1:12:43My First Million · Talking Head
We Tested Instinct, Muse, and Grokbot
Shaan Puri and Sam Parr compare three AI personal assistants and map out the billion-dollar war breaking out over who controls the buying decision.
September 30thThe argument in one line.
AI will be a hundred times bigger than the internet, and the founders who win it will pick hard, binary problems because those are only a few times harder than easy ones while paying off a million times more.
Read if. Skip if.
READ IF YOU ARE…
- A founder deciding between a comfortable niche and a swing that could be enormous or zero, who wants to hear the case for the swing from someone who took it three times.
- A builder working on AI agents who wants a frank view on why computer use beats APIs and MCP for general-purpose assistants.
- Anyone trying to separate real humanoid-robot progress from demo videos and wants concrete numbers from a company shipping them.
- An operator curious how AI labs recruit, what the talent actually costs, and why a $36 million Meta offer beats Series A stock.
- Someone in a long stretch of things not working who wants a usable method for getting through a low period.
SKIP IF…
- You want a hands-on tutorial or a product demo; this is a conversation about strategy, hiring and predictions with no screen share.
- You are looking for balanced skepticism about AI timelines; the guest speaks in absolutes and the hosts mostly let him.
The full version, fast.
The guest argues that AI is moving faster than anything in his fifteen years of software and will end up a hundred times bigger than the internet, split into humanoid robots in the physical world and a Jarvis-style assistant with perfect memory in the digital one. His new lab Hark bets that assistants must use a screen, mouse and keyboard like a human because only one in a thousand websites has an API, and that the phone and laptop are the wrong interface. Figure's robots already sort packages at 2.9 seconds each for 200 hours straight, but the real bottleneck is training data for unseen homes, not manufacturing. He grades his four 2026 predictions as mostly on track, defends Meta's talent buying as smart, and shares the rules that got him through three rock bottoms: build a punch list, go day by day, and choose hard problems because they are only a few times harder with a vastly larger payoff.
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Create a free account →Who's talking.
00:00hostSam Parr
02:42cohostShaan Puri
00:08guestBrett Adcock
Where the time goes.
00:00 – 01:21
01 · Cold open
Net worth question, the shrug, and a recap of the four companies: Vettery, Archer, Figure, Cover, now Hark.
01:21 – 03:29
02 · Binary by design
He does not care about the number; he cares about Figure becoming the biggest company in the world. Either the robots scale or they do not, and all his energy goes to the thousand-x path.
03:29 – 06:47
03 · What Hark is
AI will be a hundred times bigger than the internet. Hark is a lab building a digital AI-human pairing, and the first unlock is general computer use because only one in a thousand sites has an API.
06:47 – 07:26
04 · Sponsor break
HubSpot resurfaces Sam's millionaire-timeline database as a free download.
07:26 – 11:39
05 · Two directions for AI
Physical AI in a humanoid body, and a Jarvis-style digital assistant with perfect memory. Both need better models and a new interface, because the phone and MacBook are twenty-year-old hardware.
11:39 – 14:08
06 · Mega devices, not glasses
Only phones and computers sell a billion units a year. Hark is rebuilding that middle. Meta glasses are one of the worst products he has bought; the end state is a brain interface, not eyewear.
14:08 – 17:54
07 · Zero constraints
Start from what deep learning makes ten times better: a human in a box that sees, talks, remembers and uses computers. Then design around that, from the hammer to the handyman, and rapidly prototype everything.
17:54 – 21:37
08 · The real bottleneck is intelligence
Chinese robots are joystick toys. The constraint is not manufacturing; humanoid parts fit in your hand, unlike cars. Figure just built its thousandth Figure 3 EVT unit.
21:37 – 23:45
09 · What the robots do today
Logistics and manufacturing for customers dying from labor turnover. The livestreamed package sort was a real use case: 200 hours straight at 2.9 seconds per package, already an ROI.
23:45 – 26:22
10 · Fact versus fiction
The market is full of noise. What matters is autonomous useful work over long horizons, not backflips, dancing or parades.
26:22 – 30:10
11 · Finding the best people
Ninety percent of Bay Area engineers are not good at their jobs. The tell is whether they did the work. Ten mechanical case studies a week for six months, zero hires.
30:10 – 33:11
12 · The $36 million offer
A senior AI infra candidate chose a guaranteed $36 million at Meta over $15 to $20 million of Hark Series A stock. Maybe twenty to thirty people in California can really build models.
33:11 – 33:35
13 · Buying your way into the race
He calls Meta's strategy smart and says he would have done the same, but a great model team is twenty to forty people, and mercenaries care less than believers.
33:35 – 39:41
14 · Auditing the four predictions
Robots doing unsupervised multi-day work in unseen homes: on track. Multimodal voice agents with memory: shipping in a month. Speech Turing test: 2027. Cover's weapon-scanning system: maybe a quarter late because of custom chip lead times.
39:41 – 41:55
15 · Three buckets, keep two
Work, family, and everything else. He dropped the third bucket entirely, down to turning away a college roommate in town for ten days.
41:55 – 44:37
16 · Home screen and workflow
Hark reads his Slack and texts him what matters. To-do list used to live in a Google Doc called replanning, updated on Sundays. Quarterly bloodwork and scans, but no time to exercise.
44:37 – 48:42
17 · Rock bottom three times
Negative net worth in New York in 2015, then a $110 million exit. Nobody would fund Archer. He funded Figure with falling Archer stock and a second mortgage. Punch list, day by day, the ultramarathon trick.
48:42 – 51:26
18 · Hard things are easier
Less competition, better people, binary-payoff investors. Humanoids are three or four times harder than robot dogs with a million times the payoff. Most AI harness startups will not make it.
51:26 – 54:11
19 · Is a $10 million business a win?
The hosts push back on binary thinking. He concedes it is hard and admirable, then says the real cost of his first company was the billion-dollar products he built internally and never shipped.
54:11 – 56:46
20 · Energy, devices and heroes
The next secular trend is energy generation. No AI device has impressed him yet except Whisper Flow. Jordan, Jobs, Bezos and Jensen; Bezos told him this is game time.
56:46 – 58:10
21 · From a knee to a robot
Four years ago the office had a single working knee and five engineers. Now the robots run onboard inference. Next leg: scale.
Lines worth screenshotting.
- Only one in a thousand websites has an API, so a general-purpose AI assistant has to use a screen, mouse and keyboard the way a human does.
- The phone and the laptop were designed twenty years ago for a different kind of computer, which makes them the wrong interface for AI.
- A device category only matters if it can reach a billion units a year, and today only phones and computers qualify; glasses and pendants are accessories.
- Figure's package-sorting robot ran 200 hours straight at 2.9 seconds per package against a customer target of 3 seconds, so it already pays back at human speed.
- The hard part of home robotics is not folding laundry but folding it in a house the model has never seen, which is a data collection problem, not a code problem.
- Cars are brutal to manufacture because no human can hold the part; a humanoid robot's parts fit in your hand, which puts it closer to phone manufacturing than car manufacturing.
- Roughly twenty to thirty people in California actually know how to build frontier AI models, which is why Meta paying $36 million for one engineer is rational.
- A good AI model team is twenty to forty people, not three hundred, so mission-driven recruiting can compete with pure cash if you find the right few.
- You can tell who has done the work because the details are a scar they carry: they can reverse-engineer every decision without pausing to think.
- Ten mechanical-engineering case studies a week for six months produced zero hires, and that bar is the point.
- When things are going badly, build a punch list and get to tomorrow; planning to Friday is already too far.
- Of roughly fifty companies in one incubator cohort, two returned more than zero dollars five years later.
- Humanoid robots are maybe three or four times harder to build than robot dogs, but the payoff is a million times larger because dogs have no real market.
- Hard problems attract less competition, better people and binary-payoff investors, so they are often easier to win than easy ones.
- If your only time buckets are family and work, every coffee with an old friend is a minute stolen from one of them, and the founder chose to stop pretending otherwise.
- A full speech Turing test, where you cannot tell an AI phone call from a human one, is a 2027 event.
Pick the hard problem and go day by day.
WHAT TO LEARN
A founder who has bet everything three times argues that hard problems are only a few times harder than easy ones, that AI needs a new interface, and that surviving the low stretch is a daily punch list.
05Two directions for AI
- Build an AI assistant around what deep learning makes ten times better, not two times better: vision, speech, perfect memory and computer use.
- Design agents to use a screen, mouse and keyboard, because only one in a thousand websites has an API and most of the economy lives in a browser.
- Treat the phone and laptop as a twenty-year-old interface that AI will eventually replace, with accessories like glasses orbiting a new center.
06Mega devices, not glasses
- A device category only matters at a billion units a year; everything else is an accessory to the platform, not the platform.
10Fact versus fiction
- Judge robot progress by autonomous useful work over long time horizons, not by backflips, dancing or parade demos.
- The home-robot bottleneck is training data for rooms the model has never seen, which has to be collected in the world because it is not on the internet.
- A customer-ready robot task has a number attached: 2.9 seconds per package for 200 hours straight against a 3-second target.
11Finding the best people
- Assess candidates on whether they did the work; people who did can reverse-engineer every detail without pausing, people who watched collapse one layer in.
- Hold the bar even when it hurts: six months of weekly case studies with zero hires is the price of a team that can build actuators from scratch.
13Buying your way into the race
- If twenty to thirty people can build frontier models, overpaying for them is rational, but a committed team of twenty to forty beats mercenaries at three hundred.
14Auditing the four predictions
- Write predictions with dates and grade them in public; on track, shipping in a month, a quarter late because of chip lead times.
- A full speech Turing test on a phone call is coming by 2027; plan for voice-first, memory-rich assistants rather than text chat.
15Three buckets, keep two
- Decide which life buckets get an A and stop pretending the others fit; every hour belongs to one of the two you kept.
16Home screen and workflow
- Keep your weekly plan in one living document you revisit on a fixed day, and let an assistant triage the inbox so you only read what matters.
17Rock bottom three times
- When things are breaking, build a punch list and get to tomorrow; looking ahead to Friday is already too far.
- Borrow the ultramarathon trick: pick a point a few hundred yards away and agree to reconsider quitting only when you reach it.
- Expect most of your cohort to go to zero; two of fifty is the base rate, so conviction has to come from having done the work.
18Hard things are easier
- Hard problems have less competition, better applicants and binary-payoff investors, and they are often only three or four times harder with a far larger payoff.
19Is a $10 million business a win?
- Before committing a decade, map the probability-weighted outcomes, because the real cost of a comfortable business is the bigger product you never ship.
20Energy, devices and heroes
- The next secular trend after AI is energy generation; entrepreneurs who can raise living standards through it will have a long runway.
Terms worth knowing.
- Computer use
- An AI capability where the model looks at a screen, moves the cursor and types, so it can operate ordinary websites and apps without a programmatic interface.
- MCP
- Model Context Protocol, a standard that lets AI models call tools and services through structured connectors rather than by driving a user interface.
- Pixels to torques
- A robot control approach where a neural network takes raw camera input and directly outputs motor commands, with no hand-written planning in between.
- Out of distribution
- A situation a machine learning model was never trained on, such as a new room with different lighting and table height, where its behavior becomes unreliable.
- Post-training
- The stage after a model's initial pre-training where it is refined with techniques like reinforcement learning to perform specific tasks well.
- EVT
- Engineering validation test, an early manufacturing phase where the first full-spec units are built to prove the design before mass production.
- BCI
- Brain-computer interface, a direct link between the nervous system and a computer, which the guest calls the eventual end state after a decade of AI devices.
- Punch list
- A construction term for the short list of remaining tasks that must be finished; here, a daily list that lets you grind through a low period one item at a time.
- Vesting
- The schedule over which an employee earns their stock grant, typically four years; the guest's companies use five.
Things they pointed at.
00:26productArcher Aviation
04:32toolOpenClaw
55:13toolWhisper Flow
52:25productOutreach.io
55:28channelThe Last Dance (Michael Jordan documentary)
Lines you could clip.
03:53
“You would never hire an assistant that couldn't use a computer.”
09:05
“You have AI over here and a human, and you have an old hardware system in between. It's called a MacBook or iPhone. They were designed 20 years ago. They're complete rubbish for AI.”
13:08
“The meta glasses are probably one of the worst products I've ever bought.”
16:56
“The phone is like a tool and it's like a hammer. The next generation is basically like having a handyman next to you at all times.”
23:05
“We did that 200 hours straight at 2.9 seconds a package. So we're already at human speeds.”
26:57
“Even in the Bay Area, 90% of everybody out here is not good at their jobs.”
27:40
“If you've done the work, it's like a scar you carry with you. You know all the details. You can talk about it freely. The folks that haven't done it get one layer and they just instantly blow up.”
30:50
“There's probably like 20 to 30 people in California that know how to build really good AI models.”
38:21
“In 2027 you'll be able to take a phone call from an AI system and I don't think you guys will be able to tell the difference.”
45:45
“You got to build a punch list and you just got to get through it. The only way out is through. You can't look at Friday. You got to go every day, get to the next day.”
48:00
“Five years later, me and one other guy are the only two people that made greater than $0. 48 companies went to zero.”
49:30
“The hard things are not 10 or 100 times harder. They're three or four times harder. So you might have 100 times better payoff, but it might be like three or four times harder.”
Where the conversation goes.
00:00 – 03:29steadyFounder mindset and binary outcomes
03:29 – 17:54denseHark: computer-using AI and post-phone hardware
17:54 – 26:22denseFigure: robot capabilities, manufacturing, customers
26:22 – 33:35denseHiring, AI talent wars, Meta
33:35 – 39:41dense2026 predictions audit
39:41 – 44:37steadyPersonal operating system: buckets, tools, health
44:37 – 54:11denseRock bottom, hard things, what to spend a decade on
54:11 – 58:10sparseFuture trends, inspirations, close
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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 at all. I give like zero shits about that.
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 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 dollars. You have another thing called Cover, which stops or aims to stop school shootings. And then now you have a new thing called Hark, which you've raised money at in the billions of dollars.
And you seem worn out. Great. He was like, busy, man.
So you've been on, this is your third time on, I think you've been on one time each year the last three years. You said you were telling a story about how I think it was right when Figures started. You basically said like I had, I was worth, I don't know how much, tens of millions of dollars.
I put almost all of it into Figure to get started. And at one point you were like, I have a mortgage on my house and the rest of my money is in Figure. And some of the money is in Archer and that's not doing so great right now.
And since then. i think if you google brett adcock net worth according to fortune you're worth 19 billion dollars so that's like a pretty good swing how does 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 i'm i 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 i care a lot about figure being the biggest company in the world you talk about like you definitely have this like napoleon energy of like i want to be the best i want to conquer i think the way i would characterize is like we're just like we're just now like these companies of mine are just 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 thousand 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. We 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 the internet.
It's just like everything is just so... it 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 is going to be like, I think figure is going to be like the max ceiling of AGI of like being able to put that out. And then there's going to be a version of this in the digital world.
That's going to be like a human is going to have this like AI 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 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 like give things out to it that can like do everything you can do.
financial models, book flights, like order DoorDash, whatever you need to do. You need to be able to do it all autonomously. But only one in a thousand websites have APIs.
So in most, you know, most computers 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 like go off, go online and like maybe, 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 like. general purposeness around a computer is you can't rely on an 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 of things we did a little differently. First is like, everybody's tackling this from like using APIs and MCPs.
Like the reason why OpenClaw got so great, it was like, it could only, it couldn't use the browser. It couldn't like go on and use DoorDash end to end because DoorDash has no consumer API. So we tried to figure out how to use like a, how to look at a screen.
And one is we spin up a virtual computer for every agent. So they don't need like a MacBook or anything. So you can just spin up as many of these environments as you want in the sandboxes.
And then you need to give it the ability to look at a screen and move the cursor and use the keyboard. Yeah, but I used ChatGP'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 wasn't trying to use APIs or MCP. It was trying to use the internet.
Yeah, I don't know if I, yeah, it's got to work well. I mean, that's the whole point. But like, if it goes to like fumbles internet, it's like the whole point is like, so that's what I'm saying.
What'd you guys do to make it work? Well, was it like an algorithmic breakthrough? Is it, it was in our post -training, right?
It was in our, we have a reinforcement learning process that we think is maybe nobody else in the world has done. 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. Well, I mean, that's kind of all we had of time. We started.
So, okay, what is Hark?
I think the best way to become successful 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, successful and just want some more inspiration, check it out. I strongly believe like AI will head in two directions.
Like, 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 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 want one piece of hardware that can like, you know.
the hardware capable of doing everything. And you put like smart AI into it and it'll go off and do everything in the world. That's what 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, have access to all your accounts and systems and be able to actually go do things for like a superhuman assistant.
It'll be like, maybe the closest thing is like Jarvis from Iron Man. And it will be able to do like, it'll be like superhuman in almost every way. It'll know everything about your life.
You'll be able to access it at any moment whenever you need it. It'll be in the background helping you out at all times. If you're on a flight with like a long layover or a flight with like maybe say a short layover and you miss it, it'll like already have backup plans, already help you like figure that out.
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 model side.
It's got to be just better than text chat. It's got to 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.
You have 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 AI over here and a human, and you have an old hardware system in between.
It's called a MacBook or iPhone. They were designed 20 years ago. They're complete rubbish for AI.
They're not the right interface. So we went out and we are out there designing what we think comes like after the iPhone for AI. And it's like an upgrade cycle.
We see this all the time in startups. You guys see it, right? Like we're in an upgrade cycle with the computers and phones.
They're just going to go away. They're going to be a new ones. 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 they'll 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 uh 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 uh pairing and we hired an incredible team it seems like you know maybe like 80 or 90 now the guy that leads uh hardware design abs Previously designed for the last several generations of iPhone, MacBook, MacBook Pro.
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.
The text encoding is just not enough for us to really have a real AGI feeling with AI. So we're working on that. We did our first research preview of our computer using AGI.
that we came out last 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 uh they'll keep getting better this will keep getting better and better like every month we'll just like it'll be better and smarter using a computer and faster we're working on a couple other different types of technologies internally on the ai side and then we'll launch the ability to use hark on like traditional browser and iphone and android in about a month so um you'll be able to start using it and we'll have hardware coming we're working on now we actually have hardware in the lab now we're using testing this It's crazy shit.
The stuff is like a sci -fi movie hardware. What do you think those devices look like? People have been speculating because Johnny Ive, his shop got acquired by OpenAI, and you've seen the videos of the puck, and then this little puck, and then there's an earring.
I don't know if that's real or if that's fake. There was 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 really changed my mood on this a lot in the last 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.
They kind of meet that. I call it 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 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 phone.
that's better and replaces your existing systems end -to -end. Then what's around there is things that you will have, we will even have at Hark that helps with the family of devices that are not a billion units a year, but important for the ecosystem. My understanding, you're saying the next device, it might be like a phone, it's just going to be an AI native first phone.
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.
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. 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, like, that's the path, and it's not glasses.
Glasses, I think, I don't even know if classes will make our top 10 list of devices. When you and your team are brainstorming, do you have a framework on how you can think outside of pre -existing 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. I think of AI as a new type of computer. And you talk about automation.
That's here. The automation can do a few things that are 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.
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 as a human can. Over time, it'll be just as good as a human and faster in terms of success rate. So you have a system that's almost like human -like in capabilities.
It also can like have memory, meaning you can put memory into it and it won't forget anything. I mean, you're perfect over time. So you have a system that's almost like that has all the same like affordances a human has.
And it's almost like the ability of like, you almost like if you could bring a little human around with a computer on your shoulder everywhere you went, that'd be insane. Like we're just like, it was only for Sam though. Only Sam could see it.
Only Sam could talk to you. And only was like there to help with Sam. And that was like your whole life.
And it was getting smarter and better along the way. And it had perfect memory and could use computers and talk to you and see. You'd be like, damn, that thing would be like, it'd be like be able to do anything you do on a computer.
Okay. So your first step with your team is like, 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 we see and hear 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 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, 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 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, eh, yeah, I do. Like, let's get, let's order.
But you know what? Make it a double shot. today and you know like 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 like a monkey on my phone for the 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 and so you just tell them hey can you fix that window let's go fix the window You don't have to pick up the hammer and start figuring how to use it.
Start there. And then from there, you got to rapidly prototype. So when you come over, like we have like, we've 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 we were designing. So like, it's not like you look at that and you're like.
that looks like a that looks like this and it will that does over well over here so it's like it's not as easy as drawing those parallels it's like pretty quite radical but we rapidly prototype all this we have like a fabrication facility that does this stuff we like we have a whole design studio where we work on this i like you like use this stuff like over the coming like weeks and months i'll like either carry it around with me wear it whatever we end up doing it and we'll like kind of down selection we had like one of the biggest telecom ceos in the world here that actually helped um with the work with Steve Jobs on iPhone 1.
And he was here two weeks ago, and he'd just come from meeting Tim Cook. You know, Tim Cook's on his way out of his 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 them and they're complete crap.
They can't do anything. You can joystick 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 like years ago and I was like playing around with it.
And then like a day later, I was like, what do I do with this thing? And 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. It just didn't work.
I was like, what am I doing with this thing? Robots are like that now. Like where 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 of figure. Like we really want to tackle like. How do we make it so I can put it into any home?
It can do every 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, 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, 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 them, 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. 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 to BMW and a few other groups.
Like it's gnarly. The reason why cars are so hard is that you can't hold the part in your hand. Phones, you can just like always hold in your hand and go change or whatever, move and hold.
Like cars, you can't. You physically can't. So you need robots that like literally pass it to other robots that put things on the chassis.
And if any of those break across like thousands or 800 robots, your 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 consumer electronics, we're like over here. closer to like, you know, we're like, you know, the 40 % level over here by like cell phones. Like we, you know, we just made our 1000s EVT robot for figure three last week or week before that.
When you say you made a thousand, those are a thousand that go to customers like BMW or you're making prototypes internally? What does that mean? We have like two bit like large customers.
We have us as an engineering or AI research org that needs robots here. Every engineer needs a robot. 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 it maybe engineering fleet we need to go to.
And the second one is go to customers. So we have to go into both right now. We've actually shipped out robots to our third customer.
this this this week when they go to customers what do they do what what what can the robot do what maybe can't it do at this point we do a lot of like logistics stuff right now in packages uh we have other stuff we've done in manufacturing mostly just manufacturing logistics is 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 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 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, 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 a hundred percent turnover per year. It's really expensive to find talent. We have like a, just a large talent shortfall.
The talents are really expensive. Like wages are going up and we like, we don't have a solve for this. We can't figure out how to automate all of this work.
And we need you to come in and help us. We have an ability to make a lot of good money. and our contracts and the customers make like really good ROI on this.
Like you got to think like a robot can be like multiple shifts per day, work seven days a week. Like we can like have a lot of uptime. The task you saw like on the logistics line that we should live stream 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. Like 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 already have an ROI. And we're now in the early stages of getting these out to these customers and scaling it up. Over time, it will just put billions out to these groups.
Can you help me with like the kind of truth first fiction? Because one of the weird things is as an enthusiast or a lay person 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 One X 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.
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. One is the amount of like noise in the market for a signal. It's just like, it's like, like you mentioned, it's like, it's like it's out of control.
Like the, like 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.
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, 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 like autonomous work.
You can't solve it with code. You need to do it autonomously. You need to do it over long periods of time.
And you probably need to move around and use like something in your hands and move stuff through the world. You know what I mean? It's like you got to like do stuff economically.
Like you 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 till I see a figure like on a smoke break at the B &W factory. I could have been a great back in high school, but I blew it.
Now I'm working at a B &W 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 companies?
I mean, most of all, these are online. I mean, the figure master plan is on the internet, on the site. Archer's was up for a long time.
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 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 dug into you. Like you know all the details. You can talk about it freely.
You don't need to think. You'll understand how to like reverse engineer everything you've done and discuss it. The folks that haven't done it can't do that.
They just like, they can't even go like, they get one layer and they just like instantly blow up. They can't talk about it. They don't know why.
Out of a hundred candidates who sound good, how many, like their resume looks good, the recruiter thinks they're good. Out of a hundred candidates, how many 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. 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 uh it's just a very difficult thing to do uh and like really hard requirements we've been doing 10 case studies a week for six months and have not hired anybody that's insane it's insane but when you do get someone qualified And their competing offers are companies that are larger or 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? We gave an offer to somebody that was really senior. They were coming from XDAI.
XDAI completely blew up. Everybody just left about six months ago. Macro Heart 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 a really good package of Series A stock at Hark. And it was, I don't know, $15, $20 million of stock. Over four years?
We do five for my companies in the early days, and we transitioned to four a little bit later. We're still at five. And I was like, I think we can 10x Hark here pretty quick.
And so I was like, okay, you have $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 have to, obviously, it's going to be hard, but I think we can do it. He got an offer to go to Meta for $36 million of four years of our shoes.
And he's just like, it's kind of guaranteed cash. You know, I go there and I have to like weigh this, like maybe like $200 million at Hark or $20 million or maybe like $36 for sure at Meta. And he left and went to Meta.
And they've been doing that like every candidate we speak to is like making some absurd thing. They just haven't stopped. They've been at it since like for like a year or a 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 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, or rough back of the envelope is probably like 20 to 30 people in California know how to build really good AI models. Wait, so, but is that trickling down? So you said that there was a senior guy, but like, are even some of the less than senior, the 20 -somethings, the young 30 -somethings, are they still getting eight figures a year?
No, the, like the junior guys, like the guys in their 20s, you know, like the late, late twenties or something, they're getting like, 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 like, you know, or like 750 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 have done the same thing if I was Mark.
I would have bought my way into the race. And I think he's doing that now. I don't think I would have 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 like like mercenaries and so he hired a bunch of mercenaries they're just purely money driven he they came over there then no other 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 am 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 if you purely throw money at the problem. But I think if I was like, I think it was a really good strategy and it's working. I think hats off, like really good execution, their recruiting efforts and how they're structuring this stuff.
And it's like, I think it's like 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 going to, Meta's going to go down in 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 -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 got to send it to us. Yeah, can we use it?
Well, yeah, we used you guys early on that. Yeah, for sure. It's like research preview.
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, what I'm trying to say is like every week there's like five or 10 like junk AI slop startups or like things that are coming out. They're just like not very good. Like this whole space has gotten to a point where like there's just not great things coming out the door.
I think. This stuff in coding is probably really excellent right now, but everything beyond that is just kind of 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 what we're doing.
We're on track. The hard part here is we already do pixels to torques. It just means we're taking camera feeds and we output where to put the motor.
We want to tell the motor what to do to get to the hand in the right spot or the joints. So we already do that. Getting into a new house and never seeing it do work, that's the hard part of this problem.
I'm working on that every day. This is where I spend about three, four hours a day, every single day. Seven days a week on this problem.
So if a figure robot showed up in my house, what would it, what's the bottleneck right now? Like it wouldn't know what to do. It wouldn't know where to go.
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 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 is like out of distribution. It doesn't know what to do. It's like if you removed.
all the pyramid data from the pre -training of llms they wouldn't know how to talk about pyramids right 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 is a good example Hey, stupid question.
Why do all the robot companies care about folding clothes and doing laundry? Wouldn't it be commercially better just to say, hey, we're going to build like the best warehouse worker because there's already 20 million of those in the world and that represents this much buildings. And of course, that buys us the runway to like get the robot folding, you know, robot done.
But like, why do you care about that at all today? Why not just industrial work that... People don't want to do.
Companies need done. They're ready to pay. And it's not like my home where there's all these other sensitivities.
Why do you guys care about that right now? We didn't care about it in the past. When we first launched, we're like, we're going to basically do the commercial side to pay for the home long term.
And that was the strategy. It made a lot of sense. We can charge a lot more in the commercial market.
It's much easier to do. It's lower veritability. We're in a little work site.
We just work 24 -7. Just so much simpler. What I've learned now is that the home is super solvable today.
So we can not go work on that problem and just 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 that.
You can probably do that with 100 robots and a 50 -person team. So that company overnight would be a trillion -dollar market cap. That sounds good.
Do that. We're doing that. That's what we're doing.
We're going to solve that. I think we'll be the first. We call it solving general robotics.
And iRobot, don't they attack the humans? I don't remember this movie very well. Yeah, don't worry about that.
Okay, not that part of iRobot. Who can win in a fight right now? Can a human still win?
Yeah, a human can still win. Okay. What are the other predictions?
All right, other prediction. One you had on here. Daily AI usage will shift.
People will move beyond text. To highly multimodal, voice agents with persistent memory will become common, which will push AI closer to the synthetic human intelligence we've imagined in sci -fi. We're doing that at Hark.
We'll ship that in a month, and our first version of it. It'll get better and better. I think we're on track for that.
Have the labs ever, like has ChatGPT or Claude, have they ever released the data on this? Like, I use a ton of the voice things. 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... 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, 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 was, like, talking last night, and I was like, Claude, why am I so indecisive?
And then my wife was like, gay? She was like, bam, dude, she can hear everything I'm talking to Claude about now. Yeah, no, I talk all the time, but it's embarrassing.
Yeah, 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, and 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 like a human call you and a robot call you, and I don't think you guys will be able to tell the difference.
That's a 2027 event. I feel pretty strong about it. All right, what's the third and fourth?
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. We're building our full -scale system starting in October.
I think we'll bring it up before the end of the 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 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 project uh 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 other things but most of it's just like purely can you build a system that can detect weapons well it's partly like a hardware problem it's probably an ai problem it's like roughly like a large scale and it's like an instant deep in deep tech deep engineering problem to solve and my whole team is just all of engineer they're really good we actually made a pretty big change of cover 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 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 dealing with real silicon, long fabrication of very difficult chips.
Lead times now. We'll be out of this at some point, but it's not like chips you can go off and buy off a shelf. These are custom -designed cover chips that 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, 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 having kids and the companies. I had an issue where I think of my life as three pockets. I have work to care deeply about.
My family, I have like three kids. They're pretty young right now. And then I have like, they call it 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. I'm like, I didn't need to do like, if I want to do any of these well, I kind of like, I can't do all three. And what I wanted to do really well is like family and I 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 a friend, I had a friend in town from a college. It was like my 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. It's like, you know, for a long time, it'd 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 away 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 the home screen of your phone? What's your app set up? What do you got?
All notifications. Oh, well, you got to open it up. Oh, what's mine?
So you have just tons of texts. 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 Hark texts me. I think it's important.
I need to look at it with a link. So what's your setup like? 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. Tracking 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 that. So what about before Hark?
My to -do list was done in a Google Doc. I had a docker 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 gotten access to some special doctors and things now where they basically send you through the quarterly blood tests and the whole body scans and 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. For instance, you can get a CT scan of your heart for $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 and 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 and like, I got to go work. I like, you know, I want to go crush these businesses.
When 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 Vetteri, 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 ,000 or $100 ,000 loan. I was not paying myself a salary.
I was in New York City. I was like so broke. I was in the negative.
We raised the convertible note. It did not look great. And I think it was like six months later, we launched the marketplace at Vetteri and it just like completely took off.
And then a year later we sold for 110 million. And I think that period from 2012 to 2017 was just like, 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 day to day. You can't go week to week, two days.
Can't look at Friday. You got to go every day, every day, 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 a hundred 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, I 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. And then I was doing Archer.
I was like, oh man, it's made 110 million. It'd be like 12x all the adventure guys. And then I was like, we're going to raise money.
I'll be raising money. It'd be fine. And everybody's like, what are you doing?
For Archer? Yeah, everybody's like, what are you doing? We're not going to fund this.
What are you talking about? 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. I know I'm right because I've done the work. I understand it.
I'm on the floor. Yeah, but the odds are still against you, right? But that's the game.
That's when you play this game. It's like you sign up and 95 % of everybody around you will fail. Remember at Vetteri, we started at the NYU Incubator.
I was so excited. We got in one of the semesters. 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 five years later, me and one other guy are the only two people that made greater than $0.
48 companies went to zero. 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 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 bought a house and 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 . Well, at that point, it was just like, I think it went from like 10 bucks to like two.
And it's since like gone up a lot. But like I had to take a second mortgage on my house to 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. Can you explain? Like everybody's trying to do easy things.
When you work on harder things, you have like less, generally like overall, probably there's like first order, like less competition. You have probably like a hard thing probably means like it could be a potential like really big TAM. Really big exit if it works.
You have, like, just, like, you know, risk -reward trade. You have folks that probably want to work on hard things. You probably want, like, the best overachievers in the world that kind of wants to work there.
Generally, you know, hard things have this, like, binary payoff for investors. They really want to fund those things because they could have, like, a 100x return for the portfolio. And I think there's, like, a nonlinear curve to scaling here of the difficulty here.
Meaning, like, I think a lot of the hard things are not, like, 10 or 100 times harder. I think the hard things sometimes are, like, 2 or 3 or 4 times harder. They're five times harder, but they're not 100 times harder.
So you might have 100 times better payoff, but it might be like three or four times harder. I'll give you an example in robotics. I think like largely building like quadruped robots, like four -legged dog robots versus humanoids, like probably humanoids are probably like three times harder than that.
They'd be four. That's it. But like there is really no – I don't think there's like really a real market for humanoids like those dogs.
I think it's just like a niche thing. I don't think there's a real business for it. And I don't know anybody that really at this point really wants to spend a lot of time on that.
So you do humanoids, it's like, okay, three times harder, but it's probably like a million times higher payoff. Probably like a million X or a billion X higher ROI for that. You know what I mean?
For investors, for humans that want to work there, get stock and participate in the upside and for everything else. Why would you ever want to work on four -legged dogs? What economic value can a robot dog bring at scale?
If you really understand it, I think there's... Everybody's trying to do the easy work, and it just becomes really difficult. Look at all the AI slop open -claw harnesses out there today.
It's all crap. It's all not good. They're all going to go.
I don't think any of them will make it long -term. You might have some consolidation here and there for acquihires and stuff, but 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. Mark my words.
Have you guys even used OpenClaw since then? No, I don't know how to. Do you use OpenClaw?
I never trusted OpenClaw to set it up. I don't use it anymore. It's not very good.
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 going to actually go do because you're going to 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 our 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 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 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 they can do that well and you look back when you're 70 or 80, would you have asked the same person, hey, you built a really cool $5 or $10 million business. 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 worked on that business.
Would you have gone back 30 years ago and tried to take a bigger swing? Would you have taken a different swing than Vetteri? Vetteri was like that.
Vetteri is my bridge. I sat inside of Vetteri for like, seven years like we literally built like a marketing automation tool for us internally and then like a year later i was like oh man look at this it's outreach .io and it was like a billion dollar company we built that internally a year or two prior and then like watching all this different stuff happen 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 uh for startups assuming like there's a and i do think startups are like I think it is kind of binary.
Even guys that get the $10 million, there's probably like another 90 % of those folks that just didn't make it when they're out there trying. So I think it's just, I think it's just hard. And I think, dude, kudos to guys getting to like five or 10 million in business.
That's hard. Especially doing that if we maybe look a little bit of capital or no capital coming in. 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. 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 uh 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 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 trying to think through this problem of what was the world going to look like in 30 years where is there where everything's headed i think we have an energy problem like not an energy consumption like a generation problem so how we well maybe both but like ultimately how do we like generate more energy uh as like a species i think there's like there's like a there's like a secular trend here you want to go ride and really help and i think there's um a lot of work done correlating this to like like standards of living for humans so i think
There's a lot of work here. 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.
We all live through the internet. I think it's going to be 100 times bigger than the internet. It's going to be so, so big.
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 Horror Configure. It's unclear.
We're still in this spot where it's really not clear who's going to do well here in this stuff. We're in this foggy area for a lot of these stuff. There's been no breakout here.
There's been early wins and early breakouts, but there's a next leg here that we're going to go through. We're in it now. I think we'll know more in the next year or two what that really looks like.
I've used every AI device out there. I haven't been super thrilled. I don't know if you guys are seeing stuff in the market for these type of things, but I haven't been like, man, this is a crazy great product.
I like the small stuff. Whisper, Whisper Flow has 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?
I think I really admire the folks that are fully dedicated in their craft. You really watch the Michael Jordan documentary. He's just like, he's just like, I just want to be like the best in the world at this.
I think for like first startups have 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. Um, I've also got the note.
Jeff base was pretty well, he invested in figure and he's been here a lot of times. And I think, uh, I think Jeff's has been a really good. soundboard for a lot of things we've gone through um 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 uh i i think he's like he's in a lot of really good things what advice did jeff uh give you that was meaningful jeff said when last time he was here he's like listen you're at a really interesting period because like you've figured out how to do this somehow in the next year or two you're gonna figure out how to break through and really get this like working in a bigger way or you won't and like this is like you're it's game time for you now and you 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 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'd been like, I've been like, no way. Like, no way you could.
Dude, four years ago, I came to your office and you just had a knee working. And I was like, oh, that's a knee. That's cool.
All it was was a knee. You had like, there was five engineers. You're like, this guy just got done building the Tesla X or Cybertruck or something.
This guy did this amazing thing. This guy cured cancer. Look how the knee moves and the ankle has dorsal flexion.
And we were just sitting around looking at this knee. And that was like the coolest thing. I know, man.
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 while the joints go up. 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.
I didn'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 pop.
The Hook
The bait, then the rug-pull.
The cold open is a net-worth number and a shrug. The hosts recap a founder who sold a recruiting company for $110 million, took an air-taxi company public, put a second mortgage on his house to fund a humanoid-robot company now worth tens of billions, and has since started an AI lab and a school-safety company. Then they ask what he actually thinks is coming.
How they asked for the click.
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