Davos, of course, is Greenland. And all the Europeans are saying, Greenland could never possibly be mined. It's impossible to extract minerals from this frozen cold tundra. And we have some family mining operations in Minnesota where it's not nearly as cold, but still pretty damn cold. - You don't have, you know, mile one thick ice sheets. - We do not have mile thick ice sheets. But I think if you're talking about a billion and then eight billion robots, and you need the materials, and that's the only constraint, and you have robots that can operate-- - Even my nasty buddy. - Seriously. - You think we're gonna be doing asteroids before Greenland? - No, we'll do Greenland first. - You think Greenland is viable. Like I'm not talking about 20 years from now too. I'm talking like if you wanna build a billion robots and say six years from today, - It's a $50 trillion marketplace. - Yeah. - You need to demand that. - Don't you think you'd find a way to get through the ice given a million robots working on it? - It helps so, you know. I think we'd find like maybe better physics, but definitely better engineering solutions for this. And then we would be able to put unlimited amount of capacity of humans at it. - Yeah. - Through humanoid's. - Yeah, yeah. - That's what I'm thinking too. - Yeah. - 'Cause the machinery that I see, it's massively automated. It's still driven by people, it's still operated by people that doesn't need to be. - Yeah, it's like, it's crazy that shit works, right? - Yeah. - Like the humanoid, like just like the neural nets. It's like, it's just, it's, it's, it's, it's, - It's like when you make it work on unloading the dishwasher, people don't realize how close that is to work on every other task. - The digital internet. - In like folding laundry these things that we're already doing are like so hard. - Yeah. - They're like such hard tasks. Like you have like these compliant materials that are all changing with you dynamically. Everything's not in the right same place. It's like very different than being in a conveyor system or a manufacturer something like that. And they are going to do it today. We can do it. And now it's a matter of like doing it better. - Yeah. - And doing it like, you know, higher reliability across more diverse, you know, across the distribution of what humans do every day. Like that's a data play. - The thing is if you, if you achieve that goal by hacking together a hundred thousand lines of C++ and tele operating it, it would look the same, but it would be nowhere near, as conquering the other problems. - But if you did it purely, it's nothing but a neural net. And it's purely trained. That means you're within a millimeter of every task - We are impossible to find. - We feel like the millimeter here is just data. Like the only difference of why I can do the logistics and now I can learn like, you know, tail folding or while I can learn like dishes or whatever we end up showing it, manufacturing it literally is just data. And just data goes in the neural net. Now I can do this work. Cause there are about hardware is in the updates. It is a new neural net weights on board. - Yeah. - I think like we're just bound by data now. And I think that's like the, it's like, you know, it's like not a trivial thing to do. It's a great, pre-training set for this at scale, but like we have a bet that I think will work. And we've been deploying that at scale for the last three or four months. - Yeah. - And I think we'll stay tuned. I mean, we're working through it. So like, and I hope this will lead to really, I think you'll see a lot of positive transformers from a robot that's able to like, generalize to a lot of things. - Yeah, amazing. One last thing before we wrap up, I would love can we pull the camera in close and maybe give us a tour of Figure Three? - Yeah, let's do it. - Thanks for the close up and intimate tour. So, Figure One. - Figure One. So we basically, one cool thing about Figure One is we designed most of this in house. We didn't care about looks. We cared about I'm walking to AI and control steam. You know, it's like something they could use from a software perspective. - Yeah, of course. - So we designed and walked this robot under one year, so it incorporated the company. The thing is probably the fact one of the fastest times in history. - That's a lot of parts, man. - Did you draw this by hand? - Did you see their interlinked? - Yeah, David, basically our design lead, design this. Not as pretty as robot, but I think it has like, it had what we needed, which is like a functional robot we can get up off the ground and start using for like all the AI's policy deployment. We did the curic cake up with this robot. - Oh, yeah. - You can definitely move it, yeah. - Sure, there's gonna be collectors items someday. You're gonna make this thing you bought. - So it's heavy. - Yeah, it's about maybe 130, 140 pounds. - That's not that different from that. - Yeah, not bad. - It's all the most CNC. - We CNC aluminum most of the structures. - Yeah. - And then what else do we know about this before we move to figure two? - Basically we did, we wanted to care about speeds. We didn't really care about wiring, some electronics, like a lot of the design. It was mostly just like get a functional humanoid robot else we can do development on. - Right. - So we did that, we built a few of them. We did a lot of like, we did our first neural network on this robot, which is like, I think was phenomenal. We did so much development with it really quick. We also learned how to build actuators, battery systems, wiring, structures, kinematics, joints, like all this is like stuff we learned. - Yeah. - Different sensors. And then we used all this and we integrated now into figure two. - So you get the cost down, I know from two to three by 90% what was the cost from year to day? Probably another 90%. - About the same with you Frank. - Yeah a lot of it was actually testing parts and we moved out the tool parts, the three. So you did two cameras here. Two cameras here, we have a back camera. - Well we have a unit missing. - Yep, we also have the cameras right here in the torso running down. So we can see where the feeder out and you see if a box occluded. - Come take a look at the camera, the back of the robot here which is like the camera pulling down. - It's right there, the public. - So back here you've got what's going on here. So there's camera ports here. - Yep, we have basically a camera, a back-row facing camera. We have different ports for debugging. If we don't hook up a cable to it and we can also turn the robot on and off from here. - Amazing, yeah. And then basically we moved all the wires internally to this robot. We, all the structures is exoskeleton. So all the exterior loads of my aircraft are to our church, the skin, the housing to our loads. We do the same thing here. So out of the shell, to go to the loads, we have our second generation actuators. We had our third generation hands that are on this robot. We have more cameras on board. We have about, I think, double or triple the amount of compute and about double the batteries, a better classic onboard. - Yes. And the degree of beauty went up. - Yeah, like, yes. David did a good job making this much more presentable. - So what is venting heat out the arm pits? - Yeah, just like, oh, so yeah, it actually sucks there and here, we push it out through the torso and the body. - Okay, what's going on in the back of it? - Yep, those are like, we basically have these different paddings on the knees and some parts of the arms. Did you see make it so that, if you basically got your finger stuck here? - Oh, safety. - Yeah, maybe it would hurt if it wouldn't cut it off. - Yeah, I don't know. - So like, so someone, maybe we see like a car door. - Sure, today. - And here's the workhorse. - This is our, yeah, this is our figure three. - Nice. - This is our figure. - Yeah, so we basically, a couple of things. We made the robot like much skinnier and lower mass, but kept all the speeds and torques the same. So just as powerful and just as fast, but also kind of skinnier with the mass base. - This is about 135 pounds, this is about 150, a little over 150 pounds, yeah. We, - Carrying weight, how much weight can make a different hand? - About, about 20 kilos. - 20 kilos. - Yeah. Complete a different hand. The hands have a glove, tactile sensors, complaint material on it for better grass, and also a camera. All the parts, basically, are most of the robot is softwrap. You can see it kind of up here, squishing us to the chest and different parts of the robot. We have like no more like, we have very few pinch points in the robot. What else? We reduced the cost massively to have a better thermal system, compute system. We increase also compute on this robot as well from the last generation. We have new feed that have a toe. You might think the toe is like, "Yeah, no, it's a major part of the toe." It's helpful for like, it's a passive toe on the foot, but you might think of this like, it helps to walk better, but it's not just that, but when we get down here, we're on our toe box, really helps basically get the range of motion. Without that, you might need more joints. - But I think I'm just a little bit more-- - Fred, talk about the face, 'cause this is a big question of, do you develop, do you show facial features or not? And you went, what do you think? What do you had to West Wilner? Or you had to do an eyebrow? - I mean, I know. - I know. It comes across, it's beautiful, right? I had to be a beauty. And it comes across sleek, but it could have a negative, like a little dystopian feel with a black face. - So we have three screens on the robot. This is powered off. We have a main screen, two screens on the side, and then we have obviously a bunch of cameras and sensors in the head. So on the screens, we basically do anything. You could watch Netflix movie. - With the brain. - Looking to my eyes. - Look at your eyes. - Yeah, whatever you want. Like kids get bored and it's like, so some up there. - The brain is red in here, which makes a ton of sense to me. - Yeah. - And where the Romans, ancient Romans thought. - You basically need a motive on board computation. There is nowhere else to put it right now. - Yeah, exactly. And yeah, and that also is easier to vet the heat from here too. And then you just put all the sensors up here and it totally makes sense. - I guess I could put a latex face over the head. I wanted to. - Yeah, you can like, basically put a silicon face and put hair on it. (laughing) - We're gonna go. - I'm gonna get to you. - We also have other outfits. This is one of our logistics spots. Same robot, basically we were able to outfit it with different types of soft goods. And we have another robot here that we'd be seeing. I've also put the work that's wearing a jacket. This is like cut resistance. So they all have different traits. Some of these gloves are also better for grafting different materials. That might be say dusty or maybe it's like piece of sheet metal or it's slick. - Do you think it would operate in zero G? You just need a better training set. - I think so. Yeah, I think we really love to run a little bit on your scale in space. - You're gonna populate the-- - I've got my zero G airplane. We should get up to it. - Yeah, let's get these things on there. - Yeah, that would be great. - Yeah, well, we're gonna fill data centers in space very soon. Someone needs to assemble them like zero G is the operating. - And then we'll get other planets too. It'll be super important. - Yes, yes. - That's a lot of materials. - And then we'll disassemble the moon and the asteroid belt. - Yeah, it's really easy to put materials. (laughing) - I like the sun. Alex will love you so much. - Let's do it. - If you made it to the end of this episode, which you obviously did, I consider you a moonshot mate. Every week my moonshot mates and I spend a lot of energy and time to really deliver you the news that matters. If you're subscriber, thank you. If you're not a subscriber yet, please consider subscribing so you get the news as it comes out. I also want to invite you to join me on my weekly newsletter called Metatrends. I have a research team. You may not know this, but we spend the entire week looking at the Metatrends that are impacting your family, your company, your industry, your nation. And I put this into a two minute read every week. If you'd like to get access to the Metatrends newsletter every week, go to deamandis.com/metatrends. That's deamandis.com/metatrends. Thank you again for joining us today. It's a blast for us to put this together every week. (upbeat music)