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 Nvidia is? 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 acceleration. 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 technology rich. 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 platform company. 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 found us. 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 computing approach. 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 and networks. 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 to OpenAI. What year was that? Well, I guess it's like
five, six years ago, I guess. Five years ago. Yeah. 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 software engineers. 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 quite democratized. 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 technologies now. 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 sort cucumbers. 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. One architecture. 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 ourselves. 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 generative AI. 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. Pretty amazing. 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" doesn't hold. 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 Arizona? 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 export controls? 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 physical world. 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 phenomenal success. 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 long time. Wonderful. Well, thank you
for today's conversation. Thank you, Katie.
Soooo is he ever not in a leather jacket?
so that's them not giving anymore fucks about discrete gpus for gaming.
get used to the pricing, fellas. we are not their main source of income.
weird brand loyalty to a millionaire corporate CEO in this thread is 👀
[removed]
Does he wear the same clothes everyday?
Lisa Su and Jensen Huang are same people
Is that the 5090?