Why Physical AI Is the Next Frontier — Applied Intuition

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our mission is to put intelligence on a billion machines and we think that can have a profound impact on society.

Applied Intuition is a physical AI company.

We put intelligence on machines.

Cars, trucks, tanks, drones.

It's a physical moving thing.

We make an intelligent.

Digital AI, of course, is building software and optimizing ads and creating videos.

That's all interesting and good, but really where you talk about the global economy, that's physical AI.

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

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

There's no reason autonomy should be this obscure, difficult technology.

Our vision for that is a high school kid that can make iPhone apps, should be able to make autonomous systems.

That platform for designing and developing is what we're launching.

It's called Dana.

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

Which will we get first?

A perfectly simulated real world environment for training autonomous devices or a Grand Theft Auto 6?

Much of today's AI conversation is focused on large language models, but the next frontier may be physical AI.

Software that enables machines to perceive, reason, and operate in the real world.

In this episode, Mark Andreessen and I sit down with Applied Intuition co-founders, Kasir Yunus and Peter Ludwig to discuss the future of physical AI, along with the company's newest platform, Dana.

Which is designed to simplify how autonomous systems are built and deployed.

They explain why physical AI presents a fundamentally different set of engineering challenges.

Where autonomous systems are already making an impact and why the next decade could transform not just software, but the physical economy.

Kasir, Peter, welcome to the ASINZ podcast.

Well, thanks for having us.

Your name is?

We're just one of many.

I think we've all each known each other for too long.

More than I'd like to admit.

Yeah, one time.

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

And I was an investor for you even before then.

Exactly.

So let's do that a segue.

We have a lot to talk about today.

We have the biggest launch in company history to talk about today.

But first of all, we just give an update, a status.

What does Applied Intuition do for those who are...

Yeah, for the people who don't know, Applied Intuition is a physical AI company.

We put intelligence on machines.

That's the simple way of describing it and all types of machines.

So cars, trucks, tanks, drones, you name it.

It's a physical moving thing.

We make an intelligent.

And the history of the companies, we originally started by making the tools that would make the...

And then we got into the actual intelligence itself.

In some ways, like a very boring AI company in the sense of 83% of that company is engineering.

We win by making really great products.

It's not like a good sales or something like that.

I don't think we're good enough for a sales enabled company.

But yeah, over a thousand engineers and based in Silicon Valley.

But we have offices globally, 18 offices.

And our mission is to put intelligence on a billion machines.

And we think that can have a profound impact on society.

Both in the kind of...

Pity things everyone talks about, safety.

If you really talk to somebody who's been in a car accident or in a mining accident or in a farming accident.

Those are real gnarly situations.

Beyond just fixing that, if you can unlock productivity.

I think we've seen the unlock in the digital world.

And everyone's super excited about it.

And you have trillion dollar companies emerging.

I'm a pretty strong believer that I think when we look back 25 years, we look back to the internet now.

You look at the original internet companies that are doing surveying or they're doing some analytics.

And those are interesting.

But really, when you look back 25 years from now, the big monolithic companies are Amazon.

The Deliver's You stuff.

Apple.

These are the true kind of companies that come of age.

And I think when we look back 25 years in this intelligence revolution, the companies that impact the physical world might actually be bigger than the companies that impact the digital world.

I would love for you to talk about the following, which is when you first started the company.

The knock on the company, I think, was, "Oh, well, it's making cars autonomous, right?

Self-driving cars."

But it's kind of like, "Okay, there's like whatever.

There's Tesla way more building their own self-driving cars.

And then there's six or eight other car companies that matter.

And then the company just could never get that big because they're just not that many customers.

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

Yeah, I mean, even today, even if you'd put that, let's say, view on us, the automotive is 30% of our business.

So 70% already is non-automotive.

And I think if you fast forward another 10, 20 years, even the manufacturers themselves as a customer base will be a small amount.

I think that mission, just keep thinking of a billion machines becoming intelligent.

And you think about all the types of machines that exist.

Automotive is just an easy one.

I think it sticks in people's head because we all drive cars and it's a big market.

But I think it'll be a minority of the business.

And we already have a minority of business.

I think it'll be increasingly minority of the business.

But that doesn't necessarily mean it'll be small.

Automotive is still huge.

Just as a part of the globe's GDP, automotive is something like 3% of all GDP.

I think the way we always think about it is you try to get to your mission.

Initially, the manufacturers were the distribution to that intelligence to consumers.

But then you start working in defense and you start working in construction and mining and agriculture.

And suddenly the manufacturers are important, but maybe the mining operators are actually really important.

Or the Department of War is really important.

And suddenly they become customers and all of those are customers of ours as well.

Yeah, and if you split AI into digital AI and physical AI, digital AI, of course, is building software and optimizing ads and creating videos, that sort of thing.

That's all interesting and good.

But really, where you talk about global economy, that's physical AI.

And then we're talking about manufacturing and mining and logistics and transportation, all of these things that supply chains.

Yeah, supply chains, exactly.

Let's build on that for a second, which is, so things that move today, or historically things that move are things that have human beings at the wheel or at the controls in some form, right?

Airplanes have had to get designed around a human in the cockpit.

Both have had to get designed around human steering things, like in a world of autonomy.

Do we already know what the things are that move or are we going to discover that there are a lot of new things that are going to get built when you don't need a human in the driver's seat?

I think both.

The thing that you have to remember is you take a haulage system that's in a port, like a kamatsu, a dirt mover in a mine.

Those are made for 20, 25 years.

So the buyers of those products, they might not have gotten their full cycle ROI on them.

So they're not immediately going to buy something, no matter how much better it is.

So one part of our strategy is you got to make those things intelligent because they're not going anywhere.

The second is what you're talking about, which is, well, that depends on a human in the cab.

If you don't have a human in the cab, the machine can be smaller.

It can be shaped in very different ways.

You need to talk about mining underground.

The constraint actually is the human because the human needs to breathe and it's very dangerous.

And so you can build a very, very different machine.

We're doing both of those things.

And then the thing that we're not talking about is we're all talking about intelligence almost like within a system.

But the system level intelligence is where the unlock is.

And we're already doing work like that where you say, hey, let's take an entire port.

Let's take an entire mine.

Let's take an entire query.

And this heterogeneous mix of machines, they're all can talk to each other.

And they can optimize and be efficient when one machine goes down or one machine has an issue.

The rest of the mind doesn't have to stop.

When it's human driven, we don't even know the machine is going to go down because there's no analysis.

The human is not plugged into the core systems of the machine.

So a simple thing like knowing when a break system is going to break is actually huge because you can start preparing for an advancement.

This wear and tear is higher than in other mines.

This is using an example.

But the other macro point is if you look at agriculture as an example, average American farmer is 58 years old.

The number something like under 35, it's less than 10 percent of farmers are that young.

So what's going to happen?

The need for food growth is continuing to grow.

The need for rare earth materials is continuing.

These demands are only growing, but the humans who are the bottleneck are decreasing.

Trucking is the same way.

And so you can really just unlock a lot more efficiency.

So I mean, one way to think maybe think about this is imagine if the cost for food decreases because it's way, way more efficient.

What's the downstream impact?

The imagine for goods being transported.

Let's say instead of a few dollars a mile, it's 20 cents a mile.

And suddenly I think that the unlock is very, very, very big.

I think doesn't necessarily need for all the machines to be redesigned from the ground up.

Right.

Got it.

Makes sense.

And then maybe just one more question would be just give us a sense of parameterized like the scope and scale of the company today.

Yeah.

North of a thousand engineers.

And those engineers are obviously the classic software and AI engineering teams.

But we also have engineers who really know safety systems.

We also have engineers who really know hardware.

Because the important thing that we kind of just tipping around, stepping around is all this stuff is hard because it ultimately has to meet the real world.

And the real world has way more complexity and has a lot more issues.

And we have engineering teams and we've deployed our models onto 50 some platforms.

Even that sounds trivial because mostly when you think about models, you think about deploying them through a browser or on a phone and everything's abstracted away because you have iOS and you have Android and you have Windows and you have Linux.

And you have all these systems that have already taken care in the real world.

You don't have that.

And so we have engineering teams that can do that as well.

Our claim to fame is we've raised over about a billion dollars in the company's history.

All that is sitting in the bank.

And I always say that with an asterisk, which doesn't mean we're not going to spend it next month.

Good news, bad news.

Yeah, good news, bad news.

And I think we talk about scale.

We're at that phase where these giant markets are around us and we can make the decision how aggressive do we want to pursue those?

Because decade of, frankly, execution and deployment into production, I think the hallmark of our engineering team is putting products into production.

That really is.

I don't know.

How do you think about scale?

Yeah, I think that that's roughly the mission of bringing intelligence to a billion machines.

That is how we think about it.

And then thinking about, well, what are the types of machines that will have the most impact on and focusing on those areas first?

But we'll get there.

Ayo, let's go deeper into the differences between digital and physical AI and more so into where are we today?

What progress has been made?

What are some of the main major bottlenecks in physical AI?

When you unpack some of that.

Yeah, I mean, I think a lot of times people think about the progress in physical AI is limited to basically two use cases.

And they're just because they're obvious and interesting, which is robotaxis and humanoids.

They're very visceral.

They're they excite you and they're kind of sci fi.

I think they're those are very interesting and they there is real work being done by us and other people in those domains.

I think all the other all the other domains I think are going to be just as important.

I mean, you just you just think about what happens on a port.

That's there is a huge unlock there.

And that's I think that's that's the area we're really focused on.

It's like all the other nooks and crannies.

If you look at like we've talked before about the rise of Cisco and how networking kind of went from first individual machines and companies would get network and then entire countries were getting network.

There's a similar thing happening with AI.

AI is getting to that level of kind of sovereign AI is now a discussion.

Sovereign AI really is about physical AI because that's where you're talking about AI in defense.

You're talking about AI in the physical machines that are moving around.

If you look just at the example of Waymo from America and Pony from China trying to deploy in, let's say, the other countries, not America, not Europe, not China.

Every one of those spaces, they're way more they're way more hesitant of saying, yeah, thumbs up.

Your robot taxes can run unfettered on our in our country.

And so if you look back, just like kind of this arc of the Internet, you know, when the first Internet companies come, nobody's really thinking about sovereignty.

At all, it's like the browser goes everywhere.

The Internet goes everywhere.

That's almost the power of it.

Then when social media emerges, there's a bit more of hey, actually, not every social media.

And then you have China not allowing Facebook to come in and you have some then you get into the next level of like the online offline stuff.

There's more resistance to Ubers, the door dashes.

Suddenly, there's local players who are being favored very aggressively.

When we get to physical AI, I think there's going to be huge.

And also there's like a larger geopolitical theme of kind of more fracturing than globalization.

You're going to have this demand for this AI should somehow be localized.

And I think that has to play into our strategy as well.

We're a technology provider.

So we provide that technology across the globe.

And I think that's that's that's that's that's that's something that's understated in this in this conversation.

A few other things on digital versus physical eyes in digital AI.

The state of the art is you can train models effectively on the entirety of the Internet and then maybe augment that with additional data that's been collected and refined with some hired experts.

This is sort of a hot field right now.

But generally, you're talking about a foundation model that's built on Internet data in physical AI.

The Internet data is is useful, too.

However, to actually build a foundation model in physical AI, there's also a lot of private data collection.

You when we're talking about minds or logistics or any of these other other fields, the data that's useful for training models there is not necessarily available.

So we have to do a lot of work ourselves actually going going out and collecting that data.

And the other key factor is safety.

Right.

If you're talking about building a smartphone app, you don't necessarily care about is a safety critical application.

When you're talking about moving a machine that weighs many tons or think of a humanoid which could fall over on your children, you care a lot about safety and the evaluation of that safety.

And that is really sort of getting to the state of the art of physical AI and really proving out the safety case around some of these models.

Yeah.

And I think like you talk about like human data collection has been its own little area of interest.

But when you talk about collecting data like in places like Korea, where they have North South Korea, we have North Korea, they don't allow mapping companies, let alone allowing if an American company to come in and data collect.

So we've figured out over the years, whether it's the Middle East, whether it's Latin, how to get into these countries, work with the governments and get the thumbs up to collect proprietary data.

And so in the way that it is similar to other digital AI systems, proprietary data sets, scaling laws, all that stuff is the same.

It is applied in a very, very different way.

And it's almost like the way to think about it is like the diffusion of these models is very different because you can't it's not everyone can just access them through a phone.

You get in.

So that ironically is actually plays in our favor because once we have a massive proprietary data set to where we've been building, we have hundreds of petabytes of data.

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

There's kind of a chicken and egg thing, which is like in order to build an autonomous physical thing, you need a lot of data.

Together that data, you need a lot of physical autonomous things running around collecting the data.

So it's like once you have a giant network of physical things running around, you have the data that makes them all work.

Like is there a flywheel aspect to that?

And is there is there is there like what's the level of difficulty involved in kind of booting up that flywheel?

It's difficult, but it's also not difficult.

I mean, I think we have one of the largest data collection fleets on the planet, frankly speaking.

So that's how you bootstrap your way into it.

That's just money and resources and technical knowledge.

But it's not like there's probably more than five companies that have that technical knowledge.

So it's not extremely obscure.

I think what is more difficult is then how do you actually have that model, which is going to work on lots of different hardware and is, you know, is is tested.

Appropriately because the you saw it, you know, in cruise, right?

Cruise was this company that did amazing self-driving work and then one accident.

General Motors owns them and they get super scared and they pull back.

So it's like just getting these things into production is actually more difficult than than than it seems.

I think like we believed synthetic data was going to be important.

So we started our synthetic data team like five years ago now.

Plus, yeah, more than that at this point.

And like when we're a strong believer that synthetic data can accelerate autonomy development, we've just seen that.

And then there are like lots of other, you know, secondary and tertiary, like technical innovations that have obviously the transformer revolution hitting self-driving massive.

Basically everything done in self-driving pre-21, 22 relevant.

But you're almost like that's kind of the starting point.

But it's also different than today being the starting point.

Like there are those four or five years are actually there has been a lot of work done.

You can see it most clearly with Tesla, but there's other folks in that process.

The actual techniques historically and as I'm simplifying here, imitation learning was the way the game, which was collect a bunch of data and then the models would basically imitate what human drivers do.

The real state of the art right now is end to end reinforcement learning in in a closed loop in your tools.

And so it's a little simplified to say the system learns itself.

It identifies where the issues in the self-driving system are.

And essentially you then find data like that or you synthetically create data like that and then you close that loop and you see, are you performing in the same scenarios better and better?

I think if you fast forward some years, that will be a completely closed loop, like with no humans intervening.

Right now, you still have like, like what's the fog error that we saw?

We still see errors in the real world that impact self-driving.

Oh, yeah.

So it's like, well, what are the bottlenecks, right?

And the bottlenecks, there's plenty of them.

But whenever you're dealing with physical systems, you inevitably you hit a lot of gnarly hardware problems and it could be anything from overheating to a sensor being slightly miscalibrated or funny, funny issues.

What you saw yesterday was basically a fogging sensor, like fog impacting a sensor.

But these are the things that you actually have to solve for this stuff to work very reliably in the real world.

So I'm going to ask you a question and you can decide whether you guys want to engage on it or not.

It might be an opportunity or it might be a question which is, were you surprised?

So Cruise was a super high flying Silicon Valley autonomy startup that was kind of running neck and neck with Tesla early on and so forth.

And you know, very top end team and then they famously got bought by General Motors.

They got bought by General Motors.

They became the GM autonomy program.

GM got a lot of praise, at least, you know, at least in the tech circles for being like, OK, being like the legacy automaker with the biggest investor.

I called Peter when before before his announcement that and I said, hey, Cruz just got by, you know, he's also GM family.

We're both GM families.

And Peter guest.

It was a great opportunity to be able to get to the top end of the team.

And Peter guest, it was said Nvidia said no.

It said that's a go fish.

Apple said no.

I said general, explicit motors.

So that's surprising to people who are from GM that they were willing to buy.

Yeah, they did it.

OK, they did it.

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

And then they had this there was there was an accident.

There was a was I was injured or fatality or it was a fatality.

It was a serious injury.

Somebody was in drag for 20 feet.

Yeah, serious injury, bad press.

And then they put a bullet.

The GM CEO on board put a bullet in the cruise project.

And I know that at least some of the senior cruise people were extremely upset by the aftermath of that.

Was it surprising that they reacted the way that they did?

So, you know, full disclosure, General Motors is a customer.

And I went to the General Motors Institute.

So we have a lot of love for the company.

But incidentally and ironically, I'm reading a coincidence.

I'm reading this very famous book, which I had actually never read before called On a Clear Day You Can See General Motors.

And DeLorean's book.

Have you read it?

One does.

Have you read that book?

So I have years ago, I have it's one of the great ultimate titles and we should just pause as a gentleman was like what he was like the super genius of the car.

Yeah, he was going to be the next president of General Motors.

General Motors.

And then later on, he started his own car company, which was back to the future, back to the future.

And then that whole thing collapsed for a variety of reasons.

But yeah, he was like a legend.

He was like one of the main principal drivers of innovation in the car.

Exactly.

Lea Coca Bob Lutz.

This is this category.

Yeah.

And you got to remember this is the right.

So repeat the title of the book.

On a clear day, you can see General Motors.

And why was that the title of the book?

Because there's a lot of bullshit.

It's very large, complex.

Yeah, complex.

Yeah, it's like a nation state.

Yeah.

I mean, really, I mean, it is.

I think we say that like sometimes almost like flippantly.

But these companies are like extension like Hyundai is an extension of the state.

Toyota is an extension of the state.

Volkswagen is literally Volkswagen board members are members of the government.

So these are extensions of the state and almost every.

And there used to be old saying what's good for General Motors is good for America.

And you cannot understate how important General Motors is the history of the American corporation.

Sloan's, My Year's at General Motors and Adventures of White Collar, man.

If you run a large engineering organization, you should read that.

That is like this thing that we talk as a modern corporation, it just emerged.

Sloan and Kettering create.

Kettering is the head of engineering, created this with this, you know, with levels and vice presidents and how do you do functional and matrix organizations.

It's there really is like the source code comes along.

John, you know, comes on DeLorean and he says he writes, he's going to be president and he's so fed up with a company.

But what was controversial was GM was doing really well at the time.

GM was like a when we say like GM was number one, the Fortune 100, it was like number one, two and three.

It was everything.

And it was seen as the best company in America.

So somebody to openly criticize the company.

And so he has a hope he had rates this book as he quits out of out of how annoyed he was I general orders of being read, led.

He writes his book.

And then after he like sobers up, he's like, I don't want that.

And so he fights for years for his co-author, not to publish the book.

The co-author still publishes.

So it's a real true insight into a large corporation.

I'm incidentally just reading it out, even though I've worked at GM 20 some years ago and I know a lot about the company.

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

And so the question isn't the point I think for everyone to take away isn't that these people who run these companies are stupid, right?

They're not stupid.

They're, it's kind of like, you know, when you're selling to the department of war and people say, well, why are you doing that?

It's like, well, the distribution defines the business.

So like the distribution is this is a consumer product of this.

This stat may be outdated, but when I worked in safety systems 20 years ago, I remember GM used to pound in your head of the top five consumer lawsuits in American history.

Three are automotive.

We got the majority, right?

So it's a gift to be extremely careful.

We had these like weird things like inside the company, you can, it wasn't red, yellow, green.

It was like purple or like, you'd always have to decoder because you know why?

Because when they go to lawsuits, they're like, you let a safety system that was marked red, go to production.

It was like, no, it was marked magenta.

So like, can you imagine how infuriating that is?

Every time you're like, what does orange mean?

So fast forward to your meeting that system.

Well, for slogan for a very long time, I was like, it was quality is job one, right?

Yeah.

Which various safety is that?

Yeah, exactly.

That's the one to punch of automotive.

It's quality and safety, quality and safety.

And quality really becomes because the Japanese really reset that stage.

Because that's a whole separate automotive history.

We could talk about automotive history for an hour.

But the punch line is you have in a Silicon Valley company meeting this immovable object.

There is a parallel universe that cruises out there right now, even as a part of General Motors.

So I think you always have to take it into the context of where the company is, where union negotiations are happening literally that year.

And if you're the union, you're like, you can't make a billion dollars for us, but you're funding this thing that's killing people and it's sloppy.

And so I'm not saying precisely that's what happened to be very clear, but it's a multivariate problem.

My other hot take is, you know, I worked at both companies, right?

Google and General Motors, those companies are way more similar than they're different.

Way, way more similar than they're...

Literally, people don't need to know this.

The Google leveling system is the same as the General Motors leveling system.

And I used to say, I used to say this inside of Google meetings is like, hey, actually some of the engineers I knew at General Motors are better than the engineers here.

And people would look at me like, I'm saying there's no God in church.

It's like, they're like, how dare you, you metal bending monkey from Detroit?

It's like, no, actually, like making a modern combustion engine is extremely complex.

It's not just like, you know, it's not simple stuff.

And so the macro point, I think, is it's a bunch of things.

I think safety is always at the top of their list.

I do think, you know, we've hired lots of cruise people.

I think the way they dealt with that specific issue with the government, you got to dance a particular way when that happens.

And it just didn't dance exactly right.

And that just gives government bureaucrats more ammo to go after.

And you're a big target like General Motors.

It reminds me, you guys ever see that movie like Goodfellas, you know, one of the last scenes, The House of the Rising Sun, you know, all the old bosses go in the back of the courtroom and they're like, and you know, that's what happened.

They're like, the board was like, what do we do about cruise?

Like, what can we do?

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

And there's like, Q House of the Rising Sun.

People are running through a San Francisco office.

But it's just getting-- don't make that an AI video.

It's going to give mean text from Kyle.

So I think there is a universe that would survive, but it's tough.

So then a lot of what applied intuition does is kind of, as you said, like that dance.

It's like how to be a great partner to these companies.

Exactly.

He's bearing in mind their own very real issues and constraints.

I think General Motors also had the topic of business model, right?

So you have-- cruise was going after the robo-taxi concept, but GM makes its profits from personal car ownership.

Right.

And those things can be a bit odd.

So I think that was also a bit of the-- Oh, I see.

Right.

Okay.

Yeah.

And I think it wasn't clear.

By the way, you know, you, actually, all people, you spoke at YC in 2013.

I was in the audience.

I was a partner at the time.

And you said something, which I think is very recursive here.

We're feeding each other your advice.

It's the key thing in the new technology business.

Actually, everyone kind of figures out the technology, though that's still hard.

It's so hard sometimes to build really complex things.

It's when and how you deploy them into the market.

The when becomes really important.

You're two years early and you're doomed.

You're two years late.

There's too many competitors.

You have to hit it at the right spot.

And I think it's like-- I mean, a controversial thing to say is, I actually think cruise-- they were certainly moving at a much faster pace than Waymo.

They started way behind.

And you're talking about neck and neck when ultimately the plug was pulled.

So who knows what happens in the long term?

Our hypothesis in that same equation is actually the distribution.

You let the manufacturers do that.

We run self-driving trucks right now in Japan.

They carry commercial loads.

There's safety drivers there, but they're autonomously running.

And you won't know that because the brand is Isuzu.

That's the customer.

And why it's so good for us to partner with Isuzu in that case is, that company's been around for almost 100 years, right?

If I'm not mistaken, a pre-World War II company.

And they are-- they know the government.

They have test tracks.

They know safety.

They know their own trucks very well.

So when we go and provide them with the intelligence and the integration into their physical machinery, that's a fantastic one-two punch.

I think today, the world is ready to consume AI in the real world.

And that's a lot because of chat GPT and entropic and all these-- everything that's happened.

So people are no longer like, what's a self-driving car?

And there's these of Waymo and Tesla.

So the market is ready to consume.

And I think you just have to meet the market in the way that-- the best way possible.

And our view of that has always been, you go through some of the people who run the economy right now, whether it's a mining operator, whether it's the Department of War, whether it's the manufacturers.

And we work within each vertical with the right partner.

But that's a fundamentally different view than a Tesla or a Waymo, which are going to be vertical.

We're really playing the horizon.

And I think the way we can always think-- we think about our companies, we're kind of like a chip maker.

We actually look and talk and walk a lot like a silicon company, except we obviously don't make chips.

But we have design wins.

And then we have really large long-term relationships.

And then once we're in, we're in.

It's really hard to take us out.

So you need deep trust.

Our partners have really a lot of deep trust.

And we know their markets really, really well.

The things that Jensen knows is he knows his customers.

That's why Nvidia does well beyond the fact, obviously, they make very complex technology.

So how are these legacy car companies preparing for the future?

Are they making more acquisitions?

Are they going to?

Are they building and partnering with you?

How are they going to compete with tech-native companies?

It's like saying how our government's dealing with AI.

It's such a broad topic.

And each manufacturer-- even like you take Honda, Nissan, Toyota, three Japanese manufacturers with long legacies, they all approach it very differently.

They're roughly in a spectrum of we're going to build to we're going to buy.

And both extremes, more than ever, we're going to buy is the common answer because they've been trying.

And we've been there the whole time.

For the folks that are going to build, we provide them tools.

And we talk a little bit about our new product that we're announcing here.

And then on the ones that just want to buy, we sell them the actual intelligence that goes on the machines.

And so we meet the customer wherever they're ready in their journey.

The more nuanced version of that is the reality is every product is a different product.

And so the amount of silicon and amount of dollars you can put towards it, towards sensors, what the customer is willing to pay, all that depends on what actually gets in the long horizon.

All these things will be fully autonomous.

But the intermittent steps are very much what we saw in the PC, where you have this slow step up to one day that'll be like, now nobody really looks at laptop specs and even maybe frankly your phone specs.

But that's not the case from basically '85 to 2002, 2005, where finally people stop actually specking at all.

And then they're really moving to laptops.

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

Brolly, when you talk about machines, and machines becoming intelligent, fundamentally, a machine is a collection of these different components that are integrated.

And whoever does that final integration is oftentimes the company that puts their badge on it, the brand name.

But many, many companies are building technology that goes into that machine.

And so we now have a bunch of technology components and platforms that can go into these machines.

And we also sell the core technology that can be used to develop them as well.

And if you look, by the way, under the hood of a dirt mover, or combine or diesel truck, they'll have Cummins engines in them.

But nobody says, well, because all these guys buy Cummins, this means that they're, you know, whatever, Caterpillar is not a good company.

It's like, no, that's just a component that they buy.

They have a different role.

So when you look into any of these verticals, it's just a complex web of folks.

That's why I always say the chip kind of analogy actually works quite effectively.

Because none of those companies make chips.

But they all buy chips.

And so I think that's a good way to think about it.

So self-driving cars, so we've all been talking about self-driving cars for like, I think the whole thing started around, what, 2005 or something with the Darpa Grand Challenge originally.

And so, and then Google engaged in the program shortly after that.

Yeah, late double O's, yeah.

Late double O's.

So almost 20, basically around 20, less than 20 years maybe.

And there have been lots of reductions over the last 20 years of self-driving cars and then at any moment.

So I guess the bad news is we're sitting here today and most cars are not self-driving.

The good news is there are now self-driving cars.

And so the Waymo cars are driving all over, you know, in the places they're deployed.

It's become, you know, like people in San Francisco are, I think, treated now as routine that they get into self-driving cars.

And I think you can call, I think Tesla.

Tesla.

It's kind of like the AGI thing.

It's like, you know, if we're talking 20 years ago, everything we're seeing right now is like a mind-blowingly AGI.

The host keeps moving.

The Tesla stuff's amazing.

You can look at a bunch of manufacturers, Blue Cruise, Supercute, Cruise, BMW, Volvo's Pilot.

They're all quite impressive systems.

They're not full self-driving.

Right.

But yeah.

Well, it's full self-driving, whatever remote monitoring is happening.

The Tesla, we have a home in Los Angeles and you guys may recall there was a large fire in Los Angeles.

And then the California power grid was buckling even before that.

So it actually turns out, among the things cyber trucks are good at is they're very good batteries for powering your house.

And so literally we have, cyber trucks is our backup battery for the house.

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

14, yeah.

And that thing drives you.

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

Yeah, I think mean disengage, like the meantime and miles per disengagement are really high.

And I think miles is like in the thousands.

Right.

Which is like very impressive.

Yeah, for people who haven't driven the Highway 1 Big Sur, that's a stress-filled drive.

He said it was great the whole way.

Anyway, so I wouldn't have been talking to him had it not been.

Great.

[laughter] Would have gone right off the cliff.

Because he unbolted the steering wheel.

[laughter] Yeah, he got right off the cliff.

So and then Tesla's rolling out their robotaxi that is starting to show up in the wild.

And so on the one hand, those exist.

On the other hand, 99.999999% of cars are still not self-driving.

And then I would say maybe just one other would be the self-driving trucks.

There's been this recurring kind of panic in the press of like the trucks become self-driving and the employment in all these truck drivers be out of a job.

And sitting here today, I don't know, is there-- are there any trucks on the road that are self-driving that don't have at least a safety driver in the truck?

And I think the answer is probably still-- Yeah, still very few.

So let's split the-- there's multiple points that we brought up here.

One is on the, let's say, personally owned vehicles.

And why are they not more ubiquitous?

Part of that is the manufacturers are not good at deploying technology.

Part of that is they're-- they want to be safety conscious.

But most of it is cost, cost, cost.

What you're seeing in China, which is-- China is kind of a different EV ecosystem mainly because they don't care about profits.

And we're talking about business that doesn't care about profits.

It changes the entire calculus of the entire industry that doesn't care.

But what you're seeing is you're seeing L2++ systems.

So we can simplify the entire self-driving conversation to, is there a driver behind the steering wheel?

Right.

So this is a driver behind the steering wheel still there.

But generally, like Tesla drives everywhere.

They're like sub $1,000.

There's an aggressive-- that's chip sensors, the package, the software, everything.

We anticipate that there's a very aggressive-- once you get to like $500,000, the automotive OEMs will actually subsidize it for free.

They'll just give it to you.

This happened in nav systems.

If you guys remember, nav systems used to be a big thing.

If you pay $4,000, $3,500 to get a nav system, and then suddenly it became free and it just became default, I think that'll happen.

There's a weird thing, which is like actually getting into a subset of your cars costs x dollars.

And to get into all the cars costs x plus just a small incremental amount.

Because it's just a fixed cost in the way that how many vehicles and the way the assembly line comes in the way you have a mitigation, all these testing regimes, all this stuff.

So I think you'll have wait, wait, wait, and then a lot.

Every single OEM without exception, even the lowest dollar OEMs are working on an FSD competitor.

It'll come.

But it's just like with a good analogy to think about self-driving in the personally owned ecosystem is mobile phones.

We had the satellite phones, then we had the Qualcomm brick phones, then we had the Motorola Razors.

And from the late 90s to the late 2000s, the review was like, when's mobile going to come?

There was a huge like, and then it comes and by 07 from the iPhone launch, it's like four years when you get Uber, Instagram, WhatsApp, Snapchat.

Those are the killer applications.

So I think there's a very, very similar kind of wait, wait, wait, and then it's just basically ubiquitous in every vehicle.

If you had to ask me for what that number is, 28 SOP, start of production, 2930.

And then by the early 30s, it'll start becoming very cheap to free.

But routinely by the early 30s, you would just buy a car and you just assume self-driving.

Exactly.

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

Like what Tesla people have, what Tesla owners have today.

Today, exactly.

Yeah, we'll be default.

So then the other side of this is why don't we have a bunch of Waymos everywhere?

Specifically, Waymo has a different technology without getting into the nuances here.

But Tesla and many of the Chinese and applied were very much in this end-to-end model architecture.

This is a new way of doing self-driving.

Waymo, for the lack of a better word, is not that.

It doesn't mean they're not learned.

There's not one end-to-end system.

It's not one monolithic model.

One of the proclivities of their approach is it does depend on HD maps.

Therefore, there is a geofencing concept.

I think Waymo is trying hard to remove that bottleneck so they can expand geographically faster.

But the reality of today isn't there.

The other thing is when you have researchers, which Waymo really was coming out of an alphabic research organization, they didn't put commercial constraints.

So the sensors are bespoke and expensive.

The cars and the compute that are in there, they're just not economically feasible.

And they've tried a lot to get that down.

It's a lot easier to go from something that's really cheap and make it more featureful than something that's overbuilt and then trying to trim and make it really cheap.

And that's the big debate.

Who's going to get there first?

Tesla with full self-driving or Waymo with cost and geographic ubiquity.

But you know what we're not debating about?

Is it going to happen?

You know what we're not debating about?

There's a big technical breakthrough that needs to happen.

None of those things.

So now we're clearly in the engineering side of self-driving, which is just this grind down to dollar per mile efficiency.

And the moment that it's cheap, guess what?

All the OEMs are smart.

They'll just stay and just adopt it.

It's not that OEMs are resistant because they don't think consumers want it or they don't understand the technology.

It's because they want a price envelope which allows them to keep their thin raisers, thin margins, and at a scale, which is deployed across 100 plus countries in V1.

And so if you're just doing a small deployment, it's very different.

And the last thing I would say is the buyer of a Subaru or a buyer of a Suzuki have very different brand expectations than a buyer of a Tesla.

And so including the age of the consumer and what they think will happen, it will happen.

So that's also the reason.

So if you're a Suzuki, you're like, "The buyer is not going to want this stuff, so I'm not going to jam it into the car."

It's not because they're not technically competent.

It's just a different area.

What do you think of a routine?

Let's say the 200 biggest American cities, you wouldn't be routine to walk outside and you're just taken for granted that a robot taxi can come pick you up.

It's 26 now.

I mean, certainly by 30.

Yeah, certainly by 30.

And I would say the big variable there really is like...

Because what Waymo will say is that the dollars and cents per city already work.

And it's like, "Well, a company that has basically unlimited capital, why are they not already in 200 cities?"

But then you see their launch schedule is pretty aggressive.

And you're like, "That can get there."

So maybe if I was being aggressive, I would say 28.

Yeah, okay.

Like two years.

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

Sure.

Just scale up.

There's a volume.

And then also, if you live in LA, so like five years ago, I'd go to LA, people would be like, "What's applied intuition?

I don't know what self-driving cars are."

In the last couple years, now they all know self-driving and some of them even know applied intuition because they know from the other manufacturers.

I think you fast forward another two to four years, everybody knows it.

Now, does that mean everyone's taking way most exclusively?

That answer is no, actually.

Sure.

Now, there is a huge, huge...

If you look at the numbers, if you're Uber, you got to be scared.

I mean, they're just eating into ride-sharing.

Yeah, but to get 100% ubiquity, that's another...

You have to be extremely cheap.

And what about long haul trucking?

So that's the passenger side.

The long haul trucking, complete different economics, complete different business model.

There are many companies right now, I would say probably north of five that are running long haul trucks with drivers carrying loads between America and China.

If you had China, it's probably getting into double digits.

So it's there.

But the reason you don't know it and the reason it's not top of mind is it's not a consumer product.

And unlike on the Waymo and Tesla side where investors are willing to essentially give you some market cap adjustment for the potential of...

They say the trucking business is made...

You buy a car with your heartstrings.

You buy a truck with a calculator.

Yeah, it's a calculator business.

And so it's like pure dollars and cents.

So I think you as the provider of self-driving trucks, if you're doing the whole thing, like some of the companies are, which we're not, you have to show every mile and I'm going to save you this many dollars.

And it's like, for sure, for sure, for sure.

Because the buyer is unsophisticated.

And they're just like, "Well, I already got a staff that can drive."

And they're just not inclined.

Now, where we're playing in Japan, it's not random that we're doing trucking in Japan.

There's a massive labor shortage today and there's an imploding demographic situation.

And so there's a demand from almost every sector and that's why we've picked that market to really grow.

But I think you can take even more obscure...

When will all queries, literally like where you're moving cement, you're moving dirt, not queries, Q-U-A-R-S, Q-U-A-R-R-Y.

Yeah.

Queries.

Queries.

Those are rock stones.

Rock stones cement.

What are those?

I can tell you the people who own those things and run those things want it today.

Right.

So it's literally then you don't have a point.

You literally can't make this stuff fast enough.

Right.

The macro point though that people don't talk about, I think all this stuff's going to happen.

Right.

But that happened fairly soon.

It happened fairly soon.

But the macro point that in legislation and in the economics, the political economy of this conversation is AI is really...

You have this big pushback in digital AI because we're the counselor.

I don't know what's going to happen to my job and VCs, I'm sure all of your associates are very scared.

But in our universe...

They're betting whether they need us.

Yeah.

In our universe, it's the other way around.

It's like literally I'll meet these operators and they're like, "We'll give you everything."

If you can do this, we'll give you everything.

So then it's just up to us to get there as aggressively.

Well, the sheer for a long time has been for trucking for some reason triggers at least the press's imagination on apocalyptic levels of job loss.

But it's so wrong.

Go ahead.

There's not enough truck drivers and guess what?

Nobody wants to frickin' be a truck driver.

Why is that?

Explain that.

Because it's a terrible job.

Because?

Because you're like...

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

Yeah.

You know the answer?

Yeah.

Why is truck driving now?

It's like you're asking me...

You know what?

This is like talking to my kid.

He's like, "Well, why can't I put my hand on the stove?"

It's like, "Because it's going to burn your hand."

It's like, "But why?"

It's like after the third why, it's like, "Come on, buddy, let's do this."

[laughter] That's not a hard way.

Yeah.

So what's hard about...

I'm kidding.

Just to make sure everybody knows I did not do that with my son.

Yes.

So why is being a truck driver a difficult job or why would kids not want to do it when they grow up?

So let me use a parallel analogy, which is very clear.

And then you can...

You know, people will say like, "Nobody wants to work anymore."

And they say, "Well, McDonald's has all these job openings."

No, no.

Actually what it is is those people that used to work at McDonald's now, DoorDash and Uber.

Right.

Because it's better for them.

Because they can open, they can start their hours and there's no boss and they don't have to like stand in their feet and they can surf their phone in between orders and they don't...

Like, that's the reason.

It's not random.

The market is efficient.

Right.

And so in the truck driving example, why does somebody not want to be away from their family for four to eight days in a row doing long haul trucking?

The more a sharp example is in Australia, why don't people want to go literally by a plane to go to a mine and work on...

Or you go offshore oil rigs.

Those jobs exist.

If you want a job that pays six figures, they exist.

Even with such lucrative pay packages, it's not enough because people are like, "You know what?

I like kind of being around my family and I'm willing to take an incremental decrease in cost and how much money I make."

And then also like, I think today more than ever, things like back pain and being exposed to the sun and cancer and people that care about it, that's now a part of the equation.

This is the thing.

Tell me if I have this right, but I believe it's because I think commercial long haul truck drivers die of life expectancy 10 years less than their peers.

People say it's a consequence of several things.

One is some combination of nutrition and sleep.

It's very difficult to eat well and exercising.

What's your sleep score if you're a long haul trucker?

Let me get an eight sleep on that.

Exactly.

And so like obesity and then heart disease, hypertension and so forth, they're all very high.

One.

And then two is I think the vibration is very difficult, stress in the body.

And then the third is you mentioned cancer, but I think truck drivers have like a much higher rate of melanoma on their left arm.

Exactly.

Yeah.

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

A more interesting or even more stark stat.

Mining is 1% of the labor pool globally, 8% of work related fatalities.

Do you think people are rushing to work in mines when they hear stats like this?

Most major mines have a fatality regularly, which means once, twice a year, three times a year.

And if you ever visit a mine, you'll see that everything is based around safety.

Because once you experience one of your coworkers dying, then you're like, what am I doing here?

There's other jobs I can take.

And so I understand you're trying to enumerate for the audience, but these are not good jobs.

And the best evidence is, this is not a mining podcast.

This is not a podcast about, hey, long haul trucking is so great.

They're just not attractive jobs.

Yeah.

And even truckers don't want their kids to become truckers.

It's a proof for that reason.

They want their kids to be in a safe, at the very least, safer line of work.

But notwithstanding all that, how long will there be?

Do you think there'll be safety drivers in long haul trucks that are in our self-driving?

Or let's say other, even just somebody in the cab to deal with what happens when they arrive?

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

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

To be honest, it's not long.

We're talking a few years.

I think on the long end.

Yeah, on the long end.

And the thing is, there's a software technology thing, which is one part of the problem.

But the other part is, it's the redundancies that you need in hardware and the validation necessary for those redundancies.

And in many cases, that can actually be a long pull.

It's like, oh, productionizing a fully redundant steering system, fully redundant braking system, that's not in high-volume production yet.

And once you get that in high-volume production, I get the quality up and then that's validated and I can actually do these.

Need the price down.

Exactly.

Do you guys do you do like it?

The delivery robots?

Do you see a world where there's a billion of those running around?

Yeah, I think so.

I mean, the product that we're announcing, I think it's probably come out around with this time, is called Dana.

So you can just simplify everything that Applied Intuition does into two buckets, which is, we've been talking mostly about the models that go on the machines.

Then this is, we say, onboard software or onboard AI.

Then there's offboard AI.

This is the tools to design and develop these same systems.

They're the models that actually go on the machines.

Our vision for that is, and the delivery robot is a great example, is like a high school kid or a middle schooler, they can make iPhone apps.

They should be able to make autonomous systems.

So why can't they?

Just ask, that's a very simple question.

Why can't a ninth grader make a delivery robot in their home?

Well, they don't have the actual environment that they would first develop the scenarios in.

They would define the requirements.

Say, I want this robot to go on my high school campus around these, let's say, four buildings.

Then now that you define the requirements, then you have the scenarios get made where all the scenarios that can be made by using, let's say, a satellite image of the high school.

Then now you have to train the robot.

So you need some data.

Where do you get that data?

There's maybe enough publicly available data that can actually train a fairly rudimentary robot.

You got that data from online, maybe YouTube videos, a couple of other places.

Suddenly, the robot's not doing...

Now you need to deploy it onto the actual machine.

So then you deploy it onto the machine and then the robot runs into the wall.

Okay, what happened there?

The loop closes.

That platform for designing and developing is what we're launching.

It's called Dana, which is the street that Applied Intuition headquartered on.

This comes from our tooling background.

If you look at how tooling has changed in the digital AI world, if you look at what Claude did to all...

We also remember from Mixpanel to GitLab, GitHub, all these...

Now everything is moved into a very different, almost ID, frankly speaking.

We think the same thing's going to happen in the physical world.

That's what we're building.

That's what we built and that's what we're launching.

We already use it in-house to develop our autonomy system.

We're working on the most scaled, complex systems on the planet in all these different verticals.

So we're pretty confident that it's actually quite useful.

We've seen massive productivity gains.

But also, we think like other companies will use this to build their own systems because it gets to that mission, that billion intelligent machines.

Fundamentally, Dana is our agentic platform for physical AI.

Everything that we've built and developed over the past nearly a decade, every tool of every technique that's available in Dana, and it's very actually easy to use with the agentic interface.

Workflows that used to maybe take days or weeks to run, you can now run those in minutes in many cases.

This just lowers the barrier to entry to building these systems.

Just lowering the bar of what it means to develop an autonomous system.

Autonomy is still actually quite in the scope of software.

It's quite exotic because of the things that we've talked about.

We've just brought that down very aggressively.

It's kind of like the old adage of how do you make a great product in software?

It's like you either increase safety, convenience, or cost.

We want to try to do all three of those things with Dana.

Our hope is just like you said, kids can develop robots for their own use.

That extends to humanoids.

We're not just talking about land-based systems or humans that are humanoids.

You can do drones.

The fact that right now writing drone software and deploying it, it's quite obscure and almost hobbyist.

We want to just make that absolutely like to, maybe not child's play, but teenager play.

At this point in the world of a lot more experimentation and entrepreneurship and agriculture bots.

Every domain construction defense.

You just all of a sudden have a much larger number of people who are applying creativity and coming up with ideas and making things that move.

Have you seen with Claude, it's one thing just to make the engineer more efficient or bring more people into engineering.

Then when these agents really run, you're getting into...

It's just like the iPhone example of you couldn't imagine Instagram before the iPhone.

Imagine 2005 on laptops, you're like 10 years, there's going to be this app.

You can put photos and you're like, "Well, the phones don't have cameras."

It's like, "Yeah, but it's going to be social."

Like Facebook.

It's hard.

We think by lowering that barrier, you're going to get way, way more creative autonomy products.

I will start with you.

You decide whether to include this or not.

My kid is building autonomous bots in Factorio.

Oh, nice.

Yeah, he's one of his projects.

He's had it rolling because the toolkit's not available yet.

So he's actually training.

He's actually training models.

He's gathering data in the game and actually has a whole army of bots that he's developed to go out and...

And then his mother is like, "Why are you playing that game so much?"

And he explains, of course, it's a purely educational process and experience.

But it's the kind of thing.

There's no reason autonomy should be this obscure, difficult, alchemistic technology.

And I think not only does that have a huge impact on society, it also allows people to understand that these systems are not magic.

If I can develop a Roomba for myself in my house on a weekend using Dana, then it's not suddenly so scary.

And I think that's important.

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

I mean, you think about folks with disabilities.

We always think about humanoids as this very important task of folding laundry, which seems to be...

So we focus on the important task, but when you allow these tools to exist...

I mean, we started a tooling company.

I feel so importantly that tools are what separates actually advanced civilizations from less advanceable civilizations.

And our first mark for the company was a monkey's head.

And then we got a designer who said, "What?"

This is stupid.

I was like, "I thought it was pretty good."

So you were talking earlier about how when the technology got so good in mobile that there was a wave of these companies, Uber, WhatsApp, Snap, Airbnb, etc., that emerged in quick succession.

And so now that technology is getting there for the infrastructure for physical AI, what are some use cases or companies that you could...

Obviously, it's hard to predict the future, but what were you most excited for?

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

I mean, I think mid-term we want Dana, if not the short term, to really make humanoids way more real.

There's...

I mean, how many?

It's like a thousand core tasks in a home from humanoids.

And these companies, it's like such...

I mean, you talk to people who work in these companies, everything is difficult.

Every step of the way is difficult.

Collecting data is difficult.

Cleaning that data is difficult.

Training those models or deploying the model is difficult.

And the bar being, "I want a high school kid to make a humanoid."

So that's our path.

And we think there could be a lot there.

But that's like the obvious stuff.

I think the true non-obvious stuff is going to be...

We'll look back.

We'll be way more interesting.

And there's some core ingredients that we're bringing together in data.

We're making it way easier to actually get imitation learning to work, way easier to make reinforcement learning work in combination with that.

Where we have pre-trained models that can be used as a baseline for a lot of things.

World models, advanced simulation tech.

All of these things come together.

And then you're sort of limited by your creativity.

Like, "Well, what do I want to do?"

And if you think about any kind of physically eye task as...

You are understanding the world and you're manipulating something, and we can build that.

That can be built now much more easily in this tool.

And I think sometimes people ask, like, us being a tooling company, and you take soft driving trucks.

We deploy soft driving trucks, and many of the soft driving companies use our tools.

I think sometimes people ask, "Look, with Dana, are you going to enable all these competitors?"

That's great.

That's absolutely completely fine.

If you look at Google and what Google did to web applications, there was a massive internet.

Google still succeeded through search and YouTube and other web apps.

And other folks learned and used open source products, and then ultimately closed source products, and ultimately venture backed products.

And we think the same thing can happen here.

I was at a robotics startup a while back that you guys know well.

And they were training.

They were doing a training process, training one of their arms to do...

Particularly a killer app that I thought was very appealing, which was picking up dog poop.

[Laughter] Literally training over and over again, the difference.

And so, I don't know why not, right?

Yeah.

Why not have the little robot follow you around and walk you through it?

Yes.

Yes.

It's a good pickup.

Yeah.

And I think...

I know somebody built...

I forget who it was.

Somebody built a little lawn robot that would go around and pick up individual leaves.

Because you got that problem right.

"Hey, you're right.

You're right through your yard.

It's completely clean."

And then two hours later, there's 14 leaves.

And you're like...

Yeah.

And the development costs...

So send out the little bot to pick up the leaves.

It's like if development costs are zero, then people will do that.

Do you guys remember the early iPhone apps?

The hits were the beer one or the fart app.

[Laughter] If you imagine that in '98 with the Symbian Mobile, whatever...

The OS from I think it was Ericsson or somebody.

That would be impossible.

You'd need a team of 50 people to develop a beer thing for the Blackberry.

So I think there's a similar type of thing that's happening.

We really want to be part of that.

It would enable that.

And if it makes...

I think it'd still be a while before making a robotaxi is super, super easy.

But that'll happen.

But there's...

I mean, the number of bots that could be...

The number of kinds of bots that could be deployed in healthcare is almost...

Just healthcare alone is almost home care.

And then in construction, all the physical trades.

It's like us sitting in 2007 saying, "We should have an app store.

What type of apps?"

And we would come up with a list of eight.

And then they're like, "There'll be a messaging one.

And then there'll be a camera one."

And now you look at the app store and it's like, there's an app for the hotel you go to.

And it's like to order food off the menu.

Right.

Makes sense.

Yeah.

We were talking earlier about the differences between digital AI and physical AI.

We were sort of hinting at LLMs, but world models are in vogue right now.

Why don't you talk about the state of them as it relates to physical AI and how should we think about them?

So first off, world models means about a hundred different things.

And we had a team at CBPR recently and I was joking with them about just how many different ways you can define what a world model is.

But when we're thinking about a world model, we're typically thinking about it in the context of a simulation, right?

Something that is effectively...

Restart as a sim company.

Yeah.

Yeah.

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

Maybe Peter, I think it's worth being super explicit here.

We just go to just one level.

Determinism in simulators, kind of the sim to real gap physics-based rendering all the way to this generated world.

Where do we fit on it?

Describe the landscape, I think.

Yeah.

So this is like, let's say, simulation broadly, right?

There's so many different ways of doing simulation.

And so the more classical approaches of simulation, very physics-based.

And you can decompose physics in all different ways and all different levels of abstraction.

And you can simulate with sensors or without sensors.

And is it just a body simulation or are we actually simulating, for example, the light in the environment?

Almost think about the way CGI is done.

We literally had technical artists and we have technical artists who would create assets which would go in the simulator, which would mimic real road signs and have reflectivity and material properties that you would see in the real world.

But as you guys know, Hollywood is going through its own fundamental change.

And now you've generated technology.

The same thing is happening in our universe as well.

Yeah.

So that's at the far end of physics-based simulation.

And then the opposite end is purely neural simulation.

But within that spectrum, there's many different things you can do that are each useful in their own right.

And so one of those things is a Gaussian-based simulation where you have effectively a representation of the real world that has a 3D representation.

And that 3D representation is consistent, meaning that if you, let's say, have some reference point, let's say a camera, and that camera moves within that 3D world, because the Gaussian is actually representing the 3D geometry of that world, you'll actually get very high-quality output from that.

There's a lot of value in that.

And that's, let's say, one type of world model.

But when you go further on that spectrum into really into neural simulation, then you get into these where you're actually generating the video feeds.

You can think of a neural network that's actually outputting a video as what's actually coming out of the neurons of that.

And that can be reactive, which gives you some very interesting properties.

Reactive is in the ego, does something in the environment and the other agents respond to the ego?

Exactly.

However, you're not guaranteed in that reactivity that it's accurate, right?

And now it's a question of, well, how can I align this simulation, this world model, with the real world and the way that the real world would actually react?

And if you have perfect alignment between the real world and the world model, I think you've just solved the universe roughly, right?

That's a possibly difficult problem.

But as we make progress towards that, it makes training physically at models much easier because you can do more of that in simulation.

But the hardest part, though, is we're always talking about performance, right?

So I like to say the labs, they have it easy because they can make models that are trillions of parameters and those models can be super slow and that's fine.

But we don't have that luxury and physically high, right?

We deal in real time, like the actual clock real time.

And so we have so many milliseconds before we have to do something.

And those performance constraints, they actually constrain the problem in a lot of ways.

So we can have very large models.

And we do have very large models that are used in the off-world environment.

But once you go on board, all those constraints are very real.

And now we need to train a much smaller model that has these safety constraints, these determinism constraints.

And that's the hard part about physical.

That's also the moat, right?

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

Which will we get first?

A perfectly simulated real-world environment for training autonomous devices or Grand Theft Auto 6.

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

I reintroduced to Tom Petty because of that.

Will you give us some timelines?

Well, so this is...

Let's run with that for a second.

Yeah, go for it.

Well, no, look, I mean, so the whole thing with Grand Theft Auto is the big innovation was open-world sandbox gaming.

So it's a simulated city, at least in theory.

Yeah.

On that spectrum, we hire so many people about the video game world.

On that spectrum, it's absolutely real.

Tell us about that.

What's the spectrum?

So I hear, this is speculation, but I think Grand Theft Auto 6 will be perhaps the last major real-world video game that's still really developed, let's say, in that legacy era of traditional computer graphics tooling.

Technical artists.

Yeah.

Yeah.

I think that Grand Theft Auto 7 will much more likely be a world model-based video game.

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

It's possible.

But it could be the game as a concept.

It could be the real world, right?

As read as it is, you could have a complete recreation of the real world in the game.

This has kind of happened with flight simulators, hasn't it?

Yeah.

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

Is that right?

Yeah.

And that's where our bread and butter is when we started as part of the business.

We hired so many people out of the Microsoft Flight Sim.

I'm surprised they didn't.

But when you fly over New York or if you fly over Duluth in the flight simulator now, it is the real city.

Exactly.

But there's some tricks that they play there.

And a lot of that is fidelity.

The real world, the more you zoom in, it stays a certain level of fidelity.

And so the tricks that you play there is you basically are down sampling very, very aggressively.

And then as you get closer, then it becomes more high fidelity, whereas the real world isn't like that.

If you were to try to rebuild the world with this level of fidelity, it would take all the energy of the universe.

It's quite complex.

And that's probably, by the way, the best argument against us living in a simulation is among the...

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

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

Yeah, yeah.

That's true.

As far as I know, everything I think outside this room doesn't even exist.

Yeah, yeah.

You know Buddhism believes this.

It's a different type of podcast.

It's like when you open your eyes, the world is rendered and then you close your eyes, the world, that's literally religious.

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

Buddhism from first principles.

Yeah, exactly.

That'll get a lot of clicks as you call this.

Well, just going on the timeline topic, we gave us timelines on self-driving cars.

What timelines do you want to give us, if any, on other interesting things that were worth tracking, like perhaps when we'll get laundry folded or other things that emerge because of humanoids.

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

Yeah, yeah.

So to answer the first question, laundry folding, it's not terribly far from being solved to be clear.

And there is a lot of interesting research being...

And then humanity can rejoice.

That's in Proverbs 4, 16, I think.

Well, I do think housekeeping is a killer use case for physical AI, right?

Peter thinks too that he always talks about in the company, "What is housekeeping and entertainment?"

Peter's long on humanoid entertainment.

So what kind of entertainment do you want?

I would say...

I would 100% agree with that.

I think entertainment is a robotics killer.

I don't think anybody would.

Yeah, these two...

Yeah, yeah, that's quite a question.

I mean, like, I'm telling you, these Midwest white guys are really...

I'm just saying.

I think...

I just want to know when they get Westworld.

That's all I know.

No, I actually have an entertainment.

I have a little...

I have a tiny little Chinese robot dog.

So like, literally, it's just like a little...

It's just a little...

And it just like roams around and it just like...

Would you pay to see Cirque du Soleil with robots?

Like, yes.

Yes.

I want to see Kung Fu, trapeze swinging...

Focused by like a compiler who's the guy here.

I know, but...

I want Westworld.

I want Westworld.

I mean, the funny thing is I was just saying like, well, will people in like the suburbs of Detroit actually...

That passed in the test.

I bet you people in Sterling Heights would actually pay to see that.

It's actually true.

I stand corrected.

Back on laundry folding for a moment.

It's actually not far from being folded if you remove the time constraint.

And so the trick that's played, and if you look at the latest research videos, is they'll say like, "Play it at 8x real time."

Or whatever, right?

And that's for you to make it watchable.

So the question is, when can you actually show the most realistic future vision of robots?

Yeah.

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It seems like it's in the not terribly distant future.

I got a different answer.

You guys ever see that movie, Sam Rockwell, Moon?

Oh, yeah.

Yeah.

The set up.

I don't want to.

It's a great movie.

Don't watch the trailer just to watch the movie.

It's the premises.

The tagline of the movie is 250,000 miles from home.

You find who you are.

And it's one guy who works on an energy harvesting base run by applied intuition run by lunar technologies.

I don't want to be wailing you, Tony.

I don't want to be, you know, that's from from the alien franchises and then Tartarral Corporation from Blade Runner.

No, no, I want to be lunar technologies in the moon franchise.

Not in France.

It's one guy who works on this and basically runs by itself.

And he's just there to kind of mind it when things kind of some air signal.

Yeah.

The reason why it's I think so accurate is because the state of the art for AI systems is the system.

They just need the occasional grounding.

Exactly.

They'll just go off and do something crazy.

And then you're saying, no, stop doing that.

El Anjo like that too.

Yeah.

That's what coding bots are like.

And the other reason that I think it's quite accurate, it's maybe uncouth now, but Kevin Spacey is the AI, you know, smiley face.

And he's just there to kind of placate the human to assist, but to also like, he's like, oh, you seem like you're sad, Sam.

And like, you know, like that's the, but really, it's the one running the base.

And hopefully, I mean, I shouldn't say we want to be lunar technologies, but I don't know.

They're quite a positive force of nature in that, in that.

But I think massive energy farm that's completely autonomous.

That's going to be the future.

And I think everyone reacts to things like that with like fear.

And it's like, guys, that's amazing.

That means energy costs go way down.

That's an incredible positive thing.

I think the, you know, I just did this commencement speech at my undergrad.

Did you get destroyed?

No.

You know what?

I like Eric Schmidt.

Yeah.

Yeah.

Yeah.

Listen, listen, my, my, my wife started watching me.

She said, I feel like you're yelling at me.

I can't watch this.

So I, I basically, I mean, I don't, I'm not like, I don't, I'm not going to say which tech leaders who just basically avoid it by like punting and saying, I'm not going to talk about it.

I talk about this stuff.

And partly it's the General Motors Institute.

No one's like these.

These are, I don't want to, I don't, I don't, I don't want to throw judgment on, you know, the people we recruit out of, out of MIT and Stanford.

But I say GMI people are a little different and they're like, you know, they're like pragmatic people.

They under, they, you know, you don't go to a place like GMI if you believe a superficial view of what corporations do.

Corporations are just people working on projects together.

And by the way, people working on projects together in government, people working projects together in nonprofits, they all screw up.

And it's, and so it's, it's too simple to say, AI corporations are, are terrible.

And it's also, you can't say the other side, which is like, it'll all be great.

So you have a role to play.

That's basically what, you know, what my, what my message is.

And it's, that's, that's the case.

I think if you feel like, if, if, if the, the, the, the, I think the obvious, you know, abundance that comes from self-driving trucks, self-driving cars and the fact that people don't die, which is amazing.

But then you also get this efficiency of cheaper energy, et cetera.

If all those things don't still satisfy your, your fear, you, you as a person, it's up to your responsibility until really learn about that technology.

You, you, you can't just say, well, I'm afraid of it.

And my reaction is shut it down.

That's not that simple.

It's the, and I don't say this just to say that we're competing with the Chinese, but there's a confusion.

They're saying that by confusion, there's this, no hand can block the sun.

And the sun is technological progress.

If we as a society don't embrace technological progress, we will be left behind.

Right.

Somebody else is going to do it.

Somebody else is going to do it.

And it might fit out the Chinese, who knows?

It's maybe the Uzbek.

So it's the, you know, it's another country that is, is recognizing, hey, my citizens are suffering and I'm going to use this technology to move.

It is honestly, it's because we're living such a great society that we can have these like, I would say stupid conversations.

Like there still are people who don't, can't get food.

And someone will immediately quip if they were debating me.

They would say, well, there's plenty of food.

It's the capitalist system that doesn't, no, no, no.

Let's be very specific.

There's plenty of food, but getting that food to those people is difficult.

So that means we should let robots get that food to them faster.

That's just, that's just how it is.

So I think, and I think like we, we, we as like technologists, I think sometimes we, you know, it's I think an inclination just to say, leave these people behind.

I think you have to bring them along.

You have to explain it to them.

But we also have to treat folks like adults and say, if you don't get it after I explain it a couple of times, then you just don't get it.

There's like a middle ground.

It's not everyone's an idiot and we should just be, technology will just be perfect, perfect, perfect.

There's a middle ground.

Let's have that conversation to a point.

And then we just move forward and we make society better.

And then the results show it.

I mean, there's people who still shockingly believe communism is the right answer.

I mean, I just want to say why, and I'm, you know, I am a capitalist.

I can't, I can't, I cannot admit that, but there's 70 years of history there.

Like, that's not even a debate anymore.

I mean, I think it could be a debate if we're, if we're sitting here in 1965 and having a debate, you say, okay, maybe centrally controlled systems work better.

There's no debate anymore, folks.

You know, systems where individuals make decisions on their own interests actually work better for society.

And so that doesn't mean everything is perfect and you can't extrapolate that same thing with, you know, AI doesn't mean everything's going to be perfect, but net, net, it's definitely going to be better.

And that's roughly what my commencement speech was without the booze.

Mark and these guys were booing, so they just cut it out.

Eric was booing, he was throwing stuff, and they just edited it out.

You mentioned, you know, the Japan market earlier.

When you talk briefly about sort of the global ambitions and how these technologies, you know, interplay in what we're doing here.

So I think America particularly is still the most advanced in terms of when you take account the business model.

The second thing for a company like Applied Intuition, we're an extremely global company.

We work with everybody, minus, we don't have an office in China, but really everyone else on the globe.

And we're a horizontal company.

We're a technology provider.

And I think we, I think more Silicon Valley companies, I think can employ a little bit of what we do, which is work very, I would say collaboratively with the local economies.

As sovereign AI becomes more of a real thing, we have to build businesses that take that into account.

By the way, we're not the first ones to do this.

If you look at the history of America, you read the history of Standard Oil.

You'll see that this is, that was the history of companies.

You'd work internationally.

Aramco is not a random company, right?

You build based on the geopolitical realities of the time.

And so I think, you know, we've, I think, navigated it quite well.

I've lived, you know, in Japan, I lived in Germany, I lived in Dubai.

So also being Pakistani by birth.

I think that's also influenced our company.

Peter's only lived in Michigan and here, but he is a German.

So, but so I think we innately were more, we think about the globe more.

And I think when I was at both at Google and at YC, I was always surprised at how kind of almost myopic the companies are just always looking at the market.

That's just like within the 30, you know, between San Jose and San Francisco.

It's like, actually, the market is really big.

I think physical AI, the nature of it being physical, I think we have to be a very international company.

And I think we've had a lot of success being a very rare, you know, being international.

Yeah.

I had a good place to wrap.

Okay.

Peter Gasser, thanks so much for coming on the podcast.

Congrats on Big Lots with Dana.

Yeah.

Thanks for having us.

Awesome.

Great to see you.

Great.

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