NVIDIA CEO Jensen Huang Reveals Keys to AI, Leadership

Summary

Jensen Huang, co-founder and CEO of NVIDIA, chronicles the company's 30-year journey pioneering accelerated computing to solve complex problems. He outlines NVIDIA's strategy focusing on challenging technical domains rather than commodity markets to attract exceptional talent. Huang forecasts AI driving tremendous productivity growth across industries. He advocates maximizing education, citing insatiable curiosity as the wellspring of innovation. Ultimately, with compassion and wisdom, Huang concludes that realizing humanity's potential requires transcending geopolitical differences through mutual understanding.

Takeaways

• 👌 Jensen Wong co-founded Nvidia over 30 years ago and has led its transformation into a leader in graphics processing and AI.
• 👍 Accelerated computing focuses on specialized hardware/software to accelerate key applications rather than general purpose computing.
• 📈 Nvidia rode the waves from gaming to crypto mining to AI computing through strategic adaptations while retaining focus on accelerated computing.
• 💰 Investing in AI computing infrastructure generates value - it takes raw data as input and produces intelligent tokens as output.
• 😎 To build something special, work on problems nobody else has solved rather than competing directly.
• 🤝 Nvidia collaborates with all major cloud/hardware providers by providing a flexible accelerated computing platform.
• 🔬 AI will unlock huge advances - from protein engineering to automated design - not take away jobs.
• 📚 Stay in school and keep learning - statistically the best path to success.
• ✨ As a CEO, lead with strong character and strategic thinking.
• 😀 The passion and expertise for the company's key craft should be embodied in its leadership.

Q & A

How did Nvidia get its start and what was the original vision?

Nvidia was founded in 1993 to pursue accelerated computing - the idea of specialized hardware and software accelerating key applications rather than relying solely on general purpose computing. The initial application targeted was 3D graphics for video gaming.

How has Nvidia navigated major technology shifts over its history?

Through sustained focus on accelerated computing, Nvidia has successfully rode waves from gaming to crypto mining and now AI. It has continued to adapt its strategy while retaining its core advantage in specialized parallel processing.

How does accelerated computing help drive advances in AI?

Accelerated computing infrastructure like Nvidia GPU clusters allows AI models to be trained on vastly larger datasets. This has enabled breakthroughs like large language models that were previously not computationally feasible.

Does AI represent a threat in terms of eliminating jobs?

No, AI will drive major productivity advances and create new opportunities. Organizations that utilize AI well will grow faster. The key is having ideas and opportunities to productively invest the benefits rather than resort to layoffs.

What is the best path to success in business?

Statistically, staying in school as long as possible to deeply learn gives the best foundation. But there are also many entrepreneurs without formal education who succeed through grit and learning on the job.

What are the most critical capabilities for a CEO?

A CEO needs strong character to guide the difficult choices companies face. Additionally, top CEOs have superior strategic thinking abilities - anticipating trends, making connections and mobilizing action better than peers.

How does Nvidia balance collaboration and differentiation?

Nvidia provides an accelerated computing platform that technical partners can build on top of flexibly. This expands Nvidia's reach tremendously while the core specialty in accelerated computing remains its key advantage.

Where will AI drive the biggest advances in the near future?

AI will unlock major advances in domains like protein engineering, automated design and language interfaces. It represents a general purpose capability to transfer learning across modalities.

How has Nvidia cultivated its culture of innovation?

By tackling extremely difficult problems at the cutting edge, Nvidia has attracted world-class talent driven to push boundaries and create new capabilities previously unimagined.

What does Jensen see as the next platform beyond accelerated computing?

There likely won't be a singular next platform. As needs emerge, additional specialized co-processors will augment CPU + accelerated computing as the appetite for more complex applications continues rising over time.

Outlines

00:00

🎓 Introduction by the hosts

The hosts William McLain and Rpneit introduce the event and speakers - Jensen Huang the CEO of Nvidia and Rpneit the Dean of Columbia Business School. They talk about Jensen's background and achievements in building Nvidia into a leading company in graphics processing and now artificial intelligence.

05:07

🏙 Nvidia's founding story and early focus on gaming

Jensen describes how Nvidia was founded in 1993 with the idea of specialized 'accelerated computing' focused on 3D graphics for video games. He acknowledges this was an unlikely idea at the time with many challenges, but they were able to get funding and make it work, establishing gaming as their first 'killer app'.

10:07

⛏️ The crypto mining chapter

Rpneit asks about the period when crypto mining became a big application for Nvidia GPUs. Jensen explains how the parallel processing capabilities of GPUs were very useful for crypto mining algorithms like Bitcoin and Ethereum. This opened up new possibilities for 'mining' digital assets.

15:08

🧠 AI and neural networks

Jensen discusses how GPUs are powerful for training neural networks and AI models given their ability to process huge datasets in parallel. He emphasizes that building the infrastructure for AI does not have to be expensive.

20:08

⛓ Supply chain and manufacturing

In response to Rpneit's question, Jensen explains why Nvidia chooses not to manufacture its own chips. He states their priority is creating an environment to attract talented people by working on hard, unsolved problems - not competing for market share in commodity businesses.

25:12

🤖 The future of AI

Jensen shares his optimism about AI not taking away jobs but empowering people who use it. He predicts huge advances in areas like protein engineering. Overall he emphasizes that AI will transform productivity and capabilities across many domains.

30:14

🧑‍💻 Nvidia's platform strategy

Jensen elaborates on Nvidia's platform strategy - creating software, algorithms and architecture to accelerate applications across many domains, while collaborating widely rather than being a fully integrated vertical company.

35:14

🔀 Training AI models

Jensen gives details about how deep learning works by compressing huge datasets into compact neural network models. He notes the scale of computation required but emphasizes that it is quite affordable given Nvidia's business.

40:14

🚀 The future of generative AI

Jensen talks about groundbreaking techniques like word2vec that learn vector representations capturing semantics. This will enable generative applications like translating between modalities (text, images, video).

45:16

🌱 Biotech revolution with proteins

Jensen predicts one of the most impactful AI applications will be engineering proteins and enzymes for goals like bioremediation and agriculture. He positions this generation to lead a revolution in protein engineering.

50:18

📈 Moore's Law and innovating on computing

In response to an audience question, Jensen explains why Moore's Law is slowing while accelerated computing offers a path to continue rapidly innovating. He emphasizes focussing on solving problems rather than attachment to any specific computing tool.

55:18

🌎 Geopolitics and globalization

Answering another audience question, Jensen acknowledges the real challenges of geopolitics and tensions. However, he expresses hope that solutions will be found allowing continuing prosperity and openness while addressing valid national security concerns.

00:21

🎓 Advice for students

Jensen encourages the student audience to maximize their time at Columbia, soak up the knowledge, and not rush through education. He reflects on skills like strategic thinking that are useful in business while emphasizing the primacy of personal character.

Mindmap

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Keywords

💡accelerated computing

Accelerated computing refers to using specialized hardware like GPUs to accelerate certain computing tasks, rather than relying solely on general-purpose CPUs. It is a key theme in the video, as Jensen Huang discusses how Nvidia focused on accelerated computing to improve performance for graphics, AI, and other applications. He contrasts it with general computing and argues it is necessary to meet the rising computing demands.

💡AI

AI or artificial intelligence is discussed extensively, as a major application area benefiting from accelerated computing. Jensen talks about how GPUs are ideal for training neural networks and powering AI algorithms. He predicts AI will bring many productivity benefits and new capabilities across industries like design, materials science, etc.

💡deep learning

Deep learning is an AI technique mentioned where neural networks are trained on large datasets to perform tasks like image recognition. Jensen explains how deep learning was a breakthrough enabled by GPU accelerated computing, as training models requires processing huge datasets.

💡Moore's law

Moore's law predicted that transistor density on chips would double every 18 months. Jensen argues this law has slowed for general-purpose CPUs, while accelerated computing on GPUs continues rapid gains. This drives Nvidia's growth.

💡parallel processing

Parallel processing or distributing work across multiple processors is key to improved performance with accelerated computing. Jensen notes GPUs excel at parallel processing cryptographic and AI workloads.

💡generative AI

Generative AI models like DALL-E 2 and GPT-3 are discussed as a recent breakthrough in large neural networks enabled by accelerated computing. Jensen argues these models illustrate the future capabilities of AI.

💡software platforms

Jensen emphasizes that accelerated computing is dependent on both hardware and a software platform to make it accessible. Nvidia focused on building CUDA and integrations so developers could easily leverage GPU acceleration.

💡data centers

Data centers with thousands of GPUs are required to train and run modern AI models. Jensen talks about Nvidia's business model of providing accelerated computing capabilities inside cloud data centers like AWS and GCP.

💡flywheel effect

Jensen describes Nvidia's flywheel of improved GPU performance leading to more adoption, which funds more R&D, further improvement, and so on. This self-reinforcing cycle drives their rapid growth.

💡strategic thinking

Jensen emphasizes strategic thinking is critical for CEOs to set vision and direction. He argues they must spot trends early, connect dots, and mobilize action around strategies.

Highlights

The future of design is going to be very different. The future of everything will be very different.

You join companies where they have more ideas than they can afford to fund so that when AI automates their work, it's going to shift.

When productivity increases, meaning we embed AI all over Nvidia, Nvidia is going to become one giant ai. We already use AI to design our chips.

The future is going to be about AI factories and then video gear, will be powering these AI factories.

Somehow we've taught computers how to learn to represent information in numerical ways.

The next 10 years is going to be unbelievable. We were the computer, the chip engineering generation. You'll be a protein engineering generation.

Accelerated computing is first understanding what are the domains, what are the applications that matter to you?

The CEO's uniquely, uniquely in the right place to be the chief strategy officer.

The geopolitical challenges are real and national security concerns are real. So are all of the other economic market.

I would stay here and pig out on knowledge for as long as I can. If I could do it again, I'd still be here. Dean and me sitting next to each other. I'm the oldest student here.

Building companies hard, there's nothing easy about it. There's a lot of pain and sufferings, a lot of hard work.

I think CEOs first need second, but you're close. So join companies where they have more ideas, more ideas than they have money to invest.

Strategy matters. Obviously. Business matters are very different things. Finance matters, very different things. And so you got to learn all these different things in order to build a company.

The CEO should know the craft. You don't have to have founded the craft, but it's good that you know the craft. There's a lot of crap that you can learn.

If you believe in statistics, stay school. Yeah, go through the whole thing.

Transcripts

I am the William v McLain professor of business in the decision and operations

division here at the Columbia Business School.

I want to thank you all for joining us this evening for tonight's program,

which features Jensen Wong, co-founder and c e O of Nvidia Corporation,

as well as our own ris, the Dean of Columbia Business School,

and David and Lynn Sip, the professor of business.

Our two speakers tonight have much in common. In fact,

they both graduated from Stanford in electrical engineering nearly the same

time, maybe even not possibly overlapping. Jensen is the co-founder,

president officer of Nvidia. He is a businessman,

entrepreneur and electrical engineer.

And over the last 30 years through his work at nvidia,

he has revolutionized first the graphics processing unit industry and now more

recently, the artificial intelligence industry.

He's been named the world's best,

c e o by Harvard Business Review and Brand Finance as well as Fortune Magazine's

business person of the year, and one of time magazine's,

100 most influential people.

Our fireside chat today has been made possible through both the David and Lynn

Discipline Leadership series, as well as the digital finance, sorry, excuse me,

the Digital Future Initiative. And additionally,

I serve on the leadership of the Digital Future Initiative, the D F I.

Here at C V s,

the Digital Future Initiative is C V S'S new think tank,

focusing on preparing students to lead for the next century of digital

transformation,

as well as helping organizations governances and communities better understand

leverage and prosper from future ways digital structure.

Now I would like to hand it over to.

Thank you.

Very much. Thank you all for coming. So.

This an exciting topic and a topic that is near and dear,

certainly to my heart.

And it's a topic where the school,

everything that we do at the school is changing so fast, trying to keep up,

trying to change curricula,

trying to create opportunities for our students to actually

learn about technologies and how they're changing the world and be honest,

prepare for the future and there is no better person to be having to

talk about AI than Jensen Pond. Jensen,

thank you so much for making the time and coming here. Welcome.

Sun. Sun. Yes. I just love hearing

talk here. I think the expectation's going to be pretty high,

but say something smart. Well,

good luck with you. So I want to start.

By having you walk us through a little bit the history of Nvidia

and then I talk a little bit about that leadership thing you just mentioned,

but you launched that company 30 years ago and

you have led it through a transformation,

different applications, different type of products.

Walk us through a little bit that journey.

Yeah, one of the most proud moments, I'll start with the proud moment,

what happened recently,

the c e O of Denny's where with my first company,

and they learned that Vidia, not only was I dishwasher,

bus boy and worked my way up corporate ladder and became waiter

at Denny's, and they were my first company

that I still know how to take. I still done the menu well,

super, by the way, anybody know what a superbird is?

What kind of College Street were you?

Denny's is America's Diner Go.

And that Nvidia was founded by

outside our home in San Jose there.

And so they contacted me recently and the booth that I sat at is now

in Nvidia Booth and my name is Nvidia.

This is where a trillion dollar company was founded.

And so

Nvidia was founded during a time when

the EC revolution and the microprocessor

was capturing

just above the entire industry and

the world properly solved that the CPU of micro

processor revolution. And it really reshaped how the IT

companies that were successful before the micro processor revolution,

revolution and companies successful.

We started with our company during that time and our perspective was that

general purpose computing,

as incredible as it's can't sensibly be the solution for,

we wanted to believe that a way of doing computing,

we call accelerated computing,

where you would add a specialist next to the generalist.

The CPU is a generalist. If you well could do anything,

it could do everything however you can.

Obviously if you can do everything and anything,

then obviously you can't do anything very well.

And so there are some problems we felt that were not solvable,

not good solutions or not the problems to be solved by what we call.

And so we started this accelerated computing company. The problem is

if you want to create a computing platform company,

you want to create a computing platform one hasn't created since 1960,

a year after I was the b m system 360

beautifully described what the computer is in 1964,

I B M described that the System 360 had a central processing unit,

IO subsystem, direct memory access, virtual memory,

binary compatibility across a scalable architecture.

It described everything that we described computers to this day 60 years later.

And we felt that there was a new form of computing that could solve some

problems At the time it wasn't completely pure what problems we could solve,

but we felt that we felt that accelerated comput. So nonetheless,

we went out to start this company and we made a great first

decision that frankly is un to this day,

if somebody were to come up to you and said, one,

we are going to invent a new technology that the world doesn't have. Everybody

wants to go build a computer company around cpu.

We want to build the computer company around something else connected to c p

number one and the killer app.

The killer app is a video three D video game in

1993

and that application doesn't exist. And the companies who we built,

this company doesn't exist and the technology that we're trying to build doesn't

exist.

And so now you have a company that has a

technology challenge and a market challenge and an

ecosystem challenge.

And so the odds of that company succeeding is approximately 0%.

But nonetheless,

we were fortunate this because two very important people frankly,

that I had worked with and Kristen Curtis, the three of us I've worked with

were incredibly important people in the technology industry at the time called

up the most important ship capital in the world,

Valenti at the time and told Don Don and his name

was Gu, wanted to

industry don give this kid money and then figure out along the way what it's

going to work. And fortunately they did.

But that business plan,

I wouldn't fund myself today and it just has too

many dependencies and each one of them has some profitability of success.

And when you compound all of these together, we multiply all these together.

And so nonetheless,

we imagined that there would be this market called video games and this

market would be the largest entertainment industry in the world at the

time it was zero

and three D graphics we oscillated with would be

used for telling the stories of almost a sport any game.

And so in virtual world, you could have any game, any sport,

and as a result everybody would be a gamer. And so Don Valentine asked me,

so how big is this market going to be? And I said, well,

every human will be a gamer someday. Every human would be a gamer. Someday.

Also the wrong answer, quite frankly for starting a company.

So these are horrible habits, these are horrible skills.

I'm not advocating them for you, but nonetheless,

it turned out to have been true video games turned out to be the largest

entertainment industry in three D graphics.

And we've found first Pillar app for accelerating,

which brought us the time to use accelerated Comput to solve a whole bunch of

other problems, which eventually led to.

This is fantastic. Sorry. So before we go to ai,

I would like to ask a little bit about the crypto period.

So gaming was a huge

obviously journey for Nvidia.

And then at some point in time the killer app became crypto and mining.

What was that chapter?

It's already computing can solve problems that normal computers can,

and all of our GPUs, even though you use it for designing cars,

designing buildings, designing, use it for molecular dynamics,

use it for playing video games,

it has this programming model called Kuda that we invented.

And Kuda is the only computing model sits that exists today

that is as popular as an exit. It's used by the vault Boeing.

And so anyways, one of the things that Kuda can do is process

parallel processing incredibly fast.

And obviously one of the algorithms that we would do very nicely on is

cryptography. And so when Bitcoin first came out,

there were no Bitcoin asics.

And the obvious thing is to go find the fastest supercomputer in the world.

And the fastest supercomputer that also has the highest volume is Nvidia you

use,

it's available in hundreds of millions of gamers' homes. And so

by downloading an application,

you could do some mining at your house. Well,

the fact that you could buy one of our GPUs, one of our computers,

and you plug it into the wall and money starts squirting out,

that was a day that my mom figured out what I did for a living.

And so she called me one day and she said, son,

I thought you were doing something about video games.

And I finally figured out what you do. You buy NVIDIA's products,

you plug it in and money courts out. And I said, that's exactly what I do.

And that's the reason why that's the, so many people bought it,

Bitcoin works led to Ethereum.

But the idea that you would use a supercomputer,

use a super processing system like via GPUs to

either encode or compress or do something to refine data and

transfer it, transform it into a valuable token.

You guys know what that sounds like to generate valuable tokens,

Chad should bet. And so today, really one of the things that's happening,

if you extend the sensibility about Ethereum and

crypto mining,

it's kind of sensible in the sense that all of a sudden we created this new type

of industry where raw data comes in,

you apply energy to this computer and literally money comes sporting out.

And the currency is of course tokens.

And that token is int intelligence tokens.

This is one of the major industries of the future.

Now I'll describe something else and it makes perfect sense to us today,

but back then it looks strange. You take water and you move it into a building,

you apply fire to it.

And what comes out is something incredibly valuable and invisible called

electricity.

And so today we're going to move data into a data center that's going to refine

it and it's going to work on it and it's going to harness the capability of it

and produce a whole bunch of digital tokens that are going to be valuable

digital biology, it'll be valuable in physics, it'll be valuable in it.

All kinds of computing areas and social media and all kinds of things.

Computer games and all kinds of things. And it comes out in tokens.

So the future is going to be about AI factories and then video gear

will be powering these AI factories.

So we have jumped into the neural networks and I want to,

and we talked about power computing, how we render graphics,

let's say on a monitor, how we play games,

how we solve cryptographic problems for Bitcoin.

Talk to us a little bit about how the G P U is useful in training

your own networks.

But then what I wanted us to do for this audience,

tell us a little bit about what it takes to train a model like J G P T,

what it takes in terms of hardware, what it takes in terms of data,

what in terms of the size of the cluster that you're using,

the amount of money that you need to spend. Because these are huge problems.

And I think giving us a glimpse of the scale would be

fun. Well,

everybody wants you to think that it's a huge problem. It's super expensive.

It's not. It's not. And let me tell you why.

It costs our company about five,

6 billion of engineering costs to design a trip.

And then at 1.2 years, three years later,

I had enter and I sent an email to T SS M C

and I F T P, basically a large positive TS M C. And they fab it.

And that process costs our company something along the lines of the half a

billion dollars. So five and a half billion dollars, I get a chip.

And that chip of course is valuable to us, but it's no big deal.

I do it all the time. And so if somebody were to say, Hey Jensen,

you need to build a billion dollar data center and once you plug it in,

money will start squirting out the other side. I'll do it in a heartbeat.

And apparently a lot of people do.

And the reason for that is because who doesn't want to build a factory for

generating intelligence now? So a billion dollars is not that much money,

frankly,

the world spends about 250 billion a year in infrastructure computing

infrastructure and none of it's generating money. It's just storing our files,

passing our email around. And that's already 250 billion.

And so one of the reasons why our growth we're growing so fast is because after

60 years,

general purpose computing is on decline because it is not sensible to

invest another 250 billion to build another general purpose computing data

center. It's too through force in energy,

it's too slow in computation. And so now accelerated computing is here,

that 250 billion goes to build accelerated computing data centers.

And we're very, very happy to support customers to do that. And in addition to

that, accelerated computing,

you now have an infrastructure can to AI for all of the things that we're just

talking about.

Basically the way it works is you take a whole lot of data and you compress

it, you compress it.

Deep learning is like a compression algorithm and you're trying to figure out,

you're trying to learn the mathematical representation of mathematical

representation, the patterns and relationships of the data that you're studying,

and you compress it into a neural network.

So what goes in is say trillions of

bytes, trillions of pokes. So let's say a few trillion,

trillion bytes,

what comes out of it is a hundred gigabytes.

And so you've taken all of that data and you've compressed it into this little

tiny funnel. A hundred gigabytes is like two DVDs.

Two DVDs you could download on your phone and you can watch it. So you could

download this giant neural network on your phone.

And now that all of this data has been compressed into it,

the data that's compressed, your network model is a semantic model,

meaning you can interact with it,

you could ask questions and it would go back into its memory and understand what

you meant and generate text, read, have a conversation.

So at the core is kind of like that. It sounds magical,

but for all the computer scientists in a room, it's very sensible.

And don't let anybody convince you it costs a lot of money.

I'll give you a good break. Everybody go Bill aids. Go bill,

as.

The scale. If I press you a little bit on that scale,

do you need a computer that is essentially a data center to estimate

these models?

16,000 GPUs is what it took to build a g, PT four,

which is the largest one that anybody's using today. It's a billion,

and that's a check. It's not even a very big check.

Don't be afraid. Don't let anybody talk you how to building a company,

build your.

Dreams.

Let me ask you a question about the billion dollar check and the

growth that you've been experiencing.

I think you were named the best C e O by H B R.

That's entertainment. That's.

Entertainment.

I'll keep repeating it and then eventually I appreciate that and then eventually

we'll end with that line. But in some sense,

you are leading a company right now through a period of extreme growth,

hypergrowth,

something that most companies have not experienced in their life.

And I want to perhaps.

Tell us.

A little bit about what does it look like? I mean,

doubling in size in under a year or

managing supply chains, managing customers, managing growth, managing money.

How do you actually add to that?

I love the management money part of it. Just counting is fun.

You just wake up in the morning and just roll around all the cash.

Isn't that what you guys are all here to do? My understanding is.

That's the end goal.

That's the end goal, yeah.

Let's see. Building companies hard,

there's nothing easy about it. There's a lot of pain and sufferings,

a lot of hard work. If it was easy, everybody would do it.

And the truth about all companies, big or small,

ours or others in technology, you're always dying.

And the reason for that is because somebody's always trying to leapfrog you.

So you're always on the wave of business.

And if you don't internalize that sensibility,

don't internalize that belief. You will go out of business.

So I started at Denny's, as you guys know,

and Nvidia was built out of very unlikely odds.

And it took us a long time to be here. I mean, we're a 30 year old company,

and when Nvidia first found it,

the PC Windows nine five had come out 1993,

and that was the first usable pc. We didn't have email.

And so there were no such laptops or smartphones,

none of that stuff existed.

And so you could just imagine the world that we were started in and the world,

we didn't have cd, everything was CRTs. And so the world was very,

very different. Cd ROS didn't exist. We just to put it in perspective,

all this stuff,

that was the era we were founded in and it took this long for our company

to be recognized as heavy reinvented for the first time in 60 years

growing fast. Growing fast is all about people.

Obviously companies is all about people. Whether you,

if the right systems in place, you get right,

your surrounded by amazing people like I am

and the company has craft skills.

It doesn't really matter whether you ship a hundred billion dollars or 200

billion. Now the truth is that the supply chain is not easy. People think,

does anybody know what a GForce graphics card looks like?

And just show me as a hand, anybody knows what Nvidia graphics card looks like.

And so you have a feeling that the graphics card is like a cartridge that you

put into a pc, PC express slide pc.

But our graphics chips these days,

what is used in these deep lining systems is 35,000

parts. It weighs 70 pounds.

It takes robots to build 'em because they're so heavy.

It takes a supercomputer to test it because it's a supercomputer itself

and it costs $200,000. And for $200,000,

you buy one of these computers,

you replace several hundred general purpose processors that cost several million

dollars. And so for every $200,000 you save,

you say for every $200,000 you spent with Nvidia,

you save two and a half million dollars in computing.

And that's the reason why I tell you, the more you buy, the more you save

and early, it's working out really well. People are really lining up.

So that's it. That's what we do for a living.

And the supply chain is complicated.

We build the most complicated computers the world's ever seen,

but hard can it be really? And

it's really hard. It's really hard.

But at the core of it, if you're surrounded by amazing,

the simple truth is that it's all about people.

And I'm lucky to be surrounded by a great management team.

You have.

And then the CEO E O says things like, make a So number one, something.

Like that. Yeah, make it work.

Make it work, make it. So.

I want to go back to AI trends and what you think about the

future, but you mentioned the word platform earlier on.

You mentioned your software environment.

So you have the hardware infrastructure,

you have a software environment that is actually pervasive in training neural

networks. Right now

you're building in data centers or you're creating environments within data

centers that are sort of clusters of Nvidia hardware,

software and

public communication between these resources,

how important it is to be sort of a whole platform solution versus a

hardware play.

And how core is that into Nvidia Drive?

Unlike first of all, before you could build something,

you have to know what you're building and

what is the reason the first principles for its existence.

Accelerated computing is not a chip, that's why it's not called an accelerator.

Accelerated computing is about understanding how can you accelerate everything

in life? If you can accelerate everything in life,

if you can accelerate every application, that's called really fast computing.

And so accelerated computing is first understanding what are the domains,

what are the applications that matter to you?

And to understand the algorithms and the computing systems and the architecture

necessary to accelerate that application.

So it turns out that general purpose computing is a sensible

idea. Accelerating an application is a sensible idea.

So we'll give you an example. There's, you have DVD decoders,

you play DVDs or H two sixty four decoders on your phone.

It does one job and one job only,

and it does it incredibly well. Nobody knows how to do it better.

Accelerated computing is kind of this weird middle.

There are many applications that you can accelerate. So for example,

we can accelerate all kinds of image processing stuff, particle physics stuff.

We can accelerate all kinds of things. That includes literary algebra.

We can accelerate, we can accelerate many,

many domains of applications. That's a hard problem.

Accelerating one thing is easy.

Generally running everything under A is easy.

Accelerating enough domains such that if you accelerate too many

domains, so those of you accelerate every domain,

then you're back to a general purpose processor.

What makes them so dumb that they can't build just a faster chip?

And so on the one hand, on the other hand,

if you only accelerate one application,

then the market size is not big enough to fund your r d.

And so we had to find that slippery middle. And that is the

strategic journey of our company. This is where strategy meets reality.

And that's the part that Nvidia got right,

that no other company in the history of computing ever got, right?

To find a way to have a sufficiently large domain of applications that we can

accelerate that is still a hundred times, 500 times faster than the C P U

and such that the economics, the flywheel,

the flywheel of number of domains expanding the number of customers,

expanding the number of markets, expanding the sales,

which creates larger r d, which allows us to create even more amazing things,

which allows us to stay well ahead of the c p. Does that make sense?

That flywheel is insanely hard to create. Nobody's ever done it.

It's only been done just one time.

And so that is the capability. And in order to do that,

you have to understand the algorithms,

you have to understand a lot about the domains of applications. You have to

select it, right? You have to create the right architecture for it.

And then the last thing that we did right,

was that we realized that in order for you to have a computing platform,

the applications you develop for Nvidia should run on all of video.

You should have to think, does it run on this chip?

Is it going to run on that chip? It should run on every chip.

It should run on every computer with Nvidia in it.

That's the reason why every single G p that's ever been created in our company,

even though we had no customers from Kudo a long time ago,

we stayed committed to it.

We were determined to create this computing platform since the very beginning.

Customers were not. And that was the pain and suffering.

It cost the company decades and billions of dollars getting here. And if

not for all the video gamers in the room here, we would be here.

You were our day jobs.

And then at night we can go solve digital biology.

Those help people with quantum chemistry.

They'll help people with artificial intelligence and robotics and such.

And so we realized, number one,

that we were accelerating computing a software problem.

The second thing is AI is a data center, data center infrastructure problem.

And it's a very obvious, because you can't train an AI model on a laptop,

you can't train it on a cell phone. It's not big enough of a computer.

The amount of data is measured in trillions of bytes,

and you have to process that trillions bytes billions of times.

And so obviously that's going to be a large scale computer distributing the

problem across millions of GPUs.

The reason why I say millions is 16,000 inside the 16,000 or thousands.

And so we're distributing the workload across millions of processors.

There are no applications in the world today that can be distributed across

millions of processors. Excel works on one processor.

And so that computer science problem was a giant breakthrough,

utterly giant breakthrough.

And this reason why it enabled generative AI enabled large language models.

So we observed two things. One, accelerated computing is a software problem,

algorithm problem, and AI is a data center problem.

And so we're the only company that went out and built all of that stuff.

And the last part that we did was a business model choice.

We could have been a data center company ourselves and be completely vertically

integrated. However, we would recognize that no computer company,

no matter how successful will be the only computer company in the world and it's

better to be a platform computing company because we love developers. It's

better to be a platform computing company that serves every computing company in

the world than to be a computing company all by ourselves.

And so we took this data center, which is the size of this room,

whole bunch of wires and a whole bunch of switches and networking and a bunch of

software.

We disaggregated all of that and we integrated into everybody else's data

centers that are all completely different.

So a w Ss and G C P and Azure and Meta and so on and so forth,

data centers all over the world, that's an insane complexity problem.

And we figured out a way to have enough standardization where it was necessary

enough flexibility so that we could accommodate enough collaboration with all

the world's computer companies. As a result,

N v's architecture has now graft, if you will,

into every single computer company in the world.

And that has created a large footprint, larger,

larger install base, more developers, better applications,

which makes customer happier customers provide them more chips,

which increases the install base, which increases our r d budget,

so on and so forth. The flywheel, the positive feedback system.

And so that's how it works. Nice and easy. So one thing you haven't done.

And I wanted you explain to us why if you haven't invested in

fabricating your own chips

and why.

Is that? That's an excellent question.

The reason for that is

as a matter of strategic choice, the core values of our company,

my own core values, the core values of our company is about choosing.

The most important thing in life is choosing. How do you choose?

How do you choose? Well, everything. How do you choose what to do tonight?

How do you choose? Well,

our company decides to choose projects for one fundamental goal.

My goal is to create the environment and environment by which

amazing people in the world will come and work.

Amazing environment for the best people in the world who want to pursue this

field of computing and computer science and artificial intelligence to create

the conditions by which they will come and do their lives work. Well,

if I say that then now the question is how do you achieve that?

So lemme give you an example of how not to achieve that.

Nobody that I know wakes up in the morning and say, you know what?

My neighbor is doing that, and you know what I want to do?

I want to take it from them. I can do it too.

I want to take it from them. I want to capture their share.

I want to pumble them on price. I want to kick 'em in.

I want to take their share. It turns out,

no great people do that.

Everybody wakes up in the morning and says,

I want to do something that has never been done before.

That's incredibly hard to do that if successful makes it great impact in the

world. And that's what greatest core values are. One, how do we choose,

do something that the world's never done before?

Let's hope that's insanely hard to do.

The reason why you choose something insanely hard to do by the way,

so that you have lots of time to go learn it.

If something is insanely easy to do, like tic-tac dough,

I wouldn't buss over it.

And the reason for that obviously is highly competitive. And so you got to

choose something that's incredibly hard to do and that thing that's hard to do

discourages a whole bunch of all by itself because the person who's willing to

suffer the longest wins. And so we choose things that are incredibly hard to do,

and you've heard me say,

pain is suffering a lot and it's actually a positive attribute.

People who can suffer are ultimately the ones that are the most successful,

number one. Number two,

you should choose something that's somehow you're destined to do.

Either a set of qualities about your personality or your expertise or the people

you're surrounded by, your scale, whatever your perspective,

whatever you're somehow destined to do. The number three,

you better love working on that thing so much because unless so,

the pain and suffering is too great. Now, I just described to you,

I just described to you Invidia's core values. It's that simple as that.

And if that's the case, what am I doing? Making a cell phone check.

How many companies in the world can make a cell phone a lie?

Why am I making a C P U? How many more CPUs do we need?

Does that make sense? We don't need all those things.

And so we naturally selected ourselves out of commodity markets.

We naturally selected ourselves out of commodity markets.

And because we selected amazing markets, amazingly hard to do things,

amazing people joined us.

And because amazing people joined us and because we had the patience and let

them go succeed to go and do something amazing,

have the patience to let 'em do something amazing, they do something amazing.

The equation is that simple. The equation is literally that simple.

It turns out it's simple to say, it takes incredible character to do.

Does that make sense? That's why it's the most important thing to learn.

It turns out great success and greatness is all about character.

And no fabrication.

The reason why we don't do fabrication is because T SS m C does it so well,

and they're already doing it. For what reason do I go take their work?

I like the people at t c, they're great friends of mine.

Cc's a great friend of mine,

Mark's a great friend of mine just because I've got business,

I can drive into it. So what, they're doing a great job for me.

Let's not squander my time to go repeat what they've already done.

Let's go squander my time on something that nobody has done.

Does that make sense? Nobody has done, that's how you build something special.

Otherwise you're only talking about market share.

Thinking about the future, what do you think

when we're thinking about these decade.

Are these right answers? By the way,

I don't have an M B A and I didn't get a finance degree.

I read some books and I watched a lot of YouTubes. I got to tell you,

nobody watches more business YouTubes than I do.

And so you guys have nothing on me.

Are these right answers professor

version?

But yes, they're the right answers. And best, c e o. Yeah, right?

And what.

Do you think about ai?

What are you thinking about AI applications and where we're going to

see change in our lives, let's say over the next 3, 5, 7 years?

Where do you see that going and in places where we

will all potentially be affected in our daily experience?

Yeah, first of all, I'm going to go to the punchline.

AI is not going to take your jobs.

The person who used AI is going to take your job. You guys agree with that?

Okay?

So use AI as fast as you can so then you can stay gainfully employed.

Let me ask you a second thing. When productivity increases,

when productivity increases, meaning we embed AI all over Nvidia,

Nvidia is going to become one giant ai. We already use AI to design our chips.

We can't design our chips, we can't write our optimizing compilers without ai.

So we use AI all over the place.

When AI increases the productivity of your company, what happens next?

Layoffs Or you hire more people,

you hire more people.

And the reason for that is give me an example of one company that had earnings

growth because of productivity gains that said, guess what?

My gross margins just went up time for a layoff.

So why is it that people think about losing jobs?

If you think you have no new ideas, then that's the logical thing.

Does that make sense?

If you don't have any more ideas to invest your incremental earnings,

then what are you going to do? When the work is replaced? It's automated.

You lay people off.

And so join companies where they have more ideas than they can

afford to fund so that when AI automates their work,

it's going to shift. Of course, it's going to change the style of working,

AI's going to come after CEOs right away. Deans and CEOs

we're so toast.

I think CEOs first need second, but you're close.

So you join companies where they have more ideas,

more ideas than they have money to invest. And so naturally,

when earnings improve, you're going to hire more people. Ai.

So first of all, this is the giant breakthrough.

Somehow we've taught computers how to learn to

represent information in numerical ways. Okay,

so you guys, has anybody heard of this thing called word to back?

It's one of the best things I've ever word to back a word.

You take words and you learn from the words studying every single word.

It's relationship to every other word. And you learn,

read a whole lot sentences of paragraphs,

and you try to figure out what's the best number vector,

what's the best number to associate with that word?

So mother and father are close together numerically,

oranges and apples are close together. Numerically,

they're far from mom and dad. Dogs and cats are far from mom and dad,

but closer probably to mom and dad than they are from

oranges and apples chair and tables and chair.

Hard to say exactly where they lie,

but those two numbers are close to each other, far away from mom and dad,

king and queen, close to mom and dad. Does it make sense?

Imagine doing this for every single number and every time you test it, you go,

son, a gun. That's pretty good.

And when you subtract something from something else, it makes sense. Okay?

That's basically learning the representation of information.

Imagine doing this for English. Imagine doing this for every single language.

Imagine doing this for anything with structure,

meaning anything with predictability. Images have structure.

Because if there are no structure, it'd be white noise.

Physically it'd be white noise. And so there must be structure.

That's the reason why you see a cat, I see a cat, you see a tree, I see a tree.

You can identify where the tree is, you can identify where the coastline is,

where the mountains are where. And so we could learn all of that.

So obviously you could take that image and turn it into a vector.

You could take videos and turn into vector three D into vectors,

proteins into vectors, because there's obviously structure and protein,

chemicals into vectors. Genes eventually into vectors.

We can learn the vectors of everything. Well,

if you can learn everything into numbers and its meaning,

then obviously you can take ca word, c a t,

translated to the image c a t image of ca.

Obviously this is the same meaning if you can go from words to images,

that's called mid journey staple diffusion. If you can go from images to words,

that's called captioning video,

YouTube videos to words underneath videos. And so one of you went from,

what do you call it, if you go from say,

amino acids to proteins, that's called the Nobel Price.

And the reason for that is because that's alpha alcohol. Incredible.

Isn't that right? And so this is the amazing time,

the amazing time in computer science where we can literally take information

one kind and convert it, transfer it

generated into information of another kind.

And so you can go text to text a large body of text, P D F,

small body of text, a summarization of archive, which I really enjoy, right?

And so instead of reading every single paper,

I can ask it to summarize the paper.

And it has to understand images because in the archive,

the papers have a lot of images and charts and things like that. So you can take

all of that to summarize it.

And so you can now imagine all of the productivity benefits and in fact the

capabilities you can't possibly do without it. So in the near future,

you do something like this, you say, hi, I would like to design,

give you some options of a whole bunch of cars. I work for Mercedes,

I really care about the brand. This is the style of the brand.

Lemme give you a couple of sketches and maybe a couple of photographs of the

type of car I like to build. It's a four wheel,

s u v four wheel drive, SS u v, let's say, so on and so forth.

And all of a sudden it comes up with 20 10,

200 completely fully three D design

cab.

Now the reason why you want that instead of just finishing the car is because

you might want to select one of them and you say,

iterate on this one another 10 times,

and you might find select one and then modify it yourself. And so the future of

design is going to be very different.

The future of everything will be very different. Now,

if you gave that capability to designers, they would go in the same,

they would love you so much. They would love you so much.

And that's the reason why we're doing this. Now,

what's the long-term impact of this?

One of my favorite areas is if you could use language to describe a

protein and you could use language to figure out a way to synthesize protein in

the future of protein engineering is near us. And protein engineering,

as you know, creating enzymes to eat plastic,

creating enzymes to catch a carbon,

creating enzymes of all kinds to grow vegetables better,

all kinds of different enzymes could be created during your generation.

And so the next 10 years is going to be unbelievable. We were the computer,

the chip engineering generation. You'll be a protein engineering generation.

Something that we couldn't imagine doing just a few years ago.

I think we're going to open it up for q and a to the audience.

So questions,

and maybe I'll point and we have some mics that will be running okay

over there. We'll start there.

Thank you for coming tonight. Thank you.

So are you worried that Moore's law business schools are students are so

serious,

I understand that the graduates of Columbia ends up being

investment bankers and stock traders. I'm actually, look, computer science,

is that right? Is that right? And one computer science, you'll be,

and so that's what I understand. So I'm here selling stock

in the future. In the future, if somebody asks you what stock to buy and video,

go ahead. A question for you is,

are you worried that Moore's law might actually catch up to GP industry as it

did for companies like,

and can you also explain the difference between Moore's law and CO's law?

I didn't phrase Wong's law and it wouldn't be likely me to do so.

The very simple thing is this,

Moore's Law was twice the performance every year and a half approximately.

The easier math to do is 10 times every five years.

So every 10 years is about a hundred times, if that's the case.

In general, purpose computing microprocessors,

the general purpose computing was increasing in performance at 10 times every

five years, a hundred times every 10 years.

Why change the computing method a hundred times every 10 years?

Not fast enough. Are you kidding me?

If cars would go a hundred times every five years when life be good?

And so the answer is it's in fact, Moore's law is very good,

and I benefited from it. The whole industry benefited from it.

The computer industries here because of it,

but eventually set general purpose computing. Moore's law.

It is not about the number of transistors in computing,

it's about the number of transistors, how you use it for CPUs,

how you translate it ultimately to performance.

That curve is no longer 10 times every five years. That curve,

if you're lucky, is two or four times every 10 years.

Well, the problem is if that curve is two or four times every 10

years,

the demand for computing and our aspirations of using computers to solve

problems, our aspirations,

our imagination for using computers to solve problem,

it's greater than four times every 10 years. Isn't that right?

And so our imagination, our demand,

the world's consumption of all exceeds that. Well,

you could solve that problem by just buying more CPUs. You could buy more.

But the problem is these CPUs consume so much power because of general purpose.

It's like a generalist. A generalist is not as efficient.

The craft is not as great.

They're not as productive as a specialist.

If I'm ever going to have an open chest wound, I don't send me a generalist.

You guys know what I'm saying? If you guys are around, just call a specialist.

Alright? Yeah,

he's a vet, he's a generalist.

Look or do wrong specialist.

So generalist is too brute forced.

And so today it costs the world too much energy.

It costs too much to just brute force general purpose computing. Now,

thankfully, we've been working on accelerating computing for a long time,

and accelerating computing, as I mentioned, is not just about the processor,

it's really about understanding the application domain and then creating the

necessary software and algorithms and architecture and chips.

And somehow we figured out a way to do it behind one architecture.

That's the genius of the work that we've done,

that we somehow found this architecture that is both incredibly

fast. It has to accelerate the C P U a hundred times, 500 times,

sometimes a thousand times.

And yet it is not so specific that it's only used for

one singular activity. Does that make sense?

And so you need to be sufficiently broad so that you have large markets,

but you need to be sufficiently narrow so you can accelerate the application.

That fine line,

that razor's edge is what caused the video to be here.

It's almost impossible, if I can explain the 30 years ago,

nobody would've believed it. And in fact, if you did, to be honest,

it took a long time and we just stuck with it and stuck with it and stuck with

it. And we started with a seismic processing,

molecular dynamics, image processing, of course, computer graphics.

And we just kept working on and working on and working on weather simulation,

fluid dynamics, particle physics, quantum chemistry,

and then all of a sudden one day and deep learning

and then transformers,

and then the next will be some form of reinforcement learning transformers,

and then there'll be some multi-step reasoning systems. And so all of these

things are we just one application,

somehow we figured out a way to create an architecture and solve all.

And so will this new law end.

And I don't think so. And the reason for that is this.

It doesn't replace the C P U, it augments the cpu.

And so the question is, what comes next to augment us?

We'll just connect it next to it. We're just connect it next to it.

And so when the time comes, we'll know,

we'll know that there's another tool that we should be using to solve the

problem because we are in service of the problems we're trying to solve.

We're not trying to build a knife and make everybody use it.

We're not trying to build a acquire, make everybody use it.

We're in service of accelerated computings in service of the problem.

And so this is one of the things that all of you learn.

Make sure your mission is right.

Make sure that your mission is not build trains,

but enable transportation. Does that make sense?

Our mission is not build GPUs.

Our mission is to accelerate applications,

solve problems that normal computers cannot.

If your mission is articulated right and you're focused on the right thing,

it'll last forever. Thank you. Okay.

Up there. Someone? Yes,

that guy right there is Tony. Go ahead. Tony.

Am I, Tony? What's Tony say?

Where's Tony? Tony was that guy in the middle, right? Yeah. See,

I met him just now. I'm just kidding. Straight.

My memory.

Take my chance.

I wasn't, I wasn't trying to give Tony the mic.

I was just demonstrating my incredible memory for Tony.

Go ahead.

Thanks again.

Now there's a push for onshoring,

the supply chains for semiconductors.

Then there are also restrictions on the export supply countries.

How do you think that would affect Nvidia in the short term,

but also how would that affect us as consumers in the long term?

Yeah, really excellent question. You guys all heard a question.

It's all repeated relates to geopolitics and geopolitical tension and such.

The geopolitical tension,

the geopolitical challenges will affect every industry will affect every human.

We deeply, we the company deeply believed in national security.

We are all here because our countries are known for security.

We believe in national security,

but we also simultaneously believe in economic security.

The fact of the matter is most families don't wake up in the morning and say,

good gosh, I feel so vulnerable because of the lack of military.

They feel vulnerable because of economic survivability.

And so we also believe in human rights and the

ability to be able to create a prosperous life is part of human rights.

And as you know,

the United States believe in the human rights of the people that live here as

well as the people that don't live here. And so the country believes in all of

those things simultaneously. And we do too.

The challenge with the geopolitical tensions,

the immediate challenge is that if we're too unilateral about deciding

that we decide on the prosperity of others, then there will be backlash.

There'll be unintended consequences. But I am optimistic.

I want to be hopeful that the people who are thinking through this are thinking

through all the consequences and unintended consequences.

But one of the things that has done is that it has caused every country to

believe to deeply internalize

its sovereign rights.

Every country is talking about their own sovereign rights.

And that's another way of saying everybody's thinking about themselves and

as it applies to us. On the one hand,

it might restrict the use of our technology in China and the export control

there. On the other hand,

because of sovereignty and every country wanting to build its own sovereign AI

infrastructure, and not all of them are enemies of the United States,

and not all of 'em have a difficult relationship with the United States,

we would help 'em build AI infrastructure everywhere. And so in a lot of ways,

this weird thing about geopolitical,

it limits the market opportunities for us in some way.

It opens the market opportunities in other ways. But for people,

for people, I am just really hopeful.

I really hope not hopeful. I really hope that

we don't allow our tension with China result

into tension with Chinese.

That we don't allow our tension with the Middle East turn into tension with

Muslims. Does that make sense? We are more sophisticated than that.

We can't allow ourselves to fall into that trap. And so

a little bit about that. I worry about that as a slippery slope.

One of our greatest sources of intellectual property for our country as

foreign students. I see many of 'em here. I hope that you stay here.

It is one of our country's single greatest advantage.

If we don't allow foreign students in the brightest minds in the world to come

to Columbia and keep you here in New York City,

we're not going to be able to retain the great intellectual property of the

world. And so this is our fundamental core advantage.

And I really do hope that we don't ruin that. So as you can see,

the geopolitical challenges are real and national security concerns are real.

So are all of the other economic market. Social technology matters, technology,

leadership matters, market leadership matters. All that stuff matters.

The world's just a complicated place.

And so I don't have a simple answer for that. We will all be affected.

So we'll take one more question there.

But in the meantime, stay focused on your school. Do a good job,

just study.

Hi there.

So I actually started off working as an engineer at a semiconductor company at

Houston in entrepreneurship.

And now I'm here as someone like yourself that is fundamentally technologist,

an engineer, started a company,

very successfully learned finance from YouTube videos.

What do you think of MBAs?

Oh, I think it's terrific. You should be, first of all,

you'll likely live until you're a hundred. And so that's the problem.

What are you going to do for the last 70 years or 60 years?

And this isn't something I'm telling you,

it's something I tell everybody care about. Look to the best of your ability.

Education. When you come here, you're forced by education.

How good can that be after you leave? Like me?

I got to go scour the planet for knowledge

and I've got to go through a lot of junk.

That gets to some good stuff. You're in school,

you've got all these amazing professors who are curating the knowledge for you

and present it to you in a platter. My goodness,

I would stay here and pig out on knowledge for as long as I can.

If I could do it again, I'd still be here.

Dean and me sitting next to each other. I'm the oldest student here.

I'm just preparing for that big step function when I graduate,

just go instantaneously. Success.

I'm just a little kidding about that.

You have to leave at some point and your parents won't appreciate it.

But don't be in a hurry, I think. Learn as much as you can.

There's no one right answer to getting there.

Obviously I have friends who never graduated from college and they're

insanely successful. And so there are multiple ways to get there.

But statistically, I still think this is the best way to get there,

statistically. And so if you believe in stat in math and statistics,

stay school. Yeah, go through the whole thing.

And so I got a.

Virtual b a by working through it, not because of choice.

When I first graduated from school, I thought I was going to be an engineer.

Nobody says, Hey, Jensen, here's your diploma. You're going to be a c e O.

And so I didn't know that. So when I got there, I learned

M B A. And there's a lot of different ways to learn. Business strategy matters.

Obviously. Business matters are very different things. Finance matters,

very different things.

And so you got to learn all these different things in order to build a company.

But if you're surrounded by amazing people like I am,

they end up teaching you along the way.

And so there's some things that depending on what role you want to play,

that's critical. Yours, okay? And so for a C e O,

there are some things that are critically, it's not only my job,

but it's critical that I lead with it. And that's character.

There's something about your character, about the choices that you make,

how you deal with success, how you deal with failure.

And Norma said that how you make choices,

those kind of things matter a lot. Now,

from a skill and craft perspective,

the most important thing for a C is strategic thinking.

There's just no alternative. The company needs you to be strategic.

And the reason for that is because you see the most.

You should be able to look around corners better than anybody.

You should be able to connect dots better than anybody.

And you should be able to mobilize. Remember what a strategy is, action.

It doesn't matter what the rhetoric says, it matters what you do.

And so nobody can mobilize the company better than the CEO O. And so therefore,

the CEO's uniquely,

uniquely in the right place to be the chief strategy officer, if you'll,

and so those two things, I would say, from my perspective,

two of the most important things.

The rest of it has a lot of skills and things like that.

And you'll learn the skills. And maybe if I could just add one more thing.

I do believe that a company is about some

particular craft. You make some unique contribution to society.

You make something and you make something. You ought to be good at it.

You should appreciate the craft. You should love the craft.

You should know something about the craft, where it came from,

where it is today, and where it's going to go. Someday.

You should try to embody the passion for that craft.

And I hope today I get a little bit embodying the passion and the expertise of

that craft. I know a lot about the space that I'm in,

and so if it is possible,

the CEO should know the craft. You don't have to have founded the craft,

but it's good that you know the craft. There's a lot of crap that you can learn.

And so you want to be an expert in that field.

But those are some of the things you can learn that here. Ideally,

you can learn on the job, you can learn that from friends.

You can learn that a lot of different ways to do it. But stay in school.

So before I thank the best c e o,

I want to thank the Digital Future Initiative, the David Hilton Speaker Series,

but mostly thank you gentlemen for coming.

We all understand why you were voted the best, c e o now. Thank you.