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Optional services and products may have fees or charges. See Chime.com/feesinfo. Advertised annual percentage yield with Chime Plus status only. Otherwise 1.00% APY applies. No man balance required. Chime card on-time payment history may have a positive impact on your credit score. Results may vary. For details and applicable terms. Brett Adcock. Welcome to the show. The first one is the most important. In the show. Thank you. He didn't give me any tips on that, did he? I'm not sure. The most important thing is the coolest thing I've ever seen as far as giving somebody a gift on the show. That was awesome. Thank you. Brett, we got a lot to talk about here. So, man. How many companies are you running now? Man, I'm not sleeping. I get too many. All that. Just like kids and work and just like that thing is amazing. Never sleeping anymore. All that. Yeah, what do you think of the robot? I think it's incredible. I can't wait to talk more about it. A couple things. Just one more thing to knock out here before we get into it. I got a Patreon account. It's a subscription account. It's quite the community. There are honestly the reason that I get to sit here with you today. They get the opportunity to ask every single guest a question. This is from Stephen Casey. In today's marketplace, we find that AI platforms can sometimes invent answers rather than emitting to a lack of information. Combining this in the physical realm of robotic action seems to multiply the downside effects exponentially. What safeguards are in place that we can put our trust in to prevent the potential for downstream harm to humans as a result of bad programming or computing errors. Yeah. Yeah, we don't want to terminate our popping out here when we definitely not. That's where it's work, right? I think we're chatting about this outside. I think one thing to say four years ago when we started the company, there was no path for humanoid robots to make into people's homes in the next 10 years. There's no good story. You had big hydraulic humanoids out there. They were all hand-coded to do certain tasks. We really needed a cheaper electric humanoid that basically can use neural nets. He used basically an AI first strategy with. There's none of that existed. I think we're thankful now. Looking back, it feels like we somehow pulled 10 years of the future forward. We have electric humanoids at a reasonably price that can do useful human work with neural nets. It's just an incredible place to be in. I think those questions, which is how do we make this work now at scale in a safe way? That's the spot we want to be in, not trying to make this work for 20 years. It's a very tough problem. We have to get the product cheap enough. We have to make enough of them. We have to make it the performance work in very complicated things. Walk around a house and do dishes, laundry, very complex things. All kids can do this. It takes adults to do this level of work. We need that all done in a mechanical system that doesn't have any humans around, or maybe most of this, that does it autonomously and not makes any mistakes. Your fan mentioned, we have to do it safely over time. It's just man. It's just incredibly complex problem. I think for us, we have a safety strategy. Both intrinsically, we want the robot hardware and the robots around humans to just be safe all times. There's a bunch of semantic safety and other things that we have either put in place or put in place now to make the robot just work safe in the environment. You have a candle at home. You don't want the robot to accidentally knock it over. That's like an intelligence thing in a lot of ways. There's a boiling pot of water making sure we're very safe around it. There's the intrinsic safety of making sure this mechanical thing in your house is safe around it. Everybody around it. I think the drug answers are still a lot of wood chop of getting this thing to a point where it's like we trust it to be autonomous next to my kids all day long in my house. That's a common in your house. We had many robots now throughout my house in testing for last like a year or so. I've had them near my kids in some aspects, but we're always monitoring it. What are your kids think? Man, it's just like kind of normal for them now. They try to talk to them or like, yeah, talk to it. Yeah, they want to go, they want to go like, they want to go like, jump on it and touch it. And you know, when do kids think, you know what I mean? Like they want to go touch it and talk to it and be around it. And we're still at that stage yet where I feel comfortable enough to be like let loose and say here, you know, here's a robot. They're my kids are there and I feel okay and we're not there yet. I think we will be in the next several years. What's the longest they've been around any one particular robot? We've had a robot in my house for like maybe a couple months doing work kind of on and off, daily, sometimes every other day. And you know, the kids are kind of at school or sometimes at home. So they're always around whenever the robots are running. But a lot of times and you know, that was just like our home robot. Do they get attached to them? They name it emotionally attached. They have different names for the robot. and um. - Yeah, they love it. And it's actually question Rossian's in office of like give a robot in the home and it's like, it's got some like character too in little wear and tear. Do you like wanna keep that robot? Do you want like a new one? - That's what I'm wondering. - Yeah, I think kids are like the perfect. - My kids wanted it. - My kids wanted it. They wanted it there. - We're not getting rid of this guy. - Yeah, he's got a little subaiged up a little bit here and there and has a tear here and they just like, they loved it. - That is wild, man. - Yeah. - That is wild. - And in our lifetime we will be fortunate enough for every human to I think have a humanoid. Like almost like a phone and car. - Wow. Yeah, we were talking, I mean, just some of the stuff that you just mentioned, I mean the complexity of the problem that you're solving here, I mean all these little problems that I didn't even like, knockin' over a boiling pot of water. I never would have like, - It's just like, - Just thinking about something that it happens every day and then you think of all the things that happen every day and just a regular household and it's like problem city, man. It's like a fun house of problems. There's just problems everywhere. It's like hardware problems, AI problems, problem scaling and commercializing and getting the system reliable, manufacturing problems. Like we have a problem fun house if you wanna come by campus here. - I'll bet and check it out. - I'll bet you do. I'll bet you do. Well, some people say, I, some people say AI is that isn't an economic bubble. And as of this recording, Pauli Market says there's gonna be an 18% chance that the AI bubble will burst by December 31st, 2026. What do you think about that? Is AI in a bubble? - Absolutely. - Like the, I think you'll see some of the most transformative events and technology happen over the next like 36 months we've ever seen in our, like ever. - Ever. - I don't feel like we're in a bubble here. - I feel like we're in a very, we're watching the surf. - I'm watching AI in a human body do human work. Early, it's early. We don't have, you know, we don't, at some point here this year we'll have thousands of robots. We have like, you know, we have hundreds now. Like we need like millions of robots to make an impact. That's just gonna take some time and it's gonna be crazy cool. So we're at the start line of that happening, which is like, how do we get AI out into the physical world at scale? That'll for sure work and it'll go really far in a lot of times. And then separately, we have AI now that can use computers like humans and can think, I'll show you a little bit of that here before the show. And you know, that will manifest in a point where like, both in the physical and digital world, you basically have these little many humans that can do human like work and then can think and use computers and use machines. And I mean, that's gonna lead to such a productivity. Like we measure like GDP per capita, like per human. But if you're able to make like as many synthetic humans, like millions, billions, tens of billions of synthetic humans, in the case of the digital world, maybe trillions, that'll lead to the, I mean, I think the greatest increase and productivity we've ever seen in our lifetime and ultimately like reduce goods and service processes to unprecedented levels. Like a true age of abundance. - Wow, wow. - What do you, I mean, I'm just curious, what do you think, what will humans be doing? - I mean, I hope I don't have to, I woke up today, I was like, I'm in the dishwasher, getting my kids breakfast, like just like busy work that, like my kids are sitting there, I'm like doing work, you know what I mean? I wish I was just like, yeah, I just, wish I wasn't doing that with stuff. And then I'm like, I'll do it my day, I'm like trying to call the car service and then trying to get on my flight and you know, coming here and it's like order and launch, like all this stuff I'm doing, all the way. And I don't want to do any of that. I don't be like fully free. - I get it. - With that burden. - And I just want to be like clear headed. And I want like my AI to run a little bread adcock operating system and run my life. And all these things I have in my head about what to order and pay a tax bill and like do this meeting and I have to go back and do an engineering stand up. I want to all that stuff to be in my operating system and like a human in a box. - So you're basically saying, the way this is going to turn out is your brain, I'm going to butcher those. You're basically exporting your brain and all the tasks that are going on in your brain. You're disseminating it to robots. - And they're delegating all this out. - That's amazing. That's like, we'll do that in like 24 months. Like we'll have all this stuff so good that you won't like go order food anymore, like book stuff, like do a lot of work behind a computer, like physical stuff in the world of like doing laundry and dishes and- - Use the bullshit leg work. - Yeah, I don't like, is anyone want to do that? Like I don't want to do it. - I don't. - Yeah. - Yeah, I got it. - So like, you clear all that from my life? Like I got to spend time with my kids, like enjoy life, like kind of be like, like I guess like clear headed. Two stuff I really love, like I love working, but I don't like doing all this busy work. - Yeah. - It's just like not, it's just like manual, like just like the labor I'm doing behind computers or like in the physical world. And just like, I want to delegate that out to my AI to do and fully automate it out. - That's, I don't know why I've never thought about that. I've never thought about it like, I've always looked at it as fear. I've always been like, oh shit, they're gonna take everything over. - It's a compression algorithm. Like we're basically running a large scale compression. So like I think, you know, my way I look at it now is, we basically have built like synthetic human intelligence that can use computers and machines. So like I'm gonna delegate out all this busy work on both my digital life and physical life to robots. And they'll just do all of it. But it's good. I mean like there's like, we have AI systems now in our lab at Hark that can use computers like a human can. It can talk to you. It can like, I just, I made a phone call to ours. Before we started and talked about my schedule and how to ask for things and ask it for things and how to do things. - Yeah, and a chicken salad order to deliver deer's office. - Yeah, exactly. - Exactly, but nothing besides a single like, hey, make this order. And you can spin up computers to do that virtually and then physically, like I'll have all this work done by my robotics. Both in, you'll have in the commercial workforce and the billions like manufacturing and healthcare and construction and in every human at some point we'll have a humanoid just to do all that busy work for you. And not only that, but like something to come home to that you can talk to you that will like, will know you. - Wild. - Yeah, it's like the, yeah, it's gonna happen now, which is like really gonna be fun. - Yeah, yeah. (thunder rumbling) I'm excited to introduce to you the newest member of my family. We call them Stanley. We got Stanley this past Christmas and pretty quickly my focus became making sure he was safe while still giving him the freedom to actually be a dog. I went looking for the top rated GPS fence, the number one, and that's how I found spot on. Stanley, where's their Nova collar? With spot on, I set up a GPS fence for Stanley right on my phone. No physical fence, no leash. I just walk my property line or draw it on the map and that's the boundary the collar recognizes. I can create multiple fences, save them, and adjust them whenever I need to. So whether we're at home or traveling, Stanley always knows where his boundaries are. Another thing that's set out to me is these collars are designed right here in the USA and assembled a new Hampshire. By a team that's been working with high precision GPS technology for years and you can tell a lot of attention went into making this thing reliable. The Nova collar uses a dual band GPS system connected to more than 150 satellites. Along with an antenna that's over five times larger than typical GPS fence collars. That keeps the boundary accurate, even around trees, terrain, and changing conditions. Spot on's true location technology has been independently tested and delivers 99.3% containment, which matters when you're trusting something with your dog's safety. It's incredibly durable and on top of that, I can check his location in real time. Send voice commands directly to the collar and track his activity through the day. I use Spot on so Stanley gets the freedom to run and explore and I get the peace of mind knowing he's safe. Let your dog roam with Spot on. Go to spotonfence.com/srs and use code srs for $50 off the Nova collar. That spotonfence.com/srs and use code srs for $50 off. I would like to do a little bit of a life story on you. Does that sound good to you? Yeah, let's do it. Where'd you grow up? Central Illinois. Central Illinois? Yeah, like a small town, like 700 people. 700 people. Yeah, wow. I'm a week one. That's even smaller than where I grew up. Yeah, where'd you grow up? I grew up in small town, Chilacothymusurri. How small? About 8,000 people at a time. Yeah. Yeah, we didn't have anything. 700 people. What were you into? Yeah, kids, sports, computers, like-- I got into computers really early, did bunch of sports. I grew up in a farm. So it was corn and soybeans. My family was third generation of this. So-- No, OK. Yeah. Yeah. Third generation-- Three generations of farmers. Three generation agriculture farming. And then we switch over to--
- Yeah, yeah. - Yeah, we're doing like humanoid robots now and AI systems. But yeah, I got really interested in computers like really young. Start a bunch of like startups in high school and college. But it's a lot of startups. - At first just like mostly things on the web, like selling things did a bunch of like different types like products I was selling on the internet for like throughout like high school and college. Small like drop shipping, retail, electronics, all kinds of things. Legion marketing and just fun stuff. I was like nothing serious, you know? Just like planning on the internet, trying to make some money. Just I didn't grow up with money. So it was like, internet was a way to like, like you know, maybe make some money. Like it was really fun. You know, I love like the ability to go out and create things and kind of control my destiny. So it was just something I attached to really early on. - Right on, right on. - Do you have brothers and sisters? - I have a brother, yeah. - What, you see a farmer? - Colby, no, he actually runs an AI defense company called Scout. - No kidding. - They're doing, yeah, basically building autonomy and like AI models for defense in the military. - So you guys both got into AI. - We both got into AI. We live like a block away from each other today. - Like a serious? - Yeah, we grew up together really close. We're like different ages a couple years apart. And then we were in New York for about 15 years together. And then he just moved out to California. We live literally a block away, see him almost every weekend. He has started like basically 10 minutes away from, you know, right now. And he's doing great. - Man, what do your parents think when you're coming home with what you guys are involved in and what you're creating? - We did use to such a wild, you know, to mean from farmer to this. - Honestly, like, I think one thing my parents both drilled and I think both of us, like really early was like, you know, farming is like very entrepreneurial. Like my dad was like, you know, ran his own business like, you know, you kind of have to go out there and put the work in or you're not gonna get paid. So early on he's like, listen, if you want to control your destiny and if you want to make money and, you know, like be able to actually, you know, do what you really want in life. You need to like run your own business. And that was like beating your heads like growing up, like, you know, at some point you need to, you know, you need to get, probably get out of here, get out of farming. It's not doing well. And you need to start someone your own. And so just kind of just like by default, I was like, okay, this is what I'm gonna, I'm gonna go do since I was a kid. - Yeah, Tom. - But you got some proud parents, man. - Yeah, parents are great. - Wow. - Yeah, they're like, what the hell's going on here? What are you doing? But I've been doing pretty crazy stuff for a while now. So I think it's like, it's gotten to a point where it's like, you know, even at Archery, we're building like 6,000 pound electric aircraft. And before that doing, you know, internet startup stuff. But it's kind of been, you know, working on crazier stuff now for a little over a decade. - Were you rebuilding stuff as a kid too? - Yeah, constantly building stuff. - What kind of stuff? - Stuff on the farm, building stuff on like, in software and internet. Just like, I just love building stuff all day. And like very like, big in a science and mathematics, like, you know, I'm like, I'm like a more of a visual learner too. Like I like building stuff and seeing it and touching things. And even like, honestly doing internet for like, I did like, I was like, I did work in the internet software for like 10 years. I just like always sat there every day, like wishing I was working on hardware. Stuff like I'd like touch on my hands. Stuff like when growing up was like, you know, I was like rebuilding computers or just like on the farm and building stuff. I always like, envy things that you can go touch and build. - Wow. - Basically like atoms. - Man. So where do you go? Where'd you go to school? - I went to university, Florida. - University of Florida. - Yep. - Where'd you go from there? - So after school, I moved to New York and I started working on software startups. And during college, I was working on basically a bunch of like, side, small like internet things. And then, kind of like shortly after college, I started a company called Veteri. And the goal was to basically build like a, it really kind of going through college is like, you gotta look for a job. You gotta go find something full time and got caught up in like the whole interviewing process of like looking for job. I just thought was so broken. Like applying for jobs and like never hearing back. And like, you have to go through headhunters. And then it basically became like a, some of this like, you know, boys club of like, trying to figure out where you went to school and then like certain people knew other folks of like how to get in. And it was just like a, it wasn't very much a meritocracy. And I just thought the whole process was extremely broken. And so sort of Veteri is, we were seeing AI recruiting marketplace. So the goal was like, if we can get all the world's talent and hiring on one platform, understand their knees really well, can we make matches at scale, like without like any humans involved. And like the head of any industry is like hundreds of billions of dollars a year. People are just like, I won't even, I won't use it. No, like I know. So I just, I keep here and everybody gets ripped off. But I don't know if it's so expensive. Like pay like $50,000 a higher. It's like insane. And then the, then the coax, the guy out that they just brought to you and have go to nothing. That's like the, like the force in this role. So they get paid a commission. So Veteri, Veteri is a connector. Yeah, a connector. Well, funny enough, we ended up selling to the world's largest recruiting company that does staffing. But like, let's do that for a minute. But we basically started in 2012. And, and the goal was like, how do we put like a lot of job seekers and a lot of employers on a platform, understand their preferences and match them at scale? Like just like, how do we use algorithms? At the time, we were like, let's use AI. But it was basically like, how do we use a lot of algorithms to figure out like what people want and then make matches. So you just have a push of a button, connect the right folks, and then make placements. And then we ended up charging, most of our revenue came from subscriptions from big companies like Big Bangs or startups or tech companies basically looking for talent. We started just in tech in the, in the US. So at one point we had about like, think about little under 20 or so cities globally that we were operating in. But most of it was tech talent. The tech space is, you know, at that point. How long ago was this? Start in 2012 and then it had been selling the business in 2017 or 2018. So about five years, six years. Right on. Yeah. Then more do we go. OK, so so, that, that was like a really tough. I was like, basically went like, I didn't have much money. Went like, fully all in the business. I went into debt at one point in 2015. The business was having a tough time. And then, I ended up selling, ended up going, doing really well. The business like completely hockey sticked in growth. We got all the things figured out. And just like, battery, battery was. And then ended up getting approached by the world's largest recruiting company. The same groups you like, you and I are talking about like, like, the same groups we were trying to think of a business. And they were like, oh, we want to acquire the, you know, acquire the company. And at the time we were like, I was like, completely dead broke and put everything out of the business. It was like, I think it was at point, almost seven years in. And, and, you know, they, you know, we were excited about an acquisition a year before that $10 million. One of the big tech companies. And they came in at $110 million. And, and it was a, 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 recruiting company. And, but you, you, you didn't even have a for sale. They just approached you. Yeah, we didn't hire bank or anything. Listen, at the time we were doing like, I don't know, 20, 30,000 interview requests like a week. Like, so that was like no humans involved. Like, think about how many humans would take to do like 20 or 30,000 interview requests. Yeah, it's like, you know, to mean the manage all that processes. So we were like, it would, the growth was just unbelievable. And, and there was like, there's something better to like a human jamming you in roles, right? Like, it's just like, you need like a, and then to extent you can get, you know, all the world's like talent there and all the world's companies looking. And 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. It's like a really black box, like trying both finding talent and looking for a job. It's just a terrible experience. So we kind of, that clicked. The other roles are just recruiting company came in. And it's like, we got to buy this thing. And, yeah, I bet they did. Yeah, I want to, I sold the business. And, and it was great. It was a good time for me. I really, at the point, 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, but time I got the term she to sell to when we actually sold and closed. It's a long process you have to go through, like tons of docs and 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, basic 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. And I was like, man, there's got to be something to do here. And then secondly, I really wanted to work on like flying cars. Having to watch a lot less 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 a security in schools, K through 12, mostly in the US. And then, and then how do we, I want to work on flying cars. And I ended up making the decision to work on flying cars at the time. So in 2018, shortly after the sale of veterinary, I started arch-raviation. 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. Think about like a normal helicopter can take off from a building or a helipad. 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 the middle of the century, 70% of the world. And you just like can get around like it's just gridlocked everywhere in major cities. It's just like socks to go like 20, 30 miles. It takes like an hour in most cases. So basically you can build a design aircraft that can take off vertically and then fly like an airplane. So you can get like a lot of distance. And you can basically then re-architects 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 like a lot like 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 like calling it Uber or driving that might take you an hour in LA or SF or New York, you basically can fly there in 10 minutes. And if we, if we can pool everybody together like in a kind of like an Uber pool style, business model, you can do it for like cheap as an Uber. But the problem was like I didn't know anything about how to build electric aircraft. I mean, where do you, yeah, 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, how to do that and run engineering and run, run the company. And there was like a lot of through trial and error. And I just felt like, I just felt like I learned it. I started in industrial system engineering at University of Florida. So, and then, you know, ran engineering and ran the company at veterinary. So I basically just hit the books. I try to learn as much as possible about three subject areas. First was like electrification, which let like, you know, at the time like electric vehicles were like really doing well. And even drones, vertical takeoff and landing, vertical lift, which is like traditional like rotor craft or helicopter. And the third is like winged aircraft like airplanes. You really need wings. Like so you be okay. So you basically have to learn about those three subjects. So I started I basically bought my, my, my basement downstairs at home. Or it's like every possible book on these subjects you could match in and sort of read as much as I possibly could. This is during the year transition. As I was transitioning like out of, out of veterinary into archery. I was reading every possible thing. And then I found a small community of folks that were like hosting onsite like either half week or week long courses for this. And so I would go to these sometimes response by NASA or by colleges or whatever would be on like basic rotor craft or electric propulsion. Or winged aircraft aerodynamics. And I would basically like try to like a learn as much as possible. It got to the point where I was like completely obsessed with this algorithm I was building on electric aircraft sizing. Like how, how do you actually like, how would you actually build an electric aircraft? So electric aircraft, which interesting is like in rotor craft. Like you basically want to make to create the most efficient lifting device possible. You need as much of like as the rotor disk area like in terms of surface areas you possibly can. It's why the helicopter rotors are so large. We want to be really large. That'll reduce power and get you off the ground. And electric aircraft, the problem you're starting with is you have like one 30th of the energy is as you do in kerosene and a battery pack. So you just like you're off the bat of one 30th less range or left one 30th less energy. And so power becomes like the dominating factor of like I've had a basic build electric aircraft. Like how do you get power down as much as possible? You really want a lot of disk area. A lot of disk area is one it could be good for power, but it's also bad because you're like no redundancy in the system. You have like one rotor blade that if it didn't go doesn't go well you go down with electrification. So electrification you can basically build much smaller like basically rotors and male fully electric. And the reason you can't do that with a traditional kind of like turbo fans or engines is it gets too inefficient at these sizes. You can't build 12 like propellers on a helicopter. Yeah, true 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 size of your chair, same efficiency. When you do that you create a lot of redundancy across the system so you can build like an aircraft of 12 electric motors. So this is a the rotors are underneath. You can see like the problem here is you can design it however you want. You could 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. So how do you design it? That's the problem I hit in 2018 was how do you actually do this. And so 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's actually at a I was at a higher Regency hotel in Atlanta in 2018. It was on it was an electric propulsion week long design course and like an aerodynamic scores for winged aircraft. And I met a guy there that that was basically in the engineering department at University of Florida. He was doing his PhD in aerospace and asked somebody was doing there and say come from University of Florida. I was like oh I went to school there as well. And he's like I'm like what what are you doing here? He's like oh I want to go like do a career in e-vita aircraft. And this is called electric vertical take off a landing. So you know a helicopter is a V-tall and we put a little e-infrared over electric. And I asked me what I'm doing here. I'm sorry I'm sorry I'm 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 buildings 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 engineer in aerospace. And long story short is I ended up taking over like his lab. And and me and him and his team started building aircraft in 2018 and 2019 down in University of Florida. And I temporary moved down there with my my daughter at the time and my wife living in Gainesville, Florida. Like and and it was great. Like we ended up building I ended up funding a lab. And I spent the next like year, year and a half basically like modeling and building electric e-v-tall aircraft. Holy shit. Yeah. And it was it was a great time in my life. And like the problem is there was no intersection of folks at new electric, new rotor craft or new airplanes. And I had no wind diagram of overlap. So there was nobody in the world that understood how this all stuff that works. So I had to go from scratch like learn it from first principles. And 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 built bigger aircraft. I took the company public within three years of starting it. We're about six billion dollar publicly traded company today. Yeah, designed basically like now 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 it was it was hard. Even going public was like probably one of the hardest experiences of my life. It was really why is that? We we went public through a SPAC process. So you know SPACs at time like four or five years ago, we're like all the rage. Okay. And I 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 a software. I had never done hardware before. So it was like hard to raise capital. And B, there was nobody funding like deep tech electric vertical takeoff. Like companies like, you know, I mean, the big V's venture capital groups were not funding SPACs or Tesla or Rivian. Like, nobody's were getting funded by traditional investors. They weren't raising money from the named investors we all know about now. Are they always behind like that? They're the mandate for most of these VCs in the Bay Area or Silicon Valley and stuff are not to do hardware. Gotcha. They don't really and they do hardware that do they don't do deep tech. They don't do like rockets and autonomous vehicles. And I don't think there's a single top VC in the US that's invested in a humanoid company. Like, no shit. And as of last last six months ago now, nothing like this. They just don't do this stuff. And so I end up going all. So I, you know, made just made $110 million and or just sold the company for $110 million. I made a lot of money personally. And I'm going all in on archer through the IPO, like through going public. I put like all the money I basically, I bought a house and the rest of the money went all into it. And it was a stressful period. So we went public through SPAC and the reason it was stuff is we end up getting a point where we just couldn't raise enough money privately. Like it was either like raise, you know, $100 million privately at like, you know, some valuation, $3, $400 or $500 million. Or is it, or is like go public and raise like a billion dollars. Wow. And we end up going public and raising a billion dollars. Wow. You've got a huge appetite for risk.
and we got sued during it. - Oh really? - Yeah, like we got sued by basically like Boeing and a big startup that was founded by Larry Page, Google Founder. And-- - That's gotta be intimidating. - Yeah, it was woke up to like a front page in New York Times article about-- - Oh shit. - Yeah, it was crazy. I mean the backstory is I took, so Larry Page started a company and in the Bay Area, about 10 years ago called Kitty Hawk. And they did a great work over like 10 years in electric like V-Tala aircraft. And I ended up taking basically the core, like the core 10 to 15 folks that were there all came over to Archer, like within the first two years. Wow. And they retaliated by just like trying to harass us. What are we going public? And so yeah, just a crazy story, a getting public, I ended up getting public, you know, billion dollars on the balance sheet and we just started building like aircraft. And started building the service, like thinking about the app and how you're gonna check in and how you're gonna build places like real estate to fly into. And yeah, and then like the engineering work we had that you're 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 have a, we tilt the front, the leading edge, six motors, 90 degrees for basically take off vertically and then go into four flight. And then all the propellers, fan blades on, have variable pitch propellers. So it's like a highly overactuated system that needs like a really good software. I know human can like fly it basically without really good control software. - What altitude is it flying? - About a few thousand feet. So about two to 3000 feet above ground level. - And that's what it would normally be? - Yeah, like traditional helicopters fly at these levels. - I mean, what I think about, I think a lot about Tesla and all the EV vehicles that are coming out and you know, it's, the government just seems so far behind on AI. You know, and you just brought up gridlock and all the cities. I've always wondered why aren't, why don't we gonna go full EV? I know there's a lot of pushback about that, you know, for an over-each standpoint. But if you just think about the traffic in the cities and if you have the AI processing all this, that 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 half or so installed cars, we make like 80 million or so cars a year in the world. I think she like, you know, on order of like 20 years to replace all the cars. So 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. - 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, and 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. - Yeah. - It might be more dangerous. - We're just in this transitory state right now where in like five years, like everything will be like fully autonomous and trusted and fine and you won't have to do that. And we're just in this transitory. So we're in this chapter in the book for the technology roadmap here. We're like, we're living through it and it's like a little messy. And it's not quite like straightforward. And we don't quite know where it's headed next, but where it's headed is at some point in like five whatever years where, you know, when our kids grow up, like they're never gonna have to think about this, it's just gonna be autonomous from a start. It's gonna be like, you know, by default native. And it'll be trusted and easy and safe. I think we're just like living through this period right now which is like a weird thing. But like, because our eyes long enough, you'll have this autonomy and gentrification everywhere. - How long do you think it'll be? - I mean, so I live in the Bay Area, like you can take Waymos now. I can take Waymos everywhere and it's unbelievable. - They're running all over over there. - They're everywhere, yeah, they're in my, I'm in South Bay. But they're in the city for a while. And now they're in, you know, they're in their Palo Alto, Milo Parque, like San Jose, like all of the place. They're really great. I take it, it's like a, my wife and I would go to dinner and stuff on the weekends we take Waymos. It's like, it's so fun. It's like, it's like, it's so, like, it sounds so basic. Like, you take Waymos, it's fine. It's just, it's awesome, man. Like, it's great. You have like, it's 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. (laughing) You know, I go, it's like so many like, whatever ubers and stuff and common car smells or it's dirty or whatever else. And it's just like, you know, this is, it's just easy. It's really cool. So, like, technology is like, like, in like the early chapters, but it's all here. Like, we're gonna have autonomy at scale, like everywhere. And it's just gonna take some time to roll that out. So, it's the time it takes to get the technology mature enough where they can run enough cities and 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 gonna 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. The thing is like, Tesla's got like, 10 million cars in the road and like, maybe there's thousands or so of like, waymos. You have like, over a billion cars on the planet. So, you need to like, like, like, you know, make a large fraction of that autonomous. So, you're looking at like, this isn't gonna happen any year or two. It's gonna take some time. - One of we're gonna see your vehicles. - They are craft. We have them now. We fly every week in California. The challenging part with archers that we are governed by the federal airspace. So, to fly passengers and charge money, we have to have like, basically, type certification from the FAA. That process moves the speed of like the post office. And the FAA is not incentivized to put anything in the air unless they know for sure it's gonna be really safe. The safety standard for us that we wanna certify to is one times 10 to the minus nine in terms of hours of reliability before a catastrophic event. So, that's one in a billion hours. - One in a billion hours. - Yeah. You can't be able to, that is like a, that's the standard when we fly. It's like one of the, it's the safest form of transportation we take. And it's because of those standards like, 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. So, that's like the, that's the long pull in the tent for us. And that's wherever you go. If you go to, you know, Europe, it's YASA, or CA in China, wherever you're gonna go, there's like, there's federal mandates to get busy in 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. And then we're also dual tracking out against a couple of different entities globally now to make sure we can get certified and get in there. But it'll, it'll happen, man, the aircraft, it just, again, we're like this chapter, we're like flying cars, electric, you know, aircraft, or just like it's early, it's earlier than like, A-Vs or EVs, Thomas vehicles, electric vehicles. Now, it'll happen, it'll happen in our lifetime. We'll be taking these things around. I mean, what are you envision? Let's fast forward 20, 30 years. What are you envision? What does it look like? Do we have roads? Do those get ripped up? Is what does the sky look like? Yeah. What does everyday life look like? Yeah. The really important thing to hear about the air spaces, is 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 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. So 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 just like this. It's just getting worse. It's like the arteries are hardening here and around this. It's getting worse and worse. And it's just like it's some of the worst time to spin on a road in traffic. It's like so soul-sucking. It's just like the worst. It's just like the worst time to lose. So the good news about the air is it's three-dimensional. You can stack like, basically an infinite amount of like say roads in the air. Different altitudes. Yes. Different altitudes. And even laterally. So you can basically build like little tunnels in the sky. And you can basically stack them. And you basically can put like orders and magnitudes more things in the air than you can in the road. It's the same for a blow ground with tunnels. So the future of travel in cities is blow ground in tunnels and above ground in the sky. And the Goring Company. Yeah, exactly. Just like dig tunnels. And it's great. The only problem with tunnels with the node
system on the ground with, like, say, we call them vertiports, but basically, like real estate for flying cars. As you can, let's say you had like, you know, 10 different, or even like, you know, five, you say 10 different vertiports inside of a city, places to like take off and land from. You can travel between any one of those routes. So opens up, like, you know, basically exponentially more places to go to. I can go to like any node on the system at any time. So you hold on. So you, 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 a coup sticks 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 like checking in and stuff like that. They'll be like, they'll be like in your neighborhood. And you'll like, you'll like, you'll like waymo there or walk or take a bike. And then, uh, and then you'll get on these and they will go to any node on the network. You can't do that with tunnels. Tunnels have to go to A to B. You can't like, you go from like, you can't jump to another tunnel downstream. Like, you know, I want to jump to another tunnel like a hundred meters down. Like, it doesn't happen in tunnels. You can do that with a sky. You can basically jump to any node on the network. It's exponentially more routes. So you can basically do with like less real state. And then you can basically stack as like, you know, orders of magnitude more traffic and humans in the sky. So my envision is that like, you're going to be for most, most trips that, you know, that you're traveling over 20 minutes. All that will move to the sky. And not only that, but you will, you will have us, you'll have like cities being re, like, um, being transitioned to a point where you can live well outside of cities and get to cities really fast. The reason we live in cities is because we're like, 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, or social creatures. We want to be there next other humans or some of us are. And so, uh, yeah. So anyways, uh, 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. You don't have to like travel different distances. You're going straight from A to B in most cases. So you're like, you're removing 10 or 20% of the top, basically 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. Uh, you can live like far outside of cities and get down to city center in under 30 minutes. Uh, point so with these meat personally owned or with these meat, this will be like an Uber service. It'll be like a new service. Okay. To get cost down, the year, you'll, you'll basically just like 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 at that time. You'll hit a button. It'll be on demand. You'll ride your bike over a walk. You'll get in one and it'll leave in seven minutes and then you're busy flying right down the 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 your design. The whole, electrification allows you to reduce the cost and the safety burden of all this. Wow. We have like, 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. None. You can lose a motor. You can lose a battery pack on on board and still fly safe without, without having this. And so like it just like from a safety, from a part count, from a cost, from a acoustic signature, it's not like helicopters are loud and very noisy and it's just a much better technology for this. Have you been in Monia? We haven't flown inside an inside of Arsia. I've also been inside of our aircraft. And we basically have professional, basically pilots at the company. Test pilots. Yeah, test pilots. And they do their career test spots 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. Everything's going electric and it's so crazy. Yeah, it's crazy. It works. 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 Arsia now is we've demonstrated the hard part is like being in the wrong, the hard part is like making sure you're in the right decade. No one like go do this and you find out it's like a 2040 event. You can't get out done. It's just like a waste of time. And so the good news for Arsia is we're in a sweet spot here where this is going to happen. Aircraft now work. We're certifying now with the, you know, government bodies like the FAA to make it happen. We have a good balance sheet with cash. It teams 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 the start of it, but yeah, hopeful to make the same work. Where we go next. Humanize. Let's yes. Let's do it. Yeah. How did this idea start? Yeah. So, you know, it's been like five or six years working on like a pretty crazy robotics work at Archer. And like the ultimate like meta problem in robotic spaces, 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 a 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. I mean, if we get dropped in a Mars today, we're going to build like coffee cups that we can hold and stairs and doors. And we're going to build the stuff again. And it's like the, it's like the human operating system, building things we can like use and operate in. That makes it like easy for our lives. And we built it around the form factor that we are. I mean, if we look differently, the world will look different. Our espresso machine would be like different looking. We might not even like espresso or coffee in this case. So we built this whole world around us. The holy grail for robotics is can you basically build a genopropos machine that can do what humans can? Which for me is like a humanoid robot. And a humanoid robot is just a robot that has like a human form. So has legs so we 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. We can manipulate objects and do things like, you know, grab a stuff, open open to these gummies and, you know, fold laundry and do real work. And then we have the right sensor 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. And, you know, heavy works on like, you know, kind of like aircraft or, you know, now five or six years, I, I thought it was a pretty possible to go build an electric humanoid robot. And electric is important for cost. And it's important for safety and it's important because the performance will be much greater. And at the time, even when the best humanoid robots that then was probably like the Boston Dynamics Atlas, it had like a hydraulic system. It was like really heavy and big and high torque and very leaky like the oils everywhere. And also didn't run very like maybe ran for 20 minutes on a single charge. So you need to kind of radically transform the hardware. And then you needed to figure out a way to build like an AI brain. The humanoid is so complex. It has, has like, let's call it like a like 40 degrees of freedom. A degree of freedom is like a joint. So like an elbows, a degree of freedom, you know, shoulders got three. A ball and socket has three like a pitch on roll. And our robot has about those clock 40 degrees of freedom in it. Each degree of freedom is a motor that can spend 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 position and keep moving, amount of states. It's the mathematically, it's 360 degrees, the power of 40 actuators. So there are more states in the robot than atoms in the universe. There's more positions the body can be in. No shit. By far, it's much greater number. Done the math a few times. Very confident in this, even though it just sounds ridiculous. So you just can't code your way out of this problem. Like how do you suppose to write code? Like, how's a human's post like right, like, you know, C++ or code to tell the robot at any given time stamp what to go do? Like if I want to grab this, like I need to move like my whole upper body and maybe lean over in a movie, my fingertips and my hand, like my, you know, my hand, my wrist and hand, getting positioned to grab this. Like it's a, it's an intractable problem for code. So I'd be you were saying earlier, I'm gonna butcher this, but it's updating the foot 200 times a second. Yeah, our controller is running for balance or whole controller. So we have a main computer is processing what to tell the joints to do. 200 maybe more than 200 times a second to make sure we can just balance and then we can like do do the task. It could be reaching over and grabbing this or balancing. If we run that too slow, we just like don't have enough feedback. Then we just fall over just like, yeah, we have to fully balance, you know, it's dynamic. So it's if you, if you, generally if you powered off mid run, it's gonna just fall down. It's like a four-legged dog or quadripeda robot where like any given point, she's like statically stable. So it makes it
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's moving like all of it becomes very complicated now. It's not just like moving my hand, it's like moving my whole body to get my hand in the right spot. So every joint, all those 40 joints have basically position encoder. So we know exactly what position the motor is at or even the case of the knee or this. And we have force, force sensing, force sensing on board. We have the ability to detect all the forces at that knee as seen. It could be really high when it's walking or it could be like, you know, it could be powered off and have no forces on the leg. All of that feedback is being sent to 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 in six, five, six thousand times a second to the motor control on board. And we do the motor control, the brain for all the motors is 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 control software that tells the rest of the whole body what to go do at every timestamp to keep balance. Starting something new isn't just hard, it's terrifying. Before I launch the Sean Ryan show, those what ifs were loud, what if nobody listens, what if this fails? Walking away from what's familiar to build your own thing takes real faith, but it ended up being one of the best decisions I've ever made. Whether you're starting a podcast or launching a store, it helps to have a partner like Shopify on your side to help ease those worries with their expertise and tools. 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It's time to turn those what ifs into with Shopify today. Sign up for your $1 per month trial at Shopify.com/srs. Go to Shopify.com/srs. That's Shopify.com/srs. So, getting back to your original thing is my bet, you know, we're like three and a half years old or something like that figure. I bet three and a half years ago in 2022 and I started the company was that this was possible now. And my view is like it was I don't over some 10 or 20 years of this will work. And so I basically started on this endeavor to go basically rebuild from the ground up, humanoid robots and AI software to try to see if you could make this work. At the time, there was no there was no good precedent. There was no precedent for showing that there was no AI that had ever worked on the human or robot in history. And there was no human or electric humanoid hardware that was remotely okay to show it would work. And there was no hands or is none of this stuff. Wow. So I actually had a lot of trouble even re so I had a lot of trouble early on even like getting people excited about this because they're like what the hell are you doing? And so I ended up having to like basically self fund a lot of it in the first I 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 of burn in month four. And but it was like I knew what to do. We built a 40% team in like as fast as possible. And I knew I had to spend up hardware and software. I mean, you know, the key characteristics of robotics, like electric motors, battery systems, you know, control software embedded systems and sensors. And then even with electric motors, we build actuators. They have like a rotor and stator and a gearbox and sensors electronics and wiring and connectors and multiple sensors inside of there. And then firmware lives on the like say the micro controller lives inside the on the motor control side. And then we have a thermal characteristic is hot. And then you got to 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 want to run on highway speeds. Okay. They love that. Like whether it's a generator, like something, you know, an appliance in your home or like a electric car, they want to like run at like highway speeds. They're designed to run at full RPM. So that's when they're the most efficient. Okay. When motors are stuck in that moving, but holding power and holding forces, they're, they're all it's a really bad point of the torque speed curve. So they're not they're not built well for this. So humanized use all his 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 like just just motors. And so we have whole teams in those areas, just in that one little area here doing like rotor design, electromagnetic design, satire design, gearbox design, sensor design, motor control design, like all of this inside the teams. It was a enormous lift to just get the team members there to do it. So we spent 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, two billion or so like now it's a much different story we have, you know, we ended up building figure one with our first generation robot and had that walking in under 12 months. So from when we incorporated from inception to wow. Yeah, from when we, we had basically incorporated the company in 2022, our goal was like, can we get a robot walking by itself and these dynamics in under 12 months and we did it. We did it with like two days left in the year. At the time, I think it was the fastest time in history for anybody to do this. And 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, and I think one thing we did before we kind of moved even to while designing Gen two is, I think it's probably like 2023 the time we did a demonstration where we basically wanted to put this cake up, cake up, inside this curic and run it. It was just on a very pretty simple like nothing crazy, but we had a, you know, curic machine, coffee cup and a cake up and we had to go grab the cake up, open, you know, open the curic, put it in, close it, run it, and then, you know, make coffee. And, you know, it sounds simple, but like 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 you to afford your own. Yeah, exactly. I mean, the dexterity on the hands of that thing is just to hand you the bag of gubbi bears. Yeah. Yeah. Yeah. Then can you do with neural nets on board? It's crazy. You're 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? No 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 know, I don't know. I think it's probably one of the first examples in the world to ever have shown that. And, and I, you know, this was like game on. This is like we have, let's go build a really good human on hardware. Let's make it cheap and really reliable. Let's make sure I 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. And the hardware doesn't need to change in the same exact hardware. Like human is only like new, I don't 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 of 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, the hot damn, this is going to, this is going to really work. This is going to be humanoid robots, hardware gets good. And then you're basically going to be, this is going to be a data play, to train neural networks to run on humanoid hardware.
and do what humans do. And then we launched, you know, we launched Figure Two, we did a lot, basically more work. We started on Ville and 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, a lot of day on low dishes and full laundry. We had Figure Two's at BNW last year that worked six months every single day. Every six months every single day. Every single day it worked, a tenar shift every day for six months. And we had, it was just, it was like the first time for us getting robots out of the real world doing real stuff. Like, 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 who 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 like, you know, quitting or not show up to work, but sometimes that does happen. So it's like, 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 basic body shop that basically builds like X3 and X5s. And January of 2020, was it 2025? We started building our first BNW X3s on the line. And I bought the first four that did that. They're at the, oh my campus now. It went to my house. And they didn't build the, obviously the whole car. 'Cause like, there's like, ton of parts, but like we built, we did a part of the whole process. And what's BNW's feedback? They're great. I mean, the BNW is like, if you go into like a car manufacturing company, they're like the best robot assist in the world. There's robots everywhere. There's like these giant 12 foot, Cucam manufacturing robot arms on the floor. They're bolstered 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, human can't hold it and pass it around. So you basically have machines that are building the car and then moving the car. So the whole body shop line is only automated end end. And it's like, not end end, but there's humans involved. But like the car is being like built by machines. And then there's machines everywhere. There'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 apart. These end effectors are size of my chair. They're switching them out in seconds. Wow. One or two seconds. They're doing it really fast. And then they're basically building a car with this. And so like BMW, like they're like, 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. What we're doing is I'm very complicated. But the car manufacturing is just like no joke. It looks specifically where the figure is doing. Yeah, there's a body shop line called the basically building the rear header. It's like the back plate. So the body shop, they basically build the car by putting basic sheet metal together, welding them onto the chassis. And then they basically build in the car around that. Like you ended 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 header on this fixture. So 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 10 hours full shift. And there's three different parts on. Those three parts go on. This thing rotates in this big giant Cooke a machine. Like robot arm goes in spot welds it and switches it, switches it out to another effecter and then grabs it and puts it down the line. So like 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? Are we gonna get our ass handed to us? You know, is it gonna be easy? Is it gonna be hard? And I think it was in the middle. I think we like, we got the robot to a great spot where it was brand every day was great. I think the biggest learning lesson we took away is we had, we really cared about if we could do that and could 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 bad and means like we need to rework our plan and go back to the office? Does it do it incredibly well and 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 like 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, they couldn't last a week in these kind of environments. And you know, wear and tear, like it's running like a, you know, 40 degrees of freedom motor every single day can they operate really well? I think from a hardware perspective, it did a plus job. I think from a software perspective, we did like a, I would say a B, B job, B plus. We, and that's mostly from like my perspective, like the architecture decisions I made to scale, about half the stack we had, like traditional, like code in heuristics end. So like the controller to walk was done by a C plus plus controller. It was done by code. The walking he saw today, we had back then was done in code. - Okay. - The rest of this, we had a bunch of other stuff in there were done by neural nets. And like some of the perception stacks, some like, you know, some of the parts around everything else. And you know, this was year and a half or so ago when we were first launching. And I was like, man, the biggest problems we're having is the coding parts get stuck. The robot like either doesn't see like the right, like doesn't like see something right on the part and misses the object detector. Doesn't really understand what's going on. The controller, when it gets out of bounds of like what it's ever seen before, like he have carpet in here now and it's like really squishy. The robot's doing fine, which is great. But I think our old controller would not do well. It's like he 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 that was like, we had a really difficult time seeing that even though it did well every day, seeing that scale to like lots of robots. So we went back to the office, this is about a year and a half ago. And so we need to basically refactor everything into a neural network. And one of the big, like, and I think we just announced Helix 2 or 3 months ago now, I forget like, in the last year and it's basically entirely down the stack including the controllers and a neural net now. It's there's like no code left really on the robot. There's some code in certain pieces, but mostly just almost all of the thing is like a neural net at this point. Remove the need for like almost like over almost over 100,000 lines of code at the when we launch Helix 2. And so what you saw today was just like a robot that can, you know, that we can put now back in to say the factory and these places that will run all the way on a neural net. And 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 like basically now in 24/7 shifts without stopping, without any faults for days and days. We just went over, yeah, we just had like record time this past week on the robot running until we saw like a faults like almost, yeah, basically a whole week. And they be sort of like they can run like four hours or so, five hours and we need to charge. And then the robot knows that steps in, steps behind the robot, say, and gets ready for work the robot then backs off, the robot swaps in spot like in the next like 10 seconds it's doing to work again. So we're gonna 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 hard. - What kind of stuff are they doing at the office? - We do a few things. We have a logistics use case that we run in 24/7 shifts. Cossily, we really like it. It's done with the neural net. It's moving packages around. And it's a really good use case. We like it and we wanna run them for months and have a failures. And we see failures right now. And most of it's in software. 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 greaders and visitor bots that walk around the office all day 24/7. So you're over the office, you're getting lunch or walk around or 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. - Greaders? - Yeah, they be seeing it. - How do they greet you? - Come talk to you. They'll just come talk to you. - Yeah, they'll come talk to you. And like you can go talk to it and ask it for things. We really wanted to go like, you know, at the end state for us is like it's gonna replace like somebody like me and the candidates that are interviewing there, taking them to the conference room, getting them water or coffee, like all of that end to end and whole experience.
Yeah, right now they're walking right now, they're walking in the office at night time, 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. And it's hard. And this is a new 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 tests them like crazy by running them all the time. What's the 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 like these conversational AI as you talk to you that don't know anything about you. It's like not much to talk about. It's like what's the weather like you ask like things about Wikipedia or something. It's like you know the way to work. It's just it's kind of nonsense. I kind of feel really stupid to me. So we've been working a lot on like deep memory. So it will actually get to know you. Oh yeah. Yeah. And he's a know who you are like who am I talking to is a Sean Brett. And then based on Sean like do you have their permissions to tell the robot to go go do something or not. Like if you're visiting no, I might be able to get coffee or water but like you want to have it like go do something new like won't do it. I have not even done that either. Yeah. Like we want to be able to come and do so. Yeah. What are the permissioning systems and authentication to the robots? I mean like you know like robots in my house. My kids are going to be like hey give me ice cream every every every 10 minutes and we can't have the robot doing that right. Yeah. Get over work and the kids are just like you know through pines of ice cream and the robots are just getting whatever they need like it just be chaos. Yeah. So what is a voice recognition? Yeah. You have to do voice for something. Something's voices are enough where 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. You have to do a higher level of authentication. How would you do that? Facial recognition. Okay. And then if you have a perhaps even a finger-priced scanning is possible too but facial is what you really want to do. So those are not all those systems are not like robust enough right now we're working through them and I think the goal is like to get it's like super robust but like you know 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 like hey man go make me like go make me like go make me food or like when I'm gone today do the laundry after you like after you like I'll know the dishwasher like you know do laundry and like my kids room today or something like that or text it. So like language is super important UI so we're like spending a lot of time on speech. You can text it too. Yeah every robot we have is 5G by default on board. We actually run 5G by default now so every robot off the line has 5G enabled. Like we have a T-Mobile 5G T-Mobile is on the best servers and every robot has an e-sim card for for T-Mobile 5G so it comes of a line we use 5G for all the main network. So if you want to like you know if our like if our systems want to tell the robot what to do or commit a new bus do something we do it through 5G and so yeah you can like you can text it. So you can 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. Yeah we can do that in right now but like that's the goal is like we got it to get there like we got to get to a point where like that is certainly possible and you 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 would even know that. Go check the mail have it on the counter when I do it like it won't feed the dog. Yeah everything like watch the dog make sure dog's okay. Yeah holy shit. So it's it's it's it's a nanny housekeeper gardener. It's the Jetsons all of it. Yeah it's going to 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 like optional in the future so you'll like you might like gardening you might like mowing the lawn you just like mowing lawn if you don't want mowing lawn like all this will be a choice holy shit. Yeah and you said it it's going to it'll download apps for different 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 connect do new things now like it's got my bank account now can you bank account stuff or you got a down you got a calculator can you calculate your stuff. You really want to treat the hardware like this where you basically similar to phone where you 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 a loading the dishwasher. Making coffee on a curate like all this like walking the dog like these are like like almost like the matrix where you get like plugged into a system that re uploads like weights into the like neural net weights in the robot where you 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 we train our helix neural net for a week and then we loaded 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 seven and do logistics work and package work. Wow. Nothing changes. Where's this going to go first? Consumers business. You'll ship into businesses first. It's the engineering complexity that we have the ship is like proportional to the variability that we see on site. So the variability at homes is like extremely high. It's like my home is chaos like kids are just like dismantling the house. It's like basically in real time. And then there's just like food or they're eating snacks toys like it's just like it's just chaos. And then like you know if we go to your house in my house we probably have like different appliances or different toasters and different microwaves all different everywhere we go. So the home is just like this like tons of entropy like tons of variability a wide distribution of tasks. It's like the it's like the ultimate like challenge for robotics in the home. That's like the hardest most variable thing we got we got going. And in the workforce it's like you have this like work cell that you're doing. So like if you do like manufacturing logistics or you know a lot of tasks you have like this area you're doing working and you can basically kind of write down on a piece of paper like how to do every step. And the home you can't do that. You can't write down a piece of paper. I can't write down a piece of paper like how I can interact your house. I mean you've seen it. Yeah. But like the next assembly line and the next like conveyor system like it's like I kind of know what to do. Package I flip it down and I need you to 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 be like highway driving for autonomous vehicles. That's just happened sooner because the variability is lower than in the city. Gotcha. So it'll happen first of scale and then the national thing has a good 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 see you can go near home and do everything. The longest pull from there is going to be safety. Like me and you feel safe like being like having this here with our kids. And that is that that's going to be the hardest challenge by far. And that's going to take some time. It's very there's some trust that needs to build is a track or going to need 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 be like 500 bucks a month. You're Carly's. You do 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. So I think the commercial workforce you can charge like 10 times more. So like so it's just like the commercial and then the commercial market for humanoid is like you know I mean half of GDP is human labor. Like maybe a little under half. So it's like three billion humans in the workforce is like contributes to like 40 something percent of GDP. Wow. So like you talk about the largest market in the world is sitting in the commercial workforce. Wow. So you have like 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. Yeah. 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 dollar company. And my answer for that is just like I just want to I want robots in the home. So 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. Not every home in 10 years but we will have pretty close we will have in 10 years. You 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. 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 become like the largest company in the world. And you can do that with maybe a hundred robots. No, shit. Yeah, 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. Maybe a hundred. Also, 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 is 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 like take the laundry, put it on my bed and we can fold it all. And then, so we're doing like little spots of it. And it's pretty good. And there's a lot more spots to go fill for like long highs and 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, don't wash my jeans. Like that type of thing and all these different like veritability that you might look 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, it'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 empty in the dishwasher, the cups go here, the plates go here, the silver wears 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. Is that how it works? Is it? Yeah, you're going to, you're going to, you're going to robot in a box. You open it up, robot get out. It'll start talking to you. It'll ask you to show you the house. And you'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'll tell it all that. Like you would, let's, let's, let's, let's say you'll say it, you'll say it a friend saying for two weeks at your house, that, you know, needed to like cook and use your stuff like, you know, you want 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, this is recycling here. This is where trash is at. Like here's, here's where you get water. Like the trash goes out every, you know, every Monday. You know, this is, you know, we do blankets on the couch, but like we want them in the cabinet when they're done, you know what I mean? Like, or we want these over folded and put it over here. Like all these things you have in your home that are you know important and and you know, just like you would like walk in somebody a human around for the first time. That's what you'll do. And the robot will semantically understand, like we'll a have like, we'll remember all of this and and it will like, it will learn based on what you want, your preferences, like what to go do. Yeah, it'll be that's, so it's just like trying to keep me. This is like not this is not 10 years. Well, this is this is really soon. Like I think in the next like, I mean, I'm hoping this year we can like drop over about your home and do a good amount of stuff. It's just, we'll see. I mean, this is like, this is like solving like the Holy Grail 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. It was 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. We know we 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 not like like move deeper into pre training for 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 seen internally the we think the right direction to make this work. When you were talking about, you know, trust in the robot with your kids, what what are I'm just curious, what are your concerns? Yeah, the archer. I've always I haven't thought about this. I think an archer was always like, I'll never like feel safe. I never feel comfortable like recommending archer like people to fly an archer and letting people fly an archer. Until I like would fly an archer across my kids. That's a level of safety we need to get to. It's like a really high bar. That's what you want that right to take a aircraft like that around. So I think the same thing for figure here is we'll be we'll be safe when or to me, it will be safe when I feel comfortable putting the robot around my kids. I have a one year old and four year old and seven, you know what I mean? I have a young kids. They're like, I want to jump on everything and you know, it's like they're like, yeah, in the robot like, you know, the robot needs to be extremely safe there. So that's another hurdle. It's like getting to general, like solving general purposeness, getting safety to work and then making enough of them. Those are kind of like the equations from here. We listen, we have a good plan. Only what to go do here. But now it's like execution that we got to go do to show it works. Right on. Yeah, it 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. 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[email protected]/discount/srs. Again, that's srs for 20% off @ronutrition.com/discount/srs. Want more from the Sean Ryan show? Join our Patreon today for more clips and exclusive content. You'll get an exclusive look behind the scenes where you can watch the guests interact with the team and explore the studio before every episode. Plus, unlock bonus content like our extra intel segments where we ask our guests additional questions. Our new srs on-site specials and access to an entire tactical training library you will not find anywhere else. In the best part, Patreon members can ask our guests questions directly. Your insights can help shape the show. Join us on Patreon now, support the mission and become part of the Sean Ryan show's story. All right, this is our figure three, humanoid robot. We actually unveiled it last year. It's about 130 pounds, 5 foot six. We've been busy designing it to do most things, like a lot of things humans do. 130 pounds. It's full laundry, do dishes, do manufacturing logistics. I think a few things here that we made improvements on. This is our third time, basically running through three generations of robots. We reduced the weight and mass. We made the robot skinnier, but also same strength and speeds. We upgraded the sensors on the robot at BCC through cameras. We have our BCR's fifth generation hands on board that have a camera, tactile sensors, BC improved grip. We also have on the robot, like BC 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. They all have kind of the squishy layer of foam on it. So, yeah, go ahead. So, if you, let's say somebody pushed it over, fell over, I mean, what's the durability of these? I mean, it depends how hard you push it, but like for the most part, we fall. Robot can get back up, just continue to do work. It depends how you fall. Sometimes we break next. Sometimes it's fine. All right, turn around. Another thing too is we basically, the robot is almost all 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 it? 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. Wow. And then basically the robot is basically left with basically 40.
40 joints. So all basic electric motors and motors have like basic tons of sensors on it for balancing and doing work. Alright turn around. We can walk with it for a minute. Alright let's do it. All this walking and all the robot movements are all done again through a neural net. There's no code helping this do this. Holy shit. You're gonna have some. You want one? What's that? You want one of these? I want a couple of them. A couple of them. Okay great. Dude. Whoa. Yeah. Let's go back this way. Let's turn around. Can it run? Let's see if I can go. We have run it. We have, I don't know what we're on, running remote, but let's go as fast as we can. We do draw with the robots outside. Really? I can't miss you. I think it's also like looks cool right? Yeah. High tops on. Looks awesome. Yeah. Did you say there's cameras in the hands? Yeah, cameras in the pumps. Right here in the palm. So we can see the fingertips when it's like grabbing objects and then every single fingertip has a tactile sensor inside. So we can be see touch like touch forces as we're grabbing objects. Can you shake my hand? I don't know. Maybe. And it squeezed my hand. There you go. There you go. Would crush my hand? No. It's like gonna 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. So that is wild. Yeah. This is the power button? Yeah, don't push that. Okay. We had somebody in the officer day. It was like I feel like I need to push this so like it's literally gonna 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 in 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 what would you do in a lot of time in the office? We'll step another robot in during the meantime. Wow. Yeah. We actually charge here inductively through the feet. So the feet have like we basically have like charging pads. We're about steps on two and we charge wirelessly. We can charge that 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 he's to stand a lot, which is standing on a mat. We designed it in house. It's like an iPhone charger. Yeah, I can charge about a kilowatt per foot. It's about two kilowatts that can charge. When is this going to be available to the consumer market? As soon as we like make it work really well. And so I can send into my house and my kids will ask for ice cream every single day. And yeah, so we're working on 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 have a BMW. We work with one of the largest logistics companies in the world. We work with Brookfield. They're one of the largest field state companies in the world. They have a giant portfolio of companies. And then we have like two more customers will be announcing the next 60 days. Congratulations. 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 like 90 minutes or so now. You can make one of these in 90 minutes. Yeah, we run the line. The line's running about every 90 minutes we make one. And then that'll greatly increase even the next like several months here. Wow. Yeah. Wow. Yeah. What do you I mean, it full capacity like? 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. We want to get to like a million units a year and you know like within this decade. A million units a year. Yeah. For sure. I mean, you sell like we're selling. 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, a style manufacturing. 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. What? Yeah. Yeah. There's a better balance than I do. Yeah. Same. Dude. That's crazy. Yeah. This is three and a half years. We had we had this walking in three years. So it started the company. It was crazy. You're like basically the week of year three. We were walking this thing at the office. This one like the thing is this is like a we're going to go through like the whole iPhone lineup where it's you know iPhone one iPhone two iPhone three just gets better and better. Yeah. And I think human is a lot of things like more radical steps between those. Every every year we're roughly building a robot every year. We'll just get like dramatically better than this. Damn. Yeah. Our step up from here even to the future robots will be I think perhaps the most dramatic step up we ever make. Yeah. Wild. You want to take some pictures with them? Let's do it. Dude. That is insane. That's awesome. I want one. You want one? Yeah. Let's get you on, man. Wow. Like so that in the sense that you said the hands can sense three grams of pressure. Yeah. We basically have fingers. I mean, 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 can be like something we're touching. And then within there every joint can kind of also feel sense and and track the position every like you know part of the hand. So the hands like. The hands are really good. Honestly, we're working on hands now for like close to four years. It'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 tease a couple of weeks ago that has like basically full. I think I think it's 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 curly are. And the hand now can like full laundry and you know. But we think it'll hit a point where it can outperform a human more dexterity in a hand than a human. I 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 a. Humans have a lot of degrees of freedom. We have like 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 that'd be a huge win. We you'd have robots everywhere. And you know and then we're going to still have a lot of trouble getting to a huge like full human range of motion. Like small things like you would reach inside of a you know a washer and you kind of like move your head as you're like getting in. Yeah. I was like some people get on like you know get down to the ground and kind of get in the washer to grab some of the back or so we do a lot of crazy stuff. Yeah that is. And you know so it's like like even like a 12 year old can kind of do like most things in a house you know I mean like and 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 they led my so Sam and OpenAI led my series B. I co-led my series B with Microsoft. That was a few years ago now so we raised about a little under 700 million in our series B. I was a second round of funding and they joined my board and then we ended up spending basically a year with them working on well I mean I'll give you the background. The goal was to like try to advance AI models for human order robots together and you know they're they have some like great folks that have worked on like LMS and chatbots and things and um in the time we had like a we you know we still do we had our made a full like AI team internally so we were basically working weekly daily on like basically how do we advance uh state-of-the-art kind of like language models for robotics and um you know like uh yeah I ended up firing them. I know. I year a year later but in splitting ways but like listen they're a great team. I like the senior leadership and everybody there same included like we're great to interact with. The issue lied for us is like um there's nobody's ever put like advanced like language models into these systems and made it we have to like produce like action output out of the robot and it's like a very different thing than like um
next token prediction for language models. We ended up finding that the team we had in place, my team lead, the folks we have here all from Google DeepMind, or certain areas of top AI programs, and they're really good. The team now we have is over 50 or so on the AI, our Heelix team internally. We just found that that team we had internally, we just ran circles around them like every day. We had a hard time getting, and robot is getting run the robot, see how it does. You know, like you have a one-of-one new AI experiment, or do some embellations, and some evals, you need to run the robot into the day and see how it does. Like Sim is one thing, you can get certain far-running simulations, and looking at lost curves and stuff, and in the day, do we need to see how the robot does? And we just had a hard time getting them in the office, we had a hard time basically advancing stuff together as a team. In it up, the strategy we had internally, and the team we had was just like, complete superstars. They're the best robot learning folks in the planet that sit a figure. And they got to a point where, you know, I got to call one day, it was just like, you know, we were like also, we were like showing them how we were doing all this work. And I got to call one day saying like, hey, we're like, you know, we've been watching your progress is unbelievable. And, you know, we're thinking about doing robotics work internally. And I was just like, this is over. Yeah, I just get out of here. Like, we're teaching you how to do robot learning. You're seeing our progress. We had like a couple of, you know, Sam and a couple of co-founders on site, one point right before this, and they saw it and they were like, wow, this was like, it was doing like this neural network on table, and they were just like, Jesus, this is amazing. 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 like got no value out of the whole relationship for a very little. I mean, it was helpful having them lead the route, fully the round. It was like, there was some, there was like some good brand association there, but like beyond like that, there wasn't much. So we ended up, you know, we're going to try our own territory. We're going to do AI ourselves here. It was also just became like a, 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, like on the AI team, and we bring candidates in and they'd be like, oh, you guys do the robot and open the ideas and models. And I'm like, oh, no, not really now. We like, like, holy AI team internally. We do model development here ourselves. You know, like we're advancing also ourselves and it just wasn't the perception from the outside. It 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 was a really helpful for us long term if we're going to be competitors. So we decided to split ways. I decided to split ways, but they have like great team. I think they're doing robotics now internally. Sounds like it. Yeah, exactly. Yeah, exactly. I was like, I got a call saying like, yeah, like, you know, partly like, partly I feel like I heard was like, we've made so much progress at figure and they've seen that that they were, you know, they opening it started out as a robotics program. They were trying to solve a GI through a lot of first three, four years. They were just like all in robots. If you Google like, open AI robotics, it's like old 2016, 2017, 2019, you know, maybe like, maybe like, 2019, 2020, something like that. They ended up pivoting into like large language models made 2021, something like this. But they're in robotics from I think 2016, 2017 for like many years, maybe three or four years, trying to solve like a GI through robotics. There's, you know, there's this other, we don't need to get into it, but like it's, you know, it's unclear if you need an embodiment or not or you know, at the time it was unclear, but you need an embodiment or not to like truly get to like above like peak human intelligence. And they had a hard time in there, 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, when I'm four years old yet, four years old and into May or something like that. Like, I, we 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 like, let alone like the real stuff it's doing now, like 24/7 commercial work in the home, like, it's neural net driven, like we can make them every 90 minutes that the, you know, when our lines are up, like it's just like, it's crazy. So yeah, we started to spark, part, we started to part ways. Man, I mean, I don't think there's too many people in the world that can say they fired the biggest AI company in the, in on earth. I mean, that's that's a ball zoo 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 a, it's just very surreal for me to, to unveil some of them. I mean, I know we didn't unveil this, but it's the first podcast it's ever been. Sean, I have not taken a robot to like a podcast, like, I get asked every week to do this is the first time. And, uh, like, love your show and want to get him here in Tennessee. Uh, this is the first time bots been out here to, something like this. Thank you. Yeah, it's, it's, it's, it's, it's, it's really cool to be able to do this, like, once in a lifetime opportunity type stuff. Thank you. What about military application? Yeah, we've, um, we decided not to do military stuff today. Um, and the, not to say like the robots won't be good in military or helpful or like, uh, my, my belief right now is like, it's just too difficult to, um, to do both, like the ship into the home, ship to like, you know, top fortune 100 companies in the US. Uh-huh. And then also put like, you know, like militarized robots, I think it's just too hard in one umbrella. Um, I think there's a huge opportunity like to save lives and help on the military side. I think it becomes like, you know, we, we do have, you know, we do have like a very advanced system here. The system can, you know, unlike a car if a car became sent to you, like, you know, you can like walk in your house, walk upstairs, go in your room. It's like not going to come. Jacea. 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. You've got to be very careful with it. Um, so things, because of some of that and some other things, uh, we like, we know, we've drawn a line here to say like, uh, you know, we want to stick with the, uh, you know, consumer market commercial market and go to hard and the paint with that. Um, I think there are, we'll be, again, credible opportunities for companies like to go into the military to be frank is 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, uh, going to close quarters and houses and, you know, um, yeah, that stuff is like extremely dangerous. Humanos be great at that stuff. Like opening doors and just making sure the house is, you know, cleared, like clear house, you know what I mean? I mean, I could see it for a whole ton of stuff. Yeah. I mean, not even just going on target, but centuries, gate guards. Yeah. I mean, roving patrols. I mean, all of it, totally just armed security. It's, it's, it's, wow. You know, I mean, you kind of have some of a, a, a, a charitable asset too. You can basically, I think you can make them relatively cheap, make a lot of them just put them out the work. Do you think you'll get into it in the future? I don't know. Um, as I now know, but like, um, you know, as a part of it that you'll, as 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 of like these major companies like coming to see the robots and saying, we're going to announce this relationship with figure and we're going to announce humanoid robots in our facilities. And 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 us, I'll make that announcement. I think it's fucking awesome. It's awesome. But like, I know, it's like, it is like, uh, you know, it's, and then that makes it that much harder than if we have like any military side of things. Why do you think there is a, is it, is it replacement of human jobs? I mean, Jack Dorsi just, I mean, he just let go. What? 10,000 people. Yeah. That like, almost half of his, half of his personnel because of AI. Yeah. And it's stock going up because I think, you know, I think it's probably because the robot is human like and can do human like work. So I think it's just scary for, you know, it's a scary thing that I like like do what humans can. Um, I think it's, you know, you have similar scaringness folks have around like digital AI and how that will like basically like, you know, manifest in the future. So I think it's a real thing. Like I think the robots can do human like work. And that will continue every year to do more and more human like work. So, but like that, you know, we just got to, we just want to be very careful about how we position this and what we do and, and also how we communicate it. Yeah. Yeah. What's next for the robots? We want to solve general robotics at figure. We want to, um, we think of ourselves truly as like, uh, like a, at the, at the frontier of like this robotics AI lab that needs to build common sense reasoning into the robot that can put it in every home. How do we, um, drop it into your
your home has never been and you can just communicate with it and give us our 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 to the commercial workforce. You should military, as you mentioned. There's like, there is a path to build a business doing that. But the biggest business in the world is if you solve like general purpose robotics where just through speech and talking to the robot, it feels like you have a human in a body suit. I can understand you, nod, go off and do things now after your task. That's the problem we want to solve at figure. That's like a large scale. It's like an AI lab problem at this point. We talk a lot about how we're trying to give AI a body here at figure. We have this embodiment. We need to put really sophisticated AI into it to be able to be able to command it. 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. It's a very, very difficult problem. At this point, it's largely constrained by getting the appropriate data into the network. That scale. I think if we get snapper 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, general. About figure. There's a lot of stuff going on with China and manufacturing a few other things. I think 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. It's only until people can come online and watch our stuff we put out. When people come to the office and experience it and see the robots and 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. It's crazy. It feels like we're living in the future. It just feels like we're living here. It's crazy at work. It's crazy it's working. We're now in the we now have line of sight to make this happen. Which is exciting in my perspective, super exciting. I think it's going to be super transformative for the world. I think what we're going to try to do the next year or two is try to get this out further at scale and get everybody to feel this more and more. You feel when you come to our office and you feel when you're next to the robots, but it's hard for the we're such early innings about this yet for take off that it's hard for the whole world to really feel this. If you've seen the robots interact with each other, what does that look like? Right now they communicate with each other when they need to. We have robots that are running these 24/7 shifts. When we're in a robot, it 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 docket before it's like, you know, 1% or something like that. It's at 10%. The other robot will get ready like to sub in. It will come walk over, sit right behind it. And then when the robot is ready and knows that this there, it will then back away and the robot will go into do operations and do work. The 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 to like, I basically like the hospital in our office. So they'll go to a certain place. When they get, when they get, when they know they're going to the hospital, we have another robot coming in to the main docs, just 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 really robust. We 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 could be hardware failures or software failures, whatever. Let's say it's a knee. Lose your knee. I can't, can't stand anymore. You know what I mean? You like you fall. Today it doesn't happen. We can lose a knee. We can hold it's position. We lose full, full comms of the knee. We can stiffen the joint and we can limp off. Hopefully. 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 lims off stage. Like off like the, you know, the main like line is on, heading to the hospital. It'll limp all the way there. While it's limp in there, another group from like the healthy part of the hospital will then come in and resub it in from the on the doc while the another one undocks while it just let losses knee to go and do work. All that's happening through robot communication levels. You can be like literally asleep. Let's just happening. We run them 24/7. It can be at three in the morning. And it will happen. It's, it's, it's, it's insane. This is happening like, I saw this in the last like few months. This is happening right now. This is not even like the future stuff. Future stuff is going to be robots building robots. We're designing robots. We 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 to work. You'll speed to them. Sometimes you won't need to do and those 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 human-wise in humans in the next 10 years. For sure. I can't even imagine what that's going to be like. It'd be weird. Do you think that they will bring? Do you think, 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 like, you know, like make shoes, make toys, like things like that. I don't, I don't think we want, I don't think we have the will to do this. I don't think we have to know how to do this as well as like some of the Asian manufacturing groups. When I'm over, I'm over, so I've like walked a lot of like the high volume consumer electronics lines and stuff overseas. Some of the most impressive things I've ever seen in my life. There's like, it's like, it's like you walk these lines and they're just shipping electrons like crazy and they have every line they have like this box of automation inside of it. I call a little tiny robot inside of there. It's moving some like whatever a phone enclosure or something like that. And it's doing it with an automated way and moving it around a little conveyor and it's moving to the next station. Maybe humans doing something and it's going down the lines going into a next station. It's got a robotic system in there. Completely customized and different way to solve. They have lines and lines in 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. 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 future is to be back flying cars. I want to bring back like humanoid robots. The stuff is like highly dynamic, very intelligent systems. Like the next generation, like manufacturing 2.0 stuff. So we're doing that right now in California on our campus. We have a fairly large campus in in the Bay Area and we manufacture right now like whenever 90 minutes or so. And that will continue to spin that up and then we'll put, you know, we'll talk about more about it. But we have like, we'll put more investment here into US manufacturing for the future. So we're going to design humanore. It's here. So these are all these are all manufactured. Manufacturers in California. Right on man. Yeah, man. They walk like they walk off the lines. They walk over. It's like, it's like, it's like, 90 days ago you come we're like making a little bit. But now we make like, there'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 burdens all OEL 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 okay. 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 gotten that process really dialed. I mean, dialed in. We still have issues, but like it's fairly dialed in. And so the robots come out, do a couple of our check and then they're done and just walk over. And at some point we'd love to get like for them to get inside their own box. And another one like get it ready to go and put it on a palette and we can just start shipping them out. So good. We'll get in its own box. For sure. And another one will throw it on the palette and ship it out. For sure. Yeah. That's not hard things though. Like these are like, uh, that's not that, you know what I mean? Yeah, she's just interesting. I don't know. I just like, I feel like it comes off the line, gets in its own box, gets loaded on by another robot and then shipped off. The scary thing for me is like, those are like very, like rigid body things like cardboard and like moving boxes and maybe using machines and stuff. Like those are like easy. The scary stuff a couple years ago was like laundry. That like little
moves. It's like I really never in the same spot. It's like when you touch it, it's like actually moving or we do like these like packages on this manufacturing conveyor system that like you grab it. It's literally moving white. 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 like really difficult for robotics because they're not like stationary when you touch them. So those are things that were like man that's going to be really tough to fold laundry and for with code it's been impossible. The reason you haven't seen like packages or just six and stuff, some of the stuff automated is because like these bags are just like heart they're compliant they're just tough you can't model them. And now we have like we put it all in a neural net. They basically instantly worked. When we were working with our we have a little justice customer working with. They was like soft packages and we signed them and like we want you to move these packages on the conveyor system. And we've put videos out about it and stuff. The first month we signed them inga who runs you know accounts was like we need to do 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 touching their their something hard inside some were squishy they're like there's tons of them going to move every three seconds we're going to find the barcode put it down and put it in the middle of the conveyor every three seconds of package. I was like 50 50 shots works and it's it's got to be with a neural net and we got a bunch of data trained in 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 like high veritability environments. That's like 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 replaning very fast as your move as these things are all moving it's doing that in real time. It's just like it just works deep deep learning just works on humanoid hardware. Yeah. Crazy crazy stuff. We've all seen it. The Department of War is operating in a world that's changing faster than ever. That's why so many guests on my show talk about the importance of continued innovation and technology in the military. But here's the problem working with the Department of War can be complex. For many companies the process isn't always transparent. It's hard to know who the right stakeholders are where the decisions are made or how funding actually moves. 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Crazy, 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. Can you give us the synopsis? Yeah. Back when I saw a bit of a battery I got to obsess about a few different areas of working on when we're flying cars. The macro environment turned extremely poor for school shootings. It went from like it's really hard to track but it went from like 30 to 40 events per year in the US like 300. That was over like a span of 10 years. It's also really hard to understand why. It's like another thing that we could spend time on but there's just like a 10X mostly in the US. You didn't really see this a lot internationally. We started looking at it. I basically started reading a bunch of research reports and other things. I stumbled upon this technology, this like basic technology and kind of like tear hurts radar. So basically like or sometimes it's also called a millimeter wave technology where it's basically a high frequency like it's basically like it's radio RF. It's radio frequencies. The basic data very high frequency. I can do two to three 400 gigahertz and it's basically like similar to you know when you're in airport and you go in there and you like hold your hands up like the LG system scan you. There are a couple of feet away. They can see like anything anything you have like you know if you have naive gone they pen whatever. I read a research report that showed in my goal is like if you want to put it in the 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. You can like change like there's like a regulation side of 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 like almost all of them are some kid bringing a gun in habitually it's like their uncle's gun and they bring it in the school like every day for like three months. They get in a fight at recess and they shoot something they shoot at the gun sometimes shoot somebody somebody shoot it. And that is majority of all thing all gun events. The ones where you see like a planned event that's like on like CNN where somebody's like coming in with a machine gun or an equipment is if 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 plan ones are very difficult and maybe impossible to stop. But the 90 some percent of all other shootings you can actually avoid I think you can avoid those meaning like you can prevent them by knowing if somebody has a gun on them. You can do it the old-fashioned way which is like metal detectors and all the stuff. But like it's just like not we don't want the kids to go into school like that. That's just like not I want my kids going up. So basically the reason why I got obsessed with like Terrahurt's imaging is you could basically do this at like 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 it's like it's like a three-dimensional camera image almost but it's done in like a radio frequency. You can like look at almost like an optical image. And the reason that's interesting is because like if it's basically people are bringing guns in eventually and you can scan them at entrances you're always coming into a few doors of the school. You're not going in anywhere anymore and the schools also have all procedures now for this like for this stuff. You basically can do like offset scanning at you know five or 10 whatever meters away. You can scan people as they're walking in passively like just like as like you know just walking in don't need to stop anybody and you can basically scan you know 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 like stop it will find it will find a gun in a backpack. Yeah no shit. Yeah so it's it's amazing it'll find a concealed weapon anywhere. There's like you know yes it's there's a practice. Yes you can find them in backpack. She can find them in waistbands and pockets. So the story is that 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 we develop this technology for standoff distance detection for that Iraq and Afghanistan war. It was funded by the US government. We worked on it for 10 years and when the war stopped like funding dropped to zero and we like we were done. We didn't work on it anymore. And I'm like oh sucks. And then I'm like okay well guess keep me posted if you know I've been saying ever works out and then there it turns in like oh you want to go see it. I'm like what do you mean see it. It gets 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. Walk down to the basement. It's like it's tarp over this machine. Took a tarp off. They had a guy with a man like a mannequin this and they're with a gun underneath a shirt like I don't know three or four meters away. It turned this machine on. It was built like 10 years ago. I had like a computer tower inside of it. And then it had like a little screen next to it. So start 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 in 2D, you could see in power. There's a bunch of other ways we can look at the data, but it's 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. And 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, you know, this was a big endeavor for me, like going from software and like, you know, deep tech, hardware. So basically you decided to put cover on hold and, 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 brand show gun, you 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 on this path and here and he kind of like, he 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 fans and the news and just like, kind of anybody can go in, you know what I mean? So like, how I was like shit, you know, I gotta 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. The OG team that built is with me now. - No way. - We put an office in Pasadena. That's the main offices 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 I'll go as well by end of year, we're made a testing in school. - Wow. - And we'll put in that figure campus first even. - Wow. - This is like an optical play, I can you see it? This is an AI play saying can you detect it now? There's 130,000 KTHY12 schools in the US. There's like 60 or 80 million KTHY12 students. It's huge. And but it's not just schools, it's stadiums and airports, hospitals, malls, any venue you can move to theaters. - I'm a last baby a year ago. Just like anybody can walk in the hospital. It just like, it doesn't matter. It won't check yet. It's just like scary. And so anyway, 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, we designed the whole system I saw seven years ago last year. But it was just too expensive. The 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. - $50,000. (laughs) - There's only a few groups in the world that could make them a design them. We co-design them, we worked on the design with them, made them a fabricate them. And we have them now in our office, they work. We use many different, we use a lot of chips, but they're really cheap. And that's important. So KTH12 will have 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 gonna ask. I mean, how are you gonna get this in school? A lot of schools won't do. They won't even hire a security guard. - Yeah. There are big budgets, both at the federal and municipal level. Like a lot of money that are going into school. Like schools are getting subsidized for it to put in a lot of stuff. They're putting in CCTVs, like cameras. They're putting in ballistic chalkboards, all kinds of stuff in the schools. There'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 materials, like the cost. - Wow. - It's just like that's needed to go big and make this really work well. - Let's talk about something that actually brings a lot of stress this time of year, banking. We're talking about products like MyPay, which lets you access up to $500 of your paycheck anytime and getting paid up to two days early with direct deposit. It's the cash bag card that helps you build credit history with your own money. Plus, when you get qualifying direct deposits, you get 1.5% cash back on eligible Chime card purchases. Beyond that, you're looking at a savings APY that's seven times higher than traditional banks and five-star customer service with real humans available 24/7. It just takes a few minutes to sign up head to chime.com/srs. That's chime.com/srs. - Chime is a financial technology company, not a bank. Banking services, a secured Chime Visa credit card, and my pay line of credit provided by the bank or bank NA or stride bank NA. My pay eligibility requirements apply and credit limit ranges $20 to $500. See chime.com/feasonfo advertised annual percentage yield with Chime Plus status only. Chime card on time payment history may have a positive impact on your credit score. Results may vary. See chime.com for details and applicable terms. (cow mooing) Now let's start the key-cooking phase. The key-cooking phase is a key-cooking phase. And the video is a key-cooking phase for the key-cooking phase. And the key-cooking phase is a key-cooking phase. The price of the key-cooking phase is $100. It's the most important key-cooking phase. It's a key-cooking phase. (cow mooing) I think we'll. Are you gonna put anything else into it? You know, there. 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, went to school there, but not at the time. You know, and so if they would have had some type of facial recognition on top of what you had, that's. Yeah. This person doesn't go here. This person has a gun. Yep. 100% We'll have cameras. Maybe some audio, like mics. Cameras will be really huge. Like you can really do a lot with like just RGB cameras and understand what's really going on. You also get a lot of semantic understanding because guns are like. They're hidden somewhere. They're concealed. People are not walking in with like handguns and shotguns like into school. They're like. They're in like in a waistband and 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. So 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, is 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 understanding 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. We 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, you know, 130,000 schools every year. I think we're finding what we're finding now is a very, very small percentage of them were 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 like a student that has a gun, they're going it. They're going to Juve. So we're also finding out we think a large percentage of, like 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, I could like put you know, in a man, like I could 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, we think there's like maybe tens of thousands, maybe hundreds of thousands of guns that are being brought in every year through the school.
Wow, we're finding, you're reporting thousands and you're seeing hundreds of shootings. So our view is that we think it's actually happening like, you know, as a percentage is low but as an absolute number, it's quite high. Yeah, so I'm excited about this. We, in some way, I write this prediction every end of every year for what will happen in the spaces on man, which is like flying cars, like robotics, like AI and weapon detection and stuff like that. And 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, it just, I think it hits in a really good way with a lot of folks, maybe especially parents. So everybody's worried, we're homeschooling. Yeah, because of the shit. That's a big reason why we're homeschooling. Yeah, I hear you, my wife and I, when we think about or 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, can't recover from that, you know what I mean? I mean, she's every school I go to, I'm like, man, like you guys gotta, let's just happen down the road. I had a good buddy like, had his house breaking in too. He's got family and stuff. It was like six months ago and he just told me on what I was talking about. 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 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 just like, we just never, we're never gonna be able to get it back. At the, at the, at the, at the, and 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 gonna prevent all of them. I think you can prevent a lot of them. And then if, 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 gotta bring a gun in here now. There's like real sophisticated AI. It's in all these schools that can catch it. Yeah. I think that's another big thing. You have that at like TSA, we can go and precheck. Yeah, it's a deterrent. So you have like that, but we can also find it. Find it. We can see underneath through backpacks and stuff. It happens at specialized, like radio frequencies. Happens at like, you know, 200 to 300 gigahertz, then it happens to get 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. So actually quite a hard technical feat. I, 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 it to do it 10 times higher. So it seems like in you look at archer, like shit man, that looks really complicated. Or like a figure. Cover is just like super niche area of folks that it haven't, like there's, the folks spending like in this space are like, they're doing like, they're doing work in weather and space. And they're not doing this for like shootings and like security and stuff. There's like, there is no industry for this. And luckily we have like the world's best tear hurts experts at cover that are working every day on this. And they're really passionate. They're probably not getting paid enough and they're just like super passionate about solving this problem. And so anyway, 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 like schools, like hope on the West Coast and maybe one or two. And we'll see how it goes. There's, you know, there's a more, how do we market this? Like what do we tell the students, like teach parents is 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 like, partially occluded on certain areas of the person or the weapon. And then we need like 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 like a just guns everywhere. And they're all, they're all bricked. You can't like, you actually shoot them. But we 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, and then detecting the response when I hit something. - Yeah, that's exactly what it's doing. It's like, it's basically shooting out a radio frequency. It's like a electromagnetic, it's like a little wave, a phone that goes out, that goes out. Very similar to how your Wi-Fi works in your home, or 5G, same type of concept. It's just on a higher, like different radio frequency level. But think about your Wi-Fi, and like you want to like, in order of like 20X or so, like the radio frequency level, it's like operating in a few gigahertz, like that, but we operate it 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, how long it took to come back, and we use beam form in a couple of our techniques to figure out what happened. But you're basically, it's same as like, traditional radar technology, but we can shoot out, comes back. It's not nice, it won't hurt you. Like it's perfectly fine to be around your Wi-Fi. 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 on 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. In the case of your chest, you have a lot of like water in your skin, and it'll somewhat attenuate in your chest. So then we'll get back an image from this, and we'll reconstruct it really fast. And we can reconstruct into like, somewhat of a 3D dimensional point cloud like you do a camera. So you can give us a, like almost like, look, like 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 be just be like the handle or the side of a gun in different places of it, but you can see it through materials. Like it could be backpacks, it could be clothing or jacket, but most guns are all in the waistband pockets or backpacks, which makes sense, right? You're not wearing it right on your neck on a outside or shirt or things like this. So that's where like most weapons are in our school. We've done a lot of, we have a, probably one of the best data scientists in the world that is obsessed with school shooting. And he puts up the best school shooting in a Linux. 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. Where would data, we find on where guns are at, what type of guns and weapons are there? There's, I think it was like 200 nice stabbings last year. - 200? - Yeah, it's so high and so dangerous. Like we're trying to, we can detect knives. Like there's, you know, vape pins, whatever, whatever. It's not a metallic thing. It's like, we can, it just 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 and sense of like, it's RF technology, like radio frequency technology. And you get back like an image. And we can use that image to, 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 on making sure we're accurate in those readings or not. If we're not, we're gonna cause havoc. And we're for right a lot of times we could basically start saving lives. And that's, I mean, there's a, on average, a one shooting every single day. And more than that, like there's, you know, there's over 300 or more so shootings roughly a year. If you look back less a couple of years. So 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 US. That's just that K through 12, not colleges. But that's about, that's not looking at the 130,000 K through 12 schools in the US. - I can't, you think this will be out in a couple of years. - Yeah, I think we'll get it out in a couple of years. We have a teamwork and day and night on this. We'll, you know, we'll probably, I'll probably increase funding into it this year, significant lean. And we'll, we'll take a bigger push in head count. Yeah, but like right now, well, like right now, all things are on like, can we get the first full system in a really stable spot that works? And we've had to do a lot to increase the field of view 'cause like schools are, you know, several meters wide, multiple doors, sometimes double doors to get in. Like 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. Real solid work. Let's talk about Hark. Let's do it. - Right. - Okay, so I mean, I think my pitch here is like, I've been, I've been working on like one of the hardest AI, I think humanoid AI is like one of the hardest.
hardest AI technologies and you plan it. It's just like an incredibly difficult problem that I've been my team and I have been working through day and night for the last four years. So it's like, okay, we wanna go like build like a crazy sci-fi future with like flying cars, AI humanoids, and then on my other half of my life, I'm like using like an AI chatbot, like a frontier lab, like Gemini or Chatchee Bt. And it's so stupid. It doesn't know me at all, doesn't remember anything I'm saying, I can't see what I'm doing. It can't use tools very well. I'll use the internet like really poorly. Can't even order me a sandwich if I need to one right now. And like, it doesn't feel very futuristic. It 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? Does it know any of this stuff? Meanwhile, I think like for me, like I was just been sitting here for three years singing 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 listen and speak. Now it's like a human, it can see the world, it can do, 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. We 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 to like, there's two things here that are extremely broken. One on the AI side, we have like extreme, we have like a lot of gaps to get to, to get to like, like extremely personalized AI intelligence. There's just like a lot of, there's like a lot of, like missed opportunity now, classic few years, I wanna have like a lot of gaps there. The second thing is we're like interacting with these AI systems to like old pre-AI computers. Like you're pre-intif your phone or your Mac or your computer, it's like they're all designed like 20 years ago. It'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. I'd be 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, I 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 and 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, Abidur on the team. I mean, design iPhone 15, 16, 17. So this is gonna blend in a device? Is it gonna 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. Like 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, - It'll hold you accountable. - Hold you accountable. - Yeah. - Yeah. - Yeah, we have, we've been, we have hardware in our lab. We have, we're gonna have models now, like stuff is crazy cool. And 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. - And we're self-funding it right now. - You're still funding this one too? - Yeah, I'm still funding it right now. And yeah, the team's great man. We're, 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. Yeah. - What are you most excited about? - I just wanna like wake up to, like I always like think about, I just wanna wake up to a world that's like, that I'm like excited and inspired. I just like, you know, I love doing this stuff. I could have retired like 10 years, 12 years ago, 15 years ago. Like I think I just wanna 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, how do you keep it all together? - I don't see you're innovating. - You just, the trick is just, - The trick is just to not sleep and always work. - I'm good at that. - You know what I mean? - You 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 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 really, I do like a few podcasts a year and not a much. I'm excited to come here because like I'd love to show and get the story out too. You're great at it. And so, you know, I just like protect my time and just like I go all in on these things like my, my kids and my work kids, you know what I mean? Like and so like, which are like, you know, these are my like, like, kind of like babies. Like 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 while, like just like not gonna spend the half a day with you on Saturday if you're in town and I'm a senior engineer. So 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, happy with like things are going to work. And you know what I mean? 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, and you know, I got billions behind it, make like going for it. The great teams that work like their asses off. Like teams that are, you know, here it came with. And it's great. And I like fired up to come every day and work to try to make this thing happen. And 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. So. - American Dream is still very much alive and well. - Yeah, that's fucking cool to see. - It's cool. I feel just internally grateful to had 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 just, I'm taking a, I'm probably up, 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 mean, you know, doing veteraries, like there's 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. - Make it work. I'm doing a pretty damn good job, I think. All 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 Unitry H1 malfunction that showed how violently a humanoid system can lose control. Plus longstanding research warnings that robots and homes can create privacy and security vulnerabilities in ongoing global debate over autonomous weapons. So as 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 to, it goes outside every day, if you're not scared. (laughing) (laughing) So I think like, the futures is, this future, it can be molded and morphed and it's what we wanna do with our time. And if we want a future full of robotics and stuff,
that can help us out and free us of our times and things like this. Like 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 human rights in the planet, it's just going to be such an magical and important thing for the world. Are we going to have like, you know, 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. But 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. Like I think in some way this AI stuff of like we're generating AI systems that can be embodied and can use computers. It's going to 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. So I think my view here is yes, there's like a lot of like really difficult things that could go wrong that perhaps could maybe will go wrong. But I think we need this. Just like we need cars and I think just like we need like, you know, a lot of things in life. Airplanes and things. I think these are like important technologies that really move society forward. So anyway, I happen to believe that this is like extremely important save lives and like I think increased prosperity across like 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, you know, like I said, it's going to be a really hard road. Yeah. I mean, it's just an incredible advancement that and you know, I know there's a lot of fear around AI. I have a lot of fear around AI. We're going to go through one way or another. And you know, 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 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 chatbot. Like I think yeah, if it's like there's a different path to go down from here that could be good or bad. I think my bets on high probability of really great. It was obviously always pathic and 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 like what we have to do at this point. Like this is not like something we can turn off. We have turn off the internet. Yeah. You're going to stop people from trying to build like systems that like make us more productive and do work. I don't think that's it's not happening. So like all we can do is basically do it the right way that has the best positive effect on the world. Yeah. You know, another thing that comes to my mind is what we're talking about interacting with the humanoids. People, you know, and I've had this discussion on other podcasts too, but people are going to look at that for advice, you know, relationship advice. And I mean, I think there's a, you know, a lot of important things are going to be talking to this thing too about advice, certain people. And I think that's a big fear of a lot of folks too. It's already happening with chat, you be doing all these other cloud and all these other things anyways. Who are they getting for advice from before that probably? I think you know what I mean? It's, it's, I think it's the caliber of person. Yeah, totally. But yeah, so he's been time with. Yeah. Yeah. Last question. What advice do you have for future founders? Yeah, a few things. I think I wish I could like maybe sit differently also like pass down like young Brett like 20 years ago. I think one is like, just go, just start building. I feel like a lot of folks get too caught up in this thing that's like going to be hard. It might not work. And you can just like, it's just so easy to start a company these days. So many great tools. Just go learn. I think there's 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 a little stares and climbing over and over throughout time. And so if I just wouldn't have started and wouldn't have moved like 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 like trying to ship a product or getting feedback from customers like I'm just getting I think I'm getting it's like a, it's like a, it's like a sports player getting better with more practice. And so I think the most important thing is just like just go. I also think the thing I learned a lot in my lifetime is like what you work on is really a defining moment for like for founders and it could be founders of any in an industry, tech, non-tack or whatever. You're generally going to go and just try to like have this like have his kid that needs a lot of attention. And then at some point it's like you just can't abandon this thing and you got to keep like spending more time with it. And it needs a lot. 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 when I'm working on the hardest most pernicious problems at the company. So you got to really love it. And it's not like you can be there for a year or two. It'd be there for sometimes a really long time. And even for a successful and even if you sell your company or whatever go public, you're getting your stock locked up or you're you're investing out over a minute periods of time. It's you're going to be in it for quite a while. And I find that for me, the things I work on as like probably the most important things I could be doing with my decision making. And that's happening at a micro level inside the company's whatever go 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. And 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 like a nonlinear effect here for like starting companies that are like easier versus harder meaning starting something that could be like a hundred times higher outcome is generally not a hundred times harder. So like doing figures not a hundred times harder than doing another robot companies probably like three times harder maybe five times harder. But the total noticeable market and opportunities probably millions of times bigger than another like robot that's like on a simply line moving back and forth. And so I think there's like this nonlinear effect to like decision making here that is pretty 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 in 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 mean into the right to equipment or people or personnel or whatever marketing to basically make you more successful. And then you're generally worth working inside of bigger dressable markets like Tams that you know potential acquires 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 like a hundred X or a thousand X they don't like a two X. And generally for venture like ninety ninety five percent 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 and and then I would try to be as ambitious as possible. There's capital for that and there's there's humans for that that want to work out really crazy shit. We have them at my companies and they're 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 their best in the world what they do. But they want to come here they want to try to do something like they've never been done before they don't want to go off and design the next car like you know or do the next AI product everybody else is doing they want to be here signing something revolutionary. So I think that's like somebody don't stress enough and I think last thing is like there's no rulebook for this which is like really unfair. And there's 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 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 like what to do and what's what successful means for building a team or hiring engineers or like executing a product it's very difficult and very few people in the world know how to do it really well consistently. And so I found over time it's been really hard for me to get the right advice. And 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 I've never had somebody for 20 years I could call and just like wish I'd do in this since since you Asian I never have had it. And I wish I had there's no book there's nobody to call. Yeah. And I think that makes it really hard. But
but it's possible. You can just 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. And you can tell the folks that want it. If I talk to people, I say, well, this is hard, that's 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. There is no failure. Those are the folks that do well here. And you can bend the world and you can basically can mold the future to how you kind of want to if you try hard enough. And the goal, the goal 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, been playing this now for 20 years, still playing it. I feel like I'm in the early, any and so my career now, I want to go ship at scale these systems. I haven't done that yet. We're like in any, we're bread in any one. Wow. And so like, but for everybody out there that's in that, I just think it's, I believe in you here. You can do it. It's a great advice, man. Cool. Well, Brad, fascinating interview. Love everything you're doing, man. Like incredible stuff, huge advancements. Shama, 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. [MUSIC PLAYING]. No matter where you're watching the Sean Ryan show from, if you get anything out of this at all, anything, please like, comment, and subscribe. And most importantly, share this everywhere you possibly can. And if you're feeling extra generous, head to Apple Podcasts and Spotify and leave us a review.
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