$39B founder says his company could 100x in 5 years

Building high-stakes companies

0:00I think if you Google Brett Adcock net worth, according to Fortune, you're worth $19 billion. So that's like a pretty good swing. How does that make you feel? I don't care about that at all. I give like zero shits about that. I feel like I can rule the world. I know I could be what I want to. I put my all in it like no days off. On the road, let's travel now. Okay, so you, Brett Adcock, the short of it is that you were raised in a rural area of Illinois. You started a company called Vetteri, which we sold for over $100 million.

0:30Then you took a company public called Archer, which is like unmanned flying planes, I guess, helicopters. And then now you have a company called Figure, which is worth, I don't know how much, 40-something, 30-something, 50-something billion dollars. You have another thing called Cover, which stops or aims to stop school shootings. And then now you have a new thing called Hark, which you've raised money at in the billions of dollars. And you seem worn out. Great. You're just like busy, man. So you've been on, this is your third time on, I think you've been on one time each year the last three years.

1:01You said you were telling a story about how I think it was right when Figure started. You basically said like I had, I was worth, I don't know how much, tens of millions of dollars. I put almost all of it into Figure to get started. And at one point you were like, I have a mortgage on my house and the rest of my money is in Figure. And some of the money is in Archer, and that's not doing so great right now. And since then, I think if you Google Brett Adcock net worth, according to Fortune, you're worth $19 billion. So that's like a pretty good swing. How does that make you feel?

1:32I don't care about that at all. I give like zero shits about that. You're a super competitive guy. I think you said something like, I just want to, you said like win a bunch of times last time we hung out. It was like, I want to win for these reasons. I'm very competitive. I want to kick ass. I think that like you definitely have to care about this a little bit. And you actually have to, I think you care a lot about Figure being the biggest company in the world. You talk about like, you definitely have this like Napoleon energy of like, I want to be the best. I want to conquer. I think the way I would characterize is like, we're just like, we're just now like these companies of mine are just now hitting the inflection point.

2:08And they're really early. Like they can be like really big. So if it works, this will like 100x, 1000x from here. So most of my energy is like, how do I make sure that works? There is no flat line here. It's either like it goes down or goes up, right? Either like it's binary. Either the robots go out of scale or they don't go out of scale. So in like five years time, it's either going to be a very big thing or very bad. And so all my energy is going into making this like 1000 or a million X from where we're at here. And so it's like the pressure's on to like really just deliver.

2:40Where are you now? What's the outlook now for the next five years then? I think last time you were on three years ago, we said that, I think I said it. I was like, you'll probably be in the 40 to $50 million valuation range, which I think you are now. But in terms of like, you're still lacking output of robots. Like you still need, we still need that to come. When, where are you going to be in five years?

Hark and computer use

3:00What's your prediction? I think at a high level, I think the AI work that we're seeing here now is going to be so much, it's going to be like 100x bigger than the internet. It's just like everything is just so, it's just working so well. Like the system is working well. Like deep learning works. And everything's happening faster than I would think. And my, like, you know, having done like 15 years of like software and internet, like it was just like, nothing was happening faster on a trend line. Here it's happening like that in AI. Can you give an example of something that has happened that's blown you away?

3:32We started at, so Hark, I have a new AI lab called Hark. About a year ago, I was like very interested in this idea of like kind of building this AI to human symbiosis digitally. It's like, um, figure is going to be like, I think figure is going to be like the max ceiling of AGI of like being able to put that out. And then there's going to be a version of this in the digital world. It's going to be like a human is going to have this like AI pairing. It's going to have like, ultimately, maybe your own AI weights, your own memories, maybe your own hardware. It seemed like really close. And fundamental to that thesis was like, you got to figure out how to get AI to use computers general purpose.

4:03You would never hire an assistant that couldn't use a computer. So you got to be able to like give things out to it that can like do everything you can do. Financial models, book flights, like order DoorDash, whatever you need to do. You need to be able to do it all autonomously. But only one in a thousand websites have APIs. So in most, you know, like most computers globally is on an internet and browser. My inclination within two or three years, you'd have a system that you'd be able to talk to and say, go do this or do that. And to be able to like go off, go online and like maybe, maybe like use the internet really well.

4:33Like almost like a robot would where you can like move the mouse and use the keyboard. That's what you have to do to solve like general purposeness for around a computer is you, you can't rely on API or MCP. You have to figure out how to like navigate like a human can. Now at Hark, we've like, we just released our first kind of model and research preview last week. It's really hard for us to find now something that we tell it to go do on the internet and it can't do. What did you guys do differently than the other? Because everyone's trying to do computer use, right? So like, I think Elon's got macro hard and ChatGPT had their computer use thing.

5:06Everybody's doing it. You guys feel like you've cracked something. What'd you guys do differently? Okay. There's a couple of things we did a little differently. First is like, everybody's tackling this from like using APIs and MCPs. Like the reason why OpenClaw got so great, it was like, it could only, it couldn't use the browser. It couldn't like go on and use DoorDash end to end because DoorDash has no consumer API. So we tried to figure out how to use like a, how to look at a screen. And one is we spin up a virtual computer for every agent. So they don't need like a MacBook or anything. So you can just spin up as many of these environments as you want in the sandboxes. And then you would need to give it an ability to like look at a screen and use like move the cursor and use the keyboard.

5:41Yeah, but I used ChatGPT's computer use and it was doing that. I was like, hey, book a massage and it opened up a browser and I saw the mouse going. It was trying to type the thing and it would scroll the results. It was bad. It didn't work well, but it was, it wasn't trying to use APIs or, or, it was trying to use the internet. Yeah. I don't know if I was, yeah, it's got to work well. I mean, that's the whole point. But like, if it goes somewhere and fumbles the internet, it's like the whole point is like. So that's what I was saying. What'd you guys do to make it work well? Was it like an algorithmic breakthrough? Is it? It was in our post-training, right? It was in our, we have a reinforcement learning process that we think is maybe nobody else in the world has done.

6:13Well, let's get some context behind this. Okay. So figure that is shockingly easy to understand. Humanoid robots and that business is going to be massive if it works. If you can crack the code, I think you said there's unbounded demand. Hark, I don't entirely understand what that is. Can you kind of explain like an idiot? Because Sean, you should see, I got the deck and it was just you talking for like an hour in front of a screen. And then there was a list, there was a list of a team and it was like a hundred guys who just moved here from China who had like the greatest backgrounds ever.

6:49And you, it seemed like you pretty much just raised money because the team was amazing. And that's all the deck was. It was just you talking in a video. Well, I mean, that's kind of all we had of time. We started. So, okay, what is Hark? I think the best way to become successful is to see how other people did it, whether you're going to copy them or just use it as inspiration, because then now you know what's possible. So starting at the age of 24, I did this relentlessly and I was very methodical about it. And I created a spreadsheet where I tracked roughly 50 people who were uber successful. And I looked at the year that they were born, the year that they started their apprenticeship, and then the year that they started the first thing that made them successful, finally the year that they broke through.

7:30And I aggregated all this data along with the stories of what they did to be an apprentice and what they did to finally break through. And I put it together in a database. And HubSpot went and found this thing that I frankly even forgot about, but it did change my life. And they resurfaced it, they made it even better, and they put it into a thing that you can download for free right now. So if you click the link in the description or click the QR code right here, you can see this database that I made when I was 24 and it changed my life. And so if you're looking to become successful or you're already successful and just want some more inspiration, check it out.

Hardware after the iPhone

8:00I strongly believe AI will head in two directions. And then at some point, maybe even head together. The first is, love AI out in the physical world that will do everything in the environment for you. Like laundry, dishes, cooking, like run the supply chain end-to-end, be in healthcare. The vessel for that is a humanoid robot. It's just a human form. And it will just go out and do like, you like want one piece of hardware that can like, you know, the hardware capable of doing everything. And you put like smart AI into it and it'll go off and do everything in the world. That's what a figure is working on. Step earlier than that, there's going to be this like really close like digital like AI to human symbiosis that forms.

8:36You're going to have like this very special thing that you can like talk to that's with you everywhere you go. That will know all your stuff, have access to all your memories, have access to all your accounts and systems and be able to actually go do things for like a superhuman assistant. It'll be like, maybe the closest thing is like Jarvis from Iron Man. And it will be able to do like, it'll be like superhuman in almost every way. It'll know everything about your life. You'll be able to access it at any moment whenever you need it. It'll be in the background helping you out at all times. If you're on a flight with like a long layover or a flight with like maybe say a short layover and you miss it, it'll like already have backup plans, already help you like figure that out.

9:11Like it'll just be something with you everywhere you go. We don't have that. We have like really good coding agents. We have really good chatbots. But we don't have like something that can go off and like be my Jarvis. In order to get there, we need to work on the like model side. It's got to be just better than text chat. It's got to be able to use computers, have like basically near perfect memory, be able to talk to you like just like a human would back and forth. And we have to have vision in the system. It's really like look at the world and understand what you're seeing with it. And I think secondly, you need to have, you need to fix the interface to AI.

9:42You have like AI over here and a human and you have like an old hardware system in between, like a call like a MacBook or iPhone. They were designed 20 years ago. They're complete rubbish for AI. They're not the right interface. So we went out and we are out there designing what we think comes like after the iPhone for AI. And it's like an upgrade cycle. We see this all the time in startups. You guys see it, right? Like we're in an upgrade cycle with the computers and phones. They're just going to go away. They're going to be a new ones are going to be all AI computers and phones and systems.

10:14And they're going to be great. They're going to be all real time. You can always access them. And you want to always be like understanding what's happening. They always be able to reference things, what's going on. You'll be able to abstract away most apps. You'll probably not have an app store. You'll probably have an AI operating system. It'll be perfect for you. You'll ultimately have your own weights on your own devices that you'll own and have with you everywhere you go. It'll be like a really great pairing. And we hired an incredible team. Teams like, you know, maybe like 80 or 90 now. The guy that leads hardware design, ABS, previously designed for the last several generations of iPhone, MacBook, MacBook Pro.

10:47Like he's just like the, he's a stud. He's great. So we're designing what we think are the next generation of AI devices that will kill the phone and computer. And then we're designing the next generation of AI models. The models need to get a lot more multimodal. They need to get a lot more expressive. Like the text and coding is just not enough for us to like really have like a real AGI feeling with AI. So we're working on that. We have our first AI, we did our first research preview or computer using AGI that we came out last week. I think we were like top on some of like the leading like, you know, browser or computer use benchmarks in the world.

11:19And it'll keep getting better. This will keep getting better and better. Like every month we'll just like, it'll be better and smarter using a computer and faster. We're working on a couple other different types of technologies internally on the AI side. And then we'll launch the ability to use Hark on like traditional browser and iPhone and Android in about a month. So you'll be able to start using it. And then we'll have hardware coming. We're working on now. We actually have hardware in the lab now. We're using testing. And it's crazy shit. Like the stuff is like a sci-fi movie hardware. What do you think those devices look like? You know, people have been speculating because Johnny Ive, you know, got his shop got acquired by OpenAI.

11:51And you've seen the videos of the puck and then like this little puck and then there's like an earring. I don't know if that's real or if that's fake. There was like a leaked commercial for the Super Bowl. Again, is that real or is that fake? What's the story of that? And then what do you think these devices end up looking like? Are these watches, glasses, something else altogether? I think I've like really changed my mood on this a lot the last like year or so. But we have a really strong opinion here internally. Our opinion is that what sits in the middle is devices that could possibly reach a billion units a year. In the world.

12:22The only kind of things that we have like that in the world right now are computers and phones. They kind of meet that. I call it like mega devices. And then you have things on the ancillary around it like orbiting this like big thing. That are like AirPods and, you know, like a watch or things like this that are like they don't sell a billion units a year. They're like 3% of like Apple's revenue and they help the ecosystem as a platform. What we care about at Hark is trying to solve what's in the big middle piece. To solve that, you got to take down the computer and the phone.

12:53There's no way around that. So you have to rebuild a new computer or a new phone that's better and replaces your existing systems. End to end. And then what's around there is things that like you will have, we will even have at Hark that are like it helps with a family of devices that are not a billion units a year but important for the ecosystem. My understanding, right, you're kind of saying the next device, it might be like a phone.

Re-architecting personal devices

13:17It's just going to be an AI native first phone, right? You're not going to try to change the form factor. No, I'm not saying that at all. You're going to want to like really radically rethink everything. The first version hardware we have now in our lab is like unlike anything I've ever seen in my whole life. Okay. What lives outside of here on the edge are like glasses and pendants and wearables and things. They're not the main show. In fact, like the Meta Glasses are probably one of the worst products I've ever bought. They're just horrible. They're horrible. I can't even like figure out how to use it. It doesn't have its own network. It piggybacks on the iPhone network.

13:49It means your app needs to be open on your phone. The pairing's long. Like it doesn't work well. Like I can't think of any reason why I would need this thing strapped to my head for 14 hours a day. Like it's just like the wrong device. It's not. Like the end state is BCI in the brain. And we're going to have like AI language devices for the next 10 years before that. And like that's the path. And it's not glasses. Glasses, I think, I don't even know if glasses will make our top like 10 list of devices. When you and your team are like brainstorming, do you have a framework on how you can think outside of pre-existing norms?

14:22Because when you're talking about, like I literally can't imagine at all what you're talking about. Let's get down to like the substrate level here. Like first order, what has changed? What's changed is we have like a new type of computer, which is like I think of AI as a new type of computer. New type of automation. That's here. The automation can do a few things that are like, like when we're designing this, we want to design around like key principles that could be like 10x better. If it's like one or two times better than your phone or computer, you're not going to use it. It's going to be like literally 10x better. What are things now that like a deep learning brings that are like 10x better?

14:54There's a few of them. Like one is AI can like basically now like think and use computers and systems for you, just like a human can. It can like talk to you. It can like see. It has like visual understanding. It has a real-time speech-to-speech. It can use computers and systems for you as close to as fast, around as fast as a human can. Over time, it'll be just as good as a human and faster in terms of success rate. So you have a system that's like almost like human-like in capabilities. It also can like have memory, meaning you can put memory into it. It won't forget anything. I mean, you're perfect over time.

15:24So you have a system that's almost like a human in a box that has all the same like affordances a human has. And it's almost like the ability of like, you almost like if you could bring a little human around with a computer on your shoulder everywhere you went, that'd be insane. Like it was just like, it was only for Sam though. Only Sam could see it. Only Sam could talk to you. And only was like there to help with Sam. And that was like your whole life. And it was getting smarter and better along the way and had perfect memory and could use computers and talk to you and see. You'd be like, damn, that thing would be like, it'd be like be able to do anything you do on a computer. Okay. So your first step with your team is like,

15:56just like, let's just get rid of like any constraint ever. What would be the coolest magical thing? If we had like a little guy on our shoulder that was AI all-knowing and could see and hear everything we see in here and then give advice to us? Like, like what is the thing that's going to bring that's going to fundamentally reshape all this? Okay. And then from there, like we got to like, we got to design around that system. The competitive advantages here are that it is human-like capabilities and it has almost near perfect memory. It can go back in reference over time. My phone doesn't have that. Like I put a contact in my phone like last week

16:27and I was like, I was like busy when I was like putting the phone number in. And like a day later, like somebody's like, hey, did you call that person? I'm like, I don't even know the name. I forgot. I can't even ask my phone. Like it's just like, it's so stupid. Like the whole system is. And then I go in there, like order DoorDash like a monkey, like every day now I'm pushing things. Like I don't do any of that now with Hark. It does it end to end for me on my drive to work. I just like say, order me coffee and it's just done. It does it all for me in the background. I don't have to touch anything. It's all abstracted away. And it's like, if you had that little human with you everywhere you go, you would just say like, you would even predict probably Brett, you want coffee today?

16:58And I'd be like, eh, yeah, I do. Like, let's get, let's order. But you know what? Make it a double shot today. And, you know, like route it to the Hark office instead of figure. Like I can like, I would just, and done. I got it. Let me take care of it. I'll stay like a monkey on my phone for the next like three minutes, like trying to do checkout DoorDash. It's almost like the phone is like a tool and it's like a hammer, right? If you want the hammer to do anything functional, you have to pick up the hammer and start swinging it. Whereas the next generation is basically like having a handyman next to you at all times. And so you just tell them, hey, can you fix that window? Yeah. Let's go fix the window.

17:29You don't have to pick up the hammer and start figuring out how to use it. Start there. And then from there, you got to rapidly prototype. So when you come over, like we have like, we've, we've designed everything you could possibly think of. We 3D printed it. What were the designs that didn't work, but were kind of cool? What were designs that didn't work that were kind of cool? The thing is we're building like many different devices now. They cover like a pretty wide area of this. We have some pretty crazy stuff that we were like designing. So like, it's not like you look at that and you're like, that looks like a, that looks like this. And it will, that does over, well over here. So it's like, it's not as easy as drawing those parallels.

18:01It's like pretty quite radical. But we, we rapidly prototype all this. We have like a fabrication facility that does this stuff. We like, we have a whole design studio where we work on this. I like, you like use this stuff like over the coming like weeks and months. I'll like either carry it around with me, wear it, whatever we end up doing it. And we'll like kind of down selection. We had like one of the biggest telecom CEOs in the world here that actually helped with, with the work with like Steve Jobs on iPhone one. And he was here two weeks ago and he'd just come from meeting Tim Cook. You know, Tim Cook's on his way out as Apple, but he's like, was over there at Apple and

18:31came over here and we, he saw our stuff. And he's just like, holy shit, man, this is the first time I've ever seen anybody that could possibly take out, like take out the big guys. Well, is it true to say that with like Archer, Figure and Hark, the, the hard problem seems

Manufacturing humanoid robots

18:46like, can I just mass produce this? The hard problem is not that. We think, we believe now the most important constraint to really solve is like building a really intelligent robot system that can put out to the world. Like there's a bunch of robots you can go buy now, you can buy some from China and you get them and they're complete crap. They can't do anything. And you're like, you can joystick around. That's all you can do. And you like hit a button and it waves. It's got no hands. It's got nubs. And you're like, what do I do with this thing? It's a toy. It's like, it's like, it's like when, like early when I bought a, I bought a DGI drone like years ago and I was like playing around with it. And then like a day later, I was like, what do I do with this thing?

19:17And it was like, it was like hard to set up. It didn't really work well. Like, you know, whatever. It's just like, I floated a bunch of trees. Like it just didn't work. I was like, this is, what am I doing with this thing? Robots are like that now. Like where you can, we can go manufacture a ton of them, but like, if they're not really smart, like it's not really going to be that helpful. We're trying to crack like the true human level intelligence that figure. Like we really want to tackle, like, how do we make it so I can put it into any home? It can do every, every, every job I'd want it to do. That's what we're working on. We think that's the largest, like, like, you know, think about the largest, like gap in the schedule of what we need to go solve for.

19:48Like it's that. But then beyond that, like, you know, people generally sometimes confuse like consumer electronics manufacturing with car manufacturing. There's no company, big company in the world that would look like, say like, uh, uh, I'm scared of manufacturing this consumer electronics at high rate if there's so much demand. Like this is just possible to go do. I mean, you can make them, you make, we make a billion phones almost like, you know, pseudo by hand in the world and then with some automation, but cars is a different story. Cars, like you will die trying to manufacture cars. There's like, there's like a lot of companies, like you just like, it's so,

20:19and having seen like, you know, BMW is a commercial customer of us. I haven't been to BMW and a few other groups. Like it's gnarly. The reason why cars are so hard is that you can't hold the part in your hand. Phones, you can just like always hold in your hand and go change or whatever, move and hold like cars. You can't, you physically can't. So you need robots that like literally pass it to other robots that put things on the chassis. And if any of those break across like thousands or 800 robots, your debt is, your whole line's down. And so it's just like, just like huge giant robot you're building. That's building the car.

20:49And with figure, you can hold any part in your hand. So I think we're like, if we're like between cars and like consumer electronics, we're like over here. Closer to like, you know, we're like, you know, the 40% level over here by like cell phones. Like we, you know, we just made our 1000s EVT robot for figure three last week or week before that. When you say you made a thousand, those are a thousand that go to customers like BMW or you're making prototypes internally? What does that mean? We have like two bit like large customers. Uh, we have a, us as a, as like an engineering or like AI research org that needs like robots

21:21like here. Like every engineer needs a robot. We need like every lab needs robots. Like we need like to do tons of testing. Uh, there's just a lot of work we need to go do, uh, internally. We call it like maybe like engineering fleet we need to go to. And the second one is go to customers. So we haven't been going to both right now. Uh, we've actually shipped out robots to our third customer this, this, this week. When they go to customers, what do they do? What, what, what, what can the robot do? What maybe can't it do at this point? We do a lot of like logistics stuff right now and packages. Uh, we have other stuff we've done in manufacturing, mostly just manufacturing logistics, just stuff

21:53we've done in the past. Um, but like we're also talking to folks about other industries. And at this point when it goes to a customer and it's doing, I don't know what you said, like packaging work or what, what is that like sorting or carrying or what is it doing? They just did a live YouTube video and they had hundreds of thousands, maybe millions of views of people watching this robot sort packages off of a conveyor belt. Yeah, I saw that. So is, is that the type, is that like, would that, give me an example of one of the jobs that just, yeah, that's, that's an example of like a, uh, like a very, very close to like one of the, one of the works we do.

22:23Is that customer like, oh, this is awesome because I can't find the labor to do this. Uh, it's too expensive to humans. This is way cheaper. Or is it just like, Hey, look today, it's not faster, cheaper or better necessarily, but like, it's an investment in the future where two years from now that cost curve is going to work and it will be faster, cheaper, you know, whatever. No, no, no. It's like, uh, it's the pitches, like they come to us and they're saying, like, we're dying with labor. It's like, we're like, we have like really high turnover. Some areas have over a hundred percent turnover per year. It's really expensive to find talent. We have like a, just a large talent shortfall.

22:55The talents are really expensive. Like wages are going up and we like, we don't have a solve for this. We can't figure out how to automate all of this work. And, uh, we need you to come in and help us. We have an ability to make a lot of good money in our contracts and the customers make like really good ROI, uh, on this. Like you got to think like a robot can do like multiple shifts per day, work seven days a week. Like we can like have a lot of uptime. Uh, the task you saw, like on the, on the, on the, like, um, logistics line that we should live stream, it was actually a real use case for one of our customers. Uh, that needs to be done at three seconds a package.

23:26And it initially needs to be done five hours a day. Like, I think it was like five days a week. We did that 200 hours straight at 2.9 seconds a package. So we're already at human speeds. We're already doing this here. Now they're already having ROI and, uh, we're now in the early stages of like getting these out to these customers and scaling it up. Uh, over time it will just put billions out to these groups. Hey, let's take a quick break. You know, that feeling when strategy is done, the brief is written, everyone's aligned and you realize someone still has to sit down and actually create all the content that someone

23:57is usually you and it's due tomorrow. Well, the breeze assistant from HubSpot can help. It works right inside HubSpot. You can draft a campaign copy, blog posts, emails, all in your brand voice, all using your actual customer data. So you don't create just content. You create content that converts. Check out HubSpot.com, the agentic customer platform for growing businesses. Can you help me with like the kind of truth first fiction? Because, uh, one of the weird things is as an enthusiast or a lay person who's, who's excited about this future, you can't really, it's like really expensive or hard to test

24:28this, right? So I'll see like a, a Chinese robot and it's 20 grand. If I want to buy this robot, I have no idea really what it can do. I see, uh, you know, Elon will go out there and say, we're going to, we're going to build a million of these things in the next year. We're going to ship them. Uh, then you get like one X and they're showing their hand and they're like, look at our hand. This is the best hand you've ever seen. And then there's this service in San Francisco where they'll send a robot in to clean your apartment. And they're like, yeah, that works today. So can you help me separate fact from fiction? It seems really hard compared to most categories where I can just try the products quickly online or buy them and test them out.

Separating signal from noise

25:00Yeah. A hundred percent. Um, so, uh, I think a few things. One is, um, the amount of like noise in the market for a signal is just like, it's like, like you mentioned is like, it's like, it's, uh, it's out of control. Like the, I think there's just so much bullshit out there in the market. It's like really hard to tell what the hell's going on. So let me summarize what I think is like the most important and work backwards. Okay. What I think the most important thing to do is, uh, to be able to ship robots autonomously at scale and useful work environments.

25:30Like they can like, you know, cook you dinner, like, like clean your dishes, like, um, make your bed, like run the supply chain end to end, work in healthcare, build a building, like your logistics, like that sort of stuff. That stuff requires, uh, fundamentally onboard AI that you can run. So you can do like autonomous work. You can't solve it with code. You need to do it autonomously. You need to do it over long periods of time. And you probably need to move around and use like something in your hands and move, move, move stuff through the world. You know what I mean? It's like, you gotta like do, do stuff economically.

26:01Like you gotta move like electrons around. So I think at a high level, like what we care about is not like the best robot that's doing backflips and running the fastest mile or dancing or in a parade or running outside in the woods. Like, you know, we don't care about that stuff. Dude, I can't wait till I see a figure like on a smoke break at the B&W fact. Like I could have been a great back in high school, but now I'm working at a B&W factory. Yeah. So like, like for us, like we really want to show and demonstrate the ability to do like real things over long periods of time autonomously.

26:33Who's closest to doing that? Who's kind of most, most bullshit not doing that. Man, there's like so many groups out there. I don't want to name names. They're all teleoperating the robots and all their videos. And it's like. Dude, you get accused of that though all the time. We, we haven't put out a single thing ever. It's ever teleoperated. I don't think we've ever done it. Like we, we used some teleop internally for like data collection and for like, it's, it's good for like, you can like, you can do a lot of testing on some of the robots sometimes, but like, it's gotta be like this bad thing now. It's like the equivalent of a self-driving of like some dude in like Kentucky is driving

27:05the car and it's driving around the passenger. Everybody's taking a video of it. They're posting on the internet. I'm like, look at this. And it's just like, dude, that's like completely faked. But one thing that I've never understood. And it's, I've been wrong many times is how fraud happens. But, but I'm always like when you have hundreds of employees and you're doing something fraudulent or you're doing something sketchy or you're doing something that's highly exaggerated. My hope, and I think this happens most of the time, is people are like, hey, uh, I work here. This is bullshit. I can't, I'm not going to do this. I think there's like this, like in somehow in the robotics space, it's become, become

27:36okay. And it's like, for me, I'm just like, this is the worst thing I've ever seen in any industry I've been in as an entrepreneur. My whole life. How do you know when you watch those that that is teleoperated or you just. They'll say it's not, the ones that are like running fully autonomously will like write that in the headline. Cause it's so hard. I remember like Tesla put a video out one time of a guy, like the robot folding and then like in the bottom, right. It was like a, like a teleoperation hand glove you could see in the video. Uh, so there's like people get caught like doing that. You just know the industry.

28:06People will comment at the company later. Like, Hey, this is like, no, it wasn't autonomous. So like, if you're in the industry, you know, like you can follow it. It's also really tough. I don't know if there's any other company in the world that we've seen that you can watch do autonomous work with AI on board on useful work with a humanoid. Like I, we haven't really seen it. I don't, maybe there is. I don't, I haven't seen it. I've made jokes with you before where I was like, you started with vetery, which is just like a job recruitment thing. Now you're on these world changing things. And you were like, well, vetery actually is world changing. And here's why. And you gave this pitch. It was very good. You're very good at pitching.

28:37You're very good at raising money. You're very good at, um, being charismatic and convincing people of stuff. When you're crafting a pitch to recruit and convince people to change their lives, to uproot their lives and to trust in you and to come and build a company. How do you craft that pitch? And what was that pitch for some of your companies? I mean, most of all, these are online. I mean, the figure master plan is on the internet, on the site. Archer's was up for a long time. Like I posted about it. Like, I think like deep down, I really want to find folks that really care and are obsessed.

29:08And I'm like, I think most of my time is not, I know you want to know about the pitch. Most of my time is trying to find those folks. I found that even in the Bay Area, where it was probably like the richest AI and engineering, like, uh, folks in the world. 90% of everybody out here is not good at their jobs. How do you tell who's good and who's not?

Technical hiring and talent wars

29:27I technically assess them. All of them. Yeah. To do that, does that mean you need to be as good or better than them technically to be able to assess somebody? I need to know like a certain guiding principles. Like for instance, I need to know like if A, if you did the work or if you like watch somebody do the work. If you've done the work, it's like, it's like, it's like a scar you carry with you. It's like, it's like dug into you. Like, you know, all the details. You can talk about it freely. You don't need to think. You'll understand how to like reverse engineer everything you've done and discuss it.

29:57The folks that haven't done it, can't do that. They just like, they can't even go like, they get one layer and then it's like constantly blow up. They can't talk about it. They don't know why. Out of a hundred candidates who sound good, how many, like their resume looks good. The recruiter thinks they're good. Out of a hundred candidates, how many do you, would you say actually hit that bar? I'll give you an example. We have like a really challenging process to go through to be a mechanical engineer here at a figure. You have to be able to build like actuators from scratch. There's bearings and motors and, you know, we have a, we have a, we have a gearbox.

30:28We have like other sensors inside the system. It's a really, it's very compact. You know, it's just a very difficult thing to do and like really hard requirements. We've been doing 10 case studies a week for six months and have not hired anybody. That's insane. It's insane. But when you do get someone qualified and their competing offers are companies that are larger or, or more liquid than you. And the offers are, I, I, I think they're like tens of millions of dollars a year.

30:59Right. The AI side is certainly like that, but the AI side has gotten, um, and it's, it's mostly all driven from meta. Like at Hark, like I've never seen, I thought maybe like meta was like paying these people for like, like a year ago and it was like, it would go away. They've not stopped. So what are they, like, what's a crazy story that you've heard? We gave an offer to somebody that was really senior. That was like, they were coming from XDI, like XDI completely blew up. Like everybody just left and about six months ago, it's just like every, it was just like like macro heart, like got fully disbanded. Like there was a basically a bunch of stuff that happened.

31:31We interviewed a pretty senior guy on the AI info side. It was great. I think I gave him like a really good package of series A stock at Hark. And it was, I don't know, I don't know, 15, $20 million of stock. Over four years? Over, we do five, like for my companies in the early days. Then we transitioned to four a little bit later. So we're still a five. And, you know, I was like, I think we're like, I think we can like 10X Hark here pretty quick. And so I was like, okay, you have like, you know, 15, 20 million. I think 10X, you have a few hundred million dollars. I mean, 10X more time, you have a few billion dollars.

32:02And I think we can do it. I think we like, we have to like, obviously it's gonna be hard, but I think we can do it. And he got an offer for, to go to Meta for 36 million of four years of our shoes. And he's just like, it's kind of guaranteed cash. You know, I go there and I have to like weigh this, like maybe like $200 million at Hark or $20 million or maybe like 36 for sure at Meta. And he left and went to Meta. And they've been doing that, like every candidate we spoke to speak to is like making some absurd, absurd thing. It just hasn't stopped. They've just been, they've been at it since like for like a year, a year now. They've been buying talent.

32:33They've been buying their way into the AI race. What do you think of that strategy? Like, you know, even if you kind of hate it, do you respect it? Do you just think it's a fool's errand? What do you think of that? I really like it. I think like the AI space is, what I found is the folks that really understand how to do like language pre-training and mid-training and post-training, especially pre-training. And the infra around supercomputing and data and evals and all the right stuff you need to get put in place to do that right and the amount of folks that really understand the right kind of like recipes that transformers do well in and, you know, around MOE or whatever you're going to look at, I think it's really hard to find.

33:07It's actually really hard to find the actual folks that know what they're doing. I think there's probably, my rough calculus now is probably like, or rough back of the envelope, is probably like 20 to 30 people in California know how to build really good AI models. Wait, so, but is that trickling down? So, you said that there was a senior guy, but like are even some of the less than senior, the 20-somethings, the young 30-somethings, are they still getting eight figures a year? No, the, like the junior guys, like the guys in like their 20s, you know, like the late 20s or something are getting like, they're making like a few million total. So, they're making like 200, 250 in base.

33:40They're making like another million or whatever, like in a year in like our shoes every year. And so, they're going to pay like a million to, you know, like 750 to like 2 million or so range per year. And that's been driven up by meta. And but then all of the other labs have like followed comp. When I asked you, what do you think of that? You said, I like it. Were you being sarcastic or you're saying, no, actually, that is smart given how hard it is to get this talent? I think it was really smart. And I would have done the same thing if I was Mark.

34:11I would have bought my way into the race. And I think he's like, he's doing that now. I don't think I would have done that. I want to understand it. And I want to like first order, like find the right folks that really care deeply about this and not hire like, like mercenaries. And so, he hired a bunch of mercenaries. They're just purely money driven. They came over there. Then nobody wants to go to meta. They just, they're going there because they're getting paid a guaranteed RSU package by sitting around. And what's happening is like you don't need like 1,000 people or 500 or 300 to design AO models.

34:43You need like a really good team of 20 or 30 or 40 people. And that, you can get there without doing this. And those people probably would care more deeply about the mission and where you're at and be more committed than just if you purely throw money at the problem. But I think if I was like, I think it was a really good strategy and it's working. I think hats off, like really good execution. Their recruiting efforts and how they're structuring this stuff. And it's like, I think it's like, I think it's like paying off for them. Jury's still out if they can like actually ship real products. I think like the problem I have with those groups is they've just traditionally have not been able to do things new well.

35:18I mean, I think Facebook is probably going to, meta is going to go down to like one of the greatest acquirers in all time. With like, you know, Instagram and WhatsApp and different ways. They've like bought their way into those spaces. But like, you know, if you look at like the Ray-Bans and everything I was doing, it's just like, it's not great work. And so I think the question really is, how do you really do great work here? I think like, we're even talking like, we're using like the Hark system right now and it's so good. It's so much better than anything I use today. You got to send it to us. Yeah, can we use it? Well, yeah, we used you guys early on that. Yeah, for sure. It's like research preview.

35:48There's like 500 PhDs and then me and Sam. Yeah, exactly. No, we'll like, but like every other platform. Hark, what's the weather outside? I can answer that. Yeah, no problem. I think what I'm trying to say is like every week there's like five or 10 like junk AI slop startups or like things that are coming out. They're just like not very good. Like this whole space has gotten to a point where like there's just not great things coming out the door. I think the stuff in coding is probably really excellent right now, but everything beyond that is like just kind of like not great.

Four predictions for the year

36:15On January 1st of this year, you've made four predictions for the year. I want to check in and see how you think they're going. First one, number one, humanoid robots will perform unsupervised multi-day tasks in homes they've never seen before, driven entirely by neural networks, long time horizons going straight from pixels to torques. How are we doing on that one? On track, off track, or done? On track. On track? Yeah, four months. Yeah, I see every day like what we're doing. Like we're on track. The hard part here is we already do pixels to torques.

36:47It just means like we're taking camera feeds and we output like where to put the motor, like put the, you know, we want to put a, we want to like tell the motor like what to, what to do to get to the hand in the right spot or the joints. So we already do that. Getting into a new house is never seen to do work. That's the hard part of this problem. We're working on that. I'm working on that every day. It's where I spend about three, four hours a day, every single day, seven days a week on this problem. So if a figure robot showed up in my house, what would it, what's the bottleneck right now? Like it wouldn't know what to do. It wouldn't know where to go.

37:17It wouldn't be able to, you know, fine tune, handle my dishes. Where would it suck for me? We can fold laundry like as an example, but then going to a new place where we're folding in a different location with different lighting and maybe different like table height and different types of laundry and different like types of scenarios it's never seen before. It's like the model is like out of distribution. It doesn't know what to do. It's like if you removed all the pyramid data from the pre-training of LLMs, it wouldn't know how to talk about pyramids. And we just like, we don't have enough of that data out there. It's not on the internet. So you have to go out and collect it.

37:48So what we need to know is like how much of that data we have to go sample in the world to be able to train the model, to be able to go into your house and say fold clothes is a good example. Hey, stupid question. Why do all the robot companies care about folding clothes and doing laundry? Wouldn't it be commercially better just to say, hey, we're going to build like the best warehouse worker because there's already 20 million of those in the world and that represents this much buildings. And of course, that buys us the runway to like get the robot folding, you know, robot done. But like why do you care about that at all today? Why not just industrial work that people don't want to do, companies need done, they're ready to pay and it's not like my home where there's all these other sensitivities.

38:26Why do you guys care about that right now? Yeah, we didn't care about it in the past. We like when we first launched, we're like, we're going to basically do the commercial side to pay for the home long term. And that was the strategy. It made a lot of sense. Like there's like we can charge a lot more in the commercial market. It's like much easier to do. It's like lower veritability. We're in like a little work site. You just work 24-7, just so much simpler. What I've learned now is that the home is super solvable today. So like we can like not go work on that problem and just like sit here and work in a warehouse. But me or none of my guys want to solve that problem.

38:58We want to solve a robot that can go into any environment just through language and do work. We want to be the first to do that. You can probably do that with 100 robots and a 50-person team. So that company overnight would be a trillion dollar market cap. That sounds good. Do that. We're doing that. That's what we're doing. Like we're going to solve that. I think we'll be the first. We call it like solving general robotics. And iRobot, don't they attack the humans? I don't remember this movie very well. Yeah, don't worry about that. Okay, not that part of iRobot. Who could win in a fight right now? Can a human still win?

39:28Yeah, a human can still win. Okay. What are the other predictions? Other prediction. One you had on here. Daily AI usage will shift. People will move beyond text to highly multimodal. Voice agents with persistent memory will become common, which will push AI closer to the synthetic human intelligence we've imagined in sci-fi. We're doing that at Hark. We'll ship that in a month. And our first version of it. It'll get better and better. I think we're on track for that. Have the labs ever, like has ChatGPT or Claude, have they ever released the data on this? Like I use a ton of the voice thing.

39:58Sam, do you use the voice stuff a lot? Yeah, I don't type really at all. Yeah, I wonder. It's probably already a huge percentage. It's gotten to the point where like offices need to change. Like these open air offices that are like popular in startups. They're kind of whack right now because like I want to talk in private. Yeah. Yeah, a lot of engineers have microphones now where they're whispering and they're just like in hushed tones whispering to their computers. Yeah, like I didn't, I was like talking last night. I was like, Claude, why am I so indecisive? And then my wife was like, gay.

40:30Like she was like, bam, dude, she can hear everything I'm talking to Claude about now. Yeah, no, I talk all the time, but it's embarrassing. Yeah, like even speech still like sucks. It's still not great. Like it's almost like you set up, you have to go there, you have to turn on, like it doesn't really remember what you just talked to it about. Like it can't do tool calling and computer use very well. Like it's just like limited and he's like, you have to use it for a certain session. I don't know if we'll hit it this year, but certainly in 2027, you will hit like a full human Turing test with speech. You'll be able to take a phone call from an AI system on your phone and I'll be able to fool you guys.

41:03I'll be able to have like a human call you and a robot call you. And I don't think you guys will be able to tell the difference. That's a 2027 event. I feel pretty strong about it. Today's podcast is brought to you by my friends at Mercury. They make the world's best banking product. I think you know this already. I use Mercury for all of my businesses. I think I have like maybe seven or eight businesses. We use Mercury as our business banking across all of them. And now they actually just launched a personal banking account. So I have my personal account there. I moved off of Wells Fargo and Chase. I'm just all in on Mercury. Why? I like products that are easy to use.

41:34I like products that get me and the problems that I have. So like very easy to make a joint account with my wife. Very easy to spin up virtual cards. One click and I get savings yield. It just has all the stuff that I need in one place. So if you're looking for the best banking product on the market, it's definitely Mercury. I will fist fight anybody who disagrees with me on that. Go to mercury.com slash personal and learn more. Mercury is a fintech, not an FDIC insured bank. Banking services are provided through Choice Financial Group and column NA members FDIC. All right. What's the third and fourth? You had over the past 10 years, school shootings have increased by 10x.

42:07In 2026, the first full scanning system capable of detecting weapons from a 20-foot standoff will be built and beta tested in a K-12 school. Oh, man. We're going to be – we'll have our first – we're building our full-scale system starting in October. And I think it'll – well, I think we'll bring it up before in the year. I don't know if it will be at a K-12 school. So we might miss this one by a quarter. Do you have separate CEO running that one or are you the CEO also of that company? I have a chief engineer from JPL at NASA that's, like, really good. And it's mostly a pure engineering project.

42:38There's, like, really not much to do on the business side. Like, there's – you know, we have, like, some supply chain stuff and other things. But most of it's just, like, purely can you build a system that can detect weapons well. It's partly, like, a hardware problem. It's partly an AI problem. It's, like, roughly, like, a large-scale – it's, like, a deep tech, deep engineering problem to solve. And my whole team is just all of engineers. They're really good. We actually made a pretty big change of cover. We would already be in market by now. And, like, I pivoted the whole technology system about a year ago. We were building this, like – we basically – I found a way to do everything very cheaply in silicon, in chips.

43:15And reduced the price by, like, 90%, make it much more scalable, make it work better. And we pivoted. The problem was that the fabrication times for designing our own chips and getting them out took about a year. So we just got those chips in, like, a couple months ago. And we're testing them, and they're awesome. Now we need to make more, and there's another six-month lead time to make even more of them. So we're, like, dealing with, like, real silicon, long fabrication of very difficult chips. Lead times now. We'll be out of this at some point. But it's not, like, chips you can go off and buy it, you know, off a shelf.

43:46These are, like, custom-designed cover chips that, like, nobody's really ever designed before. We had a special fabricator in Europe that had to go make them. And it took about a year.

Personal trade-offs and daily workflow

43:56Hey, you are firing on all cylinders right now, professionally, it seems. And I actually would like to know, like, what's the tradeoff for the life that you're living right now? Because you're very optimistic. You seem excited. But what are all the tradeoffs? Yeah, about five years ago, I had, like, a, like, you know, like, having kids in the companies. I had an issue where, like, I think of my life as, like, three pockets. I have, like, work to care deeply about my family. I have, like, three kids. They're pretty young right now. And then I have, like, the other stuff where it's, like, a friend's in town, or you need to go on the annual golf trip, or, like, it's a bachelor party, or, like, you know, it's a wedding in, like, Europe, or whatever it is, like, in this bucket over here.

44:37And I felt like I needed to make a decision on, like, I didn't need to do, like, if I want to do any of these well, I kind of, like, I can't do all three. And what I wanted to do really well is, like, family, and I wanted to do, like, business stuff. I wanted to just, like, I wanted to be, like, A-plus in those areas. And so I basically stopped the third bucket. I don't, like, I don't, like, do anything anymore over here. So, like, I had a friend in town from a college. It was, like, my, like, freshman roommate. And he was, like, I'm in town for 10 days in the Bay Area. I want to meet up. I haven't seen him, like, you know, for a long time.

45:07It was going to be great to get a coffee. And I was just, like, oh, man, I'm going to be real. I don't have any time. I can't meet you. He's, like, I'll make myself available, come to you. I was, like, I literally have no time. Every minute I'm away from one of these two is a minute I'm away from my family or work. And there's almost a limited amount of time I can put in both those buckets. Can I ask you about your workflow? You made a joke. You're, like, I don't use Slack. If you're comfortable, could you just, like, hold up your phone right now? What's on the home screen of your phone? What's your app set up? What do you got? All notifications. Oh, well, you got to open it up. Oh, what's my?

45:37Oh, so you have just tons of text. This is, like, my, those are all, I think, Slacks and texts. I mean, we use Slack. I just, like, can't get through it during the day. I have Hark going through it. And then Hark texts me, hey, it's important. I need to look at it with a link. So what's your setup like? What's your, like, day to day when you're, do you use a laptop at all or are you only on the phone? I use a laptop, yes. Laptop a lot.

45:59Laptop and phone. I would say I use Hark now for all my AI stuff end-to-end, even, like, tracking, like, stuff I'm doing on engineering projects, recruiting, all of it. I do a track. It's in my email. It's in my Slack. What about your to-do list? That's all in Hark. Hark made it all there. So what about before Hark? I would, my to-do list was done in a Google Doc. I had, like, a docket called Replanning, and it would constantly keep updating every week. I would come in on Sundays, usually, and update my plans for the week, and I updated there.

46:29And what about health? Are you doing anything for health? Yeah, I do, like, I've gotten, like, access to some special doctors and things now, where they basically send you through, like, the quarterly blood tests and, like, the whole body scans and, like, the CT scans of the heart, everything. And it's been, honestly, pretty unbelievable. What was unbelievable about it? The amount of data you get back and the thoroughness of all this, like, for instance, like, you know, if you get a, you can get a CT scan of your heart for, like, 100 bucks. I think you can basically prevent heart attacks.

47:00You can get a full-body MRI, and I think you can, like, have early cancer detection. A lot of blood work can find some anomalies that you can go fix and better for your health. So there's, like, maybe, like, a dozen of those. Yeah, but the solution to all those things are probably things you're unwilling to do. It's, like, you're probably willing to eat whole foods, but, like, it's, like, get up, go for walks, exercise. And that was outside of your buckets of focus. Yeah, unfortunately, I haven't been able to have enough time to exercise enough. But, you know, eat right. Like, I've, like, I eat pretty well now.

47:31Yeah. I mean, listen, like, someone's got to give. I can't sit here all day. Yeah. I mean, like, I got to go work. I, you know, I want to go crush these businesses. When you, you wrote in, like, our prep doc, you said, I went all in on my first three startups and I pretty much hit rock bottom every year.

Navigating rock bottom

47:48Can you describe what you mean by rock bottom and what is your method of dealing with rock bottom? What's the conversation you have with yourself or kind of the entrepreneurial strategy you have when you kind of hit those lows? Yeah. I, I basically, almost for, like, 15 years was, like, always running out of money. You know, at Vetteri, we, we had a couple pivots early on. We ended up raising, like, a $500,000 convertible note in 2015.

48:17I, at that point, I think I took out, like, a $50,000 or $100,000 loan. I was not paying myself a salary. I was in New York City. I was, like, so broke. I was in the negative. We raised the convertible note. It did not look great. And I think it was, like, six months later, we launched the marketplace at Vetteri and it just, like, completely took off. And then a year later, we sold for $110 million. And I think that period from 2012 to 2017 was just, like, was, like, I, like, I had, like, basically, like, debt.

48:49Things weren't working. And it's hard. And what's, what's the inner monologue? What do you tell yourself? The inner monologue is, like, this really sucks. It's super painful. I think at that point, you just got to go, like, day for day. You just got to make it, like, day. You got to make, when things get really bad like that, you got to build a punch list and you just got to get through it. Like, there's only a way out is through. So you need to build a punch list and you need to get to day to day. You got to go day to day. You can't go week to week, two days. You can't look at Friday. You got to go every, every, you got to get to the next day. Pile through it. I was training for this ultra marathon and I hate, like, really long distance running.

49:21And I read this story about this guy who kind of helped me. And he was like, just all you got to do is, like, pick something, like, it doesn't matter if it's 100 feet or half a mile in the distance. Even though you have 49 miles left to go in the race, just pick something half a mile away and tell yourself once you get there, then you'll consider quitting. And then you get there and you're like, okay, maybe I have a little bit more. And you pick another thing, just, like, only 200 yards away. You're like, okay, I'll consider quitting when I get to that. No, it was great. I think it's exactly how I thought about it. But it was like, it's like, okay, so then I sold Vetteri and then I was doing Archer.

49:51I was like, oh, man, I just made $110 million. It'd be, like, 12x all the adventure guys. And then I was like, we're going to raise money. I'll be raising money. It'd be fine. And, like, everybody's like, what are you doing? For Archer? Yeah. Everybody's like, what are you doing? We're not going to fund this. What are you talking about?

50:06Everyone says you're stupid and wrong and this is silly. Just go do software. It's coming from a place of conviction. Like, I know I'm right because I've done the work. I understand it. I'm on the floor. Yeah, but the odds are still against you, right? But that's the game. That's when you play this game. It's like, you, like, sign up and, like, 95% of everybody around you will fail. Like, I remember what Vetteri, we, like, we started at the NYU Incubator. I was, like, so excited. We got in, like, one of the, like, the semesters. And there was, like, I think it was, like, 50 companies that were there. We started in Soho. It was great. We had a great time.

50:38The, I think if you look back, like, I think, like, five years later, me and one other guy are the only two people that made greater than zero dollars. One other team. 48 companies went to zero. And I was just, like, holy shit. If you're around this game for long enough, like, everybody dies. And that's everywhere. It's been like that since for 20 years now. I've been watching around. You, like, you see all the TechCrunch stuff and things on X about people raising money and all this. And just, like, over time, that all just kind of fades away. And it's just really brutal.

51:09So I had to, like, you know, I had to, like, I bought a house. And, like, I had to put all the rest of the money into Archer. And then I had, like, a stock lockup. So even while I was coming over to figure, like, the stock was, like, unlocking. I was funding figure with stock from Archer because I had no other cash. Stock was coming down. The stock was just, like, literally, like, a falling knife. Well, at that point, it was just like, I think it went from, like, 10 bucks to, like, two. And it's since, like, gone up a lot. But, like, I had to take a second mortgage on my house to do even fund figure.

51:43We asked you one of your philosophies. And you said, I believe that doing hard things is easier in many ways than doing easier things. Can you explain? Like, everybody's trying to do easy things. When you work on harder things, you have, like, less – generally, like, overall, probably there's, like, first order, like, less competition. You have probably, like, a hard thing probably means, like, it could be a potential, like, really big TAM, really big exit if it works. You have, like, just, like, you know, risk-reward trade. You have folks that probably want to work on hard things. You probably want, like, the best overachievers in the world that kind of wants to work there.

52:15Generally, like, you know, hard things have this, like, binary payoff for investors. They really want to fund those things because they could have, like, a 100x return for the portfolio. And I think there's, like, a nonlinear curve to scaling here of the difficulty here, meaning, like, I think a lot of the hard things are not, like, 10 or 100x harder. I think the hard things sometimes are, like, 2 or 3 or 4x harder, maybe 5x harder, but they're not 100x harder. So, you might have 100x better payoff, but it might be, like, 3 or 4x harder. I'll give you an example in robotics. I think, like, largely building, like, quadruped robots, like, four-legged dog robots versus humanoids, like, probably humanoids are probably, like, 3x harder than that.

52:52They'd be 4. That's it. But, like, there is really no, I don't think there's, like, really a real market for humanoid, like, those dogs. I think it's just, like, a niche thing. I don't think there's a real business for it. And I don't know anybody that really, at this point, like, really wants to spend a lot of time on that. So, like, you do humanoids, it's, like, okay, 3x harder, but it's probably, like, a million times higher payoff. Probably, like, a million X or a billion X. Higher ROI for that. You know what I mean? For investors, for humans that want to work there and get stock and participate in the upside and for everything else. Like, why would you ever, like, want to work on, like, four-legged dogs?

53:24What economic value can, like, a robot dog bring at scale? Like, if you really understand it, like, I think there's, everybody's trying to do the easy work, and it just becomes really difficult. You know, like, look at all the AI slop, like, open-claw harnesses out there today. It's all crap. It's all not good. They're all going to go, I don't think any of them will make it long-term. You might have some consolidation here and there for acquihires and stuff, but, like, that's going to go all the way. Dude, you're talking so many absolutes. Has that not gotten you in trouble ever? I don't know. I mean, mark my words.

53:55Like, you think, like, have you guys even used open-claw since then? No, I don't know how to. Oh, did you use open-claw? I never trusted open-claw to set it up. I mean, I used to use it. I don't use it anymore. It's not very good. Like, the wave's over. I don't know. I'm just trying to say, like, I think it's, like, I think the most important thing you can do as a founder is to think through what you're going to actually go do. Because you're going to spend the next 10, 15 years doing it. And it'll, like, it'll map the whole course, the probability course. It's, like, a probability-weighted decision of, like, our probability of, like, potential outcomes.

54:29Well, but you're talking about a very particular game. Like, for example, as you've said, you're like, we're going to be a trillion-dollar company or we're going to go bankrupt. Like, it's binary. Most business is not binary. You're, you know, you're playing the game where binary is the outcome, and that's what you like. But it's not like that for a lot of people. Like, for a lot of people, if they can build a really cool $10 million-a-year business, that's a massive home run. Is it? Like, if, like, things do that well, and you look back when you're 70 or 80, would you have asked the same person, hey, you built a really cool $5 or $10 million business.

55:03You did it for 30 years. You didn't do anything else. You didn't try anything else while you were doing it. You just, like, work on that business. Would you have gone back 30 years ago and tried to take a bigger swing? Like, would you have taken a different swing than Vetteri? Vetteri was like that. Vetteri is my bridge. I sat inside of Vetteri for, like, seven years. Like, we literally built, like, a marketing automation tool for us internally. And then, like, a year later, I was like, oh, man, look at this. It's outreach.io. And it was, like, a billion-dollar company. We built that internally a year or two prior. And then, like, watching all this different stuff happen, I was like, man, we, like, we actually did some of this work internally.

55:35It's, like, value less than some other groups out there. Like, this whole decision of, like, what you spend time on is, like, super critical for startups. Assuming, like, there's a – and I do think startups are, like – I think it is kind of binary. Even guys that get the $10 million, there's probably, like, another 90% of those folks that just didn't make it when they're out there trying. So I think it's just – I think it's just hard. And I think, dude, kudos to guys getting, like, $5 or $10 million in business. That's hard. Especially doing that if we maybe look a little bit of capital or no capital coming in. Hey, let me ask you real quick about your other stuff you've seen.

56:07So I'm sure because you're doing really interesting work, you meet other founders that are doing interesting things in, you know, unrelated spaces. So not humanoid robots, but equally cool, interesting, peek at the future. I think you've probably seen more of the future than us and definitely more than most of the listeners. Can you give us anything that you've seen or heard or read about, a founder you've met that's doing something that's like, oh, yeah, you guys realize, right, the future is actually going to look like this. And we're just – you know, not – it's not evenly distributed for all the rest of us yet.

56:38I like looking at – I like trying to think through this problem of what was the world going to look like in 30 years? Where is there – where everything's headed? I think we have an energy problem, like not an energy consumption, like a generation problem. So how we – well, maybe both, but like ultimately how do we like generate more energy as like a species? I think there's like a – there's like a secular trend here that you want to go ride and really help. And I think there's a lot of work done correlating this to like standards of living for humans.

57:09So I think there's a lot of work here. Like what is the next generation? Is it solar or is it wind? Is it nuclear? Like and then there's a bunch of different traits inside of here for fusion and fission and the rest. Like I think it's a really exciting area. I think it would take a long time, but you need like – you need like really great entrepreneurs like there solving that stuff. I think AI is just going to dominate a lot of stuff in the next 10 or 20 years for all of us here. I think it's going to be like 100 – we all live through the internet. Like I think it's going to be 100 times bigger than the internet. I think it's going to be so, so big. It's going to – it's going to – AI is going to eat the whole internet.

57:40It's going to eat it all up. And I think it's going to be an extremely like large trend, both physically and digitally. What – are there any products that you're looking at or companies that you're looking at now that are not already the mainstream that you think are good examples of what you're talking about? I mean, we're working on this stuff at HardConfigure. It's unclear. We're still in this spot where like it's really not clear who's going to do well here in this stuff. We're in this like foggy area for a lot of these stuff of like there's been no breakout here. There's been early wins and early breakouts, but there's like a next leg here that we're going to go through.

58:13And we're like – we're in it now. I think we'll know more in the next year or two what that really looks like. But I mean, I've used like every AI device out there. Haven't been super thrilled. I don't know if you guys are seeing stuff in the market for these type of things. But like I haven't like, you know, been like, man, this is like a crazy great product. I like the small stuff. Like Whisper, Whisper Flow has like pretty meaningfully changed how I communicate. Yeah. That's been pretty cool. I think that's been my big standout the last six months.

Operators and mentors

58:44What about – last question. What about people who inspire you? Because you're – you have very high standards. Who or what type of entrepreneur or who are they, dead or alive, that you lay in bed and you're like, how would this person react to this situation? Or how do I have an attribute similar to the attribute this person has? I think I really admire the folks that are like fully dedicated in their craft. You really watch the Michael Jordan documentary. He's just like – he's just like, I just want to be like the best in the world at this. I think for like first – startups have that same thing too.

59:16And I think first and foremost, like, you know, what I can read. I'd never met Steve Jobs. But like, my Lord. Like stories I've heard and everything else. The guy was just like an unbelievable operator and product-led founder. I've also gotten to know Jeff Bass was pretty well. He invested in Figure and he's been here a lot of times. And I think – I think Jeff's has been a really good soundboard for a lot of things we've gone through. I had Jensen in here last week again. Like, we are fairly close. And I think Jensen's just an unbelievable operator as well. He's very hands-on. He has a very unique way of managing NVIDIA and his organization the last 30 years.

59:49And it's – I think he's like – I think he's done a lot of really good things. What advice did Jeff give you that was meaningful? Jeff said the last time he was here, he's like, listen, you're at a really interesting period because, like, you've figured out how to do this somehow. And the next year or two, you're either going to figure out how to break through and really get this, like, working in a bigger way. Or you won't. And, like, this is, like, you're – it's game time for you now.

And you've got to just get wired in and, like, figure out how to break it out and make this thing work and scale it. And you're – it's a really interesting point. I don't know how you got here and I don't know why you got here, but you're here. And you need to figure out how to, like, your next – you know, you're on the big field now and your next big push is going to, like, make or break it.

1:00:24So, I think he's largely right. Like, I think we're, like – we got robots now doing this stuff autonomously with AI models, which is crazy. I think four years ago you've been, like – I've been, like, no way. Like, no way you could – Dude, four years ago I came to your office and you just had a knee working. And I was, like, oh, that's a knee. That's cool. All it was was a knee. You had, like – there was five engineers. You're, like, this guy just got done building the Tesla X or Cybertruck or something. This guy did this amazing thing. This guy cured cancer. Look how the knee moves and the ankle has dorsal flexion.

1:00:55And we were just sitting around looking at this knee. And that was, like, the coolest thing. I don't know. I mean, it's, like – and then now we have, like, AI that's working on a humanoid robot. We're taking in cameras. It's doing inference on board. It's operating where all the joints go. You know, it's unbelievable. It's unbelievable. And it's crazy it works. And, you know, the next leg up is just, like, making that work at higher scale. So, I don't know. It's been great. I think it's – I don't know. I think those are kind of some, like, I think really good folks to look up to that really, like, love their craft deeply and really care.

1:01:25Well, Brett, I think it's time for you to get back to work, my friend. Great. Thanks, guys. It was good to see you again. Thank you so much, dude. All right. That's it. That's the pod. I feel like I can rule the world. I know I could be what I want to. I put my all in it like no days off. On the road, let's travel, never looking back. All right. Let's take a quick break. I want to tell you about Marketing School. It is a podcast that is part of the HubSpot Podcast Network, and it is run by Neil Patel and Eric Su. And these guys are both marketers who are running businesses. And so, if you want real-world tactics from practitioners who are actually out there in the

1:01:58field doing it, this is the podcast for you. Check it out wherever you get your podcasts. Thank you.