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

10:53Peter H. Diamandis:are any quite accurate, but like, okay, well, you can, you can, you know, I mean, I think, you know, I met Sam and open AI team and they were just really interested in getting into robotics. And it was like in their early, like, you know, master plan is to get into like basically shipping home robots and um they you know really wanted to um kind of like you know basically uh basically work on a very intimate like relationship uh they ended up leading our like co-leading our series b along with microsoft and we started working on basically like a collaboration agreement to help like work on next generation models for humanoids and um you know we were like super like big then and we still are on like how do we like language condition the whole stack uh how do we use like an lm in a lot of ways is just like this like world model and i really understands like in the weights like basically like what things are what it should do has a lot of like good semantic understanding yeah we're trying to how do we tap that for the humanoid how does like how do we learn from this yeah uh as skill and some of those representations um and it just like the partnership just didn't work like my our team just ran circles around them yeah for basically better part of a year and it just gave me to a point where like uh 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 uh work after work the cure coffee stuff was done by us all this stuff was done internally yeah and at some point just didn't make sense to train other folks on how we basically build ai models internally for embedded systems like a

12:22Dave Blundin:humanoid did it turn out that that uh llm matters at all in physical like you could start with an

12:28Brett Adcock:open source llm and was it like a vla like a vision language action model yeah basically like

12:33Peter H. Diamandis:uh i think the lm is definitely a certain piece of this um uh like we basically want to like uh like take it like the semantic grounding then like a like a vlm yeah like the common sense yeah like how we like understand from this so you know uh which we have in you know the helix today super critical but like getting to a point where we can um understand physics in the robot and have it like uh really be able to plan and reason at fast dynamic speeds uh was something that nobody in the world it's never really done before right and i think that that's the work that we i think have been excelling at and we love it's just like how do we get it's like understand physics i think

13:09Dave Blundin:i think most of our audience probably knows this but just to rewind the tape you know the llms gbg2 gbt3 built entirely on text data scraped right off the internet yeah and then they supplemented that with a ton of other data also in text form and that creates this this machine that has tremendous amounts of common sense 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

13:35Peter H. Diamandis:actually doing yeah i mean like maybe 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 uh and it's like you know on the surface area just the math around this is like like the dimensionality space is really 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.

14:00Dave Blundin:That's a lot. So you're not going to stimulate those one by one. Yeah, exactly.

14:03Peter H. Diamandis:So like, um, 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 do I plan the, you know, to grab this is, you know, how do I put pressures on there? And I understand those representations really well. Um, you know, from observations now into actions that I'm doing a test time. and um this is not ml yeah the lm knows none of this yeah the lm knows this is a water bottle and it probably knows like i need to grab it from the side um and then but like all this like uh like you know uh like all this implied physics that we need to do here just we have to go train

14:43Dave Blundin:models to go do that it's actually kind of weird because it thinks it knows how to do it too you know the lms feel like they can do things you know intuitively and then they they completely fail i

14:52Peter H. Diamandis:I mean, you can, we've done this. You can, I have done this. You can zero shot the LMs inside a robot. We do it. We still do it actively. They just can't do it.

14:59Dave Blundin:Just for fun. Just to watch them fall. Yeah.

15:01Peter H. Diamandis:I'm like, you know, I'm kind of interested. Like a project I've been doing is like, can you just like, uh, uh, the other day I was like, can I zero shot? Like I'm working on this, um, new AI lab that I founded, uh, recently called Hark. And we have this new AI model here that are just like, it's just completely.

15:15Dave Blundin:Wait, wait, you founded a new AI lab. Well, rewind the tape here. What? Yeah. It's called Hark.

15:19Peter H. Diamandis:It's a new AI lab.

15:20Brett Adcock:um i sent you this did you i did i said yeah yeah we can send you again and we have like some new ai models and we actually put one of them into the figure robot uh like this month um and i was like

15:34Peter H. Diamandis:okay let's just zero shot let's give the lm you know let's give the model uh this is like a multi-modal model let's give this access to just like basic commands like basic like x y court. Like basically, can we give it like a acceleration and X, Y coordinates for navigation? Like basically a joystick. Can we give it a digital joystick? And I asked it to like find the exit sign and just like, go, go to the, get out of the building. And it just, uh, and unfortunately it was going the right direction and ran into like a clear glass wall. So we've like, we've stressed this. It's, uh, it just doesn't work.

16:07Peter H. Diamandis:Like you're missing so much, um, 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 but the robot we have for human rights are moving yeah the pelvis and head and torso and hands and arms like when you're reaching out to grab some over table your pelvis is moving backwards like it's like it's like very um very difficult to command like very high robotic physiology you know out of china this year

16:36Brett Adcock:some of the government employees said we've got a robot bubble. I don't know if you saw that article that came out. We have 150 plus robot companies in China. And I mean, there's a lot going on there. You know, in the U.S., I would say maybe there's 10 serious players. I mean, two or three who are extremely serious, including FIGURE. But there's a lot of potential human-eyed 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.

17:19Brett Adcock:And then this massive consolidation occurs and GM and Chrysler and Ford sort of buy and consolidate. What do you think is going to happen with all the robot companies today?

17:33Peter H. Diamandis:I think it happens in every industry like this, especially in deep tech. This will all consolidate down to a few groups globally.

17:39Brett Adcock: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.

17:51Dave Blundin:Globally. Globally. It always seems in the U.S. anyway to settle down to two, three, or four. But the borders are not obvious. Like with cars, cars, cars, car, right? And actually, you had cars and trucks, and those were kind of separate for a while.

18:03Brett Adcock:Well, you also have different designs. Like, I want the plush interior. I want the Sportster. I mean, I wonder, are robots going to be differentiated by their vertical application, their personality? Exactly.

18:17Dave Blundin:There's so much more variety possible in robotics.

18:19Peter H. Diamandis:Yeah, I think everybody's just taking for granted how difficult this is.

18:22Dave Blundin:Yeah.

18:23Peter H. Diamandis:You have to go out and build basically pretty novel, very difficult hardware.

18:28Dave Blundin:Yeah.

18:28Peter H. Diamandis: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 at scale. And then you got to manufacture at scale. And then you're going to get these products out reliably that all work every day without any human intervention. And, you know, I think we talked a lot about this, like how we're doing like K-cup 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.

18:51Brett Adcock:Yeah. I mean, by the way, a lot of the video we see is actually teleoperations. I think I wonder if people realize that. A lot of the robot companies are tele-operated versus fully autonomous. What we saw just walking around here was a four-minute long, fully autonomous operation on Helix 2, right?

19:12Peter H. Diamandis: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, I, uh, you know, when I started for a startup figure, it was stuff was coming out, but now it's like every week is somebody just teleoperating a robot and putting out a video. And it's just, it'd be the equivalent of like, I'm, 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. Self-driving, we're putting out teasers. We were in a lot of cases now there's companies selling the service.

19:47Like, um, so I think like, uh, I mean, if you want to do this, right, you gotta,

19:54Peter H. Diamandis:to believe in neural nets all the way down the stack. You've got to basically build for general purpose.

20:00Brett Adcock:So the parameters that make, it's going to define the success successful top two, three, four, the neural nets, manufacturing. Okay, I would say like,

20:11Peter H. Diamandis:what's impressive today is not manufacturing. You could probably solve, you know, we're pushing on manufacturing hard, but you can probably solve general robotics with 100 robots.

20:23Peter H. Diamandis:What's impressive is like a full end-to-end robot that is generalizing to an unseen place like you can drop it into an airbnb and be able to do long horizon work with neural nets i mean any longer hours of work in unseen

20:36Brett Adcock:places what do you define as long horizon hours days i would like to see days of work yeah yeah

20:41Peter H. Diamandis:full autonomous days of work and at least at the very least and um 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. Like, you know what I mean? We've done like the, like the open loop behaviors. It's just, there's just like, you know, any college kid in the dorm room can do this with a, with a robot. And yeah, so I think like that plus teleoperation, teleoperation is not impressive. You could build a shitty hardware and still teleoperate it and put out videos.

21:14Peter H. Diamandis:That is not hard. What's hard is to do full end to end neural network in unseen places or generalized to this. and then um if you can solve that then the next step is like how do you get that solid scale but we are still in the like who can solve general robotics phase of the human rights phase and it's just not impressive if i can build 100 000 robots right now that like need teleoperation or can just only do open lip replay like it's just not like not cool like we we if we like right your only job is to build 100 000 robots right now yeah we have the capital to do it and we can do it but like what we really want to solve is like i can i can give you 10 robots and they can go into n-sync places and do real useful work like that's what's going to differentiate so

21:53Brett Adcock:iterate that until it's right and then mass produce uh yeah you basically want to be bringing

21:57Peter H. Diamandis:up mass production in parallel because like building like high rate manufacturing for humanoids is going to be super hard and you're going to like have a 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 uh 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, um, 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 today.

22:25Peter H. Diamandis:I don't think there's anything over a minute long in the world that's doing neural nets continuously today and human.

22:30Dave Blundin:That's amazing.

22:31Peter H. Diamandis:Everything's caught. All the films are cut or teleoperated. It's pretty

22:34Dave Blundin:crazy. So I'm really glad you're telling us that you watch any video you want.

22:38Peter H. Diamandis:So you want to see it uncut. You want to see done with neural nets, like not teleoperated. Like, um, and then you want to see stuff that we showed you here in person today. They're like, they're running for hours and hours and just like we run these robots with i mean the kung fu videos whether they're

22:50Brett Adcock:tele-operated or fully autonomous are are actually fascinating and scary when you see them but the

22:55Peter H. Diamandis:technology around there is not great i mean you're basically putting somebody in a mocap suit you're having some guy like do karate chops or walking around right and then you're running that open loop i 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 yeah and it's it's super simple like there's like open source code for this you can do with like basically one gpu on your desktop uh and you can do with any robot and every robot has a very tiny amount of computer these are like these are single million parameter models are very small you don't need a lot of memory and they're very simple to execute what you really want is good closed loop control where it's reasoning like at over like kind of like 200 hertz or 200 times a second sure and it's dynamically responding to the scene yeah and that is literally uh a million times 100 000 sometimes harder than doing open loop.

23:42Brett Adcock:The human sort of cycle time is, I mean, it hurts. Oh, much lower than that.

23:49Peter H. Diamandis:Yeah.

23:49Brett Adcock:I would imagine.

23:50Peter H. Diamandis:One thing we've seen about a robot is we can like balance on one leg, like better than a human.

23:54Brett Adcock:Yeah.

23:54Peter H. Diamandis:We just have like much better, like faster, like dynamics.

23:57Brett Adcock:Can we talk about 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 locomotion 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 2025 for you i mean we launched helix in 2025 about this time last

24:30Peter H. Diamandis: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 to like you do you watch like package logistics i think you guys saw it right yeah it was like it's been running for days now and it's just like uh it's a neural net all the way down the stack it's learning how to grab packages um kind of like you know individualize them uh 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 it's doing that at very high, like, it's doing that at high, like, accuracy and it's doing that at high speed.

25:07Dave Blundin:High speed though. That's the part that, yeah, no, it's visually, it's like crazy. Well, because a lot of what you see in robotics, it's as fast as a human would.

25:14Peter H. Diamandis:Yeah. We, like our last, like we see air now, like the last one we did, we did like, we had one air over 67 hours. It's continuous.

25:21Dave Blundin:Over 67 hours.

25:22Peter H. Diamandis:Over multiple robots. This thing is, is crazy. Yeah.

25:24Dave Blundin:It's doing an operation every second or two. So 67 consecutive hours of that is a lot. So if you had to guess at,

25:33Brett Adcock:so figure three is a huge step change 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

25:42Peter H. Diamandis:build like our entire roadmap around helix 2 now we will basically now helix 2 can like go from like doing the logistics use case stationary yeah to walking and moving and basically do like long horizon full body control uh so that means the rope and then we basically have now integrated all the sensors tactile camera palm into the stack and we're seeing like um improvements overall in the policy layer so we're getting we're getting better and faster about like basically like taking data and basically running it on on board the robot now so i wanted to i wanted to

26:11Dave Blundin:ask you like what defines helix 2 because you're probably incrementally improving the neural net

26:15Peter H. Diamandis:every day yeah so what is a couple big steps one is we basically have integrated basically a fully learned um what we call like system zero which is our controller into the robot so the robot has a full body reinforcement learn controller in it okay so basically now we have like we have like literally no code right on that robot so it can like it can it can basically move the whole body uh itself using a full like uh basically like learn controller inside of uh inside of helix

26:39Dave Blundin:we call it s0 has anyone else ever done that before that's got to be um there are reinforcement

26:43Peter H. Diamandis:learn controllers out there like a lot of the karate stuff you see and things like that are that but nobody's only integrated that in the whole body for learned manipulation and perception you know we showed that actually working with like moving around and doing things that we shawl today yeah i actually don't even know if anybody showed it stationary standing and doing learned policies actually probably not in the world so like getting it integrated into a stack now that we actually use uh going forward i think one of the things we learned that like we were in bmw last year and we were there for like we did six months like redeployed our figure two robots every single day yeah the biggest thing we learned there is like the stack we had i think about 80 percent of the things we got right and 20 percent 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 uh every single work day and we it worked um but then we learned 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 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 so he looks one had the c code in it still so we had what he looks one had a lower body controller um that was still written in c++ and everything else full upper body was full neural nets okay and so we basically completed now the full body okay uh and then in doing so we also can like did some work on our system level a system one level where we integrated all the sensor modalities now from the hands and the rest of robot into the stack so like for example we now have tactile sensors in every fingertip that we're using on figure three as well as palm cameras to understand how we're like uh we're sometimes occluded 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 occluded from from the hand your hands like literally in front of the head camera but

28:34Brett Adcock:we still really want to understand where we're going yeah so i think um so so now with your

28:39Peter H. Diamandis:with helix 2 we basically have a full stack in dan with neural nets and we feel uh we can we we feel confident in scaling the pre-training data set into Helix 2. I would even go as far as like we've designed Helix 2 for the pre-training data set. And then we've designed, and then we designed the robot for Helix 2. So we've like, we've designed everything around data.

29:01Dave Blundin:Yeah.

29:01Peter H. Diamandis:And how do we get data at scale? If you're, if you're in the neural net game, it's like a data, it's a data play. It's like how,

29:06Brett Adcock:how like high quality and diverse. So it's experience. It's just gathered in the field in all kinds of circumstances.

29:12Dave Blundin:Like where can we find it? I know everybody knows this already, but it's accumulating and it never goes away. It's incredible. Unique data. Learned progress. Well, yeah. You teach somebody how to scuba 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.

29:29Peter H. Diamandis:The reason why I think there'll be like a very few humanoid groups is like the one thing that's important here is that once one robot learns how to do a task. Yes. Every robot in the fleet knows it. And humans don't operate like this. I wish we did.

29:40Brett Adcock:I watch my kids.

29:42Peter H. Diamandis:I like kids learn how to do stuff and they just don't listen.

29:44Brett Adcock:Vulcans do. I wish we did. So, 2026 predictions. What's your boldest predictions for figure? What is your goals for this year? What do you imagine?

29:56Peter H. Diamandis:Yeah. I mean, we basically, we're spinning up Baku production enormously right now for figure three.

30:01Brett Adcock:So, you said something like a robot every 30 minutes, you expect? We're trying to get there in the near term right now.

30:08Peter H. Diamandis:Amazing. Which you guys saw. We walked through Baku today. What did you guys think of Bocu? Yeah, it's wild.

30:12Dave Blundin:A lot of humans. I wish everyone could see it. I guess it's all secret. You can't camera through there.

30:16Peter H. Diamandis:We haven't. There's a lot of IP there because you see exposed boards and actuators and stuff. Oh, people with... It's cool, right?

30:22Dave Blundin:If we get some video, maybe we can mix it in here.

30:25Brett Adcock:But so whenever... There's a lot of humans. When do we start seeing robots building robots?

30:31Peter H. Diamandis:We will put robots on our Bocu lines this year. Okay. And then phasing... Like basically like phasing humans out of there will be a combination of getting more robots there and doing more high volume like automation over in Baku. Okay. So that's the first 2026 objective. We want to scale up robots in Baku for sure. The second thing is we want to scale out robots in the industrial commercial workforce. Yep. So we have like multiple clients that we've signed. They are like buying or leasing robots from us. And we are 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 uh we want those to be figure three so we've just retired in of last year figure twos and now we're basically building the arsenal of figure threes out you know that's where it's going to 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 seven.

31:29Brett Adcock:Your ideal customers who I know a lot of people would love.

31:32Peter H. Diamandis:We have to be frank, like so much demand for customers. We have like, we've talked to 50, a hundred customers or so in the 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. 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 current customers we have now like um 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 to like ship at scale leasing versus sale um yeah we have like uh service we have like a you know we really like the leasing model uh humans are leased.

32:25Brett Adcock:So, um, you know, at least humans.

32:31Peter H. Diamandis:So we like, uh, we at least humanoids today. Um, you know, we won't be like, 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. 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 it taught us a ton about like running it every day fleet operations safety like uh repair and maintenance like there's a lot of other things that need to come or like uh 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 um 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 analogy is 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 like basically almost like you know like almost perfect memory what's really happening or what's going on in your life um that can maybe talk to you almost be your companion i mean and then go off and do things that you would like like you an everyday human would want to do and i would expect them to get up to speed on those tasks

33:33Brett Adcock:um 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 an llm for conversation and memory?

33:46Peter H. Diamandis:We believe this all comes down to like one model at the end of the day that is one omni model 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 as we really want to make this robot something you can spend time with yeah and um we've been really focused on getting the core building blocks built but like over the next year or two i think you'll see us um i think i just want a robot at my home i can talk to sure i can remember things talk to my kids my kids come home like sad from school or something i want the robot to understand that i have the eq like self-awareness to see that talk to them like i think all this is like something we want to spend more we're spending

34:38Dave Blundin:more time on now internally is it is there already a big moe model where it'll have different like depending on the task you're doing it'll run different parts of the neural net or does it

34:46Peter H. Diamandis: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 that's interesting so there's no like a dishes neural net or like uh or like

34:56Dave Blundin:logistics neural net you saw here yeah because it because you know at scale like if you teach the thing every physical motion there's massive number of combinations the storage is actually dirt cheap yeah but the processing is very expensive yeah even better we've basically

35:10Peter H. Diamandis:seen that we've seen positive transfer now with all this data yeah like coming in the robot can

35:15Dave Blundin:generalize better with more information even like more knowledge is better it does it does cross like playing piano makes you a slightly better soccer player 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

35:29Peter H. Diamandis:problem yeah you don't want to you don't want to nuke it yeah for sure 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 a lot of stuff you saw today in a diversity that is super important for a humanoid robot needs

35:49Dave Blundin:to be able to do everything a human can which is like yeah and you know the distribution curve it's

35:52Peter H. Diamandis:like you know we probably do like billions or trillions of unique very unique things in the

35:56Dave Blundin:world one of the things you said 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 inference time compute is on super super fast dedicated non h100 non you know gb300 hardware yeah which has got to be you know at least a factor of 10 or 100 cheaper and faster yeah it's also it's also running fully

36:19Peter H. Diamandis:on board and it's running fully on board so you can basically like do like very fast uh inference

36:24Dave Blundin:and policy deployment yeah and it's also not sucking down the entire power of the robot

36:29Peter H. Diamandis:yeah yeah i mean you also have an issue where like you know um we've also run models off board the robot but if we lose communications or have some so i wanted to go there you know what i mean like if you like lose internet it's like hard to do work and it's like yeah we hit on supply

36:44Brett Adcock:chain batteries and comms so on the comm side um 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

36:55Peter H. Diamandis:yeah yeah we have uh so from a network perspective or you know uh it comes a communication back to the robot we have wi-fi on board we have a 5g and sim card uh e-sim on board so we can the robot you can text the robot uh you can have james yes they can have a network outside of like a wi-fi condition and then we also have bluetooth uh on board so almost like a walking phone or something like that you would think of um so we want that i mean you really want like connection at all times but you also want i mean ideally you want to catch all the time you also want the robot to be able to

37:24Dave Blundin:perform work without a connection yeah so you really want like a lot of on-board intelligence

37:29Peter H. Diamandis:you know that we basically in case you lose internet the robots like not bricked I mean humans for the most part can do work without their cell phone not teenagers that's going

37:39Brett Adcock:okay so so batteries I mean they've been improving what's the battery life right now I love the charging mechanism by the way for those who don't know you're charging basically through your feet through your feet yeah no connector you just yeah yeah that's great 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 um so today it's what yeah we run basically around like four to five hours

38:07Peter H. Diamandis:per like full full charge in the battery and uh um if we're starting at full battery life um and then and then through full depth of discharge and then we can like charge wirelessly about two kilowatts through the feet uh inductively so it's about we have about two kilowatt hour battery pack So it's about an hour or so for a full charge on the robot. So we can do like, you know, four or five hours on, an hour off.

38:31Dave Blundin:That's great.

38:32Peter H. Diamandis:Yeah, it'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 that many tasks. Humans take like a few hours in. You're not like, you know, you go take a little break, like do this stuff. So it was like, I think there's, um, you know, ample time to do opportunistic charging, maybe send another robot in. Uh, we also can charge it. We basically can put like this little thin mat, uh, anywhere in the world. Like it could be like a conveyor system or wherever else could be at home and for the kitchen and you can just charge there while doing work, which is really cool.

39:08Peter H. Diamandis:Um, so you don't have to like have any wires or things like that you're pulling from the.

39:12Dave Blundin: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 can you believe it can 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 tele-operated it or did it actually learn this yeah i think

39:36Peter H. Diamandis:most of those are open loop they're just like replay buttons yeah exactly and it's just so hard

39:40Dave Blundin:so when people say well how long does it run with one charge on the 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 yeah i

39:49Peter H. Diamandis:think you just gotta like the summary here is just like i need to see like real open like uh i see like real closed loop control of a robot moving around touching and moving things like a human would yeah and that's where the that's where the hardest problems all sit and that's where we've seen um we've seen this huge wave of like human origin explosion like you said out of china and things like this yeah 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 keurig or something like that uh uncut uh closed loop yeah real time yeah like and i just like you just haven't seen that and i think um i think you will and i think there's like there's then there's like a lot more levels to go from there um 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 watt room scale autonomy and that was two years of working seven days a week we're here like a lot of nights uh 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's a lot of both hardware low level like 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

41:09Brett Adcock:today on a robot that we could go buy today off the show you vertically integrated i made the choice to vertically integrate but supply chain how much supply chain ties back to china

41:20Peter H. Diamandis:um i think and like the next like i think by summer we'll have almost none of our uh supply

41:25Brett Adcock:chain in china anymore and are you do you buy into the u.s versus china sort of ai and robot

41:32Peter H. Diamandis:competition how do you think about that um i don't like i just like i spend a decent amount of time in china i love it china it's great like i go there and it's uh i know you're like you're watching tv here in the u.s and just like this massive conflict and battle and everything and then you go to china and everybody's just like trying to help and win and trying to work and collaborate and uh feels like a startup incubator and it's just like a one-way fun one-way competition

41:55Brett Adcock:it just feels like everybody's team human yeah team humanity to go win and it's so great when

42:00Peter H. Diamandis:you come back here you're like poisoned with all this like 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 like as one uh and go win um and i just like love that spirit of like trying to like just progress this technology as a giant lever arm for humanity to bring like like you know um to bring abundance basically for everybody and just make it make it like a sci-fi future we all want to live it which is like oh my god it is that is we want to speed run star trek

42:29Brett Adcock:is what we talk about exactly it's like yeah i see figure on the moon figure in orbit yeah on the

42:34Dave Blundin:ocean floor um 100 so the equivalent like uh 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 the supply chain just doesn't exist to give you the parts here yeah in china i mean you're talking about the

42:50Brett Adcock:you were just talking about this the improvements made between figure two and figure three because you have all of the ability to iterate in terms of speed and cost. I mean, the numbers that you shared on cost, I was like a 90 % reduction.

43:06Peter H. Diamandis:Yeah, we reduced cost like crazy on figure three. It's crazy. I think, listen, we vertically integrated, we had to. It would be great if we can go off and buy motors and we can plop them in the robot. It doesn't work like that. It'd be great if we can go by hand and just 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 um you know low-level firmware embedded software like it just like there's like one of the costs something breaks and our reliability something breaks in that equation and you're like left with like hopefully the vendor fixes it or you die um it just doesn't work uh none of the stuff like the technology readiness of these things are really low um we would love to have gone out and like bottle of stuff in the early days we tried and we basically just failed at all of it so we have like okay we need to go design ourselves and then now we manufacture like we do all final simply and everything here yeah and we all we do that you know in some cases because like nobody knows how to do that well uh we do that a little bit for ip like we really want to control that here and understand like what uh we have like people have access to and then um we also want to get good at making a lot of robots like what we need to get good at long term is like um probably a few things getting data at scale that can run neural nets.

44:18Dave Blundin:Yeah.

44:18Peter H. Diamandis:And then, uh, you know, basically doing Helix really well and then making a lot of robots and then getting those things out at the world at scale, like a pretty simple equation in the day.

44:28Dave Blundin:So it feels like the journey of getting figure up and running must've 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. Cause if you'd been locked into a supply chain, It only has certain models.

44:47Peter H. Diamandis:It's like, it's like, even if we used like an existing supply chain for all this stuff, the robot wouldn't be able to do what you saw today.

44:52Dave Blundin:Yeah.

44:52Peter H. Diamandis:Just can't do it. If you go out and buy like a robot, a humanoid robot, the shelf today, we can't get it to do this.

44:57Dave Blundin:Yeah.

44:58Peter H. Diamandis:We've like, we've, we've, we've bought robots off 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. They don't have compute or thermals. They don't have the right, the, the, the hardware, the hands, the head, like the, all of these are built around our neural net stack yeah well that's the new thing too the

45:14Dave Blundin:neural net is is incredibly integrated with a specific hardware if you watch folks that are

45:19Peter H. Diamandis:trying to buy these robots off the shelf like say from china yeah they'll come they'll end up retrofitting them themselves with these giant backpacks they'll have like power they'll have compute there they'll have thermals and off a wire hanging out they're gonna hook that into the back he probably has his own local battery they'll hook it in the back of the robot uh like they have to like take it and they have to overclock it and it's just like it's just like a um it's just the wrong way of doing this yeah it's it's like a hard thing it's like a it's like buying it's like doing rockets and like buying a rocket and you know here's we're gonna put stage two on the side or something like that it just doesn't really work yeah scale um it works for like uh 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 gonna have to go design yourself yeah looking

45:58Brett Adcock:at the companies coming out of china unitary uh engine ai and so forth do you have any which are ones that you're most interested in excited uh as friendly competition if you would yeah i think

46:10Peter H. Diamandis:one thing that's great about china is we're just seeing like it's 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 the robots. We'll sell them. And they're doing a ton of like basically open loop.

46:40Peter H. Diamandis:Yeah, they're hand controllers. Yeah. So I think like doing that is very, it's almost like it's orthogonal work from designing the system the right way for a full 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. And, you know. For manufacturing. I mean, for human lacoste labor? I think just for humanoids, like we really don't see anybody else besides China as a real competitive threat today.

47:09Brett Adcock:Fascinating.

47:10Peter H. Diamandis:Rumors about Apple getting into the business?

47:14Brett Adcock:They cut down their car project and the rumors are that they're heading towards humanoids. Have you heard that?

47:21Peter H. Diamandis:We've heard this. We've had every major, I've been in conversation with every major tech company in the world last 12 months.

47:29Brett Adcock:um and then nvidia and google and even sam i mean everybody's making noises about meta amazon yeah

47:37Peter H. Diamandis:yeah listen this is like going to be the largest economy in the world it's like half a gd roughly

47:42Brett Adcock:a little under half the gdp is human labor 50 trillion dollars yeah this is like the next great

47:46Peter H. Diamandis:place to be i think um it's gonna be super impactful business it'll lead to like ubiquitous goods and services for anybody in age of abundance and um it's gonna be a super fun business too that's going to build a sci-fi future we all want. It's going to feel like 2080 up in here. So what you're seeing is every major group in the world will get in this space. You have to. You have no choice.

48:09Dave Blundin:You have to have a major group being Apple, Microsoft, Google.

48:12Peter H. Diamandis:I think every major player that wants to do this. I think the thing that I think is going to be hard is we're doing rocket-type difficulty in design here. So it's like if Meta is 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 uh you know building like electric aircraft and that was hard yeah that was a very either a 6 ,000 pound aircraft 12 motors six independent battery systems we built our own control stack and embedded systems like um 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 it let me ask you about that because we were talking

48:55Dave Blundin:backstage at abundance 360 last year and you had a there the basic tech stack had six layers of competency you could probably rattle them off the top of your head actually but yeah this is for archer just for for robotics prior to neural nets i guess so it applied to archer and figure sure

49:10Peter H. Diamandis:but what were they again it was i mean like archer was basically like a flying aircraft uh so you know i basically build electric vertical takeoff and landing aircraft right um that is basically like a sorry it's like a flying robot is what i meant yeah um that has um you know it basically has battery systems on board as electric motors yeah electric motors just basically a stator rotor gearbox yeah pretty simple we have a little bit more sensors in our actuators than that but like uh for the most part like and there's like you know those encoders and stuff in there things like that um you have a like basically like a control software like how do we like control this thing and make it move around and in the case of archer and figure it's very overactuated system so archer has like 24 degrees of freedom we have like propellers like tilting we have like uh we have pitch on the on the blades like you have um flaps on both the tail and the wing uh tail um in the wing so um and then you know figure we have over 40 or so on the system yeah uh you have embedded software on board and sensors so how do you get to compute sensors and embedded software i'll talk to each other yeah uh then you have like structures okay um so those are like kind of the core ingredients of like a robot or something like physically

50:13Dave Blundin:moving through the world yeah so then my question is you know traditionally the employee base would be like experts in one two three four five and six yeah and like they'd be really really good yeah so then you come in and overlay this with helix and you've got this massive neural network thing is that is that a seventh competency or is that something that permeates the other like or did you take all of your microcontroller experts and start training them on neural networks like

50:37Peter H. Diamandis:the like the next thing archer is like how are you gonna like plan and you do through a pilot guard we have my aircraft midnight is a piloted four passenger aircraft so like who's doing the planning and uh like you know basically a higher level like basically higher level behaviors in the stack it's like a lower level control and code what to do uh and here at figure it's been it's been changing over time but now it's entirely neural nets if he looks too yeah uh so it's like who's gonna i mean what is the highest level behavior telling the rest of the stack what to go do yeah where's that it can come from a human it can come from a joystick uh it can come from an open loop behavior which we see like we talked about before or it can come from like a neural net that's like doing the planning and reasoning so like the you know the kitchen demonstration you guys saw today and we released like the what's telling the robot what to go do next and what to know to pull the rack out of the dishes and to go grab the cups and not the not the coffee cups but the like the water cups that's a neural net making that planning yeah in the case of my aircraft at archer it's a pilot determining like when to take off uh when to hover and when to transition into full flight yeah and then how to sort of descent what's a different type of neural net it's a human

51:39Brett Adcock:This episode is brought to you by Blitzy, autonomous software development with infinite code context. Blitzy 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 Blitzy platform, bringing in their development requirements. The Blitzy platform provides a plan, then generates and pre-compiles code for each task. Blitzy 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.

52:19Brett Adcock:enterprises are achieving a 5x engineering velocity increase when incorporating blitzy 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 blitzy.com to schedule a demo and start building with blitzy today let's talk into application layers so So we're seeing your movement into the home besides the industrial base and such. And healthcare is going to be a big part of this. Elder care, helping people stay healthy at home. By the way, you just came through Fountain. Yeah, I did. How was the experience for you?

53:06Peter H. Diamandis:Thanks for referring me.

53:06Brett Adcock:Yeah.

53:07Peter H. Diamandis:It was great. I went down to a clinic a couple weeks ago. Which one? Orlando? Orlando.

53:12Brett Adcock:Yeah. Headquarters.

53:13Peter H. Diamandis:I didn't know what to expect. I've done full-body MRIs and CT scans and blood work before, but got there, it was basically a full stack. I mean, you know this, but it's a full stack.

53:24Brett Adcock:Everything measurable about you. Yeah, exactly. 200 gigabytes of data.

53:27Peter H. Diamandis:Exactly. And spent like five hours there left. Got the download last week. It was just unbelievable. What was great about it was that I could get a comprehensive understanding of my body, what's happening, but also somebody there reporting it out. And talk to me through how I understand it, what to do next. and a plan and basically build a plan from there. Um, it was, it was great. Like, uh, I actually purchased it from like, uh, I purchased it as well for my, my, my parents and things like this. I think it's just a great gift. Um, yeah.

53:57Dave Blundin:Dave, we need to get you there too. Why Orlando and why not somewhere else?

54:00Peter H. Diamandis:Like I was on the East coast. So I popped down to Orlando and, um, so it was just easy for me.

54:07Brett Adcock:Yeah. We got New York, Orlando, Naples, Dallas, Houston's opening, Miami and LA. Anyway, back to the conversation here. I can imagine this is going to up the value of health in the home a lot. One of my visions of the future is you're constantly being monitored for your blood biochemistries, your protein levels, your vitamin levels, and so forth. 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. How do you think about that?

54:42Peter H. Diamandis:Yeah. Growing up, I grew up on a farm, Midwest, and then my parents got into independent assisted living 15 years ago. So I kind of grew up around senior care a little bit in my life.

54:54Brett Adcock:You got into that business?

54:55Peter H. Diamandis:Yeah. My parents own and operate senior housing facilities in the Midwest. So wait, they're still in Illinois? Yeah, still in the Midwest.

55:04Dave Blundin:Wikipedia says your hometown has 2 ,000 people.

55:06Peter H. Diamandis:I grew up in like Moeco, Illinois. I think it was like 1 ,800 people when I grew up. Far more. Yeah, like middle of 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. Yeah.

55:24Dave Blundin:Do they have parades for you when you go back home?

55:27Peter H. Diamandis:Man, it's just like... They have robot parades going through the streets.

55:32Brett Adcock:Can you imagine? Yeah. So you understand the value of a fully autonomous humanoid robot.

55:39Peter H. Diamandis:Yeah. I'm really passionate about figuring out how to be able to ship robots into senior care and letting people age in place at home. Yes. It's hard to get people to move into assisted independent living facilities. How does that work?

55:54Dave Blundin:So you sold out 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, you know, like, okay.

56:08Peter H. Diamandis:Maybe 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 humanoids. We believe we found it internally and we understand what that is. Yeah. And we believe we know how to make robots now and put them out. and we're going to run them really hard this year. We're going to run them.

56:35Brett Adcock:You showed us, what do you call it? The grid?

56:37Peter H. Diamandis:Yeah.

56:38Brett Adcock:Grid. Yeah.

56:39Peter H. Diamandis:Could you describe what we saw? The grid is like my favorite place here. It's like, 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 going to expand like hundreds of robots into that will run 24 seven. And it has 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 going to be doing both home and commercial workforce. We're spinning up right now. The facility just got open like this week. You guys saw it's like squeaky clean.

57:10Peter H. Diamandis:And we'll start shipping figure threes into it like this month.

57:13Brett Adcock:So model homes, model factories, model operations.

57:16Dave Blundin: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 into mission control too. So if the robot, you know, is 200 and how many in there at any given time? A couple hundred? 250, 300. 250, 300 robots building a house or doing, and all that video and telemetry comes back into mission control as they do it.

57:44Brett Adcock:Do you believe that AGI requires embodiment? There's a lot of conversation that's been put forward on that note.

57:53Peter H. Diamandis:I think my definition, I'm getting the chance right now to spend a lot of time on both the physical AI and also digital AI at Hark. So, kind of both a bit. And I think when I talk to AI today or use it, I just feel like it's so dumb. It just feels like you're starting a new chat. You're basically asking for knowledge retrieval. it's like an advanced google search engine um you know what i view is like i kind of like think about like um we want to build like the future we want to be like jarvis or we want to build like jessons i i want this thing i want to talk to it so bad yeah i wanted to talk to me i want to reason i want to have like perfect memory i wanted to be able to um 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 um we have hark now designing cad from scratch it's going out and finding you ask it to go build a cad thing i asked it to build uh basically a monster truck for my son in cad and it's going out it's like finding a cad package it's installing it it's opening 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 does it does it we can do that in under an hour now um fully end and just um clean sheet clean sheet from a single prompt 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 about all the tools that figure uses for like uh for cad for fea all this different stuff and it's

59:16Dave Blundin:going to learn all this and was that the inspiration for hark the fact that you know there's a lot of llms out there doing a lot of things but none of them are really connected to to cad and you have so much experience you know from your my inspiration for hark is i feel like

59:29Peter H. Diamandis:we're like chasing like all the big frontier labs are chasing this like very abstract version of like um like reasoning um well specifically anthropic wants to dominate coding and code

59:41Dave Blundin:self-improvement and then open ai wants to dominate i want to dominate like a sci-fi ai future i want like jarvis everyone knows jarvis i want jarvis like i want like the smartest person in the world with everybody yeah um we have like the von neumann it's like the idea that that these things go out into the solar system and then ultimately out in the galaxy yeah and start

59:59Peter H. Diamandis:making themselves out of raw materials doing this everybody's like 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 yeah and like 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 which is what we have now it's terrible and in one aspect it's great because like this new alien technology like 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 like the the There's so much the models can do now.

1:00:34Peter H. Diamandis:There's such an overhang in the product capabilities. And, you know, 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 work on the 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.

1:01:00Brett Adcock:Have you been tracking ClaudeBot now, MaltBot? Yeah, I've been tracking ClaudeBot. It's really cool. Yeah, they renamed it to MaltBot. I think it just shows you how complacent a lot of the frontier labs have been. Yeah.

1:01:12Peter H. Diamandis:Where you have such incredible capabilities that can be with a very simple harness and very simple markdown files and very simple tools. You can give it on the back of Opus or whatever you're going to use. It can do 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 ai model for this they basically just put some harnessing and some you know mcp and apis around this and it like basically went out and can like basically be your executive assistant it's really awesome and there's there's a huge area here to give that to every person in the world and make it easy yeah and we're doing some model development now at hark that is like i think truly state-of-the-art um 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 um the next 12 to 18 months i think will be like the largest ai transformation we've ever seen yeah and getting back to your point about like what do we do with health care and robots we're gonna make a shit ton of robots like we're spinning up resources right now both at baku you're seeing now and future baku to basically be able to make like millions of robots

1:02:25Brett Adcock:How long before these robots are your physician, your surgeon able to actually support all the complexity of a medical procedure?

1:02:36Peter H. Diamandis:I think from a hardware perspective, in 2026, we'll be able to do, like from a hardware work, what surgeons can do. And I think I see no, you know, giving more with our roadmap and things like that with figure. I see no reason we can't 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 can basically, if you could like teleoperate or something like that, you could like basically be able to do like real surgery. Depends what type, but I think like most things.

1:03:07Brett Adcock:And then the AI system is just layering on top of that.

1:03:11Peter H. Diamandis:Yeah. Then you got to 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.

1:03:17Brett Adcock:Let me ask you, federated learning gives you an incredible amount of knowledge.

1:03:22Peter H. Diamandis: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, you know, the simple like hack is if you can tell you have the robot to do it, we can learn it.

1:03:35Brett Adcock:Yeah. I mean, that's, it's an important point. People need to send if you can tell you operate the robot, if the mechanical systems, the motors, the, you know, the fidelity can be done. Yeah.

1:03:43Peter H. Diamandis:We were just like, and then we're like dumping on teleop, but like teleop has got one good, a couple of good things. We're like, it's a really good testing tool. It proves out. And it proves out the hardware. And if the, if the, if you can't tell you operated, you're not gonna learn it. I mean, if there's restrictions in the range of motion or payload, you pick up something heavy nobody can't do it during teleoperation it's not gonna be able to do it when you learn policy um so so i think if you can tell you you can learn it from a harder perspective i think we'll be able we'll be there in terms of like more dexterous type things we talked about here and then i think 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 it's capable of and then you can add infrared ultraviolet you can add all kinds of additional sensors into the system for sure i mean we have it now we have it with tactile like with like the palm camera is a good example like humans are pop cameras and we've been we've been now seeing we've been now seeing a boost in performance maybe i do a lot of cool things we're reaching in a cabinet now we can use

1:04:32Dave Blundin:as soon as the on the tour as soon as i heard it's like duh totally makes sense great yeah i mean how many times a day are you like reaching yes you can't we're gonna you know blind your

1:04:42Brett Adcock:phone down there to get the camera to look at it yeah i'm sure we would have evolved an eye right here if it were physically possible it is interesting question for you you've got the cameras in the head again mirroring a mirroring a human and the hands why aren't their cameras rear-facing or 360 degree yes there are i just bought i just bought a amazing drone the anti-gravity drone have you seen it it's the vr headset it's got 360 above 360 below backwards forwards and it's extraordinary so yeah what how do you think we do we have we have on the robot you do they all have backward facing cameras okay i have to ask this question for our

1:05:18Peter H. Diamandis:If you just like, you know, go over and look behind them. They have cameras in the back of that. Okay. Do they?

1:05:23Brett Adcock:I'm seeing it rotate here on here. So one of our moonshot mates, Salim Ismail, you might know him. He's one of the co-founders with Ray at Singularity University. He's like, why in the world are there only two hands? Why don't we see robots with like four hands or six hands?

1:05:38Peter H. Diamandis:So to put that to bed, what's the whole person? Yeah, we get asked this a lot. It's like, why not? Like, you know, why not like superhuman and all these different things? which is a lot of the questions i think um my summary to this is like our goal is to be able to do what humans can and then you want to do it the cheapest and like lightest possible way you can um like the lighter the better for safety the cheapest uh is obviously very important uh all those will affect manufacturability and scale um 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 or if you can do backflip if it's like like you know a bunch of arms it's gonna make the robot really heavy it's make it really costly it's gonna be really hard to manufacture and 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 uh you got to 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 um so uh you know in that case we basically have another three to five x in terms of speed and the actuators that we can run the software is not enabling because 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 because we need the whole body to run yeah we can run the robots that like we look at like in terms like radians a second maybe we traditionally look at rpms yeah we look at radians second here we have another three to five times headroom and the actuators that you're seeing now i would love to see a robot the thing is the cost of a mistake

1:07:13Dave Blundin:like you know when you're unloading the dishwasher at the current rate of speed the cost of mistake is relatively low you start running three to five x faster and you're like it's just that thing

1:07:23Peter H. Diamandis:glitches 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 be a harder manufacturer and then you're going to have like a, you know, over time, you're going to get the robots down to 10, $20 ,000. So you're gonna have a 10 to 20 ,000 robot there. And you're gonna have a really expensive robot. Let's call it$50 ,000. And like, and cost is really a function of manufacturing volumes. So you really want to build like the car. Well, that's why going after the industrial

1:07:45Dave Blundin:use case is such a no brainer. Like the home needs 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, it's,

1:08:03Brett Adcock:I remember I was interviewing you for my next book, which comes out in April. Here it is. We are as God. We've talked about this, but I'm super excited about, and of course you and, and figure are prominent in the book because this is God-like. I mean, it's extraordinary. We're giving life to new systems. I was interviewing you about how many and what the price point is. And I want to just double down on that because the numbers are pretty staggering and they make sense so if you're actually getting the price down to twenty thousand dollars a robot i haven't heard ten thousand a robot but twenty thousand a robot you're leasing a robot for like 300 bucks a month ten dollars a day 40 cents an hour and then the and you ask the question okay if it's really 10 bucks a day how many would you own or would you have you end up with a lot of robots so what's your estimate on the number of robots on planet earth uh 2035 2040 where do you think that's going i mean i think it's relatively straightforward to

1:09:04Peter H. Diamandis:think that every human should have a humanoid to do all your work and then we should have maybe an order of like five to seven maybe 10 billion in the commercial workforce um so i think i think like i think if all goes well i think you could basically build tens of billions of humanoids on the planet okay yeah i mean you're basically building like a

1:09:22Dave Blundin:replica of a human that's really cheap that works 24 7 yeah and so like there's really no um and then you know we will be at a point i hope in 24 months where all the robots will build all the robots well that's where i wanted to ask about skill because you said you know we're going to 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 yeah so then you're like okay the self-improvement loop is

1:09:50Peter H. Diamandis:going to be incredible here yeah you also need like it's funny we talk about this but you also need like tons of working capital if you want a billion robots on the planet even if they're let's call it twenty thousand dollars a piece you're talking twenty trillion dollars of working capital i mean you're not that what's the plan there's a billion cars on the planet right now

1:10:04Brett Adcock:it's not like more than that but if you tried to if you tried to build them in five years it took

1:10:10Dave Blundin:it took 80 years to accumulate those cars some of those cars are 30 40 years old cars on the planet

1:10:15Peter H. Diamandis:but we have like we make a billion or more cell phones a year so like yeah uh and i think this is more cell phone like where it's going to be personal like you're like i don't we even go back and forth on like if your robot breaks do you want like a brand new refurbished robot or do you want the old robot you used to have because you've known it and you understand it it's got a personality it's like i think it's going to be with you it's going to know everything about you why would 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 that you know it's just like it's your thing and it's got like a little bit of a feeling but yeah they're like for sure I think that'll be fine.

1:10:48Dave Blundin:Let me ask the geeky finance question, though, just before we lose the topic here. So 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 into the future, the demand is on the order of billions, not millions. Like you said, to build that out in one iteration, you use the cell phone as an analogy, But Apple had 15 years to profitably ramp up production to a billion units 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.

1:11:27Brett Adcock:But that's no longer an insane amount of capital. I mean, we're seeing.

1:11:31Dave Blundin:So what do you do? You leave the world starved asking for the robot for five years? Or do you raise the trillion dollars?

1:11:35Peter H. Diamandis:If you look at like credit card receivables or car leasing, these are trillion dollar markets in terms of financing. So I think the financing market is there for this. What do you do? I think one is you've got to solve the neural net game. You have to be able to scale with neural nets, and you have to solve pre-training, and you have to solve generalization. So you have to solve for a general purpose robot. That is like tables. You have to solve this. That's why we're so obsessed with trying to solve it here, figure. 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.

1:12:06Peter H. Diamandis:So those two things have to be solved, and you have to design the robot in order to make sure it's it's uh it can hopefully design itself yeah at the end of the day so like we like um there's a bunch of stuff we're putting in place in terms of like manufacturing execution software the lines all the design of it so we can at scale have humanoids go in building other humanoids and give them off the line yeah and um so i think like i don't think i think this is like uh 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 billion today yeah say again i think we should have a billion today yeah i totally agree so basically it comes down like can you get the neural nets to work at scale can you get the models good enough to generalize so this is scale yeah real general purpose call it a general purpose robot like a human in suit yeah and then can you get robots in the loop but another robot well the other thing is that's really

1:12:58Dave Blundin: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 mother ship and all the training is happening centrally Like, you know, the Star Trek Genesis project, right? You got a little capsule. It has basically the germ of DNA. 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 infinite scale.

1:13:29Brett Adcock:The innermost loop is energy and AI and intelligence.

1:13:35Dave Blundin: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 while all of the IP is still flowing back. 100x in the GDP. To train the neural net centrally. 100x the GDP of that jurisdiction. There's latent capital all over the world.

1:13:53Brett Adcock: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 has shifted now to well no this is going to create massive abundance and universal high income and that happens if in fact 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 get triple my salary because it's working three shifts yeah and it's doing that for me and And then it earns enough to get a second robot working for me. And so the question becomes, where is that capital captured?

1:14:31Brett Adcock:And is it inside the hyperscalers? Is it inside of the individual? So that's going to be the interesting conversation coming up. How do you think about that, Brett?

1:14:41Peter H. Diamandis:I mean, we're going to sell robots at scale. You're going to be able to deploy as many robots as you want to whatever you want to do.

1:14:45Dave Blundin:Yeah.

1:14:46Peter H. Diamandis:It'll just 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. It'll reason. The future is going to be really fun. Safety and privacy.

1:14:59Brett Adcock:Let's talk about safety in the home and privacy in the home. You know, there were lawsuits over the last years with Google and Amazon of it's listening to you in your bedroom and so forth. How do you address safety and privacy? Or is it just going to happen? It's just too early because we're not?

1:15:19Peter H. Diamandis:I think they're just really hard questions to answer in one go because like there's a bunch of different safety implications here that are like just uh it's safety is like probably the number one thing to tackle to get robots into the helmet scale yeah um there's like a semantic understanding of safety like if there's a you know a candle lit and i knock it over by accident 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 um um intrinsic safety of like can the robot be with humans and animals and pets and uh be safe yeah like those those like that has to be solved we can talk like at length about like how we're going to solve those problems and then you have the whole privacy cyber security other aspects of this that need to be like uh like uh with good intention like like how do we solve those problems um we are working on on all of those now they are very difficult uh things to go get right um I do see a path where we can build intrinsically like really safe robots around people and pets.

1:16:23Dave Blundin:Yeah.

1:16:24Peter H. Diamandis:We have a plan for how we're going to do that.

1:16:26Brett Adcock:I mean, they could be safer than humans by a large margin, just like autonomous cars are safer than humans. End of the day.

1:16:33Peter H. Diamandis:Yeah. These have like superhuman perception. We can see basically all around us at all times. We're always on. We're always computing like what to go do. were um you know so i think you know assuming nobody's trying to be like like you know mean to the robots or things like that i think we should be extremely safe around everything we're doing and then as we're on privacy like you know these are going to be in your home so being up front about what what data we're collecting and where that data is going and how we're um uh keeping that data private and encrypting that data it's like all this is super important we have a we have entire uh team on cyber security here in house on both most of the product and commercial side, corporate side that are working through how do we think about this at scale?

1:17:15Peter H. Diamandis:Right now, they're great. They're from the big companies that 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.

1:17:24Brett Adcock:Your facility here, which is your prototype manufacturing facility, 50 ,000 robots a year, you imagine?

1:17:31Peter H. Diamandis:That facility can support about four lines. Each line can do about 12 ,000 units a year. It's a little under 50 ,000 units a year.

1:17:37Brett Adcock:What's your next step up, do you think?

1:17:40Peter H. Diamandis:I mean, we're building like thousands of robots right now. So like, that's a big push we're doing, right? I mean, you just, you saw it today. It's like, that's the figure C stuff we're doing off the lines today. 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. This facility will top out 50 ,000, a little under 50 ,000 units a year at full capacity. so kind of think about a long term like our you probably be a low volume when we look back in five

1:18:10Dave Blundin:or ten years and be like do you think you might franchise out the neural net and the circuitry around it you know because all these other people are saying oh i'm building a robot that cleans industrial pipes i'm building a robot you know all these different form factors no no just i think

1:18:22Peter H. Diamandis: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 at archer when we were doing archer like uh building archer out like i think um it's like it's like a safety critical system yeah uh especially like archer and since they're like licensing out to their folks and stuff like that is like very problematic yeah i think here it's like same thing like human hasn't been done right like we have a fiduciary duty to our civilization to build like really safe human robots at scale yeah and uh 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

1:19:00Dave Blundin:And when do you branch out into other form factors? Like, you know, things that work underwater, things that work.

1:19:05Peter H. Diamandis:I don't think, I think the amount of, I think in the future, everything that'll move will be a robot besides humans. And within that, I think humanoids will dominate the plurality of all robots. It'll just be so big a percentage of them. Like the other robots will be like niche and expensive and done. They're like super duty trucks that you have out mining. They'll just be made for specific areas, maybe underwater, as you said.

1:19:36Dave Blundin:Or like heart surgery or brain surgery. You've got these very, very fine-tuned. It's like a robot controlling a robot.

1:19:43Peter H. Diamandis:I think you're left with very expensive equipment that's very siloed. You really want to build a general purpose machine that 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 humanoid robots on humanoid robots everywhere on the planet. And there will be other robots there, but it'll just be like a niche businesses.

1:20:16Brett Adcock:When I was flying up here, I posted your video that you released on Helix 2 today. And then we asked the community for questions. and they just blew up with a whole bunch of amazing questions. So one of the questions is, do you have a blooper reel? And 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.

1:20:42Peter H. Diamandis:Ben Casper, nice. The weirdest task did not work. Well, like weirdest task.

1:20:54Brett Adcock:listen every jogging was interesting well okay jogging was fun jogging was cool because we like

1:20:58Peter H. Diamandis: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 which i actually have a gift for you it kind of goes okay two uh two figure dead mouse hats what's that mean uh we basically uh We opened at Red Rock late last year at a Deadmau5 concert and had robots on stage. So we generally don't venture out into weird of stuff. There you go. Nice. And we actually had Deadmau5 at our last two holiday parties, I figure, which is like fun. And we generally are pretty much like, how do we design something really useful?

1:21:38Peter H. Diamandis:But then we've had some pockets of time to do fun stuff like this a bit. So I think having robots on stage at Deadmau5 and Red Rocks was just... Oh, that's fantastic. I flew in for it. It was just, it was unbelievable. And you had them on stage. We had them on stage. We had several figure twos on stage, just 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.

1:22:01Brett Adcock:I had you on stage last year at the Abundance Summit, but figure wasn't with you. So 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 a customer's home? The next question. Yeah.

1:22:18Peter H. Diamandis:We want to, we want to, I want to ship robots when they're really ready. I don't want to ship slop.

1:22:22Brett Adcock:Best guess. Earliest, latest window.

1:22:25Peter H. Diamandis:We, we, we probably, I think last year I said, you know, in this year in 2020, um, in 2025, we, uh, in 26, we launch, we launch a robot to do like end to end homework, like an alpha testing, like in my home to do like full,

1:22:41Dave Blundin:like mopping, cleaning, full scale, like long horizon work.

1:22:45Peter H. Diamandis: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 some 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 it's ever seen and also make that really work well and i want to be able to talk to it and i want it 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 27 28 29 my my best guess is i think you know i think like well i'll tell you what we're working until midnight every night to solve this problem it's like it's like it's like we are here every weekend every night to try to figure out how to solve this this is this question we kind of want to try robotics this is kind of where we want to head um i think by end of the year we will 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 is it 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 of 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 um so i think anybody tells you like hey we're going to ship them or teleop them in the home or we're going to ship them in at scale in a year like there's you you've got to ship in 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 uh roadmap which we have here we need to learn so it's going to work well at one this is going to work well at 10 homes it's going to work well 100 it's going to work well a thousand there's gonna be 10 000 there's gonna be 100 000 gonna be a 10 million so i think it's going to be like um uh super substantial growth curve exponential

1:24:28Dave Blundin:growth so is there anything to worry about there in terms of time to market is because you know the industrial use you like i said you're sold out for years to come anyway is competition going

1:24:36Brett Adcock:to come in and grab market before yeah we we feel with your kids or something the work we show today

1:24:43Peter H. Diamandis:and the work we showed two years ago has never been done in my mind whether any other human or company in history yeah and so that if that's the marker it's whenever somebody can do the keurig test for a couple minutes with uncut film and i can like watch it closed loop do it yeah with by man even not even just standing um that's you're two years away from where we're at 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 inside the robot maybe even as soon as this year like um i mean listen it could happen in a couple months we are we are we have the right stack now we are we are um we're 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 um like uh like this year how many gpus in here we we are going a lot we have we have 3 000 b200s that went like that are going live and we have another set of uh much larger gpus that we plan to put out uh here and um training or we just use it for pre-training yeah pre-training do it here physically here uh

1:25:52Brett Adcock:we no we do not use a lot of power yeah a lot of power so j create asks a question to the science fiction geeks amongst us. So what's beyond the three Asimov's laws for you? Have you thought about that? Have you thought about sort of fundamental laws to program into your robots?

1:26:09Peter H. Diamandis:I think you really want to put these rules down into the kind of non-viatile memory on board the robot at the chip level, at the substrate level. Yeah. I mean, you must have thought about that. We've been thinking about this quite a lot. like um and you know it's in one hand we still want to solve like general purpose-ness 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 we're 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 in financing all of it need to be packaged ready to go so we're working through all these now um i don't know it's funny it's like it's like you know asimov got a lot a lot of things right and i feel like a lot of the three like you know like these foundational rules for how do we uh treat humans is like um you know we 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

1:27:15Dave Blundin:good work and and uh is this something everyone learns internally in corporate training and

1:27:19Peter H. Diamandis:memorizes and all that uh it's something that we want to put we we put and we're going to continue

1:27:23Brett Adcock:to put on all the robots so you have a newborn uh you have a newborn child oh yeah so the question here from kk says when would you trust figure to hold your newborn yeah that's an interesting so

1:27:38Dave Blundin:the new the figure three is soft uh it looks like it's designed for the home but it's still about

1:27:42Peter H. Diamandis:i think i think this is the same i like this question a lot because at archer i always say like until I put my, me and my kids and family on the board, it's not safe enough to fly anybody.

1:27:52Dave Blundin:Yeah.

1:27:53Peter H. Diamandis:And, um, like I wouldn't do that today at Archer and, uh, I hope soon I could do that. Um, I figure here, I think it's the same question as like when I feel safe enough to have a robot in my home, uh, but like, you know, I've been there, we, we've had folks there and you know, um, we, we monitor it. Yeah. Um, I think we're like truly safe and, um, we're not there now. And I think that's a good bar for us to hit. It's like a, when I can put a robot in my home fully autonomously and to end around all my kids, I think that's a point where I would trust it. Um, I think that's a point I would say like, this is ready for everybody.

1:28:28Peter H. Diamandis: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 reign 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, 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. um and the robots have been totally safe uh which is great but like the one is we need to build like a system safety architecture that's really really fault tolerant and redundant in real time and we we've done that and we're doing a better job of that in the future and two is you have you just

1:29:00Dave Blundin:have to build a safety track record for this there's nothing better than like actually proving this thing is can be safe well it's a nice barrier to entry too if you you know kind of take the apple road too it's got to be a great out-of-the-box experience well that means not stepping on the cat. Totally. Certainly 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 of all companies I've met, you're perfectly positioned to get that reputation.

1:29:26Brett Adcock:Yeah.

1:29:26Dave Blundin:Don't make a mistake along the way. 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.

1:29:37Peter H. Diamandis: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 uh that you have to get right to build this scale your main message you want to get across here to everybody 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 have a clue of how

1:29:58Brett Adcock:fast this transition time is going i mean just go to our youtube and watch our videos last two years

1:30:03Peter H. Diamandis:they like and like watch them side by side it's dramatic the change every single year yeah i mean And you saw it today in person. And our robots now have been in customer sites and things. It's been out and we're going to continue to show up more. But it is hard to feel because you don't see it every day. But at some point, you're going to walk out. Probably in San Francisco would be the first. And you'll see more humanoids than humans. And I think that'll be an amazing day.

1:30:27Brett Adcock:Right now, I'm driving in Santa Monica. By the way, we just did a podcast earlier this morning with Kathy Wood, who sends her best. Oh, cool. She's a huge fan of yours.

1:30:35Peter H. Diamandis:I think she invested in me at both Archer and Figure. And she's great.

1:30:40Brett Adcock:Yeah. She feels the same way. Yeah, true. She is. Very proud to be an investor in Figure. And I was telling her, you know, when I'm out with my kids right now in Santa Monica, we do something like counting the number of Waymos that we see. Isn't that crazy? We'll see like 10 Waymos. And then the Cocoa robots, little ground robots, like Starship bots and such. I mean, they're all over the place. Crazy. And it's interesting, right? because uh you first time you see it you play out your phone 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 uh anytime

1:31:15Peter H. Diamandis:we go like we had like last last weekend with date night and uh took away mo downtown and it was just it was it's just so unbelievable and the experience feels like uh it's you just as a you know as an engineer like working on these like hard projects i feel like the like the like the amount of engineering work they had to go do to put it together safely google did decide you know

1:31:35Dave Blundin:you control the controlling of 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 beautiful job

1:31:47Brett Adcock:taking the product right i mean larry page saw the product win the darpa grand challenge back in 2005 and committed to it and brought the team.

1:31:59Peter H. Diamandis:I mean, I think it's been like 16, 17 years.

1:32:01Brett Adcock:Yeah, and just they stuck with it. You know, and Astro Teller at X basically built it out. And then Waymo is an amazing, amazing product.

1:32:09Peter H. Diamandis:They've been like undeterred for like 16, 17 years. Like, don't worry about it. We're just going to make it. And they did it. And it's unbelievable.

1:32:15Brett Adcock:Yeah, no, amazing.

1:32:16Peter H. Diamandis:It's very inspirational. Kudos to them.

1:32:17Dave Blundin:Can I ask you my geeky sci-fi meets geopolitics question du jour? So I just got back from Davos on Friday. Today's Tuesday. So nine time zones away. 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. Yeah. 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 mild, thick ice sheets. We do not have mild, thick ice sheets. but I think if you're talking about a billion and then 8 billion robots and you need the materials and that's the only constraint and you have robots that can operate.

1:33:06Dave Blundin:We're going to mine asteroids, buddy. Seriously. You think we're going to be doing asteroids before Greenland? No, we'll do Greenland first. But you think Greenland is viable? Like I'm not talking about 20 years from now too. I'm talking like if you want to build a billion robots and say six years from today.

1:33:19Brett Adcock:It's a$50 trillion marketplace.

1:33:21Dave Blundin:Yeah.

1:33:21Brett Adcock:So that demand that drives.

1:33:23Dave Blundin:Don't you think you'd find a way to get through the ice, given a million robots working on it?

1:33:29Peter H. Diamandis:I'd hope so, yeah. I think we'd find 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 through humanoids. Yeah.

1:33:44Dave Blundin:That's what I'm thinking, too. Because the machinery that I see is massively automated. It's still driven by people. It's still operated by people. but it doesn't need to be yeah it's just like um it's crazy that shit works right yeah like the

1:33:57Peter H. Diamandis:humanoid like they're just like the neural nets it's like it's just um it's it's just the thing

1:34:01Dave Blundin:is when you when you make it work on unloading the dishwasher people don't realize how close that is to working on every other task the dishwasher and like folding laundry these

1:34:09Peter H. Diamandis: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 uh everything's not in the right same place uh it's like very different than being on a conveyor system or manufacturing something like that and they are gonna do it today yeah 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 what humans do every day like that's a data play

1:34:34Dave Blundin:the thing is if you if you achieve that goal by hacking together a hundred thousand lines of c plus plus yeah and teleoperating it it would look the same but it would be nowhere near as conquering every other problem 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 could possibly define.

1:34:54Peter H. Diamandis: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, towel folding or why I can learn like dishes or whatever we end up showing at manufacturing, literally it's just data. It's just data goes in the neural net. Now I can do this work because the robot hardware doesn't need any updates. It just needs a new neural net weights on board. You know, 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 to get the right pre-training set for this at scale.

1:35:25Peter H. Diamandis: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.

1:35:34Dave Blundin:Yeah.

1:35:34Peter H. Diamandis:And I think, well, 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 transfer emerged from a robot that's able to like generalize a lot of things.

1:35:50Brett Adcock: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?

1:35:58Peter H. Diamandis:Yeah, let's do it.

1:35:58Brett Adcock:Thanks for the close up and intimate tour. So figure one.

1:36:02Peter H. Diamandis:Figure one. so we basically one cool thing about figure one is we designed most of the system in house we didn't care about looks we cared about I'm walking the AI and controls team it's like something they could use from a software perspective so we designed and walked this robot in under one year so I incorporated the company I think it's probably one of the fastest times in history that's a lot of parts

1:36:22Brett Adcock:did you draw did you draw this by hand these are internally made out of it yeah

1:36:27Peter H. Diamandis:David basically our design lead designed 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 AOS policy deployment. We did the Keurig K-Cup with this robot. And they moved the hand too? You can definitely move it, yeah.

1:36:46Dave Blundin:Are you sure this is going to be a collector's item someday? You bring this thing, you bought it. So it's heavy.

1:36:52Peter H. Diamandis:Yeah, it's about maybe 130, 140 pounds total.

1:36:54Dave Blundin:That's not that different from that.

1:36:56Peter H. Diamandis:Yeah, not bad. That's all aluminum. It's all aluminum. All CNC. We CNC aluminum most of the structures. Yeah.

1:37:01Brett Adcock:yeah uh and then what else do we know about this before we move to figure two um we basically we

1:37:06Peter H. Diamandis:uh we didn't we wanted to care about speed so we didn't really care about wiring some electronics uh like a lot of the design it was mostly just like get a functional humanoid robot out so we can do development on right um so we did that we built a few of them uh we did a lot of like we did our first neural network on this robot which is like i think was was was phenomenal we did so much development with it really quick yeah 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.

1:37:37Dave Blundin:So you got the cost down, I know, from two to three by 90%. What was the cost from here to there? Probably another 90%.

1:37:43Peter H. Diamandis:About the same would be frank.

1:37:44Dave Blundin:Wow. Yeah.

1:37:45Peter H. Diamandis:A lot of it was machine parts and we moved out the tool parts, the three. So you've got two cameras here. Two cameras here. we have a back camera. We have a unit. You can see? Yep. We also have cameras right here in the torso pointing down. So we can see where the feet are at in case you have a box occluded.

1:38:01Brett Adcock:Come take a look at the camera at the back of the robot here one second. The camera pointing down.

1:38:05Peter H. Diamandis:It's right there in the public.

1:38:06Brett Adcock:So back here you've got what's going on here. So there's camera ports here?

1:38:11Peter H. Diamandis:Yep. We basically have a camera, a backward facing camera. We have different ports for like debugging. If we need to hook up like a cable to it and we can also turn the robot on and off from here.

1:38:19Brett Adcock:Amazing.

1:38:19Peter H. Diamandis:Yeah. And then basically we moved all the wires internally into this robot. All the structures is exoskeleton, so all the exterior loads, almost like my aircraft to Archer, the skin, the outside housing took all the loads. We do the same thing here. So all the outer shell took all 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 battery capacity on board.

1:38:48Brett Adcock:Yes. And the degree of beauty went up.

1:38:52Peter H. Diamandis:Yeah. like uh yes yes uh david did a good job making this like uh much much like more presentable uh

1:38:58Dave Blundin:so funny this venting heat out the armpits yeah just like so yeah it actually sucks there and here

1:39:04Peter H. Diamandis:and push it out through the torso and the bottom 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 to basically make it so that um if you basically got your finger stuck here oh safety yeah maybe it would maybe it hurt it but wouldn't like cut it off yeah i mean so like uh so somewhere maybe what you see like a car door sure today um and here's the workhorse this is our yeah this is our figure three um yeah so we basically uh a couple things we made the robot like much skinnier and lower mass but kept all the speeds and torques the same so it's just as powerful and just as fast but also like kind of skinnier that's the last nice this is about 135 pounds 35 this is about 150 a little over 150 pounds yeah um we carrying weight how much weight can they or a different hand about about uh 20 kilos 20 kilos yeah um completely different hand the hands have a glove tactile sensors compliant material on it for better grass and also a camera all the parts basically or most of the robot is soft wrapped you can see it kind of up here a squishiness to the chest and different parts of the robot uh we have like no more like uh like our very few pinch points in the robot um what else uh we reduce the cost massively we have a better thermal system compute system We increase also compute on this robot as well from the last generation.

1:40:19Peter H. Diamandis:We have new feet that have a toe You might think of the toes like yeah, no, it's a major part of the help 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 on her, you know get down here We're on our toe box really helps basically get the range of motion without that you might need more joints

1:40:38Brett Adcock:talk about the face because this is a big question of you know do you develop do you show facial features or not and you went what do you think what do you head westworld or you irobot wow i mean i i it it comes across it's beautiful right i had to be a beauty and it comes across um sleek but it could have like a negative like a little dystopian feel with a black face.

1:41:08Peter H. Diamandis:So we have three screens on the robot. This is powered off. We have a main screen. We have 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 can do anything. You could watch a Netflix movie. Other than the brain. Look into my eyes. Yeah, whatever you want. Like kids get bored. It's like, let's throw some up there.

1:41:25Dave Blundin:So the brain is read in here, which makes a ton of sense to me. Yeah. And it's where the Romans, ancient Romans thought.

1:41:31Peter H. Diamandis:You basically need a lot of on-board computation. There is nowhere else to put it right now. Yeah, exactly.

1:41:36Dave Blundin:And that also So it's easier to get the heat from here too. And then you just put all the sensors up here and it just totally makes sense. I guess I could put a latex face over the head if I wanted.

1:41:43Peter H. Diamandis:Yeah, you can basically put a silicon face and put hair on it. We're good to go. We also have other outfits. This is one of our logistics spots. Same robot, basically we're able to outfit it with different types of soft goods. And we have another robot here that we basically have 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 a piece of sheet metal or it's slick do you think it would operate in zero g you just need a better training set and i think so yeah i think we really love to run i've got a robot scale in space you're going to populate

1:42:20Dave Blundin:i've got my zero g airplane we could we should we should take it outside yeah let's get these things on there yeah that'd be a great test yeah well look we're going to build data centers in space very soon someone needs to assemble them like zero g is the operating and then we'll get

1:42:33Peter H. Diamandis:other planets too. It'll be super important.

1:42:34Dave Blundin:Yes.

1:42:35Brett Adcock:Yes. And then we'll disassemble the moon and the asteroid belt and we'll use it for materials. 100%. Alex will love you said that. 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 a 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.

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