The Company Automating Everything | Ep. 067 Lemonade Stand 🍋

Summary

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Lemonade Stand Ep. 067: "The Company Automating Everything"

Vox Media Podcast Network
Date: June 17, 2026
Hosts: Aiden, Atrioc, DougDoug
Special Guests:

• Vikram (Applied Intuition, Machine Learning Lead)
• Kasser Yunus (CEO & Founder, Applied Intuition)

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Episode Overview

This episode dives deep into the world of automation, specifically the technology fueling autonomous vehicles and other "physical AI" systems. The hosts visit and tour Applied Intuition, a company building foundational software (an "operating system") for automation across cars, trucks, tractors, drones, boats, and even construction and mining equipment. The conversation covers the why and how of automating physical machines, technical trade-offs, the challenges of integrating such tech across industries, societal impacts—like safety and jobs—and the broader picture of where vehicle autonomy is heading.

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Key Sections & Timestamps

1. Introduction and Framing the Issue: Why Automate? (03:39–06:44)
2. Who is Applied Intuition and What Are They Building? (07:00–09:44)
3. Technical Deep Dive: How “Physical AI” Works (09:44–16:37)
4. Design, Sensors, and Unifying Car Architecture (19:20–27:45)
5. The Business Model: Horizontal Plays, Industry Impact, and Partnerships (35:57–60:04)
6. Societal Impact: Jobs, Safety, and Regulation (60:05–69:19)
7. International Dynamics: Why Not China? (75:01–79:58)
8. Competitive Edge & Company DNA (80:22–84:11)
9. Reflections, Takeaways & Off-the-Record (89:31–99:23)
10. End-of-Episode Banter & Quick News (99:24–105:19)

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1. Introduction and Framing the Issue: Why Automate?

(03:39–06:44)

• Safety Crisis:

  • Aiden lays out stark stats: 6 million crashes/year in the US; 40,000+ deaths annually; 3.8 million ER visits; half of the fatalities involve speeding or drunk driving.
  • Quote (Aiden, 03:18):

    "Over 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... Half of those involve people speeding or drunk driving."

• Systemic Inefficiency:

  • Beyond safety, “phantom traffic” and congestion are caused by human error.
  • Reference to studies (University of Illinois) showing a small percentage of automated vehicles can dramatically reduce traffic jams.

• Labor Challenges:

  • Mining: 50% of US mining workforce retiring soon.
  • Japan: Severe trucking shortage; farming workforce aging and shrinking globally.

• Why automation matters:

  • Not just about replacing drivers. Automation addresses both dangerous conditions (mining, trucking) and a shrinking labor pool.

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2. Who is Applied Intuition and What Are They Building?

(07:00–09:44)

• Not just another “self-driving car company.”
• Applied Intuition is making "an operating system for automation" across many industries.
• Rather than each company reinventing autonomy, the Applied stack lets any vehicle type (from cars to tractors to boats to mining trucks) install core automation—just add sensors as needed.

Memorable moment:

• Doug:

  "So if you have a 2017 beat to shit Honda Fit, can I install Applied Intuition and it can drive it?"
  (08:40)
  (Spoiler: It’s not quite that simple, but the idea is to enable broad retrofitting.)

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3. Technical Deep Dive: How “Physical AI” Works

(09:44–16:37)

• Why companies don’t just pick one vertical:

  • Vikram explains that focusing on many vehicle types lets the company build business resilience and use data/capabilities from one vertical to subsidize and strengthen others.

• Data and AI evolution:

  • Many AV players failed after burning billions on R&D for narrow use-cases.
  • Modern AI architectures (“transformers”/general world models) scale across environments, collecting diverse data from varied vehicles on-the-road and off-road.

• Sensor Suite: Camera-first approach

  • Most automation is using seven or so cameras (vs Waymo or legacy systems with 20+ and LIDAR).
  • Cost-effectiveness and AI progress means camera-only (with redundancies) is "good enough”—a move first proven by Tesla and now widely adopted.
  • Quote (Vikram, 16:37):

    "Only now can a true camera only system be built and scaled, which is why we ended up doing that today ... we believe we can hit the same performance guarantees, but the same level of redundancy without it."

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4. Design, Sensors, and Unifying Car Architecture

(19:20–27:45)

• Car = Rolling Network:

  • A modern car has ~150 micro-computers (“modules”) for every system (brakes, steering, dashboard, etc)—they’re all proprietary and don’t “talk well” together.
  • Applied’s vision: Reduce modules from ~150 to 5–10, making car electronics simpler, lighter, easier to maintain, and easier to update (software-style).

• Industry Analogy:

  • Aiden likens it to the cell phone industry pre-Android: every company made their own broken OS, until a shared platform enabled better apps and integration.

• Implications:

  • With a unified “vehicle OS,” both automation and new features (like AI assistants, better infotainment, even “Tron theater mode” in cars or tractors) become possible and updatable.

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5. The Business Model: Horizontal Plays, Industry Impact, and Partnerships

(35:57–60:04)

• Vertical vs. Horizontal:

  • Tesla/Waymo are “vertical”: build the full stack for their own vehicles.
  • Applied Intuition is “horizontal”: builds foundational tech for many manufacturers (like Nvidia or Android).
  • This spreads risk and enables them to “bet on the whole industry,” not just one application or form factor.

• Transformers change everything:

  • Kasser (CEO) explains how advances in AI (like transformer models behind ChatGPT) now allow systems to draw on data from all environments (cars, trucks, mines, boats, aircraft).
  • Quote (Kasser, 48:46):

    "That same technology also entered self driving... today you can feed in data from mines, from cars ... and that actually makes this model which runs on lots of different hardware better."

• Scale of Adoption:

  • Applied Intuition is already a supplier to 18 of the world’s top 20 automakers, working across both autonomous driving tech and AI-powered in-cabin experiences.

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6. Societal Impact: Jobs, Safety, and Regulation

(60:05–69:19)

• Safety:

  • Core value: automation must reduce injury and death.
  • Kasser: "There's nobody who's like, 'less injuries and less deaths isn't a positive thing.'" (36:09)

• Jobs and Economics:

  • In most sectors (trucking, mining, farming), the issue is a shrinking or aging workforce—there aren’t enough people, so automation is welcome, not feared.
  • Quote (Kasser, 62:27):

    "Japan... driver shortages are shutting down [trucking]. They had to put caps on overtime hours because people are working themselves to death because there are not enough truck drivers."

• Concerns & Labor Displacement:

  • Some anxiety remains, especially for urban taxi drivers.
  • Broadly, job destruction and creation are constants in the economy, but the scale and pace may be different as AI ramps up.
  • Kasser references DoorDash and gig work:

    "The economy finds itself... there will be other jobs that will naturally emerge because humans always need problems solved." (64:39)

• Regulation:

  • Industry and regulators (esp. in mining, trucking) are involved—regulations are crucial for safety, but always lag technological change.
  • Fun trivia: The stop sign wasn’t standardized in the US until 1930, decades after cars hit the streets.

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7. International Dynamics: Why Not China?

(75:01–79:58)

• Not in China:

  • “China is the only major market we don’t play in.”

  • Reasons:

    • Chinese automotive industry is protected/insular—foreign tech companies don’t compete on a level playing field.
    • IP theft is rampant in the sector.
    • Not primarily because of defense work, though that's a factor.

    "IP’s not respected... there's no legal system that will intervene ... they always win. And so, we just don’t want to participate in an environment where that's not going to work for us." (Kasser, 78:48)

• Competitive Landscape:

  • Chinese AV firms approach physical AI in their own unique ways, not directly comparable to US or European companies.

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8. Competitive Edge & Company DNA

(80:22–84:11)

• Why is Applied Intuition winning?

  • Technical challenge: Automation at this scale is extremely hard.
  • Deep industry knowledge: Founders and key staff have roots in the automotive world, not just software.
  • Quote (Kasser, 80:36):

    "The actual answer is these systems are incredibly complex to make… there’s less countries that have robo-taxis than have nuclear weapons. I mean these are extremely complex technologies."

• Mix of Tech and Business Acumen:

  • Successful because they marry best-in-class AI/software abilities with deep operational understanding of the automotive and industrial world.
  • (Lighthearted analogy to why certain podcasts succeed: It's a mix—timing, expertise, chemistry.)

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9. Reflections, Takeaways & Off-the-Record

(89:31–99:23)

• Cultural observations:

  • Applied Intuition’s employees seemed sincerely passionate—not the vibe of "forced fun."
  • Broad adoption of automation tech (across sectors and countries) is seen as a major lever for improving safety and efficiency worldwide.

• Waymo vs. Tesla vs. Applied:

  • Waymo is seen as the “expensive, mapped-every-corner” solution—safe but not as easily scalable.

  • The real societal value may be in automating all vehicles, not just replacing taxis/Uber 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." –Aiden (94:02)

• Mining/extraction urgency:

  • Automation in “unsexy” industries like mining and agriculture is critical for both safety and to maintain domestic industrial capacity.

• The competition:

  • Applied's real competition is Tesla (and others building horizontal, scalable solutions)—not necessarily the hyper-premium, tightly-geofenced Waymo model.

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10. End-of-Episode Banter & Quick News

(99:24–105:19)

Rapid-fire news bites:

• SpaceX's explosive valuation growth.
• Anthropic's Mythos model and US government intervention—new frontiers in AI regulation.
• Iran conflict possibly ending (but hosts are skeptical).
• “Nordic Fun Fact of the Week”: Iceland may join the EU, giving up its currency due to inflation; would be the first new EU member in over a decade.
• Playful closing: Future episodes teased based on population (India/Indonesia for the win?).

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Notable Quotes

• On World Models / AI Scaling:
  "Today you can feed in data from mines and from cars, human driven cars. And that actually makes this model which runs on lots of different hardware better."
  –Kasser Yunus (48:46)

• On Safety as the Core Value:
  "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."
  –Kasser Yunus (36:09)

• On the Economics of Labor:
  "The labor pool is choosing to move from McDonald's or wherever ... to Uber and DoorDash, the point I'm making is ... the economy kind of finds itself."
  –Kasser Yunus (64:39)

• On Why Not China:
  "IP’s not respected... there's no legal system that will intervene ... they always win. And so, we just don’t want to participate in an environment where that's not going to work for us."
  –Kasser Yunus (78:48)

• On Competitive Advantage:
  "There’s less countries that have robo-taxis than have nuclear weapons. I mean these are extremely complex technologies."
  –Kasser Yunus (80:36)

• On Societal Adoption:
  "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 ... that can get this into a lot of vehicles simultaneously."
  –Aiden (90:42)

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Takeaways for the Uninitiated

• Autonomous vehicle technology reaches far beyond robotaxis; it’s about making all physical work—especially in dangerous industries—safer and more efficient.
• The next revolution is platforms that any manufacturer can use, not just consumer car brands.
• Big advances in AI architectures (transformers) have enabled this “horizontalization”—meaning learnings and safety gains from mining, boating, etc., improve consumer vehicle models too.
• Societal resistance and fears about job loss are valid, but in trucking, mining, and farming, it’s lack of workers that’s prompting urgency for automation.
• Diffusion (getting the tech into all the old machines) is now a harder problem than inventing it—retrofits and partnerships are key.
• Regulation lags tech. Cultural, political, and economic impacts will be as disruptive as the core technology.
• Applied Intuition is a major behind-the-scenes player already working with nearly all major carmakers globally.

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This episode is a must-listen for anyone interested in how AI is moving from software—“the cloud”—to the physical world, and what that means for our future on and off the road.

Transcript

[Kasser Yunus]

How many third party vendors does your company use? 2200 thanks to AI.

[Brandon]

Someone on your team probably added three

[Kasser Yunus]

more this week and your security lead has no idea.

[Brandon]

Traditional third party risk management can't keep up.

[Kasser Yunus]

Vanta gives you continuous coverage across every vendor automatically. So you actually know what's in your

[Brandon]

stack and what to do about it.

[Kasser Yunus]

AI on risk off vanta.com TPRM.

[Aiden]

Support for this show comes from BetterHelp. Summer can feel like a sprint. Kids home trips to plan, routines flipped upside down. It's easy to slip into survival mode just trying to get through it. Then suddenly it's over and you're wishing you enjoyed the days just a little bit more.

[Kasser Yunus]

Therapy can help you slow down and

[Aiden]

actually be present for the moments that matter. With better help, you can connect with

[Kasser Yunus]

a licensed therapist from anywhere on your schedule. Don't just survive this summer thrive.

[Aiden]

Visit betterhelp.com VoxPods Ladies and gentlemen, welcome to Lemonade Stand.

[Doug]

I have been sitting alone in this room for one week because you guys went on a trip without me and

[Aiden]

I've been real sad and it was an awesome trip and I was just

[Doug]

waiting for you guys to text me that you're gonna show up and no one's here. So I just been working. I have a lot of slides ready to go.

[Brandon]

You waited here the whole time for us?

[Doug]

Snacks and our drink. 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 ask you questions. I want you guys to explain what's going on. So what the hell's going on in the world?

[Aiden]

That's right. Brandon, did you know that cars are going to drive themselves soon?

[Doug]

Well, yes, that we do know.

[Aiden]

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.

[Vikram]

And.

[Aiden]

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.

[Doug]

These cars have wheels or is that.

[Aiden]

No, that's the new thing.

[Brandon]

It's all flying. That's the approach of their business. They said wheel, it's out.

[Aiden]

Well, it's one wheel, like those weird skateboards. It's like super dangerous but cool.

[Brandon]

It's a gigantic automated mining truck, but it's just one giant wheel at the bottom.

[Doug]

I think the best self driving but you're non negotiable. And it has. So it's more dangerous.

[Brandon]

They're already sending it out to Casey Neistat for a review.

[Aiden]

Okay, cool.

[Vikram]

All right, so.

[Aiden]

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 6 million reported crashes. 2.4 million people are injured. It's the number one reason that people go to the emerg room. There's 3.8 million ER visits a year. That's from the CDC. It causes hundreds of billions of dollars economic damage. And you might be wondering, Brandon, are all these people sober when they do this?

[Doug]

I was wondering that.

[Aiden]

Yeah, no drive in L. A.

[Doug]

And I look around, I think these catgabies.

[Aiden]

Over 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.

[Doug]

What about texting? I was driving away this podcast studio. I saw someone texting in their car on the highway.

[Aiden]

I know because you texted me right after and said, this is crazy.

[Doug]

I text you a live stream. I was streaming at the time.

[Aiden]

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.

[Brandon]

Now.

[Aiden]

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. And I mean, they're incredibly dangerous. So making vehicles that are autonomous and safer. Oh, you've got it locked down for most people.

[Doug]

Yeah, but I'm like, a little tougher. I'm built a little different.

[Aiden]

When you get On a big rig at a quarry, when you're mining for

[Doug]

coaxes, I get one in each hand and I spin them like this. And then.

[Aiden]

Yeah, okay.

[Doug]

So for me, it's not a problem. But I see what you're saying.

[Brandon]

Yeah.

[Doug]

I just feel like you have to add the asterisk, otherwise it's gonna feel weird.

[Aiden]

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, there's a. A Netherlands study showed that 20% of traffic is just phantom traffic jams. You know, where there's no actual reason for there to be traffic. It's like human beings.

[Brandon]

Yeah.

[Aiden]

Well, no, it's like. It's when somebody, like, starts and stops.

[Doug]

Oh, just someone is slow.

[Aiden]

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 US 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.

[Brandon]

Or in industries like farming where the average age of a farmer is often above, you know, above 55, above 60, depending on the countries that you look at. 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.

[Aiden]

Yeah. So all of that leads to make

[Doug]

farming cool for Gen Z. Gen Z

[Aiden]

doesn't want to farm.

[Doug]

We made the right ad campaign that made farming, like, sick, which is weird.

[Aiden]

We did a psyop called Stardew Valley and they still won't go work in Iowa.

[Doug]

I guess it started making corn. But I'm wondering if, like, farm talk could, like, get. If you think that Seem cool enough.

[Brandon]

All of. All of this interest in automation sans farm doc. I. I reached out to a friend of mine named Vikram, who you might know from Smash at. Smash@xanadu. 249 grand finals, I was gonna say, right. Yeah, yeah.

[Doug]

He crushed in that grand final and he.

[Brandon]

Well, he did lose, but

[Doug]

he crushed expectations to get there.

[Brandon]

To get there together was a big deal.

[Doug]

That was crazy. You let me finish.

[Brandon]

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, vikram, can you me about your job? And it transformed into this full on invite to come tour Applied Intuition and its facilities.

[Doug]

And they paid you a bunch of money to say exactly what you wanted to say.

[Brandon]

Yeah, but we're not supposed to talk about that on the show.

[Aiden]

To be clear, this is not sponsored. We were not paid.

[Brandon]

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 V 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.

[Aiden]

Yeah, I think that the real big picture 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 any vehicle?

[Doug]

And that's the Nvidia's trying to do that.

[Brandon]

Right.

[Doug]

Or you have to buy later.

[Aiden]

Nvidia is sort of trying to do that. Except. Except they're really selling the hardware stack where they're saying we use our chips and then our software we'll talk about 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.

[Doug]

So if you have a 2017 beat to shit Honda Fit, can I install Applied Intuition and it can drive it?

[Aiden]

We literally asked them something like that.

[Brandon]

Yeah, yeah.

[Aiden]

So, Aiden, what did we do with Applied Intuition?

[Brandon]

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 we 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?

[Vikram]

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 and 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 you 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.

[Brandon]

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 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, how do you supplement, how do you collect that?

[Vikram]

They have different conditions as well.

[Kasser Yunus]

Yeah.

[Vikram]

Imagine if you're in the field or you're in a mining site, there's actually 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 cases, you just stop. If you're on a highway going 65, you can't just stop. Which means though for a lot of these off road 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, the pitfall we don't want to be in is build a perfect solution and then go find the market for it. The market already exists, kind of grow alongside with it. And the technology that we have today is in a good enough spot where it can still satisfy 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. The same thing is happening with language right now, or with voice data, etc. We've found a model architecture that scales with data and scales with compute. So now let's throw data and compute at the problem. And that's why 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.

[Aiden]

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.

[Vikram]

And it's funny and like in the industry or not, 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 we use the same system across everything.

[Aiden]

So what we learned in mining today

[Vikram]

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.

[Aiden]

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 over the past couple of 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, which has 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.

[Doug]

They all got wheels. How difficult they be.

[Aiden]

No, they don't. There's boats. I don't know if you were listening.

[Doug]

They actually.

[Aiden]

That's like literally the point of what I'm saying.

[Brandon]

Drones, boats. So many of them don't have wheels.

[Doug]

Toasters, you said.

[Aiden]

That's, that's. Yeah, that's.

[Brandon]

But one thing, 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.

[Vikram]

Yeah.

[Aiden]

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

[Vikram]

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 primarily that allows you to do L2 based driving. Yeah, I think that vehicle actually has it. Oh, we can just go there. So what a Segway duck. What can we say? So there's a few example, for example, camera here, camera there, camera on the other side.

[Aiden]

I mean, this is three cameras, right? Or do you consider this one camera? I'm just seeing lenses everywhere.

[Vikram]

Technically three in that sense. Okay, yeah. Okay. So we tend to have more than we need in the case of testing because you just have more information that you can do something interesting with in a production case. Seven is the de facto. Another camera there, another set of cameras here and then two back here.

[Aiden]

And so this amount of cameras would be able to run the autonomous system.

[Vikram]

That is correct.

[Brandon]

Man.

[Aiden]

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.

[Vikram]

Yeah, so that's also the big debate between L2 systems, which think of it as driver assist systems versus L4 systems which is full driver disengage 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, but the same level of redundancy without it.

[Aiden]

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? What changed that caused that confidence?

[Vikram]

I think a lot of it was it being proven out. Tesla's an example. The Chinese ecosystem is another example of there being many different OEMs globally that show that it's possible.

[Kasser Yunus]

Okay.

[Vikram]

And a lot of it is that effect where once one person does it, everyone else sees that that path is there and they can continue down that route. You may see for example front facing lidars, radars, et cetera, as redundancy on vehicles. All of that is still very much tbd. But you should generally expect that the form factor of sensors becomes 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.

[Aiden]

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.

[Vikram]

The thesis is that it could evolve and get to that point.

[Brandon]

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 same 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.

[Aiden]

Yeah. Then 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 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.

[Brandon]

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.

[Aiden]

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

[Vikram]

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, 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 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.

[Brandon]

Wait, so how does this. Are you going to one particular car manufacturer and getting the insides of a particular model of car, or you said earlier you're sourcing these from a ton of different places and then building it.

[Vikram]

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 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.

[Aiden]

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?

[Vikram]

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 assisted voice assistant. That could be everything from controlling H Vac in the car. So that's a big piece of it. You just simplify what you need to actually do in the vehicle.

[Brandon]

This isn't just about, I think coming into automation vehicles, people think about self driving, but this isn't just about that. This Is about controlling a bunch of different aspects of your vehicle.

[Vikram]

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.

[Brandon]

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 driving?

[Vikram]

No, but everything else. Right.

[Brandon]

Sorry sir.

[Vikram]

It does have wheels, kind of wheels. Right, but it's everything else. It could be how we control this using a mobile application. Yeah, right. Because that's now a cool thing for people to integrate into cars. It could be the infotainment screen in and of itself. And each of these need also better compute at the end of the day. So if we take what's happening in the broader LLM space as a comparable how everyone's focusing on compute data and the right architecture to do the right things there, it's the exact same approach, but now physical.

[Aiden]

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?

[Vikram]

There's about 150 on this example.

[Aiden]

Oh my God.

[Vikram]

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.

[Aiden]

So for an oem, how hard is it to go from this very complex looking thing to this very complex but simpler looking thing?

[Vikram]

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

[Aiden]

Okay.

[Vikram]

It's how do you actually simplify the car? Because there's many advantages. One example is weight everything on there is a few additional pounds which can matter if you're buying a car, 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 it fixed, 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 this stuff will be on the road for a long time. So you better make sure that you're kind of future proofing.

[Brandon]

I'm kind of wondering. The software in this case, I assume is all proprietary. Like that's the value of the company. I wouldn't be able to like launch my own homebrew software on the vehicle. Probably or probably not a production car.

[Vikram]

Yeah, like, I don't, I don't think it's as complicated as you probably think of it as. In general, this is just a giant moving computer 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.

[Aiden]

Yeah, so I thought that was pretty fascinating seeing the difference of a car. And again, if you're on, you know, an audio listener like this is just physically much less stuff. So it's pretty interesting even just setting aside this particular company, applied intuition, like, you know, across the industry, a lot of car manufacturers are dealing with this and everybody kind of has this question of. It's almost like the Android or cell phones, you know, 10, 20 years ago, where it used to be that every phone manufacturer had their own operating system. It was this awful fragmented thing where, you know, Samsung and Nokia, everybody's making their own operating system for everything and integrating other apps is this major problem. So even setting aside applied intuition, the idea of, you know, people building sort of their own solutions and that can drop into any car is something that's really, really valuable, I think rather than the idea of like literally every car company and every tractor company, every boat company is trying to do this themselves simultaneously. So I thought that was pretty interesting of how much you can simplify a modern car if you have kind of one operating system.

[Doug]

Yeah, I wouldn't trust John Deere self driving. I don't know if they got the

[Aiden]

best engineers working on that.

[Brandon]

You know, I think is really funny, is even like on the lower Scale of the apps that they were talking about that you could launch. You could have something like, you know, an assistant that walks you through a solution that you would otherwise be looking in like an owner's manual for. I was like, oh, that's like pretty, you know, that's pretty helpful. But then they had like a theater mode where they all the lights like went down in the car and then Tron came on. On the screen.

[Aiden]

Yeah, yeah.

[Brandon]

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.

[Doug]

You guys are getting high in the Tron car. You didn't invite me.

[Brandon]

Yeah, we hopped on you guys with

[Doug]

cat, with cash, crazy with CEOs.

[Aiden]

The important thing is that this tech can go into your toaster. Your toaster can have Tron mode. Okay? You're both going to have Tron mode.

[Brandon]

Go to, go to Patreon if you want to see the footage of us hot boxing 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, so. And they were like. Well, I mean, I guess. Yeah. Like I don't think they're concerned about that as like the primary market for the farmer who watches Tron.

[Doug]

I guess farmers don't enjoy Tron. I guess farmers can't enjoy a good movie.

[Aiden]

Fine, they can. We out of touch. Silicon Valley elites Friday night lights mode. Okay, we'll activate it.

[Doug]

Yeah, there we go.

[Aiden]

This is, I think this is just particularly interesting to see an example of how the whole industry could sort of make strides. And not just, oh, Tesla is doing this. Waymo is doing this. Which I think is cool. It is worth noting though, you know, there's obviously other players in this industry as well. So I think I forget if we mentioned it, but Nvidia is trying to do their own self driving stack that they can drop into a car, but that's going to be sort of different than an operating system. There are companies that are trying to make like simulation tooling, which is what applied intuition started at, actually.

[Doug]

I remember they'd have these damn self driving cars clogging up the parking lot you couldn't park. And they would have all the different models of car they were working with.

[Kasser Yunus]

Yeah.

[Doug]

And then they did these digital. They replicated the whole city of San Jose digitally. And then they were just running it a million times. That's.

[Brandon]

Yeah, yeah.

[Aiden]

And that's so again, so Nvidia is doing this like Applied Intuition is doing this. Obviously, Tesla has it for their own cars, Waymo for their own cars. So there's a lot of people who are all doing this. And then again, the question is like, how could you propagate this to many different companies? And if a company could successfully make something where it's like, hey, we can retrofit a car, you only need this number of sensors. And then you get access to a broad range of apps, or to put it differently, you have a software layer that any other company can come in and put their apps on. 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. This is a way that you see autonomous vehicles become approachable or accessible beyond just Waymo or Tesla. It's this type of thing.

[Doug]

This episode of Lemonade Stand is brought to you by True Work.

[Brandon]

Well, you guys know, I've been extremely satisfied with the True Work product. The pants and the shorts that they've given us. And you actually, you bought yours, which is.

[Aiden]

Oh, you're wearing them this time.

[Brandon]

Because they kept sending free stuff that I would keep for myself.

[Aiden]

This is true.

[Doug]

This is actually true.

[Brandon]

And they have these awesome pants that have, like, these built in, like, knee pad, sort of. It's more cushiony around the knees.

[Vikram]

Sure.

[Brandon]

And I like the way they look and feel. And I've been keeping them to myself.

[Doug]

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, like, take this. He tossed me the scrap and I had.

[Brandon]

What do you do?

[Aiden]

I already worn.

[Brandon]

I'd already worn them several times.

[Doug]

You threw me used pants.

[Brandon]

But they're good for a bunch of different weather conditions, different environments. If you're. If you're a hard worker, True Worker, great for you. If you're a podcaster, True Worker, great for you.

[Aiden]

They go out camping, go hiking with them.

[Brandon]

I actually Wore my hiking two weeks ago.

[Doug]

Yeah, they're nice. You can get 15 off your first order at true work.com with code LEMONADE. That's T R U W-E-R-K.com code LEMONADE.

[Aiden]

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[Doug]

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[Aiden]

And also because a track read it from the wrong part. The work doesn't stop just because the Weather changes. Upgrade to the T2 workman and stay comfortable no matter what the day brings.

[Brandon]

Support for the show comes from Fora.

[Doug]

Aiden, you're dumb. You're dumb as hell. And I've been wanting to say it.

[Brandon]

I'm not dumb. You know what I've been doing? I've been going out to the street corner and I've been advertising my expert ability to plan travel for people. And I've already started raking in the dough.

[Vikram]

Oh.

[Brandon]

Boots on the ground. Boots on the ground. Marketing clients. There's no better way to do it, I would argue.

[Doug]

No, there is a better way. That's why you're dumb. You could use Fora. You could. They can help you learn how to

[Brandon]

become a travel agent. What are you talking about?

[Aiden]

Not only can you give other people instructions on how to plan their trip and help them, you could even use the website to go on trips yourself. Aidan, instead of standing on a corner,

[Brandon]

I have a giant client list.

[Doug]

Who is your client list and why would they pay you? Adjust. What did you. What travel was that?

[Brandon]

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

[Aiden]

Wait, were you selling lemonade? Become a fora advisor [e-mail] lemonade. That's F O R A travel.com lemonade. Make sure you tell them we save for travel.com lemonade or just get out there.

[Brandon]

Just get out there and try it.

[Doug]

Sport for this show comes from Shopify.

[Brandon]

You bailed on the year of health after trying out let's be real. But. But there's one thing.

[Aiden]

It's funny.

[Brandon]

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 like 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.

[Aiden]

Truly, there's no way they have built in marketing tools.

[Brandon]

Yeah. So if you want to get that new candy out there, Shopify could help you push it.

[Doug]

But don't do it, because I don't

[Kasser Yunus]

want to eat more candy.

[Doug]

So you shopify for good. Make a health food targeted towards streamers who are not good at sticking.

[Brandon]

We're going to get you to cave. Turn those what ifs into Cha Ching with Shopify today. Sign up for your $1 per month trial today at shopify.com/lemonade. Go to shopify.com/lemonade that shopify.com/leMonade. Okay, so we have a longer conversation we want to show you guys with the CEO Casser, where he answers a bunch more of these topics with a lot more depth. But now we're gonna get into it and I really hope you guys enjoy this.

[Aiden]

Ladies and gentlemen, welcome to Lemonade Stand. Today we have a very special guest, Kasser Yunus, the CEO and founder of Applied Intuition.

[Kasser Yunus]

They did your homework. You pronounced it right.

[Aiden]

I actually had to ask right before this. I was like, wait, what's the last name?

[Kasser Yunus]

This is, you know, that's like my own mental way now. You know, I said years ago I did this like MIT interview. It was like a bunch of MIT kids. We were recruiting from it and they had prepared and they did this. So I said that at the beginning, for a long time that was the only like content that I had. So every time I would meet people were particularly like, they'd watch that first three minutes of that episode and they'd be very precise with the name. And I was like, you clearly saw this MIT talk. I did.

[Aiden]

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 might not care about them, why is something like this important on a broad level?

[Brandon]

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?

[Kasser Yunus]

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, 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, when you say self driving. Depending on if I grew up in Detroit, depending on if you're in Detroit in the car business, you're immediately thinking, okay, this is like a 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. 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 robotaxi. 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, to mines. Autonomous hauling in mining has been happening for conservatively a decade, but really like 15, 20 years. Your 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, it's quite unsophisticated. It's just like essentially it's following a route. You talk to somebody in a factory, they've seen mobile robots that follow the ground. I worked in factories 25 years ago. You'd have robots that are 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. Yeah, 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's 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 L2, L2 plus plus L3L, we don't need to get into that. It's just really. Is there a human sitting in the driver's seat or is not? We can simplify that saying by saying Tesla and Waymo.

[Vikram]

Right?

[Kasser Yunus]

And so like, that's the most simple way and easy way to think about it. What Tesla really does is and leaving this, you know, cyber cab out. This is the Tesla Zoonba as it currently. Yeah, currently this is like you buy it and it drives, generally speaking for you and it'll go hundreds, sometimes thousands of miles without you needing to intervene. And it'll go from point to point and they'll navigate from your home all the way to your office. And maybe you want 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 out, you know, there's some construction site or some, something unique is happening, the traffic lights are not working because it rained and some, you know, there's electricity, you know, down or something. Then the human comes in and they basically take care of the last. I'll just be exaggerate, you know, I'll 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, 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 like AI of all this is, is 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 like back up and turn around. Like it's a completely, you know, out of, out of the, out of the blue scenario. So, and that's what, so, so that's kind of the scene within self driving. The big question that is always asked is well, when are we all going to have Tesla like things or when are all getting waymos, you know, every

[Aiden]

car drive me to wherever I want

[Kasser Yunus]

and that's a, 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.

[Brandon]

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

[Aiden]

Yeah, 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.

[Kasser Yunus]

Yeah, so yeah, so like I'm just using the Tesla Waymo example because everyone, like, you know, most people are not farmers and most people are not working, you know, as commercial truck drivers, but maybe the next kind of closest is commercial truck driving. 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, on Japanese highways right now. They're moving cargo right now as we speak.

[Brandon]

Is that with no one, like, no one there is a safety driver?

[Vikram]

Yeah.

[Kasser Yunus]

Okay. 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. But so in your mind as you think, if you know nothing about self driving, you understand the self driving truck thing. Now let's go to something more, let's say unique. It's like a construction site then. Now that's you're not really driving and it's not really, you know, so there, or maybe even like a more kind of a difficult thing to understand is a battlefield. So how does self driving work in that situation? Let me use the 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 fight, are injured, you're incapacitated and you need that vehicle to leave 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, exit autonomously. So broadly speaking, you can just think about self driving and really it's just taking intelligence and putting it into a physical moving machine. A lot of times when people say physical AI, especially in Silicon Valley, they're always talking about humanoids. And I think the way we at Applied Intuition think about physical AI is actually just taking intelligence into all these existing machines. There are north of a billion of these moving machines on the planet right now. They're just not intelligent. The human is providing the intelligence.

[Aiden]

And you mean across both cars, trucks, combines, boats.

[Kasser Yunus]

We do, we do Work in maritime, we do work on drones. You know, drones are probably a good example of where almost like because a human cannot sit in the physical thing, intelligence is really, you know, embodied in it, in, 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, the most impressive stuff is Waymo's robo taxis, which are like, they can basically handle anything that's thrown at them within their geographic constraint.

[Aiden]

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 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.

[Doug]

Yeah.

[Aiden]

So on a high level, could you just break down, why do that? That seems, you know, to a layman like us, insane. Why wouldn't 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?

[Kasser Yunus]

Yeah, the. So I'm an engineer originally, but I also did an mba, so I'm going to use some of my mba. Yeah, no business administration jargon. Yeah, yeah, like. Well, you're talking about a Tesla or a Waymo as an example. These are vertical companies. They're doing everything, they're making the sensors. And Waymo doesn't make the cars, but Tesla makes the cars all the way down to the compute. Right. 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, an Nvidia or Qualcomm or amd. These companies are providing technology which goes into lots and lots of devices. On the software side, we're like an Android and Android runs across thousands of hardware devices. And so we're for and for the engineers who are listening at home, we're both literally, we make an operating system. And also proverbially we, you know, like colloquially this, 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, 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 Robo Taxi, you're, you're making a decision locked in, you're locked into that and it's a very expensive decision. I mean Waymo has spent north of $25 billion developing that technology. To be super clear, there's not many things on the planet that companies or governments have spent $25 billion on for research and development. Like, I mean like the, the tallest, you know, the tallest building in the world. The, the Burj Khalifa in I think it's dubai, that costs 1.5 billion. So like when you're, when we just throw these numbers around, like 25 billion, like that is a huge amount of capital. Yeah. And so we, you know, we didn't have Uncle Google. We were starting our, you know, we're, we're like the, we're, we're the scrappy band. We're not, we're not funded by, you know, we're not Interscope Records in, you know, in Venice. And so we gotta, we gotta start making hits and we gotta distribute them. And the way that we started that business was applied. Intuition was 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. And we're not then betting on a specific form factor. We're just betting the entire industry will somehow get autonomous. Maybe trucks will come faster, maybe construction will come faster, maybe robo taxes 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.

[Aiden]

Okay.

[Kasser Yunus]

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. Then I was like, oh, actually you can do the same thing in trucking and in defense, et cetera. Then something really important happened. There's a technical technology shift. So I don't want to get too much in the weeds. But you know, there was this Research paper attention is all that you need. That Google, that Google published, the OpenAI guy saw it, that led to the trend, you know, this LLM boom, which is like post Transformers is 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 when you're 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.

[Aiden]

So when Waymark Transformers became a broad system that applied across the world.

[Kasser Yunus]

Yeah, think about chatbots. You remember you have chatbots that were like just for finance and just for customer service and now you have this generalized language model which does everything. And that's because of the underlying technical architecture. And so today you can feed in data from mines and from, you know, from cars, human driven cars. And that actually makes this model which runs on lots of different hardware better. Yeah, including models that are running on boats and models that are running, you know, on, on. We literally have flown F16 autonomously like on planes. And so there's almost like the, 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, what's going to work. We're just kind of betting on the, on the industry and then transform than the actual technological advantage which we've certainly got lucky and benefit from.

[Brandon]

I think there was an, from our tour earlier.

[Kasser Yunus]

Sorry for getting long answers. I feel like I'm giving 10 minutes.

[Aiden]

No, you can tell you're very like excited and passionate. Yeah, yeah, yeah. No, it's great.

[Brandon]

When we did the tour earlier, we had the opportunity to speak with the deputy cto. We it. I think something that was unexpected to me was this idea that all of these different verticals can be complimentary 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 is not necessarily unhelpful to the semi truck or the regular car that it is helpful.

[Kasser Yunus]

Yeah, it's the opposite. Diverse data improves models faster. So it's like you actually want a diversity of data. And let me use a more salient example. You could have just collect highway data. Yeah. And imagine you're, you're a human, not an AI, you're a human. You only drive on highways. Well, then you get thrown into, like, a, like, city traffic in, you know, in Karachi, you would be overwhelmed by that. In. 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.

[Aiden]

Yeah, I almost killed a guy in New Zealand.

[Kasser Yunus]

Cut that out. Cut that out. Cut that out.

[Aiden]

One to three days is generous. You're responsible for that.

[Kasser Yunus]

You're asking why we're doing this. You know, exhibit A,

[Brandon]

to save kiwis from duck, specifically.

[Kasser Yunus]

That poor man is just living, you know, his life. He doesn't realize that Doug almost took him out.

[Aiden]

Surely I'm safe on this side of the road where people don't normally drive.

[Kasser Yunus]

Yeah.

[Aiden]

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

[Kasser Yunus]

not limited to the output. Well, I'm talking.

[Aiden]

Sorry, sorry. The transformer architecture.

[Kasser Yunus]

Excuse me.

[Aiden]

Yeah, yeah. So I guess am I correct in understanding that you guys are sort of building this system that can ultimately run on many different vehicles and understand many different environments, and that as you pull data from all of the different environments that you're testing on, all of them are feeding and growing and maturing. A single, like, world model. Is that what's fundamentally going on?

[Kasser Yunus]

Yeah, world model has a different technical definition, so I won't use that. A physical AI model, which is understanding the world around it, making decisions, and then telling the machine to act, you know, to literally, like, do this thing, like, you know, accelerate or move in a different direction, but yes, the answer is yes. Yeah.

[Brandon]

Do you have kind of a large.

[Kasser Yunus]

Which is, like, pretty amazing when you think about it.

[Aiden]

Unintuitive. I mean, I've watched the three blue, one brown, if you know him.

[Kasser Yunus]

Yeah, yeah, absolutely.

[Aiden]

And, you know, learn the transform. And in my brain, I don't understand the leap from that to running a construction rig, but it's amazing that it works.

[Kasser Yunus]

I mean, think about it this way. I mean, just think about it. You, as a human, you drive a car. And so once you drive a car, if you sat in a truck, you don't know, maybe you don't know how to drive a manual or specifically, like a large class A vehicle or something like that. But you have an understanding of this is the steering wheel, that's the gas, that's the brake and I'm going to go on the road in these lane lines. So it is similar. There's a lot of transferred learning there.

[Aiden]

Gotcha.

[Kasser Yunus]

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, it isn't. 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 a, we have an understanding of the properties of this table and gravity. The model has to learn all of those things. And so you want to expose it to diverse data. But it's like the classic AI. So the scaling laws really work and there's a lot of effort that goes into actually making these really intelligent systems. But we think it's obviously a really big deal. I think one of the mistakes that people make is self driving will only be in new things. We have to buy a brand new thing that has self driving. But actually like you go to a mine, those machines are there for 25 years, they're being bought to run for decades in that mine. So we can't wait till the turnover so then we can retrofit those machines with hardware to make them intelligent.

[Brandon]

Is there something, I think what I'm imagining is like the platform that you guys are developing has been deployed to 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 vision, Whereas something like self driving for commercial vehicle, like for my car is maybe very difficult, but also practically seems very far along and seems close to the end division of what that's supposed to be. So is there something where you guys feel like you're short or missing Out.

[Kasser Yunus]

Yeah, I would disagree with 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.

[Vikram]

Yeah.

[Kasser Yunus]

So they don't have that. But let's say the other 98% got it. When you ask about what's difficult getting those other 98% and getting the companies and you know, just not to pick on anything, but designing a car is hard enough, let alone, you know, making it an attractive car that, you know, people want to buy, that has. But now putting real intelligence inside it in a way that's easy to use, that's going to take many, many, many, many, many years. Like, that's 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 hard part.

[Brandon]

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

[Kasser Yunus]

Yeah.

[Brandon]

And your guys's 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.

[Vikram]

Yeah.

[Brandon]

What is that? How is that playing out over like the next few years?

[Kasser Yunus]

We just announced it just because this is topical. But of the top 20 global automakers, 18 our customers were. We're in vast, vast majority of the brands you ever think of. And you look at a parking lot where we're working with them, just can

[Aiden]

you also dive into that? What does that mean? If you're partnered with these brands, does that mean they're just using your software? Are they adding sensors to be able to like, what is the.

[Kasser Yunus]

Yeah.

[Aiden]

As we, 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?

[Kasser Yunus]

Yeah, there's, there's a, let's talk about how to build a self driving.

[Brandon]

If we, if we could just give people some brief context. The tour that we did before this, we got in some cars with your guys's operating system installed. We're able to interact with these vehicles in ways that that same model, if you bought it right now at like a dealership down the street, you wouldn't be able to do. So like how close is it to this partnership making that dealership car launch with that software that I'm looking at in the garage?

[Kasser Yunus]

Like, so that's, that's a whole business. It's a, you're taking like, let me There just, there's two different topics here. So one is just the in cabin experience, the intelligent 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 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 Stellantis 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 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 could 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 Komatsu and you'd make construction equipment. You're like, actually, the in cabin stuff we also want and the self driving stuff we also want.

[Brandon]

Right?

[Kasser Yunus]

And then we can sell them that.

[Aiden]

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?

[Kasser Yunus]

Yeah, absolutely. Now the reason it's, it's hard to talk about generalizations is every company has a different strategy.

[Aiden]

Okay.

[Kasser Yunus]

Some people are like, hey, actually we want to buy yourself driving, but we want to do the in cabin stuff ourselves.

[Vikram]

Okay?

[Kasser Yunus]

So we were like, we'll do that. You will buy your in cabin stuff, but we'll, we'll buy the, you know, we'll make our own 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 sell us all the engineering tools. We're going to make the IP ourselves. So we, you know, we're in that way it's like we're truly a technology provider.

[Aiden]

Gotcha.

[Kasser Yunus]

And now for the people who are like at home, like this is what like the bolts of a technology company are.

[Brandon]

I think, I think many people who are, you know, broadly cynical about the technology have these fears of the, of AI, of, of AI, broadly. But even just autonomous vehicles, like the, the consequences of job loss in the short term, how that's going to affect things. I'm curious what you feel about that and if there's a sense of responsibility in the way that you work on things here that comes with that understanding.

[Kasser Yunus]

Yeah, a couple of areas. Absolutely. You have responsibility. We're like members of society and like we're not like, you know, we're not just like abstracted away from like this is the area, you know, place 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. So and then there's responsibility. What you're talking about, the downstream almost you could say like economic impacts or like the social impacts. AI in the knowledge worker space is actually I think a way more difficult answer. Like what happens to accountants and what happens to you know, in the white collar fields, in the blue collar fields, long haul trucking and farming. I mean the average American farmer is 58 years old. Like there's, 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. The, the you take mining example. 1% of the globe's jobs, you know, workforce are in mining. But 8% of fatalities are in mining. Mining is an extremely dangerous job. And also by the way it's in the middle of nowhere. I mean the punchline being is like people are rushing into these jobs and so how do you fix the farming problem or how do you fix the mining problem or the long haul trucking problem is another example. 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.

[Aiden]

They had to put caps on overtime hours because people are like working themselves to death because there are not enough truck drivers.

[Kasser Yunus]

There's not enough truck drivers. So this area of AI which is putting intelligence on physical moving machines, there's a lot less of that heartburn, 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 Robotaxi, I think those are big, big questions that have to be figured out. I think, again, this is where we started the, you know, conversation of I'm cynical and I'm, I'm an optimist. This is where, like, I don't, I'm not a. I'm certainly not a market fundamentalist. Like, like some folks in the Valley tend to be or Harvard MBAs or whatever. Whatever group you, you want to associate me negatively with is. I do think I'll use an example of something I've seen in real time when I was at Y. Com. So before I did this company, I was at Y Combinator, where, you know, of many things. OpenAI was started. Sam Altman was the president, I was the CEO. 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 there's a couple of 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 Bell's and they're much more driving for DoorDash and Uber. And so when you look at some of these restaurant, restaurant, like, you know, franchisees, they say, oh, we have a hard time getting people to work here. The reason isn't partly it's wages, they're not paying enough, but partly it's because the job is actually worse. When you're driving for yourself, you can start and end whenever you want. You have an annoying boss. I literally worked at McDonald's. You don't have anyone tell. Like, I remember one of the first days I worked at McDonald's, I had my hands in my pockets and my, my boss, I won't say her name now is, you know, she's, she's a real human out there.

[Aiden]

She watches the show.

[Kasser Yunus]

Yeah, sure, yeah. She was like, she was like, she was like, she was like, hey, get your hands out of your pockets. And anyone with hands in their pockets that are not doing real work. And I was just like, there's no customers there. But it's like, you know, that's the kind of stuff you do it. So you work. We drive for Uber. Guess what? No one's telling you. And it's. That means the labor pool is choosing to move from McDonald's or wherever, Wendy's or wherever, to Uber. And DoorDash, the point I'm making is I think when we funded DoorDash, you could have made this point which is like, well, this is going to impact all these restaurants because people are going to start driving franchisees because people are going to start driving. But it's like the economy kind of finds itself. The most fundamental question broadly is will our problems be done? 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 finally begrudgingly got an assistant. I am not doing less work. I'm doing the same amount of work. I'm just doing a different type of work. And so my optimist view is, I say this as a South Asian man who has family members who drive for Uber and you know, were taxi drivers before. Like, there will be other jobs that will naturally emerge because humans always need problem solved. I don't know what those answers. My brain cannot like, compute all the different variables of where those job. Job pools will go. That's my hope. In the other stuff, farming and agriculture, it's more pretty pretty straightforward, I think.

[Brandon]

I think it seems like there's two categories of like, 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 they're all. Aren't that many people filling those jobs in the first place.

[Kasser Yunus]

But even like that concept of displacement, I'm not an economist, so. And I think I'm always like, I kind of always roll my eyes when I see like Silicon Valley guys who are like, you know, pontificating in areas way outside their area of expertise. So I want to be like super thoughtful. The stuff that I know, I know Detroit and I know the car business, I know you know, Y Combinator and funding companies and starting companies and I know physical AI. So I'm putting that caveat on there. The displacement issue is if you look at jobs on a quarterly basis and you'll see the US created 50,000 jobs or lost 50,000 jobs, that's actually only the net difference. Every quarter, millions of jobs get created and destroyed. It's just millions are also. 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. You hear hesitation in my voice because I'm not trying to propagate, like it'll all be perfect and okay. I do think these are like new times. And if you look at the Industrial revolution, which is a often talked about 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 industrial outputs of industrialization. You have the antitrust kind of revolution that happens in America in the industrial post industrial revolution. You have two world wars that happen post industrial revolution. Do they all happen because of the steam engine and then ultimately the dynamo? Yes and no. Yes and no. Like kind of, 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. You think the 60s were less divisive? You think the Civil War where the country literally fought each other was less divisive? No, we've had very divisive periods. It doesn't mean it's the end of, 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.

[Aiden]

Are you like when you are working on technology like this, whether it's required or voluntary, you working with government regulators? I mean how much of this involves like so for. In Japan where there's this.

[Kasser Yunus]

It's required to be the answers is required. Yeah, yeah, yeah.

[Aiden]

And so how much like I guess what types of conversations they're having because presumably people are aware of this, governments are aware of this. And maybe in the areas like mining or trucking in certain countries where there's just a straight up labor shortage and it is causing problems, that's probably a lot easier.

[Kasser Yunus]

But yeah, regulations manifest themselves in different industries in very different ways. In, 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 of years ago I went to Bolivia, just a backpack and I went to a mine in Bolivia which was completely unregulated. It was the essentially Bolivia. There's roughly a, like a socialist kind of view which was a. These international mining companies are exploiting workers and the workers are just going to run the mines themselves. Hint, the most dangerous place I've workplace I've ever seen because there's nobody holding any rules to accord. So as much as governments are, you know, we especially Americans, I think just hate government in general. No matter what you are, I mean, or companies are hated. It was also like a common thing in America. They also do bring in rules and they bring in, you know, when a mistake gets made, rules are made. So when you think about regulations, they're always under, they always kind of look backwards. So the regulations that we see on cars and robo taxis and trucks are all around who can drive and how can they drive and how, how are they. 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 the, in 1886 in Germany, the stop sign, which is just the octagon, red white letters in bold. 1930. That's when it finally becomes consistent across the US after the roaring twenties.

[Aiden]

We had the whole Roaring Twenties.

[Kasser Yunus]

No, it was just they were all done in different ways.

[Aiden]

Okay.

[Kasser Yunus]

So there was like finally. And then the nhtsa, 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, etc. They're just groups of people that are working on projects together. And so I think you, when you kind of dissolve this like, you know, NHTSA or the FAA or General Motors or whatever, and you dissolve, 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 really good in having a really high safety bar and kind of almost setting an industry standard. I think that's been super positive.

[Brandon]

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[Aiden]

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[Kasser Yunus]

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[Kasser Yunus]

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[Kasser Yunus]

Various.

[Aiden]

18.

[Brandon]

I'm kind of deciding where I want to go from here.

[Kasser Yunus]

There's a lot of topics.

[Brandon]

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. 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.

[Kasser Yunus]

Yeah.

[Brandon]

And I think the world is.

[Kasser Yunus]

I think it's the only major market we don't play in.

[Brandon]

And China in the US 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? Kind of your guys's, like, relationship with that country and why there's no presence.

[Kasser Yunus]

Yeah, yeah. It's. It's complicated, I'm sure. Like, like everything. I tend to want to get into a lot of nuance, so. But I will, because these are. These are like, they need nuance. Let me answer 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. You know, the 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. Is there companies in China that will compete with us, but not the country? And so I think what you're broadly speaking as, like, hey, does America compete with China? I think everybody competes with everybody. China competes with Korea, Korea competes with Japan, Japan competes with America, America competes with Germany. But we also all work together. And I think that's like the League of Nations, you know, kind of view of like, how are we going. 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 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 at. And I think something like one out

[Vikram]

of four

[Kasser Yunus]

employees of Huawei are members of the government. And they. The founder has said, our goal is not to make profits. Our goal is to grow market share and influence around the globe. Can you imagine Apple? A quarter of the Apple's name is make America great again. And 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. It's, you know, it's not. So what I'm trying to say is you should not compare Apple to Huawei because Huawei is not Apple. Apple's not 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 Wise? That's. They're not. And they're not apples and apples. These are very, very different things.

[Brandon]

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.

[Kasser Yunus]

Yeah, absolutely. Now, the reason that we don't play, and just to answer that question very directly, is so, yeah, broadly there are competitors and there's like, there's no one to one competitor. There isn't like a precise company, but there's many. And that, by the way, exists in the US and that exists in Europe as well. But in terms of specifically, like, why don't we have an office there and like, compete? You know, we have offices basically everywhere else.

[Doug]

The.

[Kasser Yunus]

We start in automotive. The Chinese automotive industry is extremely insular and the government puts their thumb on the scale for Chinese companies. You can't just go in on an even. You can't compete on an even playing, you know, field. And all the way to, like, IPs not respected. Like, literally, people will steal your ideas and you, 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 is 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 speak, you know, also like, I think this gets overblown is, you know, we do defense work for the US and people think, oh, that's the reason you're not in China. That's probably the least reason. I mean, frankly speaking, because we're a dual use company. So all the technology we're building, it's not like we're building defense specific tech. We're building tech that is actually commercially available in lots of areas and then we're putting it in defense machines, which is different than being like a defense contractor, I guess.

[Aiden]

I'm curious now something that I realize we haven't touched on. You talked about IP or strategy being leaked potentially. What is your guys's unique advantage over other companies? 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.

[Kasser Yunus]

There's everywhere.

[Aiden]

Yeah.

[Kasser Yunus]

I mean we're running site software or

[Aiden]

the software system broadly called a car

[Brandon]

into the garage with a button.

[Aiden]

Yeah.

[Kasser Yunus]

So yeah, I mean we're, yeah.

[Aiden]

So like you know, what's, what's the edge? Why are you guys successful? Why are you one of the leaders right now?

[Kasser Yunus]

Yeah, there's, there's, I'll, I'll give, I think what the actual answer is and, 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 OpenAI the only two that are like that? Or because they're really hard to do what they're doing. Like it's where 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 robo taxis than have nuclear weapons. I mean these are extremely complex technologies. Yeah. I mean and as we just like, we just like hand wave over it like, like what a Waymo does or what a 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, 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 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 a 991 GT3 and a 992 GT3 touring. Like I can tell you the spec differences. I'm not talking about that. I'm talking about the actual industry. How do you make a car? How do you price it?

[Aiden]

How do you worked like the V6 line, right?

[Kasser Yunus]

Yeah, I did. I did not as labor. I worked as a manufacturing engineer.

[Aiden]

Okay.

[Kasser Yunus]

But I also did other on the labor side it was Buicks. But this is where getting built a

[Aiden]

part of helping run factories that make things. You don't just have a car in your garage.

[Kasser Yunus]

Yeah, I love the car business and 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 war fighter and they're, they were deployed, they understand defense in a way that you as a layman will never understand. So then if they, if you can marry product or technology with their understanding the market, you can do some really special things because you. So I think like the non technical point, it's like we really know our business, we know the markets we play in and we know how our buyers are going to buy and so good products and understand the market. The VC answer because each of these answers it depends on who your changes. 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 is. Why are certain podcasts successful? Why are they not. There's 50 reasons. It could be. They started before everybody. It could be the hosts are, you know, whatever. We're famous or call.

[Aiden]

And handsome.

[Kasser Yunus]

Yes.

[Brandon]

Yeah.

[Kasser Yunus]

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

[Aiden]

Their guests are tall and handsome as well.

[Vikram]

Yeah.

[Kasser Yunus]

You know, what's that saying? It's like failure is an orphan, but success has, 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'll be successful, but if we were failing, you wouldn't. You'd be like, oh, you know, we like crickets. Yeah.

[Aiden]

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'd you choose to do that? Seems a lot harder.

[Kasser Yunus]

Yeah.

[Aiden]

Seems maybe a little more painful, a little less immediately rewarding. Even though these are both obviously very hard. So. Yeah. How does your, like, life experience lead up to that? And.

[Kasser Yunus]

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 we spend on compute and what the technical abilities of the.

[Doug]

Yeah.

[Aiden]

You're building in. So it's not like you're building.

[Kasser Yunus]

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 just 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 other. I'm so happy we didn't. You know, it's like, I think people think this. You know, typically I only do these Talks for founders. That's, that's usually my audience. I really love founders.

[Vikram]

And,

[Kasser Yunus]

and founders are just basically a small business owner, except they do it within this concept that they can raise capital and scale, because software scales really well. That's fundamentally the difference between a laundromat and 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 this. You know, 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, to be very explicit, as somebody likes tractors. Getting traction on the. Somebody wants the thing that you want. You're doing a podcast. Somebody's actually listening. You know, small group people are actually listening, and then they tell other people. And then that's what, 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 of that, 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's not, 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

[Aiden]

think we'll call it there. Kasser, thank you so much for joining us. This is fascinating. It was a fascinating industry. I feel like we could have talked for like, three more hours.

[Brandon]

Yeah, 100%.

[Kasser Yunus]

I have, like a hundred questions here.

[Aiden]

I wanted to hit.

[Kasser Yunus]

And there's a. And we still were pretty high level. There's a lot of nuance and all the, 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, you know, and you're Trying to like maybe listen to this, to, to learn to just engage with the products yourself as much as you can and you start seeing the limitations. And there, there are a bunch of like YouTube videos on like trying to get like chatgpt just to count to a thousand and it's like, it's hopeless. So. So the reason I say is you, if you get close to the technology and you learn, I do think it lowers your anxiety a little bit. I mean fear, the root of fear is lack of understanding. So try to understand, try to understand. That doesn't mean 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.

[Aiden]

Thank you so much for joining us.

[Brandon]

Thank you so much.

[Kasser Yunus]

Yeah, thanks for watching.

[Brandon]

Oh my God. We flew back from San Jose, so. That was so fast, man. San Jose is, it's a bad place.

[Doug]

It's a bad city, bro.

[Aiden]

I fled.

[Doug]

I fled like I'm fleeing a country, a war torn country.

[Brandon]

Loved the interview, Loved the company tour. San Jose needs to go.

[Aiden]

Oh, okay. It was funny driving, dude. We walked into the airplane, we got off the airplane and it's just like every AI company on every billboard everywhere. I was like, oh, I forgot what it's like to be in Silicon Valley. This is so funny.

[Brandon]

Yeah, it's so crazy. You see every big name company you could ever think of in like the 10 minute drive.

[Aiden]

Yeah. It's like. And then there's intel and then there's Microsoft. It's like just right across the street.

[Doug]

Oh yeah. The San Jose airport always has a gigantic wall to wall, business to business, AI solutions, ad or something.

[Aiden]

It's all.

[Doug]

And it's like if you need, if you're a corporate CMO who needs to upgrade your data analytics, try Duplic. It's just some. Speaking of, it changes every time. It's a new company that's clearly gone out of business.

[Aiden]

Speaking of Gumbla, 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 give you assistance and it was like, it's very funny. But anyway, this is 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 Caster interview other Things before we move on to off the record.

[Kasser Yunus]

Anything.

[Doug]

Anything. Do you sneak behind any corridors? Did you open any?

[Brandon]

The employees were too, too fucking happy. It was funny because a bunch of the other employees said something really similar and they. 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 stare. I'm just like staring at Vikram to see if he's.

[Doug]

He's like, is there a gun in his back?

[Brandon]

Like, wink if they're holding you here Type of thing.

[Aiden]

But they just.

[Brandon]

They just love it.

[Doug]

That's cool.

[Aiden]

Yeah.

[Vikram]

Yeah, that's pretty cool.

[Aiden]

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've got the exact 12 total.

[Doug]

But yeah, there's 12.

[Vikram]

Yeah.

[Aiden]

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.

[Doug]

Nobody.

[Brandon]

Yeah, the sexy kid.

[Doug]

Never heard of the sexy kid on the block.

[Brandon]

If Taylor Swift can put it in a lyric, why can't we? On the lemonade stand, there's a Taylor

[Doug]

Swift lyric about the sexy kid.

[Brandon]

Keep going, keep going, Doug.

[Aiden]

So a Waymo with huge lidar, big curvaceous cameras, it rolls into above legal age.

[Doug]

Most importantly, the Waymo is.

[Brandon]

You should have led with that.

[Aiden]

They have been developing it for over 18 years. No, you know what's interesting is they apparently Waymo is like so expensive and so intense with how they run things. They need to like really, really meticulously map out every single place.

[Doug]

Is that what you want though? I'm like, what?

[Aiden]

Okay, but sorry, go ahead.

[Doug]

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 as 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.

[Aiden]

Yes. So, okay, so an important clarification there. Uh, all of the self driving companies are going and mapping places before they go send the car out. Tesla and Elon have like proposed the dream of, oh, you take a Tesla into wherever that has no idea where you are. But all the companies, including Tesla, will use, for example, LIDAR and these other things to go map stuff out in

[Doug]

like a geofenced area.

[Aiden]

Yes. The challenge with what Waymo has done is they are like meticulously building a map that is like, you know, pixel accurate of the entire city that they operate in. And then the reason that they're able to do so well is because they are assuming that their map is accurate. But if the physical city changes in any way, which it does, and suddenly the mapping that they did six months ago, America, we don't build.

[Doug]

So actually we were, we've planned for this. We're so China's going to have so much new construction, they'll never get it down.

[Brandon]

They're falling behind already.

[Doug]

Here in America, we keep it Steam for 100 years.

[Aiden]

So as appealing as it is, the idea of like, we are going to do this incredibly extremely 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, the idea was like, what Waymo's doing is extraordinarily successful in its own right, in its own way. But they are doing a system that is kind of not scalable by default. So it's like, it's like cool. But really what you want is a way for this to be accessible to anybody as well as affordable.

[Brandon]

Right.

[Aiden]

If the idea is, 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 us the tens or hundreds of thousands of people are dying every year. You get the bad drivers into systems that stop, that have automatic braking and they're gonna drive for them and that, you know, if they get in the wheel drunk, it's going to drive for them.

[Brandon]

Or in the industries where humans just don't want to work in the first place.

[Kasser Yunus]

Yeah.

[Aiden]

Or they're fading 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 the work. We'll just not do it. Like, if we want to have a clean energy future, you have to, you have to mine and build. Like, we need stuff and if we don't do it, it's going to go to countries that are going to do it in, you know, polluting, unsafe ways. For example, famously, 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.

[Brandon]

The kids are going to be out of jobs.

[Doug]

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.

[Aiden]

Yeah. 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.

[Doug]

For them.

[Aiden]

Yeah, for them. And again for other companies that are trying to do this type of thing.

[Brandon]

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

[Aiden]

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.

[Doug]

Put it down. It's not an ad.

[Brandon]

It gives you way.

[Aiden]

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, Pig. Patreon. Excuse me, your name is Brandon. Brandon.

[Doug]

You call me Pig.

[Brandon]

Call him a pig.

[Aiden]

I was mixing Patreon and Brandon all

[Brandon]

simultaneously in 2025 Red Bull sold 14

[Aiden]

Pigman. What do you got?

[Brandon]

Quick bite.

[Doug]

Quick bite. Bites. 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 1 trillion, then 2 trillion, then 3 trillion, basically.

[Aiden]

How much is Elon Musk worth right now? As of this recording?

[Doug]

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 who's 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 it deeper. There was the US government banning Claude in a overnight.

[Aiden]

Yes. So we'll dive into this more on the Patreon because it's just, it's a long conversation with a lot of angles to go into. But we've talked a little bit about their new like, Mythos model, which is the insanely powerful model which is gonna break all the cybersecurity. And then they sort of outta nowhere launched it. This would have been what, two weeks ago? It was on June 9, 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 12, this last Friday, the government sent them a letter from Howard Lutnick, who sent them a letter and said, you need to restrict access of your model to anybody who isn't an American national, including people in America. Which is not something you can enforce. Right.

[Doug]

It's impossible.

[Aiden]

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. 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.

[Doug]

Yeah, we'll do a deeper dive on that. And also next week on the main episode, we'll follow up on all these stories. And the last thing I want to say is the Iran war could possibly actually, maybe finally. Actually maybe one time for reals. V. One final. Underscore, underscore, final be over.

[Aiden]

What's your. What's your over. Under. Do you think this one sticks?

[Doug]

It 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.

[Aiden]

It's the length of Israel's refractory period.

[Doug]

Yeah, I guess. I guess between bombings, right? Yeah, it's down to that. That's what it's coming down to. So.

[Brandon]

Israeli strike kills 4 in southern Lebanon amid ceasefire talks 1 hour ago.

[Aiden]

Isn't that insane?

[Doug]

Isn't that insane? Oh, boy, they're not even high. They're just literally trying to do whatever they can to stop this peace deal from happening. I. I don't know. I don't know what happens, but this seems like this one is different. In May, in fact hold because both sides did agree and publicly announced it, which has not happened yet. We'll get into the whole details of the specifics, but that, that's a story we should follow up on next week because a lot's gonna happen between now and Then so, I mean, those are the three big dick topics. I, I and, and, and, and the Nordic fun fact of the week. We can't do an episode, even the field trip episode, without a Nordic fun fact of the week. So Aiden, please close our show out with the. By the way that people are becoming

[Aiden]

fans of this, demanding the Nordic.

[Doug]

Some of the comments, they're like, I only come here for the Nordic fun fact of the week. I'm here.

[Aiden]

We have Nordic people using this as their litmus to understand what's going on. They're lost without us. They're rudderless.

[Doug]

Immigration application. You said you are putting in your immigration application that you do the Nordic fun fact of the week. So you should be given Swedish citizenship.

[Brandon]

Yeah, dude, I'm about this shit.

[Aiden]

Which is, by the way, Nordic reporter shouldn't talk about the work that they're doing. The important work.

[Doug]

That's what he's. You know what? I want you to get the citizenship and then I want Sweden to go to hell. You know, come crawling back.

[Brandon]

You know, they made $15 billion.

[Doug]

I'd rather do theirs in the Red Bull segment. What's the Nordic fun fact of the week?

[Brandon]

The so Iceland considering joining the European Union and leaving their currency behind for

[Doug]

the first currencies of the.

[Brandon]

The krona, the kroner. The Kroner. They are taking a vote on August 29 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.

[Doug]

The reverse Brexit. The ice.

[Brandon]

It's so weird to call it reverse Brexit because it's just. Yeah, it's joining the thing.

[Aiden]

Why would you describe it already?

[Doug]

No, no, you guys are wrong.

[Aiden]

There's only one word, the only way. So when you, when you do pick up, you do pick up Brexit ball at the, at the court.

[Doug]

Yeah, everything's Brexit.

[Aiden]

You guys cool by reverse Brexit in your game?

[Vikram]

Yes, I say that.

[Aiden]

There's actually so many rooms in a voice.

[Doug]

That's how I talk. Yes.

[Brandon]

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.

[Kasser Yunus]

All 12 of them are.

[Brandon]

To be honest with you, I have not read much about this. Like trying to read stuff about this right now. I have seen very little news outside of the fact that this vote is happening and that people are weighing the trade offs specifically of changing the currency after maintaining their own for so long. So I think it's an interesting story to follow and we'll find out at the end of August if that process is gonna 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.

[Doug]

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.

[Brandon]

I mean, not the top of my head. I can't. I know that countries have tried to. It wasn't joined NATO. Hungary.

[Aiden]

It's like something I wanted. Sweden joined NATO in 2024.

[Brandon]

So Hungary joined in 2004. Most recent EU country.

[Doug]

Dude, I searched it and then the freaking AI summary said, yes, the EU populations increased.

[Aiden]

So there you go. Thank you, AI.

[Brandon]

So the last time was Croatia in 2013.

[Doug]

Really?

[Brandon]

There's been a minute, dude, I think there's been attempts. Right? Like there's been, you know, there's a

[Aiden]

limited number of countries in Europe though, right. It was gonna stop at some point finding new ones. I don't think this is a shock that it's slowed down.

[Doug]

You keep digging, you'll find a country in there. Probably a fucking with 40 population that Aiden wants to cover. Extensively.

[Brandon]

Sorry that I'm not avoiding. 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.

[Doug]

It would be if we did the news by population. It would be China India podcast.

[Brandon]

I'm actually kind of interested in that.

[Doug]

All right, next week we'll do the. The Mumbai minute.

[Brandon]

You get India, I'll get Indonesia. They're up there.

[Vikram]

All right.

[Doug]

We'll get all the big ones.

[Brandon]

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/lemonade sand. 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.

[Doug]

Bye guys.

[Aiden]

Thanks everybody. Your next chapter in healthcare starts at Carrington College's School of Nursing in Portland. Join us for our open house on

[Brandon]

Tuesday, January 13th from 4 to 7pm

[Aiden]

you'll tour our campus, see live demos, meet instructors, and learn about our Associate Degree in Nursing program that prepares you to become a registered nurse.

[Brandon]

Take the first step toward your nursing career. Save your spot now at Carrington.

[Aiden]

Edu Events. For information on program outcomes, visit carrington. Edu Sci.