NVIDIA CEO Jensen Huang

Transcript from https://podmenti.com/t/e3bbb26d03cba449

I will say, David, I would love to have NVIDIA's full.
production team.
Every episode.
It was nice not having to worry about turning the cameras on and off and making sure that nothing bad happened myself while we were recording this.
Yeah, just the gear. I mean the
Drives that came out of the camera. All right, uh red cameras for the home studio starting next episode.
Yeah. Okay. All right. Let's do it.
Who got the truth?
Is it you, is it you, is it you Who got the truth now?
Is it you, is it you, is it you
Me down, say it straight
Another story
Welcome to this episode of Acquired, the podcast about great technology companies and the stories and playbooks behind them.
I'm Ben Gilbert. I'm David Rosenthal.
And we are your hosts.
Listeners, just so we don't bury the lead.
This episode was insanely cool for David and I. Yeah.
After researching NVIDIA for something like five hundred hours over the last two years, we flew down to NVIDIA headquarters to sit down with Jensen himself.
And Jensen, of course, is the founder and CEO of NVIDIA, the company powering this whole AI explosion. At the time of recording, NVIDIA is worth
one point one trillion dollars and is the sixth most valuable company in the entire world.
And right now is a crucible moment for the company.
Expectations are set high.
I mean sky high. They have about the most impressive strategic position and lead against their competitors of
any company that we've ever studied.
But here's the question that everyone is wondering.
Will NVIDIA's insane prosperity continue for years to come?
Is AI gonna be the next trillion dollar technology wave? How sure are we of that? And if so,
Can NVIDIA actually maintain their ridiculous dominance as this market comes to take shape?
So Jensen takes us down memory lane with stories of how they went from graphics to the data center to AI.
How they survived multiple near death experiences.
He also has plenty of advice for founders and he shared an emotional side to the founder journey.
Toward the end of the episode.
Yeah, I got new perspective on the company and on
him as a founder and a leader just from doing this despite, you know, we thought we knew everything before we came in advance, and uh it turned out we didn't. Turns out the protagonist actually knows more. Yes.
All right, well, listeners, join the Slack. There is incredible discussion of everything about this company, AI, the whole ecosystem, and a bunch of other episodes that we've done recently going on in there right now. So that is acquired.fm slash slack. We would love to see you.
And without further ado, this show is not investment advice. David and I may have investments in the companies we discuss in this show is for informational and entertainment purposes only.
On to Jensen.
So Jensen, this is acquired, so we want to start with story time. So we want to wind the clock all the way back to I believe it was nineteen ninety seven.
You're getting ready to ship the Riva one twenty eight, which is
One of the largest
graphics chips ever created in the history of computing.
It is the first fully 3D accelerated
graphics pipeline for a computer. Yeah. And uh
You guys have months of cash left.
And so you decide to do the entire testing in simulation.
rather than ever receiving a physical prototype.
you commission the production run sight on scene with the rest of the company's money. So you're betting it all right here on the Riva one twenty eight. Yeah. It comes back and of the thirty two DirectX blend modes, it supports
eight of them.
And you have to convince
The market.
To buy it.
And you gotta convince developers.
Not to use anything but those eight blend modes.
Walk us through what that's the first time.
Okay, so wait, wait, first question. Was that the plan all along? Like when when did you realize that you should have implemented what? I realized I didn't learn about it until it was too late. We should have implemented all thirty two. Yeah. But but it we built what we built and so we had to make the best of it.
That was really an extraordinary time. Remember, Revo one twenty e was M V three.
M V one and M V two were based on
Forward texture mapping.
No triangles but curves.
And it tessellated the curves.
And because we were rendering higher level objects, we
Essentially avoided using Z buffers.
And we thought that that was going to be a r good rendering approach. And turns out to have been completely the wrong answer.
And so what Revo Run twenty eight was was a reset of our company. Now remember at the time that we started the company in nineteen ninety three.
We were the only consumer three D graphics company ever created and we
We were focused on transforming the PC into an accelerated PC because at the time.
Windows was really a software rendered system.
And so anyways.
Riva one twenty eight.
was a reset of our company because
By the time that we realized we had gone down the wrong road.
Microsoft had already rolled out Direct X. It was fundamentally incompatible with NVIDIA's architecture.
Thirty competitors have already shown up, uh, even though we were the first company at the time that we've we're founded.
So the world was a completely a different place.
The question about what to do as a company strategy
At that point.
I would have said that we made a whole bunch of
Wrong decisions, but on that day that mattered, we made
A sequence of extraordinarily good decisions.
And that time, nineteen ninety seven.
was probably invideous.
best moment. And the reason for that was our backs were up against the wall. We were running out of time. We're running out of money.
For a lot of employees running out of hope.
And the question is what do we do? Well the first thing that we did was we decided that look direct access now here.
We're not gonna fight it.
Let's go figure out a way to build the best thing in the world uh for it.
And
Riva one twenty eight is the world's first
uh fully accelerated hardware accelerated pipeline.
For the rendering three D.
And so
The transform, the projection.
Every single element all the way down to the frame buffer was completely hardware accelerated.
Uh we implemented uh a uh a texture cache.
We took the bus limit, the frame buffer limit to as big as as uh physics could afford at the time.
We made the biggest chip that anybody had ever
Imagine building.
We use the fastest memories.
Basically if we built that chip.
There could be nothing that could be faster.
And we also chose a cost point.
That is
substantially higher than the highest price that we think that any of our competitors would be willing to go.
If we built it right.
We accelerated everything, we implement everything uh in direct X that we knew of.
And we build it as large as we possibly could.
Then obviously nobody can build something faster than that.
Today, in a way, you kinda do that here at NVIDIA too.
You were
A consumer products company back then, right? It was
And consumers who were gonna have to pay the money to buy this. That's right. But we observed that there was a segment of the market where people were because at the time the the PC industry was still coming up.
And it wasn't good enough.
Everybody was clamoring for the next fastest thing.
And so if your performance was
Ten times higher this year.
than what was available.
There's a whole large market of enthusiasts who who we believe would would have gone after it. And we were absolutely right. That the PC industry
had a substantially large enthusiast market.
That would buy the best of everything.
To this day it's kinda remains true.
And for certain segments of the market where the technology is never good enough, like three D graphics, when we chose the right technology, three D graphics is never good enough.
And we call it back then.
Three D gives us sustainable technology opportunity because it's never good enough. And so your technology can keep getting better.
We chose that.
Uh we also made the decision to use this technology called emulation. There was a a company called
I cows.
And on the day that I called them, they were just shutting the company down because they had no customers.
And I said, Hey look, uh I'll buy what you have inventory.
And uh you know, uh no promises are necessary.
And the reason why we needed that emulator is because
If you figure out how much money that we have, if we taped out a chip.
And we uh got it back from the Fab.
And we started working on our software.
By the time that we found all the bugs because we did the software.
Then we taped out the chip again.
Well we would have been out of business already.
Yeah.
Caught up. Well, not to mention we wouldn't have been out of business. Who cares? Exactly. So if you're gonna be out of business anyways.
That plan obviously wasn't the plan.


You know, build the chip, write the software.
Fix the bugs.
tape out a new chip, so on so forth.
That method wasn't gonna work.
And so the question is.
If we only had six months
And you get the tape out just one time.
then obviously you're gonna tape out a perfect ship.
So I so I remember having conversation with our leaders and they said, But Jensen, how do you know it's gonna be perfect? I said, I know it's gonna be perfect because if it's not, we'll be out of business.
And so let's make it perfect. We get one shot.
We essentially virtually prototype the chip by buying this emulator.
And Dwight and the software team wrote our software the entire stack.
And ran it on this emulator.
And just sat in the lab.
Waiting for windows to paint.
You know and when it was like easy. I actually think that was an hour per frame, something like that.
And so we would just sit there and watch it paint. And so on the day that we decided to tape out
I assumed that the chip was perfect. And everything that that we could have tested, we test it in advance.
And told everybody this is it. We're gonna tape out the chip. It's gonna be perfect.
Well, if you're going to tape out a chip and you know it's perfect, then what else would you do?
That's actually the good question.
If you knew that you hit enter, you taped out a chip and you knew it was going to be perfect, then what else would you do? Well the answer, obviously, go to production.
And marketing blitz. Yeah, yeah, and kick everything off. Kick everything off.
Because you got a perfect job.
And so we got it in our head that we have a perfect chip.
How much of this was you and how much of this was like
Your co founders, the rest of the company, the board, was everybody telling you you were crazy?
No, everybody was clear we had no shot. There's the the not doing it would be crazy.
Because otherwise you might go you're gonna be out of business anyways. So
Anything aside from that is crazy. So it seemed like a fairly logical thing and quite frankly, right now just I'm describing it every you're probably thinking, Yeah, it's pretty sensible.
Well it worked.
Yeah.
And so we take that out and went directly to production.
So is the lesson for founders out there
When you have conviction on something like the Revo one twenty eight.
Or uh Cuda.
Go bet the company on it.
And this keeps working for you.
Your lesson learned from this is, yes, keep pushing all the chips in because so far it's worked every time. No. How do you think about that? No, no. When you push your chips in, um I I know it's gonna work.
Notice.
We assume that we taped out a perfect chip.
The reason why we taped on a perfect chip is because we emulated the whole chip.
Before we taped it out.
We developed the entire software stack.
We ran QA on all the drivers and all the software. We ran all the games we had.
We ran every VGA application we had. And so when you push your chips in
What you're really doing is you're when you bet the farm you're saying
I'm gonna take everything in the future, all the risky things, and I pull in in advance.
And that is probably the lesson, and to this day.
Everything that we can prefetch.
Everything in the future that we can simulate today.
Oh we prefetch it.
We talk about this a lot. We were just talking about this on our Costco episode.
You want to push your chips in when you know it's gonna work.
So every time we see you make a bet the company move. Yeah.
You've already simulated it. You know. Yeah, yeah, yeah.
Do you feel like that was the case with CUDA?
Uh yeah.
In fact, before there was CUDA, there was uh CG.
Right.
And so we were already playing with the concept of
How do we create an abstraction layer above
Artship that is expressible in a higher level language and higher level expression and
And how can we use our GPU for uh things like C T reconstruction, image processing? We were already down that path. And so
There were some positive feedback.
And some intuitive positive feedback that that we think that the general purpose computing could be possible in.
If you just looked at the pipeline of a programmable shader, it is a processor and is highly parallel and
It is uh massively threaded and it is the only processor in the world that does that. And so there were a lot of characteristics about programmable shading.
That would suggest that CUDA has a great opportunity to succeed.
And that is true.
If
there was a large market of machine learning practitioners who would eventually show up and want to do all this great scientific computing and accelerated computing.
But at the time when you were starting to invest
What is now something like ten thousand person years in building that platform. Yeah.
Did you ever feel like, oh man, we might have invested ahead of the demand for machine learning since we're like a decade before.
The whole world is realizing it.
I guess yes and no.
You know, when we saw Deep Learning, when we saw Elisnet.
And
Realized it's
Incrediveness in computer vision.
We had the good sense, if you will.
to go back to first principles and ask, you know, what is it about this thing that made it so successful?
When a new software technology or new algorithm comes along.
And somehow leapfrogs
thirty years of computer vision work.
You have to take a step back and ask yourself, but why?
And fundamentally is is it scalable? And if it's scalable, what other problems can it solve?
And there were several observations that we made.
Is that
If you have a whole lot of example data
You could

To make predictions. Well, what we've basically done is discovered a universal function approximator.
Because the dimensionality could be as high as you want it to be and because each layer is trained one layer at a time.
There's no reason why you can't make
Very very deep.
uh neural networks.
Okay, so now you just reasoned your way through. Okay, so now I go back to
Twelve years ago.
Yeah, you could just imagine the reasoning I'm going through in my head.
That we've discovered a universal function approximator. In fact we might have discovered
With a couple more technologies.
A universal computer.
And you've been paying attention to the ImageNet competition every year leading up to this? Yeah, yeah. And the reason for that is because we were already working on computer vision at the time.
And we were trying to get CUDA to be a good computer vision system.
or most of the algorithms that were creative for computer vision are a good fit for CUDA. And so we're sitting there trying to figure it out.
All of a sudden Alex Net shows up.
And so that was incredibly intriguing.
It's so effective that it makes you take a step back and ask yourself
Why is that happening?
So by the time that you reason your way through this, you you go, Well, what are the kind of problems in a world where a universal function approximator
Yeah, I think.
Right.
Well, we know that most of our algorithms uh start from
Principle sciences.
Okay. You wanna understand the causality.
And from the causality you create a a simulation.
Algorithm that allows us to scale.
Well
For a lot of problems.
We kinda don't care about the causality. We just care about
The predictability of it.
Like
Do I really care?
For what reason?
You prefer this toothpaste over that.
I don't really care the causality. I just want to know that this is the one you would have predicted.
Do I really care that the fundamental cause of
Somebody who buys a hot dog buys ketchup and mustard.
It doesn't really
matter.
It only matters that I can project it.
It applies to predicting movies, predicting
Music.
It applies to predicting
Quite frankly.
Weather.
We understand thermal dynamics. We understand
radiation from the sun. We understand cloud effects. We understand oceanic effects. We understand all these different things.
We just wanna know whether we should we're
sweater or not, isn't that right? Yep. And so causality for a lot of problems in in the world doesn't matter. We just want to emulate the system.
And predict the outcome.
In it can be an incredibly lucrative market if you can predict.
What the next best performing
uh feed item to serve into a social media feed. Turns out that's a hugely. I love the examples you pulled. Two paste.
Catch up, music, movies.
When you realize this, you realize, hang hang on a second.
A universal functional approximator, a machine learning system.
You know, something that learns from examples.
could have tremendous opportunities because of just the number of applications is quite enormous.
And
Everything from obviously we just are talking about commerce all the way to science.
And so you realize that maybe
This could affect a very large part of the world's
Industries.
almost every piece of software in the world would would eventually be programmed this way.
And if that's the case, then how you build a computer and how you build a chip in fact can be completely changed.
Um
Realizing that.
The rest of it is just comes with, you know, do you have the courage to put your chips behind it? So that's where we are.
Today.
Um
And that's where NVIDIA is today.
But I'm curious in the you know, there's a couple of years after Alex net. Mm-hmm.
And this is when Ben and I work.
getting into the technology industry and the venture industry ourselves. I started at Microsoft in twenty twelve. So right after Alex Snap, but before
anyone was talking about machine learning and even the mainstream engineering community.
There were those couple of gears there where
To a lot of the rest of the world, these looked like science projects. Yeah.
The technology companies here in Silicon Valley.
Particularly the social media companies.
They were just realising.
huge economic value out of this. The Google's, the Facebooks, the Netflixes, et cetera. Yeah. And obviously that led to lots of things, including open AI, a couple of years later. But during those couple of years
When you saw just that.
Huge economic
value unlock here in Silicon Valley.
How are you feeling during those times?
The first thought was of course reasoning about uh how we we should change our computing stack.
The second thought is where can we find
Earliest
possibilities of use.
If we were to go build this computer
what would people use it to do? And we were fortunate that working with the world's universities and researchers was was innate in our company because
We were already working on CUDA and CUDA's
Early adopters were researchers.
Because we democratize supercomputing.
You know, CUDA is not just used as you know for AI, Cuda is used for almost all fields of science.
Everything from molecular dynamics to imaging C T reconstruction to
uh uh seismic processing to, you know, weather simulations.
Quantum chemistry, the list goes on, right? And so
the number of applications of CUDA in research was very high. And so when the time came and we realized that deep learning could be really interesting.
Uh, it was natural for us to go back to the researchers.
And find every single AI researcher on the planet and say, How can we help you advance your work?
And that included
Yan LeCun and Andrew Eng and
Jeff Hinton and
That's how I met all these people and and I used to go to all the AI conferences and that's where, you know, I met Ilias Suscover there for the first time. Yeah.
And so it was really about at that point
What are the systems that we can build and the software stacks we can build to help you be more successful?
to advance the research because at the time it it looked like a toy.
But we had confidence that
Even GAN, the first time I met Goodfellow, the GAN was
It was like thirty two by thirty two.
And it was just a you know blurry image of a cat.
You know? But how far can it go? And so
We believed in it. We believe that one, you could scale deep learning because obviously it's trained layer by layer and
You could make the data sets larger and you could make the models larger and we believe that if you made that larger and larger, it would get better and better.
Yeah. Kind of sensible. And
I think the
discussions and the engagements with the researchers
was the exact positive feedback system that we needed.
I would go back to research. It was that's where it all happened.
When open AI was
Found it in twenty.
Fifteen? Yeah.
I mean that was such
An important moment that's obvious today now, but at the time.
I I think most people, even people in tech, who are like
What is this? Yeah. Were you were you involved in it at all? Like, you know, because you were so connected to the researchers to Ilia.
Taking that talent.
Out of.
Google and Facebook to be blunt, but yeah.
reseeding the research community and opening it up. Um
What's such an important moment. Were you involved in it at all?
I wasn't involved in the founding of it, but I knew uh a lot of the people there and um uh Elon of course uh I knew and uh uh Peter Beale was there and Ilya was there and
Oh, we have we have some great employees today that were there in the beginning and
I knew that they needed this amazing computer that we were building and we're building the first version of the DGX, which, you know, today when you see a hopper
It's seventy pounds, thirty five thousand parts, ten thousand amps.
But
DGX, the first version that we built was uh
Used internally and I delivered the first one to open AI. Yeah, that was a fun day, but
Most of our success was
Aligned around
um in the beginning uh just about helping the researchers.
Get to the next level.
I knew it wasn't very useful in its current state.
But I also believe that in a few clicks it could be really remarkable.
And that belief system came from
the interactions with all these amazing researchers and
It came from just seeing the incremental progress. At first the papers were coming out every three months and then
Then papers today are coming out every day, right?
So you could just monitor
the archive papers and I took an interest in learning about the progress of deep learning and and and to the best of my ability read these papers.
And you could just see the progress happening.
You know, in real time, exponentially in real time.
It even seems like within the industry
From some
researchers we spoke with
It seemed like
No one predicted how useful language models would become when you just
Increase the size of the models.
They thought, oh, there has to be some algorithmic change that needs to happen. But once you cross that 10 billion parameter mark, and certainly once you cross the 100 billion,
They just magically got much more accurate, much more useful, much more lifelike.
Were you shocked by that the first time you saw a truly large language model and do you remember that feeling?
Yeah.
My first feeling about the language model was how

to just mask out words and and uh make it predict the next word.
It's self supervised learning at its best.
We have all this text.
You know, I know what the answer is.
I'll just make you guess it.
And so my first impression of Bert was really how clever it was. And now the question is how can you scale that?
You know, the first observation all almost everything is interesting and then and then try to understand intuitively why it works. And then the next step of course is
From first principles, how would you extrapolate that?
Yeah.
And so obviously we knew that bird was gonna be a lot larger.
No.
One of the things about these language models is it's encoding information, isn't that right? It's compressing information. And so within the world
Languages and text.
There's a fair amount of reasoning that's encoded in it.
We describe a lot of reasoning things and and so if you were
To say that uh few step reasoning.
is somehow learnable from just reading things.
I wouldn't be surprised.
No you know, for a a lot of us.
uh we get our common sense and we get our our reasoning ability by reading.
And so I went a
Machine learning model also learn some of the reasoning.
capabilities from that and from reasoning capabilities you could have emergent capabilities. Right. Emergent abilities.
are consistent with intuitively from reasoning. And so
Some of it could be predictable.
But still.
It's still amazing.
The fact that it's sensible.
Doesn't make it any less amazing.
Right.
I could visualize literally.
The entire computer
Um and and all the b modules in a self driving car.
And the fact that it's still
Keeping lanes.
makes me insanely happy. And so I even remember that from my first operating systems class in college when I finally
figured out all the way from programming language to the electrical engineering classes bridged in the middle by that OS class. I'm like
Oh, I think I understand how the von Neumann computer works soup to nuts.
And it's still a miracle. Yeah, yeah. Yeah, yeah, exactly.
Yeah, yeah.
When you put it all together, it's still a miracle. Yeah.
All right listeners.
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So the founders did exactly what great founders do.
operate with obsessive customer focus.
They embedded inside a massive law firm for months.
They sat with the lawyers just watching how the work really gets done.
And that's how you get features that customers love, like tabular review where you
Drop in a folder of hundreds of contracts and it pulls every key term into a grid a lawyer can actually work with. Legor's bed here is interesting, since it lets each lawyer handle more complexity, any given person can increase the quality of their work and do higher value work. And this means that the pie can grow even as each individual task takes less time.
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If your legal team wants to check it out, whether you're a law firm or you're in house at a company,
You can learn more at Lagora.com slash acquired
And just tell'em that Ben and David sent you.
We have some questions we want to ask you. Uh some are cultural about NVIDIA.
but um others are generalizable to company building broadly.
And the first one that we wanted to ask is uh we've heard that you have forty plus direct reports and that this org chart uh works a lot differently.
than a traditional company org chart.
Do you think there's something special about NVIDIA?
That
makes you able to have so many direct reports, not worry about coddling or focusing on career growth of your executives. And you're like, no, you're just here to do your freaking best work.
And the most important thing in the world now go.
A, is that correct? And B, is there something special about NVIDIA that enables that?
I don't think it's something special with NVIDIA. I think that we had the courage to build a system like this.
NVIDIA's not built like a military it's not built like a like the armed forces.
Where you have
Yeah, generals and colonels and we just we're not set up like that.
We're not set up in a command and control and information distribution system from the top down.
We're really built much more like a computing stack.
And
A computing stack, the lowest layer is our architecture and then there's our chip and then there's our software and
And on top of it there are all these different modules and
Each one of these layers of modules are people.
And so the architecture of the company to me
is a computer.
with a computing stack with um uh people managing different parts of the system.
And
Who reports to whom
your title is not related to anywhere you are in the stack. It just happens to be who is the best at running that module on that function.
On that layer.
It is in charge. And that person is the pilot in command.
And so that's one characteristic.
Yeah.
Thought about the company this way? Even from the earliest days. Yes. Yeah.
And the reason for that is because your organization should be the
architecture of the machinery of building the product.
Right. That's what a company is. Yep.
And yet
Everybody's company look exactly the same, but they all do different things. How does that make any sense?
Mm.
Do you see what I'm saying? Yeah. You know, how you make fried chicken versus how you fill burgers versus how you make, you know
Chinese fried rice is different. And so why would the machinery, why would the process be exactly the same? And so it's not sensible to me that if you look at the org charts of most companies,
It all kinda looks like this.
And then the you have one group that's for a business, and you have another for another business, you have another for another business, and they're all kind of
Supposedly autonomous.
And so none of that stuff makes any sense to me. It just depends on
What is it that we're trying to build?
And what is the architecture of the company that best suits to go build it?
That's so that's number one. In terms of information system
And how do you enable collaboration? We kinda wired up like a neural network. And the way that we say is that there's a phrase in the company called mission is the boss.
And so we figure out what is the mission of what is the mission.
And we go wire up.
the best skills and the best teams and the best resources.
to achieve that mission. And it cuts across the entire organization in a way that
Doesn't make any sense.
But it's looks like a little bit like a neural network.
You know, I'm going to do it. And when you say mission, do you mean mission like
Nvidia's mission is Bill Hopper. Yeah, okay. So it's not like
Further accelerated computing. It's like worshipping DGX Cloud. Build Hopper or somebody else's uh build a system for Hopper. Somebody is
Uh build Cuda for Hopper. Somebody's job is build Cuda and N for CUDA for Hopper. Somebody's job is
The mission, right?
So
You know, your mission is to do something.
What are the trade-offs associated with that versus the traditional structure?
The downside is
The pressure on the leaders is fairly high.
And the reason for that is because
In a command and control system, the person who you report to has more power than you.
And the reason why they have more power than you is because they're closer to the source of information than you are.
Mm-hmm. In our company.
The information is disseminated.
A fairly
quickly to a lot of different people. And usually at a team level. So for example, just now I was in I was in our robotics meeting.
And we're talking about certain things and we're making some decisions.
And there are new college grads in a room, there's three vice presidents in a room, there's two E staffs in a room, and at the moment that we decided together, we reasoned through some stuff, we made a decision.
Everybody heard it ex exactly the same time.
So
Nobody has more power than anybody else. Does that make sense? The new college grad learned at exact same same time as
The East F
And so the the executive staff and the and the leaders that that work for me
And myself.
You earn the right.

Based on your ability to reason through problems and helping other people succeed.
And and it's not because you have some privilege information that I knew the answer was three point seven and only I knew. You know. Everybody knew.
When we did our
Most recent episode in video part three that we we just released.
We sort of did this thought exercise, um
Especially over the last couple of years.
Your product shipping cycle.

Especially given the
level of technology that you are working with and the difficulty of this all.
We sort of like
Could you imagine
Apple.
Shipping two iPhones a year.
And we say that for illustrative purposes. For illustrative purposes, not to pick on Apple or what. A large tech company shipping two flagship products or their flagship product twice per year. Yeah. Or you know two WWE DCs a year. Yeah.
There seems to be something in. You can't really imagine that whereas that happens here.
Are there
Other companies
Either current or
historically that you
look up to, admire, maybe took some of this inspiration from
In the last thirty years I've read my fair share of business books.
And as in everything you read, you you're supposed to you're supposed to to first of all enjoy it.
Right. Enjoy it. Be inspired by it.
But not to adopt it.
That's not the whole point of these books. The whole point of these books is to share their experiences.
And and you you're supposed to ask, you know, what does it mean to me?
in my world and what does it mean to me in the context of what I'm going through?
What does this mean to me and the environment that I'm in and what does this mean to me and what I'm trying to achieve and
What does this mean to NVIDIA and the age of our company and the capability of our company? And so you're supposed to ask yourself, what does it mean to you?
And then from that point being informed by all these different things that we're learning.
Uh, we're supposed to come up with our own strategies.
Yeah, what I just described is kind of
How I go about everything.
You're supposed to be inspired to learn from every everybody else and
And the education's free.
You know?
when somebody talks about a new product, you're supposed to go listen to it. You're not supposed to ignore it. You're supposed to go learn from it.
And uh it could be a competitor, it could be uh adjacent industry, it could be nothing to do with us.
No, the more we're we learn from uh what's happening on the world
The better.
But then you're supposed to come back and ask yourself, you know, what does this mean to us? Yeah, you don't just want to imitate those. That's right. Yeah. I love this tee up of
Learning but not imitating and learning from a
Wide array of sources.
There's this sort of um
Unbelievable.
Third.
element I think to
what NVIDIA has become today and
That's the data center.
It's certainly not obvious. I can't reason from
Alex Nett.
And your engagement with the research community.
And and you know social media feedback matters too.
Yeah. You deciding and the company deciding. We're gonna go on a five year all in journey on the data center. Yeah, yeah.
How did that happen?
Our journey to the data center happened I would say almost seventeen years ago.
I'm always being asked, I mean, what what are the challenges that the company could see someday.
And
And I've always felt

Technology.
Is
Plugged into
A computer.
And that computer has to sit next to you.
Because it has to be connected to a monitor.
That will limit our opportunity someday.
Because there are only so many desktop PCs that
Plug a GPU into.
And uh there's only so many CRTs and and and the time L C Ds that we could possibly drive.
So the question is
Wouldn't it be amazing if our computer doesn't have to be connected to
The viewing device.
That the the separation of it.
Um made it possible for us to compute somewhere else.
Yeah, one of our engineers came and showed it to me one day.
And it was really capturing the frame buffer.
Encoding it into video.
And streaming it.
Um to a a receiver device separating computing from the viewing.
In many ways that is cloud gaming. In fact, that was when we started GFN. We knew that GFN was going to be
Um a journey that would take a long time because you you're fighting you're fighting all kinds of problems including including the speed of light.
And latency everywhere you look. That's right. Yeah, GForceNow. That's your first cloud product. That's right. And look at look at GeForceNow, wasn't Video's first data center product?
And our second data center product was remote graphics, putting our GPUs in in the world's enterprise data centers.
Mm which then led us to our third product.
Which combined CUDA plus R GPU, which became a supercomputer.
Which then work towards
You know, more and more and more.
And the reason why it's so important is because
The disconnection.
Between war and videos.
Uh
computing is done versus where it's enjoyed.
If you can separate that, your market opportunity explodes. Yeah.
Yeah. And it was completely true.
And so we're no longer limited by the physical
constraints of the desktop PC sitting by your desk.
Um you know, and and we're not limited by one GPU per person. And so
Uh it doesn't matter where it is anymore. And so that was really the great observation. It's a good reminder.
The data center segment of NVIDIA's business to me has become synonymous with
How's AI going? And that's
A false equivalence and it's interesting that
You were only this ready to sort of explode in
AI in the data center because you had
three plus previous products where you learned how to build data center computers. Exactly. Even though those markets weren't these like
gigantic world changing technology shifts the way that AI is. That's how you learned. Yeah. That's right.
You want to pave the way to future opportunities.
You can't wait until the opportunity is sitting in front of you.
For you to reach out for it. And so you have to
anticipate. You know, our job as CO is to look around corners and
And to anticipate where will opportunities be someday.
And
Even if I'm not exactly sure what and when
How do I position the company to be near it?
To be just standing kinda near under the tree.
And we can do a diving catch when the apple falls. You guys know what I'm saying? Yeah. But you've got to be close enough to do the diving catch.
Rewind it twenty fifteen and
Open AI.
If you hadn't been laying this groundwork in the data center,
You wouldn't be powering open AI right now. Yeah. But the idea that computing will be mostly done away from the viewing device.
that the vast majority of computing will be done away from the computer itself.
That insight was good.
In fact cloud computing everything about today's computing is about separation of that.
And by putting it in a data center we can overcome this latency problem, meaning
You're not gonna overcome speed of light.
Speed of light end to end is only one hundred and twenty milliseconds or something like that. It's not that long.
From a data center to a intervention. Yeah. Yeah. And so which literally across the planet. Yeah, right. So if you could solve that problem approximately something like that. I I forget the number, but it's seventy millisecond seconds, hundred milliseconds. But it's not that long.
And so my point is
If you could
Remove the obstacles everywhere else.
Then speed of light should be
You know, perfectly fine. And you could build data centers as m as large alike and you could do amazing things and
And uh this little tiny device that we use as a computer or you know, your T V as a computer, whatever computer.
But they all k they can all instantly become amazing.
And so that insight
You know, fifteen years ago it was a good one.
So speaking of the speed of light in FiniBand, like Dav David's like begging me to go here.
You totally saw that InfiniBand would be way more useful way sooner than anyone else realized. Acquiring Melanox
I think
you uniquely saw that this was required to train large language models, and you were super aggressive in acquiring that company.
Why did you see that when no one else saw that?
Well uh there were several reasons for that. First
Um
If you want to be a data center company, build building the processing chip isn't the way to do it.
A data center is distinguished from a desktop computer versus a cell phone.
Not by the processor in it.
Yeah. A desktop computer in a data center uses the same CPUs. Use the same GPUs, apparently, right? Very close.
And so it's not the chip, it's not the processing chip that this describes it, but it's the networking of it, it's the infrastructure of it, it's the you know, how the the the computing is distributed.
how security is provided, how networking is done.
You know, so on and so forth. And so
So it those characteristics
are associated with Melinox.
Not NVIDIA.
And so the day that I concluded that really NVIDIA wants to be a you know, build
Computers of the future and computers of the future are gonna be data centers, embodied in data centers.
Then we then if we want to be data center oriented company then then we really need to get into networking. And so that was one. The second thing is
Observation that
Whereas
Cloud computing started in hyperscale, which is about taking commodity components, a lot of users, and virtualizing
many users uh on top of one computer.
AI is really about distributed computing where one job
One training job
Um
Is orchestrated across millions of
Processors.
And so it's the inverse of hyperscale almost. And the way that you design a hyperscale computer with
with off the shelf commodity Ethernet.
Which is just fine for Hadoop, but it's just fine for
Search queries is just fine for all of those things. But not when you're sharding a model across the thing. And so
Uh that observation says that the type of networking you want to do is not exactly Ethernet.
And the way that we do networking for supercomputing is really quite ideal.
And so the combination of those two ideas, um uh you know, convinced me that that Mellinox is is absolutely the right the right company because they were they're the world's leading high performance networking company. And
Yeah, we worked with them in so many different areas in in uh high performance computing already.
Plus I I really like the people.
Um uh the the the Israel team is world class. Uh we have some three thousand two hundred people there now and
It was one of the best strategic decisions I ever made.
When we were
Researching particularly
Part three of our NVIDIA series. We talked to a lot of people.
And many people told us.
The Mellon Ox acquisition.
One of if not the best.
Of all time. By any technology company. I think so too, yeah. And it's so disconnected from the the work that we normally do, it was surprising to everybody.
But framed this way, you were you were standing near where the action was. Yeah. So you could figure out as soon as that apple sort of becomes available to purchase, like, oh
LLMs are about to blow up. I'm gonna need that. Everyone's gonna need that. I think I know that before anyone else does.
You want to position yourself.
near opportunities, you don't have to be that perfect.
You know, is you you want to position yourself near the tree.
And even if you don't catch the the the apple.
before it hits the ground, so long as you're the first one to pick it up.
You want to position yourself close to the opportunity.
And so that's kind of
A lot of my work is positioning the company near opportunities and and um uh having the the
Uh
the the company having the skills to to um monetize each one of the steps along the way so that we can be sustainable.
What you just said reminds me of a great uh aphorism from uh Buffett and Munger, which is it's better to be approximately right than exactly wrong. Yeah, there you go. Yeah, that's a good one. That's a good one.
Yeah.
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I want to move away from NVIDIA, if you're okay with it, and ask you some questions, since we have a lot of founders that listen to this show, sort of advice for company building.
The first one is
When you're starting a startup in the earliest days, your biggest competitor is
Uh you don't make anything people want.
Like your company's likely to die just because people don't actually care as much as you do about where they're.
Yeah. In the later days you actually have to be very thought about competitive strategy.
And I'm curious, what would be your advice to companies that
you know, have product market fit, that are starting to grow, they're in interesting growing markets.
Um where should they look
f for competition and how should they handle it.
Well, there are all kinds of ways to think about competition.
We prefer
To
Position ourselves.
In a way that
Serves a need that usually
hasn't emerged.
I've heard uh you or others in the video I think use the phrase zero billion dollar market. That's exactly right. Yeah. It's our way of saying
There's no market yet, but we believe there will be one.
And and usually when you're positioned there.
Everybody
Everybody's trying to figure out why are you here.
Right. Because when we first got into automotive because we believe that in the future the car is gonna be largely software.
And if it's gonna be largely software.
Um a a really incredible computer is necessary. And so
So when we positioned ourselves there, most people I I I still remember one one of the one of the CTOs told me, you know what, cars
cannot tolerate the blue screen of death.
I don't think anybody can tolerate that, but but it doesn't change the fact that
Some day every car will be a software defined car and I I think you know
Uh fifteen years later we're we're we're were largely right. And
So oftentimes there's non consumption.
And we like to navigate our company there.
And by doing that.
Um
By the time that you
Uh
the the market emerges.
It's very it's very likely there aren't that many competitors shaped that way. And so
We were early in P C gaming and today uh NVIDIA is very large in P C gaming.
Uh we uh re imagined what a what a uh design work station would be like and today
Just about every war station on the planet uses NVIDIA's technology.
Uh we re reimagine um how supercomputing ought to be done and who should who should benefit from supercomputing that we would democratize it and look today NVIDIA's and and accelerated computing is is um quite large and
we reimagine how software would be done.
Yeah, and today it's called machine learning and
How computing would be done, we call it AI and so
We reimagine these kind of things, uh, try to try to do that about a decade in advance. And so we spend about a decade in zero billion dollar markets. And
Today I spent a lot of time on Omniverse.
And Omniverse is a, you know, classic example of a zero billion dollar business.
And there's like 40 customers now. Amazon, BMW. It's cool. It's cool. So let's say you do get this great 10-year lead, but then other people figure it out and you got people nipping at your heels.
What are some structural things that
someone who's building a business can do to sort of
stay ahead and you can just keep your pedal to the metal and say, we're gonna outwork them and we're gonna be smarter. And like that works to some extent, but those are tactics. What strategically can you do to sort of make sure that you can maintain that lead?
Oftentimes if you created the market
You ended up having
You know, what what people describe as motes.
Because
If you build your product right.
And it's enabled.
Uh an entire ecosystem around you.
To help serve that in market.
You've essentially created a platform. Sometimes it's a
It's a product based platform, sometimes it's a service based platform, sometimes a technology based platform.
But if you were you were early there and you
You you were mindful about helping the ecosystem.
Um succeed with you.
You ended up having this network of networks.
And all these developers and all these customers who are who are built around you.
And that network is essentially your moat.
And so
You know, I I don't love thinking about it in the context of a moat.
Um and the reason for that is because you're now focused on building stuff
Around your castle.
I tend to like thinking about things in the context of building a network.
And that network is about enabling other people.
to enjoy the success.
of the final market, you know, that you're not the only company that enjoys it, but you're enjoying it with a whole bunch of other people, including me. I'm so glad you brought this up'cause I wanted to ask you um
In my mind at least, and
Sounds like in years too. NVIDIA is absolutely a platform company of which there are
Gary.
few meaningful platform companies in the world.
I
Think.
It's also fair to say that when you started for the first few years you were a technology company and
Not a platform company.
Every example I can think of of a company that tried to start as a platform company fails. You gotta start
As a technology first. When did you think about making that transition to being a platform? Like your first graphic cards were technology. They weren't there was no CUDA, there was no platform.
What you observed is not wrong.
However, inside our company we were always a platform company. And the reason for that
Is because from the very first day
Of our company we had this architecture called UDA.
It's the U of Kuda.
Could it compute unified device architecture? That's right. And the reason for that is because
What we've done, what we what we essentially did in the beginning.
Even though Revo one twenty eight only had computer graphics.
The architecture described accelerators of all kinds.
Yeah.
And we would
Take that architecture.
And developers would program to it.
In fact, N video's first
Strategy, business strategy.
was we were going to be a game console.
Inside.
The PC.
And a game console needs developers.
Which is the reason why NVIDIA a long time ago
One of our first employees was a developer relations person.
And so it it's the reason why we knew all the game developers and all the three D developers and we knew we knew everything. So was the original business plan to like
Sort of like to build direct X. Yeah, compete with Nintendo and Sega as like with PCs. Original NVIDIA architecture was called direct.
Envy.
Direct NVIDIA.
Yeah.
And DirectX was an API that made it possible for operating system.

Yeah. But directly when you started NVIDIA, right? And that's what made your strategy run for the first company.
Yeah. And which in nineteen ninety
five became, you know, well, direct X came out.
So this is an important lesson. We weren't always always a developer oriented company. The initial attempt was we will get the developers to build on direct N V and then they'll build for our chips and then we'll have a platform.
And what played out is Microsoft already had all these developer relationships. So you learned the lesson the hard way of like yikes, we just got to Microsoft did back in the day. They're like, oh that could be a developer platform. We'll take that, thank you. No, but they had a lot.
They did it very differently and and they did a lot of things right. We did a lot of things wrong, but
But having competing against Microsoft in the nineties. I mean that's uh NVIDIA today. No, it's a lot different, but I appreciate that.
But we were we were nowhere near near competing with them.
If you look now, when CUDA came along and there was OpenGL that was direct X.
Um but there's there's still another uh extension, if you will, and that extension is CUDA. And that CUDA extension
Allows
A chip.
That
got paid for running direct access in OpenGL.
to create an install base for CUDA.
Yeah. And so that's
That is why you are so
Militant and
I think from our research it really was you.
being militant that
Every NVIDIA tip will run CUDA.
Yeah, if you're a computing platform, everything's gotta be compatible.
We are the only accelerator on the planet where every single accelerator is c arch architecturally compatible with the others.
Nana has ever existed.
There are literally a couple of hundred million, right?
Two hundred fifty million, three hundred million installed base of
Active Kula.
GPUs being used in the world today?
And they're all architecturally compatible.
How would you have a computing platform if
If you know M V thirty and M V thirty five and thirty nine and M V forty, they're all different.
Mm-hmm.
Yeah.
Thirty years it's all completely compatible.
And so
That's the only unnegotiable rule in our company. Everything else is negotiable.
I mean and I guess uh CUDA was a rebirth of UDA, but
Understanding this now, UDA going all the way back.
Yeah. It really goes all the way back to all the chips you've ever got. Yeah, yeah, yeah. UDA goes all the way back to all of our chips today.
Oh.
For the record, I didn't help any of the the founding CEOs that that are listening.
I gotta tell you, you know, while you were asking that question, what what lessons would I impart?
Um I I I don't know.
I mean there the the characteristics of successful
companies and successful CEOs I think are are fairly well described. There are a whole bunch of them.
I just think starting successful companies are insanely hard.
It's just insanely hard.
And when I see
These amazing companies get built.
Uh I I have nothing but admiration and respect because I I just know that it's insanely hard.
And I think that everybody did
Many similar things.
There are some good, uh smart things that people do. There are some dumb things that you can do.
Um but
But you could do all the right smart things and still fail.
You could do a whole bunch of dumb things and I did many of them and still succeed.
So obviously th that's not exactly right.
You know, just I think skills are are the things that you can learn along the way.
But
at important moments certain circumstances have to come together and and I do think that
that the market has to
You know, be one of the agents. Yeah.
To help you succeed.
It's not enough, obviously, because a lot of people still fail.
Do you remember any moments in NVIDIA's history where you're like, ooh, we made a bunch of wrong decisions, but somehow we got saved.
Because
You know, it takes the sum of all the luck and all the skill in order to succeed. Do you remember any moments where you're like that you started with Rew Revo one twenty eight was the spot on.
Uh Rebo one twenty eight.

Smart decisions we made.
Which are smart to this day.
How we design ships is exactly the same to this day.
Because
Gosh, you know, nobody's ever done it back then.
And we pulled every trick in the book.
In a desperation.
Because we had no other choice.
Well guess what?
That's the way things ought to be done.
And now everybody does it that way.
Right. Everybody does that.
Why should you do things twice if you can do it once?
Why tape out a chip seven times if you could tape it out one time?
Right.
The most efficient, the most cost effective, the most competitive um
Uh speed is technology, right? Speed is performance.
Time to market his performance.
All of those things apply. So why do things twice if you could do it once?
Yeah.
Yeah.
Rebo one twenty eight made a lot of great decisions and how we spec products, um how we how we think about market needs and and lack of and
How do we judge markets and
All of those man, we made some amazing amazingly good decisions.
Yeah, we were, you know, back against the wall. We only had one more shot to do it.
But
Once you pull out all the stops and you see what you're capable of, why would you put stops in exactly? Like those keep the stops out all the time. That's right. Every time. That's right. Is it fair to say though maybe on the luck side of the equation?
Thinking back to nineteen ninety seven.
That that was the moment where
Consumers
tip to really, really valuing three D graphical performance in games. Oh yeah. So for example luck. Let's let's talk about luck.
Um i if uh Carmack uh hadn't um
uh decided to use acceleration because
Remember, Doom was a completely software rendered and the NVIDIA philosophy
was that although general purpose computing is a is a fabulous thing and is gonna enable software and IT and everything.
Um we felt that there were there were applications that would be possible.
Or it would be costly if it wasn't accelerated. It should be accelerated. And three D graphics was one of them, but it wasn't the only one.
And it was just happens to be the first one and a really great one.
And I still remember the first times we met John, he was quite emphatic about using CPUs and
And a software renderer was really good. I mean, quite frankly, if you look at look at Doom.
Uh the performance of Doom was really hard to achieve, even with accelerators at the time.
You know, if you didn't filter, if you didn't have to do bi linear filtering
Um it did a pretty good job.
The problem with Doom though was you needed Car Mac to program it. Yeah, you needed CarMac to program it. Exactly. It was it was a genius piece of code.
Um but nonetheless software renderers did a really good job and but and if he hadn'
Decided to go to OpenGL.
And accelerate uh accelerate for Quake.
Uh frankly, you know, what would be the killer app that put us here?
Right.
And so
Carmack and Sweeney both between uh Unreal and Quake.
created the first two uh killer applications for for consumer three D.
Yeah. And so
I I I owe owe them a great deal.
I want to come back real quick too.
And he said.
You told these stories and you're like, Well, I don't know what founders can take from that. I I actually
Do you think um.
You know, if you look at all the big tech companies today.
Perhaps with the exception of Google. They did all start and understanding this now about you.
by addressing developers, planning to build a platform and tools for developers.
Um
You know, all of them.
Apple.
Not Amazon.
Wow.
I guess. That's how AWS started.
So I think that actually is a lesson to your point of like that won't guarantee success by any means. Right. But that'll get you hanging around a tree if the apple falls. Yeah.
As many good ideas as we have.
Um you don't have all the world's good ideas.
And and the benefit of having developers is you get to see a lot of good ideas.
Yeah.
Yeah.
Well, as we we start to drift toward the end here.
We spent a lot of time on the past.
think about the future a little bit. I'm sure you spend a lot of time on this being
on the cutting edge of AI.
You know, we're moving into an era where the productivity that software can accomplish when a person is using software can massively amplify.
the impact and the value that they're creating. Which
has to be amazing for humanity in the long run.
In the short term, it's going to be inevitably bumpy as we sort of figure out what that means.
What do you think some of the solutions are as AI gets more and more powerful and better at accelerating productivity?
Uh
for all the displaced jobs that are going to come from it.
Well, first of all, we have to keep AI safe.
And there's a couple of different areas of AI safety. Um
That's really important, obviously.
I
And robotics and self driving car.
There's a whole field of AI safety and we've dedicated ourselves to functional safety and active safety and all kinds of different different areas of safety.
Um when to apply human in the loop, when is it okay for a human not to be in the loop? Uh uh
You know, how do you get to a point where
where um uh increasingly human doesn't have to be in the loop, but human largely in the loop.
Yeah.
In the case of information safety, obviously bias, false information and appreciating the the rights of artists and and creators.
Um that that whole area uh deserves a lot of attention. And
And you've seen some of the work that we've done.
Instead of scraping the internet, um, we we partnered with Getty and Shutterstock to
create commercially fair way of applying artificial intelligence generation to the eye.
In the area of uh large language models and the and the future of increasingly greater agency AI.
Clearly the answer is for as long as it's sensible and I think it's gonna be sensible for a long time is a human in the loop.
the ability
An AI to
self learn and improve and change.
Uh out in the wild.
Uh i in the digital form, uh should be avoided.
And and um uh we should collect data, we should carry the data, we should train the model, we should you know, test the model, validate the model before we release it on the wild again. So human is in the loop. Yep.
There are a lot of different
industries that have already demonstrated
how to build systems that are safe and good for humanity and
Obviously the way uh
autopilot works for for a plane and
And two pilot system and then air traffic control and
Um redundancy and diversity and
And all of the basic philosophies of designing safe systems um apply uh as well in self driving cars and and so on and so forth. And
And so I I think there's a lot of models of of creating safe AI.
And and I think we need to apply them.
With respect to automation
My feeling is that
Yeah, and we'll see but
It is more likely.
that AI is gonna create more jobs.
And in the near term. The question is what's the definition of near term? And the reason for that is
Is um
Uh the first thing that that happens with productivity is prosperity.
and prosperity when the companies get get more successful, they hire more people because they want to expand into more areas.
And so the question is.
If you think about a company and say, Okay, if we improve the productivity, then they need they need fewer people.
Well that's because the company has no more ideas, but that's not true for most companies. Um if you become more productive and the company becomes more profitable, usually
They hire more people to expand into new areas.
And so long as we believe that there are more areas to expand into that the the the
There are more ideas in drugs, this drug discovery, there are more ideas in transportation, there are more ideas in retail, there are more ideas in entertainment, that there's more ideas.
And technology.
So long as we believe that there are more ideas.
The prosperity of the industry.
Which comes from improved productivity.
Results in hiring more people.
More ideas.
Now you go back in history.
We can fairly say that today's industry is larger than the industry w the the world's industries a thousand years ago.
And the reason for that is because obviously humans have a lot of ideas.
And I think that there's
Plenty of ideas yet.
for prosperity and plenty of ideas.
that can be be got from productivity improvements. But then my sense is that it's likely to generate jobs.
Now obviously.
N net generation of jobs doesn't
Guarantee that any one human doesn't get fired. Okay. I mean that that's obviously true. And
And it's more likely that someone
We'll lose a job to someone else.
Some other human that uses an AI.
You know, and not not likely to an AI, but to some other human that uses an AI. And so I think the the first thing that everybody should do is learn how to use AI so that they can augment their own productivity.
And every company should augment their own productivity to be more productive so that they could have more prosperity, hire more people.
And so I think jobs will change.
My guess is that we'll actually have higher employment.
We'll create more jobs.
I think industries will be more more productive.
Um and many of the industries that are currently suffering from lack of lack of uh labor.
Well, workforce is likely to uh use AI to get themselves off their feet and and
and get back to growth and prosperity.
So I see it a little bit differently.
But I do think that jobs will be affected.
Um and I I'd encourage everybody just to learn AI.
This is uh appropriate. There's a version of um
Something we talk about a lot on Acquired. We call it the uh Moritz corollary to Moore's Law. After Mike Moritz from uh from uh Sequoia.
And Sequoia was w the and first investor in our company. Yeah, of course. Yeah. The great story behind it is that uh when Mike was
taking over for Don Valentine with with Doug. He was sitting and looking at Sequoia's returns and he was
Looking at fun three or four. I think it was four maybe that had Cisco in it.
How are we ever gonna top that? You know, I can't
I can't, you know, Don's gonna have us beat, we're never gonna beat that.
He thought about it and he realized that.
Well,
As compute gets cheaper.
And it can access
More.
areas of the economy because it gets cheaper and can it get adopted more widely.
Well then
the markets that we can address.
Should get bigger. Yeah.
And AI your argument is basically AI will do the same thing. Exactly. I just gave you exactly the same example.
That in fact.
Productivity doesn't result in us doing less.
Productivity usually results in us doing more.
Yeah.
Everything we do will be easier.
But we'll end up doing more. Yeah.
Because we have infinite ambition. You know, the the the world has infinite ambition. And so
So if if a company is more profitable, they tend to hire more people to do more.
Yep.
Yeah. That's true. Technology is a lever and the the the place where the idea kind of falls down is that like
that we would be satisfied. Yeah. Like Yeah. Humans have never ending ambition. No. Humans will always expand and consume more energy and uh attempt to pursue more ideas. That has always been true of every version of our species. Yeah over time.
All right listeners.
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We have a few.
Blade round questions we want to ask you. We'll open with an easy one based on all these uh conference rooms we see named around here. Favorite sci-fi book.
I've never read a f sci fi book before. No, come on. Yeah.
What's the obsession with Star Trek and just you know I watched a TV show. Favorite TV series.
Uh
Well Star Trek's my favorite. Yeah, Star Trek's my favorite. I saw Vger out there on the way in. It's a good conference room name. V'jor is an excellent one. Yeah. Yeah.
What car is your daily driver these days?
And related question, do you still have the Supra?
Oh this is one of my favorite cars. Um and also favorite memories. You guys might not know this, but but uh
Uh Lori and I got engaged.
Um I
Christmas uh one year and we drove back in my
my brand new Supra and we total it.
We were this close to the end.
But but nonetheless, uh it wasn't my fault. It wasn't w wasn't the super's fault, but but uh it it's a remark. I I love that. I love that car.
I'm driven these days for for security reasons and others, but um uh
Uh I'm driven in the uh Mercedes EQS. It's a great car. Uh yeah, great car.
Thanks.
Yeah.
Using NVIDIA technology?
Yeah it has yeah, we're in we're in the in the uh uh the the uh
Where the central computer.
Yeah.
So we
I know we already talked a little bit about business books, but one or two favorites that you've taken something from.
Clay Christensen, I think, has s the the series is the best. I mean, there's just no no two ways about it and
And the reason for that is be is because it's so intuitive and so sensible. It's it it's approachable.
But uh I read a whole bunch of them and I read just about all of them. I really enjoyed And Andrew Grove's books. They're all really good.
Awesome.
Favorite.
characteristic of Don Valentine.
Grumpy but endearing.
And uh what he said to me.
The last time does he uh decide to invest in our company, he says, If you lose my money, I'll kill you.
And then
uh over the course of of the decades, uh uh the years I've followed.
Uh when something is nice written about us in Mercury News, um it seems like he wrote it in a crayon. He you know, he'll say he'll say, Good job, Don.
You know, just right right over the newspaper and just good job done and he s mails it to me and
And uh I I hope we I've kept them, but anyways, uh y you could tell he's a he's a real sweetheart and and um uh but
But uh he cares about the companies.
He's a special character. Yeah. He's incredible.
What is something that you believe today?
That forty year old Jensen.
would have pushed back on and said, No, I disagree.
Um there's plenty of time.
Mm.
Yeah.
There's plenty of time.
If you prioritize yourself uh properly and and you make sure that you you uh
You don't let Outlook be the controller of your time.
There's plenty of time.
Plenty of time.
In the day.
Plenty of time.
Just don't do everything.
Prioritize your life.
Make sacrifices.
Don't let outload control.
What you do every day.
Notice I was late to our meeting.
And the reason for that by the time I looked up
I
Oh my gosh.
Yeah, Ben and Dave are waiting.
Yeah.
We have time.
Yeah exactly. Didn't stop this from being a great chest. No, but you have to prioritize your time really carefully.
And don't let out look.
That determine that.
Love that.
What are you afraid of, if anything?
I'm afraid of the same things today that I was
I was uh in in in the very beginning of this company, which is letting the employees down.
Yeah.
You have a lot of people who joined your company because they believe in your hopes and dreams and
And they've adopted it as their hopes and dreams and
And uh you you wanna be right for them. You wanna be successful th for them, you want them to be able to
Uh build a great life as as well as help you build a great company.
And be able to great career. You want them to have to enjoy all of that.
And these days I want them to be able to enjoy the the things I've had the benefit of enjoying and um all the great success I've enjoyed. I want them to be able to enjoy all of that. And so
So I think I think the uh the greatest fear is that that uh you let them down.
Mm-hmm.
Point.
Did you realize that
You weren't gonna have another.
Job.
That like
This was it.
I just I don't change jobs.
You know, if it wasn't because of
Chris and Curtis convincing me to do
do NVIDIA. I would still be at LSI Logic today, I'm certain of it.
Wow. Really? Yeah, yeah.
I'm certain of it.
I would keep doing what I'm doing.
And at the time that I was there, I was completely d dedicated and focused on on helping L S I Logic be the best company it could be.
And I was L S I Logic's best ambassador. I've got great friends that to this day.
uh that I've known from from LSI Logic. Uh it's a company I I loved uh then, I love dearly today.
I know exactly why I went. Um
uh the revolutionary impact it had on chip design and system design and computer design.
In my estimation, one of the most important companies that that ever came to Silicon Valley and changed everything about how computers were made.
Uh it put me in the in the epicenter of some of the most important events in computer industry.
led me to meeting Chris and Curtis and Andy Bechtelsheim and
John Rubinstein and
You know, some of the most important people in the world and
And Frank that I I was with the other day and just I mean the list goes on and and so
Uh.
uh L S I Logic was really important to me and and uh I would still be there.
I I would you know, who knows what L S I logic would have become if I were still there, right? And
And so that's kinda how my my mind works. Um AI of the world. Yeah, exactly. I mean I I might be doing the same thing I'm doing today.
Yeah.
But until until I'm fired.
This is this is my last job.
LSI logic might have also changed your um
perspective and philosophy about computing too.
The sense I we got from the research was that
When
Right out of school.
And when you first went to AMD first, right? Yeah.
You believed that
Like kind of a version of the
Terry Sanders, real men have fabs. Like you you need to do the whole stack. Like you gotta do everything in that LSA logic.
What LSI Logic did was
was uh realis that you can express
Um transistors and logical gates and chip.
functionality in high level languages.
That bite.
Raising the level of abstraction.
And
what it's now called high level design.
It was coined by
uh Harvey Jones who's on a on Nvidia's board and I met met him uh way back in the early days of synopsis. But
But uh during that time there was this belief that you can express chip design in high level languages.
And by doing so, you could take advantage of optimizing compilers and optimization logic and and and tools.
Um and and be a lot more productive.
That
Logic was so sensible to me.
And I was twenty one years old at the time and I I wanna pursue that vision.
Uh frankly that that idea happened in
And um
uh machine learning, it happened in software programming and
I want to see it happen in digital biology.
So that we can we can think about uh biology in a much higher level language.
Uh.
probably a large language model um would be the the way to make it make it representable.
That transition was so revolutionary.
I thought that was the best thing ever happened to the industry and I was po I was really happy to be part of it and I was at ground zero. And so
So I I saw one industry
um change revolutionizing another industry.
And i if not for
LSI Logic doing the work that it did.
Синопсис шорти ар.
Then why would the computer industry be where it is today?
Yeah.
It it's uh really, really terrific. I was I was uh at the right place at the right time to see all that.
That's super cool. Yeah.
And it
Sounded like the CO of LSI Logic, uh
Put a good word for in for you uh with Don Valentine too. I do know how to write a business plan.
Which it turns out is not actually important. No. No, no. It turns out that making a financial forecast
that nobody knows uh is gonna be right or wrong turns out not to be that important.
But the important things that a business plan.
Probably could have teased out. I I think that the the art of writing a business plan ought to be much, much shorter.
And it forces you to condense, you know, what what is the true problem you're trying to solve? What is the unmet need that you you believe will emerge? And what is it that you're gonna do that is sufficiently hard that when everybody else finds out is a good idea, they're they're not gonna swarm it.
And you know, make you obsolete. And so
It has to be sufficiently hard to do.
Um
Uh there there are a whole bunch of other skills that are involved in just, you know.
product and positioning and pricing and go to market and you know, all that kind of stuff. But those are skills and you can learn those things easily.
The stuff that is really, really hard is the essence of what I described and
I did that.
Okay.
But I had no idea how to write the business plan and um
Uh and and I was fortunate that Wolf Corgan
was so pleased with me and the work that I did when I was at Ellison Logic.
he called up Don Valentine and and told Don, you know, invest in this kid.
And um he's gonna come your way and and uh
Uh so I was
You know, I was I was set up for success from that moment and got it got us on the ground.
Yeah. As long as you didn't lose the money.
Have they held through today?
The V C partner uh is still on the board, Mark Stevens. Yeah. Yeah. Yeah. Yeah.
All these years. The two founding VCs are still on the board.
Sutter Hill and Sequoia. Yeah, Tench Cox and Mark Stevens. I don't think that ever happens.
Yeah. We are singular in that in that circumstance, I believe.
They've added value this whole time, uh, been inspiring this whole time, uh uh uh gave great wisdom and and uh uh great support.
Well, but they they also uh were so entertained. But they've they've been entertained, you know, by the company, inspired by the company and and enriched by the company and so they stayed with it. And I I'm I'm really grateful.
Well, in that being
Alright.
Final question for you.
It's twenty twenty three. Mm-hmm.
Thirty years anniversary of the founding of Nvidia.
If you were
Magically thirty years old.
again today in twenty twenty three.
And you were going to Denny's
With your two best friends who are the two smartest people you know.
And you're talking about starting a company.
What are you talking about starting?
I wouldn't do it.
Ha ha ha.
I know.
And the reason for that is really quite simple.
Ignor the company that we would start.
First of all, I'm not exactly sure.
The reason why I wouldn't do it.
And it goes back to why it's so hard.
Yeah.
Building a company and building a video
turned out to have been a million times harder than I expected it to be. Any of us expected it to be.
And at that time if we realised the pain and suffering
And just how vulnerable you you're gonna feel.
Um and the challenges that you're gonna endure.
Uh the embarrassment and the shame and you know, the list of all the things that that go wrong.
I don't think anybody would start it.
company. Nobody in their right mind would do it.
And
I think that that's kinda the the superpower of a entrepreneur.
They don't know how hard it is.
And they only ask themselves, how hard can it be?
And to this day.
I
I
trick my brain into thinking how hard can it be.
Because you have to.
Still when you wake up in the morning. How hard can it be?
Everything that we're doing. How hard can it be?
Omniverse, how hard can it be?
Yeah.
Planning to retire anytime soon, though. No, you're still.
The um that's that's really the trick of an entrepreneur.
You have to get yourself to believe that it's not that hard.
Because it's way harder than you think. And so
If I go taking all of my knowledge now and I go back
And I said,
I'm gonna endure that whole journey again. I think that's too much.
It is just too much.
Do you have any suggestions on any kind of support system or a way to get through the emotional trauma that comes with building something like this?
Uh family and friends and and all the colleagues we have here.
Uh I'm surrounded by people who've been here for thirty years.
Right. Chris has been here for thirty years and
Uh, Jeff Fisher's been here thirty years, Dwight's been here thirty years and
Uh, Jonah and Brian have been here, you know, twenty five some years and
Uh probably longer than that and you know, Joe Greco's been here thirty years. I'm surrounded by these people that never one time gave up.
And they never one time gave up on me. And
That's the entire ball of wax.
You know, and
And to be able to go home and
And uh
uh have your family be fully committed to to everything that you're trying to do and
Um
Uh thick or thin. They're they're proud of you and proud of the company and
You kinda need that. You need the unwavering support of people around you. You know, Jim Gaither's and the more you know, the the Tench Coxes and the Mark Stevens and the
You know, Harvey Jones and all the the early people of our company, the Bill Millers.
They uh uh not one time gave up on the company and us and
And you kinda you need that.
Yeah, not kinda need that. You need that.
I'm pretty sure that almost every
successful company and
entrepreneurs that that have gone through some difficult challenges.
they they had that support system around them.
I can only imagine how.
Meaningful that I mean I know how meaningful that is.
In any company, but
For you.
Given I I feel like the NVIDIA journey is um
Particularly amplified on these dimensions, right?
Two
Two if not three.
Eighty percent plus drawdowns in the public markets. To have investors who've stuck with you from day one through that must be just like
So much support. Yeah, yeah. It is incredible. And
You hate that any of that stuff happened and and most of it you you know
Most of it is is is out of your control.
But
Yeah.
Eighty percent fall.
Mm.
It's an extraordinary thing, no no matter how you look at it. And
I forget exactly but
I mean we we traded.


Two, three billion dollars.
In market value for a while because of the decision we made in going into CUDA and all that work and
Your belief system has to be really, really strong.
You know, you have to really, really believe it and really, really want it.
Otherwise it's just too much to endure. I mean because
You know?
Everybody's questioning you and
Employees aren't questioning you, but employees have questions.
Right. Um people outside are questioning you.
And uh
It's a little embarrassing.
Yeah, it's like, you know, when your stock price gets hit, it's embarrassing no matter how you think about it. And
It's hard to explain, you know? And so
There there's no good good answers to any of that stuff. You know, the CEOs are human and companies are built of humans and
And uh
These challenges are hard to endure.
Ben had an appropriate comment on our uh
Most recent episode on you all where uh
We're talking about
Yeah.
The current situation in Vivian.
I think you said.
For any other company, this would be a
In a precarious spot to be in.
But for NVIDIA. Then this is kind of old hat. Yeah. You guys are f familiar familiar with these large swings in amplitude.
Yeah. The thing that that to keep in mind is at all times
I
W what is the market opportunity that
That you're engaging.
And that help that informs your size.
Yeah, it was
I was told a long time ago that NVIDIA can never be larger than a billion dollars.
Obviously it's an under estimation under
Under imagination of the size of the opportunity.
It is the case that no chip company
Can ever be so big.
And so
But if you're not a chip company then then why is that why does that apply to you?
And this is the extraordinary thing about technology right now, is technology
is a tool.
And it's only so large.
What's what's unique about our current cir circumstance today
is that we're in the manufacturing of intelligence. We're in the manufacturing of work.
World.
That's AI.
And
The world of tasks doing work.
Productive.
Generative AI work, generative intelligent work.
That market size is enormous. It's measured in trillions.
One way to think about that is
If you build a chip.
for a car, how many cars are there and how many chips would they consume? That's one one way to think about that.
However, if you
If you build a
System
That
Uh whenever needed.
assistant in the driving of
The car.
Um
And you know, what's the value of a autonomous chauffeur?
Um every now and then.
And so now the the market
uh obviously the problem becomes much larger, the opportunity becomes larger.
Um you know, what would it be like if we if we were to to magically conjure up
Um a chauffeur for everybody uh who has a car.
And you know, how big is that market and obviously
obviously that that that's a much, much larger market. And so
the technology industry is at the uh you know, where what we discovered, what NVIDIA is discovered and what some of the discovered
Is that by separating ourselves from being a chip company
Um but but building on top of a chip and you're now at an AI company.
the the market opportunity has has grown by probably a thousand times.
Yeah, don't be surprised if technology companies larger in the future because
Because uh what you produce.
Uh it it's something very different.
And and that that's the kind of the
The uh
Uh

You know, how large can your opportunity how large can you be?
Has everything to do with the size of the opportunity.
Yeah.
Well, Jensen, thank you so much.
Thank you.
All right listeners.
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Ooh, David, that was awesome.
So fun.
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I know the hamburger bar on our website is expanding. Expanding, I know. That's how you know we're becoming enterprise. We have a a mega menu, a menu of menus, if you will. What is the acquired solution that we can sell?
That's true. We gotta find that. All right. With that, listeners, acquire.fm slash slack to join the Slack and discuss this episode. Acquire dot fm slash store.
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Who got the truth?
Is it you, is it you, is it you who got the truth now?