· 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.
· Qasar Younis
[00:00:00] S1: Use an example. If you're building an app for an iPhone, high school kids can do that now because there's all these things that exist and that make it easy. And with the new coding platforms, like vibe coding platforms, it's easier than ever to make a web app. Super, super simple. It's very hard to do that in terms of robotics. If you wanted to build a delivery robot for college campuses or a little vacuum that cleans your house, it's a pretty daunting thing even for hobbyists and computer scientists. You have to patch together lots and lots of disparate products and tools, and then you have to somehow figure out how to deploy that software onto that physical machine. Dana really is that, along with the fact that it brings a lot of that agentic power in writing software purely for web applications. The thing that Peter's really emphasizing, and the thing that we really want to do, is lower the bar so anybody out there, starting with engineers, but ultimately really anybody, can develop robots. And I think that really takes us much, much closer to that mission. I think, frankly speaking, it wasn't possible a few years ago because we didn't have the intelligence, literally the models that would help us create Dana, and then for end users to use Dana to actually create intelligence.
· S0, Qasar Younis
[00:00:00] S0: How much of the drive to build Dana was building where the puck is going, and how much of it was these are customer pain points or friction points and maybe we can enumerate some of them and then we need to go ahead and build the solution for them. And this is like the next evolution of the stack that we're building. Tools, OS, autonomy stack. And now we have Dana, how much of it was outside in versus inside out? [00:00:00] S1: Both. I'd say both — we use our own tools to develop autonomy as well. So we're our own customer and those are different parts of the company. And I would say our most aggressive customer feedback comes from internally where there's very little patience for anything that doesn't work quickly and fast and on first try. We're an enterprise company and for almost a decade we've been deploying tools to customers and we get feedback from many thousands of engineers who depend on us. They're also using things like Claude. So then it's very natural if you're using Cursor and Claude and say, hey, where is the Claude cursor thing but in physical AI? And we're the company to develop that. This is, frankly speaking, our bread and butter. I think if you're in the space, it's very obvious it should be that easy, like it is to use one of the major coding platforms.
· Qasar Younis, S0
[00:00:00] S1: Both. I'd say both — we use our own tools to develop autonomy as well. So we're our own customer and those are different parts of the company. And I would say our most aggressive customer feedback comes from internally where there's very little patience for anything that doesn't work quickly and fast and on first try. We're an enterprise company and for almost a decade we've been deploying tools to customers and we get feedback from many thousands of engineers who depend on us. They're also using things like Claude. So then it's very natural if you're using Cursor and Claude and say, hey, where is the Claude cursor thing but in physical AI? And we're the company to develop that. This is, frankly speaking, our bread and butter. I think if you're in the space, it's very obvious it should be that easy, like it is to use one of the major coding platforms. [00:00:00] S0: Help us understand why Applied Intuition is uniquely positioned to go ahead and build this rather than another company, or as you said, there are other generic AI assistants or coding agents that could potentially do some of this. What's so different about Dana and why Applied Intuition specifically?
· Peter Ludwig
[00:00:00] S2: So it's important to understand a bit more about the technology itself. So the general-purpose models that come from companies like Anthropic or OpenAI, those are great and they're very useful for very general-purpose tasks. But when you're dealing with things that have a very deep safety-critical component, things where lives are literally on the line based on what is being developed, and they require a very complex development toolchain. Just the model is not enough. That's like 1% of the full solution. The Dana platform itself, this is building on top of everything that we've built over the past ten years. Everything has an API, and has been re-architected to work in a model with AI agents at the forefront. These very, very complex workflows to actually build physical AI, they maybe would require switching between 20 different tools in the past for different tasks. And deeply understanding this precise flow of information between all of those things to accomplish your end goal. So to actually make a physical AI system work, there's many, many layers to that technology stack. For example, it's like, why couldn't you use Claude to build the Linux kernel? It's like, well, because the Linux kernel is actually a very, very complex piece of technology that's been built out over many years and using many, many other different tools. The same thing in physical AI. Fundamentally, these technologies are very, very complex. And now with Dana, all of those complex workflows can be very seamlessly orchestrated from an agentic interface where you're able to write things in plain English and receive answers in plain English and do very detail-oriented things that are very deep in data science and production deployment of this type of AI.
· Qasar Younis
[00:00:00] S1: Use an example. Let's use an autonomous lawnmower. What are all the things that you need? First, you need some sensors, you need some compute, then you need to create a software package that understands this is the yard I'm going to work in, the physical space I'm going to work in and don't go in other places. So now the sensors have to understand that physical space. Then you want to create scenarios that lawnmower has to successfully pass in simulation. So you need a simulation framework. And so how do you simulate your backyard? And so that also is complex. Simulation itself is a massive industry. There are companies that are worth tens of billions of dollars that only do simulation. And that's where we started. Our bread and butter was in simulation, in the world model universe. So now that you have a simulated backyard, you have scenarios in the simulated backyard that you can run again and again, and you have to do that in the cloud. That is a whole orchestration that needs to happen. And then ultimately, once you're performing at a certain level of efficiency and fidelity, then you're going to deploy that first version onto the physical lawnmower. And then a bunch of things are not going to work and you have to figure out, well, why didn't it work? Why didn't the actuation happen as you thought it would? Why are the control systems maybe not behaving? And so then there's a whole loop of feedback. You can't do that all in an LLM. It's not made for that. LLMs are really made for a different environment. So we keep hammering things like the hardware interface or the safety criticality, but it's, frankly speaking, underemphasizing just how different it is to build a web app versus an AI product in the physical world.