Shawn Ryan Show - #292 Brett Adcock - Shawn Ryan Meets a Humanoid Robot

Episode Date: March 30, 2026

Brett Adcock is a technology entrepreneur focused on building companies in robotics, artificial intelligence, and aerospace. Born and raised on a third-generation farm in central Illinois, he developed an early fascination with technology and building systems from the ground up. After attending the University of Florida, he set out to tackle ambitious, capital-intensive industries with the goal of reshaping transportation, labor, and human-machine collaboration. At 26, Adcock founded Vettery, an AI-powered talent marketplace that matched thousands of companies with highly qualified candidates. The company scaled rapidly and was acquired in 2018 for $110 million by The Adecco Group, the world’s largest recruiting firm. In 2018, he founded Archer Aviation to develop electric vertical takeoff and landing (eVTOL) aircraft aimed at transforming urban air mobility. During his time leading the company, Adcock helped architect, engineer, and flight-test five generations of aircraft, vertically integrating key technologies including flight software, electric motors, actuation systems, and battery systems. Archer secured a $1.5 billion partnership with United Airlines and positioned itself at the forefront of next-generation aviation. In 2022, Adcock founded Figure, where he serves as Founder & CEO. Figure is building general-purpose humanoid robots designed to address global labor shortages and work alongside humans in manufacturing, logistics, warehousing, retail, and the home. Backed by leading investors including Andreessen Horowitz and Sequoia Capital, the company has raised billions in venture capital and is focused on deploying embodied AI systems at scale. He is also the founder of Cover (2023–present), an AI security company developing non-intrusive scanners in partnership with NASA’s Jet Propulsion Laboratory. The technology is designed to passively detect concealed weapons in crowded environments, with the goal of improving public safety without invasive screening. Follow the market: https://polymarket.com/event/ai-bubble-burst-by Shawn Ryan Show Sponsors: SpotOn GPS Fence — trusted by Shawn Ryan for his dog Stanley. The most reliable GPS dog fence: 100% secure from backyard to backcountry with virtual boundaries you control from your phone. No wires, no digging. Sets up in minutes, any size, any shape, anywhere. Learn more: https://spotonfence.com/srs Sign up for your $1 per month trial today at https://shopify.com/srs Get 20% off Rho Nutrition Liposomal NAD+ for clean, sustained energy and sharper focus with code SRS at https://rhonutrition.com/discount/SRS risk-free 60-day money-back guarantee. If you're serious about selling to the Department of War, go to https://SBIRAdvisors.com and mention Shawn Ryan for your first month free. Brett Adcock Links: X - https://x.com/adcock_brett IG - https://www.instagram.com/brett_adcock WEB - https://www.brettadcock.com LI - https://www.linkedin.com/in/brettadcock Learn more about your ad choices. Visit podcastchoices.com/adchoices

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When West Jet first took flight in 1996, the vibes were a bit different. People thought denim on denim was peak fashion, inline skates were everywhere, and two out of three women rocked, the Rachel. While those things stayed in the 90s, one thing that hasn't is that fuzzy feeling you get when WestJet welcomes you on board. Here's to West Jetting since 96. Travel back in time with us and actually travel with us at westjet.com slash 30 years. Brett Adcock, welcome to the show.

Thanks for having me on. I've been looking forward to this for a long time. The robotics guy. Yeah. Let me give you an intro here real quick before we get started. Brett Adcock, a serial entrepreneur and founder and CEO of Figure AI, building general purpose humanoid robots for labor automation. Founded Vetteri in AI-driven talent marketplace, which was acquired for approximately $100 million.

Co-founder of Archer Aviation, developing electric, vertical takeoff and land. landing EVTOL aircraft. Found cover an AI security company using NASA Jet Propulsion Laboratory Technology to detect concealed weapons in K-12 schools. That's amazing. In late 2025, you launched Hark, a new AI lab self-funded with $100 million to build, which you call human-centric AI. You've raised billions in venture capital and time named you one of the 100 most influential

people in AI in 2024, married and a father of three children. And before we get too far into it, we always start off with a gift. Thank you. He didn't give you any tips on that, did he? All right. I got to, I got to hit one. What do you think? You can leave this guy here if you want to. This guy, this guy's staying. That is the coolest thing I've ever seen as far as giving somebody a gift on this show. That was awesome. Yeah. That was awesome.

I got you another gift. Oh. I love gifts. Keep here, put on the shelves. Thank you. Yeah. No way. Yeah.

A little robot. That is awesome. Thank you. Yeah, no problem. Very cool. Well, Brett, we've got a lot to talk about here. So, man, how many companies are you running now? Yeah, man, I'm not, I'm not sleeping.

I got too many. I'll bet. Too many. Just like kids and work and just like, yeah. That thing is amazing. Never sleeping anymore. I'll bet. I'll bet.

Yeah, what did you think of the robot? I think it's incredible. I can't wait to talk more about it. So 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. And it's quite the community.

And they're honestly the reason that I get to sit here with you today. So they get the opportunity to ask, single guest a question this is from stephen casey in today's marketplace we find that a i platforms can sometimes invent answers rather than admitting to look 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 are popping out here when we...

Definitely not. This work, right? I mean, I think, like, we were chatting about this outside. Like, you know, I think one thing to say, like, four years ago when I started the company, there was, like, no path for humanoid robots, like, to make into, like, people's homes in the next, like, 10 years. There was, there's no good story. There was, you had, like, big hydraulic humanoids out there.

They were all, like, hand-coded to do certain tasks. what you really need is like a cheaper electric humanoid that like you basically can like use neural nets, like use like basically an AI first strategy with there was there's just none of that existed. I think we're like we're thankful now like looking back like that we like it feels like we somehow pulled like 10 years of the future forward. We have like electric humanoid that like a reasonably price that can do like a useful human work with neural nets.

And it's just like I think it's just an incredible it's an incredible place to be in. giving those questions, which is like, how do we make this work now at scale in a safe way? Because, you know, that's the spot we want to be in, not like trying to make this work for 20 years. Yeah. So I think it's a very, I mean, this is a very, very tough problem. We have to get the product cheap enough. We have to make enough of them. We have to make it like, but the performance work in like very complicated things, like walk around a house and, like, do dishes, like laundry, like very complex things.

like small kids can't do this. Like it takes adults to kind of do like this level of work. And we need that all done in a mechanical system that doesn't have any humans around for maybe most of this that does it autonomously and not makes any mistakes. And then like like your fan mentioned, like we have to do it safely over time. It's just, man, it's just like incredibly complex problem. I think for us like we have a safety strategy both intrinsically. We want the robot hardware and the robots around humans to just be safe all times.

And separately we have a, there's a bunch of things. like semantic safety and other things that we need, like, we have either put in place or putting it in place now to make the robot just like work safe in the environment. Like 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. Or there's a boiling pot of water, like making sure we're like very safe around it. And then there's like the intrinsic safety of like making sure like this mechanical thing in your house is like safe around everybody here around it.

I think the direct answers is like 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. You have one in your house? We've had many robots now throughout my house in testing for last like a year or so. And I've had it like, you know, kind of near my kids

in some aspects, but we're always like monitoring it. What are your kids like? Man, they like, it's just like kind of normal for them now. Did they try to talk to them or like? Yeah, I talk to it. Yeah, they want to like, they want to go, They want to go like jump on it and touch it. And, you know, I wouldn't do kids' things.

You know what I mean? Like, they want to go touch it and talk to it and be around it. And we're still not at that stage yet where I feel comfortable enough to be like let loose and say, here, you know, here's a robot that 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, you know, daily, sometimes every other day. And, you know, the kids are kind of at school or sometimes at home. So not always, they weren't always around whenever the robots were 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. Like emotionally attached? They all had different names for the robot.

And yeah, they love it. And it's actually a question where I see an office of like, you have a robot in the home and it's like, it's got some like character to a little wear and tear. Do you like want to keep that robot or do you want to like a robot? or do you want like a new one? That's what I'm wondering. Yeah. What's the emotional attachment? I think kids are like the perfect test case.

My kids wanted it. My kids wanted it there. We're not getting rid of this guy? Yeah. He's got a little banged up a little bit here and there and has a tear here and they just like loved it. That is, that's wild, man. Yeah. That is wild.

Honestly, 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. 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 are even like knocking 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. 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,

uh, problems scaling and commercializing and getting the system reliable. manufacturing problems. Like we we have a problem fun house if you want to come by campus here 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, Polly Market says there's going to 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 not. Like the I think, I think, 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 very scraped. We're barely scratching the surface. I'm watching AI in a human body do human work early. It's early. We don't have, you know, we don't, we, at some point here this year we'll have thousands of robots.

We have hundreds now, like, we need millions of robots to make an impact. That's just going to take some time and it's going to 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 our lifetimes. And then separately, we have AI now that can use computers like humans and can think. I've shown you a little bit of that here before the show. And that will manifest in a point where, like, you know,

both in the physical and digital world, you basically have these little mini-humans that can do human-like work and they can think and use computers and use machines. And I mean, that's going to lead to such a productivity. Like we measure GDP per capita, like per human, but if you're able to make 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 in productivity we've ever seen in our lifetime and ultimately, like, reduce goods and service prices 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, like, I woke up today. I was like, I'm on the dishwasher, getting my kids breakfast, like, just like busy work. Like, my kids are sitting there. I'm like doing work. You know what I mean? Like, I wish I was just like, yeah, I just,

I wish I wasn't doing that stuff. And then I'm like, I'll think, about my day, I'm like trying to call, you know, call the car service and then trying to get on my flight and, you know, coming here and it's like order and lunch, like all the stuff I'm doing all day. And I don't want to do any of that. I don't be like fully free. I get it. All that burden. No, I totally. I love it. And I just want to be like clearheaded. 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 us tax bill and like do this meeting and I have to go back and do an engineering

stand up. I want all that stuff to be. in my, like, 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 this. You're basically exporting your brain and all the tasks that are going on in your brain. You're disseminating it to robots. I'm going to delegate all this out to robots. 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'll, like, 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. Just the bullshit legwork. Yeah. I don't like know. Is anybody want to do that?

Like, I don't want to do it. I don't. I don't. Yeah. So like, you clear all that for my life. Like I got to spend time with my kids, like enjoy life. Like kind of be like, I guess like clearheaded. 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 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 going to take everything over. It's a compression algorithm. Like, we're basically running a large scale compression. So like, I think, you know, my, my, my, my, my, the way I look at it now is, we basically have built a like synthetic human intelligence that can use computers and machines. So like I'm going to delegate out

all this busy work on both my digital life and physical life to like 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 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 have it do things. Yeah, you had a chicken salad ordered to deliver to your office. Yeah, exactly. Exactly.

But no, like 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 robotics. Both in the commercial workforce and the billions, like manufacturing and health care and construction and every human at some point will 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 that will, like, we'll know you.

Wild. Yeah, it's like the, yeah, it's gonna happen now, which is like really gonna be fun. Yeah, yeah. I'm excited to introduce to you the newest member of my family. We call him 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,

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Go to spot on fence.com slash SRS and use code SRS for $50 off the Nova caller. That's spot on fence.com slash SRS and use code SRS for $50 off. Well, I would like to do a little bit of a live story on you if that's, 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. Called Mouiqua, Illinois. That's even smaller than where I grew up. Yeah, where'd you grow up? I grew up in small town, Chilacothe, Missouri. How small?

About 8,000 people at the time. Yeah. Yeah, we didn't have anything. 700 people. Man, what were you into? Yeah, so, you know, kids. sports, computers, like, I got into computers really early, did a bunch of sports. You know, I grew up on a farm, so it was corn and soybeans.

My family was third generation of this, so. No kidding. Yeah. Yeah. Third generation, three generations of farmers. Three generation of agriculture farming. And then we switch over to AI. Yeah.

And we're doing like humanoid robots now and AI systems. But yeah, no, I got really interested in computers, like, really young. Started a bunch of, like, startups and, like, in, like, you know, in high school and college. What kind of startups? You know, at first, just, like, mostly things on the web, like, selling things, did a bunch of, like, different types of, like, products I was selling on the Internet for, like, throughout, like, high school and college. Small, like, drop shipping, retail electronics, like, all kinds of, all kinds of things.

Legion marketing and just fun stuff. I was like, nothing serious, you know, just like, playing around the internet, trying to make some money. I didn't grow up with money. So it was like, internet was a way to like, you know, maybe make some money. Like it was really fun. You know, I loved 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, I mean, what, is he a farmer? Colby? No, he actually runs an AI defense company called Scout. No kidding. 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. Are you serious? Yeah, we grew up together really close. We went to same college. 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. I see him almost every weekend. He has a startup like basically 10 minutes away from, from mine of, you know, where I'm at now. And he's doing great. Man, what, I mean, what do your parents think when you're coming home with what you guys are involved in and what you're creating? I mean, such a wild, you know what I mean, from farmer to this.

Honestly, like, I think one thing my parents both drilled into, I think both of us, 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 going to get paid. So early on, he's like, listen, if you want to control your destiny and if you, you know, if you want to make money and, you know, like be able to actually, you know, like do what you really want in life.

You need to, like, run your own business. And that was like beating to our heads, like, growing up. Like, you know, at some point you need to, you know, you need to get probably get out of here, you're gonna farm mean, it's not doing well. And you need to start something on 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, 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 a point where it's like, you know, even at Archer, 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 building 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. I'm like very like, big into science and mathematics.

Like, you know, I'm like, I'm like 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 in software for like 10 years. I just like always sat there every day, like wishing I was working on hardware. Stuff I could like touch with 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 envied things that you can go touch and build. Wow. Basically like Adams. Man. So where do you go? Where did you go to school? I went to University of Florida. University of Florida.

Yep. Where do you go from there? So after school, I don't know. 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 Vetteri. And the goal was to basically build like a, I got really kind of going through college is like, you got to look for a job. You had to go find something full time and got caught up in like the whole interviewing process of like looking for job.

I just thought it 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, somewhat 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, it wasn't very much a meritocracy. And I just thought the whole process was extremely broken. And so I started Vetteries.

We were basically in an AI recruiting marketplace. So the goal was, like, if we can get all the world's talent and hiring on one platform, understand their needs really well, can we make matches at scale, like, without, like, any humans involved? And, like, the head of the industry is, like, hundreds of billions of dollars a year. I won't even...

I won't use it. No, like, I know. Because I just keep here and everybody gets ripped off. Ripped off. It's so expensive. Like, pay, like, $50,000 a hire. It's, like, insane. And then they'll coax the guy out

that they just brought to you and have them go to another job. I was like, I'm not doing it. They'll be, like, they'll force you in this role so they get paid a commission. So Vetteri is a connector. Yeah, connector. Well, funny enough, we ended up selling to the world's largest recruiting company that does staffing. But like, let's leave that for a minute. But we basically started in 2012. 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 to use algorithms. At a time we were like, let's use AI. But it was basically like how to use a lot of algorithms to figure out like what people want and then make matches. So you can just push up a button, connect the right folks. And then, you can just push up 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 banks or startups or tech companies basically looking for talent. We started just in tech in the US. So at one point we had about, I think about a little under 20 or so cities globally that we were operating in. Wow. But most of it was tech talent, tech space is, you know,

at that point. How long ago was this? Started in 2012 and then ended up selling the business in 2017 or 2018. So about five years, six years. Right on. Yeah. Then where do we go?

Okay. So, so, um, veteran was like a really tough. I like basically went like, I didn't have much money, went like fully all in the business. Uh, went into debt at one point in 2015. The business was having a tough time. And then, um, we end up selling, end up things, end up going, doing really well. The business like completely hockey sticked and growth when we got all the things figured out and just like, Vetteri?

Betterie was. And then, um, and, um, and, um, ended up getting approached by the world's largest recruiting company, the same groups you, like, you and I were talking about, like, the same groups we were trying to take out of 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 the point of almost seven years in. And, you know, we were excited about an acquisition a year before that, $10 million from one of the big tech companies.

and they came in at $110 million. And it was a good time for me. I felt like the business was doing well. I learned a lot and I was kind of ready for my next chapter. So we ended up selling that business to the Deco Group. It's like the world's largest recruiter and recruiting company. But you didn't even have it for sale. They just approached you.

Yeah. We didn't hire a bank or anything. Listen, at the time we were doing like, I don't know, 20, 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 it's like you know what I mean and then managed all that processes so we were like the growth was just unbelievable and um and there's like there's something better to like a human jamming you in roles right like it's just like you need like and then to extent you can get you know all the world's like talent there and all the world's companies looking you can really create an amazing environment

where you can get people to like the right jobs and right now it's not like that it's like a really black box, like trying to, both finding talent and looking for a job. It's just a terrible experience. So we kind of, that clicked. Yeah, the world's largest recruiting company came in and said, we got to buy this thing. And, um... Yeah, I'll bet they did. Yeah, I sold the business.

And, um, and it was great. It was a good time for me. I really, at the point, was a point of my life where I really wanted to do something much bigger. Um, and, uh, so I took about, I basically took about a year. And, um, so it took about a year of the time I got the term sheet to sale, uh, to when we actually sold and close. 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 been interested in robotics and aviation and some areas of security for like basically since college. And I basically spent a lot of time trying to figure out if I was either going to work on. At the time, school, shooting is like basically 10xed and I was like man there's got to be something to do here and we can you know and and then secondly I really wanted to work on like flying cars like having a lot's less sci-fi as a kid is like man like I really want to go there's a there's a near term problem of like we got to go help with 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 a decision to work on

flying cars at the time so in 2018 shortly after the sale of veterinary or Archer Aviation. And basically, like, the story here is, um, 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. Like, think about like a normal, like a helicopter can take off from a building or a helipad or an airport.

And, uh, you take off, 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, be like 70% of the world. And you just, like, can't get around. Like, it's just gridlocked everywhere in major cities. It just, like, sucks to go, like, 20, 30 miles.

It takes, like, an hour in most cases. So basically, you can design an aircraft that can take off vertically and then fly, like, an airplane. So you can get, like, a lot of distance. 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.

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 an Uber or driving that might take you an hour in LA or SF or New York, you basically can fly there in 10 minutes. 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 as cheap as an Uber. But the problem was like I didn't know 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 do that and run engineering and run, run the company. And there was, like, a lot of trial and error. And I just felt like, I just felt like. I just felt like I could learn it. I started in industrial and system engineering at University of Florida. And then, you know, ran engineering and ran the company at Vetteri. So I basically just hit the books.

I try to learn as much as possible about three subject areas. First was like electrification, which at the time, like electric vehicles were like really doing well. And even drones. Vertical takeoff and landing, like vertical lift, which is like traditional like rotorcraft or helicopter. And the third is like winged aircraft like airplanes. You really need wings. So you basically have to learn about those three subjects. So I basically bought my basement downstairs at home

where there's like every possible book on these subjects you could match in and started reading as much as I possibly could. This was during the year transition. I was transitioning like out of veterry into archer. I was reading every possible thing. And then I found a small community of folks that were hosting on-site, like either half-week or week-long courses for this.

And so I would go to these sometimes responsible by NASA or by colleges or whatever it would be on like basically rotorcraft 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 do you actually like how would you actually build an electric aircraft? So electric aircraft, which interesting is like in rotorcraft like you basically want to make

to create the most efficient lifting device possible. You need as much 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 that to be really large. That'll reduce power and get you up the ground. In electric aircraft, the problem you're starting with is you have like 1.30th of the energy as you do in kerosene in a battery pack. So you're off the bat, you have 1.30th less range or 1.30th less energy.

And so power becomes like the dominating factor of like, of how to basically 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, one, it could be good for power, but it's also bad because you have like no redundancy in the system. You have like one rotor blade.

If it didn't go, doesn't go well, you go down. With electrification, you can basically build much smaller, like basically rotors and be able to fully electric. And the reason you can't do that with like 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. Gotcha.

The efficiency just drops, like, to nothing. So with electrification, you can, you can size down electric motors to small sizes and they're still 90% efficient. So, like, small electric motor on the table or a big one that 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, the rotors are underneath? Are they actually 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. Like so how do you design it?

That's the problem I hit in 2018 was how do you actually do this? And so it basically was like a crazy man trying to design this algorithm to like what is the ideal aircraft design? And then how do I go build it? So it was actually at a I was at a high air. Regency Hotel in Atlanta in 2018. It was an electric propulsion week-long design course and like aerodynamics course for winged aircraft. And I met a guy there that was basically in the engineering department

at University of Florida. He was doing his PhD in aerospace and asked him what he was doing there. And he's like, I'm from University of Florida. I was like, oh, I went to school there as well. And he's like, I'm like, what are you doing here? He's like, oh, I want to go like do a career in EVTile aircraft. and it's called electric vertical takeoff and landing. So, you know, a helicopter is a VTOL.

And you put a little E in front, it's over electric. And he's like, he asked me what I'm doing here. I was like, oh, I'm sorry to accompany him to do this and I need to figure out how to go build these things. And he's like, well, listen, my professor runs a small drone lab. He's got a full building. He's got 12 PhDs. Why don't you come down and, like, meet him and see if you can start building aircraft

with him? So I flew down that weekend to go meet his professor that runs all of basically mechanical and general. engineering and aerospace. And a long story short is I ended up taking over his, like, his lab. And, and me and him and his team started building an aircraft in 2018 and 2019 down to University of Florida. And I temporarily moved down there with my daughter at the time and my wife, living in Gainesville, Florida. And it was great. Like, we ended up building, I ended up funding a lab right off of Archer Road, a new lab, because we needed more space. We ended up calling

the business Archer Aviation, it was the main road down of University of Florida. And I spent the next like year, year and a half, basically like modeling and building electric EVTEL 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 that knew electric, new rotorcraft or new airplanes.

There was no Venn diagram of overlap. Gotcha. So there was nobody in the world that understood how did this all stuff that works. So I had to go from scratch like learn it. first principles. And then ended up moving the company out to California, basically a few years into the business. And then, you know, things took off from there. We'd, like, built bigger aircraft. I took the company public within three years of starting it. We're like a $6 billion to our publicly traded company today. And, yeah, designed basically like four or five generations

of aircraft at Archer. And it was hard. You know, it really set me up well for like, you know, doing figure and cover and the rest of stuff, we can talk about later, but like, it was, it was hard. Even going public was like probably like one of the hardest experiences of my life. Really? Why is that? We went public through a SPAC process.

So you know SPACs at a time like four or five years ago. We're like all the rage. Okay. And it was a special purpose acquisition company. So it was basically companies that were like going public through a merger, like a reverse merger. And it was hard because in 2018, 2019 coming off of software, I had never done hardware before.

So A, it was like hard to raise capital. And B, there was nobody funding like deep tech electric vertical takeoff and landing, like companies. Like you know what I mean? The big venture capital groups were not funding SpaceX or Tesla or Rivian. Like none of these were getting funded by traditional investors. They weren't raising money from the named investors we all know about now. Oh shit.

Are they always behind like that? 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 if they do hardware, they 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, you know, top VC in the U.S. that's invested in a humanoid company. No shit.

And as of the last six months ago now, nothing. Like, they just don't do this stuff. And so, like, I end up, I end up going all. So, you know, like, you know, made, just made $110 million and, or just sold the company for $110 million. I made a lot of money, like personally. It ended up going all in on Archer through the IPO, like through going public. I put like all the money.

I basically bought a house and the rest of the money went all into it. And it was a stressful period. So we went public through us back. And the reason it was stuff is we ended 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, three, four, or five hundred million dollars or it's 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, huh? 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 got to be intimidating. Yeah, It was, I woke up to like a front page of New York Times article about. Oh, shit. Yeah, it was, it was crazy. I mean, the backstory is I, the, I took, so Larry Page started a company and, in the Bay Area, about 10 years ago called Kitty Hawk.

And they were the, like, they did a great work over like 10 years in electric, like, VTile aircraft. And I ended up taking, 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 while we were going public. And so, yeah, it was just a crazy story, like getting public. End 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 going to check in and how you're going to build places like real estate to fly into. And yeah. And then like the engineering work we had to do around there of designing, you know, it's basically a really. 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 like we have a, we tilt the front, the leading edge, six motors, 90 degrees for basically take off vertically and then going to forward 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. Like no human can like fly it basically without a really good control software. What altitude is it flying? About few thousand feet. So about two to three thousand feet

above ground level. And that's what it would normally be? Yeah, like traditional helicopters fly at these levels. I mean, what, 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, when are we going to go full EV? I know there's a lot of pushback about that.

you know, for, from an overreach standpoint. But if you just think about the traffic in the cities and if you have the AI, you know, 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. Yeah. 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 a half or so installed cars. We make like 80 million or so cars a year in the world. It takes you like, you know, on an 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. Well, I mean, if you look at Tesla, for example, I mean, it can self-drive, right? It can come get you. But when you're driving, if you take your eyes off the very, road it wakes you up, you have to come back. I mean, it seems it's, it's, it's inviting more error into the road by doing that in my, in my opinion. Yeah. Am I wrong? It's almost like someone could be more

dangerous. Uh, 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. We're in this chapter in the 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 going to have to think about this.

It's just going to be autonomous from a start. It's going to 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, if you close our eyes long enough, you'll have this autonomy and electrification everywhere. How long do you think it'll be?

I mean, so I live in the Bay Area. Like, you can take Waymos now, like, I can take Waymo's everywhere. It's unbelievable. They're 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 Palo Alto, Midlo Park, like San Jose, like, all this, all over the place. They're really great. I take it's like a, my wife and I want to go to dinner and stuff on the weekends. We take Waymo. It's like, it's so fun. It's like, it sounds so like, like, it sounds so basic. Like, you take a Waymo, it's fine. It's, it's awesome, man.

Like, it's great. You have like, um, it's, uh, the car drives stuff. so human-like, and it's such a great experience, like, not having a human there, to be frank. Like, I love it. You know, like, I order so many, like, whatever, Uber's and stuff in common, the car smells or it's dirty or whatever else, and it's just like, you know, it's just easy. It's really cool. So, like, technology is, like, in, like, the early chapters, but it's all here.

Like, we're going to have autonomy as scale, like, everywhere. It's just going to 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, enough places and then it's the time it takes to get the install base of autonomous hardware and software in all these places. That's going to take some time too. We just can't snap our fingers. We just don't have enough install base of autonomous vehicles in the world. I think 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 a over a billion cars on the planet. So you need to like like a like you know make a large fraction

of that all autonomous. So you're looking at like a this isn't going to happen in a year or two. It's going to take some time. When are we going to see your vehicles? The aircraft. 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 a 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 the air unless they know for sure it's going to be really safe. The safety standard for us that we want to 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. We can have a failure. Yeah. You can't be able to, that that is like that's the standards when we fly, it's like one of the safest form of transportation we take. And it's because of those standards like governed by the FAA,

which is great. I'm like, you know, we're like, that's, that's the state, we're like, that's 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 long pole in the tent for us. And that's wherever you go. If you go to Europe, it's Yasa or, you know, CIA in China, wherever you're going to go. There's like there's federal mandates to get basically an aircraft to take passengers. So we're in the middle of the FAA certification now.

We hope to be certified in the coming, you know, as soon as, possible, basically. But it's like, it's not something you can like, there's not like a date on the calendar, 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 that against a couple 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's just, again, we're in like this chapter. We're like flying cars, electric, you know, aircraft or just like it's early. It's earlier than like AVs or EVs, autonomous vehicles, electric vehicles.

But it'll happen. It'll happen in our lifetime. We'll be taking these things around. Well, I mean, what do you envision? Let's fast forward 20, 30 years. Yeah. What do you envision? What does it look like?

Do we have roads? Do those get ripped up? What does the sky look like? Yeah. What does everyday life look like? Yeah. The really important thing to hear about the airspace is that it's three-dimensional. and the roads are not.

They're 2D, and we've built cities now and houses and restaurants all around these places. You can't, like, there's nowhere to go. There's, like, no more roads to build in these cities. So you have left with no choice if you, and then humanity are moving to cities. We have this, like,

secular trend where we all want to live in cities right now. It's like half the world lives in cities. It'll be, like, 70% by 2050. So we're, like, all moving to cities. The roads can't grow anymore, and we want, like, we're, like, we're, We're constantly moving around, going to work or going to restaurants, and just like, it's like, it's like this, it's, it's getting, it's basically getting worse. It's like the arteries are hardening here around the, 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. Yeah. Like, it's just like the worst, like, the worst, like, time to lose. So the good news about the air is just three-dimensional. You can stack, like, basically an infinite amount of, like, say, roads in the air. Different altitudes.

at different altitudes and even laterally. So you can basically build little tunnels in the sky, and you can basically stack them, and you basically can put like orders of magnitude more things in the air than you can in the road. It's the same for a below ground with tunnels. So the future of travel in cities is below ground and tunnels and above ground in the sky.

The boring company. Yeah, exactly, just like dig tunnels. And it's great. The only problem with tunnels, With the node system on the ground, or sorry, on the, on the ground with, with like, say, we call them vertiports, but basically, like, real estate for flying cars is 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, like places to like take off and land from, you can travel between any one of those routes. So it opens up, like, you know, basically exponentially. more places to go to. I can go to like any note on the system at any time. So hold on. So you're saying in order to take off and land, you'll have to go to specific

locations. You won't be able to do it from your home. Yeah, you're not going to take off and land from your home. Okay. Just because like acoustics in the neighborhood, it's going to be too loud. You need like a decent amount of infrastructure for that for charging and for passengers and cleaning and 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, you'll like Waymo there or walk or take a bike. And 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 100 meters down. Like it doesn't happen in tunnels. You can do that with the sky. You can basically jump to any node on the network. It's exponentially more routes. You can basically do with like less real estate. 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, you'll have, like, cities being, like, 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, yeah, it's like, I want to be like, I want to go to dinner with somebody. I want to see my buddies over here. And we want to go to work over here or, like, go to the mall over here. It's like, everything's there. And that's what we want to be, we're social creatures. We want to be there next to us. humans or some of us are. And so yeah, so anyways, but like, you know, now that you can fly this 150 miles an hour in the air with no traffic, point to point, like no stop signs, no construction,

no things jumping out in front of you, 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 percent 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. You can live like far outside of cities and get down a city center in under 30 minutes. So will these be personally owned or will this be like an Uber service? It'll be like an Uber service. Okay.

To get cost down, 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 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 or walk. You'll get in one.

It'll leave in seven minutes and then you're basically flying right down to town. Holy shit. And you're saying this will be in every neighborhood. This will be very accessible to everybody. Yeah, yeah. You're designing the whole. Electrification allows you to reduce the cost and the safety burden of all this. Wow.

We have like a normal helicopter could have like 100, 200, like safety critical components that any component gives out. The helicopter can go down. An electric aircraft has none. You can lose a motor, you can lose a battery, on board and still fly safe without having this. And so, like, just like from a safety, from a park count, from a cost, from an acoustic signature, it's not going to, like, helicopters are loud and very noisy. And it's just a much better technology for this.

Have you been in one yet? We, I haven't flown inside of ours yet. I've also been inside of our aircraft. And we basically have professional, basically, pilots at the company. Test pilots? Yeah, test pilots. And they do their career test pilots and they're unbelievable. All 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 us happen. Yeah. And, you know, the good thing about

you know, Archer now, as we've, like, we've demonstrated, the hard part is, like, being in the wrong, like, the hard part is, like, making sure you're in the right decade. You don't want to, like, go do this, and you find out, it's like, oh, it's like a 2040 event, and you can't have done. It's just like a, it's just like a waste of time. And so the good news, you know, for Archer's, we're, like, we're in a sweet spot here where, uh, this is going to happen.

Aircraft now work. Uh, we're certifying now with the, you know, government bodies like the FAA to make it happen. We have a good balance sheet with it with cash. the team's great and so it's just like you know get certified and get this thing going

damn that is you're really changing the world well we're the start of it but um hopeful to make this thing work where are we going next um humanoid

let's yes let's do it yeah um how did this idea start yeah so um 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 like weird biological species. Like we look, we're like, you know, we have these weird hands and arms and legs and certain height and sensors. And then we ended up building this world around us so we can interact with it. I mean, if we get dropped into Mars today, we're going to build like coffee cups that we

can hold and stairs and doors and we're going to build this stuff again. And it's like the, it's like the human operating system. We're 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. Meaning if we look differently, the world will look different. Our espresso machine would be different looking. We might not even like express.

or caffeine in this case. So we built this whole world around us. The holy grail for robotics is can you basically build a general purpose machine that can do what humans can, which for me is like a humanoid robot. And a humanoid robot is just a robot that has like a human form. So it has legs so it can walk upstairs and walk over like, you know, uneven terrain or say things on the ground and bend down, which are important, legs are important for or reach up. has like arms and hands so we can manipulate objects and do things like, you know,

grab his stuff, open, open these gummies and, you know, fold laundry and do do real work. And the other right sensor so we can like see the world and understand what to go do and use, you know, our biological neural net to kind of figure out how to reason from. And, you know, having worked on like, you know, kind of like aircraft for, you know, now five or six years, I thought it was pretty possible to go build an electric humanoid robot. And electric's important for cost, and it's important for safety, and it's important because the performance will be much greater. And at the time, even one of the best humanoid robots 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 like, let's call it like 40 degrees of freedom. A degrees of freedom is like a joint.

So like an elbow is a degrees of freedom. You know, shoulders got three. A ball and socket has three, like a pitch on roll. And our robot has about, let's call like 40 degrees of freedom in it. Each degree of freedom is a motor that can spin 360 degrees. So if you only look at how many positions the body could be in at any given time, like this is a position, this position, and keep moving, the 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 a much greater number. I've done the math a few times, very confident in this, even though it sounds ridiculous. So you just can't code your way out of this problem. Like, how do you supposed to write code?

Like, how's a human supposed to write, like, you know, C++ or code to tell the robot at any given timestamp 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 and I'm moving my fingertips and my hand, like, my, you know, my wrist and hand getting positioned to grab this. Like, it's an intractable problem for code. So, I mean, you were saying earlier,

I'm going to butcher this, but it's updating the foot 200 times a second. Yeah, our controller is running. For balance. Our whole controller, so we have a main computer, is processing what to tell all the joints to do, 200, like a little, maybe more than 200 times a second to make sure we can just balance. And then we can, like, do the task.

It could be 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, if you, if, uh, generally if you powered off mid run, it's going to just fall down. It's not like a four-legged dog or quadruped robot where like any given point, it's usually statically stable. So it makes it very difficult because you have to be able to even move your hand.

I'm moving my pelvis and my whole body, my torso's moving, my head's moving, like, all of it becomes very complicated now. It's not just like move my hand. It's like move my whole body to get my hand in the right spot. So every joint, all those 40 joints have basically position. encoders, so we know exactly what position the motors at, or even the case of the knee, are this. And we have force sensing, torque sensing on board. We have the ability to detect all the forces that that knee is seeing. It could be really high when it's walking or it could be like,

you know, it could be powered off and have no forces on the leg. All of that feedback and is as being sent in the main computer. And then we're telling all the joints what to do over 200 times a second. Some of the other feedback is happening at like five or six kilohertz. So the force feedback is happening five, six thousand times a second to the motor control on board. And we do the motor control. The brain for all the motors has done it locally at the motor level because it needs to happen so fast. That's being fed back to a main computer that runs a control software that tells the rest of the whole body what to go do at every timestamp to keep balance. Starting something new isn't just hard.

It's terrifying. I launched 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. Shopify is the commerce platform behind millions of millions of businesses around the world and 10% of all e-commerce in the U.S. From household names like figs to brands that are just getting started today, one of the biggest

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conversions, which means fewer abandoned carts and more sales for your business. It's time to turn those what ifs into with Shopify today. Sign up for your $1 per month trial at Shopify.com slash SRS. Go to Shopify.com slash SRS. That's Shopify.com slash SRS. So getting back to the original thing is my bet for you know, we're like three and a half years old or something like that at figure. I bet three and a half years ago in 2022 when I started the company was that

this was possible now. And my view is like it was possible. I don't, over some 10 or 20 years, this will work. And so I basically started on this endeavor to go basically build, 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 good precedent.

There was no precedent for, showing that there was no AI that had ever worked on a human or robot in history. And there was no electric humanoid hardware that was remotely okay to show it would work. And there was no hands. There was none of this stuff. Wow. So I actually had a lot of trouble even really. 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 end up having to like basically sell. fund a lot of it in the first year I self-funded all of it. No kidding. Yeah. And it was a lot. I mean, we got to 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-person team in like as fast as possible. And I knew how to spend up hardware and software. Again, 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 a rotor and stator and a gearbox and sensors, electronics and wiring and connectors and multiple sensors inside of there. And then firmware, it lives on the, like I say, the microcontroller,

lives inside the motor control side. And then we have like thermal characteristics as hot. And then you got to all make that work at very 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 like a generator, like something, you know, an appliance in your home or like an electric car, they want to like run at like highway speeds. They're designed to run at full RPMs. That's when they're the most efficient. Okay. When motors are stuck and not moving, but holding power and holding forces, they're all,

it's a really bad point of the torque speed curve. So they're not built well for this. So human always use all the time. Like we're like, when we're standing, we're like not moving but holding forces. When we're like holding something out and like giving me the gummies. Like it's not moving now but holding forces.

It's just a really hard engineering problem. Just that one little aspect, which is like hardware umbrella, just like just motors. And so we have whole teams in those areas, just in that one little area here doing like rotor design, electromagnetic design, satyr design, gear box design, sensor design, motor control design, like all of this inside of teams.

It was an enormous lift to just get the team members there to do it. So we spent up a team, 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, it's our first generation robot and had that walking in under 12 months. So from when we incorporate from inception to, wow. Yeah, from when we, uh, we had basically incorporated the company in 2022. Our goal is like, can we get a robot walking by itself at these dynamics? And 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 then from there, continue to build the capabilities. We built Generation 2, Figure 2, which is that guy right there, is our second generation robot. And I think one thing we did before we kind of moved even while designing Gen 2 is, I think it's probably like 2023

at the time we did a demonstration, where we basically wanted to put this K cup inside this curic and run it. It was just on a very pretty simple, like nothing crazy, but we had a curic machine, coffee cup and a K cup, and we had to go grab the K cup, open the K cup, 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 humans. Give it to a four-year-old. Yeah, exactly. I mean, the dexterity on the hands of that thing is just to hand you that back at guppy bears. Yeah. Yeah.

Yeah, then can you do with neural nets on board? It's crazy. Do you not code your way out of it? Can you have a, can you take in camera pixels and then output trajectories for the motors through a neural network? No code. And we did that in 2023 on figure one. And it was like 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 thrown on a humanoid? I don't know. I think it's probably one of the first examples in the world to ever have shown that. And, you know, this was like game on. This is like, we have, let's go build really good human on hardware. Let's make it cheap and really reliable.

Let's make sure it can do what humans can from a hardware perspective. Meaning you want to look at like a phone, where you can just add new apps, to it, like the do laundry app. And the hardware doesn't need to change in the same exact hardware. Like human is only like new, I don't need like new hardware to be able to go off and like learn how to do new skill now in the physical world.

So you want to build the human 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 we're like, hot damn, this is going to this is going to really work. This is going to be human-order 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 human-dard hardware and do what humans do. And then we launched, you know, we launched Figure 2. We did a lot, basically, more work. We started unveil on Helix, which is our neural network stack internally that we do here.

And now we've designed Figure 3, which is our third-generation robot you have here, which is like the best human hardware in the world by far. And we're now running robots that do like, I watched it, you know, the other day, unload dishes and fold laundry. We had figure twos at BMW last year that worked six months every single day. Six months every single day. Every single day it worked, a 10-hour shift every day for six months.

And we had, it was just, it was like the first time for us getting robots out to 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 do clients fire us? Do they love it? Does it work?

And the goals we have for clients is hard because we have to do human work. So we get, like, human KPIs in terms of speed and performance. Like humans, like, you know, in the case of manufacturing, they don't like, you might mess up every once in a while, but you, like, refix it. So you're not messing up every single time. You're pretty fast.

In most cases, the humans are there, not like, you know, quitting or not show up to work, but sometimes that does happen. So it's like, it's a 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 on the manufacturing line. They have a, they basically have a basic body shop that basically builds like X3 and X5s. And January of 2020, what was it, 2025? We started building our first B&W X3s on the line. Are you serious? I bought the first four that did that. They're on my campus now. One's in my house. And they didn't build, obviously, the whole car.

There's a ton of parts, but we built, we did a part of the whole process. What's BMW's feedback? They're great. I mean, if you go into like a car manufacturing company, they're like the best roboticists in the world. There's robots everywhere. There's like these giant 12. foot, Kuka, like, manufacturing, like, robot arms on the floor.

They're bolted the ground. They're massive. These things are carrying car chassis around, like, they're kids' toys. The cars are so big and so heavy, you can't, human can't hold it and pass it around. So you basically have machines that are building the car and then moving the car. So the whole body shop line is only automated end-to-end. And it's like, not end-to-end, but there's humans involved, but like the car is being, like, 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, like big end effector. The end effector is something that is grabbing apart. These endifctors are the size of my soap, like 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. There's robots everywhere. And so like B&W, like they're like, it's been a privilege to see how. like much automation has gone into automotive. It's unbelievable. These things, these machines like kind of make what we're doing sometimes look like little toys. No kidding. We're doing something very complicated, but they're the car manufacturing is just like no joke.

So what specifically were the bigger figures doing? Yeah, we had a, there's a there's a body shop line called, that's basically building the rear header. It's like the back plate. So the the body shop, they basically build the car by putting basic sheet metal together, welding them onto the chassis and then they basically building the car around that like you ended up putting the seats in bolting him down putting the car doors in wiring them up the harnessing and we were in the body shop line helping basically attach the rear head like basically putting the rear header on this fixture so we so today we basically are last year when we were there we basically take a piece of

sheet metal and we basically put on this fixture and we do that over and over again and they do that you know ten hours full shift and there's three different parts on those three parts go on this thing rotates and this big giant like Kuka machine, like robot arm, goes and spot welds it and switches it, switches out to another effector and then grabs it and puts it down the line. Wow. So like these, these facilities are being like fed these parts into the machine. And we were like, we were a piece of that.

And the goal was just like, can we run robots every day? You know, are we going to get like ass handed to us? You know, is it going to be easy? It's going to be hard. And I think it was in the middle. I think we like, we got the robot to a great spot where brand every day was great. I think the biggest learning lesson we took away is we had, we really, I really cared about if, can we do that and can we like clone it times 1,000, times 10,000?

Would we have any issues scaling? That was the part for me. Like, is it just like, you know, does it completely shit the bed and he's like, we need to rework our plan and go back to the office? Does it do it incredibly well 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 robots, the robots,

that started the first day at the start of six months, and the robot that ended the shift that day was the same, even though we had multiple robots in operation every day, we had the same robot that did the start and the finish. Wow. And it was cool. Like, you know, it was the same robot ended six months later. And this was the thing where, you know,

the worry was that humanoid robots couldn't last a month, couldn't last a week in these kind of environments. And, you know, wear and tear. It's like it's running like, you know, 40 degrees of freedom motors every single day. Can they operate really well? I think from a hardware perspective, it did an A plus job. I think from a software perspective, we did like a, I would say a B job, B plus.

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 and heuristics in. So like the controller to walk was done by a C++ controller. It was done by code. The walking you saw today we had back then was done in code. Okay. The rest of this, we had a bunch of other stuff in there done by neural nets. And like some of the perception stacks, some like, you know, some of how new parts around, everything else.

And, you know, this was a 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, 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 what it's ever seen before, like, you 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, you have, like, really shaggy carpet here. And it's like, yeah. And so, like, and it's like, not very, 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, and I think we just announced Helix 2 or three months ago now, I forget, like end of last year. And it's basically entirely down the stack, including the controllers, a neural net now. There's like no code left really on the robot.

Maybe some code in certain pieces, but mostly just almost all of the thing is like a neural net at this point. We remove the need for like almost over 100,000 lines of code at the, when we launched Helix. too. And so what you saw today was just like a, like a robot that can, you know, that we can, we can put now back in the say the factory in these places that will run all the on an neural net. And I think, I think we're running these robots right now. We're getting ready for deployment to customers and they're running incredibly well. We've, we have robots running like basically now in 24-7 shifts without stopping, without any faults for like days and days. We just, we just went like over. We just, we just had like record time.

this past week on the robot running until we saw like a fault, like almost, yeah, basically a whole week. And they basically like they can run like four hours or so, five hours and they need to charge. Another 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 is doing work again. So we can run that now in 24-7 shifts where they're talking to each other, all autonomous,

no humans or you can go to bed or whatever. and they're running that shifts all day and all night. And we do it across multiple use cases now at the office and 24-7 shifts. And it's just like we're running them hard. What kind of stuff are they doing at your office? We do a few things. We have a logistics use case that we run in 24-7 shifts constantly. We really like it.

It's done with the neural net. It's moving packages around. And it's a really good use case. We like it. And we want to like, I want to run it for months and have failures. And we still, we see failures right now. And most of it's in software. A robot gets to some spot where it feels unsafe,

he doesn't know what to do, and it'll stop for a little bit. And that the robot's not on the line for a couple minutes, we call out a failure and we're not happy with it. We have robots that are greeters and visitor bots that walk around the office all day, 24-7. So you're over the office, you're getting lunch or walking around or interviewing with us.

You see robots everywhere. And those run on 24-7 shifts all day, all night, weekends, Christmas Day, whatever, we run them. Greeters? Yeah, they basically... How do they greet you? Come talk to you. They'll just come talk to you.

Yeah, they'll come talk to you. And like, you'll, you can go talk to it and ask it for things where we really wanted to go, like, you know, at, you know, the in-state for us is like it's going to replace, like, somebody, like, meeting the candidates that are interviewing there, taking them in the conference room, getting them water or coffee, like, all of that end-to-end and whole experience. Yeah. Yeah, right now, they're walking, I mean, right now, they're walking in the office, like at nighttime, they're walking to 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 news thing. It's not like these things have been around for decades.

And we like understand that they're really mature. They're not. So we really stress tests on like crazy by running them all the time. What's a conversation you've had with a robot? We've been really working on like deep memory because I think one thing I really don't like is that. like these conversational AIs you talk to you that don't know anything about you. It's like, it's 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 feels really stupid to me. So we've been working a lot on like deep memory. It will actually get to know you. Oh, yeah.

Yeah, it needs to know who you are. Like, who am I talking to? This is Sean and then based on Sean, like, do you have their permissions to tell the robot to go do something or not? Like, if you're visiting, no. You might be able to get coffee or water, but, like, you want to have it, like, go do something new.

Like, won't do it. I've not even thought of that either. Yeah, yeah. Like, we won't even understand that. You want to be able to command you. Yeah, 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 10 minutes. And you can't have the robot doing that, right? You know, get home from work and the kids are just, like, you know, through pints of ice cream. and the robots are just getting whatever they need, like it'd just be chaos. Yeah. So what is it?

Voice recognition? Yeah, you have to do voice for something. Something's voice isn't enough where, like, if you, like, think about it, like, an extreme example, you want it to go, like, order food or spend money or send a wire, like, voice recognition won't be enough. You'll have to do a higher level of authentication. How would you do that? Facial recognition.

Okay. And then if you have a perhaps even finger press scanning is possible to, but facial is what you really want to do. Gotcha. So all those systems are not, like, robust enough right now. We're working through them. And, like, the goal is, like, to get it's, like, super robust. But, like, you know, we want to have conversations with a robot.

We want to ask it to go do things. Like, you really, you 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, do laundry in like my kids' rooms today or something like that or text it. So like language is like super important UI. So we're like spent a lot of time on speech. You can text it too. Yeah, every robot we have has 5G by default on board. We actually run 5G by default now. So every robot off the

line has 5G enabled. We have like a T-Mobile 5G. TeamMobile is an investor of ours and every robot has an e-sim card for TeamMobile 5G. So it comes to the 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 can either 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. I mean, we can do that in right now, but, like, that's the goal is, like, we've got to get there. Like, we got to get to a point where, like, that is certainly impossible. And we want that to happen. You want to be like, yeah, I'm at work. When the groceries come, make sure you put them inside and put him 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. For sure. Feed the dog. Yeah. Everything. Like watch the dog.

Make sure the dog's okay. Yeah. Holy shit. So it's a nanny housekeeper, gardener. It's the Jutson's. All of it. Yeah, it's going to be all of it. I mean, you might want to garden.

You might want to do that. All this physical labor we do today, I think, will be optional in the future. So you'll, like, you might like gardening. You know, you might like mowing the lawn. You know, if you don't mow on mow lawn, don't mow lawn. Like, all this will be a choice. Holy shit. Yeah.

And you said 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, So for us, like, what's so powerful about a humanoid is you don't want to go out and change hardware. Whenever we have a new app on your phone, you just like download it and can do new things now. Like he's got my bank account now. I can do bank account stuff or you got to download it. You got a calculator.

You can do calculator 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, you know, like unloading the dishwasher, making coffee on a curate, like all this, like walking the dog. Like these are like, almost like the matrix

where you get like plugged into a system that re-uploads like weights into the, like neural net weights into the robot, where it can like learn new things. So that's what we do now. Like if the robot like, if we can't do package logistics well, we get data for package logistics. We train our helix neural net for a week.

And then we load it to the robot. and it can like then the same robot that was like folding towels like the week before can now just sit there for 24-7 and do logistics work and package work. Wow. Nothing changes. Where is this going to go first? Consumers, businesses. You'll ship into businesses first.

It's the engineering complexity that we have to ship is like proportional to the variability that we see on site. So the variability at homes is like extremely high. It's like it's my home is chaos. Like, kids are just like, just dismantling the house. It was 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 and my house, we probably have, like, different appliances or different toasters and different microwaves, all a little different everywhere we go. So the home is just like this, like, tons of entropy, like, tons of veritability, a wide distribution of tasks. It's like the, it's like the ultimate, like, challenge for robotics. in the home. It's like the hardest, most variable thing we got going. And in the workforce, it's like you have this like work cell that you're doing. So like if you're doing like manufacturing

logistics or, you know, a lot of tasks, you have like this area you're doing work in. And you can basically kind of write down on a piece of paper like how to do every step. 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. Like the next assembly line or the next like conveyor system, like, it's like I kind of know what to do. It's like I get the package. flip it down and I need it every three seconds. You kind of have like, you know, good understanding what to go do. So it just makes it easier.

It's like the analogy of like highway driving for autonomous vehicles. That's just happened sooner because the veritability is lower than in the city. Gotcha. So it'll happen first to scale. And then the industrial thing has a good thing where it's like you kind of have your own work area so the safety areas are not as high. The hardest thing in the home will be once you figure out how to get performance, there, meaning it's capable of doing everything in the home.

Like, see, you can go to your home and do everything. The longest pull from there is going to be safety. Or, like, me and you feel safe, like, being, like having this here with our kids. And that is, 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. There's a tracker going 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, you can be able to be, you can be a very, 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 to be like 500 bucks a month. You're Carleys. And 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. Yeah, so I think, and then 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, it's like, you know, I mean, half of GDP is human labor. You know, 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're talking 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 company? And my answer for that is just, like, I just want robots in the home.

So don't really care. You know, like, we've got to make that work. Yeah. I mean, you say in 10 years, every home will have a humanoid. I don't have 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 100 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 100 how so at this point the issue we have

so we can go into my home today and we can do little pockets of work. We can do like, I can unload the full dishwasher. I can, once the laundry's in the basket, I can take it, walk it, and fill up the washer, and run it. And we can do pockets of work. We can take the laundry, put it on my bed,

and we can fold it all. And then, so we're doing like little spots of it. And it's pretty good. And, but there's a lot more spots to go fill. for like long horizon work, just that. And we have to be like extremely robust to maybe different types of clothes or like different types of like,

I don't wash my jeans, like that type of thing and all these different like veritability that you might look see. And we haven't been able to, as of today, that's like that's the hill we gotta 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. I put them in the home. How does it, I mean, do I train it? Do I personally train it? Hey, when you're emptying the dishwasher, the cups go here, the plates go here, the silverware's 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 jeans drawer is where I hang my shirt. Yeah. Is that how it works? Yeah, you'll get a robot. 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 say like you know it'll say like you know can you walk me through your home and it'll follow you around and you will tell it all that

like you would let's let's let's see it you'll see it a friend staying for two weeks at your house that you know needed to like cook and use your stuff like you're like you know you wanted to wash clothes and stay in one of your rooms like you'd walk that person around and you'd be like hey man this is

this is recycling here this is where trash is at Like here's where you get water. Like the trash goes out every, you know, every Monday. You know, this is, I, you know, we do blankets on the couch, but like you, 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, like, all these things you have in your home that are like, you know, important.

And, and you're like, just like you would, like walking somebody like human around for the first time. That's what you'll do. And the robot will semantically understand, like, will, A, have, like, we'll remember all of this. And, uh, and it will, like, it will learn based on what you want, your preferences, like, what to go do. Oh, shit. It'll be that.

So it's just like turning a human being. This is like, not, this is not 10 years. We'll do this. This is really soon. Like, I think in the next, like, I mean, I'm hoping this year we could, like drop a robot in your home and do a good amount of stuff. It's just, um, we'll see. I mean, this is like, this is like solving, like,

the holy grail robotics. This is like solving for a general purpose humanoid robot. Maybe we don't solve it this year. Maybe we solve it this year. Maybe we solve it next year. Maybe we don't solve it. 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 learn how to generalize. We need not like move deeper into pre-training. We know the directions we need to go ahead. We think to solve this and we're seeing a lot of both positive transfer and a lot of just like we're seeing internally the we think the right direction to make this work. When you were talking about trust in the robot with your kids, what are, I'm just

curious, what are your concerns? I haven't thought about this. I think at Archer was always like, I'll never like feel safe. I never feel comfortable like recommending Arch like people to fly in Archer and letting people fly in Archer until like I like would fly in Archer. I like would fly an archer aircraft than 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, though, right, to take a aircraft like that around. So I think the same thing for figure here is we'll be safe when, 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 like want to jump on everything. And, you know, it's like they're like, yeah, in the robot, like, you know, you know, the robot needs to be extremely safe there. So that's another hurdles, like getting into 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 gotta go do to show that it works. Right on, right on. You want to take a walk around this thing?

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support the mission, and become part of the Sean Ryan Show's story. All right, this is our uh figure three humanoid robot. It's, uh, we actually un-billed it last year. My God. Yeah. It's about 130 pounds, 5'6. And, uh, we basically designed it to do most things, like a lot of things humans do. 130 pounds.

135 pounds, yeah. It's a fold laundry, do dishes, do manufacturing logistics. Um, you know, I think a few things here that we like made improvements on. This is our third time, basically running through three generations of robots. Uh, we've like, we reduced the, reduce the weight and mass. We made the robot skinnier, but also same strength and speeds. We upgraded the sensors on the robot.

It basically sees through cameras. We have better, basically our fifth generation hands on board that have a camera, tactile sensors, basically improved grip. We also have on the robot, like basically more compute on board for running our Helix neural network. We also spend a lot of time on just basically making the robot more safe. So they all have kind of the squishy layer of foam on it. So, yep, go ahead.

So if you, let's say somebody pushed it over and fell over, I mean, what's the durability of these? I mean, it depends how hard you push it. But 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 necks. Sometimes it's fine.

All right, turn around. Another thing too is like we basically, the robot's almost fully softwrapped. 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 joints. So all basically electric motors and the motors have like basically tons of sensors on it for balancing and doing work. All right, turn around. We can walk with it for a minute. All right, 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.

What you think? You're going to... You want one? What's that? You want one of these? I want a couple of them. You want 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 how fast it can go. We have running...

I don't know what we're on running mode, but let's go as fast as we can. We do jog with the robots outside. Really? At campus, yeah. I think it's also like looks cool, right? Got high tops on. Looks awesome. Yeah.

And you said there's cameras in the hands too. Yeah, cameras in the palms. Right here in the palm. So we can see the fingertips when it's like grabbing objects. And then every single fingertip has a tactile sensor inside. So we can basically touch like touch forces as we're grabbing objects. Can you shake my hand? I don't know.

Maybe. Can it squeeze my hand? There you go. There you go. Will it crush my hand? And no, it's not going to crush your head. Dude, that's pretty stern. That's like, I can't move it.

We can pick up like 40 pound boxes off the floor, and we can also fold a T-shirt. That is wild. Yeah. Is this the power button? Yeah, don't push that. Okay. We had somebody in the office our day.

It was like, I feel like I need to push this. I'm like, it's literally going to turn off if you push the button. And how long does it hold the chairman? charge? It depends on what we do, but anywhere from four and five hours. How long does it take to charge? It takes about an hour to charge. So we can do about four or five hours on, we can charge for an hour.

You know, humans take breaks during the day to eat and do other stuff. Or what should do a lot of time in, we'll stub 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, robot steps onto, and we charge wirelessly. We can charge it. We can basically charge in one hour through that whole process. Holy shit. Just by standing. So in case the robot has a task where it needs to stand a lot, we can do it

So he just stands on a mat? Changed on a mat. We designed it in house an iPhone charger. It's like an iPhone charger. It's like an iPhone charger. Yeah, and I can charge about a kilowatt per foot. It's about two kilowatts that can charge. When is this gonna be available to consumer markets? As soon as we like make it work really well. And so I can send it 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 uh, you know, 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, we work with BMW, we work with one of the largest logistics companies in the world. I work with Brookfield, they're like one of the largest real estate companies in the world.

They have a giant portfolio of companies. And then we have like two more customers will be announcing the next 60 days. Yeah, congratulations. Yeah, thanks. That's amazing. Thanks, we're going to try to ship as many as possible as we can this year. Wow. We also make these on site next door at Baku.

It's our production manufacturing facility. And we make about one every kind of like 90 minutes or so now. You can make one of these in 90 minutes. Yeah, when we run the line, the lines are running about every 90 minutes, we make one. And then that'll greatly increase even in the next like several months here. Wow. Wow.

Yeah. What do you, I mean, at full capacity, like, what do you think you'll, how many do you think you'll grind out a year? Yeah, our facility there can do, maybe upwards of like 40 to 50,000 a year at like full capacity. But we need to design for much higher. Like we want to get to like a million units a year and you know like within this decade. A million units a year.

Yeah, for sure. I mean, you sell like we're sell over. It's like building a country. It's not, I mean like you sell over a billion phones a year easy. So I think it's going to be like a robot for every human. So you'll need like a cell phone quality style manufacturing. Shit. Yeah.

So I can put. 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. Dude. What? Yeah. Yeah. It's better balance than I do. Yeah, same. Dude, that's crazy. Yeah. This is three and a half years. We had this walking in three years since the start of the company. It was crazy. You're like basically the week of year three. We were walking this thing. at the office. The thing is, this is like, we're going to go to like this whole iPhone lineup

where it's, you know, iPhone 1, iPhone 2, iPhone 3. It just gets better and better. And I think Kimonos will take, like, more radical steps between those. Every year, we're roughly building a new 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. Wild. You want to take some. pictures with them. Let's do it. Dude, that is insane. What do you think? Awesome. I want one.

You want one? Yeah. Let's get you one, man. Wow. Like, so that in the sense, you said the hands can sense three grams of pressure? Yeah, we basically have the fingers in it. We have tactile sensors on every fingertip, and they're really sensitive. And we have a camera in the hand that can detect when the fingertips are in contact with some surface.

It could be like something we're touching. And then within there, every joint can kind of also feel sense and track the position of every, like, you know, part of the hand. So the hands are really good. Honestly, we're working on hands down 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 teased a couple weeks ago that has like

Basically, full, I think it gets a full human level dexterity with this hand. Are you serious? It's got as many joints on the hand as a human hand has. There's still a lot of work to go do, but it's now, it's now a huge step up where we actually even currently are. And the hand now can, like, fold laundry and, you know. Do you think it'll hit a point where it can outperform a human, more dexterity in a hand than a human?

and better balance, faster, stronger? We already have better balance than a human. The robot on one leg could balance better than a human can. I don't know about like, humans have a lot of degrees of freedom. We have like hundreds, a few hundred degrees of freedom. Our hands are very dexterous. I would say if we can do close to human dexterity in terms of like, that would be a huge win. You'd have robots everywhere.

And, you know, and then we're going to still. have a lot of trouble getting to a full human range of motion. Like small things, like you reach inside of a, you know, a washer and you kind of like move your head as you're like getting in or sometimes like some people will get on like, you know, get down to the ground and like kind of get in the washer to grab some on the back. Because 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 what I mean? Like in they can jump up on countertops and all kinds of crazy stuff. Humans be tough.

But I think we can get very soon, we'll get pretty close to most of what humans can. You had a pretty close relationship with OpenAI, correct? Yeah, they led my, so Sam and OpenAI led my Series B, co-led my Series B with Microsoft. That was a few years ago now. So we raised about a little under 700 million in our Series B, our second round of funding.

And they joined my board, and then we ended up spinning basically a year with them working on, well, I mean, I'll give you the background. The goal was to try to advance AI models for humanoid robots together. And, you know, they have some great folks that have worked on like LLMs and chatbots and things. And in the time, we had like a, you know, we still do, but we had our, we had a full, like, AI team internally. So we were basically working weekly daily on like basically how do we advance state of the art kind of like language models for robotics. And, you know, like, yeah, I ended up firing them. I know. A year or later.

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

The team now we have is over 50. or so on the AI or Helix team internally. We just found that like that team we had internally, we just found like kind of circles around them, like every day. We had a hard time getting, like, you know, in robotics, you get like run the robot, see how it does. You know, like you have like, when I run a new like AI experiment or do some ablations, like, and some evals, you need to like run the robot at the day and see how it, see how it does.

Like, sim is one thing. You can get certain far running simulations and looking at loss curves and stuff, but we need at, at the day, like, do we need to get, like, see how the robot does? And we had it, we just had a hard time getting them in the office. We had a hard time like basically like, like basically, you know, advancing stuff together as a team. Ended up, we, like the strategy we had internally and the team we had was just like complete superstars. They're the best robot learning folks on the planet that sit a figure. And it got to a point where, you know, and I got a call one day.

It was just like, you know, we were like also week to week 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, ah, this is over. Yeah, I just get out of here. Like, this is like, we're teaching you how to do, like, robot learning.

You're seeing our progress. We had like a couple of, you know, Sam and a couple of the co-founders on site at one point. We were right before this and they saw it. And they were like, wow, this is 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 or very little.

I mean, it was helpful having them lead the round. It was like there was some good brand association there. But like beyond like that, there wasn't much. So we ended up, you know, we're going to chart 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 on the AI team and we bring candidates in and they'd be like, oh, you guys do the robot and opening I do some

models. I'm like, oh, no, not really. We have a whole AI team internally. We do model development here ourselves. You know, like we're advancing ourselves and it just wasn't the perception from the outside. 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 wasn't really helpful for us long term if we're gonna be competitors. So we decided to split ways. I decided specifically to split ways. But they have a great team. I think they're doing robotics now internally. Sounds like it. Yeah, exactly.

Yeah, yeah, exactly. I was like, I got a call saying like, yeah, like, you know, partly like, part of the feedback I heard was like, we've made so much progress that figure and they've seen that they were, you know, opening started out as a robotics program. They were trying to solve AGI through a lot of it. First three, four years, they were just like all in on robots. If you Google like Open AI robotics, it's like old 2016, 2017, 2018, like, you know,

maybe like 2019, maybe like 2019, something like that. They end up pivoting into like large language models, maybe 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 AGI 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, you know, at the time, it was unclear or they 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, like, somewhat humbled is, like, it's, we made, like, I think we made, like, 10, I don't know, five to 10 years of progress in like three years, four years. Like, we just, like, it just felt like, this should have taken 10.

Even right now, it feels like, we're not even four years old yet. Four years old in end of May or something like that. Like, I, 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, do the stuff it's doing here, but, like, let alone, like, the real stuff it's doing now and, 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 decided it's 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, on Earth. I mean, that's, that's a balsy move, but it makes perfect sense. And, 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 unveil some of them. I mean, I know we didn't unveil this, but it's the first podcast that's ever been on.

Dude, Sean, I have not taken a robot to, like, a podcast. Like, I get asked every week to do this. This is the first time. And, uh, love a show and wanting to get him here in Tennessee. It's 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, it's, it's really cool to be able to do this, like, once-in-a-lifetime opportunity type stuff. Thank you. No problem.

What about military application? Yeah, we've, um, we've 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, and then also put like, you know, like militarizer robots. I think it's just too hard into one umbrella. I think there's a huge opportunity, like,

to save lives and help on the military side. But I think it becomes like, you know, we do have, you know, we do have like a very advanced system here. The system can, you know, unlike a car, if a car became sentient, like, you know, you can like, like, you can like, walk in your house, walk upstairs, go in your room, it's like not going to come chase you. Like a robot will just walk right up your stairs and open your door, the humanoid robot. You know, this is a very different technology.

You've got to be very careful with it. So I think because of some of that and some other things, we like, we know we've drawn a line here to say like, you know, we want to stick with the, you know, consumer market, commercial market and go to harden the paint with that. I think there are and will be like incredible 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, going to close quarters and houses and, you know, that stuff is like extremely dangerous. Human knows would be great at that stuff. Like opening doors and just making sure the house is, you know, cleared, like clear a house. You know what I mean?

I mean, I could see it for a whole ton of stuff. Yeah. Not even just going on target, but centuries, gate-go. I mean, roving patrols. I mean, all of it. Totally. Just armed security. It's, wow.

You know, I mean, you kind of have somewhat of a, a treatable asset, too. You can basically, I think you can make it relatively cheap, make a lot of them, just put about the work. Do you think you'll get into it in the future? I don't know. As of now, no, but like, there's a part of it that you'll, is a part of the story here where you're like,

Like, you could make this, like, obviously really safe for humans there. But 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 want to I'll make that announcement yeah I think it's fucking awesome it's awesome but like I know just like it is like a 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 they're hesitant is it is it replacement of human jobs I mean

Jack Dorsey just I mean he just let go what 10,000 people yeah like almost half of his yeah half of his personnel because of AI yeah And it's stock one up because... 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 can, like, do what humans can. I think it's, you know, you have similar scariness folks have around, like, digital AI and how that will, like, basically, like, you know, manifest in the future.

So I think that's a real thing. Like, I think the robots can do human-like work. And it will continue every year to do more and more human-like work. So... But like that, you know, we just want to be very careful. about how we position this and what we do and also how we communicate it. Yeah. Yeah. What's next for the robots?

We want to solve general robotics, that figure. We think of ourselves truly as like a, at the frontier of like this robotics AI lab that needs to build common sense reasoning into the, in a robot that can put in every home. How do we drop it into your home it's never been and you can just communicate with it and get to start doing work. That's the problem we want to solve here. That's the problem. If you solve it, you can ship billions and millions of robots. There's also a business where if you don't want to solve that, you can definitely ship robots. You could ship them in the commercial workforce. You should ship them in the military, as you mentioned. There is a path to go, like,

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'd feel like you had like a human in a body suit. They can like understand you, nod, like go off and do things now after your task. Like that's the problem we want to solve a figure. That's like a large scale like, it's like an AI lab problem at this point. We like we talk a lot about how we're trying to like, we're trying to give AI a body here at figure.

And so we have this embodiment. We need to put like really sophisticated AI into it to be able to command it. And that's the, 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 like as hard as I possibly can. And it's a very, very difficult problem. At this point, it's largely constrained by getting the appropriate data into the network at scale. I think if we could snap our fingers and get a pile of data that we really needed into helix stack, I think we would solve general robotics right now. Wow.

What should I be asking you that I haven't asked yet? About figure or general? About figure. I mean, there's a lot of stuff going out with China and manufacturing a few other things. But like, I think, you know, I think maybe to summarize, I think where we're at is, I think if I had to like, if I was like watching this and I wasn't following the story, I think the one thing I would like to convey that, you know, is like we are so. close to making this happen now. And it's only until, you know, people can come online and, like, watch our stuff we put out, you know.

But when people come to the office and experience it and see the robots and you can talk to them and some of the stuff you're doing here today, it's just like a full, like, emotional experience that is, like, really hard to convey. It's just crazy to just, it's, it feels like we're living in the future. It just feels like we're living here. Yeah, just like, well, it's like, it's crazy it works. It's crazy as working. But we're like, we're now in the, we now have like line aside to make this happen.

And which is exciting in my perspective. Super exciting. I think it's going to be super transformative for the world. And I think what we're going to try to do over the next year or two is try to like get this out further at scale and get everybody to feel this more and more. Like you feel it when you come to our office and you feel it when you're next to the robots. But it's like hard for the, we're such early innings about about this yet for takeoff that it's. It's hard for the whole world to really feel this. Yeah.

Yeah. Have you seen, do the robots interact with each other? Yeah. What does that look like? Right now they communicate with each other when they need to like, so we have like robots that are running these 24-7 shifts. When one robot gets like down to like low state of charge, let's say it's like 10%, and it's a few percentages away from, we'll dock it before it's at 1% or something like that.

It's at 10%. The other robot will get ready to sub in. It will come walk over, sit right behind it. And then when the robot is ready and knows that this is there, it will then back away. And the robot will go into due operations and do work. That other robot will then go over and start charging. If any of those robots have any problems throughout, it could be hardware or software, they will go and go to like basically like the hospital in our office.

So they'll go to a certain place. When they get, when they know they're going to the hospital, we have another robot coming in to the main docks to start subbing in and getting ready. to go. All this communication has happening like robot to robot. And it's unbelievable. And the robots are getting 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, like can't stand anymore. You know what I mean? You, like you fall. Today, that doesn't happen. We can lose a knee. We can

hold its position. We lose full calms with the knee. We can stiff in the joint and we can limp off. Holy shit. Yeah. Actually, I'll post some of the next week publicly about this. I've never, it's like, holy shit. So we can lose like a lower body motor and it literally limps off stage, like off like the, you know, the main like

line it's on. Heading to the hospital. It'll limp all the way there. While it's limping there, another group from like the healthy part of the hospital will then come in and re-sub it in on the dock, while the nether one un-docs while it just lost his knee to go in and do work. All that's happening through robot communication levels. You can be like literally asleep while this is happening.

We run them 24-7. It could be at three in the morning, and it will happen. It's insane. This is happening like, I saw this in last like a few months that's happening out right now. This is not even like the future stuff. Future stuff is gonna 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 do work. You'll speak to them sometimes. Sometimes you won't need to do and they'll just do work and they'll just be like everywhere. I say this again, but I think we'll walk out. It'll happen first in probably the Bay Area.

We're based in the Bay and a lot of companies are in there for robotics. But I think you'll go to the Bay area at some point and you'll see more human noise than humans. in the next 10 years for sure. That is, that is, 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 U.S.? 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 think we want, I don't think we have the will to do this. I don't think we have the know-how to do. to do this as well as like some of the Asian manufacturing groups. When I'm overseas, 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 out

electronics like crazy and they have every line, they have like this box of automation inside of it, like a little tiny robot inside of there, it's moving some like whatever, a phone enclosure or something like that. Oh, kidding. And it's doing it through an automated way and moving it around. a little conveyor and it's moving to the next station. Maybe a human is doing something and it's going down the line. It's going into a next station that's got a robotic system in there,

completely customized and different way you just saw. And they have lines and lines in floors and floors of this. And then buildings and buildings. And you're like, holy shit, each one of those boxes is like a figure style complexity. And they have like hundreds of them. Wow. And they need to run them at high rate. It's just like, it's unbelievable, actually.

It's not trivial. It's very complex. And they've been doing it for several decades on these lines. So I think one is like, I don't think that stuff we want to move back. I think we want to move back the high-end robotic stuff that's going to be like super transformative for us in the future. All the futuristic. Yeah.

We want to bring back flying cars. I want to bring back like humanoid robots. Like the stuff that's like highly dynamic, very intelligent systems, like the next generation, like manufacturing 2.0 stuff. Gotcha. So we're doing that right now in California on, on our campus. We have a fairly large campus in the Bay Area. And we manufacture right now, like whenever 90 minutes or so. And 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 U.S. manufacturing for the future. Right on. So we're going to design human rights here. So these are all these are all manufactured. We manufacture those in California. Right on, man. Yeah, man. They walk like, they walk off the lines, they walk over, 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 OEOL checks.

So they're self-looking each other, self-calibrating. 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 down and ever have. have 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 hour check and then when they're done, 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 it will 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, are like, these are like, uh, that's not, you know what I mean?

Yeah, it's just interesting to think about it. I don't know. I just like, I feel like, 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 literally moves. It's like 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 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. Yeah.

So those are things that were like, man, that's going to be really tough to fold laundry. And with code, it's been impossible. The reason you haven't seen like package logistics and stuff, some of this stuff automated is because like these bags are just like hard. 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 logistics customer we're working with. They're like soft packages. And we sign them. They're like, we want you to move these packages on the Cumbar system.

And we've put videos out about it and stuff. In the first month we, we, uh, Inga who runs, you know, accounts was like, we need,

uh, 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,

uh, compliant material that is moving while you're touching, some of them touch and there's something hard inside, some of them are squishy, there's tons of them. We're gonna move every three seconds. We gotta find the barcode, put it down, and put in the middle of the conveyor, every three seconds of package.

I was like, it was 50-50 shots works. And it's gotta be with a neural net. And we got a bunch of data, trained it policy, and right away it worked. And I was like, holy shit, this is like it worked really good. And for some reason, and the neural nets do extremely well

under those high veritability environments that's extremely diverse. They can learn the representations extremely well across like a wider distribution and they just love it. Folding t-shirts, towels, like packages like no problem. Wow.

Stuff that would like, you know, you're replanting very fast as these things are all moving. It's doing that in real time. It's just like it just works. Deep learning just works. Wow. Evenoid hardware.

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I love this. Cover like, yes. Yeah. Yes. Can you give us the synopsis? Yeah. So back when I sold battery, I mean, I mentioned I got obsessed about a few different areas of like working on, you know, always want to work on flying cars.

But like the macro environment turned like extremely poor for like school shootings. Like it went from like, you know, it's really hard to track. We went from like 30 to 40 events per year in the U.S. to like 300. and that was over like a span of 10 years. And it's also really hard to understand why. It's like another thing that we could like spend time on. But it's there's just like a 10X, mostly in the U.S. You didn't really see this like a lot internationally.

And, you know, we sort of looking at it, looking at it, I basically started reading a bunch of like research reports and other things. And I stumbled upon this technology, this like basically, technology. technology and kind of like terrahertz radar. So basically like, or sometimes also called millimeter wave technology where it's basically like, like basically a like, basically high frequency like, like, it's radio, RF, it's like radio frequencies, basically done like very high frequency. Like in the 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 and like the LG system scan you, like a couple feet away, they can see like anything.

anything you have. Like, you know, if you have knife, gun, vaping, whatever. I read a research report that showed in my goal is like if you want to put it in schools you can't scare the kids. You have to be able to...

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 that some people chase. which we're not chasing.

And then there's like a, how do we actually like know if people have guns on them? Because if you know a kid has a gun on them, you can go like take it away. And then majority of all school shootings are unplanned, most of them. Like almost all of them. Are some kid bringing a gun in habitually?

It's like their uncle's gun and they bring it in the school like every day for like three months. They get to fight at recess and they shoot something. They shoot it the gun. Sometimes shoot somebody, somebody to shoot it. And that is majority of all things, all gun events. The ones where you see like a planned event that's like on like CNN

where somebody is like coming in with a machine gun or automatic weapon, it happens like one or two times a year. It's on the front page of the news. The majority of all the cases, like 90-something percent, is all happening from unplanned folks are bringing in guns all the time and then they're shooting it. So you basically, what you can do is you can stop all those. The planned ones are very difficult.

I may be 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 this other stuff. But it's just like, we don't want the kids to go into school like that. That's just like not how I want my kids growing up. So basically the reason why I got obsessed with like Terrahertz 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 a three-dimensional camera image almost, but it's done in like a radio frequency. You can look at like almost like an optical image. And the reason that's interesting is because like if it's basically people bringing guns in habitually

and you can scan them at entrances, you're always coming in through a few doors at a school. 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 ten whatever meters away you can scan people as are 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 are 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 So it's amazing. It'll find a concealed weapon anywhere. There's like, you know, yes. There's printing and a backpack. Yes, you can find them in backpacks. You can find them in waistbands and pockets.

So the story is, so I found this research report done by a few of these guys, you know, that were at NASA Jet Propulsion Lab. I write these two guys and they said, you know, sure, we'd love to have you over. I get over there and they're like, they tell me the whole backstory. They're like, listen, we develop this technology for standoff distance. detection for the Iraq and Afghanistan war. It was funded by the U.S. government. We worked on it for 10 years. And when the war stopped, like, funding dropped to zero and we like, we're done. We didn't work on it anymore. And I'm like, oh, it sucks. And then I'm like, okay, well, I guess Kimmy posted

if, you know, if this thing ever works out. And then there are towards the end, like, oh, you want to go see it. I'm like, what do you mean, see it? They're going to get some basement. It's done. We did it. And this is in 2017, 2018. So I was like, oh, yeah, let's walk down. Walked down to the basement. There's like this tarp over this machine. Took a tarp off. They had a guy with a manic, like a mannequin that sitting there 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. It had like a computer tower inside of it. And then it had like a little screen next to it. So I started this machine and they basically moved over to the screen. And the screen showed like as clear as day, like a photo of the, you can see the exact gun. You could see it in 3D. You could see it in 2D.

You could see it in power. There's a bunch of other ways we can look at the data. But it'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 a, like, you know, this was a big endeavor for me, like going from software, like, you know, deep tech hardware. So I basically decided to put cover on hold and, you know, Chase, Tase Archer. And then about two years ago, somebody came to my office, one of my investors and was like, hey, I'm like looking at like trying to solve school shootings. I was just back from L.A. and I'm like trying to solve it with CCTV, like the security cameras. He's like the problem is like, you can't, you won't know until the gun goes off. And you won't like brandish a 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. I told him the story about

how I went down this path and hearing. He kind of looked me dead in the eyes. He's like, I have kids and you have kids like you have a fiduciary duty to go build this. And it was right when my daughter was also applying first grade and we were worried about it at schools. You know what I mean? Just looking at like the fence and everything goes in. Just like kind of anybody can go in. You know what I mean. So, like, I was like, shit, you know, I got to go do this. I end 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 it is with, is with me now. No way. We put an office in Pasadena. That's the main office is right next to JPL. And we've been working on this now for two years. I've been self-funding

the whole thing. And we will have, we have a prototype that already works last year. And we'll have a full-scale prototype out, like, I hope, by summer, like, in our lab. And then we hopefully, if all goes well by end of year, we're beta testing in the school. Wow. And we'll put in that figure campus first, even. Wow. This is an AI, this is like an optical play. Can you see it?

This is an AI play saying, can you detect it now? There's 130,000 K-12 schools in the U.S. There's like 60 or 80 million K-12 students. It's huge. And, but it's not just schools, it's stadiums and airports. Everywhere. Hospitals, malls. Any venue you can movie theaters.

I had my last baby a year ago, just like anybody can walk in the hospital. It's just like, it doesn't matter. It don't check you in. 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,

redesign 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.

There's only a few groups in the world that could make them and design them. We co-designed them. We worked on the design with them. made them, fabricate them, and we have them now in our office. They work. We need a lot, you know, we use many different, we use like a lot of chips, but they're like really cheap. And that's important. So we, you know, K-12 will have like a large budget. And we need to be able to, get the cost down to make it affordable for every school. That's what I was going to ask. I mean, how are you going to get this in school? A lot of, a lot of schools won't do,

they won't even hire a security guard. Yeah. There, um, there are big budgets, both of the federal and municipal level, like a lot of money that are going into school, like schools are getting subsidized for it, put in a lot of stuff. They're putting in CCTVs, like cameras. They're putting in like 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 a reasonable amount per student that both public and private

schools can afford. But it's a rat. We could have already had our systems beta testing in some schools by now. If we didn't pivot a year and a year ago, we spent the last year trying to like 90% decrease, like, decrease the bill of materials, like the cost. Wow. It's just like nuts needed to go big and make this really work well. A lot of people don't realize how much outdated banking is costing them. Monthly fees, overdraft charges, and minimum balances that punish you for not having enough.

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Would there be a way to... maybe facial recognition. Who's enrolled here? Who's not? Just for example, like the shooter that happened up at Nashville a couple years ago, the Covenant School. Yeah. Went to school there, but not at the time.

Yeah. You know, and so if they would have had some type of facial recognition on top of what you had, that's, that's, like, this person doesn't go here. This person hasn't gotten this. Yep. 100%. We'll have cameras, maybe even some audio, like mics. Like, 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? 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 it into the models to really help like understand if there's threats or not.

The schools are set up really well to do like random locker checks now. And like, okay, this doesn't look okay or not. Like the schools are really well equipped for that. It's just like we don't know what's happening. 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 are found that are brought in. And then from there, what we're also

finding is actually a similar small percentage are actually being reported. Because if you report like a student that has a gun, they're going to, they're going to Juvie. 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 what I mean? 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 schools. Wow. We're finding, you're reporting thousands. Wow. And you're seeing hundreds of shootings. So our view is that we think is actually happening, like, you know, as a percentage it's low, but as an absolute number, it's quite high.

Yeah, so I'm excited about this. We, it's in some way, like, I, you know, I write this prediction every end of every year for, like, what, you know, what will happen in the spaces I'm in, 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. got like publicly was just for cover. It just like, I think it, I think it, you know, just like hits, I think it hits in a really good way with a lot of folks, maybe especially parents. So,

everybody's word. We're homeschooling. Yeah. Because of the shit. That's a big reason why we're homeschooling. Yeah. I, uh, I hear you. We're, my wife and I, when we think about where we're put our kids and stuff too, it's just like something we talk about every time too. And it's like, uh, you know, and it's like, it's probably like a low occurrence rate. But if it did happen, And it's just like, can you can't recover from that, you know what I mean? It's just every school I go to. I'm like, man, like, you guys got to, it's just happened down the road. I had a good buddy, like, had his house breaking into.

He's got family and stuff. It was like six months ago and he just told me when I was talking last, he's like, the sense of security we have now in our home is just like, we'll never get back. And I just like, I didn't know, I didn't know what that felt like, like feeling like we were secure before, but we lost it now. And now we can definitely see it and feel it. And just like, we're never going to be able to get back 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 going to prevent all of them. I think you can prevent a lot of them. And then if you even have, like, there's no real security there at all right now, but even if you have security, there's also like a sense of like, shit, I got to bring a gun in here now.

There's like real sophisticated AI that's in all these schools that can catch it. Yeah. I think that's another big thing. You have that at like TSA. Determerechuk. Yeah, it's a deterrent. So you have like that, but we can also find you. Find it.

We can see underneath through backpacks and stuff. It happens at specialized like radio frequencies. It happens at like, you know, 200 to 300 gigahertz. And 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 it's actually quite a hard technical feat. One of the reasons I didn't do it and did Archer is because I thought the cover stuff was actually harder than doing flying cars. And I actually think it still is. We still like it's hard to it's like it seems like pretty obvious. Like it's like a what airports have I do it 10 times higher. So it seems like and you look at Archer like shit man that looks really complicated or like a figure. Cover is just like super niche area of folks that it haven't like there's the folks spending like in like in this space 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 a there is no industry for this. And luckily we have like the world's best terror 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'll have it at, like, we'll have it at figure campus first, and then we'll put it in like schools, like hopefully on the West Coast and maybe one or two and we'll see how it goes. There's, you know, how do we market this? Like what do we tell the students, like,

like parents, there's like, you know, there's a lot of stuff here. We need to get right. But if it goes well from there, And we're getting low false positives. Like what we really want to do is make sure we don't freak the kids out. We don't want to, you know, think it's a gun, but it's a crayon box. That'd be terrible.

So like that's really an AI problem. So we basically want to make sure like we have like low false positives around the whole system stack. That's a really hard problem to solve, especially for us where you can be 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 just guns everywhere.

And they're all bricked. You can't like you actually shoot him. 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 detecting the response when it hits something solid? Yeah, that's exactly what it's doing. It's like it's basically shooting out of radio frequency.

It's like electromagnetic, like a little wave format goes out to the other, 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, different, like, radio frequency level. But think about your Wi-Fi, and, like, you want to, like, you know, an order of, like, 20x or so, like, the radio frequency level, it's, like, operating in a few gigahertz, something like that, but we operate at much higher frequencies, like 300 gigahertz.

So you want to, like, whatever, call it 50, maybe it's 50, 100 times this. And then basically it shoots us out, and this waveform comes back, and we review it. And we look how long it took to come back, and we use beam forming a couple other techniques to figure out what happened. But you're basically, it's the same as traditional radar technology. But we can shoot it out, comes back. It's not ionizing. It won't hurt you.

It's perfectly fine to be around, like your Wi-Fi. And we can basically get both a 2D image, what's happening, and a 3D point cloud. The 3D point cloud is what's really important. So if you have like a weapon on you like in your pocket for whatever, or say I have one in like my chest, for example, we will start getting back the signals back from the top surface of the gun before we get your chest stuff back. 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 three-dimensional point cloud that you do a camera. So you basically get like almost like it looks like, So 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 visually see what's really happening. In the case of 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 or different places of it, but you can see it through materials. Like it could be backpacks, it could be clothing or a jacket. But most guns are all in the waistband pockets or backpacks, which makes sense, right? You're not wearing it around your neck on a outside of your shirt or... Like things like this.

So that's where like most weapons are entering 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 analytics. He does it daily. He's done it for five years. He's working with us through this. And we've done so much work on how people, how students enter schools, how they exit, emergency responses. What solutions are on campus now for this?

Like where, what data we find? on like 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 like, it's so high and so dangerous, like we're trying to, like, we can detect knives. Like there's, you know, vape pens, whatever, whatever. It's not a metallic thing.

It's like we can, it doesn't matter what the object it looks like. Different metallics will actually like, like, come back to the radar system a little bit differently. So you can kind of maybe sometimes tell if there's like a gun, like a metallic, like a metallic. signature or not coming from the material. But the technology is like really kind of straightforward in a 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 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 going to

cause havoc. And we're for right a lot of times we could basically start saving lives. And that's, I mean, there's, there's a, on average, one shooting every, every single day in the, like, more than that. Like, there's, you know, there's over 300 or more or so shootings roughly a year, if you look back the last couple 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 U.S. That's just at K-12, not colleges, but that's about, that's looking at the 130,000 of K-12 schools in the U.S.

If I can't, you think this will be out in a couple years? Yeah, I think we'll get out a couple years. We have a teamwork in day and night on this. We'll probably, I'll probably increase funding into it this year significantly. And we'll take a bigger push and head count. Yeah, but like right now, 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 because like schools are, you know, several meters wide,

multiple doors, sometimes double doors they 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. Yeah. Real solid work. Let's talk about Hark. Let's do it. Ready?

Okay, so, I mean, I think, my pitch here is like I've been I've been working on like one of the hardest AI I think it's I I think humanoid AI is like one of the hardest AI technologies in the planet it's just like an incredibly difficult problem that I've been my team and I have been working through day and I for last four years so it's like okay we want to go like build like a crazy sci-fi future with the with like flying cars AI humanoids and then on my other half my life I'm like using like an AI chat bot like a frontier lab like Jim and I or chat GPT and it's so

so stupid. It doesn't know me at all. Doesn't remember anything I'm saying, can't see what I'm doing, it can't use tools very well, use the internet really poorly, can't even order me a sandwich if I needed 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 I have access to my accounts? 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 going to get, like, Jarvis out of this from Ironman. We're going to get something crazy out of AI. It's going to move to a point where it can, like, it can, like, listen

and speak. Naturally, like a human. It can see the world. It can do, uh, it can use tools, like a browser and terminal. It can do real work for you and help you out. It'll know you really well. I know Sean, I'll know everything you're ever doing, all your stuff and be really personal to you. We don't have to be. We don't have to. We don't have. You don't have to anything like that now. I got like this stupid chat bot that doesn't remember the last thing I said to it. And so I decided to like, I said like there's two things here that are extremely broken. One on the AI side, we have like extreme, we have like we have like a lot of gaps to get to to get to like, like, like extremely personalized like AI intelligence.

There's just like a lot of, there's a lot of like missed up, like missed opportunity now last like a few years. I want to have a lot of gaps there. The second thing is we're like interacting with these AI systems to like old pre-AI computers. If you're pre-up your phone or your Mac or your computer, it's like they're all designed like 20 years ago. It's like an really old interface. The chatbot's an old interface.

It's the wrong interface to AGI. You're not going to 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 sitting here for like a year and a half,

being like somebody's going to do this really well and I can't wait for it. And nobody's doing it. I mean, look at Apple. I'm 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 going to basically We design what comes after the iPhone for AI, and we're going to design new models that are extremely multimodal that can solve this. No shit. Yeah. And we have, like, some of the world's best AI folks of all time, and we have, we have the lead designer from the iPhone, Abadur on the team. I mean, you sign iPhone 15, 16, 17.

So this is going to wind up being a device? Is it going to be a device? A family of devices. Yeah. And this will go really far. It'll replace your phone and computer. 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're what you like and don't like and be able to even like. act as a coach and say like, hey, you said you do this over 90 days and you're not doing this over here. It'll just, 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've been working on AM models now. Like, stuff is crazy cool. And, yeah, we're gonna... I think we'll probably come out of stealth by the time this thing airs here between you and me. Holy shit. And, um... We're self-funding it right now. You're self-funding this one, too?

Yeah, I'm self-funding right now. And yeah, the team's great, man. I think it's, yeah, I think it's going to be a massive opportunity. And I see the frontier laps 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 want to like wake up to like I always like think about I just want to wake up to a world that's like that I'm like excited and inspired.

I just like um you know I love doing this stuff. I could have retired like 10 years 12 years ago 15 years ago. So like I think um I just want to work on cool crazy shit. And um I'm just excited for a world of flying cars and humanoid robots and helping prevent school shootings and Jarvis. I mean, how do you keep it all together? I don't sleep. So you're innovating you're running out four major things. 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. It's super simple. Um, no, I mean, like, listen, I, I, uh, I mean, to be honest, like, I've had to make some like tons of personal sacrifices. Like,

you know, I think 10 years ago, I would have like a part of my life that would like be dedicated to like golf trips and, you know, doing the annual, like, college trip with, like, my friends and stuff. Like, I don't do that anymore. I spend, I have, like, my family and I have my companies. And that's all I do. And, you know, I really, I do, like, a few podcasts a year, not of much. I'm excited to come here because, like, I love your 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 kids and I have my work kids, you know what I mean? Like, and so, like, which are like, you know, these are my like, like, they're like kind of like babies.

I go, you know, make them, you need constant care and attention. So I have like this family and I need to like, I go all in on it and I do everything else less good. You know what I mean? I'm like a shitty college friend if you like, you know what I mean? If I had seen you a little while, like just like not going to spend the half a day with you on Saturday if you're in town. I haven't seen you in 10 years.

So it's 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. going to work and you know to be frank i just i i was born or raised on a farm man and i get to do like work on this cool shit every day and um and you know i i got billions behind it make like going for it great teams that work like their asses off like teams that are you know here it came with and uh it's

great and i like um 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 uh 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 park. I mean, it's... 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, it's, and it's great. I just, yeah, I just want, I'm taking a, I'm probably, I feel like peak career and my, my team with me is like peak team, peak resources. The stuff I'm working on, I feel like is very important for the world, which is also great. I didn't, you know, doing veterry is like, there was a part of me saying like, okay, is this like the thing I want to spend my whole life doing? And I have that here, which is great. These are like the things I want to spend all my time on for the next, like 20, 30, 40 years.

So it's good. I'm just like, I just don't want to screw it up now, you know? Oh, yeah. It can work. We're 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 unitary 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 is the bigger threat, not conscious machines at all, but obedient machines that can still malfunction, be hacked, surveilled through remotely controlled or turned into tools of intimidation, assassination, or state power? I don't know how that person gets up and goes to get, goes outside every day.

I feel that scared. So I think like, the future is this future, it can be like molded and. morphed and like it's what we want to do with our time. If we want a future full of like robotic systems that can help us out and free us of our times and things like this, like we're going to wheel our way to make that happen. I'm a pretty pretty like optimistic person. I feel that having millions and then billions of humanoid robots on the planet is 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, you know, 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 like, We're generating AI systems that can be embodied and can use computers. Like, 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, 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 we'll 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, will 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, like I said, it's gonna be a really hard road. Yeah, I mean, it's just an incredible advancement. And, you know, I know there's a lot of fear around AI. I have a lot of fear around AI, but we're gonna go through it one way or another. And, you know, I do think things are gonna be a lot better on the other side of that.

You're not stopping it now. It's like the, it's like, it's go time. It's gonna happen for sure. I think it's to be fine. Like, I think, you know, I use AI every day. It's like, it's fine. It's like, you know, like, it's a chat bot. Like, I think, yeah, if, 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. There's obviously always path that could, like, not go well, but, like, being conscious of that and, like, basically doing everything possible to steer it in the right direction is, like, what we like what we have to do at this point. Like this is not like something we can turn off. You're gonna turn off the internet. Yeah. You're gonna stop people from trying to build like systems

that like make us more productive and do work. I don't think that'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 when we're talking about interacting with the humanoids. Yeah. People, you know, and I've had this discussion on other podcasts too,

but people are going to look at that for advice, relationship advice. And I mean, I think there's a, you know, a lot of important things. They're 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, GPT, and all these other clod and all these other things anyways. But who were they getting for vice from before that? probably I think you know what I mean it's it's it's I think it's the caliber of person yeah totally but yeah yeah so he's been time with yeah last question what advice do you have for

future founders I have a few things I think are I wish I could like maybe say differently also like passed down like young Brett like 20 years ago um I think one is like uh 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 stairs I'm 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 sports player. You're 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 any industry, tech, non-tech or whatever. You're generally going to go and just try to like have this like,

have this kid that needs a lot of attention. And then at some point it's like you just can't abandon this thing and you got to keep like spending more time with it. And it needs a lot. And it's like constantly working on the problems with it. So it's like not the fun things. You're working on all the hard things. It's like this problem funnel I have where I work 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's been there for sometimes a really long time.

And if you're successful, and even if you sell your company or whatever go public, you're getting your stock locked up or you're investing out over many periods of times. You've got to be in it for quite a while. And I find that for me, the things I work on as probably the most important things I could be doing with my decision making.

And that's happening at a micro level inside the companies with whatever I work on week to week, month to month. But it's happening at a macro level where, like, where do I spend my time? Like, I'm 39 right now. Like, where do I spend my time as 39-year-old Brett? 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, there's like a non-linear effect here for, like, starting companies that are, like, easier versus harder. Meaning, starting something that could be, like, a hundred-time-time higher outcome is generally not a hundred times harder.

So like doing figure is not a hundred times harder than doing another robot company. It's probably like three times harder, maybe five times harder. But the total restable market, the opportunity is probably millions of times bigger than another like robot that's like on assembly line moving back and forth. And so I think there's like this nonlinear effect to like decision making here that is really important where harder things that have like larger outcomes are like usually easier to recruit the best talent in the world. That gives you a better lift to build a better product and a better team.

That team and better product and maybe even a bigger industry because it's harder will give you like more capital coming at you for disposal to be able to like make the right investments you need into the right, say, equipment or people or personnel or whatever, marketing to basically make you more successful. And then you're generally worth like working inside a 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 a high risk reward.

They want to, you know, investors and things, and even people. They want to like go in, like if it works, they want like 100 X or a thousand X. They don't want like a 2X. And generally for venture, like 95% of people fail. So if it works, you really want to go, you want to hit a grand slam. So I think my philosophy is like,

like, choose wisely what, like, a young Brett, choose wisely what you work on, young entrepreneurs. And then I would try to be as ambitious as possible. There's capital for that, and there's humans for that that want to work at really crazy shit. We have them at my companies, and they're incredible. You met some of them today. They're just like, you know, my design lead and a bunch of other folks here that are just unbelievable of what they do.

They're the best in the world of what they do. But they want to come here, they want to try to do something like it's never been done before. They don't want to go off and design the next car or do the next AI product everybody else is doing. They want to be here signing something revolutionary. So I think that's like somebody not stressed 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 done it before. And the signal of noise out there is just so high or so low. Like, 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, what should I do in this situation? I never have had it. And I wish I had.

But there's no book, there's nobody to call. Yeah. And I think that makes it really hard. But it's possible. You can just 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, they 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 going to 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 butts.

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 of what 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 basically can mold the future to how you kind of want to if you try hard enough. And the goal at the end of the day is just to not die. If you don't quit, you won't die. So like, anyway, I think, I think it's, listen, been playing this now for 20 years, still playing it. I feel like I'm in the early endings of 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 bred anyone. 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. That's great advice, man. Cool. Well, Brett, fascinating interview. Love everything you're doing, man. Like, incredible stuff. Huge advancements. Sean, I'm a huge fan of you and just everything you do. So, I mean, having me here and, I mean, going through all this is just, uh, it's been, great. Thanks for having me. Thank you. It's been an honor.

Great. Cheers. Yeah. 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. of year, the school calendar really starts to fill up, spring activities, testing season, and that final push toward the end of the year. It's a great moment for kids to stay focused

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