· 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.
· Peter Ludwig
[00:00:00] S2: Firstly, I would say something that we knew when we started almost ten years ago was that the technology is still going to change a lot. There's a lot of advanced engineering and research that works in this entire field. A way that you can be part of that, but not be, let's say, overly exposed to any specific implementation is to think more horizontally. And so for us, that meant initially really focusing on tools and then building tools in such a way that we could continue adding on to the platform and recognizing that the technology itself, when we talk about physical AI and advanced autonomous systems, almost every two years there's some sort of breakthrough that changes how you have to think about these things. If you're not dynamic enough in understanding how to adapt that latest technique, that latest breakthrough, then you can become almost obsolete in that sense. And that's been really baked into Applied's DNA. It's almost like this internal disruption that we have to do to ourselves to make sure that we stay on top of things. And so tools was a great way of doing that initially and doing that horizontally across all these industries. What happened after a few years of working on tools is you hit a point deploying this technology onto machines that the problem's much larger than just the tools. And you have to think, well, what are the bottlenecks? What are the rate-limiting factors? Again, with this North Star of we want to have a big impact. We want to make a billion machines autonomous. We want to do this safely and efficiently. You hit this point where all of a sudden the operating system actually becomes a bottleneck, deploying this technology onto the vehicle itself. It sort of forced us into that business, and we had to build a really good solution that allows you to deploy software onto machines, to update that software reliably, to have all the right diagnostics. And running advanced models, neural networks on machines, is extremely complicated. Sometimes it gets trivialized and people think about, oh, it's just about the model, but really there's about 1,000 different problems you have to solve to make this all work. And the operating system piece is a really big part of that. And so we had to solve that.
· Peter Ludwig
And once we had those two components, we had the tooling platform, we also had the operating system platform, then we have to start thinking about creating more of that full solution. And that's what brought us really into the vertical autonomy stack, doing more of these models ourselves. Making those available to customers. And now we have a really complete and very compelling offering in a lot of these areas.
· Qasar Younis
[00:00:00] S1: Yeah, I think also worth highlighting in this concept of a horizontal company versus a vertical company, the vertical companies, it's easy to understand — like a Tesla is a vertical company. A horizontal company is like an Nvidia. It's a company that sells this technology across a broad base of customers who then package it together into something and take it to market. It was important that the stuff that we built for one vertical could be used in other verticals. And so we also put that constraint on. So again, for the founders at home, it's not enough that you have ambition in building a company. Your ideas also have to be correct. Almost ten years ago, the question always was, well, why tools is a bad business and why be horizontal? Vertical is the right answer. I really implore everybody to really think from first principles and our results speak for ourselves. In the company's history, we raised about $1 billion, and all of that is in the bank. We've never used any money we've ever raised. And there's not an AI company like us in the business that is financially stable and healthy. And I attribute that to our technical strategy of putting the stuff that we make in automotive onto defense, putting the stuff we make in defense onto construction and mining, and so on.
· S0, Peter Ludwig
[00:00:00] S0: I know you guys have this grand vision — and you guys mentioned it — of getting to a billion intelligent machines over the next decade. We're here to talk about Dana and how that's going to enable and unlock that possibility. Maybe talk about what Dana is and how that's going to help us get to that future. [00:00:00] S2: Yeah. So Dana is our new agentic platform for physical AI. This really is the culmination of pretty much everything we've worked on now over the last decade, and it makes developing these systems so much easier than it has been in the past. It's important to understand why that's important at the outset, though. So building physical AI is extremely complicated. If you ask, why don't we have intelligent robots and intelligent vehicles everywhere today? It really comes down to building this stuff is really hard. That is the limiting factor in it. We know how to make the chips, we know how to make the hardware for these systems. It's more so actually developing all of this technology and getting it to work is very, very difficult. Our engineering tools over the last ten years, they are addressing parts of this, but now with modern AI and with this new Dana platform, we're really drastically reducing the barrier to entry to building physical AI and deploying it in the real world. With that lowering of the barrier to entry, I think it's going to make it far easier to build a very large variety of solutions and really supercharge our customers.