Our mission is to put intelligence on a
billion machines. And we think that can
have a profound impact on society.
Applied Intuition is a physical AI
company. We put intelligence on
machines. Cars, trucks, tanks, drones,
it's a physical moving thing. We make it
intelligent.
>> Digital AI of course is building
software and optimizing ads and creating
videos. That's all interesting and good,
but really we talk about the global
economy, that's physical AI.
>> In this intelligence revolution, the
companies that impact the physical world
might actually be bigger
>> [music]
>> than the companies that impact the
digital world.
>> How many things are there where the idea
of physical AI, physical intelligence
are going to matter?
>> There's no reason autonomy should be
this obscure, difficult technology. Our
vision for that is a high school kid
that can make iPhone apps should be able
to make autonomous systems. That
platform for designing and developing is
what we're launching. It's called Dana.
>> Everything that we've built and
developed over the past nearly a decade,
that's available in Dana.
>> Which will we get first? A perfectly
simulated real world environment for
training autonomous devices or Grand
Theft Auto 6?
>> [laughter]
>> Kash, Peter, welcome to the a16z
podcast.
>> Well, thanks for having us. Your name
is?
>> [laughter]
>> Yeah, which is one of many.
>> We've I think we've all each known each
other for
too long, more than I'd like to admit.
>> [laughter]
>> Long time. We're lucky to both be the
first investor or among the first
investor in the first round. Of course,
different check sizes, but
>> And I was an investor in you even before
then.
>> Exactly. Yeah. Um so, let's do that as a
segue. We have a lot to talk about
today. We've, you know, the the biggest
launch in company history to talk about
today. But first, why don't we just give
an update or a status [clears throat]
What does Applied Intuition do for those
who don't
>> Yeah, for the for the people who don't
know, Applied Intuition is a physical AI
company. We put intelligence on
machines. That's the the simple way of
describing it.
Um and all types of machines. So, cars,
trucks, tanks, drones, you name it. It's
a physical moving thing. We we make it
intelligent. And uh the history of the
company is we originally started by
making the tools that would make the
intel and then we got into the actual
intelligence itself. Um in a very like
in some ways like a very uh boring AI
company in sense of, you know, 83% of
the company is engineering. We win by
making really great products. It's not
like a
good sales or something like that. I
don't think we're good enough for for uh
for a sales enabled company, but uh
yeah, over a thousand engineers um and
based in Silicon Valley. Uh but we have
offices globally, 18 offices. And uh you
know, our mission is to to put
intelligence on a billion machines. And
uh we think that can have a profound
impact on society both in the kind of
busy things everyone talks about safety,
you know, if you really talk to somebody
who's been in a car accident or in a
mining accident or, you know, uh in a
farming accident. You Those Those are
real
gnarly situations. Beyond just fixing
that, you just if you can unlock
productivity,
I think, you know, we've seen the unlock
in the digital world. And everyone's
super excited about it and you have
trillion dollar companies emerging. I'm
a pretty strong believer that I think
when we look back 25 years If you look
back at the internet now, you know,
people actually If you look at the
original internet companies that are
doing, you know, serving or they're
they're doing some analytics and those
are interesting, but really when you
look back 25 years from now, the big
monolithic companies are Amazon that
delivers you stuff, you know,
Apple. These are the true kind of
companies that come of age. And I think
when we look back 25 years in this
intelligent revolution,
the companies that impact the physical
world,
you know, might actually be bigger than
the companies that impact the digital
world.
>> I would love for you to talk about the
following, which is when you first
started the company, you know, the knock
on the company I think was, oh, well,
it's it's like, you know, car it's
making making cars autonomous, right?
Self-driving cars, but it's kind of
like, okay, there's like whatever,
you know, there's there's Tesla and
Waymo building their own self-driving
cars and then and then there's like six
or eight other car companies that
matter. And then the company just could
never get that big cuz there just aren't
that many customers.
>> Yeah.
>> So, what's the like how should people
think about like
how many things are there that are
things that move where the idea of
physical AI physical intelligence are
going to matter?
>> Yeah, I mean even today even if you do
put that you know let's say
view on us the automotive is like 30% of
our business so 70% already is
non-automotive and I think if you fast
forward another 10 20 years
even the manufacturers themselves as a
customer base will be a small amount. I
think our that mission just keep
thinking a thought you know a billion
machines becoming intelligent.
And you think about all the types of
machines that exist. Automotive is just
an easy one. I think it sticks in
people's heads because we all drive cars
and it's a big market
but I think it's it'll be a minority of
of the business minority is a minority
business. I think it'll be increasingly
minority business but that doesn't
necessarily mean it'll be small.
Automotive is still huge just as a part
of the globe's GDP automotive is
something like 3%
of all GDP.
I think the way we always think about it
like as you try to get to your mission
initially the manufacturers were the
distribution to that intelligence to
consumers but then you start working in
defense and you start working in
construction and mining and agriculture
and suddenly the manufacturers are
important but maybe the mining operators
actually really important or the
Department of War is really important
and suddenly they become customers and
all of those are customers of ours as
well.
>> Yeah, I think if you split AI into
digital AI and physical AI right digital
AI of course is building software and
optimizing ads and creating videos that
sort of thing.
That's all interesting and good but
really when you talk about the global
economy that's physical AI. Then we're
talking about manufacturing and and
mining and logistics and transportation
all of these things that supply chains.
>> Yeah, supply chains exactly.
>> Let me let's build on that for a second
which is like so things that move today
or you know historically things that
move are things that have human beings
at the wheel or at the controls in some
form right? And so and you know
airplanes [snorts] have had to get
designed around a human in the cockpit
boats have had to you know, get designed
around a human, you know, steering
things. Um
like in a world where in a world of
autonomy like
Do we already know what the things are
that move? Or are we going to discover
that there are a lot of new things that
are going to get built when you don't
need a a human in the in the driver's
seat?
>> I think both. Well, the the the the
thing that you have to remember is like
you take like a a haulage system that's
on a in a in a port. Um
like a caterpillar Komatsu, the dirt
mover
in a mine. Those are made for 20 25
years.
>> Mhm.
>> So, the buyers of that of those
products, they're they might not have
gotten their full cycle, you know,
ROI on them. So, they're not immediately
going to buy something new no matter how
much better it is. So, one part of our
strategy is you got to make those things
intelligent cuz they're not going
anywhere. The second is what you're
talking about, which is well that
depends on a human in a cab. If you
don't have a human in a cab, you can run
the the machine can be smaller. It can
be shaped in very different ways. You
need to talk about mining underground.
>> Right.
>> The constraint actually is the human
because the human needs to breathe and
it needs to it's very dangerous. And so,
you can build a very very different
machine.
We're doing both of those things. And
and then the thing that you were not
talking about is we're all talking about
intelligence almost like within a
system, but the system level
intelligence is where the unlock is. And
we're already doing work like that where
you say, "Hey, let's take an entire
port. Let's take an entire mine. Let's
take an entire quarry."
And this heterogeneous mix of machines
they're all can talk to each other.
And they can optimize and be efficient
when one machine goes down or one
machine has an issue. The rest of the
mine doesn't have to stop. When it's
human driven, we don't even know the
machine's going to go down because
there's no announce. The human is not
plugged into the core
systems of the machine.
>> Right.
>> So, like a simple thing like knowing
when a brake system is going to break
is actually huge cuz you can start
preparing for it in advance. You're
like, "Oh, this wear and tear is higher
than in other mines. He was using
example. But like the the other macro
point is if you look at agriculture an
example, you know, average American
farmer is 58 years old.
The there's that the number something
like under 35 it's like less than 10% of
farmers are that young. So, what's going
to happen? The need for food growth is
continuing to grow. The need for
rare earth materials is coming so these
these demands are only growing, but the
humans who are the bottleneck are
decreasing. Trucking is the same way.
Um and so
you can
you can really just unlock a lot more
efficiency. So, I mean one way to think
maybe think about this is like imagine
if the cost for food decreases because
it's way way more efficient. What what's
the downstream impact? Then imagine for
goods being transported. Let's say
you know, instead of a few dollars a
mile it's 20 cents a mile. And suddenly
it's it's I think that the unlock is
very very very big. Um and that isn't
that I think doesn't necessarily need
for all the machines to be redesigned
from the ground up.
>> Right. Right. Got it. Makes sense.
>> And then maybe just one one more
question would be just give give a give
us a sense of parameterize like the
scope and scale of the company today.
>> Yeah,
uh north of a thousand engineers. And
those engineers are, you know, obviously
the classic, you know,
software and AI engineering teams. But
we also have engineers who really know
safety systems. We also have engineers
who really know hardware. Because the
important thing that we we're kind of
just tipping around stepping around is
all this stuff is hard because it
ultimately has to meet the real world.
And the real world is has way more
complexity and and and has a lot more
issues. And we have engineering teams
that can I mean we've deployed our our
our models onto like 50 some platforms.
Like even that sounds trivial because
when you think about mostly when you
think about models you think about
deploying them through a browser or on a
phone and everything's abstracted away
because you have iOS and you have
Android and you have Windows and you
have Linux and you have all these
systems that have already taken care. In
the real world you don't have that.
>> Right.
>> And so we we have engineering teams that
can do that as well. Our kind of a you
know, claim to fame is we've raised over
about a billion dollars in the company's
history. All that is sitting in the bank
and I think that's I always say that
with an asterisk because it doesn't mean
we're not going to spend it, you know,
next next month.
>> good news, bad
Good news, bad news.
>> Yeah, good news, bad news. It's but it's
the the and I think like we're at that
phase, you know, you talk about scale.
We're at that phase where
these giant markets are around us and we
can make the decision how aggressive do
we want to pursue those because
decade of frankly execution and
deployment into production. I think the
the hallmark of our engineering team is
is putting
you know, products into production. That
that that that that really is is a big I
don't know how do you think about scale?
>> Yeah, I think that that's roughly it. I
mean the the the mission of bringing
intelligence to a billion machines. That
is how we think about it. And then
thinking about well, what are the types
of machines that will have the most
impact on and focusing on those areas
first, but we'll we'll get there.
>> I
let's go deeper into the differences
between digital and physical AI and more
so into
where where are we today? What progress
has has been made? What are some of the
main major bottlenecks in in physical
AI? When you
unpack some of that.
>> Yeah, I mean I think a lot of times
people think about the progress in
physical AI is limited to basically two
case use cases in there just cuz they're
obvious and interesting, which is
robo-taxis and humanoids.
They're very visceral. They're they're
they excite you and they're kind of
sci-fi.
I think they're
those are very interesting
and they there is real work being done
by us and other people in in those
domains. I think all the other uh
all the other domains I think are going
to be just as important. I mean you just
you just think about what happens on a
port. That's
there's a huge unlock there
and that's I think that's the that's the
area where where where really focused
on. It's like all the other nooks and
crannies.
If you look at like we we've talked
before about uh the rise of Cisco and
how, you know, networking kind of went
from, you know, first individual
machines and companies would get network
and then entire countries were getting
network. There's a similar thing
happening with AI. AI is getting to that
level of kind of sovereign AI is now a
discussion. Sovereign AI really is about
physical AI. Because that's where you're
talking about AI in defense, you're
talking about AI in the physical
machines that are moving around. If you
look just at the example of of Waymo
from America and Pony from China,
they're trying to deploy in, let's say,
the other country, so not America, not
Europe, not China, every one of those uh
spaces,
they're way more uh
they're way more hesitant of saying,
"Yeah, thumbs up, your robot taxis can
run unfettered on our in our country."
And so, if you look back just at kind of
this arc of the internet,
you know, when the first internet
companies come, nobody's really thinking
about sovereignty at all. It's like, the
browser goes everywhere, the internet
goes everywhere, that's almost the power
of it. Then when social media emerges,
there's a bit more of, "Hey, actually
not every social media." And then you
have China not allowing Facebook to come
in and then you have some Then you get
into the next level of like the online
offline stuff. There's more resistance
to Ubers, to DoorDashes. Suddenly,
there's local players who are being
favored very aggressively.
When we get to physical AI, I think
there's going to be huge and also
there's like a larger
geo you know, geopolitical theme of of
kind of
more fracturing than globalization.
You're going to have this demand for
this AI should somehow be localized. And
I think that has to play into our
strategy as well. We're technology
provider. So, we can provide that
technology across the globe.
And I think that's that's that's that's
that's that's something that's
understated in this in this
conversation.
>> Yeah, a few other things on on digital
versus physical AI. So, in digital AI,
the state of the art is you you can
train models effectively on the entirety
of the internet and then maybe augment
that with additional data that's been
collected and refined with some hired
experts, right? This is sort of a hot
field right now. Uh but generally you're
talking about a foundation model that's
built on internet data. In physical AI,
the internet data is is useful, too.
However, to actually build a foundation
model in physical AI, there's also a lot
of private data collection. You uh when
we're talking about
mines or or logistics or any of these
other
uh other fields, the the data that's
useful for training models there is not
necessarily available. So, we have to do
a lot of work ourselves actually going
going out and collecting that data. And
then the other key factor is safety,
right? Uh if you're talking about uh
building a smartphone app, you don't
necessarily care about uh
is is a safety-critical application, but
when you're talking about moving a a
machine that weighs many tons uh or
think of a humanoid which could fall
over on your children, uh you care a lot
about safety and and the evaluation of
that safety and and that is really sort
of getting to the state of the art of
physical AI and and really proving out
uh
the safety case around some of these
state-of-the-art models.
>> Yeah, and I think like you know, you you
talk about like humanoid data collection
has been its own, you know, uh little uh
area of interest. But when you talk
about collecting data like in places
like Korea where they have North South
Korea where you have North Korea,
they don't allow mapping companies, let
alone allowing a you know, a an American
company to come in and data collect. So,
we've figured out over the years whether
it's the Middle East, whether it's uh
LatAm, how to get into these countries,
work with the governments, and get the
thumbs up to collect proprietary data.
And so, in in the way that it's it is
similar to very you know, other uh
digital AI systems, your
proprietary data sets, scaling laws, all
that stuff is the same. It's just
applied in a very very different way.
And uh
it's almost like the way to think about
it is like the diffusion of these models
is very different because you can't it's
not everyone can just access them
through a phone. You're you're going to
And so so that
ironically is actually plays in our
favor. Because once we have a massive
proprietary data set which we've been
building, we already have hundreds of
petabytes of data.
Um and then we have our own tools which
are like, you know, synthetic data
tools, uh neural sim. We can use our own
tools and our own proprietary data and
that allows us to build some of the best
systems in the business.
>> Cuz is it
There's kind of a chicken and egg thing
which is like in order to build an
autonomous physical thing, you need a
lot of data.
To gather that data, you need a lot of
physical autonomous things running
around collecting the data. So it's like
once you have a giant network of
physical things running around, you have
the data that makes them all work. Like
is there a Is there a flywheel aspect to
that? And is there Is there Is there
like what what what's the level of
difficulty involved in kind of booting
up that flywheel?
>> Uh it's it's it's difficult but it's
also not difficult. I mean, I think we
we have one of the largest data
collection fleets on the planet, frankly
speaking. Um so that's how you bootstrap
your way into it. That's just money and
resource and and technical knowledge.
It's but it's not like
there's probably more than five
companies that have that technical
knowledge. So it's not extremely
obscure. I think what is more difficult
is then
how do you actually have that model
which is going to work on lots of
different hardware and is, you know, is
is tested appropriately? Because the You
saw it, you know, in Cruise, right?
Cruise was this company that did amazing
self-driving work and then one accident.
General Motors owns them super scared
and they pull back. So it's like just
getting these things into production is
actually more difficult than than the
than it seems. Um
I think like we believed synthetic data
was going to be important. So we started
our synthetic data team like 5 years ago
now plus.
>> than that, yeah.
>> Yeah, more than that at this point. Uh
and like when we were a strong believer
that synthetic data can accelerate
autonomy development. We've just seen
that. And then there are like lots of
other secondary and tertiary like um
technical innovations that have
Obviously,
the transformer revolution hitting
self-driving, massive. Basically,
everything done in self-driving pre-21,
'22, relevant, but you're almost like
that's kind of the starting point. But
it's also different than today being the
starting point. Like there the those
four, five years are actually there has
been a lot of work done. You can see it
most clearly with Tesla, but there's
other folks.
In that process, the actual techniques
historically and let me just simplifying
here. Imitation learning was the way the
way the game, which was collect a bunch
of data and then the models would
basically imitate what human drivers do.
The real state of the art right now is
end-to-end reinforcement learning in in
a closed loop in in your tools. And so,
it's a little simplified to say the
system learns itself. It identifies
where the issues in the self-driving
system are and essentially you then find
data like that or you synthetically
create data like that. And then you you
know, you close that loop and you see
are you performing the same scenarios
better and better.
I think if you fast forward some years,
that will be a completely closed loop
like with no humans intervening. Right
now you still have like like what's the
the fog error that we saw uh we still
see errors in the real world that impact
self-driving.
>> Oh yeah, so so it's like what what what
what are the bottlenecks, right? And and
the the bottlenecks
there's plenty of them, but whenever
you're dealing with physical systems,
you inevitably you hit a lot of
gnarly hardware problems and it could be
anything from overheating to
sensor being slightly miscalibrated or
funny funny issues I saw yesterday was
basically a fogging sensor like fog
impacting a sensor. And these are the
things that you actually have to solve
for this stuff to work very reliably in
the real world.
>> Yeah. So I'm I'm going to ask you a
three-quarter a question and you can we
can decide whether you guys want to
engage on it or not. It might be an
opportunity or might hit the question
which is
Were surprised So Cruise was a super
high-flying Silicon Valley autonomy
startup that was kind of running neck
and neck with Tesla early on and so
forth and you know, very top-end team
and then they famously got bought by
General Motors and they
>> One of my first distributions
personally, so I had enjoyed it.
>> we go. Y Combinator Y Combinator Y
Combinator Y Combinator company. Um
and um and you know, top-end team and
they they were you know, by all accounts
making excellent progress. They got
bought by General General Motors. They
became the GM autonomy program. GM got a
lot of praise at least you know, at
least in the in the in the in tech
circles for being like Okay, being like
the the legacy automaker with the
biggest investment
>> I I called Peter when uh before before
it was announced on that and I said,
"Hey, Cruise just got bought." You know,
he's also GM family. We're both GM
families. And uh
Peter guessed it was said, "Nvidia?" I
said, "No." He said, I said, "Go fish."
He said, "Apple?" I said, "No." I said,
"General explicit of Motors."
>> [laughter]
>> So, that's surprising to people who are
from GM.
>> That they were willing to buy the thing.
>> Yeah, that they did it.
>> Okay, that they did it. And then by all
accounts they were I mean, as far as I
as far as I ever heard like they were
making excellent progress.
>> Yeah.
>> And then they had this There was a There
was an accident. There was a Was there
Was there a injury or fatality or
>> Uh it wasn't a fatality, but it was a
serious injury. Somebody was dragged
[clears throat] for 20 ft.
>> Yeah, serious injury, bad press and then
and then they put a bullet the the GM
CEO and board put a bullet in the Cruise
project and I know the at least some of
the senior Cruise people were extremely
upset you know,
>> [clears throat]
>> by the aftermath of that. Was it
surprising that they reacted the way
that they did?
>> Uh so, you know, full disclosure General
Motors is a customer and I went to the
General Motors Institute. So, we have a
lot of love for the company. Uh but
incidentally and ironically, I'm reading
uh or coincidentally I should say, I'm
reading this very famous book which I
had actually never read before called On
a Clear Day You Can See General Motors.
>> [laughter]
>> And DeLorean's book. Have you read it?
>> does.
>> Have you read that book?
>> So, I have years ago I have. It's one of
the great all-time book titles and we
should just pause to say John DeLorean
was like what he was like the super
genius of the car industry.
>> Yeah, he was going to be the next
president of General Motors.
>> of General Motors and then later on he
started his own car company which Which
in Back to the Future and then, that
whole thing collapsed for a variety of
reasons. Uh but yeah, he he was like a
legend. He was like one of the main
principal drivers of innovation in the
car industry.
>> Exactly. Lee Iacocca bought lots of
these categories.
>> Yeah. And you got to remember this is in
this rate
>> Sorry, repeat the ti- repeat the title
of the book.
>> On a Clear Day You Can See General
Motors.
>> why was that the title of the book?
>> Because
uh there's a lot of
>> [laughter]
>> It's a very large complex.
>> Yeah, complex. Yeah, yeah.
>> It's like a It's like a nation state.
>> Yeah, it I mean, really. I mean, it It
really
I think we say that like sometimes
almost like flippantly. But these
companies are like extension like
Hyundai is an extension of the state.
Kia is an extension of the state.
Volkswagen is literally Volkswagen board
members are members of the government.
So these are extensions of the state in
in in almost every uh And there used to
be an old saying, "What's good for
General Motors is good for America."
>> Right.
>> And you cannot understate
how important General Motors is to the
history of the American corporation.
Sloan's uh My Years at General Motors
and Adventures of a White-Collar Man. If
you run a large engineering
organization, you should read that. That
is the this like you this
com- this thing that that we talk as a
modern corporation didn't just emerge.
Sloan and Kettering create Kettering is
a head of engineering created this with
this arc you know, with levels and vice
presidents and how do you do functional
and matrix organizations? It's There
really is like the source code.
Comes along John uh you know, comes on
DeLorean and he says
um
he writes his he's going to be president
and he's so fed up with the company.
But what was controversial was GM was
doing really well at the time. GM
was like a When we say like GM was
number one in the Fortune 100, it was
like number one, two, and three. It was
everything and it was seen as the best
company in America. So somebody to
openly criticize the company. And so he
has a whole he had writes this book as
he quits out of out of uh
how annoyed he was at General Motors for
being read uh led. Uh he writes this
book and then after he like sobers up,
he's like, "I don't want that book
published." [laughter]
And so he fights for a years for his
co-author not to publish the book. The
co-author still publishes. So it's a
real true insight into a large
corporation.
Um incidentally just reading it out even
though I've you know worked at GM 20
some years ago and had you know know a
lot about the company.
And what's shocking is it's not only
about GM, most of the major
manufacturers actually still operate
that way on the inside. And so
the question isn't the the the point I
think for everyone to take away isn't
these people who run these companies are
stupid. They're not stupid. They're It's
kind of like you know when you're
selling to the Department of War and
people say, "Well, why are you doing
that?" It's like, "Well, the
distribution defines the business."
That like so like the distribution is
this is a consumer product. Uh
this this stat might be out of date, but
when I worked in safety systems 20 years
ago I remember GM used to pound in your
head of the top five consumer lawsuits
in in American history, three are
automotive. We got the majority, right?
So it's like you have to be extremely
careful. We had these like weird things
like inside the company you can It
wasn't red, yellow, green. It was like
purple or like you'd always have those
decoder because you know why? Because
when they go to lawsuit they're like,
"You let a safety system that was marked
red go to production." I was like, "No,
it was marked magenta."
>> [laughter]
>> Like so like can you imagine how
infuriating that is? Every time you're
like
"What does orange mean? Does
this mean I have to like So fast forward
to you're meeting that system."
>> Well, for the
Ford slogan for a very long time was it
was quality is job one, right?
>> Yeah, yeah, yeah. Which figures with
safety as well.
>> Yeah, yeah, exactly. And and that's the
one-two punch of automotive. It's
quality and safety. Quality and safety.
And quality really becomes because the
Japanese really reset that that stage in
in because
the that's a whole separate automotive
history. We could talk about automotive
history for for an hour, but the
punchline is you have the Silicon Valley
company meeting this immovable object.
There is a parallel universe that
cruises out there right now
even as a part of General Motors. So, I
think you always have to take it into
the context of where the company is,
where union negotiations are happening
literally that year. And if you're the
union, you're like, you can't make a
million dollars for us, but you're
funding this thing
>> Right.
>> that's killing people and it's sloppy.
And so, I'm not saying precisely that's
what happened, to be very clear. But,
that it's a multi-varied problem. My
other hot take is, you know, I worked at
both companies, right? Google Google and
General Motors. Those companies are way
more similar than they are different.
Way way more similar than they are.
Literally, people don't need you know
this, the Google leveling system is the
same as the General Motors [laughter]
leveling system. And I used to say, I
used to say you know, this inside of
Google meetings. It was like, "Hey,
actually some of the engineers I knew at
General Motors are better than the
engineers here." And people would look
at me like I'm saying there's no God in
church. It's like, they're like,
"How dare you, you metal butt bending
monkey from Detroit?"
>> [laughter]
>> I was like, "No, actually like making a
modern combustion engine is extremely
complex. It's not just like, you know,
it's it's not simple stuff." And so,
the macro point I think is
it's a bunch of things. I think safety
is always at the top top of their top of
their list. I do think, you know,
we've hired lots of Cruise people. I
think the way they dealt with that
specific issue with the government, you
got to you got to dance a particular way
when that happens. And they just didn't
dance exactly right. And that just gives
government bureaucrats
more ammo to go after. And you're big
target like General Motors, you got to
you know,
it it reminded you guys ever see that
movie like
Goodfellas? You know,
one of the last scenes of the House of
the Rising Sun, you know, all the old
bosses go in the back of the courtroom
and they're like,
And you know, that's what happened. They
were like,
the board was like, "What do we do about
Cruise?" They're like,
"What can we do?"
>> [laughter]
>> It's like, "Kyle's a GOOD GUY, BUT
>> [laughter]
>> AND THEN IT'S LIKE, cue House of the
Rising Sun.
People are running through the San
Francisco just kidding. [laughter]
Don't don't make that an AI video.
It's going to get a
>> [laughter]
>> going to get a mean text from Kyle.
So, I think I think there is a universe
where it would it would survive, but
it's it's it's it's tough.
>> So, then a lot of a lot of what Applied
Intuition does is kind of as you said,
like that dance. It's like how to how to
be a great partner to these companies.
>> Exactly.
>> Bearing in mind their own real very real
issues and constraints.
>> I I I think GM was also had the topic of
business model, right? So, you have
Cruise was going after the robot taxi
concept, but GM makes its profits from
personal car ownership. And those things
can be a bit odd. So, I think that was
also a bit of the
>> Oh, right.
>> Yeah, and I think it wasn't clear. I
mean, the by the way, you know, um
you actually, of all people, you spoke
at YC at 20 in 2013. I was in the
audience. I was a partner at the time.
And um you said something which I think
is it's it's very like recursive here.
We're we're feeding each other your own
advice. It's uh the key thing in in new
technology in the new technology
business, actually everyone kind of
figures out the technology. Though,
that's still hard. It's still hard
sometimes to build really complex
things. It's when and how you deploy
them into the market. The when becomes
really important. You're 2 years early
and you're doomed. You're 2 years late,
there's too many competitors. You have
to like hit it at the right spot. And I
think it's like like I mean, a
controversial thing to say is like I
actually think Cruise, you know, they
were certainly moving at a much faster
pace than Waymo. They started way behind
and you're talking about neck and neck
when, you know, ultimately the the the
plug was pulled. So, who knows what
happens in the long term. Our hypothesis
in that same equation is actually the
distribution, you let the manufacturers
do that. Like we run self-driving trucks
right now in Japan. They carry
commercial loads. They're they're, you
know, they're safety drivers there, but
they're autonomously running. And but
you won't know that because the brand is
Isuzu.
That's the customer. And and why it's so
good for us to partner with Isuzu in
that case is
that company's been around
almost 100 years, right? If I find if
I'm not mistaken, uh, pre-pre-World War
II company, and they are, uh, you know,
they know the government. They have test
tracks. They know safety. They know
their own trucks very well. So, when we
go and provide them with the
intelligence and the integration into
their physical machinery, that's a
fantastic one-two punch. I think today
the world is ready to consume AI in the
real world. And that's a lot because of
ChatGPT and Anthropic and all these, you
know, everything that's happened. So,
people are no longer like, "What's a
self-driving car?" And this is because
of Waymo and Tesla.
So, the market is ready to consume. And
I think you just have to meet the market
in the way that the best way possible.
In our view, that has always been you go
through some of the people who run the
economy right now. Whether it's a mining
operator, whether it's a Department of
War, whether it's, uh, the
manufacturers. And we work with, you
know, within each vertical, uh, with
with the right partner. But that's a
fundamentally different view than a
Tesla or a Waymo, which are going to be
vertical.
Where we're really playing the
horizontal. And I think the way we can
always think of we think about that our
company. We're kind of like a like a
chip maker. You know, we we we actually
look and talk and walk a lot like a
silicon company, except we don't
obviously we don't make chips. But, you
know, we're our we have design wins and
then we have really large long-term
relationships. And once we're in, we're
in. It's really hard to, you know, to
take us out. So, you need deep trust.
Our partners have really a lot a lot of
deep trust. And we know their markets
really really well. I think the things
that Jensen knows is he knows his
customers.
That's why Nvidia does well beyond the
fact obviously they make a very complex
technology.
>> So, how are these legacy car companies
preparing for the future? Are they
making or more acquisitions or going to?
Are they building and, you know,
partnering with you? Or they how are
they going to compete with, you know,
tech tech native companies?
>> It's like saying like how are
governments dealing with AI? It's such a
it's the such a broad topic. Um, and
each manufacturer, like even like you
take Honda, Nissan, Toyota, three
Japanese manufacturers with, you know,
long legacies. They all approach it very
differently. They're roughly in a
spectrum of we're going to build to
we're going to buy Um,
and other both extremes more than ever
we're going to buy is the common answer
because they've been trying.
And we've been there the whole time.
For the folks that are going to build we
provide them tools
and we talk a little bit about our our
our new product that we're
announcing here. And then on the ones
that just want to buy we we sell them
the actual intelligence that goes on the
machines. And so we meet the customer
wherever they're ready in their in their
journey. The more nuanced version of
that is you know the reality is like
every every product is a different
product and so the amount of silicon and
amount of dollars you can put towards it
towards sensors what the customer's
willing to pay
all that depends on what actually
gets in in the long horizon all these
things will be fully autonomous.
But the intermittent steps are very much
what we saw in the PC.
Where you you have the slow step up to
one day that'll be like now nobody
really looks at laptop specs and even
maybe frankly your phone specs. But
that's not the case from basically 85 to
2002 2005 where finally people stop
actually specking at all and and and
then they're really moving to laptops.
But there's a similar kind of 20 year I
think horizon there.
>> And Brody when you talk about machines
and machines becoming intelligent right
fundamentally a machine is it's a
collection of these different components
that are integrated right and and
whoever does that final integration is
often times the company that puts their
their badge on it the brand name but
many many companies are building
technology that goes into that machines.
And so we now have a bunch of technology
components and platforms that can go
into these machines. And but we also
sell the core technology that can be
used to develop them as well.
>> And if you look by the way under the
hood of a of a dirt mover or like
combine or diesel truck they'll have
Cummins engines in them. But nobody says
well because all these guys buy Cummins
this this means that they're you know
whatever Caterpillar's is a good
company. It's like no, that's just a
component that they buy. Uh
they they had they have a different
role. So, if you when you look in the
any of these verticals, there's it's
just a complex web of folks. That's why
I always say like the the chip kind of
analogy actually works quite effectively
because some none of those companies
make chips, but they all buy chips.
Uh and so I think that that's it's a
good way to think about it.
>> So,
self-driving cars, so you know, we've
all been talking about self-driving cars
for like I think the whole thing started
like around 2012 2005 or something with
the DARPA Grand Challenge originally.
And so and then Google engaged on the
program shortly after that.
>> Yeah, late double O's, yeah.
>> Late double O's. Um uh so almost 20 you
basically around 20 less than 20 years,
maybe.
Um and there've been lots of predictions
over the last 20 years of like
self-driving cars are imminent at any
moment. Um so I guess the bad news is
we're sitting here today and most cars
are not self-driving. The good news is
there are now self-driving cars.
>> Yeah.
>> And so the Waymo cars are driving all
over, you know, in the places they're
deployed. It's become a you know, I
think people in San Francisco are I
think treated now as routine that they
get into a car.
>> think you can call I think Tesla It's
kind of like the AGI thing. It's like,
you know, if we're talking 20 years ago,
everything we're seeing right now is
like mind-blowingly AGI. The the host
keeps moving. The Tesla stuff's amazing.
You can look at a bunch of
manufacturers. Blue Cruise, Supercruise,
BMW, Volvo's Pilot. They're all
[clears throat] quite impressive
systems. They're not full self-driving.
>> Right.
>> But yeah.
>> Well, it's it's full self-driving X
whatever remote
>> [snorts]
>> monitoring is happening. [laughter] Um
uh the the Tesla uh we have we have a
home in in in Los Angeles and if you
guys may recall, there was a there was a
large fire in Los Angeles last
>> Yeah, yeah, yeah.
>> And the power
and then the California power grid was
buckling even before that. So, it
actually turns out among the things
Cybertrucks are good at is they're
they're very good batteries
>> Yeah.
>> uh for powering your house.
>> Yeah.
>> Um and so literally we have a Cybertruck
as our backup battery for the house. And
as of last year, whatever the FSD
release, I forget the exact one, but
there was one where it like at least a
lot of people thought it like really
turned the corner.
>> 14, yeah.
>> And like that thing drives people I
talked to somebody yesterday uh talked
to somebody yesterday who has a Model Y
who let the let the thing let the thing
do the full the full route all the way
up highway one through Big Sur.
>> Yeah, I think mean disengagement like uh
the the
mean time and
like miles per disengagement are are
really high and I think miles is like in
the thousands. Yeah, which is like very
impressive.
>> Yeah, I know the Big Sur for people who
have driven the Big Sur highway one Big
Sur like that's a that's a that's a
stressful drive. He said it was great
the the whole anyway. So and I wouldn't
have been talking to him had it not been
>> [laughter]
>> would have gone right off right off
>> Cuz he unbolted the steering wheel so
>> right off right off the
>> cliff.
>> So
and then you know Tesla's rolling out
their robo-taxi, you know, that it's
it's starting starting to show up in in
the wild. So so on the one hand those
exist on the other hand, you know,
99.999999%
of cars are are still not self-driving.
And then I would say maybe just one
other would be the self-driving trucks.
There's been this recurring kind of
panic in the press of like this trucks
become self-driving and the employment,
you know, all these truck drivers will
be out of a job. And sitting here today,
I don't think I don't know is there
are there any trucks on the road that
are self-driving that don't have at
least a safety driver in the truck? And
I think the answer is probably still
>> Yeah, Silver if you So let's let's split
let's split the there's multiple points
that were brought up here. One is on the
let's say personally owned vehicles and
why are they not more ubiquitous?
The part of that is the manufacturers
are not good at deploying technology.
Part of that is they're they're they
want to be safety conscious but most of
it is cost cost cost.
The the what you're seeing in China,
which is a China's kind of a different
EV ecosystem mainly because they don't
care about profits. When you're talking
about business that doesn't care about
profits, it changes the entire calculus
of the entire industry that that doesn't
care.
But what you're seeing is you're seeing
L2++ systems. So we can simplify the
entire self-driving conversation to is
there a driver behind the steering
wheel? Right. So this is a driver behind
the steering wheel still there but
generally like Tesla drives everywhere.
They're like sub $1,000. There's an
aggressive That's that's chip, sensors,
the package, the software, everything.
We, you know, anticipate that there
there's a very aggressive Once you get
to like 500, the automotive OEMs will
actually subsidize it for free. They'll
just give it to you. This happened in
nav systems. If you guys remember nav
systems, it used to be a big thing where
you'd pay four grand, 3,500, to get a
nav system, and then suddenly it became
free and it just became default. I think
that'll happen. The There's a weird
thing which is like actually getting
into a subset of your cars costs X
dollars, and to get into all the cars
costs X plus just a small incremental
amount. Because there's a fixed cost and
the way that how many vehicles and the
way the assembly line comes in the way
you have homologation, all these testing
regimes, all this stuff. So, I think
you'll have wait, wait, wait, and then a
lot.
>> Yeah.
>> Every single OEM without exception, even
the lowest dollar OEMs are working on an
FSD competitor. So, it'll
come. But it's just like, you know, with
a good analogy to think about
self-driving in the personally owned
ecosystem is mobile phones.
We had We had the the satellite phones,
then we had the Qualcomm, you know,
brick phones, then we had the Motorola
Razrs, and from, you know, the late '90s
to the late to double zeros, there was
like, "When's mobile going to come?"
There was a huge like, and then it it
comes and we
by by '07 from the iPhone launch, like
it's like four years when you get Uber,
Instagram, WhatsApp, Snapchat. Those are
the killer applications. So, I think
there's a very, very similar kind of
wait, wait, wait, and then it's just
basically ubiquitous in every vehicle.
If you had to ask me for what that
number is,
28 SOP, 29 start of production, 29, 30,
and then by the early '30s, then it'll
start becoming very cheap to free.
>> I think
routinely by by the early '30s, you
would just you buy a car and you just
assume it's self-driving.
>> Exactly. Or it has the driver in seat
L2++ system, being very specific.
>> Like like Cybertruck or Tesla the Tesla
equivalent of what Tesla people have
what Tesla owners have today.
>> Today, exactly. Yeah, it'll be
default. So, then the question then the
the the you know, the the other side of
this is why don't we have a bunch of
Waymos everywhere.
Specifically, Waymo has a different
technology. Without getting into the
nuances here, but Tesla and many of the
Chinese and applied were very much in
this
end-to-end model architecture.
This is a new way of doing self-driving.
Waymo, for the lack of a better word, is
not that. It's not doesn't mean they're
not learned. It doesn't It just It's not
one end-to-end system. It's not one
monolithic model.
One of the proclivities of that of their
approach is it does depend on HD maps.
Therefore, there is a geofencing
concept. I think Waymo's trying hard to
remove that bottleneck so they can
expand geographically faster, but the
reality of today isn't there. The other
thing is when you have researchers,
which they which Waymo really was coming
out of
an Alphabet research organization, they
didn't put commercial constraints. So,
the sensors are bespoke and expensive.
The cars and the then the compute that
are in there, they're they're just not
economically feasible. And they've tried
a lot to get that down, but it's kind of
like it's a lot easier to go from
something that's really cheap and make
it more uh you know, more featureful
than something that's overbuilt and then
trying to trim and make it really really
cheap. And that's the big debate. Who's
going to get there first? Tesla with
full self-driving or Waymo with cost and
geographic ubiquity?
Uh but you know what we're not debating
about? Is it going to happen?
>> Right.
>> You know what we're not debating about?
Like is is is uh is there a big
technical breakthrough that needs to
happen? None of those things. So, now
we're clearly in the engineering side of
self-driving, which is just this grind
down to uh like dollar per mile
efficiency. And the moment that it's
cheap, guess what? All the OEMs are
smart, they'll just they just adopt it.
It's not that OEMs are resistant because
they don't think consumers want it or
they don't understand the technology,
it's because they want a
price envelope, which allows them to
keep their thin razor-thin margins and
at a scale, which is deployed across 100
plus countries in V1. And so, if you're
just doing a small deployment, it's very
different. And I think And the last
thing I would say is the buyer of a
Subaru or a buyer of a Suzuki have very
different brand expectations than a
buyer of a Tesla. And so, including the
age of the consumer and what they think
will happen or won't happen. Um so, that
that also reason. So, if you're Suzuki,
you're like, "Well, my buyer is like not
doesn't want this stuff, so I'm not
going to jam it into the car." It's not
because they're not like technically
competent. This is a different area.
>> When do you think When do you think
it'll be routine? Let's say the the 200
biggest American cities, like um when
will it be routine to walk [snorts]
outside and you just you just take it
for granted that a robot taxi can come
pick you up?
>> Uh it's 26 now. Um
I mean, certainly by 30.
>> Sorry. Okay.
>> Yeah, certainly by 30. And I would say
the the big like variable there really
is like
cuz you What Waymo will say is that the
dollars and cents per city already work.
And it's like, "Well, a company that has
basically unlimited capital, why are
they not already in 200 cities?" But
then you see their launch schedule's
pretty aggressive. You're like, "That
can that can get there." So,
maybe it has been aggressive. I'd say
28.
>> Yeah, okay.
>> Like 2 years.
>> I I would say I would say available in
30 but routine in maybe like 32 or 33.
>> Cuz there's a scale up, there's a
volume.
>> And then also if if you
you got you know, you're living in LA,
so
like
5 years ago, I'd go to LA, people would
be like, "What's Applied Intuition? I
don't know what self-driving cars are."
Uh in the last couple years, now they
all know self-driving and some of them
even know Applied Intuition because they
know from the other manufacturers.
Uh I think you fast forward another 2 to
4 years, everybody knows it. Now, does
that mean everyone's taking Waymos
exclusively?
The answer is no, actually. Now, there
is a a
huge If you look at the numbers, if
you're Uber, you got to be scared. I
mean, they're just eating into into ride
sharing. Um
Yeah, but to get 100% equity, I mean,
that's that's that's another Yes, to be
extremely cheap.
>> And what about long-haul trucking?
>> So long So that's So that's what we
That's So that's the pa- passenger side.
The long-haul trucking, completely
different economics, completely
different um uh business model. Um there
are many companies right now. I would
say probably north of five that are
running long-haul trucks with drivers
carrying loads between America and
China. I think China's probably getting
into double digits. So it's there, but
the reason uh you don't know it and the
reason it's not top of mind is it's not
a consumer product. And unlike uh on the
Waymo and Tesla side, where investors
are willing to essentially give you uh
you know, some um market cap uh you
know, uh adjustment for the potential of
the the They say the the trucking
business is like, you know, made What's
the word?
>> You buy a car with your heartstrings.
You buy a truck with a calculator.
>> Yeah. It's a calculator [laughter]
business. And they And so it's like pure
dollars and cents. And so I think um
you as the provider of self-driving
trucks, if you're doing the whole thing,
like some of the companies are, which
we're not,
you have to show every mile I'm going to
I'm going to save you this many dollars.
And it's like, for sure, for sure, for
sure, cuz the buyer's unsophisticated.
And they're just like, well, I already
got a staff that can drive. And it's
like, and they're like they're just not
inclined.
Now, where we're playing in Japan's not
random that we're doing trucking in
Japan. There's a massive labor shortage
today, and there's an imploding
demographic uh
uh you know, a situation. And so there's
a demand from almost every sector, and
that's why we've we've picked that
market to to to to really grow. But I
think like
you can take like even more uh obscure
like when we'll all
uh quarries, you know, literally like
where you would you know, you're you're
you're moving cement, you're moving
you're moving dirt. Uh
not quarries, q u e a r s q u a r r y.
>> Yeah. Quarries. Quarries.
>> Those are rock rock stone.
>> Rock stone cement.
When when are those?
I can tell you the people who own those
things and run those things wanted it
today.
>> Right.
>> So it's literally then you don't you
don't have a We're literally we can't
make this stuff fast enough.
>> Right.
>> Um the macro point though that people
don't talk about I think all this
stuff's going to happen.
>> Right.
>> It'll happen pretty soon.
>> It'll happen fairly soon. But the macro
point that in in legislation and and and
kind of in in the in the kind of
economics the political economy of of
this conversation is AI is really you
see you have this big pushback in
digital AI because we're like counselor
like I don't know what's going to happen
to my job and VCs I'm sure all of your
associates are very scared.
But like in our universe
>> They're debating whether they need us.
>> Yeah yeah yeah.
In our in our universe it's the other
way around. It's like you They're
literally I'll I'll I'll meet these you
know operators and they're like
We'll give you everything. Like if you
can do this we'll give you everything.
So then it's just up to us to like get
there as as you know aggressively and
physically as possible.
>> Well you know the the fear for a long
time has been for
trucking for some reason triggers the at
least the press's imagination on like
you know sort of apocalyptic levels of
job loss. Like
will there
>> But that's it's so wrong.
>> Go ahead.
>> There's not There's not enough truck
drivers and guess what? Nobody wants to
freaking be a truck driver.
>> Why is that? Let me explain.
>> Because it's a terrible job. It's like
You're like you're like you're like
you're
>> I I by the way I grew up my the main
feature of the town where I grew up was
a truck stop so I
>> It's like
>> But yeah yeah yeah. Why is truck Why is
truck driving not attractive to you?
>> asking me it's you know what this is?
This is like you know talking to my kid
who's like well why can't I put my hand
on the stove? It's like
because it's going to burn your hand.
>> [laughter]
>> It's like but why? It's like after the
third why it's like Come on buddy let's
do this.
>> [laughter]
>> Yeah let's [clears throat]
>> So what So what what's hard about
>> This is make sure everybody knows I did
not do that with [laughter] my son.
>> Yes yes. So what's hard Why why is being
a truck driver a difficult job or why
would kids not want to do it when they
grow up?
>> So, what the let me use a parallel
analogy which is very clear and then you
can
what what you know people will say like
nobody wants to work anymore and they
say well, you know, McDonald's has all
these job openings. No, no, actually
what it is is those people that used to
work at McDonald's now DoorDash and
Uber.
>> Right.
>> Because it's better for them because
they can open they can still start their
hours and end their hours and they don't
have to
there's no boss and they don't have to
like stand on their feet and they can
surf their phone in between, you know,
orders and they don't like that's the
reason. It's not random. The market is
efficient and so in the truck driving
example, why does somebody not want to
be away from their family for
4 to 8 days in a row doing long haul
trucking? The more sharp example is in
Australia, why don't people want to
go literally by a plane to go to a mine
and work on or you go off
offshore oil rigs. Those jobs exist. If
you want a job that pays six figures,
they exist. Even with such lucrative pay
packages, it's not enough cuz people are
like, you know what?
I like kind of being around my family
and I'm willing to take an incremental
decrease in cost and and how much money
I make and and then also like I think
today more than ever things like
back pain and like being exposed to the
sun and cancer and people that care
about that's now a part of the
>> This is the thing so tell me if I I have
this right but I I believe it's because
I think commercial long haul truck
drivers die at life expectancy 10 years
less than
their peers and I think it's a
consequence of several things. So, one
is some combination of nutrition and
sleep. It's you know, it's a basically,
you know,
>> It's yeah, it's
>> It's very difficult It's very difficult
to eat well and exercise and
>> What's your sleep score if you're a long
haul trucker? Let me guess, they don't
eat sleep on that on that screen.
>> Exactly and so like obesity and then
heart disease, hypertension and so forth
are all very high
one and then two is I think the
vibration is very difficult stress in
the the and then the third is you
mentioned cancer but I think it's the I
think they have truck drivers have like
a much higher rate of melanoma on their
left arm.
>> Exactly. Yeah, there's photos of like a
truck driver who's been driving for 30
years one half of their face the other
half of their face cuz it's exposed to
the sun.
A more interesting even more stark stat,
mining is 1% of the labor pool globally
8% of work related fatalities. Do you
think people are rushing to work in
mines when they hear stats like this?
Most major mines have a fatality
regularly which means once twice a year
three times a year. And if you ever
visit a mine you'll see that everything
is based around safety because once you
experience one of your co-workers dying
then you're like
what am I doing here?
>> Yeah.
>> Like
there's other jobs I can take and so
it's I I understand you're trying to
enumerate for the audience like but
these are not good jobs. And
the best evidence is this this is not a
mining podcast. This is not a podcast
about hey long haul trucking is so
great. It they're just not attractive
jobs.
>> even trucker even truckers don't want
their kids to become truckers like it's
it's a very for that reason they would
you know they want their kids to be in a
at the very least like safe safer safer
line of work. But do the do the
notwithstanding all that do they how
long will there be do you think there'll
be safety drivers in long haul trucks
that are that are self-driving? Or or or
let's say other even just somebody in
the cab to deal with what happens when
they arrive?
>> We we know multiple companies that have
driver out goals right now. So like
they're they're working to get drivers
out right now.
You know, without going into our own
details.
>> [laughter]
>> To be honest with you it's not long.
We're talking we're talking a few years
and
>> I think on the long end.
>> Yeah, on the long end. And the thing is
it's it's there there's a software
technology thing which is one part of
the problem, but the other part is it's
the redundancies that you need in
hardware and the validation necessary
for those redundancies. And in many
cases that can actually be a long pull.
It's like oh there
productionizing a fully redundant
steering system fully redundant braking
system that's that's not in high that's
not in high volume production yet and
once you get that in high volume
production now you get the quality up
and then that's validated and now you
can actually do these. Need the
>> price down.
>> Exactly.
>> Do do you guys do do you like it's the
you know these little delivery robots?
Like is that you do you see a world
where there's a billion of those running
around?
>> Yeah, I think so. I mean the uh the the
the product that we're announcing uh I
think it's probably come out around with
this time is called uh Dana. So there's
you can just simplify everything that
Applied Intuition does into two buckets,
which is the uh we've been talking
mostly about the models that go on the
machines. Then there's we say onboard
software or onboard AI. Then there's
offboard AI. This is the tools to design
and develop these same systems. The the
the models that actually go on the
machines. Our uh you know vision for
that is in the the delivery robot is a
great example is like a high school kid
or a middle schooler they can make
iPhone apps they should be able to make
autonomous systems. So why can't they?
Just ask that very simple question. Why
can't a
ninth grader make a delivery robot in
their in their home? Well, they don't
have the
the actual environment that they would
first develop the scenarios in. They
would define the requirements. Hey, I
want this robot to go on my high school
campus around these let's say four
buildings.
Uh then how Okay, now that you do define
the the requirements then you have the
scenarios get made. What are all the
scenarios that I can that can uh that
can be made
by using let's say satellite image of
the high school. Uh then now you have to
train the robot. So you need some data.
Where do you get that data? There's
maybe enough publicly available data
that can actually train a fairly uh
rudimentary robot. You know, you got
that data from online, maybe YouTube
videos, couple of other places. Suddenly
this robot's not doing Now you need to
deploy it onto the actual machine. So
then you deploy it onto the machine and
then the robot runs into the wall. Okay,
what happened there?
The loop closes. That platform for
designing and developing is what we're
launching. It's called Dana,
uh which is the street that Applied
Intuition is
>> [laughter]
>> uh headquartered on. Uh and uh and
our our our you know, this comes from
our tooling background. And if you look
at like how tooling has changed in the
digital AI world, if you look at like
what Claude did to all you you all
remember like, you know, from Mixpanel
to, you know, GitLab, GitHub, all these
Now everything has moved into a very
different almost IDE, frankly speaking.
We think the same thing's going to
happen in the physical world. And so,
uh that that that's yeah, that's what
we're that's what we're building. That's
what we're that's what we built and
that's what we're launching in. We
already use it in-house to develop our
autonomy system, which is you know, and
we're working on the most kind of scaled
complex systems on the planet in all
these different verticals. So, we're
pretty confident that it's actually
quite useful. And we've seen massive
productivity gains.
Uh but also, you know, we think like
other companies will use this to to
build our own systems cuz it gets to
that mission that
a billion intelligent machines.
>> Yeah, fundamentally our Dana is our
agentic platform for physical AI. And
and everything that we've built and
developed over the past nearly a decade,
every tool, every
>> technique that's available in Dana. And
it's very actually easy to use with the
agentic interface. And so, workflows
that used to maybe take
days or weeks to run, you can now run
those in in minutes in many cases. And
this just lowers the barrier to entry to
building these systems. And so,
>> And we're just lowering the bar of like,
you know, what it means to develop an
autonomous system. Autonomy is still
actually quite in the scope of software
is quite exotic. It's not because of the
things that we've talked about. And
we've just brought that down very very
aggressively. And it's kind of like, you
know, the old adage of like how do you
make a great product in software? It's
like you either increase safety,
convenience, or cost. And we want to try
to do all three of those things with
with Dana.
And our hope is just like you said,
like, you know,
uh kids can develop robots for their for
their own use. And and that extends to
humanoids. So, we're not just talking
about like land-based systems or or, you
know, the ones that are so you can
humanoids you can do drones. The fact
that right now writing drone software
and deploying it
quite obscure and almost hobbyist.
We want to just make that
absolutely like that, you know,
maybe not child's play but like teenager
play.
>> So this one still world like just fly
like a lot more experimentation and
entrepreneurship and like agriculture
>> everything
>> bots and like you basically in every
domain construction
>> Yeah.
>> defense
just all of a sudden have a much larger
number of people who are applying
creativity and coming up with ideas and
making things that move.
>> Yeah, and have you seen like with Claude
it's like it's one thing just to make
the
engineer more efficient or bring more
people into engineering but then when
these agents really run you're getting
into
just like the iPhone example of you
couldn't imagine Instagram
before
like
iPhone. It's like imagine 2005 on
laptops you're like you did
10 years is going to be this this app.
And you can put photos in like well the
phones don't have cameras like yeah, but
it's going to be like social like what
the hell like so like Facebook it's like
see it's just hard to
so we think
by lowering that barrier you're going to
get way way more creative autonomy
products.
>> Yeah. Okay, so I want to say that you
can decide whether to include this or
not. So my kid is building autonomous
bots in
Factorio.
>> Oh, nice.
>> That's one of his projects and so and
then but he's he's you know, he's had a
role in cuz the tool kit's not available
yet. So he's actually training and he's
actually he's actually training models.
>> Yeah.
>> He's gathering data in the game and
actually he has like a whole army of
like bots that he's developed to go.
>> Yeah. So like
>> And then his mother is like why are you
playing that game so much and he
explains of course it's a purely
educational
process and experience but but it's you
know, it's the kind of thing
>> Like there's no reason autonomy should
be this like you know,
obscure difficult you know, alchemistic
you know,
technology And
I think not only does that have a huge
impact on on society, it also allows
people to understand that these systems
are not like
you know,
magic. It's like if I can develop a
a Roomba for myself in my house on a
weekend using Dana,
then a why then it's not suddenly so
scary. And I think that's like that's
that's that's important.
>> It can it can it can support it can
support people in all kinds of ways that
we haven't even imagined yet cuz they
yeah.
>> Absolutely.
I mean you think about like you know,
folks with disabilities. You know, we
always think about humanoids as like
this
very important task of folding laundry
which seems to be
>> [laughter]
>> So we focus on you know, the important
task but the when you allow these tools
to exist, I mean I I you know, we
started a tooling company. I mean I feel
so importantly that tools are like what
separates actually advanced
civilizations from you know, less
advanced civil civilizations. And our
our first
mark for the company was a monkey's head
and then we got a designer who said
we're good.
>> [laughter]
>> Like this is stupid.
I was like I thought it was pretty good.
>> So you were talking earlier about how
when you know, um
the technology got got so good in mobile
that there was a wave of these
companies, you know, Uber, WhatsApp,
Snap, you know, Airbnb etc. that emerged
in quick succession. And so now the
technology is getting there for the
infrastructure for physical AI, what are
some use cases or companies that you can
obviously it's hard to predict the
future but what were you most excited
for? Like what what could we be talking
about the equivalent here of in quick
succession?
>> I mean I think
uh
you know, mid-term we want Dana if not
the short-term to really
you know, make humanoids way more real.
Uh there's I mean how many it's like a
thousand core tasks in a home from from
humanoids. And these companies it's like
such
I mean I'm sure if talk to people who
work in these companies, it's everything
is difficult. Every step of the way is
difficult. Collecting data is difficult.
You know, cleaning that data is
difficult. Training those models or
deploying the model is difficult.
And the bar being I want a high school
kid to make a humanoid.
So, that that's our our our path. And we
think there there could be a lot there.
But, that's like these the obvious
stuff. I think the true non-obvious
stuff is going to be we'll look back
will be will be way way more
interesting.
>> And there there's there's some core
ingredients that we're bringing together
in data, right? We're we're making it
way easier to to actually get imitation
learning to work. Way easier to make
reinforcement learning work in
combination with that.
Um we're we we have pre-trained models
that can be used as a baseline for a lot
of things. Um world models, advanced
simulation technique, all of these
things come together and then you're
sort of limited by your crea-
creativity. Like, well, what what do I
want to do? And if you think about any
kind of physically AI task as it's it's
a
you are understanding the world and
you're manipulating something and and we
can build that. That can be built now
much more easily in this in this tool.
>> And I think sometimes people ask like us
being a tooling company and like you
take self-driving trucks. We deploy
self-driving trucks and many of the
self-driving truck companies use our
tools. I I I think sometimes people ask,
well, look, you know,
with Dana, are you going to like enable
all these competitors? That's great.
>> Right.
>> That's absolutely completely fine. If
you look at Google and what Google did
to web applications, there was a massive
internet. Google still succeeded
through, you know, search and YouTube
and and and other web apps. And other
folks learned and used open source
products and then ultimately closed
source products and ultimately venture
back products. So, we think we think the
the same thing can happen here.
>> I was at a robotic startup a while back
that you you guys know well. And they
had they were training, you know, they
were doing their training process,
training one of their arms to do the
particularly killer app that I thought
was very appealing, which was picking up
dog poop.
>> [laughter]
>> Literally, you know, training over and
over again with different tools for the
And so, you know, I don't know, why Why
right?
>> Yeah.
>> Why not have the little Why not have the
little robot follow you around when you
walk the dog?
And you'll pick up the poop.
>> Yeah. And I think like like you
>> I I know somebody who built a I forget
who it was, but somebody built a a
little lawn robot that would go around
and individual pick up individual
leaves.
>> Yeah.
>> Cuz you got that problem, right? You you
rake you you rake you
you rake your yard, it's completely
clean, and then like 2 hours later
there's like 14 leaves, and you're like
>> Yeah, yeah.
>> I'm just going to send out the little
bot to pick up the leaves.
>> It's like if development costs are zero,
then people will do that. I mean you
you guys remember like the early iPhone
apps that hit were like the beer one or
the fart app. If you
>> [laughter]
>> imagine that in like yeah, if you
imagine that in '98 with you know, with
the Symbian mobile, you know, whatever
it was the OS from I think it was
Ericsson or somebody. That'd be
impossible. You didn't need a team of
like 50 people to develop a like the
beer thing for the Blackberry. So I
think there's a similar type of thing
that's happening where you know, we
really want to be a
part of that and we want to enable that.
And if it like makes making like I think
it'd still be a while before like making
a robo taxi is like super super easy.
>> Yeah.
>> But that'll happen.
>> But there's I mean the number of bots
that could be the number of kinds of
bots that could be deployed in health
care is is almost endless. Health care
alone is is endless. Home care home
care.
>> Yeah.
>> Um and then in construction, you know,
in all the physical trades.
>> us sitting in 2007 and saying, "Let's
we we should have an app store. What
type of apps?" And we would come up with
like a list of eight.
And then they're like, "There'll be a
messaging one and then there'll be a
camera one." It's like and now you look
at the app store and it's like, you
know, there's an app for like the hotel
you go to and it's like, you know, to to
order, you know, food off the menu.
>> Right.
>> Yeah.
>> Yeah, makes sense.
>> Yeah. D, we're talking, you know,
earlier about the the differences
between digital AI and physical AI.
We're sort of hinting at LLMs, but world
models are you know, in vogue right now.
Want to talk about sort of the the state
of them in as it relates to physical AI
and how should we think about them?
>> So so first off, world models means
about a hundred different things and
we had a a team at CVPR recently and and
I was joking with them about just how
many different ways you can define what
a what a world model is. But when we're
thinking about a world model, right,
we're we're typically thinking about it
in the context of of a simulation,
right? So something that is effectively
>> We started a sim company.
>> Yeah. Yeah, so so something that is like
sufficiently able to represent the real
world and and is reactive in a sense
where you can actually have, let's say,
a an autonomous agent that's acting in
this world and the world model is is
behaving appropriately in response to
that autonomous agent.
>> Maybe uh Peter, I think it's worth ex-
being super explicit here. Or we just go
just one level lower into Uh this
you know
determinism in simulators, kind of the
sim-to-real gap, physics-based, you
know, rendering all the way to like this
generated world.
>> Yeah, yeah.
>> Where do we fit on it or or where, you
know, yeah, just describe the landscape,
I think, maybe.
>> Yeah, yeah, so so this is like let's say
simulation broadly, right? There's
there's so many different ways of doing
simulation. And so the the more
classical approaches of simulation
very physics-based and and you can
decompose physics in all different ways
and all different levels of abstraction,
and you can simulate with sensors or
without sensors, and and is it is just a
body simulation or are we actually
simulating, for example, the light in in
the environment or the
>> Almost think about like the way CGI is
done. If we literally had technical
artists, and we have technical artists
who would create assets which would go
in the simulator, which would mimic real
like road signs and have you know,
reflectivity and material properties
that you would see in the real world.
But as you guys know
Hollywood [clears throat] is going
through its own fundamental change. And
yet now you've generated uh the same
thing is happening in our universe as
well.
>> Yeah, so so that's sort of on the that's
at the far end of physics-based
simulation. And and then the opposite
end is is purely neural simulation, but
within that spectrum, there's there's
many different things you can do that
are each useful in their own right. And
so one of those things is a
Gaussian-based simulation, right? Where
you have
uh a a
effectively a representation of the real
world that that has a a a 3D
representation.
And that 3D representation is
consistent, meaning that if if you let's
say have some reference point, let's say
a camera, and that camera moves within
that 3D world, because the Gaussian is
actually representing the 3D geometry of
that world, you'll actually get very
high-quality output from that. There's a
lot of value in that, and and that's
what I'd say one type of of world model.
But when you go further on that on that
spectrum into the really into neural
simulation, then you get into these
where you're actually generating the
video feeds. You can think of a neural
network that's actually outputting
a video.
As what's actually coming out of the
neurons of that. And and
that can be reactive, which gives you
some very interesting properties.
Reactive as in the ego
>> does something in the environment and
the other agents respond to the ego.
>> Exactly.
However, you're not guaranteed in that
reactivity that it's it's accurate,
right? And and and now it's a question
of well, how can I align this this
simulation, this world model, with the
real world and the way that the real
world would actually react? And if you
have perfect alignment between the real
world and and the world model, I mean I
think you've just sort of solved the
universe roughly, right? It's that's an
impossibly difficult problem.
But but as we make progress towards
that,
it makes
training physically accurate models much
easier because you can do more of that
in simulation. But the hardest part
though is we're always thinking about
performance, right? So I like to say
that the the labs, they they have it
easy because they can they can make
models that are trillions of parameters,
and those models can be super slow, and
that's fine. But we don't have that
luxury in physically AI, right? We deal
We we deal in real time. Like the actual
actual clock real time. And so we we
have so many milliseconds before we have
to do something. And and those
performance constraints, they actually
constrain the problem in a lot of ways.
So we can have very large models, and we
do have very large models that are used
in the off-world environment. But when
once you go on board, all of those
constraints are very real. And now we
need to train a much smaller model that
has these safety constraints, these
determinism constraints. And
and that's the hard part about physical
That's also the moat, right? It's It's
It is what makes our tooling and and our
our competencies valuable
because it's just really hard to to meet
all of these constraints in in a
physical system.
>> When will you Which will we get first, a
perfect perfectly simulated real-world
environment for training autonomy
autonomous devices or Grand Theft Auto
6?
>> [laughter]
>> You know, I as long as they keep putting
out great trailers, I mean, I've I was
I feel like I'm getting entertained
without paying a dollar. I am
re-introduced to Tom Petty because of
>> [laughter]
>> Will you give us some timelines?
>> Soon as
>> Well, well, so so this is
>> Let's run with that for a second.
>> Yeah, let's go for it. Go for [laughter]
it.
Well, no, look, I mean, so the whole
thing with the whole thing with Grand
Theft Auto is the big innovation was
open open world open world sandbox
gaming. So it's a It's a simi- it's a
simulated city, and at least in theory.
>> On that spectrum, I mean, we hire so
many people out of the video game world.
On that spectrum, it's absolutely real
>> Well, then tell tell us about that.
Yeah, what's the spectrum?
>> So I here I I This is This is
speculation, but I think Grand Theft
Grand Theft Auto 6 will be perhaps the
last major real-world video game that's
still really developed, let's say, in
that legacy era of traditional computer
graphics tooling.
>> Technical artists and yeah.
>> Like I I think that that that Grand
Theft Auto 7 will much more likely be
like a world model-based video game.
>> Right.
>> Um and where where you you can imagine
as as AI tech evolves here, you you have
like this this concept of this video
game world model, and there's there's
like some sort of baseline, let's say,
data store that represents the real
world in somehow, and then and then you
have a some translation layer that's
actually turning that data store into
something that you can see and and run
around in. Like it's It's possible.
>> the game is a constant It could be the
real world, right? As we said, you could
you could have a complete recreation of
the real in the game. Well, this has
kind of happened with flight simulators,
hasn't it? Isn't it? The most recent
flight simulators are literally is the
entire planet rendered accurately, is my
understanding, at least from the air. Is
that right?
>> Yeah, yeah. And I mean, you really
that's where our our bread and butter is
when we started started the business. We
hired so many people out of the
Microsoft Flight Sim. I'm going to say
I'm surprised they didn't you know
>> But when you fly over New You know,
whatever, New York or when you fly over
Duluth in the flight simulator now, it
is the real city, right?
>> Exactly. But there there's some tricks
that they play there. And a lot of that
is fidelity. You know, the real world,
the more you zoom in, it stays a certain
level of fidelity. And and so they the
tricks that you play there is you you
basically are
down sampling very very aggressively.
And then as you get closer, you know,
then it becomes more more high fidelity.
Where the real world isn't like that.
The
if you were to try to rebuild the world
with this level of fidelity, it would
you know, it would take the all the
energy of the universe, right? It's it's
it's it's it's quite complex. And that's
probably by the way the best argument
against us being living in a simulation
is among the But of course then you
would say, well, the simulator we're in
doesn't follow the laws of physics that
were
at that point
Yeah, yeah. Yeah.
>> [laughter]
>> As far as I know, everything happening
outside this room doesn't even exist.
>> Yeah, this is I mean, you know, you
know, like Buddhism believes this. We're
This is a different type of podcast.
It's like, you know, when you open your
eyes, the world is rendered. And then
you close your eyes, the world That's
literally religious.
>> [laughter]
>> I don't see why I don't see why it's
necessary for it to keep rendering if
I'm not there.
>> [laughter]
>> Buddhism from first principles.
>> Yeah, exactly. That's what you should
have That'll get a lot of clicks. That's
what you should call this.
>> [laughter]
>> Yeah. Well, just going to be on the
timeline topic, you know, you gave us
timelines on self-driving cars. What
timelines do you want to give us, if
any, on the sort of
you know, other interesting things that
were worth tracking, like perhaps when
we'll get laundry folded or
other you know, things that emerge
because of humanoids or I think also
maybe just just touching a little bit on
world models, where we see world models
going, cuz I think it's it's fundamental
to all the work we do.
>> Yeah, pretty sure.
>> Yeah, yeah, so so so to ask the the
first question
so laundry folding, it's
it's it's not terribly far from being
solved to to be clear.
And there is a lot of interesting
research being
>> And then humanity can rejoice. That's in
Proverbs 4:16, I think. [laughter]
>> Well, here I I I
I do think I do think housekeeping is is
a killer use case for physically I,
right?
>> Peter thinks two that he always talks
about in the company. One is
housekeeping and entertainment. He
Peter's long on humanoid entertainment.
>> What is it like?
>> What kind of entertainment?
>> I 100% [laughter] agree with that. I
think entertainment is the robot as
killer app. I don't think anybody else
>> Yeah, these are your admissions.
>> I mean like
>> [laughter]
>> So these
Midwest white guys are really into this.
>> I'm just saying, but I think I think
>> I just want to know when I get
Westworld. That's all I want.
>> Yes.
>> No, I actually have an entertainer. I
have a little I have a little a tiny
little Chinese robot dog that's like
literally it's just like a little it's
just a little and it just like runs
around and it just like first base a
little bit.
>> you pay to see Cirque du Soleil with
robots? Like yes.
>> [laughter]
>> Yes.
>> I I want to see I want to see kung fu
trapeze swinging.
>> Spoken by like a compiler's guy.
>> [laughter]
>> I know, but but
>> I want Westworld. I want Westworld.
>> I mean the the turn The funny thing is I
was I was just saying like well will
people in like the suburbs of Detroit
actually that pass the test? I bet you
people in Sterling Heights would
actually pay to see that. It's actually
pro.
I stand corrected. I stand
>> [laughter]
>> But but but back on laundry folding for
a moment
it's it's actually not far from being
folded if you remove the time
constraint. And so the the trick that's
played in if you look at the latest
research videos is they'll say like play
it at 8x real time or whatever, right?
And
and that's for you to make it watchable.
So so
the question is when can you actually
achieve human parity of performance?
That's that's further off.
>> When you decouple models from just the
hardware the hardware can do it now.
That used to be a constraint. So, the
hardware is very fast and accurate now,
which was actually
>> There there's still overheating issues
that are still being dealt with, but
it's it's not it's not terribly far off.
Like, these are solved.
>> I mean, it's far off from like, you
know, when I was a mechy, that was like
fantasy. Like, there's like nothing can
>> What's the movie that has the most
realistic future vision of robots?
>> Oh, man. Bicentennial Man.
>> Is it Okay.
>> Yeah, he's he's realistic.
>> [laughter]
>> Why that one? I actually haven't seen
that
>> Well, I like I like that scene. I think
it's I, Robot when Will Smith jumps in
the car and uh it's his his you know,
whatever his like accomplice is in the
car and he's like puts the car in manual
and she's like, "What are you going to
drive this thing yourself?" Like, out of
like, you know, she's Yeah, she's like,
"Yeah, are you crazy? What are you going
to drive this thing yourself?" Like,
that's what we're That's the flight of
intuition's
you know, like goal.
>> Well, again, by the way, I I haven't
seen this movie in a long time, probably
since it came out. So, my recollection
of it is probably a bit incorrect.
>> Don't worry, the internet will correct
you.
>> [laughter]
>> But, I I think I think Bicentennial Man
has
fully self-driving cars. Uh and it also
has the housekeeping robot, which which
is played by Robin Williams. And that's
it's sort of like the the friendly guy
that will the friendly robot that will
clean up and also babysit your kids and
stuff like that. And it seems like it's
it's in the not terribly distant future.
>> I got a different answer. You guys ever
see that movie uh Sam Rockwell Moon?
>> Oh, yeah.
>> Yeah. The the setup I don't want to It's
a great movie. Don't watch the trailer.
Just to watch the movie.
Uh it's the the premise is the tagline
of the movie is 250,000 mi from home you
find who you are. And it's one guy who
works on an energy harvesting base run
by Pliant Intuition run by
Lunar Technologies.
>> [laughter]
>> I'm I
I don't want to be Weyland I don't want
to be Weyland-Yutani. I don't want to
be, you know, that's from from the Alien
franchises. And then Tyrell Corporation
from Blade Runner. No, no. I want to be
Lunar Technologies in the Moon
franchise. Not even franchise. What
There's one guy who works on this in the
base basically runs by itself. And he's
just there to kind of mind it when
things kind of some you know error
signal
>> the the the reason why it's it's I think
so accurate is because the state of the
art for AI systems is like these systems
they just need the occasional grounding.
>> Exactly. They'll just go off and do
something crazy and then you have to
say, "No, no, stop doing that."
>> to like that too. I mean that's that's a
coding bot sort of like, right?
>> And then the reason other reasons I
think it's quite accurate that it's
maybe uncouth now but Kevin Spacey is
the
the AI you know smiley face and he's
he's just there to kind of placate the
human to to assist but to also like he's
like, "Oh, you're you you seem like
you're sad Sam." And like you know like
that's the the the the but really it's
the one running the base.
And
hopefully I mean I shouldn't say we want
to be lunar technologies because I don't
know if they're they're quite a positive
force in nature in that in that but I
think massive energy farm that's
completely autonomous
that's going to be the future. And I and
I think everyone like everyone reacts to
things like that with like fear. And
it's like guys, that's amazing. That
means energy costs go way down. Like
that's an incredible positive thing. I
think I think the you know I just did
this commencement speech at my my
undergrad.
>> Did you get uh destroyed?
>> No. You know what? I I
>> Unlike Eric Schmidt.
>> Yeah, yeah, yeah. Yeah, yeah. I
Listen, listen. My my
the situation my my wife started
watching and she said, "I feel like
you're yelling at me. I can't watch
[laughter] this."
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
to say which tech leaders who just
basically avoided by like punting and
saying I'm not going to talk about it. I
I talk about this stuff. And partly it's
the General Motors Institute. No one's
like these these are I I I don't want to
I don't I don't want to I don't want to
throw judgement on you know the people
we recruit out of out of MIT and
Stanford but I'd say GMI people are a
little different and they're like you
know they're like pragmatic people and
they under they you know you don't go to
a place like GMI if you believe a
superficial view of what corporations
do. Corporations are just people working
on projects together. And by the way,
people working on projects together in
government, people working on projects
together in nonprofits, they all screw
up. It's And so it's it's too simple to
say AI corporations are are terrible.
It's also you can't say the other side,
which is like it'll all be great. So you
have a role to play. That's basically
what you know, what my what what my
message is. And it's that's that's the
case. I think if you feel like if if it
if the the of the the I think the
obvious uh you know, uh abundance that
comes from self-driving trucks
self-driving cars and the fact that
people don't die, which is amazing.
But then you also get this efficiency of
cheaper energy etc. If all those things
don't still satisfy your your fear, you
you as a person, it's up to your
responsibility until really learn about
that technology. You you you can't just
say, "Well, I'm afraid of it and my
reaction is shut it down." That's not
it's simply it's the
uh and I don't I don't say this just to
say that that we're competing with the
Chinese, but there's a Confucian
>> [laughter]
>> the saying by my Confucius is just no
hand can block the sun.
>> Mhm.
>> And [clears throat] the sun is
technological progress. And if we as a
society don't embrace technological
progress, we will be left behind.
>> Somebody else is going to do it.
>> Somebody else is going to do it. And if
it's not the Chinese, who knows? It's
maybe it's the Uzbeks or the you know,
it's another country that is is
recognizing, "Hey, my citizens are
suffering and I'm going to use this
technology to remove the" It is
honestly, it's because we're living in
such a great society that we can have
these like
>> Yeah.
>> I would say stupid conversations. Like
there still are people who don't can't
get food.
>> Yep.
>> And and and and someone will immediately
equip if they were debating me, they
would say, "Well, there's plenty of
food. It's it's the capitalist system
that doesn't" No, no, no. Let's let's be
very specific. There's plenty of food,
but getting that food to those people is
difficult.
>> Yep.
>> So that means we should let robots get
that food to them faster.
>> Yep.
>> That's just that's just how it is. So I
think I'm I'm And I think like we we we
as like technologists, I think sometimes
we, you know, it's I think an
inclination just to say, "Leave these
people behind." I think you have to
bring them along. You have to explain it
to them. But, we also have to treat
folks like adults and say,
"If you don't get it after I explain it
a couple times, then you just don't get
it." So, there's like a middle ground.
It's not everyone's an idiot and every
or we should just be technology will
just be perfect perfect perfect. There's
a middle ground. Let's have that
conversation to a point.
>> Yeah.
>> And then we just move forward and make
society better. And then the results
show it. I mean, there's people who
still
shockingly believe communism is the
right answer. I mean, I just want to say
why And I'm I'm, you know, I I am a
capitalist. I can't Yeah, I can't I
can't not admit that. But,
there's 70 years of history there. Like,
we're not That's not a debate anymore. I
mean, that that I think it could be a
debate if we're in if we're sitting here
in 1965 and having a debate, you say,
"Okay,
maybe centrally controlled systems work
better." There's no debate anymore,
folks. You know, systems where
individuals
make decisions on their own interest
actually work better for society. And
so, that doesn't mean everything is
perfect. And you can't extrapolate.
That's same thing with, you know, AI.
Doesn't mean everything's going to be
perfect, but net net, it's definitely
going to be better. And that's roughly
what my commencement speech was without
the booze.
>> [laughter]
>> They Mark and these guys were booing.
So, they just cut it out.
Eric was booing and throwing stuff. They
just edited it out.
>> You mentioned
uh you know, the Japan market earlier.
When you talk briefly about sort of the
global ambitions and how these
technologies, you know, interplay and
and what we're doing here.
>> So, I think uh America particularly is
still the most advanced in terms of when
you take account of business model. The
second thing for a company like Applied
Intuition, we're an extremely global
company. We work with everybody uh uh uh
uh
minus we don't have an office in China,
but really everyone else on the globe.
Uh and
we're a horizontal company. We're a
technology provider. And I think we
I think more Silicon Valley companies I
think can employ a little bit of what we
do which is
work very I would say collaboratively
with the local economies. As sovereign
AI becomes more of a real thing
we have to
uh you know, build businesses that take
that into account. By the way, we're not
the first ones to do this. If you look
at the history of America, you read the
history of Standard Oil, you'll see that
this is this is that was the history of
companies. You'd work internationally.
Aramco is not a random company, right?
You you build based on the real
geopolitical realities of the time. And
so I think we you know, we've I think
navigated it quite well. I've I've lived
you know, in Japan, I lived in Germany,
I lived in Dubai. So I also being
Pakistani by birth, I think that's also
influenced our company. Peter's only
lived in Michigan and [laughter] here.
But he is
a German, so
But so I think innately we're more we
think about the globe more. I think when
I was at both at Google and at YC, I was
always surprised at how
uh kind of almost myopic
the companies are. They're always
looking at the market that's just like
within the 30 you know, between San Jose
and San Francisco. I was like, actually
the market is really big. I think
physical AI, the nature of it being
physical, I think we we have to be a
very international company. And I think
we've had a lot of success being a very
very you know, being international.
Yeah.
>> Cool.
All right, that's a good place to wrap.
>> Okay.
>> Peter Gasser, thanks so much for coming
on the podcast and congrats on big lots
of data.
>> Yeah, thanks for having us.
>> Awesome. Great to see you guys.
>> Okay, great.