Why Physical AI Is the Next Frontier | The a16z Show

Applied Intuition has spent the past decade building the software that powers intelligent machines, from passenger vehicles and trucks to defense systems, mining equipment, and industrial robots. In this conversation, Marc Andreessen and Erik Torenberg sit down with Applied Intuition cofounders Qasar Younis and Peter Ludwig to discuss the emergence of physical AI and the company's latest launch, Dana, a new platform designed to accelerate the development of autonomous systems. They explore autonomous vehicles, robotics, world models, simulation, AI infrastructure, and the engineering challenges of deploying intelligence safely in the physical world. Along the way, they discuss self-driving cars, humanoid robots, global competition, and why lowering the barrier to building physical AI could unlock an entirely new generation of products and companies. Timestamps: 00:00 - Intro 01:07 - What Applied Intuition Actually Does 03:32 - Beyond Automotive: How Big Is Physical AI? 05:33 - Why Autonomy Changes What Machines Look Like 19:20 - Selling to Legacy Automakers & the GM Culture 33:10 - The State of Self-Driving Cars in 2025 42:22 - Long-Haul Trucking, Mining & Why Nobody Wants Those Jobs 49:53 - Introducing Dana: Autonomy for High Schoolers 56:47 - What Comes Next: Humanoids, Home Bots & the Long Tail Resources: Follow Qasar Younis on X: https://x.com/qasar Follow Peter Ludwig on LinkedIn: https://www.linkedin.com/in/peterwludwig/ Follow Marc Andreessen on X: https://x.com/pmarca Stay Updated: If you enjoyed this episode, be sure to like, subscribe, and share with your friends! Find a16z on X:https://x.com/a16z Find a16z on LinkedIn: https://www.linkedin.com/company/a16z Listen to the a16z Podcast on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYX Listen to the a16z Podcast on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711 Follow our host:https://x.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see http://a16z.com/disclosure.

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Qasar Younis?

  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.

• 0:16Speaker 01

  Digital AI, of course, is building software and optimizing ads and creating videos. That's all interesting and good, but really where you talk about the global economy, that's physical AI.

• 0:25Qasar Younis?

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

• 0:32Marc Andreessen?

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

• 0:36Qasar Younis?

  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.

• 0:51Speaker 01

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

• 0:56Marc Andreessen?

  Which will we get first? A perfectly simulated real world environment for training autonomous devices? Or a Grand Theft Auto VI?

• 1:05Speaker 03

  Much of today's AI conversation is focused on large language models. But the next frontier may be physical AI, software that enables machines to perceive, reason, and operate in the real world. In this episode, Marc Andreessen and I sit down with Applied Intuition co-founders Kassar Yunus and Peter Ludwig to discuss the future of physical AI, along with the company's newest platform, Dana, which is designed to simplify how autonomous systems are built and deployed. They explain why physical AI presents a fundamentally different set of engineering challenges, where autonomous systems are already making an impact, and why the next decade could transform not just software, but the physical economy.

• 1:48Erik Torenberg?

  Kassar, Peter, welcome to the A-Z Podcast.

• 1:51Qasar Younis?

  Well, thanks for having us. Your name is?

• 1:55Erik Torenberg?

  Which is one of many.

• 1:56Qasar Younis?

  I feel like I think we've all each known each other for too long, more than I'd like to admit.

• 2:03Erik Torenberg?

  We're lucky to both be the first investor or among the first investor in the first round. Of course, different check sizes.

• 2:07Qasar Younis?

  And I was an investor, or you even before then.

• 2:10Erik Torenberg?

  Exactly. So let's do that as segue. We have a lot to talk about today. We have the biggest launching company in history to talk about today. But first, why don't we just give an update, a status? What does Applied Intuition do for those who are new?

• 2:19Qasar Younis?

  Yeah, 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. In some ways, like a very boring AI company in the sense of 83% of that company is engineering. We win by making really great products. It's not like a good sales or something like that. I don't think we're good enough for a sales-enabled company. But yeah, over 1,000 engineers and based in Silicon Valley. But we have offices globally, 18 offices. And our mission is to put intelligence on a billion machines.

• 3:02Qasar Younis?

  And we think that can have a profound impact on society, both in the kind of pissy things everyone talks about, safety. If you really talk to somebody who's been in a car accident or in a mining accident or in a farming accident, those are real gnarly situations. Beyond just fixing that, if you can unlock productivity, I think we've seen the unlock in the digital world. And everyone's super excited about it. And you have trillion-dollar companies emerging. I'm a pretty strong believer that I think when we look back 25 years, if you look back to the internet now, if you look at the original internet companies that are doing survey or they're doing some analytics and those are interesting. But really, when you look back 25 years, one of the big monolithic companies are Amazon, that delivers you stuff, Apple. These are the true kind of companies that come of age.

• 3:47Qasar Younis?

  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.

• 3:56Marc Andreessen?

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

• 4:12Qasar Younis?

  Yeah.

• 4:13Marc Andreessen?

  So how should people think about, like, how many things are there that are things that move where the idea of physical AI, physical intelligence are going to matter?

• 4:20Qasar Younis?

  Yeah, I mean, even today, even if you'd put that, let's say, view on us, the automotive is 30% of our business, so 70% already is non-automotive. And I think if you fast forward another 10, 20 years, even the manufacturers themselves as a customer base will be a small amount. I think that mission, just keep thinking, a billion machines becoming intelligent. And you think about all the types of machines that exist. Automotive is just an easy one. I think it sticks in people's heads because we all drive cars and it's a big market. But I think it'll be a minority of the business. A minority is a minority of the business. I think it'll be increasingly a minority of the business. But that doesn't necessarily mean it'll be small.

• 4:53Marc Andreessen?

  Right.

• 4:54Qasar Younis?

  Automotive is still huge. Just as a part of the globe's GDP, automotive is something like 3% of all GDP. I think the way we always think about it, as you try to get to your mission, initially, the manufacturers were the distribution to that intelligence to consumers. But then you start working 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 and all of those are customers of ours as well.

• 5:24Speaker 01

  Yeah, I think if you split AI into digital AI and physical AI, right, 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 global economy, that's physical AI. And we're talking about manufacturing and mining and logistics and transportation, all of these things.

• 5:44Qasar Younis?

  That's supply chain.

• 5:45Speaker 01

  Yeah, supply chain, exactly.

• 5:46Marc Andreessen?

  Let's build on that, though, for a second, which is, so things that move today, or historically, things that move are things that have human beings at the wheel or at the controls in some form, right? Airplanes have had to get designed around a human in the cockpit. Boats have had to get designed around a human steering things. Like, in a world of autonomy, do we already know what the things are that move? Or are we going to discover that there are a lot of new things that are going to get built when you don't need a human in the driver's seat?

• 6:07Qasar Younis?

  I think both. The thing that you have to remember is, like, you take like a haulage system that's in a port, like a Kamatsu, a dirt mover in a mine. Those are made for 20, 25 years. So the buyers of those products, they might not have gotten their full cycle ROI on them. So they're not immediately going to buy something new, no matter how much better it is. So one part of our strategy is you got to make those things intelligent because they're not going anywhere. The second is what you're talking about, which is, well, that depends on a human in a cab. If you don't have a human in cab, the machine can be smaller. It can be shaped in very different ways. You need to talk about mining underground.

• 6:45Marc Andreessen?

  Right.

• 6:45Qasar Younis?

  The constraint actually is the human because the human needs to breathe and it's very dangerous. 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, hey, let's take an entire port. Let's take an entire mine. Let's take an entire quarry. And this heterogeneous mix of machines, they're all can talk to each other. And they can optimize and be efficient when one machine goes down or one machine has an issue. The rest of the mine doesn't have to stop. When it's human-driven, we don'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.

• 7:29Qasar Younis?

  So a simple thing like knowing when a brake 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. 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 is something like under 35. It's less than 10% of farmers are that young. So what's going to happen?

• 7:53Marc Andreessen?

  Right.

• 7:53Qasar Younis?

  The need for food growth is continuing to grow. The need for rare earth materials is continuing. So these demands are only growing, but the humans who are the bottleneck are decreasing. Trucking is the same way. And so you can really just unlock a lot more efficiency. So I mean, one way to maybe think about this is imagine if the cost for food decreases because it's way, way more efficient. So what's the downstream impact? Imagine for goods being transported. Let's say, instead of a few dollars a mile, it's 20 cents a mile. And suddenly, I think that the unlock is very, very, very big. I think it doesn't necessarily need for all the machines to be redesigned from the ground up.

• 8:30Marc Andreessen?

  Right, right, got it. Makes sense. And then maybe just one more question would be, just give us a sense of parameterized, like, the scope and scale of the company today.

• 8:36Qasar Younis?

  Yeah, north of 1,000 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're 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 has way more complexity and has a lot more issues. And we have engineering teams. And we've deployed our models onto 50-some platforms. Even that sounds trivial because mostly, when you think about models, you think about deploying them through a browser or on a phone. And everything'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. And so we have engineering teams that can do that as well.

• 9:21Qasar Younis?

  Our claim to fame is we've raised over about a billion dollars in the company's history. All that is sitting in the bank. And I always say that with an asterisk, which is, it doesn't mean we're not going to spend it next month.

• 9:31Marc Andreessen?

  Good news, bad news.

• 9:31Qasar Younis?

  Yeah, good news, bad news. And I think we talk about scale. We're at that phase where these giant markets are around us and we can make the decision how aggressive 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, I don't know, how do you think about scale?

• 9:54Speaker 01

  Yeah, I think that that's roughly, I mean, the mission of bringing intelligence to a billion machines, that is how we think about it. And then thinking about, 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.

• 10:06Erik Torenberg?

  I O, 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? Want to unpack some of that?

• 10:19Qasar Younis?

  Yeah, I mean, I think a lot of times people think about the progress in physical AI is limited to basically two case use cases and they're just because they're obvious and interesting, which is robotaxis and humanoids. They're very visceral, they're they excite you and they're kind of sci fi. I think they're, those are very interesting and they, there is real work being done by us and other people in those domains. I think all the other, all the other domains I think are going to be just as important. I mean, you just, you just think about what happens on a port, that's, there's a huge unlock there. And that's, I think that's, 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

• 11:00Qasar Younis?

  before about the rise of Cisco and how, you know, networking kind of went from, you know, first individual machines and companies would get network and then entire countries were getting network. There's a similar thing happening with AI. AI is getting to that level of kind of sovereign AI is now a discussion. Sovereign AI really is about physical AI because that's where you're talking about AI in defense, you're talking about AI in the physical machines that are moving around. If you look just at the example of, of Waymo from America and Pony from China, trying to deploy in, let's say, the other country, so not America, not Europe, not China, every one of those spaces,

• 11:40Qasar Younis?

  they're way more, they're way more hesitant of saying, yeah, thumbs up, your robotaxis can run unfettered on our, in our country. And so if you look back, just a kind of this arc of the internet, you know, when the first internet companies come, nobody's really thinking about sovereignty at all. It's like, the browser goes everywhere, the internet goes everywhere, that's almost the power of it. Then when social media emerges, there's a bit more of, hey, actually, not every social media. And then you have China not allowing Facebook to come in and you have some, then you get into the next level of like the online offline stuff. There's more resistance to Ubers, to Door dashes, suddenly there's local players who are being favored very aggressively.

• 12:22Qasar Younis?

  When we get to physical AI, I think there's going to be huge, and also there's like a larger, you know, geopolitical theme of, of kind of more fracturing than globalization. You're going to have this demand for this AI should somehow be localized. And I think that has to play into our strategy as well. We're a technology provider, so we can provide that technology across the globe. And I think that's, that's, that's, that's, that's something that's understated in this, in this conversation.

• 12:50Speaker 01

  A few other things on digital versus physical AI. So in digital AI, the state of the art is you can train models effectively on the entirety of the internet and then maybe augment that with additional data that's been collected and refined with some hired experts, 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. However, to actually build a foundation model in physical AI, there's also a lot of private data collection. You, when we're talking about mines or logistics or any of these other, 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,

• 13:33Speaker 01

  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. 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.

• 14:02Qasar Younis?

  Yeah. And I think like, you know, you talk about like humanoid 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, where you have North Korea, they don't allow mapping companies, let alone allowing if, 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. And so in the way that it is similar to other digital AI systems, your proprietary data sets, scaling laws, all that stuff is the same. It's just applied in a very, very different way.

• 14:44Qasar Younis?

  And it's almost like the way to think about it is like the diffusion of these models is very different because you can't, it's not everyone can just access them through a phone. You're gonna, and so, so that ironically is actually plays in our favor because once we have a massive proprietary data set to where we've been building, we already have hundreds of petabytes of data. And then we have our own tools, which are like, you know, synthetic data tools, neural SIM, we can use our own tools, our own proprietary data, and that allows us to build some of the best systems in the business.

• 15:17Marc Andreessen?

  Because 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. Like, is there, is there a flywheel aspect to that? And is there, is there, is there, like, what's the level of difficulty involved in kind of booting up that flywheel?

• 15:41Qasar Younis?

  It's, it's, 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? 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

• 16:26Qasar Younis?

  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.

• 16:34Speaker 01

  Definitely more than that.

• 16:35Qasar Younis?

  Yeah, more than that at this point. And like, when we're a strong believer that synthetic data can accelerate autonomy development, we've just seen that. And then there are like lots of other secondary and tertiary like technical innovations that happen. Obviously, the transformer revolution hitting self-driving massive, basically everything done in self-driving pre 21, 22, relevant, but you're almost like, that's kind of the starting point. But it's also different than today being the starting point. Like there, those four or five years are actually, there has been a lot of work done. You can see it most clearly with Tesla, but there's other folks. In that process, the actual techniques historically have been simplifying here. Imitation learning was the way,

• 17:17Qasar Younis?

  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, 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? 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 fog error that we saw? We still see errors in the real world that impact self-driving.

• 18:02Speaker 01

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

• 18:32Marc Andreessen?

  Yeah. So I'll ask you a threat, a question and you can, 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 and.

• 18:50Qasar Younis?

  One of my first distributions personally, so I.

• 18:53Marc Andreessen?

  There we go. Y Combinator, 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 tech circles for being like, okay, being like the, the legacy automaker with the biggest investment.

• 19:12Qasar Younis?

  I called Peter when, before, before it was announced on that. And I said, hey, Cruise just got bought, 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. This is Apple, I said, no. I said, general, explicit motors.

• 19:29Qasar Younis?

  So that's surprising to people who are from GM.

• 19:31Marc Andreessen?

  That they were willing to buy.

• 19:32Qasar Younis?

  Yeah, that they did it.

• 19:33Marc Andreessen?

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

• 19:38Qasar Younis?

  Yeah.

• 19:38Marc Andreessen?

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

• 19:44Qasar Younis?

  Uh, it wasn't a fatality, but it was a serious injury. Somebody was dragged for 20 feet.

• 19:47Marc Andreessen?

  Yeah, ser- serious injury, bad press. And then, and then they put a bullet, the, the GM, CEO and board put a bullet in the cruise project.

• 19:54Qasar Younis?

  And yeah.

• 19:54Marc Andreessen?

  I know that, at least some of the senior cruise people were extremely upset, you know, by, by the aftermath of that. Was it surprising that they reacted the way that they did?

• 20:03Qasar Younis?

  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, uh, 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?

• 20:20Marc Andreessen?

  As one does.

• 20:21Qasar Younis?

  Have you read that book?

• 20:23Marc Andreessen?

  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.

• 20:29Qasar Younis?

  He was gonna be the next president of General Motors.

• 20:31Marc Andreessen?

  Of General Motors. And then later on, he started his own car company, which was in the back to the future. Back to the future. And then that whole thing collapsed for a variety of reasons. But yeah, he, he was like a legend. He was like one of the main principal drivers of innovation in the car industry.

• 20:44Qasar Younis?

  Exactly. Lee Iacocca, Bob Lutz, there's this category.

• 20:47Marc Andreessen?

  Yeah.

• 20:47Qasar Younis?

  And you gotta remember, this is, this is right.

• 20:49Marc Andreessen?

  Sorry, repeat the title, repeat the title of the book.

• 20:51Qasar Younis?

  On a clear day, you can see General Motors.

• 20:52Marc Andreessen?

  And why, why was that the title of the book?

• 20:53Qasar Younis?

  Because, uh, there's a lot of bullshit.

• 20:58Marc Andreessen?

  It's very large, complex.

• 21:00Qasar Younis?

  Yeah, complex. Yeah.

• 21:01Marc Andreessen?

  It's like, it's like a nation state.

• 21:02Qasar Younis?

  Yeah, it, I mean, really, I mean, it is. Like, I think we say that, like, sometimes, almost like flippantly.

• 21:08Marc Andreessen?

  Right.

• 21:09Qasar Younis?

  But these companies are like extension, like Hyundai's an extension of the state. Toyota's an extension of the state. Volkswagen is, literally Volkswagen board members are members of the government. So these are extensions of the state and, and almost every, uh, and there used to be an old saying, what's good for General Motors is good for America.

• 21:23Marc Andreessen?

  Right.

• 21:23Qasar Younis?

  And you cannot understate how important General Motors is, the history of the American corporation. Sloan's, uh, My Years at General Motors, and Adventures of a White Collar Man. If you run a large engineering organization, you should read that. That is, this, like, do you, this, this thing that we talk as a modern corporation didn't just emerge. Sloan and Kettering create, Kettering's 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, uh, you know, comes on DeLorean and he says, um, he writes, he's gonna be president and he's so fed up with the company. But what was controversial was GM was doing really well at the time.

• 22:07Qasar Younis?

  GM was like a, when we say like GM was number one in 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, uh, how annoyed he was at General Motors of being read, led. Uh, 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 to publish the book. The co-author still publishes it. 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 GM 20 some years ago and had, you know, know a lot about the company.

• 22:48Qasar Younis?

  And what's shocking is it's not only by GM, most of the major manufacturers actually still operate that way, uh, 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. 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.

• 23:10Marc Andreessen?

  Right.

• 23:11Qasar Younis?

  That like, so like the distribution is this is a consumer product of, of, this, this stat might be outdated, but when I worked in safety systems 20 years ago, I remember GM used to pound your head of the top five consumer lawsuits in, in American history, three are automotive. We got the majority, right? So it's a gift 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, like you'd always have to as decoder because you know why? Cause 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.

• 23:46Qasar Younis?

  Like, so like, can you imagine how infuriating that is? Every time you're like, what does orange mean? This is me, I have to like, so fast forward to, you're meeting that system.

• 23:56Marc Andreessen?

  Well, for then the, the forward slogan for a very long time was that it was quality is job one, right?

• 24:01Qasar Younis?

  Yeah, yeah, yeah.

• 24:02Marc Andreessen?

  Which is safety is about.

• 24:03Qasar Younis?

  Yeah, yeah, exactly. And, 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 in, and because then that's a whole separate automotive history. We could talk about automotive history for, for an hour. But the punchline is you have then a 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. And if you're the union, you're like, you can't make a billion dollars for us, but you're funding this thing?

• 24:39Marc Andreessen?

  Right.

• 24:39Qasar Younis?

  That's killing people and it's sloppy. And so I'm not saying precisely that's what happened, to be very, very clear. But the, it's a multivariate problem. My other hot take is, you know, I worked at both companies, right? Google, 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 to know this. 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?

• 25:16Qasar Younis?

  I was like, no, actually, like, making a modern combustion engine is extremely complex. It's not just like, you know, it's, it's not simple stuff. And so the, the, the macro point I think is it's a bunch of things. I think safety is always at the to- top of their, uh, 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. And that just gives government bureaucrats more ammo to go after. And your big target like General Motors, you gotta, you know, it, it reminds me, you guys ever see that movie like, uh, Goodfellas, uh, you know?

• 25:58Qasar Younis?

  Uh, there, there, the, the, one of the last scenes of the House of the Rising Sun, you know, the, 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, the, the board was like, what do we do about cruise? Like, what can we do?

• 26:12Qasar Younis?

  It's like, Kyle's a good guy, but.

• 26:16Qasar Younis?

  And then it's like, cue House of the Rising Sun. People running through a San Francisco office, just kidding.

• 26:23Qasar Younis?

  Don't, don't make that an AI video.

• 26:26Qasar Younis?

  It's gonna get, gonna get a mean text from Kyle. So I think, I think there is a universe that would survive, but it's, it's, it's tough.

• 26:33Marc Andreessen?

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

• 26:39Qasar Younis?

  Exactly.

• 26:39Marc Andreessen?

  Bearing in mind their own real, very real issues and constraints.

• 26:43Speaker 01

  I, I think General Motors also had the topic of business model, right? So you have, uh, Cruise was going after the robot 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, bit of the equation.

• 26:56Qasar Younis?

  Yeah. And I think it wasn't clear. I mean, the, by the way, you know, um, you, actually of all people, you spoke at Y C at 20 and 2013, I was in the audience. I was a partner at the time. And, um, you said something which I think is, it's, it's, it's very like recursive here. We're, we're feeding each other your advice. It's, uh, the key thing in, in new techno, in the new technology business. Actually, everyone kind of figures out the technology, 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.

• 27:41Qasar Younis?

  They started way behind and you're talking about neck and neck when, you know, ultimately the, the plug was pulled. So who knows what happens in the long term? Our hypothesis in that same equation is actually the distribution, you let the manufacturers do that. Like we run self-driving trucks right now in Japan. They carry commercial loads. They're, they're, you know, they're safety drivers there, but they're autonomously running. And, but you won't know that because the brand is Isuzu. That's the customer. And, and why it's so good for us to partner with Isuzu in that case is that company's been around for almost a hundred years, right? If I, if I'm not mistaken, a pre, pre-World War II company. And they are, uh, you know, they know the government. They have test tracks. They know safety. They know their, their own trucks very well.

• 28:24Qasar Younis?

  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 Chat GPT and Anthropic and all these, you know, everything that's happened. So people are no longer like, what's a self-driving car? And that's because 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 of 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, uh, the manufacturers. And we work with, you know, within each vertical, uh, with, with the right partner. But that's a fundamentally different view than a Tesla or a Waymo, which are gonna be vertical.

• 29:09Qasar Younis?

  We're, 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 actually look and talk and walk a lot like a silicon company, except we 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, it's really hard to, you know, to take us out. So you need deep trust. Our, our partners have really a lot, a lot of deep trust. And we know their markets really, really well. The, 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.

• 29:45Erik Torenberg?

  So how are these legacy car companies preparing for the future? Are they making more acquisitions they're going to? Are they building and partnering with you? Or how are they going to compete with tech native companies?

• 29:56Qasar Younis?

  It's like saying like, how are governments dealing with AI? It's such a broad topic. And each manufacturer, like even like you take Honda, Nissan, Toyota, three Japanese manufacturers with, you know, long legacies. They all approach it very differently. They're roughly in a spectrum of we're going to build to 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 talk a little bit about our new product that we're announcing here. 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.

• 30:39Qasar Younis?

  The more nuanced version of that is, you know, the reality is like every product is a different product. And so the amount of silicon and 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. 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 specking at all. And then they're really moving to laptops.

• 31:23Qasar Younis?

  But there's a similar kind of 20 year, I think, horizon there.

• 31:27Speaker 01

  Broadly, when you talk about machines and machines becoming intelligent, right? Fundamentally, a machine is it's a collection of these different components that are integrated, right? And whoever does that final integration is oftentimes the company that puts their badge on it, 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.

• 31:53Qasar Younis?

  And if you look, by the way, under the hood of a dirt mover or like combine or diesel truck, they'll have Cummins engines in them. But nobody says, well, because all these guys buy Cummins, this means that they're, you know, whatever Caterpillar is not a good company. It's like, no, that's just a component that they buy. They have a different role. So if you, when you look in any of these verticals, there's it's just a complex web of folks. That's why I always say like the chip kind of analogy actually works quite effectively because some, none of those companies make chips, but they all buy chips. And so I think that that's a good way to think about it.

• 32:28Marc Andreessen?

  So self-driving cars, so you know, we've all been talking about self-driving cars for like, I think the whole thing started like around, what, 2005 or something with the DARPA Grand Challenge originally. And so, and then Google engaged in the program shortly after that.

• 32:40Qasar Younis?

  Yeah, late double O's, yeah.

• 32:41Marc Andreessen?

  Late double O's. So almost 20, basically around 20, 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.

• 32:55Qasar Younis?

  Yeah.

• 32:56Marc Andreessen?

  And so the Waymo cars are driving all over, you know, in the places they're deployed. It's become, you know, like people in San Francisco are, I think, treated now as routine that they get into.

• 33:03Qasar Younis?

  And I think you can call, I think Tesla, it's 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.

• 33:12Marc Andreessen?

  Right.

• 33:12Qasar Younis?

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

• 33:22Marc Andreessen?

  Right.

• 33:23Qasar Younis?

  But yeah.

• 33:23Marc Andreessen?

  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, there was a large fire in Los Angeles.

• 33:34Qasar Younis?

  Yeah.

• 33:35Marc Andreessen?

  And then the power, and then the California power grid was buckling even before that. And so it actually turns out, among the things Cybertrucks are good at is their very good batteries.

• 33:42Qasar Younis?

  Yeah.

• 33:43Marc Andreessen?

  For powering your house.

• 33:44Qasar Younis?

  Yeah.

• 33:45Marc Andreessen?

  And so literally we have Cybertrucks as our backup battery for the house. And as of last year, whatever, the FSD release, I forget the exact one, but there was one where like, at least a lot of people thought it like really turned a corner.

• 33:53Qasar Younis?

  14, yeah.

• 33:54Marc Andreessen?

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

• 34:02Qasar Younis?

  Yeah, I think mean disengaged, like the meantime and like miles per disengagement are really high. And I think miles is like in the thousands. Yeah, which is like very impressive.

• 34:13Marc Andreessen?

  Yeah, I know, for people who haven't driven the Big, Highway 1 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 I wouldn't have been talking to him had it not been.

• 34:23Qasar Younis?

  Yeah.

• 34:25Marc Andreessen?

  Would have gone right off.

• 34:26Speaker 01

  Cause he unbolted the steering wheel, so.

• 34:27Marc Andreessen?

  Right off the cliff. So, and then, you know, Tesla's rolling out their robotaxi, you know, that is 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. And then I would say maybe just one other would be the self-driving trucks. There's been this recurring kind of panic in the press of like, the trucks become self-driving and the employment, you know, all these truck drivers are going to be out of a job. And sitting here today, I don't think, I don't know, is there, are there any trucks on the road that are self-driving that don't have at least a safety driver in the truck? And I think the answer is probably still.

• 35:00Qasar Younis?

  Yeah, still very few. So let's split, 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're, they're, they want to be safety conscious, but most of it is cost, cost, cost. The, what you're seeing in China, which is a, China's kind of a different EV ecosystem, mainly because they don't care about profits. And you're talking about business that doesn't care about profits. It changes the entire calculus of the entire industry that doesn't care. But what you're seeing is you're seeing L2++ systems. So we can simplify the entire self-driving conversation to, is there a driver behind the steering wheel?

• 35:43Marc Andreessen?

  Right.

• 35:44Qasar Younis?

  So this is a driver behind the steering wheel, still there, but generally like Tesla drives everywhere. They're like sub $1,000. There's an aggressive, that's chip, sensors, the package, the software, everything. We anticipate that there's a very aggressive, once you get to like 500, the automotive OEMs will actually subsidize it for free. They'll just give it to you. This happened in NAV systems. If you guys remember NAV systems, these were a big thing. You pay four grand, 3,500 to get a NAV system. And then suddenly it became free and it just became default. I think that'll happen. The, there's a weird thing, which is like actually 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 a moaligation,

• 36:29Qasar Younis?

  all these testing regimes, all this stuff. 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 the, 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, you know, double zeros, the review was like, when's mobile going to come? 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, Instagram, WhatsApp, Snapchat.

• 37:13Qasar Younis?

  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, I think it'll start becoming very cheap to free.

• 37:32Marc Andreessen?

  Routinely by the early 30s, you would just buy a car and you're just self-driving.

• 37:36Qasar Younis?

  Exactly. Or it has the driver in seat L2++ system being very specific.

• 37:40Marc Andreessen?

  Like Cybertruck.

• 37:41Qasar Younis?

  Yeah.

• 37:42Marc Andreessen?

  What Tesla people have, what Tesla owners have today will be some common.

• 37:46Qasar Younis?

  Yeah, will be default. So then the question, then the, you know, 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. It's not, doesn't mean they're not learned. It doesn't, it just, there's 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.

• 38:29Qasar Younis?

  I think Waymo is trying hard to remove that bottleneck so they can expand geographically faster, but the reality of today isn't there. The other thing is when you have researchers, which Waymo really was, coming out of an alphabet research organization, they didn't put commercial constraints. So the sensors are bespoke and expensive. The cars and then 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?

• 39:09Qasar Younis?

  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 what we're not debating about? Like is, is, 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, they'll just, they just adopt it. It's not that OEMs are resistant because they don't think consumers want it or they don't understand the technology. It's because they want a price envelope, which allows them to keep their, then raise it to thin margins and at a scale, which is deployed across 100 plus countries in V1.

• 39:53Qasar Younis?

  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. And so including the age of the consumer and what they think will happen or won't happen. So that also a 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. This is a different area.

• 40:25Marc Andreessen?

  When do you think, when do you think it'll be routine? Let's say the 200 biggest American cities, like would it be routine to walk outside and you're just, you're just taking for granted that a robotaxi can come pick you up?

• 40:36Qasar Younis?

  It's 26 now. I mean, certainly by 30.

• 40:41Marc Andreessen?

  All right, okay.

• 40:41Qasar Younis?

  Yeah, certainly by 30. And I would say the big like variable there really is like, because 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 cities? But then you see their launch schedule is pretty aggressive. And you're like, that can, that can get there. So. Maybe if I was being aggressive, I would say 28.

• 41:04Marc Andreessen?

  Yeah, okay.

• 41:05Speaker 01

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

• 41:11Marc Andreessen?

  Sure, because there's a scale up, there's a volume.

• 41:13Qasar Younis?

  And also, if you, you, you, you live in LA, 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.

• 41:24Qasar Younis?

  In the last couple of years, now they all know self-driving, and some of them even know applied intuition, because they know it from the other manufacturers. I think you fast forward another two to four years, everybody knows it now. Does that mean everyone's taking Waymos exclusively?

• 41:37Marc Andreessen?

  Right.

• 41:37Qasar Younis?

  The answer is no, actually. Now, there is a huge, huge, if you look at the numbers, if you're Uber, you gotta be scared. I mean, they're just eating into, into ride sharing.

• 41:47Qasar Younis?

  Yeah, but to get 100% ubiquity, I mean, that's, that's, that's another, it has to be extremely cheap.

• 41:51Marc Andreessen?

  Good. And what about long-haul trucking?

• 41:53Qasar Younis?

  So long, so that's, that's the passenger side, the long-haul trucking, completely different economics, completely different business model.

• 42:03Qasar Younis?

  There are many companies right now, I would say probably north of five, that are running long-haul trucks with drivers carrying loads between America and China. If 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. And unlike on the Waymo and Tesla side, where investors are willing to essentially give you, you know, some market cap, you know, adjustment for the potential of, they say the trucking business is like, you know, made...

• 42:37Speaker 01

  You buy a car with your heartstrings, you buy a truck with a calculator.

• 42:40Qasar Younis?

  Yeah, it's a calculator business. And they, and so it's like pure dollars and cents. And so I think you, as the provider of self-driving trucks, if you're doing the whole thing, like some of the companies are, which we're not, you have to show every mile, I'm gonna, I'm gonna save you this many dollars. And it's like, for sure, for sure, for sure, because the buyer's unsophisticated. And they're just like, well, I already got a staff, they can drive, 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, we've picked that market to, to, to really grow.

• 43:24Qasar Younis?

  But I think like, you can take like even more obscure, like, what are all queries? You know, literally like where you, you know, you're moving cement, you're moving, you're moving dirt. Not queries, Q U E A R, it's Q U A R R Y.

• 43:40Speaker 01

  Yeah.

• 43:41Qasar Younis?

  Quaries, queries.

• 43:41Speaker 01

  The rock, rockstone.

• 43:42Qasar Younis?

  Rockstone, cement. When, when are those? I can tell you, the people who own those things and run those things want it today.

• 43:49Marc Andreessen?

  Right.

• 43:50Qasar Younis?

  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, I think all this stuff's gonna happen.

• 44:00Marc Andreessen?

  Right.

• 44:01Speaker 01

  It happened pretty soon.

• 44:02Qasar Younis?

  It happened fairly soon. But the macro point that in, in legislation and, and, and kind of in, in the, in the kind of economics, the political economy of this conversation is, AI is really, you see, you have this big pushback in digital AI, because the accounts are like, I don't know if this is gonna happen to my job, and VCs, I'm sure all of your associates are very scared. But like, in our universe...

• 44:25Marc Andreessen?

  They're betting whether they need us.

• 44:26Qasar Younis?

  Yeah, yeah, yeah. In our, in our universe, it's the other way around. It's like, you, literally, I'll, I'll, 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, as, you know, aggressively.

• 44:41Marc Andreessen?

  You know, the fear for a long time has been, trucking for some reason triggers the, at least the person's imagination on like, you know, sort of apocalyptic levels of job loss, like, will they...

• 44:49Qasar Younis?

  It's so wrong.

• 44:50Marc Andreessen?

  Go ahead.

• 44:50Qasar Younis?

  There's not, there's not enough truck drivers, and guess what? Nobody wants to fricking be a truck driver.

• 44:55Marc Andreessen?

  Why is that? Explain that.

• 44:56Qasar Younis?

  Because it's a terrible job. It's like, you're like, you're like...

• 45:00Marc Andreessen?

  By the way, I grew up, the, the main feature of the town where I grew up was a truck stop, so I...

• 45:05Marc Andreessen?

  But why is truck, why is truck driving not a truck job for kids?

• 45:07Qasar Younis?

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

• 45:24Marc Andreessen?

  So what, so what's hard...

• 45:25Qasar Younis?

  I'm kidding, just to make sure everybody knows, I did not do that with my son.

• 45:28Marc Andreessen?

  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?

• 45:33Qasar Younis?

  So let me use a parallel analogy, which is very clear, and then you can, you know, people will say like, nobody wants to work anymore. Then they say, well, you know, McDonald's has all these job openings. No, no, actually, what it is, is those people that used to work at McDonald's, now DoorDash and Uber.

• 45:49Marc Andreessen?

  Right.

• 45:49Qasar Younis?

  Because it's better for them, because they can open, they can start their hours and 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 four to eight days in a row, doing long-haul trucking? 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, offshore oil rigs? Those jobs exist. If you want a job that pays six figures, they exist.

• 46:30Qasar Younis?

  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 part of the equation.

• 46:54Marc Andreessen?

  This is the thing, so tell me if I have this right, but I believe this, because I think long-haul truck drivers die, 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...

• 47:08Qasar Younis?

  It's, yeah.

• 47:09Marc Andreessen?

  It's very difficult, it's very difficult to eat well and exercise and get sleep.

• 47:12Qasar Younis?

  What's your sleep score if you're a long-haul trucker? Let me, yeah, there's no eight sleep on that.

• 47:16Marc Andreessen?

  And so like obesity, and then heart disease, hypertension, and so forth, they're all very high. One, and then two is, I think the vibration is very difficult, stress in the body. And then the third is, you mentioned cancer, but I think it's the, I think the, truck drivers have like a much higher rate of melanoma on their left arm.

• 47:30Qasar Younis?

  Exactly, yeah. There's photos of like a truck driver who's been driving for 30 years, and one half their face, the other half's face is exposed to the sun.

• 47:35Marc Andreessen?

  Right.

• 47:36Qasar Younis?

  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 coworkers dying, then you're like, what am I doing here?

• 48:02Marc Andreessen?

  Yeah.

• 48:03Qasar Younis?

  Like, there's other jobs I can take. And so it's, I, I understand you're trying to enumerate for the audience, like, but these are not good jobs.

• 48:12Marc Andreessen?

  Yeah.

• 48:12Qasar Younis?

  And, and, 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.

• 48:20Marc Andreessen?

  Yeah, and even trucker, even truckers don't want their kids to become truckers, like, it's, it's for that reason, they, you know, they want their kids to be, at the very least, like safer, 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, that are self-driving? Or, or, or let's say, other, even just somebody in the cab to deal with what happens when they arrive?

• 48:40Qasar Younis?

  We know multiple companies that have driver-out goals, right now.

• 48:43Marc Andreessen?

  Okay.

• 48:43Qasar Younis?

  So like, they're, they're working to get drivers out right now, you know, without going into our own details.

• 48:51Speaker 01

  To be honest, it's, it's not long. We're talking a few years, and...

• 48:54Qasar Younis?

  I think on the long end.

• 48:55Speaker 01

  Yeah, the long end. And the thing is, it's, it's, 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, that's not in high, that's not in high-volume production yet. And once you get that in high-volume production, I get the quality up, and then that's validated, and I can actually do these.

• 49:21Qasar Younis?

  And the price downs.

• 49:22Speaker 01

  Exactly.

• 49:23Marc Andreessen?

  Do you guys, do you 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?

• 49:28Qasar Younis?

  Yeah, I think so. I mean, the, the, the product that we're announcing, I think it's 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, onboard AI, then there's offboard AI. This is the tools to design and develop these same systems, the, the models that actually go on the machines. Our, you know, vision for that is, and the, the delivery robot is a great example, is like a high school kid or a middle schooler, they can make iPhone apps, they should be able to make autonomous systems. So why can't they? Just ask that's a very simple question. Why can't a...

• 50:10Qasar Younis?

  Ninth grader make a delivery robot in their, in their home while they don't have the, the actual environment that they would 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. Then how, okay, now that you define the, the requirements, then you have the scenarios get made where all the scenarios that I can, that can, uh, that can be made by using, let's say, a satellite image of the high school. Uh, 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.

• 50:53Qasar Younis?

  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, uh, which is the street that Applied Intuition has headquartered on. Uh, and, uh, and our, our, you know, this comes from our tooling background. And if you look at like how tooling has changed in the digital AI world, if you look at like what Claude did to all, I mean, you, you also remember like, you know, from Mixpanel to, you know, GitLab, GitHub, all these, now everything has moved into a very different almost IDE, frankly speaking. We think the same thing's gonna happen in the physical world. And so, uh, that, that, that's, yeah, that's what we're, that's what we're building, that's

• 51:38Qasar Younis?

  what we're, that's what we've built and that's what we're launching. And we already use it in-house, uh, to develop our autonomy system, which is, you know, and we're working on the most kind of scaled, complex systems, uh, on the planet in all these different verticals. So we're pretty confident that it's actually quite useful. We've seen massive productivity gains, uh, but also, uh, you know, we think like, uh, other companies will use this to, to build their own systems. Cause it gets to that mission, that, uh, billion intelligent machines.

• 52:04Speaker 01

  Fundamentally, where Dana is our agentic platform for physical AI and, and everything that we've built and developed over the past nearly a decade, every, every tool, every technique that's available in Dana. And it's very actually easy to use, uh, with the agentic interface. And so workflows that used to maybe take days or weeks to run.

• 52:25Qasar Younis?

  Right.

• 52:25Speaker 01

  You can now run those in, in minutes, uh, in many cases.

• 52:28Qasar Younis?

  Right.

• 52:28Speaker 01

  And, uh, and this just lowers the barrier to entry to building these systems.

• 52:31Qasar Younis?

  And just lowering the bar of like, you know, what it means to develop an autonomous system. Autonomy is still actually quite in the scope of software is quite exotic. It's not, because of the things that we've talked about. And we've just brought that down very, very aggressively. And it's kind of like, you know, the old adage of like, how do you make a great product in software? It's like you either increase safety, convenience or cost. And we want to try to do all three of those things with, with Dana. And our hope is just like you said, like, you know, uh, kids can develop robots for their, uh, for their own use and, 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, uh, so you have humanoids, you can do drones.

• 53:15Qasar Younis?

  The fact that right now, writing drone software and deploying it at, at, it's quite obscure and almost hobbyist. Uh, we wanted to make that absolutely like to, you know, maybe not child's play, but like teenager play.

• 53:30Marc Andreessen?

  So this place is a world of like, just a lot, like a lot more experimentation and entrepreneurship and like agriculture.

• 53:35Qasar Younis?

  Everything.

• 53:35Marc Andreessen?

  Bots and like basically every domain, construction.

• 53:38Qasar Younis?

  Yeah.

• 53:39Marc Andreessen?

  Defense.

• 53:39Qasar Younis?

  Exactly.

• 53:40Marc Andreessen?

  You 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.

• 53:45Qasar Younis?

  Yeah. And if you've 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's just like the iPhone example of, you couldn't imagine Instagram.

• 54:00Marc Andreessen?

  Yeah.

• 54:01Qasar Younis?

  Before, like the iPhone. It's like, imagine 2005 on laptops, you're like, in it, yeah, 10 years, there's gonna be this, this app.

• 54:08Marc Andreessen?

  Yeah.

• 54:08Qasar Younis?

  Where, and you can put photos into like, well, the phones only have, have cameras. Like, yeah, but it's gonna be like social, like what the hell? Like, so like Facebook, it's like, see, it's just hard to, and so we think by lowering that barrier, you're gonna get way, way more creative, uh, autonomy products.

• 54:23Marc Andreessen?

  Right.

• 54:24Qasar Younis?

  Yeah.

• 54:24Marc Andreessen?

  I will definitely decide whether to include this or not. So my, my kid is building autonomous bots in, um, uh, Factorio.

• 54:31Qasar Younis?

  Oh, nice.

• 54:32Marc Andreessen?

  Uh, is one of his projects.

• 54:33Qasar Younis?

  Yes.

• 54:33Marc Andreessen?

  And then, but he's, he's, you know, he's had it rolling because the, the toolkit's not available yet. So he's actually training and he's actually, he's actually training models.

• 54:39Qasar Younis?

  Yeah.

• 54:39Marc Andreessen?

  Uh, he's gathering data in the game. Um, and actually has like a whole army of like bots that he's developed and gone.

• 54:44Qasar Younis?

  Yeah. So like.

• 54:46Marc Andreessen?

  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 it's, but it's, you know, it's the kind of thing, it's like, yeah, it's like, you know.

• 54:55Qasar Younis?

  Like there's no reason autonomy should be this like, uh, you know, uh, obscure, difficult, you know, alchemistic, you know, uh, uh, technology. And, um, 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, uh, magic. It's like, if I can develop a, a Roomba for myself in my house on a weekend, using Dana, then why, then it's not suddenly so scary.

• 55:30Marc Andreessen?

  Yeah.

• 55:30Qasar Younis?

  And I think that's like, that's, that's important.

• 55:32Marc Andreessen?

  And we can, it can, it can support, it can support people in all kinds of ways that we haven't even imagined yet. Cause I, yeah.

• 55:36Qasar Younis?

  Absolutely.

• 55:36Marc Andreessen?

  Yeah.

• 55:37Qasar Younis?

  I mean, you think about like, uh, you know, folks with disabilities, you know, we always think about humanoids as like this, uh, very important task of folding laundry, which seems to be a lot.

• 55:49Qasar Younis?

  So we focus on, you know, the important task, but the, when you allow these tools to exist. I mean, I, I, you know, we started a tooling company. I mean, I feel so importantly that tools are like what separates actually advanced civilizations from, you know, less advanced civilizations. And our, our first, uh, mark for the company was a monkey's head. And then we got a designer, we said, what's this?

• 56:11Qasar Younis?

  This is stupid. I was like, I thought it was pretty good.

• 56:16Erik Torenberg?

  So you were talking earlier about how when, you know, um, 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 the technology is getting there for 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, what could we be talking about? The equivalent here of in quick succession?

• 56:42Qasar Younis?

  I mean, I think, uh, you know, midterm, we want Dana, if not the, the short term, to really, you know, make humanoids way more real. Uh, there's, I mean, how many, it's like a thousand core tasks in a home from, uh, from humanoids. And these companies, it's like such, I mean, if you talk to people who work in these companies, it's, everything is difficult. Every step of the way is difficult. Collecting data is difficult, uh, you know, cleaning that data is difficult, training these models, deploying the model is difficult. And the bar being, I want a high school kid to make a humanoid. So that, that's our, our, our path. And we think there, there could be a lot there. But that's like these, the obvious stuff. I think the true non-obvious stuff is gonna be, we'll look back, will be, will be way, way more interesting.

• 57:24Speaker 01

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

• 58:00Qasar Younis?

  And I think sometimes people ask like, us being a tooling company and like you take self-driving trucks, we deploy self-driving trucks and many of the self-driving trucking companies use our tools. I, I, I think sometimes people ask, go like, you know, with Dana, are you gonna like enable all these competitors? That's great.

• 58:15Marc Andreessen?

  Right.

• 58:15Qasar Younis?

  That's absolutely completely fine. If you look at Google and what Google did to web applications, there was a massive internet. Uh, Google still succeeded through, you know, search and YouTube and, and, and other web apps. And other folks learned and used open source products and then ultimately closed source products and ultimately venture-backed products. So we think, we think this, the, the, the, the same thing can happen here.

• 58:35Marc Andreessen?

  I was at a robotics startup, uh, a while back that you, 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.

• 58:50Marc Andreessen?

  Literally, you know, training over and over again.

• 58:51Qasar Younis?

  Yeah.

• 58:51Marc Andreessen?

  The difference. And so, you know, I don't know, why not, right?

• 58:54Qasar Younis?

  Yeah.

• 58:55Marc Andreessen?

  Why don't have the little, I don't have the little robot follow you around when you walked along.

• 58:58Qasar Younis?

  Yes, yes.

• 58:58Marc Andreessen?

  Pick up.

• 58:59Qasar Younis?

  Yeah, and I think like, like.

• 59:01Marc Andreessen?

  I know somebody who built, I forget who it was, somebody built a, a little lawn robot that would go around and individual, pick up individual leaves.

• 59:07Qasar Younis?

  Yeah.

• 59:08Marc Andreessen?

  Cause you got that problem, right? Okay, you rake, you rake, you rake your yard, it's completely clean, and then like two hours later, there's like 14 leaves. And you're like.

• 59:15Qasar Younis?

  Yeah.

• 59:15Marc Andreessen?

  So it's like send out the little bot to pick up the leaves.

• 59:17Qasar Younis?

  It's like if development costs are zero, then people will do that. I mean, I, I, you guys remember like the early iPhone apps that hits were like the beer one or the fart app? If you imagine that in like, yeah, if you imagine that in 98 with a, you know, with the Symbian mobile, you know, whatever. I was the OS from I think who's Ericsson or somebody, that'd be possible. You didn't need a team of like 50 people to develop a, like the beer thing for the BlackBerry.

• 59:41Marc Andreessen?

  Right.

• 59:42Qasar Younis?

  So I think there's a similar type of thing that's happening where, you know, we really want to be a part of that. It will enable that. And if it like makes, making, like, I think it'd still be a while before like making a robot taxi is like super, super easy.

• 59:55Marc Andreessen?

  Yeah.

• 59:56Qasar Younis?

  But that'll happen.

• 59:56Marc Andreessen?

  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, is almost, just healthcare alone is, is almost.

• 1:00:02Qasar Younis?

  Exactly.

• 1:00:03Marc Andreessen?

  Home care.

• 1:00:04Qasar Younis?

  Yeah.

• 1:00:04Marc Andreessen?

  Um, and then, um, in, um, construction, um, you know, and all the physical trades.

• 1:00:08Qasar Younis?

  It's like us sitting in 2007 and saying, let's, uh, we, we should have an app store.

• 1:00:12Marc Andreessen?

  Right.

• 1:00:13Qasar Younis?

  What type of the apps? And we would come up with like a list of eight. And then they're like, uh, there'll be a messaging one, and then there'll be a camera one. 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, to order, you know, food off the menu.

• 1:00:28Marc Andreessen?

  Right.

• 1:00:29Qasar Younis?

  Yeah.

• 1:00:29Marc Andreessen?

  Makes sense.

• 1:00:30Erik Torenberg?

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

• 1:00:43Speaker 01

  So, so first off, world models means about a hundred different things. And, uh, we had a team at CBPR recently and I was joking with them about just how many different ways you can define what a, what a world model is. But, uh, when we're thinking about a world model, right, we're typically thinking about it in the context of, of a simulation, right? Something that is effectively.

• 1:01:01Qasar Younis?

  We started as a sim company.

• 1:01:02Speaker 01

  Yeah. Yeah, so something that is like sufficiently able to represent the real world and, and is reactive in a sense where you can actually have, let's say, a, uh, an autonomous agent that's acting in this world and the world model is, is behaving appropriately in response to that autonomous agent.

• 1:01:17Qasar Younis?

  Maybe, uh, Peter, I think it's worth being super explicit here, or we just go just one level lower. And, uh, this, uh, 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.

• 1:01:32Speaker 01

  Yeah.

• 1:01:32Qasar Younis?

  Where do we fit on it or, or where, you know, yeah, describe the landscape, I think, maybe.

• 1:01:38Speaker 01

  Yeah, so this is like, let's say simulation broadly, right? There, there's, there's so many different ways of doing simulation. And so the, the more classical approaches of simulation, very physics based. And, 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, and is it, is just a body simulation or are we actually simulating, for example, the light in, in the environment or the?

• 1:02:00Qasar Younis?

  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 changes. And now you've generated, uh, technology, the same thing is happening in our universe as well.

• 1:02:23Speaker 01

  Yeah, so that's sort of on the, that's at the far end of physics based simulation. And, and then the opposite end is, is purely neural simulation. But you, within that spectrum, there's, 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, uh, a, a, effectively a representation of the real world that, uh, that has a, a, a 3D representation. Um, and that 3D representation is consistent, meaning that if, 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, uh, is actually representing the 3D geometry of that world, you'll actually get very high quality output from that.

• 1:03:05Speaker 01

  There's a lot of value in that, uh, and, and that's, let's say, one type of, of world model. But when you go further on that, uh, on that spectrum into the, really into neural simulation, then you get into these, uh, where you're actually generating the video feeds. You think of a neural network that's actually outputting, uh, a video, uh, as what's actually coming out of the neurons of that. And, and that can be reactive, uh, which gives you some very interesting properties.

• 1:03:30Qasar Younis?

  Reactive as in the ego does something in the environment and the other agents respond to the ego.

• 1:03:36Speaker 01

  Exactly. Uh, however, you're not guaranteed in that reactivity that it's, it's accurate, right? And, and now it's a question of, well, how can I align this, this simulation, this world 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, and the world model, I mean, I think you've just sort of solved the universe roughly, right? It, it's a possibly difficult problem. Um, but, but as we make progress towards that, uh, it, it makes, uh, training physically I models much easier because you can do more of that in simulation. But the hardest part though is we're always talking about performance, right? So, uh, I, I like to say the, the, the labs, they have it easy because they, they can, they can make models that are trillions of parameters

• 1:04:21Speaker 01

  and those models can be super slow. And that's fine, but we don't have that luxury in physically I, right? We deal, we, we deal in real time, like the actual, actual clock, real time. And so we, we have so many milliseconds before we have to do something. And, and those performance constraints, they actually constrain the problem in a lot of ways. So we can have very large models and we do have very large models that are used in the off 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, uh, and, and that's the hard part about physical, that's also the moat, right? It's, it's, it is what makes our tooling and, and our, our competencies valuable, uh, because it's just really hard to, to meet all of these constraints in, in a physical system.

• 1:05:05Marc Andreessen?

  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 VI?

• 1:05:14Qasar Younis?

  You know, I, as long as they keep putting out great trailers, I mean, I, I feel like I'm getting entertained without paying a dollar. I reintroduce a Tom Petty because of.

• 1:05:27Erik Torenberg?

  Will you give us some timelines?

• 1:05:30Speaker 01

  Let's run with that for a second.

• 1:05:31Marc Andreessen?

  Yeah, go for it, go for it. 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, it's a sim, it's a simulated city and at least in theory.

• 1:05:42Qasar Younis?

  On that spectrum, we hire so many people out of the video game world, on that spectrum, it's absolutely real.

• 1:05:47Marc Andreessen?

  Well, go tell, tell us about that. Yeah. What's, what's the spectrum?

• 1:05:49Speaker 01

  So I, here, I, I, this is, this is speculation, but I think Grand Theft, Grand Theft Auto VI will be perhaps the last major, uh, real world video game that's still really developed, let's say, in that legacy era of traditional computer graphics tooling.

• 1:06:04Qasar Younis?

  Technical artists and, yeah.

• 1:06:06Speaker 01

  Like I, I think that, that Grand Theft Auto VII will much more likely be like a world model based video game.

• 1:06:11Marc Andreessen?

  Right.

• 1:06:11Speaker 01

  Um, and we're, you, you can imagine as, as AI tech evolves here, you have like this, this concept of this video game world model. And there's, there's like some sort of baseline, let's say, data store, uh, that represents, uh, the real world and somehow, and then, and then you have a, some translation layer that's actually turning that data store into something that you can see and, and run around in, like, it, it's.

• 1:06:31Marc Andreessen?

  But it could be, the game as a concept, it could be the real world, right? Is, is where this is at. You could have a complete recreation of the real world in the game. Well, this has kind of happened with flight simulators, hasn't it?

• 1:06:39Speaker 01

  Yeah.

• 1:06:40Marc Andreessen?

  The most recent flight simulators are literally, is the entire planet rendered accurately, is my understanding, at least from the air, is that right?

• 1:06:46Qasar Younis?

  Yeah, yeah. And, I mean, you're really, that's where our bread and butter is when we started, uh, started the business. We hired so many people out of the Microsoft flights. I'm surprised it didn't, you know.

• 1:06:56Marc Andreessen?

  But when you fly over, you know, whatever, New York or if you need to fly over Duluth in the flight simulator now, it is the real city, right?

• 1:07:01Qasar Younis?

  Exactly, but there, there's some tricks that they play there. Uh, and a lot of that is fidelity, you know, the real world, the more you zoom in.

• 1:07:08Marc Andreessen?

  Right.

• 1:07:08Qasar Younis?

  It stays a certain level of fidelity. And, uh, and so the, the tricks that you play there is you, you basically are, uh, 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. But if you were to try to rebuild the world with this level of fidelity, it would, you know, would, would take the, all the energy of the universe, right? It's, it's, it's, it's quite complex. And that's probably, by the way, the best argument against us being, living in a simulation is among the.

• 1:07:38Marc Andreessen?

  Right.

• 1:07:38Qasar Younis?

  But of course, then you would say, well, the simulator we're in doesn't follow the laws of physics that were.

• 1:07:44Marc Andreessen?

  How, how do we know that the simulator that we're in is rendering all the stuff that we can't see?

• 1:07:48Qasar Younis?

  Yeah, yeah.

• 1:07:49Marc Andreessen?

  Like as far, as far as I know, everything happening outside this room doesn't even exist.

• 1:07:52Qasar Younis?

  Yeah, this is, I mean, you know, like Buddhism believes this. This is 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.

• 1:08:04Marc Andreessen?

  I don't see why, I don't see why it's necessary for it to keep rendering if I'm not there.

• 1:08:08Erik Torenberg?

  Buddhism from first principles.

• 1:08:10Qasar Younis?

  Yeah, exactly. That's what you should, that'll get a lot of clicks, that's what you need to call this. Yeah.

• 1:08:15Erik Torenberg?

  Well, just going 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 the sort of, uh, you know, other interesting things that were worth tracking? Like perhaps when we'll get laundry folded or, uh, other, you know, things that emerged because of humanoid.

• 1:08:29Qasar Younis?

  And I think also maybe just, uh, just touching a little bit of world models, where we see world models go. Cause I think it's, it's fundamental to what the work we do.

• 1:08:36Erik Torenberg?

  Yeah, sure.

• 1:08:36Speaker 01

  Yeah, yeah. So, so to answer the, the first question, um, so laundry folding, it's, uh, it's, it's not terribly far from being solved, to be, to be clear. Uh, and there is a lot of interesting research being.

• 1:08:48Qasar Younis?

  And then humanity can rejoice. That's in Proverbs four, 16, I think.

• 1:08:55Speaker 01

  Well, here, I, I, I do think, I do think housekeeping is, is a killer use case for physically I, right?

• 1:09:01Qasar Younis?

  Peter thinks too, that he always talks about in the company, when is housekeeping and it's entertainment? Peter's long on humanoid entertainment.

• 1:09:07Speaker 01

  I would, I would.

• 1:09:09Marc Andreessen?

  I 100% agree with that. I think entertainment is the robot's killer app. I don't think anybody.

• 1:09:14Qasar Younis?

  I mean, like, telling these Midwest, Midwest white guys are really into this.

• 1:09:19Speaker 01

  I'm just saying, what I think, I think.

• 1:09:20Marc Andreessen?

  I just want to know when the West world, that's all I want to know. 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.

• 1:09:32Speaker 01

  Would you pay to see Cirque du Soleil with robots? Like, yes. Yes. I, I want to see, I want to see Kung Fu, trapeze swinging.

• 1:09:41Qasar Younis?

  spoken by like a compiler's guy.

• 1:09:47Qasar Younis?

  I want Westworld. I want Westworld. 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.

• 1:10:02Speaker 01

  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, they'll say like, played at 8x real time, or whatever. And that's for you to make it watchable. So the question is, when can you actually human parody of performance? That's further off.

• 1:10:23Qasar Younis?

  When you decouple 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.

• 1:10:30Speaker 01

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

• 1:10:35Qasar Younis?

  I mean, it's far off from like, you know, when I was a mekki, that was like fantasy. Like, there's like nothing. What's the movie that has the most realistic future vision of robots?

• 1:10:43Speaker 01

  Oh, man, Bicentennial Man.

• 1:10:45Qasar Younis?

  Is it?

• 1:10:45Speaker 01

  Okay.

• 1:10:45Qasar Younis?

  Yeah, he's. Tell us about it.

• 1:10:48Qasar Younis?

  Why that one? I actually haven't seen. Well, I like that scene, I think it's I, Robot, when Will Smith jumps in the car and 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 this thing yourself? Like, that's what we're, that's applied intuition's, you know, like goal.

• 1:11:09Speaker 01

  Well, again, by the way, I haven't seen this movie in a long time, probably since it came out. So my recollection of it is probably a bit incorrect.

• 1:11:16Qasar Younis?

  Don't worry, the internet will correct you.

• 1:11:18Speaker 01

  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.

• 1:11:38Qasar Younis?

  I got a different answer. You guys ever see that movie, Sam Rockwell, Moon? Oh, yeah. Yeah, the setup, I don't wonder, it's a great movie. Don't watch the trailer, just to watch the movie. It's the premises, 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 base run by Applied Intuition, run by Lunar Technologies.

• 1:12:02Qasar Younis?

  I'm, I, I don't want to be, I don't want to be Weyland-Yutani. I don't want to be, you know, that's from, from the Alien franchises, and then Tarell 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 error signal.

• 1:12:25Speaker 01

  The reason why it's, I think, so accurate is because the state of the art for AI systems is, like, these systems, they just need the occasional grounding.

• 1:12:33Qasar Younis?

  Exactly.

• 1:12:33Speaker 01

  They'll just go off and do something crazy. And then you say, no, no, stop doing that.

• 1:12:37Qasar Younis?

  L1s are like that, too.

• 1:12:38Speaker 01

  Yeah.

• 1:12:38Qasar Younis?

  That's what coding bots are like. 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're, 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, because I don't know, they're quite a positive force of nature in that, in that. 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

• 1:13:19Qasar Younis?

  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 undergrad. Did you get destroyed? No. You know what? I, I. Unlike Eric Schmidt. Yeah, yeah, yeah. You not get booed? Listen, listen, my, my, this is true, my wife started watching, and she said, I feel like you were yelling at me. I can't watch this. So I, I basically, I mean, I don't, 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'd talk about this stuff. And partly, it's the General Motors Institute. No one's, like, these, these are, I don't want to, I don't, I don't want to

• 1:14:01Qasar Younis?

  throw judgment on, you know, the people we recruit out of MIT and Stanford, but I'd say GMI people are a little different. And they're like, they're like pragmatic people. 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. And by the way, people working on projects together in government, people working on projects together in nonprofits, they all screw up. And so it's too simple to say, AI corporations are terrible. There's 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,

• 1:14:41Qasar Younis?

  if, if, if the, of 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 amazing. But then you also get this efficiency of cheaper energy, et cetera. If all those things don't still satisfy your, your fear, you, you as a person, it's up to your responsibility to really learn about that technology. You, you can't just say, well, I'm afraid of it, and my reaction is shut it down. That's not, that's simply, it's, and I don't say this just to say that we're competing with the Chinese, but there's a Confucian, they're saying by Confucius, is no hand can block the sun. And the sun is technological progress. And

• 1:15:25Qasar Younis?

  if we as a society don't embrace technological progress, we will be left behind. Somebody else is gonna do it. Somebody else is gonna do it. And if it's not the Chinese, who knows? It's maybe the Uzbek, or the, you know, it's another country that is, is recognizing, hey, my citizens are suffering, and I'm going to use this technology to remove them. It is, honestly, it's 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. Yep. And, and, and someone will immediately quip if they were debating me, they would say, well, there's plenty of food, it's, 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

• 1:16:08Qasar Younis?

  faster. That's just, that's just how it is. So I think 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. 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 of times, to me, you just don't get it. 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. And then we just move forward and we make society better, and then the results show it. I mean, there's people who still, shockingly, believe communism is the right answer. I mean, I just want to say, why, and I'm, you know, I am a capitalist.

• 1:16:51Qasar Younis?

  I can't, I cannot admit that. But there's 70 years of history there. Like, 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. There's no debate anymore, folks. The, you know, systems where individuals make decisions on their own interests actually work better for society. And so that doesn't mean everything is perfect. And you can't extrapolate. That same thing with, you know, AI. It doesn't mean everything's going to be perfect. But net, net, it's definitely going to be better. And that's roughly what my commencement speech was, without the boos. Mark and, these guys were booing, so they just cut it out. Eric was booing and throwing stuff, and they just added it out.

• 1:17:34Speaker 03

  You mentioned, you know, the Japan market earlier. Why don't you talk briefly about sort of the global ambitions and how these technologies, you know, interplay and what we're doing here?

• 1:17:43Qasar Younis?

  So I think America particularly is still the most advanced in terms of, when you take account of the business model. The second thing for a company like Applied Intuition, we're an extremely global company. We work with everybody, minus, we don't have an office in China, but really everyone else on the globe. And we're a horizontal company, we're a technology provider. And I think we, 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. As sovereign AI becomes more of a real thing, we have to, you know, 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

• 1:18:24Qasar Younis?

  Oil, you'll see that this is, this is, that was the history of companies. You'd work internationally. Aramco is not a random company, right? You, you build based on the reality, 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.

• 1:18:54Qasar Younis?

  But so I think innately, we're more, 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. Just always looking at the market that's just like within the 30, you know, between San Jose and San Francisco. It's like, actually, the market is really big. I think with physical AI, the nature of it being physical, I think we, we have to be a very international company. And I think we've had a lot of success being a very, you know, being international. Yeah.

• 1:19:22Speaker 03

  Cool. I had a good place to wrap.

• 1:19:23Qasar Younis?

  Okay.

• 1:19:24Speaker 03

  Peter Gass, thanks so much for coming on the podcast. Congrats on a big launch with Dana.

• 1:19:27Qasar Younis?

  Yeah, thanks for having us. Awesome. Great to see you. Okay, great.

• 1:19:32Speaker 03

  Thanks for listening to this episode of the A16Z Podcast. If you liked this episode, be sure to like, comment, subscribe, leave us a rating or review, and share it with your friends and family. For more episodes, go to YouTube, Apple Podcasts, and Spotify. Follow us on X at A16Z, and subscribe to our Substack at A16Z.substack.com. Thanks again for listening, and I'll see you in the next episode.

• 1:19:58Speaker 03

  As a reminder, the content here is for informational purposes only, should not be taken as legal, business, tax, or investment advice, or be used to evaluate any investment or security, and is not directed at any investors or potential investors in any A16Z fund. Please note that A16Z and its affiliates may also maintain investments in the companies discussed in this podcast. For more details, including a link to our investments, please see A16Z.com/disclosures.