實戰練英文 | NVIDIA 黃仁勳榮獲2025霍金獎，分享AI革命與創業心路；AI將改變所有的工作，讓你重回工作重點；想成功，一定要告訴自已這句話 | 劍橋大學演講 | 長文、註解、大字版、中英對照

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

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

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,

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

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 in inviting the 2025 Professor Steven Hawking Fellow, Mr. Jensen Juan.

Thank you. Thank you.

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 much.

Thank you.

Maybe I'll have my wife hold it.

>> Oh, perfect. Please.

>> I feel safer with her holding as far as he goes. Thank you.

he goes. 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 where it was a beautiful place? the fireplace was uh

beautiful place? the fireplace was uh was going and and uh I sat in a must have been a 30,000 year old chair uh

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

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 challenge 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.

The three of us, three friends in a townhouse in 1993, uh set out with an idea that we'd 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

accelerated computing. We invented new strategies to take that technology and that architecture and proliferate it 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 to 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

reinvented. The entire tech industry is being reinvented. And in fact, we do

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.

perfect? I think we're fine. Wonderful.

H 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 have been able to do so far.

>> You're making me nervous.

>> I promise you I'm on my best behavior today. Taking

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

beginning. It's a very good place to start. So, being born in Taipei and

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

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

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

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

beautifully. Let's talk about the decision that led to you becoming CEO of the company. So the dream and plan for

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

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

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

difficult times. It's mostly about sacrifice. It's about strategy. And

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

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

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. I 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

modest. Um and uh uh moving to United States, you know, was quite difficult for us in 19 1973.

But somehow we found we we made our way through it. And I think the the uh the

through it. And I think the the uh the the life the life of struggle endeavor nothing for granted having having to

earn everything. Uh that I think was

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

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

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? Cuz you said that you had

going on? Cuz you said that you had never fundraised before at such a high level. You'd never pitched such a

level. You'd never pitched such a brilliant idea. if you had never had a

brilliant idea. if 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

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 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

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 is you're kind of hard to um dissu 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,

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

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

game. And so, so I think it's it's not a complicated equation. oftentimes

complicated equation. oftentimes 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 I I stand up and you know video today we have 50,000

people and I declare something about the future you know I I I describe what I I think is 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

it constantly. I'm saying it constantly because you can't just say one 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

intellectually honest. you know, if I know that if I know that that um we ought to we have to change our mind and I don't um that's a that's a character question, you know, it's an ego

question. And you know, somehow I'm

question. And 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

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,

right. That's not our job. You know,

we're we're not our job isn't to be right. Our job is to help other people

right. Our job is to help other people succeed. It's related, not the same. And

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

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

little bit. Yeah, he's still right. And

so, you know, people just want you to succeed. and and um you want to create

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

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,

software. Prior to us, prior to the GPU, graphics accelerators were fixed function. Grow shading with gross

function. Grow shading with gross shading. Fun shading was function

shading. Fun shading was function shading. The way it, you know, whatever

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

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,

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

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.

phones. And so they were called phones.

And then now they're called smart mo smart smartphones. No phone company made

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

generation. And we constantly reinvented ourselves. We've now 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

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

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

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

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

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

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

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

time. I thought it would have been a great idea. I still think it would have

great idea. I still think it would have been a great idea.

And 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

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

molecule chemical. Um and it understands the meaning of it. its vocabulary, its meaning. It understands its

meaning. It understands its functionality, for example, you know, understand the the semantics. It understands 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

context. I've just used a bunch of words that when you apply to chatbt is very very obvious. But

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 computer understand the nature of proteins, the meaning of proteins, the structure of it, its dynamics. Um it should be able to

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

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

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

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 we 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

quite profound. So, you know, we we've created um what touring, you know, imagined, right? Artificial

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

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

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,

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

a chip. We go fab a chip and we hope it works. Today, these chips are a billion

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

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

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

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?

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

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 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

design it. And the reason for that is this. Although we although we are we're

this. Although we although we are we're all a little different, we're largely the same. So there's obviously structure

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

intelligence. Just like that. We sat

there and reasoned about it step by step by step. Um, usually we have the benefit

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.

>> Ling, 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

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, Karma 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

succeed. Remember we're a platform company. We're a tools company. We're a

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 the 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

you know. So, but but is this it is uh this you you really have an extraordinary moment. But for some

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

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

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

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

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

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. 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

labor. we have shortage of labor in just about every single company, every single field. And so although we talk about

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

before. And that's that's certainly the hope and I and I believe in it.

>> Just the last question from you before we hand over to the floor in the live audience Q&A. Many of the young people

audience Q&A. Many of the young people sat here today like you have a passion for science. Cambridge is a university

for science. Cambridge is a university much like your alma mart Stanford that has produced 126 Nobel Prize laureates 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

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 time plenty of time to do that. And so people have asked me would

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 29 year 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

those things. You're going to learn everything you need to learn along the way. And just, you know, if you if

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

and high. And then when called upon to My word. And then when called upon to do

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

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

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

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 uh 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

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

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

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

talking about. It's courage. It's

intellectual honesty. It's the absence of ego. the ability to be vulnerable in

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

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.

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

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,

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

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

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 interesting as a

result more more people getting hired.

uh the uh pragmat 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.

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

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

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, uh, just hope in humanity,

things. Now, uh, 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

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 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 is 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 L.

>> 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 Jaja Business School. I also run my own

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

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

the future. That's called the sci-fi movie. And and to use the the science

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 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

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 try

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

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 Heat. Heat.

Heat. Heat.