NVIDIA CEO Jensen Huang

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I will say David I would love to have nvidia's full production Team every episode it was nice not having to worry about turning the cameras on and off and making sure that nothing bad happened myself while we were recording this yeah just the gear I mean the drives that came out of the camera all right uh RED cameras for the home studio starting next episode yeah great all right let's do it who got the truth is it you is it you is it you who got got the truth now is it you is it you is it you sit me down say it straight another story on the way welcome to this episode of acquired

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the podcast about great technology companies and the stories and playbooks behind them I'm Ben Gilbert I'm David Rosenthal and we are your hosts listeners just so we don't bury the lead this episode was insanely cool for David and I yeah after researching Nvidia for something like 500 hours over the last two years we flew down to Nvidia headquarters to sit down with Jensen himself and Jensen of course is the founder and CEO of Nvidia the company powering this whole AI explosion at the time of recording Nvidia is worth $1.1 trillion and is the sixth most valuable company in the entire world and right now is a crucible moment for the company

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expectations are set High I mean Skyhigh they have about the most impressive strategic position and Lead against their competitors of any company that we've ever studied but here's the question that everyone is wondering will nvidia's insane Prosperity continue for years to come is AI going to be the next trillion dollar technology wave how sure are we of that and if so can Nvidia actually maintain their ridiculous dominance as this Market comes to take shape so Jensen takes us down memory lane with stories of how they went from graph iics to the data center to AI how they survived multiple near-death experiences

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he also has plenty of advice for Founders and he shared an emotional side to the founder journey toward the end of the episode yeah I got new perspective on the company and on him as a founder and a leader just from doing this despite you know we thought we knew everything before we came in advance and uh it turned out we didn't turns out the protagonist actually knows more yes all right well listeners join the slack there is incredible discussion of everything about this company AI the whole ecosystem and a bunch of other episodes that we've done recently going on in there right now so that is acquired. fm/ slack we would love to see

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you and without further Ado this show is not investment advice David and I may have investments in the companies we discuss and this show is for informational and entertainment purposes only on to Jensen so Jensen this is acquired so we want to start with story time so we want to wind the clock all the way back to I believe it was 1997 you're getting ready to ship the Reva 128 which is one of the largest Graphics chips ever created in the history of computing it is the first fully 3D accelerated Graphics pipeline for a computer yeah and you guys have about months of cash left and so you decide to do the entire testing in

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simulation rather than ever receiving a physical prototype you commission the production run sight on scene with the rest of the company's money so you're betting it all right here on the revo 128 yeah it comes back and of the 32 DirectX blend modes it supports eight of them and you have to convince the market to buy it and you got to convince developers not to use anything but those eight blend modes walk us through what that the other 24 weren't that important okay so wait wait first was that the plan all along like when when did you realize that I realized I didn't learn about it until it was too late we should have implemented all 32

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yeah but but we buil we built and so we had to make the best of it that was really an extraordinary time remember Revo 120 was mv3 mv1 and mv2 were based on forward texture mapping no triangles but curves and it tesselated the curves and because we were rendering higher level objects we essentially avoided using Z buffers and we thought that that was going to be a good rendering approach and turns out to have been completely the wrong answer and so what Revo run 28 was was a reset of our company now remember at the time that we started the company 1993 we were the only consumer 3D Graphics Company ever created and we we were focused on

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transforming the PC into an accelerated PC because at the time Windows was really a software rendered system and so anyways Reva 128 was a reset of our company Because by the time that we realized we had gone down the wrong road Microsoft had already rolled out DirectX it was fundamentally incompatible with nvidia's architecture 30 competitors have already shown up uh even though we were the first company at the time that we were founded so the world was a completely different place the question about what to do as a company strategy at that point I would have said that we made a whole bunch of wrong decisions but on

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that day that mattered we made a sequence of extraordinarily good decisions and that time 1997 was probably nvidia's best moment and the reason for that was our backs were up against the wall we were running out of time we were running out of money for a lot of employees running out of Hope and the question is what do we do well the first thing that we did was we decided that look direct ex is now here we're not going to fight it let's go figure out a way to build the best thing in the world uh for it and and Revo 128 is the world's first uh fully accelerated Hardware accelerated pipeline for rendering 3D and so the transform the

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projection every single element all the way down to the frame buffer was completely Hardware accelerated uh we implemented a uh a texture cache we took the bus limit the frame buffer limit to as big as as uh physics could afford at the time we made the biggest chip that anybody had ever imagined building we use the fastest memories basically if we built that chip there could be nothing that could be faster and we also chose a cost point that is substantially higher than the highest price that we think that any of our competitors will be willing to go if we built it right we accelerated

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everything we Implement everything in direct X that we knew of and we build it as larger as we possibly could then obviously nobody can build something faster than that today in a way you kind of do that here at Nvidia too you were a consumer Products Company back then right it was end consumers who were going to have to pay the money to buy this that's right but we observed that there was a segment of the market where people were because at the time the the PC industry was still coming up and it wasn't good enough everybody was clamoring for the next fastest thing and so if your performance was 10 times higher this year than what was available

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there's a whole large Market of enthusiasts who who we believe with would have gone after it and we were absolutely right that the PC industry had a substantially large Enthusiast Market that would buy the best of everything to this day it's kind of remains true and for certain segments of the market where the technology is never good enough like 3D graphics and we chose the right technology 3D Graphics is never good enough and we call it back then 3D gives us sustainable technology opportunity because it's never good enough and so your technology can keep getting better which chose that uh we also made the decision to use this

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technology called emulation there was a company called iOS and on the day that I called them they were just shutting the company down because they had no customers and I said hey look uh I'll buy what you have in inventory and uh you know no promises are necessary and the reason why we needed that emulator is because if you figure out how much money that we have we taped out a chip and we uh got it back from the Fab and we started working on our software by the time that we found all the bugs because we did the software then we taped out the chip again well we would have been out of business already yeah and so I knew and your competitors

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would have caught up well not to mention we would have been out of business who cares exactly so if you're going to be out of business anyways that plan obviously wasn't the plan the plan that companies normally go through which is you know build the chip write the software fix the bugs tape out a new chip so on so forth that method wasn't going to work and so the question is if we only had six months and you get to tape out just one time then obviously you're going to tape out a perfect chip so I so I remember having conversation with our leaders and they said but Jensen how do you know it's going to be perfect I said I know it's

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going to be perfect because if it's not we'll be out of business and so let's make it perfect we get one shot we essentially virtually prototype the chip by buying this emulator and Dwight and the software team wrote Our software the entire stack and ran it on this emulator and just sat in the lab waiting for Windows to paint you know and it was like 60 seconds per a frame or something like that EAS I actually think that it was an hour per frame something like that and so we would just sit there and watch a paint and so on the day that we decided to tape out I assumed that the chip was perfect and everything that that we could have tested we tested into

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advance and told everybody this is it we're going to tape out the chip it's going to be perfect well if you're going to tape out a chip and you know it's perfect then what else would you do that's actually the good question if you knew that you hit enter you taped out a chip and you knew it was going to be perfect then what else would you do well the answer obviously go to production and marketing Blitz yeah yeah and develop Rel everything off KCK everything off because you got a perfect chip and so we got in our head that we have a perfect chip how much of this was you and how much of this was like your co-founders the rest of the company the

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board was everybody telling you you were crazy no everybody was clear we had no shot not doing it would be crazy because otherwise you might as go home you're going to be out of business anyways so anything aside from that is crazy so it seemed like a fairly logical thing and quite frankly right now I'm describing it every you're probably thinking yeah it's pretty sensible well it worked yeah and so we take that out and went directly to production so is the lesson for Founders out there when you have conviction on something like the revo 128 or uh Cuda uh go bet the company on it and this keeps working for you so it

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seems like your lesson learned from this is yes keep pushing all the chips in because so far it's worked every time no how do you think about that no no when you push your chips in um I I know it's going to work notice we assume that we taped out a perfect chip the reason why we taped out a perfect chip is because we emulated the whole chip before we taped it out we developed the entire software stack we ran QA on all the drivers and all the software we ran all the games we had we ran every VGA application we had and so when you push your chips in what you're really doing is you're when you bet the farm you're saying I'm going to take everything in

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the future all the risky things and going to pull it in advance and that is probably the lesson and to this day everything that we can prefetch everything in the future that we can simulate today uh we prefet it we talk about this a lot we were just talking about this on our Costco episode you want to push your chips in when you know it's going to work so every time we see you make a bet the company move yeah you've already simulated it you know yeah yeah yeah do you feel like that was the case with Cuda uh yeah in fact before there was Cuda there was a CG right and so we were already playing with the concept of how

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do we create an abstraction layer above our chip that is expressible in a higher level language and higher level expression and and how can we use our GP for uh things like CT reconstruction image processing we were already down that path and so there were some positive feedback and some intuitive positive feedback that that we think that that general purpose Computing could be possible and if you just looked at the pipeline of a programmable Shader it is a processor and is highly parallel and it is uh massively threaded and it is the only processor in the world that does that and so there were a lot of characteristics about programmable

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shading that would suggest that Cuda has a great opportunity to succeed and that is true if there was a large Market of machine learning practitioners who would eventually show up and want to do all this great scientific Computing and accelerated Computing but at the time when you were starting to invest what is now something like 10,000 person years in building that platform yeah did you ever feel like oh man we might have invested ahead of the demand for machine learning since we're like a decade before the whole world is realizing it I guess yes and no you know when we saw deep learning when we saw alexnet and realized it's incredible

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Effectiveness and computer vision we had the good sense if you will to go back to First principles and ask you know what is it about this thing that made it so successful when a new software technology a new algorithm comes along and somehow Leap Frogs 30 years of computer vision work you have to take a step back and ask yourself but why and fundamentally is is it scalable and if it's scalable what other problems can it solve and there were several observations that we made the first observation of course is that if you have a whole lot of example data you could teach this function to make predictions well what we basically done

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is discovered a universal function approximator because the dimensionality could be as high as you wanted to be and because each layer is trained one layer at a time there's no reason why you can't make very very deep uh neural networks okay so now you just reason your way through right okay so I go back to 12 years ago you could just imagine the reasoning I'm going through my head that we've discovered a universal function approximator in fact we might have discovered with a couple more Technologies a universal computer that have you been paying attention to the image competition you're leading up to this yeah yeah and the reason for that

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is because we were already working on computer vision at the time and we were trying to get Cuda to be a good computer vision system or most of the algorithm that were created for computer vision aren't a good fit for Cuda and so what we're sitting there trying to figure it out all of a sudden Alex net shows up and so that was incredibly intriguing it's so effective that it makes you take a step back and ask yourself why is that happening so by the time that you reason your way through this you you go well what are the kind of problems in a world where a universal function approximator solve right well we know that most of our algorithms uh start

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from principled Sciences okay you want to understand the causality and from the causality you create a simulation algorithm that allows it to scale well for a lot of problems we kind of don't care about the causality we just care about the predictability of it like do I really care for what reason you prefer this toothpaste over that I don't really care the causality I just want to know that this is the one you would have predicted do I really care that the fundamental cause of somebody who buys a hot dog buys ketchup and mustard it doesn't really matter it only matters that I can predict it it applies to predicting movies predicting music it

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applies to predicting quite frankly weather we understand thermodynamics we understand radiation from the sun we understand Cloud effects we understand Oceanic effects we understand all these different things we just want to know whether we should wear Swit or not isn't that right Y and so causality for a lot of problems in the world doesn't matter we just want to emulate the system and predict the outcome and it can be an incredibly lucrative Market if you can predict what the next best performing uh feed item to serve into a social media feed turns out that's a hug I to go with I love the examples you pulled it you know toothpaste catchup music movies

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when you realize this you realize hang hang on a second a universal function approximator a machine Learning System you know something that learns from examples could have tremendous opportunities because just the number of applications is quite enormous and everything from obviously we just talking about Commerce all the way to science and so you realize that maybe this could affect a very large part of the world's Industries almost every piece of software in the world would would eventually be programmed this way and if that's the case then how you build a computer and how you build a chip in fact can be completely changed

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and realizing that the rest of it is just comes with you know do you have the courage to put your chips behind it so that's where we are today um and that's where Nvidia is today but I'm curious in that you know there's couple years after Alex net and this is when Ben and I were getting into the technology industry and the Venture industry ourselves I started at Microsoft in 2012 so right after Alex snap but before anyone was talking about machine learning and even the mainstream engineering community there were those couple years there where to a lot of the rest of the world these looked like science projects yeah the technology companies here in Silicon

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Valley particularly the social media companies they were just realizing huge economic value out of this the Googles the Facebooks the Netflix's Etc and obviously that led to lots of things including open AI a couple years later but during those couple years when you saw just that huge economic value unlock here in Silicon Valley how are you feeling during those times the first thought was of course reasoning about uh how we we should change our Computing stack the second thought is where can we find earliest possibilities of use if we were to go build this computer what would people use it to do and we were fortunate that

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working with the world's universities and researchers was was innate in our company because we were already working on Cuda and cuda's early adopters were researchers because we democratize supercomputing you know Cuda is not just used as you know for AI Cuda is used for almost all fields of science everything from molecular Dynamics to Imaging CT reconstruction to um seismic processing to you know weather simulations quantum chemistry the list goes on right and so the number of applications of Cuda in research was very high and so when the time came and we realized that deep learning could be really interesting uh it was natural for us to go back to the

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researchers and find every single AI researcher on the planet and say how can we help you advance your work and that included Yan Lun and Andrew and Jeff Hinton and that's how I met all these people and and I used to go to all the AI conferences and that's where you know I met Ilia sus there for the first time yeah and so it was really about at that point what are the systems that we can build and the software Stacks we can build to help you be more successful to advance the research because at the time it looked like a toy but we had confidence that even Gan the first time I met goodfella the Gan was was like 32 by

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32 and it was just a you know blurry image of a cat you know but how far can it go and so we believed in it we believe that one you could scale deep learning because obviously it's trained layer by layer and you can make the data sets larger and you could make the models larger and we believe that if you made that larger and larger it would get better and better yeah kind of sensible and I think the discussions and the engagements with the researchers was the exact positive feedback system that we needed I would go back to research it was that's where it all happened when open AI was founded in 2015 yeah I mean that was such an

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important moment that's obvious today now but at the time I I think most people even people in Tech were like what is this yeah yeah were you involved in it at all like you know because you were so connected to the researchers to Ilia taking that Talent out of Google and Facebook to be blunt but receding the research community and opening it up um was such an important moment were you involved in it at all I wasn't involved in the founding of it but I knew a lot of the people there and um Elon of course I knew and uh Peter Beal was there and Ilia was there and we have we have some great employees today that were there in the beginning and I knew

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that they needed this amazing computer that we were building and we're building the first version of the dgx which you know today when you see a hopper it's 70 lbs 35,000 Parts 10,000 amps but dgx the first version that we built was uh used internally and I delivered the first one to open Ai and that was a fun day but most of our success was aligned around um in the beginning just about helping the researchers get to the next level I knew it wasn't very useful in its current state but I also beli that in a few clicks it could be really remarkable and that belief system came from the interactions with all these amazing researchers and it came from just seeing

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the incremental progress at first the papers were coming out every 3 months and then then papers today are coming out every day right so you could just monitor the archive papers and I took an interest in learning about the progress of deep learning and and and to the best of my ability read these papers and you could just see the progress happening you know in real time exponentially in real time it even seems like within the industry from some researchers we spoke with it's seemed like no one predicted how useful language models would become when you just increase the size of the models they thought oh there has to be some algorithmic change that needs to

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happen but once you cross that 10 billion parameter Mark and certainly once you cross the 100 billion they just magically got much more accurate much more useful much more lifelike were you shocked by that the first time you saw a truly large language model and do you remember that feeling well my first feeling about the language model was how clever it was to just mask out words and and U make it predict the next word it's self-supervised learning at its best we have all this text you know I know what the answer is I'll just make you guess it and so my first impression of Bert was really how clever it was and now the question is how can you scale that you

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know the first observation almost everything is interesting and then and then try to understand intuitively why it works and then the next step of course is from first principles how would you extrapolate that yep and so obviously we knew that Bert was going to be a lot larger now one of the things about these language models is it's encoding information isn't that right it's compressing information and So within the world's languages and text there's a fair amount of reasoning that's encoded in it and we describe a lot of reasoning things and and so if you were to say that uh fep reasoning is somehow learnable from just reading

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things I wouldn't be surprised you know for a lot of us uh we get our common sense and we get our our reasoning ability by reading and so why wouldn't a machine learning model also learn some of the reasoning capabilities from that and from reasoning capabilities you could have emergent capabilities right emergent abilities are consistent with intuitively from reasoning and so some of it could be predictable but still it's still amazing the fact that it's sensible doesn't make it any less amazing right I could visualize literally the entire computer um and and all the modules in a self-driving car and the fact that it's still keeping

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Lanes makes me insanely happy and so I even remember that from my first operating systems class in college when I finally figured out all the way from programming language to the electrical engineering classes bridged in the middle by that OS class I'm like oh I think I understand how the Von noyman Computer Works Soup To Nuts and it's still a miracle yeah yeah yeah yeah exactly yeah yeah when you put it all together it's still a miracle yeah now is a great time time to talk about one of our favorite companies stat Sig and we have some tech history for you yes so in our Nvidia part three episode we talked about how the AI research teams

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at Google and Facebook drove incredible business outcomes with cuttingedge ML models and these models powered features like the Facebook news feed Google ads and the YouTube next video recommendation in the process transforming Google and Facebook into the juggernauts that we know today and while we talked all about the research we didn't touch on how these models were actually deployed yeah the most common way to deploy new models was through experimentation AB testing when the research team created a new model product Engineers would deploy the model to a subset of users and measure the impact of the model on core product

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metrics great experimentation tools transformed the machine learning development process they drisk releases since each model could be released to a small set of users they sped up release Cycles researchers could suddenly get quick feedback from real user data data and most importantly they created a pragmatic datadriven culture since researchers were rewarded for driving actual product improvements and over time these experimentation tools gave Facebook and Google a huge Edge because they really became a requirement for leading ml teams yep so now you're probably thinking well that's great for Facebook and Google but my team can't

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build out our own internal experimentation platform well you don't have to thanks to stat Sig so stat Sig was literally founded by ex Facebook Engineers who did all this they've built a best-in-class experimentation feature flagging and product analytics platform that's available to anyone and surprise surprise a ton of AI companies are now using stat Sig to improve and deploy their models including open Ai and anthropic yep so whether you're building with AI or not stat Sig can help your team ship faster and make better data driven product decisions they have a very generous free tier and a special program for venture-backed companies

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simple pricing for Enterprises and no seat-based fees if you're in the acquired Community there's a special offer you get 5 million free events a month and white glove onboarding support So visit stats.com Acquired and get started on your datadriven journey we have some questions we want to ask you uh some are cultural about Nvidia but um others are generalizable to company building broadly and the first one that we wanted to ask is uh we've heard that you have 40 plus direct reports and that this org chart works a lot differently than a traditional company org chart do you think there's something special about Nvidia that makes you able to have

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so many direct reports not worry about coddling or focusing on Career growth of your Executives and you're like no you're just here to do your freaking best work and the most important thing in the world now go a is that correct and B is there something special about Nvidia that enables that I don't think it's something special about Nvidia I think that we had the courage to build a system like this Nvidia is not buil like a military it's not buil like a like the Armed Forces where you have you know generals and Colonels you we just we're not set up like that we're not set up in a command and control and information distribution system from the top down

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we're really built much more like a Computing stack and a Computing stack the lowest layer is our architecture and then there's our chip and then there's our software and and on top of it there are all these different modules and each one of these layers of modules are people and so the architecture of the company to me is a computer with a Computing stack with um uh people managing different parts of the system and who reports to whom your title is not related to anywhere you are in the stack it just happens to be who's the best at running that module on that function on that layer

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it is in charge and that person is the pilot in command and so that's one characteristic and have you always thought about the company this way even from the earliest days yeah pretty much yeah and the reason for that is because your organization should be the architecture of the Machinery of building the product right that's what a company is yep and yet everybody's company look exactly the same but they all build different things how does that make any sense do you see what I'm saying yeah you know how you make Fried Chicken versus how you flip burgers versus how you make you know Chinese fried rice is

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different and so why would the Machinery why would the process be exactly the same and so it's not sensible to me that if you look at the or charts of most companies it all kind of looks like this and then that you have one group that's for a business and you have another for another business you have another for another business and they're all kind of supposedly autonomous and so none of that stuff makes any sense to me it just depends on what is it that we're trying to build and what is the architecture of the company that best suits to go build it that's so that's number one in terms of information system and how do you enable collaboration we kind of wir it

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up like a neural network and the way that we say is that there's a phrase in the company called mission is the boss and so we figure out what is the mission of what is the mission and we go wire up the best skills and the best teams and the best resources to achieve that mission and it cuts across the entire organization in a way that doesn't make any sense but it's looks like a little bit like a neuron Network you know and when you say mission do you mean Mission like nvidia's mission is yeah okay so it's not like further accelerated Computing it's like we're shipping djx Cloud uh build Hopper or somebody else has uh build a system for Hopper

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somebody has uh built Cuda for Hopper somebody's job is build cudnn for Cuda for Hopper somebody's job is the mission right is is so you know your mission is to do something what are the trade-offs associated with that versus the traditional structure the downside is is the pressure on the leaders is fairly High and the reason for that is because in a command and control system the person who you reports to has more power than you and the reason why they have more power than you is because they're closer to the source of information than you are in our company the information is disseminated fairly quickly to a lot of different people and usually at A

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Team level so for example just now was I was in our robotics meeting and we're talking about certain things and we're making some decisions and there are new College grads in a room there's three vice presidents in the room there's two e staffs in a room and at the moment that we decided together we reason through some stuff we made a decision everybody heard at exactly the same time so nobody has more power than anybody else does that make sense the new college grad learned at exactly the same time as the eastaff and so the executive staff and and the leaders that that work for me and myself you earned the right to have your job based on your ability

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to reason through problems and helping other people succeed and and it's not because you have some privilege information that I knew the answer was 3.7 and only I knew you know everybody knew when we did our most recent episode Nidia part three that we we just released we sort of did this thought exercise um especially over the last couple years your product shipping cycle has been very impressive especially given the level of technology that you are working with and the difficulty of this all we sort of said like could you imagine Apple shipping two iPhones a year and we said that for illustrative purposes for illustrative

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purposes not to pick on Apple or what not a large tech company two Flagship products or their Flagship product twice per year yeah or you know two wwc's a year yeah there seems to be something like you can't really imagine that whereas that happens here are there other companies either current or historically MH that you look up to admire Maybe took some of this inspiration from in the last 30 years I've read my fair share of business books and as in everything you read you you're supposed to you're supposed to to first of all enjoy it right enjoy it be inspired by it but not to adopt it that's not the whole point of these

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books the whole point of these books is to share their experiences and and you you're supposed to ask you know what does it mean to me in my world and what does it mean to me in the context of what I'm going through what does this mean to me in the environment that I'm in and what does this mean to me in what I'm trying to achieve and what does this mean to Invidia in the age of our company and the capability of our company and so you're supposed to ask yourself what does it mean to you and then from that point being informed by all these different things that we're learning uh we're supposed to come up with our own

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strategies you know what I just described is kind of how I go about everything you're supposed to be inspired and learn from every everybody else and and the education's free you know when somebody talks about a new product you're supposed to go listen Len to it you're not supposed to ignore it you're supposed to go learn from it and it could be a competitor it could be adjacent industry it could be nothing to do with us the more we're we learn from uh what's happening out in the world uh the better but then you're just supposed to come back and ask yourself you know what does this mean to us yeah you don't just want to imitate them that's right

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yeah yeah I love this teup of learning but not imitating and learning from a wide array of sources there's this sort of um unbelievable third element I think to what Nvidia has become today and that's the data center it's certainly not obvious I can't reason from Alex net and your engagement with the research Community uh and and you know social media feed recers to you deciding and the company deciding we're going to go on a 5year Allin Journey on the data center yeah how did that happen yeah our journey to the data center happened I would say almost 17 years ago I'm always being asked I mean what are the challenges that the company could

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see someday and and I've always felt that the fact that nvidia's technology is plugged into a computer and that computer has to sit next to you because it has to be connected to a monitor that will limit our opportunity someday because there are only so many desktop PCS that plug a GPU into and uh there's only so many CRTs and and in the time LCDs that we could possibly drive so the question is wouldn't it be amazing if our computer doesn't have to be connected to the viewing device that that the separation of it um made it possible for us to compute somewhere else and one of our Engineers came and show it to me one day

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and it was really capturing the frame buffer and coding it into video and streaming it um to a a receiver device separating Computing from the viewing in many ways that's Cloud G in fact in fact in fact that was when we started gfn we knew that gfn was going to be um a journey that would take a long time because you you're fighting you're fighting all kinds of problems including including the speed of light and latency everywhere you look that's right for listen to gfn GeForce now GeForce now yeah GeForce now and and we've been working on your first Cloud product that's right and and look at look at GeForce now was nvidia's First

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Data Center product and our second Data Center product was remote Graphics putting our gpus in in the world's Enterprise data centers which then led us to our third product which combined Cuda plus our GPU which became a supercomputer which then worked towards you know more and more and more and the reason why it's so important is because the disconnection between where nvidia's uh Computing is done versus where it's enjoyed if you can separate that your Market opportunity explodes yeah yeah and it was completely true and so we're no no longer limited by the physical constraints of the desktop PC sitting by your desk um you know and we're not

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limited by one GPU per person and so it doesn't matter where it is anymore and so that was really the great observation it's a good reminder I you know the data center segment of nvidia's business to me has become synonymous with how's AI going and that's a false equivalence and it's interesting that you were only this ready to sort of explode in Ai and data center because you had three plus previous products where you learned how to build data center computers even though those markets weren't these like gigantic world changing technology shifts the way that AI is that's how you learned yeah that's right you want to PVE the way to Future

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opportunities you can't wait until the opportunity is sitting in front of you for you to reach out for it and so you have to anticipate you know our job as CEO is to look around corners and and anticipate where will opportunities be someday and even if I'm not exactly sure what and when how do I position the company to be near it to be just standing kind of near under the tree and we can do a diving catch when the Apple Falls you guys know what I'm saying yeah but you got to be close enough to do the diving catch rewinded 2015 in open AI if you hadn't been laying this groundwork in the data center yeah you wouldn't be powering open AI right now yeah but the

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idea that computing will be mostly done away from the viewing device that the vast majority of computing will be done away from the computer itself that Insight was good in fact cloud computing everything about today's Computing is about separation of that and by putting it in a data center we can overcome this latency problem meaning you're not going to overcome speed of light speed of light end to end is only 120 millisecs or something like that it's not that long from a data center to a internet anywhere on the planet yeah and so we could I see and literally across the planet yeah right so if you could solve that problem

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approximately something like that I forget the number but 70 M seconds 100 milliseconds but it's not that long and so my point is if you could remove the obstacles everywhere else then speed of light should be you know perfectly fine and you could build data centers as as large you like and you could do amazing things and and this little tiny device that we use as a computer or you know your TV as a computer whatever computer they all can all instantly become amazing and so that Insight you know 15 years ago was a good one so speaking of the speed of light infiniband yeah like David's like begging me to go here the same you

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totally saw that infin band would be way more useful way sooner than anyone else realized acquiring melanox I think you uniquely saw that this was required to train large language models and you were super aggressive in acquiring that company why did you see that when no one else saw that well uh there are several reasons for that first um if you want to be a data center company build building the processing chip isn't the way to do it a data center is distinguished from a desktop computer versus a cell phone not by the processor in it yeah a desktop computer and a data center uses the same CPUs uses the same gpus apparently right very close and so it's not the chip it's

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not the processing ship that describes it but it's the networking of it it's the infrastructure of it it's the you know how the the the Computing is distributed how security is provided how networking is done you know so on so forth and so so it those characteristics are associated with melanox not Nvidia and so the day that I concluded that really Nvidia wants to be a you know build computers of the future and computers of the future are going to be data centers embodied in data centers then then if we want to be data center oriented company then then we really need to get into networking and so that was one the second thing is observation

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that whereas cloud computing started in hyperscale which is about taking commodity components a lot of users and virtualizing many users uh on top of one computer AI is really about distributed computing where one job one training job um is orchestrated across millions of processors and so it's the inverse of hyperscale almost and the way that you design a hyperscale computer with with off the-shelf commodity ethernet which is just fine for Hadoop it's just fine for search queries it's just fine for all of those things but not when you're sharting a model across when you're sharting a model across right and so that observation says that the type of

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networking you want to do was not exactly ethernet and the way that we do networking for supercomputing is really quite ideal and so the combination of those two ideas um you know convince me that that melanox is is absolutely the right right company because they were they're the world's leading high performance networking company and and we worked with them in so many different areas in in high performance Computing already plus I I really like the people um uh the the Israel team is world class uh we have some 3,200 people there now and it was one of the best strategic decisions i' ever made when we were researching particularly part three of

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our Nvidia series we talked to a lot of people and many people told us the melanox acquisition is one of if not the best of all time by any technology company I think so too yeah and it's so disconnected from the work that we normally do it was surprising to everybody but frame this way you were you were standing near where the action was so you could figure out as soon as that Apple sort of becomes available to purchase like oh llms are about to blow up I'm going to need that everyone's going to need that I think I know that before anyone else does yeah you want to position yourself near opportunities you

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don't have to be that perfect you know know is you want to position yourself near the tree and even if you don't catch the the the Apple before it hits the ground so long as you're the first one to pick it up you want to position yourself close to the opportunities now and so that's kind of a lot of my work is positioning the company near opportunities and and um having the the uh the company having the skills to to um monetize each one of the steps along the way so that we can be sustainable what you just said reminds me of a great uh aphorism from Buffett and Munger which is it's better to be approximately right than exactly

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wrong yeah there you go yeah that's a good one good one yeah all right listeners we are here to tell you about a company that literally couldn't be more perfect for this episode cruso yes cruso as you know by now is a cloud provider built specifically for AI workloads and powered by Clean energy and in video is a major partner of cuso their data centers are filled with A1 100s and h100s and as you probably know with the rising demand for AI there's been a huge surge in the need for high performing gpus leading to a noticeable scarcity of Nvidia gpus in the market cruso has been ahead of the curve and is among the first Cloud providers to offer

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nvidia's h100s at scale they have a very straightforward strategy create the best AI Cloud solution for customers using the very best GPU Hardware on the market the customers ask for like Nvidia and invest heavily in an optimized Cloud software stack yep to illustrate they already have several customers already running large scale generative AI workloads on clusters of Nvidia h100 gpus which are interconnected with 3200 gbit infiniband and leveraging kuso's network attached block storage solution and because their cloud is run on wasted stranded or clean energy they can provide significantly better performance produ dollar than traditional Cloud

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providers yep ultimately this results in a huge win-win they take what is otherwise a huge amount of energy waste that causes environmental harm and use it to power massive Ai workloads and it's worth noting that through their operations cruso is actually reducing more emissions than they would generate in fact in 2022 cruso captured over 4 billion cubic feet of gas which led to the avoidance of approximately 500,000 metric tons of CO2 emissions that's equivalent to taking about 160,000 cars off the road amazing if you your company or your portfolio companies could use lower cost and more performant infrastructure for your AI workloads go

45:60-46:23

to cruso cloud.com acquired that's cruso cloud.com acquired or click the link in the show notes I want to move away from Nvidia if you're okay with it and ask you some questions since we have a lot of Founders that listen to this show sort of advice for company building the first one is when you're starting a startup in the earliest days your biggest competitor is uh you don't make anything people want like your company's likely to die just because people don't actually care as much as you do about what you're building in the later days you actually have to be very thoughtful about competitive strategy and I'm curious what would be your advice to

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companies that you know have product Market fit that are starting to grow they're in interesting growing markets um where should they look for competition and how should they handle it well there are all kinds of ways to think about competition we prefer to position ourselves in a way that serves a need that usually hasn't emerged I've heard uh you are others in video I think Ed the phrase Z billion dollar market that's exactly right yeah it's our way of saying there's no market yet but we believe there will be one and and usually when you're positioned there everybody everybody's trying to figure out why are you here

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right because when we first got into Automotive because we believe that in the future the car is going to be largely software and if it's going to be largely software um a really incredible computer is necessary and so so when we positioned ourselves there most people I I still remember one one of the one of the CTO told me you know what cars cannot tolerate the blue screen to death and I I don't think anybody can tolerate that but but it doesn't change the fact that someday every car will be a software defined car and I think you know 15 years later we we're largely right and so often times there's non-c consumption and we like to navigate our

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company there and by doing that um by the time that you uh that the market emerges it's very it's very likely there aren't that many competitors shaped that way and so we were early in PC gaming and today uh inidia is very large in PC gaming uh we uh reimagined what a what a design workstation would be like and today just about every workstation on the planet uses Nvidia technology U we re reimagine um how supercomputing ought to be done and who should who should benefit from supercomputing that we would democratize it and look today Nvidia is in accelerated Computing is is um quite large and we reimagined how software would be done and today it's

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called machine learning and how Computing would' be done we call it Ai and so we reimagine these kind of things uh try to try to do that about a decade in advance and so we spent about a decade in Z billion doll markets and today I spent a lot of time on Omniverse and Omniverse is a you know classic example of a z billion dollar business and there's like 40 customers now something like thaty Amazon BMW it's cool it's cool so let's say you do get this great 10-year lead but then other people figure it out and you got people nipping at your heels what are some structural things that someone who's building a business can do to sort of

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stay ahead and you can just keep your pedal to the metal and say we're going to outwork them and we're going to be smarter and like that works to some extent but those are tactics what strategically can you do to sort of make sure that you can maintain that lead often times if you created the market you ended up having you know what what people describe as Moes because if you build your product right and it's enabled uh an entire ecosystem around you to help serve that end Market you've essentially created a platform MH sometimes it's a it's a product based platform sometimes it's a service based platform sometimes a Technology based

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platform but if you're you were early there and you you you were mindful about helping the ecosystem um succeed with you you ended up having this network of networks and all these developers and all these customers who are who are built around you y and that network is essentially your remote and so you know I I don't love thinking about it in the context of a moat um and the reason for that is because you're now focused on building stuff around your Castle I tend to like thinking about things in the context of building a network and that network is about enabling other people to enjoy the success of the final Market you know that you're not the only

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company that enjoys it but you're enjoying it with a whole bunch of other people including yeah I'm so glad you brought this up because I wanted to ask you um in my mind at least and sounds like in yours too MH Nvidia is absolutely a platform company of which there are very few meaningful platform companies in the world I think it's also fair to say that when you started for the first few years you were a technology company and not a platform company every example I can think of of a company that tried to start as a platform company fails you got to start as a technology first when did you think about making that transition to being a

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platform like your first graphics cards were technology they weren't there was no Cuda there was no platform yeah what you observed is not wrong however inside our company we were always a platform company and the reason for that is because from the very first day of our company we had this architecture called UDA it's the UDA of Cuda Cuda is compute unified yeah device architecture that's right and the reason for that is because what we've done what we what we essentially did in the beginning even though Reva 128 only had computer Graphics the architecture described accelerators of all kind L and we would take that architecture

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and developers would program to it in fact nvidia's first strategy business strategy was we were going to be a game console inside the PC and a game console needs developers which is the reason why Nvidia a long time ago one of our first employees was a developer relations person and so it's the reason why we knew all the game developers and all the 3D developers and we knew so wait so was the original business plan to like sort of like to build direct X yeah compete with Nintendo and Sega as like the original Nvidia architecture was called direct Envy direct Nvidia yeah and direct X was an API that made it possible for

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operating system to directly address Hardware yeah but direct didn't ex when you started Nvidia right and that's what made your strategy wrong for the first c93 we had Direct Nvidia yeah huh and which in 1995 became you know well direct X came out so this is an important lesson you we were always always a developer oriented company the initial attempt was we will get the developers to build on Direct envy and then they'll build for our chips and then we'll have a platform and yeah exactly what played out is Microsoft already had all these developer relationships so you learn the lesson the hard way of like yikes we

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just got that's what Microsoft did back in the day they're like oh that could be a developer platform we'll take that thank you you know no but they had a lot they did it very differently and and they did a lot of things right we did a lot of things wrong but but having said competing against Microsoft in the 90s I mean that's uh like trying to comp against Nvidia today no it's a lot different but I appreciate that but but we were we were nowhere near near competing with them if you look now when Cuda came along there was open G there was direct X um but there's there's still another extension if you will and that extension is Cuda and that Cuda

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extension allows a chip that got paid for running direct X and openg to create an install base for Cuda yeah and so that's the why you were so militant and I think from our research it really was you being militant that every Nvidia chip will run Cuda yeah if you're a Computing platform everything's got to be compatible we are the only accelerator on the planet where every single accelerator is AR architecturally compatible with the others none has ever existed there are literally a couple of hundred million right 250 million 300 million installed base of active Cuda gpus being used in the world today and

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they're all architecturally compatible how would you have a Computing platform if if you know mv3 and mv35 and 39 and MV 40 they're all different right that 30 years it's all completely compatible and so that's the only unnegotiable rule in our company everything else is negotiable I mean I guess uh Kudo was a rebirth of UDA but understanding this now UDA going all the way back yeah it really is all the way back to all the chips you've ever had yeah yeah yeah in fact it UDA goes all the way back to all of our chips today wow for the record I didn't help any of the the founding CEOs that that are listening I got to tell you you know while you were asking that

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question what what lessons would I impart um I I don't know I mean there the characteristics of successful companies and successful CEOs I think are are fairly well described there are a whole bunch of them I just think starting successful companies are insanely hard it's just insanely hard and when I see these amazing companies getting built uh I have nothing but admiration and respect because I I just know that it's insanely hard and I think that everybody did many similar things there are some good uh smart things that people do there are some dumb things that you can do uh but but you could do all the right smart

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things and still fail you could do a whole bunch of dumb things and I did many of them and still succeed so obviously that's not exactly right you know just I think skills are are the things that you can learn along the way but an important moment certain circumstances have to come together and and I do think that that the market has to you know be one of the agents yeah to help you succeed it's not enough obviously because a lot of people still fail do you remember any moments in nvidia's history where you're like oh we made a bunch of wrong decisions but somehow we got saved because you know it takes the sum of all the luck and all

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the skill in order to succeed do you remember any moments where I just thought that you starting with re Revo 120 was spot on uh Revo 128 as I mentioned the number of smart decisions we made which are smart to this day how we design chips is exactly the same to this day because gosh you know nobody's ever done it back then and we pulled every trick in the book in a desperation because we had no other choice well guess what that's the way things ought to be done and now everybody does it that way right everybody does it because why should you do things twice if you can do it once why tape out a chip seven times

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if you could tape it out one time right and so the most efficient the most cost effective the most competitive um uh speed is technology right speed is performance time to Market his performance all of those things apply so why do things twice if you could do it once Y and so Revo 128 made a lot of great decisions and how we spec products um how we how we think about Market needs and and lack of and how do we judge markets and all of those man we made some amazing amazingly good decisions yeah we were you know Back Against The Wall we only had one more shot to do it but once you pull out all the stops and you see what you're

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capable of why would you put stops in exactly keep stops out all the time right every time that's right is it fair to say though maybe on the luck side of the equation thinking back to 1997 that that was the moment where consumers tipped to really really valuing 3D graphical performance in games oh yeah so for example luck let's let's talk about luck um if if carac had had um decided to use acceleration because remember Doom was completely software rendered and the Nvidia philosophy was that although general purpose Computing is is a fabulous thing and it's going to enable software and it and everything um we felt that there

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were there were applications that wouldn't be possible or it would be costly if it wasn't accelerated it should be accelerated and 3D Graphics was one of them but it wasn't the only one and it was just happens to be the first one and a really great one and I still remember the first times we met John he was quite emphatic about using CPUs and and the software renderer was really good I mean quite frankly if you look at look at Doom uh the performance of Doom was really hard to achieve even with accelerators at the time you know if you didn't filter if you didn't have to do Bor filtering um it did a pretty good job

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the problem with doom though was you needed carmac to program it yeah you needed carmac to program it exactly it was it was a genius piece of code and um but nonetheless software renderers did a really good job and but and if he had decided to go to opengl and accelerate accelerate for Quake uh frankly you know what would be the Killer app that put us here right and so carac and Sweeney both between uh unreal and Quake created the first two killer applications for for Consumer 3D yeah and so I ow owe them a great deal I want to come back real quick to you know you said you told these stories and you're like well I don't know what Founders can take from

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that I I actually do think um you know if you look at all the big tech companies today perhaps with the exception of Google they did all start and understanding this now about you by addressing developers planning to build a platform and tools for developers um you know all of them Apple Amazon well I guess with AWS that's how AWS started so I think that actually is a lesson to your point of like that won't guarantee success by any means but that'll get you hanging around a tree if the Apple Falls yeah as many good ideas as we have um you don't have all the world's good ideas and and the benefit of having developers is you get to see a lot of

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good ideas yep yeah well as we we start to drift toward the end here we spent a lot of time on the past and I want to think about the future a little bit I'm sure you spend a lot of time on this being on The Cutting Edge of AI you know we're moving into an era where the productivity that software can accomplish when a person is using software can massively amplify the impact and the value that they're creating which has to be amazing for Humanity in the long run in the short term it's going to be inevitably bumpy as we sort of figure out what that means what do you think some of the solutions are as AI gets more and more powerful

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and better at accelerating productivity uh for all the displaced jobs that are going to come from it well first of all we have to keep AI safe and there's a couple of different areas of AI safety U that's really important obviously uh in robotics and self-driving car there's a whole field of AI safety and we've dedicated ourselves to functional safety and active safety and all kinds of different different areas of safety um when to apply human in the loop when is it okay for human not to be in the loop uh uh you know how do you get to a point where where um uh increasingly human doesn't have to be in the loop but human largely in the loop yeah in the case of

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information safety obviously bias false information and appreciating the the rights of artists and and creators um that that whole area uh deserves a lot of attention and and you've seen some of the work that we've done instead of scraping the internet um we we partnered with Getty and shutter stock to create commercially Fairway of applying artificial intelligence G of AI yep in the area of large language models and the and the future of increasingly greater agency AI clearly the answer is for as long as it's sensible and I think it's going to be sensible for a long time is human in the loop the ability for an AI to self-learn and improve and

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change uh out in the wild uh in a digital form uh should be avoided and and um we should collect data we should carry the data we should train the model we should you know test the model validate the model before we release it on the wild again so human is in the loop yep there are a lot of different industries that have already demonstrated how to build systems that are safe and good for Humanity and obviously the way uh autopilot works for for a plane and and two pilot system and then air traffic control and um you know redundancy and diversity and and all of the basic philosophies of Designing Safe Systems um apply uh as well in

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self-driving cars and and so on so forth and and so I I think there's a lot of models of of creating safe Ai and and I think we need to apply them with respect to automation my feeling is that and we'll see but it is more likely that AI is going to create more jobs and in the near term the question is what's the definition of near term and the reason for that is is um uh the first thing that that happens with product activity is prosperity and prosperity when the companies get get more successful they hire more people because they want to expand into more areas and so the question is if you think about a company and say okay if we improve the

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productivity then they need they need fewer people well that's because the company has no more ideas but that's not true for most companies um if you become more productive and the company becomes more profitable usually they hire more people to expand into new areas and so long as we believe that there are more areas to expand into that the the the there are more ideas in drugs this drug Discovery there are more ideas in transportation there are more ideas in retail there more ideas in entertainment that there's more ideas in technology so long as we believe that there are more ideas the prosperity of the industry which comes from improved productivity

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results in hiring more people more ideas now you go back in history we can fairly say that today's industry is larger than the industry was the the world's Industries a thousand years ago and the reason for that is because obviously humans have a lot of ideas and I think that there's plenty of ideas yet for prosperity and plenty of ideas that can be be get from productivity improvements but that my sense is that it's likely to generate jobs now obviously net generation of jobs doesn't guarantee that any one human doesn't get fired okay I mean that's obviously true and and it's more likely that someone U will lose a job to someone else some

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other human that uses an AI you know and not not likely to an AI but to some other human that uses an AI and so I think the the first thing that everybody should do is learn how to use AI so that they can augment their own productivity and every company should augment their own productivity to be more productive so that they could have more Prosperity hire more people and so I think jobs will change my guess is that we'll actually have higher employment will create more jobs I think Industries will be more more productive um and many of the industries that are currently suffering from lack of lack of uh labor workforce is likely to uh use AI to get

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themselves off the feet and and get back to growth and prosperity so I see it a little bit differently but I do think that jobs will be affected um and I I'd encourage everybody just to learn AI this is appropriate this a version of um something we talk about a lot unacquired we call it the meritz Cory to Mo's law after Mike meritz from seoa seya was first investor in our company yeah of course yeah the great story behind it is that uh when Mike was taking over for Don Valentine with with Doug he was sitting and looking at se's returns and he was looking at fund three or four I think it was four maybe that

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had Cisco in it he was like how are we ever going to top that you know I can't I can't you know Don's goingon to have us beat we're never going to beat that he thought about it and he realized that well as compute gets cheaper and it can access more areas of the economy because it gets cheaper and can get adopted more widely well then the markets that we can address should get bigger yeah and AI your argument is basically AI will do the same thing exactly I just gave you exactly the same example that in fact productivity doesn't result in us doing less productivity usually results in us doing more everything we do will be

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easier but we'll end up doing more y because we have infinite ambition you know the the world has infinite ambition and so so if a company is more profitable they tend to hire more people to do more Yep yeah that's true technology is a lever and the the place where the idea kind of falls down is that like that we would be satisfied like uh humans have never ending ambition no humans will always expand and consume more energy and uh attempt to pursue more ideas that has always been true of every version of our species yeah over time now is a great time to share something new from our friends at blinkist and go one that is

67:00-67:60

very appropriate to this episode yes so personal story time I a few weeks ago was scouring the web to find Jensen's favorite business books which was proving to be difficult I really wanted blink to make blinks of each of those books so you could all access them and I think I found one or two in random articles but that just wasn't enough so finally before I gave up as a last resort I asked an AI chatbot specifically Bard to provide me a list and cite the sources of Jensen's favorite business books and miraculously it worked Bard found books that Jensen had called out in public forums over the past several decades so if you click the

67:60-68:22

link in the show notes or go to blinkist.com Jensen you can get the blinks of all five of those books plus a few more that Jensen specifically told us about later in the episode yes and we also have an offer from blinkist and Goan that goes beyond personal learning link has handpicked a collection of books related to the themes of this episode so Tech Innovation leadership the Dynamics of Acquisitions these books offer the mental models to adapt to a rapidly changing technology environment and just like all other episodes blinkist is giving acquired listeners an exclusive 50% discount on all premium content this gives you key insights from

68:22-68:84

thousands of books at your fingertips all condensed into easy to digest summaries and if you're a Founder a team lead or an L&D manager blinkist also incl includes curated reading lists and progress tracking features all overseen by a dedicated customer success manager to help your team flourish as you grow yes so to claim the whole free collection unlock the 50% discount and explore blinkist Enterprise solution simply visit blinkist.com Jensen and use the promo code Jensen blinkist and their parent company go1 are truly awesome resources for your company and your teams as they develop from small startup to Enterprise our

68:84-69:51

thanks to them and seriously this offer is pretty awesome go take him up on it we have a few lightning round questions we want to ask you and then we have a and then we have a very fun can't think that fast we'll open an easy one based on all these conference rooms we see named around here favorite SciFi book I've never read a Sci-Fi book before no oh come on yeah yeah what's with like the obsession with Star Trek and just you know watch the TV show you favorite TV series uh well Star Trek is my favorite yeah Star Trek's my favorite I saw verer out there on the way in it's a good it's a good conference room name verer is an

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excellent one yeah yeah what car is your daily driver these days and related question do you still have the Supra oh I is one of my favorite cars um and also favorite memories you guys might not know this but but uh uh Lori and I got engaged um uh Christmas one year and we drove back in my my brand new super Supra and we totaled it we were this close to the end thank God you didn't but but nonetheless it wasn't my fault it wasn't wasn't the supra's fault but but uh it's a remark I love the one time when it wasn't the supra's fault yeah I love that car I'm driven these days for for security reasons and others but um uh uh I'm driven in the U Mercedes eqs

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it's a great car ah ni yeah great car nice y using Nvidia technology yeah it has yeah we're we're in the in the uh the the U we're the Central Computer y sweet I know we already talked a little bit about business books but one or two favorites that you've taken something from Klay Christensen I think has the the series is the best I mean there's just no no two ways about it and and the reason for that is is because it's so intuitive and so sensible it's it it's approachable but uh I read a whole bunch of them and I read just about all of them I really enjoyed and Andy Grove's books they're all really good awesome favorite characteristic of Don

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Valentine grumpy but endearing and uh what he said to me the last time as he uh decided to invest in our company he says if you lose my money I'll kill you of course he did uh and then uh over the course of of the Decades uh uh the years I followed uh when something is nice written about us in Mercury News um it seems like he wrote it in a crayon he you know he'll say he'll say good job done you know just write right over the newspaper and just good job Don he mails it to me and and I hope I've kept them but anyways you could tell he's a he's a real sweetheart and and um U but but he cares about the companies he's a special

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character yeah he's incredible what is something that you believe today that 40-year-old Jensen would have pushed back on and said no I disagree um there's plenty of time yeah there's plenty of time if you prioritize yourself uh properly and and you make sure that you you uh you don't let Outlook be the controller of your time there's plenty of time plenty of time in the day plenty of time to achieve thing like get just don't do everything prioritize your life make sacrifices don't let Outlook control what you do every day notice I was late toour meting and the reason for that by the time I looked up I oh my gosh you

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know Ben and Dave are waiting you know that's already we had time yeah exactly and so didn't stop this from being a great chat no but you have to prioritize your time really carefully and don't let Outlook determine that love that what are you afraid of if anything I'm afraid of the same things today that I was I was uh in in the very beginning of this company which is letting the employees down you know you have a lot of people who joined your company because they believe in your hopes and dreams and and they've adopted it as their hopes and dreams and and uh you you want to be right for them you want to be successful for them you

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want them to be able to uh build a great life as as well as help you build a great company and be able to build a great career you want them to have to enjoy all of that and these days I want them to be able to enjoy the the things I've had the benefit of enjoying and um all the great success I've enjoyed I want them to be would enjoy all of that and so so I think I think the uh the greatest fear is that that you let them down what point did you realize that you weren't going to have another job that like this was it I just I don't change jobs you know if if it wasn't because of Chris and Curtis convincing me to do do Nvidia I would still be at LSI logic

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today I'm certain of it wow really yeah yeah yeah I'm certain of it I would keep doing what I'm doing and at the time that I was there I was completely dedicated and focused on on helping LSI logic be the best company could be and I was LSI logic's best Ambassador I've got great friends that to this day uh that I've known from from LS logic it's a company I I loved uh then I love dearly today I know exactly why I went um uh the Revolutionary impact it had on chip design and system design and computer design in my estimation one of the most important companies that that ever came to Silicon Valley and changed everything about how

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computers were made uh it put me in the in the epicenter of some of the most important events in computer industry it led me to meeting Chris and Curtis and Andy beos and John Rubenstein and you know some of the most important people in the world and Ed Frank that I I was with the other day and just I mean the list goes on and and so uh LSI logic was really important to me and and uh I would still be there I I would you know who knows what LSI logic would have become if I were still there right and and so that's kind of how my my mind works um powering the AI of the world yeah exactly I mean I might be doing the same thing I'm doing today I got the

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sense from remembering back to part one of our series on Nvidia but until until I'm fired I'm this is this is my last job this is I got the sense that um LSI logic might have also changed your um perspective and philosophy about computing too the sense I we got from the research was that when right out of school and when you first went to AMD first right yeah you believed that like kind of a version of the was it the Jerry Sanders real men have Fabs like you need to do the whole stack like you got to do everything and that LSI logic changed you what LSI logic did was was U realized that you can express um transistors and logical Gates

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and Chip functionality in high level languages that by raising the level of abstraction in what is now called highle design it was coined by uh Harvey Jones who's on nvidia's board and I met met him uh way back in the early days of synopsis but but during that time there was this belief that you can express chip design in high level languages and by doing so you could take advantage of optimizing compilers and optimization logic and and and tools um and and be a lot more productive that logic was so sensible to me and I was 21 years old all the time and I I wanted pursue that Vision uh frankly

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that that idea happened in in um uh machine learning it happened in you know software programming it I want to see it happen in digital biology so that we can we can think about uh biology in a much higher level language uh probably a large language model um would be the the way to make it make it representable that transition was so revolutionary I thought that was the best thing ever happened to the industry and I was I was really happy to be part of it and I was at Ground Zero and so so I I saw one industry um change revolutionize another industry and if not for LSI logic doing the work that it did uh synopsis shortly after then why would the computer

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industry be where it is today yeah it it's uh really really terrific I was I was uh at the right place at the right time to see all that that's super cool yeah and it sounded like the CEO of LSI logic uh put a good word in for you with Don Valentin I didn't know how to write a business plan and which it turns out is not actually important no no no it it turns out that making a financial forecast that nobody knows uh it's going to be right or wrong turns out not to be that important but the important things that a business plan probably could have teased out I I think that the art of writing a business plan out of be much much shorter and it forces you to

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condense you know what what is the true problem you're trying to solve what is the unmet need that you you believe will emerge and what is it that you're going to do that is sufficiently hard that when everybody else finds out is a good idea they're they're not going to swarm it and you know make you Obsolete and so it has to be sufficiently hard to do um there there are a whole bunch of other skills that are involved in just you know product and positioning and pricing and go to market and you know all that kind of stuff but those are skills and you can learn those things easily the stuff that is really really hard is the essence what I described and I did that

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okay but I had no idea how to write the business plan and um and and I was fortunate that wol coryan was so pleased with me and the work that I did when I was at LS logic he called up Don Valentine and and told Don you know invest in this kid and um he's going to come your way and and uh uh so so I was you know I was I was set up for Success from that moment moment and got it got us off the ground yeah as long as he didn't lose the money no I think sequa did okay yeah we we I I think we probably are one of the best investments they've ever made have they held through today the VC partner is still on the board

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Mark Stevens Mark H yeah yeah yeah yeah all these years the two founding VCS are still on the board Suter Hill and seoa yeah t Cox and Mark Stevens I don't think that ever happens yeah we are singular in that in that circumstance I believe they've added value this whole time uh been inspiring this whole time uh uh uh gave great wisdom and and uh a great support uh but they they also were no not yet but they they've been entertained you know by the company inspired by the company and and enriched by the company and so they stayed with it and I I'm I'm really grateful well and that being our final question for you it's 2023 30 years anniversary of

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the founding of Nvidia if you were magically 30 years old again today in 2023 and you were going to Denny's with your two best friends who are the two smartest people you know and you're talking about starting a company what are you talking about starting I wouldn't do it I know and the reason for that is really quite simple ignoring the company that we would start first of all I'm not exactly sure the reason why I wouldn't do it and it goes back to why it's so hard is build build a company and building aidia turned out to have been a million times harder than I expected it to be any of us expected it to be and at

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that time if we realize the pain and suffering and just how vulnerable you you're going to feel um and the challenges that you're going to endure uh the embarrassment and the shame and you know the list of all the things that that go wrong I don't think anybody would start a company nobody in their right mind would do it and I think that that's kind of the the superpower of a entrepreneur they don't know how hard it is and they only ask themselves how hard can it be and to this day I I tricked my brain into thinking how hard can it be because you have to still when you wake up in the morning yep how hard can it be

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everything that we're doing how hard can it be Omniverse how hard can it be you know in terms I get a sense though that you're um planning to retire anytime soon though you're still still I'm still you could choose to say like whoa this is too hard the trick is still working you're still the trick is still working I'm still enjoying myself immensely and I'm adding a little bit of value but but the the um that's that's really the trick of an entrepreneur you have to get yourself to believe that it's not that hard because it's way harder than you think and so if I go taking all of my knowledge now and I go back and I said I'm going to endure that whole journey

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again I think it's too much it is just too much do you have any suggestions on any kind of support system or a way to get through the emotional trauma that comes with building something like this family and friends and and all the colleagues we have here I'm surrounded by people who've been here for 30 years right Chris has been here for 30 years and uh Jeff Fisher's been here 30 years Dwight's been here 30 years and uh Jonah and briyan have been here you know 25 some years and probably longer than that and you know Joe Greco's been here 30 years I'm surrounded by these people that never one time gave up and they never one time gave up on me and that's

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the entire ball of wax you know and and to be able to go home and and uh uh have your family be fully committed to to everything that you're trying to do and um uh thick or thin they're they're proud of you and proud of the company and you kind of need that you need the unwavering support of people around you you know Jim Gaithers and the you know the the tench coxes and Mark Stevens and you know Harvey Jones and all the the early people people of our company the Bill Millers they uh not one time gave up on the company and us and and you kind of you need that not going to need that you need that and I'm pretty sure that almost every successful company and

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entrepreneurs that that have gone through some difficult challenges they they had that support system around them I can only imagine how meaningful that I mean I know how meaningful that is in any company but for you given I feel like the Nvidia journey is um particularly Amplified on these Dimensions right and like you know you went through two two if not three 80% plus draw Downs in the public markets and to have investors who've stuck with you from day one through that must be just like so much support yeah yeah it is incredible and you hate that any of that stuff happened and and most of it you you know most of it is is out of

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your control but you know 80% fall it it it's an extraordinary thing no no matter how you look at it and I forget exactly but I mean we we traded down at about a couple of two three billion dollars in market value for a while because of the decision we made and going into Cuda and all that work and your belief system has to be really really strong you know you have to really really believe it and really really want it otherwise it's just too much to door I mean because you know everybody's questioning you and employees aren't questioning you but employees have questions right um people outside are questioning you and uh it's a little embarrassing it's like you know

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when your stock price gets hit it's embarrassing no matter how you think about it and it's hard to explain you know and so there there's no good good answers to any of that stuff you know CEOs are human and companies are build of humans and and uh these challenges are hard to endure Ben had an appropriate comment on our uh most recent episode on you all where uh we were talking about you know the current situation in Nvidia and I think you said for any other company this would be a you know precarious spot to be in but for NVIDIA this is kind of old hat you know you guys are familiar familiar with these large swings and amplitude yeah

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the thing that that to keep in mind is at all times uh what is the market opportunity that that you're engaging and that help that informs your size you know I was I was told a long time ago that Nvidia can never be larger than a billion dollars obviously it's an underestimation under under imagination of the size of the opportunity yeah it is the case that no chip company can ever be so big and so but if you're not a chip company then then why is that why ises that apply to you and this is the extraordinary thing about technology right now is technology is a tool and it's only so large what's what's unique about our current circumstance today is

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that we're in the manufacturing of intelligence we're in the manufacturing of work world that's Ai and the world of tasks doing work productive generative AI work generative intelligent work that market size is enormous it's measured in trillions one way to think about that is if you built a chip for a car how many cars are there and how many chips would they consume that's one one way to think about that however if you if you built a uh A system that uh whenever needed uh assisted in the driving of the car um and you know what's the value of a autonomous chauffeur um every now and then and so now the the market obviously the problem becomes much larger the

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opportunity becomes larger um you know what would it be like if we if we were to magically conjure up um a chauffeur for everybody uh who has a car and you know how big is that market and obviously obviously that that that's a much much larger market and so the technology industry is at the you know where what we've discovered what Nvidia has discovered and what some of the discovered is that by separating ourselves from being a chip company um but but building on top of a chip and you're now an AI company the the market opportunity has has grown by probably a thousand times you know don't be surprised if technology companies become

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much larger in the future because because uh what you produce uh is something very different and and that that's the kind of the the uh uh the way to think about you know how large can your opportunity how large can you be it has everything to do with the size of the opportunity yep well Jensen thank you so much thank you oh David that was awesome so fun well listeners we want to tell you that you should totally sign up for our email list of course it is notifications when we drop a new email but we've added something new we're including little tidbits that we learn after releasing the episode including listener Corrections and we also have

88:03-88:63

been sort of teasing what the next episode will be so if you want to play the little guessing game along with the rest of the acquired Community sign up at acquired. fmil our huge thank you to blinkist stat Sig and cruso all the links in the show notes are available to learn more and get the exclusive offers for the acquire Community from each of them you should check out acq which is available at any podcast player as these main acquired episodes get longer and come out uh you know once a month instead of once every couple weeks it's a little bit more of a rarity these days we've been upleveling our production process and that takes

88:63-89:24

time yes acq 2 has become the place to get more from David and I and we've just got some awesome episodes coming up that we are excited about if you want to come deeper into the acquired kitchen become an LP acquired. fmlp P once every couple months or so we'll be doing a call with all of you on Zoom just for LPS to get the uh inside scoop of what's going on in acquired land and get to know David and I a little bit better and once a season you'll get to help us pick a future episode so that's acquired. fmlp anyone should join the slack acquire. fm/ slack God we've got a lot of things now David I know the hamburger bar on our website is expanding

89:24-89:93

expanding I know that's how you know we're becoming Enterprise we have a mega menu a menu of menus if you will what is the acquired solution that we can sell that's true we got to find that all right with that listeners acquired. fm/ slack to join the slack and discuss this episode acquire. fm/ store to get some of that sweet merch that everyone is talking about and with that listeners we will see you next time we'll see you next time who got the truth is it you is it you is it you who got the truth now [Music] huh [Music]

Key Themes, Chapters & Summary

Key Themes

  • NVIDIA's Foundational Challenges

  • Strategic Shift to AI and Data Centers

  • Leadership and Company Culture

  • Innovation and Market Positioning

  • Developer Community Engagement

  • Future of Artificial Intelligence

  • Ethical Considerations in AI Development

  • NVIDIA's Role in Technology Evolution

Chapters

  • The Early Days of NVIDIA

  • Transition from Graphics to AI

  • Jensen Huang's Leadership Philosophy

  • Fostering Innovation at NVIDIA

  • Building Relationships with Developers

  • NVIDIA and the AI Revolution

  • Addressing AI Safety and Ethics

  • NVIDIA's Impact on the Tech Industry

Summary

The document is a transcript of an extensive interview with Jensen Huang, the founder and CEO of NVIDIA, conducted by Ben Gilbert and David Rosenthal for their podcast "Acquired." The interview offers an in-depth exploration of Huang's journey in building NVIDIA, the company's evolution, strategic decisions, and its position at the forefront of the AI revolution.


The interview starts with a reflection on the early stages of NVIDIA, particularly focusing on the challenges faced during the development of the Riva 128 graphics chip. Huang describes the risky but innovative decisions taken to ensure the company's survival, highlighting a moment of crisis where NVIDIA bet its future on the successful development and market acceptance of the chip.


A major theme of the discussion is NVIDIA's strategic shift from graphics hardware to broader applications in AI and data centers. Huang shares insights into how NVIDIA navigated through various technological and market challenges, emphasizing the company's ability to adapt and innovate. He particularly notes the significance of the CUDA platform in enabling NVIDIA to expand its influence beyond traditional graphics processing into the realms of high-performance computing and AI.


The conversation also delves into Huang's leadership style and NVIDIA's unique company culture. He explains his approach to managing a large team of direct reports and fostering a collaborative and innovative work environment. Huang emphasizes the importance of positioning the company in markets before they fully emerge, a strategy that has enabled NVIDIA to establish dominance in several key technology areas.


Huang reflects on the importance of developing and maintaining a strong relationship with the developer community. He attributes much of NVIDIA's success to its early focus on creating a platform for developers, enabling a broad ecosystem of applications and innovations built on NVIDIA's technology.


The interview touches on the future of AI and its potential impacts on society and the economy. Huang discusses the importance of AI safety, ethical considerations, and the potential for AI to create new job opportunities through increased productivity and innovation.


Overall, the interview provides a comprehensive view of Jensen Huang's vision and leadership, and the strategic moves that have positioned NVIDIA as a leader in the technology industry, particularly in the field of artificial intelligence and computing.