Applied Intuition: A Billion Intelligent Machines - [Business Breakdowns, EP.248]

21:48How much of the drive to build data was building where the puck is going? Or 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 just like the next evolution of the stack that we're building, right? Tools, OS, autonomy stack, and now we have data. How much of it was like outside in versus inside out? Both.

22:10Qasar Younis: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?

22:48Qasar Younis: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. 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? So it's important to understand a bit more about the technology itself.

23:19So 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.

23:55These 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 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.

24:27The 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.

24:47Qasar Younis: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. This is 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 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.

25:20Qasar Younis:There are companies that are worth tens of billions of dollars that only do simulation. So then 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.

25:51Qasar Younis: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 of 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. Also in that process, right, you're doing model training and evaluation.

26:25You'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. 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 me.

27:00I would love for you just to kind of describe that loop and how that helps build a general purpose, broader platform.

27:06Qasar Younis: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, and 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.

27:40Qasar Younis: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 step. And then 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.

28:12Qasar Younis: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 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.

28:49Qasar Younis:Chatbots used to be very, very specific. Transformers happened. In 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. 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's the model company has built, and that's usually text data.

29:22But 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. 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 Megabrain 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.

29:59We 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 moat 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 scale physical AI.

30:40So 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.

31:13And 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. 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.

31:47And 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 you're building that worries you?

32:02Qasar Younis:The diffusion 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, like the actual dollars.

32:40Qasar Younis: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 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.

33:15Qasar Younis:And so the diffusion of this intelligence in the 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 disadvantaged and the advantages of those realities. This is a business podcast, so we definitely need to talk about the actual business.

33:53I'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.

34:07Qasar Younis: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 scope of how big the company is. I always say, you know, we want to be very innovative on our technology and 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 and you're very boring product, 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.

34:43Qasar Younis: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 and customers, let's make them better. And we make them better by putting some intelligence into them.

35:14Can 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, Cassidy, 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.

35:41Qasar Younis: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.

36:10Qasar Younis: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.

36:43Qasar Younis: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. I've lived in Japan and Germany, and that obviously opening offices in Detroit, Japan, and Germany as literally the first three offices for us make 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.

37:16Qasar Younis:This is not just, as Peter mentioned earlier, you have to know AI, and you have to know how to 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. 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. They're 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.

37:53Qasar Younis: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 robo-taxi to consumers. Now, if we did a robo-taxi to consumers or Waymo 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.

38:30Qasar Younis: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 a thousand people are going to go to one shoe store or two shoe stores.

39:07Qasar Younis: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 subcompetitors 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.

39:38Qasar Younis:It's just there to understand that the earth is disrelation 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 big, 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.

40:14Qasar Younis: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 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. 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.

40:52And 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. 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.

41:25If 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 use cases.

41:49Qasar Younis: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.

42:22Qasar Younis:Definitely, we are also a software company, but it doesn't necessarily mean anything as ominous and as negative as that 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? Just to be very clear, we've tried to spend it. We've been fortunate enough to grow faster than that.

42:55Qasar Younis:In every fundraise, we always start off with like we intend to spend this money. We don't and tend 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 an 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 because 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.

43:30Qasar Younis: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 and in the mission. And when we see it being constraint, 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.

44:05Qasar Younis: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 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 would you point them to? There's not many publicly traded companies around, frankly, anyone that directly plays in this physical AI world.

44:41Qasar Younis: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. The companies 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, industrial manufacturing, etc.

45:16And 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?

45:26Qasar Younis: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 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.

46:07Qasar Younis: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. Peter Kassert, it's been a pleasure. Thank you for your time. Yeah. Thanks for having us. It was fun. Thanks for having us. to find more episodes of breakdowns ranging from Costco to Visa to Moderna or to sign up for our weekly summary check out join colossus.com that's j-o-i-n-c-o-l-o-s-s-u-s.com