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

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

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

• 0:00Peter Diamandis?

  Archer was amazing. Then you jump into arguably what could be described as one of the most difficult businesses to get into. Why'd you start Figure?

• 0:08Speaker 01

  The humanoid robot is like the ultimate deployment vector for AGI.

• 0:12Speaker 02

  It is truly my honor and pleasure to introduce to you Brett Adcock, founder and CEO of Figure.

• 0:19Peter Diamandis?

  You went from a cold start in 31 months to shipping your first robot.

• 0:25Speaker 01

  We are designing a new hardware platform every 12 to 18 months. By the time I filed for the C corp, we had the robot walking in under 12 months. I think you're going to see it in the coming years, be put into homes just through speech, be able to do like very long horizon, hours of work without any problems. It was like an iPhone moment happening with humanoids. Like it's going to be, this is going to happen right now.

• 0:50Peter Diamandis?

  I think most of you know that the news media is delivering negative news to us all the time because we pay 10 times more attention to negative news than positive news. For me, the only news worthwhile that's true and impacting humanity is the news of science and technology. And that's what I pay attention to. And every week I put out two blogs, one on AI and exponential tech and one on longevity. If this is of interest to you and it's available totally for free, please join me. Subscribe at diamandus.com/subscribe. That's diamandus.com/subscribe. All right, let's go back to the episode.

• 1:31Peter Diamandis?

  Thank you for being here.

• 1:32Speaker 01

  Yeah, thanks for having me.

• 1:32Peter Diamandis?

  I know with three young kids and a robot factory and production and incredible team of engineers, you're really busy. And I don't take it for granted that you joined us here.

• 1:43Speaker 01

  Yeah.

• 1:44Peter Diamandis?

  My only request is next time I want a Figure robot with you.

• 1:47Speaker 01

  Yeah, loud and clear.

• 1:48Peter Diamandis?

  I begged him. And BMW has been taking the lion's share of them.

• 1:55Speaker 01

  Yep, we do have a lot. We actually have them running every, every day now. So like they're there today running and they're largest plant.

• 2:03Peter Diamandis?

  Why'd you start Figure?

• 2:06Peter Diamandis?

  I mean, you had this incredible, you have a few incredible successes and Archer was amazing. And then you jump into arguably what could be described as one of the most difficult businesses to get into.

• 2:22Speaker 01

  Yeah, I think um

• 2:25Speaker 01

  we really need to figure out a way to give like AGI a body here.

• 2:31Speaker 01

  I think it's like a really negative or like most like dystopian future if we figure out how to solve AGI and it lives in a server somewhere. And it's like, you know, more intelligent than the than all of human like, like everybody. And ultimately, if it wants to do something in the physical world, it'll ask, it'll have to ask our boss or human to do it. And um, the humanoid robot is like the ultimate deployment vector for AGI. It's um, you can't solve this with anything else besides a human, like a mechanical human. You need a, you need something that is a single platform that with no hardware changes can do everything a human can. And you need something that can also be good for the neural nets, like the neural net here in a humanoid can basically

• 3:17Speaker 01

  learn, like basically learn from transfer learning. It can basically multitask across a variety of different applications, which is like really good for a neural net. So we basically can build like one single neural net, like foundation model that can power the whole robot to do everything end to end.

• 3:33Peter Diamandis?

  I mean, you know, massive congrats, you went from a cold start in 31 months to shipping your first robot. Which is, which is extraordinary. I mean, a lot of companies get their PowerPoint decks ready and raise their first capital in that period of time. And we're going to be seeing some of the robots in back here. When I visited you up north, you know, you showed me around, we did a podcast together. And you showed me figure one, and here's figure two, and here's the designs for figure three.

• 4:08Peter Diamandis?

  One of the things I truly find amazing is the speed of your iteration. Can you speak to that and how important rapid iteration in hardware is because hardware is hard?

• 4:18Speaker 01

  Yeah, this is a hard problem. We have to figure out how to do something that's never been done before. And it's like a very complex system, like definitely more complex from an engineering perspective than Archer was, like building an electric aircraft.

• 4:31Speaker 01

  So yeah, my rule of thumb is like the first or second generation hardware is always going to suck.

• 4:37Speaker 01

  You know, like the first iPhone was not great, like the first, first time you make something like you're never gonna get it right. In hardware, you have to do that, like, um, you have to see like five years in the future. You have to know exactly what the product does, and then you have to clean sheet design it for that exact thing day one. And if you mess up any of those, you can't go back and fix it through the design process. You have like long lead time, supply chain, everything else. So we are designing a new hardware platform every twelve to eighteen months.

• 5:00Peter Diamandis?

  By the way, that's pretty amazing just to hear that, right? Every twelve to eighteen months of bragging you iteration.

• 5:07Speaker 01

  I mean, yeah, we had a figure one walking, like by the time I filed for the C corp, we had the robot walking in under twelve months.

• 5:14Peter Diamandis?

  Another thing you've done is you've completely vertically integrated.

• 5:18Speaker 01

  Yeah, that was out of necessity, like there was, there's no supply chain for humanoid robots. Like there's no like motor vendors, actuator vendors, sensors, battery systems, structures, like kinematics, like all the software, which is like pretty vast, it's like firmware embedded systems, operating systems, middleware, controls.

• 5:34Peter Diamandis?

  So walk me through your fa- your factory, you walked me through it before, but like what are the different segments of what's going on there?

• 5:41Speaker 01

  Yeah, in terms of like design for how we're.

• 5:43Peter Diamandis?

  Yeah, I mean, you've got, you've got component building, testing, integration.

• 5:48Speaker 01

  Yeah, so we, um, so we clean, clean sheet design everything from basically the ground up. Uh, like all the hardware is like clean sheet design. We look at like, ultimately, what does the product need to do? The product needs to, um, you, you basically want to talk to a robot and you want it to just do things without any human intervention. You just wanted to go out and do stuff in the world. So we're designing it for a capable robot that can go out and do everything from putting robots in a home to walk your dog, make coffee, do the laundry, and then the commercial workforce, which is like roughly half of GDP is human labor. So it's like the largest market in the world.

• 6:20Peter Diamandis?

  Yeah, a hundred and ten, a hundred and twenty trillion dollars of global GDP. Your TAM is like fifty to sixty trillion dollars, that's pretty good.

• 6:29Speaker 01

  Yeah, it's like the, it's gonna, it's gonna build the biggest business in the world by a long shot, like this, in our lifetime. Like the, this, the, the space. Uh, yeah, so we have, basically, we, um, so we're looking at like the end markets where the robot needs to go. We do all the hardware design, which is like kinematic design, joins, motors, uh, battery systems, sensors. We do all the software, firmware, embedded systems, controls, all the AI work end to end. Um, and then we do all the testing and manufacturing and integration and fleet operations and deliver those to the clients. So we have robots now. We have two commercial customers. The first is BMW. We have robots there that are operating every single day. They're in Spartanburg, South Carolina. They're helping to build cars.

• 7:10Peter Diamandis?

  We've got some video, I think, from the BMW plant, if we can roll in background or repeat that video.

• 7:18Speaker 01

  Yeah, we'll show that. And we have a second customer we just signed. And then within 30 days of like, starting the work, we were doing the work all end to end with neural nets. And this is like one of the largest logistics companies in the world. And then we're also pushing really hard on the home. So yeah, here's a quick update for BMW. So we have just robots here that are basically doing, like basically putting sheet metal on fixtures. This is a job that every major manufacturing company in the world does. Our robots have been that fully autonomously at the speeds we need to basically hit high performance with no human intervention, no faults, no failures.

• 7:56Peter Diamandis?

  And no drug testing, no sick days?

• 8:00Speaker 01

  Yeah, no days off.

• 8:02Peter Diamandis?

  No days off?

• 8:02Speaker 01

  No days off, yeah.

• 8:03Peter Diamandis?

  24/7?

• 8:04Speaker 01

  Totally.

• 8:05Peter Diamandis?

  I mean, it's an interesting thing, right? Think about this. Let me jump into one thing. In volume, in the future, I believe I heard you say you'll see these at a price point of $20,000 to $30,000. Do you still hold that?

• 8:19Speaker 01

  Yeah, we've done a lot of work on the bill materials. Like if you start breaking this down like to the bare, like you've got to basically got to line item by line item of what it really looks like and what basically what it looks like in like high rate manufacturing. There's really nothing in the system right now that would show that this product should be very extremely expensive.

• 8:36Peter Diamandis?

  The calculation I do is if I was going to lease a $30,000 car, it's about 300 bucks a month, which is, by the way, $10 a day and 40 cents an hour. So here's my question. How many of these humanoid robots would you own at 300 bucks a month?

• 8:57Peter Diamandis?

  Operating 24/7, no complaints, no fights with their girlfriends or boyfriends. I mean, the number could well be multiple per human.

• 9:07Speaker 01

  Yeah, you're gonna want one. They're gonna see, like, I woke up, like, I wake up every morning and help unload the dishwasher and pick up kids' toys. Like, I never want to do any of that ever again. Like, you know, it's just like not like something I need to be doing when I get home or I'm at the house. Uh, we really haven't had a lot of innovation in the home for like almost 50, 70 years. We're like same appliances, same stuff. Like, we need to-

• 9:28Peter Diamandis?

  We had old robots. We call them dishwashers now.

• 9:30Speaker 01

  Yeah, they're just like been around for a long time. Yeah. And we're, us humans are having to like work with it, right? Like, we have to work with that machine every day. And it's just like not something you'll do anymore in the future. You'll just like talk to the robot and have it do it. It'll be on a schedule. Any moment, you can just call it, text it, talk to it, and it's asking to do stuff, and it'll just go do it. It'll know you better than, it'll know you just like yourself.

• 9:51Peter Diamandis?

  I remember a couple of years ago, I'm very proud, uh, Bold is an early investor in, uh, in Figure, and I, I brought Timor to, to meet you. And I said, listen, the thing, first of all, Brett's an incredible operator, multiple successes. What's one of the best predictors of the future? It's what a person's done in their past, right? It is very much one of the best predictors. Uh, but what I found amazing, uh, that sold me instantly beyond your charm, uh, is the team you pulled together.

• 10:28Peter Diamandis?

  Can you talk about that? Cause it's, I think a lot of people in the audience here are focused on their moon shots. This is very much is a moon shot.

• 10:34Speaker 01

  Yeah.

• 10:37Peter Diamandis?

  You, you exit, Archer. How did you capitalize? What did you start? How do you pull your team together? You described that early moment.

• 10:46Speaker 01

  Yeah, like, um- So, you know, I haven't found a lot of companies in my lifetime. I get to like go back every time. And like, what did I mess up on? What did I get right? Trying to make things better. Fundamentally, the things that I spend a lot of my time on is just like building. You basically in order to build like one of the world's greatest products, you need like one of the world's greatest teams. And then you need, um, you, you need to align that team with like what the shared vision is. And everybody needs to be accountable for that and understand it. And then you gotta figure out how to hit the gas pedal, like really hard. So the entire culture at Figure, even at Archer, when I built, um, initial team was, um, was like very deliberate. And even at Figure, if you go to the website now, we have like the culture deck, we have the master plan, we have like things laid out that are like really unique.

• 11:30Speaker 01

  Uh, we're in Silicon Valley, but almost like the anti Silicon Valley. You have to work every day in the office. We work five to seven days a week. We work really hard. Um, and not a lot of people want to do that. And that's fine. There's just not the right people for us. We've assembled now a couple, like hundreds of like the best engineers in AI robotics in the world. There's just like no, nobody even close to what we've done.

• 11:49Peter Diamandis?

  I mean, seriously, like incredible.

• 11:52Speaker 01

  Yeah, like it's unbelievable. Like my whole business team's been with me at a Vettery, Archer, now Figure. They're just, I mean, we've spent 15 years together. There's unbelievable operators. They give me the ability to like spend basically all my time on product engineering, uh, to basically build the best product possible. And they help scale the business, which is great. Um, hiring, just, uh, recruiting, HR, like, uh, legal, just like finance across the board. They're great. So yeah, the team's insane. Uh, but what's even better is like the culture is just absolutely like dialed in. Like everybody knows what they should be doing. Uh, I don't do one-on-ones, things like that. Um, we have like a clear shared vision of what to do and we work really hard to go get there. And the dopamine that we all get is the same. Like we, um, we want to ship product.

• 12:35Speaker 01

  That's what we're aligned to. Like that's what everybody like basically, yeah, gets their dopamine, which is really great. So it's like this shared, uh, fuel that we have to ship product. And this is such a hard thing, like this humanoid stuff is like, it's like, uh, maybe one of the most complex things I could have worked on. And, um, you just, you have to have that fundamentally or there's literally zero shot. This is gonna work.

• 12:56Peter Diamandis?

  You know, we're going to hear from Travis Klanic tomorrow, who's going to say very much the same thing that your, your, what we call your massive transformative purpose, that, that clear mission vision. And then aligning your team and culture around that, which starts with you.

• 13:15Peter Diamandis?

  So you made a commitment of your own capital to get it going. And then you started calling people at other companies and what was your pitch?

• 13:25Speaker 01

  To raise capital.

• 13:26Peter Diamandis?

  What's that?

• 13:27Speaker 01

  To raise capital or?

• 13:28Peter Diamandis?

  No, no, no, to, to, to get those employees on board.

• 13:31Speaker 01

  Oh, um. The pitch in 2022 was I'm gonna fund this whole thing for many years. You know, we, we, and it was expensive, like we got to a million a month of burn in six months. So it wasn't like a, but I was like full pedal to the metal from day one. I just like knew exactly what to do. I mean, Archer is kind of like a flying robot in a lot of ways. So I, I knew how to build teams and know how to, like, we knew the product, what to do. I knew the technical understanding of like the powertrain and control systems and embedded software and sensors. So it was like, you know, we just like went really quickly out of there. The pitch was like, hey, I'm gonna fund it. So there's like no funding risk, at least in the near term, like next couple of years. There's a good chance for us to build like the next, it was like, it was like an iPhone

• 14:16Speaker 01

  moment happening with humanoids. Like it's gonna be, this is gonna happen right now.

• 14:20Peter Diamandis?

  And what did you tell them the probability of success was?

• 14:23Speaker 01

  Uh, pretty low. Like, uh, the thing, the thing that we had to do was like through, we had to do, we, we needed to prove like three things that have never been done before, that you had to go get all three of those right in the next like sub, like, you know, sub five years, or you fail for sure. You have to build like incredible hardware for humanoid, that's like extremely complex, that can never fail, it's always gotta work, and it's gotta work at human speeds with human range of motion. Nobody's ever done that before. Like, most robots that walk around can't even walk right, like they fall all over the time. It's very complex, like, maybe like rocket turbofan level complexity in terms of hardware systems. The second is you need to be able, this is a neural net problem, not a control problem. You can't write code your way out of this. You can't hire a PhD with a robot and solve every problem. You have to basically ingest like, uh, human-like data in the robot through a neural net, and

• 15:08Speaker 01

  it's gotta be able to then imitate what the humans do. So you have to solve that, which has never been solved on a humanoid system of like, you know, it's like a high dimensionality system, not like a robot arm on a table, which most of, none of those have AI. And then the third thing you have to do is you have to figure out how to generalize. You have to do something that's the holy grail of robotics. You have to figure out how to look at something you've never seen before through speech, tell the robot how to do it, and then be able to execute that task fully end to end, just with one neural net. So the, you know, and I wrote about this in the master plan in 2022. It's like, we need to solve those. If you can solve those, you're in the right decade. You're going to go build the iPhone moment for this whole space. And we're in full liftoff. But like, but those looked pretty dire at the time in 2022. There was just nothing out there. I mean, you had Boston Dynamics that was like leaping around and doing backflips and parkour

• 15:53Speaker 01

  and stuff, but like nowhere near the level of like manipulation and dexterity you needed for humanoid robots to enter the home. So I think we can confidently say now we've like, we have solved or we're making substantial progress on all of those.

• 16:05Peter Diamandis?

  Amazing.

• 16:05Speaker 01

  So, which is great. So like, I think like, yeah.

• 16:07Peter Diamandis?

  Yes.

• 16:11Peter Diamandis?

  There was a pivotal moment late last year where you said, OpenAI was a large investor and you were baselining OpenAI's AI systems. And you made a critical decision, say, nope, we have to build our own AI internally. Helix, can you speak to that moment? And I'd like to show the video of Figure at Home along that lines.

• 16:38Speaker 01

  Yeah, that'd be great.

• 16:41Speaker 01

  Okay, so what you're seeing is Helix. This is our like, this is our like, large scale AI internally. It's like a basically a large scale like vision, language, action model. And this is public, it's on our YouTube. So the prompt here that Corey gave, he leads the Helix team, was putting groceries on the table and the prompt was just put the groceries away. Not telling you where they go, not telling you where they are, just put them away. And the trick here, like the, the tricky part for the robots, they never have seen any of the groceries before in training. We purposely withheld all of these items. So it's like the first time the robot has ever seen these in its life with its own cameras and sensors. And so you basically have to solve like the generalization problem in a home. Every home is different.

• 17:25Speaker 01

  Like, you know, we all have different like toaster ovens. We have different appliances. We have different like spatulas and silverware. Like every and this it's located differently and things are changing throughout the day. So you really have to solve this like some I call like semantic intelligence, but like it's like this semantic grounding that's needed from a human world to robot world. And Helix, we can talk about why I was able to do that, is able to communicate on a single neural net on each robot and collectively together able to put these all away with just a single English problem. And so I think this is like the first signs of life. I think I will go even like more maybe of a bolder claim.

• 18:07Speaker 01

  I think this is probably the most important AI update for robotics in human history. Everything in the future that moves will be a robot and it will be powered by AI agents like this. This was trained on also very little data. It's like 500 hours of data trained in this.

• 18:21Peter Diamandis?

  I love the way they're like looking at each other to confirm like, yes, I get it. Like, oh, where are you putting that thing? Yeah, I think that's a good, a good idea to put it up there.

• 18:33Speaker 01

  Yeah, actually, it's.

• 18:34Peter Diamandis?

  I mean, is that, is that a created, you know, like they're about to look at each other here as he passes it over like.

• 18:42Speaker 01

  I get like, listen, a part of this was like, it's funny, a part of this was like emergent from training. So when the robots are doing handovers, they actually look at each other. There's actually a very split second where like one robot needs to release the package of the item. Other robot needs to grab it so it doesn't lose like basically like hold of the item and doesn't fall down. So what happened emergent from training is the robots actually look at each other as the clearing, clearing way signal for like, we should be releasing the item into each other's hands, which is like really interesting. The other stuff of like robots looking at each other and moving around like, I think it's just overall important. There's like a certain level of communication that needs to happen from a robot in terms of like, uh, interaction design with humans. So you don't want like, you know, you don't want to walk in a room and have a robot just like not move.

• 19:25Speaker 01

  And like not look at you like humans look and like do nods and gestures like all of this is extremely important to learn like we need to learn these expressions of humans. Uh, just like we need to learn how to grab items. Um, it's going to be super important as we at scale integrate robots into the entire world that this happens.

• 19:41Peter Diamandis?

  I have a thousand questions for you. Let me hit a few rapid style here.

• 19:44Speaker 01

  Yeah, let's do it.

• 19:44Peter Diamandis?

  So figure three, when do I get to see, I saw the designs. When does figure three get shown?

• 19:51Speaker 01

  Yeah, you keep asking this. You like this one.

• 19:53Peter Diamandis?

  I do. It was a beauty. It was a, I mean, you know, a degree of beauty was increasing.

• 19:58Speaker 01

  Yeah, I don't think people understand this, how like incredible. Well, they don't because we haven't showed it, but we, um, so we like, we're on, this is like the ones robots you saw here on the videos on stage were figure two. So second generation robot. Um, you can like, kind of, I guess figure one's like online a little bit, but it's like, it's a little bit more gnarly. It's like got a wires outside of it and it's a little bit more fast and. It was a much more, um, quicker design cycle to get this to our engineers to start doing real use case work. Um, but figure two was like a feature complete robot that was supposed to be able, is, is able to do almost anything a human can or vast majority of it. Um, you know, we haven't talked about this publicly a lot, but we, we're done now with figure three design. Um, I think we'll, we'll probably show an update next week. It's a quick minor, like a minor update, not, not, not anything material.

• 20:40Speaker 01

  Um, as it relates to how like what we're going about for, uh, that process. Um, figure three is like, you look at like figure one to figure two and it's like a huge step up. You're like, wow. So from a college dorm room project to a real like, like pretty decent robot. And you like the magnitude of this stuff that was pretty material. That same magnitude happened again on figure three. So if you were to see it, it's just unbelievable. We spent like 18 months designing it from scratch. Uh, the high level, it's just like 90% cheaper. Uh, it's smaller, it's less mass. Um, it's got better sensors. It's hands, head and feet were designed for neural nets. It's a completely, uh, I would say like, you know, figure two is probably the best humanoid around the market. Maybe, you know, probably not by a ton, but like, I think it's the best. 10%, 20%.

• 21:25Speaker 01

  Figure three is just like next level design. Like we've spent, um, it's definitely like the most like for me, like the most proud moment I've had in engineering in my career, like looking at that robot. And so, uh, we're going into production, uh, manufacturing with that this year. I'll have some more updates on that soon. Um, that's the robot that we want to send everywhere into the world. Uh, we want to make it at low cost, very high rate. Um, it's even better just on almost like so many dimensions.

• 21:49Peter Diamandis?

  What can you about production rates over the next three, four years and when I'm going to see it in the home?

• 21:56Speaker 01

  Yeah, so we have like two tracks. We have a like workforce track, which is like, um, and then we have the home track. Like the, what, what most people don't get is like the workforce is the big business. Like it's half of GDP. We can charge meaningfully more per robot in the house. Um, and it's also easier that the, the, the things that the robot does is just like the same things almost on a repeat. Uh, the home is like the wild west. It's like extremely hard. Uh, we have a huge safety area of like not falling on like any human or hurting people. There's a semantic in safety of like not knocking over the candle and burning the house down. There's like, the home is just like vastly harder. Like, um, maybe in self-driving, it's like driving on the highway is like workforce for us. And driving to the city is like the home. It's just like unbelievably difficult.

• 22:38Speaker 01

  Um, between our two first commercial customers, which are very large businesses, uh, we have demand. Like if we had a hundred thousand robots today that all worked, they would take a hundred thousand robots today. And so, and then, and then we have like 50 customers I could sign by the weekend that are all Fortune 100 companies that we've like literally visited. We know them. We just like, we can't, I, I've, you know, done a bunch of meetings today at lunch. Everybody's like, what do you think about helping out here in healthcare? All sound great. Like we're just like bombarded with the amount of demand here. You're thinking about like the workforce, you have like a certain, certain number of supply of humans. It's literally going down. Demographically, maybe we're retiring, so you have less humans in the workforce. There's labor pains everywhere.

• 23:18Speaker 01

  And, you know, like there's a lot of job shortages, like we can like, so anyway, we see like just unbounded demand. I, I think we could ship a million robots to this month if we like had them all working and they're ready to go. And I think one thing that we're going to maybe add before you, sorry, I knew you wanted to rocket fire. But, um, you guys saw BMW and you saw our second commercial customer. It took us a year to do BMW fully end to end at high, like high speeds. Like last summer, if you look at figure one, it was four minutes. Now we're down like 40 seconds. And just a lot of great engineering work into it. We started working on Helix. It was just completely transformative, like completely. And then we said, okay, well, what if we use Helix for this next use case for the new 92nd customer? And we did that whole thing end to end in under 30 days from scratch. Had nothing. And I think if we had to do it all over again,

• 24:03Speaker 01

  we could maybe do it in less than 40 hours. And so the robots are going to learn how to do something in like the matter of hours here. Not like 10 years from now, like this year. And I think that has pushed our timeline left multiple years for the home. Like the hardest thing, like the long pole and the temp for the home is like, is like semantic intelligence. Like can it understand what the hell is going on anywhere it goes? So.

• 24:31Peter Diamandis?

  Under over on the home is what?

• 24:33Speaker 01

  We'll start alpha testing in the home this year, which means like our, we'll be doing internal work on the home, like my home, or like engineers homes.

• 24:40Peter Diamandis?

  You want to get rid of that dishwashing duty off?

• 24:42Speaker 01

  Dude, I can't do it anymore. Just like, what am I doing? Uh, it's just like not something I want to do. Like, I want to spend time with the family and kids and life, you know, it's like, just, it's no bueno. So yeah, we got to fix that. Um, I feel, I mean, at this point, we just feel data bound in the home. Like, we think if we just like increase the data set that we trained Helix with by like, a couple orders of magnitude, it would probably, like, right now, Helix, we put it in like a, we put a little note on the website about Helix, and one of the things we put in is just drop like small household objects in front of it. It can pick up, pick up almost every object we put in front of it. Like, we put up this like weird cactus like toy, like from one of the kids' rooms, and it was like singing, and we're like, pick up the desert item. And it's kind of like, it's got to relate,

• 25:22Speaker 01

  like, a cactus to a desert, like, you know, plant. And it was a toy, and it was singing, it was moving, and I picked it up. So like, all of that is like in the weights. And it has like a very large, like, L and backbone to it. So it really understands the world's semantic grounding. So we think just like, we just need more data now, like basically data bound for it. So I guess there's a lot of confidence that you're seeing a sign of life now that you haven't seen in history, that a robot, intelligent robot in the, in the world can be built. And the question is, we just got to keep extrapolating that on like the curve far enough to where it's entering. And I think it's like this decade, I think you're going to see it in the coming years, being put into homes, just through speech, be able to do like very long horizon, hours of work without any prompt, with any fix.

• 26:06Peter Diamandis?

  Everybody, thanks for listening to Moon Shots. You know, this is the content I love sharing with the world. Every week I put out two blogs, a lot of it from the content here, but these are my personal journals, the things that I'm learning, the conversations I'm having about AI, about longevity, about the important technology transforming all of our worlds. If you're interested, again, please join me and subscribe at dmantis.com/subscribe. That's dmantis.com/subscribe. See you next week on Moon Shots.