Why Physical AI Is the Next Frontier | Applied Intuition

Our mission is to put intelligence on a billion machines and that we think that can have a profound impact on society. Oplot intuition is a physical AI company. We put intelligence on machines. Cars, trucks, tanks, drones, it's a physical moving thing. We make an intelligent digital AI, of course, is building software and optimizing ads and creating videos. That's all interesting and good, but really where you talk about the global economy, that's physical AI. In the intelligence revolution, the companies that impact the physical world might actually be bigger than the companies that impact the digital world. How many things are there where the idea of physical AI, physical intelligence, or better matter? There's no reason the autonomy should be this obscure, difficult technology. Our vision for that is a high school kid that can make iPhone apps, should be able to make autonomous systems. That platform for designing and developing is what we're launching. It's called Dana. Everything that we've built and developed over the past nearly a decade, that's available in Dana. Which will we get first? A perfectly simulated role-own environment for training autonomous devices or a Granthe autosix. [Laughter] Much of today's AI conversation is focused on large language models. But the next frontier may be physical AI, software that enables machines to perceive, reason, and operate in the real world. In this episode, Mark and Dresan and I sit down with a plaid intuition co-founders, Cassar Unis, and Peter Ludwig, to discuss the future of physical AI, along with the company's newest platform, Dana, which is designed to simplify how autonomous systems are built and deployed. They explain why physical AI presents a fundamentally different set of engineering challenges, where autonomous systems are already making an impact, and why the next decade could transform not just software, but the physical economy. Cassar, Peter, welcome to ACNZ Podcast. Well, thanks for having us. Your name is? [Laughter] Just one of many. I think we've all each known each other for too long, more than I'd like to admit. Yeah, one time. We're lucky to both be the first investor, or among the first investor in the first round, of course, different check sizes. And I was an investor for you, even before I-- Exactly. So let's do that in a second. We have a lot to talk about today. The biggest launching company history to talk about today. But first of all, we just give an update status. What is the planning to do for those who are-- Yeah, for the people who don't know, a planning tuition is a physical AI company. That's the simple way of describing it. And all types of machines. So cars, trucks, tanks, drones, you name it. It's a physical moving thing. We make an intelligent. And the history of the company is we originally started by making the tools that would make the intent. And then we got into the actual intelligence itself. In some ways, like a very boring AI company, in some 83% of that company is engineering. We win by making really great products. It's not like a good sales or something like that. I don't think we're good enough. We're a sales-enabled company. But yeah, over 1,000 engineers and based in Silicon Valley. But we have offices globally, 18 offices. And our mission is to put intelligence on a billion machines. And we think that can have a profound impact on society. Both in the kind of busy things everyone talks about safety. If you really talk to somebody who's been in a car accident or in a mining accident or in a farming accident, those are real, gnarly situations. Beyond just fixing that, if you can unlock productivity, I think we've seen the unlock in the digital world. And everyone's super excited about it. And you have trillion dollar companies emerging. I'm a pretty strong believer that I think when we look back 25 years, we look back to the internet now. You look at the regional internet companies that are doing surveying or they're doing some analytics and those are interesting. But really when you look back 25 years, we're the big monolithic companies are Amazon, delivers you stuff. Apple, these are the true kind of companies that come of age. And I think when we look back 25 years in the intelligence revolution, the companies that impact the physical world might actually be bigger than the companies that impact the digital world. I would love for you to talk about the following, which is when you first started the company, the knock on the company, I think was, oh, well, it's making cars autonomous, right? Self-driving cars would just kind of like, okay, there's like whatever. There's Tesla way more building their own self-driving cars. And then there's six or eight other car companies that matter. And then the company just could ever get that big because they're just not that many customers. So how should people think about, like, how many things are there that are things that move where the idea of physical AI, physical intelligence are going to matter? Yeah, I mean, even today, even if you put that, let's say a view on us, the automotive is 30% of our business. So 70% already is non-automotive. And I think if you fast forward another 10, 20 years, even the manufacturers themselves as a customer base will be a small amount. I think that mission, just keep thinking of a billion machines becoming intelligent. And you think about all the types of machines that exist. Automotive is just an easy one. I think it sticks in people's head because we all drive cars and it's a big market. But I think it'll be a minority of the business. And we already have a minority business. And I think it'll be increasingly minority business. But that doesn't necessarily mean it'll be small. Automotive is still huge. Just as a part of the globe's GDP, automotive is something like 3% of all GDP. I think the way we always think about it, as you try to get to your mission, initially the manufacturers were the distribution to that intelligence to consumers. But then you start working defense and you start working in construction and mining and agriculture. And suddenly, the manufacturers are important, but maybe the mining operators are actually really important. Or the department of war is really important. And suddenly they become customers and all of those are customers as well. They can be split AI into digital AI and physical AI. Digital AI, of course, is building software and optimizing ads and creating videos. That's all interesting and good. But really, when you talk about global economy, that's physical AI. And we're talking about manufacturing and mining and logistics and transportation. All of these things that supply change. Yeah, supply change. It was build on that for a second, which is so things that move today, or historically things that move are things that have human beings at the wheel or at the controls in some form. Airplanes have had to get designed around a human in the cockpit. Both have had to get designed around human steering things like in a world of autonomy. Do we already know what the things are that move? Or are we going to discover that there are a lot of new things that are going to get built when you don't need a human in the driver's seat? I think both. The thing that you have to remember is like you take like a holly system that's in a port, like a Kamehatsu dirt mover in a mine. Those are made for 20, 25 years. So the buyers of those products, they might not have gotten their full cycle ROI on them. So they're not immediately going to buy something no matter how much better it is. So one part of our strategy is you got to make those things intelligent, because they're not going anywhere. The second is what you're talking about, which is well, that depends on a human in the cab. If you don't have even a cab, the machine can be smaller. It can be shaped in very different ways. You need to talk about mining underground. The constraint actually is the human, because the human needs to be. Then it needs to be very dangerous. And so you can build a very, very different machine. We're doing both of those things. And then the thing that you were not talking about is we're all talking about intelligence almost like within a system. But the system level intelligence is where the unlock is. And we're already doing work like that where you say, hey, let's take an entire port. Let's take an entire mine. Let's take an entire query. And as heterogeneous mix of machines, they're all can talk to each other. And they can optimize and be efficient when one machine goes down or one machine has an issue. The rest of the mind doesn't have to stop when it's human driven. We do even know the machine is going to go down because there's no analysis. The human is not plugged into the core systems of the machine. So a simple thing like knowing when a break system is going to break is actually huge. Because you can start preparing for an advancement. Like, oh, this is where in Terra is higher than in other mines. It was using an example. But the other macro point is if you look at agriculture example, average American farmers, 58 years old, the number something like under 35, it's less than 10% of farmers are that young. So what's going to happen? The need for food growth is continuing to grow. The need for rare earth materials is going to be so these demands are only growing. But the humans who are the bottleneck are decreasing. Trucking is the same way. And so you can really just unlock a lot more efficiency. So I mean, one way to think maybe think about this is imagine if the cost for food decreases. Bees is way way more efficient. So what's the downstream impact? Imagine for goods being transported. Let's say instead of a few dollars a mile, it's 20 cents a mile. And suddenly, I think that the unlock is very, very, very big. I think it doesn't necessarily need for all the machines to be redesigned from the ground up. Right, right. Right. Right. Right. Make sense. And then maybe just one more question. I'll just give us a sense of parameterized like the scope and scale of the company today. Yeah, north of a thousand engineers and those engineers are obviously the classic software and AI engineering teams. But we also have engineers who really know safety systems. We also have engineers who really know hardware. And the world has way more complexity and has a lot more issues. And we have engineering teams. And we've deployed our models on the 50 some platforms. Even that sounds trivial. Because mostly when you think about models, you think about deploying them through a browser or on a phone. And everything is abstracted away because you have iOS and you have Android and you have windows and you have Linux. And you have all these systems that have already taken care in the real world. You don't have that. And so we have engineering teams that can do that as well. And our claim to fame is we've raised over about a billion dollars in the company's history. All that is sitting in the bank. And I always say that with an asterisk, which doesn't mean we're not going to spend it next month. Good news, bad news. Yeah, good news, bad news. And I think we talk about scale. We're at that phase where these giant markets are around us. And we can make the decision how aggressive we want to pursue those because frankly execution and deployment into production. I think the hallmark of our engineering team is putting products into production that really is, is it, I don't know how do you think about scale? Yeah, I think that that's roughly, I mean the mission of bringing intelligence to a billion machines. That is how we think about it. And then thinking about what we're doing. Well, one of the types of machines that will have the most impact on and focusing on those areas first, but we'll get there. Right. I/O, let's go deeper into the differences between digital and physical AI and more so into, where are we today? What progress as we made, what are some of the main major bottlenecks in physical AI? Why don't you unpack some of that? Yeah, I mean, I think a lot of times people think about the progress in physical AI is limited to basically two use cases and they're just because they're obvious and interesting, which is robot taxes and humanoids. They're very visceral. They're, they're, they excite you and they're kind of sci-fi. I think they're, those are very interesting and they, there is real work being done by us and other people in those domains. I think all the other, all the other domains I think are going to be just as important. I mean, you just, you just think about what happens on a port that's, there's a huge unlock there. And that's, I think that's the, that's the area we're, we're, we're really focused on. It's like all the other Nixon crannies. If you look at like we, we've talked before about the rise of Cisco and how, you know, networking kind of went from, you know, first individual machines and companies would get network and then entire countries were getting network. There's a similar thing happening with AI. AI is getting to that level of kind of sovereign AI is now a discussion. Sovereign AI really is about physical AI because that's where you're talking about AI in defense. But AI in the physical machines that are moving around, if you look just at the example of, of Waymo from America and Pony from China, trying to deploy in, let's say, the other country. So not America, not Europe, not China. Every one of those spaces, they're way more, they're way more hesitant of saying, yeah, thumbs up, your robot taxis can run unfettered on our, on our country. And so if you look back just the kind of this arc of the internet, you know, when the first internet companies come, nobody's really thinking about sovereignty at all. It's like the browser goes everywhere, the internet goes everywhere. That's almost the power of it. Then when social media emerges, there's a bit more of hey, actually not every social media and then you have China not allowing Facebook to come in and then you have some, then you get into the next level of like the online offline stuff. There's more resistance to Uber's, to door dashes. Suddenly, there's local players who are being favored very aggressively. When you get to physical AI, I think there's going to be huge. And also there's like a larger geopolitical theme of kind of more fracturing than globalization. You're going to have this demand for this AI should somehow be localized. And I think that has to play into our strategy as well. We're a technology provider. So we provide that technology across the globe. And I think that's something that's understated in this conversation. A few other things on digital versus physical AI. So in digital AI, the state of the art is you can train models effectively on the entirety of the internet and then maybe augment that with additional data that's been collected and refined with some hired experts. This is sort of a hot field right now. But generally you're talking about a foundation model that's built on internet data. In physical AI, the internet data is useful too. However, to actually build a foundation model in physical AI, there's also a lot of private data collection. When we're talking about minds or logistics or any of these other fields, the data that's useful for training models there is not necessarily available. So we have to do a lot of work ourselves actually going out and collecting that data. And the other key factor is safety. If you're talking about building a smartphone app, you don't necessarily care about is a safety critical application. But when you're talking about moving a machine that weighs many tons or think of a humanoid which could fall over on your children, you care a lot about safety and the evaluation of that safety. And that is really sort of getting to the city of the art of physical AI and really proving out the safety case around some of these city-bearer models. Yeah. And I think like, you know, you talk about like human data collection has been its own, you know, a little area of interest. And when you talk about collecting data like in places like Korea, where they have North South Korea, where you have North Korea, they don't allow mapping companies, let alone allowing a, you know, an American company to come in and data collect. So we've figured out over the years whether it's the Middle East, whether it's Latin, how to get into these countries, work with the governments and get the thumbs up to collect proprietary data. And so in the way that it is similar to other digital AI systems, your proprietary data sets, scaling laws, all that stuff is the same. It is applied in a very, very different way. And it's almost like the way to think about it is like the diffusion of these models is very different because you can't, it's not everyone can just access them through a phone. You're going to end. And so, so that ironically is actually plays in our favor because once we have a massive proprietary data sector where we've been building, we already have hundreds of petabytes of data. And then we have our own tools, which are like synthetic data tools, neural sim. We can use our own tools, our own proprietary data, and then allows us to build some of the best systems in the business. >> Because it's kind of a chicken and egg thing, which is like in order to build an autonomous physical thing, you need a lot of data. Together with that data, you need a lot of physical autonomous things running around collecting the data. So it's like once you have a giant network of physical things running around, you have the data that makes small work. Is there a flywheel aspect of that? What's the level of difficulty involved in kind of booting up that flywheel? >> It's difficult, but it's also not difficult. I mean, I think we have one of the largest data collection fleets on the planet, frankly speaking. So that's how you bootstrap your way into it. That's just money and resource and technical knowledge. But it's not like there's probably more than five companies that have that technical knowledge. So it's not extremely obscure. I think what is more difficult is then how do you actually have that model which is going to work on lots of different hardware and is tested appropriately? Because you saw it in cruise. I cruise with this company that did amazing self-driving work and then one accident, General Motors owns them and they get super scared and they pull back. So it's like just getting these things into production is actually more difficult than it seems. I think we believed synthetic data was going to be important. So we started our synthetic data team like five years ago now, plus more than that at this point. And we're a strong believer that synthetic data can accelerate autonomy development. We've just seen that. And then there are lots of other things, secondary and tertiary like technical innovations that have obviously the transformer revolution hitting self-driving massive. Everything done in self-driving pre-21, 22 relevant, but you're almost like that's kind of the starting point. But it's also different than today being the starting point. Like those four or five years are actually there has been a lot of work done. You can see it most clearly with Tesla, but there's other folks in that process, the actual techniques, historically, and it was simplifying here. Imutation learning was the way the game, which was collect a bunch of data and then the models would basically imitate what human drivers do. The real state of the art right now is end-to-end reinforcement learning in the closed loop in your tools. And so it's a little simplified to say the system learns itself. It identifies where the issues in the self-driving system are. And essentially you then find data like that or you synthetically create data like that. And then you close that loop and you see, are you performing those same scenarios better and better? I think if you fast forward some years, that will be a completely closed loop with no humans intervening. Right now you still have like, what's the fog error that we saw? We still see errors in the real world that impact self-driving. Oh yes, it's like, well, what are the bottlenecks, right? And the bottlenecks, there's plenty of them. But whenever you're dealing with physical systems, inevitably you had a lot of gnarly harbor problems and it could be anything from overheating to sensor being slightly miscalibrated or funny issue you saw yesterday was basically a fogging sensor, like fog impacting a sensor. And these are the things that you actually have to solve for this stuff to work very reliably in the real world. So I've asked you a question and you can decide whether you guys want to engage on it or not. It might be an opportunity or might hate the question, which is, you're surprised. So cruise was a super high flying Silicon Valley autonomy startup that was kind of running neck and neck with Tesla early on and so forth and very top end team and then they famously got bought by general motors and they went to my first distributions personally side. There we go. Why come in there? Why come in there? Why come in there? And you know, top end team and they were, you know, by all accounts making excellent progress, they got bought by general motors. They became the GM autonomy program. GM got a lot of praise, at least in the tech circles for being like, okay, being like the legacy automaker with the biggest investment. I called Peter when before, before his announced on that and I said, hey, cruise has got, you know, he's also GM family. We're both GM families. And Peter guessed it was said in video, I said, no, it's a go fish. These apples had no. I said general, explicit motors. So that's surprising to people who are from GM that they were willing to buy. Yeah, they did it. Okay. And then by all accounts, they were, I mean, as far as I ever heard, they were making excellent progress. And then they had this, there was an accident. There was an injury or fatality or was it a fatality? It was a serious injury. It was a drag for 20 feet. Yeah, serious injury, bad press. And then they put a bullet, GM, you know, and bought a bullet in the cruise project. And, yeah, I know that only some of the senior cruise people were extremely upset, you know, by the aftermath of that. Is it surprising that they reacted the way that they did? - So, full disclosure, gentlemen, this is a customer and I went to the General Motors Institute, so we have a lot of love for the company. But incidentally and ironically, I'm reading a coincidental, I should say, I'm reading this very famous book, which I had never actually read before called on a clear day you can see General Motors. - And Delorean's book. - Has someone does? Has one of you read that book? - So years ago I have, it's one of the great all-time book titles. I mean, she just pauses, John Delorean was like what he was like, the super genius of the car industry. - Yeah, he was gonna be the next president of General Motors. - A General Motors and then later on, he started his own car company, which was in the back to the future. - He was in the back to the future and then to get that whole thing collapsed, right, a variety of reasons. But yeah, he was like a legend. He was like one of the main principal drivers of innovation in the car industry. - Exactly, got it. - Leia Coco Boblots, this is this category. And you gotta remember, this is Linda's story. - But repeat the title. - Repeat the title on a clear day you can see General Motors. - And why was that the title of the book? - Because there's a lot of bullshit. (laughing) - It's a very large complex. - Yeah, complex, yeah. It's like a nation state. - Yeah, it, oh, it mean really. I mean, it is. - Right, right. - I think who we say that like, sometimes almost like, flip-it-ly. But these companies are like extension, like Hyundai's an extension of the state. Toyota's an extension of the state. Volkswagen is, literally Volkswagen board members are members of the government. So these are extensions of the state and almost every, and there used to be old saying, what's good for General Motors and good for America. And you cannot understate how important General Motors is the history of the American corporation. Sloans, my years of General Motors, and Adventures of Y. Colour Man, if you run a large engineering organization, you should read that. That is the, this, like do you, this, this thing that we talk as a modern corporation and just emerge. Sloan and Kettering create. Kettering is the head of engineering. Created this, with this, you know, with levels and vice presidents and how do you do functional and matrix organizations. It's, there really is like the source code. It comes along, John, you know, it comes onto Lorian and he says, he writes, he's gonna be president and he's so fed up with a company. But what was controversial was GM was doing really well at the time. GM was like a, when we say like, GM was number one, the fortune 100. It was like number one, two and three. It was everything and it was seen as the best company in America. So somebody to openly criticize the company. And so he has a hope, he writes this book as he quits out of, out of how annoyed he was as a general person being read, led. He writes his book and then after he like sobers up, he's like, I don't want that but published. And so he fights for years for his co-author, not to publish the book, the co-author still publishes. So it's a real true insight into a large corporation. Incidentally, just reading it out, even though I've, you know, worked at GM 27 years ago and had, you know, know a lot about the company. And what's shocking is it's not only by GM, most of the major manufacturers actually still operate that way on the inside. And so the question isn't, the point I think for everyone to take away isn't that these people who run these companies are stupid. They're not stupid. They're, it's kind of like, you know, when you're selling to the department of war and people say, well, why are you doing that? It's like, well, the distribution defines the business. Right. So like the distribution is this is a consumer product of the, the, the stat may be out of date, but when I worked in safety systems 20 years ago, I remember GM used a pound in your head of the top five consumer lawsuits in American history. Three are automotive. We got the majority, right? So it's a gift to be extremely careful with these like, weird things like inside the company, you couldn't, it wasn't red yellow green. It was like purple, like you'd always have to as decoder because you know why? Because when they go to lawsuits, they're like, you let a safety system that was marked red. Go to production. It was like, no, it was marked magenta. (laughing) Like, so like, can you imagine I'm furating that is every time you're like, what does orange mean? This is the, I have to like, so fast forward to, you're meeting that system. Well, for the, the Ford slogan for a very long time was like, it was quality as job one, right? Yeah, yeah, yeah. It was a safety, it was job. Yeah, yeah, exactly. And that's the one, two punch of automotive. It's quality in safety, quality in safety. And quality really because, because the Japanese really reset that, that stage in, and because, that's a whole separate automotive history. We could talk about automotive history for, for an hour. But the punch line is you have the Nassilkan Valley company meeting this immovable object. There is a parallel universe that cruises out there right now, even as a part of General Motors. So I think you always have to take it into the context of where the company is, where you need negotiations are, happening literally that year. And if you're the union, you're like, you can't make a billion dollars for us, but you're funding this thing. That's killing people and it's sloppy. And so I'm not saying precisely that's what happened. To be very clear. But it's a multi-variate problem. My other hot take is, you know, I worked at those companies, right? Google, Google, General Motors, those companies are way more similar than they're different. Way, way more similar than they're, literally people don't, and you know, the Google leveling system is the same as the General Motors leveling system. And I used to say, I used to say, you know, this inside of Google meetings is like, hey, actually some of the engineers I knew at General Motors are better than the engineers here. And people would look at me like, I'm saying there's no God in church. It's like, they're like, how dare you. You metal-bending monkey from Detroit. (laughing) It's like, no, actually like making a modern combustion engine is extremely complex. It's not just like, you know, it's not simple stuff. And so the macro point I think is, it's a bunch of things. I think safety is always at the top of their list. I do think, you know, we've hired lots of crews, people. I think the way they dealt with that specific issue with the government, you got to dance in a particular way when that happens. And they just didn't dance exactly right. And that just gives government bureaucrats more ammo to go after. And your big target like General Motors, you gotta, you know, it reminds you, you guys ever see that movie like Goodfellas, you know, they're one of the last scenes of the House of the Rising Sun, you know, all the old bosses go in the back of the courtroom and they're like, and you know, that's what happened. They're like, the board was like, what are we gonna do about crews? Like, what can we do? (laughing) It's like, guys, a good guy. (laughing) And there's like two House of the Rising Sun. You know, people running through a San Francisco house and it's just, don't make that an AI video. (laughing) It's gonna get a mean text from Kyle. (laughing) So I think there is a universe that would survive, but it's tough. So then a lot of what a play at Intuition does is kind of, as he said, like that dance. It's like how to be a great partner to these companies. Exactly. And they're very, very real issues and constraints. I think gentlemen has also had the topic of business model, right? So you have crews was going after the robotaxi concept, but GM makes its profits from personal car ownership. And those things can be a bit odd. So I think that was also a bit of a video-wise. Oh, it's a bit of a agent, right? Okay. Yeah, and I think it wasn't clear. I mean, by the way, you know, you, actually, all people, you spoke at YC at 20 and 2013. I was an audience. I was a partner at the time. And you said something which I think is, it's very like a recursive where we're feeding each other your own advice. It's the key thing in new technology business. Actually, everyone kind of figures out the technology, though that's still hard. It's still hard sometimes to build really complex things. It's when and how you deploy them into the market. The when becomes really important. You're two years early and you're doomed. You're two years late. There's too many competitors. You have to like hit it at the right spot. And I think it's like, like, I mean, a controversial thing that says like, I actually think crews, you know, they were certainly moving at a much faster pace than Waymo. They started way behind and you're talking about neck and neck when, you know, ultimately the plug was pulled. So who knows what happens in the long term. Our hypothesis in that same equation is actually the distribution. You let the manufacturers do that. Like we run self-driving trucks right now in Japan. They carry commercial loads. They're, they're, you know, they're safety drivers there, but they're autonomously running. And, but you won't know that because the brand is a Suzu. That's the customer. And why it's so good for us to partner with Suzu in that case is that company's been around for almost a hundred years, right? If I find from not mistaken, a pre pre pre world war two company and they are, you know, they know the government. They have test tracks. They know they're, they know their own trucks very well. So when we go and provide them with the intelligence and the integration into their physical machinery, that's a fantastic one to punch. I think today the world is ready to consume AI in the real world. And that's a lot because of chat. UBT and then Throbbick and all these, you know, everything that's happened. So people are no longer like what's the self-driving car. And there's a reason of Waymo and Tesla. So the market is ready to consume. And I think you just have to meet the market in the way that, the best repository in our view that has always been, you go through some of the people who run the economy right now, whether it's a mining operator, whether it's a departmental war, whether it's the manufacturers. And we work with, you know, within each vertical, with the right partner. But that's a fundamentally different view than a Tesla or a Waymo, which are going to be vertical. We're really playing the horizontal. And I think the way we can always think about that are companies and we're kind of like a chip maker. You know, we actually look and talk and walk a lot like a Silicon company, except we do obviously, we don't make chips. But, you know, we have design wins and then we have really large long-term relationships. And once we're in, we're in. It's really hard to take us out. So you need deep trust. Our partners have really a lot of deep trust. And we know their markets really, really well. The things that Jensen knows is he knows his customers. That's why Nvidia does well. Beyond the fact, obviously, they make a very complex technology. So how are these legacy car companies preparing for the future? Are they making more acquisitions? Are they going to? Are they building a partnering with you? Or are they going to compete with tech, tech, tech, tech, tech, tech, tech companies? It's like saying, like, how are governments dealing with AI? It's such a broad topic. and each manufacturer, like even like you take Honda Nissan Toyota, three Japanese manufacturers with long legacies, they all approach it very differently. They're roughly in a spectrum of, we're gonna build to, we're gonna buy, and both extremes, more than ever, we're gonna buy is the common answer because they've been trying, and we've been there the whole time. For the folks that are gonna build, we provide them tools, and we talk a little bit about our new product that we're announcing here, and then on the ones that just wanna buy, we sell them the actual intelligence that goes on the machines, and so we meet the customer wherever they're ready in their journey. The more nuanced version of that is, you know, the reality is like every product is a different product, and so the amount of silicon and the amount of dollars you can put towards it, towards sensors, what the customer's willing to pay, all that depends on what actually gets, and the long horizon, all these things will be fully autonomous, but the intermittent steps are very much what we saw in the PC, where you have this slow step up to, one day that'll be like, now nobody really looks at laptop specs, and even maybe frankly, your phone specs, but that's not the case from basically 85 to 2002, 2005, where finally people stop actually speccing at all, and then they're really moving to laptops, but there's a similar kind of 20 year, I think, horizon there. - Brody, when you talk about machines, and machines becoming intelligent, right? Fundamentally, a machine is a collection of these different components that are integrated, right? And whoever does that final integration is oftentimes the company that puts their badge on it, or the brand name, but many, many companies are building technology that goes into that machines, and so we now have a bunch of technology components, of platforms that can go into these machines, but we also sell the core technology that can be used to develop them as well. - And if you look by the way, under the hood of a dirt mover, or like combine or diesel truck, they'll have Cummins engines in them, but nobody says well, because all these guys buy Cummins, this means that they're, you know, whatever caterpillar is not a good company, it's like, no, that's just a component that they buy. They have a different role. So if you, when you look in the, any of these verticals, there's, it's just a complex web of folks. That's why I always say like the chip kind of analogy actually works quite effectively, because some, none of those companies make chips, but they all buy chips. And so I think that's, it's a good way to think about it. - So self driving cars, so you know, we've all been talking about self driving cars, for like, I think the whole thing started like around 2005, or something with the DARPA Grand Challenge originally. And so in the Google, the engage of the program thoroughly after that. - Yeah, late double os, yeah. - Late double os. So almost 20, basically around 20, a little less than 20 years movie. And there have been lots of reductions over the last 20 years of like self driving cars are in at any moment. So I guess the bad news is, we're sitting here today in most cars, we're not self driving. The good news is there are now self driving cars. - Yeah. - And so the way my cars are driving all over, you know, in the places they're deployed, it's become, you know, like people in separate scoards, I think it treated out as routine that they get into. - And I think you can call, I think Tesla, it's kind of like the AGI thing. It's like, you know, if we were talking 20 years ago, everything we're seeing right now is like a mind blowingly AGI, the host keeps moving, the Tesla stuff's amazing. You can look at bunch of manufacturers, blue cruise, supercute cruise, BMW, Volvo's pilot. They're all quite impressive systems. They're not full self driving. - Right. - Well, it's, it's self driving X, whatever remote monitoring is happening. - Yeah. - The Tesla, we have a home in Los Angeles, and you guys may recall there was a large fire in Los Angeles. - Yeah. - And then the power, then then the California power grid was buckling even before that. And so it actually turns out, among the things that we're talking so good at is they're very good batteries for powering your house. - Yeah. - And so literally we have type of trucks as our backup battery for the house. - And as of last year, whatever, the FSD really is suffering at the exact one, but there was one where it, like, it leads to a lot of people thought it, like really turning the corner 14, yeah. - And like that thing for us, you know, I talked to somebody yesterday, talked to somebody yesterday who has a model why, we let this thing do the full route all the way up. Highway once you're a big sir. - Yeah, I think mean disengaged, like the mean time and like miles per disengaged are really high. I think miles is like in the thousands. - Yeah, which is like very impressive. - Yeah, I know, for real, I haven't driven the big sir highway one big sir. Like that's a, that's a, that's a stressful drive. - Yeah. - He said it was great. Anyway, so I wouldn't have been talking to him had it not been. (laughing) - He would have gone right off right off. - He unbolted this steering wheel. So, he had to go right off the cliff. - So, and then, you know, Tesla's rolling out there, Robotaxi, you know, it started turning a stroke in the wild. So, on the one hand, those exist. On the other hand, you know, 99.9999, 99% of cars are still not self driving. And then I would say maybe just one other would be the self driving trucks. There's been this recurring kind of panic in the press of like this. The truck becomes self driving and the employment in all these truck drivers will be out of a job. And sitting here today, I don't think, I don't know, is there, are there any trucks on the road that are self driving that don't have at least a safety driver in the truck? And I think the answer is probably still. - Yeah, so very few. So, let's look at the, let's split the, there's multiple points to Ubrata-Pur. One is on the, let's say, personally owned vehicles. And why are they not more ubiquitous? The part of that is the manufacturers are not good at deploying technology. Part of that is they're, they're, they want to be safety conscious. But most of it is cost, cost, cost. The, the, what you're seeing in China, which is a, China is kind of a different EV ecosystem mainly because they don't care about profits. And you're talking about business that doesn't care about profits. It changes the entire calculus of the entire industry. That doesn't care. But what you're seeing is you're seeing L2 plus plus systems. So we can simplify the entire self driving conversation to is there a driver behind the steering wheel? - Right. - So this is a driver behind the steering wheel still there. But generally like Tesla drives everywhere. They're like sub $1,000. There's an aggressive, that's chip sensors, the package, the software, everything. We anticipate that there's a very aggressive. Once you get to like 500, the automotive OEMs will actually subsidize it for free. They'll just give it to you. This happened in nav systems. If you guys remember nav systems, these would be a big thing. If you paid four grand, 3,500 to get a nav system. And then suddenly it became free and it just became default. I think that'll happen. The, there's a weird thing which is like, actually get into a subset of your cars costs X dollars. And to get into all the cars costs X plus just a small incremental amount. These are just a fixed cost in the way that how many vehicles and the way the assembly line comes in the way you have a obligation, all these testing, regimes, all this stuff. So I think you'll have weight, weight, weight, and then a lot. - Yeah. - Every single OEM without exception. Even the lowest dollar OEMs are working on an FSD competitor. - Yeah. - So it'll come. It's just like, you know, with a good analogy to think about self-driving in the personally owned ecosystem is mobile phones. We had the satellite phones. Then we had the Qualcomm, you know, brick phones. Then we had the Motorola Razors. And from, you know, the late '90s to the late '200s, the review was like, when's mobile gonna come? There was a huge like, and then it comes. And it'll be by '07 from the iPhone launch. Like it's like four years when you get Uber, Instagram, WhatsApp, Snapchat. Those are the killer applications. So I think there's a very, very similar kind of weight, weight, weight. And then it's basically ubiquitous in every vehicle. If you had to ask me for what that number is, 28 SOP, 29, it's sort of production, 29, 30. And then by the early 30s, I think it'll start becoming very cheap to free. - But you were teamly by the early 30s, you would just be by a car and you just, - Yeah. - So it's self-driving. - Exactly. It has the driver in-seat L2 plus plus system being very specific. - Like, separate truck, it's like Tesla. - It has a lot of people. - It has a lot of people today. - Today, it's that common. - Yeah, we'll be default. So then the question, then the other side of this is, why don't we have a bunch of weimos everywhere? - Specifically weimo has a different technology. Without getting into the nuances here, but Tesla and many of the Chinese and applied were very much in this end to end model architecture. This is a new way of doing self-driving. Weimo, for the lack of a better word, is not that. It's not, doesn't mean they're not learned. It just, it's not one end to end system. It's not one monolithic model. One of the proclivities of their approaches, it does depend on HD maps. Therefore, there is a geofencing concept. I think weimos trying hard to remove that bottleneck so they can expand geographically faster. But the reality of today is in there. The other thing is when you have researchers, which Weimo really was coming out of an in the Alphabetic Research Organization, they didn't put commercial constraints. So the sensors are bespoke and expensive. The cars and the compute that are in there, they're just not economically feasible. And they've tried a lot to get that down. But it's kind of like, it's a lot easier to go from something that's really cheap and make it more, you know, more feature-full than something that's overbuilt and then trying to trim and make it really, really cheap. And that's the big debate. Who's going to get their first? Tesla with full self-driving or Weimo with cost and geographic ubiquity. But, you know what we're not debating about? Is it going to happen? You know what we're not debating about? Like, is there a big technical breakthrough that needs to happen? None of those things. So now we're clearly in the engineering side of self-driving, which is just as grind down to like dollar per mile efficiency. And the moment that it's cheap, guess what? All the OEMs are smart. They'll just stay just adopted. It's not that OEMs are resistant because they don't think consumers want it or they don't understand the technology. It's because they want a price envelope which allows them to keep their thin, raised-to-thin margins and at a scale, which is deployed across 100 plus countries in V1. And so if you're just doing a small deployment, it's very different. And I think, and that was the last thing I would say is, the buyer of a Subaru or a buyer of a Suzuki have very different brand expectations that are buyer of a Tesla. And so including the age of the consumer and what they think will happen, or what will happen. So that's also the reason. So if you're Suzuki, you're like, my buyers like not, that they want this stuff. So I'm not gonna jam it into the car. It's not because they're not like technically competent. This is a different area. - When do you think, when do you think of a routine? Let's say they have 200 biggest American cities, like you would only be routine to walk outside and you just take it for granted that a robot taxi can come pick you up. - It's 26 now. I mean, certainly by 30. - All right, okay. - Yeah, certainly by 30. And I would say, the big like variable there really is like, 'cause what Wayma will say is that the dollars and cents per city already work. And it's like, well, a company that has basically unlimited capital, why are they not already in 200 cities? But then you see their launch schedules pretty aggressive. And you're like, that can get there. So maybe because of being aggressive, I would say 28. - Yeah, okay. - Like two years. - I would say available in 30, but routine and maybe like 32, 33. - Sure, just scale up, there's a volume. And then also if you live in LA. So like five years ago, I'd go to LA, people would be like, what's applied into it? And I don't know what self-driving cars are. In the last couple of years, now they all know self-driving. And some of them even know applied intuition because they know from the other manufacturers. I think you fast forward another two to four years. Everybody knows it now. Does that mean everyone's taking Waymos exclusively? Then there's no actually. Now there is a huge, if you look at the numbers, if you're Uber, you gotta be scared. I mean, they're just eating into ride-sharing. Yeah, but to get 100% ubiquity, even that's another, yes, extremely cheap. - And what about long haul trucking? - So long haul, so that's what we, that's the path passenger side. So the long haul trucking, completely different economics, completely different business model. There are many companies right now. I would say probably north of five that are running long haul trucks with drivers, carrying loads between America and China. If she had China, it's probably getting into double digits. So it's there, but the reason, you don't know it and the reason it's not top of mind is it's not a consumer product. And unlike on the Waymo and Tesla side where investors are willing to essentially give you, you know, some market cap, you know, adjustment for the potential of, they say the trucking business is like, you know, made, what was it? - You buy a car with your heartstrings, you buy a truck with a calculator. - Yeah, it's a calculator business. And so it's like, pure dollars and cents. And so I think you as the provider of self-driving trucks, if you're doing the whole thing, like some of the companies are, which we're not, you have to show every mile, I'm gonna save you this many dollars. And it's like, for sure, for sure, for sure, 'cause the buyer's unsophisticated. And they're just like, well, I already got a staff, it can drive and it's like, and they're just not inclined. Now, where we're playing in Japan, it's not random that we're doing trucking Japan. There's a massive labor shortage today, and there's an imploding demographic, you know, a situation. And so there's a demand from almost every sector. And that's why we've picked that market to really grow. But I think like, you can take like even more obscure, like when we'll all queries, you know, literally like where you're moving cement, you're moving dirt, not queries QAR, QU, ARY, the queries, queries. - Those are rock stone, right? - Rock stone cement. When are those? I can tell you the people who own those things and run those things wanted today. So it's literally, then you don't have a point, which can't make this stuff fast enough. The macro point though that people don't talk about, I think all this stuff's gonna happen. But that happened fairly soon. - It happened fairly soon. But the macro point that in legislation and kind of in the kind of economics, the political economy of this conversation is AI is really, you see, you have this big pushback in digital AI, 'cause when it comes to like, I don't know what's gonna happen to my job and VCs, I'm sure all of you are associates are very scared. But like, you know, there's many ways that need us. - Yeah. - In our universe, it's the other way around. It's like literally, I'll meet these operators and they're like, we'll give you everything. Like if you do this, we'll give you everything. So then it's just up to us to like get there as you know, aggressively into this. - Well, the sheer furl on time has been first. It's trucking for some reason, triggers the, at least the press's imagination on like, you know, sort of apocalyptic levels of job loss. Like, will there be, but it's so wrong. There's not enough truck drivers. And guess what? Nobody wants to frickin' be a truck driver. - Why is that? - Because it's a terrible job. It's like a cause. You're like, you're like, you're like, - By the way, I grew up, the main teacher of the town of I grew up was a truck stop side. - Yeah, it's a laser. - Why is truck driving not truck driver? - Yeah, it's like, you're asking me, it's, you know what, this is like a, you know, talking to my kid. At least like, well, why can't I put my hand on the stove? It's like, because it's gonna burn your hand. It's like, well, why? It's like after the third life, it's like, come on, buddy, let's do this. (laughing) - That's not an hard one. - Yeah, so what's hard? - I'm kidding, just to make sure everybody knows I did not get out of this. - I did, I did. - It's not, yes. - So what's hard, why is being a truck driver a difficult job or why would kids not want to do it when they grow? - So, let me use a parallel analogy, which is very clear. And then you can, why, you know, people will say like, nobody wants to work anymore. Then they say, well, you know, McDonald's is all these job openings. Then actually what it is is, those people that used to work with McDonald's now, DoorDash and Uber. Because it's better for them. Because they can open, they can start their hours and they don't have to, there's no boss and they don't have to like stand in their feet and they can serve their phone in between, you know, orders and they don't, like, that's the reason. It's not random. The market is efficient. And so in the truck driving example, why does somebody not want to be away from their family for four to eight days in a row doing long haul trucking? And more a sharp example is in Australia, why don't people want to go literally by a plane to go to a mine and work on, or you go offshore oil rigs. Those jobs exist. If you want a job that pays six figures, they exist. Even with such lucrative pay packages, it's not enough because people are like, you know what? I like kind of being around my family. And I'm willing to take an incremental decrease in cost and how much money I make. And then also like, I think today more than ever, things like back pain and like being exposed to the sun and cancer and people that care about, that's now a part of these other things. - It's a thing, so I have this right, but I believe it's because I think, a lot of hot truck drivers die of glyphosurctency 10 years less than their peers. And I think it's a, people say it's a consequence to several things. So one is some combination of nutrition and sleep. It's, you know, it's basically, you know, it's yeah, it's very difficult. - It's very difficult to eat well and exercising. - It's your sleep score if you're a long haul trucker. Let me get another note eight sleep on that. - Exactly. - And so like obesity and then heart disease, hypertension and so forth, they're all very high. One, and then two is I think the vibration is very difficult, stress in the body. And then the third is you mentioned cancer, but I think it's the, I think they have. Truck drivers have like a much higher rate of melanoma on their left arm. - Exactly. Yeah, there's photos like a truck driver who's been driving for 30 years, one half their face, the other half's face that's exposed to the sun. - Right. - A more interesting, or even more stark stat. Mining is 1% of the labor poor globally, 8% of correlated fatalities. Do you think people are rushing to work in minds when they hear stats like this? Most major minds have a fatality regularly, which means once, twice a year, three times a year. And if you ever visit a mine, you'll see that everything is based around safety. Because once you experience one of your coworkers dying, then you're like, what am I doing here? - Yeah. - Like there's other jobs I can take. And so it's, I understand you're trying to enumerate for the audience like, but these are not good jobs. And best evidence is, this is not a mining podcast. This is not a podcast about, hey, long haul trucking is so great. They're just not attractive jobs. - Yeah, and even truckers don't want their kids to become truckers. It's a prayer for that reason. They want their kids to be at the very least safer, safer when it work. But now it's standing all that. Do they, how long will there be, do you think there'll be safety drivers in long haul trucks that are self driving? Or let's say, even just somebody in the cab to deal with what happens when they drive. - We know multiple companies that have driver our goals right now. So like they're working to get drivers out right now, without going into our own details. (laughing) - To be honest, it's not long. We're talking a few years. - I think I'm the long end. - Yeah, the long end. And the thing is, there's a software technology thing which is one part of the problem. But the other part is, it's the redundancies that you need in hardware. And the validation necessary for those redundancies. And in many cases that can actually be a long pull. Take a look, productionizing a fully redundant steering system, fully redundant braking system. That's not an high volume production yet. And once you get that high volume production, I get the quality up and then that's validated. And I can actually do these. - Need the price downs, exactly. - Do you guys, do you look like it's, you know, the delivery robots? Like is that, do you see a world where there's a billion of those running around? - Yeah, I think so. I mean, the product that we're announcing, I think it's probably come out around with, this time it's called a Dana. So you can just simplify everything that applied intuition does into two buckets, which is the, we've been talking mostly about the models that go on the machines. Then this is, we say on board software, on board AI, then there's off board AI. This is the tools to design and develop these same systems. So the models that actually go on the machines. Our, you know, vision for that is, and the delivery robot is a great example is, like. a high school kid or a middle schooler, they can make iPhone apps. They should be able to make autonomous systems. So why can't they just ask that's a very simple question. Why can't a ninth grader make a delivery robot in their in their home? Well, they don't have the actual environment that they would first develop the scenarios in. They would define the requirements. I want this robot to go on my high school campus around these, let's say, four buildings. Then how okay, now that you define the requirements, then you have the scenarios get made, where all the scenarios that I can that can that can be made by using, let's say, satellite image of the high school. Then now you have to train the robot. So you need some data. Where do you get that data? There's maybe enough publicly available data that can actually train a fairly rudimentary robot. Okay, now you got that data from online. Maybe YouTube videos, some other places. Suddenly the robot's not doing, now you need to deploy onto the actual machine. So then you deploy onto the machine and then the robot runs into the wall. Okay, what happened there? The loop closes. It's called Dana, which is the street that applied intuition as headquartered on. This comes from our tooling background. And if you look at how tooling has changed in the digitally I world, if you look at what Claude did to all of you, we also remember from mixed panel to GitLab, GitHub, all these, now everything is moved into a very different, almost ID, frankly speaking. We think the same thing's going to happen in the physical world. And so, yeah, that's what we're building. That's what we built and we're launching. And we already use it in-house to develop our autonomy system, which is, and we're working on the most kind of scaled complex systems on the planet in all these different verticals. So we're pretty confident that it's actually quite useful. And we've seen massive productivity gains, but also, you know, we think like other companies will use this to build our systems because it gets that mission, the billion intelligent machines. Funnily, where Dana is our agentic platform for physical AI and everything that we've built and developed over the past nearly a decade, every tool of every technique that's available in Dana. And it's very actually easy to use with the agentic interface. And so workflows that used to maybe take days or weeks to run, you can now run those in minutes, in many cases. And this just lowers the barrier to entry to building these systems. And we're just lowering the bar of like, you know, what it means to develop an autonomous system. Autonomy is still actually quite in the scope of software is quite exotic. It's not because of the things that we've talked about. And we've just brought that down very, very aggressively. And it's kind of like, you know, the old adage of like, how do you make a great product in software? It's like you either increase safety, convenience, or cost. And we want to try to do all three of those things with Dana. And our hope is just like you said, like, you know, kids can develop robots for their, for their own use. And that extends to humanoids. So we're not just talking about like land-based systems or, or, you know, ones that are, that are, so you know, humanoids, you can do drones. The fact that right now, writing drone software and deploying it at the time, it's quite obscure and almost hobbyist, we want to just make that absolutely like, you know, maybe that child's play, but like teenager play. So this points to the world of like, just like a lot more experimentation and entrepreneurship and like agriculture, everything, bots and like basically every domain construction. Yeah, defense. Exactly. Just have a sudden have a much larger number of people who are applying creativity and coming up with ideas and making things that move. And if you seem like with Claude, it's like, it's one thing just to make the engineer more efficient or bring more people into engineering. But then when these agents really run, you're getting into, yeah, it's just like the iPhone example of you couldn't imagine Instagram before, like the iPhone. It's like imagine 2005 on laptops, you're like a 10 years, there's going to be this app where you can put photos like, well, the phones don't have cameras like, yeah, but it's going to be like social. Like what the hell? Like some like Facebook. It's like, it's hard. It's hard. So we think by lowering that barrier, you're going to get way, way more creative autonomy products. Yeah. I want to decide whether to include this or not. So my kid is building autonomous bots in factorial. Oh, nice. Yeah. He's one of his projects. Yeah. But he's had a rolling because the toolkit's not available yet. So he's actually training and he's actually training models. Yeah. He's gathering data in the game. And actually has like a whole army of like bots that he's developed. Yeah. Yeah. So like and his mother is like, why are you playing that game so much? And he explains, of course, it's a purely educational process and experience. But it's interesting. It's the kind of thing. It's like, yeah, it's like, there's no reason autonomy should be this like, you know, obscure, difficult, you know, alchemistic, you know, technology. And I think not only does that have a huge impact on society, it also allows people to understand that these systems are not like, you know, magic. If I can develop a, a roomba for myself in my house and a weekend using data, then why then it's not suddenly so scary. Yeah. And I think that's like, that's that's a support. And we can support it can support people in all kinds of ways that we haven't even imagined yet. Yeah. Absolutely. Yeah. Exactly. I mean, you think about like, you know, folks with disabilities, you know, we always think about humanoid as like this very important task of holding laundry, which is the way we focus on, you know, the important task of it, when you allow these tools to exist, I mean, I, you know, we started a tooling company. I mean, I feel so importantly that tools are like what separates actually advanced civilizations from, you know, less advanced civilizations. And our first, uh, uh, mark for the company was a monkey's head. And then we got a designer. We said, what this, this is stupid. I was like, that was pretty good. So you were talking earlier about how when, you know, um, the technology got so good and mobile, that there was a wave of these companies, you know, Uber, WhatsApp, Snap, you know, Airbnb, etc. that emerged in quick succession. And so now the technology is getting there for the infrastructure for a physical AI. What are some use cases or companies that you could have obviously started predict the future, but where are you most excited for like what, what could we be talking about the equivalent here of in quick succession? I mean, I think, uh, you know, midterm we want Dana, if not the short term to really, you know, make humanoids way more real. Uh, there's, I mean, how many, it's like a thousand core tasks in a home from, uh, from humanoids and these companies, it's like such. I mean, I'm sure you talked to people who work in these companies. It's everything is difficult. Every step of the way is difficult collecting data is difficult. Uh, you know, cleaning that data is difficult, training is models are deploying the model is difficult. And the bar being, I want a high school kid to make a humanoid. So that that's our, our, our path. And we think there could be a lot there. But that's like these obvious stuff. I think the true non-obvious stuff is going to be, we'll look back, we'll be, we'll be way more interesting. And there, there's, there's some core ingredients that we're bringing together in data, right? We're making it way easier to, to actually get imitation learning to work way easier to make reinforcement learning work in combination with that. Um, where we have pre-trained models that can be used as a baseline for a lot of things. Um, world models, advanced simulation tech, they go, all of these things come together. And then you're sort of limited by your creativity, like, well, what, what do I want to do? And if you think about any kind of physically eye task as it's, it's a, you are understanding the world and you're manipulating something and, and we can build that. That can be built now much more easily in this, in this tool. And I think sometimes people ask like, I was being a tooling company and like you take self-driving trucks, we deploy self-driving trucks and many of the self-driving truck companies use their tools. I, I think I sometimes don't have to be a last goal. Like, you know, with Dana, are you going to like enable all these competitors? That's great. That's absolutely completely fine. If you look at Google and what Google did to web applications, there was a massive internet. Uh, Google still succeeded through, you know, search and YouTube and, and, and other web apps and other folks learned and used open source products and then ultimately close source products and ultimately venture back products. So we think we think the, the, the, the, the same thing had happened here. I was at a robotic sternum, a while back that you, you guys know well, um, and they had, they were training, you know, they were dig, they were training process training. They were one of their arms to do, particularly a killer app that I thought was very appealing, which was picking up dog poop. We don't, you know, training over and over again. Yeah. Yeah. I was further. And so, you know, I don't know, why not? Right? Yeah. Why don't have the little, I don't have the little robot fall you around me walked it on. Yes. Yes. Yeah. And I think like like, I know somebody who built a, I forget who was, somebody built a, a little lawn robot that would go around and into this pick up individual leaves. Yeah. Yeah. Because you got that problem ready. Hey, you're, you're, you're, you're, you're, you're, you're, you're, you're, you're, you're, you're, you're, you're, you're, you're, it's completely clean. And then like two hours later, there's like 14 leaves. Yeah. Yeah. Yeah. And the, that was like, set up the little bot to pick up. It's like if development costs are zero, then people will do that. I mean, I don't know. You guys remember like the early iPhone apps that hits were like the beer, one or the fart app. If you imagine that in like, yeah, if you imagine that in 98 with, you know, with the Symbian mobile, you know, whatever, I was the OS from I think was Eric's center somebody that'd be possible. You didn't need a team of like 50 people to develop a, like the beer thing for the blackberry. So I think there's a similar type of thing that's happening. We're, you know, we really want to be a part of that. It wouldn't enable that. And if it like makes making, like I think it still be a while before like making a robot taxi is like super, super easy. Yeah. But that'll happen. But there's, I mean, the number of bots that could be, the number of kinds of bots that could be to, - The way in health care is almost, and just to get our loan is almost home care. - Yeah, and then in construction, you know, and all the physical traits. - It's like us sitting in 2007 saying, let's, we should have an app store. What type of apps? And we would come up with a list of eight. (laughing) And then they're like, there'll be a messaging one. And then there'll be a camera one. And it's like, now you look at the app store and it's like, you know, there's an app for like, the hotel you go to. And it's like, you know, to order food off the menu. - Yeah. - It makes sense. We were talking earlier about the differences between digital AI and physical AI, we were sort of hinting at LLM's, but world models are, you know, in Vogue right now. What do you talk about sort of the state of them as it relates to physical AI and how should we think about them? - So first off, world models means about a hundred of things. - Yes. - And we had a team at CBPR recently and I was joking with them about just how many different ways you can define what a world model is. But when we're thinking about a world model, we're typically thinking about it in the context of a simulation, right? Something that is effectively - Restarts as a sim company. - Yeah. - Yeah. - Something that is sufficiently able to represent the real world and is reactive in a sense where you can actually have, let's say, an autonomous agent that's acting in this world and the world model is behaving appropriately in response to that autonomous agent. - Maybe a Peter, I think it's worth being super explicit error. We just go to one level lower into, - Yeah. - You know, determinism in simulators, kind of the sim-to-real gap, physics-based rendering all the way to this generated world. - Yeah. - Where do we fit on it or where, you know, yeah, describe the landscape, I think maybe. - Yeah, so this is like, let's say simulation broadly, right? There's so many different ways of doing simulation and so the more classical approaches of simulation, very physics-based and you can decompose physics in all different ways and all different levels of abstraction and you can simulate with sensors or without sensors and is just the body simulation or are we actually simulating, for example, the light in the environment or the air? - Almost think about like the way CGI is done. If we literally had technical artists and we have technical artists who would create assets which would go in the simulator, which would mimic real like road signs and have, you know, roof-flectivity and material properties that you would see in the real world. But as you guys know, Hollywood is going through its own fundamental change, and now you've generated technology. The same thing is happening in our universe as well. - Yeah, so that's sort of on the far end of physics-based simulation and the opposite end is purely neural simulation. But within that spectrum, there's many different things you can do that are each useful in their own right. And so one of those things is a Gaussian-based simulation where you have effectively a representation of the real world that has a 3D representation and that 3D representation is consistent, meaning that if you have some reference point and let's say camera and that camera moves within that 3D world, because the Gaussian is actually representing the 3D geometry of that world, you'll actually get very high quality output from that. There's a lot of value in that and that's one type of world model. But when you go further on that spectrum and to really into neural simulation, then you get into these where you're actually generating the video feeds. You can think of a neural network that's actually outputting a video as what's actually coming out of the neurons of that. And that can be reactive, which gives you some very interesting properties. Reactive is in the ego does something in the environment and the other agents respond to the ego. Exactly. However, you're not guaranteed in that reactivity that it's accurate, right? And now it's a question of, well, how can I align this simulation, this world model with the real world and the way that the real world actually react? And if you have perfect alignment between the real world and the world model, I think you just sort of saw the universe roughly, right? It's a possibly difficult problem. But as we make progress towards that, it makes training physically on models much easier because you can do more of that in simulation. But the hardest part, though, is we're always doing what performance, right? So I like to say that the labs, they have it easy because they can make models that are trillions of parameters and those models can be super slow. And that's fine. But we don't have that luxury and physical AI, right? We deal in real time, like the actual clock real time. And so we have so many milliseconds before we have to do something. And those performance constraints, they actually constrain the problem in a lot of ways. So we can have very large models. And we do have very large models that are used in the off-ward environment. But once you go on board, all of those constraints are very real. And now we need to train a much smaller model that has the safety constraints, these determinism constraints. And that's the hard part about physical AI. That's also the moat, right? It is what makes our tooling and our competencies valuable because it's just really hard to meet all of these constraints in the physical system. When will you-- which will we get first? A perfectly simulated real world environment for training autonomous devices or a Grand Theft Auto 6? [LAUGHS] As long as they keep putting out great trailers, I mean, I'm going to be like, I'm getting entertained without paying a dollar. I'm reintroduced to Tom Petty because of that. [LAUGHS] Will you give us some timeline? Well, so this is-- Let's just run that for a second. Yeah, let's go for it. Well, look, I mean, so the whole thing was-- The whole thing for Grand Theft Auto was the big innovation was open world sandbox gaming. So it's a simulated city, at least in theory. On that spectrum, we hire so many people out of the video game world. On that spectrum, it's absolutely real. Well, go tell us about that. Yeah, what's the spectrum? So I hear-- this is a speculation. But I think Grand Theft Auto 6 will be, perhaps, the last major real world of video game that's still really developed, let's say, in that legacy era of traditional computer graphics tooling, tech-wild artists. Yeah. I think that Grand Theft Auto 7 will much more likely be like a world model-based video game. Right. And you could imagine, as AI tech evolves here, you would have this concept of this video game world model. And there's some sort of baseline, let's say, data store that represents the real world and somehow. And then you have some translation layer that's actually turning that data store into something that you can see and run around in. But it could be the game as a concept. It could be the real world, right? It's rather the same. You could have a complete recreation of the real world in the game. Well, this has kind of happened with flight simulator. So it wasn't-- Yeah. So those recent flight simulators are literally, as the entire planet rendered accurately, is understanding, at least from the air, is that right? Yeah, yeah. And you're really-- that's where our bread and butter is when we started as part of the business. We hired so many people out of the Microsoft Flight Simpsons. I'm surprised. You know, when you fly over New York, or when you fly over to Los, then the flight simulator now-- It is the real city, right? Exactly. But there's some tricks that they play there. And a lot of that is fidelity. You know, the real world, the more you zoom in, it stays a certain level of fidelity. And so the tricks that you play there is you basically are down sampling very, very aggressively. And then as you get closer, then it becomes more high fidelity, where the real world isn't like that. If you were to try to rebuild the world with this level of fidelity, it would take all the energy of the universe, right? It's quite complex. And that's probably, by the way, the best argument against us being living in a simulation is among the-- but of course, and you would say, well, the simulator we're in doesn't call it laws of physics that we're-- How do we know that the simulator that we're in is rendering all the stuff that we can't see? Yeah, yeah. That's true. As far as I know, everything else outside this room does everything else. You know, Buddhism believes this. There's a different type of path that's like, when you open your eyes, the world is rendered, and then you close your eyes, the world. That's literally religious. I don't see why. I don't see why I see this. I prefer to keep rendering. I find that, sir. [LAUGHTER] Buddhism from press principles. Yeah, exactly. That's what you should. That'll get a lot of clicks that you call this. Yeah. Well, it just goes-- it was going to be on the timeline topic. We give us timelines on self-driving cars. What timelines do you want to give us, if any, on sort of other interesting things, if we're tracking perhaps when we'll get laundry full-date or other things that emerge because of humanoid surveillance? And then also maybe just touching a little bit of world model, where we see world models come. Because I think it's fundamental to what the work we do. Yeah, yeah. So to ask the first question, laundry holding, it's not terribly far from being solved to be clear. And there is a lot of interesting research being-- And then humanity can rejoice. That's a proverbs 4. 16, I think. [LAUGHTER] Well, here, I do think housekeeping is a killer use case for physical AI. Computer thinks, too, that he always talks about in the company. What is housekeeping and entertainment? He had Peter's long on humanoid entertainment. Well, I think entertainment is about a killer. I don't think anybody would have-- What do you do? I mean, like, so these Midwest white guys are really-- I'm just saying. I think-- I just want to know when the Northwest world is all in the-- Yes. No, I actually haven't entertaining-- I have a tiny little Chinese robot dog. So like, literally, it's just like a little-- And it's just like rooms around. And it's just like-- Are you paid to see Cirque du Soleil with robots? Like, yes. Yes. I want to see Kung Fu. Trap-Ease swinging. Look, and by like a compiler's the guy here. [LAUGHTER] I know. But my time-- One Westworld. I want Westworld. I mean, the drug-of-the-bunny thing is I was just saying, well, will people in the suburbs-- actually, that passed the test. I bet you people in sterling heights would actually pay for that. It's actually true. Stan Croft said. Stan Croft said. But back on laundry folding for a moment, it's actually not far from being folded if you remove the time constraint. And so the trick that's played, and if you look at the latest research videos, as they'll say, play at 8x real time or whatever, right? And that's for you to make it washable. So the question is, when can you actually human parity of performance? That's further off. When you decouple models from just the hardware, the hardware can do it now. That used to be a constraint. But the hardware is very fast and accurate now, which was actually-- There's still overheating issues that are still being dealt with, but it's not terribly far off. I mean, it's far off from when I was a mechie, that was fantasy. There's nothing can do. What's the movie that has the most realistic future vision of robots? Oh, man. Bicentennial man. Yeah, he's-- He's-- Realist-- why that one? I actually haven't seen it. Well, I like that scene. I think it's I-Robot, when Will Smith jumps in the car, and his-- whatever, his accomplice of the car, and he's like puts the car in manual, and she's like, what are you going to drive this thing to yourself? Like, out of like-- You know, she's crazy. Yeah, you crazy? What are you going to drive this thing to yourself? Like, that's what we're-- that's a flight to-- to-- it should be-- you know, like goal. Well, get by the way, I haven't seen this movie in a long time, probably since it came out. So my recollection of it's probably a bit incorrect. Don't worry, the internet will correct you. But I think Bicentennial man has fully self-driving cars. And it also has the housekeeping robot, which is played by Robin Williams. And it's like the friendly guy that will-- the friendly robot that will clean up and also babysit your kids and stuff like that. It seems like it's in the not terribly distant future. I got a different answer. You guys ever see that movie, a Sam Rockwell moon? Oh, yeah. Yeah. The set up. I don't wonder-- it's a great movie. Don't watch the trailer. Just watch the movie. It's the premises, the tagline of the movie's 250,000 miles from home you find who you are. And it's one guy who works in an energy harvesting based run by Plei-Tin-Twish-- run by lunar technologies. [LAUGHTER] I don't want to be whale. I don't want to be whale in Utah. I don't want to-- you know, that's from the alien franchises and then tar-terral corporation from Blade Runner. No, no. I want to be lunar technologies in the moon franchise. The one that's one guy who works on this and the base basically runs by itself. And he's just there to kind of mind it when things kind of-- some, you know, air signal. Yeah, the reason why it's-- I think so accurate is because the state of the art for AI systems is-- like, the system, they just need the occasional ground. Exactly. They'll just go off and do something crazy. Yeah. And then you're saying, no, no, stop doing that. Oh, I'm sure I like that too. Yeah. That's what coding about so late. Yeah. And the reason-- other reason that I think it's quite accurate, it's maybe uncouth now, but Kevin Spacey is the AI, you know, smiley face. And he's just there to kind of placate the human to assist, but to also like-- he's like, oh, you're-- you seem like you're sad Sam. And like, you know, like, that's the-- the-- the-- the-- but really, it's the one running the base. And hopefully-- I mean, I shouldn't say we want to be lunar technologies. I don't know if they're quite a positive force in nature in that-- but I think massive energy farm that's complete autonomous. That's going to be the future. And I-- and I think everyone, like, everyone reacts to things like that with, like, fear. And it's like, guys, that's amazing. That means energy costs go way down. That's incredible. Like, that's an incredible positive thing. I think, I think the, you know, I just did this commencement speech at my-- my underdraged speech. Did you get destroyed? No. You know what? I-- Unlike Eric Schmidt. Yeah, yeah, yeah. Yeah, yeah, yeah. I-- listen, listen, listen, my-- my-- my-- my wife started watching me see that happy like you were yelling at me. Oh, what's this? So I-- I basically-- I don't-- I'm not like-- I don't-- I'm not going to say which tech leaders who just basically avoid it by, like, punting and saying I'm not going to talk about it. I talk about this stuff. And partly it's the General Motors Institute. No one's-- like, these are-- I don't want to-- I don't-- I don't-- I don't-- I don't want to throw judgment on, you know, the people we recruit out of-- out of MIT and Stanford. But let's say GMI people are a little different. And they're like, you know, they're like pragmatic people. And they-- they-- you know, you don't go to a place like GMI if you believe a superficial view of what corporations do. Corporations are just people working on projects together. And by the way, if people working on projects in other government, people working on projects together and nonprofits, they all screw up. And so it's-- it's too simple to say AI corporations are terrible. This also, you can't say the other side, which is like, it'll all be great. So you have a role to play. That's basically what-- you know, what my-- what my message is. And it's-- that's-- that's the case. I think if you feel like-- if the-- if the-- of the-- the-- I think the obvious, you know, abundance that comes from self-driving-- self-driving cars and the fact that people don't die, which is amazing. But then you also get this efficiency of cheaper energy, et cetera. If all those things don't still satisfy your-- your fear, you-- you as a person, it's up to your responsibility until really learn about that technology. You-- you can't just say, well, I'm afraid of it. And my reaction is shut it down. That's not-- that's simple. It's-- and I don't say this just to say that we're competing with the Chinese, but there's a confusion. You know, saying about my confusion, there's this no hand can block the sun. And the sun is technological progress. And if we as a society don't embrace technological progress, we will be left behind. Somebody else is going to do it. And if it's not the Chinese who knows, it's maybe the Uzbek. Or it's another country that is recognizing, hey, my citizens are suffering. And I'm going to use this technology to remove it. It is honestly, it's because we're living such a great society that we can have these like-- I would say stupid conversations. There still are people who don't can get food. And someone will immediately equip if they were debating me. They would say, well, there's plenty of food. It's the capitalist system that doesn't-- then I don't know. Let's-- let's be very specific. There's plenty of food beginning that food to those people is difficult. So that means we should let robots get that food to them faster. That's just how it is. So I think-- and I think like we, we as like technologists, I think sometimes we, you know, it's I think an inclination to say, leave these people behind. I think you have to bring them along if they explain it to them. But we also have to treat folks like adults and say, if you don't get it after I explain it a couple times, team, you just don't get it. There's like a middle ground. It's not everyone's an idiot and we should just be technology will just be perfect, perfect, perfect. There's a middle ground. Let's have that conversation to a point. And then we just move forward and make society better. And then the results show it. I mean, there's people who still shockingly believe communism is the right answer. I mean, I just want to say why-- and I'm, you know, I am a capitalist. I can't admit that. But there's 70 years of history there. Like, word, that's not even a debate anymore. I mean, I think it could be a debate. If we're sitting here in 1965 and having a debate, you say, look, maybe central control systems work better. There's no debate anymore, folks. Systems where individuals make decisions on their own interests actually work better for society. And so that doesn't mean everything is perfect. And you can't extrapolate. That same thing with AI. It doesn't mean everything's going to be perfect. But net net, it's definitely going to be better. And that's roughly what my commencement speech was without the booze. The market and these guys were booing. So they just cut it out. Eric was booing, he was throwing stuff, and they just started it out. You mentioned the Japan market earlier. When you talk briefly about sort of the global ambitions and how these technologies, you know, interplay and what we're doing here. So I think America particularly is still the most advanced in terms of when you take account the business model. The second thing for a company like Applied Intuition, we're an extremely global company. We work with everybody minus we don't have an office in China, but really everyone else on the globe. And we're a horizontal company. And I think we, I think more Silicon Valley companies that they can employ a little bit of what we do. So it just worked very, I would say collaboratively with the local economies. As sovereign AI becomes more of a real thing, we have to build businesses that take that in account. By the way, we're not the first ones to do this. If you look at the history of America, you read the history of Standard Oil. You'll see that this is, this is, that was the history of companies. You'd work internationally. Aramco is not a random company, right? You build based on the real geopolitical realities of the time. And so I think we've, you know, we've, I think navigated quite well. I've lived, you know, in Japan, I lived in Germany, I lived in Dubai. So also being Pakistani by birth. I think that's also influenced our company. Peter's only lived in Michigan and here. But he is a German. So. So. But so I think innately we're more, we think about the globe more. I think when I was at, both at Google and at OIC, I was always surprised at how kind of almost myopic the companies are just always looking at the market that's just like within the 30, you know, between San Jose and San Francisco. It's like actually the market is really big. I think physical AI, the nature of it being physical. I think we, we have to be a very international company. And I think we've had a lot of success being a very, you know, being international. Yeah. Come on. I think it's a good place to wrap. Okay. Peter Kaster, thanks so much for coming on the podcast and congrats on big lots of data. Yeah. Thanks for having us. Awesome. Great. Okay. Great. Thanks for listening to this episode of the A16Z podcast. If you liked this episode, be sure to like, comment, subscribe, leave us a rating or review and share it with your friends and family. For more episodes, go to YouTube, Apple Podcasts and Spotify. Follow us on X, A16Z and subscribe to our substack at a16z.substack.com. Thanks again for listening and I'll see you in the next episode. As a reminder, the content here is for information. purposes only. Should not be taken as legal business, tax, or investment advice, or be used to evaluate any investment or security, and is not directed at any investors or potential investors in any A16Z fund. Please note that A16Z and its affiliates may also maintain investments in the company's discussed in this podcast. 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