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A
This episode is brought to you by Redfin. You're listening to a podcast, which means you're probably multitasking, maybe even scrolling home listings on Redfin, saving homes without expecting to get them. But Redfin isn't just built for endless browsing. It's built to help you find and own a home with agents who close
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B
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A
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B
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A
our new protein lattes or matcha. Try it today at Starbucks.
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B
Brett Adcock, welcome to the show.
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A
Thanks for having me on.
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B
I've been looking forward to this for a long time. The robotics guy. Yeah, let me give you an intro here real quick before we get. 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 and AI driven talent marketplace, which was acquired for approximately $100 million. Co founder of Archer Aviation. Developing electric vertical takeoff and landing EVTOL 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 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.
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A
Thank you.
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B
He didn't give you any tips on that, did he?
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A
All right, I got. I gotta hit one.
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B
What do you think?
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A
They're great.
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B
You can leave this guy here if you want to.
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A
This guy. This guy's staying.
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B
That is the coolest thing I've ever seen as far as giving somebody a gift on the show. That was awesome.
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A
Yeah, that was awesome. I got you another gift. Oh, something you keep here, put on the shelves.
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B
Thank you.
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A
Yeah.
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B
No way.
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A
Yeah. Little robot.
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B
That is awesome. Thank you.
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A
Yeah, no problem.
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B
Very cool. Well, Brett, we got a lot to talk about here, so. Man, how many companies are you running now,
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man? I'm like not. I'm not sleeping. I got too many. I'll bet too many. Just like kids and work and just like.
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B
Yeah, that thing is amazing.
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A
Never sleeping anymore.
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B
I'll bet. I'll bet.
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A
Yeah. What do you Think of the robot.
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B
I. I think it's incredible. I want to. I can't wait to talk more about it. So, a couple of 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. In today's marketplace, we find that AI platforms can sometimes invent answers rather than admitting 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?
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A
Yeah, yeah, we don't want the Terminator popping out here when we Definitely not this work. Right. I mean, I think like we were chatting about this outside, like, you know, I think one thing to say, like four years ago when I like, you know, we started the company, there was like no path for humanoid robots, like to make into like people's homes in the next like 10 years. There was, there's no good story. There was, you had like big hydraulic humanoids out there. They were all like hand coded to do certain tasks. What you really need is like a cheaper electric humanoid that like you basically can use like neural nets, like use like basically an AI first strategy with. There was, there was just none of that existed. I think we're like, we're thankful now, like looking back like that we like 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 useful human work with neural nets. And it's just like, I think it's just an incredible, it's an incredible place to be in getting those questions, which is like, how do we make this work now at scale, in a safe way? Because that's the spot we want to be in, not like trying to make this work for 20 years.
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B
Yeah.
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A
So I think it's a very, I mean this is a very, very tough problem. We have to get the product cheap enough. We have to make enough of them. We have to make it like. But the performance work in like very complicated things, like walk around a house and like do dishes, like laundry. Like very complex things. Like small kids can't do this. Like it takes adults to kind of do like this level of work and we need that all done in a mechanical system that doesn't have any humans around for maybe most of this, that does it autonomously and not makes any mistakes. And then like, like, like your fan mentioned, like, we have to do it safely over time. It's just, man, it's just like incredibly complex problem. Um, I think for us, we have a safety strategy both intrinsically. We want the robot hardware and the robots around humans to just be safe all times. And separately, there's a bunch of semantic safety and other things that we have either put in place or putting in place now to make the robot just work safe in the environment. You have a candle at home. You don't want the robot to accidentally knock it over. That's like an intelligence thing in a lot of ways. Or there's a boiling pot of water, like, making sure we're very safe around it. And then there's like the intrinsic safety of making sure this mechanical thing in your house is safer on everybody around it. I think the direct answers. There's still a lot of wood chop of getting this thing to a point where it's like, we trust it to be autonomous. Next to my kids all day long in my house. That's the kind of one.
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B
In your house.
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A
We've had many robots now throughout my house in testing for the last year or so, and I've had like, you know, kind of near my kids in some aspects, but we're always like. We're always like, monitoring it and what
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B
do your kids think?
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Man, they like, it's just like, kind of normal for them now.
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B
Do they try to talk to them or like.
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A
Yeah, 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, you know, when do kids things, you know what I mean? Like, they want to go touch it and talk to it and be around it. And we're still at that stage yet where I feel comfortable enough to, like, let loose and say, here, you know, here's a robot, or my kids are there and I feel okay and not there yet. I. I think we will be the next several years.
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B
What. What's the longest they've been around any one particular robot?
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We've had a robot in my house for, like, maybe a couple months doing work kind of on and off, you know, daily, sometimes every other day. And, you know, the kids are kind of at school or sometimes at home, so not always. They weren't always around whenever the robots are running, but a lot of times And. And, you know, that was just like our home robot.
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B
Do they get attached to them?
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They named it emotionally different names for the robot. And, yeah, they love it. And it's actually a question we're asking in the office of, like, you ever about in the home? And it's like, it's got some, like, character to it, little wear and tear. Do you, like, want to keep that robot or do you want, like, a new one?
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That's what I'm wondering. What's the emotional attachment? I think kids are like, the perfect test case.
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My kids wanted it. They wanted it there.
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B
We're not getting rid of this guy.
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Yeah, he'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.
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B
That is. That's wild, man. Yeah, that is wild.
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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.
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B
Wow. Yeah. We were talking, I mean, just some of the stuff that you just mentioned. I mean, the complexity of the problem that you're solving here. I mean, all these little problems that I didn't, like knocking over a boiling pot of water, I never would have. Like, it's just like, just thinking about something that it happens every day, and then you think of all the things that happen every day in just a regular household, and it's like, problem city, man.
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It's like a fun house of problems. There's just problems everywhere. It's like hardware problems, AI problems. Problem scaling and commercializing and getting the system reliable, manufacturing problems. Like, we. We have a problem fun house. If you want to come by campus here and check it out.
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B
I'll bet you do. I'll bet you do.
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A
Well,
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some people say AI is in an economic bubble. And as of this recording, Polymarket says there's going to be an 18% chance that the AI bubble will burst by December 31, 2026. What do you think about that? Is AI in a bubble?
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A
I think you'll see some of the most transformative events in technology happen over the next 36 months we've ever seen in. In our, like, ever, ever.
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I don't. I don't feel like we're in a bubble here. I feel like. I mean, like, we're very scraped.
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We're watching scratch of the surface. I'm watching AI in a human body do human work. Like, early. It's early. We don't have, you know, we. We don't. We. 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 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. 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 we have AI now that can use computers like humans and can think. I was showing you a little bit of that here before the show. And you know that, that, that will manifest in a point where like you know, both in the physical and digital world you basically have these little mini humans that can do human like work and they can think and use computers and use machines. And I mean that's going to lead to such a productivity. Like we measure 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.
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B
Wow. Wow. What do you, I mean I'm just curious, what do you think? What will humans be doing?
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A
I mean I hope I, 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 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 wish I wasn't doing that stuff. And then I'm like 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.
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B
I get it.
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A
That burden.
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No, I totally and I just want
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to be like clear headed and I want like my AI to run a little bread adcock operating system and run my life and all these things I have in my head about what to order and pay his tax bill and like do this meeting and I have to go back and do an engineering stand up. I want all that stuff to be in my operating System and like a human in a box.
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B
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 disseminating it to, to robots
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and they're gonna delegate all this out.
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That's amazing.
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That's like, we'll, we'll do that in like 24 months. Like we'll have all this stuff so good that you'll like, you won't like go order food anymore, like book stuff. Like do a lot of work behind a computer, like physical stuff in the world of like doing laundry and dishes
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and just the legwork.
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Yeah, I don't like, does anybody want to do that? Like, I don't want to do it.
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I don't.
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Yeah, yeah. So like, you clear all that from my life. Like I gotta spend time with my kids, like enjoy life. Like kind of be 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. Yeah, it's just like not, it's just like manual like, just like labor I'm doing behind computers or like in the physical world and just like I want to delegate that out to my AI to do and fully automate it out.
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That's. I don't know why I've never thought about that. I've never thought about it. Like, I've always looked at it as fear. I've always been like, oh shit, they're going to take everything over.
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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 going to delegate out all this busy work on both my digital life and physical life to like, to robots and they'll just do all of it. But it's, it's good. I mean like there's like, we have AI systems now in our lab at HARC that can use computers like a human can. It can talk to you. It can. Like I just, I made a phone call to ours right before we started and talked about my schedule and how to ask for things and ask it for things and have it do things.
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B
Yeah, you had a chicken, chicken salad ordered to deliver to your office.
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Yeah, exactly, exactly. But no like nothing besides a single, like, hey, make this order and you can Spin up computers to do that virtually. And then physically like I'll have all this work done by my, by robotics, both in. You'll have it in the commercial workforce in the billions, like manufacturing and healthcare and construction. And in every human at some point will have a humanoid just to do all that busy work for you. And not only that, but like something to come home to that you can talk to that like will know you wild. Yeah, it's like the. Yeah, it's going to happen now, which is like really going to be fun.
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Yeah. Yeah. I'm excited to introduce to you the newest member of my family. We call him Stanley. We got Stanley this past Christmas and pretty quickly my focus became making sure he was safe while still giving him the freedom to actually be a dog. I went looking for the top rated GPS fence, the number one. And that's how I found Spoton. Stanley wears their Nova collar. With Spoton, I set up a GPS fence for Stanley right on my phone. No physical fence, no leash. I just walk my property line or draw it on the map and that's the boundary the collar recognizes. I can create multiple fences, save them and adjust them whenever I need to. So whether we're at home or traveling, Stanley always knows where his boundaries are. Another thing that stood out to me is these collars are designed right here in the USA and assembled in New Hampshire by a team that's been working with high precision GPS technology for years. And you can tell a lot of attention went into making this thing reliable. The Nova collar uses a dual band GPS system connected to more than 150 satellites along with an antenna that's over five times larger than typical GPS fence collars. That keeps the boundary accurate even around trees, terrain and changing conditions. Spoton's trulocation technology has been independently tested and delivers 99.3% containment. Which matters when you're trusting something with your dog's safety. It's incredibly durable. And on top of that, I can check his location in real time, send voice commands directly to the collar, and track his activity through the day. I use Spoton so Stanley gets the freedom to run and explore and I get the peace of mind knowing he's safe. Let your dog roam with spoton. Go to spotonfence.com SRS and use code SRS for $50 off. The Nova Collar, that's spotonfence.comSRS and use code SRS for dollar fifty off. Well, I would like to do a little bit of a life story on you if that's does that sound good to you?
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A
Yeah, let's do it.
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B
Where'd you grow up?
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A
Central Illinois.
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B
Central Illinois?
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A
Yeah. Like a small town, like 700 people.
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B
700 people?
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A
Yeah.
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B
Wow, that's even smaller than where I grew up.
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A
Yeah, Where'd you grow up?
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B
I grew up in small town, Chillicothe, Missouri.
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A
How small?
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B
About 8, 000 people at the time.
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A
Yeah, we didn't have. We didn't have anything. 700 people, man.
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B
Were you into?
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A
Yeah, so like, you know, kids, sports, computers, like got into computers really early, did a bunch of sports, you know, we. I grew up on a farm, so it was corn and soybeans. My, my, My family was third generation of this, so. No kidding. Yeah, yeah. So third generation. Three generations of farmers, three generation agricult agriculture farming.
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B
And then we switch over to.
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A
Yeah, yeah, we're doing like humanoid robots now and AI. AI systems. But yeah, no, I got, I got really interested in computers like really young. Started a bunch of like startups and like in like you know, in high school and college. But.
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B
What kind of startups?
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A
You know, at first just like mostly things on the web, like selling things. Did a bunch of like different types of like products I was selling on the Internet for like throughout like high school and college. Small, like drop shipping, retail electronics, like all kinds of. All kinds of things. Legion marketing and just fun stuff. It was like nothing serious, you know, just like 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, like, you know, maybe make some money. Like it was really fun, you know, I loved like the ability to go out and create things and kind of control my destiny. So it was just something I attached to really early on.
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B
Right on, right on. Do you have brothers and sisters?
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A
I have a brother, yeah.
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B
Well, I mean what is he, a farmer?
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A
Colby? No, he actually runs an AI defense company called Scout. They're doing. Yeah, basically building autonomy and like AI models for, for defense in the military.
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B
So you guys both got into AI?
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A
We both got into AI. We live like a block away from each other today. Like serious. Yeah, we grew up together, really close. Went to same with same college. We're like different ages, a couple years apart. And then, and then we were in New York for about 15 years together and then he just moved out to California. We live literally a block away. See him almost every weekend. He hasn't started like basically 10 minutes away from, from mine, you know where I'm at now. And he's, he's doing great, man.
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B
What, I mean, 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? This is such a wild. You know what I mean? From farmer to, to this.
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A
Honestly, like, I think one thing 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 is like, you know, ran his own business. Like, you know, you kind of have to go out there and put the work in or you're not going to get paid. So early on he's like, listen, if you want to control your destiny and if you, you, 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 beat into our heads, like, growing up. Like, you know, at some point you need to, you know, you needed to get. 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 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.
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B
Yeah, but you got some proud parents, man.
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A
Yeah, parents are great. Wow. 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.
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B
Were you rebuilding stuff as a kid too?
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A
Yeah, constantly building stuff.
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B
What kind of stuff?
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A
Stuff on the farm. Building stuff on like in software and Internet. Just like, I just love building stuff all day. I'm like, 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, even, like, honestly doing Internet for like, I did like, I, I was like, I did work in the like 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.
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B
Wow.
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A
Basically like atoms, man.
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B
So where do you go? Where'd you go to school?
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A
I went to University of Florida.
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B
University of Florida?
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A
Yep.
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B
Where do you go from there?
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A
So after school I moved to New York and I started working on software startups. And during college I was working on basically a bunch of like side small like Internet things and. And then kind of like shortly after college, I started a company called Vettery and the goal was to basically build like a. I got really kind of going through college is like, you gotta look for a job, you gotta go find something full time. And got caught up in like the whole interviewing process of like looking for 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 how to get in and it was just like a. It wasn't very much a meritocracy. And I just thought the whole process was extremely broken. And so I started Veteries. 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 like the head hunting industry is like hundreds of billions of dollars a year.
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B
I won't, I won't even. I won't use it.
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A
No. Like, I know.
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B
I just. I keep hearing everybody gets ripped off.
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A
Ripped off. It's so expensive. Like pay like $50,000 a hire. It's like insane.
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B
And then they'll. And then they'll coax the guy out that they just brought to you and have go to another job.
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A
Yeah, like the fortune is roll, so they get paid a commission.
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B
So. Veteran veterans Connector.
- [23:44]
A
Yeah, connector. Well, funny enough, we ended up selling to like the world's largest recruiting company that does staffing. But like, let's leave that for a minute. But we basically started in 2012 and the goal was like, how do we put like a lot of job seekers and a lot of employers on a platform understand their preferences and match them at scale? Like just like, how do we use algorithms? At the time we were like, let's use AI. But it was basically like, how do we use a lot of algorithms to figure out like what people want and then make matches? So you can just push of a button, connect the right folks and then make placements. And then we ended up charging most of our revenue came from subscriptions from big companies like big banks or startups or tech companies. Basically looking for talent. We started just in tech in the U. In the US So at one point we had about, I think about a little under 20 or so cities globally that we were operating in.
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B
Wow.
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A
But most of it was tech talent, tech spaces, you know at that point.
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B
How long ago was this?
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A
Started in 2012 and then ended up selling the business in 2017 or 2018. So about five years, six years.
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B
Right on.
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A
Yeah.
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B
Then where do we go?
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A
Okay. So Veteran was like a really tough. I like basically went like I didn't have much money. Went like fully all in the business. I went into debt at one point in 2015 the business was having a tough time and then we ended up selling end up things ended up going doing really well. The business like completely hockey sticked in growth when we got all the things figured out and just like vettery Veteran was and then ended up getting approached by the world's largest recruiting company. The same groups you like you and I are talking about the same groups who were trying to take out a business and they were like, like oh we want to acquire the. You know, acquire the company. And at the time we were like I was like completely dead broke and put everything out of the business. It was like I think it was at the point almost seven years in and and you know we were excited about an acquisition a year before that. $10 million from one of the big tech companies and they came in at 110 million and. And it was, it was a good time for me. I felt like the business was doing well. I learned a lot and I was kind of ready for my next chapter. So end up selling that business to the Deco Group. It's like the world's largest recruiter company and.
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B
But you didn't even have a for sale. They just approached you.
- [25:59]
A
Yeah, we didn't hire a bank or anything. Listen, at the time we were doing like I don't know, 20, 30,000 interview requests like a week. Like so that was like no humans involved. Like think about how many humans would take to do like 20 or 30,000 interview requests. It's like, you know what I mean? And then manage all that processes. So we were like the growth was just unbelievable and. And there was like there's something better to like a human jam in you in roles. Right. Like it's just like you need like, and then to extent you can get, you know, all the world's like talent there and all the world's companies looking, you can really create an amazing environment where you can get people to like, the right jobs. And right now it's not like that. It's like a really black box, like trying both finding talent and looking for a job. It's just a terrible experience. So we kind of. That clicked. Yeah. The world's largest recruiting company came in and said we got to buy this thing. And yeah, I'll bet they did. Yeah, I wanted. I sold the business and it was great. It was a good time for me. I really at the point was a point in my life where I really wanted to do something much bigger. And so I took about. I basically took about a year and so it took about a year 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 industries that I've. I've been interested in robotics and aviation and some areas of security for, like basically since college. And I basically spent a lot of time trying to figure out if I was either going to work on at the time, school shootings, like basically 10x. 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 lots of sci fi as a kid is like, man, like, I really want to go. There's a near term problem of like, we got to go help with security in schools K through 12, mostly in the US and then, and then how do we. I want to work on flying cars. And I ended up making the decision to work on flying cars at the time. So in 2018, shortly after the sale of Veteri, I started Archer Aviation. And basically like the story here is you can build like an electric aircraft that can take off, like a helicopter. If you take off like a helicopter, you don't need to place the airports outside of cities. You can place them inside of cities. Think about like a normal hell, 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 basically nestle the aircraft Inside of cities. Half the world lives in cities today. It's you know, by like middle of middle of the century, It'd be like 70% of the world and you just like can't get around. Like it's just gridlocked everywhere. In major cities it's just like sucks to go like 20, 30 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 re architecture the whole aircraft to be fully electric. The reason you want to do that is for cost and safety. You basically can make it like, like a lot, like less expensive. You can put a lot less parts in the aircraft that are also good for safety. So basically you can build like an electric flying car that you can move around. So instead of like calling an Uber or driving that might take you an hour in LA or SF or New York, you basically can fly there in 10 minutes. If we, if we can pull everybody together like in a kind of like an Uber pool style business model, you can do it for as cheap as an Uber. But the problem was like, I didn't know, I didn't know anything about how to build electric aircraft. I mean, where do you.
- [29:35]
B
Yeah, I'm just, I know you just sold your business for $110 million, but where do you get the confidence to, where do you, where do you get the confidence to go? I'm going to build vertical takeoff and landing flying cars now.
- [29:50]
A
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 a lot through trial and error and I just felt like, I just felt I could learn it. I started in industrial and system engineering at University of Florida and then ran engineering and ran the company at Veteri. So I basically just hit the books. I tried to learn as much as possible about three subject areas. First was electrification, which at the time electric vehicles were really doing. Um, and, 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. Uh, you really need wings, like so you. Okay, so you basically have to learn about those three subjects. So I started, I b. I basically bought my, my, my basement downstairs at home where it's like every possible book on these subjects you could match in, and started reading as much as I possibly could. And this was during the year transition, as I was transitioning like out of, out of veterinary into Archer, I was reading every possible thing and then I found a small community of folks that were like hosting on site 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 learn as much as possible. It got to the point where I was like completely obsessed with this algorithm. I was building on electric aircraft. Sizing, like how, how do you actually, like how would you actually build electric aircraft? So electric aircraft, what's interesting is like in rotorcraft, like you basically want to create the most efficient lifting device possible. You need as much of like as the rotor disc area, like, like in terms of surface area as you possibly can. It's why the helicopter rotors are so large. We want that to be really large. That'll reduce power and get you up off the ground. In electric aircraft, the problem you're starting with is you have like 1/30 of the energy as, as you do in kerosene in a battery pack. So you just like you're off the BAT of 1/30 less range or 1/30 less energy. And so power becomes like the dominating factor of like, of how to basically build electric aircraft. Like how do you get power down as much as possible? You really want a lot of disc area, a lot of disk 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. If it doesn't go well, you go down. With electrification you can basically build much smaller like basically rotors and be able fully electric. And the reason you can't do that with traditional kind of like turbofans or engines is it gets too inefficient at these sizes. You can't build 12 propellers on a helicopter. Gotcha. The efficiency just drops 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.
- [32:59]
B
So, so this is, the rotors are underneath.
- [33:04]
A
Like the problem here is you can design it however you want. You could put A bunch of rotors along the wings. You can put them like laterally across the fuselage. You can make one big one, you can make 30 small ones. Like so how do you design it? That's the problem I hit in 2018 was how do you actually do this? And so it basically was like a crazy man trying to design this algorithm to like, what is the ideal aircraft design and then how do I go build it? So it was actually at a, I was at a Hyatt Regency hotel in Atlanta in 2018. I was on, it was an electric propulsion week long design course and like a aerodynamics course for winged aircraft. And I met a guy there that, that was basically in the engineering department at University of Florida. He was doing his PhD in aerospace and asked him what he was doing there and he's like, I'm from University of Florida. I was like, oh, I went to school there as well. And he's like, I'm like what are you doing here? He's like oh, I want to go do a career in EVTOL aircraft. And it's called electric vertical takeoff and landing. So a helicopter is a vtol and he put a little E in front so we're electric. And I was, he's like, ask me what I'm doing here. I was like, oh, I'm. I'm starting a company to do this and I need to figure out how to go build these things. And he's like, well listen, my professor runs a small drone lab. He's got a full building, he's got 12 PhDs. Why don't you come down and like meet him and see if you can start building aircraft with him. So I flew down that weekend to go meet his professor that runs all of basically mechanical engineering and aerospace. And long story short is I ended up taking over his like his lab and, and me and him and his team started building aircraft in 2018 and 2019 down to university of Florida. And I temporary moved down there with my, my daughter at the time and my wife living in Gainesville, Florida. Like, and, and it was great. Like we ended up building, I ended up funding a lab right off of Archer Road, a new lab because we needed more space. And we ended up calling the business Archer Aviation is the main road down at University of Florida. And I spent the next like year, year and a half basically like modeling and building electric EVTOL aircraft.
- [35:11]
B
Holy shit.
- [35:12]
A
Yeah. And it was, it was a, it was a great time in my life. And the problem is there was no intersection of folks that knew electric New rotorcraft or new airplanes. There was no Venn diagram of overlap.
- [35:26]
B
Gotcha.
- [35:26]
A
So there was nobody in the world that understood how to this all stuff that works. So I had to go from scratch, like learn it from first principles. And then ended up moving the company out to California basically a few years into the business. And then things took off from there. We built bigger aircraft. I took the company public within three years of starting it. We're $6 billion publicly traded company today. And yeah, designed basically four or five generations of aircraft at Archer. And it was hard. You know, it really set me up well for like, you know, doing figure and cover and the rest of stuff we can talk about later. But like it was, it was, it was hard. Even going public was like probably like one of the hardest experiences of my life. It was just really.
- [36:14]
B
Why is that?
- [36:15]
A
We, we went public through a SPAC process. So, you know, SPACs at the time, like four or five years ago, like all the rage.
- [36:21]
B
Okay.
- [36:23]
A
And it was a special purpose acquisition company. So it was basically companies that were like going public through a merger, like a reverse merger. And it was hard because in 2018, 2019, coming off of software, I had never done hardware before. So A was like hard to raise capital and B, there was nobody funding like deep tech, electric, vertical takeoff and landing companies like, you know what I mean? The big venture capital groups were not funding SpaceX or Tesla or Rivian. Like none of these were getting funded by traditional investors. They weren't raising money from the named investors we all know about now.
- [36:59]
B
Oh shit. Are they always behind like that?
- [37:01]
A
The mandate for Most of these VCs in the bay Area or Silicon Valley and stuff are not to do hardware. They don't really. And if they do hardware, they do. They don't do deep tech. They don't do like rockets and autonomous vehicles. And I don't think there's a single, you know, top VC in the US that's invested in a humanoid company. Like, no. And as of like last six months ago, now, nothing like this. Don't. It's just. They just don't do this stuff. And, and so like I end up, I end up going all. So I, you know, like, you know, made. Just made $110 million and I just sold the company for $110 million. I made a lot of money like personally ended up going all in on Archer through the ipo, like 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, and it was a stressful period. So we went public through this back. And the reason it was tough is we ended up getting to a point where we just couldn't raise enough money privately. Like it was, it was either like raise, you know, $100 million privately at like, you know, some valuation, 3, 4 or $500 million or, or it was like go public and raise like a billion dollars.
- [38:08]
B
Wow.
- [38:09]
A
And we end up going public and raising a billion dollars.
- [38:11]
B
Wow. You've got a huge appetite for risk, huh?
- [38:14]
A
And we got sued during it.
- [38:16]
B
Oh, really?
- [38:17]
A
Yeah, like we got sued by basically like Boeing in a big, big startup that was founded by Larry Page, Google founder.
- [38:24]
B
And that's got to be intimidating.
- [38:26]
A
Yeah, it was woke up to like a front page in New York Times article about.
- [38:30]
B
Oh shit.
- [38:31]
A
Yeah, it was, it was crazy. I mean the backstory is I, the, I took. So Larry Page started a company and in the bay area about 10 years ago called Kitty Hawk. And they were the, they did a great work over like 10 years in electric VTOL aircraft. And I ended up taking, I ended up taking basically the core, like the core 10 to 15 folks that were there all came over to Archer like within the first two years. Wow. And they retaliated by just like trying to harass us while we were going public. And so, yeah, it was just a crazy story getting public. Ended up getting public. Billion dollars on the balance sheet. And we just started building aircraft and started building the service. Thinking about the app and how you're going to check in and how you're going to build places like real estate to fly into and. Yeah, and then the engineering work we had to do around there of designing. It's basically a flying robot. You have 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. 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, we tilt the front, the leading edge, six motors 90 degrees for basically take off vertically and then go into forward flight and, and then all the propellers, fan blades on, have variable pitch propellers. So it's like a highly overactuated system that needs like really good software. Like no human can like fly it basically without really good control software.
- [40:12]
B
What, Ella, what altitude is it flying?
- [40:14]
A
About a few thousand feet. So about 2 to 3,000ft above ground level.
- [40:18]
B
And that's what it would normally be.
- [40:19]
A
Yeah, like traditional helicopters fly at these
- [40:21]
B
levels, I mean, what, what I think about, I think a lot about Tesla and all the EV vehicles that are coming out.
- [40:30]
A
And you know, it's.
- [40:33]
B
The government just seems so far behind on AI, you know, and you just brought up gridlock and all the cities. I've always wondered, why aren't, when are we going to go full ev? I know there's a lot of pushback about that, you know, for, from an overreach standpoint, but if you just think about the traffic in the cities and if you have the AI, you know, processing all this, that even, even without air vehicles, I feel like a lot of that would go away because the, the, the, the AI will route you the quickest.
- [41:04]
A
Yeah.
- [41:04]
B
And take all the traffic patterns into account and it would just flow.
- [41:08]
A
Yeah.
- [41:09]
B
A lot easier.
- [41:10]
A
Yeah.
- [41:11]
B
But there's all this government regulation.
- [41:15]
A
I think it's also hard because, like, if you look at the number of installed cars in the world, like billion 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 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, it's a hard problem. Well, I mean, if you look at
- [41:39]
B
Tesla, for example, I mean, it can self drive, right. It can come get you. But when you're driving, if you take your eyes off the road, it wakes you up. You have to come back. I mean, it seems, it's, it's, it's, it's inviting more error into the road by, by doing that. In my, in my opinion.
- [41:56]
A
Yeah, it's almost like wrong could be more dangerous. We're just in this transitory state right now where in like five years, like everything will be like fully autonomous and trusted and fine. And you won't have to do that. And we're just in this transitory, 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, in our, 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.
- [42:31]
B
Yeah.
- [42:31]
A
And it'll be trusted and easy and safe. I think we're just like, living through this period right now, which is like a weird thing, but, like, if you close our eyes long enough, you'll have this autonomy and electrification everywhere.
- [42:42]
B
How long do you think it'll be?
- [42:44]
A
I mean, so I live in the Bay area. Like, you can take whomos now. Like, I can take way more everywhere. It's unbelievable.
- [42:49]
B
All over, over there.
- [42:50]
A
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, you know, Palo Alto, Menlo park, like San Jose. Like, all this all over the place. They're really great. I take. It's like my wife and I, we go to dinner and stuff on the weekends. We take Waymo. It's like, it's so fun. It's like, oh, shit. It sounds so, like. Like, it sounds so basic. Like, you take away mo. It's fine. It's just. It's. It's awesome, man. Like, it's. It's great. You have, like, it's. The car drives so human, like, and it's such a great experience, like, not having a human there, to be frank. Like, I love it. You know, I order 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. It's really cool. So, like, technology is like in, like the early chapters, but it's all here. Like, we're going to have autonomy at scale, like, everywhere. It's just going to take some time to roll that out. It's the time it takes to get the technology mature enough, where they can run enough cities, enough places. And then it's the time it takes to get the install base of autonomous hardware in software in all these places. That's going to take some time, too. We just can't snap our fingers. We just don't have enough install base of autonomous vehicles in the world. I think Tesla's got like 10 million cars on the road, and maybe there's thousands or so of Waymos, but you have over a billion cars on the planet, so you need to make a large fraction of that all autonomous. So you're looking at. This isn't going to happen in a year or two. It's going to take some time.
- [44:19]
B
When are we going to see your
- [44:21]
A
Vehicles, the aircraft, we have them now, we fly every week in California. The challenging part with Archer is that we are governed by the federal airspace. So to fly passengers and charge money, we have to have basically a type certification from the faa. That process moves at the speed of the post office. And the FAA is not incentivized to put anything in the air unless they know for sure it's going to be really safe. The safety standard for us that we want to Certify to is 1 times 10 to the minus 9 in terms of hours of reliability before a catastrophic event. So that's one in a billion hours. One in a billion hours. Yeah. You can't be able to. That, that, that is like, that's the, that's the standards when we fly. It's like one of the safest form of transportation we take. And it's because of those standards, like, governed by the faa, which is great. I'm like, you know, we're like, that's, that's, that's the bar you need to be at, and that's the bar you need to hit, especially taking passengers over cities with aircraft overhead. You need to be at those levels. So that's like the, that's the long 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 basically an aircraft to take passengers. So we're in the middle of FAA certification now. We hope to be certified in the coming, you know, as soon as possible, basically. But it's like, it's not something you can, like there's not like a date on the calendar like you'll be certified here. You have to work through a very, like, very long and slow process with FAA to get through this. And then we're also dual tracking that against a couple different entities globally now to make sure we can get certified and get in there. But it'll, it'll happen, man. The aircraft. It 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. But it'll happen. It'll happen in our lifetime. We'll be taking these things around.
- [46:23]
B
Well, I mean, what do you envision? Let's, let's fast forward 20, 30 years.
- [46:28]
A
Yeah.
- [46:28]
B
What do you, what do you envision? What does it look like, do we have roads? Do those get ripped up? What does the sky look like? What does everyday life look like?
- [46:37]
A
Yeah, the really important thing you hear about the airspace is it's three dimensional. And the roads are not, they're 2D. And we built cities now and houses and restaurants all around these places. You can't like, there's nowhere to go. There's like no more roads to build in these cities. So you have left with no choice. And then humanity are moving to cities. We have this like secular trend where we all want to live in cities. Right now it's like half the world lives in cities. It'll be like 70% by 2050. So we're like all moving to cities. The roads can't grow anymore and we want like. We're like, we're constantly moving around, going to work or going to restaurants and just like it just, it's like, it's like, it's like this. It's basically getting worse. It's like this. The arteries are hardening here, around the, around this and it's getting worse and worse. And it's just like, it's some of the worst time to spin on a road in traffic. It's like so soul sucking. It's just like the worst, it's just like the worst time to lose. So the good news about the air is it's three dimensional. You can stack basically an infinite amount of say roads in the air. Different altitudes. Yes, at different altitudes and even laterally. So you can basically build little tunnels in the sky and you can basically stack them and you basically can put orders of magnitude more things in the air than you can on the road. It's the same for a below ground ground with tunnels. So the future of travel in cities is below ground in tunnels and above ground in the sky and the boring company. Yeah, exactly. Like just like dig tunnels and it's great. The problem with, the only problem with tunnels with this, with, with the, with the node system on, on. On the ground or sorry on the ground with, with. With like say like we call invert a ports but basically like real estate for flying cars is you can, let's say you had like you know, 10 different or even like you know, five, you say 10 different vertiports inside of a city. Like places to like take off and land from. You can travel between any one of those routes. So 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.
- [48:44]
B
So 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.
- [48:52]
A
Yeah, you're not going to take off and land from your home.
- [48:54]
B
Okay.
- [48:55]
A
Just because like acoustics in the neighborhood, it's gonna be too loud. You need like a decent amount of infrastructure for that, for charging and for passengers and cleaning and like checking in and stuff like that. They'll be 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, and then you'll get on these and they will go to any node on the network. You can't do that with tunnels. Tunnels have to go to A to B. You can't like, you go from like, you can't jump to another tunnel downstream. Like, you know, I want to jump to another tunnel like a hundred meters down. Like it doesn't happen in tunnels. You can do that with the sky. You can basically jump to any node on the network. It's exponentially more routes. You can basically do with like less real estate. And then you can basically stack as like, you know, orders of magnitude more traffic and humans in the sky. So my envision is that like you're going to be for most, most trips that you know that you're traveling over 20 minutes, all of that will move to the sky. And not only that, but you will, you will have a. You'll have like cities being re. Like being transitioned to a point where you can live well outside of cities and get to cities really fast. The reason we live in cities is because we're like, we're working there and we have friends there and we have. It's. Yeah, it's like I want to be like, I want to go to dinner with somebody. I want to see my buddies over here and we want to go to work over here or like go to the mall over here. It's like everything's there and that's what we want to be. We're social creatures. We want to be there next to other humans or some of us are. And so, yeah, so anyways, but like, you know, now that you can fly this 150 miles an hour in the air with no traffic point to point, like no stop signs, no construction, no things jumping out in front of you, 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, basically the distance just by going point to point. And then you have nothing stopping you. Going 150 miles an hour most of the way there. You can live like far outside of cities and get down to city center in under 30 minutes.
- [50:43]
B
So will these be personally owned or will these be. This will be like an Uber service.
- [50:47]
A
It'll be like an Uber service.
- [50:48]
B
Okay.
- [50:48]
A
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. That time you'll hit a button, it'll be on demand. You'll ride your bike over or walk. You'll get in one, it'll leave in seven minutes, and then you're basically flying right down to town. Holy.
- [51:10]
B
And you're saying this will be in every neighborhood. This will be very accessible to everybody.
- [51:16]
A
Yeah, that's. You're designing the whole electrification allows you to reduce the cost and the safety burden of all this.
- [51:23]
B
Wow.
- [51:24]
A
We have like, like a normal helicopter could have like 100, 200, like safety critical components that any component gives out. The helicopter can go down. An electric aircraft has none. You can lose a motor, you can lose a battery pack on board and still fly safe without having this. And so just from a safety, from a part count, from a cost, from acoustic signature, helicopters are loud and very noisy and it's just a much better technology for this.
- [51:58]
B
Have you been in one yet?
- [52:00]
A
I haven't flown inside of ours yet. I've also been inside of our aircraft and we basically have professional, basically pilots at the company.
- [52:12]
B
Test pilots.
- [52:13]
A
Yeah, test pilots. And they do their career test pilots. And they're unbelievable. I'll bet you know a lot from the law, from the military, a lot from the big aerospace groups. And they're, they're just professionals.
- [52:23]
B
What do they think?
- [52:24]
A
I love it, man. This is the future of aviation. Everything's going electric and it's so crazy. Yeah, it's crazy. It works like, it's, it's crazy. It works and crazy. We're in the right time period to make this happen. Yeah. And you know, the good thing about, you know, Archer now is we've, like, we've demonstrated the hard part is like being in the wrong. Like, the hard part is like making sure you're in the right decade. 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 so the good news, you know, for archers, we, like, we are. We're in a sweet spot here where this is going to happen. Aircraft now work. We're certifying now with the government bodies, like the faa, to make it happen. We have a good balance sheet with cash. The team's great. And so it's just like, get certified and get this thing going.
- [53:14]
B
Damn, that is. You're really changing the world.
- [53:17]
A
Well, we're at the start of it, but hopeful to make this thing work.
- [53:21]
B
Where are we going next?
- [53:26]
A
Humanoids.
- [53:26]
B
Let's. Yes, let's do it.
- [53:29]
A
Yeah.
- [53:30]
B
How did this idea start?
- [53:33]
A
Yeah. So I spent, like five or six years working on, like, a pretty crazy robotics work at Archer. And, like, the ultimate, like, meta problem in robotic space is can you. Can you build, like, a general purpose machine to do everything in the world, like, much of what, say, humans can in the world? And I have this big belief that, you know, we, like, we have a, like, weird biological species. Like, we look, we're like, you know, we have these weird hands and arms and legs and certain height and sensors. And then we ended up building this world around us so we can interact with it. I mean, if we get dropped into Mars today, we're going to build, like, coffee cups that we can hold and stairs and doors, and we're going to build this stuff again. And it's like the. It's like the human operating system. We're building things we can use and operate in that makes it easy for our lives. And we built it around the form factor that we are, meaning if we look differently, the world will look different. Our espresso machine would be different looking. We might not even like espresso or caffeine in this case. So we built this whole world around us. The holy grail for robotics is can you basically build a general purpose machine that can do what humans can, which for me is like a humanoid robot. And a humanoid robot's 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 legs are important for, or reach up, has, like, arms and hands so we can manipulate objects and do things like, you know, grab his stuff, open, open these gummies and, you know, fold laundry and do, do real work and then we have the right sensor so we can like, see the world and understand what to go do and use a, you know, our biological neural net to kind of figure out how to reason from. And, you know, 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. 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 needed 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 a, like 40 degrees of freedom. And degrees of freedom is like a joint. So like an elbow is a degrees of freedom. And 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 position and keep moving amount of states, mathematically it's 360 degrees to the power of 40 actuators. So there are more states in the robot than atoms in the universe. There's more positions the body can be in. No shit, by far. It's much greater number. Done the math a few times. Very confident in this, even though it sounds ridiculous. So you just can't code your way out of this problem. Like, how do you supposed to write code? How's a human supposed to write C or code to tell the robot at any given timestamp what to go do. If I want to grab this, I need to move my whole upper body and maybe lean over and I'm moving my fingertips and my hand, my wrist and hand get in position to grab this. It's an intractable problem for code. So I mean, you were saying earlier,
- [57:32]
B
I'm gonna butcher this, but it's updating the foot 200 times a second.
- [57:38]
A
Yeah, Our controller is running for balance, our whole controller. So we have a main Computer is processing what to tell all the joints to do. 200, a little, maybe more than 200 times a second to make sure we can just balance and then we can like do, do the task. It could be reaching over and grabbing this or balancing. If we run that too slow, we just like don't have enough feedback. Then we just fall over just like. Yeah, we have to fully balance. You know, we're, it's dynamic so it's, if you, if you, if generally if you power it off mid run, it's going to fall down. It's not like a four legged dog or quadruped robot where like at any given point it's usually statically stable. So it makes it very difficult because you have to be able to even move your hand. I'm moving my pelvis and my whole body, my torso is moving, my head's moving. Like all of it becomes very complicated. Now it's not just like move my hand, it's like move my whole body to get my hand in the right spot. So every joint, all those 40 joints have basically position encoders. So we know exactly what position the, the motor is at. Or even the case of the knee or this. And we have force, force sensing, torque sensing on board. We have the ability to detect all the forces that that knee is seeing. It could be really high when it's walking or it could be like, you know, it could be like it could be powered off and have no forces on the leg. All of that feedback is being sent in the main computer and then we're telling all the joints what to do over 200 times a second. Some of the other feedback is happening at like 5 or 6 kilohertz. So the force feedback's happening 6, 5, 6,000 times a second to the motor control on board. And we do the motor control. The brain for all the motors is done it locally at the motor level because it needs to happen so fast. That's being fed back to a main computer that runs a control software that tells the rest of the whole body what to go do at every timestamp to keep balance.
- [59:29]
B
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- [62:03]
A
So getting back to your original thing is my bet. You know, we're like three and a half years old or something like that at figure I bet three and a half years ago in 2022 when I started the company was that this was possible now. And my like, 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 rebuild from the ground up humanoid robots in AI software to try to see if you could make this work. At the time there was no good precedent, there was no precedent for showing that there was no AI that had ever worked on a humanoid robot in history. And there was no electric humanoid hardware that was remotely okay to show it would work. And there was no hands. There was none of this stuff.
- [62:52]
B
Wow.
- [62:53]
A
So I actually had a lot of trouble even I had a lot of trouble early on even 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.
- [63:10]
B
No kidding.
- [63:11]
A
Yeah. And it was a lot. I mean we got the business to a million a month of burn in month four and but it was like I knew what to do. We built a 40 person team and like as fast as possible and I knew how to spin up Hardware and software and, you know, the key characteristics of robotics, like electric motors, battery systems, control software, embedded systems, and sensors. And then even within electric motors, we build actuators. They have a rotor and stator and a gearbox and sensors, electronics and wiring and connectors and multiple sensors inside of there. And then firmware, it lives on, say the microcontroller lives inside on the motor control side. And then we have thermal characteristics. It's hot. And then you gotta all make that work at very, like, high speeds and high torques. Meaning motors don't, like working when they're not moving. Motors hate not moving. Motors want to run on highway speeds. Okay, they love that. Like, whether it's like a generator, like something, you know, an appliance in your home, or like an electric car, they want to, like, run at, like, highway speeds. They're designed to run at full RPMs. That's when they're the most efficient. Okay, when motors are stuck and not moving but holding power and holding forces, they're. They're all. It's a really bad point of the torque speed curve. So they're not built well for this. So humanoids use all the time. Like, we're like, when we're standing, we're, like, not moving, but holding forces. When we're, like, holding something out and, like, giving me the gummies. Like, it's not moving now, but holding forces. It's just a really hard engineering problem. Just that one little aspect, which is like, hardware, umbrella, just motors. And so we have whole teams in those areas. Just in that one little area here doing, like, rotor design, electromagnetics design, satyr design, gearbo design, sensor design, motor control design. Like, all. All of this inside of teams. It's. It was an enormous lift to just get the team members there to do it. So we sp up a team to go operate that really quick. And then, you know, now, like, looking back, I think we raised, you know, you know, 2 billion or so. Like, now it's a much different story. We have, you know, we ended up building figure 1, which is our first generation robot, and had that walking in under 12 months. So from when we.
- [65:24]
B
From inception to. Wow.
- [65:26]
A
Yeah, from when we. We basically incorporated the company in 2022, our goal was like, can we get a robot walking by itself? These Dynamics in under 12 months. And we did it. We did it with, like, two days left in the year. At the time, I think it was the fastest time in history for anybody to do this. And, you know, and then from there, continue to build the capabilities. We built built Generation two, figure two, which is that guy right there, is our second generation robot. And, and I think one thing we did before we kind of moved even to gen while designing gen 2 is I think it's 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 you know, it sounds simple, but like for a humanoid robot to do that is extremely hard. And then we wanted to do all of that with just neural networks.
- [66:33]
B
It sounds simple, but I mean, simple task for kids. Give it to a four year old.
- [66:37]
A
Yeah, exactly.
- [66:38]
B
I mean the dexterity on the hands of that thing is just to hand you the bag of gummy bears.
- [66:43]
A
Yeah, yeah, yeah. Then can you do it with neural nets on board?
- [66:48]
B
It's crazy.
- [66:48]
A
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 we did that in 2023 on Figure 1. And it was like probably the, probably the most significant demonstration we've done in four years now, almost four years where we were like internally, we were like, you know, how do we get neural nets to run on a humanoid? 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 you know, this was like, game on. This is like, we have, let's go build 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. Meaning you want to look at it like a phone, where you can just add new apps to it like the Do Laundry app. And the hardware doesn't need to change the same exact hardware. Like humans don't need like new, I don't need new hardware to be able to go off and like learn how to do new skill now in the physical world. So you want to build the humanoid hardware so it's like can do everything basically a human can or as most of possible. And then you want to go all in on neural networks because you just can't code your way out of this problem. And that was the first moment in 2023, we're like, hot damn, this is going to really work. This is going to be humanoid robots. Hardware gets good and then you're basically going to be, this is going to be a data play to train neural networks to run on humanoid hardware and do what humans do. And then we launched, you know, we launched figure 2. We did a lot basically more work. We started unveiling Helix, which is our neural network stack internally that we do here. And now we've designed figure three, which is our third generation robot you have here, which is like the best human hardware in the world by far. And we're now running robots that do like I watched it, you know, the other day, unload dishes and fold laundry. We had figure twos at BMW last year that worked six months every single day. Every six months. Every single day, Every single day. It worked a 10 hour shift every day for six months. And we had, it was just, it was like the first time for us getting robots out to the real world doing real stuff. It's fun doing demos at the office and showing it can really work. But the real level boss is 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 human KPIs in terms of speed and performance. Humans, in the case of manufacturing, you might mess up every once in a while, but you refix it so you're like, 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 and we have to wake up every day and be able to do that. And so we had robots on the manufacturing line. They basically have a basic body shop that basically builds like X3 and X5s. And January of 2020 was it 2025, we started building our first BMW X3s on the line. And 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 car. It's like there's a ton of parts but like we built, we did, we did part of the whole process.
- [70:15]
B
And what's BMW's feedback?
- [70:20]
A
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 kuka like manufacturing like, like robot arms on the floor. They're bolted to the ground. They're massive. These things are carrying car chassis around like they're kids toys. The cars are so big and so heavy, you can't, human can't hold it and pass it around. So you basically have machines that are building the car and then moving the car. So the whole body shop line is only automated end to end. And it's like not end to end but there's humans involved. But the car is being built by machines and then there's machines everywhere. There's special end effectors on every machine. They're switching these things out basically in real time, like grabbing a big end effector. The end effector is something that is grabbing apart. These in effectors are the size of my. So like my chair. They're switching them out in seconds.
- [71:20]
B
Wow.
- [71:21]
A
One or two seconds. They're doing it really fast. And then they're basically building a car with this. There's robots everywhere. And so like BMW like they're like. It's, it's been a privilege to see how like much automation has gone into automotive. It's unbelievable. These machines like kind of make what we're doing sometimes look like little toys.
- [71:40]
B
No kidding.
- [71:41]
A
Or we're doing something very complicated. But the car manufacturing is just like no joke.
- [71:45]
B
So what specifically were bigger figures doing?
- [71:48]
A
Yeah, we had a, there's a, there's a body shop line called. That's basically building the rear header. It's like the, the back plate. So the, the body shop, they basically build the car by putting basic sheet metal together, welding them onto the chassis and then they're basically building the car around that. Like ended up putting the seats in, bolting them down, putting the car doors in, wiring them up, the harnessing. And we were in the body shop line helping basically attach the rear head, like basically putting the rear header on this fixture. So we, so today we basically, or last year when we're there, we basically take a piece of sheet metal and we basically put on this fixture and we do that over and over again. And they do that, you know, 10 hours full shift. And there's three different parts on those three parts go on. This thing rotates and this big giant Kuka machine like this robot arm goes and spot welds it and switches it switches out to another effector and then grabs it and puts it down the line. So like these, these, these facilities Are being like, fed these parts into the machine. And we were like, we were a piece of that. And the goal was just like, can we run robots every day? You know, are we gonna get like 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, we got the robot to a great spot where brand every day was great. I think the biggest learning lesson we took away is we had, we really, I really cared about if can we do that and can we like clone it times a thousand, times ten thousand, Would we have any issues scaling? That was the part for me. Like, is it just like, you know, does it completely shit the bed? And he's like, we need to rework our plan and go back to the office. Does it do it incredibly well and you can just copy paste this thing to everywhere in the world? Like, how, 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.
- [73:43]
B
Wow.
- [73:44]
A
And it was cool. Like, you know, like, it was. Same robot ended six months later. And this was a 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 job. I think from a software perspective, we did, I would say a B, B job, B plus. And that's mostly from my perspective. The architecture decisions I made to scale about half the stack we had traditional code and heuristics in. So the controller to walk was done by a C controller. It was done by code. The walking you saw today we had back then was done in code.
- [74:30]
B
Oh, good.
- [74:30]
A
The rest of this, we had a bunch of other stuff in there done by neural nets and some of the perception stacks, some of how to move parts around everything else. And this was a year and a half or so ago when we were first launching. And I was like, man, the biggest problems we're having is the coding parts get stuck. The robot like either doesn't see like the right, like doesn't like see something right on the part and misses the object detector doesn't really Understand what's going on the controller when it gets out of bounds of, like, what it's ever seen before. Like, you have carpet in here now, and it's, like, really squishy. The robot is doing fine, which is great, but I think our old controller would not do well. It's like, you have, like, really shaggy carpet here, and it's like. Yeah. And so, like. And it's, like, not very. It's like. It's, like, hard, you know, it's harder for a robot to walk around. And so that was, like. We had a really difficult time seeing that, even though it did well every day, 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. And I think we just announced Helix 2 two or three months ago now. I forget, like, end of last year. And it's basically entirely down the stack, including the controllers and neural net now. There's no code left, really, on the robot. Some code in certain pieces, but mostly just almost all of the thing is, like, a neural net at this point. We removed the need for almost over 100,000 lines of code when we launched Helix 2. And so what you saw today was just a robot that we can put now back in, say, the factory in these places that will run only on a neural net. And I think we're running these robots right now. We're getting ready for deployment to customers, and they're running incredibly well. We have robots running basically now in 24, seven shifts without stopping, without any faults, for, like, days and days. We just went, like, over. Yeah, we just had, like, record time this past week on the robot running until we saw, like, a fault, like, almost. Yeah, basically a whole week. And they basically, like, they can run, like, four hours or so, five hours. And when you charge, another robot knows that, steps in, steps behind the robot, say, and gets ready for work. The robot then backs off. The other robot swaps in spot, like, in the next, like, 10 seconds, is doing work again. So we can run that now in 24, seven shifts where they're talking to each other, all autonomous, no humans are even. Go to bed, whatever. And they're running night shifts all day and all night. And we do it across multiple use cases now at the office and 24, seven shifts, and it's just like we're running them hard.
- [77:05]
B
What kind of stuff are they doing at your office?
- [77:07]
A
We do a few things. We have a Logistics use case that we run in 247 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, we want to like. I want to run it for months and have failures. And we still, we still, we see failures right now. And it's, most of it's in software. Robot gets to some spot where it feels unsafe, doesn't know what to do and it'll stop for a little bit. And if the, you know, the robot's not on the line for a couple minutes, we call out a failure and we're not happy with it. We have robots that are greeters and visitor bots that walk around the office all day, 24 7. So you have the office, you're getting lunch or you're walking around, you're interviewing with us. You see robots everywhere. And Those run in 247 shifts. All day, all night, weekends, Christmas day, whatever. We run them greeters. Yeah, they basically.
- [77:53]
B
How do they greet you?
- [77:54]
A
Come talk to you?
- [77:55]
B
They'll just come talk to you?
- [77:56]
A
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 meeting the candidates that are interviewing there, taking them to the conference room, getting them water, coffee, like all of that end to end and whole experience shit. 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. And 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.
- [78:42]
B
What's the conversation you've had with a robot?
- [78:45]
A
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 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, on the way to work, it 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.
- [79:07]
B
It will actually get to know you.
- [79:10]
A
Oh yeah, yeah. It needs to know who you are. Like, who am I talking to? So Sean and Brett, and then based on Sean, like, do you have the permissions to tell the robot to go, go do something or not? Like if you're visiting. No, you might be able to get coffee or water, but like, you want to have it. Like go do something new. Like, won't do it.
- [79:27]
B
I've not even thought of that either.
- [79:28]
A
Yeah, yeah.
- [79:29]
B
Like we want to be able to command it.
- [79:30]
A
Yeah. What are the permissioning systems and authentications of the robots? I mean, like, like, you know, like, like robots in my house. My kids are going to be like, hey, give me ice cream every, every, every 10 minutes. And you can't have the robot doing that. Right. Imagine 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.
- [79:52]
B
So what is it? Voice recognition.
- [79:54]
A
Yeah. You have to do voice for something. Something's voice isn't enough. Where, like, if you like think about it like an extreme example, you wanted to go like order food or spend money or send a wire. Like voice recognition won't be enough. You'll have to do a higher level of authentication.
- [80:09]
B
How would you do that?
- [80:10]
A
Facial recognition.
- [80:11]
B
Okay.
- [80:11]
A
And then if you have a, perhaps even fingerprint scanning, it's possible to. But facial is what you really want to do. Gotcha. So those are not all those systems are not like robust enough right now. We're working through them and like the goal is like to get, it's like super robust. But like, you know, we want to have conversations with the robot. I want to ask it to go do things. Like you really, 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, 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, do laundry in like my kids room today or something like that. Or text it. So like language is super important. Ui. So we're like spend a lot of time on speech.
- [80:55]
B
You can text it too.
- [80:57]
A
Yeah. Every, every Robot we have as 5G by default on board. We actually run 5G by default now. So every robot off the line has 5G enabled. Like we have like a T mobile 5G. T mobile's an investor of ours, and every robot has an ESIM card for t mobile 5G. So it comes to the line. We use 5G for all the main network. So if you want to, like, you know, if our, like, if our systems want to tell the robot what to do or commit to do something, we do it through 5G. And so, yeah, you can, like, you can text it.
- [81:32]
B
So you could be at work and say, hey, I want. Go get the pizza out of the freezer, put it in the oven.
- [81:38]
A
Yeah.
- [81:38]
B
425 degrees, 15 minutes.
- [81:41]
A
Yeah, maybe we can't 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 you want that to happen. You want to be like, yeah, I'm at work when the groceries come, make sure you put them inside and put them in the fridge and do. Do all this. Or it would even know that.
- [81:59]
B
Go check the mail, have it on the counter. When I hear, like, all feed the dog.
- [82:03]
A
Yeah, everything, like, watch the dog, make sure dog's okay. Yeah.
- [82:06]
B
Holy. So it's nanny, housekeeper, gardener.
- [82:14]
A
It's the Jetsons.
- [82:15]
B
All of it.
- [82:17]
A
Yeah, it's going to be all of it. I mean, you might want to garden. You might want to do it. All this physical labor we do today, I think will be optional in the future. So you'll like, you might like gardening, you might like mowing the lawn. You just like, mow lawn. If you don't want to mow lawn, don't mow the lawn. Like all of this will be a choice. Holy shit.
- [82:36]
B
Yeah. And you said it's gonna. It'll download apps for different.
- [82:41]
A
You wanna think about the software layer. Like, you wanna think about that. So for us, what's so powerful about a humanoid is you don't wanna go out and change hardware. Whenever we have a new app on your phone, you just download it and it can do new things now. Like, it's got my bank account. Now I can do bank account stuff. Or you gotta download. You got a calculator can do calculator stuff. You really wanna treat the hardware like this, where you. Basically similar to phone, where you, you don't have to change the hardware for new capabilities. You wanted to learn how to do, like, you know, complex towel folding or like unloading the dishwasher, making coffee on a keurig. Like all this, like, walking the dog. Like, these are like almost like the matrix where you get, like, plugged into a system that re uploads, like, weights into the, like, neural net weights into the robot where it can, like, learn new things. So that's what we do now. Like, if the robot, like, if we can't do package logistics, well, we get data for package logistics. We train our Helix neural net for a week, and then we load it to the robot and it can, like, then the same robot that was, like, folding towels like the week before can now just sit there for 24, 7 and do logistics work and package work.
- [83:51]
B
Wow.
- [83:52]
A
Nothing changes.
- [83:53]
B
Where is this going to go first? Consumers, businesses.
- [83:57]
A
You'll ship into businesses first. It's the engineering complexity that we have to ship is, like, proportional to the variability that we see on site. So the veritability at homes is, like, extremely high. It's like, it's. My home is chaos. Like, kids are just, like, just dismantling the house. It's like, basically in real time. And then there's just, like, food or they're eating snacks, toys. Like, it's just, like. It's just chaos. And then, like, you know, if we go to your house and my house, we probably have, like, different appliances or different toasters and different microwaves, all a little different everywhere we go. So the home is just like this, like, tons of entropy, like, tons of variability, a wide distribution of tasks. 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 in the workforce, it's like you have this, like, work cell that you're doing. So, like, if you're doing, like, manufacturing logistics or, you know, a lot of tasks, you have, like, this area you're doing work in, and you can basically kind of write down on a piece of paper, like, how to do every step in the home. You can't do that. You can't write down a piece of paper. I can't write down a piece of paper. Like, how I can interact your house. I haven't even seen it. Yeah, but, like, the next assembly line or the next, like, conveyor system, like, it's like, I kind of know what to do. It's like, I get the package, I flip it down, and I need to do it every three seconds. Like, you kind of have, like, you know, good understanding what to go do. So it just makes it easier. It's like the analogy would be like, highway driving for autonomous vehicles. That's just happened sooner because the veritability is lower than in a city. Gotcha. So it'll happen first at scale. And then the industrial thing has a good thing where it's like you kind of have your own work area. So the safety areas are not as high. The hardest thing in the home will be once you figure out how to get performance there. Meaning it's capable of doing everything in the home. Like so you can go 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, that that's going to be the hardest challenge by far and that's going to take some time. It's a very, there's some trust that needs to build. There's a track record that needs to be built. There's like system safety engineering that needs to be done extremely well so that just. And then the home like you can charge like 10x. You can charge like 10x more in the commercial market than you can the home. Home needs be like 500 bucks a month. Your car lease, those will be, you
- [86:23]
B
think those will be around 500 bucks a month?
- [86:25]
A
Yeah, I think it'll be like that level like, you know that like order of like more magnitude. Yeah, yeah. So I think. And then the commercial workforce, you can charge like 10 times more. So like, so it's just like the commercial. And then the commercial market for humanoids, like you know, I mean half of GDP is human labor. 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.
- [86:54]
B
Wow.
- [86:54]
A
So like you talking about the largest market in the world is sitting in the commercial workforce.
- [86:58]
B
Wow.
- [86:59]
A
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?
- [87:09]
B
Yeah.
- [87:10]
A
You know what I mean? Like why would you like spend time over here when you can just go over here and build like a 20 trillion dollar company? And my answer for that is just like I just want to, I want robots in the home. So don't really care, you know, like we got to make that work. Yeah.
- [87:27]
B
I mean you say in 10 years every home will have a humanoid of
- [87:34]
A
every home in 10 years. But we will have pretty close. We will have in 10 years. You have like two long poles. You have like a long pole with manufacturing enough volumes for this and then you have a long pole where you can actually technically do the work fully end to end. My belief is that the hardest thing in the stack is not manufacturing. The hardest thing in the stack is. Sorry. The hardest hill right now is can you put a robot into your home today and do the five hours of work you need without ever seeing your home before? The first group to do that, I think will be like, become like the largest company in the world. And you can do that with maybe 100 robots?
- [88:24]
B
No.
- [88:25]
A
Yeah. I think you can solve a general purpose humanoid robo. I think you can solve general purpose robotics with maybe like hundreds or low thousands of robots. Maybe a hundred.
- [88:35]
B
How so
- [88:38]
A
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, the washer and run it. And we can do pockets of work. We can like, take the laundry, put on my, like, my bed, and we can fold it all. And then so we're doing like little spots of it, and it's pretty good. And. But there's a lot more spots to go fill for like Long Horizon work. Just that. And we have to be like, extremely robust to maybe different types of clothes or like different types of like, I don't, don't wash my jeans, like that type of thing. And all these different, like, veritability that you might look, see, and we haven't been able to. We as of today. That's like, that's the hill. We got to go solve that hill. Looks really hard.
- [89:35]
B
So how. I mean, 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 the laundry, I want these ones washed cold. I want these ones washed hot. This is where they go. This is where the, the jeans drawer is. This is where I hang my shirt.
- [90:04]
A
Yeah.
- [90:05]
B
Is that how it works?
- [90:05]
A
Is it just. You got a robot in a box, you open it up, robot, get out. It'll start talking to you. It'll ask you to show you the house. And you'll, 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. Let's say, let's, let's say you'll see it a friend saying 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 recycling here. This is where trash is at. Like, here's where you get water. Like, the trash goes out every Monday. We do blankets on the couch, but we want them in the cabinet when they're done. You know what I mean? Or we want these folded and put over here. All these things you have in your home that are important. And just like you would walking somebody, a human around for the first time, that's what you'll do. And the robot will, semantically will a have, like, will remember all of this. And it will learn based on what you want and your preferences, like, what to go.it'll be that.
- [91:22]
B
So it's just like training a human being.
- [91:23]
A
This is like not. This is not 10 years we'll do. This is really soon. Like, I think in the next, like, I mean, I'm hoping this year we could like, drop robot in your home and do a good amount of stuff. It's just. We'll see. I mean, this is like. This is like solving like the holy grail of robotics. This is like solving for a good general purpose humanoid robot. Maybe we don't solve it this year, maybe we solve it next year, maybe we don't solve it next year. But it's 2020. I don't know. Like, we're close. We feel like we're in the red zone with like, we feel like we know the architecture, we have the hardware, we know how to get the data. We put the data in, the robot does it. We need to, like, now learn how to generalize. We need not like, like move deeper into pre training. For we, we know the directions. We need to go ahead, we think to solve this. And we're seeing a lot of both positive transfer and a lot of just like we're seeing internally the we think the right direction to make this work.
- [92:18]
B
When you were talking about trust in the robot with your kids, what, what are. I'm just curious. What are your concerns?
- [92:26]
A
Yeah, there are tries.
- [92:27]
B
I haven't thought about this.
- [92:29]
A
I think an Archer was always like, I'll never, like, feel safe. I never feel comfortable recommending people to fly an Archer and letting people fly in Archer until I would fly an Archer Aircraft to my kids, 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 will 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.
- [93:02]
B
Yeah.
- [93:03]
A
Four year old and seven. You know, I mean, I have young kids, they want to jump on everything and you know, it's like they're like, yeah. And the robot, like, you know, the robot needs to be extremely safe there. So that's another hurdle. It's like getting to general, like solving general purposeness, getting safety to work and then making enough of them. Those are kind of like the equations from here we listen, we have a good plan only what to go do here. But now it's like execution that we got to go do to show it works.
- [93:27]
B
Right on, right on. You want to take a walk around this thing?
- [93:31]
A
Yeah. Let's do it.
- [93:32]
B
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- [95:55]
A
All right, this is our figure three humanoid robot. It's. We actually unveiled it last year. My God. Yeah, it's about 130 pounds, five foot six. And we basically designed it to do most things, like a lot of things humans do. 130 pounds, 135 pounds. Yeah, it's fold laundry, do dishes, do manufacturing, logistics. You know, I think a few things here that we, like, made improvements on. This is our third time basically running through three generations of robots. We've like, we reduced, reduced the weight and mass. We made the robot skinnier, but also same strength and speeds. We upgraded the sensors on the robot. It basically sees through cameras. We have better or basically our fifth generation hands on board that have a camera, tactile sensors, basically improved grip. We also have on the robot basically more compute on board for running our Helix neural network. We also spend a lot of time on just basically making the robot more safe. So they all have kind of the squishy layer of foam on it, so. Yep, go ahead.
- [97:04]
B
So if you. Let's say somebody pushed it over, fell over. I mean, what's the durability of these?
- [97:10]
A
I mean, depends how hard you push it. But like, for the most part, we, we fall, robot can get back up, just continue to do work. It depends how you fall. Sometimes we break necks, sometimes it's fine. All right, turn around. Another thing too is like we, we basically, the robot's almost all fully soft wrapped. One thing we can do here is we basically can make clothes for the robot, which we do for both our customers. And internally, the clothes can be put on by like any person. So we can basically unzip it, take clothes off, put clothes back on. We don't need tools to do so.
- [97:46]
B
And can we see, can we see what's in there?
- [97:49]
A
Yeah, basically it's the torso.
- [97:52]
B
Oh, you can't see any of the internals.
- [97:55]
A
They're all inside the structure. So inside of here we have basically a battery, GPUs, computer power distribution. Basically the brains and all the energy are in the torso. And then basically the robot is basically left with basically 40, 40 joints. So all basically electric, electric motors. And those motors have like basically tons of sensors on it for balancing and doing work. All right, turn around. All right, we can walk with it for a minute.
- [98:30]
B
All right, let's do it.
- [98:34]
A
All this walking and all the robot movements are all done again through A neural net. There's no code helping us do this. Holy. What do you think? You going to.
- [98:44]
B
Awesome.
- [98:45]
A
You want one?
- [98:46]
B
What's that?
- [98:46]
A
You want one of these?
- [98:47]
B
I want a couple of them.
- [98:48]
A
A couple of them. Okay. Great.
- [98:50]
B
Dude. Whoa.
- [98:54]
A
Yeah. We.
- [98:55]
B
Let's go back this way.
- [98:56]
A
Let's turn around.
- [99:00]
B
Can it run?
- [99:01]
A
Let's see how fast it can go. We have running. We have. I don't know what. We're on the running mode, but let's go as fast as we can. We do jog with the robots outside.
- [99:10]
B
Really?
- [99:10]
A
On campus. Yeah. I think it just also, like, looks cool, right? High tops on.
- [99:28]
B
Looks awesome.
- [99:29]
A
Yeah.
- [99:29]
B
And you said there's cameras in the hands?
- [99:31]
A
Yeah, cameras in the palms. Right here in the palm. So we can see the fingertips when it's like, grabbing objects. And then every single fingertip has a tactile sensor inside. So we can basically touch, like, touch forces as we're grabbing objects.
- [99:43]
B
Can it shake my hand?
- [99:45]
A
I don't know. Maybe.
- [99:46]
B
And it squeezed my hand.
- [99:48]
A
There you go. There you go.
- [99:50]
B
Will it crush my hand?
- [99:52]
A
No, it's not gonna crush your hand, dude.
- [99:55]
B
That's pretty sturdy. That's like. Yeah, I can't move it.
- [99:58]
A
We can pick up like 40 pound boxes off the floor. And we can also fold a T shirt. So that is wild.
- [100:07]
B
Yeah. This is the power button.
- [100:10]
A
Yeah. Don't push that.
- [100:11]
B
Okay.
- [100:13]
A
We had a. We had somebody in the office. Our day was like, I. I feel like I need to push this. I'm like, it's literally going to turn off if you push the button. And how.
- [100:22]
B
How long does it hold the charge?
- [100:25]
A
It depends on what we do, but anywhere from four and five hours.
- [100:27]
B
How long does it take to charge?
- [100:28]
A
It takes about an hour to charge. We can do about four or five hours on. We can charge for an hour. You know, humans take breaks during the day to eat and do other stuff. And. Or. Or we should do a lot of time in the office. We'll sub another robot in during the meantime.
- [100:42]
B
Wow.
- [100:43]
A
Yeah. We actually charge here inductively through the feet. So the feet have like, basically have like, charging pads. Robot steps onto. And we charge wirelessly. We can charge. We can basically charge in one hour through that whole process. Holy. Just by standing. So in case the robot has a task where it needs to stand a lot.
- [100:59]
B
We can do stands on a mat.
- [101:01]
A
Change on the mat. We designed it.
- [101:02]
B
An iPhone charger.
- [101:03]
A
It's like an iPhone charger. Yeah. And I can charge about a kilowatt per foot. It's about 2kW it can charge.
- [101:07]
B
When is this going to be available to this consumer market?
- [101:10]
A
As soon as we like make it work really well. And so I can send it to my house and my kids don't ask for ice cream every single day. And. Yeah, so we're working really hard, I think, you know, we've been testing in my home fairly recently and we'll be shipping these robots out to commercial customers here really shortly.
- [101:31]
B
Can I ask who the commercial customers are?
- [101:33]
A
Yeah, we have. We work with BMW. We work with one of the largest logistics companies in the world. I work with Brookfield. They're like one of the largest real estate companies in the world. They have a giant portfolio of companies. And then we have like two more customers we'll be announcing in the next like 60 days.
- [101:47]
B
Congratulations.
- [101:48]
A
Yeah, thanks.
- [101:49]
B
That's amazing.
- [101:49]
A
So we're gonna try to ship as many as possible we can this year.
- [101:52]
B
Wow.
- [101:52]
A
We also make these on site next door at Baku. It's our production manufacturing facility. And we make about one every kind of like 90, like 90 minutes or so.
- [102:02]
B
Now you can make one of these in 90 minutes?
- [102:04]
A
Yeah, when we run the line. The lines are running about every 90 minutes. We make one and then that'll greatly increase even the next like several months here. Wow. Yeah. Wow. Yeah.
- [102:15]
B
What do you. I mean, at full capacity, like what do you think?
- [102:19]
A
Our office. Our facility. Yeah, our facility there can do maybe upwards of like 40 to 50,000 a year at like full capacity. But we need to design for much higher. Like we want to get to like a million units. Like a million units a year and you know, like within this decade.
- [102:34]
B
A million units a year?
- [102:35]
A
Yeah, for sure. I mean, you sell like we're sell over.
- [102:37]
B
It's like building a country.
- [102:39]
A
It's not. I mean like you sell 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 cell phone style manufacturing. Yeah.
- [102:51]
B
So I can push this?
- [102:53]
A
Oh yeah, it has. Push recovery. Give it a little push. I mean, a little harder than that might be nice. Harder. Harder.
- [103:02]
B
Dude, what? Yeah, it's better balance than I do.
- [103:12]
A
Yeah, same.
- [103:17]
B
Dude. That's crazy.
- [103:20]
A
Yeah.
- [103:21]
B
This is three and a half years we had.
- [103:23]
A
We had this walking in three years since I started the company. It was crazy. You're like basically the week of year three, we were walking this thing at the office. This was like the thing is, this is like a. We're going to go through like this whole iPhone lineup where it's you know, iPhone1, iPhone2, iPhone3. It just gets better and better. And I think humanoids 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. Damn. Yeah. Our step up from here, even to the future robots will be, I think, perhaps the most dramatic step up we ever make.
- [103:59]
B
Wild, you want to take some pictures with us?
- [104:02]
A
Let's do it.
- [104:06]
B
Dude, that is insane.
- [104:09]
A
What do you think?
- [104:11]
B
Awesome.
- [104:12]
A
I want one. You want one?
- [104:14]
B
Yeah.
- [104:14]
A
Let's get you one, man. Wow.
- [104:18]
B
Like, so that in the same that you said the hands can sense three grams of pressure.
- [104:24]
A
Yeah, we basically have fingers. I mean, we have tactile sensors on every fingertip and they're really sensitive. And we have a camera in the hand that can detect when the fingertips are in contact with some surface. Could be like something we're touching. And then within there, every joint can kind of also feel sensing and track the position of every, like, you know, part of the hand. So the hands, like, the hands are really good. Honestly, we're working on hands now for like, close to four years. It's. It's probably one of the hardest engineering problems we have on the hardware side. It's probably as hard. And we have our next generation hand that we kind of teased a couple weeks ago that has like, basically full. I think, I think it gets a full human level dexterity with this hand.
- [105:11]
B
Are you serious?
- [105:12]
A
It's got as many joints on the hand as a human hand has. There's still a lot of work to go do, but, like, it's now, it's now a huge step up where we actually even currently are. And the hand now can like fold laundry. And, you know, you think it'll hit
- [105:29]
B
a point where it can outperform a human. More dexterity in a hand than a human.
- [105:34]
A
I don't know.
- [105:35]
B
Better balance, faster, stronger.
- [105:37]
A
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, there's. 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, that'd be a huge win. We. You. You'd have robots everywhere and, you know, and then we're gonna still have a lot of trouble getting to hu. Like full human range of motion. Like small things, like you reach inside of a, you know, a washer. And you kind of like move your head as you're like getting in or sometimes like some people get on like, you know, get down to the ground and kind of get in the washer to grab some on the back. We do a lot of crazy stuff.
- [106:21]
B
Yeah, that is.
- [106:22]
A
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 they can jump up on countertops and all kinds of crazy stuff. Humans be tough but like I think we can get. Very soon we'll get to like pretty close to most of what humans can.
- [106:42]
B
You had a pretty close relationship with OpenAI, correct?
- [106:46]
A
Yeah, they, 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 ended up spending basically a year with them working on. Well, I mean I'll give you the background. The goal was to try to advance AI models for humanoid robots together. And they have some great folks that have worked on LLMs and chatbots and things. And at the time we still do, but we had a full AI team internally. So we were basically working weekly, daily on basically how do we advance state of the art kind of like language models for robotics. And you know, like, you know, I ended up firing them I know a year, a year later, but in splitting ways. But listen, they're a great team. I like the senior leadership and everybody there, Sam included, like were great to interact with. The issue lied for us of there was nobody that's ever put advanced language models into these systems. We have to produce action output of the robot and it's a very different thing than next token prediction for language models. We ended up finding that the team we had in place, my team lead, the folks we have here, all from Google, DeepMind or certain areas of, you know, top AI programs and they're really good. The team now we have is over 50 or so on the AI or Helix team. Internally we just found that like that team we had internally, we just, 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, you know, like, like you have like want to run a new like AI experiment or do some ablations like and some evals. You need to like run the robot the end of the day and See how it. See how it does. Like, sim is one thing. You can get certain fire 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, like, basically, you know, advancing stuff together as a team. Ended up. We, like the strategy we had internally and the team we had was just like complete superstars. They're the best robot learning folks on the planet. That set a figure. And it got to a point where, you know, I got a call one day. It just like, you know, we were like, also week to week, like, showing them how we were doing all this work. And I got a call one day saying, like, hey, we're like, you know, we've been watching your progress. It's unbelievable. And, you know, we're thinking about doing robotics work internally. And I was just like, this is over. Like, 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. And I was like, you know, they were still at a point where they continue to want to work together after this. And I was like, there's no way we're going to teach you how to do this stuff anymore. And also, we just, like, got no value out of the whole relationship for very little. I mean, listen, it was helpful having them lead the route, the round. It was like, there was some. There was some good brand association there, but beyond that, there wasn't much. So we ended up, we're going to chart our own territory. We're going to do AI ourselves here. It was also just became, to be frank, it became really hard to recruit. We were like, I have to spend a lot of my time hiring on the AI team. And we'd bring candidates in and they'd be like, oh, you guys do the robot and OpenAI does the models. And I'm like, oh, no, not really. No. We have a whole AI team internally. We do model development here ourselves. You know, like, we're advancing all this ourselves. And it just wasn't the perception from the outside. It was just hard. So that also wasn't helpful for us both Hiring was not great and we were like, you know, there was like an information passing back that I think wasn't really helpful for us long term if we're going to be competitors. So we decided to split ways. I decided physically to split ways. But they have a great team. I think they're doing robotics now internally.
- [111:10]
B
Sounds like it.
- [111:11]
A
Yeah, exactly. Yeah. Yeah, exactly. I was like. I got a call saying, like, yeah, like, you know, partly, like, partly the feedback I heard was like, we've made so much progress at Figure. And they've seen that, that they were, you know, OpenAI started out as a robotics program. They were trying to solve AGI through first three, four years. They were just like all in on robots. If you Google, like, OpenAI Robotics, it's like old 2016-2017-2018-2019. Like, you know, maybe like, maybe like 2019, 2020, something like that. They end up pivoting into, like large language models. Maybe 2021, something like this. But they're in robotics from, I think, 2016, 2017 for like many years, maybe three or four years trying to solve, like, AGI through robotics. There's, you know, there's this other. We don't need to get into it, but, like, it's, it's, you know, it's unclear if you need an embodiment or not. Or, you know, at the time it was unclear whether you need an embodiment or not to like, truly get to like, above, like, peak human intelligence. And they had a hard time in there. But there was like, part of their thesis was like, get back into robotics at some point. And I think we just, we accelerated that here at Figure. And, you know, I think to be fair, like, to be like, somewhat humbled is like, it's. We made, like. I think we made like 10, I don't know, five to 10 years of progress in, like three years, four years. Like, we just, like. It just felt like this should have taken 10. Even right now it feels like we're not even four years old yet. Four years old into May or something like that. Like, I, we, like. I couldn't believe when we started the company three and a half years ago, we'd be at a point where you can get a humanoid robot even here, doing the stuff it's doing here, but, like, let alone like the real stuff it's doing now and like 24, seven commercial work in the home. Like, it's neural net driven. Like, we can make them every 90 minutes at the, you know, when our lines are up. Like, it's just like, it's crazy. So, yeah, we started part beside us part ways, man.
- [113:03]
B
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 man, again, just congratulations on, on everything. I mean, that is, that's just crazy. You know, I've done, I've done a, it's just very surreal for me to, to, to unveil some of that. I mean, I know we didn't unveil this, but it, it's the first podcast
- [113:33]
A
it's ever been on, dude. Sean, I have not taken a robot to like a podcast. Like, I get asked every week to do this. This is the first time and like love your show and want to get him here in Tennessee. This is the first time bot's been out here to something like this.
- [113:47]
B
Thank you. It's, it's, it's really cool to be able to do this like once in a lifetime opportunity type stuff. Thank you.
- [113:57]
A
No problem.
- [113:58]
B
What about military application?
- [114:02]
A
Yeah, we've, we've decided not to do military stuff today and not to say like, the robots won't be good in military or helpful or like my belief right now is like, it's just too difficult to, to do both. Like the ship into the home ship to like, you know, top Fortune 100 companies in the US and then also put like, you know, like militarizer robots. I think it's just too hard under 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. Like, a robot will just walk right up your stairs and open your door. The humanoid robot, you know, this is a very different technology. We've got to be very careful with it. So I think because of some of that and some other things, we, like, we know we've drawn a line here to say like, you know, we want to stick with, 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 into the military. To be frank, these, these robots would be great. They're like, they can just like, they can, you know, like, some of the most dangerous missions are like, you know, going to close quarters and houses and, you know, you know, that stuff is like extremely dangerous. Humanoids be great at that stuff, like opening doors and just making sure the house is, you know, cleared, like, clear house. You know what I mean?
- [115:40]
B
It's like I, I could see it for a whole ton of stuff. Not, not even just going on target, but centuries. Gate guards.
- [115:48]
A
Yeah.
- [115:48]
B
I mean, roving patrols. I mean, all of it. Totally just armed security in.
- [115:56]
A
It's.
- [115:57]
B
It's.
- [115:58]
A
Wow. You know, I mean, you kind of have somewhat of a. A tradable asset too. You can basically. I think you can make them relatively cheap. Make a lot of them just put them out to work.
- [116:06]
B
Do you think you'll get into it in the future?
- [116:08]
A
I don't know. As of now, no. But like, there's a part of it that you'll. There's a part of the story here where you're like, you could make this, like, obviously really safe for humans. There, 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 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, I'll make that announcement. Yeah, I think it's awesome. It's awesome. But like, I know just like it is like a, you know, and then that makes it that much harder than if we have like, any military side of things.
- [117:02]
B
Why do you think they're hesitant? Is it replacement of human jobs? I mean, Jack Dorsey just, I mean, he just let go, what, 10,000 people? Yeah, like almost half of his. Yeah, half of his personnel because of AI and his stock went up because,
- [117:17]
A
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 can like, do what humans can. I think it's, you know, you have similar scariness folks have around like, digital AI and how that will like, basically like, you know, manifest in the future. So I think it's a real thing. Like, I think the robots can do human like work and it will continue every year to do more and more human like work. So, but like that, you know, we just got to, we, we just want to be very careful about how we position this and what we do and also how we communicate it.
- [117:49]
B
Yeah, yeah. What's next for the robots?
- [117:54]
A
We want to solve general robotics at Figure. We think of ourselves truly as the frontier of this robotics AI lab that needs to build common sense reasoning in a robot that can put in every home. How do we drop it into your home? It's never been. And you can just communicate with it and get to start doing work. That's the problem we want to solve here. That's the problem. If you solve it, you can ship billions and millions of robots. There 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 can ship them in the military. As you mentioned, there is a path to go, like build a business doing that. But the biggest business in the world is if you solve general purpose robotics, where just through speech and talking to the robot, it'd feel like you had like a human in a bodysuit that can like understand you, nod, like go off and do things. Now after tasks like, that's the problem we want to solve at Figure. That's like a large scale. Like it's like an AI lab problem at this point. We 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 biggest problem we're trying to solve. If you're with me in the office every day, I am working that down with no sleep, basically as hard as I possibly can. It's a very, very difficult problem at this point. It's largely constrained by getting the appropriate data into the network 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.
- [119:47]
B
Wow. What should I be asking you that I haven't asked yet about figure or general about figure.
- [120:00]
A
I mean, there's a lot of stuff going on with China and manufacturing, a few other things. But like, I think, you know, I think maybe to summarize, I think where we're at is I think if I had to like, if I was like watching this and I wasn't following the story, I think the one thing I would like to convey that you know, is like, we are so close to like making this happen now. And it's only until, you know, people can come online and like watch our stuff we put out or watching, 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.
- [120:44]
B
It's. And it's just crazy to just. It's. It feels like we're living in the future.
- [120:50]
A
It just feels like we're living here. Yeah, just like, it's like. It's crazy. It works. It's crazy. It's working. But we're like, we're now in the, we now have line of sight to making this happen, which is exciting in my perspective. Super exciting. And I think it's going to be super transformative for the world. And I think what we're going to try to do over the next year or two is try to get this out further at scale and get everybody to feel this more and more. You feel it when you come to our office and you feel it when you're next to the robots. But it's hard for the. We're such early innings about this yet for takeoff that it's hard for the whole world to really feel this.
- [121:26]
B
Yeah, yeah. Have you seen do the robots interact with each other?
- [121:31]
A
Yeah.
- [121:31]
B
What does that look like right now?
- [121:34]
A
They communicate with each other when they need to. Like, so we have like robots that are running these 247 shifts. When run robot gets like down to like low state of charge. Let's say it's like 10% and it's a few percentages away from. We'll dock it before it's at, you know, 1% or something like that. It's at 10%. The other robot will get ready like to sub in. It will like come, walk, walk over, sit right behind it. And then when the robot is ready and knows that this there, it will then back away and the other robot will go in to 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 go to like a basically like the hospital in our office. So they'll go to a certain place when they, when they get, when they get, when they know they're going to the hospital. We have another robot coming in to the main docks to start subbing in and getting ready to go. All this communication is happening like robot to robot. And it's unbelievable. And the robots are getting really robust. We can, like a year or two ago, we would, like, there would be like, certain motors that you would lose communications with or other types of comms or could be hardware failures or software failures, whatever. Let's say it's a knee. Lose your knee. Like, can't, can't stand anymore. You know what I mean? Like, you fall. Um, today it doesn't happen. We can lose a knee. We can hold its position. We lose full, full comms of the knee. We can stiffen the joint and we can limp off the hospital.
- [122:59]
B
Holy.
- [123:00]
A
Yeah, actually I'll, I'll post some of the next, like, week publicly about this. I've never. It's like, holy. 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'll limp all the way there. While it's limping there, another group from, like, the healthy part of the hospital will then come in and 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. You can be like, literally asleep while this is happening. We run them 24, 7. It could be at 3 in the morning and it will happen. It's, it's, it's a, it's insane. This is happening. Like, I saw this in the last, like, few months that's happening right now. This is not even like the future stuff. Future stuff is going to be robots building robots. We're designing robots. We will have robots building robots here. And then they will go out and they will just do autonomous work and they will like, charge themselves. They will go 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, because 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.
- [124:23]
B
That is that is, I can't even imagine what that's going to be like.
- [124:28]
A
It'd be weird.
- [124:29]
B
Do you think that they will bring, do you think, do you think manufacturing will come back to the U.S. yeah,
- [124:35]
A
we're going to bring back because of this. My view is we don't want to bring back manufacturing that's already overseas. We don't want to like, you know, like make shoes, make toys, like things like that. I don't, I don't think we want, I don't think we have the will to do this. I don't think we have the know how to, to do this as well as like some of the Asian manufacturing groups when I'm, when I'm overseas. So I've like walked a lot of like the high volume consumer electronics lines and stuff overseas. Some of the most impressive things I've ever seen in my life. There's like, it's like, it's like you walk these lines and they're just shipping 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 that's moving some like whatever, a phone enclosure or something like that. And it's doing it through an automated way and moving it around a little conveyor and it's moving to the next station. Maybe a human's doing something and it's going down the line, it's going to a next station. Got a robotic system in there, completely customized and different from what 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.
- [125:43]
B
Wow.
- [125:44]
A
And they're 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 the, 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.
- [126:06]
B
All the futuristic.
- [126:07]
A
Yeah, we want to bring back flying cars. I want to bring back like, like humanoid robots. Like the stuff that's like highly dynamic, very intelligent systems like the next generation like manufacturing 2.0 stuff. Gotcha. So we're doing that right now in California on, on our campus. We have a fairly large campus in, 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, 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.
- [126:41]
B
Right.
- [126:42]
A
So we're going to design humanoids here.
- [126:43]
B
So these are all, these are all manufactured.
- [126:45]
A
Manufacture those in California.
- [126:47]
B
Right on, man.
- [126:48]
A
Yeah, man. They walk like, they walk off the lines. They walk over. It's like, it's 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 OOL checks. So they're self looking at each other self calibrating. They're doing, they're doing like burpees and other shit to make sure they like, they're okay. If they fail, they go into a triage place. We understand why to fail like that shouldn't happen. We should always fix that and it should not fail again. Like how do we fix the manufacturing process so the next one doesn't come down 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 hour check and then when they're done, they just walk over. And at some point we'd love to get like, for them to get inside their own box and another one like get it ready to go and put it on a pallet and we can just start shipping them out.
- [127:41]
B
So it will get in its own box for sure and another one will throw it on the pallet and ship
- [127:45]
A
it out for sure. Yeah. That's not hard things though, like these are like. That's not. You know what I mean?
- [127:52]
B
Yeah, it's just interesting.
- [127:53]
A
I don't know. Just like, I feel like comes off
- [127:55]
B
the line, gets in its own box, gets loaded on by another robot and then shipped off.
- [128:01]
A
The scary thing for me is like those are like very like rigid body things like cardboard and like moving boxes and maybe using machines and stuff. Like those are like easy. Like the scary stuff a couple years ago was like laundry that like literally moves. It's like literally 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 white. It's moving because the conveyor is moving down and then the packages are squishing each other and then the package itself is moving because it's plastic when you're grabbing it. Those are the hard things that are compliant that are like really difficult for robotics because they're not like, they're not like stationary when you touch them.
- [128:35]
B
Yeah.
- [128:36]
A
So those are things that we're like, man, that's going to be really tough to fold laundry and for. With code. It's been impossible. The reason you haven't seen like package logistics and stuff, some of this stuff automated is because, like these bags are just like hard. They're compliant, they're just tough. You can't model them.
- [128:50]
B
Yeah.
- [128:51]
A
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 were working with. They like soft packages and we signed them. They're like, we want you to move these packages on the conveyor system. And we've put videos out about it and stuff. The first month we signed them, Inga, who runs, you know, accounts, was like, we need to do this for them or they're gonna be really unhappy. And I was like, I was like, damn, that's like a compliant material that is moving while you're touching. Some of them touch and they're something hard inside. Some of them are squishy. They're like, there's tons of them. We're gonna move every three seconds. We gotta find the barcode, put it down, and put in the middle of the conveyor every three seconds a package. I was like, it was 50, 50 shots works. And it's gotta be with a neural net. And we got a bunch of data, trained IT policy, and right away it worked. And I was like, holy shit. This is like. It worked really good. And for some reason, 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. Wow. 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 learning just works on humanoid hardware. Yeah.
- [130:23]
B
Crazy, crazy stuff. We've all seen it. The Department of War is operating in a world that's changing faster than ever. That's why so many Guests on my show talk about the importance of continued innovation and technology in the military. But here's the problem. Working with the Department of War can be complex. For many companies, the process isn't always transparent. It's hard to know who the right stakeholders are, where the decisions are made, or how funding actually moves. Even when you get the right conversation started, you often hear the same responses. We like the technology, but funding is already allocated. Or check back next fiscal year. That leaves a lot of capable teams with strong products but no clear path forward. That's where my friends at SBIR Advisors come in. They've built a team of over 60 former acquisition officers who spent their careers inside that black box. They help you find the right buyers in the Department of War, find the money, and write winning proposals so you can get on the right contract and fast. Since 2020, they've helped small businesses like yours win over $600 million in government contracts. And they're a 100% veteran team dedicated to one thing. Getting the best technology into the hands of the people who need it the most, the war fighters. If you're serious about selling to the Department of war, go to sbiradvisors.com that's sbir advisors.com and if you mention my name, you'll get the first month free. Crazy, crazy stuff. Let's talk about your venture to save kids in schools. Ready to move on to that?
- [132:19]
A
Let's do it.
- [132:21]
B
I love this. Yes.
- [132:23]
A
Yeah.
- [132:24]
B
Yes. Can you give us the synopsis?
- [132:26]
A
Yeah. So back when I sold battery, I got. I mean, I mentioned I got obsessed about a few different areas of, like, working on, you know, I always want to work on flying cars, but, like, the. The macro environment turned, like, extremely poor for, like, school shootings. Like, it went from, like, you know, you know, it's really hard to track. We went from like 30 to 40 events per year in the U.S. to like 300. And that was over, like, a span of 10 years. And it's also really hard to understand why that's like, another thing that we could, like, spend time on. But it's. There's just like, you know, a 10x mostly in the US you didn't really see this, like, a lot internationally. And, you know, we. I started looking at it. Looking at it. I basically started reading a bunch of, like, research reports and other things, and I found I stumbled upon this technology, this basically technology and kind of like terahertz radar, so basically sometimes also called millimeter wave technology, where it's basically high frequency. It's Radio rf. It's like radio frequencies basically done at a very high frequency in the 2 to 3400 gigahertz. And it's basically similar to when you're at an airport and you go in there and you hold your hands up and like the LG system scan you like a couple feet away. They can see like anything, anything you have like, you know, if you have knife, gun, vape, pen, whatever. I read a research report that showed my goal is like if you want to put 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, some people chase, which we're not chasing. And then there's like a. How do we actually like know if people have guns on them? Because if you know a kid has a gun on them, you can go like take it away and then. Majority of all school shootings are unplanned. Most of them like, like almost all of them are some kid bringing a gun in habitually, it's like their uncle's gun and they bring it into school like every day for like three months. They get in a fight at recess and they shoot something. They shoot at the gun, sometimes shoot somebody, somebody who shoot it. And that is majority of all things. All gun events, the ones where you see like a planned event that's like on like CNN where somebody's like coming in with a machine gun or automatic weapon. It happens like one or two times a year. It's on the front page of the news. The majority of all the cases like 90 something percent is all happening from unplanned. Folks are bringing in guns all the time and then they're shooting it. So you, basically what you can do is you can stop all those. The, the, the plan ones are very difficult and maybe impossible to stop. But the 90 some percent of all other shootings you can actually avoid. I think you can avoid those meaning like you can prevent them by knowing if somebody has a gun on them, you can do it the old fashioned way which is like metal detectors and all this other stuff. But like it's just like that's not, we don't want the kids to go into school like that. That's just like not how I want my kids growing up. So basically the reason why I got obsessed with terahertz imaging is you could basically do this at a larger offset 10, 20, 30 meters away. You can do it at a high frame rate and you basically get back a point cloud. You basically get back an image. It's like a three dimensional camera image almost, but it's done in a radio frequency. You could look at almost an optical image. And the reason that's interesting is because, like, if it's basically people bringing guns in habitually and you can scan them at entrances, you're always coming in through a few doors. At a school, you're not going in anywhere anymore. And schools also have all procedures now for this, like, for this stuff. You basically can do like offset scanning at, you know, five or ten, whatever meters away. You can scan people as 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 it.
- [136:41]
B
Will find, it. Will find a gun in a backpack.
- [136:43]
A
Yeah. No, yeah. So it's, it's, it's amazing.
- [136:47]
B
It'll find a concealed weapon anywhere.
- [136:50]
A
There's like, you know. Yes, it's, there's printing in a backpack. Yes, you can find them in backpacks. You can find them in waistbands and pockets. So the story is I found this research report done by a few of these guys that were at NASA Jet Propulsion Lab. I write these two guys and they said, sure, we'd love to have you over. I get over there and they're like, they tell me the whole backstory. They're like, listen. We developed this technology for standoff distance detection for the Iraq and Afghanistan war. It was funded by the US government. We worked on it for 10 years. And when the war stopped, funding dropped to zero. And we're done, we didn't work on it anymore. And I'm like, oh, sucks. And then I'm like, okay, well, I guess keep me posted if this thing ever works out. And then there are, towards the end, like, oh, you want to go see it? I'm like, what do you mean, see it? Like, it's in the basement. It's done. We did it. And this is in 2017, 2018. So this is like. I was like, oh, yeah, let's walk down, walk down to the basement. There's like this tarp over this machine Took a tarp off. They had a guy with a man, 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 he 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 in 2D. You could see in power. There's a bunch of other ways we can look at the data, but it was just, like, crystal clear.
- [138:29]
B
Wow.
- [138:30]
A
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 note, like, you know, deep tech hardware. So I basically decided to put cover on hold and, you know, Chase. Chase Archer. And then about two years ago, somebody came to my office, one of my investors, and was like, hey, I'm, like, looking at, like, trying to solve school shootings. I was just back from LA and I'm, like, trying to solve it with CCTVs, like, the security cameras. He's like, the problem is, like, you can't. You won't know until the gun goes off. And you won't, like, brandish a gun. You won't pull the gun up until you're, like, trying to, like, shoot it. So it's just, like, way too late. I told him the story about how I went down this path and 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. 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, I gotta go do this. I end up spinning the technology out of jet propulsion lab at Caltech, and I own it and started cover two years ago. The OG team that built it is with me now.
- [139:54]
B
No way.
- [139:55]
A
We put an office in Pasadena. It's the main office is right next to JPL and we've been working on this now for two years. I've been self funding the whole thing and we have a prototype that already works last year and we'll have a full scale prototype out like I hope by summer like in our lab and then we hopefully if all goes well by end of year we're beta testing in school.
- [140:17]
B
Wow.
- [140:18]
A
And we'll put him at figure campus first even.
- [140:20]
B
Wow.
- [140:21]
A
This is like an optical play. Can you see it? This is an AI play saying can you detect it? Now there's 130,000 K through 12 schools in the US there's like 60 or 80 million K through 12 students. It's a, it's huge. And but it's not just schools. It's stadiums and airports everywhere.
- [140:41]
B
Hospitals, airports, malls, any venue you can, movie theaters.
- [140:46]
A
I had my last baby a year ago. Just like anybody can walk in the hospital. It's just like, just doesn't matter. They don't check you in. It's just like scary. And so anyway we're getting close here and the technologies we designed are incredible. Actually we designed all of it. Like we designed the whole system that I saw redesigned the whole system I saw seven years ago last year. But it was just too expensive. The systems we were using were like certain parts on it were like 50, 60 thousand dollars. 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. Yeah. There's only a few groups in the world that can make them and design them. We co designed them, we worked on the design with them, made them, fabricate 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, K12s will have like a large budget and we need to be able to get the cost down to make it affordable for every school.
- [141:54]
B
That's what I was going to ask. I mean how are you going to, how are you going to get this in school? A lot of, a lot of schools won't do, they won't even hire a security guard.
- [142:01]
A
Yeah, there are, there are big budgets both at the federal municipal level. Like hundred, 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 good. They're putting in CCTVs, like cameras, they're putting in like ballistic chalkboards, all kinds of stuff in the schools. There's a lot of cash there. The schools also spend a decent amount per student and I think we get the cost down a reasonable amount per student that both public and private schools can afford. But it's a rat. We're like, we could have already had our systems beta testing in some schools by now if we didn't pivot. A year and a half year ago, we spent the last year trying to like 90% decrease, like decrease the bill of materials, like the cost.
- [142:47]
B
Wow.
- [142:48]
A
It's just like that's needed to go big and make this really work.
- [142:50]
B
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- [144:27]
A
chime is a financial technology company, not a bank. Banking services. A secured Chime Visa credit card and my pay line of credit provide the Bancor Bank NA or Stride Bank NA. MyPay eligibility requirements apply and credit limit ranges $20 to $500. Optional services and products may have fees or charges. See chime.com feesinfo advertised annual percentage yield with Chime+status only. Otherwise 1.00% APY applies. No min balance required. Chime card on time. Payment history may have a positive impact on your credit score. Results may vary. See chime.com for details and applicable terms. Score more with the college branded Venmo debit card and earn up to 5% cash back with Venmo Stash Got paid back with the Venmo debit card, you can instantly access your balance and spend on what you want, like game day, snacks, gear, tickets and more. The more you do, the more cash back you can earn. Plus, there's no monthly fee or minimum balance. Sign up now@venmo.com collegecard the Venmo Mastercard is issued by the Bancorp Bank NA Select Schools available Venmo Stash terms and exclusions apply at Venmo me stash terms max $100 cash back per month. I think we'll.
- [145:19]
B
Are you going to put anything else into it? Any other. Like, here's an example when I think of this, it's Would there be a way to maybe facial recognition who's enrolled here, who's not? Just for example, like the shooter that happened up at Nashville a couple years ago at the Covenant School went to school there, but not at the time, you know. And so if they would have had some type of facial recognition on top of what you had, that's, that's yeah, like this person doesn't go here. This person has a gun.
- [145:51]
A
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 like anything in their pockets when they're walking in, but they have a backpack we can be thoughtful about. Like, we probably need to scan a backpack. So. And we maybe need to spend more time getting higher frame rate on this area. And then as you mentioned, like a lot of understanding about like, is this person belong here or not? Is this 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. 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, perhaps like tens of thousands of guns that are being brought into schools in the US across 130,000 schools every year. I think what we're finding now is a very, very small percentage of them are found that are brought in. And then from there, what we're also finding is actually a similar small percentage are actually being reported because if you report like, a student that has a gun, they're going to. They're going to juvie. So we're also finding out, we think a large percentage of, like, we're finding a large percentage of guns are even found. And then of that, we think a large percentage are not even reported because, like, you know, could like, put, you know what I mean, could, like, wreck this kid's life, which is unclear what we should do here for that. That's terrible. But we think there's maybe tens of thousands, maybe hundreds of thousands of guns that are being brought in every year through the schools.
- [147:55]
B
Wow.
- [147:56]
A
You're reporting thousands and you're seeing hundreds of shootings. So our view is that we think it's actually happening as a percentage. It's low, but as an absolute number, it's quite high. Yeah. So I'm excited about this in some way. Like, I, 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, like robotics, like AI and weapon detection and stuff like that. And like, I did a post in December, like, here's what I think on these four areas. And like, overwhelmingly, like, the most support I got, like, publicly was just for cover. It just, like, I think it. I think it, you know, it just like hits. I think it hits in a really good way with a lot of folks, maybe especially parents. So everybody's worried we're homeschooling. Yeah.
- [148:44]
B
Because of this. That's a big reason why we're homeschooling.
- [148:47]
A
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, like, can't recover from that,
- [149:01]
B
you know, I mean, it's just every school I go to, I'm like, man, like, you guys gotta. This just happened down the road.
- [149:07]
A
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, the sense of security we have now in our home is just like, we'll never get back. And I just, like, I didn't know. I didn't know what that felt like. Like feeling like we were secure before, but we lost it now. And now we definitely see it and feel it and 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. It's terrible. And so, you know, my agenda here is. I think it can be prevented. I don't know if you're going to prevent all of them. I think you can prevent a lot of them. And then if. And then if you even have, like, there's no real security there at all right now. But even if you have security, there's also like a sense of like, shit, I got to bring a gun in here. Now there's like, real sophisticated AI. It's in all these schools that can catch it. Yeah, I think that's another big thing. You have that at, like, TSA PreCheck. Yeah, it's a deterrent. So you have like that. But we can also find. You find it. We can see underneath through backpacks and stuff. It happens at specialized like. Like radio frequencies. Happens at, like, you know, 200 or 300 gigahertz, and it happens again at 600 gigahertz. And in between those bands, there's either FCC rules that prevent you from doing it, or there's atmospheric attenuation, meaning sometimes there's enough moisture in the atmosphere at certain radio frequencies that, like, the. The radio frequencies don't do well and perform well. Um, they perform well at these certain radio frequencies for the imaging stuff we do. So actually quite a hard technical feat. I. One of the reasons I didn't do it and did Archer is because I thought the COVID stuff was actually harder than doing flying cars and.
- [150:44]
B
No kidding.
- [150:45]
A
I actually think it still is. We still, like, it's hard to. It's like. It seems like pretty obvious. Like, it's like a way airports have but do it 10 times higher. So it seems like. And you look at Archer, like, man, that looks really complicated. Or like a figure cover is just like, super niche area of folks that haven't. Like, there's. The folks spinning like, in this space are like, they're doing like, they're doing work in weather and space, and they're not doing this for, like, shootings and like, security and stuff. There's. There is no industry for this. And luckily we have like, the world's best terahertz experts at cover that are working every day on this, and they're really passionate. They're probably not getting paid enough and they're just like, super passionate about solving this problem. And so anyway, I think. I think the through line for covers, I think it'll work. I think we'll be able to demonstrate it hopefully by end of this year. Like, we'll be able to say, like, we have it at, like, we'll have it at figure campus first, and then we'll put it in, like, schools, like hopefully on the west coast and maybe, maybe one or two and we'll see how it goes. There's, you know, there's a. How do we market this? Like, what do we tell the students? Like, teach like parents? There's, like, you know, there's a lot of stuff here we need to get right. But if it goes well from there and we're getting low false positives, like, what we really want to do is make sure we don't freak the kids out. We don't want to, you know, think it's a gun, but it's a crayon box. That'd be terrible. So, like, that's really an AI problem. So we basically want to make sure, like, we have, like, low false positives around the whole stack. That's a really hard problem to solve, especially for us, where you can be like, partially occluded on certain areas of the person or the weapon. And then we need, like, know what we see. It's actually is. Is it. Is it real or not? And so funny enough, if you come to our lab, we have like a. Just guns everywhere, and they're all. They're all bricked. You can't, like, you actually shoot them. But. But we, we all, we all day, we. We try to figure out how to put guns on humans or mannequins, and we try to figure out how to detect.
- [152:38]
B
So how does it work? Is it shooting frequency? And then. And then it's like detecting the response when it hits something solid.
- [152:45]
A
That's exactly what it's doing. It's like it's basically shooting out a radio frequency. It's like electromagnetic, like it's like a little wave format goes, that goes out very similar to how your WI fi works in your home. Or 5G same like same, same type of concept. It's just on a higher like different, different like radio frequency level. But, but think about your WI fi and you want to order of 20x or so the radio frequency level that's operating in a few gigahertz, something like that. But we operate at much higher frequencies, 300 gigahertz. So you want to whatever call it 50, maybe it's 50, 100 times this. And then basically it shoots us out and this waveform comes back and we review it and we look how long it took to come back and we use beamforming, a couple other techniques to figure out what happened. But you're basically, it's same as like traditional radar technology but we can shoot 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 a 2D image of what's happening in a 3D point cloud. The 3D point cloud is what's really important. So if you have like a weapon on you like in your pocket or whatever, or say I have one on like my chest for example, we will start getting back the signals back from the top surface of the gun before we get your chest, 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. So then we'll get back an image from this and we can re 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 look like an op I showed you earlier today. The vision from this, it looks like a kind of a camera image is what you get back. And so from there you can kind of like visually see what's really happening. In the case of the gun you can see the gun, you can see the trigger in some cases. Yeah. And sometimes it might be just be like the handle or the side of a gun or different places of it. But you can see it through materials. Like it could be backpacks, it could be clothing or a jacket. But most guns are all in the waistband, pockets or backpacks, which makes sense, right? You're not like wearing it around your neck on a Outside your shirt or things like this. So that's where like most weapons are entering a school. We've done a lot of. We have a. Probably one of the best data scientists in the world that is obsessed with school shooting. And he puts up the best school shooting analytics. He does it daily. He's done it for five years. He's working with us through this. And we've done so much work on how people, how students enter schools, how they exit, emergency responses, what solutions are on campus now for this, what data we find on where guns are at, what type of guns and weapons are there. There's I think it was like 200 nice stabbings last year.
- [155:29]
B
200?
- [155:30]
A
Yeah. It's like, it's so high and so dangerous. Like we're trying to, like we can detect knives. Like there's, you know, vape pens, whatever, whatever. It's not a metallic thing. It's like we can, it doesn't matter what the object it looks like. Different metallics will actually like, like come back to the radar system a little bit differently. So you can kind of maybe sometimes tell if there's a couple. A metallic signature or not coming from the material. But the technology is really kind of straightforward in the sense of it's RF technology, radio frequency technology, and you get back an image and we can use that image to build a neural network to then look at it and say, what is this thing? What time of day is it? Who is this human? Is this a dangerous threat or not? And we need to do a really good job of making sure we're accurate in those readings or not. If we're not, we're going to cause havoc and we're for. Right. A lot of times we could basically start saving lives and that's, I mean there's, there's a, on average one shooting every, every single day in the, like, more than that. Like there's, you know, there's over three, 300 or more or so shootings roughly a year if you look back last couple years. So like every single day, I mean there's less school days in a year than 365. But like roughly every day there's a school shooting in the U.S. that's just at K through 12, not colleges, but that's about, that's looking at 130,000 K through 12 schools in the U.S. i can't.
- [156:47]
B
You think this will be out in a couple years?
- [156:48]
A
Yeah, I think we'll get it out in a couple years. We have a team working day and night on this. We'll you know, we'll probably, I'll probably increase funding into it this year significantly and we'll, we'll take a bigger push in headcount. Yeah. But like right now, we'll, like right now all things are on, like, can we get the first full system in a really stable spot that works? And we've had to do a lot to increase the field of view because like schools are, you know, several meters wide, multiple doors, sometimes double doors to get in. Like we need to scan all of that all the way through. So it's like a natural aperture that students are walking into, which is good. You're not walking inside of a building, you know, like through a brick wall. You have to walk into a door entrance. And we're trying to, yeah, basically we're trying to that get that fully complete this year.
- [157:36]
B
Man, that is solid work. Real solid work. Let's talk about Hark.
- [157:42]
A
Let's do it. Okay. So I mean my, I think my pitch here is like, I've been, I've been working on like one of the hardest AI. I think it's, I think humanoid AI is like the, one of the hardest AI technologies in 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 Gemini or ChatGPT. And it's so stupid. It doesn't know me at all, doesn't remember anything I'm saying. Can't see what I'm doing. It can't use tools very well. Uses the Internet like really poorly. Can't even order me a sandwich if I needed one right now. And like, it doesn't feel very futuristic. 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. Doesn't have access to my accounts, doesn't know any of this stuff. Meanwhile, I think like, for me, like I, I was just been sitting here for three years thinking like, 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. 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 Ashana 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 a lot of gaps to get to. To get to like, like extremely personalized, like AI intelligence. There's just like a lot of. There's a lot of. There's a lot of like, like missed, missed opportunity now, last, like few years. A lot of gaps there. The second thing is we're interacting with these AI systems through old pre AI computers. You're bringing up your phone or your Mac or your computer. It's like they're all designed 20 years ago. It's like a really old interface. The chatbot's an old interface. It's the wrong interface to AGI. You're not going to get to Jarvis with those. So we have to go rebuild all the hardware from scratch.
- [160:21]
B
Holy.
- [160:22]
A
Yeah. And I don't see anybody. I've been waiting. I've been sitting here for like a year and a half being like, somebody's going to do this really well, and I can't wait for it. And nobody's doing it. I mean, look at Apple. They're just like, what are they doing? Like, I. So I started a new lab last summer called Hark, and it's an AI lab. And we're going to basically design what comes after the iPhone for AI. And we're going to design new models that are extremely multimodal that can solve this.
- [160:55]
B
No.
- [160:56]
A
Yeah. And we have like, the world, some of the. I think some of the world's best AI folks of all time. And we have, we have like, we have the lead designer from the iPhone, Abadur, on the team, I mean, design iPhone 15, 16, 17.
- [161:13]
B
So this is going to wind up being a device. Is it going to be a device?
- [161:17]
A
A family of devices? Yeah. And this will go really far. It'll replace your phone and computer and you'll have like, native AI systems that are always on, always thinking, always understanding, always there to help, like, doing stuff in the background. Like, we'll. 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'd do this over 90 days and you're not doing this over here. It'll just, like, it'll just, it'll hold you accountable. Hold you accountable. Yeah, yeah, yeah, 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 yeah, we're gonna, we're gonna. I think we'll probably come out of stealth by the time this thing airs here. Between you and me.
- [162:13]
B
Holy.
- [162:14]
A
And we're, I'm self funding it right now.
- [162:19]
B
You're still funding this one, too?
- [162:21]
A
Yeah, I'm still funding right now. And, and yeah, the team's great, man. We're, I think we're, Yeah, I think it, I think it just, I think it's going to be a massive opportunity. And I see the frontier laps heading in a, a really great place for them, but a very different place than where we're headed. Yeah.
- [162:45]
B
What are you most excited about?
- [162:48]
A
I just want to, like, wake up to, like, I always, like, think about. I just want to wake up to a world that's like, that. I'm excited and inspired. It's like, you know, I love doing this stuff. I could have retired, like 10 years, 12 years ago, 15 years ago. So, like, I think I just want to work on cool, crazy shit. And I'm just excited for a world of flying cars and humanoid robots and helping prevent school shootings and Jarvis.
- [163:20]
B
I mean, how do you, how do you, how do you keep it all together?
- [163:24]
A
I know you're innovating. You just.
- [163:27]
B
The trick is just four major things.
- [163:29]
A
The trick is just to not sleep and always work.
- [163:32]
B
I'm good at that.
- [163:33]
A
You know what I mean? You get me. Yeah. Like, just that's, that's how you do it. Super simple. No, I mean, like, listen, I, I, I mean, to be honest, like, I've had to make some, like, tons of personal sacrifices. Like, you know, I think 10 years ago I would have, like, a part of my life that would, like, be dedicated to, like, golf trips and, you know, doing the annual, like, college trip with, like, my friends and stuff. Like, I don't do that anymore. I spend, I have, like, my family and I have my companies, and that's all I do. And, you know, I really, I do like, a few podcasts a year, not a much. I'm excited to come here because, like, I love your show and get the story out too. You're great at it. And so, you know, I just like, protect my time and just like, I go all in on these things. Like my. My kids and I have my work kids, you know what I mean? Like, and so like, which are like, you know, these are my, like, like, they're like kind of like babies. I go, you know, make them constant care and attention. So I have like this family and I need to, like, I go all in on it and I do everything else less good. You know what I mean? I'm like a shitty college friend, if you, like, you know what I mean? If I seen you in a while, like, just like not gonna spend the half a day with you on Saturday if you're in town. I haven't seen you in 10 years, so. Which is unfortunate. I wish I. But like, you know, I care about these things more. I care about doing this stuff really well, and I'm really happy at it. You know, I'm happy with family, happy with like, things going 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. And you know, I. I got billions behind it. Make like, going for it great teams that work like their asses off, like teams that are, you know, here came with and it's great. And I like, fired up to come every day to work to try to make this thing happen. And I hope these things all work. It's just. But like, I know these are also hard businesses, so it's pretty incredible.
- [165:20]
B
I mean, a farm boy from a town of 700 people now, right? Building that thing, flying cars, keeping kids safe and park.
- [165:30]
A
I mean, yeah, it's.
- [165:34]
B
American dream is still very much alive and well. Yeah, that's cool to see.
- [165:38]
A
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 way you get a shot to go do this stuff. You know, it's. And it's great. I just. Yeah, I just. I'm taking a. 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 veterans, 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 I have that Here, which is great. These are like the things I want to spend all my time on for the next like 20, 30, 40 years. So it's good. I'm just like, I just don't want to, don't want to screw it up now, you know. Oh yeah, make them work.
- [166:24]
B
We're doing a pretty damn good job, I think. All right, we're wrapping up the interview. I got a hot question to ask you. You ready?
- [166:32]
A
Let's do it.
- [166:34]
B
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 long standing research warnings that robots and homes can create privacy and security vulnerabilities in ongoing global debate over autonomous weapons. So is the bigger threat not conscious machines at all, but obedient machines that can still malfunction, be hacked, surveilled through remotely controlled or turned into tools of intimidation, assassination or state power?
- [167:28]
A
I don't know how that person gets up and goes to get, goes outside every day. Feel that scary. So I think like. The futures is this future. It can be molded and morphed and it's what we want to do with our time. If we want a future full of robotic systems that can help us out and free us of our times and things like this, we're going to will our way to make that happen. I'm a pretty optimistic person. I feel that having millions and then billions of humanoid robots on the planet is just going to be such a magical and important thing for the world. Are we going to have bumps along the way? For sure. Are they going to hurt somebody at some point? I think that's bound to happen at some point with enough scale. But I think the spirit here for humanity to get this done I think is here. And I think it's going to be one of the most important technologies of our lifetime. Like I think in some way this AI stuff of like we're like we're generating AI systems that can be embodied and can use computers. Like it's going to be like one of the most transformative technologies we've ever been through. Like we're building synthetic humans at scale and it's, it's, it's both scary but also like very, I'm like very excited about that future. So I think my view here is, yes, there's, like, a lot of, like, really difficult things that could go wrong that perhaps could, like, maybe will go wrong. But I think we need this. Just like we need cars, and I think just like we need, like, you know, a lot of things in life. Airplanes and things. I think these are, like, important technologies that really move society forward. Forward. So anyway, I happen to believe that this is extremely important, will save lives and I think increase prosperity across all of human civilization. And I think I'm excited to be working on it, but I think there is a lot of truth to what. Like I said, it's going to be a really hard road.
- [169:55]
B
Yeah. I mean, it's just a incredible advancement. And I know there's a lot of fear around AI. I have a lot of fear around AI, but. But we're gonna go through it one way or another. Yeah. And, you know, I do think things are gonna be a lot better on the other side of that.
- [170:10]
A
You're not stopping it now. It's like the. Exactly. It's. It's like it's go time. It's gonna happen for sure. And I think it's gonna 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. If, like, there's like, different paths to go down from here that could be good or bad. I think my bets on high probability of really great. There's obviously always path that could, like, not go well, but, like, being conscious of that and like, basically doing everything possible to steer it in the right direction is like, will we, like, what we have to do at this point, like, this is not like something we can turn off, you know, turn off the Internet. Yeah. You're going to stop people from trying to build, like, systems that make us more productive and do more work. I don't think it's not happening. So, like, all we can do is basically do it the right way. That has the best positive effect on the world.
- [171:05]
B
Yeah. You know, another thing that comes to my mind is when we're talking about interacting with the Humanoids people, you know, and I've had this discussion on other podcasts too, but people are going to look at that for advice. Yeah. Relationship advice. And I mean, I think there's a. You know, a lot of important things are going to be talking to this thing too, about advice, certain people. And I think that's a big fear of a lot Of a lot of folks too. It's already happening with chat, GPT and all these other cloud and all these other things anyways. But who are they getting advice from before that? Probably, I think. You know what I mean, it's, it's. I think it's the caliber of person.
- [171:50]
A
Yeah, totally. But yeah. So you spend time with. Yeah, yeah.
- [171:54]
B
Last question. What advice do you have for future founders?
- [172:04]
A
I have a few things I think are. I wish I could like maybe say differently also like pass down to like young Brett like 20 years ago. I think one is like just go, just start building. I feel like a lot of folks get too caught up in this thing that's like going to be hard, it might not work and you can just like it's just so easy to start a company these days. So many great tools. Just go learn. I think there's never been a situation where I haven't like done something and then learned a bunch and then haven't reset from that feedback. So almost like little stairs I'm climbing over and over throughout time and so if I just wouldn't have started and wouldn't have moved like I wouldn't have learned this information. So it's like a lot of information coming in recursively, self improving and getting better over time. This could be simple things like hiring and doing accounting or running an engineering team or like trying to ship a product or getting feedback from customers. Like I'm just getting, I think I'm getting. It's like a, it's like a, it's like a sports player getting better with more practice. And so I think the most important thing is just like just go. I also think the thing I learned a lot in my lifetime is like what you work on is really a defining moment for like for founders. And it could be founders of any, in any industry, tech, non tech or whatever. You're generally going to go and just try to like have this like have this kid that needs a lot of attention. And then at some point it's like you just can't abandon this thing and you got to keep like spending more time with it. And it needs a lot and it's like constantly working on the problems with it. So it's like not the fun things. You're working on all the hard things. It's like this problem funnel I have where I work on the hardest, most pernicious problems at the company. So you gotta like really love it and it's not like you can be there for a year or two. You have to Be there for sometimes a really long time. And even if you're successful, and even if you sell your company or whatever, go public, you're getting your stock locked up or you're vesting out over many periods of time, it's, you're gotta 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 of what I work on week to week, month to month. But it's happening at a macro level where like where do I spend my time? Like I'm 39 right now. Like where do I spend my time as 39 year old Brett? And where does like 20 year old Brett and 25 year old Brett spend my time as an entrepreneur? And I generally have this philosophy that, that harder things are easier. Meaning there's a non linear effect here for starting companies that are easier versus harder. Meaning starting something that could be 100 times higher outcome is generally not 100 times harder. So doing figure is not 100 times harder than doing another robot company. It's probably three times harder, maybe five times harder. But the total addressable market and opportunity is probably millions of times bigger than another robot that's on assembly line moving back and forth. And so I think there's this non linear effect to decision making here that is extremely important. Where harder things that have larger outcomes are 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 more capital coming at you for disposal. To be able to 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 working inside of bigger addressable markets like tams that potential acquirers or public markets or other folks really want to see and have basically a disproportionate outcome. They want a high risk reward. They want to, you know, investors and things and even people they want to like go in and like if it works, they want like 100x or a thousandx. They don't like a 2x. And generally for venture like 90, 95% of people fail. So if it works, you really want to go, you want to hit a grand slam. So I think my philosophy is like spin like choose wisely. What? Like a young brat, choose wisely. What you work on young entrepreneurs. And then I would try to be as ambitious as possible. There's capital for that and there's humans for that that want to work at really crazy shit. We have them at my companies and they're incredible. You met some of them today. They're just like 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 that's never been done before. They don't want to go off and design the next car or do the next AI product everybody else is doing. They want to be here signing something revolutionary. So I think that's like something don't stress enough. And I think last thing is, like, there's no rule book for this, which is, like, really unfair. And there's. 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 or so low. Like, I mean, you're getting a lot of noise out of, like, in the market, it's very noisy. About what to do and what successful means for building a team or hiring engineers or executing on product. It's very difficult and very few people in the world know how to do it really well consistently. And so I found over time, it's been really hard for me to get the right advice. And so I think it's been a lonely path. And for folks out there that are on that path, it's lonely. But I believe in you. You can do it. And I think that's. I've never had somebody for 20 years I could call and just like, what should I do in this situation? I never have had it, and I wish I had, but there's no book, there's nobody to call. Yeah. And I think that makes it really hard. But. 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 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 people will fail. And it's. It's only for the folks that will, like, I will, like, you know, I will do whatever it takes to go make sure I make this happen. There is no failure. Those are the folks that do well here. And you can bend the world, and you basically can mold the. The future to how you kind of want to if you try hard enough. And the end of the go, the goal at the end of the day is just to not die. If you don't quit, you won't die. So, like, anyway, I think. I think it's. Listen. Been playing this now for 20 years, still playing it. I feel like I'm in the early innings of my career now. I want to go ship at scale, these systems. I haven't done that yet. We're, like, in any. We're bred in anyone.
- [179:14]
B
Wow.
- [179:14]
A
And so, like, but for everybody out there, that's in that. I just think it's. I believe in you here. You can do it.
- [179:22]
B
That's great advice, man.
- [179:23]
A
Cool.
- [179:23]
B
Well, Brett, fascinating interview. Love everything you're doing, man. Like, incredible stuff, huge advancements.
- [179:32]
A
Sean, I'm a huge fan of you and just everything you do. So, I mean, having me here and I mean, going through all this is just. It's been great. Thanks for having me.
- [179:40]
B
Thank you. It's been an honor.
- [179:42]
A
Great. Cheers. Foreign.
- [179:55]
B
No matter where you're watching the Sean Ryan show from, if you get anything out of this at all, anything, please, like, comment and subscribe. And most importantly, share this everywhere you possibly can. And if you're feeling extra generous, head to Apple Podcasts and Spotify and leave us a review.
- [180:19]
A
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A
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