Jensen Huang, Founder and CEO of NVIDIA

Summary

In this inspiring interview, Jensen Huang, CEO of NVIDIA, shares his remarkable journey of transforming a startup into a pioneering tech giant. He reveals the core principles that have guided his leadership, emphasizing the importance of empowering employees, fostering a culture of innovation, and staying true to one's beliefs. Huang reflects on overcoming challenging times, such as the 80% market cap loss during the financial crisis, and highlights the role of 'early indicators of future success' in driving the company forward. He discusses the disruptive potential of generative AI and encourages a first-principles approach to harnessing its power responsibly. Throughout the interview, Huang's wisdom and passion for pushing the boundaries of computing shine through, inspiring viewers to redefine their own tomorrows.

Takeaways

• 😀 Have a strong core belief and vision, but be willing to adapt and reinvent along the way based on new information and opportunities.
• 🤖 Aggressively pursue new technologies and applications that can solve problems current general computing cannot address, even if the market doesn't exist yet.
• 📚 Continuously learn and study from first principles - treat challenges as opportunities to gain new knowledge and insights.
• 🏆 Seek out 'early indicators of future success' rather than getting fixated on short-term financial metrics.
• 🧠 Empower employees by being transparent, informing them of the broader context, and showing how you reason through problems.
• 🌳 Design the organizational structure from first principles based on what you are trying to build, not legacy hierarchical models.
• 🔭 Look backwards from the future you envision for your company and work backwards to get there.
• 🚀 Embrace generative AI as a paradigm shift that will fundamentally change computing and disrupts industries.
• ⚖️ Support tailored regulation for AI products/services by existing bodies, not blanket regulation across domains.
• 👥 Surround yourself with people you love and take them on the journey with your core beliefs as the guiding force.

Q & A

What motivated Jensen Huang to leave LSI Logic and become a founder?

Jensen was motivated by the opportunity to build a company focused on solving problems that normal computers powered by general-purpose computing couldn't, as suggested by his colleagues Chris and Curtis.

How did Nvidia choose its first killer app and what was it?

Nvidia's first killer app was decided to be 3D graphics, targeting the application of video games. This decision was based on the intersection of the challenge of making 3D graphics affordable and the non-existent video game market for PCs at the time.

What was Nvidia's approach to overcoming the challenge of their technology becoming incompatible with Microsoft's direct 3D?

Nvidia reset the company by adopting the OpenGL standard for computer graphics after their initial technology became incompatible with Microsoft's direct 3D, allowing them to innovate and create something the world had never seen before.

How did Jensen Huang ensure Nvidia stayed on track during challenging times, such as when its market cap dropped 80%?

Jensen focused on core beliefs and the fundamentals of the business, maintaining the same approach to work and leadership, and ensuring the company remained focused on its mission despite external market pressures.

What role does generative AI play in Nvidia's vision for the future?

Generative AI is seen as a revolutionary technology that will change the nature of computing, enabling software to understand and generate new content, which will have broad implications for various applications and industries.

What were the key principles behind the creation of Nvidia's organizational structure?

Nvidia's organizational structure is designed to be flat, prioritizing empowerment and information accessibility across all levels to ensure every employee can contribute effectively to the company's mission.

How does Jensen Huang view the potential challenges and regulations surrounding AI?

While acknowledging the need for technological advancements to address safety and ethical considerations, Jensen emphasizes the importance of context-specific regulations across different industries, rather than overarching regulations that might stifle innovation.

What was Nvidia's strategy for entering and growing the gaming market for PCs?

Nvidia focused on creating technology and markets simultaneously, recognizing the need to both innovate in 3D graphics technology and actively develop the gaming market for PCs to ensure the company's success.

How did Nvidia adapt when realizing their initial vision wasn't working as expected?

Nvidia was flexible in its approach, shifting focus from their initial vision to explore and capitalize on the emerging market of 3D graphics for gaming, demonstrating the company's ability to pivot based on market demands and technological possibilities.

What does Jensen Huang consider as the most important aspect of his leadership role at Nvidia?

Jensen Huang believes his most important role is to create the conditions that allow his employees to do their life's work, focusing on empowerment, information sharing, and fostering an environment where everyone can contribute to Nvidia's mission.

Outlines

00:00

👨‍💼 Founding Nvidia and Securing Initial Funding

Jensen describes how he and his co-founders Curtis Priem and Chris Malachowsky decided to start Nvidia during the microprocessor revolution in the early 1990s. Their goal was to build a company that could solve problems that normal computers couldn't handle. Jensen shares the story of how he secured funding from Don Valentine, a renowned Silicon Valley investor, with the help of his former boss at LSI Logic, Wilfred Corrigan.

05:00

🎮 Creating the 3D Gaming Market for PCs

Jensen explains how Nvidia's initial focus was on creating the technology and market for 3D graphics in video games on PCs. This involved developing affordable hardware and software solutions to enable 3D graphics, which were previously only available on expensive workstations. Nvidia had to create both the technology and the market, working with companies like Electronic Arts to drive adoption.

10:02

💡 Pivoting and Implementing the OpenGL Pipeline

Jensen discusses how Nvidia had to pivot and reset the company when their initial 3D graphics technology became incompatible with Microsoft's Direct3D standard. They implemented the OpenGL pipeline by studying a textbook on how Silicon Graphics did computer graphics. This pivotal moment gave the company confidence that they could succeed by learning and reinventing from first principles.

15:03

📈 Expanding into New Markets and Applications

Jensen talks about Nvidia's efforts to expand into new markets and applications beyond 3D graphics, such as computational drug design, weather simulation, and artificial intelligence. He highlights the importance of identifying 'early indicators of future success' to guide the company's investments in emerging technologies, even when the markets don't yet exist.

20:04

🤖 The Impact of Deep Learning and AI

Jensen discusses how Nvidia recognized the potential of deep learning early on and worked with researchers to develop tools and languages for expressing neural network algorithms on their processors. He emphasizes the importance of reasoning from first principles to understand the implications of new technologies like AI and how they could fundamentally change computing and software development.

25:05

💼 Leadership During Challenging Times

Jensen shares his approach to leading Nvidia through challenging times, such as when the company lost 80% of its market cap during the financial crisis. He emphasizes the importance of gut-checking core beliefs, focusing on the company's mission, and being transparent with employees to keep them motivated and empowered.

30:06

🗺️ Creating a Flat and Empowered Organization

Jensen explains his philosophy behind creating a flat and highly empowered organization at Nvidia. He believes in minimizing layers of information flow and trusting employees with important information to enable them to make smart decisions. Jensen's goal is to create conditions where employees can do their life's work and contribute to the company's mission.

35:06

🔮 The Future of Generative AI and Computing

Jensen discusses the implications of generative AI and its ability to understand and generate content across different modalities (text, images, speech, etc.). He explains how this technology could fundamentally change the way we process information, write software, and approach computing. Jensen encourages reasoning from first principles to understand how different industries and applications might be disrupted by generative AI.

40:08

🌍 Redefining Tomorrow and Nvidia's Contribution

When asked about redefining tomorrow, Jensen emphasizes the importance of making a unique contribution to advancing the future of computing, which he believes is the most important instrument for humanity. He envisions Nvidia being remembered as a company that changed everything through its work in creating new technologies and enabling new markets.

45:10

🤖 Addressing Concerns About AI Development Pace and Regulation

Jensen acknowledges the concerns about the pace of AI development and the need for regulation. He emphasizes the importance of advancing technologies for grounding, safety, and security to mitigate risks. Jensen advocates for product and service-specific regulations rather than an overarching 'super regulation' and highlights the need for social implications of AI to be addressed.

50:11

🤵 Rapid-Fire Questions and Advice

Jensen responds to a series of rapid-fire questions, including recalling his fondest memory from working at Denny's, what he would wear if there were a shortage of black leather jackets, and what he would title a hypothetical textbook. As a parting piece of advice, he encourages having a core belief, gut-checking it every day, pursuing it with all one's might, surrounding oneself with loved ones, and taking them on that journey.

55:13

🙏 Closing Remarks

The session concludes with Jensen expressing gratitude for the opportunity to share his insights and experiences.

Keywords

💡Founder

The term 'Founder' refers to an individual or group of individuals who establish a company or startup. In the video, Jensen discusses his journey from working at LSI Logic to becoming a founder of Nvidia alongside Chris and Curtis. This transition highlights the leap from being an employee to taking on the entrepreneurial challenge of building a new company, motivated by the desire to solve unique problems that conventional computing couldn't address at the time.

💡Accelerated Computing

Accelerated Computing refers to the use of specialized hardware to perform computational tasks more efficiently than traditional CPUs. The video narrative describes Nvidia's focus on accelerated computing as a cornerstone for its mission, aiming to build computers that solve problems normal computers can't. Jensen discusses the early days of the PC revolution and how the company decided to contribute to it by focusing on areas like computational drug design, weather simulation, and AI, highlighting the foundational goal of Nvidia.

💡3D Graphics

3D Graphics is used in the context of Nvidia's initial killer application, identified by Jensen and his team as the technology focus for their company. This choice was driven by the challenge and opportunity of making high-cost image generation technologies, like those used in workstations, more accessible for the video game market. The narrative emphasizes how this decision shaped Nvidia's early direction and success, underlining the importance of selecting impactful applications for emerging technologies.

💡Microprocessor Revolution

The Microprocessor Revolution refers to a pivotal period in computing history characterized by rapid advancements in microprocessor technology, leading to the widespread adoption of personal computers. Jensen recounts Nvidia's inception during this transformative era, highlighting the environment of innovation and the anticipation of significant change, such as the launch of Windows 95 and the Pentium processor, that Nvidia was born into and aimed to contribute to through accelerated computing and graphics.

💡Entrepreneurship

Entrepreneurship in this context refers to the process of starting and building a new business venture to innovate and solve problems. Jensen's narrative embodies the entrepreneurial spirit, from taking the risk of leaving a stable job to tackle the uncertainties of founding Nvidia, to navigating the challenges of identifying markets and technologies that could leverage the company's strengths. His story illustrates the essence of entrepreneurship - vision, risk-taking, and perseverance.

💡Computational Drug Design

Computational Drug Design is mentioned as one of the problems Nvidia set out to solve with its technology. This field involves using computer algorithms to discover, enhance, or study drugs and biological systems. Jensen's inclusion of this area underlines Nvidia's broader mission to impact sectors beyond traditional computing, demonstrating the company's ambition to contribute to advancing scientific research and healthcare through accelerated computing and AI.

💡AI (Artificial Intelligence)

AI is a central theme in Jensen's discussion, reflecting Nvidia's pivot and significant contribution to the field through GPU acceleration. Jensen discusses AI in the context of Nvidia driving down computational costs, enabling new software development paradigms where computers can generate code, leading to advancements in autonomous vehicles, robotics, and more. The mention of AI showcases Nvidia's role in the evolution of computing from graphics to general computational acceleration, facilitating groundbreaking advancements in technology.

💡Business Plan

The Business Plan segment offers insights into Jensen's approach to entrepreneurship and the practical challenges of starting a company. He humorously recounts his attempt to learn how to write a business plan from a book, only to realize the urgency and complexity of his endeavor required a more direct approach. This anecdote serves to underscore the hands-on, learn-as-you-go aspect of building a startup, emphasizing the importance of adaptability and vision over formal planning in the early stages.

💡Innovation

Innovation is a key concept throughout the video, reflecting Nvidia's commitment to pioneering new technologies and markets. Jensen details how Nvidia's willingness to tackle hard problems, like making 3D graphics accessible or venturing into AI and deep learning, showcases the company's innovation DNA. The story of Nvidia's adaptation and success in multiple technology revolutions exemplifies how continuous innovation is crucial for staying relevant and leading in the tech industry.

💡Ecosystem

Ecosystem in this context refers to the comprehensive range of Nvidia's technologies, software, and platforms that together enable various applications in computing, AI, gaming, and more. Jensen's discussion about Nvidia creating not just technology but markets emphasizes the importance of building an ecosystem where different elements complement and enhance each other, facilitating broader adoption and enabling new capabilities across industries.

Highlights

Jensen Huang explains his motivation for co-founding NVIDIA, driven by the belief that they could create a computer that solves problems regular CPUs couldn't handle and the aspiration to work on impactful projects that would make the world better.

Huang emphasizes the importance of finding early indicators of future success when investing in a long-term vision, even when the market doesn't exist yet. He cites examples like the first fuzzy cat image from deep learning research as an early sign of its potential impact.

During challenging times like NVIDIA's 80% market cap drop, Huang advocates for going back to core beliefs, gut-checking assumptions, and continuing the work if those foundational principles remain unchanged.

Huang attributes NVIDIA's success to creating a highly empowered organization where employees understand the context and can make smart decisions by being transparent with information and trusting them to handle complex situations.

Huang explains how generative AI represents a fundamental shift in computing, moving from retrieval-based models to generation-based models, and the implications this will have on industries, applications, and how we think about computing overall.

Huang advocates for going back to first principles and reasoning through the core implications of new technologies like generative AI, rather than being constrained by existing organizational structures or ways of thinking.

Huang believes that regulations for AI should be focused on products and services, leveraging existing regulatory bodies like the FDA and FAA, rather than creating overarching, cross-cutting regulations that may impede progress.

Huang advises having a core belief, gut-checking it every day, pursuing it with all your might for a very long time, and surrounding yourself with people you love on that journey.

Huang emphasizes the importance of creating the conditions for employees to do their life's work, which involves empowering them with information, transparency, and trust, rather than limiting information flow.

Huang highlights the value of learning from textbooks and archive papers, going back to first principles, and reinventing solutions based on current conditions and motivations, rather than incrementally building on past approaches.

Huang discusses the early decision to focus on 3D graphics and video games as the initial killer app for NVIDIA, despite the technology being expensive and the market being non-existent at the time, driven by the belief in the importance of the work.

Huang emphasizes the importance of making a unique contribution that advances the field of computing, which he sees as the most important instrument of humanity, and being remembered for doing something insanely hard that changed everything.

Huang advises against being constrained by existing organizational structures or hierarchies, and instead designing organizations based on first principles, considering the inputs, outputs, and environmental conditions the company operates in.

Huang shares his approach of looking forward in time and reasoning backwards, imagining the impact NVIDIA will have made and working backwards to achieve that desired end result, rather than looking forward from the present.

Huang highlights the importance of surrounding himself with genius-level talent across all domains, acknowledging that NVIDIA has the deepest technology management team the world has ever seen.

Transcripts

[Music]

Jensen this is such an honor thank you

for being here I'm delighted to be here

thank you in honor of your return to

Stanford I decided we'd start talking

about the time when you first left you

joined LSI logic and that was one of the

most exciting companies at the time

you're building a phenomenal reputation

with some of the biggest names in Tech

and yet you decide to leave to become a

Founder what motivated

you uh uh Chris and Curtis Chris and

Curtis uh uh I was an engineer at LS

logic and Chris and Curtis were at Sun

and I was working with with uh some of

the brightest Minds in computer science

at the time of all time uh including

andyto shim and others uh building

building workstations and Graphics

workstations and so on so forth and uh

Chris and Curtis uh uh said one day that

they like to leave some son and they

like uh me to go figure out what they're

going to go leave

four and and um I had a great

job but they they insisted that I uh

figure out you know with them how to how

to build a company and so so we hung out

at Denny when whenever they Dro by and

and uh uh which was which is by the way

my alma marter my my first

company uh you know my first job before

for before CEO was a was a dishwasher

and so and and I did that very

well and and so anyways uh we got

together and and we we DEC and it was

during the the microprocessor Revolution

this is 1993 and and 1992 when we were

getting together the PC Revolution was

just getting going you you know that

Windows 95 obviously which is the

Revolutionary version of Windows uh

didn't even come to the market yet and

Pentium wasn't even announced yet and so

and this is this is all before the right

before the PC Revolution and it was it

was pretty clear that that uh the

microprocessor was going to be very

important and we we thought you know why

don't we build a company uh to go solve

problems that a normal computer that is

powered by general purpose Computing

can't and and so that that became the

company's Mission uh to go to go build a

computer uh the type of computers and

solve problems that normal computers

can't and to this day uh we're focusing

on that and if you look at all the the

problems that that um and the markets

that we opened up as a result uh it's

you know things like uh computational

drug design um uh weather simulation

materials design these are all things

that we're really really proud of uh

robotics uh self-driving cars uh

autonomous autonomous uh software we

call artificial intelligence and then

all you know of course uh we uh we drove

the the uh U the techn techology so hard

that that eventually the computational

cost uh uh went to approximately zero

and then enabled enabled a whole new way

of developing software where the

computer wrote the software itself

artificial intelligence as we know it

today and so so I that was that was it

that was the journey yeah thank you all

for

[Laughter]

coming well these applications are on

all of our minds today but back then the

CEO of LSI logic

convinced his biggest investor Don

Valentine to meet with you he is

obviously the founder of seoa yeah now I

can see a lot of Founders here edging

forward in anticipation but how did you

convince the most sought-after investor

in Silicon Valley to invest in a team of

firsttime Founders building a new

product for a market that doesn't even

exist I I didn't know how to write a

business

plan and and uh uh so I went to a went

to a book

bookstore and back then there were

bookstores and and and um in the

business book section there was this

book and it was written by somebody I

knew Gordon Bell and this book I should

go find it again but it's a very large

book and the book says how to write a

business

plan and and that was you know a highly

specific title for a very niche market

and it seems like he wrote it for like

you know 14 people and I was one of them

and and so I I bought the book I I

should have known right away that that

it was a bad idea because that you know

Gordon is super super smart and super

smart people have a lot to say and and

they wanted you know and I I'm pretty

sure Gordon wants to teach me how to

write a business plan uh completely and

so I I picked up this book it's like 450

pages long well I never got through it

not even close I I flipped through it a

few pages and I go you know what by the

time I'm done reading this this thing

I'll be out of business I'll be out of

money and and uh Lori and I only had

about 6 months uh in the bank and we had

already Spencer Madison and and uh and a

dog and so the five of us had to live

off of you know uh whatever money we had

in the bank and and so I didn't have

much time uh and so instead of writing

the business plan uh I just went to talk

to to W Coran he turn he called me one

day and said hey you know you left the

company you didn't even tell me what you

were doing I want you to come back and

explain it to me and so I went back and

I explained it to Wi and wi wi at the

end of it he he said I have no idea what

you

said and and um that's one of the worst

elevator pitches I've ever

heard um and then he picked up the phone

and he called Don Valentine and he he

called Don and he says Don I want you to

give I'm going to send a kid over I want

you to give him

money he's one of the best employees l

logic ever ever had and um I and and so

the thing I learned is is

uh uh you you can make up a great

interview you could even have a bad

interview but you can't run away from

your past and so have a good past you

know try to have a good past and and and

in a lot of ways I was serious when I

said I was a good dishwasher I was

probably Denny's best

dishwasher um I I planned my work I was

organized you know I was Misan plus and

then I washed The Living Daylights out

of the dishes and then and then you know

they promoted me to bus I was certain

I'm the best bus boy Denny's ever had

you know I was I never left a station

with empty-handed I never came back

empty-handed I was very efficient and

then they and so anyways eventually I

became you know a CEO I'm working I'm

still working on being being a good CEO

but you talk about being the bad you

needed to be the best among 89 other

companies that were funded after you to

build the same thing and then with 6 to9

months of Runway left you realized that

the initial Vision was just not going to

work MH how did you decide what to do

next to save the company when the cards

were so stacked against

you well we started uh this company

called for Accelerated Computing and the

question is what is it for what's the

killer app and and uh that was that that

came our first great

decision um and this is what sequa

funded the first great decision was the

first killer app was going to be 3D

graphics and the the the technology was

going to be 3D graphics and the

application was going to be video games

at the Time 3D Graphics was impossible

to make cheap it was Million

dooll image generators from Silicon

graphics and the video and so it was a

million dollars and and it's hard to

make cheap um and the video game Market

was0 billion doar so you have this

incredible technology that's hard to uh

commoditize and commercialize and then

you have this Market that doesn't exist

that was that intersection was the

founding of our company and and I still

remember uh when when Don at the end of

my

presentation uh you know Don was still

kind of he he said you know know one of

the things he said to me which made a

lot of sense back then makes a lot of

sense today he says startups don't

invest in startups or startups don't

partner with startups and his point is

that in order for NVIDIA to succeed we

needed another startup to succeed and

that other startup was Electronic

Arts and and then on the way out he he

reminded me that electronic arts's

CTO is 14 years old and had to be driven

to work by his

mom and he just wanted to remind me that

that's who I'm relying on that that and

then and uh and then after that he said

if you lose my money I'll kill you and

that that was that was kind of my

memories of that first

meeting uh but nonetheless uh we created

we created something uh we went on uh

the next several years to go create the

market to create the gaming market for

PCs and it took a long time to do so

we're still doing it today uh we realize

that not only do you have to create the

technology and uh invent a new way of

doing computer Graphics so that what was

a million dollars is now you know three

400 $500 um that fits in the computer

and you have to go create this new

market so we have to create technology

create markets the idea that a company

would create technology create markets

defines Nvidia today almost everything

we do we create technology we create

markets that's that's the reason why

people say we have a you know people

call it a stack an ecosystem words like

that um but that's basically it at the

core for 30 years what Nvidia realized

we had to do is in order to uh create

the conditions by which somebody could

buy our products we had to go invent

this new market and uh it's the reason

why we were early in autonomous driving

it was the reason why we're early in

deep learning it was the reason why

we're early and just about all these

things including uh computational drug

disc drug design and and Discovery um

all these different areas we're trying

to create the market while we're

creating the technology and so that

that's um uh okay and then we got we got

going and and then and then um Microsoft

introduced uh a standard called direct

3D and that spawned off hundreds of

companies and we found ourselves a

couple years later competing with just

about everybody and and the thing that

that we invented the company the

technology we invented uh 3D graphics

with the consumerized 3D with turns out

to be incompatible with direct 3D so we

started this company we had this 3D

Graphics thing we million-dollar thing

we're trying to make it consumerized and

so we invented all this technology and

then shortly after it became

incompatible and um uh so we had to

reset the company uh or go out of

business but we didn't know how to we

didn't know how to build it the way that

Microsoft had defined it and um and I

remember I remember a meeting at at you

know on a weekend and the conversation

was you know we now have 89

competitors uh I understand that the way

we do it is not not right but we don't

know how to do it the right

way and and um thankfully there was

another bookstore and

um and the bookstore is called fries

Fries electronics I don't think I don't

know if it's still here um and so I had

I had I had um I I I think I drove madis

and my daughter on a weekend to fries

and and it was sitting right

there the openg manual uh which would

defined uh how silicon Graphics did

computer graphics and so it was it was

right there it was like $68 a book and

so I had a couple hundred dollar I

bought three books I took it back to the

office and I said guys I found it our

future and I handed out I had three

versions of it handed out had a big nice

centerfold you know the centerfold is

the opengl pipeline which is the

computer Graphics Pipeline and um uh and

I handed it to uh the same Geniuses that

I founded the company with and we

implemented the openg pipeline like

nobody had ever implemented the opengl

pipeline and we built something the

world never seen and so uh a lot of

lessons are right there that moment in

time for our company

uh gave us so much confidence and the

reason for that is you can

succeed in doing something inventing a

future even if you were not informed

about it at all and is kind of the my

attitude about everything now you know

when somebody tells me about something

and I've never heard of it before or if

I've heard of it never don't understand

how it works at all my first thought is

always you know how hard can it be

and it's probably just a textbook away

you know you're probably one archive

paper away from figuring this out and so

I spent a lot of time reading archive

papers and um and it it's true it's true

you can you can um now of course you

can't learn how somebody else does

something and do it exactly the same way

and hope to have a different outcome but

you could learn how something can be

done and then go back to First

principles and ask yourself um giving

the conditions today given my motivation

given the instruments the tools um given

you know how things have changed how

would I redo

this how would I reinvent this whole

thing how would I design a how would I

build a car today would I build it

incrementally from 1950s and 1900s how

would I build a computer today how would

I write software today does that make

sense and so I go back to First

principles all the time uh even in the

company today and just reset ourselves

you know because the world has changed

and U the way we wrote software in the

past was monolithic and it's designed

for supercomputers but now it's

disaggregated it's you know so on so

forth and how we think about software

today how we think about computers today

how we think just always cause your

company always cause yourself to go back

to first first principles and it creates

lots and lots of opportunities yeah the

way you applied this technology turns to

be revolutionary you get all the

momentum that you need to IPO and then

some more because you grow your Revenue

nine times in the next four years but in

the middle of all of this success you

decide to Pivot a little bit the focus

of innovation happening at Nvidia based

on a phone call you have with this

chemistry professor can you tell us

about that phone call and how you

connected the dots from what you heard

to where you

went uh remember at the core the company

was uh pioneering a new way of doing

Computing computer Graphics was the

first application uh but we already

always knew that there would be other

applications and so image processing

came particle physics came fluids came

so on so forth all kinds of interesting

things that we wanted to do uh we made

the processor more programmable so that

we could express more algorithms if you

will and then one day we invented um uh

programable shaders which made all forms

of Imaging and computer Graphics

programmable that was a great

breakthrough so we invented Ed that on

top of that we invented uh we we tried

to look for ways to express um uh more

comp more sophisticated algorithms uh

that could be computation that could be

computed on our processor which is very

different than a CPU and so we we

created this thing called CG this I

think it was 2003 or so C for

gpus it predated Cuda by about three

years um the same person who wrote The

textbook that saved the company Mark

Hilgard wrote that

textbook and um I and so CG was was

super cool we wrote textbooks about it

we started teaching people how to use it

we developed tools and such um and then

several several researchers discovered

it uh many of the researchers here

students here at Stanford was using it

um many of the the engineers that that

then became uh engineers at Nvidia were

were uh playing with it uh

uh a doctor a couple of doctors at at

Mass General picked it up and used it

for uh CT reconstruction so I flew out

and saw them and said you know what are

you guys doing with this thing and uh

they told me about that and then and

then uh a um uh a

computational a Quantum chemist uh used

it to um uh Express his his algorithms

and so I I realized that that there's

there's some evidence that people might

want to use this

uh and and it gave it gave us gave us

you know incrementally more more

confidence that that we ought to go do

this that that this field this form of

computing could solve problems that

normal computers really can't and and um

reinforced our belief and and kept us

going every time you heard something new

you really savored that surprise and

that seems to be a theme throughout your

leadership at Nvidia U it feels like you

make the these bets so far in advance of

Technology inflections that when the

Apple finally falls from the tree you're

standing right there in your black

leather jacket waiting to catch

it how do you find the conv always seems

like a diving catch oh it does seem like

a diving catch you do things based on

core beliefs you know we we uh we we

deeply believe that that we uh we

could create a computer that solves

problems Norm processing can't do that

there are limits to what a CPU can do

there are limits to what general purpose

Computing can do and then there are

interesting problems uh that we can go

solve the question the question is

always are those in interesting problems

only or are they can they also be

interesting markets because if they're

not interesting markets it's not

sustainable and Nvidia went through

about a decade where we were investing

in this future and the markets didn't

exist there was only One Market at the

time was computer Graphics uh for 10 15

years the markets that fuels Nvidia

today just didn't exist and so so how do

you

continue um uh with all of the people

around you you know our company and you

know nvidia's management team and all of

the amazing Engineers that they're

creating this future with me um all of

your shareholders your board of

directors all your partners you're

you're taking everybody with you and

there's no evidence uh of a market that

is really really challenging you know

the fact that the technology can solve

problems and the fact that you have

research papers that that are used that

that are made possible because of it are

interesting but you're always looking

for that market but nonetheless before a

market exists you still need early

indicators of future

success you know we we have this phrase

in the company is is you know there's a

phrase called key performance indicators

unfortunately kpis are hard to

understand I find kpis hard to

understand what's a good

kpi you know a lot of people you know

when when we look for kpis we go gross

margins that's not a kpi that's a

result you know you're looking for

something that's an early indicators of

future positive results okay and as

early as possible and the reason for

that is because you want early indic

that early sign that you're going in the

right direction

and so we have this phrase is called EO

ifs FS you know early indicators e FS

early indicators of future success and

and um and it helps people uh uh because

I was using it all the time to give the

company hope that hey look we solved

this problem we solved that problem we

solved this problem the markets didn't

exist but there were important problems

and that's what the company's about to

solve these problems uh we want to be

sustainable

and therefore the markets have to exist

at some point but you you want you want

to decouple the result from um uh from

evidence that you're doing the right

thing okay and so so so that's how you

that's how you kind of solve this

problem of investing into something

that's very very far away um and having

the the conviction uh to stay on the

road is to find as early as possible the

indicators that you're doing the right

things and so uh start with a core

belief unless something you know changes

your mind you continue to believe in it

and um look for early indicators of

future success what are some of those

early indicators that have been used by

product teams at

Nvidia uh all kinds

um uh uh I saw I saw I saw a uh a paper

uh long before I saw the paper I met

some people that needed my help on on um

uh on this thing called Deep learning at

a time I didn't even know what deep

learning Le was and um and they needed

us to create a domain specific language

so that um all of their algorithms could

be expressed easily on our on our

processors and we created this thing

called

cdnn and it's essentially the SQL um uh

SQL is in in storage Computing this is

um neuron network computing and uh we

created a a language if you will domain

specific language for that you know kind

of like the openg GL of of uh deep

learning and so we we uh they needed us

to do that so that they they could

express their mathematics and uh they

didn't understand Cuda but they

understood their deep learning and so we

created this thing in the middle for

them uh and the reason why we did it was

because uh even though there were zero I

mean this you know these researchers had

no money uh and and this is kind of one

of the the great skills of our company

that that you're willing to do something

even though the financial returns are

complet completely non-existent or maybe

very very far out even if you believed

in it uh we we ask ourselves you know is

this worthy work to do um does this

Advance a field of science somewhere

that matters notice this is something

that I I've been talking about you know

since the very beginning of time uh we

ex we we find inspiration uh not from

the size of a market from but from the

importance of the

work uh because the importance of the

work is the early indicators of a future

Market

and nobody has to write a nobody has to

do a a um a business case on it nobody

has to show me a a pnl uh nobody has to

show me a financial forecast the only

question is is this important work and

if we didn't do it uh would it happen

without us now if we didn't do something

and something could happen without us it

gives me tremendous Joy actually and the

reason for that is could you imagine the

world got better you didn't have to lift

a finger that's the definition of you

know of of uh ultimate laziness and and

and in a lot of ways in a lot of ways

you want that habit and the reason for

that is

this uh you want the company to be lazy

about doing things that other people

always do can do if somebody else can do

it let them do it we should go select

the things that if we didn't do it the

world the world would fall apart you

have to convince yourself of that that

if I don't do this it won't get

done that is Inc and and if that work is

hard and that work is impactful and

important then it gives you a sense of

purpose does that make sense and so our

company has been selecting these

projects deep learning was just one of

them and the first indicator of of the

success of that was this you know fuzzy

cat that that Andrew an came up with and

um then Alex

kvki uh detected cats um you know not

all the time but you know successfully

enough that it was you know this might

take us somewhere and then we reasoned

about the structure of deep learning and

you know we're computer scientists and

we understand how things work and and so

we we uh we convinced ourselves this

could change everything and and um and

anyhow that but that's an that's an

example so these selections that you've

made they've paid huge dividends both

literally and figuratively um but you've

had to steer the company through some

very challenging times like when it lost

80% of its market cap amid the financial

crisis cuz what Wall Street didn't

believe in your bet on ML um in times

like these how do you steer the company

and keep the employees motivated at the

task at hand uh it's this is the my

reaction during that time is the same

reaction I had about this week uh

earlier today you asked me about this

week my pulse was exactly the

same this week is no different than last

week or the week before that um and so

the opposite of that you know when you

drop it

80% um it don't get me

wrong when when your share price drops

80% it's a little embarrassing okay and

and um you just want to you just want to

wear a t-shirt that says wasn't my

fault

um but even more than that you just you

just don't want to you you don't want to

get out of your bed you don't want to

leave the house um all of that is true

all of that is true um but then you go

back to go back to just doing your job I

woke up at the same time I prioritize my

day in the same way uh I go back to what

do I believe uh you got to gut check

always gut check back to the court you

know what do you believe uh what are the

most important things uh and uh just

check them off you know sometimes

sometimes it's helpful to you know

family loves me okay check um you know

double you know right and so you just

got to check it off and and you go back

to your core um and then go back to work

and and then every conversations go back

to the core uh keep the company focused

back on the core do you believe in it

did something change the stock price

changed but did something else change

the physics change the gravity

change did did all of the things that

that that we assumed uh that we believed

that led to our decision did any of

those things change because if those

things change you got to change

everything but if none of those things

change you change nothing you keep on

going yeah yeah that's how you do

it in speaking with your employees they

say that you try to avoid the

public in speaking with your employees

they've said that your leadership

including the employees I'm just

kidding no le lead leaders have to be

seen unfortunately that's the hard

that's the hard part you know I I I was

I was I was at I was I was an electrical

engineering student and I was quite

Young when I went to school um when I

went to went to College I was I was

still 16 years old and so I was I was

young when I did everything and and so I

was a bit of an introvert kind of you

know I'm shy I don't enjoy public

speaking I'm delighted to be here I'm

not suggesting um but but it's it's not

something that I do naturally and and um

I and so so when when things are

challenging um uh it's not easy to be in

front of precisely the people that you

care most about

you know and the reason for that is

because could you imagine a company

meeting we just our stock prices dropped

by

80% and the most important thing I have

to do as the CEO is this to come and

face you explain it and partly you're

not sure why

partly you're not sure how long uh how

bad yeah you just don't know these

things and and but you still got to

explain it face face all these people

and you know what they're thinking you

know you you know some of them are

probably thinking we're doomed uh some

people are probably thinking you're an

idiot and some people are probably

thinking you know something else and so

I um there are a lot of things that

people are thinking and you know that

they're thinking those things but you

still have to get in front of them and

and and deal you know do the hard work

they may be thinking of those things but

yet not a single person of your

leadership team left during times like

this and in fact

unemployable

that's what I keep reminding them I'm

just kidding I'm surrounded by Geniuses

I'm surrounded by Geniuses yeah other

Geniuses un un unbelievable uh Nvidia is

well known to have singularly the best

management team on the planet this is

the deepest technology management team

the world's ever seen I'm surrounded by

a whole bunch of them and they're just

genius business teams marketing teams

sales teams just incredible and

engineering teams my research teams

unbelievable yeah your employees say

that your leadership style is very

engaged you have 50 direct reports you

encourage people across all parts of the

organization to send you the top five

things on their mind and you constantly

remind people that no task is beneath

you can you tell us why you've

purposefully designed such a flat

organization and how should we be

thinking about our organizations that we

designed in the

future uh no task is is to me no task is

beneath me because remember I used to be

a dishwasher and I and I mean that I

used to clean toilets I mean you know I

cleaned a lot of toilets I've cleaned

more toilets than all of you

combined and and some of them just can't

[Laughter]

unsee I don't know I I don't know what

to tell you you know that's life and and

so so uh uh you can't show me and you

can't show me a task that is that's

beneath me um now I'm not doing it I'm

not doing it uh only because because of

uh you know whether it's beneath me or

not beneath me U if you send me

something and you want my input on it

and I can be of service to you and in my

in my review of IT share with you how I

reason through it uh I've made a

contribution to you I've made I've made

it possible for you to see how I reason

through something and and by reasoning

as you know how someone reasons through

something empowers you you go oh my gosh

that's how you reason through something

like this it's not as complicated as it

seems this is how you reason through

something that's super ambiguous this is

how you reason through something that's

incalculable this is how you reason

through something that you know seems to

be very scary this is how you seem do

you understand and so I show people how

to reason through things all the

time strategy things you know how to

forecast something how to break a

problem down uh and you're just you're

empowering people all over the place and

so that's how I see it if you send me

something you want me to help review it

uh I'll do my best and I'll show you how

I would do it um I in the process of

doing that of course I learned a lot

from you is that right you gave me a

seat of a lot of information I learned a

lot and so I I feel rewarded by the

process um it does take a lot of energy

sometimes because you know you got in

order to add value to somebody and

they're incredibly smart as a starting

point and I'm surrounded by incredibly

smart people you have to at least get to

their plane you know you have to get

into their head space and that's really

hard that's really hard um and that

takes just an enormous amount of

emotional and intellectual energy and so

I feel exhausted after after I I work on

things like that um I'm surrounded by by

a lot of great people a CEO should have

the most direct report rep s um uh by

definition because the people that

reports to the CEO requires the least

amount of

management it makes no sense to me that

CEOs have so few people reporting to

them except for one fact that I know to

be true the the knowledge the

information of a CEO is supposedly so so

valuable so secretive you can only share

with two other people or

three and their information is so

invaluable so incredibly secretive that

they can only share with a couple

more well

um I don't believe in in in a culture an

environment where the information that

you possess is the reason why you have

power I would like us all to to to

contribute to the company and our

position in the company should have

something to do with our ability to

reason through complicated things lead

other people to um achieve greatness um

Inspire Empower other people um support

other people those are the reasons why

the the management team exists in

service of all of the other people that

work in the company to create the

conditions by which all of the all of

these amazing people who volunteer to

come work for you instead of all the

other amazing high-tech companies around

the world they elected they volunteer to

work for you and so you should create

the conditions by which they could do

their life's work which is

Mission you know you probably heard it

i' I've said that you know pretty

clearly and I and I believe that what my

job is is very simply to create the

conditions by which you could do your

life's work and so how do I do that what

does that condition look like well that

condition should um result in great deal

of empowerment you should you can only

be empowered if you understand the

circumstance isn't it right you have to

understand the cont you have to

understand the context of the situation

you're in in order for you to come up

with great ideas

and so I have to create a circumstance

where you understand the context which

means you have to be

informed and the best way to be informed

is for there to be as little layers

of information

mutilation right between us and so

that's the reason why it's very often

that I'm reasoning through things like

in an audience like this I say first of

all this is the beginning facts these

are the data that we have um this is how

I would reason through it these are some

of the assumptions these are some of the

unknowns these are some of the

knowns and so you reason through it and

now you've created an organization

that's highly empowered nvidia's 30,000

people we're the smallest large company

in the world we're tiny little company

but every employee is so empowered and

they're making smart decisions on my

behalf every single day and the reason

for that is because you know they

understand that they understand my

condition

they understand my condition I'm very

transparent with people um and uh and I

believe that that I can trust you with

the information often times the

information is hard to hear and uh the

the situations are complicated uh but I

trust that you can handle it you're you

know a lot of people hear me say you

know these you're adults here you can

handle this sometimes they're not really

adults they just

graduated I'm just kidding I know that

when I first graduated was barely an

adult and um I but I was I was fortunate

that I was trusted with with uh with uh

important information so I want to do

that I want to create the conditions for

people to do

that I do want to now address the topic

that is on everybody's mind AI last week

you said that generative Ai and

accelerated Computing have hit the

Tipping Point so as this technology

becomes more mainstream what are the

applications that you personally are

most excited about

well you have to go back to First

principles and ask yourself what is

generative AI what happened um what

happened was we have a we now have the

ability to have software that can

understand something they they can

understand why you know what is first of

all we digitized everything that was you

know like for example Gene sequencing

you digitized genes but what does it

mean that sequence of genes what does it

mean we've digitized amino acids um but

what does it mean uh and so we now have

the ability we dig digitize words we

digitize sounds uh we digitize images

and videos we digitize a lot of things

but what does it mean we now have the

ability through um a lot of study a lot

of Da data and from their patterns and

relationships we We Now understand what

they mean not only do we understand what

they mean we we can translate between

them because we learned about the

meaning of these things in the same

world we didn't learn about them

separately so we we learned about speech

and and words and and paragraphs and

vocabulary in the same context and so we

found correlations between them and

they're all you know registered if you

will registered to each other and so now

we uh not only do we understand uh the

modality the meaning of each modality we

can understand how to translate between

them and so uh for obvious things you

could caption video to text that's

captioning uh text to uh images M

Journey uh text to text chat GPT I

amazing things and so so we now we now

know that uh we understand meaning and

we can translate uh the translation of

something is generation of information

and and um uh and all of a sudden you

you have to take your you take a step

back and ask yourself um uh what is the

implication in every single layer of

everything that we do and so I'm

exercising in front of you I'm reasoning

in front of you uh the same thing I did

a quarter uh 15 years ago when I first

saw um uh alexnet some 13 14 years ago I

guess um I how I reasoned through it uh

what did I

see how interesting what can it

do very cool but then most importantly

what does it mean what does it mean what

does it mean to every single layer of

computing because you know we're in the

world of computing and so what it means

is that that the way that we um process

information fundamentally will be

different in the future that's what

Nvidia builds you know chips and system

the way we write software will be

fundamentally different in the future

the type of software we'll be able to

write write in the future will be

different new applications and then ALS

also the processing of those

applications will be different what was

historically a retrieval based model

where uh in uh information was pre

pre-recorded if you will almost you know

we wrote the text pre-recorded and we

retrieved it based on uh some

recommender system algorithm in the

future uh some seed of information will

be will be uh the starting point we call

them prompts you as you guys know and

then we generate the rest of it and so

the future of computing will be highly

generated well let me give you an

example of what's

happening for example uh we're having a

conversation right now very little of

the information I'm trans I'm conveying

to you is Retreat most of it is

generated it's called intelligence

and so in the future we're going to have

a lot more generative our computers will

will perform in that way it's going to

be highly generative instead of Highly

retrieval based you go back and you got

to ask yourself you know now for for you

know entrepreneurs you got to ask

yourself uh what industries will be

disrupted therefore will we think about

networking the same way will we think

about storage the same way will we think

about would we be as abusive of internet

traffic as we are today probably not

notice we're having a conversation right

now and and I to get in my car every

every

question so we don't have to be as

abusive of of transformation information

transporting as we used to um uh what's

going to be more what's going to be less

uh what kind of applications you know

etc etc so you can go through the entire

industrial spread and ask yourself

what's going to get disrupted what's

going to get be different what's going

to get NED you know so on so forth and

and that reasoning starts from what is

happening what is generative

AI Foundation Al what is happening go

back to First principles with all things

there was something I was going to tell

you about organization you asked the

question and I forgot to answer it the

way you create an organization by the

way someday um don't worry about how

other companies or charts look you start

from first principles remember what an

organization is designed to

do the organizations of the past where

there's a king you know

CE and then then you have all all these

you know the Royal subjects you know the

Royal Court and then eaff and then you

keep working your way down eventually

they're employees well the reason why it

was designed that way is because they

they wanted the employees to have as low

information as possible because their

fundamental purpose of the soldiers is

to die in the field of

battle to die without asking questions

you guys know

this I don't I only have 30,000

employees I would like them none of them

to die

I would like them to question everything

does that make sense and so the way you

organize in the past and the way you

organize today is very different to

Second the question is what is nid what

does Nvidia build an organization is

designed so that we could build what it

whatever it is we build

better and so if we all build different

things why why are we organized the same

way why would why would this

organizational Machinery be exactly the

same irrespective of what you build it

doesn't make make any sense you build

computers you organize this way you

build healthare Services you build

exactly the same way it makes no sense

whatsoever and so you had to go back to

First principles just ask yourself what

kind of Machinery what what is the input

what is the output what are the

properties of this environment you know

what what is the what is the what is the

forest that this animal has to live in

what is this characteristics is it

stable most of the time you're trying to

squeeze out the last drop of water or is

it changing all the time being attacked

by everybody and so you got to

understand you know you're the CEO your

job is to architect this company that's

my first job to create the conditions by

which you can do your life's work and

the architecture has to be right and so

you have to go back to First principles

and think about those things and I was

fortunate that that when I was 29 years

old you know I had the benefit of of of

taking a step back and asking myself you

know how would I build this company for

the future and what would it look like

and you know what's the operating system

which is called culture what do we what

kind of behavior do we en encourage

enhance and what what do we discourage

and not enhance you know so on so forth

and anyways I want to save time for

audience questions but um this year's

theme for view from the top is

redefining tomorrow and one question

we've asked all of our guests is Jensen

as the co-founder and CEO of Nvidia if

you were to close your eyes and

magically change one thing about

tomorrow what would it

be

were we supposed to think about this in

advance I I'm going to give you a

horrible answer

um I I don't know that it's one thing

look there are a lot of things we don't

control you know there are a lot of

things we don't control um your job is

to make a unique contribution live a

life of

purpose to do something that nobody else

in the world would do or can do to make

a unique contribution so that in the

event that after you done

um everybody says you know the world was

better because you were

here and so I think that that to me um I

live I live my life kind of like this I

go forward in time and I Look Backwards

so you asked me a question that's

exactly from a from a computer vision

pose perspective exactly the opposite of

how I think I never look forward from

where I am I go forward in time and look

backwards and the reason for that is

it's

easier I would look backwards and kind

of read my

history we did this and we did that way

and we broke that prom down does that

make sense and so it's a little bit like

um how you guys solve problems you

figure figure out what is the end result

that you're looking for and you work

backwards to achieve it and so I imagine

Nvidia uh making a unique contribution

to advancing the the future of of uh of

computing which is the single most

important instrument of all

Humanity now it's not about our self

self-importance but this is just what

we're good at and it's incredibly hard

to do and we believe we can make an

absolute unique contribution it's taken

US 31 years to be here and we're still

just beginning our journey and so this

is insanely hard to

do and uh uh When I Look Backwards I

believe that we made I believe that that

we're going to be remembered as a

company that kind of changed everything

not because we went out and changed

everything through all the things that

we said but because we did this one

thing that was insanely hard to do that

we're incredibly good at doing that we

loved doing we did for a long time I'm

part of the GSP lead I graduated in 2023

so my question is how do you see see

your company in the next decade as what

challenges do you see your company would

face and how you are positioned for that

first of all can I just tell you what

was going on through my head as you say

what

challenges the list that flew by my

head was so so large uh that that I was

trying to figure out what to

select um now the honest truth is is

that when you ask that question

most of the challenges that showed up

for me were technical

challenges and the reason for that is

because that was my

morning if you were to you know chosen

yesterday um it might have been Market

creation

challenges there are some markets that I

gosh I just desperately would love to

create I just can't we just do it

already you know but we can't do it

alone Nvidia is a technology platform

company we're here in service of a whole

bunch of other the companies so that

they could realize if you will our hopes

and dreams through

them and and so some of the things that

I would love I would love for the world

of biology to to be at a point where

it's kind of like the world of Chip

design 40 years ago computer AED and

design um Eda that entire industry

really made possible for us today and I

believe we're going to make possible for

them tomorrow

computer AED drug design because we're

able to now represent genes and proteins

and even cells now very very close to be

able to represent and understand the

meaning of a cell a combination of a

whole bunch of genes um what is a cell

mean it's kind of like what does that

paragraph mean well if we could

understand a a cell like we can

understand a paragraph imagine what we

could do and so uh so so I'm I'm anxious

for that to happen you know I'm kind of

excited about that uh there's some that

I'm just excited about that I know we

around the corner on for example uh

humanoid

robotics very very close around the

corner and the reason for that is

because if you can tokenize and

understand speech why can't you tokenize

and understand uh

manipulation and so so these kind of

computer science techniques you once you

figure something out you ask yourself

well if got do that why can't I do that

and so I'm excited about those kind of

things um and so that challenge is kind

of a happy

challenge uh some of the some of the

other challenges some of the other

challenges of course are industrial and

geopolitical and they're social and and

but you've heard all that stuff before

these are all true you know the social

issues in in the world uh the

geopolitical issues in the world uh why

can't we just get along uh things in the

world why do I have to say those kind of

things in the world um why do we have to

say those things and then amplify them

in the world uh why do we have to judge

people so much in the world uh you you

know all those things you guys all know

that I don't have to say those things

over again my name is Jose I'm a class

of the 2023 uh from the GSB my question

is uh are you worried at all about the

pace at which we're developing AI um and

do you believe that any sort of

Regulation might be needed thank you uh

yeah that's uh the answer is yes and no

um we need uh you you know that the the

the greatest breakthrough in uh modern

AI of course deep learning and it

enabled great progress but another

incredible breakthrough is something

that that humans know and we practice

all the time uh and we just invented it

for uh for language models called uh

grounding reinforcement learning human

feedback um I provide reinforcement

learning human feedback every day that's

my job um and their for their parents in

the room uh you're providing

reinforcement learning human feedback

all the time okay now we just figured

out how to do that um at a system

systematic level for artificial

intelligence there are a whole bunch of

other technology necessary to uh

guardrail uh

fine-tune ground for example how do I

generate um how do I generate uh uh uh

tokens that obey the laws of physics you

know right now things are floating in

space and doing things and they don't

they don't obey the laws of physics um

how do that requires technology Guard

railing requires technology fine-tuning

requires technology alignment requires

technology safety requires technology

the reason why planes are so safe is

because you know all of the autopilot

systems are are surrounded by diversity

and redundancy and all kinds of

different functional safety and active

safety systems that were

invented I need all of that to be

invented much much faster uh you also

know that that the border between

security and artificial intelligence

cyber security and artificial

intelligence is going to become blurry

and blurry we need technology to advance

very very quickly in the area of cyber

security in in order to protect us from

artificial intelligence and so so in a

lot of ways we need technology to go

faster a lot faster okay uh regulation

there's two types of Regulation uh

there's social regulation I don't know

what to do about that but there's

product and services regulation know

exactly what to do about that okay so um

the fa the FAA the FDA the uh Nitsa you

name it all the the fs and all the NS

and all the you know fcc's the they all

have regulations for products and

services that are have particular use

cases uh um uh bar exams and doctors and

you know so on so forth um you all have

uh qual qualification exams you all have

standards that you have to reach you all

have to uh continuously be certified uh

accountants and so on so forth whether

it's a product or a service there are

lots and lots of

regulations please do not add a super

regulation that cuts across of it the

regulator who is regulating accounting

should not be the regulator that

regulates a

doctor you know I love accountants um

but I I just you know if I ever need an

open heart surgery the fact that they

can close books is interesting but not

sufficient and so and so I I would like

I would like um all of those all of

those fields that already have products

and services um to also enhance their

regulation in context of in the context

of AI okay but I left out this one very

big one which is this the social

implication of AI and how do you how do

you deal with that I don't have great

answers for that um but you know enough

people are talking about it but it's

important to subdivide all of this into

chunks does that make sense so that we

don't we don't become super hyperfocused

on this one thing at the expense of a

whole bunch of routine things that we

could have done and as a result people

are getting killed by cars and planes

and you know it doesn't make any sense

we should make sure that we we do the

right things there okay very practical

things may I take one more question well

we have some rapid fire questions for

you as view from the tradition

okay I was trying to avoid

that okay all right far away far away

well your first job was at Denny's they

now have a booth dedicated to you what

was your fondest memory of working my

second job was AMD by the

way is there Booth dedicated to me there

I'm just

kidding um I'm I love my job there I did

I love there it's a great company yeah

yeah um if there were a worldwide

shortage of black leather jackets what

would we be see you

wearing oh no I've I've got a large

reservoir of black

jackets I'm the I'll be the only person

who is who is not

concerned um you spoke a lot about

textbooks if you had to write one what

would it be

called I wouldn't write

one you're asking me a hypothetical

question that has no possibility of of

of uh that's fair and finally if you

could share one parting piece of advice

to broadcast across Stanford what would

it

be uh it's not a word but but um I you

know have a core belief

um gut check it every

day I pursue it with all your

might pursue it for a very long

time surround yourself with people you

love and take them on that right so

that's the story of Nvidia Jensen this

last hour has been a treat thank you for

spending thank you very

much

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