Applied Intuition: A Billion Intelligent Machines

Business Breakdowns

Co-founders Qasar Younis and Peter Ludwig discuss Applied Intuition's mission to provide the 'brains' for a billion intelligent machines. They

Key takeaways

• Applied Intuition utilizes a horizontal business model similar to Nvidia, selling intelligence into various machines across automotive, defense, and agriculture.
• Physical AI presents unique challenges compared to digital AI, specifically regarding real-time constraints, safety criticality, and cost envelopes.
• The company's new platform, Dana, is an agentic platform designed to lower the barrier to entry for developing and deploying physical AI systems.
• A significant competitive moat is built through a proprietary data engine that uses feedback loops from diverse machines like drones and trucks.
• The founders emphasize that timing is critical in deep tech, noting that being too early often leads to failure due to market unreadiness.

Main topics

• Physical AI vs Digital AI
• Horizontal vs Vertical Business Models
• The Dana Agentic Platform

Notable quotes

Our mission is to make a billion machines intelligent.

Conclusion

The episode highlights how software-driven intelligence will be the fo

Transcript preview

Speaker 1 (0:00) This episode is brought to you by Portrait. Portrait was built by former BuySide investors, and they understand great investing isn't just about having more information from low-quality sources. It's about having the right information organized the right way. And if you listen to the show, you appreciate diligence consists of many things. Diving into the history of a business, framing the nuanced competitive dynamics, tracking key signposts around your thesis. And historically, that would take up material time that you do not have. But portrait is basically like adding an army of analysts to your team. It's powered by an AI system specifically designed for investment research workflows. So you get nuanced idea generation. Portrait assesses the same types of qualitative attributes that we discuss on this show. And that can help identify businesses which fit your frameworks. Portrait also customizes research report generation. And third, there's intelligent thesis monitoring. And that's where Portrait assesses thousands of data points across value chains each day, extracting the insights, driving the business. Again, all this work would typically take hours and hours and hours. It's at your fingertips now. Visit portraitresearch.com to start your free trial today. Speaker 2 (1:28) This is Business Breakdowns. Business Breakdowns is a series of conversations with investors and operators diving deep into a single business. For each business, we explore its history, its business model, its competitive advantages, and what makes it tick. Speaker 2 (1:47) We believe every business has lessons and secrets that investors and operators can learn from, and we are here to bring them to you. To find more episodes of Breakdowns, check out joincolossus.com. All opinions expressed by hosts and podcast guests are solely their own opinions. Hosts, podcast guests, their employers, or affiliates may maintain positions in the securities discussed in this podcast. This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions. Speaker 5 (2:16) Today, we are breaking down applied intuition. Our guests are co-founders Kassir Yunus and Peter Ludwig, who started the company in 2017 with a mission to make a billion machines intelligent. The simplest way to understand applied intuition is that it builds the brains for machines and the tools other companies use to build those brains. If a manufacturer wants its tractor, truck, or mining vehicle to drive itself, it can buy the intelligence from applied intuition or use its platform to develop its own. Just as Nvidia sells chips into everyone else's machines, Applied Intuition sells intelligence into everyone else's machines across automotive, defense, mining, agriculture, and robotics without building any single machine itself. We discuss why the most important companies of the next 25 years will all be physical AI companies, Dana, their new agentic platform for developing and deploying these systems, and how the company raised a billion dollars without spending any of it. Please enjoy this breakdown of Applied Intuition. Speaker 5 (3:11) I know a lot of the story we're going to tell today is going to be about a single business, Applied Intuition. But it's also really the story of the physical AI market and how far autonomous technology has come. And you too see this across as many industries as about anyone. Maybe just describe the state of the physical AI market, how the whole landscape feels to you now in 2026, and maybe some of the important key hash marks on the timeline since when you started the company in 2017. Speaker 3 (3:38) In our case, in Applied Intuition's case, our mission is to make a billion machines intelligent. One simple way of that you could think of is self-driving cars. Those are intelligent machines. But it's one example, like Instagram is an app on the phone. There's many also other apps. So physical AI is this intersection of AI and hardware typically, but in the real world. humanoids falls into this as well as a category. The particular technical challenges of physical AI are quite different from digital AI, which is like your LLMs and your information retrieval systems, like chatbots, stuff like that. Because you have the constraints of the real world, you have the safety criticality of the physical world. Often when we're talking about moving machines, they're moving in a time and space with humans, and suddenly that becomes... something where you have to really think about safety, the real-time nature of the problem. So if you ask the chatbot, tell me about Peter Ludwig, it can take 20 seconds to process that information. But when you're flying down the highway, a humanoid is making a decision, there's very hard real-time constraints there. And then probably an underreported aspect of physical AI is the dollars. You need to put this on machines and on silicon that is affordable within the use case that you're talking about. You don't just throw endless compute at processing something. You have to do it in a compute envelope, not only a time envelope, Speaker 4 (5:10) but also like a cost envelope. It's useful also to think about the separation of digital AI and physical AI, right? So digital AI, typically thinking about what you're using on your desktop or your mobile phone. There's a screen that's showing you the result of the AI. And then where that crosses into physical AI is when anything is in the real world actually moving. And this is where the real impact on the economy will happen when you talk about all of the industries that fundamentally have moving things. So think about anything from industrial companies, manufacturing, use cases in healthcare and energy. There's so many different fields where in order to get the benefits of AI, you actually have to impact these physical systems. Just to add on to that, by putting Speaker 3 (5:49) self-driving and intelligence on machines, we're really making some of the worst jobs on the planet easier. In digital AI, there's a lot of like teeth mashing and hand wringing about what's going to happen to, you know, accountants and maybe even podcast hosts. In the physical AI world, it's very, very different. Like the AI can't get there fast enough. If you look at like farming, the average American farmer is 58 years old. We have record shortages in long-haul trucking. Mining, as an example, is 1 % of the world's workforce, but accounts for 8 % of work-related fatalities. I mean, you kind of peel that back, like why do people not want to work on mines? These are difficult jobs. People are just choosing. They don't want to be away from family in unsafe circumstances. The impact can be very, very quick and very, very significant in areas. where there's a lot of demand. And so I think that's one of the important things to keep in mind, which is very different in physical AI than frankly, digital AI. I think when we look back 25 years from now about this particular state of business and technology phase that we're going through, we're really going through a lot of changes very, very quickly. My hunch is I don't think we're going to only be talking about code complete. products. Those are important. And you can kind of see how big of an impact just that use case is making as a proxy to how big of an impact that AI can have on society. I think when you look back 25 years from now, I think physical AI companies are going to be the ones that really dominate everyone's mind. Speaker 5 (7:19) And Kester, I know you have this whole notion that the physical AI market is going to be in orders of magnitude much larger than the digital AI market. Maybe just unpack why you think that and just help us appreciate this market. Speaker 3 (7:30) Look at industrials as a category, a huge category of the economy, roughly 5 % of GDP. Automotive of that is the largest of the industrials, which is 3 % of global GDP, which is an astronomically high number. And so when automotive, which is personally owned passenger vehicles, when they become intelligent, the impact to all the people that interact with cars every day, which is essentially everybody on the planet, is really, really big. If you're sitting in an airport and you look around the gate, how many people have interacted with a car versus how many people wrote software that day? So then you go into the, let's say, other verticals. So Applied Intuition, we play in all of these verticals. We are the only company on the planet that does this. This includes the Chinese ecosystem and others. So the other verticals that we're in are commercial trucking, defense, construction, mining, agriculture. robotics. And each of those verticals is very, very large in itself in whichever way you want to define it. In just pure GDP numbers, growth numbers, number of people employed in those sectors, almost in every measurable way. Sometimes the markets get so big, they're almost like hard for folks to put their head around. But I mean, you can just use like RoboTaxi as one instantiation of self-driving cars, and you have Waymo being valued at $126 billion by fairly sophisticated investors. They're not valuing Waymo at $126 because they just want a high valuation. It's because the impact can be very, very large. And that's one part of one of those markets. Speaker 5 (9:05) And before we go any deeper, Applied Intuition, in my sense, almost has this like Palantir mystique around it, where it's actually very hard to describe what it is and what you guys do. So maybe just to orient us. literally describe as simply as you can what you guys actually build and sell. Speaker 3 (9:21) 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 and 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 10 years. The company's almost 10 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. We kind of do that, except we're doing it with the intelligence on top. And then the other half of the company is all the off-board software, the development tools that you would use to develop that intelligence. And 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. We really are two big product areas. We have forward deployed engineers, we call them something else, but most vast majority of our workers, we're like a product company. In that way, we're much more actually like a silicon company. Like 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. Speaker 4 (11:31) 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, right? So the machine, let's say, has sensors and actuators and wanted to do something intelligently. How do you actually do that? Well, if you want to develop the technology yourself, you're going to need a really strong tooling platform to actually do that development. And so Applied Intuition, we make and we 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. Then we license this technology out to the industry. Speaker 5 (12:21) 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, etc. That if we had them, it would be amazing and we could immediately see how valuable the potential would be. But it's very hard to actually predict how long in the timelines these things play out on. And you've been able to develop tools across a bunch of these