Why Physical AI Is the Next Frontier | The a16z Show

0:00 Our mission is to put intelligence on a billion machines. And we think that can have a profound impact on society. Applied 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

0:17 building software and optimizing ads and creating videos. That's all interesting and good, but really when you talk about the global economy, that's physical AI.

0:25 In this intelligence revolution, the companies that impact the physical world might actually be bigger than the companies that impact the digital world.

0:32 How many things are there where the idea of physical AI, physical intelligence are going to matter?

0:36 There's no reason 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.

0:51 Everything that we've built and developed over the past nearly a decade, that's available in Dana.

0:56 Which will we get first? A perfectly simulated real world environment for training autonomous devices or Grand Theft Auto 6.

1:07 Casser?

1:08 Peter, welcome to the Ace Z podcast.

1:10 Well, thanks for having us. Your name is.

1:14 Which is one of many like we've.

1:16 I think we've all each known each other for too long. More than I'd like to admit.

1:21 Yeah, long time.

1:22 We're lucky to both be the first investors or among the first investor in the first round. Of course. Different check sizes, but.

1:27 And I was an investor for you even before then.

1:30 Exactly. So let's do that segue. We have a lot to talk about today. We've, you know, the biggest launch in company history to talk about today. But first, why don't we just give an update or a status. What does Applied Intuition do for those who.

1:40 Yeah. For the, for the people who don't know. Applied Intuition is a physical AI company. We put intelligence on machines. 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 intelligence. Then we got into the actual intelligence itself. Innov. Very like, in some ways like a very boring AI company in the sense of, you know, 83% of the company is engineering.

2:11 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 for a sales enabled company, but yeah, over a thousand engineers and based in Silicon Valley, but We have offices globally, 18 offices. And you know, 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 pithy things everyone talks about safety.

2:39 You know, if you really talk to somebody who's been in a car accident or in a mining accident or you know, in a farming accident, those are real gnarly situations. Beyond just fixing that, you just, if you can unlock productivity. I think, you know, 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 to 25 years, if you look back at the Internet now, people actually if you look at the original Internet companies that are doing surveying or they're doing some analytics and those are interesting.

3:13 But really when you look back 25 years from the big monolithic companies are Amazon, that 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 this intelligence revolution, the companies that impact the physical world might actually be bigger than the companies that impact the digital world.

3:32 I would love for you to talk about the following which is when you first started the company, you know, the knock on the company I think was oh well it's like, you know, it's making, making cars autonomous. Right. Self driving cars. But it's kind of like okay, there's like whatever, you know there's, there's Tesla way more building their own self driving cars and then, and then there's like six or eight other car companies that matter and then the company just could never get that big because they're just not that many customers.

3:50 Yeah.

3:51 So what's the, like 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?

4:00 Yeah. I mean even, even if you put that, you know, let's say view on us, the Automotive is like 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 our, that mission just keep thinking, you know, a billion machines becoming intelligent and you think about all the types of machines that exist.

4:25 Automotive is just an easy one. I think it sticks in people's heads. We all drive cars and it's a big market but I think it's, it'll be a minority of the business. Is a minority business I think it'll be increasingly a minority in the business, but that doesn't necessarily mean it'll be small.

4:38 Right.

4:38 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, like as you try to get to your mission, initially the manufacturers were the distribution to that intelligence, to consumers. But then you start working in defense, and you start working in construction and mining and agriculture, and suddenly the manufacturers are important, but maybe the mining operator is actually really important or the Department of War is really important, and suddenly they become customers.

5:07 And all of those are customers of ours as well.

5:09 Yeah, I think if you split AI into digital AI and physical AI. Digital AI, of course, is building software and optimizing ads and creating videos, that sort of thing. That's all interesting and good, but really, where you talk about the global economy, that's physical AI. And then we're talking about manufacturing and mining and logistics and transportation, all of these things that.

5:31 Supply chains.

5:32 Yeah, supply chains, exactly.

5:33 Let's build on that, though, for a second, which is like, so things that move today or historically that move are things that have human beings at the wheel or at the controls in some form. Right. And so, and, you know, airplanes have had to get designed around a human in the cockpit. Boats have had to, you know, get designed around human, you know, steering things. Like, in a world where, in a world of autonomy, like, 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?

6:02 I think both. Well, the, the thing that you have to remember is, like, you take like a haulage system that's on a. In a. In a port, like a catapult, Komatsu Dirt Mover in a mine, those are made for 20, 25 years. So the buyers of those products there, they might not have gotten their full cycle, you know, ROI on them, so they're not immediately going to buy something new, no matter how much better it is.

6:28 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 a cab. If you don't have a human in a cab, you can run. 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 breathe and it needs very dangerous.

6:50 And so you can build a very, very different machine. We're doing both of those things and then the thing that we're 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 he take an entire port, let's take an entire mine, let's take an entire query. And this heterogeneous mix of machines, they all can talk to each other and they can optimize and be efficient.

7:18 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 didn't even know the machine's going to go down because there's no analysis. The human is not plugged into the core systems of the machine. So like a simple thing like knowing when a break system is going to break is actually huge because you can start preparing for it in advance. You're like, oh, this wear and tear is higher than in other mines.

7:43 This is using an example. But the other macro point is if you look at agriculture as an example, average American farmer is 58 years old. The number something like under 35. It's like 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. So these, these demands are only growing. But the humans who are the bottleneck are decreasing.

8:09 Trucking is the same way. And so you can, you can really just unlock a lot more efficiency. So I mean one way to think, maybe think about this is like imagine if the cost for food decreases because it's way, way more efficient. What's the downstream impact? The imagine for goods being transported. Let's say, you know, instead of a few dollars a mile, it's 20 cents a mile and suddenly it's, It' unlock is very, very, very big.

8:37 And that isn't, I think, doesn't necessarily need for all the machines to be redesigned from the ground up.

8:41 Right, right, Got it. Makes sense. And then maybe just one more question would be just give us a sense of parameterize, like the scope and scale of the company today.

8:48 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. Because the important thing that we were kind of just tipping around, stepping around is all this stuff is Hard because it ultimately has to meet the real world. And the real world is, has way more complexity and, and has a lot more issues.

9:16 And we have engineering teams that can, I mean we've deployed our, our, our models onto like 50 some platforms. Like even that sounds trivial because when you think about, mostly when you think about models, you think about deploying them through a browser or on a phone and everything's 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.

9:37 And so we have engineering teams that can do that as well. Our kind of a 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 think that's, I always say that with an asterisk, which doesn't mean we're not going to spend it next month.

9:55 Good news, bad news.

9:56 Yeah, good news, bad news. But it's the, and I think like we're at that phase, you know, you talk about scale. We're at that phase where these giant markets are around us and we can make the decision how aggressive do we want to pursue those? Because decade of frankly execution and deployment into production. I think the hallmark of our engineering team is putting products into production. That really is a big thing. I don't know. How do you think about scale?

10:24 Yeah, I think that's roughly it. I mean the mission of bringing intelligence to a billion machines. That is how we think about it. And then thinking about, well, what are the types of machines that will have the most impact on and focusing on those areas first. But we'll get there.

10:37 Let's go deeper into the differences between digital and physical AI and more so into where are we today? What progress has been made? What are some of the main major bottlenecks in physical AI? When you unpack some of that.

10:49 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 robo taxis and humanoids. They're very visceral, they excite you and they're kind of sci f. I think those are very interesting and there is real work being done by us and other people in those domains. I think all the other domains I think are going to be just as important.

11:17 I mean you just think about what happens on a port. There's a huge unlock there and I Think that's the area we're really focused on. It's like all the other nooks and crannies. If you look at, like, 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 are getting networked. There's a similar thing happening with AI.

11:43 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. You're talking about AI in the physical machines that are moving around. If you look just at the example of Waymo from America and Pony from China trying to deploy in, let's say, the other countries. So not America, not Europe, not China. Every one of those spaces, they're way more hesitant of saying, yeah, thumbs up your robo.

12:17 Taxis can run unfettered in our country. And so if you look back just at kind of this arc of the Internet, 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 you have some.

12:44 Then you get into the next level of, like, the online offline stuff. There's more resistance to Ubers, to doordashes. 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, you know, 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.

13:11 We're a technology provider, so we can provide that technology across the globe. And I think that's something that's understated in this conversation.

13:21 Yeah, a few other things on digital versus physical AI. So in digital AI, the state of the art is, you know, 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. Right. 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.

13:45 However, to actually build a foundation model in physical AI, there's also a lot of Private data collection. When we're talking about mines 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 then the other key factor is safety. Right. If you're talking about building a smartphone app you don't necessarily care about is a safety critical application.

14:15 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 state of the art of physical AI and really proving out the safety case around some of these state of the art models.

14:33 Yeah, and I think like, you know, you talk about like human data collection has been its own, you know, little area of interest. But when you talk about collecting data, like in places like Korea, where they have North, South Korea, we 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 latam, how to get into these countries, work with the governments and get the thumbs up to collect proprietary data.

15:03 And so in the way that it is similar to, you know, other digital AI systems, your proprietary data sets, scaling laws, all that stuff is the same, it's just 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 can. And so that ironically actually plays in our favor because once we have a massive proprietary data center where we've been building, we already have hundreds of petabytes of data.

15:38 And then we have our own tools, which are like synthetic data tools, neural sim. We can use our own tools with our own proprietary data. And that allows us to build some of the best systems in the business.

15:49 There'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 to gather 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 them all work. Is there a flywheel aspect to that? And is there like, what's the level of difficulty involved in kind of booting

16:10 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 resources 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, you know, is, is tested appropriately because the, you saw it, you know, in Cruise, right?

16:45 Cruise was 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, than, than it seems. I think like we believed synthetic data was going to be important so we started our synthetic data team like five years ago now. Plus yeah, more than that at this point. And like, and we're a strong believer that Synthet accelerate autonomy development.

17:14 We've just seen that. And then there are like lots of other secondary and tertiary technical innovations that happen. Obviously the transformer revolution hitting self driving massive. Basically everything done in self driving pre 2122 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.

17:40 But there's other folks in that process. The actual techniques historically and as I'm simplifying here, imitation learning was the way of 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 a 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, you know, you close that loop and you see are you performing in those same scenarios better and better.

18:20 I think if you fast forward some years that will be a completely closed loop like with no humans intervening right now you still have like, like what's the, the fog error that we saw? We, we still see errors in the real world that impact self driving.

18:33 Oh yeah.

18:33 So it's like well what, what are the bottlenecks? Right? And, and the the bottlenecks, there's plenty of them. But whenever you're dealing with physical systems, you inevitably you hit a lot of gnarly hardware problems. And it could be anything from overheating to sensor being slightly miscalibrated or a funny issue we saw yesterday was basically a fogging sensor, like fog impacting a sensor. But these are the things that you actually have to solve for this stuff to work very reliably in the real world.

19:03 Yeah.

19:03 So I want to ask you a threat, a question, and we can decide whether you guys want to engage on it or not. It might be an opportunity or might hate the question, which is, were you 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 you know, very top end team. And then they famously got bought by General Motors.

19:21 One of my first distributions, personally. Side.

19:23 There we go. Y Combinator. Y Combinator company. And, and you know, top end team. And they, 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, you know, at least in the, in the, in the, in tech circles for being like, okay, being like the, the legacy automaker with the biggest investment.

19:43 I called Peter when, before, before it was announced on that. And I said, hey, Cruise just got, you know, he's also GM family. We're both GM families. And Peter guessed it was. Said Nvidia. I said no. Said, I said, go fish. Apple. I said no. I said General Explicit Motors. So that's surprising to people who are from gm that they were willing to buy. Yeah, that they did it.

20:04 Okay, they did it. And then by all accounts they were, I mean, as far as, like, as far as I ever heard, like they were making excellent progress.

20:09 Yeah.

20:09 And then they had this. There was a, there was an accident. There was a, was that it was an injury or fatality or.

20:14 It wasn't a fatality, but it was a serious injury. Somebody was dragged for 20ft.

20:17 Yeah, serious injury, bad press. And then, and then they put a bullet. The GM CEO on board put a bullet in the Cruise project. And I know that at least some of the senior Cruise people were extremely upset, you know, by the aftermath of that. Was it surprising that they reacted the way that they did?

20:34 So, you know, full disclosure, General Motors 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 coincidentally, I should say I'm reading this very famous book which I had actually never read before called On a clear day you can see General Motors and DeLorean's book. Have you read it?

20:51 As one does?

20:52 As one does.

20:52 Have you read that book?

20:53 So I have, years ago, I have. It's one of the great all time book titles. And we should just pause and say John DeLorean was like what? He was like the super genius of the car industry.

21:00 He was going to be the next president of General Motors.

21:02 Of General Motors. And then later on he started his

21:04 own car company which was in Back to the Future.

21:06 Back to the Future. And then that whole thing collapsed for 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.

21:15 Exactly. Lee Iacocca, Bob Lutz, this category.

21:18 Yeah.

21:18 And you got to remember, this is Linda's.

21:19 Right, sorry, repeat the title, repeat the title of the book.

21:21 On a clear day, you can see General Motors.

21:23 And why was that the title of the book?

21:24 Because there's a lot of bullshit.

21:29 Very large. Complex. Yeah.

21:31 Complex.

21:31 Yeah.

21:31 It's like. It's like a nation state.

21:33 Yeah. I mean really. I mean it is like. I think we say that like sometimes almost like flippantly.

21:39 Right.

21:39 But these companies are like extension. Like Hyundai is an extension of the state. Toyota is 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 an old saying, what's good for General Motors is good for America. And you cannot understate how important General Motors is the history of the American corporation. Sloan's, My years at General Motors and Adventures of White Collar Man.

22:04 If you run a large engineering organization, you should read that. That is the. This, like you. This, this thing that we talk as a modern corporation didn't just emerge. Sloan and Kettering create. Kettering as the head of engineering, created this with this arc, 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 comes along. John, you know, comes on DeLorean and he says, he writes he's going to be president and he's so fed up with a company and.

22:35 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, how genuine was being read led.

22:59 He writes his book and then after he like sobers up, he's like, I don't want that book published. And so he fights for years for his co author, not the publisher of the book, the co author still publishes. So it's a real true insight into a large corporation. I'm incidentally just reading it out even though I've, you know, worked at GM20 some years ago and I, you know, know a lot about the company. And what's shocking is it's not only by gm.

23:21 Most of the major manufacturers actually still operate that way on the inside. And so the question isn't the, the point, I think, for everyone to take away isn't that these people who run these companies are stupid. 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. That like, so, like, the distribution is.

23:43 This is a consumer product of. This stat might be outdated, but when I worked in safety systems 20 years ago, I remember GM used to pound in your head. Of the top five consumer lawsuits in American history, three are automotive. We got the majority right. So it's like, you have to be extremely careful. We had these like, weird things, like inside the company, you couldn't. It wasn't red, yellow, green, it was like purple. Or like, you'd always have to decoder.

24:06 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. Like, so, like, can you imagine how infuriating that is every time you're like, what does orange mean? Does this mean I have to like, so fast forward to you're meeting that system?

24:27 Well, for the four, our slogan for a very long time was that it was quality is job one, Right, Yeah. Versus safety is job.

24:34 Yeah, yeah, exactly. And that's the one, two punch of automotive. It's quality and safety. Quality and safety. And quality really becomes. Because the Japanese really reset that, that stage because that's a whole separate automotive history. We could talk about automotive history for an hour. But the punchline is you have the Silicon 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 union negotiations are happening literally that year.

25:05 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 multivariate problem. My other hot take is, you know, I worked at both companies, right? Google and General Motors. Those companies are way more similar than they're different. Way, way more similar than they're literally. People don't need know this.

25:27 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. I was like, no, actually, like making a modern combustion engine is, is extremely complex.

25:53 It's not just like, you know, it's, it's, it's not simple stuff. And so the, the macro point I think is it's a bunch of things. I think safety is always at the top of their, top of their list. I do think, you know, we've hired lots of Cruise people. I think the way they dealt with that specific issue with the government, you gotta, you gotta dance a particular way when that happens, and it just didn't dance exactly right.

26:15 And that just gives government bureaucrats more ammo to go after. And you're a big target like General Motors. You gotta, you know, it reminds me, you guys ever see that movie like Goodfellas? You know, 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 doing about Cruise?

26:39 Like, what can we do? It's like, like, Kyle's a good guy. And then it's like, qhouse, the Rising sun people are running through a San Francisco office. Just kidding. Don't make that an AI video. Gonna get mean text from Kyle. So I think, I think there is a universe that would survive, but it's tough.

27:04 So then a lot of what applied intuition does is kind of, as you said, like that dance. It's like how to Be a great partner to these companies.

27:09 Exactly.

27:10 Bearing in mind their own very real issues and constraints.

27:14 I think General Motors also had the topic of business model. Right. So you have. Cruise was going after the robo taxi 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 the equation.

27:27 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 in 2013. I was in the audience, I was a partner at the time. And you said something which I think is, it's very like recursive here. We're, we're feeding each other. You're hon advice. It's the key thing in, in new technology. In the new technology business actually everyone kind of figures out the technology.

27:51 Though that's still hard. It's so 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 to say is like, I actually think Cruise, you know, they were certainly moving at a much faster pace than Waymo.

28:12 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 safety drivers there, but they're autonomously running. But you won't know that because the brand is Isuzu. That's the customer. And why it's so good for us to partner with Isuzu in that case is that company's been around for almost 100 years.

28:45 Right. If I'm not mistaken, pre World War II company. And they are, you know, they know the government, they have test tracks, they know safe, 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, two punch. I think today the world is ready to consume AI in the real world and that's a lot because of ChatGPT and Anthropic and all these, you know, everything that's happened.

29:11 So people are no longer like, what's a self driving car? And there's views 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 way possible. And 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 the Department of War, whether it's the manufacturers and we work with, with, you know, within each vertical with the right partner.

29:34 But that's a fundamentally different view than a Tesla or a Waymo, which are going to be vertical where we're really playing the horizontal. And I think the way we can always think, we think about that our companies, we're kind of like a, like a chip maker. You know, we, we, we, we actually look and talk and walk a lot like a silicon company. Except we do obviously do, we don't make chips but you know, we're are, we have design wins and then we have really large long term relationships and then once we're in, we're in.

29:58 It's really hard to, you know, take us out. So you need deep trust. Our partners have really a lot, 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.

30:15 So how are these legacy car companies preparing for the future? Are they making or more acquisitions? They're going to, are they building and you know, partnering with you or they, how are they going to compete with, with you know, tech, tech native companies.

30:27 It's like saying like how are governments dealing with AI? It's such a, it's such a broad topic and each manufacturer like, even like you take Honda, Nissan, Toyota, three Japanese manufacturers with, you know, long legacies, they all approach it very differently. They're roughly in a spectrum of we're going to build two, we're going to buy and both extremes more than ever. We're going to buy is the common answer because they've been trying and we've been there the whole time for the folks that are going to build, we provide them tools and we can talk a little bit about our new product that we're announcing here.

31:00 And then on the ones that just want to 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 the reality is like every product is a different product and so the amount of silicon and amount of dollars you can put towards it, towards Sensors, what the customer is willing to pay, all that depends on what actually gets in the long horizon.

31:30 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, and then they're really moving to laptops. But there's a similar kind of 20 year I think horizon there broadly, when

31:58 you talk about machines and machines becoming intelligent.

32:00 Right.

32:00 Fundamentally a machine is a collection of these different components that are integrated and whoever does that final integration is oftentimes the company that puts their badge on it, 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 and platforms that can go into these machines. But we also sell the core technology that can be used to develop them as well.

32:23 And if you look by the way, under the hood of a, 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 into any of these verticals there's, it's just a complex web of folks.

32:47 That's why I always say like the, the chip kind of analogy actually works quite effectively because none of those companies make chips, but they all buy chips. And so I think that's a good way to think about it.

32:59 So self driving cars. So 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, and then Google engaged in the program shortly after that.

33:10 Yeah, late double Os.

33:11 Yeah, late double Os. So almost basically around a little less than 20 years maybe. And there have been lots of predictions over the last 20 years of like self driving cars are imminent at any moment. So I guess the bad news is we're sitting here today and most cars are not self driving. The good news is there are now self driving cars.

33:26 Yeah.

33:27 And so the Waymo cars are driving all over, you know, in the places they're deployed. It's become, you know, I'd like People in San Francisco I think treat it now as routine that they get into

33:34 and I think you can call, I think Tesla kind of like the AGI thing. It's like you know, if we're talking 20 years ago, everything we're seeing right now is like a mind blowingly AGI.

33:42 Right.

33:43 The post keeps moving. The Tesla stuff's amazing. You can look at a bunch of manufacturers. Blue Cruise, Super Cruise, BMW, Volvo's Pilot. They're all quite impressive systems. They're not full self driving.

33:53 Right. But yeah, well it's full self driving X whatever remote monitoring is happening. The Tesla, we have a home in Los Angeles and you guys may recall there was a large fire in Los Angeles and then the California power grid was buckling even before that. So it actually turns out among the things cybertrucks are good at is they're very good batteries for powering your house. Yeah. And so literally we have cyprotrucks as our backup battery for the house.

34:18 And as of last year, whatever the FSD release, I forget the exact one but there was one where it like at least a lot of people thought it like really turned the corner 14.

34:24 Yeah.

34:25 And like that thing drives people. I talked to somebody yesterday, talked to somebody yesterday who has a model Y who let the, let the thing, let the thing do the full route all the way up Highway 1 through Big Sur.

34:32 Yeah. I think mean disengage like the, the meantime and like miles per disengagement are really high and I think miles is like in the thousands. Thousands, yeah. Which is like very impressive.

34:44 Yeah, I know the big for it for people who haven't driven the big highway one Big Sur like that's a, that's a, that's a stress filled drive. He said it was great the whole way anyway so. And I wouldn't have been talking to him had it not been, would have gone right off. Right.

34:57 Because he unbolted the steering wheel.

34:58 So right off the cliff so. And then you know, Tesla's rolling out their robotaxi. You know it's starting to show up in the wild. And so on the one hand those exist. On the other hand, you know, 99.999999% of cars are still not self driving. Maybe just one other would be the self driving trucks. There's been this recurring kind of panic in the press of like this trucks become self driving and the employment, you know all these truck drivers be out of a job and sitting here today.

35:23 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, silver.

35:31 So let's, let's split, let's split the. There's multiple points that we brought up here. 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 want to be safety conscious. But most of it is cost, cost, cost. What you're seeing in China, which is China's kind of a different EV ecosystem, mainly because they don't care about profits.

36:00 When you're talking about business that doesn't care about profits, it changes the entire calculus of the entire industry doesn't care. But what you're seeing is, you're seeing L2 systems. So we can simplify the entire self driving conversation to is there a behind the steering wheel?

36:14 Right?

36:14 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, that's chip sensors, the package, the software, everything. We, you know, 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 used to be a big thing.

36:36 You pay four grand, 3500 to get an app system and then suddenly it became free and it just became default. I think that'll happen. There's a weird thing which is like actually getting 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. And the way that, how many vehicles and the way the assembly line comes in, the way you have homologation, all these testing regimes, all this stuff.

37:02 So I think you'll have wait, wait, wait. And then a lot, every single OEM, without exception, even the lowest dollar OEMs are working on an FSD competitor. So it'll come, but 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, 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 to you know, double zeros, their view was like when's mobile gonna come?

37:33 There was a huge like. And then it comes. And by 07 from the iPhone launch like it's like four years when you get Uber, Uber, Instagram, WhatsApp, Snapchat, those are the killer applications. So I think there's a very, very similar kind of wait, wait, wait. And then it's just basically ubiquitous in every vehicle. If you had to ask me for what that number is.28, SOP 29, start of production, 29, 30. And then by the early 30s it'll start becoming very cheap to free routinely

38:04 by the early 30s you would just buy a car and you just assume it's self driving.

38:07 Exactly. Or it has the driver in seat L2 plus plus system being very specific

38:11 like cybertruck or what Tesla people have. What Tesla owners have today.

38:17 Yeah. So then the question then the other side of this is why don't we have a bunch of Waymos everywhere? Specifically Waymo 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. Waymo, for the lack of a better word, is not. That doesn't mean they're not learned.

38:49 It just not one end to end system. It's not one monolithic model. One of the proclivities of their approach is it does depend on HD maps. Therefore there is a geofencing concept. I think Waymo's trying hard to remove that bottleneck so they can expand geographically faster. But the reality today is in there. The other thing is when you have researchers which they, which Waymo really was coming out of an Alphabet research organization, they didn't put commercial constraints.

39:17 So the sensors are bespoke and expensive. The cars and the, and the compute that are in there, they're, 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 featureful 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 there first?

39:40 Tesla with full self driving or Waymo with cost and geographic ubiquity. But you know what we're not debating about is it going to happen? You know, 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 this grind down to like dollar per mile efficiency. And the moment that it's cheap, guess What? All the OEMs are smart.

40:05 They'll just, they just adopt it. It's not, the 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 razor 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 than a buyer of a Tesla.

40:38 And so including the age of the consumer and what they think will happen and what won't happen. So that also the reason. So if you're Suzuki, you're like, my buyer is like not doesn't want this stuff so I'm not going to jam it into the car. It's not because they're not like technically competent. It's just a different area.

40:56 You, when do you think, when do you think it'll be routine? Let's say the 200 biggest American cities, like wouldn't it be routine to walk outside and you just, you just take it for granted that a robotaxi can come pick you up.

41:07 It's 26 now. I mean certainly by 30. All right, okay, yeah, certainly by 30. And I, I would say the big like variable there really is like, because you, what Waymo 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? But then you see their launch schedule is pretty aggressive and you're like, that can get there. So maybe if I was being aggressive I would say 28.

41:35 Yeah, okay, like two years.

41:37 I would say available in 30, but routine and maybe like 32, 33.

41:42 Sure. Because there's scale up, there's a volume

41:44 and also if you live in la, and so like five years ago I'd go to la, people would be like, what's applied intuition? I don't know what self driving cars are in the last couple 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 who's exclusively.

42:07 Right.

42:08 The answer is no. Actually now there is a huge, huge. If you look at the numbers, if you're Uber, you got to be scared. I mean they're just eating into, into ride sharing. Yeah but to get a hundred percent ubiquity, I mean that's, that's, that's another. It has to be extremely cheap.

42:22 And what about long haul transfer trucking?

42:24 So long. So that's what we. So that's the passenger side, the long haul trucking. Completely different economics, completely different business model. There are many companies right now, I would say probably north of 5 that are running long haul trucks with drivers carrying loads between America and China. If you 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.

42:49 And, and unlike on the Waymo and Tesla side where investors are willing to essentially give you some market cap adjustment for the potential of. They say the trucking business is like

43:07 made you buy a car with your heartstrings. You buy a truck with a calculator.

43:11 Yeah, it's a calculator business. And so it's like pure dollars and cents. And so I think 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 going to save you this many dollars. And it's like for sure, for sure, for sure. Because the buyer is unsophisticated and they're just like well I already got a staff. They can drive.

43:35 And it's like, and they're like, they're just not inclined. Now where we're playing in Japan, it's not random that we're doing trucking in Japan. There's a massive labor shortage today and there's an imploding demographic, you know, 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 you can take even more obscure. When will all queries literally where you're moving cement, you're moving dirt, not quarries.

44:08 Q U E A R Q U A R R Y queries, quarries, rock stone, cement. When are those those. I can tell you the people who own those things and run those things want it today.

44:20 Right.

44:21 So it's literally then you don't, you don't have a. Which literally we can't make this stuff fast enough. The Macro point, though, that people don't talk about. I think all this stuff's going to happen.

44:30 Right.

44:31 But it happened pretty soon and 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. Is really. You see, you have this big pushback in digital AI because accountants are like, I don't know if this is going to happen to my job and VCs. I'm sure all of your associates are very scared. But, like, in our universe, they're debating

44:56 whether they need us. Yeah.

44:57 In our. In our universe, it's the other way around. It's like, you literally, I'll meet these, you know, operators, and they're like, we'll give you everything. Like, if you can do this, we'll give you everything. So then it's just up to us to, like, get there as, you know, aggressively as quickly.

45:12 You know, the fear for a long time has been for 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.

45:19 But that's.

45:20 It's so wrong.

45:21 Go ahead.

45:21 There's not. There's not enough truck drivers. And guess what? Nobody wants to fricking be a truck driver.

45:26 Why is that? Explain.

45:27 Because it's a terrible job. It's like you're. You're like. You're like.

45:31 By the way, I grew up. The main feature of the town where I grew up was a truck stop. So I. Yeah, but why is truck. Why is truck driving not.

45:37 Yeah, it's like.

45:38 It's like you're asking me. It's, you know what this is? This is like a, you know, talking to my kid who's like, well, why can't I put my hand on the stove? It's like, like, because it's going to burn your hand. It's like. But why? It's like after the third why. It's like, come on, buddy, let's do this.

45:55 Yeah, let's.

45:55 So what? So what's hard?

45:56 I'm kidding. Just to make sure everybody knows I did not do that on FaceTime.

45:59 Yes. Yes. So what's hard? Why. Why is being a truck driver a difficult job? Or why would kids not want to do it when they grow up?

46:04 So let me use a parallel analogy, which is very clear. And then you can. What? What. You know, people will say, like, nobody wants to work anymore. And they say, well, you know, McDonald's has all these job openings. No, no.

46:16 Is.

46:16 Is those people that Used to work at McDonald's, now DoorDash and Uber because it's better for them because they can open, they can start their hours and end their hours and they don't have to, there's no boss and they don't have to like stand on their feet and they can surf 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, for four to eight days in a row doing long haul trucking?

46:46 The more sharp example is in Australia. Why don't people want to go literally buy 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, 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

47:24 part of these things tell me if I have this right. But I believe it's because I think commercial long haul truck drivers die of life expectancy 10 years less than their peers. And I think it's a. People say it's a consequence of 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 exercise and what's

47:43 your sleep score if you're a long haul trucker? Let me get. There's no eight sleep on that.

47:47 Exactly. And so like obesity and then heart disease, hypertension, so forth are 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 truck drivers have like a much higher rate of melanoma on their left arm.

48:01 Exactly. Yeah. There's photos of like a truck driver who's been driving for 30 years. So one half their face, the other half space because they're exposed to the sun.

48:06 Right.

48:07 A more interesting or even more stark stat mining is 1% of the labor pool globally, 8% of work related fatalities. Do you think people are rushing to work in mines when they hear stats like this? Most major mines 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 co workers dying, then you're like, what am I doing here?

48:33 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.

48:42 Yeah.

48:43 And the 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.

48:51 Yeah. And even trucker, even truckers don't want their kids to become truckers. Like it's for that reason, you know, if they want their kids to be in a, at the very least, like safer, safer line of work. But do the, do the. Notwithstanding 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 other even just somebody in the cab to deal with what happens when they.

49:10 We know multiple companies that have driver out goals right now.

49:14 Okay.

49:14 So like they're working to get drivers out right now. You know, without going into our own

49:19 details, to be honest, it's not long. We're talking a few years and I think.

49:25 On the long, long end.

49:26 Yeah, on 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. It's like, oh, they're productionizing a fully redundant steering system, fully redundant braking system. That's not in high volume production yet. And once you get that in high volume production now you get the quality up and then that's validated and now you can actually do these.

49:52 Neither price does exactly.

49:53 Do you guys, do you look like it's, you know, these little delivery robots. Like, is that. Do you see a world where there's a billion of those running around?

49:59 Yeah, I think so. I mean the, the, the, the product that we're announcing I think has probably come out around with this time is called Dana. So there's. 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 onboard software or onboard AI. Then there's offboard AI, this is the tools to design and develop these same systems, the models that actually go on the machines.

50:25 Our 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 very simple question, why can't a 9th grader make a delivery robot in their home? While they don't have the actual environment that they would for first develop the scenarios in, they would define the requirements. Hey, I want this robot to go on my high school campus around these, let's say, four buildings.

50:57 Then how, okay, now that you define the requirements, then you have the scenarios get made. What are all the scenarios that can be made by using, let's say, a 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, a couple of other places.

51:23 Suddenly the robot's not doing. Now you need to deploy it onto the actual machine. So then you deploy it onto the machine and then the robot runs into the wall. Okay, what happened there? The loop closes. That platform for designing and developing is what we're launching. It's called dana, which is the street that Applied Intuition is headquartered on. And, and this comes from our tooling background. And if you look at how tooling has changed in the digital AI world, if you look at what Claude did to all, we also remember, From Mixpanel to GitLab, GitHub, all these, now everything has moved into a very different, almost ide, frankly speaking, we think the same thing's going to happen in the physical world.

52:06 And so, yeah, that's what we're building, that's what we're built, and that's what we're launching. And we already use it in house to develop our autonomy system, which is, you know, and we're working on the most kind of scale 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 their own systems because it gets to that mission that a billion intelligent machines,

52:35 fundamentally, Radana is our agentic platform for physical AI and everything that we've built and developed over the past nearly a decade, every tool, every technique that's available in Dana. And it's very, actually easy to use with the agency 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 just

53:02 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.

53:30 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 where that are, that are. So you can 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 not child's play, but like teenager play.

54:00 So this points to a world of like, just like, like a lot more experimentation and entrepreneurship and like agriculture, everything. Bots and like basically every domain. Construction, defense.

54:10 Exactly.

54:11 Just all of a sudden have a much larger number of people who are applying creativity and coming up with ideas and making things that move.

54:16 Yeah. And have you seen 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 it. Just like the iPhone example of. You couldn't imagine Instagram before for like the iPhone. It's like imagine in 2005 on laptops. You're like, in 10 years there's going to be this app and you can put photos and you're like, well, the phones don't have cameras, but it's going to be like social, like, what the hell?

54:45 Like Facebook. It's like, it's hard. And so we think by lowering that barrier, you're going to get way, way more creative Autonomy products.

54:54 Right.

54:55 Yeah.

54:55 I will stuff with you. You can decide whether to include this or not. So my kid is building autonomous bots in Factorio.

55:02 Oh, nice.

55:03 Is one of his projects. And so yes and, but he's, you know, he's had it rolling because the toolkit's not available yet. So he's actually training. And he's actually. 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 to go on.

55:15 Yeah, yeah. So, like.

55:17 And then 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. But it's interesting. You know, it's the kind of thing. It's like, yeah, it's like they're, you

55:26 know, like, there's no reason autonomy should be this, like, you know, obscure, difficult, you know, alchemistic, you know, technology. And. And I think not only does that have a huge impact on. On society, it also allows people to understand that these systems are not like, you know, magic. It's like, if I can develop a Roomba for myself in my house on a weekend using Dana, right, Then why.

55:59 Then it's not suddenly so scary? And I think that's like, that's. That's important.

56:03 And we can. It can. It can support. It can support people in all kinds of ways that we haven't even imagined yet because.

56:06 Yeah, absolutely.

56:07 Yeah, exactly.

56:08 I mean, you think about, like, you know, folks with disabilities. You know, we always think about humanoids as, like, this very important task of folding laundry, which seems to be. So we focus on, you know, the important task, but 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 advanceable civilizations. And our first mark for the company was a monkey's head.

56:38 And then we got a designer who said, what does this. This is stupid. I was like, I thought it was pretty good.

56:47 So you were talking earlier about how when, you know, the technology got. Got so good in mobile that there was a wave of these companies, you know, Uber, WhatsApp, Snap, you know, Airbnb, et cetera, that emerged in quick succession. And so now that technology is getting there, or the infrastructure for physical AI, what are some use cases or companies that you could. Obviously, it's hard to predict the future, but where are you most excited for? Like, what.

57:11 What could we be talking about the equivalent here of in quick succession?

57:13 I mean, I think, you know, midterm. We want Dana, if not the short term, to really make humanoids way more real. There's. I mean, how many. It's like a thousand core tasks in a Home from Humanoids and these companies. It's like such, I mean you talk to people who work in these companies, everything is difficult. Every step of the way is difficult. Collecting data is difficult. Cleaning that data is difficult. Training those models or deploying the model is difficult.

57:41 And the bar being I want a high school kid to make a human. So that's our path and we think there could be a lot there. But that's like the obvious stuff. I think the true non obvious stuff is going to be, we'll look back will be way more interesting.

57:55 And there's some core ingredients that we're bringing together in Dana.

57:58 Right.

57:58 We're making it way easier to actually get imitation learning to work, way easier to make reinforcement learning work. In combination with that we have pre trained models that can be used as a baseline for a lot of things. World models, advanced simulation tech. All of these things come together and then you're sort of limited by your creativity, like well what do I want to do? And if you think about any kind of physical AI task as you are understanding the world and you're manipulating something and we can build that, that can be built now much more easily in this tool.

58:30 And I think sometimes people ask us being a tooling company and you take self driving trucks, we deploy self driving trucks and, and many of the self driving trucking companies use their tools. I think sometimes people ask, oh look, 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. Google still succeeded through search and YouTube and other web apps and other folks learned and used open source products and then ultimately closed source products and ultimately venture backed products.

59:03 And we think the same thing could happen here.

59:06 I was at a robotic start a while back that you guys know well and they had, they were training, you know, they were doing, go through a training process, training their, one of their arms to do the particularly a killer app that I thought was very appealing which was picking up dog poop. Literally, you know, training over and over again. The difference for the, and so you know, I don't know why not, right?

59:25 Yeah.

59:26 Why not have the little, why not have the little robot follow you around when you walk to dog?

59:29 Yes, yes.

59:29 Pick up the poop.

59:30 Yeah.

59:31 And I think like, like I know

59:32 somebody who built a, I forget who it was but somebody built a, a little lawn robot that would go around an individual, pick up individual leaves.

59:38 Yeah, yeah.

59:39 Because you got that problem right ok. You rake your yard, it's completely clean, and then like two hours later, there's like 14 leaves and you're like, yeah, the little bot to pick up.

59:48 It's like, if development costs are zero, then people will do that. I mean, do you guys remember, like, the early iPhone apps? The 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 the OS from, I think, who's Ericsson or somebody, that'd be impossible. You need a team of like 50 people to develop a, like, the beer thing for the BlackBerry.

60:12 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 and we're going to enable that. And if it, like, makes making like, I think it'd still be a while before, like, making a robo taxi is like, super, super easy. Yeah, but that'll happen.

60:27 But there's. I mean, the number of bots that could be. The number of kinds of bots that could be deployed in healthcare is almost endless. Healthcare alone is home care. Yeah. And then in construction, you know, all

60:38 the physical training, it's like us sitting in 2007 and saying, let's. We. We should have an app store. What type of apps? And we would come up with like

60:45 a list of eight.

60:46 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, you know, food off the menu. Right, yeah.

61:00 Makes sense.

61:01 Yeah.

61:01 The.

61:01 We're talking, you know, earlier about the differences between digital AI and physical AI. We're sort of hinting at LLM, but world models are in vogue right now. Why don't you talk about sort of the state of them as it relates to physical AI and how should we should think about them?

61:14 So, first off, world models means about 100 different things. 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.

61:32 You started as a sim company. Yeah, yeah, yeah.

61:33 Something that is, like, 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.

61:48 Maybe. Peter, I think it's worth being super explicit here. We just go just one level lower, you know, determinism in simulators, kind of the sim to real gap physics based, you know, rendering all the way to like this generated world. Where do we fit on it or where you know. Yeah, describe the landscape, I think maybe.

62:09 Yeah.

62:09 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 a body simulation or are we actually simulating lighting, for example, the light in the environment or the

62:31 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, reflectivity and material properties that you would see in the real world. But as you guys know, Hollywood is going through its own fundamental change now. You've generated technology. The same thing is happening in our universe as well.

62:54 Yeah, so that's sort of on the. That's at the far end of physics based simulation. And then 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, right. 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, let's say, have some reference point, let's say a camera, and that camera moves within that 3D world because the Gaussian is actually representing the 3D geometry of that world.

63:34 You'll actually get very high quality output from that. There's a lot of value in that. And that's, let's say one type of world model. But when you go further on that spectrum 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, putting 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.

64:01 Reactive as in the ego does something in the environment and the other agents Respond to the ego.

64:06 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 model with the real world and the way that the real world would actually react? And if you have perfect alignment between the real world and the world model, I think you've just sort of solved the universe roughly. Right. That's an impossibly difficult problem. But as we make progress towards that, it makes training physical AI models much easier because you can do more of that in simulation.

64:39 But the hardest part though is we're always talking about performance. Performance, right. So I like to say 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 in physically high. 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 places, ways.

65:09 So we can have very large models and we do have very large models that are used in the off board environment. But when, once you go onboard, all of those constraints are very real. And now we need to train a much smaller model that has these safety constraints, these determinism constraints. And that's the hard part about physical. That's also the moat.

65:25 Right?

65:26 It is what makes our tooling and our competencies valuable because it's just really hard to meet all of these constraints in a physical system.

65:35 When will you. Which will we get first? A perfect, a perfectly simulated real world environment for training, autonomy, autonomous devices, or Grand Theft Auto 6.

65:45 You know, as long as I keep putting out great trailers, I mean, I feel like I'm getting entertained without paying a dollar. I'm reintroduced to Tom Petty because.

65:58 Will you give us some timelines?

66:01 Let's run with that for a second.

66:01 Yeah, go for it, Go for it.

66:03 Go for.

66:03 Well, no, look, I mean. So the whole thing was. The whole thing with Grand Theft Auto is the big innovation was open open world, open world sandbox gaming. So it's a simulated city.

66:11 And 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.

66:17 Well, tell us about that. Yeah. What's the spectrum?

66:20 So here, this is speculation, but I think Grand Theft Auto 6 will be perhaps the last major real world video game that's still really developed, let's say in that legacy era of, of traditional

66:33 computer graphics tooling, technical artists and yeah,

66:37 I think that Grand Theft Auto 7 will much more likely be like a world model based video game. And you can imagine as AI tech evolves here you 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. Is something that you can see and run around in. Like it's possible, but it could be

67:02 the game as a consequence, could be the real world. Right. Is where this is at. You could have a complete recreation of the real world in the game. This has kind of happened with flight simulators, hasn't it? Isn't it? Yeah. The most recent flight simulators are literally. It's the entire planet rendered accurately, is my understanding, at least from the air. Is that right?

67:17 Yeah, yeah. And I mean, you're really. That's where our bread and butters, when we started, started the business, we hired so many people out of the Microsoft flight sim, I'm surprised they didn't. You know, when you fly over, you

67:28 know, whatever New York or when you fly over to Luth in the flight simulator. Now it is the real city.

67:32 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, and so the, the tricks that you play there is you, you basically are down sampling very, very aggressively. And then as you get closer, you know, then it becomes more, more high fidelity. Where the real world isn't like that.

67:54 The.

67:55 If you were to try to rebuild the world with this level of fidelity, it would, you know, 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, then you would say, well, the simulator we're in doesn't follow the laws of physics that we're at that point.

68:16 How do we know that the simulator that we're in is rendering all the stuff that we can't see?

68:19 Yeah.

68:20 As far as I know, everything happening outside this room doesn't even exist.

68:23 Yeah, this is, I mean, you know, you know, like Buddhism believes this. It's a different type of podcast. It's like, you know, when you open your eyes, the world is rendered and then you close your eyes. The world. That's literally religious.

68:35 I don't see Why? I don't see why it's necessary for it to keep rendering if I'm not there.

68:39 Buddhism from first principles. Yeah, exactly.

68:41 That's what you should. That'll get a lot of clicks. That's what you call this? Yeah.

68:46 Well, just to be on the timeline topic, you know, we gave us timelines on self driving cars. What timelines do you want to give us if any. On sort of, you know, other interesting things that were worth tracking. Like perhaps when we'll get laundry folded or other, you know, things that emerge because of humanoids.

69:00 I think also maybe just, just touching a little bit on world models where we see world models going because I think it's, it's fundamental to what the work we do. Yeah, yeah, yeah.

69:08 So, so to answer the, the first question. So laundry holding, it's, it's, it's not terribly far from being solved to be clear. And there is a lot of interesting research being.

69:19 And then humanity can reject Joyce. That's in Proverbs 4. 16 I think.

69:26 Well here I, I do think, I do think housekeeping is, is a killer use case for physical AI.

69:32 Peter thinks two that he always talks about in the company. One is housekeeping and entertainment. Peter's long on humanoid entertainment.

69:40 Agree with that. I think entertainment is robotics killer. I don't think anybody.

69:45 I mean like so these Midwest white guys are really into this.

69:50 I'm just saying. I think, I think I just want

69:52 to know when I go west world.

69:53 That's all I want.

69:54 No, I actually have an entertaining. I have a little, I have a little, a tiny little Chinese robot dog that's like literally it's just like a little, it's just a little and it just like roams around and it just like.

70:03 Would you pay to see Cirque du Soleil with robots? Like yes, yes. I want to see, I want to see Kung fu trapeze swinging.

70:12 Spoken by like a compiler's guy.

70:17 I know, but I want Westworld. I want Westworld.

70:20 I mean the funny thing is I was just saying like, well, will people in like the suburbs of Detroit actually. That passes the test. I bet you people in Sterling Heights would actually pay to see that. It's actually true. I stand corrected.

70:32 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 is bit a little say like play it at 8x real time or whatever. Right. And, and that's for you to make it Watchable. So, so the question is when can you actually reach human parity of performance? That's, that's further off when you decouple

70:55 models from just the hardware. The hardware can do it. Now that used to be a constraint. So the hardware is very fast and accurate now, which was actually there's still

71:02 overheating issues that are still being dealt with, but it's, it's not, it's not terribly far off. Like these are solved.

71:06 I mean it's far off from like, you know, when I was a mech e that was is like fantasy. Like there's like nothing can.

71:11 What's the movie that has the most realistic future vision of robots?

71:14 Oh man. Bicentennial Man.

71:15 Is it okay? Yeah, he's, he's, why that one I actually haven't seen.

71:20 Well, I like, I like that scene. I think it's Irobot when Will Smith jumps in the car and his, his, you know, whatever. His like accomplice sits in the car and he's like, puts the car in manual and she's like what are you gonna drive this thing yourself? Like out of like, you know, she's yeah, she's like are you crazy? What are you gonna drive me think you're sound like that's what we're, that's the flight into intuition's, you know, like goal.

71:40 Well, 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.

71:47 Don't worry, the Internet will correct you.

71:49 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 sort of like the friendly guy that will, the friendly robot that will clean up and also babysit your kids and stuff like that. And it seems like it's in the not terribly distant future.

72:09 I got a different answer. You guys ever see that movie? Sam Rockwell Moon? Oh yeah. Yeah. The setup. I don't want to. It's a great movie. Don't watch the trailer just to watch the movie. It's the premise is the tagline of the movie is 250,000 miles from home. You find who you are. And it's one guy who works on an energy harvesting based run by Applied Intuition, run by Lunar Technologies. I, I, I don't want to be whale.

72:35 I don't want to be whaling Yutani. I don't want to be, you know, that's from, from the Alien franchises and then Terrell Corporation From Blade Runner? No, no. I want to be Lunar Technologies in the Moon franchise. Not even Franchise one. There'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, error signal.

72:56 Yeah, the, the reason why it's, it's, I think so accurate is because the state of the art for AI system systems is these systems, they just need the occasional grounding.

73:03 Exactly.

73:04 They'll just go off and do something crazy and then you're saying, no, no, stop doing that.

73:07 LLMs are like that too. That's what coding bots are like.

73:10 And the reason, other reasons, I think it's quite accurate. It's maybe uncouth now, but Kevin Spacey is the AI, you know, smiley face. And he's, he's just there to kind of placate the human, to assist, but to also, like, he's like, oh, you seem like you're sad, Sam. And like, you know, like, that's 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, in that.

73:40 But I think massive energy farm that's completely 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. Like, that's an incredible positive thing. I think, I think the, you know, I just did this commencement speech at my, my undergrad.

74:03 Did you get destroyed?

74:04 No. You know what?

74:06 I, I, unlike Eric Schmidt.

74:07 Yeah, yeah, yeah, yeah. Listen, listen. My, my, this is true story. My wife started watching, she said, I feel like you're yelling at me. I can't watch this. So I, I, I basically talk. I mean, I, I don't, I'm, I'm not like, I don't, 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.

74:28 No one's like, these, these are, I don't want to, 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 under, 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.

74:51 And by the way people working on projects that are in government, people working projects together and now profits, they all screw up. It's. And so it's, it's too simple to say AI corporations are, 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, if it, if the, the of the, the I think the obvious, you know, abundance that comes from self driving trucks, self driving cars and the fact that people don't die which is a amazing.

75:23 But then you also get this efficiency of cheaper energy etc. 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, 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 the, and I don't say this just to say that we're competing with the Chinese, but there's a confusion. The same no hand can block the sun and the sun is technological progress.

75:55 And if we as a society don't embrace technological progress, we will be left behind.

76:00 Somebody else is going to do it.

76:01 Somebody else is going to do it. And if it's not the Chinese, who knows, it's maybe it's the Uzbeks or you know, it's another country that is recognizing hey, my citizens are suffering and I'm going to use this technology to remove them. It is honestly it's because, because we're living such a great society that we can have these like I would say stupid conversations like there still are people who don't can't get food and someone will immediately quip if they were debating me they would say well there's plenty of food.

76:29 It's the capitalist system that doesn't. No, no, no, let's, let's be very specific. There's plenty of food but getting that food to those people is difficult. So that means we should let robots get that food to them faster. That's just, that's just how it is. So I think I'm, I'm. And I think like we, we, we as like technologists. I think sometimes we, you know, it's, I think, an inclination just to say, leave these people behind.

76:51 I think you have to bring them along. You have to 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, then you just don't get it. So there's like a middle ground. It's not everyone's an idiot and every. Or we should just be. Technology will just be perfect, perfect, perfect. There's a middle ground. Let's have that conversation to a point.

77:10 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, I'm, you know, I, I am a capitalist. I can't not admit that. But there's 70 years of history there. Like, we're, that's not even a debate anymore. I mean, I think it could be a debate. If we're, if we're sitting here in 1965 and having a debate, you say, okay, maybe centrally controlled systems work better.

77:36 There's no debate anymore, folks. You know, systems where individuals make decisions on their own interest actually work better for society. And so that doesn't mean everything is perfect. And you can't extrapolate that same thing with, you know, AI 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 boos. Mark and these guys were booing.

77:59 So they did.

78:00 They cut it out. Eric was booing, he was throwing stuff and they just edited it.

78:05 You mentioned the Japan market earlier. When you talk briefly about sort of the global ambitions and how these technologies interplay and what we're doing here.

78:13 So I think America particularly 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, and we're a horizontal company. We're a technology provider. And I think more Silicon Valley companies, I think, can employ a little bit of what we do, which is work very, I would say, collaboratively with the local economies.

78:44 As sovereign AI becomes more of a real thing. We have to build businesses that take that into 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.

78:57 This is.

78:58 That was the history of companies you'd work internationally. Aramco is not a random company, right?

79:03 You.

79:03 You build based on the real geopolitical realities of the time. And so I think, you know, we've, I think, navigated it quite well. I've. 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. But so I think in innately, we think about the globe more. And I think when I was at both at Google and at yc, I was always surprised at how kind of almost myopic the companies are.

79:36 Just always looking at the market that's just like within the 30, you know, between San Jose and San Francisco. And it's like, actually the market is really big. I think physical AI. The nature of it being physical. I think 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.

79:51 Yeah.

79:52 Cool.

79:53 I think it's a good place to wrap.

79:54 Okay.

79:54 Peter Gasser, thanks so much for coming on the podcast, and congrats on big launch with Dana.

79:58 Yeah, thanks for having us.

79:59 Awesome. Great to see you.

80:00 Okay, great.