Jensen Huang, Founder and CEO of NVIDIA

[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 h

ow 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 ob

viously 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 intelligen

ce 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 LS

I 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 an

d 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 supe

r 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 ide

a 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 pas

t 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 Accelerat

ed 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 dolla

rs 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 tod

ay 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 a

nd 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 pr

oducts 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 s

tandard 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 b

ecame 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 fri

es 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 version

s 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 paper

s 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 woul

d 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 t

hen 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 i

t 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 fina

lly 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 somethi

ng 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 ma

rkets 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 d

idn'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 mayb

e 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 sen

se 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 t

ask 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 mo

re 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 someti

mes 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 wa

s 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 tha

t'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 you

r 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 us

ed 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 a

nd 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 someth

ing 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 proce

ss 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 tha

t 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 peop

le 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 reas

on 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 unders

tand 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 for

tunate 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 dig

itize 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 b

etween 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 o

f 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 t

hose 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 w

ill 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 ca

n 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 worr

y 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 f

undamental 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 en

vironment 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 aud

ience 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 th

at 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 t

oday 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 break

through 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 figu

red 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 req

uires 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 the

y 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 k

now 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 [Music] than