Jensen, thank you so much for being here today.
And I want to get started just with a question that is
really basic, but I think chips have been in the
ecosystem a lot more lately, and there’s people
who probably didn’t even really know what a
semiconductor was a few years ago.
Can you give me a very basic definition of what
Wow, that’s an easy question.
Well, we are a technology company that processes
software. For applications and domains of science that
are barely possible without us.
And so because of what we do, we can make what is
barely possible, possible.
Or we can make something that is very energy
consuming, very energy efficient.
Or we could turn something that costs a lot of money
and make it much more affordable. And so we
created this thing called accelerated computing, and
that was what we pioneered about three decades ago.
And it’s taken until now to really take off.
In the early days at Denny’s with Chris and Curtis, the
dream was probably simpler.
Can you explain what it what your first dream was,
what the vision was, even though now it’s come so far
to be this accelerated computing company?
Well, at the time, if you go back 30 years, at the time
the PC revolution was just starting.
The microprocessor was starting to take off.
The CPU was starting to take off.
And there was quite a bit of debate about: what is the
future of computing and how should software be run?
And there was a large camp, and rightfully so, that
believed that CPU or general purpose software was
the best way to go and it was the best way to go for a
long time. We felt, however, that there was a
class of applications that wouldn’t be possible without
Or you couldn’t make it affordable enough for
everybody to enjoy without acceleration.
And so we started this accelerator company, this
accelerated computing company, to solve those
problems. In the beginning, there weren’t that many
applications for it, frankly, and we smartly
chose one particular combination that was a home
run. It was computer graphics, and we applied it
to video games.
And that combination turned out to have been a giant
industry. And now video games is the largest
industry in the world and the largest entertainment
industry in the world.
And it drove our technology for three decades because
making video games more and more realistic, making it
available to more people, took a long time.
And we’re still in that journey and frankly,
probably early in that journey.
There are now probably, you know, over a billion gamers
in the world, but there are 8 billion people.
Someday everybody’s going to be a gamer.
And so it’s going to be the largest by far entertainment
industry. And so it turned out to have been a fantastic
technology driver for our company.
And we step-by-step added more and more things that we
could do, to today, artificial intelligence.
Beyond gaming and graphics, Nvidia has grown immensely.
I think that there’s a lot of things people might be
surprised to hear are powered by Nvidia.
Can you just give a very simple list of some of the
use cases and big name customers that people might
be surprised to hear are powered by Nvidia?
People would probably be surprised that the most
powerful and energy efficient supercomputers in
the world, that are used for molecular dynamic
simulations to climate science research to material
science research to quantum computing research, are
powered by Nvidia.
All the way to the other extreme: a whole bunch of
robots that are powered by Nvidia in manufacturing
lines. Self-driving cars that are powered by Nvidia
to the Nintendo Switch that I’m very proud of that’s
powered by Nvidia. So we’re in very powerful systems and
we’re in very energy efficient systems.
And probably one of the most talked about systems
today are the systems at the Microsoft Azure data
centers that are powering ChatGPT.
And the work that we did with OpenAI in the very
beginning to now that Powers ChatGPT.
I think those are really quite exciting.
I’m going to come back to ChatGPT for sure, but first
I wanted to ask you about betting it all.
This is something that you have not shied away from in
the 30 years since you started the company.
It was maybe seven times that you’ve been reinvented
and faced, you know, success or utter failure.
What is the lesson here?
Well, we’re in a really fast moving industry.
You know, technology is incredible in the sense that
such enormous challenges and problems could be solved
by computing, on the one hand.
On the other hand, the technology changes.
And there are so many great companies in the world and
we’re pursuing very similar aspirations.
We want to solve the world’s greatest challenges.
And so every now and then, a technology revolution
comes along. We were started in the PC
revolution. After that, the Internet revolution came and
all of a sudden the companies before it, some of
them didn’t make it to the revolution.
And some great new companies like Google and others got
invented during that time.
And then the cloud computing revolution came.
And then the mobile cloud computing revolution came.
And now we’re talking about the AI revolution.
And so each one of these transitions, it’s very
unlikely that the companies that were great before it
are still great after it.
And there are some companies that have made the
ability to, because of their adaptability and
agility, reinvented themselves along the way.
We had to reinvent ourselves in each one of
those technology revolutions.
And, you know, agility is just really, really, really
important to companies.
And one of the things that I’m really proud about our
company is, at the core of our company is incredible
technology. We have incredible technologists.
You know, if you’re pioneering one of the most
important computing platforms in the world, from
use for scientific computing to genomics to
digital biology, all the way to video games, well
you’re going to need incredible computer
scientists. So on the one hand, we’re incredibly
On the other hand, we’re in an enormous, we’re in a
giant sea of technology companies.
And so the ability for us to adapt and reinvent
ourselves and continue to be relevant and from one
generation to another generation was really
important. And I’m very proud of that.
It hasn’t always been success.
Can you talk to me about some of the biggest stumbles
that you’ve had to overcome in the years?
Well, you know, every company makes mistakes and I
make a lot of them.
And, you know, some of them puts the company in peril,
especially in the beginning, because we were
small and were up against very, very large companies
and we’re trying to invent this brand new technology.
And, you know, when you invent something new, you
have to convince customers to use it.
You have to convince the ecosystem it’s the right
thing to use. And you’ve got developers, you know.
We’re a computing company, so developers matter a lot
to us. And so we’re trying to invent something new and
we’re barely, we barely know exactly what we’re
doing, you know?
So when you’re doing something that’s never been
done before, you’re not exactly sure what you’re
doing. And yet, on the other hand, you have these
giant companies who would like you not to disrupt the
industry. And so early on, there were product mistakes
that we made.
There were, you know, execution challenges that we
had. There were some strategy mistakes that I
made. And, you know, there’s just so many of
them. And, you know, one of the skills of resilience is
the ability to forget the past.
You know, just as coaches tell you, don’t worry about
the last down, worry about the next down.
And so I tried to make sure that the company remembers
our learnings from the mistakes.
Most founders would be very satisfied being at the helm
of such a huge industry with gaming graphics.
What signaled to you, and when, that it wasn’t enough?
Well, our ambition was always to be a computing
We selected computer graphics and video games as
our first market combination: technology,
market, product technology and market combination.
But we always believed that accelerated computing
was going to be impactful for many, many different
industries. We expanded from video games into
design. And today just about every product that’s
designed or every digital asset or movie or, you know,
almost anything that’s designed in 2D or 3D
digitally uses Nvidia somehow.
And then we extended that into scientific computing,
into physical simulation.
And it started with seismic processing, as a field
called inverse physics, to particle simulations,
molecular dynamic simulations, and so on and
so forth, and fluids.
And just about every field of science we’re in today.
And so I’m really proud of that.
And that led us to a much more general purpose type of
accelerated computing that we created.
Which then, one day, artificial intelligence
You know, this is one of the things that’s really
amazing about a computing platform.
You have a vision about what you want to create.
And for whatever reason, you differentiate in your
And maybe you made it super convenient in the cloud.
Maybe you made it possible for you to keep the computer
with you all the time: mobile cloud.
And in our case, accelerated computing makes it possible
for you to solve problems that were impossible
before, or much more energy efficient than before.
And so there’s a fundamental reason that
makes a new computing architecture successful.
And at some point, the positive feedback system
starts to work. You know, you reach now a lot of
different customers and different applications.
We’re in every cloud, made by every computer company,
and then all of a sudden one day a new application
that wasn’t possible before discovers you.
First you discover them, and then pretty soon they
discover you. And this positive feedback system
starts to feed on itself.
I assume you’re talking about the moment with
AlexNet and CUDA powering that, and sort of the big
bang of AI, if you will.
I’m curious how much of that you feel like was luck?
I mean, what you’re talking about is it finding you.
It sounds a bit like luck.
And how much of it was foresight?
Well, it wasn’t foresight.
The foresight was accelerated computing.
The foresight was making this architecture exactly
the same for everybody.
Having the discipline of staying true to that
platform for generation after generation after
generation, believing that eventually our install base
would be so large that not only would we have reach,
but applications would therefore be enabled by us.
New entire applications that weren’t possible before
would discover us.
This is the nature of cloud.
This was the nature of PC.
This was the nature of mobile cloud.
And each one of these revolutions and generations
of technology. In the beginning there was some
fundamental reason it was successful, and then at some
point it achieves a bit of a escape velocity and it
becomes exponential because these applications start to
be enabled by you and they come and discover you.
And so we made a lot of great decisions.
And the great decisions associated with the
architecture and discipline of the platform and
evangelizing it to everybody.
And we reached out to research universities all
over the world. And we just believed that some day
something new would happen.
The rest of it requires some serendipity.
But the part that was really wonderful was when we
realized that AlexNet is not just some neural
network, but it’s a whole new way of doing software.
AlexNet is profound in that way.
Not only was it a giant breakthrough in computer
vision, it was also a profoundly new way of doing
software. Some people call it software 2.0, where the
machine augments the software programmers and the
data writes the software.
Instead of humans typing in a software program, the data
creates the software.
That way of using experience or data to cause
a software to be able to make future predictions was
so profound, and we had the good wisdom to go put the
whole company behind it.
We saw early on, about a decade or so ago that this
way of doing software could change everything.
All of the software that we’ve wanted to write that
we didn’t know how to write, we can now do.
And that was a great decision.
And we changed the company from the bottom all the way
to the top and sideways.
Every chip that we made was focused on artificial
intelligence. We built a wonderful research
organization dedicated to artificial intelligence.
Our entire software stack was invented for AI and and
then all the things that we did to create large systems
Which then became this thing called an AI supercomputer.
And I remember delivering my very first AI
supercomputer. I hand delivered it myself.
I delivered it to OpenAI.
The world’s very first AI supercomputer was delivered
What year was that?
Well, I guess it’s like five, six years ago, I
guess. Five years ago.
And now here we are and OpenAI has taken the world
by storm. Do you think that your products, Nvidia, is at
the very center of this and has become the must-have
products to power this next big step?
Well we’re the world’s engine for AI.
Because of the decisions we made a decade or so ago, and
we put so much of our might and expertise into it.
We’re now in every cloud.
We’re in every country and every field of science.
35,000 companies use our AI computers to develop and
advance this field.
Giant companies like cloud and internet companies, all
the way to startups.
Thousands of startups.
They’re in all kinds of areas: consumer internet to
digital biology to robotics.
I’m really happy with the diffusion of the technology.
I’m really pleased with how we’ve democratized the
technology so that anybody can access it.
You can’t ignore the incredible vision and
dedication to the work at OpenAI.
From the very first day I saw them, they were
dedicated to wanting to do this and they’ve been
focused on it for five years.
And of course in research, even longer than that.
I’m incredibly proud of the work that they’ve done.
Yeah really terrific team.
Here in Silicon Valley, there’s a bunch of CEOs and
founders who’ve started bringing up the A100 and
kind of publicly competing with each other about who
bought more when and who saw this coming.
Sort of competing for bragging rights around the
A100. What would you want to say to them?
There’s more. Come get them.
Everybody should win.
You know, winners to all.
In the past, when you start a company, a software
company or technology company, you need a lot of
It is still true and you need amazing computer
scientists. But today, startups - and there are
some amazing startups that we’re working with right now
- where they’re 25, 30 people.
Backed up with a large data center of AI supercomputers
powered by A100s.
If you want to start a startup today, it’s you and
AI. And you’re supercharged by the AI supercomputer and
the algorithms that you have inside and all the data
that you’re going to teach it with.
And so it’s really quite a transformation in how
startups are going to get built in the future.
Now we’re onto something even larger than that, you
know, built on these AI supercomputers, these large
language models. It’s definitely a watershed event
for the AI industry.
It feels very much like the iPhone moment, when mobile
cloud really took off and all of the environmental
conditions feel exactly the same way, just larger and
much, much more industries.
Right now, generative AI is still extremely expensive to
accomplish. How do you think it’ll really take off
if only a couple big companies have true access
to do it at scale?
Well, it turns out it doesn’t cost that much.
And the reason why there are so many CEOs with
bragging rights on so many A100s is because it’s really
We took what otherwise would be a $1 billion data
center running CPUs and we shrunk it down into a data
center of $100 million.
Now $100 million is, when you put that in the cloud
and shared by 100 companies, is almost
nothing. If you take a look at how much it costs to
design a chip, so you put that in perspective, it
costs us about $2 to $3 billion to design A100.
When I hit enter and asked TSMC to help us make it,
that email is $100 million.
And then it populates these AI supercomputer data
centers. And when you train a large language model,
let’s say it costs $10 million.
So a chip, and there are 3,000 chip companies in the
world, taping out a chip is like $100 million or $50
million, $30 million, depending on the size, but
nothing less than $10 million.
And now you could build something like a large
language model, like a ChatGPT for something like
$10, $20 million.
That’s really, really affordable.
And so I think the the ability for every industry
to create their foundation model: there’s going to be a
protein foundation model, a chemical foundation model.
There will be a robotics foundation model.
There’ll be foundation models for science, for
finance, for all kinds of different applications and
different industries and different countries.
I was just in Sweden and the Berzelius supercomputer
there, we helped them with. We built an AI
supercomputer. It’s a Swedish foundation model
supercomputer. And with just tens of millions of
dollars, you can build the most powerful supercomputer
in Sweden. And so these are really, really accessible
There are always skeptics and people who are alarmed,
perhaps, by how fast AI is taking off and how powerful
it’s become with capabilities like deepfakes,
fake eye contact, for instance, that I’ve seen an
example of. What do you say to them?
Well, the first thing that everybody should do is to
take advantage of the technology and to boost
their own capability.
There’s no question that the interest behind ChatGPT
has been so great.
It is the fastest growing application in the world,
and it’s been used in all kinds of different ways.
The thing that’s really amazing about artificial
intelligence is that what ChatGPT has shown is that it
has eliminated the digital and the technology divide.
Everyone is a programmer now.
Everybody could program a computer.
During my generation, the way that you program a
computer was: started with Basic and I learned Fortran.
Then you learn C and then you move to C++ and Java and
now PyTorch or Python.
And each one of those languages, there was Ooc,
and these are really weird languages and they’re hard
to learn. And the whole time that we’ve been making
computers more and more capable, the technology
became harder and harder to use.
And the technology divide arguably has been growing,
until artificial intelligence. And you hear
about cucumber farmers who are teaching a robot how to
And a high school student did that for his mom.
And now 150 million people are programing the
computer, instead of programing the computer with
C or Python, you’re now programing the computer with
anybody’s plain language.
And you tell this computer what you want to do.
And this computer goes off and does it.
Or you tell the computer you’d like to write a Python
script, and it goes off and does it.
And so this capability has democratized computing for
the very first time.
It’s put technology, very powerful technology, in the
hands of anybody who would like to use it.
And so I think this is really genuinely the first
time in my generation that we’ve created something, or
contributed to creating something, that made our
technology accessible to everyone.
Not just to use, but to harness.
Not just to use, but to program.
And so I think every domain expert in the world will be
able to do that. And I recommend everybody just,
number one, take advantage of AI and augment your work
. Make yourself more productive.
Lift yourself, you know, power up.
Power up your own career, power up your own
capability. And then from there, you know, increase
the productivity of society and move everything along.
How do you stay ahead in an industry where some of your
customers could become your competitors?
You know, speaking about Google’s TPUs and Amazon has
their own internal chips as well.
How do you stay ahead in that landscape?
We stay ahead by, number one, doing it very well.
But also we do it very differently.
The first thing that I would say is that every data
center in the world should accelerate every workload
they can. And the reason for that is because, as you
know, the world’s data centers consume a lot of
power now. And it used to be the case that because of
Moore’s Law, even though we required more computing
throughput every year, the amount of power that the
world’s data centers consume didn’t grow that
fast. And the reason for that is because Moore’s Law.
But now that’s changed.
That has ended.
And as a result, if we want to increase the amount of
computing throughput we want, and there’s no
question that’s happening, then the amount of power
that the world needs in the data center will grow.
And you can see in the recent trends, it’s growing
very quickly and that’s a real issue for the world.
The first thing that we should do is: every data
center in the world, however you decide to do it,
for the goodness of sustainable computing,
accelerate everything you can.
Now, an ASIC is designed to be application specific.
It does nothing, it does exactly that and it does it
very well. What Nvidia does is a general purpose
accelerated computing platform.
So we could, on the one hand, simulate climate
science. On the other hand do robotics.
On the other hand, do large language models or computer
graphics and play video games and such.
And so our ability to be flexible, versatile and also
extremely performant lets us increase the versatility
and the utility, the utilization of it, inside
data center. When you build an infrastructure, the most
important thing for you is utilization.
You can’t afford to have hotels that are occupied
30%. You would like the data center even more so
because it cost billions of dollars.
Nvidia’s accelerated computing platform lets you
have versatility and utilization.
So our TCO, our cost, is actually the lowest of all.
And that’s the reason why people use it: because they
can use it on so many things. The second reason is
we’re in every cloud.
And so if you’re an enterprise customer or a
developer or a startup company and you would like
to have the ability to operate your service in
every cloud or any cloud across the world, we make it
possible for you to do it in every cloud: on prem,
hybrid cloud, all the way out to the edge.
What do you say to gamers who wish you had kept focus
entirely on the core business of gaming?
Well, if not for all of our work in physics simulation,
if not for all of our research in artificial
intelligence, what we did recently with GeForce RTX
would not have been possible. We invented the
GPU and programable shader 25 years ago, a quarter of a
century ago, and it’s remained basically the same
for the last 25 years.
About five years ago, we came to the conclusion that
in order for us to take computer graphics and video
games to the next level, we had to reinvent and disrupt
Change literally what we invented altogether.
And so we invented this new way of doing computer
graphics: ray tracing, and basically simulating the
pathways of light and simulate everything with
And so we compute one pixel and we imagine with AI
the other seven.
It’s really quite amazing.
Imagine a jigsaw puzzle and we gave you one out of eight
pieces and somehow the AI filled in the rest.
And so as a result, we increased the performance of
what made possible ray tracing.
We increased the performance by probably a
factor of five.
Or another way to think about that: we reduced the
amount of energy consumed by a factor of five.
And so that great invention completely revolutionized
video games. And the next 25 years, because of what we
did, I think we have 25 years of amazing future.
Just a couple of questions about the state of the
industry. Experts seem to say the worst of the chip
shortage is over.
How did Nvidia weather that storm?
The chip shortage was a strange one.
On the one hand, there was chip shortage.
On the other hand, about the same time, you know,
this is now, we’re now coming out of it.
But some two or three quarters ago, we had supply
challenges and demand challenges at the same time
. But not at the same customer.
Not in the same industry.
Not in the same market.
And so that was very, very challenging: to have your
foot on the gas and your foot on the brakes at
exactly the same time and full pressure on both.
Our company weathered it just fine.
We’re a strong and resilient company.
Our financial performance wasn’t as good as our
technology and contribution performance.
We did some of our best work ever in the history of
our company. A100 was replaced by H100, which
we’re in full production now.
All the work that we did with AI supercomputing and
RTX ray tracing and all of that came out during this
time. Meanwhile, our financial performance wasn’t
very good. And so I think the lesson there is: focus
on doing your good work and things will work out for
itself. And so I’m really, really pleased with the
company and the work that everybody’s done.
And going forward, I think it’s starting to ease up
now. I think we’re starting to have a lot less inventory
in channels. And the industry has more capacity
and more flexibility and we’re moving nicely into the
next generation nodes.
And so almost everything is starting to to get better.
What about a price slump?
Does that worry you?
Everything that we build is rather singular.
And the markets that we serve aren’t commodity
markets. You know, right now, more than any time, the
investment needed in AI is just off the charts.
Generative AI, this is the moment that we’ve all been
working for in the last ten years.
And now AI is about to be used to revolutionize
digital biology and genomics and transportation
and retail and all these different industries.
search. And everything about the situation we’re in right
now is really about growth and really about getting
into the next phase of computing.
And AI is at the center of that.
So I’m super excited about the moment we’re in.
I want to make sure that we take advantage of it and
capitalize on it.
The vast majority of your chips are made by TSMC.
How have you insulated against geopolitical risks
of the region in the case that the “Silicon shield”
As a company, our first priority is to make sure
that we’re as resilient as possible.
And in every area that we can, to be as resilient
through diversity and redundancy as much as we can
. In semiconductor design tools, the manufacturing of
our chips, packaging, memory, systems.
The systems that we build, AI supercomputers, these
things are like cars.
They weigh 350 pounds per computer.
They’re the heaviest computers that humans make.
And it’s complicated.
It’s got tens of thousands of unique parts.
And so we try to engineer and design, into everything
that we do, diversity and redundancy.
The fact of the matter is TSMC is a really important
company. This is a really special company.
And the world doesn’t have more than one of them.
It is imperative upon ourselves and them, also
invest in diversity and redundancy.
And the move that they made recently in building the fab
in Arizona is a very big deal.
Will you be moving any of your manufacturing to
Oh, absolutely. We’ll use Arizona.
Yeah. Yeah, absolutely.
The thing that’s really great about TSMC is every
mask runs everywhere and so they have the ability to use
all the various fabs for the masks that we have.
And so I’m excited about the investments that they’re
making so that the entire world can count on them for
diversity and redundancy.
Yeah, it’s a really special company.
About a quarter of your revenue comes from mainland
China. How do you calm investor fears over the new
Well, Nvidia’s technology is export controlled.
It’s a reflection of the importance of the technology
that we make. The first thing that we have to do is
comply with the regulations. It was a
turbulent, you know, month or so as the company went
upside down to reengineer all of our products so that
it’s compliant with the regulation and yet still be
able to serve the commercial customers that we
have in China. We’re able to serve our customers in
China with the regulated parts and delightfully
support them. And so I think we’re going to be just
fine in the ability to serve the customers there.
The customers that we have there are consumer companies
and consumer internet companies. And the
regulation is going to be just fine.
We’re going to be able to work through it.
You are famous for reinvention.
What’s the next one going to be?
The next big reinvention is probably where AI meets the
And today, all of our AI experiences are related to
digital. It’s in software.
It’s, you know, it’s information.
It’s all digital-related Al.
The next generation of AI, and where AI meets $100
trillion of the world’s industry, that’s in the
physical world. And so it could be transportation.
It could be robotic surgery .
It could be warehouses and manufacturing plant and
energy plants and fabrication plants and so on
and so forth. And in order for us to bring digital
technology and artificial intelligence technology into
that physical world where humans are, and safety is
important and resilience is important, and all of those
kind of physical world physics-related challenges,
we need a new type of software.
And we created this thing called Omniverse that
allows us to connect the digital world and the
physical world. And Omniverse is going to be a
And we have 700-plus customers who are trying it
now. And from car industry to logistics warehouse to,
you know, wind turbine plants.
And so I’m really excited about the progress there.
And it represents probably the single greatest
container of all of Nvidia’s technology:
computer graphics, artificial intelligence,
robotics and physics simulation all into one.
I have great hopes for it.
This is the last one. Just on a personal note, you are
the longest running tech CEO.
Is there any end in sight?
Well, as you can tell, I’m sprightly and quite
enthusiastic and energetic yet.
I’m surrounded by amazing people.
They keep me inspired and I feel that we could do great
things together. They give me so much confidence in
what we can do and the impact we can make.
And I feel that I’m making a real contribution to the
company and to them, and to create an environment where
we can make really amazing contributions.
And so I think for so long as I believe I could do
that, and I don’t know exactly how long that’s
going to be. But three or four decades, I would say.
In another four decades, I’ll be robotic and, maybe
another three or four decades after that.
And so hopefully, I’ll get to enjoy this for a very
Wonderful. Well, thank you for today’s conversation.
Thank you, Katie.