The Company Automating Everything | Ep. 067 Lemonade Stand 🍋

15:20Atrioc:Yeah. One more question on this. So presumably there are a lot of sensors in order to make this thing be able to do autonomous driving. Where are those? Like what exactly needs to be added on to something like this for it to be able to?

15:30Aiden:Yeah, so in this case, we'll actually show you a car later as well that has all the sensors. Seven cameras really at the end of the day. So it's a camera only system. Okay. Primarily that allows you to do L2 plus plus based driving.

15:42Atrioc:Yeah.

15:42Aiden:I think that vehicle actually has it. Oh, cool. We can just go there. What a segue, dog. What can we say? So there's a few example, for example, camera here, camera there, camera on the other side.

15:55Atrioc:I mean, this is three cameras, right? Or do you consider this one camera? I'm just seeing lenses everywhere. Technically three in that sense. Okay.

16:04Aiden:Yeah. Yeah. Okay. So we tend to have more than we need in the case of testing because you just have more information that you can do something interesting with. In a production case, seven is the de facto, another camera there, another set of cameras, here and then two back here.

16:25Atrioc:And so this amount of cameras would be able to run the autonomous system.

16:30Aiden:That is correct.

16:31Atrioc:Man. With these sensors, I guess I'm surprised by how few there are compared to something like a Waymo, which has 26 or something, whatever it is, I forget the number. And also they have LiDAR. What's the general overview of why you guys feel like you only need this many sensors and only cameras.

16:47Aiden:Yeah, so that's also the big debate between L2++ systems, which think of it as driver assist systems versus L4 systems, which is full driver disengaged related systems. The other thing to realize is a lot of the way in which we've thought about building the stack is utilizing transformer based architectures or an end to end architecture that goes from signals in to control outputs, which means from a pure camera based system, you could actually get to realistic driving behavior, whereas before you'd have to have all these submodules or subcomponents that constitute your driving stack, and each of those would have to work sequentially.

17:22Aiden:So when you compare what you need to do, only now can a true camera-only system be built and scaled, which is why we ended up doing that today. So if you have, say, many LIDARs, many radars, many cameras on your system, we're not saying that won't work. But what we believe is that's not a cost-effective way really to deploy a vehicle if we want to do so at scale. And we believe we can hit the same performance guarantees with the same level of redundancy without it.

17:51Atrioc:What was, this maybe will be too technical, but what was the key difference between we don't think cameras are enough to now we think cameras are enough? Like what changed that caused that confidence?

18:02Aiden:I think a lot of it was it being proven out. Tesla is an example. the Chinese ecosystem is another example of there being many different OEMs globally that show that it's possible. And a lot of it is that effect where once one person does it, everyone else sees that that path is there and they can continue down that route. You may see, for example, front-facing LIDARs, radars, et cetera, as redundancy on vehicles. All of that is still very much TBD, but you should generally expect that the form factor of sensors to become simpler over time. Whereas before, we wanted to get as much data and as much sensory information as possible to make the best decision we can.

18:40Aiden:So that's the evolution of self-driving really over the last few years.

18:43Atrioc:That makes sense. And then is this same amount of sensors, the idea is this can also get to L4 at some point or would that require an upgrade? And for folks listening, L4 would be the point that a human is not involved with the driving at all.

18:54Aiden:The thesis is that it could evolve and get to that point.

18:57Atrioc:Okay.

18:57Aiden:Okay, one thing to add on to that quick, I think it came up later on in the day, is that they aren't opposed to installing LiDAR on vehicles, but it does not seem as like the main way that they're going to provide autonomous driving at scale. So like they could, if a company came to them and was like, we want you to install LiDAR on a vehicle for us, they could take that approach. But the camera system that Tesla also uses is their default way of handling autonomous driving.

19:30Atrioc:Yeah. Then another interesting piece we see in this garage, you'll see in a second, is like the typical car has like 100 computers in it, which is not something I really realized. Like every piece of a car is like often a contracted out piece of computer, like a mini, for example, like the braking system is like its own little computer. And then same with the steering wheel and then same with any like visual panels and whatnot. And so what's kind of happened with car development is that the whole industry has become this weird fragmented, like a hundred different computer systems inside of a single thing.

19:59Aiden:Yeah. I think Perry was saying before we started recording the episode is that all of these electronic components in modern cars right now have their own little proprietary operating systems and things. And they're not really designed to perfectly work with each other.

20:13Atrioc:Yeah. So in this next clip, you'll see basically what a car is currently looking like. And it's literally becoming an issue with modern cars of how many computers and the weight of the wires because of just how much shit is being crammed into every car. And then what it could look like if in theory you have a sort of unified system. This is a real live car, I think.

20:32Aiden:Not a real live car, but almost a real live car. So this is garage one of a few actually just around this campus here. This is where we have a lot of different vehicle types we're going to show you around. So right here are two different vehicle rigs. Instead of you testing on a live car all the time, we don't want to do that. So we try to emulate it as close as possible. On the left hand side over there is what a car looks like today. So let's actually start with that. So this is literally the guts and innards of a car. We were talking about it right before. but it's the equivalent of you building a PC without a proper box around it.

21:06Aiden:It's just a bunch of wiring, ECUs, a mix and match of everything you can think about. You can even look on the inside if you want just to see how messy it is. So if you've ever dented your car or broken off a piece of it, this is what you'll see inside. Wait, so how does this, are you going to one particular car manufacturer and getting the insides of a particular model of car? Or you said earlier, you're sourcing these from a ton of different places and then building it. So when someone builds a car, they may need to get all these different components from separate places. Say the backup camera versus the infotainment screen versus what controls your trunk.

21:48Aiden:And there could be maybe 150 of these things inside of a vehicle, all controlled by some compute module. But that compute module has to then interact with the entire rest of the system. So now imagine you have 150 different things that you all need to make interoperable and kind of work together. And that's where you get this. That's why you have additional wiring everywhere. And that's why there's redundancy in said wiring. That's why everything looks a little bit different, even from a design perspective, sizing perspective, reliability perspective. And when someone ships a car, they have to make sure everything works together and will continue to work for the next 10, 15 years.

22:23Atrioc:Something I'm curious about. So if this is the sort of like guts and frame of a car, what needs to get added to what would have been a traditional car? What are the pieces that you guys are adding that wouldn't have been there otherwise? So a lot of it is actually we're simplifying it to some degree.

22:37Aiden:So if you look at this side, this is what the car could look like. And the reason it's a lot simpler is instead of there being, say, 150 different compute modules, you can simplify that not to one, but maybe a few modules in different zones on the vehicle. So instead of all connecting to each other in this interconnected fashion, imagine they all go to a central box or set of boxes. And those boxes have significantly better compute that can do more interesting things that could be running the latest and greatest edge models to do something like an AI-assisted voice assistant. That could be everything from controlling HVAC in the car.

23:15Aiden:So that's a big piece of it. You just simplify what you need to actually do in the vehicle. This isn't just about, I think, coming into like automation vehicles. People think about self-driving, but this isn't just about that. This is about like controlling a bunch of different aspects of your vehicle. This is what I call the compute and electrical architecture of the entire car to run anything software related on a car. Autonomy is just one example of one piece of software that could run on a car. Okay, that totally makes sense because I think that, you know, I come in and I remember seeing this in a video before and it's, oh, it's the car without the wheels.

23:52Aiden:How are they automating the driving? No, but everything else, right? Sorry, sir. It does have wheels. Kind of wheels, right? But it's everything else. It could be how we control this using a mobile application. Yeah. Right. Because that's now a cool thing for people to integrate into cars. It could be the infotainment screen in and of itself. And each of these need also better compute at the end of the day. So if we take what's happening in like the broader LLM space as a comparable, how everyone's focusing on compute data and the right architecture to do the right things there, it's the exact same approach, but now physical.

24:27Atrioc:So am I correct in understanding that as a pretty uh layman car guy saying this version the traditional car has many different electronic components basically? Correct. Okay I guess I didn't think about that. Do you know are Are there like numbers of how many of these like individualized systems are being pulled out in order to kind of pull it into one?

24:47Aiden:There's about 150 on this example of different systems. That's why if you look like you just see one view of it. But if you look around even on the side, you'll see all these different peripheries of what exists. And on here, I don't remember the exact number, but it's say around five to 10 is a good example of how many different modules you can reduce this down to.

25:06Atrioc:So for an OEM, how hard is it to go from this very complex looking thing to this very complex but simpler looking thing?

25:15Aiden:It's been difficult because over the last 10 years, that's all what they've been trying to do.

25:19Atrioc:Okay.

25:19Aiden:It's how do you actually simplify the car? Because there's many advantages. One example is weight. Everything on there is a few additional pounds, which can matter if you're buying a car or using a car. to another example is if you have 150 different modules and say something goes wrong, how do you actually fix it? Like right now you have to take your car back to servicing. You leave it there for three weeks. You may get a fix. You may not. Who knows? But imagine if you could just do a software update similar to what exists for a Tesla today for any other type of vehicle. So a lot of this is if you get the right foundation, anything that is software on a vehicle can now be built and deployed and updated in a better way, whether you're testing it or also once it's on the road for 20 years.

26:03Aiden:And I think that's the thing people don't realize is the stuff will be on the road for a long time. So you better make sure that you're kind of future-proofing. I'm kind of wondering, like the software in this case, I assume is all proprietary. Like that's the value of the company. I wouldn't be able to like launch my own homebrew software on the vehicle. probably or probably not a production car like i don't i don't think it's as complicated as you probably think of it as in general this is just a giant moving computer yeah really at the end of the day without it being a desktop in the back of a car and that's actually to its benefit and if that is the case that means you can build software in the ways that you probably think about building software today that a lot of industrials historically have not been able to do yeah so i i thought that

26:54Atrioc:was pretty fascinating seeing the difference of a car and again if you're on you know an audio listener like this is just physically much less stuff so it's pretty interesting even just setting aside this particular company applied intuition like you know across the industry a lot of car manufacturers are dealing with this and everybody kind of has this question of it's almost like the android or cell phones you know 10 20 years ago where it used to be that every phone manufacturer had their own operating system. It was this awful fragmented thing where, you know, Samsung and Nokia, everybody's making their own operating system for everything and integrating other apps is this major problem.

27:29Atrioc:So even setting aside applied intuition, the idea of, you know, people building sort of their own solutions and that can drop into any car is something that's really, really valuable, I think, rather than the idea of like literally every car company and every tractor company, every boat company is trying to do this themselves simultaneously. So I thought that was pretty interesting of how much you can simplify a modern car if you have kind of one operating system yeah i wouldn't trust john deere self-driving i don't know if they got the best

27:58Aiden:engineers working on that you know i think is really funny is even like on the lower scale of the apps that they were talking about that you could launch you could have something like you know an assistant that uh walks you through a solution that you would otherwise be looking in like an owner's manual for i was like oh that's like pretty you know that's pretty helpful but then they had like a theater mode where they all the lights like went down in the car and then Tron came on on the screen. Yeah. And then we're just watching Tron while like the lights of the car augment the movie and the sound and then we went over

28:36Atrioc:to the guys were getting high in the Tron car. You invite me. Yeah, we have with

28:46Atrioc:CEOs else and you didn't invite me the important thing is that this tech can go into your toaster your toaster gonna have tron mode okay your boat can have tron mode go to go to patreon if you want

28:56Aiden:to see the footage of us hot boxing the tractor with the ceo uh no but we went over i thought it was like a silly question but we went over the tractor later and i was like can you run theater mode on the tractor too uh so and they were like well i mean i guess yeah like yeah i don't think they're concerned about that is like the primary market for the farmer who watches i guess farmers

29:17Atrioc:don't enjoy tron i guess farmers can't enjoy a good movie fine they can we out of touch silicon valley elites friday night lights mode okay we'll activate it yeah there we go so this is i think this is just particularly interesting to see an example of how the whole industry could sort of make strides and not just oh tesla is doing this waymo is doing this which i think is cool it is worth noting though you know there's obviously other players in this industry as well so i think I forget if we mentioned it, but NVIDIA is trying to do their own self-driving stack that they can drop into a car. But that's going to be sort of different than an operating system.

29:52Atrioc:There are companies that are trying to make like simulation tooling, which is what Applied Intuition started at, actually. I remember they'd have these damn self-driving cars clogging up the parking lot. You couldn't park. And they would have all the different models of car they were working with. Yeah. And then they did these digital – they replicated the whole city of San Jose digitally. And then they were just running it a million times. that's yeah yeah and that's so again so nvidia is doing this like applied intuition is doing this obviously tesla has it for their own cars waymo for their own cars so there's a lot of people who are all doing this and then again the question is like how could you propagate this to many different companies and if a company could successfully make something where it's like hey we can retrofit a car you only need this number of sensors and then you get access to a broad range of apps or to put it differently you have a software layer that any other company can come in and put their apps on.

30:41Atrioc:What we could see is very quickly sort of all cars around the world, all vehicles, tractors, all these things, suddenly having the ability to just like plug and play different autonomous software. And that could be the software that Applied Intuition is doing. It could be others. This is a way that you see autonomous vehicles become approachable or accessible beyond just Waymo or Tesla. It's this type of thing. This episode of Eliminate Stand is brought to you by TrueWork.

31:08Aiden:Well, you guys know I've been extremely satisfied with the True Work product, the pants and the shorts that they've given us. I do know that, great. And you actually, you bought yours, which is funny. Oh, you're wearing them this time! I'm wearing them! Wow. And I like them a lot. Because they kept sending free stuff that I would keep for myself. This is true! This is actually true! And they have these awesome pants that have like these built-in like knee pads, sort of it's more cushiony around the knees. Sure. And I like the way they look and feel, and I've been keeping them to myself.

31:41Atrioc:No, I had to buy these myself, but the one pair I got of the free ones we provided, you threw at me at the office and said, here, you can have them. You take this. He tossed me the scrap. And I had kept them. I'd already worn them several times.

31:56Aiden:You already worn them. You threw me used pants. But they're good for a bunch of different weather conditions, different environments. If you're a hard worker, true worker, great for you. If you're a podcaster, true worker, great for you.

32:07Atrioc:Go out camping. Go out hiking with them.

32:09Aiden:I actually wore my hiking two weeks ago.

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32:34Aiden:And you're dumb. You're dumb as hell, and I've been wanting to say it. I'm not dumb. You know what I've been doing? I've been going out to the street corner, and I've been advertising my expert ability to plan travel for people, and I've already started raking in the dough. Oh, a little bit of cash. Boots on the ground, marketing clients. There's no better way to do it, I would argue.

32:58Atrioc:No, there is a better way. That's why you're dumb. You could use fora. They can help you learn how to become a travel agent. What are you talking about? Not only can you give other people instructions on how to plan their trip and help them, you could even use the website to go on trips yourself, Aiden, instead of standing on a corner.

33:15Aiden:I have a giant client list.

33:16Atrioc:Who is your client list? Why would they pay you? What travel was that?

33:21Aiden:I think this person driving in the middle of the street, I kind of came up to their window and they handed me this.

33:27Atrioc:Wait, were you selling lemonade? Become a 4 advisor today at 4.com slash lemonade. That's F-O-R-A travel.com slash lemonade. Make sure you tell them what you say. Foratravel.com slash lemonade.

33:40Aiden:Or just get out there. Just get out there and try it. Support for this show comes from Shopify. You bailed on the year of health after trying that. Let's be real. But there's one thing. It's funny. He's been holding strong on this. He has not been eating all the candy he used to, which I actually think is unfortunate. because there's actually this crazy trend right now where a lot of content creators have been coming out and they'll release their own snack or candy or something like that. And when you make a product like that, you probably wonder where you could sell it online, how you could sell it to a bunch of people online.

34:17Aiden:Shopify allows you to build out a store with their design tools, sell, track your inventory, fulfill your products. True. There's no way they have built-in marketing tools. Yeah. So if you want to get that new candy out there, Shopify can help you push it

34:33Atrioc:but don't do it because I don't want to eat more candy so use Shopify for good make a health food targeted towards streamers who are not good at sticking to diets we're going to get you to cave

34:41Aiden:turn those what ifs into cha-ching with Shopify today sign up for your$1 per month trial today at shopify.com slash lemonade go to shopify.com slash lemonade that's shopify.com slash lemonade cha-ching cha-ching okay so we have a longer conversation we want to show you guys with the CEO, Kasser, where he answers a bunch more of these topics with a lot more depth.

35:06Atrioc:But now we're going to get into it, and I really hope you guys enjoy this. Ladies and gentlemen, welcome to Lemonade Stand. Today we have a very special guest, Kasser Yunus, the CEO and founder of Applied Intuition. Did your homework, you pronounced it right? I actually had to ask right before this. I was like, wait, what's the last name? This is, you know, that's like my own mental way. You know, I said years ago, I did this like MIT interview. It was like a bunch of MIT kids who were recruiting from MIT, and they had prepared and they did this. So I said that at the beginning. For a long time, that was the only content that I had.

35:36So every time I would meet, people were particularly like, they'd watch that first three minutes of that episode. And they'd be very precise with the name. And I was like, you clearly saw this MIT talk I did.

35:48Atrioc:Thanks so much for sitting with us. We are here in one of the Applied Intuition garages with some of the vehicles behind us. and wanted to actually, why don't you kick it off? We wanted to kind of see like for the average person who might not be super aware of autonomous vehicles or not care about them. Why is something like this important on a broad level?

36:07Aiden:Yeah, could you paint a picture? If I am a, say I'm even a tech cynic, you know, how are autonomous vehicles going to affect my life in positive ways? Yeah, so I, my own view, I'm like an optimist cynic So I also have thoughts around some of these, let's say, anxieties that people have around technology. But I think I try to approach it in a slightly different way. It's not that hard to do in self-driving because the value of self-driving is just less injuries and less deaths. And I think there's nobody who's like, that's hard to debate that that's a positive thing. let me break down the self-driving ecosystem a little bit and then we'll like you know well then you guys can ask whatever you want so a bunch of folks when you say self-driving depending on if I grew up in Detroit depending on if you're in Detroit in the car business you're immediately thinking okay this is like highway lane keep cruise control adaptive cruise control you put some radars and you have a camera and the car kind of stays in the road it's not sophisticated, intelligent self-driving, but it's like advanced cruise control.

37:19If you talk to somebody in San Francisco or LA and they see Waymos all the time, they think self-driving, it's a big robo-taxi. There's no human in the vehicle at all. It's driving completely autonomously and it goes basically anywhere in the city. If you're out in Perth in Australia and you say self-driving, that's where you have tons of mining happening in Western Australia. And that's kind of the home of some of the big mining operators as they jump off from Perth to mines. Autonomous hauling in mining has been happening for conservatively a decade, but really like 15, 20 years, the first time you're seeing trucks that are moving dirt autonomously.

37:57Now, that type of dirt moving in that universe, it's quite unsophisticated. It's just like essentially it's following a route. You talk to somebody in a factory, they've seen mobile robots that follow the ground. I worked in factories 25 years ago. You'd have robots that are falling to ground. And they were essentially slightly better than... They're like forklifts, you can almost think of it, but they're not as heavy duty. So there's a huge universe of what self-driving is. So with that context, let me put a structure to it. So first we'll talk cars and then every other industry. In cars, the way to think about self-driving, just to simplify it is, is there a person sitting in the driver's seat or there's not a person sitting in the driver's seat?

38:39There's all of these like Society of Automotive Engineers levels, like L2, L2++, L3L. We don't need to get into that. It's just really, is there a human sitting in the driver's seat or is not? We can simplify that saying by saying Tesla and Waymo. And so like that's the most simple way and easy way to think about it. What Tesla really does is, and leaving the cyber cab out, this is the Tesla that you can buy. As it currently exists on the road. This is like you buy it and it drives, generally speaking, for you. And it'll go hundreds, sometimes thousands of miles without you needing to intervene.

39:12And it'll go from point to point and they'll navigate from your home all the way to your office. And maybe you won't for 10 different drives, you won't interact at all, but sometimes you will. And it might be because there's somebody's, there's an Uber drop off or there's a box in the road or somebody's pulled over, you know, there's some construction site or some, something unique is happening. The traffic lights are not working because it rained and something, you know, so there's electricity is down or something. Then the human comes in and they basically take care of the last, I'll just be exaggerating, like 5%, but really it's like the last half a percent of cases.

39:47But you still need the human there. Without a human there, the Tesla is not going to, it does not have the capability to understand what's happening in the scene and navigate around And the key point here, and like where the AI of all this is, is does the system perceive the environment correctly to as it is based on the sensors it has? A Tesla has less sensors than a Waymo. And therefore, it's almost like somebody who just sees and perceives a little less. On the other side, you have Waymo, which has many more sensors, and there's no driver in the seat, which means it has to tackle every variant of thing that can happen.

40:27including like a police officer shuts down the road. Right. And now suddenly everyone has to like back up and turn around. Like it's a completely, you know, out of, out of the, out of the blue scenario. So then that's what, so, so that's kind of the scene within self-driving. The big question that it's always asked is, well, when are we all going to have Tesla like things or when are we all getting Waymos, you know, and every car drive me to wherever I want. And that's a more complex when we talk about that. But let me talk about all the other areas of self-driving that people don't talk about a lot.

41:02Aiden:I think that people don't. Yeah, the average person is not engaging with or doesn't even really think about it.

41:07Atrioc:It's worth, like, for people listening purely audio, we're sitting in front of a tractor, in front of, like, construction vehicles, as well as, you know, there's a Porsche over there. There's a truck right next to me. There's this huge range of vehicles that you are working to make autonomous. And I think it's particularly interesting. So with that context. Yeah. Yeah. So like I'm just using the Tesla Waymo example because everyone like, you know, most people are not farmers and most people are not working, you know, as commercial truck drivers. But maybe the next kind of closest is commercial truck driving.

41:35So it's taking these, you know, large trucks that typically move goods on highways and making them autonomous. There's a bunch of companies doing that already. There's a bunch of tests happening. We, for example, run driverless trucks, automated trucks, I should say, on Japanese highways right now. They're moving cargo right now as we speak.

41:57Aiden:Is that with no one, like no one inside monitoring? There is a safety driver. There is a safety driver, yeah, yeah. And I think in, I don't think there is a driver out truck on the planet right now. Yeah. But that will happen very soon. Like that is not like, we're not talking about like five years from now or three years from now or maybe even a year from now. I mean, there are absolutely companies that are trying to get driver out as we speak. but you so in your mind as you think if you know nothing about self-driving you can understand the self-driving truck thing now let's go to something more uh let's say unique it's like a construction site then now that's you're not really driving and it's not really you know so there or maybe even like a more uh kind of a difficult thing to understand is a battlefield so how does self-driving work in that situation let me use a battlefield example because it's kind of the most almost out there, you're a warfighter and you're in a war zone and you are injured, you're incapacitated, and you need that vehicle to leave the theater.

42:57And you should be able to tell that vehicle, I need to get out of here. And that vehicle can leave and exit autonomously. So broadly speaking, you can just think about self-driving and really it's just taking intelligence and putting it into a physical moving machine. A lot of times when people say physical AI, especially in Silicon Valley, they're always talking about humanoids. And I think the way we at Applied Intuition think about physical AI is actually just taking intelligence into all these existing machines. There are north of a billion of these moving machines on the planet right now.

43:30They're just not intelligent. The human is providing the intelligence. And you mean across both cars, all of it? Cars, trucks, combines, boats. We do work in maritime. We do work on drones. You know, drones are probably a good example of where almost like because a human cannot sit in the physical thing, intelligence is really embodied in it in almost from the beginning. And as we like every quarter and every month and every year, that intelligence will get cheaper and it'll get more sophisticated. And so it can do more and more things. Right now, everything is quite simple. I think the most impressive stuff is Waymo's robo-taxis, which are like they can basically handle anything that's thrown at them within their geographic constraint.

44:14Atrioc:Okay, so kicking off on that, here at Applied to Intuition, you are not just doing what Waymo is currently doing and trying to tackle this specific type of self-driving car. Or you're not just going after the construction industry. You guys are instead building an operating system, a set of technology that can be installed in any vehicle. And again, not just any driving car, but into a tractor, into a drone, into agricultural equipment. So on a high level, could you just break down why do that? That seems to a layman like us insane. Why wouldn't you go for one of these verticals? Why wouldn't you pick one industry?

44:49Atrioc:And instead, you guys are deploying now across all these different industries. We've been able to see them in our factory tour. Why do that? What's the benefit? What's the strategy? Yeah. So I'm an engineer originally, but I also did an MBA. So I'm going to use some of my MBA. Yeah, no. I have some business administration. The jargon, yeah. Yeah, jargon, which is like, well, you're talking about a Tesla or a Waymo as an example. These are vertical companies. They're doing everything. They're making the sensors. Weymo doesn't make the cars, but Tesla makes the cars all the way down to the compute.

45:19So they're verticalized. There's advantages of being verticalized, especially when the technology, the subcomponents don't exist. We are a horizontal company. We're like a chip company. An NVIDIA or a Qualcomm, AMD, these companies are providing technology, which goes into lots and lots of devices. On the software side, we're like an Android. An Android runs across thousands of hardware devices. And so we're, for the engineers who are listening at home, we're both literally, we make an operating system. And also proverbially, we, you know, like colloquially, this technology sits on lots and lots of devices.

45:53Why is that? I think, I'll give you the, let's say the reason when we started the company, why we went down this route, and then kind of the reality of what it is today. When we started the company, we didn't quite know which version of self-driving was going to be the most consumed by the market. So when you pick, let's say, RoboTax, you're making a decision. You're locked into that. And it's a very expensive decision. I mean, Waymo has spent north of$25 billion developing that technology. To be super clear, there's not many things on the planet that companies or governments have spent$25 billion dollars on for research and development.

46:37Like, uh, I mean like the, the tallest, you know, the tallest building in the world, the, the, the Burj Khalifa in, in, I think it's Dubai that costs 1.5 billion. So like when you're, when we just throw these numbers around like 25 billion, like that is a huge amount of capital. Um, and so, you know, we didn't have uncle Google, we were starting our, you know, we're, we're like the, we're, we're the scrappy band. We're not, We're not Interscope Records in Venice. And so we got to start making hits and we got to distribute them. And the way that we started that business was applied intuition. It was like, hey, let's just build a tooling that all these different self-driving companies can use to build their systems.

47:18And what that really taught us is actually horizontal actually works well because we're not then betting on a specific form factor. We're just betting the entire industry. We'll somehow get autonomous. Maybe trucks will come faster. Maybe construction will come faster. Maybe robotex will come faster. And we didn't know. And I think that almost like in some finance terms, we kind of like isolated ourselves from that risk. But then as we got deeper in business, our company is almost 10 years old now. As we got deeper in the business and we built like operating systems and we started building autonomy directly because our customers asked for it.

47:51Then it's like, oh, actually, we can do the same thing across lots of verticals and lots of men. First, it was just automotive, just lots of manufacturers. And I was like, oh, actually, you can do the same thing in trucking and in defense, et cetera. Then something really important happened. There was a technical technology shift. So I don't want to get too much in the weeds, but there was this research paper, attention is all that you need, that Google published. The OpenAI guys saw it. That led to this LLM boom, which is like post-transformances type of architecture within AI, which allows for these modern chatbots, roughly.

48:27Well, that same technology also entered self-driving. That same AI architecture is now in self-driving. And the way that you'll hear about it now when you're listening to NVIDIA or somebody, they'll say end-to-end self-driving technology. That's what they're talking about. So self-driving before this very important moment of transformers, each of the verticals were actually quite discrete and different.

48:52Atrioc:But transformers became a broad system that applied across all. Chatbots. You remember you had chatbots that were like just for finance and just for customer service. Right. And now you have this like generalized language model, which does everything. Yeah. And that's because of the underlying technical architecture. And so today you can feed in data from mines and from, you know, from cars, human driven cars. And that actually makes this model, which runs on lots of different hardware, better. Yeah. Including models that are running on boats and models that are running on, we literally have flown F-16s autonomously, like on planes.

49:29And so there's almost like the survival instinct of a young company that was like, hey, let's sell to a bunch of players because we don't know what's going to work. We're just kind of betting on the industry and then the actual technological advantage, which we've certainly got lucky and benefit from.

49:48Aiden:I think there was an, from our tour earlier, long answers. I feel like I'm giving 10 minutes. No, I can tell you're very excited and passionate. Yeah, yeah, yeah. No, it's great. When we did the tour earlier, we had the opportunity to speak with the deputy CTO. We, I think something that was unexpected to me was this idea that all of these different verticals can be complimentary to each other in the data that they bring in and that the information that you're pulling from, uh, you know, a mine, a mine is not necessarily unhelpful to the semi-truck or the regular car that it's, it is helpful. Yeah.

50:25It's the opposite. Diverse data, uh, improves models faster. So it's like, you actually want a diversity of data. And as like, uh, let me use a more, uh, salient example, you could have, uh, uh, just collect highway data. Yeah. And does, and imagine you're a, you're a human, not just not an AI, you're a human, you only drive on highways. Well, then you get thrown into like a city, like city traffic in, you know, in Karachi, you would be overwhelmed by that. And by the way, there are studies where it shows like, if you've only been driving in America for a long time, and then you go to another country, it does take you like a day, two days, three days to adjust to the rules of the road.

51:07Atrioc:Yeah. I almost killed a guy in New Zealand. One to three days is generous. You're asking why we're doing this, you know, exhibit A.

51:24Aiden:To save Kiwis from Doug. That poor man is just living, you know, his life. He doesn't realize that Doug almost took him out.

51:32Atrioc:Surely I'm safe on this side of the road where people don't normally drive. Yeah. So I want to just quickly try to understand the infrastructure side of this, because I did not know that LLMs and transformers were so pivotal to this industry. I wouldn't have thought that, to be honest, even as somebody who's, you know, it's not LLMs or the output. Well, I'm talking about the transformer architecture. Yeah. So I guess, am I correct in understanding that you guys are sort of building this system that can ultimately run on many different vehicles and understand many different environments. And that as you pull data from all of the different environments that you're testing on, all of them are feeding and growing and maturing a single like world model.

52:11Atrioc:Is that what's fundamentally going on? Yeah. World model has a different technical definition. So I won't use that. A physical AI model, which is understanding the world around it, making decisions, and then telling the machine to act, you know, to literally like do this thing, uh, like, you know, accelerate, uh, uh, or move in a different direction. But yes, the answer is yes. Yeah.

52:34Aiden:Do you have kind of a, a large, which is like pretty amazing when you think about it.

52:38Atrioc:Unintuitive. I mean, I've, I've, you know, I've watched the three blue one Brown, if you know him. Yeah, absolutely. You know, learn the transfer. And in my brain, I don't understand the leap from that to running a construction rig, but it's amazing that it works. I mean, think about it you as a human, you drive a car. And so once you drive a car, if you sat in a truck, you don't know, maybe you don't know how to drive a manual or specifically like a large class A vehicle or something like that. But you have an understanding of this is the steering wheel, that's the gas, that's the brake, and I'm going to go on the road and in these lane lines.

53:11And so it is similar. There's a lot of like transferred learning there. Gotcha. Yeah. Well, what's really happening is the model is getting an understanding of the physics of the world. That's really what's happening. And that's why this is, you know, physical AI. So whereas in large language models, there's understanding of these concepts and how they relate to each other, which are words, you know, individual words. And how does, you know, when I say something like fall, based on the context, am I talking about a weather? Am I talking about somebody tripping? or am I talking about long-term capital management falling as a hedge fund, right?

53:50Those are three different, but the context tells you in the physical world, the environment tells you what's a drivable surface and what can I do and what do I expect the other things in the physical environment for me to do? It's actually a pretty tough problem. Like we assume all these things. We know because everything we grew up with that this table is not gonna move because we have an understanding of the properties of this table and gravity. A model has to learn all of those things. And so you want to expose it to diverse data, but it's like the classic AI. So the scaling laws really work and there's a lot of effort that goes into actually making these really intelligent systems.

54:33But we think it's obviously a really big deal. Like I think that one of the mistakes that people make is like self-driving will only be in new things. We have to buy a brand new thing that has self-driving. Actually, like you go to a mine, those machines are there for 25 years. They're being bought to run for decades in that mine. So we can't wait till the turnover so then we can retrofit those machines with hardware to make them intelligent.

55:02Aiden:Is there something, I think what I'm imagining is like the platform that you guys are developing has been deployed to like so many different types of things is there an expectation of something like uh the boats you guys have worked on that is very far away from like the end vision whereas something like self-driving for commercial view like for my car is maybe very difficult but also practically seems very far along yeah and seems close to the end vision of what that's supposed to be um so is there something where you guys feel Like you're short or missing. Yeah, I would disagree with that view where like, even if everybody had, let's say Tesla FSD in every one of their cars, just an example.

55:47Like 98 % of vehicles are not Teslas. Yeah. So they don't have that. But let's say the other 98 % got it. When you ask about what's difficult, getting those other 98 % and getting the companies and, you know, just... Not to pick on anything, but designing a car is hard enough, let alone making it an attractive car that people want to buy. But now putting real intelligence inside it in a way that's easy to use, that's going to take many, many, many, many, many years. The difficult part of all this stuff is not the technology, it's the diffusion of this technology into these machines. That's actually the hard part.

56:23Aiden:Well, you guys had a recent breakthrough on this front, right? like you have this, you've announced this partnership with Stellantis. Yeah. And your guys' platform is being directly integrated into a bunch of these car brands that I think people are familiar with. Things like Maserati. Jeep and stuff, yeah. What is that? How is that playing out over like the next few years? It's not that we just announced it just because this is topical, but of the top 20 global automakers, 18 our customers, we're in vast, vast majority of the brands you ever think of. and you look at a parking lot, we're working with them.

56:57Atrioc:Can you also dive into that? What does that mean? If you're partnered with these brands, does that mean they're just using your software? Are they adding sensors to be able to use? Like, what is the, as we sit here, what is the state of like what your tech and software is doing and how OEMs, the car manufacturers, are changing what they're doing? Yeah, let's talk about how to build a self-driving system.

57:19Aiden:If we could just give people some brief context, the tour that we did before this, We got in some cars with your guys' operating system installed. We're able to interact with these vehicles in ways that that same model, if you bought it right now at like a dealership down the street, you wouldn't be able to do. So like how close is it to this partnership making that dealership car launch with that software that I'm looking at in the garage? So that's the whole business. So you're taking – like let me – there's two different topics here. So one is just the in-cabin experience, the intelligent in-cabin experience, and the other self-driving.

57:54So they mix, and over time they'll converge. But those are two separate almost product groups, as you can talk about it. And we do both of those things. And we broadly call this bringing intelligence into the physical machine. So how you talk to the car and how the car interacts with you, and then how the car drives are two different things. To answer your question of what do we do, we provide that full spectrum. We're a technology provider. They think of us as just like a chip company, except we don't sell chips. So you're Stellantis or you're whatever car company you can think of, and you want to make your in-cabin experience better.

58:28You want it to be the best in the business, but you don't have AI engineers on your team. You don't have those skills in your team. Or you do have those skills, but it's really, really expensive. When we're a technology provider, we can split those costs across lots and lots of manufacturers. A way to think about this in the old car business is I used to work at Bosch. When Bosch, you know, Mercedes can do brakes. But why does Mercedes buy brakes from Bosch? Because Bosch takes all the globe's demand for brakes and they put it in one factory and they make all the brakes there and it actually lowers the cost.

59:01So Bosch will make brakes cheaper than even Mercedes can make it themselves just because they're doing volume. We're kind of doing the same thing on these platforms, both the in-cabin platforms and on the self-driving stuff. So then you're a Komatsu and you'd make construction equipment. You're like, actually, the in-cabin stuff we also want and the self-driving stuff we also want. Right. And then we can sell them that.

59:23Atrioc:Is it correct that maybe first step would be for some of these companies, they set up the software so that there's this in-cabin experience. And then you're also offering this essentially product for the software system, which is autonomous driving. Is that okay? Yeah, absolutely. Now, the reason it's hard to talk about generalizations is that every company has a different strategy. Some people are like, hey, actually, we want to buy your self-driving, but we want to do the in-cabin stuff ourselves. Some people are like, we'll buy your in-cabin stuff, but we'll make our own self-driving. Now, the reason I talk about we're a spectrum of solutions, we also, and where we started the company as, is we also make all the engineering tools to make these things.

1:00:02So some companies will just say, tell us all the engineering tools. We're going to make the IP ourselves. So we are, you know, we're, and that way it's like we're truly a technology provider. Gotcha. And not for the people who are like at home, like this is what like the bolts of a technology company are.

1:00:21Aiden:I think many people who are, you know, broadly cynical about the technology have these fears of the... Of AI. Of AI broadly, but even just autonomous vehicles, like the consequences of job loss in the short term, how that's going to affect things. I'm curious what you feel about that and if there's a sense of responsibility in the way that you work on things here that comes with that understanding. Yeah, a couple of areas. Absolutely, you have responsibility. You know, we're like members of society and like we're not just like, you know, we're not just like abstracted away from like this is the area, you know, place we grew up in Detroit.

1:01:06Like I care a lot about what happens there. There's two things. One is responsibility just from a safety side. You don't want to feel technology that is unsafe. So it's our first core value in the company is safety. So and then there's responsibility. What you're talking about, the downstream almost, you could say like economic impacts or like the social impacts. AI in the knowledge worker space is actually, I think, a way more difficult answer, like what happens to accountants and what happens to, you know, in the white collar fields. In the blue collar fields, long haul trucking and farming.

1:01:44I mean, the average American farmer is 58 years old. Like there's not a huge and our need for food is doubling over the next, I think it's 20 years. So like we need more food and the farmers are very old. You take mining example, 1 % of the globe's jobs, you know, workforce are in mining, but 8 % of fatalities are in mining. Mining is an extremely dangerous job. And also, by the way, it's in the middle of nowhere. I mean, the punchline being is like people are rushing into these jobs. And so how do you fix the farming problem or how do you fix the mining problem or the long haul trucking problem is another example.

1:02:23People don't want to be long haul truckers. Japan, the reason the government and the individual companies are so intent on getting driverless trucks out there and why we're doing it is they literally are, there's no drivers. The driver shortages are shutting down. They had to put caps on overtime hours because people are like working themselves to death because there are not enough truck drivers. There's not enough truck drivers. So this area of AI, which is putting intelligence on physical moving machines, there's a lot less of that heartburn and anxiety. there's the it's like ai can't get here fast enough autonomy can't get here fast enough so that's the broad point is i think it's a lot less contentious in this area but when you get to specific things like taxi drivers in san francisco and in new york where there is there they do want to do that job and now robo tax i think those are big big questions that have to be figured out i think again this is where we started the you know the conversation of i'm cynical and i'm an I'm an optimist.

1:03:20This is where like, I don't want, I'm not a, I'm certainly not a market fundamentalist, uh, like, like some folks in the Valley tend to be, or Harvard MBAs or whatever, whatever group you, you want to associate me negatively with, uh, is I do think I'll use an example of something I've seen in real time when I was at Y Combinator. So before I did this company, I was a Y Combinator where, you know, of many things opening, I would start Sam Altman was the president i was the coo um but relevantly we funded doordash uh and i remember when doordash was coming through it was called palo alto food delivery and i'm still in touch with with those guys tony and crew and they're really really smart guys just just there's a couple of four founders at the time and um and i remember thinking like well grubhub already exists and seamless already exists then we have the doordash story which is like we all use doordash now Now, from a labor perspective, what's happened?

1:04:13Actually, people have left the McDonald's and the Taco Bells and they're much more driving for DoorDash and Uber. And so when you look at some of these restaurant, like, you know, franchisees, they say, oh, we have a hard time getting people to work here. The reason is that partly it's wages. They're not paying enough. But partly it's because the job is actually worse. When you're driving for yourself, you can start and end whenever you want. You have an annoying boss. I literally worked at McDonald's. You don't have anyone tell, like I remember one of the first days I worked at McDonald's, I had my hands in my pockets and my, I won't say her name now, you know, she's a real human out there.

1:04:48Atrioc:Yeah, she watches the show, by the way. Yeah, sure, yeah. She was like, she was like, she was like, hey, get your hands out of your pockets. Anyone with hands in their pockets, they're not doing real work. And I was just like, there's no customers here. But it's like, you know, that's the kind of stuff to do it. So you drive for Uber, guess what? No one's telling you. And it's, that means that labor pool is choosing to move from McDonald's or wherever Wendy's or wherever to Uber and DoorDash. The point I'm making is I think when we funded DoorDash, you could have made this point, which is like, well, this is going to impact all these restaurants because people are going to start driving, restaurants and franchisees because people are going to start driving.

1:05:23It's like the economy kind of finds itself. The most fundamental question broadly is, will our problems be done? Like that's what to some degree capitalism is, and this to some degree what the money exchange is. And for vast majority of my career, I didn't have an assistant. And I finally begrudgingly got an assistant. I am not doing less work. I'm doing the same amount of work. I'm just doing a different type of work. And so my optimist view is, I say this as a South Asian man who has family members who drive for Uber and were taxi drivers before. Like there will be other jobs that will naturally emerge because humans always need problems solved.

1:06:07I don't know what those answers. My brain cannot compute all the different variables of where those job pools will go. That's my hope. In the other stuff, farming and agriculture, it's more pretty straightforward.

1:06:20Aiden:It seems like there's two categories of maybe on one hand, there will be this end result that's figured out. But on the other, in something like farming, there actually isn't this large displacement that you'd expect because there aren't that many people filling those jobs in the first place. But even like that concept of displacement, I'm not an economist. So, and I think I'm always like, I kind of always roll my eyes when I see like Silicon Valley guys who are like, you know, pontificating in areas way outside their area of expertise. So I want to be like super thoughtful. The stuff that I know, I know Detroit and I know the car business.

1:06:53I know, you know, Y Combinator and funding companies and starting companies and I know physical AI. So I'm putting that caveat on there. The displacement issue is if you look at jobs on a quarterly basis and you'll see the US created 50 ,000 jobs or lost 50 ,000 jobs, that's actually only the net difference. Every quarter, millions of jobs get created and destroyed. It's just millions are also, you know, so it's just the difference. And I think if I remember correctly, it's like literally like single digit millions every quarter get destroyed and created. So within that context, lots of companies are coming and going, lots of jobs are coming and going, and any individual job code is actually pretty small relative to the full pool of the labor market.

1:07:41But again, I'm a little hesitant to hear hesitation in my voice because I'm not trying to propagate like, it'll all be perfect and okay. I do think these are like new times. And if you look at the Industrial Revolution, which is a often talked about, you know, example, there's a lot of upheaval in the Industrial Revolution. I mean, the Soviet Union is created in the Industrial Revolution, which ultimately ends up being honestly a huge calamity for seven decades, eight decades, where you have, And then that's only one revolution. There are many other revolutions that happen as outputs of industrialization.

1:08:21You have the antitrust kind of revolution that happens in America in the post-industrial revolution. You have two world wars that happen post-industrial revolution. Do they all happen because of the steam engine and then ultimately the dynamo? Yes and no. Yes and no. Like kind of, or job displacement maybe. be like kind of, kind of not. It's also because, you know, people move from agriculture for societies to maybe cities. There's a lot of things changing. I don't know what all of these moving variables happen. But I think if you look at our politics, you do see something is going on. The political environment that we're in today is distinctly different.

1:09:01Politics has always been divisive. I think this is also like the, like you think like the 60s were less divisive. You think, you know, the civil war where the country literally fought each other less divisive. No, we've had very divisive periods. It doesn't mean it's the end of America. It doesn't mean it's the end of capitalism and democracy or something like that. But the point is, I think we should all be aware. Like these things are moving and we need to maybe come up with new solutions because the problems are going to be new.

1:09:29Atrioc:Are you, like when you are working on technology like this, whether it's required or voluntary, Are you working with government regulators? I mean, how much of this involves, like, so for in Japan, where there's this - It's required to be. The short answer is it's required. Yeah, yeah. Yeah. And so how much, like, I guess, what types of conversations they're having? Because presumably, people are aware of this. Governments are aware of this. And maybe in the areas like mining or trucking in certain countries where there's just a straight-up labor shortage and it is causing problems, that's probably a lot easier.

1:09:57Atrioc:But what are those conversations like? Regulations manifest themselves in different industries in very different ways. in mining, for example, as I mentioned earlier, regulations are really around safety, safety, safety, safety. It is an extremely dangerous job. People die all the time. And so all the rules, the government around the world are all based on literally 100 years of people dying. As an aside, a couple of years ago, I went to Bolivia, just a backpack. And I went to a mine in Bolivia, which was completely unregulated. It was essentially Bolivia, there's roughly like a socialist kind of view, which was these international mining companies that are exploiting workers, and the workers are just going to run the mines themselves.

1:10:42Hint, the most dangerous workplace I've ever seen, because there's nobody holding any rules to accord. So as much as governments are, you know, we, especially Americans, I think, just hate government in general, no matter what you are. I mean, or companies are hated. It was also like a common thing in America. They also do bring in rules and they bring in, you know, when a mistake gets made, rules are made. So when you think about regulations, they're always under, they always kind of look backwards. So the regulations that we see on cars and robo-taxis and trucks are all around who can drive and how can they drive and how are they?

1:11:21There's new rules and regulations being made literally for robo-taxis and some of these things. But regs always are far, far behind. The car is invented in 1886 in Germany. The stop sign, which is just the octagon red, white letters in bold, 1930. That's when it finally becomes consistent across the US. That was after the Roaring Twenties? We had the whole Roaring Twenties with no stop signs? No, it was just they were all done in different ways. Okay, okay. So there was like finally the like, and then the NITSA, the Highway Transportation Authority for America, starts in the late 60s. The car is invented in the late 1800s and it's in 1960.

1:12:04So regs just tend to be really far behind. So I think the way, you know, society is, we shouldn't expect the government to basically anticipate all the problems. I think it'll always be leaning. But then you're saying, like, are you just expecting the companies themselves to self-regulate? And that can also be really bad because the company motive is always simple, to make profits. That's the reason a company exists. The way I would think about all these things, regulations, governments, companies, labor unions, et cetera, they're just groups of people that are working on projects together. And so I think when you kind of dissolve this like, you know, NHTSA or the FAA or General Motors or whatever, and you dissolve it, it's just groups of people working together.

1:12:49Then you kind of get a more human understanding of like what it is. It's like everyone's just kind of stumbling their way through and trying to figure things out. I give a lot of credit to companies like Waymo who've done, you know, really good in having a really high safety bar and kind of almost setting an industry standard. I think that's been super positive. This episode is brought to you by Google Chrome. You think you know a browser, but Gemini and Chrome? That's new. It can help you with practically anything on the web, like restoring a vintage motorcycle from a 50-page restoration block, or finally break down that long article you've had open for weeks.

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1:14:32Aiden:I'm kind of deciding where I want to go from here. There's a lot of topics. Yeah. Yeah. I think it was in a different interview with you. I saw you talking about the way your presence abroad has grown. The amount of like even the truck that's operating in Japan right now. You have a presence in a ton of other countries around the world. One of the places you don't have a presence in, as far as I understand, is China. Yeah. And I think the world is kind of - only major market we don't play in and china and the u.s have kind of become these major players within this ai industry and i was wondering how you see like is applied intuition competing with some other major players in this physical ai space in china what's the reason for not having anything there in such a large market uh it's kind of your guys's like relationship with that country and why there's no presence.

1:15:31Yeah. Yeah. It's, uh, it's complicated. Uh, like, like everything I tend to want to get into a lot of nuance. Um, so I, but I will, because these are, these are like, they, they need nuance. Uh, uh, let me answer some of those questions in separate chunks. So first is like, should we think of China as competition or are they, are our competitors? Well, like no country, we don't really, you know, a definition of a competitor is somebody who is taking money out of the same bucket out of you. And a country doesn't take money. There are companies in China that will compete with us, but not the country.

1:16:06And so I think what you're broadly speaking is like, how does America compete with China? I think everybody competes with everybody. China competes with Korea. Korea competes with Japan. Japan competes with America. America competes with Germany. But we also all work together. And I think that's like the League of Nations, you know, kind of view of like, what are the rules and orders that we're going to figure out? China specifically is a communist country. And so they're capitalist in nature, but that means their goal of the government is very different. Their goal of their companies is very different.

1:16:36So we tend to project our values onto other people. So let's take a specific example. Like we hear Huawei. And Huawei has, you know, like for the folks at home, originally it was a networking company, but now it's like a broad technology company. Basically a consumer electronics company today. and you think, oh, well, consumer electronics, it must be like Samsung and it must be like Apple. Actually, Huawei isn't like that. Huawei is, the word Huawei is China's ambition. That's what it translates that. And I think something like one out of four employees of Huawei are members of the government.

1:17:16And they, the founder has said, our goal is not to make profits. Our goal is to grow market share and influence around the globe. Can you imagine Apple, a quarter of the, you know, Apple's name is Make America Great Again, and one out of four members are party members of a specific party, and they say, we don't care about profits. We care about America's influence. That's not a company. That is just, it's, you know, it's not. So what I'm trying to say is you should not compare Apple to Huawei because Huawei is not Apple. Apple's not these are very very different things they both make products but they're very different things and the mistake that we make in our debates and our dialogues and we say things like does applied intuition compete with a company x or does apple compete with company y's that's they're not and they're not apples and apples these are very very different things and so but

1:18:04Aiden:there's probably i imagine there are chinese companies that are approaching this problem of automation across all these verticals in a similar capacity like whether or not they're in pursuit of profit or not, they're still trying to have a presence in all the cars that you might be. Yeah, absolutely. Now, the reason that we don't play, and just to answer that question very directly is, so yeah, broadly, there are competitors. And there's no one-to-one competitor. There isn't a precise company, but there's many. And that, by the way, exists in the US and that exists in Europe as well. But in terms of specifically, why don't we have an office there and like compete, you know, we have offices basically everywhere else.

1:18:45We started in automotive. The Chinese automotive industry is extremely insular and the government puts their thumb on the scale for Chinese companies. You can't just go in on an even, you can't compete on an even playing, you know, field. And all the way to like IP is not respected. Like literally people will steal your ideas and you have no recourse. There's no legal system that the government will intervene on the behalf of applied intuition against the Chinese company and say, well, applied intuition, this was your IP and this company stole it and we're going to hold this company punishment.

1:19:21They're like, no, they're a Chinese company. They win. They always win. And so it's like we just don't want to participate in an environment where that's not going to work for us. And then broadly speaking, you know, also like I think this gets overblown is, you know, we do defense work for the U.S. and people think, oh, that's the reason you're not in China. That's probably the least reason. I mean, frankly speaking, because we're a dual use company. So all the technology we're building, it's not like we're building defense specific tech. We're building tech that is actually commercially available in lots of areas.

1:19:51And then we're putting it in defense machines, which is different than being like a defense contractor.

1:19:55Atrioc:I guess I'm curious now, something that I realize we haven't touched on. we talked about IP or strategy being, you know, leaked potentially. What is your guys' unique advantage over other companies? You know, there are many people trying this, and I think you are one of the most successful right now, and it's really actively being deployed in many industries right now. And my understanding is there are, I mean, in Japan, but even, like, there are cars here in this area that are running your autonomous software or the software system broadly. I called a car into the garage with a button. Yeah. So, I mean, we're, yeah.

1:20:29Atrioc:So, like, you know, what's the edge? Why are you guys successful? Why are you one of the leaders right now? Yeah, I'll give, I think, what the actual answer is, and then I'll talk about it from a non-technical audience perspective. The actual answer is these systems are incredibly complex to make. Like, why is Anthropic and OpenAI the only two that are like that or a handful? Because they're really hard to do what they're doing. Like, it's, this is not just, like, a business strategy. It's not just like, you know, it's just not like a distribution. The technology is actually difficult to make.

1:21:01There's, I mean, I've said before, there's less countries that have robo-taxis than have nuclear weapons. I mean, these are extremely complex technologies. Yeah, I mean, and as we just like, we just like hand wave over it, like what a Waymo does or what a Tesla does. It's incredible. I mean, it really is. And we should be very proud as people of Silicon Valley that these companies are local hometown heroes. So we do things that are really hard. The non-technical answer is the way I think we know our markets really well. I mean, I went to the General Motors Institute undergrad. I grew up in the car business.

1:21:41I worked at General Motors. I mean, I really love the car business. And I understand the car business. I think, I mean, when Peter, my co-founder, Peter Ludwig, also his father and grandfather worked in the car business for 20, 30 years. I mean, we are like car guys all the way down. We used to make jokes that we forgot more about the car business than lots of people know. And I'm not just talking about the car business from an enthusiast. I know the difference between a 991 GT3 and a 992 GT3 Touring. I can tell you the spec difference. I'm not talking about that. I'm talking about the actual industry.

1:22:13How do you make a car? How do you price it? You worked on the V6 line, right? Yeah, I did. I did. Not as labor. I worked as a manufacturing engineer. Okay. But I also did other. On the labor side, it was Buicks.

1:22:26Atrioc:Yeah. You've been a part of helping run factories that make things. It's like you don't just have a car in your garage. Yeah, yeah, yeah. I love the car business. But the point is, one of the reasons I think we're very successful is, so when you know an industry that deeply, it's like when you talk to people who've been in defense for 25, 30 years. they understand especially if they're like a warfighter and they're they were deployed they understand defense in a way that you as a layman will never understand so then if they if you can marry product or technology with their understanding the market you can do some really special things because you so i think like the non-technical point it's like we really know our business we know the markets we play in and we know how our buyers are going to buy and uh so good products and understand the market the vc answer because each of these answers it depends on who you're from.

1:23:11Yeah, changes. From a VC perspective, I think they would say we're working in a market that deeply wants our products and it's good you guys are smart and it's good that you work hard and you know the market but it's the market demands these products and it's just sucking sound and that's why we've done well. It's a mix of all of those things. It's kind of like why are certain podcasts successful? Why are they not? There's 50 reasons. It could be they started before everybody. It could be the hosts are, you know, whatever. we're famous or tall and handsome yeah tall and handsome that's why we're all sitting but you know there's like it's their guests are tall and handsome as well yeah you know what's that saying it's like failure is an orphan but success has a you know thousand mothers or something like that it's like some variance of that the you know there are many reasons that were you know you'll be successful but if we were failing you wouldn't you'd be like oh So, you know, we like crickets.

1:24:08Yeah.

1:24:08Atrioc:We've touched a little bit on your personal history. You grew up in Pakistan. You immigrated here, grew up in Detroit. And I'm kind of curious, you, as you mentioned, you worked at YC and kind of around the same time that folks like Sam Maltman went off to go create software AI focused companies. You essentially the exact same time went off and said, we want to make hardware and vehicles do amazing things. Why did you choose to do that? It seems a lot harder. Yeah. seems maybe a little more painful, a little less immediately rewarding, even though these are both obviously very hard. So yeah, how does your life experience lead up to that?

1:24:41Yeah, yeah. I think we're, to be clear, I think we're more like an AI company than, I mean, literally, if you look at how much you spend on compute and what the technical abilities is like, you know, you're building an OS.

1:24:54Atrioc:It's not like you're building cars. Yeah, exactly. So I think the real insight was, hey, partner with the manufacturers, which is very different than... The reason is, I think it's just what I know. I worked at General Motors. I worked at Bosch. I went to the General Motors Institute. It's merging, and it's super lucky, honestly, the two areas of my life, which is like Google and the software universe and the AI world with the industry that I grew up in. So it's more random than planned. I mean, I remember when Peter and I were starting the company, we were looking at ideas and like crypto and voice and all these other, I'm so happy we didn't go to that.

1:25:31You know, it's like, I think people think, you know, typically I only do these talks for founders. That's usually my audience. I really love founders. And founders are just basically a small business owner, except they do it within this concept that they can raise capital and scale because software scales really well. That's fundamentally the difference between a laundromat and, you know, somebody who runs a software company, they're still small business owners. But founders, I think you have to be very careful that you don't take away the wrong lessons. And you somehow, and the wrong lesson to take away from applied intuition is like, oh, we already like had this plan and we knew it was all like, you know, it was like this, you know, I think that's just disingenuous.

1:26:11I think what you're trying to do when you're a young company is you want to get some traction and traction to be very clear, to be very explicit as

1:26:18Atrioc:You're talking about the tractors getting crashed on the wheels. Somebody wants the thing that you want. You're doing a podcast. Somebody's actually listening. Small group people are actually listening, and then they tell other people, and then that's what it is. And our business is like, well, we know the car business, and we can make stuff for those guys from the stuff that we know, which is software and AI. And then once we got a little bit of revenue through literally revenue and momentum, because that allows us to hire more people. to be very clear, like what does, what do we do with the money that we make?

1:26:50We use it to pay salaries. It's not, it doesn't go into some banking account or dividends. It literally allows us to hire more people to pay for the lights and to pay for the food and to pay for the GPUs. And, and, uh, and it allows us to continue to work on the stuff that we like, which is this intersection of hardware and software. I think we'll call it there.

1:27:10Atrioc:Kassar, thank you so much for joining us. This is fascinating. It's a fascinating industry. I feel like we could have talked for like three more hours. Yeah, 100%. I have like 100 questions here I want to do. And we still were pretty high level. There's a lot of nuance in all of these things. The last thing I would say is like, whether you're talking, let's say you're somebody who doesn't work in AI or doesn't know about AI, but you're just constantly hearing about this thing and like, how do you relate to this? And you're trying to like maybe listen to this to learn. Just engage with the products yourself as much as you can.

1:27:42And you start seeing the limitations. And there are a bunch of YouTube videos on trying to get ChatGPT just to count to 1 ,000, and it's hopeless. So the reason I say is if you get close to the technology and you learn, I do think it lowers your anxiety a little bit. I mean, fear, the root of fear is lack of understanding. So try to understand, try to understand. That doesn't mean there are not real risks. It doesn't mean we as a society have to figure out all these complex things we talked about. But I think you're a bit more in the driver's seat, no pun intended.

1:28:14Aiden:thank you so much for joining us thank you so much yeah thanks for having me watching oh my god we flew back from san jose so fast man san jose is it's a bad place it's a bad city bro

1:28:28Atrioc:i fled i fled like i'm fleeing a country a war-torn country loved the interview loved the company tour san jose needs to go oh okay it was funny drive it dude we walked into the the airport we got off the airplane and it's just like every ai company on every billboard ever i was like i forgot what it's like to be in silicon valley this is so fun yeah it's so crazy

1:28:51Aiden:you see every big name company you could ever think of in like the 10 minute drive and then

1:28:56Atrioc:there's intel and then there's microsoft it's like just right across the street oh yeah the San Jose airport always has a gigantic wall to wall business to business AI solutions ad or something. It's like, and it's like, if you need, if you're a corporate CMO who needs to upgrade your data analytics, try do it. It's just some speaking of it changes every time it's a new company. It's clearly gone out of business. Speaking of Gumbler, when we were in the airport, like waiting to fly back, there was like a humanoid robot that you can tell you about your gate. No, it was just there to give you assistance.

1:29:30Atrioc:It was very funny. But anyway, this was cool. I mean, obviously, this is a more experimental kind of episode and format, but we had the opportunity, and we're just super interested in this whole kind of ecosystem. So I hope you've enjoyed this. Any takeaways you had from caster interview, other things before we move on to fun goofs? Yeah, did you guys see anything off the record? Did you sneak behind any corridors? Did you open any?

1:29:49Aiden:The employees were too fucking happy. It was funny because a bunch of the other employees said something really similar, and there seems to be this shared camaraderie there of I have fun at work because I get to tackle really difficult problems and I feel like I'm a part of something really unique. Like I'm part of this like second industrial revolution right now and that they all seem kind of motivated by that. And I was so genuinely surprised because I'm just like staring at Vecro to see if he's like -

1:30:20Atrioc:Yeah, there's a gun in his back.

1:30:22Aiden:Wink if they're holding you here type of thing. But they just love it.

1:30:27Atrioc:That's cool.

1:30:28Aiden:Yeah, yeah.

1:30:28Atrioc:That's pretty cool. Yeah. I mean, one of the things that was, I hadn't really thought about until this is, you know, however many billions of cars there are on the road or hundreds of millions, I forget the exact number. There's only 12 total, but yeah. There's 12, yeah, yeah. Of the 12 cars on the road. I mean, even if you have, you know, a couple companies like Breakout and like they make an autonomous vehicle, you know, if you want to get towards this world where people aren't killing each other with cars all the time, you need to come up with some systems, whether it's Applied Intuition or whoever, that can get this into a lot of vehicles simultaneously.

1:30:56Atrioc:Like everybody's trying to do this. And I thought what was interesting is like, yeah, we hear about these success cases of Tesla and Waymo, but like the average person has not gotten into a Waymo. Like this isn't propagated out in any real meaningful way yet. Oh, and then the other interesting thing while we were talking at lunch is about Tesla and Waymo, which is that Waymo from their perspective, or at least this person we spoke to, their opinion was that Waymo really, while they're the sexy kid on the block that everybody's talking about. Nobody.

1:31:25Aiden:Yeah, the sexy kid.

1:31:27Atrioc:I've never heard of the sexy kid on the block.

1:31:28Aiden:If Taylor Swift can put it in a lyric, why can't we on the lemonade stand?

1:31:32Atrioc:There's a Taylor Swift lyric about the sexy kid on the block. Keep going, Doug. So, a Waymo with huge LiDAR, big curvaceous cameras, it rolls in the screen. And above legal age. Above age. Most importantly, the Waymo is over. You should have led with that. They have been developing it for over 18 years. uh no you know what's interesting is they uh apparently waymo is like so expensive and so intense with how they run things they need to like in really really meticulously map out every single place what you want though like what okay no but sorry go ahead no i was just like isn't that from a consumer pov don't i want to be like spending the money and taking the time and being safe as possible like i am am agreeing with you that overall self-driving is safer and you wanted to get it to a lot of cars.

1:32:20Atrioc:But like putting a software in my shitty old Honda, even if it has computers in it, it makes me worry that it's not going to be as safe. That's my worry. Yes. So, okay. So an important clarification there. All of the self-driving companies are going and mapping places before they go send the car out. Tesla and Elon have like proposed the dream of, oh, you take a Tesla into wherever that has no idea where you are. But all the companies, including Tesla, will use, for example, LiDAR and these other things to go map stuff out. like a geofenced area yes the challenge with what waymo has done is they are like meticulously building a map that is like you know pixel accurate of the entire city that they operate in and then the reason that they're able to do so well is because they are assuming that their map is accurate but if the physical city changes in any way which it does and suddenly the mapping that they did six months ago in america we don't build so actually we were we've planned for this we're so china's gonna have so much new construction they'll never get it down they're falling behind but here in america we keep it same for a hundred years so as appealing as it is the idea of like we are going to do this incredibly expensive detailed mapping system if you then depend on that and you cannot be as flexible with whatever comes up and whatever changed in the city and whatever oh this block is different this tree fell down this car this scaffolding whatever is different, then that can also can cause these problems.

1:33:44Atrioc:And so the, the idea was like what Waymo is doing is extraordinarily successful in its own right, in its own way, but they are doing a system that is kind of not scalable by default. So it's like, it's like, cool, but really what you want is a way for this to be accessible to anybody as well as affordable, right? If the ideas you get, here's another way, the thing I've been thinking about Waymo is really cool, but ultimately they're replacing taxi drivers. What I would like personally is not to replace taxi drivers. What I would like is to replace the average dipshit on the road that can't drive well, right?

1:34:15Atrioc:That's who you want to replace. And so if you can get software like that, that dramatically reduces average, you know, person's driving faults, that is where the real value lies. And so having a system that can actually scale to multiple places still, you're going to have mapping in advance. But I think that's like the societal value to me, coming out of this and just a number of research over the past couple weeks i'm less convinced that automating away uber is like that valuable for society i think what's really valuable is you know those millions and millions of accidents that happen just in the u.s the tens or hundreds of thousands of people are dying every year you get the the bad drivers into systems that stop that have automatic braking and they're going to drive for them and that you know if they get in the wheel drunk it's going to drive for them or in the industries where humans just don't want to

1:35:03Aiden:work in the first place yeah or that fading away like yeah you know another thing is like with

1:35:09Atrioc:mining it's like i know mining isn't sexy but if we don't mine and do it safely and have environmental standards it goes to other countries like there's value in countries like ours being able to do mining operations or construction and not just say well everybody's retiring and nobody wants to do the work we'll just not do it like if we want to have a clean energy future you have to you have to mine and build like we need stuff and if we don't do it it's going to go to countries that are going to do it in you know polluting unsafe ways for example famously uh it's cobalt right that they mine in the congo with children it's like you don't really want that that's not great the kids are gonna be out of jobs you're taking the cobalt miners jobs doug these kids love that job.

1:35:56Atrioc:I've seen the movie Minecraft. I agree with you. It's a good point. There's a weird balance going on and essentially the competition here is with Tesla, not with Waymo, which I was a little bit surprised by. For them. Yeah, for them and for, again, for other companies that are trying to do this type of thing.

1:36:14Aiden:Alright, that's enough about automation for this week. We needed to squeeze in a few other stories. What do you guys have?

1:36:22Atrioc:we don't know yet. Welcome to the future from last week when we recorded the episode. Yeah. Now it's the present right now. Although it'll be the past when you're watching this down. It's not an ad.

1:36:37Atrioc:It gives you way. That was our field trip episode. We hope you enjoyed. Now, honestly, we've only got a little bit of time left in this episode. And so rather than like dive into half of a news thing, whatever, we're going to give you some quick bites, little quibby. Yeah. Just a little bit of teaser. maybe some stuff we're going to talk about on the Patreon if you are interested in that show and otherwise we shall see you more next week so Patreon excuse me your name is Brandon Brandon you call me Pig? I was mixing Pig, Patreon and Brandon all simultaneously

1:37:04Aiden:in 2025 Red Bull sold 14

1:37:11Atrioc:Pigman what do you got quick bites I mean look there's a lot of news this week we kind of picked unfortunate timing for the field trip so we'll have to cover some of this on the Patreon and Overflow but obviously we got you know So SpaceX hitting$1 trillion, then$2 trillion, then$3 trillion, basically. How much is Elon Musk worth right now as of this recording? I think he's like one and a half or something. He gained Warren Buffett's net worth in a day. Warren Buffett is the 10th most richest man in the world. That is insane. That is an insane stat. He has a man that's compounded an enormous amount of wealth for 50 years, and he gained it in a day.

1:37:48Atrioc:Yeah, the SpaceX whole IPO is crazy. We'll have to go into it deeper. There was the US government banning Claude in an overnight. Yes. So we'll dive into this more on the Patreon because it's just it's a long conversation with a lot of angles to go into. But we've talked a little bit about their new like Mythos model, which is the insanely powerful model, which is going to break all the cybersecurity. And then they sort of out of nowhere launched it. This would have been, what, two weeks ago. It was on June 9th. So like about a week ago. and everybody was like, oh my God, it's really powerful, but there were these restrictions on what you could use it for.

1:38:23Atrioc:And then on June 12th, this last Friday, the government sent them a letter. This is from Howard Lutnick who sent them a letter and said, you need to restrict access of your model to anybody who isn't an American national, including people in America, which is not something you can enforce, right? It's impossible. It's impossible to enforce. So this is, it's, you know, not only interesting in that Anthropic had to shut down this thing and it implies this sort of intense power of this model. It is also a whole new world we're entering of governments declaring that AIs are dangerous to be used, but nationals, it's a kind of crazy precedent.

1:39:01Atrioc:So there's a lot of weird angles in terms of what this does to Anthropic and their IPO, what the government legality is behind this, what this means for the IPO that Anthropic was trying to do, what it means for every other AI company and how they're going to release models and whether governments around the world or just going to try to shut it down. It's wild. It's real wild. Yeah, we'll do a deeper dive on that. And also next week on the main episode, we'll follow up on all these stories. And the last thing I want to say is the Iran war could possibly actually, maybe, finally, actually, maybe one time for reals, V1 final underscore underscore final be over.

1:39:37Atrioc:What's your over under? Do you think this one sticks? Genuinely, based on what I've seen, it depends on whether or not Israel does another bombing of Lebanon, which I think they already just did, but they are trying everything they can to stop the peace deal. Israel is openly at this point. It's the length of Israel's refractory period. Yeah, I guess between bombings. Right, right. It's down to that. That's what it's coming down to.

1:40:01Aiden:Israeli strike kills four in southern Lebanon amid ceasefire talks one hour ago.

1:40:05Atrioc:Isn't that insane? Isn't that insane? Oh, boy. They're not even hiding. They're just literally trying to do whatever they can to stop this peace deal from happening. I don't know. I don't know what happens, but it seems like this one is different and may in fact hold because both sides did agree and publicly announced it, which has not happened yet. We'll get into the whole details of the specifics, but that's a story we should follow up on next week. Cause a lot's going to happen between now and then. So, I mean, those are the three big, big topics. I, and, and, and, and the Nordic fun fact of the week.

1:40:34Atrioc:Thank you. We can't do an episode, even the field trip episode without a Nordic fun fact of the week. So Aiden, please close our show out with the, by the way, people are becoming fans of this. Demanding the Nordic fun fact. Some of the comments are like, I only come here for the Nordic fun fact of the week. I'm here. We have Nordic people using this as their litmus to understand what's going on. They're lost without us. They're rudderless. He's using it in his immigration application. You said you are putting in your immigration application that you do the Nordic fun fact of the week. So you should be given Swedish citizenship.

1:41:05Aiden:Yeah. Dude. I'm about to show you. Which is by the way.

1:41:11Atrioc:A top Nordic reporter shouldn't talk about the work that they're doing. the important work. That's what he's going to do. You know what? I want you to get the citizenship, and then I want Sweden to go to hell. I want you to come crawling back.

1:41:22Aiden:You know they made$15 billion in revenue last year. This is not a...

1:41:25Atrioc:I'd rather do theirs than the Red Bull segment. What's the Nordic Fund back of the week, Aiden?

1:41:30Aiden:So Iceland, considering joining the European Union and leaving their currency behind for the first... What's their currency? Is it the Kroner? The Kroner. The Kroner. The Kroner. they are taking a vote on August 29th to restart the process of negotiations to join the European Union they started this a long time ago in 2013 when an initial effort came through to consider this the first time but basically inflation is spiking in Iceland and a lot of people within iceland see european union membership as a pathway to getting this under control so by being more deeply embedded into european trade you can reduce the cost of the imports and also getting rid of the uh the cost of having a separate currency when those transactions for imports have to occur.

1:42:29Aiden:So this vote is coming up on August 29th. The reverse Brexit. The ice center. It's so weird to call it reverse Brexit because it's just, yeah, it's joining the thing. We have words for that already.

1:42:43Atrioc:You guys are wrong. There's only one word. The only way, so when you do pick up Brexit ball at the court. Yeah, everything's Brexit related. You guys cool if I reverse Brexit in your game?

1:42:54Aiden:Yes, I say that. There's actually so many verbs. Why are you doing this in a voice? That's how I talk. I would be curious if anyone from Iceland is listening, how much you feel like this is dominating the news cycle in your tiny country.

1:43:07Atrioc:All 12 of them are.

1:43:10Aiden:To be honest with you, I have not read much about this. Like, trying to read stuff about this right now, I have seen very little news outside of the fact that this vote is happening and that people are weighing the trade-offs specifically of changing the currency after maintaining their own for so long. So I think it's an interesting story to follow and we'll find out at the end of August if that process is going to start. And that's it. That's it. That's all it is this week. It's really simple and short. And you seem happy. I guess so.

1:43:44Atrioc:I mean, it's interesting if they join the EU. Has anyone joined the EU since post-COVID?

1:43:56Aiden:I mean, not the top of my head. I can't. I know that countries have tried to. It wasn't Hungary.

1:44:04Atrioc:It's like something I want. Sweden joined NATO in 2024. So Hungary joined in 2004.

1:44:10Aiden:Most recent EU country. I searched it.

1:44:13Atrioc:And then the freaking AI summary said, yes, the EU populations increased. There you go.

1:44:20Aiden:Thank you, AI. guy. So the last time was Croatia in 2013.

1:44:27Atrioc:Really? There's been a minute, dude. I think there's been attempts, right? Like there's been... You know there's a limited number of countries in Europe though, right? It was going to stop at some point. They're always finding new ones. I don't think this is a shock that it slowed down. Like if you keep digging, you'll find a country in there. Probably a fucking, with 40 population that Aiden wants to cover extensively. Sorry that I'm not avoiding. What do you want me to do?

1:44:50Aiden:Yeah, let's do the news by population. All of the stories will be about India next week. That's what you've...

1:44:57Atrioc:It would be hype. If we did the news by population, it would be hype. China, India podcast.

1:45:03Aiden:I'm actually kind of interested in that.

1:45:05Atrioc:Alright. Next week, we'll do the Mumbai Minute.

1:45:11Aiden:You get India. I'll get Indonesia. They're up there. Alright, we'll get all the big ones. Alright, well, if you want to hear more about those, you can join us for our extra episode. we do every week on Patreon, patreon.com slash lemonade stand. And we'd love to hear your thoughts about this specific episode because it was a field trip. We tried something new in both like the format, going to tour an actual facility. Let us know what you thought about it. And we will see you guys in the Patreon episode and on the main episode next week. Bye guys. Thanks everybody. Formula One, so hot right now.

1:45:44Aiden:It's like if traders in succession had a baby on wheels. Teams lying. Drivers beefing. Celebrities everywhere. And scandals. Lots of scandals. So we made a show about it, the Red Flags podcast, where we recap races and break down all the latest F1 headlines.

1:46:04Atrioc:But no nerdy tech talk. We only cover the stuff you want to hear about.

1:46:09Aiden:Yeah, and the only thing hotter than the drivers are our takes. And now we're doing it on Vox. Oh, we're so legit now. We're basically thought leaders. Ted Talk incoming And we do a podcast with Gunter Steiner called Venka Hours I still can't believe that's true Well, believe it There is so much for the beautiful Vox Media audience to enjoy So come check out the Red Flags podcast every Monday on YouTube or wherever you get your podcasts