I am the William v McLain professor of
business in the decision and operations division here at the
Columbia Business School. I want to thank you all for joining
us this evening for tonight's program, which features Jensen Wong, co-founder
and c e O of Nvidia Corporation, as well as our own ris, the Dean
of Columbia Business School, and David and Lynn Sip,
the professor of business. Our two speakers tonight
have much in common. In fact, they both graduated from Stanford in
electrical engineering nearly the same time, maybe even not possibly
overlapping. Jensen is the co-founder, president officer of
Nvidia. He is a businessman, entrepreneur and electrical engineer. And over the last 30 years
through his work at nvidia, he has revolutionized first the graphics
processing unit industry and now more recently, the artificial
intelligence industry. He's been named the world's best, c e o by Harvard Business Review and Brand
Finance as well as Fortune Magazine's business person of the year,
and one of time magazine's, 100 most influential people. Our fireside chat today has been made
possible through both the David and Lynn Discipline Leadership series, as well as
the digital finance, sorry, excuse me, the Digital Future
Initiative. And additionally, I serve on the leadership of the
Digital Future Initiative, the D F I. Here at C V s, the Digital Future Initiative
is C V S'S new think tank, focusing on preparing students to
lead for the next century of digital transformation, as well as helping
organizations governances and
communities better understand leverage and prosper from
future ways digital structure. Now I would like to hand it over to. Thank you. Very much. Thank you all for coming. So. This an exciting topic and a
topic that is near and dear, certainly to my heart. And it's a topic where the school, everything that we do at the school is
changing so fast, trying to keep up, trying to change curricula, trying to create opportunities
for our students to actually learn about technologies and how they're
changing the world and be honest, prepare for the future and there
is no better person to be having to talk about AI than Jensen Pond. Jensen, thank you so much for making the
time and coming here. Welcome. Sun. Sun. Yes. I just love hearing talk here. I think the expectation's
going to be pretty high, but say something smart. Well, good luck with you. So I want to start. By having you walk us through a
little bit the history of Nvidia and then I talk a little bit about that
leadership thing you just mentioned, but you launched that
company 30 years ago and you have led it through a transformation, different applications,
different type of products. Walk us through a little bit that journey. Yeah, one of the most proud moments,
I'll start with the proud moment, what happened recently, the c e O of Denny's where
with my first company, and they learned that Vidia,
not only was I dishwasher, bus boy and worked my way up
corporate ladder and became waiter at Denny's, and they were my first company that I still know how to take.
I still done the menu well, super, by the way, anybody
know what a superbird is? What kind of College Street were you? Denny's is America's Diner Go. And that Nvidia was founded by outside our home in San Jose there. And so they contacted me recently
and the booth that I sat at is now in Nvidia Booth and my name is Nvidia. This is where a trillion
dollar company was founded. And so Nvidia was founded during a time when the EC revolution and the microprocessor was capturing just above the entire industry and the world properly solved
that the CPU of micro processor revolution. And it
really reshaped how the IT companies that were successful before
the micro processor revolution, revolution and companies successful. We started with our company during
that time and our perspective was that general purpose computing, as incredible as it's can't
sensibly be the solution for, we wanted to believe that
a way of doing computing, we call accelerated computing, where you would add a specialist
next to the generalist. The CPU is a generalist. If
you well could do anything, it could do everything however you can. Obviously if you can do
everything and anything, then obviously you can't
do anything very well. And so there are some problems
we felt that were not solvable, not good solutions or not the
problems to be solved by what we call. And so we started this accelerated
computing company. The problem is if you want to create a
computing platform company, you want to create a computing
platform one hasn't created since 1960, a year after I was the b m system 360 beautifully described what
the computer is in 1964, I B M described that the System
360 had a central processing unit, IO subsystem, direct memory
access, virtual memory, binary compatibility across
a scalable architecture. It described everything that we described
computers to this day 60 years later. And we felt that there was a new form
of computing that could solve some problems At the time it wasn't completely
pure what problems we could solve, but we felt that we felt that
accelerated comput. So nonetheless, we went out to start this
company and we made a great first decision that frankly is un to this day, if somebody were to come
up to you and said, one, we are going to invent a new technology
that the world doesn't have. Everybody wants to go build a
computer company around cpu. We want to build the computer company
around something else connected to c p number one and the killer app. The killer app is a video
three D video game in 1993 and that application doesn't exist.
And the companies who we built, this company doesn't exist
and the technology that
we're trying to build doesn't exist. And so now you have a company that has a technology challenge and
a market challenge and an ecosystem challenge. And so the odds of that company
succeeding is approximately 0%. But nonetheless, we were fortunate this because
two very important people frankly, that I had worked with and Kristen
Curtis, the three of us I've worked with were incredibly important people in the
technology industry at the time called up the most important
ship capital in the world, Valenti at the time and
told Don Don and his name was Gu, wanted to industry don give this kid money and
then figure out along the way what it's going to work. And fortunately they did. But that business plan, I wouldn't fund myself
today and it just has too many dependencies and each one of them
has some profitability of success. And when you compound all of these
together, we multiply all these together. And so nonetheless, we imagined that there would be this
market called video games and this market would be the largest entertainment
industry in the world at the time it was zero and three D graphics we
oscillated with would be used for telling the stories
of almost a sport any game. And so in virtual world, you
could have any game, any sport, and as a result everybody would be a
gamer. And so Don Valentine asked me, so how big is this market
going to be? And I said, well, every human will be a gamer someday.
Every human would be a gamer. Someday. Also the wrong answer, quite
frankly for starting a company. So these are horrible habits,
these are horrible skills. I'm not advocating them
for you, but nonetheless, it turned out to have been true video
games turned out to be the largest entertainment industry
in three D graphics. And we've found first
Pillar app for accelerating, which brought us the time
to use accelerated Comput
to solve a whole bunch of other problems, which eventually led to. This is fantastic. Sorry.
So before we go to ai, I would like to ask a little
bit about the crypto period. So gaming was a huge obviously journey for Nvidia. And then at some point in time the
killer app became crypto and mining. What was that chapter? It's already computing can solve
problems that normal computers can, and all of our GPUs, even though
you use it for designing cars, designing buildings, designing,
use it for molecular dynamics, use it for playing video games, it has this programming model
called Kuda that we invented. And Kuda is the only computing
model sits that exists today that is as popular as an exit.
It's used by the vault Boeing. And so anyways, one of the things
that Kuda can do is process parallel processing incredibly fast. And obviously one of the algorithms
that we would do very nicely on is cryptography. And so when
Bitcoin first came out, there were no Bitcoin asics. And the obvious thing is to go find
the fastest supercomputer in the world. And the fastest supercomputer that also
has the highest volume is Nvidia you use, it's available in hundreds of
millions of gamers' homes. And so by downloading an application, you could do some mining
at your house. Well, the fact that you could buy one
of our GPUs, one of our computers, and you plug it into the wall
and money starts squirting out, that was a day that my mom figured
out what I did for a living. And so she called me one
day and she said, son, I thought you were doing
something about video games. And I finally figured out what
you do. You buy NVIDIA's products, you plug it in and money courts out.
And I said, that's exactly what I do. And that's the reason why that's
the, so many people bought it, Bitcoin works led to Ethereum. But the idea that you
would use a supercomputer, use a super processing
system like via GPUs to either encode or compress or
do something to refine data and transfer it, transform
it into a valuable token. You guys know what that sounds
like to generate valuable tokens, Chad should bet. And so today, really
one of the things that's happening, if you extend the sensibility
about Ethereum and crypto mining, it's kind of sensible in the sense that
all of a sudden we created this new type of industry where raw data comes in, you apply energy to this computer and
literally money comes sporting out. And the currency is of course tokens. And that token is int intelligence tokens. This is one of the major
industries of the future. Now I'll describe something else and
it makes perfect sense to us today, but back then it looks strange. You take
water and you move it into a building, you apply fire to it. And what comes out is something
incredibly valuable and invisible called electricity. And so today we're going to move data
into a data center that's going to refine it and it's going to work on it and it's
going to harness the capability of it and produce a whole bunch of digital
tokens that are going to be valuable digital biology, it'll be valuable
in physics, it'll be valuable in it. All kinds of computing areas and
social media and all kinds of things. Computer games and all kinds of
things. And it comes out in tokens. So the future is going to be about
AI factories and then video gear will be powering these AI factories. So we have jumped into the
neural networks and I want to, and we talked about power
computing, how we render graphics, let's say on a monitor, how we play games, how we solve cryptographic
problems for Bitcoin. Talk to us a little bit about how
the G P U is useful in training your own networks. But then what I wanted us
to do for this audience, tell us a little bit about what it
takes to train a model like J G P T, what it takes in terms of hardware,
what it takes in terms of data, what in terms of the size of
the cluster that you're using, the amount of money that you need to
spend. Because these are huge problems. And I think giving us a
glimpse of the scale would be fun. Well, everybody wants you to think that it's
a huge problem. It's super expensive. It's not. It's not. And
let me tell you why. It costs our company about five, 6 billion of engineering
costs to design a trip. And then at 1.2 years, three years later, I had enter and I sent
an email to T SS M C and I F T P, basically a large
positive TS M C. And they fab it. And that process costs our company
something along the lines of the half a billion dollars. So five and a
half billion dollars, I get a chip. And that chip of course is valuable
to us, but it's no big deal. I do it all the time. And so if
somebody were to say, Hey Jensen, you need to build a billion dollar
data center and once you plug it in, money will start squirting out the
other side. I'll do it in a heartbeat. And apparently a lot of people do. And the reason for that is because who
doesn't want to build a factory for generating intelligence now? So a
billion dollars is not that much money, frankly, the world spends about 250 billion
a year in infrastructure computing infrastructure and none of it's generating
money. It's just storing our files, passing our email around. And
that's already 250 billion. And so one of the reasons why our growth
we're growing so fast is because after 60 years, general purpose computing is on
decline because it is not sensible to invest another 250 billion to build
another general purpose computing data center. It's too through force in energy, it's too slow in computation. And so
now accelerated computing is here, that 250 billion goes to build
accelerated computing data centers. And we're very, very happy to support
customers to do that. And in addition to that, accelerated computing, you now have an infrastructure can to
AI for all of the things that we're just talking about. Basically the way it works is you take
a whole lot of data and you compress it, you compress it. Deep learning is like a compression
algorithm and you're trying to figure out, you're trying to learn the mathematical
representation of mathematical representation, the patterns
and relationships of the
data that you're studying, and you compress it into a neural network. So what goes in is say trillions of bytes, trillions of pokes.
So let's say a few trillion, trillion bytes, what comes out of it
is a hundred gigabytes. And so you've taken all of that data and
you've compressed it into this little tiny funnel. A hundred
gigabytes is like two DVDs. Two DVDs you could download on your
phone and you can watch it. So you could download this giant neural
network on your phone. And now that all of this data
has been compressed into it, the data that's compressed, your
network model is a semantic model, meaning you can interact with it, you could ask questions and it would go
back into its memory and understand what you meant and generate text,
read, have a conversation. So at the core is kind of
like that. It sounds magical, but for all the computer scientists
in a room, it's very sensible. And don't let anybody convince
you it costs a lot of money. I'll give you a good break.
Everybody go Bill aids. Go bill, as. The scale. If I press you
a little bit on that scale, do you need a computer that is
essentially a data center to estimate these models? 16,000 GPUs is what it
took to build a g, PT four, which is the largest one that
anybody's using today. It's a billion, and that's a check. It's
not even a very big check. Don't be afraid. Don't let anybody
talk you how to building a company, build your. Dreams. Let me ask you a question about
the billion dollar check and the growth that you've been experiencing. I think you were named
the best C e O by H B R. That's entertainment. That's. Entertainment. I'll keep repeating it and then eventually
I appreciate that and then eventually we'll end with that
line. But in some sense, you are leading a company right now
through a period of extreme growth, hypergrowth, something that most companies have
not experienced in their life. And I want to perhaps. Tell us. A little bit about what
does it look like? I mean, doubling in size in under a year or managing supply chains,
managing customers, managing
growth, managing money. How do you actually add to that? I love the management money part
of it. Just counting is fun. You just wake up in the morning
and just roll around all the cash. Isn't that what you guys are all
here to do? My understanding is. That's the end goal. That's the end goal, yeah. Let's see. Building companies hard, there's nothing easy about it.
There's a lot of pain and sufferings, a lot of hard work. If it was
easy, everybody would do it. And the truth about all
companies, big or small, ours or others in technology,
you're always dying. And the reason for that is because
somebody's always trying to leapfrog you. So you're always on the wave of business. And if you don't internalize
that sensibility, don't internalize that belief.
You will go out of business. So I started at Denny's, as you guys know, and Nvidia was built out
of very unlikely odds. And it took us a long time to be here.
I mean, we're a 30 year old company, and when Nvidia first found it, the PC Windows nine
five had come out 1993, and that was the first usable
pc. We didn't have email. And so there were no such
laptops or smartphones, none of that stuff existed. And so you could just imagine the world
that we were started in and the world, we didn't have cd, everything was
CRTs. And so the world was very, very different. Cd ROS didn't exist.
We just to put it in perspective, all this stuff, that was the era we were founded in
and it took this long for our company to be recognized as heavy reinvented
for the first time in 60 years growing fast. Growing
fast is all about people. Obviously companies is all
about people. Whether you, if the right systems in
place, you get right, your surrounded by
amazing people like I am and the company has craft skills. It doesn't really matter whether you
ship a hundred billion dollars or 200 billion. Now the truth is that the
supply chain is not easy. People think, does anybody know what a GForce
graphics card looks like? And just show me as a hand, anybody knows
what Nvidia graphics card looks like. And so you have a feeling that the
graphics card is like a cartridge that you put into a pc, PC express slide pc. But our graphics chips these days, what is used in these deep
lining systems is 35,000 parts. It weighs 70 pounds. It takes robots to build 'em
because they're so heavy. It takes a supercomputer to test it
because it's a supercomputer itself and it costs $200,000. And for $200,000, you buy one of these computers, you replace several hundred
general purpose processors
that cost several million dollars. And so for
every $200,000 you save, you say for every $200,000
you spent with Nvidia, you save two and a half
million dollars in computing. And that's the reason why I tell you,
the more you buy, the more you save and early, it's working out really
well. People are really lining up. So that's it. That's
what we do for a living. And the supply chain is complicated. We build the most complicated
computers the world's ever seen, but hard can it be really? And it's really hard. It's really hard. But at the core of it, if
you're surrounded by amazing, the simple truth is that
it's all about people. And I'm lucky to be surrounded
by a great management team. You have. And then the CEO E O says things
like, make a So number one, something. Like that. Yeah, make it work. Make it work, make it. So. I want to go back to AI trends
and what you think about the future, but you mentioned
the word platform earlier on. You mentioned your software environment. So you have the hardware infrastructure, you have a software environment that is
actually pervasive in training neural networks. Right now you're building in data centers or
you're creating environments within data centers that are sort of
clusters of Nvidia hardware, software and public communication
between these resources, how important it is to be sort of
a whole platform solution versus a hardware play. And how core is that into Nvidia Drive? Unlike first of all, before
you could build something, you have to know what you're building and what is the reason the first
principles for its existence. Accelerated computing is not a chip,
that's why it's not called an accelerator. Accelerated computing is
about understanding how can
you accelerate everything in life? If you can
accelerate everything in life, if you can accelerate every application,
that's called really fast computing. And so accelerated computing is first
understanding what are the domains, what are the applications
that matter to you? And to understand the algorithms and the
computing systems and the architecture necessary to accelerate that application. So it turns out that general
purpose computing is a sensible idea. Accelerating an
application is a sensible idea. So we'll give you an example.
There's, you have DVD decoders, you play DVDs or H two sixty
four decoders on your phone. It does one job and one job only, and it does it incredibly well.
Nobody knows how to do it better. Accelerated computing is
kind of this weird middle. There are many applications that
you can accelerate. So for example, we can accelerate all kinds of image
processing stuff, particle physics stuff. We can accelerate all kinds of things.
That includes literary algebra. We can accelerate, we can accelerate many, many domains of applications.
That's a hard problem. Accelerating one thing is easy. Generally running
everything under A is easy. Accelerating enough domains such
that if you accelerate too many domains, so those of you
accelerate every domain, then you're back to a
general purpose processor. What makes them so dumb that they
can't build just a faster chip? And so on the one hand, on the other hand, if you only accelerate one application, then the market size is not
big enough to fund your r d. And so we had to find that
slippery middle. And that is the strategic journey of our company.
This is where strategy meets reality. And that's the part that Nvidia got right, that no other company in the history
of computing ever got, right? To find a way to have a sufficiently
large domain of applications that we can accelerate that is still a hundred
times, 500 times faster than the C P U and such that the economics, the flywheel, the flywheel of number of domains
expanding the number of customers, expanding the number of
markets, expanding the sales, which creates larger r d, which allows
us to create even more amazing things, which allows us to stay well ahead
of the c p. Does that make sense? That flywheel is insanely hard
to create. Nobody's ever done it. It's only been done just one time. And so that is the capability.
And in order to do that, you have to understand the algorithms, you have to understand a lot about the
domains of applications. You have to select it, right? You have to create
the right architecture for it. And then the last thing that we did right, was that we realized that in order
for you to have a computing platform, the applications you develop for
Nvidia should run on all of video. You should have to think,
does it run on this chip? Is it going to run on that chip?
It should run on every chip. It should run on every
computer with Nvidia in it. That's the reason why every single G p
that's ever been created in our company, even though we had no customers
from Kudo a long time ago, we stayed committed to it. We were determined to create
this computing platform
since the very beginning. Customers were not. And that
was the pain and suffering. It cost the company decades and
billions of dollars getting here. And if not for all the video gamers in
the room here, we would be here. You were our day jobs. And then at night we can
go solve digital biology. Those help people with quantum chemistry. They'll help people with artificial
intelligence and robotics and such. And so we realized, number one, that we were accelerating
computing a software problem. The second thing is AI is a data center,
data center infrastructure problem. And it's a very obvious, because you
can't train an AI model on a laptop, you can't train it on a cell phone.
It's not big enough of a computer. The amount of data is measured
in trillions of bytes, and you have to process that
trillions bytes billions of times. And so obviously that's going to be a
large scale computer distributing the problem across millions of GPUs. The reason why I say millions is
16,000 inside the 16,000 or thousands. And so we're distributing the workload
across millions of processors. There are no applications in the world
today that can be distributed across millions of processors.
Excel works on one processor. And so that computer science
problem was a giant breakthrough, utterly giant breakthrough. And this reason why it enabled generative
AI enabled large language models. So we observed two things.
One, accelerated computing
is a software problem, algorithm problem, and AI
is a data center problem. And so we're the only company that
went out and built all of that stuff. And the last part that we did
was a business model choice. We could have been a data center company
ourselves and be completely vertically integrated. However, we would
recognize that no computer company, no matter how successful will be the only
computer company in the world and it's better to be a platform computing
company because we love developers. It's better to be a platform computing company
that serves every computing company in the world than to be a computing
company all by ourselves. And so we took this data center,
which is the size of this room, whole bunch of wires and a whole bunch
of switches and networking and a bunch of software. We disaggregated all of that and we
integrated into everybody else's data centers that are all completely different. So a w Ss and G C P and Azure
and Meta and so on and so forth, data centers all over the world,
that's an insane complexity problem. And we figured out a way to have enough
standardization where it was necessary enough flexibility so that we could
accommodate enough collaboration with all the world's computer
companies. As a result, N v's architecture has
now graft, if you will, into every single computer
company in the world. And that has created a
large footprint, larger, larger install base, more
developers, better applications, which makes customer happier
customers provide them more chips, which increases the install base,
which increases our r d budget, so on and so forth. The flywheel,
the positive feedback system. And so that's how it works. Nice and
easy. So one thing you haven't done. And I wanted you explain to us
why if you haven't invested in fabricating your own chips and why. Is that? That's an excellent question. The reason for that is as a matter of strategic choice,
the core values of our company, my own core values, the core values
of our company is about choosing. The most important thing in life
is choosing. How do you choose? How do you choose? Well, everything.
How do you choose what to do tonight? How do you choose? Well, our company decides to choose
projects for one fundamental goal. My goal is to create the
environment and environment by which amazing people in the
world will come and work. Amazing environment for the best people
in the world who want to pursue this field of computing and computer science
and artificial intelligence to create the conditions by which they will
come and do their lives work. Well, if I say that then now the question
is how do you achieve that? So lemme give you an example
of how not to achieve that. Nobody that I know wakes up in the
morning and say, you know what? My neighbor is doing that, and
you know what I want to do? I want to take it from
them. I can do it too. I want to take it from them.
I want to capture their share. I want to pumble them on
price. I want to kick 'em in. I want to take their share. It turns out, no great people do that. Everybody wakes up in
the morning and says, I want to do something that
has never been done before. That's incredibly hard to do that if
successful makes it great impact in the world. And that's what greatest core
values are. One, how do we choose, do something that the
world's never done before? Let's hope that's insanely hard to do. The reason why you choose something
insanely hard to do by the way, so that you have lots
of time to go learn it. If something is insanely easy
to do, like tic-tac dough, I wouldn't buss over it. And the reason for that obviously is
highly competitive. And so you got to choose something that's incredibly hard
to do and that thing that's hard to do discourages a whole bunch of all by
itself because the person who's willing to suffer the longest wins. And so we choose
things that are incredibly hard to do, and you've heard me say, pain is suffering a lot and it's
actually a positive attribute. People who can suffer are ultimately
the ones that are the most successful, number one. Number two, you should choose something that's
somehow you're destined to do. Either a set of qualities
about your personality or
your expertise or the people you're surrounded by, your
scale, whatever your perspective, whatever you're somehow destined
to do. The number three, you better love working on that
thing so much because unless so, the pain and suffering is too
great. Now, I just described to you, I just described to you Invidia's
core values. It's that simple as that. And if that's the case, what am I
doing? Making a cell phone check. How many companies in the world
can make a cell phone a lie? Why am I making a C P U? How
many more CPUs do we need? Does that make sense? We
don't need all those things. And so we naturally selected
ourselves out of commodity markets. We naturally selected ourselves
out of commodity markets. And because we selected amazing
markets, amazingly hard to do things, amazing people joined us. And because amazing people joined us
and because we had the patience and let them go succeed to go
and do something amazing, have the patience to let 'em do something
amazing, they do something amazing. The equation is that simple. The
equation is literally that simple. It turns out it's simple to say, it
takes incredible character to do. Does that make sense? That's why it's
the most important thing to learn. It turns out great success and
greatness is all about character. And no fabrication. The reason why we don't do fabrication
is because T SS m C does it so well, and they're already doing it. For
what reason do I go take their work? I like the people at t c,
they're great friends of mine. Cc's a great friend of mine, Mark's a great friend of mine
just because I've got business, I can drive into it. So what,
they're doing a great job for me. Let's not squander my time to go
repeat what they've already done. Let's go squander my time on
something that nobody has done. Does that make sense? Nobody has done,
that's how you build something special. Otherwise you're only
talking about market share. Thinking about the
future, what do you think when we're thinking about these decade. Are these right answers? By the way, I don't have an M B A and I
didn't get a finance degree. I read some books and I watched a
lot of YouTubes. I got to tell you, nobody watches more
business YouTubes than I do. And so you guys have nothing on me. Are these right answers professor version? But yes, they're the right answers.
And best, c e o. Yeah, right? And what. Do you think about ai? What are you thinking about AI
applications and where we're going to see change in our lives, let's
say over the next 3, 5, 7 years? Where do you see that going
and in places where we will all potentially be affected
in our daily experience? Yeah, first of all, I'm
going to go to the punchline. AI is not going to take your jobs. The person who used AI is going to take
your job. You guys agree with that? Okay? So use AI as fast as you can so then
you can stay gainfully employed. Let me ask you a second thing.
When productivity increases, when productivity increases,
meaning we embed AI all over Nvidia, Nvidia is going to become one giant ai.
We already use AI to design our chips. We can't design our chips, we can't write
our optimizing compilers without ai. So we use AI all over the place. When AI increases the productivity
of your company, what happens next? Layoffs Or you hire more people, you hire more people. And the reason for that is give me an
example of one company that had earnings growth because of productivity
gains that said, guess what? My gross margins just
went up time for a layoff. So why is it that people
think about losing jobs? If you think you have no new ideas,
then that's the logical thing. Does that make sense? If you don't have any more ideas to
invest your incremental earnings, then what are you going to do? When
the work is replaced? It's automated. You lay people off. And so join companies where they
have more ideas than they can afford to fund so that when
AI automates their work, it's going to shift. Of course, it's
going to change the style of working, AI's going to come after CEOs
right away. Deans and CEOs we're so toast. I think CEOs first need
second, but you're close. So you join companies
where they have more ideas, more ideas than they have money
to invest. And so naturally, when earnings improve, you're
going to hire more people. Ai. So first of all, this is
the giant breakthrough. Somehow we've taught
computers how to learn to represent information
in numerical ways. Okay, so you guys, has anybody heard of
this thing called word to back? It's one of the best things
I've ever word to back a word. You take words and you learn from
the words studying every single word. It's relationship to every
other word. And you learn, read a whole lot sentences of paragraphs, and you try to figure out
what's the best number vector, what's the best number to
associate with that word? So mother and father are
close together numerically, oranges and apples are close together.
Numerically, they're far from mom and dad. Dogs
and cats are far from mom and dad, but closer probably to mom
and dad than they are from oranges and apples chair
and tables and chair. Hard to say exactly where they lie, but those two numbers are close to
each other, far away from mom and dad, king and queen, close to mom
and dad. Does it make sense? Imagine doing this for every single
number and every time you test it, you go, son, a gun. That's pretty good. And when you subtract something from
something else, it makes sense. Okay? That's basically learning the
representation of information. Imagine doing this for English. Imagine
doing this for every single language. Imagine doing this for
anything with structure, meaning anything with predictability.
Images have structure. Because if there are no
structure, it'd be white noise. Physically it'd be white noise.
And so there must be structure. That's the reason why you see a cat, I
see a cat, you see a tree, I see a tree. You can identify where the tree is, you
can identify where the coastline is, where the mountains are where.
And so we could learn all of that. So obviously you could take that
image and turn it into a vector. You could take videos and turn
into vector three D into vectors, proteins into vectors, because there's
obviously structure and protein, chemicals into vectors. Genes
eventually into vectors. We can learn the vectors
of everything. Well, if you can learn everything
into numbers and its meaning, then obviously you can
take ca word, c a t, translated to the image c a t image of ca. Obviously this is the same meaning
if you can go from words to images, that's called mid journey
staple diffusion. If you
can go from images to words, that's called captioning video, YouTube videos to words underneath
videos. And so one of you went from, what do you call it, if you go from say, amino acids to proteins,
that's called the Nobel Price. And the reason for that is because
that's alpha alcohol. Incredible. Isn't that right? And so
this is the amazing time, the amazing time in computer science
where we can literally take information one kind and convert it, transfer it generated into information
of another kind. And so you can go text to text
a large body of text, P D F, small body of text, a summarization of
archive, which I really enjoy, right? And so instead of reading
every single paper, I can ask it to summarize the paper. And it has to understand
images because in the archive, the papers have a lot of images and charts
and things like that. So you can take all of that to summarize it. And so you can now imagine all of the
productivity benefits and in fact the capabilities you can't possibly do
without it. So in the near future, you do something like this, you
say, hi, I would like to design, give you some options of a whole
bunch of cars. I work for Mercedes, I really care about the brand.
This is the style of the brand. Lemme give you a couple of sketches and
maybe a couple of photographs of the type of car I like to
build. It's a four wheel, s u v four wheel drive, SS u v,
let's say, so on and so forth. And all of a sudden it
comes up with 20 10, 200 completely fully three D design cab. Now the reason why you want that instead
of just finishing the car is because you might want to select
one of them and you say, iterate on this one another 10 times, and you might find select one and then
modify it yourself. And so the future of design is going to be very different. The future of everything
will be very different. Now, if you gave that capability to
designers, they would go in the same, they would love you so much.
They would love you so much. And that's the reason why
we're doing this. Now, what's the long-term impact of this? One of my favorite areas is if you
could use language to describe a protein and you could use language to
figure out a way to synthesize protein in the future of protein engineering
is near us. And protein engineering, as you know, creating
enzymes to eat plastic, creating enzymes to catch a carbon, creating enzymes of all kinds
to grow vegetables better, all kinds of different enzymes could
be created during your generation. And so the next 10 years is going to
be unbelievable. We were the computer, the chip engineering generation. You'll
be a protein engineering generation. Something that we couldn't imagine
doing just a few years ago. I think we're going to open it
up for q and a to the audience. So questions, and maybe I'll point and we have
some mics that will be running okay over there. We'll start there. Thank you for coming tonight. Thank you. So are you worried that Moore's law
business schools are students are so serious, I understand that the graduates
of Columbia ends up being investment bankers and stock traders.
I'm actually, look, computer science, is that right? Is that right? And
one computer science, you'll be, and so that's what I understand.
So I'm here selling stock in the future. In the future, if somebody
asks you what stock to buy and video, go ahead. A question for you is, are you worried that Moore's law might
actually catch up to GP industry as it did for companies like, and can you also explain the difference
between Moore's law and CO's law? I didn't phrase Wong's law and it
wouldn't be likely me to do so. The very simple thing is this, Moore's Law was twice the performance
every year and a half approximately. The easier math to do is
10 times every five years. So every 10 years is about a
hundred times, if that's the case. In general, purpose
computing microprocessors, the general purpose
computing was increasing in
performance at 10 times every five years, a hundred
times every 10 years. Why change the computing method
a hundred times every 10 years? Not fast enough. Are you kidding me? If cars would go a hundred times
every five years when life be good? And so the answer is it's in
fact, Moore's law is very good, and I benefited from it. The
whole industry benefited from it. The computer industries
here because of it, but eventually set general
purpose computing. Moore's law. It is not about the number
of transistors in computing, it's about the number of
transistors, how you use it for CPUs, how you translate it
ultimately to performance. That curve is no longer 10 times
every five years. That curve, if you're lucky, is two or
four times every 10 years. Well, the problem is if that curve
is two or four times every 10 years, the demand for computing and our
aspirations of using computers to solve problems, our aspirations, our imagination for using
computers to solve problem, it's greater than four times
every 10 years. Isn't that right? And so our imagination, our demand, the world's consumption
of all exceeds that. Well, you could solve that problem by just
buying more CPUs. You could buy more. But the problem is these CPUs consume so
much power because of general purpose. It's like a generalist. A
generalist is not as efficient. The craft is not as great. They're not as productive as a specialist. If I'm ever going to have an open chest
wound, I don't send me a generalist. You guys know what I'm saying? If you
guys are around, just call a specialist. Alright? Yeah, he's a vet, he's a generalist. Look or do wrong specialist. So generalist is too brute forced. And so today it costs the
world too much energy. It costs too much to just brute
force general purpose computing. Now, thankfully, we've been working on
accelerating computing for a long time, and accelerating computing,
as I mentioned, is not
just about the processor, it's really about understanding the
application domain and then creating the necessary software and algorithms
and architecture and chips. And somehow we figured out a way
to do it behind one architecture. That's the genius of the
work that we've done, that we somehow found this
architecture that is both incredibly fast. It has to accelerate the C
P U a hundred times, 500 times, sometimes a thousand times. And yet it is not so specific
that it's only used for one singular activity.
Does that make sense? And so you need to be sufficiently
broad so that you have large markets, but you need to be sufficiently narrow
so you can accelerate the application. That fine line, that razor's edge is what
caused the video to be here. It's almost impossible, if I
can explain the 30 years ago, nobody would've believed it. And
in fact, if you did, to be honest, it took a long time and we just stuck
with it and stuck with it and stuck with it. And we started with
a seismic processing, molecular dynamics, image processing,
of course, computer graphics. And we just kept working on and working
on and working on weather simulation, fluid dynamics, particle
physics, quantum chemistry, and then all of a sudden
one day and deep learning and then transformers, and then the next will be some form of
reinforcement learning transformers, and then there'll be some multi-step
reasoning systems. And so all of these things are we just one application, somehow we figured out a way to
create an architecture and solve all. And so will this new law end. And I don't think so. And
the reason for that is this. It doesn't replace the C
P U, it augments the cpu. And so the question is, what
comes next to augment us? We'll just connect it next to it.
We're just connect it next to it. And so when the time comes, we'll know, we'll know that there's another tool
that we should be using to solve the problem because we are in service of
the problems we're trying to solve. We're not trying to build a
knife and make everybody use it. We're not trying to build a
acquire, make everybody use it. We're in service of accelerated
computings in service of the problem. And so this is one of the
things that all of you learn. Make sure your mission is right. Make sure that your mission
is not build trains, but enable transportation.
Does that make sense? Our mission is not build GPUs. Our mission is to accelerate applications, solve problems that
normal computers cannot. If your mission is articulated right
and you're focused on the right thing, it'll last forever. Thank you. Okay. Up there. Someone? Yes, that guy right there is
Tony. Go ahead. Tony. Am I, Tony? What's Tony say? Where's Tony? Tony was that guy
in the middle, right? Yeah. See, I met him just now. I'm
just kidding. Straight. My memory. Take my chance. I wasn't, I wasn't trying
to give Tony the mic. I was just demonstrating my
incredible memory for Tony. Go ahead. Thanks again. Now there's a push for onshoring, the supply chains for semiconductors. Then there are also restrictions
on the export supply countries. How do you think that would
affect Nvidia in the short term, but also how would that affect
us as consumers in the long term? Yeah, really excellent question.
You guys all heard a question. It's all repeated relates to geopolitics
and geopolitical tension and such. The geopolitical tension, the geopolitical challenges will affect
every industry will affect every human. We deeply, we the company deeply
believed in national security. We are all here because our
countries are known for security. We believe in national security, but we also simultaneously
believe in economic security. The fact of the matter is most families
don't wake up in the morning and say, good gosh, I feel so vulnerable
because of the lack of military. They feel vulnerable because
of economic survivability. And so we also believe
in human rights and the ability to be able to create a
prosperous life is part of human rights. And as you know, the United States believe in the human
rights of the people that live here as well as the people that don't live here.
And so the country believes in all of those things simultaneously.
And we do too. The challenge with the
geopolitical tensions, the immediate challenge is that if
we're too unilateral about deciding that we decide on the prosperity of
others, then there will be backlash. There'll be unintended
consequences. But I am optimistic. I want to be hopeful that the people who
are thinking through this are thinking through all the consequences
and unintended consequences. But one of the things that has done
is that it has caused every country to believe to deeply internalize its sovereign rights. Every country is talking about
their own sovereign rights. And that's another way of saying
everybody's thinking about themselves and as it applies to us. On the one hand, it might restrict the use of our
technology in China and the export control there. On the other hand, because of sovereignty and every country
wanting to build its own sovereign AI infrastructure, and not all of them
are enemies of the United States, and not all of 'em have a difficult
relationship with the United States, we would help 'em build AI infrastructure
everywhere. And so in a lot of ways, this weird thing about geopolitical, it limits the market
opportunities for us in some way. It opens the market opportunities
in other ways. But for people, for people, I am just really hopeful. I really hope not hopeful.
I really hope that we don't allow our
tension with China result into tension with Chinese. That we don't allow our tension with
the Middle East turn into tension with Muslims. Does that make sense? We
are more sophisticated than that. We can't allow ourselves to
fall into that trap. And so a little bit about that. I worry
about that as a slippery slope. One of our greatest sources of
intellectual property for our country as foreign students. I see many of 'em
here. I hope that you stay here. It is one of our country's
single greatest advantage. If we don't allow foreign students in
the brightest minds in the world to come to Columbia and keep you
here in New York City, we're not going to be able to retain
the great intellectual property of the world. And so this is our
fundamental core advantage. And I really do hope that we don't
ruin that. So as you can see, the geopolitical challenges are real
and national security concerns are real. So are all of the other economic market.
Social technology matters, technology, leadership matters, market leadership
matters. All that stuff matters. The world's just a complicated place. And so I don't have a simple answer
for that. We will all be affected. So we'll take one more question there. But in the meantime, stay focused
on your school. Do a good job, just study. Hi there. So I actually started off working as an
engineer at a semiconductor company at Houston in entrepreneurship. And now I'm here as someone like yourself
that is fundamentally technologist, an engineer, started a company, very successfully learned
finance from YouTube videos. What do you think of MBAs? Oh, I think it's terrific.
You should be, first of all, you'll likely live until you're a
hundred. And so that's the problem. What are you going to do for
the last 70 years or 60 years? And this isn't something I'm telling you, it's something I tell everybody care
about. Look to the best of your ability. Education. When you come here,
you're forced by education. How good can that be
after you leave? Like me? I got to go scour the planet for knowledge and I've got to go through a lot of junk. That gets to some good
stuff. You're in school, you've got all these amazing professors
who are curating the knowledge for you and present it to you in
a platter. My goodness, I would stay here and pig out on
knowledge for as long as I can. If I could do it again, I'd still be here. Dean and me sitting next to each
other. I'm the oldest student here. I'm just preparing for that big
step function when I graduate, just go instantaneously. Success. I'm just a little kidding about that. You have to leave at some point and
your parents won't appreciate it. But don't be in a hurry, I
think. Learn as much as you can. There's no one right
answer to getting there. Obviously I have friends who never
graduated from college and they're insanely successful. And so there
are multiple ways to get there. But statistically, I still think
this is the best way to get there, statistically. And so if you believe
in stat in math and statistics, stay school. Yeah, go
through the whole thing. And so I got a. Virtual b a by working through
it, not because of choice. When I first graduated from school, I
thought I was going to be an engineer. Nobody says, Hey, Jensen, here's your
diploma. You're going to be a c e O. And so I didn't know that. So
when I got there, I learned M B A. And there's a lot of different
ways to learn. Business strategy matters. Obviously. Business matters are very
different things. Finance matters, very different things. And so you got to learn all
these different things in
order to build a company. But if you're surrounded by
amazing people like I am, they end up teaching you along the way. And so there's some things that
depending on what role you want to play, that's critical. Yours,
okay? And so for a C e O, there are some things that are
critically, it's not only my job, but it's critical that I lead with it.
And that's character. There's something about your character,
about the choices that you make, how you deal with success,
how you deal with failure. And Norma said that how you make choices, those kind of things matter a lot. Now, from a skill and craft perspective, the most important thing for
a C is strategic thinking. There's just no alternative. The
company needs you to be strategic. And the reason for that is
because you see the most. You should be able to look around
corners better than anybody. You should be able to connect
dots better than anybody. And you should be able to mobilize.
Remember what a strategy is, action. It doesn't matter what the rhetoric
says, it matters what you do. And so nobody can mobilize the company
better than the CEO O. And so therefore, the CEO's uniquely, uniquely in the right place to be the
chief strategy officer, if you'll, and so those two things, I
would say, from my perspective, two of the most important things. The rest of it has a lot of
skills and things like that. And you'll learn the skills. And maybe
if I could just add one more thing. I do believe that a company is about some particular craft. You make some
unique contribution to society. You make something and you make
something. You ought to be good at it. You should appreciate the craft.
You should love the craft. You should know something about
the craft, where it came from, where it is today, and where
it's going to go. Someday. You should try to embody
the passion for that craft. And I hope today I get a little bit
embodying the passion and the expertise of that craft. I know a lot
about the space that I'm in, and so if it is possible, the CEO should know the craft. You
don't have to have founded the craft, but it's good that you know the craft.
There's a lot of crap that you can learn. And so you want to be
an expert in that field. But those are some of the things
you can learn that here. Ideally, you can learn on the job, you
can learn that from friends. You can learn that a lot of different
ways to do it. But stay in school. So before I thank the best c e o, I want to thank the Digital
Future Initiative, the
David Hilton Speaker Series, but mostly thank you gentlemen for coming. We all understand why you were voted
the best, c e o now. Thank you.
