My First Million - $39B founder says his company could 100x in 5 years

Episode Date: August 14, 2026

Sam's database on how long it takes to become a millionaire: https://clickhubspot.com/elft Episode 851: Sam Parr ( https://x.com/theSamParr ) and Shaan Puri ( https://x.com/ShaanVP ) talk to Brett Adcock ( https://x.com/adcock_brett ), the founder of Figure.ai. — Show Notes: (0:00) Intro (3:29) Hark, a human in a box (8:00) zero constraints (17:32) rapid prototyping (21:38) What are the robots doing? (23:45) what about the hype is real? (27:54) finding the best people (30:14) $20M dollar salaries for engineers (32:03) How Zuck is buying his way into AI (35:44) Auditing Brett's 2026 predictions (42:33) reducing your buckets (44:00) show us your home screen (46:17) hitting rock bottom 3x (57:22) who inspires Brett — Links: • Hark - https://hark.com/  • Cover - https://www.cover.ai/  • Figure - https://www.figure.ai/  — Check Out Sam's Stuff: • Hampton (joinhampton.com): My community for founders. Average member does $25m/year. Many of the guests are members. Get after it...apply: http://joinhampton.com/mfm — Check Out Shaan's Stuff: • Shaan's weekly email - https://www.shaanpuri.com  • Visit https://www.somewhere.com/mfm to hire worldwide talent like Shaan and get $500 off for being an MFM listener. Hire developers, assistants, marketing pros, sales teams and more for 80% less than US equivalents. • Mercury - Shaan uses Mercury across all of his companies. you can too: http://mercury.com/  Mercury is a fintech company, not an FDIC-insured bank. Banking services provided by Choice Financial Group, Column, N.A., Members FDIC • I run all my newsletters on Beehiiv and you should too + we're giving away $10k to our favorite newsletter, check it out: beehiiv.com/mfm-challenge My First Million is a HubSpot Original Podcast // Brought to you by HubSpot Media // Production by Arie Desormeaux // Editing by Ezra Bakker Trupiano /

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? I don't care about that. You have like zero sheds about that. I feel like I can rule the world. I know I could be what I want to. 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. dollars. 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 dollars. You have another thing called cover, which 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. 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 three years.

You said you were telling a story about how I think it was right when figure started. You basically said, like, 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, and 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. 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 of the way I would characterize is, like, we're just,

like, we're just now, like, these companies on mine are just not hitting the inflection point, and they're really early. Like, they can be, like, really big. So if it works, this will, like, 100x,000 X 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 a thousand or a 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 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 that to come. Where are you going to be in five 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 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. Am I true? Like, you know, having done like 15 years of like software,

internet like it was just like nothing was happening faster on a trend line here is happening like that in a i can you give an example of something that has happened that's blown you away we started at so hark i have a new a lab called hark about a year ago i was like very interested in this idea of like kind of building this a i to human symbiosis digitally it's like um figure is going to be like i think figure's going to be like the max ceiling of a g i of like being able put that out and then there's going to be a version of this in the digital world that's going to be like a human's going to have this like a i pairing it's going to have like Also 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've got to figure out how to get AI to use computer's 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 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 computer use globally is on the 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 it'd be able to go online and maybe, maybe, like, use the internet really well. Almost like a robot would, where you can, like, move the mouse and use the keyboard. That's what we have to do to solve, like,

general purposeness for around a computer, is you can't rely on an API or MCP. You have to figure out how to, like, navigate it, 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. 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 chatypD had their computer use thing. Everybody's doing it. You guys feel like you've cracked something. What did you guys do differently? Okay, there's a couple things we did a little differently. First is like everybody's tackling this from like using APIs and MCPs. Like the reason why open claw 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 try to figure out how to use, like, 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 you want in the sandboxes. And then we need to give it ability to look at a screen and use, like, move the cursor and use the keyboard. Yeah, but I use chat CTP'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 wasn't trying to use APIs or MCP. It was trying to use the internet.

Yeah, I don't know if I was, yeah, yeah, it's got to work well. I mean, that's the whole point. But like, if it goes to where it fumbles on the internet, it's like the whole point is like, so that's what I was 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 is done. Well, 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 of a team. And it was like a hundred guys who just moved here from China who had like the greatest backgrounds ever. And 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. Romi, 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 over 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 like AI will head in two directions. Like, and then at some point maybe even like maybe like head together. Like the first is love AI out in the physical world that will like do everything in the environment for you.

Like laundry, dishes, cooking, like run the supply chain end and 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 one piece of hardware that can like, you know, the hardware capable of doing everything. And you put like smart AI into it and go off and do everything in the world.

That's what figure is working on. Separately than that, there's going to be this like really close like digital like AI to human symbiosis that forms. You'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, ad access to all your accounts and systems and be able to actually go do things for like a superhuman assistance.

step. 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 in 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. Like 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, 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. You have like AI over here in a human and you have like an old hardware system in between, like a mac,

to call like a MacBook or iPhone. They were designed 20 years ago. They're a 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? And 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. They're going to 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.

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, 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 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 A. 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 using a computer and faster. We're working on a couple of 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 a hardware in the lab now. We're using testing. 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 I've, you know, got a, his shop got acquired by opening AI. And you've seen the videos of the puck and then the 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 less like a 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. The only kind of things that we have like that in the world right now are computers and phones that kind of meet that. I call it mega devices. And then you have things on the ancillary around it, like orbiting this big thing that are like air pods

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 got to take down the computer and the phone.

There's no way around that. So you have to rebuild a new computer or new a phone that's better and replaces your existing systems 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 the 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. It'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 anything I've ever seen 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. 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 it's not. Like what the in state is BCI and the brain and we're going to have like AI language devices for the next 10 years before that. And that like that's that's the path. And it's not classes. Classes, I think, I don't even know if classes will make our top, like, 10 list of devices. When you're, 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, 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 the, I think of AI is a new type of computer. You talk about automation.

here. That 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 in 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, deep learning brings that are, like, 10x better? There's a few of them.

Like, one is AI can, like, 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 as, like, visual understanding. It has a real-time speech-to-speech. It can use computers and systems for you as fast, close to a whole, close to a 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 like human-like and 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. So you have a system that's almost like a human in a box that has all the same like a fortis as 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.

It was only for Sam, though. Only Sam could see it, and 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 would be able to do anything you do on a computer. Okay, so your first step where your team is like, just 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 and we see in here, and then give advice to us.

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, uh, 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 and 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, 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 on there like order door dash 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 like say it's like order me and it's 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 old human with you everywhere you go you would just say like you'd even predict probably Brett you want coffee today and I'd be like yeah I do like let's get let's order about you know what make it a double shot today and you know like routed 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 here like a monkey on my phone for next, like, three minutes, like trying to do checkout, door dash. 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, just go fix the window. You don't have to pick up the hammer and start figuring out how to use it. Start there. And then from there, you got a rapidly prototype. So when you come over, like we have like,

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 many different devices now that cover like a pretty wide area of this. We have some pretty crazy stuff we were designing.

So like, it's not like you look at that. You're like, that looks like this. And that does over here. So it's like it's not as easy as drawing those parallels. It's like pretty quite radical. But we rapidly prototype all this. We have like a fabrication facility that does this stuff. We have a whole design studio where we work on this.

I 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 like kind of down selection. We had like one of the biggest telecom CEOs in the world here that actually helped with the work with like Steve Jobs on iPhone one. And he was here two weeks ago. And he just come from meeting Tim Cook. You know, Tim Cook's on his way out of Apple. But he's like, we 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, like, take out the big guys. Well, is it true to say that with like Archer, figure, and Hark, the hard problem seems like, 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 compared 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.

You can, like, you can joy stick around. That's all you can do. and you like hit a button and 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 DGI drone like years ago and I was like playing around and then like a day later I was like, what do I do with this thing? And 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. I just didn't work. I was like, 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. We really want to tackle like, 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, like, you know, think about the largest, like, the largest, like, gap in the schedule of what we need to go solve for. Like, 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 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. There's, like, there's, like, a lot of companies. Like, you just, like, it's so.

And having seen, like, you know, BMW is a commercial customer of us. I haven't been in 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. You need robots that 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 whole line's down. And so it's just 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 a certain electronics, we're like over here. Closer to like, you know, we're like, you know, at the 40% level over here by like cell phones. Like we, you know, we just made our 1,000's EVT robot for figure three last week. week or a 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 large customers. We have us as like an engineering or like AI research org that needs like robots like here. Like every engineer needs a robot. We need like every lab needs robots. Like we need like to do tons of testing. There's just a lot of work we need to go do internally.

We call like maybe like engineering fleet we need to go to. And the second one is go to customers. So we haven't to go into both right now. We've actually shipped out robots to our third customer this week. When they go to customers, what do they do? What can the robot do? What maybe can't it do at this point? We do a lot of logistics stuff right now in packages.

We have other stuff we've done in manufacturing. Mostly just manufacturing and logistics we've done in the past. But 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 is that, like sorting or carrying? 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 that the type, is that like, give me an example of one of the jobs. Yeah, that's an example of like a very close to like one of the works we do.

Is that customer like, oh, this is awesome because I can't find the labor to do this. 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, 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 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 this work. And 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 really good ROI on this. You've got to think a robot can do multiple shifts per day, work seven days a week. We can have a lot of uptime.

The task you saw on the logistics line that we should live stream, it was actually a real use case for one of our customers. That needs to be done at three seconds a package. And it needs to be done five hours a day. I think it's 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 an ROI and we're now in the early stages of getting these out to these customers and

scouting 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, first fiction? Because one of the weird things is

as an enthusiast or a layperson 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 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, you know, Elon will go out there and say, we're going to build a million of these things in the next year. We're going to ship them. Then you get like 1X and they're showing their hand.

They're like, look at our hand. Look at this. 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. 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 you mentioned is like it's like it's out of control. Like the there's just so much bullshit out there in the market. It's like really hard to tell what the hell is going on. So let me summarize what I think is like the most important and work backwards. What I think the most important thing to do is to be able to ship robots autonomously at scale and 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 health care, build a building, like do logistics, like that sort of stuff. That stuff requires fundamentally on board 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 over a long periods of time. And you probably need to move around and use like something in your hands and move stuff through the world. You know what I mean? You've got to do stuff economically.

Like, you've got to 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 until 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 I blew it. Now I'm working at a BW factory. I've made jokes with you before where I was like,

you started with Vetteri, which is just like a job recruitment thing. Now you're on these world-changing things. And you were like, well, Vetteri 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 company? companies? I mean, most of all these are online. I mean, the figure master plan is on the internet, on the site, Arches was up for a long time, you posted about it. 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 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, 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 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 a scar you to carry with you. 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 reverse engineer everything you've done and discuss it.

The folks that haven't done it can't do that. They can't even go like, they get one layer and they just like can't slowly blow up. They can't talk about it. They don't know why. 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 a really challenging process to go through to be a mechanical engineer here, 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 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 in 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 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, like, I've never seen, I thought maybe like meta was like paying these people for 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?

I think we gave an offer to somebody that was really senior that was like they were coming from x-ed-e-i like x-di-i completely blew up like everybody just left and about it six months ago it was just like every it was just like macro heart like got fully disbanded like there's basically a bunch of stuff that happened we interviewed a pretty senior guy on the AI infestide it was great I think I gave him like a really good package like a series A stock at hark and it was I don't know 15 20 million dollars of stock over four years over we do five like for my companies in the early days and we transition to four a little bit later. So we're still a five. And, you know, I was 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. And I think we can do it.

I think we like, we have to, like, obviously it's going to 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 wait this like maybe like $200 million a Hark or $20 million or maybe like 36 for sure at Meta. And he left him going to Meta. And they've been doing that like every candidate we Spock speak to is like making some absurd absurd thing. It just haven't stopped.

They've been out of it since like for like a year or year. 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 hair end? 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 super computing 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. 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 a rough back of the envelope. It's probably like 20 to 30 people in.

California know how to build really good AI models. Wait, 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, like the junior guys, like the guys in their 20s, like the late late 20s or something, they're making like a few million total. So they're making like 200, 250 in base. They'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, or like 750 to like two million or so range per year. And that's, uh, that's been driven up by meta. And but then all the other labs have, have like, 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 have done the same thing if I was, I was, I was, I was Mark. I 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 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, 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 a thousand people or 500 or 300 to design AAM models.

You 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 to be 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. They're recruiting efforts and how they're structuring this stuff.

And 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 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 going to, Meta's going to go down to like one of the greatest acquirers in all time with like, you know, Instagram and WhatsApp and different way. They've like bought their way into those spaces. But like, you know, if you look at like the Ray bands and everything I were doing it's just like 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 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, every other platform.

Hark, what's the weather outside? I can answer that. Yeah, no problem. So, like, I think what I'm trying to say is like every week there's like five or ten, 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 this stuff in coding is probably really excellent right now,

but everything beyond that is just kind of like not great. On 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 tors. It just means like we're taking camera feeds and we out put like where to put the motor, like, you know, we want to put a, we want to like tell the motor like what to do to get to the hand in the right spot or the joints.

So we're going to do that. Getting into a new house has 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. This is where I spend about three, four hours a day. every single day, 70s a week on this problem. So if a figure robot showed up in my house, what's the bottleneck right now?

It wouldn't know what to do. It wouldn't know where to go. It wouldn't be able to fine tune handled by dishes. Where would it suck for me? We can fold laundry as an example, but then going to a new place where we're folding in different location with different lighting and maybe different 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, they 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. So 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 as a good example. Hey, stupid question. Why do all the robot companies care about folding clothes and doing laundry? Wouldn't it be commercial 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 abilities. And of course, that buys us the runway to like get the robot folding, you know, robot done.

But like, why didn't 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. Why do you guys care about that right now? We didn't care about it in the past. When we first launch, 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 variability. 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. We 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. 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 going to solve that. I think we'll be the first, we call it like solving general robotics. And I robot, 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 my robot. Who could win it? Who could win in a, who can win in a fight right now? Can a human still win? Yeah, human can still win. Okay. Where did the other prediction show?

Other prediction. One new 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 Chad GPD or Claude? Have they ever released the data on this? 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. 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, gay. Like, she's She's like, like, bam, dude, you can hear everything I'm talking to clot 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, like, almost like you set up, you have to go there, you have to turn on. Like, it doesn't, like, 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 in these, 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. I'll be able to have a human call you and a robot call you, and I don't think if you guys will tell a difference. That's a 2027 event.

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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. Ah, man, we're going to be, well, we're building our full-scale system starting in October, and I think it'll bring it up before in a year. I don't know if it will be out of K-12 school. So we might miss this one by a quarter. Do you have separate CEO running that one or you're the CEO also of that company?

I have a chief engineer from JPL and NASA that's like really good and it's mostly a pure engineering project. There's like really not much to do on the business side. Like there's, you know, we have like some supply chain stuff and everything's, 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 probably an AI problem. It's like roughly like a large scale and it's like an instant 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 cover. We would already be in market by now, and 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 your 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 silken 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 off a shelf.

These are like custom design 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. Hey, you are firing on all cylinders right now, professionally, it seems. And 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 five years ago, I had, like, you know, having kids and the companies. I had an issue where, like, I think of my life is, 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 call like the other stuff Where it's like a friends in town Or you need to go the annual golf trip or like

It's a bachelor party or like you know So wedding and like Europe or whatever it is like in this bucket over here And I felt like I needed to make a decision And like I didn't do like if I wanted 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 want to do like business stuff I want to just like I want 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 there's 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's like you know for a long time it'd be great to get a coffee I was just like I'm man I'm gonna be real I don't have any time I can't I can't meet you he's like I'll make myself available coming 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 waiting 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's your app set up? What do you got? All notification. Oh, well, you got to open it up. Oh, what's my? 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, I can't get through it during the day. I have heart going through it. And then they, they're hard, text me. It's important. I need to look at it with a link. So what's your setup like? What's your day-to-day when you're, do you use a laptop at all?

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 into 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 us all there.

So what about before Hark? I would, my to-do list was done in a Google Doc. I had a docket called Replanning, and it would constantly keep updating every week. I 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 gotten, like, access to some special doctors and things now, or 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 met 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. 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 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. Yeah. 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 like, 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. 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 entrepreneurial strategy you have

when you kind of hit those lows? Yeah. I basically almost for like 15 years was like always running out of money. You know, at Vetri, we had a couple of pivots early on. We ended up raising like a $500,000 convertible note in 2015. at that point I think I took out like a 50 or 100,000 dollar loan I was not paying myself a salary of the New York City I was like so broke I was in the negative

we raised the convertible no it did not look great and I think it was like six months later we launched the marketplace at Vetri 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 team was just like, was like, I had like basically like debt. Things weren't working and it's hard. And 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 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 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 week to week, two days, can't look at Friday. You got to go every day. every, every, you get to the next day. Pile through it. I was training for this ultra-marathon, 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 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. Like, okay, I'll consider quitting when I get to that.

No, I was like, great. I think exactly how I thought about it. But it was like, it's like, okay, so then I sold Vetteri, and then I was like doing Archer. It was like, oh, man, it's made $110 million. We like 12 X to all the adventure guys. And then I was like, we're going to raise money. I'll be raised money.

It'd be fine. And like, everybody's like, what are we doing? What are you doing? What are you doing? What are you doing? We're not going to 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 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, 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 the 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.

The, 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 of our 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. And I've been watching around. You like, you see all the tech crunch 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. So I had to like, I had to like, you know, 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 out of my house.

They're even fun figure. We asked you one of your philosophies that you said, I believe that doing hard things is easier in many ways than doing easier things. Can you explain? Everybody's trying to do easy things. When you work on harder things, you have 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 that probably want to work on things, probably the best overachievers in the world that kind of wants to work there. Generally, you know, hard things have this like binary payoff for investors. They really want to fund those things because we could have like a hundred X return for the portfolio. And I think there's like a nonlinear curve to scaling here, I mean, the difficulty here.

Meaning like I think a lot of the hard things are not like 10 or 100 times harder. I think the hard things sometimes are like two or three or four times harder, maybe five times harder, but they're not a hundred times harder. So you might have a hundred times better payoff, but it might be like three or four times. times harder. I'll give an example on robotics. I think, like, largely building, like, quadruped robots, like, four-leg-a-dog robots versus humanoid. Like, probably human-oids are probably, like, three times harder than that. Like, before. That's it. But, like, there is really no, I don't think there's, like, really a real market for humanoid, like, like, those dogs. I think it's just, like,

a niche thing. I don't think it's a real business for it. And I don't know anybody really at this point, like, really wants to spend a lot of time on that. So, like, you do humanoid, it's like, okay, three times harder, but it's probably, like, a million times higher payoff. probably like a million X or billion X higher R-O-R-Wap for that. You know what I mean? For investors, for humans that want to work there, 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? What economic value can, like, a robot dog bring that at scale? Like, if you really understand it, like, I think there's, everybody's trying to do the easy work

and 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 aquilers and stuff,

but that's going to 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, you think, like, have you guys even used OpenClaw since then? No, I don't know how to. Oh, do you use OpenCla?

I never trusted OpenClaught to set it up. I mean, I used to use it. I don't use it anymore. It's not very good. Like, the waves over. I don't know. I'm just trying to say like I think it's like, I think the most important thing you do as a founder is a thing 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, it'll map the whole course, the probability course. It's like a probability weighted decision of like our probability of like potential outcomes. Well, but you're you're talking about a very particular game. Like 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? If like, if like, we think you do that well and you look back when you're seven, you're 80, would you, would you have asked the same person, hey, you go to look 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 work on that business. Would you have gone back 30 years ago and try to take a bigger swing.

Would you have taken a different swing than Vetteri? Vetteri was like that. Vetteri is my bridge. I started 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. Dot I.O.

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 happened. 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 the 10 million dollars 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 the guys getting into like five or 10 million in business those that's that's hard especially doing that if maybe a little bit of capital or no capital coming in that let me ask you real quick about 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 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, I like looking at, I like, I like trying to think through this problem of what was the world going to look like in 30 years or 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, 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. I think there's a lot of work here.

What is that next generation? Is it solar or is it when? Is it nuclear? And then there's a bunch of different traits 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

like they're 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. I think it's going to be 100 times bigger than internet. It gives me so, so big.

It's going to, it's going to, AI is going to eat the whole. internet. It's going to need 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 Hara 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. We're in this like foggy area for a lot of these stuff. 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 six months.

What about, last question, what about people who inspire you? Because you have very high standards. Who or what type of entrepreneur, 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 Micro Jordan documentary. He's just like, I just want to be like the best in the world at this.

I think for like for startups have the same thing too. And I think first and foremost, like, you know, what I can read. I'd ever 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. We are fairly close, and I think Jensen's just an unbliddenble operator as well. He's very hands-on. It has a very unique way of managing Nvidia and its organization last 30 years. And I think he's, like, I think he's had a lot of really good things.

What advice did Jeff give you that was meaningful? Jeff said last time he was here, he's like, listen, you're at a really interesting period because you figured out how to do this somehow. In the next year or two, they're going to figure out how to break through and really get this, like, working in a bigger way, or you won't.

And this is like it's game time for you now. And you got to just get wired in and like figure out how to break it out and make this thing work and scale it. And it's 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 going to like make or break it. So 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 it all it was a knee you had like there was five engineers you're like this guy just got done building the Tesla X or cyber truck or something this guy did this amazing thing this guy cured cancer look how the knee moves and the ankle has

dorsal flexion 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 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 out pretty well the joints go of 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, he'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. You don't 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 a pod. 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 Sue. 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 podcast.