Applied Intuition CEO: physical AI market is bigger than software AI — company has preserved nearly all capital raised

are from. That was certainly a playbook that was adopted by a lot of companies. Uh showing putting the supply chain on display basically uh it was the right right fit at the time but people are not not into it. The chat is not is not happy about

let's ask let's ask our dear friend

Queser Unice from Applied Intuition because he's here. He's in the TBPN Ultra Dome now. Quer, how you doing?

I'm doing great. How about you guys?

We're doing great. We have to ask you,

do you own a pair of Allirds? What's your preferred shoe when you're walking around a factory like that?

I uh don't own any Allirds. When you're in a factory, uh you have to wear

Yeah, exactly. You have to have a steel. There's no All birds at

Maybe they should Maybe that's the comeback story for them. So, they just they were worth four billion, now they're worth 40. Maybe the steeltoe allirds are what gets it done.

A steeltoe allird would look fantastic. Uh we seem to be having a video delay. I think the team will work it out, but we can't hear you. Can you hear us? Okay.

Okay, great. Yeah, I can hear you and I can see you. Okay, so fantastic. I know exactly what's going on.

Uh well, uh great to have you back on the show. I'd love for you to just uh reset with us for the the shape of the business, where the company is today, how big are you, give us, uh you know, the broad strokes, and then we'll go into the partnership today.

Yeah, thank you. Uh thanks again for having me. Uh the company applied intuition. We're a $15 billion uh company still uh uh doing what we were doing before which is taking intelligence and putting into physical machines.

Uh today we have our first ever physical AI day where we're bringing lots of investors together, bringing industry analysts, you know, bringing everybody who's kind of relevant uh in the field to talk about all the things that are happening in physical AI. We're we're pretty strong believers that the future, you know, the next kind of big thing is AI going out of screens and going into the real world.

Yeah, I couldn't agree more. Uh talk about the the most recent partnership LG.

Yeah, LG Intech. We just uh we just announced this a couple of days ago. The uh I don't know uh how many of your viewers know um but LG provides

You're putting AI in TVs. That's what you're doing. The AI is going in the TV and I'm going to be able to ask questions.

Thinnest biggest.

No, that is not that is not what we're doing.

Much more serious.

I mean, what uh what's happening in the self-driving space is there is an now the models are basically working and they're they're figuring out. So, really there's an aggressive downward pricing pressure of how to make self-driving cheaper. The the research kind of question is done and now it's just an engineering question and that's just another way of saying it's a cost question. So companies like LG who are doing you know sensors at really really uh large scales and really really cheaply.

Yeah.

You know they're they're entering the space as well and we're working together with them on self-driving.

Yeah. So uh yeah take me through uh when when people think self-driving they always think Whimo Tesla but the the the the the market map of like products that need autonomy that would be defined as vehicles. Uh give me some examples. I mean you're standing in front of something. Uh I I know that it's very broad. Uh what's in this partnership and then what else are you focused on? What's adjacent? Uh and what's you know on the road map?

Yeah. So um I think what's different about us versus let's say vertical players like a a Whimo or a Tesla is we provide this uh you know AI across all types of machines. So you see a machines behind me if you were you guys were here for physical AI day. We do we take the same models and we put them in defense. We put them in commercial trucks. We're running driverless trucks in Japan right now uh that are going into commercial operations in the next quarter. We are running in mines. Uh so both all the way from you know Arizona to Australia. So our hypothesis basically is

these this these technologies whether it's self-driving or the underlying operating system they're so expensive and they're so complex to build and maintain. The only way that you really make this a viable business is that you actually spread this across lots of manufacturers and lots of industries and lots of use cases. I mean, our kind of crazy claim to fame is, you know, our company's almost 10 years old and we've preserved basically all the capital we've ever raised.

Which is kind of, you know, it almost sounds like BS, right? Because uh the whole mantra is, you know, raise a lot of capital and then and we're a real AI company. We have real AI bills and we we figured a commercial model which is allowed to scale. We're we have over a thousand engineers and so we're one of the if not the biggest physical AI companies on the planet uh that's obviously also commercially viable. So but it all goes back to that simple thing is like you want to distribute all this cost across lots and lots of companies, lots and lots of verticals.

What about what about shared learnings? like are is a team that's working on mining are they able to find a breakthrough or discover something you can apply to trucking in Japan

like is there a lot of

absolutely that that is the heart of the company and so there's all the what you what you described as like shared learning kind of broadly but there's also technical advantages what we've seen is taking data which is just obvious also not obvious but taking really really diverse data from a mine actually makes our self-driving car system better

and taking you know data that we have from our software and car in Germany, you know, makes our defense work uh better. And so it really is it's really is

to our Yeah, I've heard so many stories about that where like there will be like exactly one instance of a a chicken being chased by a woman on a tricycle in the training set. And so it's very hard for the machine learning system to actually understand that if you see that exact scenario, you got to slow down. But that's the nature of big data and machine learning and these scaled systems. And and and it's not it sounds crazy, but it's not that crazy to imagine some weird scenario that you see in a mine actually teaching you something that you could use just on a normal street.

Yeah. Maybe getting a level level lower just just so because I always me being an engineer always bothers me to talk in pure generality because I tend to mix miss things. Uh just getting to a level lower what you're really talking about is anomaly detection and it's not necessarily like you know you need to see the chicken running across the road in Thailand and that's going to make the mind better. But what's really happening is models are getting a better understanding of the physical world around them

and the kind of parameters around them. If you look at uh you know kind of the the last kind of generation I'm crazy to say last generation but really large language models large language models really improve with diversity of data that is really like you know a kind of a big breakthrough and of course scaling laws all of that stuff is being brought in to the physical world. Yeah. And uh and we're powering that.

Yeah. I mean truly no one would have predicted or I mean of course some people did predict but I would have never predicted that like uh including poetry would help a model get to like solving math. Like I would just see those as different things and say put the poetry team over there, put the math team over there. But actually bringing all these things together worked really well. Uh play out the counterfactual for me. Uh you you haven't you haven't been a high burn company. You haven't been super capital intensive. If you'd done vertical integration and built the tractor behind you uh that would have been extremely capital intensive. Correct. Is is is that like impossible?

It's well nothing is impossible. Uh but you know I my my my my uh undergrad uh was at this obscure school called the General Motors Institute and uh as the name implies it's really about automotive. It's like the West Point for automotive and uh when you spend a lot of years in factories as as I have uh there are some deep lessons that get imparted into you and one of those lessons is holy crap these factories are extremely cost intent the capital intense and they're extremely complex and uh the strengths of Silicon Valley are actually don't quite overlap with the strengths of building a large factory. Now in terms of the core question, we had Mark and Dreon here today and we we you know we we talked about this. Mark was one of our first investors and has kind of been been along with us with the entire ride. I mean all the way to the presentation today and we asked him this question about vertical horizontal. What do you see happening in AI? What do you see happening specifically physical AI? And the punch line is you know we all of our values at applied intuition can be reduced down to two words radical pragmatism. And if there are verticals that we think that we should be a bit more vertical in, we'll we'll we'll do that. And I think it's it's kind of a false trade-off to say what we do in, you know, trucking is what we're going to do in construction, which what we do in agriculture is what we're going to do in mining. What we're really trying to do is bring intelligence out into the real world. And each of these verticals are facing really, really different problems. You take a, you know, with a tractor behind me, the average American farmer is 58 years old. there's nobody coming to replace that person. And so what is going to happen because you know if you take that person their kids have have left and they're often not coming and taking over the farm like maybe in previous generations. So that farmer needs you know we don't need to teach them how to use claw code. That's not what's going to change the farmer's trajectory. What's going to change the farmer's trajectory is the machines are intelligent and they're working harder and smarter on the on on their behalf. So he can run an entire farm with a, you know, with a swarm of machines. And that's not,

you know, that's not too far into sci-fi. One of the key components here that, you know, we're doing and we believe is you need to abstract that hardware and software away. We we as technologists, you look at like your laptop and your phone and you kind of take for granted the miracle that exists. Android runs on thousands of hardware devices flawlessly. So that's also something that apply does. We're just abstracting a hardware and software. Once you do that, you can make every machine, you know, intelligent.

Have you tried to estimate the economic impact assuming you guys, you know, stay at the, you know, at at the current kind of improvement rate or accelerate as the technology kind of starts to diffuse in in some of these industries like trucking and and mining and agriculture like what what are the downstream impacts? I mean, there's such a debate right now around what what what impact will AI have on the economy? So much the economy is like moving physical things around, producing things, shipping them.

Let's let's sep Exactly. Let's separate a little because economy is such a generalization. So when you're talking about like you know code complete and white collar work is very different than you know trucking where there is a huge labor shortage. It's very different than in mining where you know people don't want to go live in kind of remote areas doing 12-hour shifts. I mean literally labor shortages are preventing construction companies from you know collecting billions and billions in revenue. So these are industries where AI can't get there fast enough.

Yeah.

It's a very different calculus than a kind of you know I think what the normal narrative is and uh and then we're super obviously excited about that. Let's take defense as a particular example. It's a very salient example. We don't need more warf fighters in harm's way. We need less war fighters in harm's way. And no war fighter wants to go out in into that ecosystem where a autonomy is really becoming the dominant thing. And so so I think the way to think about this impact in the physical world is it's a lot less resistance. There's a lot more pull. Now the first question you asked is the size of impact. I don't want to, you know, sound like I'm pitching my own book here with the

I'm asking you. I'm asking you. I want I want the biggest number. I want the biggest number.

The numbers are absurd and ridiculous. I I but there but I can tell you this much if you think about you know the way I think about you I used to be a Y cominator before I was a COO and and and you know ran the firm and funded lots of interesting companies and one of the analogies I used to use to help founders understand market potential market sizes yeah I grew up in Detroit you're sitting in the Detroit metro airport and you're sitting in a gate you look around how many of those people are like really deeply using cloud code I mean sure

frankly speaking not many lot will be using something like chat GBT, some variant of that, maybe Gemini. Uh, but how many of those people drive?

How many of those people work at construction sites? How many of those people ride in buses? How many of those people serve in our armed forces? The point is a much much larger group. And I I I like that I I feel a little again the engineer in me feels a little awkward saying these kind of you know pitching these things but I think the market for physical AI is way way bigger purely because the surface area is much bigger and it's compounded by the fa the way that the way technology diffuses with phones and laptops creates this like rapid you know competition that you see in you know that you're seeing in all these kind of subspaces right

in physical AI you got kind of know what's going on in the car business. And I'm not saying, you know, I'm not, you know, gatekeeping and saying, you got to go to the General Motors Institute to build technology for the car business, but you bet your bottom dollar it helps.

And uh and we're doing that across a bunch of industry. I think it's I think it's, you know, I'm I'm as confident about the company as ever before. You know, the question we always get asked this question, why the hell did you raise all this money, you know, almost a billion dollars? You're just going to keep plowing it away in the bank account. We're doing it for a simp simple reason because we can if we need to we can invest very aggressively to take opportunities that we think we can accelerate you know beyond just traditional organic growth and so far that's worked. It's not to you know promise the future that we won't uh but that's those are kind of debates we have every single day.

Yeah makes a ton of sense. Well thank you so much.

I just want to say I can see the path to a hundred and then a trillion dollars in in run rate. I agree.

Well I mean uh Whimo uh you know a company that we we we love them. there are local, you know, we're we're also in Mountain View now, Sunnyville. That company, uh, you know,

is a great company, but is burning a lot of capital and, uh, is a smaller revenue base than us and just raised at $126 billion.

I mean, I love those guys. I mean, we have so many friends there. I'm not I'm not trying to talk poorly about this, but

No, we love Whimo, too. It's very impressive what they're doing. you know, 15 that we're at and 126. I I think we got room to grow.

Massive. Massive. Yeah, we got room to grow.

Tell tell Mark. Tell Mark you're ready. You're ready for the big believe me. Everybody wants to, you know, I feel like it's faux where they want to get Pete keep putting money, you know, money into the company. We we don't need anybody.

That is the best analogy for a venture capitalist. They are the farmers stuffing the goose. We we we we have our own we have our own uh farm, you know, and we're making our own money, so that that's really great. And frankly speaking, I mean, like I said, Whimo is great, but it's just robo taxis.

Yeah. Yeah.

And and and that's a small it's

so much go to go to go to Warren, Michigan.

Yeah.

And you just go go to the party shop in the corner and say, "Hey, aren't you excited about Whimo? I don't think you know it's not hit the masses yet." which just shows obviously Whimo's growth potential, but also shows I think how