· Qasar Younis
[00:00:00] S1: Applied Intuition is a physical AI company. We take intelligence, so we take AI and we put it on machines and make those machines smarter. So whether it's having them drive themselves or whether your ability to interact with them and have intelligent interactions just like you would with your phone, but with the context of the real world. So a lot more sensors, a lot more information. And then we do all the stuff that you would think in order to get there. So we develop our own models, train and deploy those models. We evaluate those models. We make sure that they're safe. That's one, let's say, half of the company. We're putting models on machines. And that's way more complex than even what I just said, because the machines have a huge diversity in them. When you're putting something on a phone or a laptop as a developer, you have these great operating systems that abstract hardware away from software. Well, if you're writing software for a combine, that's very different than writing software for a car, which is very different than writing software for a humanoid. We've done a lot of that hard work in nearly ten years. The company's almost ten years now in abstracting away all these different types of hardware from the software. And Peter was one of the early engineers on Android Automotive. Android is basically known for this. It runs on thousands of different devices, and it's the same operating system. So we do that, except we're doing it with the intelligence on top. And then the other half of the company is all the offboard software, the development tools that you would use to develop that intelligence. So we're a B2B company in the sense that we're an enterprise company. We sell to other enterprises, and enterprises meet us in one of those two ways, and sometimes in both ways. They're buying the development environment from us so they can make their own intelligent machines, or they just buy the models and they put them directly on the machine that way. It's a technology provider. And in terms of the Palantir, when the company was very young, it was funny. People would say, oh, you guys are kind of like Palantir for automotive and trucking, or Palantir for defense, which is weird because Palantir doesn't do the defense. It's not exactly a one-to-one. We're different in the sense that we really are two big product areas.
· Qasar Younis, Peter Ludwig
We have forward-deployed engineers, or what Palantir calls them. We call them something else. But the vast majority of our workers — we're like a product company in that way. We're much more actually like a silicon company — we're a technology provider. It's almost like you think about chips, they go into all these different machines and they can do all these different things, but they're kind of a platform and we're kind of like that, except our platform isn't silicon, it's intelligence. [00:00:00] S2: Imagine that you run a company that makes some kind of machine. Again, this maybe is in transportation, something in robotics or something in healthcare, and you want to make this machine intelligent. So the machine, let's say, has sensors and actuators, and you want it to do something intelligently. How do you actually do that? If you want to develop the technology yourself, you're going to need a really strong tooling platform to actually do that development. So Applied Intuition, we make and sell that tooling platform used by engineers at the company that's building that machine. Or maybe as the maker of the machine, you actually want to purchase more of a complete solution. That can make the machine intelligent almost out of the box. We also make more of those complete solutions, which we then sell to those companies as well. We have that spectrum from tools to solution, and then we license this technology out to the industry.
· S0
[00:00:00] S0: I was in San Francisco last week, and basically every other car now is a Waymo or some other autonomous vehicle. It's kind of cool to see the explosive nature of that technology, but there are also a bunch of other technologies, whether it's drones or humanoids, robotics, mining technology, farming technology, et cetera, that if we had them, would be amazing and we could immediately see how valuable the potential would be. But it's very hard to actually predict how long these things take to play out. And you've been able to develop tools across a bunch of these different technologies. I'm curious how you were able to stay flexible and be able to build tools and systems for these technologies, which are hard to predict.
· Qasar Younis
[00:00:00] S1: Before Applied Intuition, I was the COO at Y Combinator. Sam Altman was the president. I ran the firm. It was the era where OpenAI was created, and it's the era where we funded Cruise, Scale, and a bunch of other great companies that are kind of in this space now. I give that context because the most important thing, especially for founders who are listening, is timing is everything. If you build a technology that's maybe two years too early, the market is not ready to consume it and you burn a lot of money waiting for the market to mature, which by the way, I think is the default failure case. Most companies fail because they're too early. Rarely do they fail because they're too late. The opposite though is you can also be too late where it's very competitive. There's lots of players. The margins in the market are kind of being destroyed. I'm an engineer, but I also did a grad degree at HBS and I'm an MBA. That aspect of market dynamics is sometimes underemphasized as well. So with that context — nearly a decade ago, Peter and I approached making this company in an extremely intentional way. We didn't just guess our way into it. Part of it's because we're old — we'd done companies before and we'd led large engineering teams at Google and other places. We have technical experience, but part of it is also being very, very intentional about putting these constraints on. So initially, the first time we talked about working on a company together — even before I was at YC, when we were working together at Google — was, hey, we should maybe do a robotaxi company. And we concluded at that time — this is in the early teens — that simultaneously, the technology hadn't really been figured out, which means you're going to be too early. You're going to spend a lot of money waiting for the technology to manifest itself into a production product. Then secondly, the business model hasn't been figured out. How is the robotaxi going to actually be a profitable venture? I ended up at Y Combinator. Peter stayed at Google. And then fast forward, we funded Cruise at Y Combinator, and then Cruise was bought by General Motors. So I went undergrad at the General Motors Institute. I worked at General Motors. Peter's father and grandfather worked at General Motors. So we're both Detroit guys. Our family roots are very deeply in the automotive industry.
· Qasar Younis
In 2016, when Cruise was acquired — acquired by none other than General Motors — we started talking again about what's happening in this industry. This industry at the time specifically was automotive. Where automotive goes, that's where honestly defense goes, and that's where construction or mining goes, and that's where agriculture goes. Because the ways that you build a haul system, if you're Caterpillar, or Komatsu or a combine like John Deere is actually kind of like a cousin product to a car. Or if you're General Dynamics and you're building an infantry squad vehicle or troop mover or something like this. When we said, okay, where is this industry going? This industry being automotive, we're like, there's gonna be kind of like the Tesla-fication of this industry. These machines are gonna get smart, they're gonna be software first, and then you're gonna have all these tools that are gonna actually enable that to happen for fleet management, to updating software, to testing the software to make sure it's dependable. First principles. So I'm enumerating this for people who are going to start companies themselves. We thought, okay, if we make software right now and try to sell it to the manufacturers, they are not going to consume it from a little young company because they're safety-critical systems. You need a lot more track record and heft, and they're frankly very complex systems. A team of 50 people cannot build an autonomous vehicle. There's too many subcomponents and complexities. So we started with tools. And one thing we're here to talk about really is our biggest product launch in that fundamental category of the company, which is a product called Dana, which is an agentic platform in order to do everything we've been doing for the last almost ten years, but doing it in a much more AI-first way. But that's how we started.
· S0
[00:00:00] S0: Since you brought up Dana, helpful context for everyone. If we trace the evolution of the business, you mentioned that you started with tools instead of the vertical integrated route, building the autonomous vehicles. So you start with tools, you build the OS, now you have the Autonomy Stack, and now you have Dana. Peter, maybe it's helpful for you to walk us through the history of that evolution and how it all plays together.