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Our mission is to put intelligence on a

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billion machines. And we think that can

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have a profound impact on society.

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Applied Intuition is a physical
AI company. We put intelligence on

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machines. Cars, trucks, tanks, drones,
it's a physical moving thing. We make it

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intelligent. >> Digital
AI of course is building

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software and optimizing ads and creating
videos. That's all interesting and good,

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but really we talk about the global
economy, that's physical AI.

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>> In this intelligence revolution, the
companies that impact the physical world

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might actually be bigger >> [music]
>> than the companies that impact the

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digital world. >> How many
things are there where the idea

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of physical AI, physical
intelligence are going to matter?

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>> There's no reason autonomy should be

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this obscure, difficult technology. Our

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vision for that is a high school kid
that can make iPhone apps should be able

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to make autonomous systems. That platform
for designing and developing is

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what we're launching. It's called Dana.

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>> Everything that we've built and
developed over the past nearly a decade,

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that's available in Dana.
>> Which will we get first? A perfectly

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simulated real world environment
for training autonomous devices or Grand

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Theft Auto 6? >> [laughter]

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>> Kash, Peter,
welcome to the a16z podcast.

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>> Well, thanks for having us.
Your name is?

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>> [laughter] >> Yeah,
which is one of many.

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>> We've I think we've all
each known each other for

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too long, more than I'd like to admit.

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>> [laughter] >> Long time.
We're lucky to both be the

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first investor or among the first
investor in the first round. Of course,

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different check sizes, but >> And I
was an investor in you even before then.

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>> Exactly. Yeah. Um so, let's do that
as a segue. We have a lot to talk about

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today. We've, you know, the the biggest
launch in company history to talk about

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today. But first, why don't we just give
an update or a status [clears throat]

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What does Applied Intuition
do for those who don't

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

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company. We put intelligence on machines.
That's the the simple way of

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describing it. Um and all
types of machines. So, cars,

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trucks, tanks, drones, you name it. It's
a physical moving thing. We we make it

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intelligent. And uh the history
of the company is we originally started by

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making the tools that would make
the intel and then we got into the actual

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intelligence itself. Um in a very like

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in some ways like a very uh boring AI

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company in sense of, you know, 83%
of the company is engineering. We win by

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making really great products.
It's not like a

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good sales or something like that.
I don't think we're good enough for for uh

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for a sales enabled company, but uh

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yeah, over a thousand engineers um and

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based in Silicon Valley. Uh but we have

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offices globally, 18 offices. And uh you

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

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uh we think that can have a profound
impact on society both in the kind of

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busy things everyone talks about safety,
you know, if you really talk to somebody

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who's been in a car accident or in a
mining accident or, you know, uh in a

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farming accident. You Those Those are
real gnarly situations. Beyond just fixing

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that, you just if you
can unlock productivity,

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I think, you know, we've seen the unlock
in the digital world. And everyone's

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super excited about it and you have
trillion dollar companies emerging. I'm

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a pretty strong believer that I think
when we look back 25 years If you look

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back at the internet now, you know,
people actually If you look at the

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original internet companies that are
doing, you know, serving or they're

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they're doing some analytics and those
are interesting, but really when you

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look back 25 years from now, the big
monolithic companies are Amazon that

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delivers you stuff, you know,
Apple. These are the true kind of

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companies that come of age. And I
think when we look back 25 years in this

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intelligent revolution, the companies
that impact the physical world,

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you know, might actually be bigger than
the companies that impact the digital

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world. >> I would love
for you to talk about the

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following, which is when you first
started the company, you know, the knock

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on the company I think was, oh, well,
it's it's like, you know, car it's

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making making cars autonomous, right?

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Self-driving cars, but it's kind
of like, okay, there's like whatever,

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you know, there's there's Tesla
and Waymo building their own self-driving

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cars and then and then there's like
six or eight other car companies that

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matter. And then the company just could
never get that big cuz there just aren't

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that many customers. >> Yeah.
>> So, what's the like how should people

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think about like how many
things are there that are

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things that move where the idea
of physical AI physical intelligence are

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going to matter? >> Yeah,
I mean even today even if you do

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put that you know let's say view
on us the automotive is like 30% of

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our business so 70% already
is non-automotive and I think if you fast

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forward another 10 20 years even
the manufacturers themselves as a

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customer base will be a small amount.
I think our that mission just keep

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thinking a thought you know a billion
machines becoming intelligent.

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

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an easy one. I think it sticks in people's
heads because we all drive cars

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and it's a big market but I
think it's it'll be a minority of

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

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minority business but that doesn't
necessarily mean it'll be small.

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Automotive is still huge just as a part
of the globe's GDP automotive is

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something like 3% of all GDP.

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I think the way we always think about
it like as you try to get to your mission

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initially the manufacturers were
the distribution to that intelligence to

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consumers but then you start working
in defense and you start working in

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construction and mining and agriculture
and suddenly the manufacturers are

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important but maybe the mining operators
actually really important or the

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Department of War is really important
and suddenly they become customers and

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all of those are
customers of ours as well.

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>> Yeah, I think if you split AI into
digital AI and physical AI right digital

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AI of course is building software and
optimizing ads and creating videos that

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sort of thing. That's all
interesting and good but

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really when you talk about the global
economy that's physical AI. Then we're

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talking about manufacturing and and
mining and logistics and transportation

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all of these things that supply chains.

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>> Yeah, supply chains exactly.

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>> Let me let's build on that for a second
which is like so things that move today

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or you know historically things that
move are things that have human beings

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at the wheel or at the controls
in some form right? And so and you know

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airplanes [snorts] have had to get
designed around a human in the cockpit

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boats have had to you know, get designed
around a human, you know, steering

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things. Um like in a world
where in a world of

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autonomy like Do we already
know what the things are

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that move? Or are we going to discover
that there are a lot of new things that

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are going to get built when you don't need
a a human in the in the driver's seat?

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>> I think both. Well, the the the the
thing that you have to remember is like

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you take like a a haulage system that's

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on a in a in a port.
Um like a caterpillar Komatsu, the dirt

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mover in a mine.
Those are made for 20 25 years.

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>> Mhm. >> So, the buyers of that of those
products, they're they might not have

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gotten their full cycle, you know,
ROI on them. So, they're not immediately

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going to buy something new no matter how
much better it is. So, one part of our

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strategy is you got to make those
things intelligent cuz they're not going

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anywhere. The second is what you're
talking about, which is well that

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depends on a human in a cab. If you
don't have a human in a cab, you can run

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the the machine can be smaller. It can
be shaped in very different ways. You

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need to talk about mining underground.

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>> Right. >> The constraint
actually is the human

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because the human needs to breathe and it
needs to it's very dangerous. And so,

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you can build a very
very different machine.

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We're doing both of those things.
And and then the thing that you were not

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talking about is we're all talking
about intelligence almost like within a

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system, but the system level
intelligence is where the unlock is. And

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we're already doing work like that where
you say, "Hey, let's take an entire

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port. Let's take an entire mine.
Let's take an entire quarry."

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And this heterogeneous mix of machines

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they're all can talk to each other.

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And they can optimize and be efficient
when one machine goes down or one

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machine has an issue. The rest of the
mine doesn't have to stop. When it's

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human driven, we don't even know
the machine's going to go down because

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there's no announce.
The human is not plugged into the core

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systems of the machine. >> Right.

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>> So, like a simple thing like knowing

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when a brake system is going to break
is actually huge cuz you can start

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preparing for it in advance. You're like,
"Oh, this wear and tear is higher

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than in other mines. He was using example.
But like the the other macro

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point is if you look at agriculture
an example, you know, average American

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farmer is 58 years old.
The there's that the number something

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like under 35 it's like less than 10% of

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farmers are that young. So, what's going

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to happen? The need for food growth is

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continuing to grow. The need for rare
earth materials is coming so these

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these demands are only growing,
but the humans who are the bottleneck are

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decreasing. Trucking is the same way.

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Um and so you can you can
really just unlock a lot more

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efficiency. So, I mean one way to think
maybe think about this is like imagine

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if the cost for food decreases because
it's way way more efficient. What what's

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the downstream impact? Then imagine
for goods being transported. Let's say

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you know, instead of a few dollars
a mile it's 20 cents a mile. And suddenly

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it's it's I think that the unlock
is very very very big. Um and that isn't

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that I think doesn't necessarily need
for all the machines to be redesigned

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from the ground up. >> Right.
Right. Got it. Makes sense.

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>> And then maybe just one one more
question would be just give give a give

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us a sense of parameterize like
the scope and scale of the company today.

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>> Yeah, uh north of a
thousand engineers. And

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those engineers are, you know, obviously

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the classic, you know, software
and AI engineering teams. But

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we also have engineers who really know
safety systems. We also have engineers

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who really know hardware. Because
the important thing that we we're kind of

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just tipping around stepping around
is all this stuff is hard because it

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ultimately has to meet the real world.

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And the real world is has way more
complexity and and and has a lot more

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issues. And we have engineering teams
that can I mean we've deployed our our

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our models onto like 50 some platforms.

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Like even that sounds trivial because
when you think about mostly when you

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think about models you think about
deploying them through a browser or on a

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phone and everything's abstracted
away because you have iOS and you have

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Android and you have Windows and you
have Linux and you have all these

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systems that have already taken care.
In the real world you don't have that.

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>> Right. >> And so we we
have engineering teams that

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can do that as well. Our kind of a you
know, claim to fame is we've raised over

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about a billion dollars in the company's
history. All that is sitting in the bank

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and I think that's I always say that
with an asterisk because it doesn't mean

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we're not going to spend it,
you know, next next month.

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>> good news, bad Good news, bad news.

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>> Yeah, good news,
bad news. It's but it's

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the the and I think like we're at that
phase, you know, you talk about scale.

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We're at that phase where

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these giant markets are around us and we

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can make the decision how aggressive
do we want to pursue those because

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decade of frankly execution and
deployment into production. I think the

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the hallmark of our engineering
team is is putting

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you know, products into production. That
that that that that really is is a big I

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don't know how do you think about scale?

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>> Yeah, I think that that's roughly it.
I mean the the the mission of bringing

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intelligence to a billion machines.
That is how we think about it. And then

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thinking about well, what are the types
of machines that will have the most

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impact on and focusing on those areas
first, but we'll we'll get there.

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>> I let's go deeper into the differences
between digital and physical AI and more

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so into where where are
we today? What progress

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has has been made? What are some of the
main major bottlenecks in in physical

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AI? When you unpack some of that.

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>> Yeah, I mean I think a lot of times
people think about the progress in

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physical AI is limited to basically two
case use cases in there just cuz they're

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obvious and interesting,
which is robo-taxis and humanoids.

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They're very visceral. They're they're
they excite you and they're kind of

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sci-fi. I think they're
those are very interesting

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and they there is real work being
done by us and other people in in those

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domains. I think all the other uh all
the other domains I think are going

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to be just as important. I mean you just
you just think about what happens on a

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port. That's there's a huge unlock there

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and that's I think that's the that's
the area where where where really focused

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on. It's like all the other
nooks and crannies.

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If you look at like we we've talked
before about uh the rise of Cisco and

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how, you know, networking kind of went
from, you know, first individual

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machines and companies would get network
and then entire countries were getting

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network. There's a similar thing

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happening with AI. AI is getting to that

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level of kind of sovereign AI is now a

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discussion. Sovereign AI really is about

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physical AI. Because that's where you're
talking about AI in defense, you're

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talking about AI in the physical
machines that are moving around. If you

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look just at the example of of Waymo

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from America and Pony from China,
they're trying to deploy in, let's say,

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the other country, so not America, not
Europe, not China, every one of those uh

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spaces, they're way more uh
they're way more hesitant of saying,

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"Yeah, thumbs up, your robot taxis can
run unfettered on our in our country."

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And so, if you look back just
at kind of this arc of the internet,

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you know, when the first internet
companies come, nobody's really thinking

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about sovereignty at all. It's like,
the browser goes everywhere, the internet

228
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goes everywhere, that's almost the power

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of it. Then when social media emerges,
there's a bit more of, "Hey, actually

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not every social media." And then you
have China not allowing Facebook to come

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in and then you have some Then you get
into the next level of like the online

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offline stuff. There's more resistance
to Ubers, to DoorDashes. Suddenly,

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

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

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there's like a larger geo you know,
geopolitical theme of of kind of

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more fracturing than globalization.

237
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You're going to have this demand for

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this AI should somehow be localized. And

239
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I think that has to play into
our strategy as well. We're technology

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provider. So, we can provide
that technology across the globe.

241
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And I think that's that's that's
that's that's that's something that's

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understated in this in this conversation.

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>> Yeah, a few other things on on digital
versus physical AI. So, in digital AI,

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the state of the art is you you can
train models effectively on the entirety

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of the internet and then maybe augment
that with additional data that's been

246
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collected and refined with some hired
experts, right? This is sort of a hot

247
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field right now. Uh but generally you're
talking about a foundation model that's

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built on internet data. In physical AI,

249
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the internet data is is useful, too.

250
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However, to actually build a foundation
model in physical AI, there's also a lot

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of private data collection.
You uh when we're talking about

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mines or or logistics or any of these
other uh other fields, the the data that's

253
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useful for training models there is not
necessarily available. So, we have to do

254
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a lot of work ourselves actually going
going out and collecting that data. And

255
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then the other key factor is safety,
right? Uh if you're talking about uh

256
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building a smartphone app,
you don't necessarily care about uh

257
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is is a safety-critical application,
but when you're talking about moving a a

258
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machine that weighs many tons uh
or think of a humanoid which could fall

259
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over on your children, uh you care a lot
about safety and and the evaluation of

260
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that safety and and that is really sort
of getting to the state of the art of

261
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physical AI and and really proving out
uh the safety case around some of these

262
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state-of-the-art models. >> Yeah,
and I think like you know, you you

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talk about like humanoid data collection
has been its own, you know, uh little uh

264
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area of interest. But when you talk
about collecting data like in places

265
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like Korea where they have North
South Korea where you have North Korea,

266
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they don't allow mapping companies, let
alone allowing a you know, a an American

267
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company to come in and data collect. So,
we've figured out over the years whether

268
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it's the Middle East, whether it's uh
LatAm, how to get into these countries,

269
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work with the governments, and get
the thumbs up to collect proprietary data.

270
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And so, in in the way that it's it
is similar to very you know, other uh

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digital AI systems, your proprietary
data sets, scaling laws, all

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that stuff is the same. It's just
applied in a very very different way.

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And uh it's almost like
the way to think about

274
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it is like the diffusion of these models

275
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is very different because you can't it's

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not everyone can just access them
through a phone. You're you're going to

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And so so that ironically
is actually plays in our

278
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favor. Because once we have a massive
proprietary data set which we've been

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building, we already have
hundreds of petabytes of data.

280
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Um and then we have our own tools
which are like, you know, synthetic data

281
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tools, uh neural sim. We can use our own
tools and our own proprietary data and

282
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that allows us to build some
of the best systems in the business.

283
00:15:48,080 --> 00:15:50,997
>> Cuz is it There's kind
of a chicken and egg thing

284
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which is like in order to build
an autonomous physical thing, you need a

285
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lot of data. To gather
that data, you need a lot of

286
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physical autonomous things running
around collecting the data. So it's like

287
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once you have a giant network of physical
things running around, you have

288
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the data that makes them all work. Like
is there a Is there a flywheel aspect to

289
00:16:06,960 --> 00:16:09,637
that? And is there Is there Is there
like what what what's the level of

290
00:16:09,720 --> 00:16:11,397
difficulty involved in kind
of booting up that flywheel?

291
00:16:11,480 --> 00:16:15,357
>> Uh it's it's it's difficult but it's
also not difficult. I mean, I think we

292
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we have one of the largest data
collection fleets on the planet, frankly

293
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speaking. Um so that's how you bootstrap
your way into it. That's just money and

294
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resource and and technical knowledge.

295
00:16:24,920 --> 00:16:27,637
It's but it's not like
there's probably more than five

296
00:16:27,720 --> 00:16:29,917
companies that have that technical
knowledge. So it's not extremely

297
00:16:30,000 --> 00:16:33,757
obscure. I think what
is more difficult is then

298
00:16:33,840 --> 00:16:35,677
how do you actually have that model

299
00:16:35,760 --> 00:16:37,797
which is going to work on lots of

300
00:16:37,880 --> 00:16:41,037
different hardware and is, you know, is

301
00:16:41,120 --> 00:16:45,517
is tested appropriately? Because the You
saw it, you know, in Cruise, right?

302
00:16:45,600 --> 00:16:50,037
Cruise was this company that did amazing
self-driving work and then one accident.

303
00:16:50,120 --> 00:16:53,117
General Motors owns them super scared
and they pull back. So it's like just

304
00:16:53,200 --> 00:16:56,557
getting these things into production
is actually more difficult than than the

305
00:16:56,640 --> 00:17:00,837
than it seems. Um I think
like we believed synthetic data

306
00:17:00,920 --> 00:17:04,477
was going to be important. So we started
our synthetic data team like 5 years ago

307
00:17:04,560 --> 00:17:08,037
now plus. >> than that, yeah.
>> Yeah, more than that at this point. Uh

308
00:17:08,120 --> 00:17:12,917
and like when we were a strong believer
that synthetic data can accelerate

309
00:17:13,000 --> 00:17:16,717
autonomy development. We've just seen
that. And then there are like lots of

310
00:17:16,800 --> 00:17:20,677
other secondary and tertiary like um
technical innovations that have Obviously,

311
00:17:20,760 --> 00:17:23,677
the transformer revolution hitting
self-driving, massive. Basically,

312
00:17:23,760 --> 00:17:27,077
everything done in self-driving pre-21,

313
00:17:27,160 --> 00:17:30,757
'22, relevant, but you're almost like

314
00:17:30,840 --> 00:17:34,397
that's kind of the starting point. But
it's also different than today being the

315
00:17:34,480 --> 00:17:37,357
starting point. Like there the those four,
five years are actually there has

316
00:17:37,440 --> 00:17:41,157
been a lot of work done. You can see
it most clearly with Tesla, but there's

317
00:17:41,240 --> 00:17:45,077
other folks. In that process,
the actual techniques

318
00:17:45,160 --> 00:17:48,717
historically and let me just simplifying
here. Imitation learning was the way the

319
00:17:48,800 --> 00:17:52,637
way the game, which was collect a bunch
of data and then the models would

320
00:17:52,720 --> 00:17:55,717
basically imitate what human drivers do.

321
00:17:55,800 --> 00:17:57,637
The real state of the art right now is

322
00:17:57,720 --> 00:18:00,357
end-to-end reinforcement learning in in

323
00:18:00,440 --> 00:18:03,437
a closed loop in in your tools. And so,

324
00:18:03,520 --> 00:18:07,677
it's a little simplified to say
the system learns itself. It identifies

325
00:18:07,760 --> 00:18:12,197
where the issues in the self-driving
system are and essentially you then find

326
00:18:12,280 --> 00:18:15,997
data like that or you synthetically
create data like that. And then you you

327
00:18:16,080 --> 00:18:19,037
know, you close that loop and you see
are you performing the same scenarios

328
00:18:19,120 --> 00:18:22,837
better and better. I think if
you fast forward some years,

329
00:18:22,920 --> 00:18:26,517
that will be a completely closed loop
like with no humans intervening. Right

330
00:18:26,600 --> 00:18:30,877
now you still have like like what's
the the fog error that we saw uh we still

331
00:18:30,960 --> 00:18:33,397
see errors in the real world
that impact self-driving.

332
00:18:33,480 --> 00:18:36,157
>> Oh yeah, so so it's like what what what
what are the bottlenecks, right? And and

333
00:18:36,240 --> 00:18:39,557
the the bottlenecks there's
plenty of them, but whenever

334
00:18:39,640 --> 00:18:42,957
you're dealing with physical systems,
you inevitably you hit a lot of

335
00:18:43,040 --> 00:18:45,197
gnarly hardware problems and it could be

336
00:18:45,280 --> 00:18:48,397
anything from overheating to

337
00:18:48,480 --> 00:18:51,957
sensor being slightly miscalibrated or

338
00:18:52,040 --> 00:18:55,277
funny funny issues I saw yesterday was

339
00:18:55,360 --> 00:18:59,557
basically a fogging sensor like fog
impacting a sensor. And these are the

340
00:18:59,640 --> 00:19:02,437
things that you actually have to solve
for this stuff to work very reliably in

341
00:19:02,520 --> 00:19:04,597
the real world. >> Yeah.
So I'm I'm going to ask you a

342
00:19:04,680 --> 00:19:07,317
three-quarter a question and you can
we can decide whether you guys want to

343
00:19:07,400 --> 00:19:09,837
engage on it or not. It might be
an opportunity or might hit the question

344
00:19:09,920 --> 00:19:14,637
which is Were surprised So Cruise was a
super high-flying Silicon Valley autonomy

345
00:19:14,720 --> 00:19:17,437
startup that was kind of running neck
and neck with Tesla early on and so

346
00:19:17,520 --> 00:19:20,197
forth and you know, very top-end team
and then they famously got bought by

347
00:19:20,280 --> 00:19:22,237
General Motors and they >>
One of my first distributions

348
00:19:22,320 --> 00:19:23,877
personally, so I had enjoyed it.

349
00:19:23,960 --> 00:19:27,997
>> we go. Y Combinator Y Combinator
Y Combinator Y Combinator company. Um

350
00:19:28,080 --> 00:19:31,757
and um and you know, top-end team and they
they were you know, by all accounts

351
00:19:31,840 --> 00:19:34,077
making excellent progress. They got
bought by General General Motors. They

352
00:19:34,160 --> 00:19:37,837
became the GM autonomy program. GM got
a lot of praise at least you know, at

353
00:19:37,920 --> 00:19:40,557
least in the in the in the in tech
circles for being like Okay, being like

354
00:19:40,640 --> 00:19:43,517
the the legacy automaker
with the biggest investment

355
00:19:43,600 --> 00:19:46,597
>> I I called Peter when uh before before
it was announced on that and I said,

356
00:19:46,680 --> 00:19:49,117
"Hey, Cruise just got bought." You know,
he's also GM family. We're both GM

357
00:19:49,200 --> 00:19:52,597
families. And uh Peter guessed
it was said, "Nvidia?" I

358
00:19:52,680 --> 00:19:54,277
said, "No." He said, I said, "Go fish."

359
00:19:54,360 --> 00:19:58,577
He said, "Apple?" I said, "No."
I said, "General explicit of Motors."

360
00:19:58,660 --> 00:20:02,197
>> [laughter] >> So, that's
surprising to people who are from GM.

361
00:20:02,280 --> 00:20:03,157
>> That they were
willing to buy the thing.

362
00:20:03,240 --> 00:20:05,357
>> Yeah, that they did it. >> Okay,
that they did it. And then by all

363
00:20:05,440 --> 00:20:08,237
accounts they were I mean, as far as I
as far as I ever heard like they were

364
00:20:08,320 --> 00:20:09,637
making excellent progress. >> Yeah.

365
00:20:09,720 --> 00:20:10,917
>> And then they had
this There was a There

366
00:20:11,000 --> 00:20:14,677
was an accident. There was a Was there
Was there a injury or fatality or

367
00:20:14,760 --> 00:20:16,917
>> Uh it wasn't a fatality, but it
was a serious injury. Somebody was dragged

368
00:20:17,000 --> 00:20:19,757
[clears throat] for 20 ft. >> Yeah,
serious injury, bad press and then

369
00:20:19,840 --> 00:20:24,357
and then they put a bullet the the GM
CEO and board put a bullet in the Cruise

370
00:20:24,440 --> 00:20:27,677
project and I know the at least some
of the senior Cruise people were extremely

371
00:20:27,760 --> 00:20:30,837
upset you know, >> [clears throat]
>> by the aftermath of that. Was it

372
00:20:30,920 --> 00:20:33,637
surprising that they reacted
the way that they did?

373
00:20:33,720 --> 00:20:35,397
>> Uh so, you know,
full disclosure General

374
00:20:35,480 --> 00:20:37,997
Motors is a customer and I went to the
General Motors Institute. So, we have a

375
00:20:38,080 --> 00:20:42,997
lot of love for the company. Uh but
incidentally and ironically, I'm reading

376
00:20:43,080 --> 00:20:45,997
uh or coincidentally I should say,
I'm reading this very famous book which I

377
00:20:46,080 --> 00:20:49,667
had actually never read before called
On a Clear Day You Can See General Motors.

378
00:20:49,750 --> 00:20:51,717
>> [laughter] >> And DeLorean's book.
Have you read it?

379
00:20:51,800 --> 00:20:53,677
>> does. >> Have you read that book?

380
00:20:53,760 --> 00:20:56,517
>> So, I have years ago I have. It's one
of the great all-time book titles and we

381
00:20:56,600 --> 00:20:59,437
should just pause to say John DeLorean
was like what he was like the super

382
00:20:59,520 --> 00:21:01,237
genius of the car industry.
>> Yeah, he was going to be the next

383
00:21:01,320 --> 00:21:03,717
president of General Motors.
>> of General Motors and then later on he

384
00:21:03,800 --> 00:21:07,637
started his own car company which Which
in Back to the Future and then, that

385
00:21:07,720 --> 00:21:11,397
whole thing collapsed for a variety
of reasons. Uh but yeah, he he was like a

386
00:21:11,480 --> 00:21:14,757
legend. He was like one of the main
principal drivers of innovation in the

387
00:21:14,840 --> 00:21:17,997
car industry. >> Exactly. Lee Iacocca
bought lots of these categories.

388
00:21:18,080 --> 00:21:20,157
>> Yeah. And you got
to remember this is in this rate

389
00:21:20,240 --> 00:21:21,757
>> Sorry, repeat the ti-
repeat the title of the book.

390
00:21:21,840 --> 00:21:23,557
>> On a Clear Day You
Can See General Motors.

391
00:21:23,640 --> 00:21:24,557
>> why was that the title of the book?

392
00:21:24,640 --> 00:21:29,277
>> Because uh there's
a lot of >> [laughter]

393
00:21:29,360 --> 00:21:31,837
>> It's a very large complex.
>> Yeah, complex. Yeah, yeah.

394
00:21:31,920 --> 00:21:33,197
>> It's like a It's like a nation state.

395
00:21:33,280 --> 00:21:37,917
>> Yeah, it I mean, really. I mean, it It
really I think we say that like sometimes

396
00:21:38,000 --> 00:21:41,917
almost like flippantly. But these
companies are like extension like

397
00:21:42,000 --> 00:21:43,237
Hyundai is an extension of the state.

398
00:21:43,320 --> 00:21:44,397
Kia is an extension of the state.

399
00:21:44,480 --> 00:21:48,077
Volkswagen is literally Volkswagen board
members are members of the government.

400
00:21:48,160 --> 00:21:51,357
So these are extensions of the state
in in in almost every uh And there used to

401
00:21:51,440 --> 00:21:54,077
be an old saying, "What's good
for General Motors is good for America."

402
00:21:54,160 --> 00:21:59,077
>> Right. >> And you cannot understate
how important General Motors is to the

403
00:21:59,160 --> 00:22:00,397
history of the American corporation.

404
00:22:00,480 --> 00:22:04,197
Sloan's uh My Years at General Motors
and Adventures of a White-Collar Man. If

405
00:22:04,280 --> 00:22:07,077
you run a large engineering organization,
you should read that. That

406
00:22:07,160 --> 00:22:11,277
is the this like you this com-
this thing that that we talk as a

407
00:22:11,360 --> 00:22:13,077
modern corporation didn't just emerge.

408
00:22:13,160 --> 00:22:17,397
Sloan and Kettering create Kettering
is a head of engineering created this with

409
00:22:17,480 --> 00:22:21,157
this arc you know, with levels and vice
presidents and how do you do functional

410
00:22:21,240 --> 00:22:25,517
and matrix organizations?
It's There really is like the source code.

411
00:22:25,600 --> 00:22:30,397
Comes along John uh you know,
comes on DeLorean and he says

412
00:22:30,480 --> 00:22:32,757
um he writes his he's
going to be president

413
00:22:32,840 --> 00:22:34,957
and he's so fed up with the company.

414
00:22:35,040 --> 00:22:39,277
But what was controversial was GM
was doing really well at the time. GM

415
00:22:39,360 --> 00:22:43,677
was like a When we say like GM
was number one in the Fortune 100, it was

416
00:22:43,760 --> 00:22:48,277
like number one, two, and three. It
was everything and it was seen as the best

417
00:22:48,360 --> 00:22:51,957
company in America. So somebody
to openly criticize the company. And so he

418
00:22:52,040 --> 00:22:56,117
has a whole he had writes this book
as he quits out of out of uh

419
00:22:56,200 --> 00:22:59,797
how annoyed he was at General Motors
for being read uh led. Uh he writes this

420
00:22:59,880 --> 00:23:02,997
book and then after he like sobers up,
he's like, "I don't want that book

421
00:23:03,080 --> 00:23:05,757
published." [laughter] And so
he fights for a years for his

422
00:23:05,840 --> 00:23:09,597
co-author not to publish the book.
The co-author still publishes. So it's a

423
00:23:09,680 --> 00:23:12,637
real true insight into
a large corporation.

424
00:23:12,720 --> 00:23:15,437
Um incidentally just reading it out
even though I've you know worked at GM 20

425
00:23:15,520 --> 00:23:18,957
some years ago and had you know
know a lot about the company.

426
00:23:19,040 --> 00:23:22,037
And what's shocking is it's not
only about GM, most of the major

427
00:23:22,120 --> 00:23:26,717
manufacturers actually still operate
that way on the inside. And so

428
00:23:26,800 --> 00:23:30,477
the question isn't the the the point
I think for everyone to take away isn't

429
00:23:30,560 --> 00:23:34,637
these people who run these companies are
stupid. They're not stupid. They're It's

430
00:23:34,720 --> 00:23:37,437
kind of like you know when you're
selling to the Department of War and

431
00:23:37,520 --> 00:23:39,477
people say, "Well, why are you
doing that?" It's like, "Well, the

432
00:23:39,560 --> 00:23:41,797
distribution defines the business."

433
00:23:41,880 --> 00:23:46,237
That like so like the distribution
is this is a consumer product. Uh

434
00:23:46,320 --> 00:23:49,437
this this stat might be out of date, but
when I worked in safety systems 20 years

435
00:23:49,520 --> 00:23:52,837
ago I remember GM used to pound in your
head of the top five consumer lawsuits

436
00:23:52,920 --> 00:23:56,877
in in American history, three are
automotive. We got the majority, right?

437
00:23:56,960 --> 00:23:59,637
So it's like you have to be extremely
careful. We had these like weird things

438
00:23:59,720 --> 00:24:04,237
like inside the company you can It
wasn't red, yellow, green. It was like

439
00:24:04,320 --> 00:24:07,877
purple or like you'd always have those
decoder because you know why? Because

440
00:24:07,960 --> 00:24:11,317
when they go to lawsuit they're like,
"You let a safety system that was marked

441
00:24:11,400 --> 00:24:14,687
red go to production." I was like,
"No, it was marked magenta."

442
00:24:14,770 --> 00:24:18,917
>> [laughter] >> Like so
like can you imagine how

443
00:24:19,000 --> 00:24:23,237
infuriating that is? Every time you're
like "What does orange mean? Does

444
00:24:23,320 --> 00:24:27,677
this mean I have to like So fast
forward to you're meeting that system."

445
00:24:27,760 --> 00:24:30,917
>> Well, for the Ford slogan
for a very long time was it

446
00:24:31,000 --> 00:24:32,157
was quality is job one, right?

447
00:24:32,240 --> 00:24:33,957
>> Yeah, yeah, yeah.
Which figures with safety as well.

448
00:24:34,040 --> 00:24:37,637
>> Yeah, yeah, exactly. And and that's
the one-two punch of automotive. It's

449
00:24:37,720 --> 00:24:38,917
quality and safety. Quality and safety.

450
00:24:39,000 --> 00:24:42,997
And quality really becomes because the
Japanese really reset that that stage in

451
00:24:43,080 --> 00:24:45,757
in because the that's
a whole separate automotive

452
00:24:45,840 --> 00:24:48,357
history. We could talk about automotive
history for for an hour, but the

453
00:24:48,440 --> 00:24:53,037
punchline is you have the Silicon Valley
company meeting this immovable object.

454
00:24:53,120 --> 00:24:56,317
There is a parallel universe
that cruises out there right now

455
00:24:56,400 --> 00:24:59,997
even as a part of General Motors. So,
I think you always have to take it into

456
00:25:00,080 --> 00:25:03,877
the context of where the company is,
where union negotiations are happening

457
00:25:03,960 --> 00:25:07,037
literally that year. And if you're
the union, you're like, you can't make a

458
00:25:07,120 --> 00:25:10,157
million dollars for us,
but you're funding this thing >> Right.

459
00:25:10,240 --> 00:25:12,197
>> that's killing people and it's sloppy.

460
00:25:12,280 --> 00:25:15,557
And so, I'm not saying precisely that's
what happened, to be very clear. But,

461
00:25:15,640 --> 00:25:19,397
that it's a multi-varied problem. My other
hot take is, you know, I worked at

462
00:25:19,480 --> 00:25:22,797
both companies, right? Google Google and
General Motors. Those companies are way

463
00:25:22,880 --> 00:25:24,477
more similar than they are different.

464
00:25:24,560 --> 00:25:26,037
Way way more similar than they are.

465
00:25:26,120 --> 00:25:29,757
Literally, people don't need you know
this, the Google leveling system is the

466
00:25:29,840 --> 00:25:33,317
same as the General Motors [laughter]
leveling system. And I used to say, I

467
00:25:33,400 --> 00:25:35,757
used to say you know, this inside
of Google meetings. It was like, "Hey,

468
00:25:35,840 --> 00:25:37,717
actually some of the engineers I knew
at General Motors are better than the

469
00:25:37,800 --> 00:25:41,157
engineers here." And people would look
at me like I'm saying there's no God in

470
00:25:41,240 --> 00:25:44,917
church. It's like, they're like,
"How dare you, you metal butt bending

471
00:25:45,000 --> 00:25:47,157
monkey from Detroit?" >> [laughter]

472
00:25:47,240 --> 00:25:49,517
>> I was like, "No, actually like making a

473
00:25:49,600 --> 00:25:52,477
modern combustion engine is extremely

474
00:25:52,560 --> 00:25:54,477
complex. It's not just like, you know,

475
00:25:54,560 --> 00:25:59,637
it's it's not simple stuff."
And so, the macro point I think is

476
00:25:59,720 --> 00:26:03,357
it's a bunch of things. I think safety
is always at the top top of their top of

477
00:26:03,440 --> 00:26:07,797
their list. I do think, you know,
we've hired lots of Cruise people. I

478
00:26:07,880 --> 00:26:11,597
think the way they dealt with that
specific issue with the government, you

479
00:26:11,680 --> 00:26:14,477
got to you got to dance a particular way
when that happens. And they just didn't

480
00:26:14,560 --> 00:26:17,237
dance exactly right. And that just gives

481
00:26:17,320 --> 00:26:21,517
government bureaucrats more
ammo to go after. And you're big

482
00:26:21,600 --> 00:26:24,397
target like General Motors,
you got to you know,

483
00:26:24,480 --> 00:26:29,797
it it reminded you guys ever see
that movie like Goodfellas? You know,

484
00:26:29,880 --> 00:26:32,757
one of the last scenes of the House
of the Rising Sun, you know, all the old

485
00:26:32,840 --> 00:26:35,797
bosses go in the back of the
courtroom and they're like,

486
00:26:35,880 --> 00:26:37,677
And you know, that's what
happened. They were like,

487
00:26:37,760 --> 00:26:42,067
the board was like, "What do we do about
Cruise?" They're like, "What can we do?"

488
00:26:42,150 --> 00:26:47,037
>> [laughter] >> It's like,
"Kyle's a GOOD GUY, BUT >> [laughter]

489
00:26:47,120 --> 00:26:50,117
>> AND THEN IT'S LIKE,
cue House of the Rising Sun.

490
00:26:50,200 --> 00:26:54,157
People are running through the San
Francisco just kidding. [laughter]

491
00:26:54,240 --> 00:26:56,717
Don't don't make that an AI video.

492
00:26:56,800 --> 00:27:00,277
It's going to get a >> [laughter] >>
going to get a mean text from Kyle.

493
00:27:00,360 --> 00:27:03,077
So, I think I think there is a universe
where it would it would survive, but

494
00:27:03,160 --> 00:27:05,517
it's it's it's it's tough. >> So,
then a lot of a lot of what Applied

495
00:27:05,600 --> 00:27:08,237
Intuition does is kind of as you said,
like that dance. It's like how to how to

496
00:27:08,320 --> 00:27:09,837
be a great partner to these companies.

497
00:27:09,920 --> 00:27:14,077
>> Exactly. >> Bearing in mind their own
real very real issues and constraints.

498
00:27:14,160 --> 00:27:18,317
>> I I I think GM was also had the topic
of business model, right? So, you have

499
00:27:18,400 --> 00:27:22,277
Cruise was going after the robot taxi
concept, but GM makes its profits from

500
00:27:22,360 --> 00:27:25,357
personal car ownership. And those things
can be a bit odd. So, I think that was

501
00:27:25,440 --> 00:27:27,317
also a bit of the >> Oh, right.

502
00:27:27,400 --> 00:27:31,557
>> Yeah, and I think it wasn't clear.
I mean, the by the way, you know, um

503
00:27:31,640 --> 00:27:35,797
you actually, of all people, you
spoke at YC at 20 in 2013. I was in the

504
00:27:35,880 --> 00:27:37,917
audience. I was a partner at the time.

505
00:27:38,000 --> 00:27:41,997
And um you said something which I think
is it's it's very like recursive here.

506
00:27:42,080 --> 00:27:47,717
We're we're feeding each other your own
advice. It's uh the key thing in in new

507
00:27:47,800 --> 00:27:50,597
technology in the new technology
business, actually everyone kind of

508
00:27:50,680 --> 00:27:52,877
figures out the technology. Though,
that's still hard. It's still hard

509
00:27:52,960 --> 00:27:55,917
sometimes to build really complex things.
It's when and how you deploy

510
00:27:56,000 --> 00:27:59,717
them into the market. The when becomes
really important. You're 2 years early

511
00:27:59,800 --> 00:28:02,197
and you're doomed. You're 2 years late,
there's too many competitors. You have

512
00:28:02,280 --> 00:28:06,357
to like hit it at the right spot.
And I think it's like like I mean, a

513
00:28:06,440 --> 00:28:09,517
controversial thing to say is like I
actually think Cruise, you know, they

514
00:28:09,600 --> 00:28:13,477
were certainly moving at a much faster
pace than Waymo. They started way behind

515
00:28:13,560 --> 00:28:16,477
and you're talking about neck and neck
when, you know, ultimately the the the

516
00:28:16,560 --> 00:28:18,637
plug was pulled. So, who knows what

517
00:28:18,720 --> 00:28:20,917
happens in the long term. Our hypothesis

518
00:28:21,000 --> 00:28:26,117
in that same equation is actually the
distribution, you let the manufacturers

519
00:28:26,200 --> 00:28:29,517
do that. Like we run self-driving
trucks right now in Japan. They carry

520
00:28:29,600 --> 00:28:32,197
commercial loads. They're they're, you
know, they're safety drivers there, but

521
00:28:32,280 --> 00:28:36,517
they're autonomously running. And but you
won't know that because the brand is

522
00:28:36,600 --> 00:28:39,557
Isuzu. That's the customer.
And and why it's so

523
00:28:39,640 --> 00:28:44,197
good for us to partner with Isuzu
in that case is that company's been around

524
00:28:44,280 --> 00:28:48,197
almost 100 years, right? If I find if
I'm not mistaken, uh, pre-pre-World War

525
00:28:48,280 --> 00:28:52,557
II company, and they are, uh, you know,
they know the government. They have test

526
00:28:52,640 --> 00:28:55,957
tracks. They know safety. They know
their own trucks very well. So, when we

527
00:28:56,040 --> 00:28:58,997
go and provide them with the
intelligence and the integration into

528
00:28:59,080 --> 00:29:00,597
their physical machinery, that's a

529
00:29:00,680 --> 00:29:04,077
fantastic one-two punch. I think today

530
00:29:04,160 --> 00:29:08,117
the world is ready to consume AI in the
real world. And that's a lot because of

531
00:29:08,200 --> 00:29:11,397
ChatGPT and Anthropic and all these,
you know, everything that's happened. So,

532
00:29:11,480 --> 00:29:14,477
people are no longer like, "What's
a self-driving car?" And this is because

533
00:29:14,560 --> 00:29:18,717
of Waymo and Tesla. So,
the market is ready to consume. And

534
00:29:18,800 --> 00:29:21,957
I think you just have to meet the market
in the way that the best way possible.

535
00:29:22,040 --> 00:29:25,477
In our view, that has always been you
go through some of the people who run the

536
00:29:25,560 --> 00:29:28,237
economy right now. Whether it's a mining
operator, whether it's a Department of

537
00:29:28,320 --> 00:29:31,757
War, whether it's, uh, the manufacturers.
And we work with, you

538
00:29:31,840 --> 00:29:35,557
know, within each vertical, uh,
with with the right partner. But that's a

539
00:29:35,640 --> 00:29:38,837
fundamentally different view than
a Tesla or a Waymo, which are going to be

540
00:29:38,920 --> 00:29:42,677
vertical. Where we're really playing
the horizontal. And I think the way we can

541
00:29:42,760 --> 00:29:45,517
always think of we think about that
our company. We're kind of like a like a

542
00:29:45,600 --> 00:29:50,117
chip maker. You know, we we we actually
look and talk and walk a lot like a

543
00:29:50,200 --> 00:29:52,797
silicon company, except we don't
obviously we don't make chips. But, you

544
00:29:52,880 --> 00:29:56,757
know, we're our we have design wins
and then we have really large long-term

545
00:29:56,840 --> 00:30:00,557
relationships. And once we're in, we're
in. It's really hard to, you know, to

546
00:30:00,640 --> 00:30:02,437
take us out. So, you need deep trust.

547
00:30:02,520 --> 00:30:05,797
Our partners have really a lot a lot
of deep trust. And we know their markets

548
00:30:05,880 --> 00:30:09,277
really really well. I think the things
that Jensen knows is he knows his

549
00:30:09,360 --> 00:30:12,477
customers. That's why
Nvidia does well beyond the

550
00:30:12,560 --> 00:30:15,757
fact obviously they make
a very complex technology.

551
00:30:15,840 --> 00:30:20,957
>> So, how are these legacy car companies
preparing for the future? Are they

552
00:30:21,040 --> 00:30:22,677
making or more acquisitions or going to?

553
00:30:22,760 --> 00:30:24,877
Are they building and, you know,
partnering with you? Or they how are

554
00:30:24,960 --> 00:30:26,957
they going to compete with,
you know, tech tech native companies?

555
00:30:27,040 --> 00:30:29,317
>> It's like saying like how are
governments dealing with AI? It's such a

556
00:30:29,400 --> 00:30:33,237
it's the such a broad topic. Um,
and each manufacturer, like even like you

557
00:30:33,320 --> 00:30:37,237
take Honda, Nissan, Toyota, three
Japanese manufacturers with, you know,

558
00:30:37,320 --> 00:30:40,317
long legacies. They all approach it
very differently. They're roughly in a

559
00:30:40,400 --> 00:30:43,717
spectrum of we're going to build
to we're going to buy Um,

560
00:30:43,800 --> 00:30:48,517
and other both extremes more than ever
we're going to buy is the common answer

561
00:30:48,600 --> 00:30:52,317
because they've been trying.
And we've been there the whole time.

562
00:30:52,400 --> 00:30:55,557
For the folks that are going
to build we provide them tools

563
00:30:55,640 --> 00:30:59,677
and we talk a little bit about
our our our new product that we're

564
00:30:59,760 --> 00:31:03,677
announcing here. And then on the ones
that just want to buy we we sell them

565
00:31:03,760 --> 00:31:07,357
the actual intelligence that goes on
the machines. And so we meet the customer

566
00:31:07,440 --> 00:31:09,117
wherever they're ready in their in their

567
00:31:09,200 --> 00:31:11,917
journey. The more nuanced version of

568
00:31:12,000 --> 00:31:15,157
that is you know the reality is like

569
00:31:15,240 --> 00:31:20,117
every every product is a different
product and so the amount of silicon and

570
00:31:20,200 --> 00:31:24,117
amount of dollars you can put towards
it towards sensors what the customer's

571
00:31:24,200 --> 00:31:28,317
willing to pay all that
depends on what actually

572
00:31:28,400 --> 00:31:32,077
gets in in the long horizon all
these things will be fully autonomous.

573
00:31:32,160 --> 00:31:34,877
But the intermittent steps are very much

574
00:31:34,960 --> 00:31:39,277
what we saw in the PC.
Where you you have the slow step up to

575
00:31:39,360 --> 00:31:42,317
one day that'll be like now nobody
really looks at laptop specs and even

576
00:31:42,400 --> 00:31:43,997
maybe frankly your phone specs. But

577
00:31:44,080 --> 00:31:46,917
that's not the case from basically 85 to

578
00:31:47,000 --> 00:31:51,717
2002 2005 where finally people stop
actually specking at all and and and

579
00:31:51,800 --> 00:31:53,877
then they're really moving to laptops.

580
00:31:53,960 --> 00:31:57,757
But there's a similar kind
of 20 year I think horizon there.

581
00:31:57,840 --> 00:32:00,717
>> And Brody when you talk about machines
and machines becoming intelligent right

582
00:32:00,800 --> 00:32:04,397
fundamentally a machine is it's
a collection of these different components

583
00:32:04,480 --> 00:32:07,717
that are integrated right and and
whoever does that final integration is

584
00:32:07,800 --> 00:32:11,837
often times the company that puts
their their badge on it the brand name but

585
00:32:11,920 --> 00:32:14,757
many many companies are building
technology that goes into that machines.

586
00:32:14,840 --> 00:32:19,237
And so we now have a bunch of technology
components and platforms that can go

587
00:32:19,320 --> 00:32:22,637
into these machines. And but we also
sell the core technology that can be

588
00:32:22,720 --> 00:32:25,277
used to develop them as well.
>> And if you look by the way under the

589
00:32:25,360 --> 00:32:29,557
hood of a of a dirt mover or like
combine or diesel truck they'll have

590
00:32:29,640 --> 00:32:33,597
Cummins engines in them. But nobody says
well because all these guys buy Cummins

591
00:32:33,680 --> 00:32:37,517
this this means that they're you
know whatever Caterpillar's is a good

592
00:32:37,600 --> 00:32:41,037
company. It's like no, that's
just a component that they buy. Uh

593
00:32:41,120 --> 00:32:44,357
they they had they have a different role.
So, if you when you look in the

594
00:32:44,440 --> 00:32:48,037
any of these verticals, there's it's
just a complex web of folks. That's why

595
00:32:48,120 --> 00:32:52,197
I always say like the the chip kind of
analogy actually works quite effectively

596
00:32:52,280 --> 00:32:56,757
because some none of those companies
make chips, but they all buy chips.

597
00:32:56,840 --> 00:32:59,677
Uh and so I think that that's
it's a good way to think about it.

598
00:32:59,760 --> 00:33:01,637
>> So, self-driving cars,
so you know, we've

599
00:33:01,720 --> 00:33:04,357
all been talking about self-driving cars
for like I think the whole thing started

600
00:33:04,440 --> 00:33:07,717
like around 2012 2005 or something
with the DARPA Grand Challenge originally.

601
00:33:07,800 --> 00:33:10,877
And so and then Google engaged
on the program shortly after that.

602
00:33:10,960 --> 00:33:11,837
>> Yeah, late double O's, yeah.

603
00:33:11,920 --> 00:33:15,637
>> Late double O's. Um uh so almost 20 you
basically around 20 less than 20 years,

604
00:33:15,720 --> 00:33:18,637
maybe. Um and there've been lots of
predictions over the last 20 years of like

605
00:33:18,720 --> 00:33:22,797
self-driving cars are imminent at any
moment. Um so I guess the bad news is

606
00:33:22,880 --> 00:33:25,277
we're sitting here today and most cars
are not self-driving. The good news is

607
00:33:25,360 --> 00:33:26,637
there are now self-driving cars.

608
00:33:26,720 --> 00:33:28,517
>> Yeah. >> And so the Waymo
cars are driving all

609
00:33:28,600 --> 00:33:30,837
over, you know, in the places they're
deployed. It's become a you know, I

610
00:33:30,920 --> 00:33:33,717
think people in San Francisco are I
think treated now as routine that they

611
00:33:33,800 --> 00:33:35,917
get into a car. >> think you
can call I think Tesla It's

612
00:33:36,000 --> 00:33:39,557
kind of like the AGI thing. It's like,
you know, if we're talking 20 years ago,

613
00:33:39,640 --> 00:33:43,837
everything we're seeing right now
is like mind-blowingly AGI. The the host

614
00:33:43,920 --> 00:33:45,437
keeps moving. The Tesla stuff's amazing.

615
00:33:45,520 --> 00:33:49,077
You can look at a bunch of manufacturers.
Blue Cruise, Supercruise,

616
00:33:49,160 --> 00:33:51,797
BMW, Volvo's Pilot. They're all
[clears throat] quite impressive

617
00:33:51,880 --> 00:33:53,597
systems. They're not full self-driving.

618
00:33:53,680 --> 00:33:57,647
>> Right. >> But yeah. >> Well, it's
it's full self-driving X whatever remote

619
00:33:57,730 --> 00:33:59,477
>> [snorts] >> monitoring
is happening. [laughter] Um

620
00:33:59,560 --> 00:34:02,477
uh the the Tesla uh we have we have
a home in in in Los Angeles and if you

621
00:34:02,560 --> 00:34:05,037
guys may recall, there was a there
was a large fire in Los Angeles last

622
00:34:05,120 --> 00:34:08,117
>> Yeah, yeah, yeah. >> And the power
and then the California power grid was

623
00:34:08,200 --> 00:34:11,117
buckling even before that. So,
it actually turns out among the things

624
00:34:11,200 --> 00:34:13,957
Cybertrucks are good at is they're
they're very good batteries >> Yeah.

625
00:34:14,040 --> 00:34:15,397
>> uh for powering your house.

626
00:34:15,480 --> 00:34:17,157
>> Yeah. >> Um and so
literally we have a Cybertruck

627
00:34:17,240 --> 00:34:20,157
as our backup battery for the house.
And as of last year, whatever the FSD

628
00:34:20,240 --> 00:34:22,917
release, I forget the exact one,
but there was one where it like at least a

629
00:34:23,000 --> 00:34:24,557
lot of people thought it
like really turned the corner.

630
00:34:24,640 --> 00:34:26,197
>> 14, yeah. >> And like
that thing drives people I

631
00:34:26,280 --> 00:34:28,957
talked to somebody yesterday uh talked
to somebody yesterday who has a Model Y

632
00:34:29,040 --> 00:34:31,677
who let the let the thing let the thing
do the full the full route all the way

633
00:34:31,760 --> 00:34:32,877
up highway one through Big Sur.

634
00:34:32,960 --> 00:34:35,797
>> Yeah, I think mean
disengagement like uh the the

635
00:34:35,880 --> 00:34:39,957
mean time and like miles
per disengagement are are

636
00:34:40,040 --> 00:34:43,677
really high and I think miles is like
in the thousands. Yeah, which is like very

637
00:34:43,760 --> 00:34:45,197
impressive. >> Yeah,
I know the Big Sur for people who

638
00:34:45,280 --> 00:34:49,197
have driven the Big Sur highway one
Big Sur like that's a that's a that's a

639
00:34:49,280 --> 00:34:52,957
stressful drive. He said it was great
the the whole anyway. So and I wouldn't

640
00:34:53,040 --> 00:34:56,157
have been talking to him
had it not been >> [laughter]

641
00:34:56,240 --> 00:34:59,117
>> would have gone right off right off
>> Cuz he unbolted the steering wheel so

642
00:34:59,200 --> 00:35:01,197
>> right off right off the >> cliff.

643
00:35:01,280 --> 00:35:05,517
>> So and then you know Tesla's rolling
out their robo-taxi, you know, that it's

644
00:35:05,600 --> 00:35:08,797
it's starting starting to show up in
in the wild. So so on the one hand those

645
00:35:08,880 --> 00:35:12,117
exist on the other hand,
you know, 99.999999%

646
00:35:12,200 --> 00:35:13,597
of cars are are still not self-driving.

647
00:35:13,680 --> 00:35:15,997
And then I would say maybe just one
other would be the self-driving trucks.

648
00:35:16,080 --> 00:35:19,077
There's been this recurring kind
of panic in the press of like this trucks

649
00:35:19,160 --> 00:35:21,637
become self-driving and the employment,
you know, all these truck drivers will

650
00:35:21,720 --> 00:35:25,197
be out of a job. And sitting here today,
I don't think I don't know is there

651
00:35:25,280 --> 00:35:27,597
are there any trucks on the road
that are self-driving that don't have at

652
00:35:27,680 --> 00:35:31,037
least a safety driver in the truck?
And I think the answer is probably still

653
00:35:31,120 --> 00:35:33,037
>> Yeah, Silver if you
So let's let's split

654
00:35:33,120 --> 00:35:36,317
let's split the there's multiple points
that were brought up here. One is on the

655
00:35:36,400 --> 00:35:38,837
let's say personally owned vehicles and

656
00:35:38,920 --> 00:35:40,957
why are they not more ubiquitous?

657
00:35:41,040 --> 00:35:45,357
The part of that is the manufacturers
are not good at deploying technology.

658
00:35:45,440 --> 00:35:48,957
Part of that is they're they're they
want to be safety conscious but most of

659
00:35:49,040 --> 00:35:51,117
it is cost cost cost.

660
00:35:51,200 --> 00:35:53,797
The the what you're seeing in China,

661
00:35:53,880 --> 00:35:59,077
which is a China's kind of a different
EV ecosystem mainly because they don't

662
00:35:59,160 --> 00:36:01,797
care about profits. When you're talking
about business that doesn't care about

663
00:36:01,880 --> 00:36:05,637
profits, it changes the entire calculus
of the entire industry that that doesn't

664
00:36:05,720 --> 00:36:08,757
care. But what you're
seeing is you're seeing

665
00:36:08,840 --> 00:36:12,557
L2++ systems. So we can simplify
the entire self-driving conversation to is

666
00:36:12,640 --> 00:36:15,797
there a driver behind the steering wheel?
Right. So this is a driver behind

667
00:36:15,880 --> 00:36:19,357
the steering wheel still there but
generally like Tesla drives everywhere.

668
00:36:19,440 --> 00:36:21,557
They're like sub $1,000. There's an

669
00:36:21,640 --> 00:36:26,477
aggressive That's that's chip, sensors,
the package, the software, everything.

670
00:36:26,560 --> 00:36:29,917
We, you know, anticipate that there
there's a very aggressive Once you get

671
00:36:30,000 --> 00:36:33,117
to like 500, the automotive OEMs will
actually subsidize it for free. They'll

672
00:36:33,200 --> 00:36:35,877
just give it to you. This happened
in nav systems. If you guys remember nav

673
00:36:35,960 --> 00:36:39,117
systems, it used to be a big thing where
you'd pay four grand, 3,500, to get a

674
00:36:39,200 --> 00:36:42,157
nav system, and then suddenly it became
free and it just became default. I think

675
00:36:42,240 --> 00:36:46,517
that'll happen. The There's a weird
thing which is like actually getting

676
00:36:46,600 --> 00:36:49,157
into a subset of your cars costs X

677
00:36:49,240 --> 00:36:53,597
dollars, and to get into all the cars
costs X plus just a small incremental

678
00:36:53,680 --> 00:36:57,637
amount. Because there's a fixed cost
and the way that how many vehicles and the

679
00:36:57,720 --> 00:37:00,957
way the assembly line comes in the way
you have homologation, all these testing

680
00:37:01,040 --> 00:37:04,037
regimes, all this stuff. So, I think
you'll have wait, wait, wait, and then a

681
00:37:04,120 --> 00:37:08,277
lot. >> Yeah. >> Every single
OEM without exception, even

682
00:37:08,360 --> 00:37:10,117
the lowest dollar OEMs are working on an

683
00:37:10,200 --> 00:37:15,917
FSD competitor. So, it'll come.
But it's just like, you know, with

684
00:37:16,000 --> 00:37:18,677
a good analogy to think about
self-driving in the personally owned

685
00:37:18,760 --> 00:37:23,797
ecosystem is mobile phones.
We had We had the the satellite phones,

686
00:37:23,880 --> 00:37:26,877
then we had the Qualcomm, you know,
brick phones, then we had the Motorola

687
00:37:26,960 --> 00:37:31,677
Razrs, and from, you know, the late '90s
to the late to double zeros, there was

688
00:37:31,760 --> 00:37:33,397
like, "When's mobile going to come?"

689
00:37:33,480 --> 00:37:36,397
There was a huge like,
and then it it comes and we

690
00:37:36,480 --> 00:37:39,917
by by '07 from the iPhone launch, like

691
00:37:40,000 --> 00:37:44,877
it's like four years when you get Uber,
Instagram, WhatsApp, Snapchat. Those are

692
00:37:44,960 --> 00:37:48,757
the killer applications. So, I think
there's a very, very similar kind of

693
00:37:48,840 --> 00:37:52,677
wait, wait, wait, and then it's just
basically ubiquitous in every vehicle.

694
00:37:52,760 --> 00:37:54,997
If you had to ask me
for what that number is,

695
00:37:55,080 --> 00:37:58,837
28 SOP, 29 start of production, 29, 30,

696
00:37:58,920 --> 00:38:02,717
and then by the early '30s, then it'll
start becoming very cheap to free.

697
00:38:02,800 --> 00:38:06,037
>> I think routinely by by the early '30s,
you would just you buy a car and you just

698
00:38:06,120 --> 00:38:09,717
assume it's self-driving. >> Exactly.
Or it has the driver in seat

699
00:38:09,800 --> 00:38:11,677
L2++ system, being very specific.

700
00:38:11,760 --> 00:38:14,997
>> Like like Cybertruck or Tesla the Tesla
equivalent of what Tesla people have

701
00:38:15,080 --> 00:38:18,077
what Tesla owners have today.
>> Today, exactly. Yeah, it'll be

702
00:38:18,160 --> 00:38:20,517
default. So, then the question then the

703
00:38:20,600 --> 00:38:23,077
the the you know, the the other side of

704
00:38:23,160 --> 00:38:26,437
this is why don't we have a bunch of

705
00:38:26,520 --> 00:38:30,437
Waymos everywhere.
Specifically, Waymo has a different

706
00:38:30,520 --> 00:38:32,157
technology. Without getting into the

707
00:38:32,240 --> 00:38:36,077
nuances here, but Tesla and many of the

708
00:38:36,160 --> 00:38:39,677
Chinese and applied were very much in this

709
00:38:39,760 --> 00:38:41,557
end-to-end model architecture.

710
00:38:41,640 --> 00:38:44,397
This is a new way of doing self-driving.

711
00:38:44,480 --> 00:38:48,317
Waymo, for the lack of a better word,
is not that. It's not doesn't mean they're

712
00:38:48,400 --> 00:38:51,757
not learned. It doesn't It just It's
not one end-to-end system. It's not one

713
00:38:51,840 --> 00:38:55,677
monolithic model.
One of the proclivities of that of their

714
00:38:55,760 --> 00:38:57,717
approach is it does depend on HD maps.

715
00:38:57,800 --> 00:39:02,077
Therefore, there is a geofencing concept.
I think Waymo's trying hard to

716
00:39:02,160 --> 00:39:05,357
remove that bottleneck so they can
expand geographically faster, but the

717
00:39:05,440 --> 00:39:09,037
reality of today isn't there. The other
thing is when you have researchers,

718
00:39:09,120 --> 00:39:12,437
which they which Waymo
really was coming out of

719
00:39:12,520 --> 00:39:14,757
an Alphabet research organization, they

720
00:39:14,840 --> 00:39:19,637
didn't put commercial constraints. So,
the sensors are bespoke and expensive.

721
00:39:19,720 --> 00:39:23,477
The cars and the then the compute that
are in there, they're they're just not

722
00:39:23,560 --> 00:39:27,557
economically feasible. And they've tried
a lot to get that down, but it's kind of

723
00:39:27,640 --> 00:39:30,317
like it's a lot easier to go from
something that's really cheap and make

724
00:39:30,400 --> 00:39:35,317
it more uh you know, more featureful
than something that's overbuilt and then

725
00:39:35,400 --> 00:39:39,197
trying to trim and make it really really
cheap. And that's the big debate. Who's

726
00:39:39,280 --> 00:39:43,877
going to get there first? Tesla with full
self-driving or Waymo with cost and

727
00:39:43,960 --> 00:39:47,157
geographic ubiquity?
Uh but you know what we're not debating

728
00:39:47,240 --> 00:39:48,797
about? Is it going to happen? >> Right.

729
00:39:48,880 --> 00:39:49,717
>> You know what we're not debating about?

730
00:39:49,800 --> 00:39:52,837
Like is is is uh is there a big
technical breakthrough that needs to

731
00:39:52,920 --> 00:39:56,677
happen? None of those things. So, now
we're clearly in the engineering side of

732
00:39:56,760 --> 00:40:01,797
self-driving, which is just this grind
down to uh like dollar per mile

733
00:40:01,880 --> 00:40:04,877
efficiency. And the moment that it's
cheap, guess what? All the OEMs are

734
00:40:04,960 --> 00:40:06,317
smart, they'll just they just adopt it.

735
00:40:06,400 --> 00:40:10,437
It's not that OEMs are resistant because
they don't think consumers want it or

736
00:40:10,520 --> 00:40:14,477
they don't understand the technology,
it's because they want a

737
00:40:14,560 --> 00:40:16,277
price envelope, which allows them to

738
00:40:16,360 --> 00:40:18,917
keep their thin razor-thin margins and

739
00:40:19,000 --> 00:40:22,197
at a scale, which is deployed across 100

740
00:40:22,280 --> 00:40:25,797
plus countries in V1. And so, if you're

741
00:40:25,880 --> 00:40:29,917
just doing a small deployment, it's
very different. And I think And the last

742
00:40:30,000 --> 00:40:32,397
thing I would say is the buyer of a

743
00:40:32,480 --> 00:40:36,037
Subaru or a buyer of a Suzuki have very

744
00:40:36,120 --> 00:40:40,597
different brand expectations than
a buyer of a Tesla. And so, including the

745
00:40:40,680 --> 00:40:46,237
age of the consumer and what they think
will happen or won't happen. Um so, that

746
00:40:46,320 --> 00:40:50,157
that also reason. So, if you're Suzuki,
you're like, "Well, my buyer is like not

747
00:40:50,240 --> 00:40:53,477
doesn't want this stuff, so I'm not
going to jam it into the car." It's not

748
00:40:53,560 --> 00:40:56,157
because they're not like technically
competent. This is a different area.

749
00:40:56,240 --> 00:40:58,877
>> When do you think When do you think
it'll be routine? Let's say the the 200

750
00:40:58,960 --> 00:41:02,917
biggest American cities, like um when
will it be routine to walk [snorts]

751
00:41:03,000 --> 00:41:05,557
outside and you just you just take it
for granted that a robot taxi can come

752
00:41:05,640 --> 00:41:11,757
pick you up? >> Uh it's 26 now.
Um I mean, certainly by 30.

753
00:41:11,840 --> 00:41:14,797
>> Sorry. Okay. >> Yeah,
certainly by 30. And I would say

754
00:41:14,880 --> 00:41:18,997
the the big like variable
there really is like

755
00:41:19,080 --> 00:41:23,757
cuz you What Waymo will say is that the
dollars and cents per city already work.

756
00:41:23,840 --> 00:41:26,397
And it's like, "Well, a company that has
basically unlimited capital, why are

757
00:41:26,480 --> 00:41:28,917
they not already in 200 cities?"
But then you see their launch schedule's

758
00:41:29,000 --> 00:41:33,477
pretty aggressive. You're like,
"That can that can get there." So,

759
00:41:33,560 --> 00:41:35,397
maybe it has been aggressive. I'd say 28.

760
00:41:35,480 --> 00:41:36,517
>> Yeah, okay. >> Like 2 years.

761
00:41:36,600 --> 00:41:38,597
>> I I would say I would say available in

762
00:41:38,680 --> 00:41:42,317
30 but routine in maybe like 32 or 33.

763
00:41:42,400 --> 00:41:44,277
>> Cuz there's a scale up,
there's a volume.

764
00:41:44,360 --> 00:41:49,117
>> And then also if if you you got
you know, you're living in LA, so like

765
00:41:49,200 --> 00:41:52,557
5 years ago, I'd go to LA, people would
be like, "What's Applied Intuition? I

766
00:41:52,640 --> 00:41:54,037
don't know what self-driving cars are."

767
00:41:54,120 --> 00:41:57,997
Uh in the last couple years, now they
all know self-driving and some of them

768
00:41:58,080 --> 00:42:01,117
even know Applied Intuition because
they know from the other manufacturers.

769
00:42:01,200 --> 00:42:05,437
Uh I think you fast forward another 2
to 4 years, everybody knows it. Now, does

770
00:42:05,520 --> 00:42:08,197
that mean everyone's
taking Waymos exclusively?

771
00:42:08,280 --> 00:42:12,397
The answer is no, actually. Now, there
is a a huge If you look at the numbers, if

772
00:42:12,480 --> 00:42:15,797
you're Uber, you got to be scared.
I mean, they're just eating into into ride

773
00:42:15,880 --> 00:42:19,957
sharing. Um Yeah,
but to get 100% equity, I mean,

774
00:42:20,040 --> 00:42:22,357
that's that's that's another
Yes, to be extremely cheap.

775
00:42:22,440 --> 00:42:24,397
>> And what about long-haul trucking?

776
00:42:24,480 --> 00:42:27,397
>> So long So that's So that's what we
That's So that's the pa- passenger side.

777
00:42:27,480 --> 00:42:30,037
The long-haul trucking, completely
different economics, completely

778
00:42:30,120 --> 00:42:34,157
different um uh business model. Um there

779
00:42:34,240 --> 00:42:37,317
are many companies right now. I would
say probably north of five that are

780
00:42:37,400 --> 00:42:41,597
running long-haul trucks with drivers
carrying loads between America and

781
00:42:41,680 --> 00:42:44,957
China. I think China's probably getting
into double digits. So it's there, but

782
00:42:45,040 --> 00:42:46,637
the reason uh you don't know it and the

783
00:42:46,720 --> 00:42:51,997
reason it's not top of mind is it's not
a consumer product. And unlike uh on the

784
00:42:52,080 --> 00:42:53,637
Waymo and Tesla side, where investors

785
00:42:53,720 --> 00:42:56,557
are willing to essentially give you uh

786
00:42:56,640 --> 00:42:59,877
you know, some um market cap uh you

787
00:42:59,960 --> 00:43:03,877
know, uh adjustment for the potential of

788
00:43:03,960 --> 00:43:08,077
the the They say the the trucking business
is like, you know, made What's the word?

789
00:43:08,160 --> 00:43:10,037
>> You buy a car with your heartstrings.

790
00:43:10,120 --> 00:43:11,317
You buy a truck with a calculator.

791
00:43:11,400 --> 00:43:12,437
>> Yeah. It's a calculator [laughter]

792
00:43:12,520 --> 00:43:15,317
business. And they And so it's like pure

793
00:43:15,400 --> 00:43:19,517
dollars and cents. And so I think um

794
00:43:19,600 --> 00:43:23,077
you as the provider of self-driving
trucks, if you're doing the whole thing,

795
00:43:23,160 --> 00:43:25,237
like some of the companies
are, which we're not,

796
00:43:25,320 --> 00:43:28,397
you have to show every mile I'm going to
I'm going to save you this many dollars.

797
00:43:28,480 --> 00:43:32,277
And it's like, for sure, for sure,
for sure, cuz the buyer's unsophisticated.

798
00:43:32,360 --> 00:43:35,317
And they're just like, well, I already
got a staff that can drive. And it's

799
00:43:35,400 --> 00:43:38,637
like, and they're like
they're just not inclined.

800
00:43:38,720 --> 00:43:41,797
Now, where we're playing in Japan's
not random that we're doing trucking in

801
00:43:41,880 --> 00:43:45,477
Japan. There's a massive labor
shortage today, and there's an imploding

802
00:43:45,560 --> 00:43:49,917
demographic uh uh you know,
a situation. And so there's

803
00:43:50,000 --> 00:43:53,237
a demand from almost every sector,
and that's why we've we've picked that

804
00:43:53,320 --> 00:43:57,077
market to to to to really grow.
But I think like

805
00:43:57,160 --> 00:44:01,197
you can take like even more
uh obscure like when we'll all

806
00:44:01,280 --> 00:44:04,757
uh quarries, you know, literally like
where you would you know, you're you're

807
00:44:04,840 --> 00:44:07,837
you're moving cement, you're
moving you're moving dirt. Uh

808
00:44:07,920 --> 00:44:11,117
not quarries, q u e a r s q u a r r y.

809
00:44:11,200 --> 00:44:13,597
>> Yeah. Quarries. Quarries.
>> Those are rock rock stone.

810
00:44:13,680 --> 00:44:16,877
>> Rock stone cement. When when are those?

811
00:44:16,960 --> 00:44:20,277
I can tell you the people who own those
things and run those things wanted it

812
00:44:20,360 --> 00:44:22,797
today. >> Right. >> So it's
literally then you don't you

813
00:44:22,880 --> 00:44:25,637
don't have a We're literally we
can't make this stuff fast enough.

814
00:44:25,720 --> 00:44:28,557
>> Right. >> Um the macro
point though that people

815
00:44:28,640 --> 00:44:30,717
don't talk about I think all
this stuff's going to happen.

816
00:44:30,800 --> 00:44:32,917
>> Right. >> It'll happen pretty soon.

817
00:44:33,000 --> 00:44:34,917
>> It'll happen fairly soon. But the macro

818
00:44:35,000 --> 00:44:37,677
point that in in legislation and and and

819
00:44:37,760 --> 00:44:40,677
kind of in in the in the kind of

820
00:44:40,760 --> 00:44:42,877
economics the political economy of of

821
00:44:42,960 --> 00:44:47,757
this conversation is AI is really
you see you have this big pushback in

822
00:44:47,840 --> 00:44:49,717
digital AI because we're like counselor
like I don't know what's going to happen

823
00:44:49,800 --> 00:44:54,317
to my job and VCs I'm sure all
of your associates are very scared.

824
00:44:54,400 --> 00:44:56,797
But like in our universe >> They're
debating whether they need us.

825
00:44:56,880 --> 00:44:59,517
>> Yeah yeah yeah.
In our in our universe it's the other

826
00:44:59,600 --> 00:45:03,157
way around. It's like you They're
literally I'll I'll I'll meet these you

827
00:45:03,240 --> 00:45:06,997
know operators and they're like
We'll give you everything. Like if you

828
00:45:07,080 --> 00:45:08,237
can do this we'll give you everything.

829
00:45:08,320 --> 00:45:11,197
So then it's just up to us to like get
there as as you know aggressively and

830
00:45:11,280 --> 00:45:14,077
physically as possible. >> Well you know
the the fear for a long time has been for

831
00:45:14,160 --> 00:45:16,957
trucking for some reason triggers the
at least the press's imagination on like

832
00:45:17,040 --> 00:45:19,837
you know sort of apocalyptic
levels of job loss. Like will there

833
00:45:19,920 --> 00:45:21,357
>> But that's it's so wrong. >> Go ahead.

834
00:45:21,440 --> 00:45:25,277
>> There's not There's not enough truck
drivers and guess what? Nobody wants to

835
00:45:25,360 --> 00:45:27,557
freaking be a truck driver.
>> Why is that? Let me explain.

836
00:45:27,640 --> 00:45:31,277
>> Because it's a terrible job. It's like
You're like you're like you're like you're

837
00:45:31,360 --> 00:45:33,717
>> I I by the way I grew up my the main
feature of the town where I grew up was

838
00:45:33,800 --> 00:45:37,197
a truck stop so I >> It's like >>
But yeah yeah yeah. Why is truck Why is

839
00:45:37,280 --> 00:45:38,357
truck driving not attractive to you?

840
00:45:38,440 --> 00:45:39,957
>> asking me it's you know what this is?

841
00:45:40,040 --> 00:45:43,717
This is like you know talking to my kid
who's like well why can't I put my hand

842
00:45:43,800 --> 00:45:47,677
on the stove? It's like because
it's going to burn your hand.

843
00:45:47,760 --> 00:45:49,637
>> [laughter] >> It's like
but why? It's like after the

844
00:45:49,720 --> 00:45:53,567
third why it's like Come
on buddy let's do this.

845
00:45:53,650 --> 00:45:55,637
>> [laughter] >> Yeah
let's [clears throat]

846
00:45:55,720 --> 00:45:58,317
>> So what So what what's hard about >>
This is make sure everybody knows I did

847
00:45:58,400 --> 00:45:59,437
not do that with [laughter] my son.

848
00:45:59,520 --> 00:46:01,397
>> Yes yes. So what's
hard Why why is being

849
00:46:01,480 --> 00:46:03,757
a truck driver a difficult job or why
would kids not want to do it when they

850
00:46:03,840 --> 00:46:06,277
grow up? >> So,
what the let me use a parallel

851
00:46:06,360 --> 00:46:08,677
analogy which is very
clear and then you can

852
00:46:08,760 --> 00:46:12,837
what what you know people will say like
nobody wants to work anymore and they

853
00:46:12,920 --> 00:46:16,197
say well, you know, McDonald's has all
these job openings. No, no, actually

854
00:46:16,280 --> 00:46:19,797
what it is is those people that used to
work at McDonald's now DoorDash and Uber.

855
00:46:19,880 --> 00:46:21,797
>> Right. >> Because it's
better for them because

856
00:46:21,880 --> 00:46:24,637
they can open they can still start their
hours and end their hours and they don't

857
00:46:24,720 --> 00:46:26,877
have to there's no boss
and they don't have to

858
00:46:26,960 --> 00:46:30,437
like stand on their feet and they can
surf their phone in between, you know,

859
00:46:30,520 --> 00:46:32,357
orders and they don't like that's the

860
00:46:32,440 --> 00:46:35,157
reason. It's not random. The market is

861
00:46:35,240 --> 00:46:37,957
efficient and so in the truck driving

862
00:46:38,040 --> 00:46:40,317
example, why does somebody not want to

863
00:46:40,400 --> 00:46:43,077
be away from their family for

864
00:46:43,160 --> 00:46:45,837
4 to 8 days in a row doing long haul

865
00:46:45,920 --> 00:46:48,357
trucking? The more sharp example is in

866
00:46:48,440 --> 00:46:50,517
Australia, why don't people want to

867
00:46:50,600 --> 00:46:53,557
go literally by a plane to go to a mine

868
00:46:53,640 --> 00:46:58,397
and work on or you go off offshore
oil rigs. Those jobs exist. If

869
00:46:58,480 --> 00:47:03,117
you want a job that pays six figures,
they exist. Even with such lucrative pay

870
00:47:03,200 --> 00:47:06,557
packages, it's not enough cuz
people are like, you know what?

871
00:47:06,640 --> 00:47:10,317
I like kind of being around my family
and I'm willing to take an incremental

872
00:47:10,400 --> 00:47:12,637
decrease in cost and and how much money

873
00:47:12,720 --> 00:47:15,197
I make and and then also like I think

874
00:47:15,280 --> 00:47:18,197
today more than ever things like

875
00:47:18,280 --> 00:47:23,317
back pain and like being exposed
to the sun and cancer and people that care

876
00:47:23,400 --> 00:47:26,317
about that's now a part of the >>
This is the thing so tell me if I I have

877
00:47:26,400 --> 00:47:28,437
this right but I I believe it's because
I think commercial long haul truck

878
00:47:28,520 --> 00:47:33,157
drivers die at life expectancy 10 years
less than their peers and I think it's a

879
00:47:33,240 --> 00:47:37,157
consequence of several things. So,
one is some combination of nutrition and

880
00:47:37,240 --> 00:47:40,077
sleep. It's you know, it's a basically,
you know, >> It's yeah, it's

881
00:47:40,160 --> 00:47:43,157
>> It's very difficult It's very
difficult to eat well and exercise and

882
00:47:43,240 --> 00:47:44,437
>> What's your sleep
score if you're a long

883
00:47:44,520 --> 00:47:47,397
haul trucker? Let me guess, they
don't eat sleep on that on that screen.

884
00:47:47,480 --> 00:47:50,237
>> Exactly and so like obesity and then
heart disease, hypertension and so forth

885
00:47:50,320 --> 00:47:52,717
are all very high one
and then two is I think the

886
00:47:52,800 --> 00:47:56,117
vibration is very difficult stress
in the the and then the third is you

887
00:47:56,200 --> 00:47:59,317
mentioned cancer but I think it's the I
think they have truck drivers have like

888
00:47:59,400 --> 00:48:01,037
a much higher rate
of melanoma on their left arm.

889
00:48:01,120 --> 00:48:03,917
>> Exactly. Yeah, there's photos of like
a truck driver who's been driving for 30

890
00:48:04,000 --> 00:48:05,997
years one half of their face the other
half of their face cuz it's exposed to

891
00:48:06,080 --> 00:48:10,397
the sun. A more interesting
even more stark stat,

892
00:48:10,480 --> 00:48:15,557
mining is 1% of the labor pool globally
8% of work related fatalities. Do you

893
00:48:15,640 --> 00:48:18,957
think people are rushing to work
in mines when they hear stats like this?

894
00:48:19,040 --> 00:48:23,677
Most major mines have a fatality
regularly which means once twice a year

895
00:48:23,760 --> 00:48:27,117
three times a year. And if you ever
visit a mine you'll see that everything

896
00:48:27,200 --> 00:48:31,397
is based around safety because once you
experience one of your co-workers dying

897
00:48:31,480 --> 00:48:33,397
then you're like what am I doing here?

898
00:48:33,480 --> 00:48:36,997
>> Yeah. >> Like there's
other jobs I can take and so

899
00:48:37,080 --> 00:48:41,397
it's I I understand you're trying
to enumerate for the audience like but

900
00:48:41,480 --> 00:48:45,437
these are not good jobs. And the best
evidence is this this is not a

901
00:48:45,520 --> 00:48:49,277
mining podcast. This is not a podcast
about hey long haul trucking is so

902
00:48:49,360 --> 00:48:51,717
great. It they're just
not attractive jobs.

903
00:48:51,800 --> 00:48:54,317
>> even trucker even truckers don't want
their kids to become truckers like it's

904
00:48:54,400 --> 00:48:57,437
it's a very for that reason they would
you know they want their kids to be in a

905
00:48:57,520 --> 00:49:01,757
at the very least like safe safer
safer line of work. But do the do the

906
00:49:01,840 --> 00:49:04,317
notwithstanding all that do they how
long will there be do you think there'll

907
00:49:04,400 --> 00:49:07,557
be safety drivers in long haul trucks
that are that are self-driving? Or or or

908
00:49:07,640 --> 00:49:10,357
let's say other even just somebody
in the cab to deal with what happens when

909
00:49:10,440 --> 00:49:12,277
they arrive? >> We we know
multiple companies that have

910
00:49:12,360 --> 00:49:16,157
driver out goals right now. So like
they're they're working to get drivers

911
00:49:16,240 --> 00:49:20,547
out right now. You know,
without going into our own details.

912
00:49:20,630 --> 00:49:22,837
>> [laughter] >> To be
honest with you it's not long.

913
00:49:22,920 --> 00:49:26,477
We're talking we're talking a few
years and >> I think on the long end.

914
00:49:26,560 --> 00:49:29,477
>> Yeah, on the long end. And the thing
is it's it's there there's a software

915
00:49:29,560 --> 00:49:33,197
technology thing which is one part
of the problem, but the other part is it's

916
00:49:33,280 --> 00:49:36,037
the redundancies that you need
in hardware and the validation necessary

917
00:49:36,120 --> 00:49:40,037
for those redundancies. And in many
cases that can actually be a long pull.

918
00:49:40,120 --> 00:49:43,197
It's like oh there
productionizing a fully redundant

919
00:49:43,280 --> 00:49:46,957
steering system fully redundant braking
system that's that's not in high that's

920
00:49:47,040 --> 00:49:48,877
not in high volume production yet
and once you get that in high volume

921
00:49:48,960 --> 00:49:51,557
production now you get the quality up
and then that's validated and now you

922
00:49:51,640 --> 00:49:53,237
can actually do these.
Need the >> price down.

923
00:49:53,320 --> 00:49:55,517
>> Exactly. >> Do do you
guys do do you like it's the

924
00:49:55,600 --> 00:49:56,877
you know these little delivery robots?

925
00:49:56,960 --> 00:49:59,477
Like is that you do you see a world where
there's a billion of those running around?

926
00:49:59,560 --> 00:50:03,637
>> Yeah, I think so. I mean the uh the
the the product that we're announcing uh I

927
00:50:03,720 --> 00:50:06,837
think it's probably come out around with
this time is called uh Dana. So there's

928
00:50:06,920 --> 00:50:10,197
you can just simplify everything that
Applied Intuition does into two buckets,

929
00:50:10,280 --> 00:50:14,637
which is the uh we've been talking
mostly about the models that go on the

930
00:50:14,720 --> 00:50:18,717
machines. Then there's we say onboard
software or onboard AI. Then there's

931
00:50:18,800 --> 00:50:23,677
offboard AI. This is the tools to design
and develop these same systems. The the

932
00:50:23,760 --> 00:50:27,237
the models that actually go on the
machines. Our uh you know vision for

933
00:50:27,320 --> 00:50:28,997
that is in the the delivery robot is a

934
00:50:29,080 --> 00:50:32,357
great example is like a high school kid

935
00:50:32,440 --> 00:50:36,077
or a middle schooler they can make
iPhone apps they should be able to make

936
00:50:36,160 --> 00:50:38,157
autonomous systems. So why can't they?

937
00:50:38,240 --> 00:50:41,597
Just ask that very simple
question. Why can't a

938
00:50:41,680 --> 00:50:45,197
ninth grader make a delivery robot
in their in their home? Well, they don't

939
00:50:45,280 --> 00:50:50,597
have the the actual environment that they
would first develop the scenarios in. They

940
00:50:50,680 --> 00:50:54,477
would define the requirements. Hey,
I want this robot to go on my high school

941
00:50:54,560 --> 00:50:56,997
campus around these
let's say four buildings.

942
00:50:57,080 --> 00:51:00,957
Uh then how Okay, now that you do define
the the requirements then you have the

943
00:51:01,040 --> 00:51:05,037
scenarios get made. What are all
the scenarios that I can that can uh that

944
00:51:05,120 --> 00:51:07,517
can be made by using let's
say satellite image of

945
00:51:07,600 --> 00:51:10,237
the high school. Uh then now you have to

946
00:51:10,320 --> 00:51:13,157
train the robot. So you need some data.

947
00:51:13,240 --> 00:51:16,397
Where do you get that data? There's
maybe enough publicly available data

948
00:51:16,480 --> 00:51:20,237
that can actually train a fairly uh
rudimentary robot. You know, you got

949
00:51:20,320 --> 00:51:23,957
that data from online, maybe YouTube
videos, couple of other places. Suddenly

950
00:51:24,040 --> 00:51:27,277
this robot's not doing Now you need
to deploy it onto the actual machine. So

951
00:51:27,360 --> 00:51:30,797
then you deploy it onto the machine and
then the robot runs into the wall. Okay,

952
00:51:30,880 --> 00:51:33,997
what happened there?
The loop closes. That platform for

953
00:51:34,080 --> 00:51:37,797
designing and developing is what
we're launching. It's called Dana,

954
00:51:37,880 --> 00:51:40,517
uh which is the street that Applied
Intuition is >> [laughter]

955
00:51:40,600 --> 00:51:43,957
>> uh headquartered on. Uh and uh and

956
00:51:44,040 --> 00:51:45,917
our our our you know, this comes from

957
00:51:46,000 --> 00:51:47,917
our tooling background. And if you look

958
00:51:48,000 --> 00:51:51,397
at like how tooling has changed in the

959
00:51:51,480 --> 00:51:55,197
digital AI world, if you look at like
what Claude did to all you you all

960
00:51:55,280 --> 00:52:00,317
remember like, you know, from Mixpanel to,
you know, GitLab, GitHub, all these

961
00:52:00,400 --> 00:52:04,397
Now everything has moved into a very
different almost IDE, frankly speaking.

962
00:52:04,480 --> 00:52:07,397
We think the same thing's going
to happen in the physical world. And so,

963
00:52:07,480 --> 00:52:09,477
uh that that that's yeah, that's what
we're that's what we're building. That's

964
00:52:09,560 --> 00:52:11,357
what we're that's what we built
and that's what we're launching in. We

965
00:52:11,440 --> 00:52:15,557
already use it in-house to develop our
autonomy system, which is you know, and

966
00:52:15,640 --> 00:52:20,237
we're working on the most kind of scaled
complex systems on the planet in all

967
00:52:20,320 --> 00:52:22,437
these different verticals. So,
we're pretty confident that it's actually

968
00:52:22,520 --> 00:52:25,957
quite useful. And we've seen
massive productivity gains.

969
00:52:26,040 --> 00:52:30,637
Uh but also, you know, we think like
other companies will use this to to

970
00:52:30,720 --> 00:52:33,637
build our own systems cuz
it gets to that mission that

971
00:52:33,720 --> 00:52:35,357
a billion intelligent machines.

972
00:52:35,440 --> 00:52:39,237
>> Yeah, fundamentally our Dana is our
agentic platform for physical AI. And

973
00:52:39,320 --> 00:52:43,357
and everything that we've built and
developed over the past nearly a decade,

974
00:52:43,440 --> 00:52:44,917
every tool, every

975
00:52:45,000 --> 00:52:47,477
>> technique that's available in Dana. And

976
00:52:47,560 --> 00:52:52,517
it's very actually easy to use with
the agentic interface. And so, workflows

977
00:52:52,600 --> 00:52:54,357
that used to maybe take

978
00:52:54,440 --> 00:52:59,677
days or weeks to run, you can now run
those in in minutes in many cases. And

979
00:52:59,760 --> 00:53:02,237
this just lowers the barrier to entry
to building these systems. And so,

980
00:53:02,320 --> 00:53:04,797
>> And we're just
lowering the bar of like,

981
00:53:04,880 --> 00:53:07,877
you know, what it means to develop
an autonomous system. Autonomy is still

982
00:53:07,960 --> 00:53:13,317
actually quite in the scope of software
is quite exotic. It's not because of the

983
00:53:13,400 --> 00:53:16,717
things that we've talked about.
And we've just brought that down very very

984
00:53:16,800 --> 00:53:20,477
aggressively. And it's kind of like,
you know, the old adage of like how do you

985
00:53:20,560 --> 00:53:25,037
make a great product in software?
It's like you either increase safety,

986
00:53:25,120 --> 00:53:28,957
convenience, or cost. And we want to try
to do all three of those things with

987
00:53:29,040 --> 00:53:31,837
with Dana. And our hope
is just like you said,

988
00:53:31,920 --> 00:53:36,317
like, you know, uh kids can
develop robots for their for

989
00:53:36,400 --> 00:53:39,637
their own use. And and that extends
to humanoids. So, we're not just talking

990
00:53:39,720 --> 00:53:44,357
about like land-based systems or or,
you know, the ones that are so you can

991
00:53:44,440 --> 00:53:49,277
humanoids you can do drones. The fact
that right now writing drone software

992
00:53:49,360 --> 00:53:51,237
and deploying it

993
00:53:51,320 --> 00:53:54,797
quite obscure and almost hobbyist.

994
00:53:54,880 --> 00:53:58,677
We want to just make that
absolutely like that, you know,

995
00:53:58,760 --> 00:54:01,037
maybe not child's play
but like teenager play.

996
00:54:01,120 --> 00:54:03,637
>> So this one still world like just
fly like a lot more experimentation and

997
00:54:03,720 --> 00:54:06,597
entrepreneurship and like
agriculture >> everything

998
00:54:06,680 --> 00:54:09,757
>> bots and like you basically
in every domain construction >> Yeah.

999
00:54:09,840 --> 00:54:12,357
>> defense just all of a
sudden have a much larger

1000
00:54:12,440 --> 00:54:15,197
number of people who are applying
creativity and coming up with ideas and

1001
00:54:15,280 --> 00:54:17,917
making things that move. >> Yeah,
and have you seen like with Claude

1002
00:54:18,000 --> 00:54:22,197
it's like it's one thing just to make
the engineer more efficient or bring more

1003
00:54:22,280 --> 00:54:25,957
people into engineering but then when
these agents really run you're getting

1004
00:54:26,040 --> 00:54:29,037
into just like the iPhone example of you

1005
00:54:29,120 --> 00:54:33,837
couldn't imagine Instagram before like

1006
00:54:33,920 --> 00:54:37,397
iPhone. It's like imagine 2005
on laptops you're like you did

1007
00:54:37,480 --> 00:54:39,677
10 years is going to be this this app.

1008
00:54:39,760 --> 00:54:42,917
And you can put photos in like well the
phones don't have cameras like yeah, but

1009
00:54:43,000 --> 00:54:46,117
it's going to be like social like what
the hell like so like Facebook it's like

1010
00:54:46,200 --> 00:54:48,917
see it's just hard to so we think

1011
00:54:49,000 --> 00:54:53,757
by lowering that barrier you're going
to get way way more creative autonomy

1012
00:54:53,840 --> 00:54:56,397
products. >> Yeah. Okay,
so I want to say that you

1013
00:54:56,480 --> 00:55:01,397
can decide whether to include this or not.
So my kid is building autonomous bots in

1014
00:55:01,480 --> 00:55:04,717
Factorio. >> Oh, nice.
>> That's one of his projects and so and

1015
00:55:04,800 --> 00:55:07,197
then but he's he's you know, he's had
a role in cuz the tool kit's not available

1016
00:55:07,280 --> 00:55:10,037
yet. So he's actually training and he's
actually he's actually training models.

1017
00:55:10,120 --> 00:55:12,597
>> Yeah. >> He's gathering
data in the game and

1018
00:55:12,680 --> 00:55:15,317
actually he has like a whole army
of like bots that he's developed to go.

1019
00:55:15,400 --> 00:55:18,517
>> Yeah. So like >> And then
his mother is like why are you

1020
00:55:18,600 --> 00:55:22,077
playing that game so much and he explains
of course it's a purely educational

1021
00:55:22,160 --> 00:55:26,597
process and experience but but it's
you know, it's the kind of thing

1022
00:55:26,680 --> 00:55:28,117
>> Like there's no reason autonomy should

1023
00:55:28,200 --> 00:55:32,117
be this like you know,

1024
00:55:32,200 --> 00:55:36,837
obscure difficult you know,
alchemistic you know,

1025
00:55:36,920 --> 00:55:39,077
technology And

1026
00:55:39,160 --> 00:55:42,117
I think not only does that have a huge

1027
00:55:42,200 --> 00:55:46,957
impact on on society, it also allows
people to understand that these systems

1028
00:55:47,040 --> 00:55:50,557
are not like you know,

1029
00:55:50,640 --> 00:55:53,877
magic. It's like if I can develop a

1030
00:55:53,960 --> 00:55:58,637
a Roomba for myself in my house
on a weekend using Dana,

1031
00:55:58,720 --> 00:56:02,237
then a why then it's not suddenly so
scary. And I think that's like that's

1032
00:56:02,320 --> 00:56:04,277
that's that's important.
>> It can it can it can support it can

1033
00:56:04,360 --> 00:56:06,837
support people in all kinds of ways
that we haven't even imagined yet cuz they

1034
00:56:06,920 --> 00:56:09,917
yeah. >> Absolutely.
I mean you think about like you know,

1035
00:56:10,000 --> 00:56:14,677
folks with disabilities. You know, we
always think about humanoids as like this

1036
00:56:14,760 --> 00:56:19,240
very important task of folding
laundry which seems to be >> [laughter]

1037
00:56:20,160 --> 00:56:24,157
>> So we focus on you know, the important
task but the when you allow these tools

1038
00:56:24,240 --> 00:56:27,757
to exist, I mean I I you know, we
started a tooling company. I mean I feel

1039
00:56:27,840 --> 00:56:31,757
so importantly that tools are like
what separates actually advanced

1040
00:56:31,840 --> 00:56:36,597
civilizations from you know, less advanced
civil civilizations. And our our first

1041
00:56:36,680 --> 00:56:39,757
mark for the company was a monkey's
head and then we got a designer who said

1042
00:56:39,840 --> 00:56:42,397
we're good. >> [laughter]

1043
00:56:42,480 --> 00:56:47,200
>> Like this is stupid.
I was like I thought it was pretty good.

1044
00:56:47,720 --> 00:56:52,317
>> So you were talking earlier
about how when you know, um

1045
00:56:52,400 --> 00:56:55,077
the technology got got so good
in mobile that there was a wave of these

1046
00:56:55,160 --> 00:56:56,837
companies, you know, Uber, WhatsApp,

1047
00:56:56,920 --> 00:56:59,717
Snap, you know, Airbnb etc. that emerged

1048
00:56:59,800 --> 00:57:04,477
in quick succession. And so now
the technology is getting there for the

1049
00:57:04,560 --> 00:57:07,677
infrastructure for physical AI, what are
some use cases or companies that you can

1050
00:57:07,760 --> 00:57:10,317
obviously it's hard to predict
the future but what were you most excited

1051
00:57:10,400 --> 00:57:13,037
for? Like what what could we be talking
about the equivalent here of in quick

1052
00:57:13,120 --> 00:57:15,597
succession? >> I mean I think uh

1053
00:57:15,680 --> 00:57:18,037
you know, mid-term we want Dana if not

1054
00:57:18,120 --> 00:57:22,837
the short-term to really you know,
make humanoids way more real.

1055
00:57:22,920 --> 00:57:27,077
Uh there's I mean how many it's like
a thousand core tasks in a home from from

1056
00:57:27,160 --> 00:57:31,677
humanoids. And these companies it's like
such I mean I'm sure if talk to people who

1057
00:57:31,760 --> 00:57:35,157
work in these companies, it's everything
is difficult. Every step of the way is

1058
00:57:35,240 --> 00:57:36,957
difficult. Collecting data is difficult.

1059
00:57:37,040 --> 00:57:38,877
You know, cleaning that data is difficult.
Training those models or

1060
00:57:38,960 --> 00:57:40,917
deploying the model is difficult.

1061
00:57:41,000 --> 00:57:44,357
And the bar being I want a high
school kid to make a humanoid.

1062
00:57:44,440 --> 00:57:47,757
So, that that's our our our path. And we
think there there could be a lot there.

1063
00:57:47,840 --> 00:57:51,597
But, that's like these the obvious stuff.
I think the true non-obvious

1064
00:57:51,680 --> 00:57:55,357
stuff is going to be we'll look back
will be will be way way more interesting.

1065
00:57:55,440 --> 00:57:58,157
>> And there there's there's some core
ingredients that we're bringing together

1066
00:57:58,240 --> 00:58:01,917
in data, right? We're we're making it
way easier to to actually get imitation

1067
00:58:02,000 --> 00:58:05,077
learning to work. Way easier to make
reinforcement learning work in

1068
00:58:05,160 --> 00:58:08,717
combination with that.
Um we're we we have pre-trained models

1069
00:58:08,800 --> 00:58:12,437
that can be used as a baseline for a lot
of things. Um world models, advanced

1070
00:58:12,520 --> 00:58:15,517
simulation technique, all of these
things come together and then you're

1071
00:58:15,600 --> 00:58:18,037
sort of limited by your crea- creativity.
Like, well, what what do I

1072
00:58:18,120 --> 00:58:22,677
want to do? And if you think about any
kind of physically AI task as it's it's

1073
00:58:22,760 --> 00:58:27,517
a you are understanding the world and
you're manipulating something and and we

1074
00:58:27,600 --> 00:58:30,757
can build that. That can be built now
much more easily in this in this tool.

1075
00:58:30,840 --> 00:58:34,037
>> And I think sometimes people ask like
us being a tooling company and like you

1076
00:58:34,120 --> 00:58:37,437
take self-driving trucks. We deploy
self-driving trucks and many of the

1077
00:58:37,520 --> 00:58:41,157
self-driving truck companies use our
tools. I I I think sometimes people ask,

1078
00:58:41,240 --> 00:58:44,837
well, look, you know, with Dana,
are you going to like enable

1079
00:58:44,920 --> 00:58:46,197
all these competitors? That's great.

1080
00:58:46,280 --> 00:58:48,277
>> Right. >> That's
absolutely completely fine. If

1081
00:58:48,360 --> 00:58:51,757
you look at Google and what Google did
to web applications, there was a massive

1082
00:58:51,840 --> 00:58:55,517
internet. Google still succeeded
through, you know, search and YouTube

1083
00:58:55,600 --> 00:59:00,317
and and and other web apps. And other
folks learned and used open source

1084
00:59:00,400 --> 00:59:02,677
products and then ultimately closed
source products and ultimately venture

1085
00:59:02,760 --> 00:59:06,517
back products. So, we think we think
the the same thing can happen here.

1086
00:59:06,600 --> 00:59:10,957
>> I was at a robotic startup a while
back that you you guys know well. And they

1087
00:59:11,040 --> 00:59:13,197
had they were training, you know,
they were doing their training process,

1088
00:59:13,280 --> 00:59:16,597
training one of their arms to do
the particularly killer app that I thought

1089
00:59:16,680 --> 00:59:19,887
was very appealing,
which was picking up dog poop.

1090
00:59:19,970 --> 00:59:21,757
>> [laughter] >> Literally,
you know, training over and

1091
00:59:21,840 --> 00:59:25,237
over again with different tools for the
And so, you know, I don't know, why Why

1092
00:59:25,320 --> 00:59:27,437
right? >> Yeah. >> Why not
have the little Why not have the

1093
00:59:27,520 --> 00:59:29,637
little robot follow you
around when you walk the dog?

1094
00:59:29,720 --> 00:59:32,317
And you'll pick up the poop.
>> Yeah. And I think like like you

1095
00:59:32,400 --> 00:59:35,317
>> I I know somebody who built a I
forget who it was, but somebody built a a

1096
00:59:35,400 --> 00:59:38,437
little lawn robot that would go around
and individual pick up individual leaves.

1097
00:59:38,520 --> 00:59:41,797
>> Yeah. >> Cuz you got that problem,
right? You you rake you you rake you

1098
00:59:41,880 --> 00:59:44,357
you rake your yard, it's completely
clean, and then like 2 hours later

1099
00:59:44,440 --> 00:59:46,837
there's like 14 leaves,
and you're like >> Yeah, yeah.

1100
00:59:46,920 --> 00:59:48,397
>> I'm just going to send out
the little bot to pick up the leaves.

1101
00:59:48,480 --> 00:59:52,157
>> It's like if development costs are
zero, then people will do that. I mean you

1102
00:59:52,240 --> 00:59:56,277
you guys remember like the early iPhone
apps that hit were like the beer one or

1103
00:59:56,360 --> 00:59:59,677
the fart app. If you >> [laughter]
>> imagine that in like yeah, if you

1104
00:59:59,760 --> 01:00:04,477
imagine that in '98 with you know, with
the Symbian mobile, you know, whatever

1105
01:00:04,560 --> 01:00:07,357
it was the OS from I think it
was Ericsson or somebody. That'd be

1106
01:00:07,440 --> 01:00:11,077
impossible. You didn't need a team
of like 50 people to develop a like the

1107
01:00:11,160 --> 01:00:14,597
beer thing for the Blackberry.
So I think there's a similar type of thing

1108
01:00:14,680 --> 01:00:17,317
that's happening where you
know, we really want to be a

1109
01:00:17,400 --> 01:00:18,837
part of that and we want to enable that.

1110
01:00:18,920 --> 01:00:22,317
And if it like makes making like I think

1111
01:00:22,400 --> 01:00:26,117
it'd still be a while before like making
a robo taxi is like super super easy.

1112
01:00:26,200 --> 01:00:27,437
>> Yeah. >> But that'll happen.

1113
01:00:27,520 --> 01:00:29,997
>> But there's I mean the number of bots
that could be the number of kinds of

1114
01:00:30,080 --> 01:00:32,477
bots that could be deployed in health
care is is almost endless. Health care

1115
01:00:32,560 --> 01:00:34,997
alone is is endless. Home care home care.

1116
01:00:35,080 --> 01:00:39,637
>> Yeah. >> Um and then in construction,
you know, in all the physical trades.

1117
01:00:39,720 --> 01:00:43,877
>> us sitting in 2007 and saying, "Let's
we we should have an app store. What

1118
01:00:43,960 --> 01:00:46,917
type of apps?" And we would come
up with like a list of eight.

1119
01:00:47,000 --> 01:00:50,397
And then they're like, "There'll be
a messaging one and then there'll be a

1120
01:00:50,480 --> 01:00:53,357
camera one." It's like and now you
look at the app store and it's like, you

1121
01:00:53,440 --> 01:00:57,917
know, there's an app for like the hotel
you go to and it's like, you know, to to

1122
01:00:58,000 --> 01:00:59,677
order, you know, food off the menu.

1123
01:00:59,760 --> 01:01:00,957
>> Right. >> Yeah. >> Yeah, makes sense.

1124
01:01:01,040 --> 01:01:03,717
>> Yeah. D, we're talking, you know,
earlier about the the differences

1125
01:01:03,800 --> 01:01:04,997
between digital AI and physical AI.

1126
01:01:05,080 --> 01:01:09,197
We're sort of hinting at LLMs, but world
models are you know, in vogue right now.

1127
01:01:09,280 --> 01:01:12,277
Want to talk about sort of the the state
of them in as it relates to physical AI

1128
01:01:12,360 --> 01:01:13,837
and how should we think about them?

1129
01:01:13,920 --> 01:01:17,757
>> So so first off, world models means
about a hundred different things and

1130
01:01:17,840 --> 01:01:21,957
we had a a team at CVPR recently and and I
was joking with them about just how

1131
01:01:22,040 --> 01:01:25,757
many different ways you can define what
a what a world model is. But when we're

1132
01:01:25,840 --> 01:01:28,237
thinking about a world model, right,
we're we're typically thinking about it

1133
01:01:28,320 --> 01:01:31,957
in the context of of a simulation,
right? So something that is effectively

1134
01:01:32,040 --> 01:01:34,757
>> We started a sim company. >> Yeah.
Yeah, so so something that is like

1135
01:01:34,840 --> 01:01:38,877
sufficiently able to represent the real
world and and is reactive in a sense

1136
01:01:38,960 --> 01:01:40,957
where you can actually have, let's say,

1137
01:01:41,040 --> 01:01:46,317
a an autonomous agent that's acting
in this world and the world model is is

1138
01:01:46,400 --> 01:01:48,597
behaving appropriately in response
to that autonomous agent.

1139
01:01:48,680 --> 01:01:51,717
>> Maybe uh Peter, I think it's worth ex-
being super explicit here. Or we just go

1140
01:01:51,800 --> 01:01:55,597
just one level lower into Uh this you know

1141
01:01:55,680 --> 01:01:59,837
determinism in simulators, kind
of the sim-to-real gap, physics-based, you

1142
01:01:59,920 --> 01:02:02,837
know, rendering all the way
to like this generated world.

1143
01:02:02,920 --> 01:02:05,997
>> Yeah, yeah. >> Where do
we fit on it or or where, you

1144
01:02:06,080 --> 01:02:08,997
know, yeah, just describe
the landscape, I think, maybe.

1145
01:02:09,080 --> 01:02:12,117
>> Yeah, yeah, so so this is like let's
say simulation broadly, right? There's

1146
01:02:12,200 --> 01:02:15,397
there's so many different ways
of doing simulation. And so the the more

1147
01:02:15,480 --> 01:02:20,037
classical approaches of simulation
very physics-based and and you can

1148
01:02:20,120 --> 01:02:23,477
decompose physics in all different ways
and all different levels of abstraction,

1149
01:02:23,560 --> 01:02:27,157
and you can simulate with sensors or
without sensors, and and is it is just a

1150
01:02:27,240 --> 01:02:30,277
body simulation or are we actually
simulating, for example, the light in in

1151
01:02:30,360 --> 01:02:32,917
the environment or the >> Almost
think about like the way CGI is

1152
01:02:33,000 --> 01:02:36,277
done. If we literally had technical
artists, and we have technical artists

1153
01:02:36,360 --> 01:02:40,597
who would create assets which would go
in the simulator, which would mimic real

1154
01:02:40,680 --> 01:02:44,437
like road signs and have you know,
reflectivity and material properties

1155
01:02:44,520 --> 01:02:46,037
that you would see in the real world.

1156
01:02:46,120 --> 01:02:48,477
But as you guys know Hollywood
[clears throat] is going

1157
01:02:48,560 --> 01:02:52,437
through its own fundamental change.
And yet now you've generated uh the same

1158
01:02:52,520 --> 01:02:54,597
thing is happening
in our universe as well.

1159
01:02:54,680 --> 01:02:58,117
>> Yeah, so so that's sort of on the
that's at the far end of physics-based

1160
01:02:58,200 --> 01:03:03,237
simulation. And and then the opposite
end is is purely neural simulation, but

1161
01:03:03,320 --> 01:03:06,117
within that spectrum, there's there's
many different things you can do that

1162
01:03:06,200 --> 01:03:09,717
are each useful in their own right.
And so one of those things is a

1163
01:03:09,800 --> 01:03:14,037
Gaussian-based simulation,
right? Where you have uh a a

1164
01:03:14,120 --> 01:03:19,477
effectively a representation
of the real world that that has a a a 3D

1165
01:03:19,560 --> 01:03:22,197
representation.
And that 3D representation is

1166
01:03:22,280 --> 01:03:26,397
consistent, meaning that if if you let's
say have some reference point, let's say

1167
01:03:26,480 --> 01:03:31,237
a camera, and that camera moves within
that 3D world, because the Gaussian is

1168
01:03:31,320 --> 01:03:34,917
actually representing the 3D geometry
of that world, you'll actually get very

1169
01:03:35,000 --> 01:03:38,557
high-quality output from that. There's
a lot of value in that, and and that's

1170
01:03:38,640 --> 01:03:40,717
what I'd say one type of of world model.

1171
01:03:40,800 --> 01:03:42,957
But when you go further on that on that

1172
01:03:43,040 --> 01:03:47,837
spectrum into the really into neural
simulation, then you get into these

1173
01:03:47,920 --> 01:03:50,637
where you're actually generating
the video feeds. You can think of a neural

1174
01:03:50,720 --> 01:03:54,037
network that's actually
outputting a video.

1175
01:03:54,120 --> 01:03:58,037
As what's actually coming out
of the neurons of that. And and

1176
01:03:58,120 --> 01:04:00,917
that can be reactive, which gives
you some very interesting properties.

1177
01:04:01,000 --> 01:04:04,397
Reactive as in the ego >> does
something in the environment and

1178
01:04:04,480 --> 01:04:06,877
the other agents respond to the ego.

1179
01:04:06,960 --> 01:04:10,237
>> Exactly. However,
you're not guaranteed in that

1180
01:04:10,320 --> 01:04:13,557
reactivity that it's it's accurate,
right? And and and now it's a question

1181
01:04:13,640 --> 01:04:18,317
of well, how can I align this this
simulation, this world model, with the

1182
01:04:18,400 --> 01:04:21,397
real world and the way that the real
world would actually react? And if you

1183
01:04:21,480 --> 01:04:25,557
have perfect alignment between the real
world and and the world model, I mean I

1184
01:04:25,640 --> 01:04:28,477
think you've just sort of solved the
universe roughly, right? It's that's an

1185
01:04:28,560 --> 01:04:33,197
impossibly difficult problem.
But but as we make progress towards that,

1186
01:04:33,280 --> 01:04:36,517
it makes training physically
accurate models much

1187
01:04:36,600 --> 01:04:40,637
easier because you can do more of that
in simulation. But the hardest part

1188
01:04:40,720 --> 01:04:42,557
though is we're always thinking about

1189
01:04:42,640 --> 01:04:46,237
performance, right? So I like to say

1190
01:04:46,320 --> 01:04:50,117
that the the labs, they they have it
easy because they can they can make

1191
01:04:50,200 --> 01:04:54,477
models that are trillions of parameters,
and those models can be super slow, and

1192
01:04:54,560 --> 01:04:57,637
that's fine. But we don't have that
luxury in physically AI, right? We deal

1193
01:04:57,720 --> 01:05:02,197
We we deal in real time. Like the actual
actual clock real time. And so we we

1194
01:05:02,280 --> 01:05:06,077
have so many milliseconds before we
have to do something. And and those

1195
01:05:06,160 --> 01:05:09,877
performance constraints, they actually
constrain the problem in a lot of ways.

1196
01:05:09,960 --> 01:05:13,317
So we can have very large models, and we
do have very large models that are used

1197
01:05:13,400 --> 01:05:16,717
in the off-world environment. But when
once you go on board, all of those

1198
01:05:16,800 --> 01:05:19,757
constraints are very real. And now we
need to train a much smaller model that

1199
01:05:19,840 --> 01:05:23,477
has these safety constraints,
these determinism constraints. And

1200
01:05:23,560 --> 01:05:26,677
and that's the hard part about physical
That's also the moat, right? It's It's

1201
01:05:26,760 --> 01:05:28,997
It is what makes our tooling and and our

1202
01:05:29,080 --> 01:05:33,797
our competencies valuable because
it's just really hard to to meet

1203
01:05:33,880 --> 01:05:35,877
all of these constraints
in in a physical system.

1204
01:05:35,960 --> 01:05:39,557
>> When will you Which will we get first,
a perfect perfectly simulated real-world

1205
01:05:39,640 --> 01:05:43,717
environment for training autonomy
autonomous devices or Grand Theft Auto 6?

1206
01:05:43,800 --> 01:05:47,117
>> [laughter] >> You know,
I as long as they keep putting

1207
01:05:47,200 --> 01:05:50,997
out great trailers, I mean, I've I
was I feel like I'm getting entertained

1208
01:05:51,080 --> 01:05:54,847
without paying a dollar.
I am re-introduced to Tom Petty because of

1209
01:05:54,930 --> 01:05:56,950
>> [laughter]

1210
01:05:57,920 --> 01:05:59,197
>> Will you give us some timelines?

1211
01:05:59,280 --> 01:06:01,917
>> Soon as >> Well, well, so so this
is >> Let's run with that for a second.

1212
01:06:02,000 --> 01:06:03,837
>> Yeah, let's go for it.
Go for [laughter] it.

1213
01:06:03,920 --> 01:06:06,157
Well, no, look, I mean, so the whole
thing with the whole thing with Grand

1214
01:06:06,240 --> 01:06:09,197
Theft Auto is the big innovation
was open open world open world sandbox

1215
01:06:09,280 --> 01:06:12,997
gaming. So it's a It's a simi- it's
a simulated city, and at least in theory.

1216
01:06:13,080 --> 01:06:15,557
>> On that spectrum, I mean, we hire so
many people out of the video game world.

1217
01:06:15,640 --> 01:06:18,677
On that spectrum, it's absolutely real
>> Well, then tell tell us about that.

1218
01:06:18,760 --> 01:06:21,357
Yeah, what's the spectrum?
>> So I here I I This is This is

1219
01:06:21,440 --> 01:06:23,557
speculation, but I think Grand Theft

1220
01:06:23,640 --> 01:06:25,797
Grand Theft Auto 6 will be perhaps the

1221
01:06:25,880 --> 01:06:29,637
last major real-world video game that's

1222
01:06:29,720 --> 01:06:34,077
still really developed, let's say,
in that legacy era of traditional computer

1223
01:06:34,160 --> 01:06:36,757
graphics tooling.
>> Technical artists and yeah.

1224
01:06:36,840 --> 01:06:40,677
>> Like I I think that that that Grand
Theft Auto 7 will much more likely be

1225
01:06:40,760 --> 01:06:42,317
like a world model-based video game.

1226
01:06:42,400 --> 01:06:45,037
>> Right. >> Um and where
where you you can imagine

1227
01:06:45,120 --> 01:06:48,717
as as AI tech evolves here, you you
have like this this concept of this video

1228
01:06:48,800 --> 01:06:51,557
game world model, and there's there's
like some sort of baseline, let's say,

1229
01:06:51,640 --> 01:06:56,117
data store that represents the real
world in somehow, and then and then you

1230
01:06:56,200 --> 01:06:59,157
have a some translation layer that's
actually turning that data store into

1231
01:06:59,240 --> 01:07:02,557
something that you can see and and run
around in. Like it's It's possible.

1232
01:07:02,640 --> 01:07:05,917
>> the game is a constant It could be the
real world, right? As we said, you could

1233
01:07:06,000 --> 01:07:08,877
you could have a complete recreation
of the real in the game. Well, this has

1234
01:07:08,960 --> 01:07:11,317
kind of happened with flight simulators,
hasn't it? Isn't it? The most recent

1235
01:07:11,400 --> 01:07:15,517
flight simulators are literally is the
entire planet rendered accurately, is my

1236
01:07:15,600 --> 01:07:17,037
understanding, at least
from the air. Is that right?

1237
01:07:17,120 --> 01:07:20,277
>> Yeah, yeah. And I mean, you really
that's where our our bread and butter is

1238
01:07:20,360 --> 01:07:23,517
when we started started the business.
We hired so many people out of the

1239
01:07:23,600 --> 01:07:27,157
Microsoft Flight Sim. I'm going to say
I'm surprised they didn't you know

1240
01:07:27,240 --> 01:07:29,477
>> But when you fly over New You know,
whatever, New York or when you fly over

1241
01:07:29,560 --> 01:07:31,957
Duluth in the flight simulator now,
it is the real city, right?

1242
01:07:32,040 --> 01:07:35,277
>> Exactly. But there there's some tricks
that they play there. And a lot of that

1243
01:07:35,360 --> 01:07:37,517
is fidelity. You know, the real world,

1244
01:07:37,600 --> 01:07:42,637
the more you zoom in, it stays a certain
level of fidelity. And and so they the

1245
01:07:42,720 --> 01:07:46,397
tricks that you play there
is you you basically are

1246
01:07:46,480 --> 01:07:48,797
down sampling very very aggressively.

1247
01:07:48,880 --> 01:07:52,997
And then as you get closer, you know,
then it becomes more more high fidelity.

1248
01:07:53,080 --> 01:07:54,797
Where the real world isn't like that.

1249
01:07:54,880 --> 01:07:57,477
The if you were to try
to rebuild the world

1250
01:07:57,560 --> 01:08:00,837
with this level of fidelity, it would
you know, it would take the all the

1251
01:08:00,920 --> 01:08:03,877
energy of the universe, right? It's it's
it's it's it's quite complex. And that's

1252
01:08:03,960 --> 01:08:07,677
probably by the way the best argument
against us being living in a simulation

1253
01:08:07,760 --> 01:08:12,357
is among the But of course then you
would say, well, the simulator we're in

1254
01:08:12,440 --> 01:08:18,120
doesn't follow the laws
of physics that were at that point

1255
01:08:19,400 --> 01:08:21,917
Yeah, yeah. Yeah. >> [laughter] >>
As far as I know, everything happening

1256
01:08:22,000 --> 01:08:22,837
outside this room doesn't even exist.

1257
01:08:22,920 --> 01:08:26,397
>> Yeah, this is I mean, you know, you
know, like Buddhism believes this. We're

1258
01:08:26,480 --> 01:08:27,837
This is a different type of podcast.

1259
01:08:27,920 --> 01:08:30,837
It's like, you know, when you open
your eyes, the world is rendered. And then

1260
01:08:30,920 --> 01:08:34,017
you close your eyes, the world
That's literally religious.

1261
01:08:34,100 --> 01:08:36,037
>> [laughter] >> I don't
see why I don't see why it's

1262
01:08:36,120 --> 01:08:38,607
necessary for it to keep
rendering if I'm not there.

1263
01:08:38,690 --> 01:08:40,917
>> [laughter] >> Buddhism
from first principles.

1264
01:08:41,000 --> 01:08:43,117
>> Yeah, exactly. That's what you should
have That'll get a lot of clicks. That's

1265
01:08:43,200 --> 01:08:47,277
what you should call this. >> [laughter]
>> Yeah. Well, just going to be on the

1266
01:08:47,360 --> 01:08:50,757
timeline topic, you know, you gave us
timelines on self-driving cars. What

1267
01:08:50,840 --> 01:08:53,757
timelines do you want to give us,
if any, on the sort of

1268
01:08:53,840 --> 01:08:56,557
you know, other interesting things that
were worth tracking, like perhaps when

1269
01:08:56,640 --> 01:08:59,677
we'll get laundry folded or other
you know, things that emerge

1270
01:08:59,760 --> 01:09:02,797
because of humanoids or I think also
maybe just just touching a little bit on

1271
01:09:02,880 --> 01:09:05,757
world models, where we see world models
going, cuz I think it's it's fundamental

1272
01:09:05,840 --> 01:09:07,517
to all the work we do.
>> Yeah, pretty sure.

1273
01:09:07,600 --> 01:09:11,277
>> Yeah, yeah, so so so
to ask the the first question

1274
01:09:11,360 --> 01:09:15,237
so laundry folding, it's it's
it's not terribly far from being

1275
01:09:15,320 --> 01:09:18,997
solved to to be clear. And there
is a lot of interesting research being

1276
01:09:19,080 --> 01:09:25,250
>> And then humanity can rejoice. That's
in Proverbs 4:16, I think. [laughter]

1277
01:09:26,480 --> 01:09:30,277
>> Well, here I I I I do think
I do think housekeeping is is

1278
01:09:30,360 --> 01:09:32,077
a killer use case for physically I, right?

1279
01:09:32,160 --> 01:09:34,597
>> Peter thinks two that he always
talks about in the company. One is

1280
01:09:34,680 --> 01:09:38,317
housekeeping and entertainment.
He Peter's long on humanoid entertainment.

1281
01:09:38,400 --> 01:09:40,157
>> What is it like?
>> What kind of entertainment?

1282
01:09:40,240 --> 01:09:42,517
>> I 100% [laughter] agree with that.
I think entertainment is the robot as

1283
01:09:42,600 --> 01:09:45,317
killer app. I don't think anybody else
>> Yeah, these are your admissions.

1284
01:09:45,400 --> 01:09:49,997
>> I mean like >> [laughter] >> So these
Midwest white guys are really into this.

1285
01:09:50,080 --> 01:09:52,757
>> I'm just saying, but I think I
think >> I just want to know when I get

1286
01:09:52,840 --> 01:09:54,517
Westworld. That's all I want. >> Yes.

1287
01:09:54,600 --> 01:09:57,237
>> No, I actually have an entertainer.
I have a little I have a little a tiny

1288
01:09:57,320 --> 01:10:00,077
little Chinese robot dog that's like
literally it's just like a little it's

1289
01:10:00,160 --> 01:10:03,157
just a little and it just like runs around
and it just like first base a little bit.

1290
01:10:03,240 --> 01:10:06,377
>> you pay to see Cirque du
Soleil with robots? Like yes.

1291
01:10:06,460 --> 01:10:11,037
>> [laughter] >> Yes.
>> I I want to see I want to see kung fu

1292
01:10:11,120 --> 01:10:14,237
trapeze swinging.
>> Spoken by like a compiler's guy.

1293
01:10:14,320 --> 01:10:16,340
>> [laughter]

1294
01:10:17,800 --> 01:10:20,117
>> I know, but but >> I want
Westworld. I want Westworld.

1295
01:10:20,200 --> 01:10:22,917
>> I mean the the turn The funny thing
is I was I was just saying like well will

1296
01:10:23,000 --> 01:10:26,877
people in like the suburbs of Detroit
actually that pass the test? I bet you

1297
01:10:26,960 --> 01:10:30,837
people in Sterling Heights would actually
pay to see that. It's actually pro.

1298
01:10:30,920 --> 01:10:33,837
I stand corrected. I stand >> [laughter]
>> But but but back on laundry folding for

1299
01:10:33,920 --> 01:10:38,157
a moment it's it's actually not far
from being folded if you remove the time

1300
01:10:38,240 --> 01:10:41,317
constraint. And so the the trick that's
played in if you look at the latest

1301
01:10:41,400 --> 01:10:45,277
research videos is they'll say like play
it at 8x real time or whatever, right?

1302
01:10:45,360 --> 01:10:48,437
And and that's for you
to make it watchable.

1303
01:10:48,520 --> 01:10:50,717
So so the question
is when can you actually

1304
01:10:50,800 --> 01:10:52,757
achieve human parity of performance?

1305
01:10:52,840 --> 01:10:55,797
That's that's further off.
>> When you decouple models from just the

1306
01:10:55,880 --> 01:10:57,437
hardware the hardware can do it now.

1307
01:10:57,520 --> 01:11:00,717
That used to be a constraint. So, the
hardware is very fast and accurate now,

1308
01:11:00,800 --> 01:11:03,197
which was actually >> There
there's still overheating issues

1309
01:11:03,280 --> 01:11:05,797
that are still being dealt with, but it's
it's not it's not terribly far off.

1310
01:11:05,880 --> 01:11:07,717
Like, these are solved.
>> I mean, it's far off from like, you

1311
01:11:07,800 --> 01:11:11,317
know, when I was a mechy, that was like
fantasy. Like, there's like nothing can

1312
01:11:11,400 --> 01:11:14,197
>> What's the movie that has the most
realistic future vision of robots?

1313
01:11:14,280 --> 01:11:16,597
>> Oh, man. Bicentennial Man.
>> Is it Okay.

1314
01:11:16,680 --> 01:11:18,917
>> Yeah, he's he's realistic.
>> [laughter]

1315
01:11:19,000 --> 01:11:20,157
>> Why that one?
I actually haven't seen that

1316
01:11:20,240 --> 01:11:22,997
>> Well, I like I like that scene. I think
it's I, Robot when Will Smith jumps in

1317
01:11:23,080 --> 01:11:26,517
the car and uh it's his his you know,
whatever his like accomplice is in the

1318
01:11:26,600 --> 01:11:29,477
car and he's like puts the car in manual
and she's like, "What are you going to

1319
01:11:29,560 --> 01:11:33,717
drive this thing yourself?" Like, out
of like, you know, she's Yeah, she's like,

1320
01:11:33,800 --> 01:11:36,277
"Yeah, are you crazy? What are you going
to drive this thing yourself?" Like,

1321
01:11:36,360 --> 01:11:40,357
that's what we're That's the flight
of intuition's you know, like goal.

1322
01:11:40,440 --> 01:11:43,637
>> Well, again, by the way, I I haven't
seen this movie in a long time, probably

1323
01:11:43,720 --> 01:11:47,037
since it came out. So, my recollection
of it is probably a bit incorrect.

1324
01:11:47,120 --> 01:11:48,557
>> Don't worry,
the internet will correct you.

1325
01:11:48,640 --> 01:11:52,357
>> [laughter] >> But, I I think
I think Bicentennial Man has

1326
01:11:52,440 --> 01:11:57,677
fully self-driving cars. Uh and it also
has the housekeeping robot, which which

1327
01:11:57,760 --> 01:12:01,437
is played by Robin Williams. And that's
it's sort of like the the friendly guy

1328
01:12:01,520 --> 01:12:05,517
that will the friendly robot that will
clean up and also babysit your kids and

1329
01:12:05,600 --> 01:12:09,277
stuff like that. And it seems like it's
it's in the not terribly distant future.

1330
01:12:09,360 --> 01:12:13,397
>> I got a different answer. You guys
ever see that movie uh Sam Rockwell Moon?

1331
01:12:13,480 --> 01:12:15,517
>> Oh, yeah. >> Yeah.
The the setup I don't want to It's

1332
01:12:15,600 --> 01:12:17,477
a great movie. Don't watch the trailer.

1333
01:12:17,560 --> 01:12:21,317
Just to watch the movie.
Uh it's the the premise is the tagline

1334
01:12:21,400 --> 01:12:25,557
of the movie is 250,000 mi from home you
find who you are. And it's one guy who

1335
01:12:25,640 --> 01:12:29,157
works on an energy harvesting
base run by Pliant Intuition run by

1336
01:12:29,240 --> 01:12:34,317
Lunar Technologies. >> [laughter] >> I'm I

1337
01:12:34,400 --> 01:12:36,797
I don't want to be Weyland I don't want
to be Weyland-Yutani. I don't want to

1338
01:12:36,880 --> 01:12:41,037
be, you know, that's from from the Alien
franchises. And then Tyrell Corporation

1339
01:12:41,120 --> 01:12:44,757
from Blade Runner. No, no. I want
to be Lunar Technologies in the Moon

1340
01:12:44,840 --> 01:12:49,557
franchise. Not even franchise. What
There's one guy who works on this in the

1341
01:12:49,640 --> 01:12:53,997
base basically runs by itself. And he's
just there to kind of mind it when

1342
01:12:54,080 --> 01:12:56,237
things kind of some you know error signal

1343
01:12:56,320 --> 01:12:57,877
>> the the the reason
why it's it's I think

1344
01:12:57,960 --> 01:13:02,317
so accurate is because the state of the
art for AI systems is like these systems

1345
01:13:02,400 --> 01:13:03,917
they just need the occasional grounding.

1346
01:13:04,000 --> 01:13:06,717
>> Exactly. They'll just go off and do
something crazy and then you have to

1347
01:13:06,800 --> 01:13:08,237
say, "No, no, stop doing that."

1348
01:13:08,320 --> 01:13:10,397
>> to like that too. I mean that's
that's a coding bot sort of like, right?

1349
01:13:10,480 --> 01:13:14,277
>> And then the reason other reasons
I think it's quite accurate that it's

1350
01:13:14,360 --> 01:13:17,357
maybe uncouth now but Kevin Spacey is the

1351
01:13:17,440 --> 01:13:20,477
the AI you know smiley face and he's

1352
01:13:20,560 --> 01:13:25,037
he's just there to kind of placate the
human to to assist but to also like he's

1353
01:13:25,120 --> 01:13:28,317
like, "Oh, you're you you seem like
you're sad Sam." And like you know like

1354
01:13:28,400 --> 01:13:32,837
that's the the the the but really
it's the one running the base.

1355
01:13:32,920 --> 01:13:35,717
And hopefully I mean
I shouldn't say we want

1356
01:13:35,800 --> 01:13:38,237
to be lunar technologies because I don't
know if they're they're quite a positive

1357
01:13:38,320 --> 01:13:43,157
force in nature in that in that but I
think massive energy farm that's

1358
01:13:43,240 --> 01:13:46,277
completely autonomous that's
going to be the future. And I and

1359
01:13:46,360 --> 01:13:50,477
I think everyone like everyone reacts
to things like that with like fear. And

1360
01:13:50,560 --> 01:13:53,237
it's like guys, that's amazing. That

1361
01:13:53,320 --> 01:13:56,597
means energy costs go way down. Like

1362
01:13:56,680 --> 01:14:00,557
that's an incredible positive thing.
I think I think the you know I just did

1363
01:14:00,640 --> 01:14:03,437
this commencement speech
at my my undergrad.

1364
01:14:03,520 --> 01:14:07,717
>> Did you get uh destroyed? >> No.
You know what? I I >> Unlike Eric Schmidt.

1365
01:14:07,800 --> 01:14:11,077
>> Yeah, yeah, yeah. Yeah,
yeah. I Listen, listen. My my

1366
01:14:11,160 --> 01:14:13,477
the situation my my wife started
watching and she said, "I feel like

1367
01:14:13,560 --> 01:14:16,397
you're yelling at me.
I can't watch [laughter] this."

1368
01:14:16,480 --> 01:14:20,597
So I I I basically I mean I I don't I'm
not like I don't I don't I'm not going

1369
01:14:20,680 --> 01:14:23,597
to say which tech leaders who just
basically avoided by like punting and

1370
01:14:23,680 --> 01:14:27,837
saying I'm not going to talk about it.
I I talk about this stuff. And partly it's

1371
01:14:27,920 --> 01:14:31,517
the General Motors Institute. No one's
like these these are I I I don't want to

1372
01:14:31,600 --> 01:14:34,237
I don't I don't want to I don't want
to throw judgement on you know the people

1373
01:14:34,320 --> 01:14:38,117
we recruit out of out of MIT
and Stanford but I'd say GMI people are a

1374
01:14:38,200 --> 01:14:41,397
little different and they're like you
know they're like pragmatic people and

1375
01:14:41,480 --> 01:14:46,277
they under they you know you don't go
to a place like GMI if you believe a

1376
01:14:46,360 --> 01:14:50,277
superficial view of what corporations do.
Corporations are just people working

1377
01:14:50,360 --> 01:14:52,997
on projects together. And by the way,
people working on projects together in

1378
01:14:53,080 --> 01:14:55,997
government, people working on projects
together in nonprofits, they all screw

1379
01:14:56,080 --> 01:15:01,037
up. It's And so it's it's too simple
to say AI corporations are are terrible.

1380
01:15:01,120 --> 01:15:04,637
It's also you can't say the other side,
which is like it'll all be great. So you

1381
01:15:04,720 --> 01:15:07,837
have a role to play. That's basically
what you know, what my what what my

1382
01:15:07,920 --> 01:15:10,597
message is. And it's that's that's the

1383
01:15:10,680 --> 01:15:12,997
case. I think if you feel like if if it

1384
01:15:13,080 --> 01:15:18,637
if the the of the the I think
the obvious uh you know, uh abundance that

1385
01:15:18,720 --> 01:15:21,037
comes from self-driving trucks
self-driving cars and the fact that

1386
01:15:21,120 --> 01:15:23,237
people don't die, which is amazing.

1387
01:15:23,320 --> 01:15:25,477
But then you also get this efficiency of

1388
01:15:25,560 --> 01:15:28,277
cheaper energy etc. If all those things

1389
01:15:28,360 --> 01:15:33,437
don't still satisfy your your fear,
you you as a person, it's up to your

1390
01:15:33,520 --> 01:15:37,357
responsibility until really learn about
that technology. You you you can't just

1391
01:15:37,440 --> 01:15:39,797
say, "Well, I'm afraid of it and my

1392
01:15:39,880 --> 01:15:44,437
reaction is shut it down."
That's not it's simply it's the

1393
01:15:44,520 --> 01:15:46,797
uh and I don't I don't say this just
to say that that we're competing with the

1394
01:15:46,880 --> 01:15:49,557
Chinese, but there's
a Confucian >> [laughter]

1395
01:15:49,640 --> 01:15:52,397
>> the saying by my Confucius
is just no hand can block the sun.

1396
01:15:52,480 --> 01:15:56,357
>> Mhm. >> And [clears throat] the sun
is technological progress. And if we as a

1397
01:15:56,440 --> 01:16:00,357
society don't embrace technological
progress, we will be left behind.

1398
01:16:00,440 --> 01:16:00,997
>> Somebody else is going to do it.

1399
01:16:01,080 --> 01:16:03,357
>> Somebody else is going to do it. And if
it's not the Chinese, who knows? It's

1400
01:16:03,440 --> 01:16:07,437
maybe it's the Uzbeks or the you know,
it's another country that is is

1401
01:16:07,520 --> 01:16:10,797
recognizing, "Hey, my citizens are
suffering and I'm going to use this

1402
01:16:10,880 --> 01:16:12,757
technology to remove the" It is

1403
01:16:12,840 --> 01:16:17,837
honestly, it's because we're living
in such a great society that we can have

1404
01:16:17,920 --> 01:16:21,197
these like >> Yeah. >> I would
say stupid conversations. Like

1405
01:16:21,280 --> 01:16:24,557
there still are people
who don't can't get food.

1406
01:16:24,640 --> 01:16:26,997
>> Yep. >> And and and and
someone will immediately

1407
01:16:27,080 --> 01:16:29,237
equip if they were debating me,
they would say, "Well, there's plenty of

1408
01:16:29,320 --> 01:16:32,357
food. It's it's the capitalist system
that doesn't" No, no, no. Let's let's be

1409
01:16:32,440 --> 01:16:35,317
very specific. There's plenty of food,
but getting that food to those people is

1410
01:16:35,400 --> 01:16:38,477
difficult. >> Yep.
>> So that means we should let robots get

1411
01:16:38,560 --> 01:16:40,077
that food to them faster. >> Yep.

1412
01:16:40,160 --> 01:16:41,317
>> That's just that's just how it is. So I

1413
01:16:41,400 --> 01:16:45,077
think I'm I'm And I think like we we we

1414
01:16:45,160 --> 01:16:48,557
as like technologists, I think
sometimes we, you know, it's I think an

1415
01:16:48,640 --> 01:16:51,757
inclination just to say, "Leave these
people behind." I think you have to

1416
01:16:51,840 --> 01:16:55,077
bring them along. You have to explain
it to them. But, we also have to treat

1417
01:16:55,160 --> 01:16:58,717
folks like adults and say,
"If you don't get it after I explain it

1418
01:16:58,800 --> 01:17:02,277
a couple times, then you just don't get
it." So, there's like a middle ground.

1419
01:17:02,360 --> 01:17:05,677
It's not everyone's an idiot and every
or we should just be technology will

1420
01:17:05,760 --> 01:17:08,317
just be perfect perfect perfect.
There's a middle ground. Let's have that

1421
01:17:08,400 --> 01:17:10,317
conversation to a point. >> Yeah.

1422
01:17:10,400 --> 01:17:12,917
>> And then we just move forward and make
society better. And then the results

1423
01:17:13,000 --> 01:17:16,997
show it. I mean, there's people who
still shockingly believe communism is the

1424
01:17:17,080 --> 01:17:21,397
right answer. I mean, I just want to say
why And I'm I'm, you know, I I am a

1425
01:17:21,480 --> 01:17:25,157
capitalist. I can't Yeah,
I can't I can't not admit that. But,

1426
01:17:25,240 --> 01:17:28,957
there's 70 years of history there. Like,
we're not That's not a debate anymore. I

1427
01:17:29,040 --> 01:17:31,517
mean, that that I think it could be
a debate if we're in if we're sitting here

1428
01:17:31,600 --> 01:17:34,197
in 1965 and having a debate,
you say, "Okay,

1429
01:17:34,280 --> 01:17:37,917
maybe centrally controlled systems
work better." There's no debate anymore,

1430
01:17:38,000 --> 01:17:43,037
folks. You know, systems where individuals
make decisions on their own interest

1431
01:17:43,120 --> 01:17:46,197
actually work better for society.
And so, that doesn't mean everything is

1432
01:17:46,280 --> 01:17:48,317
perfect. And you can't extrapolate.

1433
01:17:48,400 --> 01:17:50,197
That's same thing with, you know, AI.

1434
01:17:50,280 --> 01:17:53,397
Doesn't mean everything's going to be
perfect, but net net, it's definitely

1435
01:17:53,480 --> 01:17:56,517
going to be better. And that's roughly
what my commencement speech was without

1436
01:17:56,600 --> 01:17:59,557
the booze. >> [laughter] >> They
Mark and these guys were booing.

1437
01:17:59,640 --> 01:18:04,197
So, they just cut it out.
Eric was booing and throwing stuff. They

1438
01:18:04,280 --> 01:18:07,997
just edited it out. >> You mentioned
uh you know, the Japan market earlier.

1439
01:18:08,080 --> 01:18:11,517
When you talk briefly about sort
of the global ambitions and how these

1440
01:18:11,600 --> 01:18:13,637
technologies, you know, interplay
and and what we're doing here.

1441
01:18:13,720 --> 01:18:15,997
>> So, I think uh America particularly is

1442
01:18:16,080 --> 01:18:20,957
still the most advanced in terms of when
you take account of business model. The

1443
01:18:21,040 --> 01:18:24,117
second thing for a company like Applied
Intuition, we're an extremely global

1444
01:18:24,200 --> 01:18:27,437
company. We work with
everybody uh uh uh uh

1445
01:18:27,520 --> 01:18:31,397
minus we don't have an office in China,
but really everyone else on the globe.

1446
01:18:31,480 --> 01:18:36,557
Uh and we're a horizontal company. We're
a technology provider. And I think we

1447
01:18:36,640 --> 01:18:39,677
I think more Silicon Valley companies I
think can employ a little bit of what we

1448
01:18:39,760 --> 01:18:42,917
do which is work very I
would say collaboratively

1449
01:18:43,000 --> 01:18:47,757
with the local economies. As sovereign
AI becomes more of a real thing

1450
01:18:47,840 --> 01:18:50,677
we have to uh you know,
build businesses that take

1451
01:18:50,760 --> 01:18:53,517
that into account. By the way, we're not
the first ones to do this. If you look

1452
01:18:53,600 --> 01:18:56,877
at the history of America, you read the
history of Standard Oil, you'll see that

1453
01:18:56,960 --> 01:19:01,117
this is this is that was the history
of companies. You'd work internationally.

1454
01:19:01,200 --> 01:19:03,197
Aramco is not a random company, right?

1455
01:19:03,280 --> 01:19:07,797
You you build based on the real
geopolitical realities of the time. And

1456
01:19:07,880 --> 01:19:10,757
so I think we you know, we've I think
navigated it quite well. I've I've lived

1457
01:19:10,840 --> 01:19:15,077
you know, in Japan, I lived in Germany,
I lived in Dubai. So I also being

1458
01:19:15,160 --> 01:19:19,157
Pakistani by birth, I think that's also
influenced our company. Peter's only

1459
01:19:19,240 --> 01:19:21,117
lived in Michigan and [laughter] here.

1460
01:19:21,200 --> 01:19:25,157
But he is a German, so

1461
01:19:25,240 --> 01:19:29,837
But so I think innately we're more we
think about the globe more. I think when

1462
01:19:29,920 --> 01:19:33,757
I was at both at Google and at YC,
I was always surprised at how

1463
01:19:33,840 --> 01:19:36,877
uh kind of almost myopic
the companies are. They're always

1464
01:19:36,960 --> 01:19:39,797
looking at the market that's just like
within the 30 you know, between San Jose

1465
01:19:39,880 --> 01:19:42,517
and San Francisco. I was like,
actually the market is really big. I think

1466
01:19:42,600 --> 01:19:46,437
physical AI, the nature of it being
physical, I think we we have to be a

1467
01:19:46,520 --> 01:19:49,757
very international company. And I think
we've had a lot of success being a very

1468
01:19:49,840 --> 01:19:51,197
very you know, being international.

1469
01:19:51,280 --> 01:19:54,437
Yeah. >> Cool. All right,
that's a good place to wrap.

1470
01:19:54,520 --> 01:19:55,997
>> Okay. >> Peter Gasser,
thanks so much for coming

1471
01:19:56,080 --> 01:19:58,157
on the podcast and congrats
on big lots of data.

1472
01:19:58,240 --> 01:19:59,557
>> Yeah, thanks for having us.

1473
01:19:59,640 --> 01:20:00,317
>> Awesome. Great to see you guys.

1474
01:20:00,400 --> 01:20:02,880
>> Okay, great.