#292 Brett Adcock - Shawn Ryan Meets a Humanoid Robot

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

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

• 0:00Speaker 00

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• 0:30Speaker 01

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• 1:04Shawn Ryan?

  Brett Adcock, welcome to the show.

• 1:06Speaker 03

  Thanks for having me on.

• 1:09Shawn Ryan?

  I've been looking forward to this for a long time. The robotics guy.

• 1:12Speaker 03

  Yeah.

• 1:13Shawn Ryan?

  Let me give you an intro here real quick before we get, 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 Vettery, an AI-driven talent marketplace, which was acquired for approximately $100 million. Co-founder of Archer Aviation, developing electric vertical takeoff and landing EV TOL aircraft. Found Cover, an AI security company using NASA jet propulsion laboratory technology to detect concealed weapons in K through 12 schools. That's amazing. In late 2025, he launched Hark, a new AI lab self-funded with $100 million

• 1:58Shawn Ryan?

  to build what 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.

• 2:38Speaker 03

  Thank you.

• 2:38Shawn Ryan?

  He didn't give you any tips on that, did he?

• 2:43Speaker 03

  Alright, I gotta, I gotta hit one.

• 2:50Shawn Ryan?

  Hmm, what do you think?

• 2:51Speaker 03

  They're great.

• 2:52Shawn Ryan?

  You can leave this guy here if you want to.

• 2:54Speaker 03

  This guy, this guy's staying.

• 2:55Shawn Ryan?

  That is the coolest thing I've ever seen as far as giving somebody a gift on the show. That was awesome. That was awesome.

• 3:04Speaker 03

  I got you another gift.

• 3:06Shawn Ryan?

  Oh.

• 3:07Speaker 03

  Something you can keep here, put on the shelves.

• 3:11Shawn Ryan?

  Thank you.

• 3:11Speaker 03

  Yeah.

• 3:17Shawn Ryan?

  No way.

• 3:18Speaker 03

  Yeah, little robot.

• 3:19Shawn Ryan?

  That is awesome.

• 3:20Speaker 03

  Mm-hmm.

• 3:21Shawn Ryan?

  Thank you.

• 3:22Speaker 03

  Yeah, no problem.

• 3:24Shawn Ryan?

  Very cool.

• 3:30Shawn Ryan?

  Well Brett, we've got a lot to talk about here. So, man, how many companies are you running now?

• 3:39Speaker 03

  Man, I'm like, I'm not sleeping. I have too many.

• 3:42Shawn Ryan?

  I'll bet.

• 3:43Speaker 03

  Too many, just like, kids and work and just like, yeah.

• 3:47Shawn Ryan?

  That thing is amazing.

• 3:48Speaker 03

  Never sleeping anymore.

• 3:49Shawn Ryan?

  I'll bet, I'll bet.

• 3:51Speaker 03

  Yeah, what'd you think of the robot?

• 3:52Shawn Ryan?

  I, I think it's incredible. I wanna, I can't wait to talk more about it.

• 3:56Speaker 03

  Yeah.

• 3:56Shawn Ryan?

  So, um, couple things, just one more thing to knock out here before, 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 every single guest a question. This is from Stephen Casey. "In today's marketplace, we find that AI platforms can sometimes invent answers rather than admitting to, 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?"

• 4:41Speaker 03

  Yeah. Yeah, we don't want the Terminator popping out here when we

• 4:44Shawn Ryan?

  Definitely not.

• 4:45Speaker 03

  This work, right? I mean, I think like, uh, we're, we're chatting about this outside, like, you know, I think one thing to say, like, four years ago, when I like, you know, we started the company, there was like no path for humanoid robots, like,

• 5:01Speaker 03

  to make it into like people's homes in the next like 10 years. There was, there's no good story. There was a, 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, uh, you basically can like use, use like neural nets, like use like basically an AI first strategy with. There was, there's none of that existed. I think we're like, we're thankful now. Like, looking back, like that we like, uh, feels like we somehow pulled like 10 years of the future forward. We have like electric humanoids that like are reasonably priced that can do like a useful human work with neural nets. And it's just like, uh, I think it's, it's just an incredible, it's an incredible place to be in getting those questions, which is like, how do we make this work now at scale in a safe way?

• 5:45Speaker 03

  Cause, um, you know, that's the, that's the spot we want to be in, not like, uh, trying to make this work for 20 years.

• 5:50Shawn Ryan?

  Yeah.

• 5:51Speaker 03

  So I think, I think the, it's a very, it's, 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, 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, uh, intrinsically, we want the robot hardware

• 6:35Speaker 03

  and the robots around humans to just be safe all times. And separately, we have a, there's a bunch of like semantic safety and other things that we need. Like that we're, we have either put in place or putting in place now to make the robot just like work is safe in the environment, like, uh, You have a candle at home. You don't want the robot to accidentally knock it over. Uh, that's like, that's like an intelligence thing, uh, 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, this mechanical thing in your house is like safe around everybody around it. I think the, the direct answer is like still a lot of wood chop. Uh, of getting this play, getting this thing to a point where it's like, uh, uh, we trust it to be autonomous next to my kids all day long in my house.

• 7:18Shawn Ryan?

  You have one in your house?

• 7:20Speaker 03

  We've had, we've had many robots now throughout my house in testing for the last like year or so. And, um, I've had it like, you know, kind of near my kids in some aspects, but we're always like, we're always like monitoring it.

• 7:30Shawn Ryan?

  What do your kids think?

• 7:32Speaker 03

  Man, they like, it's just like kind of normal for them now.

• 7:35Shawn Ryan?

  Do they try to talk to them or like?

• 7:37Speaker 03

  Yeah, I talk to it. Yeah. They want to like, they want to go, they want to go like, they want to go like, jump on it and touch it. And, uh, you know, and do kids things, you know what I mean? Like they want to go touch it and talk to it and be around it. And, uh, 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'm, I feel okay. And we're not there yet. I, I think we will be in the next several years.

• 7:58Shawn Ryan?

  What, what's the longest they've been around any one particular robot?

• 8:02Speaker 03

  We've had a robot in my house for like maybe a couple of months, uh, doing work kind of on and off. You know, daily, sometimes every other day and, um, you know, the kids are kind of at school or sometimes at home. So not, oh, they weren't always around, uh, whenever the robots were running, but a lot of times and, um, and you know, that was just like our home robot.

• 8:24Shawn Ryan?

  Do they get attached to them?

• 8:25Speaker 03

  They named it.

• 8:26Shawn Ryan?

  Emotionally attached?

• 8:27Speaker 03

  Different names for the robot and, um, yeah, they love it. And it's, it's actually a question we're asking in the office of like, you have a robot in the home and it's like, uh, it's got some like character to it, a little wear and tear. Do you like, want to keep that robot or do you want like a new one?

• 8:39Shawn Ryan?

  That's what I'm wondering. What's the emotional attachment? I think kids are like the perfect test case.

• 8:45Speaker 03

  My kids wanted it. They wanted it there.

• 8:47Shawn Ryan?

  We're not getting rid of this guy?

• 8:49Speaker 03

  Yeah. It's got a little, it's banged up a little bit here and there and it has a tear here and they just like loved it.

• 8:54Shawn Ryan?

  That is, that's wild, man.

• 8:55Speaker 03

  Yeah.

• 8:56Shawn Ryan?

  That is wild.

• 8:57Speaker 03

  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.

• 9:03Shawn Ryan?

  Wow. Yeah. We were talking, I mean, I did just some of the stuff that you just mentioned. I mean, uh, the complexity of the problem that you're solving here. I mean, all these little problems that even like knocking over a boiling pot of water. I never would have like.

• 9:19Speaker 03

  It's just like.

• 9:19Shawn Ryan?

  Just thinking about something that it happens every day. And then you think of all the things that happen every day in just a regular household and.

• 9:27Speaker 03

  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, problem scaling and commercializing and getting the system reliable. Manufacturing problems like we, we, we have a problem fun house. If you want to come by campus here.

• 9:43Shawn Ryan?

  I'll bet.

• 9:43Speaker 03

  And check it out.

• 9:44Shawn Ryan?

  Bet you do. I'll bet you do. Well, some people say I, some people say AI is that is in 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?

• 10:05Speaker 03

  Um, absolutely not. Like the, I, I, I think, um, I think you'll see the, some of the most transformative events and technology happen over the next like 36 months we've ever seen and

• 10:19Speaker 03

  in our like, um.

• 10:21Shawn Ryan?

  Ever?

• 10:21Speaker 03

  Ever.

• 10:22Shawn Ryan?

  I don't, I don't feel like we're in a bubble here. I feel like we're very scrape, we're, we're barely scratching the surface.

• 10:27Speaker 03

  I'm watching AI in a, in a human body do human work like early. It's early. We don't have, you know, we, we don't, we like at some point here this year, we'll have thousands of robots. We have like, you know, we have hundreds now, like, but like we, we need like millions of robots to make an impact. That's just going to take some time and it's going to be crazy cool. Um, so we're just, 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, that, that'll for sure work and it'll go really far in our lifetimes. And then separately, um, we have AI now that can use computers like humans and can think. We're, I've shown you a little bit of that here before the show.

• 11:04Speaker 03

  And, uh, you know, that, that, that will manifest in a point where like, uh, 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, um. I mean, that's going to lead to such a productivity. Like, we measure like GDP per capita, like per human. But if you're able to make like as many synthetic humans, like millions, billions, tens of billions of synthetic humans. In the case of the digital world, maybe trillions. That'll lead to the, I mean, I think the greatest increase 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.

• 11:47Shawn Ryan?

  Wow, wow. What do you, I mean, I'm just curious, what do you think? What will humans be doing?

• 11:53Speaker 03

  I mean, I hope I don't have to like, I woke up today, I was like, I'm in the dishwasher, getting my kids breakfast, like, just like busy work that I 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, uh, wish I wasn't doing that stuff. And then I'm like, uh, all throughout 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 ordering lunch, like all this stuff I'm doing all day. And I don't want to do any of that. I want to be like fully free.

• 12:22Shawn Ryan?

  I get it. No, I totally.

• 12:24Speaker 03

  And I just want to be like clear headed and I want like my AI to run a little Brett 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, uh, in my like operating system and like a human in a box.

• 12:43Shawn Ryan?

  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, you're, you're disseminating it to, to robots.

• 12:58Speaker 03

  I'm going to delegate all this out.

• 13:00Shawn Ryan?

  That's amazing, that's.

• 13:02Speaker 03

  Like, we'll, we'll do that in like 24 months. Like, we'll have all this stuff so good that you'll like, uh, 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, um, doing laundry and dishes and.

• 13:18Shawn Ryan?

  Just the bullshit legwork.

• 13:19Speaker 03

  Yeah, I don't like, is anybody want to do that? Like, I don't want to do it.

• 13:22Shawn Ryan?

  I don't.

• 13:22Speaker 03

  Yeah. So like, yeah, you clear all that from my life, like I gotta spend time with my kids, like enjoy life, like kind of be like, like, I guess like clear headed. Do stuff I really love, like I love working, but I don't like doing all this busy work.

• 13:34Shawn Ryan?

  Yeah.

• 13:35Speaker 03

  Um, 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.

• 13:45Shawn Ryan?

  That's I don't know why I've never thought about that. I've never thought about it like I've always looked at it as fear. I've always been like, oh shit, they're gonna take everything over.

• 13:54Speaker 03

  It's a compression algorithm. Like, we're basically running a large-scale compression. So like, I think, you know, my, my, my, the way I look at it now is, we basically had built like synthetic human intelligence that can use computers and machines. So like, I'm gonna delegate out all this busy work on both my digital life and physical life to like to robots. And they'll just do all of it. But it's, 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, uh, just, I mean, a phone call to ours before we started and talked about my schedule and how to ask for things and I ask it for things and have it do things.

• 14:36Shawn Ryan?

  Yeah, you had a chicken, chicken salad ordered to deliver to your office.

• 14:40Speaker 03

  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, and then physically, like, I'll, I'll have all this work done by my, by robotics. Both in, you'll have in the commercial workforce and the billions, like manufacturing and healthcare and construction and in every human, at some point, we'll have a humanoid just to do all that busy work for you. And not only that, but like something to come home to that you can talk to that will like will know you.

• 15:08Shawn Ryan?

  Wild.

• 15:09Speaker 03

  Yeah, it's like the, yeah, it's gonna happen now, which is like really gonna be fun.

• 15:13Shawn Ryan?

  Yeah, yeah.

• 15:17Shawn Ryan?

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• 16:02Shawn Ryan?

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• 16:41Shawn Ryan?

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• 17:23Shawn Ryan?

  That's spotonfence.com/srs and use code SRS for $50 off.

• 17:33Shawn Ryan?

  Well, I would like to do a little bit of a life story on you if that's... Does that sound good to you?

• 17:37Speaker 03

  Yeah, let's do it.

• 17:38Shawn Ryan?

  Where'd you grow up?

• 17:39Speaker 03

  Uh, central Illinois.

• 17:40Shawn Ryan?

  Central Illinois?

• 17:41Speaker 03

  Yeah, like a small town, like, uh, 700 people.

• 17:45Shawn Ryan?

  700 people?

• 17:46Speaker 03

  Yeah.

• 17:46Shawn Ryan?

  Wow.

• 17:47Speaker 03

  We called them Awikwa.

• 17:47Shawn Ryan?

  That's even smaller than where I grew up.

• 17:50Speaker 03

  Yeah, where'd you grow up?

• 17:50Shawn Ryan?

  I grew up in small town Chillicothe, Missouri.

• 17:53Speaker 03

  How small?

• 17:54Shawn Ryan?

  About 8,000 people at the time.

• 17:56Speaker 03

  Yeah. No, we didn't have, uh, we didn't have anything. 700 people.

• 18:02Shawn Ryan?

  Man, what were you into?

• 18:05Speaker 03

  Yeah, so like, you know, kids, sports, computers, like, uh, got into computers really early, uh, did a bunch of sports. Um, you know, we, I grew up on a farm, so it was corn and soybeans. My, my, uh, my family was third generation of this, so.

• 18:20Shawn Ryan?

  No kidding?

• 18:21Speaker 03

  Yeah. Yeah, so.

• 18:23Shawn Ryan?

  Third generation, three generations of farmers.

• 18:26Speaker 03

  Three generation agriculture farming.

• 18:28Shawn Ryan?

  And then we switch over to AI.

• 18:30Speaker 03

  Yeah, we're doing like, uh, humanoid robots now and AI, AI, AI systems. Um, but yeah, no, I got, I got really interested in computers like really young. Um, started a bunch of like startups and like, in like, you know, in high school and college.

• 18:45Shawn Ryan?

  What kind of startups?

• 18:46Speaker 03

  Um, you know, at first, just like, uh, mostly things on the web, like selling things. Uh, um, did a bunch of like, uh, different types of, um, like products I was selling on the internet for like throughout like high school and college. Um, small like drop shipping, retail electronics, like all kinds of, all kinds of things. Uh, Legion Marketing and just fun stuff. It was like, no, nothing serious, you know, just like, uh, playing around the internet, trying to make some money. Just, I didn't grow up with money. So it was like, internet was a way to like, uh, like, you know, maybe make some money. Like, it was really fun. Um, you know, I loved like the ability to go out and create things and, uh, kind of control my destiny. So it was just something I, I, I attached to really early on.

• 19:28Shawn Ryan?

  Right on, right on. Do you have brothers and sisters?

• 19:31Speaker 03

  I have a brother. Yeah.

• 19:32Shawn Ryan?

  Well, I mean, what, is he a farmer?

• 19:35Speaker 03

  Uh, Colby? No, he actually runs, uh, an AI defense company called Scout.

• 19:40Shawn Ryan?

  No kidding?

• 19:41Speaker 03

  Uh, yeah, basically building autonomy and, uh, like AI models for, for defense in the military.

• 19:46Shawn Ryan?

  So you guys both got into AI?

• 19:48Speaker 03

  We both got into AI. We, we live like a block away from each other today.

• 19:51Shawn Ryan?

  Like, serious?

• 19:52Speaker 03

  Yeah, we grew up together really close, uh, went to same, we same college, uh, we're like different ages, a couple of years apart. And then, uh, and then we were in New York for about 15 years together. And then, uh, he just moved out to California. We live literally a block away. Uh, see him almost every weekend. He has a startup like basically 10 minutes away from, uh, from mine of, you know, where I'm at now. And, uh, he's, he's doing great.

• 20:14Shawn Ryan?

  Man, what, I mean.

• 20:17Shawn Ryan?

  What do your parents think when you're coming home with with what you guys are involved in and what you're what you're creating? Because you're such a wild, you know what I mean, from farmer to to this.

• 20:29Speaker 03

  Honestly, like, I think one thing my my parents both drilled into, 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.

• 20:46Speaker 03

  So early on, he's like, listen, if you want to control your destiny and 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, beaten to our heads, like, growing up, like, you know, at some point, you need to, you know, you need to probably get out of here, get out of farming, it's not doing well. And you need to start something on your own. And so just, like, 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.

• 21:13Shawn Ryan?

  But you got some proud parents, man.

• 21:16Speaker 03

  Yeah, parents are great.

• 21:17Shawn Ryan?

  Wow.

• 21:18Speaker 03

  Yeah, they, they're like, what the hell's going on here? What are you, what are you doing? But I've been doing pretty crazy stuff for a while now. So I think it's like, it's gotten to a point where it's like, you know, even at 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.

• 21:37Shawn Ryan?

  Were you rebuilding stuff as a kid, too?

• 21:39Speaker 03

  Yeah, constantly building stuff.

• 21:41Shawn Ryan?

  What kind of stuff?

• 21:42Speaker 03

  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 a more of a visual learner, too, like, I like building stuff and seeing it and touching things. And even like, honestly, doing internet for like, I did like, I was like, I did work in the internet and 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.

• 22:18Shawn Ryan?

  Wow.

• 22:19Speaker 03

  Basically, like atoms.

• 22:20Shawn Ryan?

  Man, so where do you go? Where'd you go to school?

• 22:23Speaker 03

  I went to University of Florida.

• 22:25Shawn Ryan?

  University of Florida?

• 22:26Speaker 03

  Yep.

• 22:27Shawn Ryan?

  Where do you go from there?

• 22:28Speaker 03

  Um, so after school, I moved to New York and I started working on software startups.

• 22:37Speaker 03

  And during college, I was working on basically a bunch of like side, small like internet things. And, um, and then, uh, kind of like shortly after college, I started a company called Vettery. And the goal was to basically build like a, I really, um, kind of going through college is like, you gotta look for a job. You had to go find something full time and, um, got caught up in like the whole interviewing process of like looking for jobs. I just thought I was so broken, like applying for jobs and like never hearing back and like you have to go through headhunters. And then it basically became like a 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, uh, it wasn't very much a meritocracy. And I just thought the whole process was extremely broken.

• 23:21Speaker 03

  And, um, so I started Vettery is we were basically 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, uh, like the headhunter industry is like hundreds of billions of dollars a year.

• 23:39Shawn Ryan?

  I won't, I won't even, I won't, I won't use it.

• 23:42Speaker 03

  No, like I know.

• 23:43Shawn Ryan?

  I just hear, I keep hearing, everybody gets ripped off.

• 23:45Speaker 03

  Ripped off. It's so expensive. Like pay like $50,000 a hire. It's like insane.

• 23:49Shawn Ryan?

  And then they'll, and then they'll coax the guy out that they just brought to you and have him go to another job.

• 23:53Speaker 03

  Yeah, they'll like they'll force you in this role so they get paid a commission.

• 23:56Shawn Ryan?

  So Vettery, Vettery is a connector?

• 23:59Speaker 03

  Yeah, connector. Uh, well, funny enough, we ended up selling to like the world's largest recruiting company that does staffing. But like, uh, let's leave that for a minute, but we, um, basically started in, uh, 2012. And, um, 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 the time we were like, let's use AI, but it was basically like, uh, how to use a lot of algorithms to figure out like what people want and then make matches. So you can just push of a button, connect the right folks and then, uh, 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.

• 24:39Speaker 03

  Um, we started just in tech in the U, in the US. So, um, at one point we had about, I think about a little under 20 or so cities globally that we were operating in. But most of it was tech. Talent tech spaces, you know, at that point.

• 24:52Shawn Ryan?

  How long ago was this?

• 24:54Speaker 03

  Started in 2012 and then ended up selling the business in 2017 or 2018. So about five years, six years.

• 25:00Shawn Ryan?

  Right on.

• 25:01Speaker 03

  Yeah.

• 25:01Shawn Ryan?

  Then where did we go?

• 25:02Speaker 03

  Okay, so, um, vet- vettery 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 ended up selling, ended up things, ended up going, uh, doing really well. The business like completely hockey-sticked in growth and we got all the things figured out and just like.

• 25:25Shawn Ryan?

  Vettery?

• 25:26Speaker 03

  Vettery was and then, um, ended up, uh, getting approached by the world's largest recruiting company, the same groups you like, you and I were talking about. Like, like, it's the same, the same groups we were trying to take out of business. And they were like, uh, like, oh, we want to acquire, 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 that point, almost seven years in and, um, and, uh, you know, they, you know, we were, uh, excited about an acquisition a year before that, uh, $10 million from one of the big tech companies and, um, they came in at 110 million. And, um, and it was, uh, it was a good time for me. I felt like, uh, the business was doing well. I learned a lot and I was kind of ready for my next chapter. So ended up selling that business, uh, to the Deco Group. It's like the world's largest recruiter and recruiting company. Um, and, uh.

• 26:11Shawn Ryan?

  But you, you, you didn't even have it for sale. They just approached you?

• 26:14Speaker 03

  Yeah, we, we didn't hire a bank or anything. Uh, 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 manage all that processes. So we were like, it was, the growth was just unbelievable. And, um, and there was like, there was something better to like a human jam in your roles, right? Like, it's just like, you need like, um, and then to extent you can get, you know, all the world's, uh, like talent there and all the world's, uh, 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.

• 26:57Speaker 03

  So we kind of that clicked. The world's largest recruiting company came in and said, we got to buy this thing. And

• 27:05Shawn Ryan?

  Yeah, I'll bet they did.

• 27:05Speaker 03

  Yeah, I sold the business and it was great. It was a good time for me. I really at the point, it was a point in my life where I really wanted to do something much bigger. And so I took about, I basically took about a year. And so it took about a year by the time I got the term sheet to sell to when we actually sold and closed. It's a long process you have to go through, like tons of docs, 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 of industries that I've, I've been interested in robotics and aviation and some areas of security for like, basically since college. And I basically spent a lot of time trying to figure out if I was either

• 27:50Speaker 03

  going to work on, at the time, school shootings, like basically 10x. And I was like, man, there's got to be something to do here. And we can, you know, and then secondly, I really wanted to work on like flying cars. Like having watched a lot less sci fi as a kid is like, man, like, I really want to go. There's a, there was a, there was a near term problem of like, we got to go help with security in schools, K through 12, mostly in the US. And then, 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 Veterans, I started Archer Aviation. And basically, like, the story here is, you can build like an electric aircraft that can take off like a helicopter.

• 28:34Speaker 03

  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 heli, like a helicopter can take off from a, a building or a helipad, or an airport. And so you take off, if you can take off vertically, you can, you can basically nestle the aircraft inside of cities. Half the world lives in cities today. It's, it's You know, by like the middle of the century, it'd be like seventy percent of the world. And you just like can't get around like it's just gridlocked everywhere in major cities. It's just like socks to go like twenty, thirty miles. It takes like an hour in most cases. So basically you can build, 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 rearchitect the whole aircraft to be fully electric. The reason you want to do that is for cost and safety.

• 29:19Speaker 03

  You basically can make it like, like a lot like less expensive. You can put a lot less parts in the aircraft that are also good for safety. So basically you can build like an electric flying car that you can move around. So instead of like calling an Uber or driving that might take you an hour in LA or SF or New York. You basically can fly there in ten 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.

• 29:49Shawn Ryan?

  I mean, where do you, yeah, I'm just. I know you just sold your business for a hundred and ten million dollars, but where do you get the confidence to? Where do you, where do you get the confidence to go? I'm going to build vertical takeoff and landing flying cars now.

• 30:06Speaker 03

  I mean, listen, I didn't wake up to this world like learning how to build software. So like I learned how to, how to do that and run engineering and run, run the company. And there was like a lot of through trial and error. And I just felt like, well, I just felt like I could learn it. I started in industrial system engineering at University of Florida. So, and then, you know, ran engineering and ran the company at Vettery. So I basically just hit the books. I tried to learn as much as possible about three subject areas. First was like electrification, which like, you know, 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.

• 30:51Speaker 03

  You really need wings like so, okay. So you basically have to learn about those three subjects. So I started, I basically bought my, my, my basement downstairs at home. There's like every possible book on these subjects you could match in. And started reading as much as I possibly could. And this was during the year transition. As I was transitioning like out of, out of Vettery into Archer, I was reading every possible thing. And then I found a small community of folks that were like hosting onsite, like either half week or week long courses for this. And so I would go to these, sometimes they're sponsored 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, like 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.

• 31:36Speaker 03

  Like how, how do you actually, like, how would you actually build an electric aircraft? So electric aircraft, what's interesting is like in rotorcraft. Like you basically want to make, to create the most efficient lifting device possible. You need as much of like as the rotor disc area, like in terms of surface area as you possibly can. It's why the helicopter rotors are so large. We want it to be really large. That'll reduce power and get you off the ground. And electric aircraft, the problem you're starting with is you have like one thirtieth of the energy. Is as you do in kerosene in a battery pack. So you're just like, you're off the bat of one thirtieth less range or one thirtieth less energy. And so like power becomes like the dominating factor of like, of how to basically build electric aircraft.

• 32:20Speaker 03

  Like, how do you get power down as much as possible? You really want a lot of disc area.

• 32:26Speaker 03

  A lot of disc area is, well, one, it could, could be good for power, but it's also bad because you have like no, like no redundancy in the system. You have like one rotor blade that if it didn't go, doesn't go well, you go down. With electrification, you can basically build much smaller.

• 32:42Speaker 03

  Like basically rotors and they'll fully electric. And the reason you can't do that with like traditional, kind of like turbofans or engines is it gets too inefficient at these sizes. You can't build 12, like propellers on a helicopter.

• 32:56Shawn Ryan?

  Gotcha.

• 32:56Speaker 03

  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 the size of your chair, same, same efficiency. When you do that, you create a lot of redundancy across the system. So you can build like an aircraft with 12 electric motors.

• 33:14Shawn Ryan?

  So, so there's those.

• 33:16Shawn Ryan?

  The rotors are underneath.

• 33:18Speaker 03

  Um, basically 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 thirty 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, uh, how do I go build it? So it's actually at a, I was at a Hyatt Regency Hotel in Atlanta in 2018. Uh, I was on, it was an electric propulsion week long design course and like aerodynamics course for winged aircraft.

• 33:59Speaker 03

  And I met a guy there that, um, 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, um, he's like, uh, I'm like, what, what are you doing here? He's like, oh, I want to go like do a career in EVTOL aircraft. And, um, it's called electric vertical takeoff and landing. Um, so you know, a helicopter is a VTOL and you put a little E in front, it's over electric. And I was, he's like, ask me what I'm doing here. I was like, I'm sorry, I'm starting a company to do this and I need to figure out how to go build these things. And, um, 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?

• 34:42Speaker 03

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

• 35:26Shawn Ryan?

  Holy shit.

• 35:27Speaker 03

  Yeah. And it was, um, it was a, it was a great time in my life. Um, and, uh, 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.

• 35:41Shawn Ryan?

  Gotcha.

• 35:41Speaker 03

  So there was nobody in the world that understood how did this all stuff that works? So I had to go from scratch, like learn it from first principles and, um, and then ended up moving the company out to California, uh, basically a few years into the business. Um, 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 by a $6 billion publicly traded company today. And, um, yeah, designed basically like now four or five generations of aircraft at Archer. And, um, it was hard. Uh, you know, um, 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, uh, it was, um, it was, it was hard. Even going public was like, probably

• 36:25Speaker 03

  like one of the hardest experiences of my life. It was just.

• 36:28Shawn Ryan?

  Really? Why is that?

• 36:30Speaker 03

  We, we went public through a SPAC process. So you know, SPACs at the time, like four or five years ago, we're like all the rage.

• 36:36Shawn Ryan?

  Okay.

• 36:37Speaker 03

  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, um, it was hard because, um, in 2018, 2019, uh, coming off of software, I, I had never done hardware before. So A was like, like hard to raise capital and, um, uh, B, there was nobody funding like deep tech, electric vertical takeoff and landing, like companies, like, you know what I mean? The, the big V 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.

• 37:14Shawn Ryan?

  Oh shit. Are they always behind like that?

• 37:16Speaker 03

  They're, they, the mandate for most of these VCs in the Bay area or Silicon Valley and stuff are not to do hardware.

• 37:21Shawn Ryan?

  Gotcha.

• 37:22Speaker 03

  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. Uh, you know, top VC in the US is investing in a humanoid company.

• 37:35Shawn Ryan?

  No shit.

• 37:36Speaker 03

  And as of last, last six months ago now, nothing like this don't, it's just, they just don't do this stuff. And, uh, and so like I end up, I end up going all, so I, you know, like, um, you know, made, just made a hundred and 10 million dollars. And I just sold the company for a hundred and 10 million dollars. I made a lot of money, like personally. Ended up going all in on Archer through the IPO, like, uh, through going public. I, I put like all, all the money I basically, I bought a house and the rest of the money went all into it. And, um, and it was a stressful period. So we went, went, went public through us back. And the reason it was tough is, um, we ended up getting to a point where we just couldn't raise enough money privately. Like it was, uh, it was either like raise, you know, a hundred million dollars privately at like, you know, some valuation, three, four or 500 million dollars.

• 38:20Speaker 03

  Or is yours like go public and raise like a billion dollars.

• 38:23Shawn Ryan?

  Wow.

• 38:24Speaker 03

  And, uh, we ended up going public and raising a billion dollars.

• 38:26Shawn Ryan?

  Wow. You've got a huge appetite for risk, huh?

• 38:29Speaker 03

  And we got sued during it.

• 38:31Shawn Ryan?

  Oh, really?

• 38:32Speaker 03

  Yeah. Like we got sued by, uh, basically like Boeing and a big, uh, big startup that was founded by Larry Page, Google founder. And, um,

• 38:40Shawn Ryan?

  That's gotta be intimidating.

• 38:41Speaker 03

  Yeah. It was, uh, woke up to like a front page of New York Times article about.

• 38:45Shawn Ryan?

  Oh shit.

• 38:46Speaker 03

  Yeah. It was, uh, it was crazy. I mean, the backstory is I, um, the, I took, so Larry Page started a company and, um, in the Bay Area. About 10 years ago called Kitty Hawk and they were the, like, they, they did a great work over like 10 years in electric, like VTOL aircraft. And, um, I ended up taking, I ended up taking basically the, the core, like the core 10 to 15 folks that were there all came over to Archer. Like within the first two years.

• 39:13Shawn Ryan?

  Wow.

• 39:13Speaker 03

  And, uh, they retaliated by just like trying to harass us while we were going public. And, um, so yeah, it was just a crazy story, uh, getting public. Uh, I ended up getting public, you know, billion dollars on the balance sheet and we just started building like aircraft. And, um, started building the service, like thinking about the app and how you're going to check in and how you're going to build, uh, places like real estate to fly into. And, um, Yeah, and then like the engineering work we had to do around there of designing, you know, it's basically, it's basically a flying robot. You have like a battery systems, electric motors, sensors, embedded software and control systems. And basically, like the robot you saw this morning, like very, you know, that's like it's a flying,

• 39:55Speaker 03

  it's a 6,000 pound, four passenger piloted robot. And it has 24 degrees of freedom on the system, like wing, wing flaps. We like, we have a, we tilt the front, like the leading edge, six motors, 90 degrees for basically take off vertically and then go into forward flight. And then all the propellers, fan blades on, have variable pitch propellers. So it's like a highly over actuated system that needs like a really good software. Like no human can like fly it basically without a really good control software.

• 40:27Shawn Ryan?

  What, what altitude is it flying?

• 40:29Speaker 03

  About few thousand feet, so about 2,000 to 3,000 feet above ground level.

• 40:33Shawn Ryan?

  And that's what it would normally be?

• 40:34Speaker 03

  Yeah, like traditional helicopters fly at these levels.

• 40:36Shawn Ryan?

  I mean, what, what, I think about, I think a lot about Tesla and, and all the EV vehicles that are coming out and, you know, it's, the government just seems so far behind on AI, you 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, for, from an overreach standpoint. But if you, if you just think about the traffic in the cities and if you have the AI, you know, processing all this, that even, even without air vehicles, I feel like a lot of that would go away because the, the, the AI will route you the quickest.

• 41:19Speaker 03

  Yeah.

• 41:19Shawn Ryan?

  It take all the traffic patterns into account and it would just flow.

• 41:23Speaker 03

  Yeah.

• 41:24Shawn Ryan?

  A lot easier.

• 41:25Speaker 03

  Yeah.

• 41:26Shawn Ryan?

  But there's all this government regulation.

• 41:30Speaker 03

  I think it's also hard because like, if you look at the number of installed cars in the world, like billion and 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 the order of like 20 years to replace all the cars. So if all the cars were electric and autonomous today, autonomous cars have like autonomous hardware in them. It's not like you can just go out and retrofit all the cars in the world right now. Like it's a, it's a hard problem.

• 41:52Shawn Ryan?

  Well, I mean, if you look at Tesla, for example, I mean, it can self-drive, right? It can come get you.

• 42:00Shawn Ryan?

  But when you're driving, if you take your eyes off the road, it wakes you up, you have to come back. I mean, it seems it's it's it's it's inviting more error into the road by by doing that in my in my opinion.

• 42:11Speaker 03

  Yeah.

• 42:12Shawn Ryan?

  Am I wrong?

• 42:13Speaker 03

  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, 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 the start. It's going to be like, you know, by default native.

• 42:46Shawn Ryan?

  Yeah.

• 42:46Speaker 03

  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 your eyes long enough, you'll have this autonomy and electrification everywhere.

• 42:56Shawn Ryan?

  How long do you think it'll be?

• 42:59Speaker 03

  I mean, so I live in the Bay Area, like you can take Waymos now, like I can take Waymos everywhere. It's unbelievable.

• 43:04Shawn Ryan?

  They're already all over over there?

• 43:05Speaker 03

  They're everywhere. Yeah, they're in my, I'm in South Bay, but they were in the city for a while and now they're in, you know, um, they're in Palo Alto, Menlo Park, like San Jose, like all this, all over the place. They're really great. Uh, I take it's like, uh, my wife and I would go to dinner and stuff on the weekends. We take Waymo and it's like, it's so fun. It's like

• 43:22Shawn Ryan?

  No shit.

• 43:22Speaker 03

  It sounds so like, like it sounds so basic, like you take a Waymo, it's fine. It's just, it's, it's awesome, man. Like it's, it's great. You have like, um, it's, uh, the car drives so human like, and it's, uh, such a great experience, like not having a human there, to be frank, like I love it. You know, like I notice like so many like whatever Ubers and stuff in common, the car smells or it's dirty or whatever else. And it's just like, you know, this is, it's just easy. Um, it's really cool. So like technology is like, like in like the early chapters, but it's all here. Like we're, we're going to have autonomy at scale, like everywhere. It's just going to take some time to roll that out. It's like, it's the time it takes to, um, 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.

• 44:07Speaker 03

  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 um 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, like, you know, make a large fraction of that autonomous. So you're looking at like, uh, this isn't going to happen in a year or two. It's going to take some time.

• 44:34Shawn Ryan?

  When are we going to see your vehicles?

• 44:37Speaker 03

  The aircraft.

• 44:40Speaker 03

  We have them now. We fly every week in California. The challenging part with Archer is that we are governed by the federal airspace. So to fly passengers and charge money, we have to have like basically a type certification from the FAA. That process moves at the speed of like the post office. And FAA is not incentivized to put anything in 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 um one in a billion hours.

• 45:15Shawn Ryan?

  One in a billion hours.

• 45:16Speaker 03

  Yeah. You can't be able to, uh, that, that, that is like, uh, that's the, but 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 uh governed by the FAA. Which is great. I'm like, you know, we're like, that's, 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 uh, that's like the, that's the long pole in the tent for us. And that's wherever you go. If you go to, you know, Europe, it's, it's YASA or you know, CAA in China, wherever you're going to go. There's like, there's federal mandates to get, to get basically an aircraft to take passengers. So we're, we're in the middle of FAA certification now.

• 45:58Speaker 03

  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 of like, you'll be certified here. You have to work through a very like, uh, very long and slow process with FAA to get through this. And then we're also uh dual tracking that against a couple of different uh entities globally now uh to make sure we can get certified and get in there. But it'll, it'll happen, man. The aircraft, um, it just, again, we're in like this chapter, we're like flying cars, electric, you know, aircraft are just like, it's early. It's earlier than like AV, AVs or EVs, autonomous vehicles, electric vehicles.

• 46:35Speaker 03

  But it'll happen. It'll happen in our lifetime. We'll be taking these things around.

• 46:38Shawn Ryan?

  Well, I mean, what do you envision? Let's, let's fast forward twenty, thirty years.

• 46:43Speaker 03

  Yeah.

• 46:43Shawn Ryan?

  What do you, what do you envision? What does it look like? Do we have roads? Do those get ripped up? Is, what does the sky look like?

• 46:50Speaker 03

  Yeah.

• 46:50Shawn Ryan?

  What does everyday life look like?

• 46:52Speaker 03

  Yeah. The, the, the really important thing to hear about the airspace is it's three dimensional. And the roads are not. They're two D 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, uh, 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 seventy percent by twenty fifty. Um, so we're like all moving to cities. The roads can't grow anymore. And we want, like, we're like, we're constantly moving around, going to work, going to restaurants and just like, and it just, it's like, it's like, um, it's like this, uh, it's, it's, it's getting, it's basically getting worse.

• 47:35Speaker 03

  It's like this, 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 spend on a road in traffic. It's like so soul sucking. Uh, like it's just like the worst, it's just like the worst, like, like time to lose. Uh, so the good news about the air is it's three dimensional. You can stack like basically an infinite amount of like, say, roads, uh, in the air.

• 47:55Shawn Ryan?

  Different altitudes.

• 47:56Speaker 03

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

• 48:20Shawn Ryan?

  The boring company.

• 48:21Speaker 03

  Yeah, exactly. Like, just like dig tunnels and it's great. The problem with, the only problem with tunnels, um, with this, with, with, uh, with the node system, uh, uh, on, on, uh, on the ground, or sorry, in the, uh, on the, uh, on the ground with, uh, with, with like, say, we call them vertiports, but basically, uh, like real estate for flying cars. Is you can, let's say you had like, you know, ten different, um, or even like, you know, five, you say ten 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 opens up like, you know, basically exponentially more places to go to. I can go to like any node on the system at any time.

• 49:00Shawn Ryan?

  So you hold on. So you you're saying in order to take off and land, you'll have to go to specific locations. You won't be able to do it from your home.

• 49:07Speaker 03

  Yeah, you're not going to take off and land from your home.

• 49:09Shawn Ryan?

  Okay.

• 49:10Speaker 03

  Uh, just because like acoustics in the in the neighborhood is going to be too loud. Uh, 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 at like, they'll be like in your neighborhood. And you'll like, you'll like, you'll like Waymo there or walk or take a bike. And then, uh, and then you'll get on these and they will go to any node on the network. You can't do that with tunnels. Tunnels have to go to A to B. You can't like, you go from like, you can't jump to another tunnel, uh, downstream. Like, you know, I want to jump to another tunnel like a hundred meters down. Like, it doesn't happen in tunnels. You can do that with 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.

• 49:51Speaker 03

  So my ambition is that like, you're going to be for most, most trips that, you know, that you're traveling over 20 minutes. All of that will move to the sky. And not only that, but you will, you will have us, you'll have like cities being re like, um, being transitioned to a point where you can live well outside of cities and get to cities really fast. Reason we live in cities is because we're like, we're working there and we have friends there. And we have like, it's, yeah, it's like, I want to be like, I want to go to dinner with somebody. I want to see my buddies over here and, uh, we want to go to work over here or like go to the mall over here. It's like, everything is there. And that's what we want to be, we're social creatures. We want to be there next to other humans or some of us are. And, um, so, uh, yeah, so anyways, uh, but like, you know, now that you can fly this 150 miles an hour in the air with no traffic, point to point,

• 50:36Speaker 03

  like no stop signs, no construction, no things jumping out in front of you. You don't have to like travel different distances. You're going straight from A to B in most cases. So you're like, you're removing 10 or 20% of the top, like basically the distance just by going point to point. And then you have nothing stopping you going 150 miles an hour most of the way there. Uh, you can live like far outside of cities and get down to city center in under 30 minutes.

• 50:58Shawn Ryan?

  So will these be personally owned or will these be, this will be like an Uber service?

• 51:02Speaker 03

  It'll be like an Uber service.

• 51:03Shawn Ryan?

  Okay.

• 51:03Speaker 03

  To get cost down, you'll, you'll basically just like pay per trip. You'll pull up an app and you'll go like, I want to go downtown. It's whatever, it's 40 bucks and I'll be there in under 30 minutes and you'll, you'll, you'll say, great, I want to be there at that time. You'll hit a button, it'll be on demand, you'll ride your bike over or walk, you'll get in one, it'll even. Seven minutes and then you're basically flying right down to town.

• 51:24Shawn Ryan?

  Holy shit. And you're saying this will, this will be in every neighborhood, this will be very accessible to everybody.

• 51:31Speaker 03

  Yeah, that's you're designing the whole electrification allows you to reduce the costs and the safety burden of all this.

• 51:38Shawn Ryan?

  Wow.

• 51:39Speaker 03

  We have like, um, like, um, a normal helicopter could have like hundred, two hundred, like, safety critical components that any component gives out, the helicopter can go down. Uh, an electric aircraft has none, none. Uh, you can lose a motor, you can lose a battery pack on, on board and still fly safe without, without having this. And, um, and so like, uh, it just like from a safety, from a part count, from a cost, uh, from like acoustic signature, it's not like helicopters are loud and very noisy and, um, like it's, it's just a much better technology for this.

• 52:13Shawn Ryan?

  Have you been in one yet?

• 52:14Speaker 03

  Um, we, I haven't flown, uh, inside and inside of ours yet. I've also been inside of our aircraft. And, uh, we basically have professional, uh, basically, uh, pilots, uh, at the company.

• 52:27Shawn Ryan?

  Test pilots?

• 52:28Speaker 03

  Yeah, test pilots and they do their career test pilots and they're unbelievable.

• 52:31Shawn Ryan?

  I'll bet.

• 52:32Speaker 03

  You know, a lot from the, a lot from the military, a lot from the big aerospace groups and they're, they're just professionals.

• 52:38Shawn Ryan?

  What do they think?

• 52:39Speaker 03

  I love it, man. This is the future of aviation.

• 52:41Shawn Ryan?

  Man.

• 52:42Speaker 03

  Everything's going electric and, uh,

• 52:44Shawn Ryan?

  It's so crazy.

• 52:45Speaker 03

  Yeah, it's crazy. It works like it's, uh, crazy. It works and crazy. We're in the right time period to make this happen.

• 52:52Speaker 03

  Yeah. And you know what? The good thing about, you know, Archer now is we've like, we've demonstrated the hard part is like being in the wrong, like the, the hard part is like making sure you're in the right decade. No one like go do this and you find out it's like, oh, it's like a 2040 event and you can't get it done. It's just like a, it's just like a waste of time. And, um, so the good news, you know, for us is we like, we're, we're in a sweet spot here where, uh, this is going to happen. Aircraft now work, uh, we're certified now with the, you know, government bodies like the FAA to make it happen. We have a good balance sheet with, with cash. The team's great. And so it's just like, uh, you know, get certified and get this thing going.

• 53:29Shawn Ryan?

  Damn, that is, you're really changing the world.

• 53:32Speaker 03

  Well, we're, we're the start of it, but, um, yeah, hopeful to make this thing work.

• 53:36Shawn Ryan?

  Where are we going next?

• 53:39Speaker 03

  Um,

• 53:41Speaker 03

  humanoids?

• 53:41Shawn Ryan?

  Let's, yes. Let's do it.

• 53:44Speaker 03

  Yeah.

• 53:45Shawn Ryan?

  How did this idea start?

• 53:48Speaker 03

  Yeah, so um, you know, it's been like five or six years working on like a pretty crazy robotics work at Archer. And um, like the ultimate like meta problem in robotics spaces, can you

• 54:05Speaker 03

  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 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. Um, 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, um, 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.

• 54:48Speaker 03

  Our espresso machine would be like different looking. We might not even like espresso or caffeine in this case. So we build 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 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, um, or reach up. Has like arms and hands, so we can manipulate objects and do things like, you know, grab the stuff, open

• 55:29Speaker 03

  open these gummies and, um, you know, fold laundry and do, do real work. Um, and we have the right sensors so we can like see the world and understand what to go do. And use a, you know, our biological neural net to kind of figure out how to reason from. And um, You know, having worked on like, you know, kind of like aircraft for, you know, now five or six years, I, I thought it was like 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.

• 56:11Speaker 03

  It was like really heavy and big and high torque and very leaky, like the oils everywhere and also didn't run very like maybe ran for 20 minutes on a single charge. So you need to kind of radically transform the hardware. And then you needed to figure out a way to build like an AI brain. The humanoid is so complex, it has, has like, let's, let's call it like, uh, like 40 degrees of freedom. And 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 it 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 like how many positions the body could be in at any given time, like this is a position, this is a position and keep moving, amount of states.

• 56:56Speaker 03

  It's, uh, 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.

• 57:09Shawn Ryan?

  No shit.

• 57:10Speaker 03

  By far, it's much greater number.

• 57:13Speaker 03

  Done the math a few times, very confident in this, even though it sounds ridiculous.

• 57:18Speaker 03

  So you just can't code your way out of this problem. Like, how are you supposed to write code? Like, how's a human supposed to like 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 hand, my, my wrist and hand, uh, getting positioned to grab this, like, it's a. It's an intractable problem for code. So.

• 57:45Shawn Ryan?

  I mean, you were saying earlier, I'm gonna butcher this, but it's updating the foot 200 times a second.

• 57:53Speaker 03

  Yeah, our controller is running.

• 57:54Shawn Ryan?

  For balance.

• 57:55Speaker 03

  Our whole controller, so we have a main computer is processing what to tell all the joints to do 200, a little maybe more than 200 times a second to make sure we can just balance. And then we can like do, we'll 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, uh, yeah, we have to fully balance, you know, we're, it's dynamic. So it's if you, if you, if generally if you powered off mid-run, it's gonna just fall down. It's not like a four-legged dog or quadruped robot where like at any given point, she's like 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 is moving, my head's moving, like, uh, all of it becomes very complicated now. It's not just like move my hand.

• 58:39Speaker 03

  It's like move my whole body to get my hand in the right spot. So every joint, all those 40 joints have basically position encoders. We know exactly what position the, the motor is at, or even the case of the knee or this. And we have force, force sensing, torque sensing onboard. We have the ability to detect all the forces that that knee is seeing. It could be really high when it's walking or it could be like, you know, it could be like, it could be powered off and have no forces on the leg. All of that feedback and is, is being sent in the main computer. And then we're telling all the joints what to do over 200 times a second. Some of that other feedback is happening at like five or six kilohertz.

• 59:20Speaker 03

  So the force feedback is happening in six, five, 6,000 times a second to the motor control onboard. And we do the motor control, the brain for all the motors has done it locally at the motor level because it needs to happen so fast. That's being fed back to a main computer that runs a control software that tells the rest of the whole body what to go do at every timestamp to keep balance.

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• 1:02:18Speaker 03

  So getting back to the original thing is my bet, you know, we're like three and a half years old or something like that, that figure. I bet three and a half years ago in 2022, when I started the company was that this was possible now. And my, my view is like it was, 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, there was no good precedent. There was no precedent for showing that. There was no AI that had ever worked on a humanoid robot in

• 1:02:55Speaker 03

  history. And there was no human, electric humanoid hardware that was remotely okay to show it would work. And there was no hands, there was none of this stuff.

• 1:03:07Shawn Ryan?

  Wow.

• 1:03:08Speaker 03

  So I actually had, I actually had a lot of trouble even, I had a lot of trouble early on even like getting people excited about this because they were like, what the hell are you doing? And so I ended up having to like basically self-fund a lot of it in the first, the first year I self-funded all of it.

• 1:03:25Shawn Ryan?

  No kidding?

• 1:03:26Speaker 03

  Yeah. And it was a lot. I mean, we got to business to a million a month to burn in month four. And, but it was like, I knew what to do. We built a 40 person team and like as fast as possible. And I knew how to spin up hardware and software. Again, you know, the, the key characteristics of robotics, like electric motors, battery systems, you know, control software, embedded systems and sensors. And then even within like electric motors, we build actuators. They have like a rotor and stator and a gearbox and sensors, electronics and wiring and connectors and multiple sensors inside of there. And then firmware, it lives on the, the, like I say, the microcontroller lives inside the, on the motor control side. And then we have like thermal characteristics, it's hot.

• 1:04:10Speaker 03

  And then you got to all make that work at very like high speeds and high torques. Meaning motors don't like working when they're not moving. Motors hate not moving. Motors want to run on highway speeds.

• 1:04:20Shawn Ryan?

  Okay.

• 1:04:20Speaker 03

  They love that. Like whether it's 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.

• 1:04:32Shawn Ryan?

  Okay.

• 1:04:33Speaker 03

  When motors are stuck and not moving, but holding power and holding forces, they're they're all, it's a really bad point of the torque speed curve. So they're not, they're not built well for this. So humanoid 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. And just that one little aspect, which is like hardware umbrella, just like, just, just motors. And so we have whole teams in those areas, just in that one little area here, doing like rotor design, electromagnetic design, stator design, gearbox design, sensor design, electric motor control design, like all of this inside the teams.

• 1:05:15Speaker 03

  It's, it was an enormous lift to just get the team members there to do it. So we spun up a team to go operate that really quick. And then, you know, now like looking back, I think we raised, you know, you know, two billion or so, like now it's a much different story. We have, you know, we, we, we ended up building figure one. It's our first generation robot and had that walking in under 12 months. So from when we

• 1:05:39Shawn Ryan?

  From inception, wow.

• 1:05:41Speaker 03

  Yeah, from when we, uh, we had basically incorporated the company in 2022. Our goal was like, can we get a robot walking by itself at these dynamics in under 12 months? And we did it. We, we did it with like two days left in the year. And at the time is, I think it was the fastest time in history for anybody to do this. And, um, you know, and then from there, continue to build the capabilities. We built, built generation two, figure two, which is that guy right there is our second generation robot. And, um, and I think one thing we did before we kind of moved even to gen, while designing gen two is,

• 1:06:17Speaker 03

  I think it was probably like 2023, the time we we did a demonstration where we basically wanted to put this K cup, K cup inside this Keurig and run it. It was just on a very, pretty simple, like, like not, nothing crazy, but we had a, you know, Keurig machine, coffee cup, and a K cup, and we had to go grab the K cup, open, you know, open the Keurig, put it in, close it, run it, and then, you know, make coffee. And, um, you know, it sounds simple, but like for a humanoid robot to do that uh, is extremely hard. And then we wanted to do all of that with just neural networks.

• 1:06:48Shawn Ryan?

  It sounds simple, but I mean,

• 1:06:50Speaker 03

  Simple task for humans.

• 1:06:51Shawn Ryan?

  Give it to a four year old.

• 1:06:52Speaker 03

  Yeah, exactly.

• 1:06:52Shawn Ryan?

  I mean, the dexterity on the hands of that thing is just to hand you that bag of gummy bears.

• 1:06:58Speaker 03

  Yeah. Yeah. Yeah. Then can you do it with neural nets on board?

• 1:07:03Shawn Ryan?

  It's crazy.

• 1:07:03Speaker 03

  Can you not code your way out of it? Can you have a, can you take in camera pixels? And then output trajectories for the motors through a neural network? No code. And, um, we did that in 2023 on figure one. And it was like probably the probably the most significant demonstration we've done in four years now, almost four years, where we were like, uh, internally, we were like, you know, how do we get neural nets to run on a humanoid? And I, I don't, I don't know. I, I think it's probably one of the first examples in the world to ever have shown that. And and, you know, this was like game on. This is like, we have, let's go build a really good humanoid hardware. Let's make it cheap and really reliable. Let's make sure it can do what humans can from a hardware perspective.

• 1:07:46Speaker 03

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

• 1:08:30Speaker 03

  And, um, and then we launched, you know, uh,

• 1:08:35Speaker 03

  We launched Figure Two. We did a lot, basically more work. We started unveiling Helix, which is our neural network stack internally that we do here. And now we've designed Figure Three, which is our third generation robot you have here, which is like, it's the best humanoid hardware in the world by far. And we're now running robots that do like, I watched it, you know, the other day, unload dishes and fold laundry. We had Figure Twos at BMW last year that worked six months every single day, every

• 1:09:07Shawn Ryan?

  Six months every single day?

• 1:09:08Speaker 03

  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 showing up to work, but sometimes that does happen. So it's like, it's hard. It's a hard bar to go hit.

• 1:09:54Speaker 03

  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 2025, we started building our first BMW X3s on the line. And I bought, I bought the first four that did that. They're at the, on my campus now. One's at my house. And, and they, they didn't build, obviously, the whole cars. Like, there was a ton of parts, but like, we built, we did, we did a part of the whole process. And

• 1:10:31Shawn Ryan?

  What's BMW's feedback?

• 1:10:35Speaker 03

  They're great. I mean, the BMW is like, if you go into like a car manufacturing company, they're like the best roboticists in the world. There's robots everywhere. There's like these giant 12-foot KUKUM, like manufacturing, like, like robot arms on the floor. They're bolstered the ground. They're massive. These things are carrying car chassis around like they're kids toys. The cars are so big and so heavy, 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, uh, not end to end, but like there's humans involved, but like the car is being like built by machines.

• 1:11:18Speaker 03

  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, uh, end effector. The end effector is something that is grabbing a part. These end effectors are the size of my, so like my chair. They're switching them out in seconds. One or two seconds, they're doing it really fast. And then they're basically building a car with this. There's robots everywhere. And, um, so like BMW, like they're like, it's, it's been a privilege to see how like much automation has gone into automotive. It's unbelievable. It's these things, these machines like kind of make what we're doing sometimes look like little toys.

• 1:11:55Shawn Ryan?

  No kidding.

• 1:11:56Speaker 03

  Uh, we're doing some very complicated, but they, the, the car manufacturing is just like no joke.

• 1:12:00Shawn Ryan?

  So what specifically were bigger figures doing?

• 1:12:03Speaker 03

  Yeah, we had a, there's a, there's a body shop line called, um, that's basically building the rear header. It's like the, the back plate. So the, the body shop, they basically build the car by putting basically sheet metal together, welding them onto the chassis. And then they're basically building the car around that. Like you end up putting the seats in, bolting them down, putting the car doors in, wiring them up, the harnessing. And we were in the body shop line, helping basically attach the rear head, like basically putting the rear header on this fixture. So we, so today, um, we basically, or last year when we were there, uh, we basically take a piece of sheet metal and we basically put on this fixture and we do that over and over again. And they do that, you know, 10 hours, full shift.

• 1:12:41Speaker 03

  And, uh, there's three different parts on, those three parts go on, this thing rotates and this big giant Kuka machine, like this robot arm goes and spot welds it. And switches it, switches out to another effector and then grabs it and puts it down the line. So like these, these, uh, these facilities are being like fed this, 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 gonna get our ass handed to us? You know, is it gonna be easy? Is it gonna be hard? And I think it was in the middle. I think we like, we got the robot to a great spot. Where it was branding 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?

• 1:13:26Speaker 03

  And can we like clone it times a thousand, times 10,000? Would we have any issues scaling? That was the part for me. Like, is it just like, you know, does it completely shit the bed? And he's like, we need to rework our plan and go back to the office. Does it do incredibly well? And you can just copy paste this thing everywhere in the world? Like, how does it, how did it work? And the biggest learning lesson we got was that the robots, the robot that started the first day at the start of six months, and the robot that ended the shift that day was the same, like, even though we had multiple robots in operation every day, we had the same robot that did the start and the finish.

• 1:13:58Shawn Ryan?

  Wow.

• 1:13:59Speaker 03

  And it was cool. Like, you know, like, it was same robot ended six months later. And this was the thing where, you know, the worry was that humanoid robots couldn't last a month, couldn't last a week in these kind of environments. And, you know, wear and tear. It's like, it's running like a, you know, 40 degrees of freedom motors every single day. Can they operate really well? I think from a hardware perspective, it did an A plus job. I think from a software perspective, we did like a, I would say a B, 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, like code and heuristics in. So like the controller to walk was done by a C++ controller. It was done by code.

• 1:14:42Speaker 03

  The walking you saw today, we had back then was done in code.

• 1:14:45Shawn Ryan?

  Okay.

• 1:14:45Speaker 03

  The rest of this, we had a bunch of other stuff in there done by neural nets. And like some of the perception stacks, some like, you know, some of how to move parts around, everything else. And, you know, this was a year and a half or so ago when we were first launching and And I was like, man, this, the, the, the biggest problems we're having is the, the coding parts get stuck. The robot like either doesn't see like the right, uh, like, doesn't like see something right on the part and misses the, the object detector, doesn't really understand what's going on. The controller, when it gets out of bounds of like what it's ever seen before, like 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, uh, and it's like not very, it's like,

• 1:15:30Speaker 03

  it's like, you know, it's harder for a robot to walk around. And, um, so that, that, that was like, we had a really difficult time, uh, 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 said, we need to basically refactor everything into a neural network. And one of the big, like, and, and I think we just announced Helix two, two or three months ago now, I forget, like end of last year. And it's basically entirely down the stack, including the controllers and neural net now. It's, uh, there's, there's like no code left really on the robot. Um, maybe some code in certain pieces, but mostly just, almost all of the things is like a neural net at this point. Uh, we removed the need for like almost, like over, almost over a hundred thousand lines of code at the, when we launched Helix

• 1:16:13Speaker 03

  two. 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 only on a 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. Um, we've, we have robots running like basically now in 24 seven shifts without stopping, without any faults for, uh, like days and days. Uh, we just, we just went like over, uh, yeah, 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, they can run, uh, Like four hours or so, five hours, and when you need to charge, another robot knows that,

• 1:16:58Speaker 03

  steps in, steps behind the robot, say, and gets ready for work. The robot then backs off. The robot swaps in spot like in the next like 10 seconds, it's doing 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, whatever. And they're running that shifts all day and all night. And we do it across multiple use cases now at the office in 24/7 shifts. And it's just like, we're running them hard.

• 1:17:20Shawn Ryan?

  What kind of stuff are they doing at your office?

• 1:17:22Speaker 03

  We do a few things. We have a logistics use case that we run in 24/7 shifts constantly. We really like it. It's done with a neural net. It's moving packages around. And it's a really good use case. We like, we like it. And we, we want to like, I want to run it for months and have failures. And we still, we see failures right now. And it's most of it's in software. The robot gets to some spot where it feels unsafe, doesn't know what to do, and it'll stop for a little bit. And then the, you know, the robot's not on the line for a couple of 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 at the office, you're getting lunch, or walking around, or interviewing with us. You see robots everywhere. And those run in 24/7 shifts, all day, all night, weekends, Christmas Day, whatever we run them.

• 1:18:06Shawn Ryan?

  Greeters?

• 1:18:07Speaker 03

  Yeah.

• 1:18:08Shawn Ryan?

  They basically, how do they greet you?

• 1:18:09Speaker 03

  Come talk to you.

• 1:18:10Shawn Ryan?

  They'll just come talk to you?

• 1:18:11Speaker 03

  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, the end state for us is like, it's going to replace like somebody like me in the candidates that are interviewing there, taking them to the conference room, getting them water or coffee, like all of that end to end, and whole experience.

• 1:18:29Shawn Ryan?

  Holy shit.

• 1:18:30Speaker 03

  Yeah, right now, they're walking, I mean, right now, they're walking in the office. Like at nighttime, they're walking office everywhere. And it's a good, 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, it's hard. This is a new 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 test them like crazy by running them all the time.

• 1:18:56Shawn Ryan?

  What's a conversation you've had with a robot?

• 1:18:59Speaker 03

  Um, we've been really working on like deep memory. Because I think one thing I really don't like is like these conversational AIs you talk to 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. It kind of feels really stupid to me. So we've been working a lot on like deep memory. Uh, it's like

• 1:19:22Shawn Ryan?

  Who will actually get to know you?

• 1:19:25Speaker 03

  Oh, yeah. Yeah, it needs to know who you are. Like, who am I talking to? Is this Sean or Brett? And then based on Sean, like, do you have the permissions to tell the robot to go, go do something or not? Uh, like, if you're visiting, no. Uh, you might be able to get coffee or water, but like, if you wanted to have it, like, go do something new, like, won't do it. Um.

• 1:19:41Shawn Ryan?

  I have, I've not even thought of that either.

• 1:19:43Speaker 03

  Yeah, yeah, like, we want to

• 1:19:44Shawn Ryan?

  Who do you want to be able to command it?

• 1:19:45Speaker 03

  Yeah, what are the permissioning systems and authentication of the robots? I mean, like, you, like, you know, like, like, robots in my house, my kids are gonna be like, hey, uh, give me ice cream every, every, every 10 minutes, and we can't have the robot doing that, right?

• 1:19:59Speaker 03

  You know, I get, 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. Um, so we're, we're

• 1:20:08Shawn Ryan?

  What is it? Voice recognition?

• 1:20:10Speaker 03

  Yeah, you have to do voice for something. Something's voice isn't enough. Where, um, 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. Uh, you'll have to do a higher level of authentication.

• 1:20:24Shawn Ryan?

  How would you do that?

• 1:20:25Speaker 03

  Uh, facial recognition.

• 1:20:26Shawn Ryan?

  Okay.

• 1:20:26Speaker 03

  And then if you have a, perhaps even a fingerprint scanning, um, is possible too, but facial, uh, is what you really want to do.

• 1:20:34Shawn Ryan?

  Gotcha.

• 1:20:34Speaker 03

  Um, so those are not, all those systems are not, like, robust enough right now. We're working through them. And, um, like, the goal is, like, to get it, like, super robust, but like, you know, we want to have conversations with the robot, want to, want to ask it to go do things, like, you really, um, you want the main modality to be speech with, with robots, you want to just, like, hey, man, go make me, like, go make me, like, uh, go make me food, or like, do, when I'm gone today, do the laundry. After you, like, after you unload the dishwasher, like, you know, uh, do, do laundry in, like, my kids' rooms today or something like that. Or, or text it. Um, so, like, language is, like, super important UI. So we're, like, spending a lot of time on speech.

• 1:21:10Shawn Ryan?

  You can text it, too?

• 1:21:12Speaker 03

  Yeah, every, every robot we have has 5G, uh, by default onboard. We actually run 5G by default now. Uh, so every robot off the line has 5G enabled. Um, like, uh, we have, like, a T-Mobile 5G. Uh, T-Mobile's an investor of ours, and every robot has an eSIM card for, for T-Mobile 5G, so it comes off the line, we use 5G for all the main network. So if you want to, like, um, you know, if our, if our, like, if our systems want to tell the robot what to do, or command the robot to do something, we do it through 5G. And, um, and so, yeah, you can, like, you can text it.

• 1:21:47Shawn Ryan?

  So you could be at work and say, hey, I want to get the pizza out of the freezer, put it in the oven.

• 1:21:53Speaker 03

  Yeah.

• 1:21:53Shawn Ryan?

  Four hundred twenty five degrees fif- fifteen minutes.

• 1:21:56Speaker 03

  Yeah, maybe we can do that in right now, but like that's the goal is like we got to get there. Like we got to get to a point where like that is certainly possible and um. We want that to happen. You want to be like, uh, yeah, I'm at work. Uh, when the groceries come, make sure you put them inside and uh, put them in the fridge. And do that, do all this, or they would even know that.

• 1:22:14Shawn Ryan?

  Go check the mail, have it on the counter when I get it, like, oh, feed the dog.

• 1:22:18Speaker 03

  Yeah, like watch the dog, make sure the dog's okay. Yeah.

• 1:22:21Shawn Ryan?

  Holy shit. So it's it's it's a nanny, housekeeper, gardener.

• 1:22:29Speaker 03

  It's uh, the Jetsons.

• 1:22:30Shawn Ryan?

  All of it.

• 1:22:32Speaker 03

  Yeah, it's gonna be all of it. I mean, you might want to garden, you might want to do it. All this physical labor we do today, I think will be optional in the future. So you'll. Like you might like gardening, um, you know, you might like mowing the lawn, you just like mow the lawn. If you don't want to mow the lawn, don't mow the lawn. Like all this will be a choice.

• 1:22:49Shawn Ryan?

  Holy shit.

• 1:22:51Speaker 03

  Yeah.

• 1:22:51Shawn Ryan?

  And you said it's gonna, it'll download apps for different.

• 1:22:56Speaker 03

  You want to think about the software layer, like you want to think about the, like, so for us, like, um. What's so powerful about a humanoid is you. You don't want to go out and change hardware. Whenever we have a new like app on your phone, you just like download it and connect, do new things now. Like he's got my bank account now, I can do bank account stuff, or you gotta download, you got a calculator, can do calculator stuff. Uh, you really want to treat the hardware like this, where you basically similar to phone, where you um, you don't have to change the hardware for new capabilities. Uh, you want it to learn how to do uh, like, you know, complex towel folding or um, like. Uh, loading the dishwasher, um, making coffee on a Keurig, like all this, like walking the dog, like these are like. Uh, like almost like the matrix, where you get like plugged into a system.

• 1:23:41Speaker 03

  That um, reuploads 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 twenty four seven and do logistics work and package work.

• 1:24:06Shawn Ryan?

  Wow.

• 1:24:07Speaker 03

  Nothing changes.

• 1:24:08Shawn Ryan?

  Where's this gonna go first? Consumers, businesses.

• 1:24:12Speaker 03

  You'll ship into businesses first. It's um, the engineering complexity that we have to ship is like proportional to the variability that we see on site. So um, the variability at homes is extremely high. It's like, it's my home is chaos. Like kids are just like dismantling the house. It was like, it's basically in real time. Uh, 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 um, tons of entropy, like uh, tons of variability, a wide distribution of tasks.

• 1:24:52Speaker 03

  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, we got going. And um, in the workforce, it's like, you have this like work cell that you're doing. So like, and 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. In the home, you can't do that.

• 1:25:17Shawn Ryan?

  Mm hmm.

• 1:25:18Speaker 03

  Can't write down a piece of paper. I can't write down a piece of paper like how I could interact your house. I mean, you've seen it.

• 1:25:21Shawn Ryan?

  Yeah.

• 1:25:22Speaker 03

  Like the next assembly line or the next like conveyor system, like it's like, I kind of know what to do. It's like, I get the package, I flip it down and I need it every three seconds. Like you kind of have like, you know, good understanding what to go do. So um, it just makes it easier. It's like uh, the analogy would be like highway driving for autonomous vehicles. That's just happened sooner because the variability is lower than in a city.

• 1:25:45Shawn Ryan?

  Gotcha.

• 1:25:45Speaker 03

  Um, so it'll happen first to scale. And then the industrial thing has a good, good thing where it's like, you kind of have your own work area, so the safety areas are not as high. The hardest thing in the home will be uh, once you figure out how to get performance there, meaning it's capable of doing everything in the home, like so you can go to your home and do everything. The longest pull from there is going to be safety. Like me and you feel safe, like being, like having this here with our kids. And that is um, That that's gonna be the hardest challenge by far. And that's gonna take some time. It's a very, it's, there's some trust that needs to build, there's a tracker that needs to be built, there's like system safety engineering that needs to be done extremely well.

• 1:26:25Speaker 03

  So that just, and then the home, like, you can charge like 10x, you can charge like 10x more in the commercial market than you can the home. Home needs to be like 500 bucks a month. Your car lease.

• 1:26:37Shawn Ryan?

  Those will be, you think those will be around 500 bucks a month?

• 1:26:40Speaker 03

  Yeah, I think it'll be like that level, like, you know, that like order of like magnitude, yeah.

• 1:26:46Speaker 03

  Yeah, so I think then the commercial workforce, you can charge like 10 times more.

• 1:26:53Speaker 03

  So like, so it's just like the commercial, and then the commercial market for humanoids, like, you know, I mean, ha- half of GDP is human labor.

• 1:27:01Speaker 03

  You know, maybe a little under half. So it's like 3 billion humans in the workforce is like contributes to like 40 something percent of GDP.

• 1:27:09Shawn Ryan?

  Wow.

• 1:27:09Speaker 03

  So like you're talking about the largest market in the world is sitting in the commercial workforce.

• 1:27:13Shawn Ryan?

  Wow.

• 1:27:14Speaker 03

  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?

• 1:27:24Shawn Ryan?

  Yeah.

• 1:27:25Speaker 03

  You know what I mean? Like, why would you like spend time over here when you can just go over here and build like a 20 trillion dollar company?

• 1:27:32Speaker 03

  And my answer for that is just like, I just wanna, I want robots in the home, so don't really care.

• 1:27:39Speaker 03

  You know, like, we gotta make that work. Yeah.

• 1:27:42Shawn Ryan?

  I mean, you say in 10 years, every home will have a humanoid.

• 1:27:49Speaker 03

  We'll have every home in 10 years, but we will have

• 1:27:51Shawn Ryan?

  Pretty close.

• 1:27:52Speaker 03

  We will have in 10 years.

• 1:27:59Speaker 03

  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 be like, become like the largest company in the world. And you can do that with maybe a hundred robots.

• 1:28:39Shawn Ryan?

  No shit.

• 1:28:40Speaker 03

  Yeah, I think you can solve a general purpose humanoid robot. I think you can solve general purpose robotics with maybe like hundreds or low thousands of robots. Maybe a hundred.

• 1:28:50Shawn Ryan?

  How so?

• 1:28:52Speaker 03

  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 like 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 like take the laundry, put it on my bed and we can fold it all. And then, so we're doing like little spots of it. And it's pretty good. And 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

• 1:29:34Speaker 03

  types of clothes or like different types of like, I know, don't, don't wash my jeans, like that type of thing. And all these different like variability 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.

• 1:29:50Shawn Ryan?

  So how, I mean, how, how, let's say, let'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 silverwares go here, the forks go here. When you're doing, 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, is where I hang my shirt.

• 1:30:19Speaker 03

  Yeah.

• 1:30:20Shawn Ryan?

  Is that how it works?

• 1:30:21Speaker 03

  Yeah, you're gonna, you're gonna robot. You got a robot in a box. You open it up, robot get out, it'll start talking to you. Uh, it'll ask you, um, to show you the house. And, um, you'll, it'll, you'll, you'll say like, you know, it'll say like, you know, can you walk me through your home? And it'll follow you around and you will tell it all that. Like you would, um, let's say, let's, let's say it, you'll say it, a friend staying for two weeks at your house. That, you know, needed to like cook and use your stuff, like, 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, um, this is recycling here. This is where trash is at. Like here's, here's where you get water, like, uh, the, the trash goes out every, you know, every Monday.

• 1:31:02Speaker 03

  Uh, you know, this is, uh, I, you know, we do blankets, uh, on the couch, but like you, we want them in the, in the cabinet when they're done. You know what I mean? Like, or we want these all folded and put it over here. Like, like all these things you have in your home. They're like, uh, you know, important and, um, and you're like, just like you would, like walking somebody, a human around for the first time. Uh, that's what you'll do. And the robot will, uh, semantically understand, like, like, will A have like, will 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.

• 1:31:35Shawn Ryan?

  Shit, it'll be that. So it's just like turning a human being.

• 1:31:38Speaker 03

  This is like not, this is not 10 years. We'll, we'll do, this is, this is really soon. Uh, like, I think in the next, like, I mean, I'm hoping this year we can like drop a robot in your home and do a good amount of stuff. Uh, it's just, um, we'll see. I mean, this is like, this is like solving like the holy grail of robotics. This is like solving for a good general purpose humanoid robot. Um, maybe we don't solve it this year. Maybe we solve it next year. Maybe we don't solve it next year. It's 2020. I don't know. Like we're, we're close. We feel like we're in the red zone with like, we feel like we know the architecture, we have the hardware, we know, we know how to get the data. We put the data in, the robot does it. We need to like now like learn how to generalize. We need not like, like move deeper into pre-training for, we, we know the directions we need to go ahead.

• 1:32:22Speaker 03

  We think to solve this and we're seeing a lot of both positive transfer and, um, a lot of just like, uh, we're, we're seeing internally the, we think the right direction to make this work.

• 1:32:33Shawn Ryan?

  When you were talking about trusting the robot with your kids, what are, I'm just curious, what are your concerns?

• 1:32:41Speaker 03

  Yeah, Archer always

• 1:32:42Shawn Ryan?

  I haven't thought about this.

• 1:32:44Speaker 03

  I think in Archer was always like, I'll never like feel safe. I never felt comfortable like recommending people to fly in Archer and letting people fly in Archer. Until I like would fly an Archer aircraft with my kids.

• 1:32:58Speaker 03

  That's the 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, we'll be safe when, or to me, it will be safe when I feel comfortable putting the robot around my kids. I have a one-year-old and

• 1:33:17Shawn Ryan?

  Yeah.

• 1:33:18Speaker 03

  four-year-old and seven, you know, I have young kids. They like want to jump on everything and you know, it's like they're like, uh, yeah, and the robot like, you know, the robot needs to be extremely safe there. So

• 1:33:29Speaker 03

  that's another hurdle. It's like getting into general, like solving general purposeness, getting safety 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 works.

• 1:33:43Shawn Ryan?

  Right on, right on. You want to take a walk around this thing?

• 1:33:46Speaker 03

  Yeah, let's do it.

• 1:33:47Shawn Ryan?

  Perfect.

• 1:33:50Shawn Ryan?

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• 1:36:10Speaker 03

  All right, this is our Figure 3 humanoid robot. It's, uh, we actually unbilled it last year.

• 1:36:18Shawn Ryan?

  My God.

• 1:36:19Speaker 03

  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.

• 1:36:30Shawn Ryan?

  130 pounds?

• 1:36:32Speaker 03

  135 pounds, yeah. It's, uh, fold laundry, do dishes, do manufacturing logistics, um...

• 1:36:39Speaker 03

  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, reduced the weight and mass. Uh, we made the robot skinnier, but also same strength and speeds. Uh, we upgraded the sensors on the robot. It basically sees through cameras. Um, we have better, are basically our fifth generation hands onboard that have a camera, uh, tactile sensors, um, basically improved grip. Uh, we also have, uh, on the robot, like basically more compute onboard for running our Helix Neural Network. Uh, we also spent a lot of time on, uh, just basically making the robot more safe. So they all have kind of the squishy layer of foam on it. So, yep, go ahead.

• 1:37:19Shawn Ryan?

  So if you, let's say somebody pushed it over and fell over, I mean, what's the durability of these?

• 1:37:25Speaker 03

  Hmm. I mean, depends how hard you push it, but like, uh, for the most part, we, we fall, robot can get back up, just continue to do work. Uh, it depends how you fall. Uh, sometimes we break necks. Sometimes it's fine. Um, all right, turn around.

• 1:37:42Speaker 03

  Another thing too is like we, uh, we basically, uh, the robots almost all are fully soft wrapped. Uh, one thing we can do here is we basically can make clothes for the robot, which we do for both our customers and internally. Uh, the clothes can be put on by like, uh, any person. So we can basically unzip it, um, take clothes off, put clothes back on. We, we don't need tools to do so and, um,

• 1:38:02Shawn Ryan?

  Can we see it? Can we see what's in there?

• 1:38:04Speaker 03

  Uh, yeah, basically it's the torso.

• 1:38:07Shawn Ryan?

  Oh, you can't see any of the internals, they're all protected?

• 1:38:09Speaker 03

  No, they're all inside the structure. Uh, so inside of here, we have basically a battery, uh, GPUs, computer, uh, power distribution, um, basically the brains and all the energy are in the torso.

• 1:38:23Shawn Ryan?

  Wow.

• 1:38:23Speaker 03

  Um, and then, uh, and then basically the robot is basically left with basically forty, uh, forty joints. So, uh, all basically electric, uh, electric motors and the motors have like basically tons of sensors on it for balancing and doing work. All right, turn around.

• 1:38:44Speaker 03

  We can walk with it for a minute.

• 1:38:45Shawn Ryan?

  All right, let's do it.

• 1:38:49Speaker 03

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

• 1:38:57Shawn Ryan?

  Holy shit, this is awesome.

• 1:39:00Speaker 03

  You want one?

• 1:39:01Shawn Ryan?

  What's that?

• 1:39:01Speaker 03

  You want one of these?

• 1:39:02Shawn Ryan?

  I want a couple of them.

• 1:39:03Speaker 03

  A couple of them, okay, great.

• 1:39:05Shawn Ryan?

  Dude, whoa.

• 1:39:09Speaker 03

  Yeah.

• 1:39:10Shawn Ryan?

  Let's go back this way.

• 1:39:11Speaker 03

  Let's turn around.

• 1:39:15Shawn Ryan?

  Can it run?

• 1:39:16Speaker 03

  Let's see how fast it can go. We have running, we have, I don't know if we're on the running mode, but let's go as fast as we can. We do jog with the robots outside.

• 1:39:25Shawn Ryan?

  Really?

• 1:39:25Speaker 03

  At campus, yeah.

• 1:39:40Speaker 03

  I think it also like looks cool, right? Got high tops on.

• 1:39:43Shawn Ryan?

  Looks awesome.

• 1:39:43Speaker 03

  Yeah.

• 1:39:44Shawn Ryan?

  And you said there's cameras in the hands?

• 1:39:46Speaker 03

  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 uh has tactile sensor inside. Uh so we can basically touch like touch forces as we're grabbing objects.

• 1:39:58Shawn Ryan?

  Can it shake my hand?

• 1:39:59Speaker 03

  Uh, I don't know, uh, maybe.

• 1:40:01Shawn Ryan?

  Can it squeeze my hand?

• 1:40:03Speaker 03

  There you go.

• 1:40:05Speaker 03

  There you go.

• 1:40:05Shawn Ryan?

  Will it crush my hand?

• 1:40:07Speaker 03

  No, it's not gonna crush your hand.

• 1:40:10Shawn Ryan?

  Dude, that's pretty stern. That's like,

• 1:40:12Speaker 03

  Yeah.

• 1:40:12Shawn Ryan?

  I can't move it.

• 1:40:13Speaker 03

  We can pick up like 40 pound boxes off the floor and we can also fold a t-shirt, so.

• 1:40:20Shawn Ryan?

  That is wild.

• 1:40:22Speaker 03

  Yeah.

• 1:40:23Shawn Ryan?

  This is the power button?

• 1:40:25Speaker 03

  Yeah, don't push that.

• 1:40:26Shawn Ryan?

  Okay.

• 1:40:28Speaker 03

  We had a, we had somebody in the office the other day, uh, it was like, I, I feel like I need to push this. I'm like, it's literally gonna turn off if you push the button.

• 1:40:36Shawn Ryan?

  And how, how long does it hold the charge?

• 1:40:39Speaker 03

  Uh, it depends on what we do, but anywhere from four and five hours.

• 1:40:42Shawn Ryan?

  How long does it take to charge?

• 1:40:43Speaker 03

  Takes about an hour to charge. So we can do about four or five hours on, we can charge for an hour. Um, you know, humans take breaks during the day to eat and do other stuff. And uh, or, or we should do a lot of time in the office, we'll sub another robot in during the meantime.

• 1:40:57Shawn Ryan?

  Wow.

• 1:40:58Speaker 03

  Yeah. We actually charge. Uh, here inductively through the feet. So the feet have like, um, basically have like, uh, charging pads, robot steps on to. And we charge wirelessly. We can charge, we can basically charge in one hour through that whole process.

• 1:41:11Shawn Ryan?

  Holy shit.

• 1:41:11Speaker 03

  Just by standing. So in case the robot has a task where it needs to stand a lot, we can just stand there and charge.

• 1:41:14Shawn Ryan?

  So he just stands on a mat?

• 1:41:16Speaker 03

  Standing on a mat. We designed it in-house.

• 1:41:17Shawn Ryan?

  Like an iPhone charger?

• 1:41:18Speaker 03

  It's like an iPhone charger. Yeah, and it can charge about a kilowatt per foot. It's about two kilowatts it can charge.

• 1:41:22Shawn Ryan?

  When is this gonna be available to its consumer market?

• 1:41:25Speaker 03

  As soon as we like make it work really well.

• 1:41:29Speaker 03

  And so I can sit into my house and my kids ask for ice cream every single day. And yeah, so, um, we're working on it really hard. I think, uh, you know, we've been testing in, I tested my home, uh, fairly recently and we'll be shipping these robots out to commercial customers here really shortly.

• 1:41:46Shawn Ryan?

  Can I ask who the commercial customers are?

• 1:41:48Speaker 03

  Yeah, we have, uh, 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. Uh, and then we have like two more customers we'll be announcing in the next like sixty days.

• 1:42:02Shawn Ryan?

  Congratulations.

• 1:42:03Speaker 03

  Yeah, thanks.

• 1:42:04Shawn Ryan?

  That's amazing.

• 1:42:04Speaker 03

  So we're gonna try to ship as many as possible we can this year.

• 1:42:07Shawn Ryan?

  Wow.

• 1:42:07Speaker 03

  We also make these, uh, on-site next door at Baku. It's our, um, production manufacturing facility. And we make about one every, kinda like ninety, like ninety minutes or so now.

• 1:42:17Shawn Ryan?

  You can make one of these in ninety minutes?

• 1:42:18Speaker 03

  Yeah, when we run the line, um, when lines are running about every ninety minutes, we make one. And then that'll greatly increase even in the next like several months here.

• 1:42:28Shawn Ryan?

  Wow.

• 1:42:29Speaker 03

  Yeah.

• 1:42:29Shawn Ryan?

  Wow.

• 1:42:30Speaker 03

  Yeah.

• 1:42:30Shawn Ryan?

  What do you, I mean, at full capacity, like, what do you think you'll, how many do you think you'll earn out a year?

• 1:42:36Speaker 03

  Yeah, our facility there can do, uh, maybe upwards of like forty to fifty thousand a year at like full capacity. Uh, but we need to design for much higher, uh, like, we wanna get to like a million units, like a million units a year and, you know, like within this decade.

• 1:42:49Shawn Ryan?

  A million units a year?

• 1:42:50Speaker 03

  Yeah, for sure. I mean, you sell like, we're sell over-

• 1:42:52Shawn Ryan?

  It's like building a country.

• 1:42:54Speaker 03

  It's not, I mean, like, we sell like over a billion phones a year, easy, so I think it's gonna be like a, a robot for every human, so you'll need like a cellphone qual- uh, style manufacturer.

• 1:43:05Shawn Ryan?

  Shit.

• 1:43:05Speaker 03

  Yeah.

• 1:43:06Shawn Ryan?

  So I can push this?

• 1:43:08Speaker 03

  Oh yeah, it has push recovery. Give it a little push. I mean, a little harder than that might be nice.

• 1:43:12Shawn Ryan?

  Harder?

• 1:43:13Speaker 03

  Harder.

• 1:43:16Shawn Ryan?

  Dude, what?

• 1:43:19Speaker 03

  Yeah.

• 1:43:24Speaker 03

  Yeah.

• 1:43:26Shawn Ryan?

  There's a better balance than I do.

• 1:43:27Speaker 03

  Yeah, same.

• 1:43:32Shawn Ryan?

  Dude.

• 1:43:34Shawn Ryan?

  That's crazy.

• 1:43:35Speaker 03

  Yeah.

• 1:43:36Shawn Ryan?

  This is three and a half years.

• 1:43:38Speaker 03

  We had, 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. This like, the thing is, this is like, uh, we're gonna go through like this whole iPhone lineup where it's, uh, you know, iPhone one, iPhone two, iPhone three, it just gets better and better.

• 1:43:56Shawn Ryan?

  Yeah.

• 1:43:57Speaker 03

  And I think humans will take like more radical steps between those. Every, every year, we're roughly building a new robot every year. We'll just get like dramatically better than this.

• 1:44:05Shawn Ryan?

  Damn.

• 1:44:06Speaker 03

  Yeah. Our step up from here even to the future robots will be, I think perhaps the most dramatic step up we ever make.

• 1:44:14Shawn Ryan?

  Wild. You wanna take some pictures with them?

• 1:44:17Speaker 03

  Let's do it.

• 1:44:21Shawn Ryan?

  Dude. That is insane.

• 1:44:24Speaker 03

  What do you think?

• 1:44:26Shawn Ryan?

  Awesome. I want one.

• 1:44:28Speaker 03

  You want one?

• 1:44:29Shawn Ryan?

  Yeah.

• 1:44:29Speaker 03

  Let's get you one, man.

• 1:44:31Shawn Ryan?

  Um, wow, like, so that in the, you said the hands can sense three grams of pressure?

• 1:44:39Speaker 03

  Yeah, we basically have.

• 1:44:39Shawn Ryan?

  The fingers in it?

• 1:44:40Speaker 03

  We have tactile sensors on every fingertip. And they're really sensitive.

• 1:44:48Speaker 03

  And we have a camera in the hand that can detect when the fingertips are in contact with some surface. 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 hands. The hands like, the hands are really good. Uh, honestly, we were working on hands now for like close to four years. It's, 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 of weeks ago that has like basically full.

• 1:45:21Speaker 03

  I think I think it's a full human level dexterity with this hand.

• 1:45:26Shawn Ryan?

  Are you serious?

• 1:45:27Speaker 03

  It's got as many joints on the hand as a human hand has.

• 1:45:32Speaker 03

  There's still a lot of work to go do, but like it's now.

• 1:45:36Speaker 03

  It's now a huge step up where we actually even currently are in the hand now can like fold laundry and. You know.

• 1:45:43Shawn Ryan?

  Do you think it'll hit a point where it can outperform a human? More dexterity in a hand than a human.

• 1:45:49Speaker 03

  I don't know.

• 1:45:50Shawn Ryan?

  A better balance, faster, stronger.

• 1:45:52Speaker 03

  We already have better balance than a human. The robot on one leg could balance better than a human can. I don't know about like. Humans have a lot of degrees of freedom. We have like. Hundreds, a few hundred degrees of freedom. Our hands are very dexterous. I would say if we can do. Close to human dexterity in terms of like. That'd be a huge win. We'd you'd have robots everywhere. And. You know, and then we're going to still have a lot of trouble getting to a hu- like full human range of motion like. Small things like you would reach inside of a.

• 1:46:26Speaker 03

  You know, a washer and you kind of like move your head as you're like getting in or sometimes like some people get on like, you know. Get down to the ground and like kind of get in the washer to grab some on the back. Cause like we do a lot of crazy stuff.

• 1:46:36Shawn Ryan?

  Yeah, that is.

• 1:46:37Speaker 03

  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.

• 1:46:49Speaker 03

  Humans would be tough, but like I think we can get. Very soon we'll get to like pretty close to most of what humans can.

• 1:46:57Shawn Ryan?

  You had a. Pretty close relationship with OpenAI, correct?

• 1:47:01Speaker 03

  Yeah, they.

• 1:47:04Speaker 03

  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 were they joined my board and then we end up spending basically a year with them working on.

• 1:47:24Speaker 03

  Well, I mean, I'll give you the background.

• 1:47:27Speaker 03

  The goal was to like try to advance AI models for humanoid robots together. And. You know, they're they have some great folks that have worked on like LLMs and chatbots and things and. And at the time, we had like a, you know, we still do, but we had our, we had a full like AI team internally. So we were basically working weekly, daily on like, basically, how do we advance state of the art? Kind of like language models for robotics. And, um, you know, like, uh,

• 1:48:00Speaker 03

  yeah, I, I, I ended up firing them.

• 1:48:03Shawn Ryan?

  I know.

• 1:48:04Speaker 03

  A year, a year later, but, uh, uh, in splitting ways, but like, listen, they're a great team. I, I like the senior leadership and everybody there, Sam included, like, were great to interact with. The issue lied for us of like, um,

• 1:48:19Speaker 03

  there's nobody that's ever put like advanced like 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, um, next token prediction for like language models.

• 1:48:33Speaker 03

  We ended up finding that the team we had in place, you know, my, my team lead, the folks we have here all from Google DeepMind or certain areas of like, you know, top AI programs and that they're really good. The team now we have is over 50 or so on the AI, our Helix team internally. We just found that like that team we had internally, uh, we just, we just ran like kind of circles around them. Like every day, we had a hard time getting like, you know, in robotics, you gotta like run the robot, see how it does.

• 1:49:00Speaker 03

  You know, like, 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 end of the day and see how, 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 the end of the day, like, do we need to get, like, see how the robot does? And we had, we just had a hard time getting them in the office. We had a hard time, like, basically, like, uh, 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, they're the best robot learning folks on the planet, they're sort of figure. And, um, it got to a point where, uh, 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.

• 1:49:43Speaker 03

  And I got a call one day saying like, hey, we're like, you know, we've been watching your progress is unbelievable. And, um, you know, we're thinking about doing robotics work internally. And I was just like, ah, this is over.

• 1:49:57Shawn Ryan?

  You're done.

• 1:49:57Speaker 03

  Yeah, just get out of here. Like, this is like, we're like, 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 co-founders on site at one point, 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.

• 1:50:14Shawn Ryan?

  Yeah.

• 1:50:15Speaker 03

  And, uh, 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 gonna teach you how to do this stuff anymore. And, um, it's also, we just like got no value out of the whole relationship or really very little. I mean, it was helpful having them lead the route, co-lead the round. It was like, there was some, there was like some good brand association there, but like beyond like that, there wasn't much. So we ended up, you know, we're going to 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, I have to spend a lot of my time hiring, like, like on the AI team and, um, we bring candidates in and they'd be like, oh, you guys do the robot opening ideas and models. And I'm like, oh, no, not really. No, we have a whole AI team internally. We do model development here ourselves.

• 1:51:00Speaker 03

  You know, like we're advancing ourselves and it just wasn't the perception from the outside. It was just hard. Uh, so that also wasn't helpful for us. Um, both hiring was not great and we were like, you know, there was like an, like information passing back that I think wasn't really helpful for us, uh, long-term if we're going to be competitors. So, uh, we decided to split ways. Um, I decided specifically to split ways. Um, but they have a great team. I think they're doing robotics now internally.

• 1:51:25Shawn Ryan?

  Sounds like it.

• 1:51:26Speaker 03

  Yeah, exactly. Yeah, yeah, exactly. Uh, I was like, I got a call saying like, yeah, like, you know, partly like, uh, the partly feedback I heard was like, we've made so much progress at Figure and they've seen that. That they were, you know, they, the opening started out as a robotics program. They were trying to solve AGI through a body set. First three, four years, they were just like all in on robots. If you Google like OpenAI Robotics, it's like old 2016, 2017, 2018, 2019, like, you know, maybe like, it may be like 2019, 2020, 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, twenty sixteen, twenty seventeen for like many years, maybe three or four years, trying to solve like AGI through robotics.

• 1:52:08Speaker 03

  There's, you know, there's this other, we don't need to get into it, but like it's, it's, you know, it's unclear if you need an embodiment or not, or, you know, at the time it was unclear, but you need an embodiment or not to like truly get to like above like peak human intelligence. And, and they had a hard time in there, but there was like, part of their thesis is 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 somewhat humbled is like, it's, we made like, I think we made like ten, I don't know, five to ten years of progress in like three years, four years. Like, we just like, it just felt like, uh, this should have taken ten. Even right now, it feels like, we're not even four years old yet. Four years old and end of May or something like that.

• 1:52:52Speaker 03

  Like, I, we like, I couldn't believe when we started the company three and a half years ago, we'd be at a point where you can get a humanoid robot, even here, doing the stuff it's doing here, but like, let alone like the real stuff it's doing now, I'm like, twenty four, seven, commercial work in the home, like, it's neural net driven, like, uh, we can make them every ninety minutes at the, you know, when our lines are up, like, it's just like, uh, it's crazy. So, yeah, we started part ways.

• 1:53:16Shawn Ryan?

  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 ballsy move, but it makes perfect sense. And, uh, man, again, just congratulations on, on everything. I mean, that is, that's just crazy. You know, I've done, I've done a, it's just very surreal for me to unveil some of that. I mean, I know we didn't unveil this, but it's the first podcast it's ever been on.

• 1:53:49Speaker 03

  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, like, love your show and want to get him here in Tennessee. Uh, this is the first time a bot's been out here to something like this.

• 1:54:02Shawn Ryan?

  Thank you. 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.

• 1:54:12Speaker 03

  No problem.

• 1:54:13Shawn Ryan?

  What about military application?

• 1:54:17Speaker 03

  Yeah, we've, um, we've decided not to do military stuff today.

• 1:54:22Speaker 03

  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 militarize the robots. I think it's just too hard in a 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, 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 walk in your house, walk upstairs, go in your room. It's like not going to come chase you.

• 1:55:08Speaker 03

  Like a, a robot will just walk right up your stairs and open your door. The humanoid robot, you know, this is a very different technology. We've got to be very careful with it. So I think because of some of that and some other things, uh, we like, we, we know, we've drawn a line here to say like, uh, 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 incredible opportunities for companies like to go in, uh, into the military. To be frank, is these, these robots would be great there. Like they can just like, they can, you know, like some of the most dangerous missions are like, you know, uh, going into close quarters and houses and, you know, um, yeah, that, that stuff is like extremely dangerous. Humans would be great at that stuff.

• 1:55:50Speaker 03

  Like opening doors and just making sure the house is, you know, cleared, like clear house. You know what I mean?

• 1:55:55Shawn Ryan?

  I could, I could see it for a whole ton of stuff. Not, not even just going on target, but sentries, gate guards.

• 1:56:03Speaker 03

  Yeah.

• 1:56:04Shawn Ryan?

  I mean, roving patrols. I mean, all of it.

• 1:56:07Speaker 03

  Totally.

• 1:56:08Shawn Ryan?

  Just armed security. It's, it's wow.

• 1:56:13Speaker 03

  You know, I mean, you kind of have somewhat of a, a, a tritable asset too. You can basically, I think you can make them relatively cheap, make a lot of them, just put them out to work.

• 1:56:21Shawn Ryan?

  Do you think you'll get into it in the future?

• 1:56:23Speaker 03

  I don't know. Um, as of now, no, but like, um, There's a part of it that you'll, is a part of the story here where you're like, you could make this like obviously really safe for humans there. There's a whole part of the story where it's like, I think it just becomes, you know, to be frank, like the, when we sell to commercial customers even homes, like it's not like selling like a robot arm on a stand. It's like these commercial customers need like CEO approval. We can't get them through without the CEO of like these major companies like coming to see the robots and saying, we're going to announce this relationship with Figure and we're going to announce humanoid robots in our facilities. And it's just like a, it's a very, you know, it's like a, there's, you know, it's like, if you watch this.

• 1:57:07Shawn Ryan?

  I'll make that announcement. I think it's fucking awesome.

• 1:57:10Speaker 03

  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.

• 1:57:17Shawn Ryan?

  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?

• 1:57:25Speaker 03

  10,000 people.

• 1:57:26Shawn Ryan?

  Yeah, like almost half of his, half of his personnel because of AI.

• 1:57:30Speaker 03

  Yeah.

• 1:57:31Shawn Ryan?

  And his stock went up because.

• 1:57:32Speaker 03

  I think, you know, I think it's probably because the robot is human-like and can do human-like work. So I think it's just scary for, you know, it's a scary thing that like, 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, the robots can do human-like work. And it will continue every year to do more and more human-like work. So, but like that, you know, we just gotta, we, we just want to be very careful about how we position this and what we do and, and also how we communicate it.

• 1:58:04Shawn Ryan?

  Yeah.

• 1:58:05Speaker 03

  Yeah.

• 1:58:07Shawn Ryan?

  Well, what's next for the robots?

• 1:58:09Speaker 03

  We want to solve general robotics at Figure. We want to, um, we think of ourselves truly as like, uh, like a, at the, at the frontier of like this robotics AI lab that needs to build common sense reasoning into the, in a robot that can put in every home. How do we, um, 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 is 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 ship them in the military, as you mentioned. There's like, there is, there is a path to go like build a business doing that. But the biggest business in the world is if you solve like, like general purpose robotics.

• 1:58:55Speaker 03

  Where just through speech and talking to the robot, it'd be, it'd feel like you had like a human in a body suit. That can like, understand you, nod, like, go off and do things now after your task. Like, that's the problem we want to solve at Figure. That's like a large scale, like, it's, it's like an AI lab problem at this point. We, like, we, we talk a lot about how we're trying to like, we're like, we're trying to give AI a body here at Figure. And so we have this embodiment, we need to put like really sophisticated AI into it to be, to be able to command it. And that's the, that's, that's the biggest problem we're trying to solve. If you're, if you're with me in the office every day, I am working that down with no sleep, basically is like, as hard as I possibly can.

• 1:59:41Speaker 03

  It's a very, very difficult problem. At this point, it's, um, it's largely constrained by getting the appropriate data into the, into the network.

• 1:59:51Speaker 03

  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.

• 2:00:02Shawn Ryan?

  Wow. What should I be asking you that I haven't asked yet?

• 2:00:06Speaker 03

  About Figure or General?

• 2:00:07Shawn Ryan?

  About Figure.

• 2:00:09Speaker 03

  Um.

• 2:00:16Speaker 03

  I mean, there's a lot of stuff going on with China and manufacturing, a few other things, but like, I think,

• 2:00:23Speaker 03

  you know, I think maybe to summarize, I think where we're at is, I think if I had to like, um, if I was like watching this and I wasn't following the story.

• 2:00:34Speaker 03

  I think the one thing I would like to convey that, you know, is like, we are so close to like 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.

• 2:00:59Shawn Ryan?

  It's

• 2:00:59Speaker 03

  And

• 2:01:00Shawn Ryan?

  It's just crazy to just, it's, it feels like we're living in the future.

• 2:01:05Speaker 03

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

• 2:01:40Shawn Ryan?

  Yeah.

• 2:01:41Speaker 03

  Yeah.

• 2:01:42Shawn Ryan?

  Have you seen, do the robots interact with each other?

• 2:01:46Speaker 03

  Yeah.

• 2:01:46Shawn Ryan?

  What does that look like?

• 2:01:48Speaker 03

  Right now they communicate with each other when they need to like, um, 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%. Uh, and it's a few percentages away from, we'll, we'll dock it before it's at, you know, 1% or something like that. Uh, it's at 10%. The other robot will get ready. Uh, like to sub in, it will like come walk, walk over, sit right behind it. And then when the robot is ready and knows that this there, it will then back away and the other robot will go into do operations and do work. That other robot will then go over and, and, and start charging. If any of those robots have any problems throughout, it could be hardware or software. They will go and like, uh, go to like, basically like the hospital in our office. So they'll go to a certain place.

• 2:02:30Speaker 03

  When they, when they get, when they get, um, when they know they're going to the hospital, we have another robot coming in to the main docks to start subbing in and getting ready to go. All this communication is happening like robot to robot. And, uh, it's unbelievable. And the robots are getting really robust. We can like, um, 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, can't stand anymore. You know what I mean? Like you fall. Today, that doesn't happen. We can lose a knee. We can hold its position, like we lose full, full comms with the knee. We can stiffen the joint and we can limp off. Holy shit.

• 2:03:15Speaker 03

  Yeah. Actually, I'll, I'll post some of the next week publicly about this. Uh, 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, headed to the hospital. It will live all the way there. While it's limping there, another group from like the healthy part of the hospital will then come in and resub it in from the, on the dock while another one undocks while it just lost his knee to go in and do work. All that's happening through robot communication levels. We, 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. Uh, it's, it's, it's, it's insane. This is happening like, I saw this in the last like few months that's happening right now.

• 2:03:59Speaker 03

  This is not even like the future stuff. Future stuff is going to be robots building robots. We're designing robots, we, we, 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, I say this again, but I, I think we'll walk out, it'll happen first in probably the Bay Area. We're based in the Bay and a lot of companies are in there for robotics. But I think you'll go to the Bay Area at some point and you'll see more humanoids than humans in the next 10 years, for sure. That is, that is, I, I can't even imagine what that's going to be like. It'd be weird.

• 2:04:44Shawn Ryan?

  Do you think that they will bring, do you think, do you think? Manufacturing will come back to the US?

• 2:04:50Speaker 03

  Yeah, we're gonna bring back.

• 2:04:51Shawn Ryan?

  Because of this?

• 2:04:52Speaker 03

  My view is that we don't want to bring back manufacturing that's already overseas. We don't want to like, uh, you know, like make shoes, make toys, like things like that. I don't, I don't think we want, I don't think we have the will to do this. I don't think we have the know-how to do this as well as like some of the Asian manufacturing groups. Um, when I'm, when I'm overseas, I, 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. You, there is like, it's like, it's like you walk these lines and they're just shipping 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.

• 2:05:33Shawn Ryan?

  No kidding.

• 2:05:33Speaker 03

  And it's doing it with through an automated way and moving it around a little conveyor. And it's moving to the next station, maybe a human's doing something and it's going down the line. It's going to a next station that's got a robotic system in there, completely customized in different way you just saw. And they have lines and lines in, in floors and floors of this, and then buildings and buildings. And you're like, holy shit, each one of those boxes is like a figure style complexity. And they have like hundreds of them.

• 2:05:58Shawn Ryan?

  Wow.

• 2:05:59Speaker 03

  And there have been, 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, the high-end robotic stuff. That's going to be like super transformative for us in the future.

• 2:06:21Shawn Ryan?

  All the futuristic.

• 2:06:22Speaker 03

  Yeah, we want to bring back flying cars. I want to bring back like, uh, like humanoid robots, like the stuff that's like highly dynamic, very intelligent systems. Like the next generation, like manufacturing 2.0 stuff.

• 2:06:33Shawn Ryan?

  Gotcha.

• 2:06:34Speaker 03

  So we're doing that right now in California on, on our campus. We have a fairly large campus in, um, in the Bay Area. And we manufacture right now, like whenever 90 minutes or so. And that will, we'll continue to spin that up and then we'll put, you know, uh, we'll, we'll, we'll talk about more about it. But we have like, we'll put more investment here into US manufacturing for the future.

• 2:06:56Shawn Ryan?

  Right on.

• 2:06:57Speaker 03

  So we're, we're going to design humanoids here.

• 2:06:59Shawn Ryan?

  So these are all, these are all manufactured.

• 2:07:00Speaker 03

  We manufacture those in California.

• 2:07:02Shawn Ryan?

  Right on, man.

• 2:07:03Speaker 03

  Yeah, man, they walk like, they walk off the lines, they walk over. It's like, it's like, uh, like you, like, like it's 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 burn ins, all O E O L checks. So they're they're self looking at each other, self calibrating. They're doing they're doing like burpees and other shit to make sure they like they're okay. If they fail, they go into a triage place. We understand why to fail. Like that shouldn't happen. We should always fix that and it should not fail again. Like, how do we fix the manufacturing process? So the next one doesn't come out and ever 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 of our check and when they're done, it's walk over.

• 2:07:47Speaker 03

  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 pallet and we can just start shipping them out. So.

• 2:07:56Shawn Ryan?

  It will get in its own box.

• 2:07:58Speaker 03

  For sure.

• 2:07:58Shawn Ryan?

  And another one will throw it on the pallet and ship it out.

• 2:08:00Speaker 03

  For sure. Yeah. That's not hard things, though. Like, these are like, uh, that's not, you know what I mean?

• 2:08:07Shawn Ryan?

  Yeah, it's just interesting to think about.

• 2:08:09Speaker 03

  I feel like.

• 2:08:10Shawn Ryan?

  Comes off the line, gets in its own box, gets loaded on by another robot and then shipped off.

• 2:08:16Speaker 03

  The scary thing for me is like, those are like very, um, like rigid body things, like cardboard and like moving boxes and maybe using machines and stuff. Like, those are like easy, like the scary stuff a couple of 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 you grab it, it's literally moving like it's moving because the conveyor is moving down and then the packages are squishing each other. And then the package itself is moving because it's plastic when you're grabbing it. Those are the hard things that are compliant that are like really difficult for robotics. Because they're not like they're not like stationary when you touch them. So those are the things that were like, man, that's going to be really tough to fold laundry. And for with code, it's been impossible.

• 2:08:58Speaker 03

  The reason you haven't seen like package logistics and stuff, some of the stuff automated is because like these bags are just like hard, they're compliant, they're just tough. You can't model them.

• 2:09:05Shawn Ryan?

  Yeah.

• 2:09:06Speaker 03

  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 have like soft packages. And we sign them, they're like, we want you to move these packages on this conveyor system. And we've put videos out about it and stuff. The first month we, um, sign them. Inga, who runs, you know, accounts was like, we need to, we need to, we need to do, we need to do this for them, or they're gonna be really unhappy. And I was like, I was like, Dan, that's like a compliant material that is moving while you're touching. Some of them touch and there's something hard inside. Some of them are squishy. There are like, there's tons of them. We're gonna move every three seconds. We gotta find the barcode, put it down and put it in the middle of the conveyor every three seconds a package. I was like, it's 50/50 shots works. And it's, it's gotta be with a neural net.

• 2:09:52Speaker 03

  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, the neural nets do extremely well under those like high variability environments. That's like extremely diverse. They can learn the representations extremely well across like a wider distribution. And they just love it. Folding t-shirts, towels, like packages, like no problem.

• 2:10:20Shawn Ryan?

  Wow.

• 2:10:21Speaker 03

  Stuff that would like, you know, you're replanning very fast as you're, as these things are all moving. It's doing that in real time. It's just like, it just works. Deep, deep, deep learning just works on humanoid hardware. Yeah.

• 2:10:37Shawn Ryan?

  Crazy, crazy stuff.

• 2:10:43Shawn Ryan?

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• 2:11:21Shawn Ryan?

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• 2:12:27Shawn Ryan?

  Crazy, crazy stuff. Let's talk about your venture to save kids in schools. Ready to move on to that?

• 2:12:34Speaker 03

  Let's do it.

• 2:12:36Shawn Ryan?

  I love this. Yes, yes. Can you give us the synopsis?

• 2:12:41Speaker 03

  Yeah.

• 2:12:43Speaker 03

  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. But we're from like 30 to 40 events per year in the US 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.

• 2:13:13Speaker 03

  There's just like a, you know, a 10x mostly in the US. You didn't really see this like a lot internationally. And, um, you know, we sort of looking at it, looking at it, uh, I basically started reading a bunch of like research reports and other things. And I found I stumbled upon this technology, uh, this like basically technology and kind of like terahertz radar. So basically like, uh, or sometimes also called millimeter wave technology where it's basically like, um, uh, basically high frequency, like, uh, like, uh, it's basically like it's radio, uh, RF. It's like radio frequencies, but basically done at a very high frequency. Like in the two to three, 400 gigahertz. And, um, it's basically like similar to, you know, when you're in

• 2:13:57Speaker 03

  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, uh, like, you know, if you have knife, gun, vape pen, whatever. Um, I read a research report that showed, and my goal is like, if you want to put it in schools, you, you can't scare the kids. You have to be able to, so sorry, back up. My view in schools is if you want to solve it, you have to solve it from a perception perspective. Meaning you have to see if people have, you have to understand if people have guns on them or not. You can like change, like, there's like a regulation side that some people chase, which we're not chasing. Uh, and then there's like a, how do we actually like know if people have guns on them?

• 2:14:40Speaker 03

  Cause if you know a kid has a gun on them, you can go like, take it away. And then majority of all school shootings are unplanned. Most of them, like, like almost all of them. Are some kid bringing a gun in habitually. It's like their uncle's gun and they bring it into school like every day for like three months. They get in a fight at recess and they shoot something, they shoot it, the gun. Sometimes shoot somebody, somebody will shoot it. And that is majority of all things, all gun events. The ones where you see like, uh, a planned event that's like on like CNN where somebody's like coming in with a machine gun or a night weapon. There's a, 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 ninety something percent, is all happening from unplanned. Folks are bringing in guns all the time.

• 2:15:25Speaker 03

  And then they're shooting it. So you basically, what you can do is you can stop all those. The, the, the planned ones are very difficult and maybe impossible to stop. But the ninety some percent of all other shootings, you can actually, 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 like, it's just like, that's not, we don't want the kids going to school like that. That's just like, not, I want my kids growing up.

• 2:15:51Shawn Ryan?

  Yeah.

• 2:15:52Speaker 03

  So basically, uh, the reason why I got obsessed with like, uh, Terahertz imaging is you could basically do this at like, uh, at a, at a larger offset, ten, twenty, thirty 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, it's like, it's like a three dimensional camera image almost, but it's done in like a radio frequency. Um, it's just, you could like, look at like, almost like an optical image. And, um, 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.

• 2:16:37Speaker 03

  You can scan people as they're walking in, passively, like, just like, as like, you know, just walking in. Don't need to stop anybody. And you can basically scan, you know, most guns that are brought in schools 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.

• 2:16:56Shawn Ryan?

  It will find, it will find a gun in a backpack?

• 2:16:58Speaker 03

  Yeah.

• 2:16:59Shawn Ryan?

  No shit?

• 2:17:00Speaker 03

  Yeah, so, um.

• 2:17:01Shawn Ryan?

  It's, it's amazing it'll find a concealed weapon anywhere.

• 2:17:05Speaker 03

  There's like, you know, yes, it's.

• 2:17:07Shawn Ryan?

  There's print in a backpack.

• 2:17:09Speaker 03

  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. Um, you know, uh, that were at NASA Jet Propulsion Lab. I write these two guys, and they said, you know, sure, we'd love to have you over. I get over there, and they're like, they tell me the whole backstory. They're like, listen, we, we developed this technology for standoff distance detection for the Iraq and Afghanistan war. It was funded by the US government. We worked on it for ten years. And when the war stopped, like, funding dropped to zero, and we like, we were done. We didn't work on it anymore. And I'm like, oh, it sucks. And then I'm like, okay, well, I guess keep me posted if, you know, if this thing ever works out. And then they're towards the end like, oh, you want to go see it? I'm like, what do you mean, see it?

• 2:17:55Speaker 03

  They're like, it's in the basement, it's done. We did it. And this is in 2017, 2018. This is like, I was like, oh yeah, let's walk down, walk down to the basement. There's like this tarp over this, uh, machine, took a tarp off. They had a guy with a mannequin, like a mannequin that's sitting there with a gun underneath a shirt. Like, I don't know, three or four meters away. They turned this machine on, it was built like 10 years ago. It had like a computer tower inside of it. And, 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.

• 2:18:40Speaker 03

  There's a bunch of other ways we can look at the data, but it was just like crystal clear. Wow. And I was like, what, 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 to like, you know, deep tech hardware. So I basically decided to put cover on hold and, um, you know, chase, taste Archer. And then about two years ago, somebody came to my office.

• 2:19:15Speaker 03

  One of my investors was like, hey, I'm like looking at like trying to solve school shootings. I was just back from LA and I'm like trying to solve it with CCTVs, like the security cameras. He's like, the problem is like, you can't, you won't know until a gun goes off. And you, 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. And I told him the story about how I went down this path and here and he kind of looked me dead in the eyes. He's like, I have kids and you have kids, like you, you have a fiduciary duty to go build this.

• 2:19:44Speaker 03

  And it was right when my daughter was also applying first grade and we were worried about it at schools. You know what I mean? Just looking at like the fence and just like kind of anybody can go in. You know what I mean? So like, I was like, shit, you know, I gotta go do this. I ended up spinning the technology out of Jet Propulsion Lab at Caltech. And I own it and started cover two years ago. The OG team that built it is with, is with me now.

• 2:20:08Shawn Ryan?

  No way.

• 2:20:09Speaker 03

  Uh, we put an office in Pasadena. That's the main offices and 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, uh, 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 it at school.

• 2:20:32Shawn Ryan?

  Wow.

• 2:20:33Speaker 03

  And we'll put it in that figure campus first, even.

• 2:20:35Shawn Ryan?

  Wow.

• 2:20:35Speaker 03

  This is an AI, this is like an optical play. Can you see it? This is an AI play saying, can you detect it now?

• 2:20:42Speaker 03

  There's a hundred and thirty thousand K through 12 schools in the US. There's like 60 or 80 million K through 12 students. It's a, it's huge. And, um, but it's not just schools. It's stadiums and airports.

• 2:20:55Shawn Ryan?

  Everywhere.

• 2:20:56Speaker 03

  Hospitals, hospitals.

• 2:20:57Shawn Ryan?

  Malls, every, any venue, you can movie theaters.

• 2:21:01Speaker 03

  I had my last baby a year ago, just like anybody can walk in the hospital. It's just like, just doesn't matter. They'll check you in. It's just like scary. And so anyway, um, we're, we're getting close here. And, um, the technologies we designed are incredible. Actually, we designed all of it. Like we designed the whole system that I saw. Redesigned the whole system I saw up 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, 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.

• 2:21:43Shawn Ryan?

  Yeah.

• 2:21:44Speaker 03

  There's only a few groups in the world that can make them and design them. We co-designed, we worked on the design with them, made them, fabricated them, and we have them now in our office. They work. We didn't like a lot, you know, we use many different, we use like a lot of chips, but they're like really cheap. And that's important. So we, you know, K12 will have like a large budget, and we need to be able to get the cost down to make it affordable for every school.

• 2:22:09Shawn Ryan?

  That's what I was gonna ask. I mean, how are you gonna, how are you gonna get this in school? A lot of, a lot of schools won't do, they won't even hire a security guard.

• 2:22:16Speaker 03

  Yeah. There, there are big budgets, both at the federal and municipal level, like hun, like a lot of money to put, that are going into school, like schools are getting subsidized for to put in a lot of stuff.

• 2:22:26Shawn Ryan?

  Good.

• 2:22:27Speaker 03

  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, I think we get the cost down to a reasonable amount per student that, that both public and private schools can afford. But it's a rat, we're like, we, we could have already had our systems beta testing in some schools by now, if we didn't pivot a year and a half, year ago. We spent last year trying to like 90% decrease, like decrease the bill materials, like the cost.

• 2:23:02Shawn Ryan?

  Wow.

• 2:23:02Speaker 03

  It's just like, that's needed to go big and make this really work well.

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• 2:24:05Speaker 03

  Um, I think we'll...

• 2:24:08Shawn Ryan?

  Are you gonna put anything else into it? Any other... Like, here's an example. When I think of this, it's... Would there be a way to... Maybe facial recognition?

• 2:24:21Speaker 03

  Yeah.

• 2:24:22Shawn Ryan?

  Who's enrolled here, who's not?

• 2:24:24Speaker 03

  Yeah.

• 2:24:24Shawn Ryan?

  Just for example, like, the shooter that happened up at Nashville a couple of years ago, the Covenant School.

• 2:24:28Speaker 03

  Yeah.

• 2:24:29Shawn Ryan?

  Went to school there, but not at the time.

• 2:24:31Speaker 03

  Yeah.

• 2:24:31Shawn Ryan?

  You know, and so if they would've had some type of facial recognition on top of what you had, that's...

• 2:24:36Speaker 03

  Yeah.

• 2:24:37Shawn Ryan?

  This person doesn't go here. This person has a gun.

• 2:24:39Speaker 03

  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'll also get a lot of semantic understanding, because guns are like, they're hidden somewhere, they're concealed. People are not walking in with, like, handguns and shotguns, like, into school. They're like, they're in, like, in a waistband, in a pocket, backpack. We can be really thoughtful about if somebody, you know, clearly doesn't have 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, uh, 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?

• 2:25:24Speaker 03

  It's just like, is this a weird time for somebody to be, like, leaving and walking back into the school? So there's just a lot of semantic grounding we can put into the models to really help, like, understand if there's threats or not. The schools are set up really well to do, like, random locker checks now, and like, okay, this doesn't look okay or not. Like, the schools are really well-equipped for that. It's just like, we don't know what's happening. 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.

• 2:26:11Speaker 03

  Because if you report like a student that has a gun, they're going to juvie.

• 2:26:17Speaker 03

  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, could like, 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 to the schools.

• 2:26:43Shawn Ryan?

  Wow.

• 2:26:43Speaker 03

  We're finding, you're reporting thousands, and you're seeing hundreds of shootings. So our view is that we think is actually happening, like, you know, as a percentage is low, but as a 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, will happen in the spaces I'm in, which is like, flying cars, robotics, like AI and weapon detection and stuff like that. And like, I did a post in December, like, here's what I think on these four areas. And like overwhelmingly, like, the most support I got, like publicly, was just for cover. It just like, I think, I think it, you know, just like hits, I think

• 2:27:25Speaker 03

  it hits in a really good way with a lot of folks, maybe especially parents. So

• 2:27:29Shawn Ryan?

  Everybody's word. We're homeschooling.

• 2:27:32Speaker 03

  Yeah.

• 2:27:32Shawn Ryan?

  Because of this shit. That's a big reason why we're homeschooling.

• 2:27:36Speaker 03

  Yeah, I, 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, you know, and it's like, it's probably like a low occurrence rate, but if it did happen, it's just like, can't recover from that, you know?

• 2:27:49Shawn Ryan?

  I mean, it's just every school I go to, I'm like, man, like, you guys got it. It's just happened down the road.

• 2:27:55Speaker 03

  I had a good buddy, like, had his house breaking into, he's got a family and stuff. It was like six months ago, and he just told me on, when I was talking to him last, he's like,

• 2:28:03Speaker 03

  The sense of security we have now in our home is just like we'll never get back. And I'm 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 just never, we're never going to be able to get it back. 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, um, it's terrible. And so, you know, my, my agenda here is I, 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, and then if you even have like, there's no real security there at all right now, but even if you have security, there's also like a sense of like, shit, I gotta bring a gun in here now. There's like real sophisticated AI. It's in all these schools that can catch it.

• 2:28:44Shawn Ryan?

  Yeah.

• 2:28:44Speaker 03

  I think that's another big thing. You have that at like TSA.

• 2:28:47Shawn Ryan?

  Deterrant.

• 2:28:48Speaker 03

  Yeah, it's a deterrent. So you have like that, but we can also find you, find it. We can see underneath, uh, through backpacks and stuff. It happens at specialized, um, like, uh, like radio frequencies. Uh, it happens at like, you know, 200 to 300 gigahertz. Then it happens to get at 600 gigahertz. And in between those bands, there's either FCC rules that prevent you from doing it, or there's atmospheric attenuation, meaning, um, sometimes, uh, there's enough moisture in the atmosphere at certain radio frequencies that like the, the radio frequencies don't do well and perform well. Um, they perform well at these certain, uh, radio frequencies for the imaging stuff we do. So they're actually quite a hard technical feat. I, one of the reasons I didn't do it and did Archer is because I thought the cover stuff was actually harder than doing flying cars.

• 2:29:32Shawn Ryan?

  Okay.

• 2:29:33Speaker 03

  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.

• 2:29:47Speaker 03

  Cover is just like super niche area of folks that it haven't, like there's the, the folks spending. Like in like in this space or 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. Uh, they're probably not getting paid enough and they're just like super passionate about solving this problem. And um, so anyway, I think um, I think the through line for covers, I think it'll work. I think we'll be able to demonstrate it hopefully by end of this year. Like we'll be able to say like we have it at like, we'll have it at figure campus first and then we'll put it in uh, in like schools like hopefully on the west coast and maybe a maybe one or two and we'll see how it goes.

• 2:30:33Speaker 03

  There's um, you know, there's a how do we market this? Like, what do we tell the students? Like teach the 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 uh, we have like low false positives around the whole system stack. Uh, that's a really hard problem to solve, especially for us. Where you can be like partially occluded on certain areas. Uh, of the person or the weapon. And then we need like to know what we see. It's actually is it is it real or not? And so. Funny enough, if you come to our lab, we have like a just guns everywhere.

• 2:31:16Speaker 03

  And um, they're all they're all bricked. You can't like you actually shoot him, but um, but we we all 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.

• 2:31:26Shawn Ryan?

  So how does it work? Is it is it shooting frequency and then and then detecting the response when it hits something solid?

• 2:31:33Speaker 03

  That's exactly what it's doing. It's like um. It's basically shooting out a radio frequency. It's like electromagnetic, like it's like a little wave uh, format goes out uh, that goes out. Uh, very similar to how like your wifi works in your home uh, or 5G. Same like same uh, same type of concept. It's just on a higher like different different like uh, radio frequency level. But think about your wifi and like you want to like um. You know, in order of like 20x or so, like the 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. Um, so you want to like, whatever, call it 50, maybe it's 50, 100 times this. And um, and then basically shoots us out, uh, and this waveform comes back and we we review it.

• 2:32:16Speaker 03

  And we look how long, uh, like how long it took to come back. Um, and we use beamforming and a couple of other techniques to figure out, um, what happened. But you're basically, it's same as like traditional radar technology. Um, but we can shoot it out, comes back, it's not ionizing, won't hurt you, like it's perfectly fine to be around like your wifi. And we can basically get a, we can get both, um, a 2D image of 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, uh, pocket for whatever, or say I have one on like my chest, for example. We will start getting back the the signals back from the top surface of the gun before we get your chest chest stuff back. In the case of your chest, you have a lot of like water in your skin and it'll it'll somewhat attenuate in your chest.

• 2:33:00Speaker 03

  So then we'll get back an image from this and we'll reconstruct it really fast. And we can reconstruct into somewhat of a three dimensional point cloud like you do a camera. So you can basically get like, almost like it looks like, like an, I showed you earlier today, the the the vision from this, it looks like a kind of a camera image is what you get back. And, um, so from there, you can kind of like visually see what's really happening. In the case of the gun, you can, you can see the gun, you can see the trigger in some cases. Yeah, and sometimes it might be just be like the handle or the, um, 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. Um, but most guns are all in the waistband pockets or backpacks, which makes sense, right? You're not like wearing it around your neck on a, outside your shirt or, um, like things like this. So, uh, that's where, that's where like most weapons are entered into school.

• 2:33:44Speaker 03

  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 out the best school shooting analytics, he does it daily, he's done it for five years. He's, he's working with us through this and we've done so much work on how people, how how students enter schools, how they exit, emergency responses. What solutions are on campus now for this? Uh, like, uh, where, what, what data, uh, we find on like where guns are at, what type of guns and weapons are there? There's, um, I think it was like 200 knife stabbings last year.

• 2:34:17Shawn Ryan?

  200?

• 2:34:18Speaker 03

  Yeah, it's like, it's so high and so dangerous, like we're trying to, like, we can detect knives. Like there's, um, 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, um, uh, like come back to the radar system a little bit differently. So you can kind of maybe sometimes tell if there's like a gun, like a metallic signature or not coming from the material. But the technology is like really kind of straightforward in 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 try to build like a neural network. To then look at it and say like, what is this thing? What time of day is it? Who is this human? Like, is this a dangerous threat or not? And we need to do a really good job of making sure we're accurate in those readings or not.

• 2:35:02Speaker 03

  Uh, if we're not, we're going to cause havoc. And if we're right, a lot of times we could basically start saving lives. And that's, I mean, there's, there's a, on average, a one shooting every, every single day in the, like more than that. Like this, you know, there's over three, three hundred or more or so shootings roughly a year. If you look back the last couple of years. So like every single day, I mean, there's less school days in a year than 365. But like roughly every day is a school shooting in the U.S. Um, that's just at K through 12, not colleges. Uh, but that's about, that's looking at the 130,000 K through 12 schools in the U.S.

• 2:35:34Shawn Ryan?

  I can't, did you think this will be out in a couple of years?

• 2:35:36Speaker 03

  Yeah, I think we'll get it out in a couple of years. We're, we have a team working day and night on this. We'll, you know, we'll probably, I'll probably increase funding into it this year significantly. And we'll, uh, we'll, we'll take a bigger push in headcount. Uh, 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, um, 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 to get in. Like we need to scan all of that all the way through. So it's like a natural aperture that students are walking into, which is good. You're not walking inside of a building, you know, like through a brick wall. You have to walk into a door entrance.

• 2:36:19Speaker 03

  And we're trying to, yeah, basically we're trying to, uh, get that fully complete this year.

• 2:36:24Shawn Ryan?

  Man, that is solid work.

• 2:36:26Speaker 03

  Yeah.

• 2:36:26Shawn Ryan?

  Real solid work. Let's talk about HARC.

• 2:36:30Speaker 03

  Let's do it.

• 2:36:31Shawn Ryan?

  Ready?

• 2:36:34Speaker 03

  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 tech. I think it's I think humanoid AI is like the one of the hardest AI technologies on the planet. It's just like an incredibly difficult problem that I've been my my team and I have been working through day and night for the last four years. So it's like, okay, we want to go like build like a crazy sci fi future with the with like flying cars, AI humanoids, and then on my other half of my life, I'm like using like an AI chatbot, like a frontier lab like Jim and I are chat GPT. And it's so stupid. It doesn't know me at all, doesn't remember anything I'm saying, can't see what I'm doing. It can't use tools very well, uses the internet like really poorly, can't even order me a sandwich if I needed one right now.

• 2:37:19Speaker 03

  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. It doesn't have access to my accounts, doesn't know any of this stuff. Meanwhile, I think like for me, like I was just been sitting here for three years thinking like, we're gonna get like Jarvis out of this from Iron Man. We're gonna get something crazy out of AI. It's gonna move to a point where it can like, it can like listen and speak naturally like a human, it can see the world, it can do, 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, and know

• 2:38:02Speaker 03

  Sean, it'll know everything you're ever doing, all your stuff, and be really personal to you. We don't have anything like that now. I got like this stupid chatbot that doesn't remember the last thing I said to it. And so I decided to like, I said 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 AI intelligence. There's just like a lot of, there's a lot of, there's a lot of like, like missed up, like missed opportunity now, last like few years, a lot of like, a lot of gaps there. The second thing is we're like interacting with these AI systems to like old pre AI computers. Like you're

• 2:38:47Speaker 03

  prepping up your phone or your Mac or your computer, it's like they're all designed like 20 years ago. They're like the, it's like a 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.

• 2:39:05Speaker 03

  So we have to go rebuild uh all the hardware from scratch.

• 2:39:09Shawn Ryan?

  Holy shit.

• 2:39:10Speaker 03

  Yeah, and I don't see anybody. I've been waiting. I've been sitting here for like a year and a half. Be like, somebody's gonna do this really well, and I can't wait for it.

• 2:39:20Speaker 03

  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 gonna basically design what comes after the iPhone for AI. And we're gonna design new models that are extremely multimodal that can solve this.

• 2:39:43Shawn Ryan?

  No shit.

• 2:39:44Speaker 03

  Yeah, and we have like the world, some of the, I think, some of the world's best AI folks of all time, and we have we have like, we have the the lead designer from the iPhone, Abidur on the team. I mean, design iPhone fifteen, sixteen, seventeen.

• 2:40:01Shawn Ryan?

  So this is gonna wind up being a device? Is it gonna be a device?

• 2:40:05Speaker 03

  A family of devices. Yeah. And um this will go really far. It'll replace your phone and computer and you'll 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 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 ninety days and you're not doing this over here. It'll just like, it'll just

• 2:40:38Shawn Ryan?

  It'll hold you accountable.

• 2:40:39Speaker 03

  Hold you accountable.

• 2:40:41Shawn Ryan?

  Yeah.

• 2:40:42Speaker 03

  Yeah. Yeah, we have, we've been we have hardware in our lab, we have we work on AI models now, like stuff is crazy cool and um.

• 2:40:53Speaker 03

  Yeah, we're gonna uh

• 2:40:57Speaker 03

  I think we'll probably come out of stealth by the time this thing airs here between you and me.

• 2:41:01Shawn Ryan?

  Holy shit.

• 2:41:02Speaker 03

  And um we're I'm self-funding it right now.

• 2:41:07Shawn Ryan?

  You're self-funding this one too?

• 2:41:09Speaker 03

  Yeah, I'm self-funding right now and um

• 2:41:13Speaker 03

  and yeah, the team's great, man. We're uh I think we're yeah, I think I think it just I think it's gonna be a massive opportunity. And um I see the Frontier Labs heading in a a really great place for them, but a very different place than where we're headed.

• 2:41:33Speaker 03

  Yeah.

• 2:41:34Shawn Ryan?

  What are you most excited about?

• 2:41:36Speaker 03

  I just wanna like wake up to I always like think about I just wanna wake up to a world that's like that I'm like excited and inspired. I was like um

• 2:41:48Speaker 03

  I 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 wanna work on cool, crazy shit.

• 2:42:00Speaker 03

  And um I'm just excited for a world of flying cars and humanoid robots and helping prevent school shootings in Jarvis.

• 2:42:08Shawn Ryan?

  I mean, how do you how do you how do you keep it all together?

• 2:42:12Speaker 03

  I don't sleep.

• 2:42:12Shawn Ryan?

  You're innovating

• 2:42:14Speaker 03

  You just the trick is just

• 2:42:15Shawn Ryan?

  Four major things.

• 2:42:17Speaker 03

  The trick is just to not sleep and always work.

• 2:42:20Shawn Ryan?

  I'm good at that.

• 2:42:21Speaker 03

  You know what I mean? You get me. Like, just that's that's how you do it. 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 um And I rarely, I do like a few podcasts a year, not a much. I'm excited to come here cause like I love your show and get the story out too. You're great at it. And um, so I, you know, I just like protect my time and just like I go all in on these things like my, you

• 2:43:04Speaker 03

  know, my kids and I have my work kids, you know what I mean? Like, and so like, uh, which are like, you know, these are my like, like, they're like, kinda like babies. I go, you know, make them, you need constant care and attention. Uh, so I have like this family and I need to like, uh, I go all in on it and I do everything else less good. You know what I mean? I'm like a shitty college friend. If you like, you know what I mean? If I had seen you in a while, like just like not gonna spend the half a day with you on Saturday if you're in town and I haven't seen you in 10 years. So it's just unfortunate. I wish, but like, you know, I care about these things more. I care about doing this stuff really well. And I, I'm really happy at it. Uh, you know, I'm happy with family, happy with like things going at work. And, you know, to be frank, I just, I, I was born and raised on a farm, man. And I get to do like work on this cool shit every day. And, um, and

• 2:43:48Speaker 03

  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 into 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.

• 2:44:08Shawn Ryan?

  It's pretty incredible. I mean, a farm boy from a town of 700 people now.

• 2:44:12Speaker 03

  Right.

• 2:44:13Shawn Ryan?

  Building that thing.

• 2:44:14Speaker 03

  Right.

• 2:44:14Shawn Ryan?

  Flying cars, keeping kids safe and park. I mean.

• 2:44:19Speaker 03

  Yeah. So.

• 2:44:22Shawn Ryan?

  American dream is still very much alive and well.

• 2:44:25Speaker 03

  Yeah.

• 2:44:25Shawn Ryan?

  That's fucking cool to see.

• 2:44:26Speaker 03

  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, um, yeah, I just, I'm taking a, I'm, I'm probably at, 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 veterinary is like, there was a part of me saying like, okay, is this like the, the thing I want to spend my whole life doing? And, uh, 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. Um, so it's good. I'm just like, uh, I just don't wanna, don't wanna screw it up now, you know?

• 2:45:11Shawn Ryan?

  Oh, yeah.

• 2:45:12Speaker 03

  Make them work.

• 2:45:13Shawn Ryan?

  We're doing a pretty damn good job, I think.

• 2:45:16Shawn Ryan?

  All right, we're wrapping up the interview. I got a hot question to ask you. You ready?

• 2:45:20Speaker 03

  Let's do it.

• 2:45:22Shawn Ryan?

  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 1979 to the viral 2025 Unitree H1 malfunction that showed how violently a humanoid system can lose control. Plus, longstanding research warnings that robots in 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,

• 2:46:07Shawn Ryan?

  be hacked, surveilled through remotely controlled, or turned into tools of intimidation, assassination, or state power?

• 2:46:16Speaker 03

  I don't know how that person gets up and goes outside every day if they're that scared.

• 2:46:23Speaker 03

  So I think like,

• 2:46:29Speaker 03

  the future is,

• 2:46:32Speaker 03

  this future, it can be like molded and morphed and like it's what we want to do with our time.

• 2:46:41Speaker 03

  If we want a future full of like robotic systems that can help us out and freeze us of our times and things like this, like we're gonna will our way to make that happen.

• 2:46:50Speaker 03

  I'm a pretty like optimistic person. I feel that having

• 2:46:58Speaker 03

  millions and then billions of humanoid robots on the planet is just gonna be such a magical and important thing for the world.

• 2:47:06Speaker 03

  Are we gonna have like, you know, bumps along the way, like for sure? Are they gonna, you know, hurt somebody at some point? Like I think that's bound to happen at some point with enough scale.

• 2:47:18Speaker 03

  But I think like the... The spear here for humanity to get this to get this done.

• 2:47:27Speaker 03

  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 it's both scary, but also like very. I'm like very excited about that future.

• 2:47:57Speaker 03

  So I think my view here is.

• 2:48:01Speaker 03

  Yes, there's like a lot of like really difficult things that could go wrong that perhaps could maybe will go wrong. But I think we need this.

• 2:48:13Speaker 03

  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.

• 2:48:25Speaker 03

  So anyway, I.

• 2:48:28Speaker 03

  I happen to believe that this is like extremely important, will save lives and like, I think, increase prosperity across like all of human civilization. And I think. I'm excited to be working on it, but I think there is a lot of truth to what, you know, like I said, it's going to be a really hard road.

• 2:48:43Shawn Ryan?

  Yeah, I mean, it's 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 going to go through it one way or another.

• 2:48:54Speaker 03

  Yeah.

• 2:48:54Shawn Ryan?

  And, you know, I do think things are going to be a lot better on the other side of that.

• 2:48:58Speaker 03

  You're not stopping it now. It's like the.

• 2:49:01Shawn Ryan?

  Exactly.

• 2:49:02Speaker 03

  It's it's like it's go time. It's going to happen for sure.

• 2:49:07Speaker 03

  And I think it's be fine. Like, I think, you know, I use AI every day. It's like, it's fine. It's like nothing, you know, like it's a chatbot. Like, I think, yeah, if it's like, there's a different path to go down from here that could be good or bad. I think my bet's on high probability of really great. There's obviously always path that could like not go well, but like being conscious of that. And like basically doing everything possible to steer it in the right direction is like what we 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?

• 2:49:41Shawn Ryan?

  Yeah.

• 2:49:42Speaker 03

  You're gonna stop people from trying to build like systems that like make us more productive and

• 2:49:45Shawn Ryan?

  Yeah.

• 2:49:46Speaker 03

  do what will work. I don't think that's it's not happening. So like, um, all we can do is basically do it the right way that has the best positive effect on the world.

• 2:49:53Shawn Ryan?

  Yeah, you know, another thing that comes to my mind is when we're talking about interacting with the humanoids.

• 2:50:01Shawn Ryan?

  People, you know, and I've had this discussion on other podcasts too, but

• 2:50:07Shawn Ryan?

  people are gonna look at that for advice. Relationship advice. And I mean, I think there's a, you know, lot of important things. They're gonna be talking to this thing too about advice, certain people. And I think that's a big fear of a lot of, a lot of folks too. It's already happening with chat G P T and all these other Claude and all these other things anyways, but who are they getting for advice from before that? Probably, I mean, you know what I mean? It's, it's, I think it's the caliber of person.

• 2:50:38Speaker 03

  Yeah, totally.

• 2:50:39Shawn Ryan?

  But.

• 2:50:40Speaker 03

  Yeah, it's who you spend time with.

• 2:50:41Shawn Ryan?

  Yeah. Last question. What advice do you have for future founders?

• 2:50:52Speaker 03

  I have a few things I think are, I wish I could like, maybe said differently, also like pass down like young Brett like 20 years ago.

• 2:51:01Speaker 03

  I think one is like, uh, just go, just start building. I feel like, um, a lot of folks get too caught up in this thing. That's like gonna be hard and might not work. And, um, you can just like, it's just so easy to start a company these days. So many great tools. Uh, just go learn. I think, um, there's never been a, never been a situation where I haven't like done something and then learned a bunch and then, uh, haven't reset from that feedback. So almost like, um, like little stairs I'm climbing over and over throughout time. And, um, so if I just wouldn't have started and wouldn't have moved, like I wouldn't have learned this information. Um, so it's like a lot of information coming in recursively, self-improving and getting better over time.

• 2:51:42Speaker 03

  This could be simple things like hiring and doing accounting or running an engineering team or like trying to ship a product or getting feedback from customers like, I'm just getting, I think I'm getting, it's like a, it's like a, it's like a sports player. You're getting better with more practice. And so I think the most important thing is just like, just go. I also think, um, 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

• 2:52:27Speaker 03

  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 gotta like really love it. And it's not like you can be there for a year or two. It'd be there for sometimes a really long time. And even if you're successful and even if you sell your company or whatever, go public, you're getting your stock locked up or you're investing out over many periods of time. It's you're got to be in it for quite a while. And I find that for me, the things I work on as like probably the most important things I could be doing with my decision making. And that's happening at a micro level inside the companies or whatever come 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

• 2:53:10Speaker 03

  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, um, there's, uh, there's like a nonlinear effect here for like, uh, starting companies that are like easier versus harder. Meaning, um, starting something that could be like a hundred times higher outcome is generally not a hundred times harder. Um, so like doing figures not a hundred times harder than doing another robot company. It's probably like three times harder, maybe five times harder. But the total risk of market and opportunity is probably millions of times bigger than another like robot that's like on assembly line, moving back and forth.

• 2:53:52Speaker 03

  And so I think there's like this nonlinear effect to like decision making here that is extremely 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 of bigger addressable markets like TAMs that, you know, potential acquires or public markets or other folks that really want to see and have like basically a disproportionate outcome. They want to, they

• 2:54:33Speaker 03

  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 a hundred X or a thousand X. They don't like a two X. And generally for venture, like ninety, ninety-five percent of people fail. So if it works, you really want to go, you want to hit a grand slam. So I think my philosophy is like, spin, like choose wisely, like a young Brett, spin, choose wisely what you work on and young entrepreneurs. And, and then I would try to be as ambitious as possible. There's capital for that and there's, there's humans for that that want to work at really crazy shit. We have them at my companies and they're, they're, they're incredible. You met some of them today. They're just like, you know,

• 2:55:14Speaker 03

  my design lead and a bunch of other folks here that are just unbelievable at what they do. They're the best in the world at what they do. But they want to come here. They want to try to do something like has never been done before. They don't want to go off and design the next car or like, you know, or do the next AI product everybody else is doing. They want to be here signing something revolutionary. So I think that's like, um, some of you don't stress enough. And I think last thing is like, there's no rulebook for this, which is like really unfair. And there's, there's a lot of people out there that will teach like, here's what to do. And they're generally coming from folks that haven't, haven't done it before. And the signal of noise out there is just so high. Are so low. Like, I mean, you get a lot of noise out of like in the market.

• 2:55:57Speaker 03

  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

• 2:56:18Speaker 03

  so I think it's been a lonely path. And for folks out there that are on that path, it's lonely, but I believe in you. You can do it. And I think that's, um, I've never had somebody for 20 years I could call and just like, what should I do in this situation? I never have had it. And I wish I had, uh, but there's no book. There's nobody to call.

• 2:56:41Shawn Ryan?

  Yeah.

• 2:56:41Speaker 03

  And I think that makes it really hard, but, um, but it's possible. You can just like, go do these things and it works. So for the folks out there that really want it, and it filters out like everybody who doesn't really want it. 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 buds, like it's the great filter. It's 95% of everybody will fail and you'll devote your life into it and time and maybe all your money and your brand and you'll be embarrassed and you'll, you'll fail. Most will fail. And it's, uh, 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.

• 2:57:26Speaker 03

  Those are the folks that do well here. And you can bend the world and, uh, you can basically can mold the, the future to how you kind of want to, if you try hard enough. And the, the end of the, the goal at the end of the day is just to not die. If you don't quit, you won't die. So like, uh, anyway, I think, um,

• 2:57:50Speaker 03

  I think it's, listen, been playing this now for 20 years, still playing it. I feel like I'm in the early innings of my career now. I want to go ship at scale these systems. I haven't done that yet. We're like an inning, we're Brett in one. And so like, uh, but for everybody out there that's in that, I just think it's, um, I believe in you here. You can do it.

• 2:58:10Shawn Ryan?

  That's great advice, man.

• 2:58:11Speaker 03

  Cool.

• 2:58:12Shawn Ryan?

  Well, Brett, fascinating interview. Love everything you're doing, man. Like incredible stuff, huge advancements.

• 2:58:20Speaker 03

  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. Uh, thanks for having me.

• 2:58:29Shawn Ryan?

  Thank you. It's been an honor.

• 2:58:30Speaker 03

  Great.

• 2:58:31Shawn Ryan?

  Cheers.

• 2:58:43Shawn Ryan?

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