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Business Breakdowns
Applied Intuition: A Billion Intelligent Machines - [Business Breakdowns, EP.248]
00:47:13
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- [00:04]
Podcast Host
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. 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 episodes of breakdowns, check out joincolasis.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.
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Today we are breaking down Applied Intuition. Our guests are co founders Kasser 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 his 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.
- [01:47]
Interviewer / Host
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
- [02:05]
Podcast Host
physical AI market, how the whole landscape
- [02:08]
Interviewer / Host
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 in our case.
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Kasser Yunus
In Applied Intuitions 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, but also like a cost envelope.
- [03:47]
Peter Ludwig
It's useful. Also 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, where 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. I think 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.
- [04:23]
Kasser Yunus
Just to add on to that, by putting 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. You know, in the physical AI world it's very, very different. Like the AI can't get there fast enough. If you 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. When 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 and 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 codecomplete 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 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.
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Interviewer / Host
And Kasser, 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.
- [06:06]
Kasser Yunus
Look at industrials as a category, a huge category of the economy is 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. 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 like Robo Taxi 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.
- [07:41]
Interviewer / Host
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.
- [07:57]
Kasser Yunus
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 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 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 offboard 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.
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Peter Ludwig
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 you want it 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.
- [10:57]
Interviewer / Host
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, 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 different technologies. I'm curious how you were able to stay flexible and be able to build tools and systems for these technologies, which
- [11:35]
Kasser Yunus
are hard to predict out before Applied Intuition. I was the COO at Y Combinator. Sam Altman was the president. I was a coo. I ran the firm. It was the era of where OpenAI is created, and it's the era where we fund Cruise and 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 Just very competitive. There's lots of players. The margins in the market is 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 under emphasized as well. So with that context, how did we navigate. Nearly a decade ago, Peter and I took 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, so we'd done companies before and we'd led large engineering teams at Google and other places. We have, you know, technical experience. But part of it is also is just being very, very intentional about putting these constraints on. And so initially what we envisioned was the first, first time we talked about working on companies because even before I was at yc, when we were working together at Google, was, hey, we should maybe do a robotaxi company. We concluded at that time this was in the early teens, that it's simultaneously, the technology hasn'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. And then secondly, the business model hasn't been figured out. Like, how is a robotaxi going to be really a profitable venture? I ended up at Y Combinator, Peter stayed at Google, and then fast forward, we funded Cruise at Y Combinator, and Cruise was bought by General Motors. So I went undergrad at the General Motors Institute. I worked at General Motors. Peter's father, grandfather worked at General Motors. So we're, we're both Detroit guys. Our family roots are very deeply in the automotive industry. In 2016, when Cruise was acquired, it was 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 and mining goes, and that's where agriculture goes. Because the ways that you build a hall system, you know, if you're, if you're a 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 a infantry squad vehicle, a troop mover or something like this, when we said, okay, well where is this industry going? This industry being automotive? We're like, well, there's going to be kind of like the Tesla fication of this industry. These machines are going to get smart they're going to be software first and then you're going to have all these tools that are going to actually enable that to happen for fleet management, to updating software, to literally testing the software to make sure it's dependable, first principles. So I'm just kind of enumerating this for people who are going to start companies themselves. We thought, okay, well 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 like 50 people cannot build a autonomous vehicle. There's too many subcomponents and complexities. And 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 10 years, but doing it in a much more AI first way. But that's kind of how we started.
- [15:32]
Interviewer / Host
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 integrator 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 pays together.
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Peter Ludwig
Firstly, I would say something that we knew when we started almost 10 years ago was just 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 onto the platform and just 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 and 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 sort of 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, sort of 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 a thousand 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. And once we had those two components, right, 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 and making those available to customers. And now we have a really complete and very compelling offering in a lot of these areas.
- [18:07]
Kasser Yunus
Yeah, I think also worth highlighting in this concept of like a horizontal company versus a vertical company. The vertical companies, it's kind of 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, and so we also put that constraint on. So again, for the founders at home, it's not enough that you have like ambition in building a company. Your ideas also have to be like, correct. Almost like 10 years ago, the question always was like, well, why tools is like a bad business and why, why be horizontal? Vertical is the right answer. You know, I really implore everybody to really think from first principles and our results speak for ourselves. 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.
- [19:08]
Interviewer / Host
I know you guys have this, like, 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 just talk about what Dana is and how that's going to help us get to the future.
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Peter Ludwig
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 10 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.
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Kasser Yunus
Use an example, if you're building an app for an iPhone, like 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 like a delivery robot for college campuses or like a little vacuum that cleans your house, it's a pretty daunting thing even for hobbyists and computer scientists. Like, 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 just for, like, web applications. The thing that Peter's really emphasizing and thing that we really want to do is just 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.
- [21:48]
Interviewer / Host
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 that we need to go ahead and build the solution for them. This is just like the next evolution of the stack that we're building, right? Tools, OS autonomy, stack. And now we have Dana. How much of it was like outside in versus inside out?
- [22:10]
Kasser Yunus
Both. I say both is 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 like 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, I mean, this is just, frankly speaking, our bread and butter. I think if you're in the space, it's very obvious it should be that easy. Just like it is to use one of the major coding platforms.
- [23:00]
Interviewer / Host
Help us understand why Applied Intuition is uniquely positioned to go ahead and build this rather than another company. Or as you said, like 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?
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Peter Ludwig
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 tool chain. Just the model is not enough. That's like 1% of the full solution. The Dana platform itself, right? This is building on top of everything that we've built over the past 10 years. Everything has an API 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 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. Like, 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.
- [24:48]
Kasser Yunus
Use an example, let's use a 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. This is 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 that lawnmower has to successfully pass in simulation. So you need a simulation framework. So how do you simulate your backyard? 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. So, 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, okay, so now you have scenarios in a simulated backyard that you can run again and again, and you have to do that in the cloud. That is an old 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, there's a whole loop of feedback. You can't do that all in an LLM. It's not made for that. And 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, under emphasizing how just different it is to build a web app versus an AI product in the physical world.
- [26:22]
Peter Ludwig
Also in that process, right, you're doing model training and evaluation. You're doing data collection and post processing of that data. These are very complex systems. But that exact analogy, you can apply that really to any kind of machine or any kind of robot, and those same things apply.
- [26:38]
Interviewer / Host
Your team, while we were prepping, actually described this very beautiful loop where you can take information and data that you're getting from a tractor or an underground mine. And then that's relevant to, let's say, like a drone or autonomous vehicle. That's also relevant to an autonomous C vehicle. And they all kind of feed into this broader platform and inform how the platform gets made. I would love for you to just kind of describe that loop and how that helps build a general purpose, broader platform.
- [27:07]
Kasser Yunus
An AI system really is always two big components. One is the actual platform that you develop, the intelligence, and the other is the actual intelligence. So it's almost like the world model and then the actual intelligence that you'll deploy on the machine on that second half, on the intelligence that you're deploying on the machine. The way to think about it is this data engine. It's a feedback loop. So as the car is in the real world consumes data, as in it consumes the world around and creates data packages. It also is not successfully navigating specific scenarios. So you can almost mark, hey, the car had difficulty in doing this. So then how do you help the brain on the car navigate that scenario that it wasn't able to navigate last time more successfully next time? Well, you can do it a couple of ways. You can expose it to lots of scenarios that humans maybe have already literally human driven. So it imitates how humans handle that scenario. You can create a synthetic environment where you show it this is how you would navigate this type. And there's a bunch of techniques. But the macro point is there's a data loop. It's just feedback. So the machine interacts with the scenario. It can and cannot navigate that. Now step back. Don't just make that a car, make that any type of machine. It can be a drone, it can be a mining dirt mover, it can be a combine, and the same thing happens. The interesting thing we've learned in our development of intelligence and just models is as we take scenarios from let's say a drone or we run autonomous trucks right now, L4 trucks in Japan, we take data from those trucks. It actually makes the performance of those models in fairly different environments better. So what's really happening is the model is getting a sense of physics in the real world. This should elicit some corollaries in the chatbot universe. Chatbots used to be very, very specific. Transformers happen and general chatbots now actually can perform really, really well. The same thing has happened in self driving. We just benefit a lot from that because we see this diversity of data in all these different use cases. And so it feeds that data loop in the data engine.
- [29:08]
Peter Ludwig
It also gets to a bit more of the distinction between digital AI and physical AI. Right. Because in digital AI, if you're thinking about these general purpose models, those are oftentimes they're trained on the Internet, plus maybe some extra data that the model company has built, and that's usually text data. But in physical AI, almost all of the data is actually proprietary. Right. It's like data that we ourselves are collecting through our own vehicles and partnerships that we have with our customers, collecting that data because you're ultimately building these models on data that's just not available on the Internet.
- [29:39]
Interviewer / Host
While we're on the topic, Peter, maybe you could talk about the quantum of data that you're able to collect is almost unimaginable across all the different vehicles and machines and industries and applications. I'm thinking of like this mega brain or Gigabrain in a way, maybe just talk about the data that you're able to collect and then the actions that you're able to take on top of that data that maybe no one else is able to do.
- [30:00]
Peter Ludwig
We do have an enormous amount of data that is fact. It's very meaningful because it's first off, it's just expensive to do, but it's also very difficult. The actual tech stack required to do reliable high quality data collection is surprisingly deep and complex. And there's not that many companies around the world that really have a very high quality tech stack for doing data collection. For physical AI, that's a pretty fundamental remote and long term advantage that we have. There's also all of these other very deep things that are unlocked based on that data. That there's this combination of imitation learning with reinforcement learning, which we're very, very deep in. Which I think this is really the critical unlock to scaled physical AI. So a lot of the talk right now in Autonomy is this topic of end to end models where you can take data that's been collected, you can train a model off of that data using Something called imitation learning. And that allows then a machine to effectively mimic what was being done in that training data. That's great. And it's been proven that it's very effective, but oftentimes it doesn't actually get you to a fully productionizable solution. What we've now added and really innovated on in a big way and done a lot of research and actually published a lot on as well, is reinforcement learning. And so you take that base imitation learning model and you complement that with a really powerful simulation environment using very highly performant reinforcement learning. And that can actually smoothen out a lot of the problem cases that you'd get with pure imitation learning. And we see this as the technical path towards large scale, widely deployed physical AI. And I think we have some pretty unique advantages across the spectrum right now.
- [31:33]
Interviewer / Host
Are there other rate limiters that we should discuss? Obviously, Dana is a huge unlock for Peter. You were mentioning how the operating system is a rate limiter in terms of like it's just very hard to design, develop, build, test, analyze all these different systems and Dana is now going to be able to go ahead and do that. Are there other rate limiters? Whether it's anything from like the chips to the sensors to the actuators to materials, like power is like a big issue now. Anything that's around the actual technology that
- [32:01]
Podcast Host
you're building that worries you, the diffusion
- [32:04]
Kasser Yunus
of this technology will be at very different rates and very different ways. Fable comes out. It can work on your phone and your laptop because those environments are quite standardized because of the browser and the operating system and a bunch of other things, app stores and payment plan methods and stuff like that. So it's very easy to consume that intelligence as an end user. There's just impedances. In order to diffuse this intelligence into physical machines. They could be manufacturers, it could be the operators of the farm and the mine. So there's a lot of other things that kind of get in the way and maybe just as importantly, the actual dollars. When people talk about self driving cars, I always like to use passenger vehicles because everyone can kind of understand it's maybe a little harder to grok like ports in terms of your personal vehicle. Let's say you just bought a Honda Accord yesterday and then tomorrow self driving is available for free. Well, you still own that Honda Accord. Over half of Americans live on a fairly small savings account. So they're. When they buy a car, it's a big deal and it's a big purchase and they're going to use that car for 10, maybe 15 years, regardless of what the other product that's available in the market is, just because of the nature of economics and how much money that they have. And so the diffusion of this intelligence in these machines has a lot of different complexities that you won't see on a desktop or on a phone. But I think those are also moats. Like once you figure out how to make a mine autonomous or a farm autonomous, we as a technology provider in that ecosystem, I think are really advantaged because we're really in there again, just the same way silicon is so sticky. Once you're a chip maker and your chips are in a bunch of machines, that's a real deep moat. And we are both the disadvantages and advantages of those realities.
- [33:50]
Interviewer / Host
This is a business podcast, so we definitely need to talk about the actual business. I'm curious how you would break down the revenue for Applied Intuition. There could be different buckets for this. One could be software which has one margin, profile, services could be another, or consulting. Maybe just break down the different components of revenue.
- [34:08]
Kasser Yunus
We are a very classic product business. The way we make money is licensing. It's really a straightforward relationship with customers. We do kind of a weekly live all hands inside the company, and we're a little over a thousand engineers to give some some scope of how big the company is. I always say, you know, we want to be very innovative on our technology and we want to be very boring on our business model. And when it's the other way around, that's when maybe you get into trouble. When you have very boring products but very innovative ways to do accounting around them. I think probably evidenced by the fact that our last round was blackrock and Fidelity before then. So these are very traditional, conservative investors who do actual diligence. Not to say that venture investors don't. Why I bring that up is I think as a founder and as a company and I'm speaking to other founders here, it should be really easy to understand your business. Your customers should have a very clear understanding of your incentives and your motivations and how and where you make money and where you don't make money and what you don't want to do. So for us, it's let's make your products, that's our end customers. Let's make them better. And we make them better by putting some intelligence into them.
- [35:15]
Interviewer / Host
Can you talk about the actual customer base? I was reading that like 18 of the top 20 automotive manufacturers are your customers. And you guys expanded into Kasser. I think you were talking about like we're not just land autonomy anymore, we're sea, we're space. A lot of other industries that you guys are going to maybe just give the audience a sense of the different customer buckets that you guys work with. And to my understanding, it's also quite global. So give us a little bit of a feel of the different countries that you guys work with too.
- [35:41]
Kasser Yunus
Our customers typically but not exclusively are manufacturers. So they're people who make physical machines. And I say not exclusively because we also work with folks like somebody who's, let's say a mining operator or somebody who runs a port. And they're automating kind of a heterogeneous mix of machines. And those machines have to talk to each other and work with each other. And we provide again, either Dana, the platform that is the tooling side or the actual intelligence that a manufacturer would use and then kind of embed into their machines and make their machines more intelligent. On the verticals in terms of the buckets, it's all the ones that are the big verticals that make machines and deploy machines. In the real world, it's automotive, it's commercial trucking, it's defense, it's construction, mining, agriculture. I think, you know, in the short horizon, robotics, humanoids, space, like anywhere where there's a physical machine that's moving around people or goods or information. And so that's the verticals. And it's frankly fairly evenly split. I think a lot of times people think we're like an automotive only company. It's frankly a minority of our business, quite evenly split. And we're also quite international as you mentioned, so we really work across the globe. Our first international offices were almost right when the company started. Again, that's also part of the founding story. You know, I've lived in Japan and Germany and that obviously opening offices in Detroit, Japan and Germany as the, literally the first three offices for us makes sense. And then as we got into defense, going to dc, all fairly logical things. We always like to be close to our customers and that's a good reason to have international offices. But also there's a lot of engineering talent. Frankly speaking, this is not just as Peter mentioned earlier, you have to know AI and you have to know how to like optimize models. There's a lot more to our technology. We find people really around the globe that can help us succeed in our mission.
- [37:30]
Interviewer / Host
From an outside in perspective, it's actually quite hard to pin exact direct competitors. There are synthetic data providers. You could have big platforms like Nvidia you mentioned Tesla, which is like more of the vertically integrated, their full stack operators. I'm curious how you guys think about competition and whether there are some companies that you feel are in your path.
- [37:54]
Kasser Yunus
Conversations like this is important to define the word competition because there's a lot of companies that play in let's say self driving, but they don't necessarily make them competitors. Waymo is an example. We're both ex Googlers. Is Waymo a competitor? Not really. Mainly because they don't take money out of the bucket that we're taking money out of. We're selling, let's say to manufacturers. Waymo is doing a robotaxi to consumers. Now if we did a robotaxi to consumers or Waymo being sold to manufacturers, then you're more direct competitor, but it's not really a competitor. You're correct that there isn't really an applied intuition frankly out there. But there are many companies that compete with portions of our business. So there are companies that make something in construction or mining or something that'll make something in automotive. From our own team we get asked these questions all the time about, you know, how should we think about competitors and things like that. I fall into the classic YC model here which is you should be aware of the competitors, you should fight them aggressively, but you can't let them dictate your future because they are a different company with different skills. And these markets are really, really, really big. Competition becomes really important. If you are in a small town and there are a thousand people who live there and the thousand people are going to go to one shoe store or two shoe stores, then competition becomes really important because it is a little bit of a zero sum game. There's a finite amount of shoes they're going to buy. In our business the market is growing so rapidly and so aggressively that let's say you wrote down all the sub competitors for applied, all of them could be successful and Applied Intuition could be successful because the markets are so big. One way to also to think about this again, I'm talking to founders here of young companies. You know, when you, if you have kids you see like the image of the universe where they show the sun, Mars and Earth and stuff. It's just there to understand that the Earth is this relation to Saturn and Jupiter. Well if you actually have that at scale and the sun is like the size of a basketball, you know, the Earth is like many, many tens of feet away and it's like a little pin, it's like the head of a ballpoint Pen and so all this vastness is this black empty space. Markets are kind of like that. People focus a lot on how close these companies kind of look to each other, but the markets are so vast and so they actually don't really impact each other's gravity. And I fall very much into the view that if we don't succeed it's because of us. So if we execute, we're going to do fantastic. And I think this company can be honestly certainly 10x if not much, much bigger. And I think before when we used to say things like this, like there will be a multi hundred billion dollar physical AI company, people would say, well that doesn't really make sense. Now you see with companies like, like SpaceX and Anthropic and Open how big they've gotten and they're hard tech companies that are just really focused on one thing and you just see, wow, these markets are really big.
- [40:44]
Interviewer / Host
There does seem like there's a real renaissance of people building physical and hardware companies. Bezos and Prometheus, you have Travis, Kalanick and Adams and I think literally just today a company called like Terraform or something that's building robotic mining technology. It does seem like there's a lot of urgency and ambition to build into the physical world. And it's hard to imagine any of these new entrants building physical technology without some sort of intelligence baked into it. So I wonder how that's playing out and how you guys think about it and whether that may or may not dampen some of the demand for your solutions.
- [41:16]
Peter Ludwig
These are all potential customers for us. It's great that many more hardware companies are starting and so much of this has to do again with the barrier to entry that we talked about earlier. If building an intelligent hardware system is an extraordinarily daunting task, very few companies are going to do it. But once it becomes more achievable by a reasonable sized team with a reasonable amount of funding, then all of a sudden you can have hundreds or thousands of organizations building all kinds of things and we can imagine what those things could be. But a lot of it, it's going to be the creativity of humanity that comes up with these new physical AI use cases.
- [41:50]
Kasser Yunus
Just to echo Peter, I think these folks could definitely be customers of the company because we provide that platform in order to develop this technology. History is such a great way to learn about these things. Google started in 1998 when there's multiple search engines that are already public. And I think if we were having this podcast in 98 and said there's a new company coming up and you would say, well, the market's already saturated. These markets are really, really big. The instinct always is, oh, should you be worried that Jeff Bezos is going to start a physical AI company? That's like saying Jeff Bezos is starting a software company. Definitely, we are also a software company, but doesn't necessarily mean anything. As ominous and as negative as that
- [42:29]
Interviewer / Host
is, I heard this crazy stat that you basically haven't spent any of the money that you raised and you raised a non insignificant amount of capital somewhere in the range of a billion dollars and you've built this company to be self funding. So the question that's begged to be asked is why have you raised that amount of money and how do you broadly think about allocating, deploying capital?
- [42:49]
Kasser Yunus
Just to be very clear, we've tried to spend it. We've been fortunate enough to grow faster than that. In every fundraise we always start off with we intend to spend this money, we don't intend to raise it and put it in the bank. So that's one. As we look at resource allocation, we want to be very thoughtful, but not so conservative that we become vulnerable to emerging company that wants to, let's say, be less frugal or something like that. I think as we look forward, we're fortunate enough, partly is our track record, partly is what we did as technologists and engineers before we even started this company, that we could raise very significant amounts of capital from the markets if we needed to. I think the way we think about this is, hey, this is our mission. If as Peter was talking about, the bottleneck is capital, then we should take care of that. If it's technology, we should take care of that. If it's customers, we should take care of that or products that we need to build. So it's just one variable in the path, in the mission, and when we see it being constrained, we fix it. I think also we're getting to the size and scale that I think we could deploy a lot more capital much more effectively. So something we always talk about and think about. But I wouldn't say it's like the first thing I'm thinking about in the morning. The first thing I'm thinking about is how do we make sure we're making the best products in the business. If we make the best products in the business, a lot of things take care of themselves because unlike other businesses, the product really matters here. In a lot of businesses, the products can be okay, but not in safety critical.
- [44:21]
Interviewer / Host
Systems similar to maybe a challenge pointing to a direct competitor. If I force you to point to a public company or a basket of public companies, that would be helpful for an investor to value applied intuition with where you would point them to.
- [44:36]
Kasser Yunus
There's not many publicly traded companies around, frankly anyone that directly plays in this physical AI world. But I think that's why there's enthusiasm around. Applied intuition is like, I think if you listen for the last hour and had your thinking brain on, it's pretty easy to understand the problem and the solution. We're the category leader in physical AI and it's a big market. So that's the punchline why we get such enthusiasm, honestly, from engineers and from investors alike.
- [45:05]
Interviewer / Host
The company that you've been fortunate to work with are kind of a who's who of leaders across all these industries that we talked about, right? You have defense, you have automotive, you have farming, industrials, manufacturing, et cetera. And you have such a unique perch advantage point. Looking maybe three or five years out. What's the most interesting ways you think the future will be different than today?
- [45:27]
Kasser Yunus
The future is safer. And this is not to be understated or made to be pithy. If you know anyone who's gotten in a car accident or who's who's been in a workplace accident on a farm or a mine, it's absolutely devastating in a way that is hard to quantify because it impacts everything they do forever, the rest of their life. That's huge. As we started off, you mentioned how San Francisco, you see self driving all around. That's going to be way more common in many, many more cities and many more companies, not just Waymo or Tesla, who are fielding those products. And then as you go to other places, you go to college campuses, you'll see shuttles and you'll see food delivery robots more and more. You already see some of them, but you'll see this at an increasing rate and before you know it, just like having a supercomputer in your pocket is taken for granted. Having machines move around you and take care of things for you and take care of you, I think it'll be taken for granted. And that's like a very positive thing.
- [46:23]
Interviewer / Host
Peter Kassard, it's been a pleasure. Thank you for your time.
- [46:26]
Kasser Yunus
Yeah, thanks for having us. It was fun.
- [46:27]
Peter Ludwig
Thanks for having us.
- [46:29]
Podcast Host
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