Lemonade Stand - The Company Automating Everything | Ep. 067 Lemonade Stand 🍋

Episode Date: June 17, 2026

On this week's show... Atrioc eats some snacks, DougDoug holds a camera, and Aiden presses a button. We launched a Patreon! - https://www.patreon.com/lemonadestand for bonus episodes, discord access, a book club, and many more ways to interact with the show! Episode: 067 Recorded on: March 28th, 2026 with additional filming on June 9th and June 16th Clips Channel: https://www.youtube.com/channel/UCurXaZAZPKtl8EgH1ymuZgg Follow us TikTok - https://www.tiktok.com/@thelemonadecast Instagram - https://www.instagram.com/thelemonadecast/ Twitter - https://x.com/LemonadeCast The C-suite Aiden - https://x.com/aidencalvin Atrioc - https://x.com/Atrioc DougDoug - https://x.com/DougDougFood Edited by Aedish - https://x.com/aedishedits Thumbnail by Cheyenne DeWolf - https://x.com/cheyedewolf Produced by Perry - https://x.com/perry_jh Segments 0:00 Intro 1:30 Safety, Traffic, and Farmers 5:39 Applied Intuition 8:00 Tour - Cross Platforms 14:05 Tour - Cameras 18:15 Tour - Simplifying Cars 29:48 Truewerk Ad 31:15 Interview with the CEO 1:09:26 Fora Travel Ad 1:10:40 Shopify Ad 1:11:15 Interview Continued 1:25:38 Back in the Studio 1:33:44 Three Quick Topics 1:37:42 Nordic Fun Fact New takes on Business, Tech, and Politics. Squeezed fresh every Wednesday. #lemonadestand #dougdoug #atrioc #aiden Learn more about your ad choices. Visit podcastchoices.com/adchoices

For a long time, modern life meant remotes. A lot of remotes. Remotes for TVs and sound systems and DVD players and everything else in our lives. And then came the Harmony. The one remote to rule them all with promises to control your living room and maybe eventually everything else. This week on Version History, our chat show about old technology, we tell the whole story of Harmony and why it never really managed to control everything. That's Version History, wherever you get podcasts. What happens when political appointees push charges that career prosecutors don't support?

There is an element to this administration that wants to simply shout down reality. And I think shouting down reality and shouting down justice, shouting down fairness, should be a deeply concerning thing to all Americans. I'm Preet Barrara. And this week, longtime GOJ reporter Devlin Barrett joins me to discuss his new book, The Department of Revenge, how Trump took control of American justice. The episode is out now. Search and follow. Stay tuned with Preet wherever you get your podcast. Ladies and gentlemen, welcome to the Lemonade Stand. I have been sitting alone in this room for one week because you guys went on a trip without me.

And I've been real sad here. And it was an awesome trip. And I just been waiting for you guys to text me that you're going to show up and no one's here. So I just been working. I have a lot of slides ready to go. You waited here the whole time for us? Yes. Oh, that's awesome.

I'm just surviving on our snacks and our drinks. And apparently you guys went on a really cool trip. to discover something to do with autonomous something. I don't know deeper. I'm going to be the dumb guy and asking questions. I want you guys to explain what's going on in the world. That's right, Brandon. Do you know that cars are going to drive themselves soon?

Well, yes. That we do know. Thanks for watching. No, so Aiden and I have been really interested in autonomous vehicles, and we had a chance recently to go and visit a company called Applied Intuition that is working on making autonomous vehicles of all sorts around the world. And to give a kind of intro of maybe why you should care at all, We've talked about things like Tesla or Waymo self-driving, but broadly, autonomous...

Does these cars have wheels, or is that? No, that's the new thing. It's all flying. That's the approach of their business. They said, wheel, it's out. Well, it's one wheel like those weird skateboards. It's like super dangerous, but cool. It's a gigantic automated mining truck, but it's just a one giant wheel at the bottom.

You think the best self-driving, but you're non-negotiable. Yeah. So it's more dangerous. They're already sending it out to Casey Nicet for a review. Okay, cool. All right. So, okay. So let's kind of open with just, before we talk about this specific company, why, again,

does autonomous vehicles matter? We've talked about this a little bit in the past, but I think there's two real arguments for this. One, and the biggest is safety. There are so many people that die every year from car crashes. In 2023 in America, there's six million reported crashes. 2.4 million people are injured. It's the number one reason that people go to the emergency room.

There's 3.8 million ER visits a year. That's from the CDC. It causes hundreds of billions of dollars to economic damage. And you might be wondering, Brandon, are all these people sober when they do this? I was wondering that. Yeah. No, they are. Because we're driving L.A. and I look around.

I think these cat can be sober. 40,000 people die every year in America from car accidents. That is one person killed every 13 minutes. five people are injured every minute and 1,000 of those are children. Half of those involve people speeding or drunk driving. Well, oh, what about texting? I was driving the way to this podcast studio and I saw someone texting in their car on the highway. I know because you texted me right after and said, this is crazy.

I was texting a live stream. I was streaming at the time. This is, this is in America. The number of deaths and destruction, I mean, the reason car insurance goes up in price, the reason that, I mean, like everybody knows somebody who's been affected by this in some way. It is truly unbelievable the amount of damage and destruction that happens through people driving cars. And the reality is that a lot of the autonomous vehicle technology is proving to be much safer. Now, there's an asterisk around that. It depends on the information you get.

For example, Tesla doesn't share a lot of information, but Waymo does, much safer than an average human. And so there's, I think, a real safety imperative. And not only that, there's other industries besides, you know, a person driving a car on the freeway. There's things like construction. There's things like mining where you use huge vehicles in very dangerous environments. And those industries, mining construction are some of the most dangerous. I mean, they are incredibly dangerous. So making vehicles that are autonomous and safer. Oh, you've got it locked down?

For most people, yeah, but I'm like a little tougher. I'm built a little different. When you get on a big rig. Yeah, me or a big rig. When you're mining for cobalt. Sometimes I get two pickaxes. I get one in each hand and I spin them like this. And then like, okay.

So for me, it's not a problem. But I see what you're saying. Yeah, yeah. For the average people. I just feel like you have to add the asterisk. Otherwise it's going to feel weird. For other, for people other than Brandon, it can be extremely dangerous. And for people other than Brandon, there's also, dude, there's crazy things like, uh, there's a, a Netherlands study show that 20% of traffic is just phantom traffic. You know, where there's no actual reason for there to be traffic. It's like human beings. Yeah. Well, no, it's like, it's when somebody like starts and stops. Oh, just someone is slow. And then it causes a backup that exists on the freeway for a long period of time. And then a university of Illinois, study showed that only 5% of cars need to be automated where you're stopping a lot of that human error that, you know, where somebody like starts and stops and causes a whole problem, you need a very small number of cars to be automated before that goes away. So there's a lot of really interesting from the safety angle as well as, and we'll get into this a little bit later,

the jobs angle where obviously this is a big challenge in terms of replacing jobs in some industries. And in other industries, there aren't enough people working these things and it will help. Some crazy things I learned about in the U.S. mining industry, like 50% of people in our mining industry are going to retire in the next 10 years. In Japan, there's just a straight up shortage of truckers. They do not have enough people to work. They had to make laws to stop people from working themselves to death. And because of that, the whole infrastructure is like straining because they do not have enough people. Or in industries like farming, where at the average age of a farmer is often above 55, above 60, depending on the countries that you look at. And

And these are not jobs that a new generation of people are looking to step into. So automating them is considered a part of the solution. Yeah. So all of that leads to- Or we can make farming cool for Gen Z. Gen Z doesn't want to farm. I'm just saying we made the right ad campaign. They made farming like sick.

Which is weird. We did a sci-op called Stardue Valley and they still won't go work in Iowa. I guess if Stardivalli didn't do it. But I'm wondering if like farm talk could like get, if you make that seem cool enough. All of all of this interest in automation, Sands Farm Talk. I reached out to a friend of mine named Vickram, who you might know from Smash at Zanadu 249 grand finals. Oh, I was going to say.

Yeah. Yeah, he crushed in that grandfinal. And he, well, he did lose, but. He crushed expectations to get there. To get there. Together was a big deal. That was crazy. You don't let me finish.

I heard that he had been working in machine learning, specifically on vehicles for a long time. And I reached out last year. And I was like, Vickram, can you just tell me about your job? And it transformed into this full-on invite to come tour applied intuition and its facilities. And they paid

you a bunch of money to say exactly what you wanted to say. Yeah, but we're not supposed to talk about that on the show. To be clear, this is not sponsored. We were not paid. Going from hitting up my friend blindly about his job, we quickly learned that applied intuition is a company making basically an operating system that is meant to work across a bunch of different form factors of things, not just vehicles in the traditional sense,

but things like a robot, for instance. And this one operating system allows them to create a bunch of different apps that works across these different vehicles, including self-driving or like autonomous driving, as the main one that we talked about the most. Yeah, I think that the real big pitch here would be instead of every individual car company trying to reinvent self-driving on their own. It's like, okay, what if you have a company that can make a thing

that slots into like literally, literally any vehicle. And that's trying to do that, right? Or you have to buy later. Nvidia is sort of trying to do that, except they're really selling the hardware stack where they're saying, we use our chips and then our software.

We'll talk about it a little bit, but what Applied Intuition is trying to do is say, hey, all these vehicles that need all of these different disparate computers, it can now be under a single operating system, makes it way simpler, and then you can use our driving thing on top. Or if a 2017 beat to

Chit Honda Fit, can I install applied intuition and it can drive it? We literally ask them something like, like that. Yeah. Yeah. So what did we do with applied intuition? Well, they gave us a tour of their garage and an interview with their CEO to ask the most pressing questions that we had about how automation across the board works in this industry. In this episode, we're going to just show you everything that we got to ask and experience. Different type of episode than normal, but I hope you guys enjoy. You have maybe a company like Tesla, a company like Waymo. They've been working on automated driving

forever and they're basically working in this very like specific environment of like just driving on the street with like fixed set of rules that you're supposed to operate around and that has taken a long time with just a handful of models of vehicles basically but this is you know in the grand sense everything everywhere but how is that a practical approach to like automate everything at the same time in so many different environments. I think it's actually, that's the reason it makes it practical. So the alternative case is you just focus on one vertical in one way. And there are a lot of players that have actually come and gone. We remember the ones that are alive today. But there's many others that spent, say, billions of dollars on R&D to try to get to that point. And at some point,

the billions will stop coming. And that's why they'll stop. In our case, when we work across all these different platforms, and you continuously build the same platform, you can then build a real business around it because that means work in one area could help subsidize work in the other. And if you have enough of these verticals, that's how you create the real platform. Yeah, I think I can understand like the reason of the business having the business, like having everything consolidated. I think maybe this is a stupid question. Like all of the, the amount of data that you need to do like automated to operate a vehicle autonomously in all these different environments seems like it would be extraordinary. And I'm wondering like how do you supplement,

how do you collect that? They have different conditions as well. Imagine if you're in the field or you're in a mining site. There's actually, it can be restrictions on are there going to be particular pedestrians or other people or other vehicles or a dog? So you don't have to think about the 100% case of this is every possible potential future. Because the failure mode or the exit case is you to stop. If you're on a highway going 65, you can't just stop. Not there. Which means, though, for a lot of these off-row cases, it is a bit safer in that regard. Which means even if you don't necessarily have hundreds of millions of miles of data,

you can still make something that is relatively good, can solve the use cases that you have at hand, and then allow you to still collect data to make it better over time. I think the pitfall we don't want to be in is build a perfect solution and then go find the market for it. It's the market already exists, so it kind of like grow alongside it with it. And the technology that we have today is in a good enough spot where Kin still satisfied the needs. The other piece of it when you compare it to other AV players that exist today, it is the technology inflection point argument. Like the same thing is happening with language right now or with voice data, etc. We've found it on a model architecture that scales with data and

scales with compute. So now let's throw data and compute at the problem. And And that's how we have data collection fleets across the globe. Not just for cars, we also have data collection for off-road modalities, other on-road modalities, and that way you can build this better and better model. So to put that simply, you're finding that you can make models that apply across these different industries. Like you've been able to make it work, basically. Yeah, and it's funny. And like in the industry and now people are talking about this as like general world models or world foundation models and things like that.

We're basically doing that. We just don't say it in that way. We just tend to focus on what's the use case. And for us, that use case is making physical systems that can do actions autonomously. Yeah, so essentially, like, we use the same system across everything. So what we learn in mining today, we can apply an automotive tomorrow. So they all kind of benefit from each other. It's something that helps with automotive might definitely help with mining later on or trucking.

All of these things are like interconnected. And in the same way that multimodal like LLMs function, it's the same with our technology. The other fun fact, especially about the types of customers you work with, is most of them have actually worked across these different verticals in their history as a company. Like most automotive companies were defense companies 100 years ago. And a lot of them have industrial arms. So they actually are used to these kinds of problems and used to trying to have shared learnings.

But they haven't so far. Yeah. So in this tour, what I thought was particularly interesting is them confidently saying, oh, yeah, we can make a system that works across all vehicles. because I just would not have intuitively thought that a tractor software could work on something else. And as we talked with other folks at lunch, like outside of this recording,

it sounds like that just really is the case. Like, of the past couple years, there's been a major change in the way that companies have been sort of like designing their self-driving systems. We talked about Tesla and some major changes they had to do. And they actually apparently have kind of lost

their competitive advantage because the way that they were building their systems was actually kind of shit. And now they're reforming it over the last couple of years. years is just allowed everybody else to catch up. But very like unintuitive to me that you could sort of have the information from all of these different cars or vehicles. They're not wheels. How difficult are they be? No, they don't. There's boats. I don't know if you were listening.

They actually, that's like literally the point of what I'm saying. Plains drones, bones. So many that wheels. Toasters, you said. That's that's yeah, that's. But one thing they, it was interesting, one thing they did do like Tesla. If you guys remember one of our first episodes was this comparison between Waymo's approach to autonomous driving and Tesla's approach to autonomous driving. And we recapped how Tesla is approaching, like using cameras instead of the LiDAR sensors that Waymo has on them, right?

Yeah. And in the tour, it was shown as like their primary way forward, is something similar to Tesla, is installing cameras and not LiDAR as the main way they're going to be providing, autonomous driving for most of these vehicles. 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 it to be added on to something like this for it to be able to. 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++ based driving. Yeah. I think that vehicle actually, that has it. Oh cool. We can just go there. So what a Segway duck? What can we say?

So there's a few examples. For example, camera here, camera there, camera on the other side. I mean, is this is three cameras, right? Or do you consider this one camera? I'm just seeing lenses everywhere? Technically, three in that sense. Okay. Yeah.

Oh, 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. the de facto another camera there. Another set of cameras here. And then two back here. And so this amount of cameras would be able to run the autonomous system. That is correct. 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 like general overview of why you guys feel like you only need this many sensors and only cameras. Yeah, so that's also the big debate between L2++ systems, which think of it as driver 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 sub-modules or sub-components that constitute your driving stack and each of those would have to work sequentially. 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. Gotcha.

And we believe we can hit the same performance guarantees with the same level of redundancy without it. 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? 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. Okay. 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, etc., 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. So that's the evolution of self-driving really over the last few years. 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. The thesis is that it could evolve it to that point. Okay. 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 is 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. Yeah. Another interesting piece we see in this garage, you'll see in a second, is like the typical car has like a hundred 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 hundred different computer systems inside of a single thing. 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. 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.

Not a real live car, but almost a real life 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. Cool. 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. 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 a 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 build. 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. 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. 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.

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 voice assistant. That could be everything

from controlling HVAC in the car. 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. How are they automating the drive? No, but everything else. Right? Sorry, sorry, my mistake, 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 it 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 is the exact same approach, but now physical. So am I correct in understanding that as a pretty 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 there like numbers of how many of these like individualized systems are being pulled out in order to kind of pull it into one? There's about 150 on this example. Oh my God. 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 5 to 10 is a good example of how many different modules you can reduce this down to. So for an OEM, how hard is it to go from this very complex-looking thing to this very complex but simpler-looking thing? It's been difficult because over the last 10 years, that's all what they've been trying to do. Okay. It's how do you actually simplify the car? Because there's many advantages. One example is, weight. Like everything on there is a few additional pounds, which can matter if you're buying a car, using a car. To another example is if you have 150 different modules and say something goes wrong,

how do you actually fix it? 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. 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 home brew software on the vehicle, probably, or... Probably not a production car, yeah. Like, I don't think it's as complicated as you probably think of it as, in general, this is just a giant moving computer, 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 and 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 thought that was pretty fascinating,

seeing the difference of a car. And again, if you're on 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 Samsung and Nokia, everybody's making their own operating system for everything and integrating other apps is this major problem.

So even setting aside applied intuition, the idea of 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 engineers working on that. You know what 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 walks you through a solution that you would otherwise be looking in like an owners 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, 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 to the- You guys were getting high in the Tron car.

You didn't invite me? Yeah, we hop-boxing. You guys are hot-waxing the Tron car. with CEOs and you invite me? The important thing is that this tag can go into your toaster. Your toaster can have Tron mode, okay?

Your boat can have Tron mode. Go to Patreon if you want to see the footage of us hotboxing the tracker with the CEO. 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?

Yeah. And they were like, well, I mean, I guess, yeah. I don't think they're concerned about that like the primary market for the farmer who watches Toronto. I guess farmers don't enjoy Tronon. I guess farmers can't enjoy a good movie. Fine. Again, we... You out of touch Silicon Valley

elites. Friday night lights mode. Okay. We'll activate it. Yeah. There we go. 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 Invidia, 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. 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 that 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. Yeah, yeah. And that's, so, again, so invidia 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. 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. There's, uh, this is a way that you see autonomous vehicles

become approachable or accessible beyond just Waymore Tesla. It's this type of thing. This episode Eliminate Stand is brought to you by True Work. Well, you guys know I've been extremely satisfied with the True Work product, the pants in 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 don't like them. Because they kept sending free stuff that I would keep for myself. This is true. This is true.

And they have these awesome pants that have like these built-in, like, knee pad sort of, it's more cushioning around the knees. Sure. And I like the way they look and feel, and I've been keeping them myself. 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, you take this, he tossed me the

scrap. And I had kept them, I already worn them. I had already worn them several times. You already warned 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. You go all camping. Go hiking with them. I actually wore my on hiking too. weeks ago. They're nice. You can get 15% off your first order at truwork.com with code lemonade. That's TRUEWERK.com code lemonade.

True work built like it matters because it does. And also because A-TRAC read it from the wrong part. The work doesn't stop just because the weather changes. Upgrade to the T2 work pant and stay comfortable no matter what the day brings. The U.S. and Iran say they've agreed on terms to end the war and reopen the strait of Hormuz. You already see oil prices from a high of 120. $26 a barrel down to about $80 a barrel today. That's a lot of progress.

The war, of course, drove up the price of gas and other essentials and has led to some ugly polling for President Trump. 61% of adults polled by NPR, PBS, and Marist disapprove of his handling of the economy. His handling in a certain light makes sense. His priority was preventing Iran from getting nukes. But Trump's messaging was unusual, unusual for a president. Last month, the reporter asked Trump, to what extent was he thinking about Americans? finances when he negotiated with Iran.

I don't think about American financial situation. I don't think about anybody. What's he doing? Coming up on today, explained from Vox. Support for this show comes from Shopify. You bailed on the year of health after trying to.

Let's be real. 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. 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 could help you put it. But don't do it because I don't want to eat more candy. So you Shopify for good. Make a health food target towards streamers who are not good at sticking to diets. We're going to get you to cave. Turn those what ifs into 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. Chiching. 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. 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 Yunis, 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, you know, MIT kids that 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 I had. So every time I'd 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. 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? 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 that's, I also have, you know, 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, 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, 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. If you talk to somebody in San Francisco or L.A. 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. like, you know, autonomously. Now, that type of dirt moving in that universe, it's quite unsophisticated. It's just like essentially it's following a route.

If 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 following the ground. And they were like essentially like slightly better than, like they're like forklifts. You can almost think of it. But they're not as heavy duty. So there's a huge like, you know, universe of what self-driving is. So with that context, let me like put a structure to it.

So the 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 seat or there's not a person sitting in the driver's seat? There's all of these like society of automotive engineers levels like L2L2 plus plus L3L we don't need to get into that. It's just really is there a human sitting in the driver seat there's 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.

This is like you buy it and it drives, generally speaking, for you. And it will go hundreds, sometimes thousands of miles without you needing to intervene. And 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 something unique is happening.

The traffic lights are not working because it rained and so there's the electricity is down or something. Then the human comes in and they basically take care of the last, I'll just be exaggerate to, you know, exaggerate like 5%. But really it's like the last like half a percent of cases. But you still need the human there. Without a human there, the Tesla is not going to, you know. It does not have the capability to understand what's happening in the scene and navigate around it. And the key point here, and like where the AI of all this is, is this 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, including like a police officer shuts down the road. Right? And now suddenly everyone has to back up and turn around. Like it's a completely out of the blue scenario.

So then that's what, 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 or while getting Waymo's, you know, and every city. 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. I think that people don't, yeah, the average person is not engaging with or doesn't even really think about it. Yeah, and 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, so like we, 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. So 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. Is that with no one, like no one inside monitoring? 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's, there are absolutely companies that are trying to get driver out as we speak. 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, let's say, unique. It's like a construction site. So now that's, you're not really driving and it's not really, you know, or maybe even like a more 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 war fighter and you're in a war zone and you are injured you're incapacitated and you need that vehicle to leave the the theater and you should be able to tell that vehicle I need to get out of here and that vehicle can leave and exit autonomously so 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 had applied intuition thing 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. They'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 merits. time. 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, you know, 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 Robotaxis, which are like, they can basically hand. handle anything that's thrown at them within their geographic constraint. 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 not just, again, not just any driving car, but into a tractor, into a drone, into agriculture equipment. So on a high level, could you just break down? Why do that? That seems, you know, to a layman like us, insane. Why would you go for one of these verticals? Why wouldn't you pick one industry? And instead, you guys are deploying now across all these different industries. We've been able to see them in our, 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, the business administration.

The jargon, yeah, jargon, which is like, well, you're talking a Tesla or a Waymo as an example. These are vertical companies. They're doing everything. They're making the sensors. Waymo doesn't make the cars, but Tesla makes the cars all the way down to the compute. So 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. InVIDIA or Qualcomm, AMD, these companies are providing technology, which goes into lots and lots of devices. And the software side, we're like an Android. And Android runs across thousands of hardware devices. And so we're, for the engineers we're listening to 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. Why 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, Robotaxi, 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 technology. To be super clear, there's not many things on the planet that companies or governments have spent $25 billion. on for research and development.

Like, I mean, like the tallest, you know, the tallest building in the world, the, the, the Birch Khalifa in, I think is Dubai, that costs $1.5 billion. So, like, when you're, we just throw these numbers around like $25 billion, like that is a huge amount of capital. Yeah. And so, you know, we didn't have Uncle Google. We were starting our, you know, we're like the, we're the scrappy band. We're not, we're not funded by, you know, we're not Interscope records in, and, you know,

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's like, hey, let's just build a tooling that all these different self-driving companies can use to build their systems. And what that really taught us is actually horizontal actually works well because we're not, we're not been 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. Okay. But then as we got deeper in the business, our company's 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.

Then it's like, oh, actually, we can do the same thing across lots of verticals and lots of manufacturers. 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, etc. Then something, really important happen. There was a technical technology shift. So I don't want to get two

minutes in the weeds, but there was this research paper attention is all that you need. Yeah, Google published. The Open AI guy saw it that led to the, you know, this LLM boom, which is like post-transformists as a type of architecture within AI, which allows for these modern chatbots, roughly. Well, 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. But transformers became a broad system that applied across the whole.

Yeah, about chatbots. You remember you had chat bots that were like just for finance and just for customer service. 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, uh, you can feed in data from mines and from, you know, from cars, uh, human driven cars. And that actually makes this model, which runs on lots of different, uh, hardware better. Yeah. Including models that are running on boats and models that are running and, you know, on, on, we literally have flown F-16s autonomously. Like on planes. And so there's almost like the, you know, 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.

I think there was from our tour earlier. Sorry for getting long answers. I feel like I'm giving 10 minutes. No, you can tell you very excited and excited. Yeah, yeah, yeah. That'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 complementary to each other and the data that they bring in and that the information that you're pulling from, you know, a mine. A mine, yeah. Is not necessarily unhelpful to the semi-truck or the regular car that it is helpful. Yeah, yeah. It's the opposite. diverse data improves models faster. So it's like you actually want a diversity of data.

And as like, let me use a more salient example. You could have just collect highway data. Yeah. And imagine you're a human, not an AI, you're a human. You only drive on highways. Well, then you get thrown into like city traffic in, you know, in Karachi, you would be overwhelmed by, By that, 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.

Yeah, I almost killed a guy in New Zealand. Yeah, that's it. Yeah. Cut that out. Cut that out. Cut that out. One to three days is generous. You're responsible for me.

You're asking why we're doing this, you know, exhibit A. Yeah. 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. 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 under the transformer architecture. Yeah, yeah. Yeah, yeah. So I guess, am I correct and understanding that you, 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 world model. Is that what's fundamentally going on? Yeah, world model has a different technical definition. So I won't use that. Okay. Okay.

A physical AI model, which is understanding the world around it, making decisions, and then telling the machine to actually like do this thing, like, you know, accelerate or move in a different direction. But yes. The answer is yes. Yeah. Do you have kind of a large occasion? Which is like pretty amazing when you think about it. Unintuitive. I mean, 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, just think about it. I mean, just think about it you as a human. You drive a car. Yeah. 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 in these lane lines. And 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. It is, it is an, so whereas a, in large language models, there's understanding 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? Those are, those 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 going to 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, you want to, expose it to diverse data. But it's like the classic AI. So the scaling laws really work. And, you know, there's a lot of effort that goes into actually making these really intelligent systems. But we think it's obviously a really big deal.

Like, I think the 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. that they're being bought to run for decades in that mind. So we can't wait till the turnover. So then we can retrofit those machines with hardware to make them intelligent. 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 the boats you guys have worked on that is very far away from like the end? 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 and seems close to the end vision of what that's supposed to be. So is there something where you guys feel like you're short or missing? Yeah, I would disagree with that, that view where like even if, even if everybody had, let's say, Tesla FSD in every one of their cars, just an example, like 98% of vehicles are not Teslas. Yeah. So they don't have that.

let's say the other 98% got it. When you ask about what's difficult, getting those other 98% and getting the companies and 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 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 actually the the hard part.

Well, you guys had a recent breakthrough on this front, right? Like you have this, you've announced this partnership with Stalantis. 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?

Yeah, Stelda, we just announced it just because this is topical. But of the top 20 global automakers, 18-R 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. Can you also dive into that? What does that mean? If you're partner 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, there's a, let's talk about how to build a self-driving system. 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, 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? Like, so that's a whole business. You're taking, like, let me, let's just, there's two different topics here. So one is just the in-cabin experience, the intelligent and cabin experience, and the other self-driving. So 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, which is bring, 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, you know, 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 Stalantis or your whatever car company you can think of, and you want to make your in-cabin experience better. You 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 capacity, demand for brakes, and they put it in one factory, and they make all the brakes there and it actually lowers the cost. So Bosch will make brakes cheaper than even Mercedes can make of 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 Kamatsu 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. So 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 general relations was every company is 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 do that you, we'll buy your in-cabin stuff, but we'll buy the, you know, we'll make ourselves self-driving. Now, the reason I talk about like 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. So some companies will just say, tell us all the engineering tools, we're going to make the NIP ourselves. So we're, you know, And that way, it's like we're truly a technology provider. Gotcha.

Now for the people who are like at home, like this is what like the bolts of a technology company are. I think many people who are 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 and 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. 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 where I grew up in Detroit. Like I care a lot about what happens there. There's two things. One, there's a 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. 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 in farming. I 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. One percent 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 aren't 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? It's another example. People 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 the movement of goods. 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, you know, putting intelligence on physical moving machines, there's a lot less of that heartburn and anxiety. 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 robotics 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 optimist this is where like I don't want I'm not certainly not a market fundamentalist like like some folks in the valley tend to be your Harvard MBAs or whatever whatever group you you want to associate me negatively with is I do think, I'll use an example of something I've seen in real time. When I was at Ycom, so before I did this company, I was a Y Combinator where, you know, of many things opening Iowa started, Sam Altman was the president.

I was the C-O-O. But relevantly, we funded DoorDash. And I remember when DoorDash was coming through. It was called Palo Alto Food Delivery. And I'm still in touch with those guys, Tony and crew, and they're really, really smart guys. Just, just, there's a couple of, I was four founders at the time. 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. From a labor perspective, what's happened? Actually, 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 restaurants, like 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 you. I remember one of the first days I worked at McDonald's, I had my hands in my pockets. I won't say her name now. She's a real human out there. Yeah, she watches the show. Yeah, she was like, she was like, she was like, hey, get your hands out of your pockets. Anyone with the 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 to do. That means the labor pool is choosing to move from McDonald's or wherever Wendy's or wherever to Uber and DoorDash. The point of making is, I think in 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.

It's like the economy kind of finds itself. The more, most fundamental question broadly is, will our problems be done? Like, will, like, that's what to some degree capitalism is, and this is some degree what the money exchange is. And like, you know, 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 is a South Asian man who has family members who drive for Uber and, you know, or taxi drivers before. Like, there will be other jobs that will naturally emerge because humans always need problems solved. I don't know what those answers. My brain cannot like 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. It seems like there's two categories of maybe, like, 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. 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 super thoughtful. The stuff that I know, I know Detroit and I know the car business. I 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 U.S. 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 a 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.

But again, I'm a little hesitant. I know you hear hesitation in my voice because I'm not trying to like 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 you have, then that's only one revolution. There are many, other revolutions that happen as the outputs of industrialization. You 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. Because they all happen because of the steam engine and then ultimately the dynamo. Yes and no. Yes and no. Like kind of, but, you know, or job displacement maybe 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. Politics 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 was 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. 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 this required. Yeah. Yeah. So how much like, I guess what types of conversations that are 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. But what are these 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 as, you know, around the world are all based on literally 100 years of people dying. As an aside, a couple 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 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. hint the most dangerous place I've, the 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 it's made, rules are made.

So when you think about regulations, they're always under, they always kind of look backwards. So the regulations, So the regulations that we see on cars and robotaxies and trucks are all around who can drive and how can they drive and how are there. There'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 both 1930. That's when it finally becomes consistent across the U.S. That is after the roaring 20s? We had the whole roaring 20s. No stop signs. No, it was just they were all done in different ways. Okay, okay. So there was like finally the, they're like,

and then the highway, the NTSA, the Highway Transportation Authority for America, starts in the late 60s. The car is invented in the late 1800s, and it's in 1960s. So 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, like the NHTSA or the FAA or General Motors or whatever. And you dissolve it. It's just groups of people working together.

Then 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'd give a lot of credit to companies like Waymo who've done, you know, really good and having a really high safety bar and kind of almost setting an industry standard. I think that's been super positive. Hassam Piger has blown up in recent years. After the 2024 election, the popular leftist Twitch streamer became a go-to voice for the Democratic Party. But Pikers' glow-up has angered a section of Democrats who are growing louder in voice.

Hassan Piker is anti-American. He is bigoted. He's anti-Semitic. And he is deeply misogynistic. So in March, a Democratic group called Third Way published an op-ed in the Wall Street Journal's opinion section saying, quote, Democrats are too cozy with Hassan Piker. He is such an extremist that it will only do damage to Democrats and hurt their chances of beating right-wing populism. Now, Piker is controversial, no doubt. But is he toxic? I don't think this helps Republicans at all. I think, as a matter of fact, third-way's brand of politics has helped Republicans.

Their attitude has been to constantly concede on culture or issues to the Republican Party and never focus on economic populism. I'm a Sted Herndon, and this is America Actually. Catch us every Saturday on YouTube. or wherever you get your podcast. 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 has kind of... I think it's the 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? it kind of your guys's relationship with that country and why there's no presence. Yeah, yeah. It's complicated. Yeah, I'm sure.

Like everything, I tend to want to get into a lot of nuance. But I will because these are like they need nuance. Let me answer some of those questions in separate chunks. So first is like, should we think of China as competition

or are they 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. And so I think you're broadly speaking as like, it is America compete with China.

I think everybody competes with everybody. China competes with Korea. Korea competes with Japan. Japan competes with America. America competes Germany. But we also all work together. And I think that's like the League of Nations, you know, kind of view of like, how are we going to, 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. So 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. And 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 a one out of 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, 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 and Apple's not Huawei. 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 company X or does Apple compete with company Y's, that's, they're not apples and apples. These are very, very different things. And so. But 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 of we don't play and just answer that question very directly is, so yeah, broadly, there are competitors. And there's a one-to-one competitor. There isn't like a precise company, but there's many. And that, by the way, exists in the U.S., and that exists in Europe as well. But in terms of specifically, like, why don't we have an office there and, like, compete, you know, we have offices basically everywhere else. We start an automotive.

The Chinese automotive industry is extremely insomely. 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 field. And all the way to like IP's 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.

They're like, no, they're 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, 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. And then we're putting it in defense machines, which is different than being like a defense contractor. 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. There's every, yeah. We're running your autonomous software or the software system broadly. I called a car into the garage with the button. Yeah.

Yeah. So like, you know, what's the edge? Why are you guys successful? Why are you one of the leaders right now? Yeah, how are you? I'll give, I think, what the actual answers and then I 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 open AI the only two that are like that or a handbook? because they're really hard to do what they're doing. Like, 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. There's, I mean, I've said before, there's less countries that have robotaxies than have nuclear weapons. I mean, these are extremely complex technologies. Yeah, I mean, and we just like hand wave over it, like, like what a Waymo does or what

Tesla does, it's incredible. I mean, it's really is, and we should be like very proud as, as, you know, 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, we know our markets really well. I mean, I went to the General Motors Institute undergrad. I grew up in the car business. Yeah. I worked at General Motors. I mean, I really, I love the car business. And I understand in the car business, I think, I mean, when Peter, my co-founder, Peter Ludwig, is also his father and grandfather worked in the car business for 20 and 30 years. I mean, we are like car guys all the way down. Like, we used to make jokes that like we forgot more about the car business than

lots of people know. And I'm not just talking about the car business from a enthusiast. Like, I know, you know, the difference between 991 GT3 and a 992 GT3 touring. Like I can tell you, you know, the spec difference. I'm not talking about that. I'm talking about the actual industry. How do you make a car? How do you price it? How do you. How do you price it? How do you You worked on like the V6 line, right? Yeah, 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 Buick's. 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 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-technal 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 so good products and understand the market. The VC answer, because each of these answers, it depends on who you're going to. Yeah, 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, you know. It's like 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 host star, you know, whatever. We're famous or- Paul Anne Hanson.

Yeah, tall and handsome. That's why we're all sitting. But, you know, there's like, it's a, it's a, 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.

You know, there are many reasons that we're, you know, you would be successful. But if we were failing, you wouldn't, you'd be like, oh, you know, it would be like crickets. Yeah. 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 Altman 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 do you choose to do that? It seems a lot harder. It seems maybe a little more painful, a little less immediately rewarding, even though these are both obviously very hard.

So how does your life experience lead up to that? Yeah, yeah. 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 like how much you spend on compute and what the technical abilities is like, you know, a thousand plus engineers. It's not like you're building cars. Yeah, exactly. So our, I think the real insight was, hey, partner with the manufacturers, which is very different than the reason is, I think it's what I know. You know, 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, you know, 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, there's, I remember when Peter and I were starting the company, we were looking at ideas and like crypto and voice and all these ideas. I'm so happy we didn't. You know, it's like, I think people think, this is, you know, typically I only do these talks for founders.

That's usually my audience. I really love founders. And 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 a difference between laundromat and 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 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, I think that's just disingenuous.

I 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. It's very explicit as somebody likes. You're talking about the tractors getting traction 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, you know, once we got a little bit of, you know, 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? We use it to pay salaries. It doesn't go into some bank 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 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 Kasser, thank you so much for joining us. This is a fascinating industry. I feel like we could have talked for like three more hours.

Yeah, 100%. I have like 100 questions here. And there's a little, and we still were pretty high level. There's a lot of nuance and 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 are just constantly hearing about this thing and like how do you relate to relate to this, and you're trying to maybe listen to this to learn to just engage with the products yourself

as much as you can and you start seeing the limitations and there are a bunch of like YouTube videos on like trying to get like chat GPT just to count to a thousand and it's like it's hopeless so 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 to try to understand that doesn't mean they're not real risk 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. You know, no pun intended.

Thank you so much for joining us. Thank you so much. Thanks everybody for 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. 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. We walked into 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 funny. Yeah, it's so crazy. You see every big name company you could ever think of in like the 10 minute drive. Yeah, it's like in video. And then there's Intel and then there's Microsoft. It's like just right across the street. Oh, yeah. San Jose Airport always has a gigantic wall-to-wall business-to-business AI solutions.

ad or something. It's always like, it's like, if you need, if you're a corporate CMO who needs to upgrade your data analytics, try doble it. It's just some, it changes every time. It's a new company that's clearly gone out of business. Speaking of Gumbler, when we were in the airport, like, waiting to fly back, there's like a humanoid robot that can tell you about your gate. No, it was just there to like give you assistance. And it was like, it's very funny. But anyway, this was cool. I mean, obviously, this is a more experimental kind of episode and format, but, you know, we had the opportunity and we're just super interested in this whole kind of ecosystem. So, hope you've enjoyed this. Any takeaways you had from

Cassar interview, other things before we move on to fun goofs. Yeah, you guys see anything off the record. Anything, anything, anything, do you sneak behind any corridors? Did you open any? The employees were too, too fucking happy. It was funny because a bunch of the other employees said something really similar. And they, uh, there seems to be the 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 Vecroam to see if he's like, are they sort of gun in his back? Wink if they're holding you here type of thing. But they just love it. That's cool. 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. 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. 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 other, the other interesting thing while we were talking at lunch is about Tesla and Waymo, which is that Waymo from 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.

Yeah, the sexy kid. There's never heard of the sexy kid on the block. If Taylor Swift can put it in a lyric, why can't we on the line? Lemonade state. There's a Taylor's so flurring about the sexy kid in the wine. Keep going, keep going, Doug. So a Waymo with huge LIDAR, big curvaceous cameras. It rolls in the screen.

Above legal age. Most importantly, the Waymo is over. You should have led with that. They have been developing it for over 18 years. 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 meticulous. map out every single place. Isn't that what you want though? Like what? But sorry, go ahead.

No, I was just like, isn't that from a consumer POV, don't I want them to be like spending the money and taking the time and being safe as possible? Like I am agreeing with you that overall self-driving is safer and you wanted to get into a lot of cars. But like putting a software in my shitty old Honda, even if it has computers in it, that 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 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.

In like a geo-fenced area. Yes. The challenge of 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're we've planned for this. We're so... China's going to have so much new construction, they'll never get it down.

They're falling behind already. But here in America, we keep it stame for a hundred years. So as appealing as it is, the idea of like, we're 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. And so the idea was like what Waymo's doing is extraordinarily successful in its own way, 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's 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? 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 that are dying every year, you get 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 work in the first place. Yeah, or that. Fating away.

Like, you know, another thing is like with 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 their 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 going to be out of jobs. You're taking the cobalt miners jobs, Doug. These kids love that job. I've seen the movie Minecraft. I agree with you. I agree with you. It's a good point. Yeah, there's a weird, 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. Oh, for them. Yeah, for them. And for, again, for other companies that are trying to do this type of thing.

All right. That's enough about automation for this week. We needed to squeeze in a few other stories. What do you guys have? 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. Put it down.

It's not an ad. You're like, 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. Patreon, excuse me, your name is Brandon. Brandon. You call me Pig? I was mixing Pig, Patreon, and Brandon all simultaneously.

In 2025, Red Bulls sold 14. Pigman, what do you got? Quickbytes. I mean, look, there's a lot of news this week. We kind of picked a 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, SpaceX hitting one trillion, then two trillion, then three 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. Yeah.

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 is compounded an enormous amount of wealth for 50 years. And he gained it in a day. Yeah, the SpaceX whole IPO is crazy. We'll have to go into a deeper.

There was the U.S. government banning Claude in a overnight. Yes. So we'll dive into this more on the Patreon because it's just it's a long conversation or 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 with 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. And then on June 12th, this last Friday, the government sent them a letter. from Howard Lutnik, 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.

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. So there's a lot of weird angles in terms of what this does to Anthropic and their IPO, what the government like 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 are 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, you're just, you're going to be a, you're Iran war could possibly actually maybe finally, actually maybe one time for Reels V1 final underscore underscore final be over. 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 like they are trying everything they can to stop the peace deal. Israel is like openly at this point. It's the length of Israel's refractory period. Yeah, I guess, I guess between bombs. Right. Right. It's down to that. That's what's coming down to. So, uh, Israeli strike kills four in southern Lebanon amid ceasefire talks one hour ago. Isn't that insane? Isn't that insane? Oh boy. They're not even high. They're just literally trying to do whatever they came to stop this piece still from happening. I don't know. I don't know what

happens, but it seems like this one is different in may in fact hold because both sides did agree and publicly announce it. It just not happened yet. We'll get into the whole details of the specifics, but that's the story we should fall upon next week because a lot's going to happen between now and them. So, I mean, those are the three big, big topics. And, and, and, and the Nordic fun fact of the week. Thank you. We can't do an episode, even the field trip episode, without a Nordic fun fact for a week. So, Aidan, please close our show out with the, by the way, people are becoming fans of this.

Demanding the Nordic fun fact. They're like, I'll 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. immigration application. You said you are putting in your immigration application that you do the Nordic fun fact of the weeks. You should be given Swedish citizenship. Yeah. Dude, I'm about this.

If you're saying a top Nordic reporter shouldn't talk about the work that they're doing, the important work. That's what he's going. I 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. You know, they made $15 billion. This is not what I'd rather do that as in the Red Bull segment. What's the Nordic fund back to the week, Aden? So Iceland, considering joining the European Union and leaving their currency behind for the first. What's their currency? Is it the cronor?

The cronor. The croner. 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 costs of the imports, and also getting rid of the costs of having a separate currency when those

transactions for imports have to occur. 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. No, no, no. You guys are wrong.

It's there's only one word. The only way. So when you, when you do pick up, you do pick up Brexit ball at the, that's right. At the court. Yeah, everything's Brexit related. You guys cool by reverse Brexit in your game? 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? All 12 of them are. 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 tradeoffs specifically of changing the currency after, you know, 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, you seem happy. I guess so. I mean, it's interesting if they join the EU. I want, because has anyone, has anyone joined the EU in the past, since post-COVID? I mean, not, at the top of my head, I can't.

I know that countries have tried to. It wasn't going to NATO, right? Hungary. It's like something I want. Sweden joined NATO in 2024. So Hungary joined in 2004, most recent EU country.

Dude, I searched it, and then the freaking AI summary said, yes, the EU populations increased so people have joined. There you go. Thank you, AI.

So the last time was Croatia. in 2013. Really? They've 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? They were always finding new ones. I don't think that this is a shock, that it's slowed down. If you keep digging, you'll find a country in there. Probably a fucking... With 40 population, the Aden wants to cover extensively.

Sorry that I'm not. What do you want me to do? Yeah, let's do the news by population. All of the stories will be about India next week. That's what you've... It would be kind of right. If we did the news by population,

it would be hype. China, India podcast. I, I'm actually kind of interested in that. All right. Next week, we'll do the, the Mumbai minute.

You get India. I'll get Indonesia. They're up there. All right, we'll get all the big ones. All right. 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 would 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. 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. But no nerdy tech talk.

We only cover the stuff you want to hear about. 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.

That's true. Well, believe it. There is so much for the beautiful Vox Media audience to enjoy. So come check out the Red Flax podcast every Monday on YouTube or wherever you get your podcasts.