Good evening everybody. Thank you so
much for joining us for what is about to
be an exciting evening. Just before we
get into tonight's proceedings, just a
tiny in-house rule from me. Please do
resist the urge to take photographs or
videos in your personal mobile devices.
We have professional photographers in
house and I would trust you to leave it
to the professionals to take care of
that. Thank you. Okay. So, created in
2017, the Professor Steven Hawking
Fellowship was established to recognize
individuals who have made distinguished
contributions to science and technology.
Administered every year by the Hawking
Fellowship Committee, the fellowship
acts to memorialize Professor Hawings
legacy and to celebrate innovation in
the field. Fellows from past years have
included Bill Gates in 2019, Jane Goodle
in 2020 and the Open AI team in 2023.
Tonight's honore makes a stunning
addition to that list of inducted
fellows. Jensen Huang is the founder,
CEO, and president of Nvidia. Founded in
1993, Nvidia has pioneered accelerated
computing. From the invention of the GPU
in 1999 to major developments like the
GraceHopper Super Chips, this Stanford
graduate and alumni has been a
trailblazer in innovation and tech
pioneering since the very beginning.
Please give me a warm Cambridge Union
welcome in inviting the 2025 Professor
Steven Hawking Fellow, Mr. Jensen Hong.
Heat.
Thank you.
And now I would like to take the
opportunity to invite Lucy Hawking,
Professor Hawking's daughter, to present
this year's fellowship.
Thank you so
maybe I'll have my wife hold it.
>> Oh, perfect. Please.
>> I feel safer with her holding as far as
Thank you.
Wow.
This is quite a moment. You know, today
when I arrived, I was so overwhelmed by
being on Cambridge
that I decided to uh bask in the moment
and write a proper speech.
And and so I I was in is it the St.
James Lodge? Is it uh where it was a
beautiful place? the fireplace was uh
was going and and uh I sat in a must
have been a 30,000 year old chair chair
and um it was it was incredible and and
here here it is I wrote it. So Lucy,
Ivan, members of the Hawin family, and
everyone, everyone here. Incredible.
Everyone here. I'm deeply honored to
receive the Steven Hawking Fellowship.
And to receive it here at Cambridge is
profoundly humbling. Cambridge is a
cathedral, a cathedral of world changing
ideas.
Newton redefining motion and gravity.
Darwin,
questioning creation.
Maxwell, one of my favorites, uniting
light and magnetism.
Touring, another favorite,
imagining a machine that could think
and Stephen Hawking expanding our
understanding of time and universe.
Professor Hawkings life showed that
curiosity has no boundaries.
By the way, that was one of his theories
as you know, no boundaries. Even when
his body was confined, his mind traveled
beyond the stars.
He reminded us that discovery does not
come only from intellect
but from conviction and optimism.
And through his life's work
and through the way he lived his life,
he inspired us to look beyond our
limitations
to meet challenged with curiosity and
humor.
I can think of no higher compliment than
to be associated with that spirit.
Nvidia is a story of nearly impossible
odds
with the three of us, three friends in a
townhouse in 1993,
uh set out with an idea that we invent a
new way of doing computing to solve
problems that normal computers cannot.
Along the way, we invented a new product
category, the GPU. We invented in fact a
new form of computing called CUDA
accelerated computing. We invented new
strategies to take that technology and
that architecture and proliferated
literally all over the world.
And along the way we
created the instrument of scientists,
artists, designers, dreamers, and most
importantly
we sparked a new industrial revolution,
the AI industrial revolution.
We've come a long ways and our
discoveries has led to the most
impactful technology of our time and uh
probably of all time, the ability to
manufacture intelligence.
We've now seen the technology advance
incredibly in the last decade.
It is now transforming every
application,
every field of science, every industry.
Everyone will be impacted.
Every company will use it.
Every nation will build it. It will now
be part of infrastructure,
the intelligence infrastructure.
and to realize that like energy, like
the internet, we will now be building AI
infrastructure all over the world. That
observation, that realization
led to the company that you see today.
The company helping every company, every
industry, every country build out
artificial intelligence as part of their
social fabric.
And so here we are 33 years later. I'm
the longest
serving tech CEO the world's ever known.
The way you achieve that, by the way, is
uh don't get bored and don't get fired.
And in a lot of ways I feel
Nvidia and I have been uh reborn
that the company is renewed that the
entire technology industry is being
completely reinvented.
Every single layer of the single most
important instrument of humanity,
computers, is being reinvented
from chips to systems, the software,
algorithms, applications,
and the potential impact.
In no time in history has this happened.
Surely not in the last 100 years. And
now
the entire tech stack is being
reinvented. The entire tech industry is
being reinvented and in fact we do feel
completely renewed. And so in a lot of
ways I'm starting where you're starting.
We're all newbies now.
We're all looking at a future
beaming with opportunity
and equal amounts of concern.
No technology of this capability can be
advanced without thoughtfulness,
without care, without scrutiny.
And yet the opportunities ahead is
incredible.
And so I feel like a startup again. 33
years later, Nvidia is now the world's
largest startup.
And so I'm I'm incredibly incredibly
proud to receive this. It's a great
honor. and um I'm looking forward to
spending time with all of you. Thank
you.
It might be me. It might be that
one of us has to go.
Should I?
>> Perfect. I think we're fine. Wonderful.
Well, thank you very much for joining us
today. And I think that it would be a
miss if I didn't begin by saying
congratulations on receiving the 2025
Professor Steven Hawking Fellowship.
>> Thank you.
>> With such a rich career and legacy and
sense of of achievement in all that
you've been able to do so far.
>> You're making me nervous.
I promise you I'm on my best behavior
today. Taking
>> that setup that setup is too much.
>> You know, taking all of that into
consideration, it's tricky to figure out
where to start. But to quote The Sound
of Music, let's start at the very
beginning. It's a very good place to
start. So, being born in Taipei and
moving to America when you were nine.
Um, what comes really apparent in your
early years is a great sense of
determination, drive, and personal
discipline. How were you able to
cultivate that through your education,
through your time at Oregon State,
through your time at Stanford to keep
going and to continue being relentless
even in your early education?
My mom taught me English and she doesn't
speak English.
And that kind of tells you all
that tells you something about about um
the impression that parents could leave
with their children. Uh I she told me
when I was young that that uh uh that I
was special, that somehow somehow I took
tests and I was I did well on tests
and um
I and she encouraged me. Uh often times
if people tell you that you're you're
better, greater uh more capable than you
are, you might live up to that
expectation.
uh it reminds us to do the same with our
companies and it reminds us to do the
same with each other. Uh and uh
and she she left with me an impression
that that nothing could be that hard you
know to this day and people have seen me
adapt and
I think it was professor Hawin that said
uh adapt in intellect is the ability to
adapt. Wasn't didn't he say that true
intellect is the ability to adapt? Uh in
a lot of ways that that kind of defines
Nvidia kind of defines me. I I approach
almost everything from the perspective
you know how hard can it be now often
times turns out to be really hard but
you approach it with the attitude how
hard could it be? And and if you look at
all the things that we've done as a
company and what I've done, I I've never
been CEO before. This is my first CEO
gig.
Okay? And so I I I think I'm I'm going
to get it right someday. Um
but when we first started the company
and I never raised money, I never wrote
a business plan. I still haven't written
a business plan. Um and and I've never
been a CEO, never even been a manager.
in all cases.
I think I think her the the the the
fact that she was able to teach me
English and she doesn't speak English.
Uh she can't read English.
So, you got to ask yourself how did how
did she do it? Um turns out piece of
paper in a dictionary. And uh I
approached almost everything like that.
you know, how hard could it be and break
it down to first principles and you
learn along the way and and and I meant
it earlier. Uh so long as you could stay
in the game long enough to learn the
sport and staying in the game is in fact
most of it. Uh I I was able to to do
what I'm doing today because I didn't
get bored and I didn't get fired. That I
think was the magic all all of it. It's
100% of it. And I think staying on that
topic of Nvidia being the beginning of a
lot of firsts for you. It's as if you've
read my mind. This is flowing so
beautifully. Let's talk about the
decision that led to you becoming CEO of
the company. So the dream and plan for
Nvidia came together over many coffee
meetings at the chain restaurant
Denny's. Those meetings revealed to your
co- business partners Pri and Malahowski
that you were to be the CEO of Nvidia.
What do you think made them unanimously
decide that you were the best fit
amongst the group to lead Nvidia as its
CEO? I think because they didn't want
the job and they were right.
They didn't want the job and and and um
all three of us were engineers
and and I think that's the answer. They
didn't want the job. Um
in retrospect,
I I could have been smarter myself and
and uh to be CEO is a is a lifetime of
sacrifice.
And most people think that it's about
leading and being in command and being
on top and uh you none of that is true.
You're you're in service of the company.
You're creating conditions for other
people to do their life's work. Uh
you're you're inspiring through example.
Most of the examples are making
difficult decisions during very
difficult times. It's mostly about
sacrifice. It's about strategy. And
strategy as you know is not choose not
just about choosing what to do. It's
about choosing what not to do which is
sacrifice
and and the and the determination the
the conviction um the pain and suffering
that goes along with with overcoming
obstacles. That's all sacrifice. It's a
you know being the CEO. I'm not trying
to talk anybody out of the the
profession. uh if you were to do it, it
it is the greatest honor. But I you you
you have to realize that it's not about
you know fame and glory. It's mostly
about pain and suffering and and to a
lot of that um you know I I I attribute
to me growing up. It wasn't an easy easy
uh uh easy journey. Uh you know we came
to we went to we went we came to America
went to America. Um my parents wanted us
to pursue the American dream. Uh they
didn't have very much they didn't they
were they were uh quite modest. Um and
uh uh moving to United States, you know,
was quite difficult for us in 19 1973.
Uh but somehow we found we we made our
way through it. And I think the the uh
the the life the life of struggle,
endeavor, um nothing for granted, having
having to earn everything. Uh that I
think was good CEO training, you know.
And so I I think the answer is that they
were smart. They didn't want the job and
they live very high quality lives now.
And building upon what you said about
struggle and strategy and personal
development at a recent visit to
Stanford, you told an audience of
students, greatness comes from
character. Character comes out of from
people who have suffered in the
beginning years of building Nvidia
amongst many setbacks. How did you keep
going on? because you've said that you
had never fundraised before at such a
high level. You'd never pitched such a
brilliant idea. You had never had a
business plan, but there was still this
idea of strategy and duty and personal
sacrifice. I mean, it's one of those
it's it's one thing to to say it, but to
live it is often really difficult. How
did you continue to keep the faith in
those really beginning stages? I say it
to this day. We believe what we believe.
And so, we reason about we reason about
about our perspective of the future. And
you reason about from first principles
that you learn um through education and
you know fundamental first principles
and uh you have to reason all the way
back to either
the first principles of computer science
or the first principles of physics or
you know whatever first principles you
could you could hang on to and you try
to you try to reason as far back as you
can. Now once you do that and you come
to the conclusion that all of the all of
the environmental conditions and all the
information that you have uh causes you
to believe in what you believe then at
that point you believe what you believe
and you got to decide am I going to be
somebody who does something with it do
something about it or do I just become
one of those people that you know that
say something like oh yeah I knew that
or I also knew that or I've said that
before but you did nothing about it and
so I tend to be somebody that that would
would reason about these things and
believe it so deeply that frankly I
could see it in my head and once I could
see it in my head as far as I'm
concerned is might as well be real.
Everything else is just details. And so
you you manifest your belief as deeply
as you can. And after that it's you're
kind of hard to um dissuade. Now of
course every single day I gut check all
of the assumptions that I made. all the
assumptions that I used to reason about
the strategy, I'm constantly
re-evaluating. If any of that changes,
if any of the the assumptions or any of
the the principles that I used were were
flawed, um I'm quick quick to adapt, you
know. So, I'm constantly learning
through failure and I'm quick to adapt
and by adapting you get to stay in the
game. And so, so I think it's it's not a
complicated equation. Often times,
oftentimes I think I think people have a
hard time pivoting because they feel
that their ego is somehow tied to some
decision they have made or something
that they said. And that's really hard
for CEOs. You know, for CEOs, you I I
stand up and you know, as Nvidia today,
we have 50,000 people and I declare
something about the future. You know, I
I I describe what I I think as a as a
direction the company ought to go and
and and you reason about why you do do
so. And you I do do that in front of
50,000 people. And I'm doing it
constantly. I'm saying it constantly
because you can't just say something
once, you got to say a thousand times.
After you say something a thousand
times, when you discover that in fact
you were wrong,
it's fairly difficult to pivot. But over
time I I've earned the right to change
my mind
and I as soon as something is wrong and
I feel that it's and and the way we
describe it is we have we have a phrase
in the company and I I remind ourselves
of that all the time you h we have to be
intellectually honest you know if I know
that if I know that that um we had we
had to change our mind and I don't um
that's a that's a character question,
you know, it's an ego question. It, you
know, somehow I'm preventing myself from
allowing everybody else to to do the
right things. And so, so I I I'm quick
to get out of that. And over time, I
discovered that that leaders are not
meant to be right. That's not our job.
You know, we're not our job isn't to be
right. Our job is to help other people
succeed. It's related, not the same. And
so to the extent that they always
believe that I want to help people
succeed, then they want to help me
succeed. When I change my mind, nobody
thinks about it twice. It's almost like
they never forgot what I said before.
You know, this new thing that I say was,
"Yeah, he's right." And I change it a
little bit. Yeah, he's still right. And
so, you know, people just want you to
succeed. and and um you want to create
the conditions where you allow yourself
to be vulnerable uh to change your mind
uh to be wrong uh but because people
know that that um I always have their
best interest and I want the company to
succeed and I want us to realize the
future you know they they'll they'll go
along with it.
>> Wonderful. Let's talk about the GPU. So
in 1999
Nvidia creates the light bulb moment in
the tech industry when the company
manages to offload graphic rendering
from the CPU to the GPU. In 2006, you
followed up with the CUDA microservices
with over 400 libraries.
When you're creating and innovating at
NVIDIA, how do you strike the balance
between building and developing on
pre-existing technologies and deciding
like you said when to pivot into a new
into a new lane? How do you keep that
balance?
>> Uh it's really hard and the reason for
that is this. When you uh when you
reinvent some for example, let's let's
use a phone for example. When the iPhone
came along, it's still a phone and
initially it didn't do much more than a
phone. Uh it had a browser and most
people didn't need a browser all the
time and uh it had a map that was nice
and it played some music that was nice
but it cost five times more. And so the
challenge is at the time of a new
product category the cost of the
technology is much higher than the value
that it provides. And the same thing
with the GPU. We invented the GPU
because we wanted computer graphics to
be a medium that was expressable through
software. Prior to us, prior to the GPU,
graphics accelerators were fixed
function. Grow shading with gross
shading. Fun shading was function
shading. The way it, you know, whatever
specular highlight you decide, that's
exactly the way it would render it.
Well, we decided that that computer
graphics should be a artistic
storytelling medium and the software uh
it should be programmable by software
using what is called a programmable
shader. So we invented this idea called
a real-time programmable shader. And so
that that led to a bunch of future
opportunities. But on the day we
announced it, there were no
applications, but it cost twice as much.
And so here's something that you have no
need for and as the current customer
would pref prefer to have something that
is quite frankly half the half the price
than to have something that has future
promise. And so,
uh, there's there's no easy answer for
that aside from you've got to believe
what you believe. And when you create
it, you know, the rest of it is about
ecosystem development and uh, inspiring
the the developers to create
applications that realize the potential.
Um, a whole bunch of software, you know,
that kind of stuff. And that's all
that's all kind of mechanical work. Um,
but in order to go do that, you have to
believe in a future. And then crossing
that crossing that chasm is extremely
painful. It's life-threatening. Most
companies don't make it. Most companies
don't make it from classical phone to
become a smartphone. Notice none of them
did.
No feature. It's back then we call our
phones feature phones. I I don't know
phones. And so they were called phones.
And then now they're called smart mo
smart smartphones. No phone company made
it through to the smartphone. Nvidia is
the only company that made it from one
generation to another generation to
another generation to another
generation. And we constantly reinvented
ourselves. We've now reinvented
ourselves through six computing eras.
And I the mechanics of it is fairly
mundane. It's easy to explain. Um you
know I'll teach a course here one of
these days. It's a it's a five it's a
five-step program and it's not it's not
hard. But the part that is hard, it's
the courage to do it because when you
leap across to that next thing at the
moment when you're in the middle of that
canyon, your your cost is incredibly
high. Your value is incredibly
non-existent and very very rarely do
people cross to the other side. And just
that's 100% courage, the ability to
endure pain and suffering. Um the rest
of it is you know skills.
>> One of the beautiful things about
Nvidia's work is the way in which its
discoveries have reverberated across the
world and have spurred on lots of
different technologies. This year
Bristol University unveiled the Ismbard
AI the 11th fastest supercomputer in the
world powered by 5,448
GH200 Gracehopper super chips. Ismbard
AI is being lorded for being able to
power cutting edge medical and
sustainable research. How does it feel
to see the work of the Gracehopper super
chips be extrapolated upon to build what
is essentially really crucial technology
that is shifting medical research at the
highest levels? Yeah. Um before is
so so that we get the record straight
the fastest supercomput in the UK built
um I built and it was called Cambridge
1.
>> Did you guys know that? Okay. I never
got I was I I did it because I thought
um Nvidia was going to have a
headquarters here in the UK.
We uh did you guys did you guys ever
hear about the story? I almost bought a
a uh UK company. Uh it was blocked by
the UK just to I
I know breaks my heart to this day. Uh
so so anyways, we almost bought ARM. You
guys know that, right? We almost bought
ARM and uh I worked on it for a long
time. I thought it would have been a
great idea. I still think it would have
been a great idea.
I don't think it's too late. No, I'm
just kidding.
Anyways, ARM's a great company. Turned
out turned out. Uh so so anyhow
you know what what we build is the is
the most crucial instrument of knowledge
discovery and for the very first time I
we we've built a computer that can
understand the meaning of the
information that it is processing. It's
not just processing data. It understands
the meaning of the data it's processing.
So for example, it's not processing
letters, it's processing words and it
understands the words. Um it's not just
processing uh a whole bunch of numbers.
It understands that these numbers
represent
fluid flow. Um or that it it understands
that this sequence of numbers actually
represents a protein uh or a small
molecule chemical. Um and it understands
the meaning of it. its vocabulary, its
meaning. It understand its
functionality,
for example, you know, understand the
the semantics. It understands the
context that it's in and therefore uh it
understands how we would react in that
context. I've just used a bunch of words
that when you apply the chatbt is very
very obvious. But
remember the protein and those English
words to the computer is the same.
to the to the to the extent that we can
under we can help the com computer
understand the nature of proteins, the
meaning of proteins, the structure of
it, its dynamics. Um, it should be able
to understand how it interacts with
other chemicals and other proteins in
various context. We should be able to
talk to our protein in the future. What
are you? How would you behave? Are you
soluble? How do you how do you behave in
temperature, high temperature? How do
you behave in different different types
of liquids in different context? Um, how
would you react to this particular
chemical? How would you bind to it? You
could just literally talk to a protein
in the future. Now, what I just
described sounds a little ridiculous
right now, but as you know, you could
talk to an image today. Just go up to an
image and you know, what are you? I'm a
picture of a cat. What kind of cat are
you? Can you move? and and all of a
sudden the video the the image turns
into a video. And so uh notice notice
this is the world that we're in now. Not
only were you processing data, we
understand the data that we're
processing and the implications of that
in the field of of course drug discovery
or you know material sciences or any
other any form of science is really
quite profound. So, you know, we we've
created um what touring, you know,
imagined, right? Artificial
intelligence.
>> One of the key um visions at NVIDIA is
about being able to turn biology into
engineering. And NVIDIA is um alongside
other AI companies doing
>> How hard could it be?
>> Turns out super hard.
>> We've been at it for 10 years. I'm still
working on it. Nvidia alongside other um
AI companies are doing a lot of really
good work at studying um molecular
biology particularly as it pertains to
things like amino acids um and trying to
not only keep the work that you do um
limited to computers but also to do
cutting edge medical research. At what
point did you realize that Nvidia
possessed the capability to switch into
helping with the with the with the
healthcare industry? And what are your
um views on the progress that's being
made in molecular biology research using
AI?
>> 40 years ago, something really amazing
happened. uh 43 years ago,
I was the first generation of engineers
that designed a computer inside a
computer.
Before then, all of the engineers did it
practically by hand, prototyped
our systems into existence.
My generation was the generation of what
is called computer Aed design.
Well,
40 years later,
100% of what we build exists completely
as a digital twin inside a computer
before we make it. To the point where in
the old days, we would we would tape out
a chip. We go fab a chip and we hope it
works. Today, these chips are a billion
times more complicated. Tens of
thousands of engineers work on it at at
one time. We send it to the fab and when
it comes back I know it works. And the
reason why I know it works because it's
been living inside another computer for
a long time. Well, that's 40 years
later. I believe the time is about now
where we have the ability to represent
the various hierarchies of biology so
that we can have computer aided drug
design.
the idea of drug discovery. I the the
even the word is wrong. Drug discovery.
It's like, "Hey honey, uh, I'm going to
go look for mushrooms now."
And some days you come back empty-handed
most of the time. Some some days you
come back with some amazing truffles.
Look what I discovered today. I found it
today. Now, of course, drug discovery is
a little bit like that. It's much more
science and less engineering. If you
look at the way we do computers design
design today it's 100% engineering and
so we have every single year we get
better and better and better at it drug
discovery is very very hard every single
drug for every single disease almost
feels like a brand new discovery like a
brand new journey we've we have to
create the computer tools and the the
the information representation
transistors and logical gates and you
know large functions and large chips We
have different languages and different
tools to represent the different
hierarchies of electronic design. We
need to discover the hierarchy of
information representation for
biological design. Does that make sense?
And so once we have that, once we have
tools that understand that
representation and we can manipulate it,
then the world becomes drug design. And
every single year that goes by, we
become better and better and better at
it. We stand on the top, you know, on
the shoulders of giants. And one of
these days, you know, who knows? And so
that was that was I just reason I
literally just did just now what I did
10 years ago at NVIDIA. And I explained
this to a bunch of computer scientists
and and then everybody goes, "Okay,
well, let's go give it a try. How hard
can it be?" And so it's been 10 years
and I got to tell you, it's super hard,
but it's okay. But I still believe it. I
I have I I have 100% certainty that we
will discover the representation of
biology and the tools necessary to go
design it. And the reason for that is
this. Although we although we are we're
all a little different, we're largely
the same. So there's obviously structure
in biology and obviously we it's it's a
repeatable thing.
Not only is Nvidia crossing over in
terms of its legacies into different
fields, it's
>> by the way, I just wrote the entire
business plan of Nvidia going into drug
discovery and this business is now for
us billions of dollars
and I just wrote the entire business
plan just now. No number, no no
spreadsheets were used.
No numbers were applied.
No calculus was necessary.
Just like that. All simple reasoning. I
did the same with autonomous vehicles. I
did the same with robotics. I did
exactly the same for artificial
intelligence. Just like that. We sat
there and reasoned about it step by step
by step. Um, usually we have the benefit
of a whiteboard. It makes it easier to
to to communicate. After that, somebody
takes a picture of it, the company goes
off, and 10 years later, in the case of
artificial intelligence, 15 years later,
here we are.
>> Well, I mean, let's talk a little bit
more about artificial in intelligence.
So, not only is the work that Nvidia is
doing crossing over different fields of
science, but it's crossing across
borders. In September 2025, Nvidia
announced a2 billion pound investment in
the UK AI ecosystem alongside firms like
Baldin, Phoenix Cord, Excel.
>> I did it right in front of the prime
minister,
>> Kstarma.
>> Yes.
>> I mean, Kstarma championed this.
>> I'm investing your company. I'll invest
in your company. Their $2 billion went
really fast.
>> Not only did Kstarma champion it, it was
also recognized by a bunch of other
business leaders who thanked Nvidia for
its bold leadership. How does it feel to
be able to support burgeoning um
leadership in AI and creativity and
innovation particularly in the UK and
also across the world?
>> Nvidia succeeds when other companies
succeed. Remember we're a platform
company. We're a tools company. We're a
platform computing platform company.
Nobody wakes up in the morning, okay?
Nobody in your family wakes up in the
morning said this morning and go guess
what we need? We need to buy a computing
platform.
Nobody says that. And so the only reason
why we succeed is because companies and
developers who use our platform create
something incredible.
That's how we succeed. And so so in a
lot of ways I've got one of the the
really the one of the greatest jobs
ever, which is to have a company whose
mission is to desire other people to
succeed.
And this is a phrase that is always
inside the company. I'm always reminding
everybody in the company that that we
want and we need and we desire other
people to succeed and it's through their
success we get to ride their their
coattails and so that's that's it you
know and so when I when I saw that the
UK um really is a has a goldilock moment
you know this is a this is you you have
incredible researchers here this is the
home of computer science if if if
there's a country that represents
computer science if that's got to UK,
you've got you've got uh a rich uh
ecosystem of entrepreneurs
and the only thing that you don't have
as it turns out is the instrument of
knowledge, the instrument of science,
the instrument necessary to do that and
I I know how to do that. And so I felt
that that um with our involvement, we
might be able to create that spark and
um and then maybe UK becomes, you know,
one of Nvidia's headquarters after all,
you know. So, but but is this it is uh
this you you really have an
extraordinary moment. But for some
reason,
I do feel that that
the the the culture of of the UK
you're so modest,
you know, you know, in in Silicon
Valley,
what however great we are, we describe
it as a hundred times more than that.
And however, but here in the UK, however
great you are, you're onetenth that. And
so, so, um, I'm here to tell you, you
you're pretty extraordinary. And, and
look at the list of of, uh, of
the inventors and the scientists and
discoverers that have come before you
and that have that have inspired us and
uh, has uh, has made it possible for us
to do what what we do. And then just
remember
the industrial revolution was invented
here was discover it was created here
and there's a new industrial revolution
now. So take advantage of it. It's got
all the it's got everything that that
you're you're part of and everything
that you're great at. And so it this is
your moment. You've got to you've got to
capitalize on it.
>> And you've spoken earlier on in this
interview about what makes the UK such a
promising um field for AI development.
Um let's talk a little bit about South
Korea. So Nvidia has just committed over
$260,000 of its most advanced chips to
South Korea in what is being called a
$10 billion mega deal in AI. What would
you say is what makes South Korea an
ideal partner for this level of
investment and what are some of the
promising projects that you hope that
Nvidia might be able to achieve in the
in the area region?
>> South Korea has a uh is one of the major
regions major countries in the world
that has industrialized deeply. Uh South
Korea as you know manufactures chips.
They manufacture ships, chips, ships,
cars, electronics. Uh this is a country
that is incredibly good at
industrialization.
In in just a few short decades, um they
reinvented the whole nation and became a
global industrial giant. uh
South Korea also has has a unique
capability of doing software and so
the technology ecosystem is able to deal
with um hard hardware and manufacturing
on the one hand but software and the art
artistry of of developing software on
the other hand and so I I think the the
it's a real opportunity to see them uh
take advantage of artificial
intelligence and reinvent how
industrialization works.
You know this is the next era of AI.
Currently AI understands language and
numbers and images and videos. But in
the future, in the near future, we need
AI to understand the laws of physics,
understand causality and object
permanence and you know inertia and you
know that needs to understand gravity
and things like that, friction and
things like that. And so so I think the
the um when when that happens and when
we create embodied AI AI that could
operate manipulate and operate in the
world uh then then we're going to
reinvent reinvent how industry works and
and as you know the world has a ve quite
a severe shortage of labor. The world's
GDP would be much higher today if not
for the fact that we had a shortage of
labor. we have shortage of labor in just
about every single company, every single
field. And so although we talk about
about jobs being lost, it is very likely
that all jobs will be transformed and
it's very likely that AI is going to
drive a product productivity gains and
and GDP growth like we've never seen
before. And that's that's certainly the
hope and I and I believe in it.
>> Just the last question for you before we
hand over to the floor in the live
audience Q&A. Many of the young people
sat here today like you have a passion
for science. Cambridge is a university
much like your alma mart Stanford that
has produced 126 Nobel Prize laurates
with an exciting AI development scheme.
The city has produced a culture of many
eager to turn their ideas into
businesses and to top firms as you have
successfully done. What is one piece of
advice you would share to many of the
young people who've got an idea, who've
got a bullet point, who've got a bit of
code that they're excited about. How
would you encourage them today?
>> There are some skills. There are some
skills in determining whether an idea is
a good one or all. If you want to do
something, just go do it. Um, and so
long as you're you're you're
intellectually honest, you're
contextually alert, um, you're
environmentally alert, understanding
what's going on around the world and and
your industry.
uh and you're willing to pivot, then
then that's all fine. Okay? But
nonetheless, there are skills in
determining uh upfront whether an idea
is worth pursuing or not.
But I I would say that the singular best
piece of advice for entrepreneurs, and
it's served me incredibly,
is to have this
childlike
view of the future, which is optimistic.
You're curious,
and you ask yourself, you know, how hard
could it be? And don't let anybody tell
you that in fact it's really hard.
You're going to find out for yourself
that in fact it's really hard. Um but
you have plenty of time to do that. You
have plenty plenty of time to do that.
And so people have asked me would I
start the company again if I knew
everything today back then. The answer
of course not. It's too scary. It's too
painful. Too much sacrifice. How could
you take all of the, you know, all of
the feelings and everything that I've
learned, all bottled up at this moment,
transport it back into a 29year-old
body, and go, "Hey, with that, get at
it. You're never going to do it. You're
never going to do it." And so, don't
don't be don't be afraid of ignorance
and don't be afraid of being naive and
don't be afraid of, you know, all all of
those things. You're going to learn
everything you need to learn along the
way. And just, you know, if you if
you're really passionate about
something, go do it. you know, just tell
yourself how hard can it be.
>> Thank you so much, Jensen. And on that
note, we are going to open the questions
up to the floor. So, if you do have a
question, please stick your hand up nice
and high. And then when called upon to
My word. And then when called upon to do
that, we will get an EM to bring you a
microphone. Um, let's start right here
at the front with the gentleman who's
got his hand up. And then afterwards, we
will go upstairs to the man in the dark
blue jumper. So, we'll start here and
then go upstairs.
Jensen, your your point about uh
cooperating with each other and not
competing really resonated. Um but here
at the university, we spend a lot of our
time training students for exams, uh
marking them, ranking them and so on.
What would your advice be to Cambridge
University? Should this 800 and
something year old institution abolish
exams um and have a more cooperative way
of building up our student base so that
we can adapt to the future which is
coming with AI?
I I guess I guess if if there's no
um ranking and rating uh it would be
hard for you to know whether the
problems and the curriculum is even hard
enough that is sufficiently challenging
to to push the students. I get that. Um
a long time ago when I first started the
company there was a concept in
management called ranking and rating. It
was created by another Silicon Valley
company and and because because that
company was was famed for being
incredibly good at management um I took
a lot of the the learnings that were
being being talked about and I applied
it at Nvidia. I can tell you today I've
dropped 100% of them. There's no benefit
in ranking people.
Uh there are ideas called uh even back
then uh 360 peer review. There's no
benefit in that.
There's no benefit in asking people to
to rate and review you. Um I I found
that most of those techniques don't
really work. Uh but in the context of
academia,
frankly with AI,
I think
the idea of information being somehow
uh
unaccessible so that it's even
worthwhile to compete anymore to get the
right answer. I think those days are
kind of gone. Um
I I do wonder I do wonder how I I wonder
if the way you teach courses are going
to be very similar to the way we renew
ourselves at a corporate level cons
continuously learning continuously
renewing ourselves. I'm I'm quite famed
for saying I don't fire anybody.
And and the reason for that is very is
is is grounded in in in wisdom.
We want to encourage
our employees to innovate, which which
requires them to take risk, which
requires them to be vulnerable. They're
going to do something that puts
themselves out and they'll likely fail.
And if they're taking sufficient risk
that is proportional to the type of
company that we want to be, then they
should fail often.
But if if we let go of people that we
rank to the bottom, the bottom 5% rule,
you might have heard of that management
technique as well. Every single year,
remove the bottom 5%. Because if you do
that, then what's left in the soup, you
know, is consummate. Okay? But that's
complete nonsense because we love stew.
We want we want the mess of a stew, not
the p purity of a consmate. And so in
fact, we don't want that 5% to be lost
because they they happen to be precisely
the people who had just taken a risk
failed
and they learned something from it and
they might tomorrow be the outlier that
reinvented something to save your
company.
And so I think the the old cultures, the
old cultures, the old systems deserve
some re reevaluation and certainly
because intelligence is now a commodity.
We got to say that kind of out loud.
Intelligence is about to be a commodity.
Then what's left? And what's left is a
lot of the things that we were just
talking about. It's courage. It's
intellectual honesty. It's the absence
of ego. the ability to be vulnerable in
public so that you can create. You know,
as you know, artists and inventors and
creators, they're humiliated, laughed at
often because what they do isn't always
perfect. And so, you need to have that
that humility, the vulnerability, and
yet the courage to put yourself out
there and be laughed at.
And so so um some of those things I I
think are going to become more
important.
>> Can we take a question
um to in the greenest jumper right
there?
>> Yeah, indeed.
>> Hi Jensen.
>> Hi.
>> Um thank you for coming to Cambridge and
congratulations again on your
fellowship. Thank you.
>> My name is Josh. I'm in Trinity College.
>> Um you mentioned earlier that you think
that the future of jobs are going to
change. um as a result of AI and a lot
of young people, aspiring lawyers,
accountants, consultants, bankers, they
don't really share that optimism. I
think they're more pessimistic about it.
So, I was hoping you could just maybe
shed some more light onto what you think
that job transformation will look like
and what the benefits will be that will
come from it.
>> Okay. Um,
one is is um
uh
optimism in humanity. One part of the
answer, one part of the answer is uh
pragmatism and then one part of the
answer is evidence. Okay, I'll start
with evidence.
I radiology was going to be the first
industry to be completely destroyed by
artificial intelligence. And in fact,
almost every radiologist today uses AI.
And yet the number of radiologists being
hired has increased.
Why is that? Because they can now do
more things.
There are so many cases that went
undiagnosed using radiology and there's
so many cases that were not deeply
diagnosed because there's just a
bottleneck of how many radiologists
could study all those all those images
and so now the basic stuff is done very
very quickly. Now the number of cases
that they're getting, the depth of the
cases that they're getting um is much
much more much more more interesting as
a result more more people getting hired.
Uh the uh pragma prag pragmatic part of
it. Uh let's say that you have a job and
uh within your job part of it is doing
some task that
however you describe that task that task
became infinitely fast. Okay. And and it
used to take you a week to do something.
Now it takes you a second to do it.
Well, my question to you is based on
that, what is more likely to happen?
That you now have
more time to enjoy coffee
or you just became busier? It is likely
you just became busier. It's just
another example of the radiology. The
reason for that is because that thing
that you were doing is causing you to
not be able to do all the other stuff
later be past that pipeline, you know,
past that workflow or past that task.
Now that task is done infinitely fast,
all of a sudden the answer came back to
you. You're now the critical path again.
It's no different than if I were to
issue an instruction to my my my group
of people that I'm working with and
they're supposed to go off and do a
research or do some analysis or
simulation and bring that answer back to
me so that I can make the next decision.
Today, it'll take a day or a week for
them to do that. Meanwhile, I'm doing
something else. But if it takes a second
for them to come back, I'm back in the
critical path again. I'm busier than
ever. Now, the reason for that is
because I have so many ideas. We have so
many ideas to go pursue that when the
tasks become super fast, it turns out we
could do more things. We become busier.
There's some evidence that smartphones
made us busier, not less busy. There's
some evidence that computers made us
busier, not less busy. And so, it's
because we're we are now increasingly
the critical path and because we have so
many ideas, we can pursue more things.
Now, I just hope in humanity, we always
find a way to discover new things to do,
new things to be busy. And and I'm
hoping that that as a result,
um we'll go off and work on the problems
that are that are the most meaningful,
the most valuable in all of our work,
which is the ones that are poorly
defined. The poorly defined work is the
most valuable of all work, discovery
work, creativity work, the original
creativity work. I'm not talking about
change the cat, you know, to a cat with
shag r shag rag, you know, shag shag
rugs, you know, and is I'm talking about
really create creating something out of
the box. And and so the the ability for
us to use AI to solve problems that are
fairly easy to des well not it doesn't
have to be even easy that this
describable um so that we could go and
work on things that are very difficult
to describe I think is is really quite
powerful.
>> Thank you. Shall we take the question
from the member in the gallery?
>> Hi um hi Jason my name is
>> Every job will change. You will not lose
your job to AI. You will lose your job
to someone who uses AI.
Thanks for that. Uh, hi Jason. My name
is Lily. I'm a EMBA student at the Jaji
Business School. I also run my own
venture builder called FunX. Um, it's
very interesting that you just mentioned
the UK's position almost having this
Goldilock moment because we commonly
believe the AI game is actually
happening in China or happening in the
US. So what do you think the UK or the
UK startup community can do more to
leverage its current position and
benefiting more from the AI game?
>> Regulate less.
>> And I I mean that I mean that deeply and
and uh uh genuinely for the best
interest of the UK. Uh you I know I know
that I know that regulators um and
because so much so much of regulators
are are lawyers which is good um but
because they want to protect us they
want to pro protect society um they
could regulate too early
especially on technology like this where
it's hard to predict the future. You
could watch sci-fi movies but that's not
the future. That's called the sci-fi
movie. And and to use the the science
fiction movies projected into reality um
and and um through some words of some
people uh cause cause social panic uh to
the point that you cause you you
overregulate um you're stifling the UK's
ability to innovate. Uh the fact of the
matter is China as you know is a under
technology is underregulated and the
reason for that is because the leaders
in China mostly are engineers. The
leaders in United States are mo mostly
lawyers and so you can kind of tell
what's going on. Uh and the the rate of
of technology evolution and industry
evolution in China is just running
incredibly fast because they regulate
late. They wait until the problems show
up and they regulate the problems. Uh
they solve they create regulations the
way engineers solve solve problems. You
know let's not dream it up. Let's let's
observe the problem. Let's understand
the root cause of the problem and then
solve the problem. And um and so anyhow
I would say under regulate less.
>> Jensen thank you very much for this
evening. I know that we have so many
questions but in the interest of time I
think it's fitting for us to conclude
there. But I want to say a huge thank
you from us um from all of the members
gathered here today and on behalf of the
selection committee. Congratulations
again on being the twins.
>> Thank you very very much.
>> Thank you.