$39B founder says his company could 100x in 5 years
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's that make you feel?
I don't care about that at all. 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.
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 Vettery, which you sold for over $100 million. Then 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. 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 in the billions of dollars, and you seem worn out.
Feeling great. He was like, busy, man.
So you've been on, this is your third time on. I think you, I think you've been on one time each year the last 3 years. You 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's 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's that make you feel?
I don't care about that at all. 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, 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 wanna be the best, I wanna conquer.
I think maybe the way I would characterize is like, we're just the, like, we're just now, like these companies of mine are just now hitting an inflection point and they're really early. Like they can be like really big. So if it works, this will like 100x. 1,000x from here. So most of my energy is like, how do I make sure that works? There is no flatline 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 5 years' time, it's either gonna be a very big thing or very bad. And so all my energy's going into making this like 1,000 or 1 million x from where we're at here. And so it's like the pressure's on to like really just deliver.
Where are you now? What's the outlook now for the next 5 years then? I think Last time you were on 3 years ago, we said that— I think I said it, uh, 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 5 years? What'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 100 times 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?
We 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, Figure's going to be like, I think Figure's going to be like the max ceiling of AGI, of 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's going to have this AI pairing. It's going to have ultimately maybe your own AI weights, your own memories, maybe your own hardware. It's going to be really close. And fundamental to that thesis was you got to figure out how to get AI to use computers general purpose. You would never hire an assistant that couldn't use a computer. So you got to be able to give things out to it that can do everything you can do. financial models, book flights, order DoorDash, whatever you need to do. It needs to be able to do it all autonomously, but only 1 in 1,000 websites have APIs. So in most, like most computer use globally is on the internet and browser. My inclination within 2 or 3 years, you'd have a system that you'd be able to talk to and say, go do this or do that. And it'd be able to like go off, go online and like maybe like use the internet really well, like 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 general purpose around a computer is you can't rely on API or MCP. You have to figure out how to navigate like a human can. Now at Hark, 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 I think Elon's got MacroHard and ChatGPT had their computer use thing. Everybody's doing it. You guys feel like you've cracked something. What'd you guys do differently?
Okay, there's a couple things we did a little differently. First is everybody's tackling this from using APIs and MCPs. The reason why OpenCLaw got so great, it was like it couldn't use the browser, it couldn't go on and use DoorDash end to end because DoorDash has no consumer API. So we tried to figure out 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, uh, and then you would need to give it ability to like look at a screen and use like, move the, move the cursor and use the keyboard.
Yeah, 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 and 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, uh, MTP. It was trying to use the internet.
Yeah, I don't know if I, yeah, it's gotta work well. I mean, that's the whole point. But like if it goes somewhere, fumbles on the internet, it's like the whole point is like it's not.
So, 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 re— we have a reinforcement learning process that we think is maybe nobody else in the world has done.
Well, get, let's get some context behind this because, okay, so Figure, that is shockingly easy to understand. Humanoid robots and that business is gonna 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 I'm 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 100 guys who just moved here from China who had like the greatest backgrounds ever. And 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. Yeah.
Well, I mean, that's kind of all we had at the 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. And 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.
I strongly believe AI will head in 2 directions, and then at some point, maybe even head together. The first is we'll have AI out in the physical world that will do everything in the environment for you, like laundry, dishes, cooking, run the supply chain end-to-end, be in healthcare. The vessel for that's a humanoid robot. It's just a human form. And it'll just go out and do, like, you want one piece of hardware that can, the hardware's capable of doing everything, and you put smart AI into it and it'll go off and do everything in the world. That's what Figure's working on. Separately than that, there's going to be this really close digital AI-to-human symbiosis that forms. You're going to have this very special thing that you can 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 you, like a superhuman assistant. It'll be like, maybe the closest thing is like Jarvis from Iron Man. And 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 a long layover, or you're on a flight with maybe say a short layover and you miss it, it'll already have backup plans, already help you figure that out. It'll just be something with you everywhere you go. We don't have that. We have really good coding agents. We have really good chatbots. But we don't have something that can go off and be my Jarvis. In order to get there, we need to work on the model side. It's gotta be just better than text chat. It's gotta be able to use computers, have basically near-perfect memory, be able to talk to you just like a human would back and forth. And we have to have vision in the system. It has to be able to look at the world and understand what you're seeing with it. And I think secondly, you need to fix the interface to AI. You have like AI over here and a human, and you have like an old hardware system in between, like a, call it 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 gonna go away. They're gonna be new ones. They're gonna be all AI computers and phones. And systems, and they're going to be great. They're going to be all real time. You can always access them any you want. They'll always be like understanding what's happening. They'll 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. Team's like, you know, maybe like 80 or 90 now. The guy that leads hardware design, Abs, previously designed for last several generations of iPhone, MacBook, MacBook Pro. Like, 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 encoding is just not enough for us to like really have a, like a, like a real AGI feeling with AI. So we're working on that. We have our first AI, we did our first research preview of our computer-using agent that we came out last week. I think we were like top on some of like the leading, like, you know, browser computer use benchmarks in the world. And 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 at 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 Jony Ive, you know, got his shop got acquired by OpenAI, and you've seen the videos of the puck and then the, 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, what's the story of that? And then, yeah, 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 we, what sits in the middle is devices that could possibly reach a billion units a year in the world. The only kind of things that we have like that in the world right now are computers and phones that kind of meet that. 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're like, 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 gotta take down the computer and the phone. There's no way around that. So you have to rebuild a new computer or 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 helps with a family of devices that are not a billion units a year, but important for the ecosystem.
My understanding, right, you're, you're kind of saying the next, the next device, it might be like a phone, it's just gonna be an AI-native-first phone, right? You're not gonna try to change the form factor.
No, I'm not saying that at all. You're gonna wanna like really radically rethink everything. Uh, the, like 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, like glasses and pendants and wearables and things. They're not, 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. Like if you, I, I can't even like figure out how to use it. It doesn't have its own network. It piggybacks on the iPhone network. It 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 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 preexisting norms? Because when you're talking about, I literally can't imagine at all what you're talking about.
Let's get down to the substrate level here. First order, what has changed? What's changed is we have a new type of computer, which is the, I think of AI as a new type of computer, new type of automation that's here. That automation can do a few things that are like, when we were designing this, we wanted to design around key principles that could be like 10x better. If it's like 1 or 2 times better than your phone or computer, you're not going to use it. It's got to be like literally 10x better. What are things now that deep learning brings that are like 10x better? There's a few of them. One is AI can basically now think and use computers and systems for you just like a human can. It can talk to you, it can see, it has visual understanding, it has real-time speech-to-speech, it can use computers and systems for you as close to as fast or around as fast of 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 almost human-like in capabilities. It also can have memory, meaning you can put memory into it and it won't forget anything. It'll be near perfect over time. So you have a system that's almost like a human in a box that has all the same affordances a human has. And it's almost like the ability of, almost like if you could bring a little human around with a computer on your shoulder everywhere you went, That'd be insane. It was only for Sam though. Only Sam could see it and only Sam could talk to you and only was there to help with Sam. And that was 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 able to do anything you do on a computer.
Okay, so your first step with your team is just like, let's just get rid of any constraint ever. What would be the coolest magical thing if we had a little guy on our shoulder that was AI, all-knowing and could see and hear everything we see and hear and then give advice to us?
Like, what is the thing that's gonna bring, that's gonna fundamentally reshape all this?
Okay.
And then from there, like, we gotta design around that system. The competitive advantages here are that it is human-like in capabilities and it has almost near-perfect memory. It can go back and reference over time. My phone doesn't have that. Like, I put a contact in my phone like last week and 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, it would even predict probably, Brett, you want coffee today? And I'd be like, ah, 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 don't stick like a monkey on my phone for the next like 3 minutes, like trying to do checkout DoorDash.
It's almost like the phone is like a tool in the— 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 'em, hey, can you fix that window? And let's go fix the window. You don't have to pick up the hammer and start figuring out how to use
it. Start, 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 that 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 this and it does well over here. So it's not as easy as drawing those parallels. It's pretty quite radical. But we rapidly prototype all this. We have a fabrication facility that does this stuff. We have a whole design studio where we work on this. I use this stuff over the coming weeks and months. I'll either carry it around with me, wear it, whatever we end up doing it, and we'll down-selection. We had one of the biggest telecom CEOs in the world here that actually helped with the work with Steve Jobs on iPhone 1. And he was here 2 weeks ago and he'd just come from meeting Tim Cook. Tim Cook's on his way out of Apple, but he was over there at Apple and came over here and 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 the big guys.
Well, is it true to say that with Archer, Figure, and Hark, the hard problem seems like, can I just mass produce this?
The hard problem is not that. We believe now the most important constraint to really solve is building a really intelligent robot system we can put out to the world. There's a bunch of robots you can go buy now. You can buy some from China and you get 'em and they're complete crap. They can't do anything. You can enjoy sticking around. That's all you can do. And you 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 early when I bought a DJI drone years ago and I was playing around with it, and then a day later I was like, what do I do with this thing? Yeah. And it was hard to set up. It didn't really work well, whatever. It's just like, it flew into a bunch of trees. It just didn't work. I was like, what am I doing with this thing? Robots are like that now, where we can go manufacture a ton of 'em, but if they're not really smart, it's not really going to be that helpful. We're trying to crack the true human-level intelligence figure. We really want to tackle, how do we make it so I can put it into any home, it can do every job I'd want it to do? That's what we're working on. We think that's the largest, think about the largest gap in the schedule of what we need to go solve for. It's that. 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, 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 'em, we make a billion phones almost like, you know, pseudo by hand in the world and with some automation. But cars is a different story. Cars, like, you will die trying to manufacture cars. Yeah. There's like a lot of companies, like you just like, it's so, and having seen like, you know, BMW's 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, you're dead. It is. Your whole line's down. And so it's just like this like huge giant robot you're building that's building the car. And 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 1,000th, uh, EVT robot for Figure 3 last week or week before that.
When you say you made 1,000, those are 1,000 that go to customers like BMW or you're making prototypes internally? What does that mean?
We have like 2 large customers. We have us as like an engineering and AI research org that needs robots here. Every engineer needs a robot. We need, every lab needs robots. We need to do tons of testing. There's just a lot of work we need to go do internally. We call maybe engineering fleet we need to go to. And the second one is go to customers. So we have it going to both right now. We've actually shipped out robots to our 3rd customer. This, this, this week.
When they go to customers, what do they do? What, what, what, what can the robot do? What, you know, maybe can't it do at, at this point?
Uh, 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 we'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 be, gimme an example of one of the jobs. Is it just like a whole—
Yeah, that's, that's an example of like a, a, like a very, very close to like one of the, one of the works we do.
Is that customer like, oh, this is awesome because I can't find the labor to do this. Uh, it's too expensive to pay 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 2 years from now, that cost curve is gonna work and it will be faster, cheaper, you know, whatever.
No, no, no, no. It's like, uh, it's the pitch is 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 100% turnover per year. It's really expensive to find talent. We have like a, just a large talent shortfall. The talent's 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 on this. Like, you gotta think like a robot can do like multiple shifts per day, work 7 days a week. Like we can like have a lot of uptime. The task you saw like on the, on the, on the like logistics line that we should livestream was actually a real use case for one of our customers. That needs to be done at 3 seconds a package, and it initially needs to be done 5 hours a day. Like, I think it's like 5 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 already have an ROI. And we're now in the early stages of like getting these out to these customers and scaling it up. 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 is usually you, and it's due tomorrow. Well, the Breeze Assistant from HubSpot can help. It works right inside HubSpot. You can draft 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 versus fiction? Because one of the weird things is, as an enthusiast or a layperson who's, who's excited about this future, You can't really, it's like really expensive or hard to test this, right? So I'll see like a, a Chinese robot and it's $20 grand if I wanna 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 gonna, we're gonna build a million of these things in the next year. We're gonna ship 'em. Uh, then you get like 1X and they're showing their hand and they're like, look at our hand. Look at this. This is the best hand you've ever seen. Yeah. And then there's this service in San Francisco where they'll send a robot in to clean your apartment. Yeah. 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.
Yeah, 100%. So I think a few things. One is the amount of like noise in the market for a signal is just like, it's like, like you mentioned, it's like, it's like it's, 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 to be able to ship robots autonomously at scale in useful work environments. Like they can like, you know, cook you dinner, like clean your dishes, like make your bed, like run the supply chain end to end, work in healthcare, build a building, like do logistics, like that sort of stuff. That stuff requires fundamentally onboard AI that you can run so you can do 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 something in your hands and move stuff through the world. You know what I mean? It's like, you gotta do stuff economically, you gotta move electrons around. So I think at a high level, what we care about is not the best robot that's doing backflips and running the fastest mile or dancing or in a parade or running outside in the woods. 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 BMW factory.
Like,
I could have been great back in high school, but I blew it. Now I'm working at a BMW factory.
I've made jokes with you before where I was like, you started with Vettery, which was just like a job recruitment thing. Now you're on these world-changing things. And you were like, well, Vettery actually is world-changing and here's why. And you gave this pitch. It was very good. You're very good at pitching. You're very good at raising money. You're very good at 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. Archers was up for a long time. I posted about it. Like, I think like deep down, I really want to find folks that really care and are obsessed. And I'm like, I think most of my time is not— I know you wanna 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, 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?
I technically assess 'em. All of 'em? Yeah.
To do that, does that mean you need to be as good or better than them technically to be able to assess somebody?
Out of 100 candidates who sound good, how many, like, their resume looks good, the recruiter thinks they're good, out of 100 candidates, how many 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 Figure. You have to be able to build like actuators from scratch. There's bearings and motors and, you know, we have a gearbox, we 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 6 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 more liquid than you, and the offers are, I think they're like tens of millions of dollars a year, right?
The AI side is certainly like that. The AI side has gotten, and it's mostly all driven from Meta. Like at Hark, I've never seen, I thought maybe Meta was paying these people for like a year ago and 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 xAI. xAI completely blew up. Everybody just left about 6 months ago. It was just like MacroHark got fully disbanded. There was basically a bunch of stuff that happened. We interviewed a pretty senior guy on the AI infra side. It was great. I think I gave him like a really good package, like of Series A stock at Hark. And it was, I don't know, I don't know, $15, $20 million of stock.
Over 4 years?
Over, we do 5, like for my companies in the early days, and we transition to 4 a little bit later. So we're still at 5. And, you know, I was like, I think we're like a, 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. Maybe 10x one more time, you have a few billion dollars. And I think we can do it. I think we have to, obviously it's going to be hard, but I think we can do it. And he got an offer to go to Meta for $36 million, 4 years of our shoes. And he was just like, it's kind of guaranteed cash. I go there and I have to weigh this, maybe $200 million at Hark or $20 million or maybe $36 for sure at Meta. And he left and went to Meta and they've been doing that. Every candidate we speak to is making some absurd absurd thing. They just haven't stopped. They've just been at it since like for like a year, year and a half. They've been buying talent. They'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 recipes that transformers do well in, around MOE or whatever you're going to look at, I think it's really hard to find. It'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 are even some of the less than senior, the 20-somethings, the younger 30-somethings, are they still getting 8 figures a year?
No, the, like the junior guys, like the guys in like their 20s, you know, like the, like late, late 20s or something, they're getting like a, they're making like a few million total. So they're making like $200,000, $250,000 in base. They're making like another million or whatever, like in, in a year in like our shoes every year. And so they're gonna pay like a million to like, you know, or like $750,000 to like $2 million or so range per year. And that's been driven up by Meta. But then all the other labs have 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've done the same thing if I was Mark. I would've bought my way into the race. And I think he's doing that now. I don't think I would've done that. I want to understand it and I want to first order find the right folks that really care deeply about this and not hire mercenaries. And so he hired a bunch of mercenaries. They're just purely money driven. They came over there. Nobody wants to go to Meta. They just said they're going there because they're getting paid a guaranteed RSU package by sitting around. And what's happening is you don't need 1,000 people or 500 or 300 to design AI models. You can make 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, uh, 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. I mean, I think Facebook is probably gonna— Meta's gonna go down as 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 they're doing, it's just like, it's just 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 gotta send it to us.
Yeah. Can we use it?
Well, yeah, we get you guys early on that. Yeah, for sure.
It's like research preview. There's like 500 PhDs and then me and Sam.
Yeah, exactly. No, well, like, like every other platform is—
I can answer that. Yeah, no problem. But like, I, I, I think what I'm trying to say is like every week there's like 5 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.
On January 1st of this year, you've made 4 predictions for the year. I wanna check in and see how, how you think they're going. First one. Uh, number 1, 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?
You have 4 months.
Yeah, I'm like, I see every day like what we're doing. Like we're on track. The hard part here is, um, we already do pixels to torques. It just means like we're just taking camera feeds and we output like where to put the motor, like put the, you know, we wanna put a, we, we wanna 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. Um, getting into a new house has never seen you do work. That's the hard part of this problem. Um, we're working on that. I'm working on that every day. It's where I spend about 3, 4 hours a day, every single day, 7 days a week on this problem.
So if a Figure robot showed up in my house, what would it, uh, what's the bottleneck right now? Like it wouldn't know what to do, it wouldn't know where to go, it 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 types of scenarios it's never seen before, it's like the model's 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. Right. And we just don't have enough of that data out there. It's not on the internet, so you have to go out and collect it. So what we need to know is 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 gonna build like the best warehouse worker 'cause there's already 20 million of those in the world and that represents this much billions. 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 wanna do, companies need done, they're ready to pay, and it's not like, my home where there's all these other sensitivities? Why do you guys care about that right now?
Uh, we didn't care about it in the past. We, like, when we first launched, we're like, we're gonna basically do the commercial side to pay for the home long-term. And, uh, 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 variability. We're in like a little worksite, just work 24/7, just so much simpler. Uh, 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 wanna solve that problem. We wanna solve a robot that can go into any environment just through language and do work. We wanna be the first to do that. You can probably do that with 100 robots and a 50-person team so that, 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 gonna solve that. I think we'll be the first. We call it like solving general robotics.
In I, Robot, don't they attack the humans? I don't remember this movie very well.
Yeah, don't worry about it.
Okay, not that part of I,
Robot. Who could win it? Who could win in the— who can win in a fight right now? Can a human still win?
Yeah, a human can still win.
Okay. Uh, what are the other predictions?
Other prediction. Um, 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. Sam, 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 of what you use.
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 and I was like, Claude, why am I so indecisive? And then my wife was like, like,
she
was like, I'm like, damn, dude, she can hear everything I'm talking to Claude about now. Yeah, no, I, I, I, I talk all the time, but it's embarrassing.
Yeah, even speech still sucks. It's still not great. It's almost like you set up, you have to go there, you have to turn it on. It doesn't really remember what you just talked to it about. It can't do tool calling and computer use very well. It's just limited in these, 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 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. I'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 that.
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 7 or 8 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. I 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 fistfight anybody who disagrees with me on that. Go to mercury.com/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 3rd and 4th?
You had over the past 10 years, school shootings have increased by 10x. In 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 end of year. I don't know if we'll be at a K-12 school, so we might miss this one by a quarter.
Do you have a separate CEO running that one or you're the CEO also of that company?
I have a chief engineer from JPL at NASA that's really good and it's mostly a pure engineering project. There's really not much to do on the business side. We have some supply chain stuff and other things, but most of it's just purely, can you build a system that can detect weapons well? It's partly a hardware problem, it's partly an AI problem. It's roughly a large-scale, it's like a deep tech, deep engineering problem to solve. And my whole team is just all of engineering. They're really good. We actually made a pretty big change of COVID We would already be in market by now. And I 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 and chips and reduce 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 'em out took about a year. So we just got those chips in like a couple months ago and we're testing 'em and they're awesome. Now we need to make more and there's another 6-month lead time to make even more of 'em. So it's just like we're like dealing with like real silicon, long fabrication of very difficult chips lead times now. We'll be outta this at some point, but it's not like chips you can go off and buy it, you know, off a shelf. These are like custom designed cover chips. that like nobody's really ever designed before that we had a special fabricator in Europe that had to go make 'em. And it took about a year.
Hey, you are firing on all cylinders right now professionally, it seems. And I'm, and I'm, and I, I actually would like to know, like, what's the trade-off for the life that you're living right now? Because you're very optimistic, you seem excited, but what are all the trade-offs?
Yeah, about 5 years ago I had like a, like, you know, like having kids and the companies. I had an issue where like, I think of my life as like 3 pockets. I have like work, which I care deeply about, my family, I have like 3 kids, they're pretty young right now. And then I have like the, call it 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. And I felt like I needed to make a decision on like, I didn't need to do like, if I wanna do any of these well, I kind of like, I can't do all 3. 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 want to be like A+ 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 a friend, 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. I'm in the Bay Area. I want to meet up. I haven't seen him like, you know, for a long time. This would 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. 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 either with 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 your, what's on the home screen of your phone? What, what, what's your app setup? What do you got?
All notifications.
Oh, well you gotta open it up.
Oh, what's my—
Oh, so you have just tons of texts.
This is like my, those are all, I think, Slacks and texts. I mean, I use Slack, I just can't get through it during the day. I have Hark going through it and then they, 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. Laptop and phone. I would say I use Hark now for all my AI stuff end-to-end. Even like tracking stuff I'm doing on engineering projects, recruiting, all of it. I do it, it tracks, it's in my email, it's in my Slack.
What about your to-do lists?
I would, my to-do list was done in a Google Doc. I had like a doc 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.
And what about health? Are you doing anything for health?
Yeah, I do. Like, I've, 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, like for instance, like, like, you know, if you get a— you can get a CT scan of your heart for like $100. I think you can basically prevent heart attacks. You can get a full body MRI, and I think you can like have early cancer detection. A lot of bloodwork 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, I've like, I eat pretty well now. Yeah. I mean, listen, like something's gotta give. I can't sit here all day eating and like, I gotta go work. I love, I like, you know, I wanna go crush these businesses.
When you, you wrote in like our prep doc, you said, I went all in on my first 3 startups and I pretty much hit rock bottom every year. Can 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, 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 Vettery, we, we had a couple of pivots early on. We ended up raising like a $500,000 convertible note. In 2015, I, 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, did not look great. And I think it was like 6 months later, we launched the marketplace at Vettery 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, things 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. Super painful. I think at that point you just gotta go like day for day. You just gotta make it like day. You gotta make, when things get really bad like that, you gotta build a punch list and you just gotta get through it. Like there's only way out is through. So you need to build a punch list. And you need to get to day to day. You gotta go day to day. You can't go week to week, 2 days, can't look at Friday. You look, you gotta go every, every, you gotta get to the next day, pile through it.
I was training for this ultramarathon and I hate like really long distance running. And I read this story about this guy who kind of helped me and he was like, just all you gotta 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 like, great. I think it's exactly how I thought about it. But it was like, it's like, okay, so then I sold Vettery and then I was like doing Archer. I was like, oh man, I just made $110 million. We like 12x'd all the venture guys. And then I was like, we're gonna raise money. It'd be, I'll be raising money. It'd be fine. And like everybody's like, what do we do? What are you doing?
Yeah. Everybody's like, what are you doing? We're not gonna fund this. What are you talking about?
How do you fight that inner monologue where everyone says you're stupid and wrong and this is silly? Just go do software.
It's coming from a place of conviction. Like, I, I know I'm right because I've been in the— 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, that's, that, 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 Vettery, we started at the NYU incubator. I was so excited. We got in one of the semesters and there was, I think it was like 50 companies that were there. We started in SoHo. It was great. We had a great time. I think if you look back, I think like 5 years later, me and one other guy are the only 2 people that made greater than $0 on our team. 48 companies went to zero. Wow. And I was just like, holy shit, if you're around this game for long enough, everybody dies. And that's everywhere. It's been like that since for 20 years now. I've been watching around, you see all the TechCrunch stuff and things on X about people raising money and all this. And just over time, that all just kind of fades away and it's just really brutal. So I had to, I bought a house and I had to put all the rest of the money into Archer, and then I had a stock lockup. So even while I was coming over to Figure, like the stock was unlocking, I was funding Figure with stock from Archer 'cause I had no other cash. Stock was coming down. The stock was just like literally like a falling knife. At that point, it was just like, I think it went from like $10 to like $2 and it's since like gone up a lot. But like I had to take a second mortgage on my house to even fund Figure.
We asked you one of your philosophies and you said, I believe that doing hard things is easier in many ways than doing easier things. Could you explain?
Everybody's trying to do easy things. When you work on harder things, you have less, generally overall, probably there's first order, less competition. You have probably a hard thing probably means it could be a potential really big TAM, really big exit if it works. You have this risk-reward trade. You have folks that probably want to work on hard things, probably want the best overachievers in the world that wants to work there. Generally, hard things have this binary payoff for investors. They really want to fund those things because it could have a 100x return for the portfolio. And I think there's a nonlinear curve to scaling here, of the difficulty here, meaning I think a lot of the hard things are not 10 or 100 times harder. I think the hard things sometimes are 2 or 3 or 4 times harder, maybe 5 times harder, but they're not 100 times harder. So you might have 100 times better payoff, but it might be 3 or 4 times harder. I'll give you an example in robotics. I think largely building quadruped robots, like 4-legged dog robots versus humanoids, probably humanoids are probably 3 times harder than that, maybe 4. That's it. But there's really no, I don't think there's really a real market for humanoid, like those dogs. I think it's just 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 really wants to spend a lot of time on that. So you do humanoids, it's like, okay, 3 times harder, but it's probably 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, get stock and participate in upside and for everything else. Like, why would you ever like want to work on like 4-legged dogs? What economic value can 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 gonna go. I, I don't think any of 'em will make it long-term. You might have some consolidation here and there for acquihires and stuff, but like, that's gonna go all the way.
Dude, you talk in so many absolutes. Has that not gotten you in trouble ever?
I don't know. I mean, mark my words. Like, like you think, like, have you guys even used OpenClaw since then?
No, I don't know how to.
Do you use OpenClaw?
I, I never, I never trusted OpenClaw to set it up. I was not gonna use it.
Yeah, 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, I think the most important thing you can do as a founder is to think through what you're gonna actually go do, 'cause you're gonna spend the next 10, 15 years doing it. And it'll map the whole course, the probability course. It's like a probability weighted decision of like, or probability of like potential outcomes.
Well, but you're talking about a very particular game. Like for example, as you've said, you're like, we're gonna be a trillion dollar company or we're gonna 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 they can do that well and you look back when you're 70 or 80, would they, would you ask the same person, hey, you built a really cool $5 or $10 million business, you 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 try to take a bigger swing.
Vettery is my bridge. I sat inside of Vettery for like 7 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 and I was like, man, we like, we actually did some of this work internally. It'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 to $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 hard. And I think, dude, kudos to guys getting like $5 or $10 million in business. That's hard. Especially doing that if maybe like a little bit of capital or no capital. Coming in.
Hey, let me ask you real quick about your, your other stuff you've seen. So 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, 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, 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.
I 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 everything headed? I think we have an energy problem, like, not an energy consumption, like a generation problem. So how we— well, I mean both, but like, ultimately, how do we like generate more energy? as a species. I think there's 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 standards of living for humans. So I think there's a lot of work here on what is the next generation? Is it solar? Is it wind? Is it nuclear? And then there's a bunch of different trades inside of here for fusion and fission and the rest. I think it's a really exciting area. I think it would take a long time, but you need really great entrepreneurs 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 lived 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— AI is going to eat the whole internet. It's going to eat it all up. And I think it's going to be an extremely large trend, both physically and digitally.
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 hard configure. 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. And we're in this like foggy area for a lot of these stuff of like, there's no, 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. And 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, I 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 6 months. What 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, for startups have that same thing too. And I think first and foremost, like, you know, what I can read, I 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 Bezos pretty well. He invested in Figure and he's been here a lot of times. And I think Jeff 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, has a very unique way of managing Nvidia and his organization last 30 years. And 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 figured out how to do this somehow. In the next year or 2, 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 your— it's game time for you now. And you gotta just get wired in and like figure out how to break out and make this thing work and scale it. And you're at 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 gonna like make or break it. So, which 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 4 years ago you'd been like, I'd been like, no way. Like no way you could do
that. Dude, 4 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 5 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 dorsiflexion. And we were just sitting around looking at this knee and that was like the coolest thing.
I 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 outputting where all the joints go. You know, 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 there'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.
Well, 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 Siu. 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 field doing it, this is the podcast for you. Check it out wherever you get your podcasts.