#292 Brett Adcock - Shawn Ryan Meets a Humanoid Robot

24:31And then we ended up charging, most of our revenue came from subscriptions from big companies like big banks or startups or tech companies basically looking for talent. We started just in tech in the US. So at one point we had about, I think about a little under 20 or so cities globally that we were operating in. Wow. But most of it was tech, talent, tech spaces, you know, at that point. How long ago was this? Started in 2012 and then ended up selling the business in 2017 or 2018. Right on. So about five years, six years. Right on. Yeah. Then where did we go? Okay. So veteran was like a really tough, I like basically went like, I didn't have much money, went like fully all in the business.

25:12I went into debt at one point in 2015. The business was having a tough time. And then we ended up selling, ended up doing really well. The business like completely hockey sticked in growth. We got all the things figured out and just like - Vetteri? Vetteri was. And then ended up getting approached by the world's largest recruiting company. The same groups you and I were talking about. The same groups we were trying to take out of business. And they were like, oh, we want to acquire the company. And at the time, we were like, I was like completely dead broke and put everything out of the business.

25:45It was like, I think it was at the point almost seven years in. And, you know, we were excited about an acquisition a year before that,$10 million from one of the big tech companies. And they came in at$110 million. And it was a good time for me. I felt like the business was doing well. I learned a lot and I was kind of ready for my next chapter. So I ended up selling that business to the Deco Group. It's like the world's largest recruiter in the company. But you didn't even have it for sale? They just approached you? Yeah, we didn't hire a bank or anything. Listen, at the time we were doing like, I don't know, 20 ,000, 30 ,000 interview requests a week.

26:25So that was like no humans involved. Think about how many humans it would take to do like 20 ,000 or 30 ,000 interview requests. It's like, you know what I mean? And then manage all that processes. So we were like, the growth was just unbelievable. And there's something better to like a human jam and you enrolls, right? Like, it's just like you need like a, and then to the extent you can get, you know, all the world's like talent there and all the world's companies looking, you can really create an amazing environment where you can get people to like the right jobs. And right now it's not like that.

26:54It's like a really black box, like trying to both finding talent and looking for a job. It's just a terrible experience. So we kind of, that clicked. Yeah, the world's largest recruiting company came in and said, we got to buy this thing. And - Yeah, I'll bet they did. Yeah, I sold the business and it was great. It was a good time for me. I really, at the point, it was a point in my life where I really wanted to do something much bigger. And so I took about, I basically took about a year. And so it took about a year by the time I got the term sheet to sell to when we actually sold and closed. It's a long process you have to go through, like tons of docs.

27:28And then you announce the deal. Then you actually close the deal. And then it went into escrow. Then it finally hit my account. It's kind of like one of those processes. And I want to go work on something really important, hard. and a couple industries that I've, I've been interested in robotics and aviation and some areas of security for like basically since college. And I basically spent a lot of time trying to figure out if I was either going to work on, at the time school shootings, like basically 10X'd. And I was like, man, there's got to be something to do here. And we can, you know, and then secondly, I really wanted to work on like flying cars.

28:03Like having to watch lots of sci-fi as a kid is like, man, like I really want to go. There was a near-term problem of like, we got to go help with security in schools, K through 12, mostly in the US. And then how do we, I want to work on flying cars. And I ended up making a decision to work on flying cars at the time. So in 2018, shortly after the sale of Vetteri, I started Archer Aviation. And basically like the story here is you can build like an electric aircraft that can take off like a helicopter. If you take off like a helicopter, you don't need to place the airports outside of cities. You can place them inside of cities.

28:41Like think about like a normal helicopter can take off from a building or a helipad. And so if you can take off vertically, you can basically nestle the aircraft inside of cities. Half the world lives in cities today. It's, you know, by like the middle of the century, it'd be like 70 % of the world. And you just like can't get around. Like it's just gridlocked everywhere in major cities. It just sucks to go like 20, 30 miles. It takes like an hour in most cases. So basically you can design an aircraft that can take off vertically and then fly like an airplane. So you can get like a lot of distance.

29:13And you can basically then re-architect the whole aircraft to be fully electric. The reason you want to do that is for cost and safety. You basically can make it like a lot less expensive. You can put a lot less parts in the aircraft that are also good for safety. So basically you can build like an electric flying car that you can move around. So instead of calling an Uber or driving, that might take you an hour in LA or SF or New York, you basically can fly there in 10 minutes. Holy shit. And if we can pool everybody together in kind of like an Uber pool style business model, you can do it for as cheap as an Uber.

29:46But the problem was I didn't know anything about how to build electric aircraft. I mean, where do you, you know, I'm just, I know you just sold your business for$110 million, but where do you get the confidence to, where do you get the confidence to go, I'm going to build vertical takeoff and landing flying cars now? I mean, listen, I didn't wake up to this world, like, learning how to build software. So, like, I learned how to do that and run engineering and run the company. And there was, like, a lot of through trial and error.

30:20and I just felt like I could learn it. I started in industrial system engineering at University of Florida and then ran engineering and ran the company at Vetteri. So I basically just hit the books. I tried to learn as much as possible about three subject areas. First was electrification, which at the time, electric vehicles were really doing well and even drones. Vertical takeoff and landing, like vertical lift, which is like traditional like rotorcraft or helicopter. And the third is like winged aircraft, like airplanes. You really need wings. Like, so you basically have to learn about those three subjects.

30:57So I started, I basically bought my basement downstairs at home, where it's like every possible book on these subjects you could match in and started reading as much as I possibly could. And this was during the year transition. As I was transitioning like out of out of veterinary into archer, I was reading every possible thing. And then I found a small community folks that were hosting on-site either half-week or week-long courses for this. And so I would go to these, sometimes they're sponsored by NASA or by colleges or whatever it would be on basically rotorcraft or electric propulsion or winged aircraft aerodynamics.

31:28And I would basically try to learn as much as possible. It got to the point where I was completely obsessed with this algorithm I was building on electric aircraft sizing. How would you actually build an electric aircraft. So electric aircraft, which interesting is like in rotorcraft, um, like you basically want to make, to create the most efficient lifting device possible. You need as much of, uh, like as the rotor disc area, like, like in terms of surface areas, you possibly can. It's why the helicopter rotors are so large. We want them to be really large. That'll reduce power and get you off the ground.

32:02And electric aircraft, the problem you're starting with is you have like one 30th of the energy as you do in kerosene in a battery pack. So you're just like, you're off the bat, you have 1 30th less range or 1 30th less energy. And so power becomes like the dominating factor of how to basically build electric aircraft. Like how do you get power down as much as possible? You really want a lot of disc area. A lot of disc area is, well, one, it could be good for power, but it's also bad because you have like no redundancy in the system. you have like one rotor blade. If it doesn't go well, you go down.

32:38With electrification, you can basically build much smaller, like basically rotors, and be able to be fully electric. And the reason you can't do that with traditional kind of like turbo fans or engines is it gets too inefficient at these sizes. You can't build 12 propellers on a helicopter. The efficiency just drops like to nothing. So with electrification, you can. You can size electric motors to small sizes, and they're still 90 % efficient. So like small electric motor on the table or a big one the size of your chair, same efficiency. When you do that, you create a lot of redundancy across the system.

33:13So you can build like an aircraft with 12 electric motors. So this is, the rotors are underneath. Are they underneath? Like the problem here is you can design it however you want. You can put a bunch of rotors along the wings. You can put them like laterally across the fuselage. You can make one big one. You can make 30 small ones. Like, so how do you design it? That's the problem. I hit in 2018 was how do you actually do this? And so it basically was like a crazy man trying to design this algorithm to like, what is the ideal aircraft design? And then how do I go build it? So it was actually at a I was at a Hyatt Regency Hotel in Atlanta in 2018.

33:52It was an electric propulsion week long design course and like aerodynamics course for winged aircraft. and I met a guy there that was basically in the engineering department at University of Florida. He was doing his PhD in aerospace and I asked him what he was doing there and he's like, I'm from University of Florida. I was like, oh, I went to school there as well. And he's like, I'm like, what are you doing here? He's like, oh, I want to go do a career in eVTOL aircraft. And it's called electric vertical takeoff and landing. So a helicopter is a VTOL and you put little E in front, it's over electric.

34:28And he's like, I asked me what I'm doing here. I was like, I'm starting a company to do this and I need to figure out how to go build these things. And he's like, well, listen, my professor runs a small drone lab. He's got a full building, he's got 12 PhDs. Why don't you come down and like meet him and see if you can start building aircraft with him. So I flew down that weekend to go meet his professor that runs all of basically mechanical engineering aerospace. And long story short is I ended up taking over like his lab and me and him and his team started building aircraft in 2018 and 2019 down at University of Florida.

35:01And I temporarily moved down there with my daughter at the time and my wife living in Gainesville, Florida. And it was great. I ended up funding a lab right off of Archer Road, a new lab, because we needed more space. We ended up calling the business Archer Aviation. It was the main road down of University of Florida. And I spent the next year, year and a half, basically like modeling and building electric eVTOL aircraft. Holy shit. Yeah. And it was a great time in my life. And the problem is there was no intersection of folks that knew electric, new rotorcraft, or new airplanes. There was no Venn diagram of overlap.

35:42Gotcha. So there was nobody in the world that understood how did this all stuff that works. So I had to go from scratch, like learn it from first principles. and then ended up moving the company out to California basically a few years into the business. And then, you know, things took off from there. We'd like built bigger aircraft. I took the company public within three years of starting it. We're a$6 billion publicly traded company today. And yeah, designed basically like four or five generations of aircraft at Archer. And it was hard. You know, it really set me up well for like, you know, doing figure and cover and the rest of stuff, we can talk about later, but like, it was hard.

36:24Even going public was like probably one of the hardest experiences of my life. Really? Why is that? We went public through a SPAC process. So you know SPAC's a time like four or five years ago where like all the rage. Okay. And it was a special purpose acquisition company. So it was basically companies that were like going public through a merger, like reverse merger. and it was hard because in 2018, 2019, coming off of software, I had never done hardware before. So A, it was like hard to raise capital. And B, there was nobody funding like deep tech, electric vertical takeoff and landing like companies.

37:03Like, you know what I mean? The big venture capital groups were not funding SpaceX or Tesla or Rivian. Like none of these were getting funded by traditional investors. They weren't raising money from the named investors we all know about now. Oh, shit. Are they always behind like that? The mandate for most of these VCs in the Bay Area or Silicon Valley and stuff are not to do hardware. Gotcha. And if they do hardware, they don't do deep tech. They don't do, like, rockets and autonomous vehicles, and I don't think there's a single, you know, top VC in the US that's invested in a humanoid company.

37:36No shit. And as of last six months ago now, nothing. Like, they just don't do this stuff. and uh and so like i end up i end up going all so i you know like um you know made just made 110 million dollars and or just sold the company for 110 million dollars i made a lot of money like personally ended up going all in on archer through the ipo like uh through going public i i put like all the money i basically i bought a house and the rest of the money went all into it and um and it was a stressful period so we went public through a spec and the reason it was stuff is we ended up getting to a point where we just couldn't raise enough money privately.

38:14Like, it was either, like, raise, you know, $100 million privately at, like, you know, some valuation, three, four,$500 million, or it was, like, go public and raise, like, a billion dollars. Wow. And we ended up going public and raising a billion dollars. Wow. You've got a huge appetite for risk, huh? And we got sued during it. Oh, really? Yeah. Like, we got sued by basically, like, Boeing in a big startup that was founded by Larry Page, Google founder. That's got to be intimidating. Yeah, I woke up to a front page of New York Times article about - Oh, shit. Yeah, it was crazy. I mean, the backstory is I took - So Larry Page started a company in the Bay Area about 10 years ago called Kitty Hawk.

38:58And they did a great work over 10 years in electric VTOL aircraft. And I ended up taking basically the core 10 to 15 folks that were there all came over to Archer within the first two years. Wow. And they retaliated by just trying to harass us while we were going public. And so, yeah, it was just a crazy story, getting public. Ended up getting public, you know, billion dollars on the balance sheet, and we just started building aircraft. and started building the service, like thinking about the app and how you're going to check in and how you're going to build places like real estate to fly into.

39:38And yeah, and then like the engineering work we had to do around there of designing, you know, it's basically a flying robot. You have like battery systems, electric motors, sensors, embedded software and control systems. And basically like the robot you saw this morning, like very, you know, it's a flying, it's a 6 ,000 pound, four passenger piloted robot. And it has 24 degrees of freedom on the system, like wing flaps. We tilt the front, the leading edge, six motors, 90 degrees for basically take off vertically and then go into forward flight. And then all the propellers, fan blades on have variable pitch propellers.

40:20So it's like a highly overactuated system that needs like a really good software. Like no human can like fly it basically without a really good control software. What altitude is it flying? About a few thousand feet. So about two to three thousand feet above ground level. And that's what it would normally be. Yeah. Like traditional helicopters fly at these levels. I mean, what, what I think about, I think a lot about Tesla and, and all the EV vehicles that are coming out and, you know, it's the government just seems so far behind on AI, You just brought up gridlock and all the cities. I've always wondered, when are we going to go full EV?

41:01I know there's a lot of pushback about that from an overreach standpoint, but if you just think about the traffic in the cities and if you have the AI processing all this, even without air vehicles, I feel like a lot of that would go away because the AI will route you the quickest and take all the traffic patterns into account. and it would just flow a lot easier. Yeah. But there's all this government regulation. I think it's also hard because, like, if you look at the number of installed cars in the world, like a billion and a half or so installed cars, we make, like, 80 million or so cars a year in the world.

41:40It takes you, like, you know, on the order of, like, 20 years to replace all the cars. So if all the cars were electric and autonomous today, autonomous cars have, like, autonomous hardware in them. It's not like you can just go out and retrofit all the cars in the world right now. Like, it's a hard problem. Well, I mean, if you look at Tesla, for example, I mean, it can self-drive, right? It can come get you. But when you're driving, if you take your eyes off the road, it wakes you up. You have to come back. I mean, it seems it's inviting more error into the road by doing that, in my opinion.

42:13Yeah. Am I wrong? It's almost like some harm could be more dangerous. We're just in this transitory state right now where in five years, everything will be fully autonomous and trusted and fine, and you won't have to do that. And we're just in this transitory. We're in this chapter in the book for the technology roadmap here where we're living through it and it's a little messy and it's not quite straightforward. And we don't quite know where it's headed next, but where it's headed is at some point in five, whatever years, where when our kids grow up, they're never going to have to think about this.

42:43It's just going to be autonomous from the start. It's going to be by default native. Yeah. And it'll be trusted and easy and safe. I think we're just living through this period right now, which is a weird thing. But if you close your eyes long enough, you'll have this autonomy and electrification everywhere. How long do you think it'll be? I mean, so I live in the Bay Area. You can take Waymos now. I can take Waymos everywhere. It's unbelievable. They're already all over over there? They're everywhere, yeah. I'm in South Bay. But they were in the city for a while, and now they're in Palo Alto, Menlo Park.

43:14like San Jose, like all over the place. They're really great. I take it. It's like my wife and I, we go to dinner and stuff. On the weekends, we take Waymo. It's like, it's so fun. It's like - No shit. It sounds so basic. Like, you take a Waymo, it's fine. It's awesome, man. Like, it's great. You have like, the car drives so human-like and it's such a great experience like not having a human there, to be frank. Like, I love it. You know, I order so many like whatever, Ubers and stuff in common. and the car smells or it's dirty or whatever else. And it's just like, you know, it's just easy. It's really cool.

43:48So technology is like in the early chapters, but it's all here. Like we're going to have autonomy at scale like everywhere. It's just going to take some time to roll that out. It's the time it takes to get the technology mature enough where they can run enough cities, enough places. And then it's the time it takes to get the install base of autonomous hardware and software in all these places. That's going to take some time too. We just can't snap our fingers. we just don't have enough install base of autonomous vehicles in the world. I think Tesla's got like 10 million cars in the road, and maybe there's thousands or so of Waymos.

44:21You have over a billion cars on the planet. So you need to make a large fraction of that all autonomous. So you're looking at like, this isn't gonna happen in a year or two. It's gonna take some time. When are we gonna see your vehicles? The aircraft. We have them now. We fly every week in California. The challenging part with Archer is that we are governed by the federal airspace. So to fly passengers and charge money, we have to have basically a type certification from the FAA. That process moves at the speed of the post office. And the FAA is not incentivized to put anything in the air unless they know for sure it's going to be really safe.

45:04The safety standard for us that we want to certify to is 1 times 10 to the minus 9 in terms of hours of reliability before a catastrophic event. So that's one in a billion hours we can have a failure. One in a billion hours. You can't be able to, that is like, that's the standards when we fly, it's like one of the safest form of transportation we take. And it's because of those standards governed by the FAA, which is great. I'm like, you know, we're like, that's the bar you need to be at. And that's the bar you need to hit, especially taking passengers over cities with aircraft overhead. You need to be at those levels.

45:40So that's like the long pole in the tent for us. And that's wherever you go. If you go to, you know, Europe, it's YASA or, you know, CAA in China, wherever you're going to go. There's like there's federal mandates to get basically an aircraft to take passengers. So we're in the middle of the FAA certification now. We hope to be certified in the coming, you know, as soon as possible, basically. But it's like, it's not something you can like, there's not like a date on the calendar, but like, you'll be certified here. You have to work through a very, like, very long and slow process with the FAA to get through this.

46:13And then we're also dual tracking that against a couple of different entities globally now to make sure we can get certified and get in there. But it'll happen, man. The aircraft, it just, again, we're in like this chapter. were like flying cars, electric aircraft, or just like it's early. It's earlier than like AVs or EVs, autonomous vehicles, electric vehicles. But it'll happen. It'll happen in our lifetime. We'll be taking these things around. Well, I mean, what do you envision? Let's fast forward 20, 30 years. Yeah. What do you envision? What does it look like? Do we have roads? Do those get ripped up?

46:49What does the sky look like? Yeah. What does everyday life look like? Yeah. Yeah, the really important thing to hear about the airspace is that it's three-dimensional and the roads are not. They're 2D and we've built cities now and houses and restaurants all around these places. You can't like, there's nowhere to go. There's like no more roads to build in these cities. So you have left with no choice if you, and then humanity are moving to cities. We have this like secular trend where we all want to live in cities right now. It's like half the world lives in cities. It'll be like 70 % by 2050.

47:23So we're all moving to cities, the roads can't grow anymore, and we're constantly moving around, going to work, going to restaurants, and it's like this... It's basically getting worse. The arteries are hardening here around this, it's getting worse and worse. And it's just like, it's some of the worst time to spend on a road in traffic. It's so soul-sucking. It's just the worst time to lose. So the good news about the air is it's three-dimensional. You can stack basically an infinite amount of, say, roads in the air. Different altitudes. Yes, at different altitudes, and even laterally. So you can basically build little tunnels in the sky, and you can basically stack them, and you basically can put orders of magnitude more things in the air than you can in the road.

48:13It's the same for below ground with tunnels. So the future of travel in cities is below ground in tunnels and above ground in the sky. The boring company. Yeah, exactly. Just dig tunnels. And it's great. The only problem with tunnels, with the node system on the ground with, say, we call them vertiports, but basically real estate for flying cars, is you can say you had 10 different, or even like, you say 10 different vertiports inside of a city, like places to like take off and land from, you can travel between any one of those routes. So it opens up like, you know, basically exponentially more places to go to.

48:59I can go to like any node on the system at any time. So hold on. So you're saying in order to take off and land, you'll have to go to specific locations. You won't be able to do it from your home. Yeah. You're not going to take off and land from your home. Okay. Just because like acoustics in the neighborhood, it's going to be too loud. You need like a decent amount of infrastructure for that, for charging and for passengers and cleaning and checking in and stuff like that. They'll be in your neighborhood. And you'll like Waymo there or walk or take a bike. And then you'll get on these and they will go to any node on the network.

49:31You can't do that with tunnels. Tunnels have to go to A to B. You can't jump to another tunnel downstream. Like, you know, I want to jump to another tunnel like 100 meters down. Like, it doesn't happen in tunnels. You can do that with the sky. You can basically jump to any node on the network. It's exponentially more routes you can basically do with less real estate. And then you can basically stack as orders of magnitude more traffic and humans in the sky. So my ambition is that you're going to be, for most trips that you're traveling over 20 minutes, all of that will move to the sky. And not only that, but you will have cities being transitioned to a point where you can live well outside of cities and get to cities really fast.

50:10The reason we live in cities is because we're working there and we have friends there. And we have like, it's, yeah, it's like, I want to be like, I want to go to dinner with somebody. I want to see my buddies over here. And we want to go to work over here or like go to the mall over here. It's like, everything's there. And that's what we want to be. We're social creatures. We want to be there next to other humans or some of us are. And so, yeah, so anyways, but like, you know, now that you can fly this 150 miles an hour in the air with no traffic, point to point, like no stop signs, no construction, no things jumping out in front of you.

50:41You don't have to like travel different distances. is you're going straight from A to B in most cases. So you're removing 10 or 20 % of the distance just by going point to point. And then you have nothing stopping you going 150 miles an hour most of the way there. You can live far outside of cities and get down to city center in under 30 minutes. So will these be personally owned? Or will this be like an Uber service? It'll be like an Uber service. OK. To get cost down, you'll basically just pay per trip. You'll pull up an app and you'll go like, I want to go downtown to whatever. it's 40 bucks and i'll be there in under 30 minutes and you'll you'll you'll say great i want to be there that time you'll hit a button it'll be on demand you'll ride your bike over or walk you'll get in one it'll leave in seven minutes and then you're basically flying right down to town holy shit and you're saying this will this will be in every neighborhood this will be very accessible to everybody yeah that's you're designing the whole uh electrification allows you to reduce the cost and the safety burden of all this.

51:39Wow. We have like a normal helicopter could have like 100, 200 like safety critical components that any component gives out, the helicopter can go down. An electric aircraft has none. You can lose a motor, you can lose a battery pack on board and still fly safe without having this. And so, like, just from a safety, from a part count, from a cost, from an acoustic signature, it's not gonna... Like, helicopters are loud and very noisy, and it's just a much better technology for this. Have you been in one yet? We... I haven't flown inside of ours yet. I've also been inside of our aircraft. And we basically have professional, basically, pilots at the company.

52:28Test pilots? Yeah, test pilots. And they're career test pilots, and they're unbelievable. I'll bet. You know, a lot from the military, a lot from the big aerospace groups, and they're just professionals. What do they think? I love it, man. This is the future of aviation. Man. Everything's going electric, and... This is so crazy. Yeah, it's crazy. It works. Like, it's crazy it works, and crazy we're in the right time period to make this happen. Yeah. And, you know, the good thing about, you know, Archer now is we've demonstrated... The hard part is making sure you're in the right decade. You don't want to go do this and you find out, it's like a 2040 event and you can't get it done.

53:08It's just a waste of time. And so the good news for Archer is we're in a sweet spot here where this is going to happen, aircraft now work, we're certifying now with the government bodies like the FAA to make it happen. We have a good balance sheet with cash, the team's great. And so it's just like, you know, get certified and get this thing going. Damn, that is, you're really changing the world. Well, we're at the start of it, but, you know, hopeful to make this thing work. Where are we going next?

53:42Humanoids? Let's, yes, let's do it. Yeah. How did this idea start? Yeah, so, you know, I spent like five or six years working on like a pretty crazy robotics robotics work at Archer. And like the ultimate like meta problem in robotic space is can you can you build like a general purpose machine to do everything in the world like much of what say humans can in the world. And I have this big belief that, you know, we like we have like weird biological species. Like we look, we're like, you know, we have these weird hands and arms and legs and certain height and sensors. And then we ended up building this world around us so we can interact with it.

54:31I mean, if we get dropped into Mars today, we're going to build like coffee cups that we can hold and stairs and doors, and we're going to build this stuff again. And it's like the human operating system. We're building things we can like use and operate in that makes it easy for our lives. And we built it around the form factor that we are. I mean, if we look differently, the world would look different. Our espresso machine would be different looking. We might not even like espresso or caffeine in this case. So we built this whole world around us. The holy grail for robotics is can you basically build a general purpose machine that can do what humans can, which for me is like a humanoid robot.

55:08And a humanoid robot is just a robot that has like a human form. So it has legs so it can walk upstairs and walk over like, you know, uneven terrain or say things on the ground and bend down, which are important, legs are important for, or reach up, has like arms and hands so we can manipulate objects and do things like, you know, grab his stuff, open, open these gummies and, you know, fold laundry and do, do real work. And then we have the right sensors so we can like see the world and understand what to go do and use a, you know, our biological neural net to kind of figure out how to reason from.

55:46And having worked on aircraft for now five or six years, I thought it was pretty possible to go build an electric humanoid robot. And electric's important for cost, and it's important for safety, and it's important because the performance will be much greater. And at the time, I think one of the best humanoid robots then was probably the Boston Dynamics Atlas. It had a hydraulic system. It was really heavy and big and high torque and very leaky, like the oil is everywhere, and also didn't run very, like maybe ran for 20 minutes on a single charge. So you needed to kind of radically transform the hardware, and then you needed to figure out a way to build like an AI brain.

56:29The humanoid is so complex, it has like, let's call it like 40 degrees of freedom, and degrees of freedom is like a joint. So like an elbow is a degrees of freedom, shoulders got three, a ball and socket has three, like a pitch-on roll. And our robot has about, let's call it 40 degrees of freedom in it. Each degree of freedom is a motor that can spin 360 degrees. So if you only look at how many positions the body could be in at any given time, like this is a position, this is a position, and keep moving, amount of states. Mathematically, it's 360 degrees to the power of 40 actuators. So there are more states in the robot than atoms in the universe.

57:07There's more positions the body can be in. No shit. By far, it's a much greater number. Done the math a few times, very confident in this, even though it sounds ridiculous. So you just can't code your way out of this problem. Like, how are you supposed to write code? Like, how's a human supposed to write C++ or code to tell the robot at any given timestamp what to go do? Like, if I want to grab this, I need to move my whole upper body and maybe lean over, and I'm moving my fingertips and my wrist and hand, get in position to grab this. Like, it's an intractable problem for code. So, um... I mean, you were saying earlier, I'm going to butcher this, but it's updating the foot 200 times a second.

57:54Yeah, our controller is running... For balance. Our whole controller, so we have a main computer, is processing what to tell all the joints to do, 200, like, a little maybe more than 200 times a second to make sure we can just balance. And then we can, like, do the task. It could be, like, reaching over and grabbing this or balancing. If we run that too slow, we just don't have enough feedback. Then we just fall over. We have to fully balance. It's dynamic. Generally, if you power it off mid-run, it's going to fall down. It's not like a four-legged dog or quadruped robot where at any given point it's usually statically stable.

58:29It makes it very difficult because you have to be able to even move your hand. I'm moving my pelvis and my whole body. My torso is moving. My head is moving. like all of it becomes very complicated now. It's not just like move my hand, it's like move my whole body to get my hand in the right spot. So every joint, all those 40 joints have basically position encoders. We know exactly what position the motor is at or even the case of the knee or this. And we have force sensing, torque sensing on board. We have the ability to detect all the forces that that knee is seeing. It could be really high when it's walking or it could be like, you know, it could be like, it could be powered off have no forces on the leg.

59:09All of that feedback is being sent in the main computer, and then we're telling all the joints what to do over 200 times a second. Some of the other feedback is happening at like five or six kilohertz. So the force feedback is happening five, 6 ,000 times a second to the motor control on board. And we do the motor control. The brain for all the motors has done it locally at the motor level because it needs to happen so fast. That's being fed back to a main computer that runs a control software that tells the rest of the whole body what to go do at every timestamp to keep balance.

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1:02:17Video audio damage Video audio gasolina Thank you. So, getting back to the original thing, we're like three and a half years old or something like that figure. I bet three and a half years ago in 2022 when I started the company was that this was possible now. And my view is like over some 10 or 20 years, this will work. And so, I basically started on this endeavor to go basically rebuild from the ground up humanoid robots in AI software to try to see if you could make this work. At the time, there was no good precedent. There was no precedent for showing that there was no AI that ever worked on a human or robot in history.

1:02:57And there was no electric humanoid hardware that was remotely okay to show it would work. And there was no hands. There was none of this stuff. Wow. So I actually had a lot of trouble. I had a lot of trouble early on even getting people excited about this because they were like, what the hell are you doing?

1:03:20And so I ended up having to basically self-fund a lot of it. The first year I self-funded all of it. No kidding. Yeah. And it was a lot. I mean, we got the business to a million a month to burn in month four. But it was like I knew what to do. We built a 40-person team as fast as possible. And I knew how to spin up hardware and software. Again, the key characteristics of robotics, like electric motors, battery systems, control software, embedded systems, and sensors. And then even within electric motors, we build actuators. They have a rotor and stator and a gearbox and sensors, electronics and wiring and connectors, multiple sensors inside of there.

1:04:02And then firmware, it lives on the microcontroller, it lives on the motor control side. and then we have like thermal characteristics, it's hot. And then you gotta all make that work at very like high speeds and high torques, meaning motors don't like working when they're not moving. Motors hate not moving. Motors wanna run on highway speeds. Okay. They love that. Like whether it's like a generator, like something, you know, an appliance in your home or like an electric car, they wanna like run at like highway speeds. They're designed to run at full RPMs. That's when they're the most efficient.

1:04:33Okay. When motors are stuck and not moving, but holding power and holding forces, they're all, it's a really bad point of the torque speed curve. So they're not built well for this. So humanoid is all the time. Like we're like, when we're standing, we're like not moving, but holding forces. When we're like holding something out and like giving me the gummies, like it's not moving now, but holding forces. It's just a really hard engineering problem. Just that one little aspect, which is like hardware umbrella, just motors. And so we have whole teams in those areas, just in that one little area here doing like rotor design, electromagnetics design, satyr design, gearbox design, sensor design, motor control design, like all of this inside of teams.

1:05:16It was an enormous lift to just get the team members there to do it. So we split up a team to go operate that really quick. And then, you know, now like looking back, I think we raised, you know, you know, 2 billion or so like now it's a much different story. We have, you know, we ended up building figure one, it's our first generation robot and had that walking in under 12 months. So from when we - From inception? Wow. Yeah, from when we had basically incorporated the company in 2022, our goal was like, can we get a robot walking by itself, these dynamics in under 12 months? And we did it. We did it with like two days left in the year.

1:05:57At the time, I think it was the fastest time in history for anybody to do this. and um you know and then from there continue to build the capabilities we built generation two figure two which is that guy right there is our second generation robot and um and i think one thing we did before we kind of moved even to gen while designing gen two is um i think it's probably like 2023 at the time we um we did a demonstration where we basically wanted to put this k cup inside this curic and run it it was just on a very pretty simple like like not nothing crazy but we Keurig machine, coffee cup, and a K-cup.

1:06:33And we had to go grab the K-cup, open the Keurig, put it in, close it, run it, and then make coffee. And it sounds simple, but for a humanoid robot to do that is extremely hard. And then we wanted to do all of that with just neural networks. It sounds simple, but I mean... Simple task for humans. Give it to a four-year-old. Yeah, exactly. I mean, the dexterity on the hands of that thing is just to hand you the bag of gummy bears. Yeah. Yeah. Yeah. Yeah. Then can you do it with neural nets on board? It's crazy. Can you not code your way out of it? Can you have a, can you take in camera pixels and then output trajectories for the motors through a neural network?

1:07:12No code. And we did that in 2023 on figure one. And it was like probably the, probably the most significant demonstration we've done in four years now, almost four years, where we were like, internally, we were like, you know, how do we get neural nets to run on a humanoid? I don't know. I think it's probably one of the first examples in the world to ever have shown that. And, you know, this was like game on. This is like, let's go build a really good human-owned hardware. Let's make it cheap and really reliable. Let's make sure it can do what humans can from a hardware perspective. Meaning you want to look at like a phone where you can just add new apps to it, like the do laundry app.

1:07:54And the hardware doesn't need to change in the same exact hardware. like humans don't need like new hardware to be able to go off and like learn how to do new skill now in the physical world. So you want to build the humanoid hardware so it's like can do everything basically a human can or as most as possible and then you want to go all in on neural networks because you just can't code your way out of this problem and that was the first moment in 2023 where we're like hot damn this is gonna this is gonna really work. This is gonna be humanoid robots hardware gets good and then you're basically gonna be this is gonna be a data play to train neural networks to run on humanoid hardware and do what humans do.

1:08:32And then we launched, you know, we launched figure two. We did a lot, basically more work. We started unveiling Helix, which is our neural network stack internally that we do here. And now we've designed figure three, which is our third generation robot you have here, which is like the best humanoid hardware in the world by far. and we're now running robots that do like i watched it you know the other day uh unload dishes and fold laundry we had figure twos at bmw last year that worked six months every single day every uh six months every single day it worked a 10-hour shift every day for six months and um we had uh it was just it was like the first time for us getting robots out to the real world doing real stuff.

1:09:20Like, you know, it's fun doing like, you know, demos at the office and showing it can really work. But the real like level boss is like, how do we get robots out and do clients fire us? Do they love it? Does it work? And the goals we have for clients is hard because we have to do human work. So we get like human KPIs in terms of speed and performance. Like humans, like, you know, in the case of manufacturing, they don't like, you might mess up every once in a while, but you like refix it. So you're not messing up every single time. You're pretty fast. In most cases, the humans are there, not quitting or not showing up to work, but sometimes that does happen.

1:09:53So it's hard. It's a hard bar to go hit. And we have to wake up every day and be able to do that. And so we had robots in the manufacturing line. They basically have a body shop that basically builds like X3 and X5s. And January of 2020, what was it? 2025, we started building our first BMW X3s on the line. Are you serious? And I bought the first four that did that. They're on my campus now. One's at my house. And they didn't build, obviously, the whole car. There was a ton of parts, but we did a part of the whole process. What's BMW's feedback? They're great. I mean, the BMW is like, if you go into like a car manufacturing company, they're like the best roboticists in the world.

1:10:43There's robots everywhere. There's like these giant 12 foot KUKA like manufacturing, like, like robot arms on the floor. They're bolster to the ground. They're massive. These things are carrying car chassis around like they're kids toys. The cars are so big and so heavy. You can't human can't hold it and pass it around. So you You basically have machines that are building the car and then moving the car. So the whole body shop line is only automated end to end. And it's like, not end to end, but like there's humans involved, but like the car is being like built by machines. And then there's machines everywhere.

1:11:22There's special end effectors on every machine. They're switching these things out basically in real time, like grabbing a big end effector. The end effector is something that is grabbing a part. These end effectors are the size of my chair. They're switching them out in seconds. One or two seconds. They're doing it really fast. And then they're basically building a car with this. There's robots everywhere. And so like BMW, it's been a privilege to see how much automation has gone into automotive. It's unbelievable. These machines kind of make what we're doing sometimes look like little toys. No kidding.

1:11:57We're doing something very complicated, but the car manufacturing is just like no joke. So what specifically were the figures doing? Yeah, we had a, there's a body shop line called, that's basically building the rear header. It's like the back plate. So the body shop, they basically build the car by putting basically sheet metal together, welding them onto the chassis. And then they're basically building the car around that. Like you end up putting the seats in, bolting them down, putting the car doors in, wiring them up, the harnessing. And we were in the body shop line, helping basically attach the rear head, like basically putting the rear header on this fixture.

1:12:29So today, we basically, or last year when we were there, we basically take a piece of sheet metal and we basically put on this fixture and we do that over and over again. And they do that, you know, 10 hours full shift. And there's three different parts on. Those three parts go on. This thing rotates and this big, giant KUKA machine, like this robot arm, goes and spot welds it and switches out to another effector and then grabs it and puts it down the line. Wow. So these facilities are being fed these parts into the machine. And we were a piece of that. And the goal was just like, can we run robots every day?

1:13:10Are we going to get our ass handed to us? Is it going to be easy? Is it going to be hard? And I think it was in the middle. I think we got the robot to a great spot where it was brand every day. It was great. I think the biggest learning lesson we took away is we had, we really, I really cared about if, can we do that? And can we like clone it times a thousand times 10 ,000? Would we have any issues scaling? That was the part for me. Like, is it just like, you know, does it completely shit the bed and he's like, we need to rework our plan and go back to the office. Does it do it incredibly well?

1:13:41And you can just copy paste this thing to everywhere in the world. Like how does it, how did it work? And the biggest learning lesson we got was that the robots, the robot that started the first day at the start of six months, and the robot that ended the shift that day was the same. Like even though we had multiple robots in operation every day, we had the same robot that did the start and the finish. Wow. And it was cool. Like, you know, like it was same robot ended six months later. And this was the thing where, you know, the worry was that humanoid robots couldn't last a month, couldn't last a week in these kind of environments.

1:14:10And, you know, wear and tear, it's like it's running like a, you know, 40 degrees of freedom motors every single day? Can they operate really well? I think from a hardware perspective, it did an A-plus job. I think from a software perspective, we did like a, I would say a B-job, B-plus. And that's mostly from my perspective, like the architecture decisions I made to scale. About half the stack we had traditional code and heuristics in. So the controller to walk was done by a C++ controller. it was done by code the walking you saw today we had back then was done in code okay the rest of this we had a bunch of other stuff in there done by neural nets and like some of the perception stacks some like you know some of how to move parts around everything else and you know this was a year and a half or so ago when we were first launching and um and i was like man this the the the biggest problems we're having is the coding parts get stuck the robot like either doesn't see like the right, like see something right on the part and misses the object detector, doesn't really understand what's going on.

1:15:17The controller, when it gets out of bounds of like what it's ever seen before, like you have carpet in here now and it's like really squishy. The robot was doing fine, which was great, but I think our old controller would not do well. It's like you have like really shaggy carpet here. And it's like, yeah. And so like, and it's like not very, it's like, it's like, you know, it's harder for a robot to walk around. And so that was like, we had a really difficult time seeing that, even though it did well every day, I've seen that scale to lots of robots. So we went back to the office, this is about a year and a half ago, and said we need to basically refactor everything into a neural network.

1:15:49And one of the big, and I think we just announced Helix two, two or three months ago now, I forget, like end of last year. And it's basically entirely down the stack, including the controllers and neural net now. There's like no code left really on the robot. Maybe some code in certain pieces, but mostly just almost all of the thing is like a neural net at this point. We removed the need for almost over 100 ,000 lines of code when we launched Helix 2. And so what you saw today was just like a robot that we can put now back into, say, the factory in these places that will run only on a neural net.

1:16:24And I think we're running these robots right now. We're getting ready for deployment to customers, and they're running incredibly well. We have robots running basically now in 24-7 shifts without stopping, without any faults for like days and days. We just went like over, we just had like record time this past week on the robot running until we saw like a fault, like almost, yeah, basically a whole week. And they basically like, they can run like four hours or so, five hours, and we need to charge. Another robot knows that, steps in, steps behind the robot, say, and gets ready for work. The robot then backs off, Now they're about swaps and spot, like in the next 10 seconds, it's doing work again.

1:17:08So we can run that now in 24-7 shifts where they're talking to each other, all autonomous, no humans, or you can go to bed, whatever. And they're running that shifts all day and all night. And we do it across multiple use cases now at the office in 24-7 shifts. And it's just like, we're running them hard. What kind of stuff are they doing at your office? We do a few things. We have a logistics use case that we run in 24-7 shifts constantly. We really like it. It's done with a neural net. It's moving packages around and it's a really good use case. We like it and we want to like, I want to run in for months and have failures.

1:17:40And we still, we see failures right now. And most of it's in software. The robot gets to some spot where it feels unsafe, doesn't know what to do, and it'll stop for a little bit. And then the robot's not on the line for a couple of minutes. We call it a failure and we're not happy with it. We have robots that are greeters and visitor bots that walk around the office all day, 24-7. So you're over the office, you're getting lunch or you're walking around, you're interviewing with us, you see robots everywhere. And those run in 24-7 shifts, all day, all night, weekends, Christmas day, whatever we run them.

1:18:08Greeters? Yeah. They basically... How do they greet you? Come talk to you. They'll just come talk to you? Yeah, they'll come talk to you. And you can go talk to it and ask for things. We really wanted to go... The end state for us is it's going to replace somebody meeting the candidates that are interviewing there, taking them to the conference room, getting them water or coffee, all of that end to end and whole experience. Holy shit. Yeah, right now they're walking in the office. At nighttime, they're walking in the office everywhere. And it's a good stress test for us because these are neural nets that are running for navigation or planning or manipulation or whatever it would look like.

1:18:47And it's hard. This is a news thing. It's not like these things have been around for decades and we understand that they're really mature. They're not. So we really stress test them like crazy by running them all the time. What's a conversation you've had with a robot? We've been really working on deep memory because I think one thing I really don't like is these conversational AIs you talk to that don't know anything about you. It's not much to talk about. It's like, what's the weather? You ask things about Wikipedia or something. It's like the way to work. It's just, it's kind of nonsense. It kind of feels really stupid to me.

1:19:20So we've been working a lot on deep memory. It's like - So it will actually get to know you. Oh yeah. Yeah, it needs to know who you are. Like, who am I talking to? Is it Sean and Brett? And then based on Sean, do you have their permissions to tell the robot to go do something or not? Like, if you're visiting, no. You might be able to get coffee or water, but if you want to have it go do something new, it won't do it. I've not even thought of that either. Yeah, yeah. We want to understand that. What do you want to be able to command you? What are the permissioning systems and authentications of the robots?

1:19:50I mean, like robots in my house, my kids are going to be like, hey, give me ice cream every 10 minutes, and we can't have the robot doing that, right? Imagine I get home from work and the kids are just like, you know, through pints of ice cream and the robots are just getting whatever they need, like it'd just be chaos. Yeah. So we're going to - So what is it, voice recognition? Yeah, you have to do voice for something. Something's voice isn't enough, where, like if you like think about it, like an extreme example, you want it to go like order food or spend money or send a wire, like voice recognition won't be enough.

1:20:23You have to do a higher level of authentication. How would you do that? Facial recognition. Okay. And then if you have perhaps even finger press scanning is possible too, but facial is what you really want to do. Gotcha. So all those systems are not robust enough right now. We're working through them. And the goal is to get it super robust. But we want to have conversations with the robot. We want to ask it to go do things. Like you really want the main modality to be speech with robots. You want to just say, hey man, go make me food. or like when I'm gone today, do the laundry. After you unload the dishwasher, like, you know, do laundry in like my kids' rooms today or something like that, or text it.

1:21:07So like language is like super important UI. So we're like spending a lot of time on speech. You can text it too. Yeah, every robot we have has 5G by default on board. We actually run 5G by default now. So every robot off the line has 5G enabled. um like uh we have like a t-mobile 5g uh t-mobile's an investor of ours and every robot has an e-sim card for t-mobile 5g so it comes with a line we use 5g for all the main network so if you want to like um you know if our like if our systems want to tell the robot what to do or can everybody do something we do it through 5g and um and so yeah you can like you can text it so you could be at work and say, hey, I want to get the pizza out of the freezer, put it in the oven, 425 degrees, 15 minutes.

1:21:58Yeah. I mean, we can't do that right now, but like, that's the goal is like, we got to get there. Like, we got to get to a point where like, that is certainly possible. And we want that to happen. You want to be like, yeah, I'm at work. When the groceries come, make sure you put them inside and put them in the fridge and do all this. Or they wouldn't even know that go check the mail have it on the counter when i do it like it all feed the dog yeah everything like watch the dog make sure the dog's okay yeah holy shit so it's it's it's a nanny housekeeper gardener it's uh the jetsons all of it yeah it's gonna be all of it i mean you might want to garden you might want to do it all this physical labor we do today i think will be optional in the future.

1:22:41So you'll, like, you might like gardening. You know, you might like mow the lawn. You just like mow the lawn. If you don't want to mow the lawn, don't mow the lawn. Like, all this will be a choice. Holy shit. Yeah. And you said it's gonna, it'll download apps for different tasks? You want to think about the software layer. Like, you want to think about the, like, so for us, like, what's so powerful about a humanoid is you, you don't want to go out and change hardware. Whenever we have a new, like, app on your phone, you just, like, download it and it can do new things now. Like he's got my bank account now, I can do bank account stuff, or you got a calculator, can do calculator stuff.

1:23:15You really want to treat the hardware like this, where you basically similar to a phone, where you don't have to change the hardware for new capabilities. You want it to learn how to do like, you know, complex towel folding, or like unloading the dishwasher, making coffee on a Keurig, like all this, like walking the dog. Like these are like, almost like the matrix where you get like plugged into a system that re-uploads like weights into the neural net weights into the robot where it can like learn new things. So that's what we do now. Like if the robot, like if we can't do package logistics well, we get data for package logistics.

1:23:54We train our Helix neural net for a week and then we load it to the robot and it can like then the same robot that was like folding towels like the week before can now just sit there for 24-7 and do logistics work and package work. Wow. Nothing changes. Where is this going to go first? Consumers, businesses? You'll ship into businesses first. The engineering complexity that we have to ship is proportional to the variability that we see on site. So the variability at homes is extremely high. My home is chaos. Kids are just dismantling the house. It was like, it's basically in real time. And then there's just like food or they're eating snacks, toys.

1:24:38Like, it's just like, it's just chaos. And then like, you know, if we go to your house and my house, we probably have like different appliances or different toasters and different microwaves, all a little different everywhere we go. So the home is just like this, like tons of entropy, like tons of veritability, a wide distribution of tasks. It's like the, it's like the ultimate like challenge for robotics in the home. It's like the hardest, most variable thing we got going. And in the workforce, it's like you have this like work cell that you're doing. So like if you're doing like manufacturing, logistics or, you know, a lot of tasks, you have like this area you're doing work in and you can basically kind of write down on a piece of paper, like how to do every step.

1:25:18In the home, you can't do that. Can't write down a piece of paper. I can't write down a piece of paper, like how I can interact with your house. I mean, you've seen it. Like the next assembly line or the next like conveyor system, like it's like, I kind of know what to do. It's like I get the package, I flip it down, and I need to do it every three seconds. Like you kind of have like, you know, good understanding what to go do. So it just makes it easier. It's like the analogy would be like highway driving for autonomous vehicles. That's just happened sooner because the veritability is lower than in a city.

1:25:46Gotcha. So it'll happen first to scale. And then the industrial thing has a good thing where it's like you kind of have your own work area, so the safety areas are not as high. The hardest thing in the home will be, once you figure out how to get performance there, meaning it's capable of doing everything in the home, like so you can go into your home and do everything, the longest pull from there is going to be safety. We're like, me and you feel safe like having this here with our kids. And that's going to be the hardest challenge by far. And that's going to take some time. There's some trust that needs to build.

1:26:20There's a track record that needs to be built. There's like system safety engineering that needs to be done extremely well. so that just and then the home like you can charge like 10x you can charge like 10x more in the commercial market than you can the home home needs like 500 bucks a month your carly's you those will be you think those will be around 500 bucks a month yeah i think it'll be like that level like you know that like order of like more magnitude yeah um yeah so i think um And then the commercial workforce, you can charge like 10 times more. So it's just like the commercial... And then the commercial market for humanoids is like, you know, I mean, half of GDP is human labor.

1:27:02Maybe a little under half. So it's like 3 billion humans in the workforce contributes to like 40-something percent of GDP. Wow. So you're talking about the largest market in the world is sitting in the commercial workforce. Wow. So you have like that, plus the variability is lower, plus you can charge 10 times more. It's like the, like for investors are like, dude, why would you ever work? Why would you ever do homework? You know what I mean? Like, why would you like spend time over here when you can just go over here and build like a$20 trillion company? And my answer for that is just like, I just want robots in the home.

1:27:37So don't really care. You know, like we got to make that work. Yeah. I mean, you say in 10 years, every home will have a humanoid. We'll have every home in 10 years, but we will have... Pretty close. We will have in 10 years...

1:28:00You have like two long poles. You have like a long pole with manufacturing enough volumes for this. And then you have a long pole where you can actually technically do the work fully end to end. my belief is that the hardest thing in the stack is not manufacturing it the hardest thing in the stack is sorry the hardest hill right now is can you put a robot into your home today and do the five hours of work you need without ever seeing your home before the first group to do that i think will be like become like the largest company in the world and you can do that with maybe a hundred robots. No shit.

1:28:41Yeah, I think you can solve a general purpose humanoid robot. I think you can solve general purpose robotics with maybe like hundreds or low thousands of robots. How so? Maybe a hundred. How so? At this point, the issue we have, so we can go into my home today and we can do little pockets of work. We can do like, I can unload the full dishwasher. I can, once the laundry's in the basket, I can take it, walk it, and fill up the washer and run it. And we can do pockets of work. We can take the laundry, put it on my bed, and we can fold it all. And then, so we're doing little spots of it. And it's pretty good.

1:29:26But there's a lot more spots to go fill for long horizon work, just that. And we have to be like extremely robust to maybe different types of clothes or like different types of like, I don't wash my jeans, like that type of thing. And all these different like veritability that you might see. And we haven't been able to, as of today, that's like, that's the hill we got to go solve. That hill looks really hard. So how, I mean, how, let's say, let's fast forward 10 years, I'm getting one of these guys. Yeah. I put them in the home. how does it i mean do i train it do i personally train it hey when you're emptying the dishwasher the cups go here the plates go here the silverwares go here the forks go here when you're doing the laundry i want these ones washed cold i want these ones washed hot this is where they go this is where the the jeans drawer is is where i hang my shirt yeah is that how it works is it you're gonna you're gonna you're gonna robot in a box you open it up robot get out it'll start talking to you uh it'll ask you um to show you the house and um you'll it'll you'll you'll say like you know it'll say like you know can you walk me through your home and it'll follow you around and you will tell it all that like you would um let's say let's let's say you'll see you had a friend staying for two weeks at your house that you know needed to like cook and use your stuff like you're like you know you wanted to wash clothes and stay in one of your rooms like you'd walk that person around and you'd be like, hey man, this is recycling here, this is where trash is at.

1:30:58Like here's where you get water. Like the trash goes out every Monday. We do blankets on the couch, but we want them in the cabinet when they're done. You know what I mean? Or we want these over folded and put it over here. All these things you have in your home, they're important. And just like you would like walking somebody, human around for the first time. That's what you'll do. And the robot will semantically understand, will A, have like, will remember all of this. And it will learn based on what you want, your preferences, like what to go do. Holy shit. It'll be that. So it's just like turning a human being.

1:31:40This is like, this is not 10 years. We'll do this. This is really soon. Like, I think in the next, like, I mean, I'm hoping this year we could like drop a robot in your home and do a good amount of stuff. It's just, we'll see. I mean, this is like solving like the holy grail of robotics. This is like solving for a good general purpose humanoid robot. Maybe we don't solve it this year. Maybe we solve it next year. Maybe we don't solve it next year, but it's 2020. I don't know. Like we're close. We feel like we're in the red zone with like, we feel like we know the architecture. We have the hardware.

1:32:12We know how to get the data. We put the data in. The robot does it. We need to like now like learn how to generalize. We need to like move deeper into pre-training. We know the directions we need to go ahead, we think, to solve this. And we're seeing a lot of both positive transfer and a lot of just like... We're seeing internally, we think, the right direction to make this work. When you were talking about trusting the robot with your kids, what are... I'm just curious, what are your concerns? Yeah, at Archer... I haven't thought about this. I think at Archer it was always like, I'll never feel safe.

1:32:50i never feel comfortable like recommending arch like people to fly an archer and letting people fly an archer until like i like would fly an archer aircraft to my kids um that's the level of safety we need to get to it's like a really high bar um that's what you want though right to take a aircraft like that around um so i think the same thing for figure here is um we'll be we'll be safe when our to me it will be safe when i feel comfortable putting the robot around my kids. I have a one-year-old and a four-year-old. I have young kids. They want to jump on everything. They're like, yeah, and the robot, the robot needs to be extremely safe there.

1:33:31That's another hurdle. It's like solving general purposeness, getting safety to work, and then making enough of them. Those are kind of the equations from here. Listen, we have a good plan on what to go do here, but now it's like execution that we got to go do to show it works. Right on, right on. You want to take a walk around this thing? Yeah, let's do it. Perfect. Most people blame stress, sleep, or just getting older when the energy starts to fade. The brain fog, the slower recovery, the feeling of running on empty by midday. But underneath all of it, there's often a cellular reason. Your body is running low on NAD, the molecule that powers energy production at its core.

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1:36:11All right, this is our figure three humanoid robot. It's, we actually unveiled it last year. My God. Yeah. It's about 130 pounds, five foot six. And we basically designed it to do most things, like a lot of things humans do. 130 pounds. 135 pounds, yeah. It's full laundry, do dishes, do manufacturing, logistics. You know, I think a few things here that we like made improvements on. This is our third time basically running through three generations of robots. We've like, we reduced the weight and mass. We made the robot skinnier, but also same strength and speeds. We upgraded the sensors in the robot.

1:36:54It basically sees through cameras. We have better, our basically fifth generation hands on board that have a camera, tactile sensors, basically improved grip. We also have on the robot basically more compute on board for running our Helix neural network. We also spend a lot of time on just basically making the robot more safe. So they all have kind of the squishy layer of foam on it. Yep, go ahead. So if you, let's say somebody pushed it over and fell over, I mean, what's the durability of these? I mean, it depends how hard you push it. But for the most part, we fall, the robot can get back up, just continue to do work.

1:37:33It depends how you fall. Sometimes we break necks. Sometimes it's fine. All right, turn around.

1:37:43Another thing too is like we basically, the robot's almost fully soft wrapped. One thing we can do here is we basically can make clothes for the robot, which we do for both our customers and internally. The clothes can be put on by like any person. So we can basically unzip it, take clothes off, put clothes back on. We don't need tools to do so. Can we see what's in there? Yeah, basically it's the torso. You can't see any of the internals. No, they're all inside the structure. So inside of here, we have basically a battery, GPUs, computer, power distribution. Basically the brains and all the energy are in the torso.

1:38:24Wow. And then basically the robot is left with basically 40 joints. So, all basically electric motors and the motors that have like basically tons of sensors on it for balancing and doing work. All right, turn around.

1:38:45All right, we can walk with it for a minute. All right, let's do it.

1:38:50All this walking and all the robot movements are all done again through a neural net. There's no code helping us do this.

1:38:59Holy shit. What do you think? You want one? What's that? You want one of these? I want a couple of them. You want a couple of them. OK, great. Dude, whoa. Yeah. Let's go back this way. Let's turn around.

1:39:16Can it run? Let's see how fast it can go. We have running, we have, I don't know if we're on the running mode, but let's go as fast as we can. We do jog with the robots outside. Really? On campus, yeah.

1:39:41I think it also looks cool, right? Got high tops on. Looks awesome. Yeah. And you said there's cameras in the hands, too. Yeah, cameras in the palms. Right here in the palm, so we can see the fingertips when it's grabbing objects. And then every single fingertip has a tactile sensor inside. So we can basically touch forces as we're grabbing objects. Can it shake my hand? I don't know. Maybe. Can it squeeze my hand? There you go. There you go. Will it crush my hand? No, it's not going to crush your hand. Dude, that's pretty stern. That's like... Yeah. I can't move it. We can pick up like 40-pound boxes off the floor, and we can also fold a T-shirt.

1:40:20So... That is wild. Yeah. Is this the power button? Yeah, don't push that. Okay. We had somebody in the office our day. It was like, I feel like I need to push this. I'm like, it's literally going to turn off if you push the button. And how long does it hold the charge? It depends on what we do, but anywhere from four and five hours. How long does it take to charge? It takes about an hour to charge. So we can do about four or five hours on. We can charge for an hour. You know, humans take breaks during the day to eat and do other stuff. Or we should do a lot of time in the office. We'll sub another robot in during the meantime.

1:40:59Wow. Yeah. We actually charge here inductively through the feet. So the feet have like, basically have like charging pads the robot steps onto, and we charge wirelessly. And we can charge, we can basically charge in one hour through that whole process. Holy shit. Just by standing. So in case the robot has a task where it needs to stand a lot, we can just stand there So he just stands on a mat? Changed on a mat. We designed it in-house. Like an iPhone charger. It's like an iPhone charger. Yeah, and it can charge about a kilowatt per foot. It's about two kilowatts it can charge. When is this going to be available to the consumer market?

1:41:26As soon as we make it work really well. And so I can send it to my house and my kids will ask for ice cream every single day. And yeah, so we're working really hard. I think we've been testing in my home fairly recently, and we'll be shipping these robots out to commercial customers here really shortly. Can I ask who the commercial customers are? Yeah, we work with BMW. We work with one of the largest logistics companies in the world. We work with Brookfield. They're one of the largest real estate companies in the world. They have a giant portfolio of companies. And then we have like two more customers we'll be announcing in the next like 60 days.

1:42:03Congratulations. Yeah, thanks. That's amazing. We're going to try to ship as many as possible we can this year. Wow. We also make these on site next door at Baku. It's our production manufacturing facility. And we make about one every kind of like 90 minutes or so now. You can make one of these in 90 minutes. When we run the line, the lines are running about every 90 minutes, we make one. and then that'll greatly increase even in the next like several months here. Wow. Yeah. Wow. Yeah. What do you, I mean, at full capacity, like, what do you think you'll, our office, our facility, yeah, our facility there can do maybe upwards of like 40 to 50 ,000 a year at like full capacity, but we need to design for much higher, like, we want to get to like a million units, like a million units a year and, you know, like within this decade.

1:42:50A million units a year. Yeah, for sure. I mean, you sell like, we're selling over. It's like building a country. It's not. I mean, like, you sell over a billion phones a year easy. So I think it's going to be like a robot for every human. So you'll need like a cell phone style manufacturing. Shit. Yeah. So I can push this? Oh, yeah. It has push recovery. Give it a little push. I mean, a little harder than that might be nice. Harder? Harder.

1:43:18Dude, what? Yeah.

1:43:25Yeah. There's better balance than I do. Yeah, same.

1:43:33Dude. That's crazy. Yeah. This is three and a half years. We had this walking in three years since the start of the company. It was crazy. Basically, the week of year three, we were walking this thing at the office. The thing is, this is like a... We're going to go through this whole iPhone lineup, where it's, you know, iPhone 1, iPhone 2, iPhone 3. It just gets better and better. And I think human rights will take more radical steps between those. Every year, we're roughly building a new robot every year, we'll just get dramatically better than this. Damn. Our step up from here even to the future robots will be, I think, perhaps the most dramatic step up we ever make.

1:44:15Yeah. Wild. You want to take some pictures with them? Let's do it.

1:44:23Dude, that is insane. What do you think? Awesome. I want one. You want one? Yeah. Let's get you one, man. Wow. So you said the hands can sense three grams of pressure? Yeah, we basically have a... The fingers. We have tactile sensors on every fingertip, and they're really sensitive. and we have a camera in the hand that can detect when the fingertips are in contact with some surface. It could be like something we're touching. And then within there, every joint can kind of also feel sense and track the position of every part of the hand. So the hand's really good. Honestly, we're working on hands now for close to four years.

1:45:09It's probably one of the hardest engineering problems we have on the hardware side. It's probably as hard... And we have our next generation hand that we kind of teased a couple weeks ago that has like basically full, I think it gets a full human level dexterity with this hand. Are you serious? It's got as many joints on the hand as a human hand has. There's still a lot of work to go do, but like it's now, it's now a huge step up where we actually even currently are. And the hand now can like fold laundry and, you know. Do you think it'll hit a point where it can outperform a human? More dexterity in a hand than a human?

1:45:50I don't know. Better balance, faster, stronger? We already have better balance than a human. The robot on one leg could balance better than a human can. I don't know about like... Humans have a lot of degrees of freedom. We have like hundreds, a few hundred degrees of freedom. Our hands are very dexterous. I would say if we can do close to human dexterity in terms of like... That'd be a huge win. and you'd have robots everywhere. And then we're gonna still have a lot of trouble getting to full human range of motion. Small things, like you reach inside of a washer and you kinda move your head as you're getting in.

1:46:31Some people get down to the ground and kinda get in the washer to grab some on the back. We do a lot of crazy stuff. Yeah, that is. And so it's like, even a 12 year old can kind of do like most things in a house. You know what I mean? They can jump up on countertops and all kinds of crazy stuff. Humans would be tough, but like, I think we can get, like very soon we'll get to like pretty close to most of what humans can. You had a pretty close relationship with OpenAI, correct? Yeah, they led my, so Sam and OpenAI led my Series B, co-led my Series B with Microsoft. That was a few years ago now.

1:47:14So we raised about a little under 700 million in our series B, our second round of funding. And they joined my board and then we ended up spending basically a year with them working on, well, I'll give you the background. The goal was to try to advance AI models for humanoid robots together. And they have some great folks that have worked on like LLMs and chatbots and things. And at the time we had like a, you know, we still do, but we had our, we had a full like AI team internally. So we were basically working weekly, daily on like basically how do we advance state-of-the-art kind of like language models for robotics.

1:47:57And, you know, like, yeah, I ended up firing them. I know. A year later, but in splitting ways. But listen, they're a great team. The senior leadership and everybody there, Sam included, were great to interact with. The issue lied for us of like, there's nobody that's ever put advanced language models into these systems and made it. We have to produce action output on the robot. And it's a very different thing than next token prediction for language models. We ended up finding that the team we had in place, you know, my team lead, the folks we have here all from Google DeepMind or certain areas of like, you know, top AI programs and they're really good.

1:48:45The team now we have is over 50 or so on the AI or Helix team internally. We just found that like that team we had internally, we just found like kind of circles around them, like every day. We had a hard time getting, like, you know, in robotics you get like run the robot, see how it does. You know, like, you have like, when I run a new like AI experiment or do some ablations, like in some evals, you need to run the robot at the end of the day and see how it does. Like, sim is one thing. You can get certain far running simulations and looking at loss curves and stuff, but at the end of the day, we need to see how the robot does.

1:49:16And we just had a hard time getting them in the office. We had a hard time, like, basically, like, like, basically, you know, advancing stuff together as a team. Ended up, we like the strategy we had internally and the team we had was just like complete superstars. They're the best robot learning folks on the planet that sit a figure. and um it got to a point where uh you know i got a call one day it just like you know we were like also week to week like showing them how we were doing all this work and i got a call one day saying like hey we're like you know we've been watching your progress it's unbelievable and um you know we're thinking about doing robotics work internally and i was just like uh this is over like yeah just get out of here like this is like we're we're teaching you how to do robot learning.

1:50:03You're seeing our progress. We had a couple of the, you know, Sam and a couple of the co-founders on site at one point, right before this, and they saw it and they were like, wow, it was doing this neural network on the table. And they were just like, Jesus, this is amazing. Shit. And I was like, you know, they were still at a point where they continue to want to work together after this. And I was like, there's no way we're going to teach you how to do this stuff anymore. And also, we just got no value out of the whole relationship, very little. I mean, it was helpful having them lead the round.

1:50:32It was like there was some good brand association there, but beyond that, there wasn't much. So we ended up, we're going to chart our own territory. We're going to do AI ourselves here. It was also just became like, to be frank, like it became like really hard to recruit. We were like, you know, I have to spend a lot of my time hiring like on the AI team and we bring candidates in and they'd be like, oh, you guys are the robot and open AI to some models. And I'm like, oh, no, not really. No, we have a whole AI team internally. we do model development here ourselves, you know, like we're advancing ourselves and it just wasn't the perception from the outside.

1:51:05It was just hard. So that also wasn't helpful for us. Both hiring was not great. And we were like, you know, there was like an information passing back that I think wasn't really helpful for us long-term if we're going to be competitors. So we decided to split ways. I decided specifically to split ways, but they have a great team. I think they're doing robotics now internally. Sounds like it. Yeah, exactly. Yeah, yeah, exactly. I was like, I got a call saying like, yeah, like, you know, partly like, partly the feedback I heard was like, we've made so much progress at Figure, and they've seen that, that they were, you know, OpenAI started out as a robotics program.

1:51:42They were trying to solve AGI through a robotics. I realize that. First three, four years, they were just like all in on robots. If you Google like OpenAI robotics, it's like old 2016, 2017, 2018, 2019, like, you know, maybe like, maybe like 2019, 2020, something like that. they end up pivoting into large language models, maybe 2021, something like this. But they're in robotics from, I think, 2016, 2017 for many years, maybe three or four years, trying to solve AGI through robotics. There's this other, we don't need to get into it, but it's unclear if you need an embodiment or not, or at the time it was unclear whether you need an embodiment or not to truly get to above peak human intelligence.

1:52:23And they had a hard time in but there was like part of their thesis was like get back into robotics at some point. And I think we just, we accelerated that here at Figure. And you know, I think to be fair, like to be hum, like somewhat humbled is like, it's, we made like, I think we made like 10, I don't know, five to 10 years of progress in like three years, four years. Like we just like, it just felt like this should have taken 10. Even right now it feels like we're not even four years old yet. Four years old and end of May or something like that. Like, I couldn't believe when we started the company three and a half years ago, we'd be at a point where you can get a humanoid robot even here, doing the stuff it's doing here, but let alone the real stuff it's doing now, like, 24-7 commercial work in the home, like, it's neural net driven.

1:53:09Like, we can make them every 90 minutes at the, you know, when our lines are up. Like, it's just like, it's crazy. So, yeah, we decided to start part ways. Man. I mean, I don't think there's too many people in the world that can say they uh fired the biggest ai company in the wind on earth i mean that's that's a ballsy move but it makes perfect sense and uh man again just congratulations on on everything i mean that is that's just crazy you know i've done i've done a it's just very surreal for me to to unveil some of that i mean i know we didn't unveil this but it's the first podcast it's ever been dude sean i have not taken a robot to like a podcast like i get asked every week to do this it's the first time and uh like love your show and want to get him here in tennessee uh it's the first time bots been out here to something like this thank you yeah it's it's it's it's really cool to be able to do this like once in a lifetime opportunity type stuff.

1:54:13Thank you. No problem. What about military application? Yeah, we've decided not to do military stuff today. And not to say like the robots won't be good in military or helpful or like, my belief right now is like, it's just too difficult to do both, like the ship into the home, ship to like, you know, top fortune 100 companies in the US, and then also put like, you know, like militarize the robots. I think it's just too hard in one umbrella. I think there's a huge opportunity like to save lives and help on the military side. But I think it becomes like, you know, we do have, you know, we do have like a very advanced system here.

1:54:57The system can, you know, unlike a car, if a car became sentient, like, you know, you can like walk in your house, walk upstairs, go in your room. It's like not going to come chase you. Like a robot will just walk right up your stairs and open your door, the humanoid robot. You know, this is a very different technology. We've got to be very careful with it. So I think because of some of that and some other things, we know we've drawn a line here to say like, you know, we want to stick with the, you know, consumer market, commercial market and go just harden the paint with that. I think there are and will be like incredible opportunities for companies like to go into the military.

1:55:36To be frank, these robots would be great there. Like, they can just, like, they can, you know, like, some of the most dangerous missions are, like, you know, going to close quarters and houses and, you know, that stuff is, like, extremely dangerous. Human rights would be great at that stuff. Like, opening doors and just making sure the house is, you know, cleared. Like, clear a house. You know what I mean? I could see it for a whole ton of stuff. I mean, not even just going on target, but sentries, gate guards. Yeah. I mean, roving patrols. I mean, all of it. Totally. Just armed security. It's, wow.

1:56:14You know what I mean? You kind of have somewhat of a treatable asset too. You can basically, I think you can make them relatively cheap, make a lot of them, just put them out to work. Do you think you'll get into it in the future? I don't know. As of now, no. But like, there's a part of it that you'll, there's a part of the story here where you're like, you could make this like obviously really safe for humans there. um there's a whole part of the story where it's like i think it just becomes you know to be frank like the when we sell to commercial customers even homes like it's not like selling like a robot arm on a stand it's like these commercial customers need like ceo approval we can't get them through without the ceo like these major companies like coming to see the robots and saying we're going to announce this relationship with figure and we're announce humanoid robots in our facilities.

1:57:03And it's just like a, it's a very, you know, it's like a, there's, you know, it's like, if you watch this - I'll make that announcement. I think it's fucking awesome. It's awesome. But like, I know it's just like, it is like a, you know, and then that makes it that much harder than if we have like a new military side of things. Why do you think they're hesitant? Is it, is it replacement of human jobs? I mean, Jack Dorsey just, I mean, he just let go of what? 10 ,000 people. Yeah. that almost half of his personnel because of AI and his stock went up because of it. I think it's probably because the robot is human-like and can do human-like work.

1:57:37So I think it's just scary for, it's a scary thing that I can do what humans can. I think you have similar scariness folks have around digital AI and how that will basically manifest in the future. So I think it's a real thing. I think the robots can do human-like work and it will continue every year to do more and more human-like work. So, but like that, you know, we just gotta, we just wanna be very careful about how we position this and what we do and also how we communicate it. Yeah. Yeah. What's next for the robots? We wanna solve general robotics figure. We wanna, we think of ourselves truly as like, like at the frontier of like this robotics AI lab that needs to build common sense reasoning into a robot that can be put in every home.

1:58:27How do we drop it into your home it's never been and you can just communicate with it and you can just start doing work? That's the problem we want to solve here. That's the problem if you solve it, you can ship billions and millions of robots. There's also a business where if you don't want to solve that, you can definitely ship robots. You can ship them in the commercial workforce, you can ship them in the military, as you mentioned. There is a path to go build a business doing that. But the biggest business in the world is if you solve general purpose robotics, where just through speech and talking to the robot, it'd feel like you had like a human in a bodysuit that can like understand you, nod, like go off and do things now after your task.

1:59:08Like that's the problem we want to solve at Figure. That's like a large scale, like it's like an AI lab problem at this point. We talk a lot about how we're trying to like, we're like, we're trying to give AI a body here at Figure. And so we have this embodiment. we need to put like really sophisticated AI into it to be able to command it. And that's the biggest problem we're trying to solve. If you're with me in the office every day, I am working that down with no sleep, basically as hard as I possibly can. And it's a very, very difficult problem. At this point, it's largely constrained by getting the appropriate data into the network.

1:59:52That scale. I think if we could snap our fingers and get a pile of data that we really needed into Helix stack, I think we would solve general robotics right now. Wow. What should I be asking you that I haven't asked yet? About figure or general? About figure.

2:00:16I mean, there's a lot of stuff going on with China and manufacturing and a few other things, but I think maybe to summarize, I think where we're at is, I think if I was watching this and I wasn't following the story, I think the one thing I would like to convey is we are so close to making this happen now. and it's only until people can come online and watch our stuff we put out. But when people come to the office and experience it and see the robots and you can talk to them and some of the stuff you're doing here today, it's just like a full emotional experience that is really hard to convey.

2:01:02It's just crazy. It feels like we're living in the future. It just feels like we're living here. Yeah, it's crazy it works. It's crazy it's working. But we're like, we're now in the, we now have like line of sight to make this happen. And which is exciting in my perspective, super exciting. I think it's gonna be super transformative for the world. And I think what we're gonna try to do over the next year or two is try to like get this out further at scale and get everybody to feel this more and more. Like you feel it when you come to our office and you feel it when you're next to the robots, but it's like hard for the, we're such early innings about this yet for takeoff that it's hard for the whole world to really feel this.

2:01:42Yeah. Yeah. Have you seen, do the robots interact with each other? Yeah. What does that look like? Right now they communicate with each other when they need to like, so we have like robots that are running these 24, seven shifts. When one robot gets like down to like low state of charge, let's say it's like 10 % and it's a few percentages away from, we'll dock it before it's at, you know, 1 % or something like that. It's at 10%. The other robot will get ready like to sub in. It will like come walk, walk over, sit right behind it. And then when the robot is ready and knows that it's there, it will then back away.

2:02:16And then the robot will go in to do operations and do work. That other robot will then go over and start charging. If any of those robots have any problems throughout, it could be hardware or software, they will go and like go to like basically like the hospital in our office. So they'll go to a certain place when they, when they get, when they get, when they know they're going to the hospital, we have another robot coming in to the main docks to start subbing in and getting ready to go. All this communication is happening like robot to robot. And it's unbelievable. And the robots are getting really robust.

2:02:46We can like, a year or two ago, we would like, there would be like certain motors that you would lose communications with or other types of comms, or it could be hardware failures or software failures, whatever, let's say it's a knee, lose your knee, like can't stand anymore. You know what I mean? Like you fall. Today that doesn't happen. We can lose a knee. We can hold its position. We lose full comms with the knee. We can stiffen the joint, and we can limp off. Holy shit. Yeah. Actually, I'll post some of the next week publicly about this. I've never... It's like, holy shit. So we can lose like a lower body motor, and it literally limps offstage, like off like the main line it's on, headed to the hospital.

2:03:32It will limp all the way there. While it's limping there, another group from like the healthy part of the hospital will then come in and resub it in on the dock, while another one undocks while it just lost its knee to go in and do work. All that's happening through robot communication levels. You can be like literally asleep while this is happening. We run them 24-7. It can be at 3 in the morning and it will happen. It's insane. This is happening like, I saw this in the last few months that's happening right now. This is not even like the future stuff. is going to be robots building robots. We're designing robots.

2:04:08We will have robots building robots here. And then they will go out and they will just do autonomous work. And they will like charge themselves. They will go do work. You'll speak to them sometimes. Sometimes you won't need to do and they'll just do work and they'll just be like everywhere. I say this again, but I think we'll walk out. It'll happen first in probably the Bay Area. We're based in the Bay and a lot of companies are in there for robotics. But I think you'll go to the Bay Area at some point and you'll see more humanoids than humans in the next 10 years, for sure. That is... I can't even imagine what that's going to be like.

2:04:44It'd be weird. Do you think that they will bring... Do you think manufacturing will come back to the US? Yeah, we're going to bring back... Because of this? My view is that we don't want to bring back manufacturing that's already overseas. We don't want to make shoes, make toys, things like that. I don't think we have the will to do this. I don't think we have the know-how to do this, as well as some of the Asian manufacturing groups. So I've walked a lot of the high volume consumer electronics lines and stuff overseas. Some of the most impressive things I've ever seen in my life. It's like you walk these lines and they're just shipping electronics like crazy, They have every line, they have like this box of automation inside of it, like a little tiny robot inside of there.

2:05:30It's moving some like whatever, a phone enclosure or something like that. No kidding. And it's doing it through an automated way and moving it around a little conveyor. And it's moving to the next station. Maybe a human's doing something and it's going down the line. It's going to a next station that's got a robotic system in there, completely customized and different from what you just saw. And they have lines and lines in floors and floors of this, and then buildings and buildings. And you're like, holy shit, each one of those boxes is like a figure style complexity. And they have like hundreds of them.

2:06:00Wow. And they need to run them at high rate. It's just like, it's unbelievable, actually. It's not trivial. It's very complex. And they've been doing it for several decades on these lines. So I think one is like, I don't think that stuff, we want to move back. I think we want to move back the high-end robotic stuff that's going to be like super transformative for us in the future. All the futuristic. Yeah, we want to bring back flying cars. I want to bring back like humanoid robots, like the stuff that's like highly dynamic, very intelligent systems, like the next generation, like manufacturing 2.0 stuff.

2:06:35Gotcha. So we're doing that right now in California on our campus. We have a fairly large campus in the Bay Area and we manufacture right now, like whenever 90 minutes or so. And we'll continue to spin that up and then we'll put, you know, we'll talk more about it, but we'll put more investment here into U.S. manufacturing for the future. Right on. So we're going to design humanoids here. So these are all manufactured, right? We manufacture those in California. Right on, man. Yeah, man. They walk off the lines, they walk over. It's like... 90 days ago, you come, we're making a little bit, but now we make like...

2:07:14There's like seven robots that are all doing like end of line checkout by themselves for like an hour and a half. They do their own burn ins, all OEOL checks. So they're self looking at each other, self calibrating. They're doing they're doing like burpees and other shit to make sure they like they're OK. If they fail, they go into a triage place. We understand why to fail like that shouldn't happen. We should always fix that and it should not fail again. Like how do we fix the manufacturing process so the next one doesn't come out and ever had that failure. And now we've got that process really dialed.

2:07:40I mean, dialed in. We still have issues, but like it's fairly dialed in. And so the robots come out, do a couple-hour check, and when they're done, they just walk over. And at some point, we'd love for them to get inside their own box and another one get it ready to go and put it on a pallet, and we can just start shipping them out. It will get in its own box. For sure. And another one will throw it on the pallet and ship it out. For sure. Yeah. That's not hard things, though. These are like, that's not that hard. You know what I mean? Yeah, it's just interesting to think about. I feel like it comes off the line, gets in its own box, Gets loaded on by another robot and then shipped off.

2:08:17The scary thing for me is those are very rigid body things, like cardboard and moving boxes and maybe using machines and stuff. Those are easy. The scary stuff a couple of years ago was laundry that literally moves. It's literally never in the same spot. It's like when you touch it, it's actually moving. Or we do these packages on this manufacturing conveyor system that you grab it, it's literally moving. It's moving because the conveyor is moving down and then the packages are squishing each other. and then the package itself is moving because it's plastic when you're grabbing it. Those are the hard things that are compliant that are really difficult for robotics because they're not stationary when you touch them.

2:08:52So those are things that were like, man, that's going to be really tough to fold laundry. And with code, it's been impossible. The reason you haven't seen package logistics and stuff, some of this stuff automated, is because these bags are just hard, they're compliant, they're just tough, you can't model them. And now we have, we put it all in a neural net, they basically instantly worked. When we were working with our, we have a logistics customer we're working with, it was like soft packages and we signed them. They're like, we want you to move these packages on the scenario system. And we've put videos out about it and stuff.

2:09:21The first month we signed them, Inga who runs, you know, accounts was like, we need to, we need to do, we need to do this for them or they're going to be really unhappy. And I was like, I was like, Dan, that's like a compliant material that is moving while you're touching. Some of them touch in there, there's something hard inside, some of them are squishy. There's tons of them. We're going to move every three seconds. We've got to find the barcode, put it down, and put it in the middle of the conveyor every three seconds of package. I was like, it's 50-50 shots works. And it's got to be with a neural net.

2:09:53And we got a bunch of data, trained it, policy, and right away it worked. And I was like, holy shit. This is like, it worked really good. And for some reason, the neural nets do extremely well under those high veritability environments that's extremely diverse. They can learn the representations extremely well across like a wider distribution. And they just love it. Folding t-shirts, towels, like packages, like no problem. Wow. Stuff that would like, you know, you're replanning very fast as these things are all moving. It's doing that in real time. It's just like, it just works. Deep learning just works on humanoid hardware.

2:10:35Yeah. Crazy, crazy stuff.

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2:12:28Crazy, crazy stuff. Let's talk about your venture to save kids in schools. Ready to move on to that? Let's do it. I love this. Yes. Yes. Can you give us the synopsis? Yeah. So back when I sold BetterE, I mean, I mentioned I got obsessed about a few different areas. of like working on, you know, always want to work on flying cars, but like the macro environment turned like extremely poor for like school shootings. Like I went from like, you know, it's really hard to track, but we went from like 30 to 40 events per year in the U.S. to like 300. And that was over like a span of 10 years. And it's also really hard to understand why.

2:13:11It's like another thing that we could like spend time on, but it's, there's just like you know, a 10X, mostly in the US. You didn't really see this like a lot internationally. And, you know, we started looking at it, I basically started reading a bunch of like research reports and other things. And I stumbled upon this technology, this like basically technology and kind of like terahertz radar. So basically like, or sometimes also called millimeter wave of technology where it's basically a high frequency, like it's radio RF, it's like radio frequencies, but basically done at a very high frequency, like in the two to three, 400 gigahertz.

2:13:56And it's basically like similar to, you know, when you're in an airport and you go in there and you like hold your hands up and like the LG system scan you, they're like a couple of feet away. They can see like anything you have. like, you know, if you have knife, gun, vape pen, whatever. I read a research report that showed, and my goal is like, if you want to put it in schools, you can't scare the kids. You have to be able to, so, sorry, back up. My view in schools is if you want to solve it, you have to solve it from a perception perspective, meaning you have to see if people have, you have to understand if people have guns on them or not.

2:14:32You can like change, like there's like a regulation side that some people chase, which we're not chasing. And then there's like a, how do we actually like know if people have guns on them? Because if you know a kid has a gun on them, you can go like take it away. And then majority of all school shootings are unplanned. Most of them, like almost all of them are some kid bringing a gun in habitually. It's like their uncle's gun and they bring it into school like every day for like three months. They get in a fight at recess and they shoot it, the gun. Sometimes they shoot somebody, somebody they shoot it.

2:15:05And that is majority of all gun events. The ones where you see like a planned event that's like on like CNN, where somebody is coming in with a machine gun or an automatic weapon, it happens like one or two times a year. It's on the front page of the news. The majority of all the cases, like 90 something percent, is all happening from unplanned. Folks are bringing in guns all the time and then they're shooting it. So you basically, what you can do is you can stop all those. The planned ones are very difficult and maybe impossible to stop. But the 90-some percent of all other shootings, I think you can avoid those, meaning you can prevent them by knowing if somebody has a gun on them.

2:15:43You can do it the old-fashioned way, which is metal detectors and all this other stuff. But we don't want the kids to go into school like that. That's just not how I want my kids going up. So basically, the reason why I got obsessed with terahertz imaging is you could basically do this at a larger offset, 10, 20, 30 meters away. You can do it at a high frame rate, and you basically get back a point cloud. You basically get back an image. It's like a three-dimensional camera image almost, but it's done in a radio frequency. You can look at it almost like an optical image. And the reason that's interesting is because if it's basically people bringing guns in habitually, and you can scan them at entrances, you're always coming in through a few doors at a school.

2:16:27You're not going in anywhere anymore. and the schools also have all procedures now for this stuff. You basically can do offset scanning at five or 10, whatever, meters away. You can scan people as they're walking in, possibly. Just walking in, don't need to stop anybody, and you can basically scan most guns that are brought in schools or either in your pocket, waistband, or backpack. It's like most of all guns are being brought in there. And you can basically find them. And if you know that, you can basically stop. It will find a gun in a backpack? Yeah. No shit. Yeah, so... It's amazing. It'll find a concealed weapon anywhere.

2:17:06There's like, you know, yes. There's printing in a backpack. Yes, you can find them in backpacks. You can find them in waistbands and pockets. So the story is, so I found this research report done by a few of these guys, you know, that were at NASA Jet Propulsion Lab. I write these two guys and they said, you know, sure, we'd love to have you over. I get over there and they're like, they tell me the whole backstory. They're like, listen, we developed this technology for standoff distance detection for the Iraq and Afghanistan war. It was funded by the US government. We worked on it for 10 years.

2:17:39And when the war stopped, funding dropped to zero. And we were done. We didn't work on it anymore. And I'm like, oh, it sucks. And then I'm like, okay, well, I guess Kimi posted if this thing ever works out. And then towards the end, they're like, oh, you want to go see it? I'm like, what do you mean, see it? They're like, it's in the basement, it's done. We did it. And this is in 2017, 2018. So this is like, I was like, oh yeah, let's walk down. Walked down to the basement. There's like this tarp over this machine, took a tarp off. They had a guy with a mannequin that's sitting there with a gun underneath a shirt, like, I don't know, three or four meters away.

2:18:19They turned this machine on, it was built like 10 years ago. It had like a computer tower inside of it. And then it had like a little screen next to it. So I started this machine and they basically moved over to the screen. And the screen showed like as clear as day, like a photo of the, you can see the exact gun. You could see it in 3D, you could see it in 2D, you could see it in power. There's a bunch of other ways we can look at the data, but it was just like crystal clear. Wow. And I was like, what happened here? Like we basically got to the end of this program and we don't have any more funding, so it's done.

2:18:54and I basically made the decision, you know, long story short, I ended up chasing Archer at the time. I went and built Archer and at the time I only had like a, like, you know, this was a big endeavor for me, like going from software to like, you know, deep tech hardware. So I basically decided to put cover on hold and, um, you know, chase Archer. And then about two years ago, somebody came to my office, one of my investors was like, Hey, I'm like looking at like trying to solve school shootings. I was just back from LA and I'm like trying to solve it with CCTVs, like the security cameras. He's like, the problem is like, you can't, you won't know until the gun goes off and you won't like brandish your gun.

2:19:30You won't pull the gun up until you're like trying to like shoot it. So it's just like way too late. And I told him a story about how I went down this path and he kind of looked me dead in the eyes. He's like, I have kids and you have kids. Like you, you have a fiduciary duty to go build this. And it was right when my daughter was also applying first grade and we were worried about it at schools. You know what I mean? Just looking at like the fence and just like kind of anybody can go in, you know what I mean? So like, I was like, shit, you know, I got to go do this. I ended up spinning the technology out of Jet Propulsion Lab at Caltech and I own it and started cover two years ago.

2:20:07The OG team that built it is with me now. No way. We put an office in Pasadena. That's the main office is in right next to JPL. And we've been working on this now for two years. I've been self-funding the whole thing. And we will have, we have a prototype that already works last year. And we'll have a full scale prototype out, like I hope by summer, like in our lab. And then we hopefully, if all goes well by end of year, we're beta testing in school. Wow. And we'll put it in that figure campus first even. Wow. This is an AI, this is like an optical play. Can you see it? This is an AI play saying, can you detect it now?

2:20:43um there's 130 000 k-12 schools in the u.s there's like 60 or 80 million k-12 students it's a it's huge and um but it's not just schools it's stadiums and airports everywhere hospitals airports malls every any venue you can movie theaters i had my last baby a year ago just like anybody can walk in the hospital it's just like just doesn't matter they don't check you in it's just like scary. And so anyway, we're, we're getting close here and the technologies we designed are incredible. Actually, we designed all of it. Like we designed the whole system that I saw, redesigned the whole system I saw seven years ago last year, but it was just too expensive.

2:21:26The systems we were using were like certain parts on it were like 50,$60 ,000. So we moved all of that into a chip. And we spent last year and a half doing that work. Those chips are in our office now and working. Those chips are like $7 instead of$50 ,000. Yeah. There's only a few groups in the world that can make them and design them. We co-designed them. We worked on the design with them, made them, fabricated them, and we have them now in our office. They work. We didn't like a lot. You know, we use many different, we use like a lot of chips, but they're like really cheap. And that's important.

2:22:03So we, you know, K-12s will have like a large budget and we need to be able to get the cost down to make it affordable for every school. That's what I was going to ask. I mean, how are you going to, how are you going to get this in school? A lot of, a lot of schools won't do, they won't even hire a security guard. Yeah. There, there are big budgets, both at the federal and municipal level, like a lot of money to put that are going into school, like schools are getting subsidized for to put in a lot of stuff. They're putting in CCTVs, cameras. They're putting in ballistic chalkboards, all kinds of stuff in the schools.

2:22:36There's a lot of cash there. The schools also spend a decent amount per student. And I think we get the cost down to a reasonable amount per student that both public and private schools can afford. But we could have already had our systems beta testing in some schools by now if we didn't pivot a year and a half year ago, we spent the last year trying to like 90 % decrease, like decrease the bill of materials, like the cost. Wow. It's just like that's needed to go big and make this really work well. Get in the game with the college branded Venmo debit card. Wreck your team with every tap and earn up to 5 % cash back with Venmo Stash, a new rewards program from Venmo.

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2:23:40In a classroom of sodas, most stay quiet. Then there's Mr. Pibb. Sweet cherry, bold outbursts, the kind of flavor that gets attention. Bold kick of cherry. Hey, yo, Mr. Pibb. I think we'll... Are you going to put anything else into it? Any other... Like, here's an example. When I think of this, it's... Would there be a way to... Maybe facial recognition? Who's enrolled here, who's not? Just for example, like the shooter that happened up at Nashville a couple years ago, the Covenant School. Yeah. Went to school there, but not at the time. Yeah. You know, and so if they would have had some type of facial recognition on top of what you had.

2:24:23That's like, this person doesn't go here. This person has a gun. Yep, 100%. We'll have cameras, maybe even some audio, like mics. Cameras will be really huge. You can really do a lot with just RGB cameras and understand what's really going on. You'll also get a lot of semantic understanding because guns are like, they're hidden somewhere. They're concealed. People are not walking in with handguns and shotguns into school. They're in a waistband, in a pocket, backpack. we can be really thoughtful about if somebody, you know, clearly doesn't have anything, like, anything in their pockets when they're walking in, but they have a backpack, we can be thoughtful about, like, we probably need to scan a backpack.

2:24:59So, and we maybe need to spend more time, like, getting higher frame rate on this area. And then, as you mentioned, like, a lot of understanding about, like, does this person belong here or not? It's just, like, is this a weird time for somebody to be like leaving and walking back into the school. So there's just a lot of semantic grounding we can put into the models to really help like understand if there's threats or not. The schools are set up really well to do like random locker checks now and like, okay, this doesn't look okay or not. Like the schools are really well equipped for that. It's just like, we don't know what's happening.

2:25:32We actually think now that there's probably like, like perhaps like tens of thousands of guns that are being brought into schools in the US across 130 ,000 schools every year. I think what we're finding now is a very, very small percentage of them are found that are brought in. And then from there, what we're also finding is actually a similar small percentage are actually being reported. Because if you report a student that has a gun, they're going to juvie. So we're also finding out, we think a large percentage of, we're finding a large percentage of guns are even found. And then of that, we think a large percentage are not even reported because, like, you know, could, like, put, you know what I mean?

2:26:15Could, like, wreck this kid's life, which is, you know, unclear, like, what we should do here, you know, for that. That's, like, that's terrible. But we think there's, like, maybe tens of thousands, maybe hundreds of thousands of guns that are being brought in every year to the schools. Wow. We're finding, you're reporting thousands. Wow. And you're seeing hundreds of shootings. So our view is that we think is actually happening, like, you know, as a percentage, it's low, but as an absolute number, it's quite high. Yeah, so I'm excited about this. In some way, like, I write this prediction every end of every year for, like, what, you know, will happen in the spaces I'm in, which is, like, flying cars, robotics, like AI, and weapon detection and stuff like that.

2:26:58And, like, I did a post in December, like, here's what I think on these four areas. And like, overwhelmingly, like, the most support I got, like, publicly was just for cover. It just like, I think it, you know, just like hits, I think it hits in a really good way with a lot of folks, maybe especially parents. Everybody's worried. We're homeschooling. Yeah. Because of this shit. That's a big reason why we're homeschooling. Yeah, I hear you. My wife and I, when we think about where we put our kids and stuff too, it's just like something we talk about every time too. And it's like, you know, and it's like, it's probably like a low occurrence rate, but if it did happen, it's just like, you can't recover from that, you know what I mean?

2:27:37I mean, it's just every school I go to, I'm like, man, like you guys got to... It's just happened down the road. I had a good buddy, like, had his house breaking into. He's got family and stuff. It was like six months ago, and he told me when I was talking to him last, he's like, the sense of security we have now in our home is just like, we'll never get back. And I just like, I didn't know, I didn't know what that felt like, like feeling like we were secure before, but we lost it now. And now we definitely see it and feel it. And it's like, we just never, we're never going to be able to get it back.

2:28:04And I've had like, you know, I've had like some close people I know that have been involved around this stuff. And it's just like, it's terrible. And so, you know, my agenda here is I think it can be prevented. I don't know if you're going to prevent all of them. I think you can prevent a lot of them. And then if you even have like, there's no real security there at all right now. But even if you have security, there's also like a sense of like, shit, I got to bring a gun in here now. There's like real sophisticated AI that's in all these schools that can catch it. I think that's another big thing.

2:28:33You have that at like TSA when you go through PreCheck. Yeah, it's a deterrent. So you have like that, but we can also find it. We can see underneath through backpacks and stuff. It happens at specialized radio frequencies. Happens at like 200 to 300 gigahertz, then it happens again at 600 gigahertz. And in between those bands, there's either FCC rules that prevent you from doing it or there's atmospheric attenuation, meaning sometimes there's enough moisture in the atmosphere at certain radio frequencies that like the radio frequencies don't do well and perform well. They perform well at these certain radio frequencies for the imaging stuff we do.

2:29:08So it's actually quite a hard technical feat. One of the reasons I didn't do it and did Archer is because I thought the cover stuff was actually harder than doing flying cars. And I actually think it still is. We still like, it's hard to, it's like, it seems like pretty obvious. Like it's like a way airports have, I do it 10 times higher. So it seems like, and you look at Archer, like shit, man, that looks really complicated. Or like a figure. Um, cover is just like super niche area of folks that it haven't, like there's the folks spending like in, like in this space or like they're doing like, like they're doing work in weather and space and they're not doing this for like shootings and like security and that there's a, there is no industry for this.

2:29:52And luckily we have like the world's best terahertz experts at cover that are working every day on this. And they're really passionate. Uh, they're probably not getting paid enough and they're just like super passionate about solving this problem. And, um, so anyway, I think, um, I think the through line for covers, I think it'll work. I think we'll be able to demonstrate it hopefully by end of this year. Like we'll be able to say like, we have it at like, we'll have it at figure campus first and then we'll put it in, uh, in like schools, like hopefully on the West coast and maybe, maybe one or two and we'll see how it goes.

2:30:21There's, you know, there's a, how do we market this? Like what do we tell the students, like teach parents? There's like, you know, there's a lot of stuff here we need to get right. But if it goes well from there and we're getting low false positives, like what we really want to do is make sure we don't freak the kids out. We don't want to, you know, think it's a gun, but it's a crayon box. That'd be terrible. So like that's really an AI problem. So we basically want to make sure like we have like low false positives around the whole system stack. That's a really hard problem to solve, especially for us, where you can be partially occluded on certain areas of the person or the weapon.

2:30:54And then we need to know what we see. It's actually, is it real or not? And so funny enough, if you come to our lab, we have just guns everywhere. And they're all bricked. You can't actually shoot them. But all day, we try to figure out how to put guns on humans or mannequins, and we try to figure out how to detect them. So how does it work? Is it shooting frequency and then detecting the response when it hits something solid? Yeah, that's exactly what it's doing. It's basically shooting out a radio frequency. It's like electromagnetic, it's like a little wave format that goes out. Very similar to how your Wi-Fi works in your home or 5G.

2:31:34Same type of concept. It's just on a higher, different radio frequency level. But think about your Wi-Fi and you want to, in an order of 20x or so, like the radio frequency level, it's like operating in a few gigahertz, something like that. But we operate at much higher frequencies, like 300 gigahertz. So you want to like, whatever, call it 50, maybe it's 50, 100 times this. And then basically it shoots us out and this waveform comes back and we review it. And we look how long it took to come back. And we use beamforming a couple other techniques to figure out what happened. But you're basically, it's same as like traditional radar technology.

2:32:15But we can shoot it out, comes back. It's not ionizing, it won't hurt you. Like it's perfectly fine to be around like your wifi. And we can basically get a, we can get both a 2D image. What's happening in a 3D point cloud. The 3D point cloud is what's really important. So if you have like a weapon on you, like in your pocket for whatever, or say I have one in like my chest, for example, we will start getting back the signals back from the top surface of the gun before we get your chest stuff back. And in the case of your chest, you have a lot of water in your skin, and it'll somewhat attenuate in your chest.

2:32:47So then we'll get back an image from this, and we'll reconstruct it really fast. And we can reconstruct into somewhat of a three-dimensional point cloud like you do a camera. So you can basically get almost like it looks like an op. I showed you earlier today the vision from this. It looks like kind of a camera image is what you get back. And so from there, you can kind of like visually see what's really happening. In the case of the gun, you can see the gun. You can see the trigger in some cases. Yeah, and sometimes it might just be like the handle or the sight of a gun or different places of it, but you can see it through materials.

2:33:16Like it could be backpacks, it could be clothing or a jacket, but most guns are all in the waistband pockets or backpacks, which makes sense, right? You're not wearing it around your neck on the outside of your shirt or things like this. So that's where most weapons are entering school. We have probably one of the best data scientists in the world that is obsessed with school shooting, and he puts out the best school shooting analytics. He does it daily. He's done it for five years. He's working with us through this, and we've done so much work on how people, how students enter schools, how they exit, emergency responses, what solutions are on campus now for this, like, what data we find on where guns are at, what type of guns and weapons are there.

2:34:00There's, I think it was like 200 nice stabbings last year. 200? Yeah. It's like, it's so high and so dangerous. like we're trying to, like we can detect knives. Like there's, you know, vape pens, whatever, whatever. It's not a metallic thing. It's like we can, it doesn't matter what the object, it looks like. Different metallics will actually like, like come back to the radar system a little bit differently. So you can kind of maybe sometimes tell if there's like a gun, like a metallic signature or not coming from the material. But the technology is like really kind of straightforward in the sense of like, it's RF technology, like radio frequency technology.

2:34:34And you get back like an image. and we can use that image to build like a neural network to then look at it and say like, what is this thing? What time of day is it? Who is this human? Like, is this a dangerous threat or not? And we need to do a really good job of making sure we're accurate in those readings or not. If we're not, we're going to cause havoc and if we're right, a lot of times we could basically start saving lives. And that's, I mean, there's on average one shooting every single day, more than that. Like, there's over 300 or more or so shootings roughly a year, if you look back the last couple years.

2:35:07So like every single day, I mean, there's less school days in a year than 365, but like roughly every day there's a school shooting in the U.S. That's just at K-12, not colleges. That's looking at the 130 ,000 K-12 schools in the U.S. I can't. Do you think this will be out in a couple years? Yeah, I think we'll get it out in a couple years. We have a team working day and night on this. We'll, you know, we'll probably, I'll probably increase funding into it this year significantly. and we'll take a bigger push and head count. Yeah, but right now all things are on, like can we get the first full system in a really stable spot that works?

2:35:46And we've had to do a lot to increase the field of view because schools are several meters wide, multiple doors, sometimes double doors to get in. We need to scan all of that all the way through. So it's like a natural aperture that students are walking into, which is good. You're not walking inside of a building, you know, like through a brick wall. You have to walk into a door entrance. And we're trying to, yeah, basically we're trying to get that fully complete this year. Man, that is solid work. Yeah. Real solid work. Let's talk about Hark. Let's do it. Ready? Okay, so, I mean, I think my pitch here is like, I've been working on like one of the hardest AI tech, I think humanoid AI is one of the hardest AI technologies on the planet.

2:36:32It's just an incredibly difficult problem that my team and I have been working through day and night for the last four years. So it's like, okay, we want to go build a crazy sci-fi future with flying cars, AI humanoids, and then on the other half of my life, I'm using an AI chatbot. Like a frontier lab, like Gemini or ChatGPT. And it's so stupid. It doesn't know me at all. doesn't remember anything I'm saying, can't see what I'm doing. It can't use tools very well, uses the internet like really poorly, can't even order me a sandwich if I needed one right now. And like, it doesn't feel very futuristic.

2:37:13It felt futuristic three years ago, but now anymore, it's just like, it's just not very good. It feels like I'm like in an incognito window searching Google. That's all I can do. Does it have access to my accounts? Doesn't know any of this stuff. meanwhile i think like for me like i was just been sitting here for three years thinking like we're gonna get like jarvis out of this from iron man we're gonna get something crazy out of ai it's gonna move to a point where it can like it can like listen and speak naturally like a human it can see the world it can do uh it can use tools like a browser and terminal it can do real work for you and help you out it'll know you really well i know sean i know everything you're ever doing all your stuff and be really personal to you.

2:37:54We don't have anything like that now. I got like this stupid chatbot that doesn't remember the last thing I said to it. And so I decided to like, I said like there's two things here that are extremely broken. One on the AI side, we have like extreme, we have like, we have like a lot of gaps to get to, to get to like, like, like extremely personalized like AI intelligence. There's a lot of missed opportunity now, last few years, a lot of gaps there. And the second thing is we're interacting with these AI systems to old pre-AI computers. If you're putting up your phone or your Mac or your computer, they're all designed 20 years ago.

2:38:42It's like a really old interface. The chatbot's an old interface. it's the wrong interface to AGI. You're not gonna get to Jarvis with those. So we have to go rebuild all the hardware from scratch. Holy shit. Yeah, and I don't see anybody, I've been waiting, I've been sitting here for like a year and a half, being like somebody's gonna do this really well, and I can't wait for it. And nobody's doing it. I mean, look at Apple, they're just like, what are they doing? Like I, so I started a new lab, last summer called Hark and it's an AI lab. And we're gonna basically design what comes after the iPhone for AI.

2:39:26And we're gonna design new models that are extremely multimodal that can solve this. No shit. Yeah. And we have like the world, some of the, I think some of the world's best AI folks of all time. And we have, we have like, we have the lead designer from the iPhone, Abadur on the team. I mean, he's an iPhone 15, 16, 17. So this is going to wind up being a device? Is it going to be a device? A family of devices. Yeah. And this will go really far. It'll replace your phone and computer. And you'll have like native AI systems that are always on, always thinking, always understanding, always there to help, like doing stuff in the background.

2:40:11Like, we'll have near-perfect memory. We'll know everything about your life and what you like and don't like, and be able to even, like, act as a coach and say, like, hey, you said you'd do this over 90 days, and you're not doing this over here. It'll just, like, it'll just... Whoa, it'll hold you accountable. Hold you accountable. Oh, shit. Yeah. Yeah, we have, we've been... We have hardware in our lab. We have... We work on AM models now. Like, stuff is crazy cool. And, um... Yeah, we're gonna... I think we'll probably come out of stealth by the time this thing airs here between you and me. Holy shit.

2:40:49And we're self-funding it right now. You're self-funding this one too? Yeah, I'm self-funding right now. And yeah, the team's great, man. I think it's gonna be a massive opportunity and I see the Frontier Labs heading in a really great place for them, but a very different place than where we're headed.

2:41:20Yeah. What are you most excited about? I just want to wake up to... I always think about... I just want to wake up to a world that I'm excited and inspired. It's like... You know, I love doing this stuff. I could have retired like 10 years, 12 years ago, 15 years ago. So I think I just want to work on cool, crazy shit. And I'm just excited for a world of flying cars and humanoid robots and helping prevent school shootings and Jarvis. I mean, how do you keep it all together? I don't sleep. You're innovating four major things. The trick is just to not sleep and always work. I'm good at that. You know what I mean?

2:42:09You get me. Like just that's how you do it. Super simple. No, I mean, like, listen, I, I, I mean, to be honest, like I've had to make some like tons of personal sacrifices. Like, you know, I think 10 years ago, I would have like a part of my life that would like be dedicated to like golf trips and, you know, doing the annual like college trip with like my friends and stuff. Like I don't do that anymore. I spend, I have like my family and I have my companies and that's all I do. And I rarely, I do like a few podcasts a year, not much. I'm excited to come here because like I love your show and get the story out too.

2:42:45You're great at it. And so I, you know, I just like protect my time and just like I go all in on these things, like my kids and I have my work kids, you know what I mean? Like, and so like, which are like, you know, these are my like, like, they're like kind of like babies. I go, you know, make them, you need constant care and attention. So I have like this family and I need to like, I go all in on it and I do everything else less good. You know what I mean? I'm like a shitty college friend, if you like, you know what I mean? If I had seen you in a little while, like just like not gonna spend the half a day with you on Saturday if you're in town and haven't seen you in 10 years.

2:43:15So it's just unfortunate. I wish, but like, you know, I care about these things more. I care about doing this stuff really well and I'm really happy at it. You know, I'm happy with family and happy with like things going to work. And, you know, to be frank, I just, I was born and raised on a farm, man. And I get to do like work on this cool shit every day. And, you know, I got billions behind it, like, going for it. Great teams that work, like, their asses off. Like, teams that are, you know, here, came with. And it's great. And I, like, fired up to come every day to work to try to make this thing happen.

2:43:48And I hope these things all work. It's just, but, like, I don't know, these are also hard businesses, so. It's pretty incredible. I mean, a farm boy from a town of 700 people now. Right. Building that thing. Right. Flying cars. Keeping kids safe. and Hark. I mean... Yeah, it's... American Dream is still very much alive and well. That's fucking cool to see. It's cool. I feel just internally grateful to have a shot to do this. I feel like, you know, young entrepreneur Brett 20 years ago had been like, no fucking way you get a shot to go do this stuff, you know, and it's great. I just, yeah, I'm taking a...

2:44:30I'm probably at, I feel like, peak career and my team with me is like peak team, peak resources. The stuff I'm working on, I feel like is very important for the world, which is also great. I didn't, you know, doing vetery, it was like, there was a part of me saying like, okay, is this like the thing I want to spend my whole life doing? And I have that here, which is great. These are like the things I want to spend all my time on for the next like 20, 30, 40 years. So it's good. I'm just like, I just don't want to, don't want to screw it up now, you know? Oh yeah. It can work. We're doing a pretty damn good job, I think.

2:45:03All right, we're wrapping up the interview. I got a hot question to ask you. You ready? Let's do it. For decades, movies taught us to fear robots becoming self-aware and turning on people. But in the real world, we still don't have public evidence of conscious machines. What we do have are real cases of robots harming people from Robert Williams being killed by a Ford industrial robot in 1979 to the viral 2025 Unitree H1 malfunction that showed how violently a humanoid system can lose control, plus long-standing research warnings that robots in homes can create privacy and security vulnerabilities in ongoing global debate over autonomous weapons.

2:45:48So is the bigger threat not conscious machines at all, but obedient machines that can still malfunction, be hacked, surveilled through remotely controlled, or turned into tools of intimidation, assassination, or state power? I don't know how that person gets up and goes outside every day if you're that scared. I do. So I think like...

2:46:16The future is... This future, it can be like molded and morphed morphed and like, it's what we want to do with our time. If we want a future full of like robotic systems that can help us out and free us of our times and things like this, we're going to wheel our way to make that happen. I'm a pretty like optimistic person. I feel that having millions and then billions of humanoid robots on the planet is just going to be such a magical and important thing for the world. Are we going to have bumps along the way? Like, for sure. Are they going to hurt somebody at some point? Like, I think that's bound to happen at some point with enough scale.

2:47:05But I think, like, the spirit here for humanity to get this done, I think is here, and I think it's going to be one of the most important technologies of our lifetime. I think in some way this AI stuff of like, we're generating AI systems that can be embodied and can use computers. Like it's gonna be like one of the most transformative technologies we've ever been through. Like we're building synthetic humans at scale. And it's both scary, but also like very, I'm like very excited about that future. Mm-hmm. So I think my view here is, yes, there's a lot of really difficult things that could go wrong, that perhaps maybe will go wrong, but I think we need this, just like we need cars.

2:48:01And I think just like we need a lot of things in life, airplanes and things. I think these are important technologies that really move society forward. So anyway, I happen to believe that this is extremely important, will save lives, and I think increase prosperity across all of human civilization. And I think I'm excited to be working on it, but I think there is a lot of truth to what, like I said, it's going to be a really hard road. Yeah. I mean, it's just an incredible advancement. And I know there's a lot of fear around AI. I have a lot of fear around AI, but we're going to go through it one way or another.

2:48:43And I do think things are going to be a lot better on the other side of that. You're not stopping it now. It's like the... Exactly. It's like, it's go time. It's going to happen for sure. And I think it's going to be fine. Like, I think, you know, I use AI every day. It's like, it's fine. It's like nothing, you know, like it's a chat bot. Like, I think, yeah, if there's a different path to go down from here that could be good or bad, I think my bet's on high probability of really great. There's obviously always path that could, like, not go well, but, like, being conscious of that and, like, basically doing everything possible to steer it in the right direction is, like, what we have to do at this point.

2:49:24Like, this is not, like, something we can turn off. You know, turn off the internet? Yeah. You're going to stop people from trying to build systems that make us more productive and do what will work. I don't think that's not happening. So all we can do is basically do it the right way that has the best positive effect on the world. Yeah. Another thing that comes to my mind is when we're talking about interacting with the humanoids, people, and I've had this discussion on other podcasts too, But people are going to look at that for advice, relationship advice. And I mean, I think there's a lot of important things they're going to be talking to this thing, too, about advice, certain people.

2:50:09And I think that's a big fear of a lot of folks, too. It's already happening with ChatGPT and all these other, Claude and all these other things anyways. But who were they getting advice from before that? problem i think you know what i mean it's it's i think it's the caliber of person yeah totally but yeah it's who you spend time with yeah yeah last question what advice do you have for future founders

2:50:39i have a few things i think are i wish i could like maybe said differently also like pass down to like young Brett, like 20 years ago. Um, I think one is like, uh, just go, just start building. I feel like, um, a lot of folks get too caught up in this thing. That's like going to be hard. It might not work. And, um, you can just like, it's just so easy to start a company these days. So many great tools. Uh, just go learn. I think, um, there's never been as never been a situation where I haven't like done something and then learned a bunch and then, have it reset from that feedback. So almost like little stairs I'm climbing over and over throughout time.

2:51:20And so if I just wouldn't have started and wouldn't have moved, I wouldn't have learned this information. So it's like a lot of information coming in, recursively self-improving and getting better over time. This could be simple things like hiring and doing accounting or running an engineering team or trying to ship a product or getting feedback from customers. I'm just getting, I think I'm getting, it's like a sports player. You're getting better with more practice. And so I think the most important thing is just go. I also think the thing I learned a lot in my lifetime is what you work on is really a defining moment for founders.

2:51:55And it could be founders of any industry, tech, non-tech, or whatever. You're generally gonna go and just try to have this kid that needs a lot of attention. And then at some point, it's like, you just can't abandon this thing. And you gotta keep spending more time with it. And it needs a lot. and it's like constantly working on the problems with it. So it's like not the fun things. You're working on all the hard things. It's like this problem funnel I have where I worked on the hardest, most pernicious problems at the company. So you got to like really love it. And it's not like you can be there for a year or two.

2:52:26You have to be there for sometimes a really long time. And even if you're successful and even if you sell your company or whatever, go public, you're getting your stock locked up or you're investing out over many periods of time. You're got to be in it for quite a while. And I find that for me, the things I work on as probably the most important things I could be doing with my decision making. And that's happening at a micro level inside the companies where I work on week to week, month to month. But it's happening at a macro level where like, where do I spend my time? Like I'm 39 right now. Like where do I spend my time as 39 year old Brett?

2:52:59And where does like 20 year old Brett and 25 year old Brett spend my time as an entrepreneur? And I generally have this philosophy that harder things are easier. Like meaning there's there's like a non-linear effect here for like starting companies that are like easier versus harder. Meaning starting something that could be like 100 times higher outcome is generally not 100 times harder. So like doing figure is not 100 times harder than doing another robot company. It's probably like three times harder, maybe five times harder. But the total responsible market and opportunity is probably millions of times bigger than another like robot that's like on assembly line moving back and forth.

2:53:39And so I think there's like this non-linear effect to like decision-making here that is really important where harder things that have like larger outcomes are like usually easier to recruit the best talent in the world. That gives you a better lift to build a better product and a better team. That team and better product and maybe even a bigger industry because it's harder will give you like more capital coming at you for disposal to be able to like make the right investments you need into the right, say, equipment or people or personnel or whatever marketing to basically make you more successful.

2:54:07and then you're generally worth working inside of bigger dressable markets like TAMS that, you know, potential acquirers or public markets or other folks like really want to see and have like basically a disproportionate outcome. They want to, they want a high risk reward. They want to, you know, investors and things and even people, they want to like go in and like if it works, they want to like a hundred X or a thousand X. They don't want like a two X. And generally for venture, like 90, 95 % of people fail. So if it works, you really want to go, you want to hit a grand slam. So I think my philosophy is like, spin, like choose wisely, like a young Brett, spin, choose wisely what you work on, young entrepreneurs.

2:54:48And then I would try to be as ambitious as possible. There's capital for that. And there's humans for that, that want to work at really crazy shit. We have them at my companies and they're incredible. You met some of them today. They're just like my design lead and a bunch of other folks here that are just unbelievable. at what they do. They're the best in the world at what they do. But they want to come here and they want to try to do something like it's never been done before. They don't want to go off and design the next car or do the next AI product everybody else is doing. They want to be here designing something revolutionary.

2:55:20So I think that's like, something we don't stress enough. And I think last thing is like, there's no rule book for this, which is like really unfair. And there's a lot of people out there that will teach like, here's what to do. And they're generally coming from folks that haven't, haven't done it before. And the signal and noise out there is just so high, or so low. I mean, you get a lot of noise out of, like, in the market. It's very noisy about what to do and what successful means for building a team or hiring engineers or executing a product. It's very difficult. And very few people in the world know how to do it really well, consistently.

2:55:58And so I found over time, it's been really hard for me to get the right advice. and um so i think it's been a lonely path and for folks out there that are on that path it's lonely but i believe in you you can do it and i think that's um i've never had somebody for 20 years i could call and just like what should i do in this situation i never have had it and i wish i had uh there's no book there's nobody to call yeah and i think that makes it really hard but it's possible. You can just like go do these things and it works. So for the folks out there that really want it and it filters out like everybody who doesn't really want it.

2:56:41And you can tell the folks that want it. If I talk to people, they say, well, this is hard ass hard. I'm like, you just don't want it. You shouldn't be doing this. You're gonna get completely wiped out. You are. And it's like, it's the great filter. It's the folks that, you know, you went through buds. Like it's the great filter. It's 95 % of everybody will fail and you'll devote your life into it. and time and maybe all your money and your brand and you'll be embarrassed and you'll fail. Most will fail. And it's only for the folks that will like, I will like, you know, I will do whatever it takes to go make sure I make this happen.

2:57:12There is no failure. Those are the folks that do well here. And you can bend the world and you basically can mold the future to how you kind of want to if you try hard enough. and the goal at the end of the day is just to not die. If you don't quit, you won't die. So like, anyway, I think, I think it's, listen, I've been playing this now for 20 years, still playing it. I feel like I'm in the early innings of my career now. I want to go ship at scale these systems. I haven't done that yet. We're like an inning, we're bred in one. Wow. And so like, but for everybody out there that's in that, I just think it's, I believe in you here.

2:57:55You can do it. That's great advice, man. Cool. Well, Brett, fascinating interview. Love everything you're doing, man. Like, incredible stuff. Huge advancements. Sean, I'm a huge fan of you and just everything you do. So, I mean, having me here and, I mean, going through all this is just, it's been great. Thanks for having me. Thank you. It's been an honor. Great. Cheers. Yeah.

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