NVIDIA CEO Jensen Huang

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 to myself while we were recording this. Yeah, just the gear. I mean, the drives that came out of the camera. All right, red cameras for the home studio starting next episode.

Yeah, good. All right, let's do it. Who got the truth? Is it you, is it you, is it you, who got the truth now? Is it you, is it you, is it you?

Sit it down, stay straight. Another story on the way. Who got the truth? Welcome to this episode of Acquired, 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. Expectations are set high. I mean, sky high. 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 graphics to the data center to AI, how they survived multiple near-death experiences. 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 it turned out we didn't. Turns out the protagonist actually knows more. Yes. All right, well listeners, join this lack. 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 at .fm slash Slack. We would love to see you. And without further ado, this show is not investment advice. David and I may have investments in the companies we discuss. And the 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 RIVA 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 six months of cash left. And so you decide to do the entire testing in simulation rather than ever receiving a physical prototype. You commission the production run site on scene with the rest of the company's money. So you're betting it all right here on the RIVA 128. It comes back and of the 32 direct X 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 felt like. The other 24 weren't that important. Okay. So wait, wait, wait.

Was that the plan all along? Like when did you realize the way you did what? I realized I didn't learn about it until it was too late. We should have implemented all 32. Yeah.

But we built what we built and so we had to make the best of it. That was really an extraordinary time. Remember, RIVA 128 was M3. NV1 and NV2 were based on forward texture mapping, no triangles, but curves and it translated the curves. And because we were rendering higher level objects, we essentially avoided using Z-Buffers.

And we thought that was going to be a good rendering approach and it turns out to have been completely the wrong answer. And so what RIVA 128 was was a reset of our company. Now remember at the time that we started the company in 1993, we were the only consumer 3D graphics company ever created. And we were focused on transforming the PC into an accelerated PC because at the time, Windows was really a software rendered system. And so anyways, RIVA 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 direct X.

It was fundamentally incompatible with Nvidia's architecture. 30 competitors have already shown up 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 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 backswear 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 luck, direct X 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 for it. And RIVA 128 is the world's first fully accelerated hardware accelerated pipeline for the rendering 3D. And so the transform, the projection, every single element all the way down to the frame buffer was completely hard work accelerated. We implemented a texture cache. We took the bus limit, the frame buffer limit to as big as physics could afford at the time.

We made the biggest chip that anybody had ever imagined building. We used 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 would be willing to go. If we built it right, we accelerated everything, we implemented everything in direct X that we knew of, and we built it as large as we possibly could, then obviously nobody can build something faster than that.

Today, in a way, you kind of do that here in 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 it. That's right. But we observed that there was a segment of the market where people were, because at the time, 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, there's a whole large market enthusiast who we believe 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 kind of remains true. And for certain segments of the market where the technology is never good enough, like 3D graphics, when 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 shows that. We also made a decision to use this technology called emulation, that was a company called iCoS.

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, I'll buy what you have inventory, and no promises are necessary. And the reason why we needed that emulator is because if you figure out how much money that we have, if we taped out a chip and we 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 a chip again. Well, we would have been out of business already. Yeah.

And so I knew your competitors would have caught up. Well, not to mention we would have been out of business. Who cares? 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 build a chip, write the software, fix the bugs, tape on the new chip, so on and 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 remember having a conversation with our leaders and they said, but chances are how do you know it's going to be perfect? I said, I know it's 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 prototyped the chip by buying the simulator. 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 we... It was like 60 seconds frame or something like that. I actually think that was an hour of proof frame, something like that. And so we 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 we could have tested, we tested in 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 tape 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 start kicking everything off, kick 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 board, was everybody telling you you were crazy? No, everybody was clear. We had no shot. They're not doing it would be crazy.

Because otherwise 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 just describing it. You're probably thinking, yeah, it's pretty sensible. Well, it worked.

Yeah. And so we take that out and we directly to production. So is the lesson for founders out there when you have conviction on something like the Revo 128 or CUDA go bet the company on it? And this keeps working for you. So it 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 a chips in, I know it's going to work. Notice we assume that we take that out a perfect chip.

The reason why we take that out a perfect chip is because we emulated the whole chip before we take it out. We develop 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 a chips in, what you're really doing is you're going to bet the form you're saying, I'm going to take everything in the future, all the risky things and I pull in advance. And that is probably the lesson. To this day, everything that we can prefetch, everything in the future that we can simulate today, we prefetch it. We talk about this a lot. We're just talking about this on our casket episode.

You want to push your chips in when you know it's going to work. So every time we see you make the company move, you've already simulated it. Yeah. Yeah. Do you feel like that was the case with CUDA?

Yeah. In fact, before there was CUDA, there was a CG. Right. And so we were already playing with the concept of how do we create an abstraction layer above our chip that is expressible in a higher level language and higher level expression. And how can we use our GPU for things like CTP reconstruction, image processing, we're already down that path.

And so there were some positive feedback and some intuitive positive feedback that we think that that general purpose computing could be possible. And if you just let that pipeline of a programmable shader, it is a processor and is a highly parallel. It is massively threaded. And it is the only processor in the world that does that. And so there were a lot of characteristics about programmable 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, 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 effectiveness in 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 leapfrogs 30 years of computer vision work, you have to take a step back and ask yourself, but why? And fundamentally, is it scalable? And if it's scalable, when other problems can 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 is discovered a universal function approximator, because the dimensionality could be as high as you want it 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 neural networks. Okay, so now you just reason your way through, right? Okay, so now go back to 12 years ago, you can just imagine the reasoning I'm going through in 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 you can change attention to the image, competition, if you're leading up to this.

Yeah, yeah, and the reason for that is because we're already working on computer vision at the time, and we were trying to get CUDA to be a computer vision system. Or most of the algorithms that were created for computer vision are in good fit for CUDA. And so we're sitting there trying to figure it out. All of this analytics 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 go, well, what are the kind of problems in the world where a universal functional approximator can solve, right? Well, we know that most of our algorithms start from principled sciences. Okay, you want to understand the causality and from the causality, you create a simulation algorithm that allows us 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 a catch up and mustard?

It doesn't really matter. It only matters that I can predict it. It applies to predicting movies, predicting music. It 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 it, sweat or not.

Is that right? Yeah. 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. It can be an incredibly lucrative market.

If you can predict what the next best performing feed item to serve into a social media feed, turns out that's a hugely valuable idea. This is where we're going to go with that. I love the examples you believe. Too paced, catch up, music, movies. When you realize this, you realize, hang on a second, a universal functional products meter, a machine learning system, something that learns from examples could have tremendous opportunities because it's just a number of applications.

It's quite enormous. And everything from obviously, we just talked 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 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.

And realizing that the rest of it just comes with, you have to courage to put your chips behind it. So that's where we are today. And that's where NVIDIA is today. But I'm curious, there's a couple of years after AlexNet. 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 AlexNet, but before anyone was talking about machine learning, even the mainstream engineering community. There were those couple of years there where, to a lot of the rest of the world, these looked like science projects. The technology companies here in Silicon Valley, particularly the social media companies, they were just realizing huge economic value out of this, the Google, the Facebook, Netflix, etc. And obviously that led to lots of things, including Open AI a couple of years later.

But during those couple of 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 how 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 working with the world's universities and researchers was innate in our company because we were already working on CUDA and CUDA's early adopters were researchers, because we democratized super computing.

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, CTB construction to seismic processing to, whether simulations, quantum chemistry, the list goes on. 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, it was natural for us to go back to the researchers and find every single AI researcher on the planet and say, how can we help you advance your work? And that included Jan Lekon and Andrew Ng and Jeff Hinton, and that's how I met all these people. And I used to go to all the AI conferences and that's where I met Ilya Suska over there for the first time.

Yeah. And so it was really about, at that point, what are the systems that we can build in 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 Goodfellow, the GAN was like 32 by 32. And it was just a blurry image of a cat. But how far can it go?

And so we believed in it. We believed that one, you could scale deep learning because obviously it's trained later by later. And you could make the data sets larger and you could make the models larger. And we believed 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. How we go back to research, that's where it all happened. When OpenAI was founded in 2015, that was such an important moment. That's obvious today now. But at the time, I think most people even people in tech

what is this? Were you involved in it at all? Because you were so connected to the researchers to Iliya taking that talent out of Google Space, which we want, but receding the research community and opening it up. What's 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 Elon, of course, I knew and Peter Beale was there and Iliya was there. And we have some great employees today that were there in the beginning. And I knew that they needed this amazing computer that we were building. And we're building the first version of the DGX, which today when you see a hopper, it's £75,000, 35,000 parts, 10,000 amps. But DGX, the first version that we built was

used internally. And I delivered the first one to OpenAI. And that was a fun day. But most of our success was aligned around 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 believe 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 the incremental progress at first, the papers were coming out every three months. And then papers today are coming out every day. So you could just monitor the archive papers. And I took an interest in learning about the progress of

deep learning and to the best of my ability to read these papers. And you could just see the progress happening in real time, exponentially in real time. It even seems like within the industry, from some researchers we spoke with, it 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 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 make it predict the next word. It's self-supervised learning at its best. We have all this text. I know

what the answer is. I'll just make you guess it. And so my first impression of Burt was really how clever was. And now the question is how can you scale that? The first observation almost everything is interesting and then try to understand intuitively why it works. And then the next step,

of course, is from first principles, how was your extrapolate that? And so obviously, we knew that Burt was going to be a lot larger. Now, one of the things about these language models is it's encoding information. 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. We describe a

lot of reasoning things. And so if you were to say that a few step reasoning is somehow learnable from just reading things, I wouldn't be surprised. For a lot of us, we get our common sense and we get our reasoning ability by reading. And so I went to a machine learning model, also learned some of the reasoning capabilities from that. And from reasoning capabilities, you could have emergent capabilities.

Immersion 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 and all the modules in a self-driving car.

And the fact that it's still keeping lanes makes me insanely happy. I 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 to west class, I'm like, oh, I think I understand how the von Neumann computer works soup to nuts. And it's still a miracle. Yeah, yeah, yeah, exactly. When you put it all together, it's still

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sent you. We have some questions we want to ask you some are cultural about NVIDIA, but others are generalizable to company building broadly. And the first one that we wanted to ask is 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 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 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 in NVIDIA. I think that we had the courage to build a system like this. NVIDIA is not built like a military, it's not built like the armed forces where you have generals and criminals. 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. We're really built much more like a computing stack.

And a computing stack, the lowest layer is our architecture and then the job chip and then there's our software 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 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 is in charge and that person is the pilot in command. And so that's one characteristic. And you always thought about the company this way, even from the earliest days. And the reason for that is because your organization should be the

architecture of the machinery of building the product. That's what a company is. And yet everybody's company looking exactly the same, but they all feel different things. How does that make sense? Do you see what I'm saying? How you make fried chicken versus how you feel burgers versus

how you make Chinese fried rice is 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 org charts of most companies, it all kind of looks like this. And then 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 build it. That's the number one in terms of information system and how do you enable collaboration. We kind of wired 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 best resources to achieve that mission. And it cuts across the entire organization in a way that doesn't make any sense. But it looks like a little bit like a neural network. And when you say, do you mean mission like the video's mission is? So it's not like further accelerated computing. It's like we're sitting in a cloud.

Build Hopper or somebody else is build a system for Hopper. Somebody is build Kuda for Hopper. Somebody's job is build Kudian and for Kuda for Hopper. Somebody's job is the mission. So your mission is to do something. What are the trade offs associated with that versus the

traditional structure? The downside 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 report 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 that you are. In our company, the information is disseminated fairly quickly to a lot of different people. It's usually at a team level. So for example, just now 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 the room. There's three vice presidents in the room. There's two east hours in the room. And at the moment we decided together, we reasoned through some stuff. We made a decision.

Everybody heard it exactly the same time. So nobody has more power than anybody else. Does that make sense? The new college grad learned the exact same time as the east staff. And so the executive staff and the leaders that worked for me and myself, you earned the right to have your job based on your ability to reason through problems and helping other people succeed. And it's not because you have some privilege information that I knew. The answer was

3.7 and only I knew. Everybody knew. When we did our most recent episode of video part three, we just released, we started this thought exercise, especially over the last couple of 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, could you imagine Apple shipping two iPhones a year? And we said that for illustrative purposes, not pick on Apple. A large tech company,

two flagship products, or their flagship product twice per year. Or two WWDCs a year. Yeah. There seems to be something in you. You can't really imagine that whereas that happens here.

Are there other companies, either current or historically, that you look up to and 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're supposed 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 books.

The whole point of these books is to share their experiences. And you're supposed to ask, 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 it mean to me in the environment that I'm in? What does it mean to me and what I'm trying to achieve? And what does it mean to envision 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, we're supposed to come up with our own strategies. What I just described is how I go about everything. You're supposed to be inspired to learn from everybody else. And the education is free. When somebody talks about a new product,

you're supposed to go listen 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 a JSON industry. It could be nothing to do with

us. The more where we learn from what's happening on the world, the better. But then you're supposed to come back and ask yourself, what does this mean to us? Yeah, you don't just want to imitate that. That's right. Yeah. I love this tip of learning, but not imitating and learning from a wide array of

sources. There's this sort of unbelievable third element, I think, to winning videos become today. And that's the data center. It's certainly not obvious. I can't reason from AlexNet and your engagement with the research community and social media providers, too. You deciding and

the company deciding. We're going to go in a five-year all-in journey on the data center. Yeah. 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, what are the challenges that the company could see someday? 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 there's only so many CRTs and the time LCDs that we could possibly drive. So the question is, when it'd be amazing if our computer doesn't have to be connected to the viewing device, that the separation of it made it possible for us to compute somewhere else. And one of our engineers

came and showed it to me one day, and it was really capturing the frame buffer and coding it into video and streaming it to a receiver device, separating computing from the viewing. In many ways, that's cloud gaming. In fact, that was when we started GFN. We knew that GFN was going to be a journey that would take a long time because you're fighting all kinds of problems, including speed of light. It's late and see everywhere you look. That's right. That was a TFN. T4.

T4 is now. Yeah. T4 is now. And we've been working on it. That all makes sense. Your first cloud product.

That's right. And look at G4 now was NVIDIA's first data center product. And our second data center product was remote graphics, putting our GPUs in the world's enterprise data centers, which then led us to our third product, which combined CUDA plus our GPU, which became a super computer, which then worked towards more and more and more. And the reason why it's so important is because the disconnection between where NVIDIA is computing is done versus where it's enjoyed, if you can separate that, your market opportunity explodes. And it was completely true. And so we're

no longer limited by the physical constraints of the desktop PC sitting by your desk. And we're not 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. The data center segment of NVIDIA's

business to me has become synonymous with how is AI going? And that's a false equivalence. And it's interesting that you were only this ready to explode in AI in the 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 gigantic world-changing technology shifts the way the AI is. That's how you learn. Yeah. That's right. You want to pave the way to future 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 our job as CEOs to look around corners 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 near under the tree and we can do a diving catch when the Apple calls are going to say, but you've got to be close enough to do the diving catch. Remind to 2015 and open AI. If you hadn't been laying this groundwork in the data center,

you wouldn't be powering open AI right now. But the 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 is 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 tend to end only 120 milliseconds

or something like that. It's not that long. From a data center to anywhere. Anywhere. Yeah. And so we could literally across the planet. Yeah. Right. So if you could solve that problem,

approximately something like that, I forget the number, but it's 70 milliseconds, 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 perfectly fine. And you could build data centers as large like, and you can do amazing things. And this little tiny device that we use as a computer or, you know, your TV as a computer, whatever computer, they can instantly become amazing.

And so that insight, you know, 15 years ago was a good one. So speaking of the speed of light in Finneban, David's like, I can go here. You totally saw that in Finneban would be way more useful, way sooner than anyone else realized. But firing 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, there were several reasons for that. First, if you want to be a data center company building the processing chip as a way to do it, a data center is distinguished from a desktop computer versus a cell phone, not by the processor in it. A desktop computer in the data center uses the same CPUs, uses the same GPUs, apparently, right? Very close. And so it's not the chip. It's not

the processing chip that describes it, but it's the networking of it is the infrastructure of it. It's the, you know, how the computing is distributed, how security is provided, how networking is done, you know, so on and so forth. And so those characteristics are associated with melanox, not NVIDIA. And so the day that I concluded that really NVIDIA wants to be, you know, build computers of the future and computers of the future are going to be data centers and body to data centers. Then we want to be data center oriented company, then we really need to get into networking. And so that was one. The second thing is observation that whereas cloud computing started

in hyperscale, which is about taking commodity components, a lot of users and virtualizing many users on top of one computer, AI is really about distributed computing, where one job, one training job 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 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 now when you're sharding a model across, not when you're sharding a model across, right? And so now that observation says that the type of networking you want to do is not exactly ethernet. And the way that we do not working for supercomputing is really quite ideal. And so the

combination of those two ideas, you know, convinced me that melanox is absolutely the right company, because they were the world's leading high performance networking company. And we worked with them in so many different areas in high performance computing already. Plus, I really liked the people that the Israel team is world class. 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 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. Yeah, 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.

You want to position yourself near opportunities. You don't have to be that perfect. You want to position yourself near the tree. And even if you don't catch the Apple before it hits the ground, as long as you first want to pick it up. You want to position so close to the opportunities.

Now, and so that's kind of a lot of my work is positioning the company near opportunities and the company having the skills to monetize each one of the steps along the way so that we can be sustainable. What you just remind me of a great aphorism from Buffet and Munger, which is it's better to be approximately right than exactly wrong. Yeah, there you go. Yeah, that's a good one. It's a good one. Yeah. All right, listeners, now is a great time to thank our longtime friend

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more than 20 years building the operational backbone of the enterprise. The workflows, governance, approval, security controls, and institutional knowledge that power how work actually gets done across IT, HR, customer service, finance, and security. ServiceNow already runs more than 100 billion workflows annually and trillions of transactions from more than 85% of the Fortune 500. So when companies need a place to govern AI at enterprise scale, they're building on a platform at the center of how their business already operates. And in a future, that isn't going to be one AI, it's going to be thousands of AI agents working across every function of the company. But the question is,

who's managing them all? So if you're trying to turn AI ambition into real business outcomes and make it work safely, securely at scale, go check out ServiceNow.com slash acquired and tell them that Ben and David sent you. 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 the 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 you don't make anything people want. Like your company's likely to die of consumption just because people don't actually care as much as you do about what's going on.

In the later days, you actually have to be very thoughtful about competitive strategy. And I'm curious, what would be your advice to companies that have product market fit that are starting to grow, they're in interesting growing markets, 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 you are others in NVIDIA. I think you said zero billion dollars.

That's exactly right. It's our way of saying there's no market yet, but we believe there will be one. And usually when you're positioned there, everybody's trying to figure out why are you here. 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,

a really incredible computer is necessary. And so when we positioned ourselves there, most people, I remember one of the CTOs told me, what cars cannot tolerate the blue screen of death. I said, I don't think anybody can tolerate that, but that doesn't change the fact that someday every car will be a software-defined car. And I think 15 years later, we're largely right. And so oftentimes, there's non-consumption. And we like to navigate our company there. And by doing that,

by the time that the market emerges, it's very likely there aren't that many competitors shaped that way. And so we were early in PC gaming. And today, NVIDIA is very large in PC gaming. We reimagined what a design workstation would be like. And today, just by reworkstation on the planet uses NVIDIA's technology, we reimagined how supercomputing ought to be done and who should benefit from supercomputing that we would democratize it and look today, NVIDIA's in excel-ready computing is quite large. And we reimagined how software would be done. And today, it's commission learning and

how computer would be done, we call it AI. And so we reimagined these kind of things, try to do that about a decade in advance. And so we spend about a decade in zero billion on markets. And today, I spend a lot of time on Omniverse. And Omniverse is a classic example of a zero billion on our business. There's like 40 customers now. It was on BMW. Yeah, it's cool. So let's say you

do get this great tenure lead, but then other people figured out, you got people nipping at your heels. What are some structural things that someone who's building a business can do to sort of stay ahead? And you can just keep your pedal to the metal and say, we're going to outwork them. 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 create at the market, you ended up having, you know, what people describe as moats. Because if you build your product and it's enabled an entire ecosystem around you to help serve that in market, you've essentially created the platform. Sometimes it's a product based platform, sometimes it's a service based platform, sometimes a technology based platform. But if you were early there and you were mindful about helping the ecosystem succeed with you, you ended up having this network of networks and all these developers and all these customers who are built around you. And that network is

essentially your moat. And so, you know, I don't love thinking about it in the context of a moat. 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 company that enjoys it, but you're enjoying it with a whole bunch of other people, including me.

I'm so glad you brought this up because I wanted to ask you, in my mind, at least, it sounds like in years to, in videos, 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 a company that tried to start a platform company fails. You got to start as a technology person. When did you think about making that transition to a platform? Like your first graphics cards were technology. There

was no 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, right.

That's right. And the reason for that is because what we've done, what we essentially did in the beginning, even though we've only had computer graphics, the architecture described accelerators of all kinds. And we would take that architecture and developers would program to it. In fact, the video'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. So it was the original business plan to like sort of like to go direct X. Yeah, compete with Nintendo and Sega as like in PC. The original Nvidia architecture was called direct envy. Direct Nvidia. Yeah. And direct X was an API that made it possible for operating system to directly

connect hardware. Yeah, but direct X to be started in video, right? And that's why made your strategy wrong. It was key 93. Yeah. We had direct Nvidia. Yeah. And which in 1995 became,

you know, well, direct X came out. So this is an important lesson. You we were always a developer oriented company. The initial attempt was we will get the developers to build on direct and then they'll build for our chips and then we'll have a platform and yeah, exactly played out is Microsoft or he had all these developer relationships. So you learn the less and the hard way of like, yeah, yeah. That's why I did that in the day. They're like, oh,

that could be a developer platform. We'll take that. Thank you. No, but they had a lot. They did it very differently. And they did a lot of things right. We did a lot of things wrong. But

what have you been getting? It's Microsoft in the 90s. I mean, that's, yeah, having a good time today. Yeah, it's a lot different. But I appreciate that. But we were

nowhere near competing with them. If you look now, when Kuda came along and it was opening all, there was direct X, but there's there's still another extension, if you will, an extension is Kuda and that Kuda extension allows a chip that got paid for running direct X and open GL to create an install base for Kuda. Yeah. And so that's the idea. You were so militant. And I think from our

research, it really was you being militant that every invidiotip will run Kuda. Yeah, if you're computing platform, everything's got to be compatible. We are the only accelerator on the planet where every single accelerator is 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 Kuda GPS being used in the world today. And they're all architecturally compatible. How would you have a computing platform if, you know, envy 30 and envy 35 and 39 and envy 40, they're all different, right? 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 Kuda was a rebirth

of UDA, but understanding this now, UDA going all the way back, really, all the way back to all the chips. Yeah, yeah. In fact, UDA goes all the way back to all of our chips today. For the record, I didn't help any of the founding CEOs that are listening. I got to tell you, while you were asking that question, what lessons would I impart? I don't know. I mean, the characteristics of successful

companies and successful CEOs, I think, are fairly well-described, 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, I have nothing but admiration and respect, because I just know that it's insanely hard. And I think that everybody may similar things. There are some

good, smart things that people do. There are some dumb things that you can do. But you could do all the right, smart 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. I think skills are

things that you can learn along the way. But at important moments, certain circumstances have to come together. And I do think that the market has to be one of the agents 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 it takes the sum of all the luck and all the skill in order to succeed. Do you remember any moments where you were like...

You're starting with what Revo 128 was that, spot on. 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, nobody's ever done it back then. And we pulled every trick into 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. Everybody does it because why should you do things twice if you can do it once? Why tape out a chip seven times if you can tape it out one time? And so, the most efficient, the most cost effective, the most competitive speed is technology, right? Speed is performance, time to market is performance. All of those things apply. So,

why do things twice if you can do it once? And so, Revo 128 made a lot of great decisions and how we spec products, how we think about market needs and lack of, and how do we judge markets, and all of those. Maybe we made some amazingly good decisions. Yeah, we were 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 capable of, why would you put stops in? Exactly. Like, keep stops out all the time, every time. That's right. Is it fair to say that maybe on the left side of the equation, thinking back to 1997, that that was the moment where consumers tipped a really, really valuable 3D graphical performance in Gage. Oh, yeah. So, for example, luck. Let's have a look. If Carmack

had decided to use acceleration, because remember, Doom was completely software rendered. And the NVIDIA philosophy was that although John purpose computing is a fabulous thing, it's going to enable software and IT and everything, we felt that 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 just happens to be the first one, and a really great one. And I still remember the first time we met John, he was quite emphatic about using CPUs, and a software render was really good. I mean, quite frankly, if you look at Doom, the performance of Doom was

really hard to achieve even with accelerators at the time. If you didn't filter, if you didn't have to do volume filtering, it did a pretty good job. The problem with Doom, though, was you needed Carmack to program it. Yeah, you needed Carmack to program it. Exactly. It was a genius piece of

code. But nonetheless, software renders did a really good job. And if he hadn't decided to go to open GL and accelerate for Quake, frankly, what would be the killer app that put us here? Right? And so Carmack and Sweeney, both between Unreal and Quake, created the first two killer applications for consumer 3D. Yeah. And so I owe them a great deal. I want to come back real quick too. He said,

you told these stories and you're like, well, I don't know what founders can take from that. I actually do think if you look at all the big tech companies today, perhaps with the exception of Google, they did all start and understanding this now by you by addressing developers, planning to build a platform and tools for developers. All of them. Apple. Not some. Well, I guess what AWS is how

AWS started. So I think that actually is a lesson. So you're like, that won't guarantee success by means. But that'll get you hanging around the tree if the apple falls. Yeah. As many good ideas as we

have, you don't have all the world's good ideas. And the benefit of having developers is you get to see a lot of good ideas. Yeah. Yeah. Well, as we start to drift toward the end here, we spend 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 and better in accelerating productivity 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. That's really important. Obviously, 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 areas of safety. When to apply human in blue, when is it okay for human not to be in the loop? You know, how do you get to a point where increasingly human doesn't have to be in a loop, but human largely in a loop? In the case of

information safety, obviously bias, false information and appreciating the rights of artists and creators, that whole area deserves a lot of attention. And you've seen some of the work that we've done instead of scraping the internet. We partnered with Getty and Sharer-Sag to create commercially fair way of applying artificial intelligence to the AI. In the area of large language models in 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 change out in the wild in a digital form should be avoided. And we

should collect data, we should carry the data, we should train the model, we should test the model, validate the model before we release it on the wild again. So human is in the loop. 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 autopilot works for applying and to pilot system and air traffic control and redundancy and diversity and all of the basic philosophies of designing safe systems apply as well in self-driving cars and so on and so forth. And so I think there's a lot of models of creating safe AI 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 in your term? And the reason for that is

the first thing that happens with productivity is prosperity and prosperity when the companies 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 productivity, then they need fewer people. Well, that's because the company has no more ideas but that's not true from what happens. 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 there are more ideas and drugs, there's drug discovery, there are more ideas in transportation, there are more ideas in retail, there are more ideas in entertainment, 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, results in hiring more people, more ideas. Now, go back in history, we can fairly say that today's industry is larger than the industry that the world's industry is 1000 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 get from productivity improvements. But 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 it's more likely that someone will lose a job to someone else, some other human that uses an AI, you know, and not likely to an AI, but to some other human that uses an AI. And so I think 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 can have more prosperity, hire more people. And so I think jobs will change, my guess is that we'll actually have higher employment

or create more jobs. I think industries will be more productive. And many of the industries that are currently suffering from lack of lack of labor workforce is likely to use AI to get themselves off the street and get back to growth and prosperity. So I see it a little bit differently, but I do think that jobs will be affected. And I'd encourage everybody just to learn AI.

This is appropriate. There's a version of something we talk about a lot on acquired. We call it the Moritz Corollary to Moore's Law. Mike Moritz from Sequoia. And first investor in our company.

Yeah, of course. Yeah. The great story behind it is that when Mike was taking over for down Valentine with Doug, he was sitting looking at Sequoia's returns and he was looking at fun three or four. I think it was four, maybe that had Sisko in it. He's like, how are we ever going to top that?

You know, I can't, I can't, you know, Don's going to have his beaver, never going to beat that. They thought about it and you realize that, well, as compute gets cheaper and it can access more areas of the economy because it gets cheaper and can it get adopted more widely, well, then the markets that we can address should get bigger. Yeah. And you're argument is basically exactly 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 easier, but we'll end up doing more. Yeah. Because we have infinite ambition, you know, the world has infinite vision. And so, so if a company is more profitable,

they tend to hire more people to do more. Yeah. 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. Yeah.

Yeah. Humans have never ending ambition. No. Humans will always expand and consume more energy and attempt to pursue more ideas. That's always been true of every version of our species. Yeah. Over time.

All right. Listeners. Now is a great time to talk about one of our favorite companies, Lagora, the agent operating system for legal work. Yes. Most of you have dropped a contract into a chatbot by now, but as a lawyer, how far can that really take you? It's never seen your firm's

precedence. It doesn't respect confidentiality and you can't check where its answer came from. So Lagora's founders approached this the way that great founders do. Early on, the team moved into the office of a big law firm in Stockholm and learned four months how the work actually gets done. A couple of thousands of enterprise customers and a hundred thousand users later, they still work that way. They employ a hundred legal engineers on staff. These are lawyers

sitting inside of firms and legal departments to deploy Lagora onto live situations like the deal closing this week or the policy review do Friday. The product has gotten very powerful in a short period of time. A year ago, the goal was a good answer from a chatbot. With a Lagora agent, you hand it the work and it comes back with a plan tasks like running diligence on a data room or reviewing any vendor contract get completed inside the tool you use every day like Microsoft Word and against your own precedence and playbooks. Every finding is sourced, the lawyer directs the work and signs off before the system executes any steps. Every legal team can get access to the

same AI models. So the models themselves do not give you an edge. What you can do with your own data, people, playbooks, that's the whole game. Today, over a hundred thousand legal professionals at more than 1,800 law firms and in-house legal teams in over 50 markets use Lagora. And when Lagora goes head to head with its top competitor in a pilot, it wins 70% of the time.

A demo is one thing, but what matters most is an associate who reaches for it at two in the morning during a closing or whether a general counsel trusts what it says before the board meeting. So if your legal team wants to check it out, whether you're at a firm or in-house, go to Lagora.com slash acquired and be sure to tell them that Ben and David sent you. We have a few waiting round questions. We have a very fun, we'll open it easy one based on all these conference rooms we see in the name right here. Favorite sci-fi book? I've never read a sci-fi book before.

No. Come on. Yeah. What's like the obsession with Star Trek and... Oh, just watch the TV show.

Okay. Favorite sci-fi TV shows? Star Trek is my favorite. Yeah, Star Trek is my favorite. It's not V-ger.

Now they're on the way out. It's a good conference room. V-ger is an excellent one. Yeah. Yeah.

What car is your daily driver these days and really the question? These days, Supra. Oh, it is one of my favorite cars and also favorite memories. You guys might not know this, but Lori and I got engaged Christmas one year and we drove back in my brand new Supra and we totaled it. We were just close to the end.

I think I did. But nonetheless, it wasn't the Supra's fault. But it's a mark. The one time when it wasn't the Supra's fault. I love that car.

I'm driven these days for security reasons and others, but I'm driven in the Mercedes EQS. It's a great car. Yeah, great car. Thanks. Using NVIDIA technology?

Yeah, we're in the central computer. Yeah. Sweet. I know we already talked a little bit about business books, but one or two favorites that you've taken something from. Clay Christensen, I think, has been the series is the best.

There's no two ways about it. And the reason for that is because it's so intuitive and so sensible. It's approachable. But I read a whole bunch of them and I read just about all of them. I really enjoyed Andy Groves' books.

They're all really good. Awesome. Favorite characteristic of Don Valentine? Grumpy but endearing. And what he said to me the last time as he decided to invest in our company, says, if you lose my money, I'll kill you.

And then over the course of the decades, the years I've followed, when something is nice written about us in Mercury News, it seems like he voted in a crayon. He'll say, good job, Don. And I just write over the newspaper. It doesn't good job, Don. He's mails it to you.

And I hope I've kept them. But anyways, you could tell he's a real sweetheart. And so, but he cares about the companies. It's a special 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? There's plenty of time. Yeah. There's plenty of time.

If you prioritize yourself properly and you make sure that you don't let out look be the controller of your time, there's plenty of time. Plenty of time in the day, plenty of time. Do anything. Do anything. Do anything.

Do anything. Just don't do everything. Prioritize your life. Make sacrifices. Don't let outload control what you do every day.

Notice I was late to our meeting. And the reason for that, by the time I looked up, I, oh my gosh, you know, Ben and David, we're waiting. That's already. We have time. Yeah.

Exactly. That's a. Didn't stop this from being a great job. No, but you have to prioritize your time really carefully. And don't let outload the determinant.

Love that. What are you afraid of, if anything? I'm afraid of the same things today that I was, I was in the very beginning of this company, which is letting the employees down. You know, you have a lot of people who join your company because they believe in your hopes and dreams and they've adopted it as their hopes and dreams. And and you want to be right for them.

You want to be successful for them. You want them to be able to build a great life 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 things I've had the benefit of enjoying and all the great success I've enjoyed. I want them to be able to enjoy all of that.

And so I think the greatest fear is 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 it wasn't because of Chris and Curtis convincing me to do doing video, I would still be a LSI logic today. I'm certain of it.

Wow. Really? 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 helping LSI logic be the best company could be. And I was LSI logic's best ambassador. I've got great friends to this day that I've known from LSI logic. It's a company I loved then I loved dearly today.

I know exactly why I went. The revolutionary impact of head on ship design and system design and computer design in my estimation, one of the most important companies that ever came to Silicon Valley and changed everything about how computers were made. It put me in the epicenter of some of the most important events in computer industry. It led me to meeting Chris and Curtis and Andy Bechterstein and John Rubenstein. You know, some of the most important people in the world.

And Frank that I was with the other day and just, I mean, the list goes on. And so LSI logic was really important to me. And I would still be there. I would, you know, who knows what LSI logic would have become if I were still there, right? And so that's kind of how my mind works.

Powering AI of the world. Yeah. Exactly. I might be doing the same thing. I'm doing the same thing.

I got the sense from remembering back to part one of our series on NVIDIA. Until I'm fired. This is my last job. I got the sense that LSI logic might have also changed your perspective and philosophy about computing too. The sense we got from research was that right at school, when you first came to D first, you believed that kind of a version of that was it the Jerry Sanders Real Men have fabs.

Like you need to do the whole stack. Like you got to do everything and then LSI logic changed you. What LSI logic did was realized that you can express transistors and logical gates and chip functionality in high level languages. That by raising the level of abstraction and what is now called high level design, it was coined by Harvey Jones who's on NVIDIA's board. I met him way back in the early days of synopsis.

During that time, there was this belief that you can express chip design in high level languages. By doing so, you could take advantage of optimizing compilers and optimization logic and tools and be a lot more productive. That logic was so sensible to me. I was between one years and a lot of time. I want to pursue that vision.

Frankly, that idea happened in machine learning. It happened in software programming. I want to see it happen in digital biology so that we can think about biology in a much higher level language. Probably a large language model would be the way to make it representable. That transition was so revolutionary.

I thought that was the best thing ever happened to the industry. I was really happy to be part of it. I was at ground zero. I saw one industry change revolutionized in other industry. If not for LSI logic doing the work that it did, synopsis shortly after, then why would the computer industry be worried today?

It's really, really terrific. I was at the right place at the right time to see all that. That's super cool. It sounded like the C.O. of LSI logic put a good word for you.

I didn't know how to write a business plan. Which it turns out is not actually important. No. It turns out that making a financial forecast that nobody knows is 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 think that the hard of writing a business plan ought to be much, much shorter.

It forces you to condense. What is the true problem you're trying to solve? What is the unmet need that you believe will emerge? What is it that you're going to do that is sufficiently hard that when everybody else finds out it's a good idea, they're not going to swarm it and make you obsolete. And so it has to be sufficiently hard to do.

There are a whole bunch of other skills that are involved in just product and positioning and pricing and go to market and all that kind of stuff. Those are skills and you can learn those things easily. The stuff that is really, really hard is the essence when I described them. I did that. But I know I had to write a business plan.

And I was fortunate that Wolf Korgen was so pleased with me and the work that I did when I was at LSI logic. He called up Don Valentine and told Don, you know, invest in this kid. And he's going to come your way. And so I was set up for success from that moment and got us on the ground. As long as he doesn't lose the money.

I think Sequoia did OK. 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. Marcie?

Marcie. Yeah. Yeah. All these years. The two founding VCs are still on the board.

Sutter Hill and Sequoia? Yeah. Tetch Cox and Marcie. I don't think that ever happens. Yeah.

We are singular in that circumstance, I believe. They've added value this whole time. I've been inspiring this whole time. I gave great wisdom and great support. But they also were...

They were so creative. No, no, no. But they've been entertained by the company, inspired by the company, and enriched by the company. And so they stayed with it. And I'm really grateful.

Well, in that being our final question for you, it's 20, 30 years in the anniversary of 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.

If you're not wearing 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 building a company and building a video turned out to have been a million times harder than I expected it to be. Any of us expected it to be. And at that time, if we realized the pain and suffering, and just a vulnerable, you're going to feel, and the challenges that you're going to endure, the embarrassment and the shame, and the list of all the things 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 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 trick my brain into thinking how hard can it be, because you have to. Still, you have to do it.

Yeah, hard can it be. Everything that we're doing, hard can be. Underverse how hard can it be. You know, in terms of the sense that you're playing to retire anytime soon, though. No, no, no.

You could choose to say like, whoa, this is too hard. The trick is still working. You're still working. No, I'm still enjoying myself immensely, and I'm adding a little bit of value. But 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 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? I have family and friends 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 Jeff Fisher's been here 30 years, Dwight's been here 30 years, and Jonah and Brian have been here, you know, 25 some years, and probably longer than that. And 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 the entire ball of wax, you know, and to be able to go home.

And have your family be fully committed to everything that you're trying to do, and I'm sick or thin, 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 Tenge Cosses and Mark Steves and Harvey Jones and all the early people of our company, the Bill Millers, they not one time gave up on the company and us. And you kind of, you need that, not kind of need that, you need that.

And I'm pretty sure that almost every successful company and entrepreneurs that have gone through some difficult challenges, they had that support system around them. I can only imagine how meaningful that is in any company. But for you, I feel like the NVIDIA journey is particularly amplified on these dimensions, right? Not normal. You know, you went through two, if not three, 80% plus drawdowns in the public markets that 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 most of it is out of your control. But 80% fall, it's an extraordinary thing. No matter how you look at it.

And I forget exactly, but I mean, we traded down at about a couple of two, three billion dollars in market value for a while because of the decision we made in 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 endure. I mean, because, you know, everybody's questioning you and police aren't questioning you, but in police are questions, people outside are questioning you.

And it's all embarrassing. It's like, you know, when your stock price gets hit, it's embarrassing about how you think about it. And it's hard to explain, you know? And so there's no good answers to any of that stuff. You know, CEOs are human and companies are built of humans.

And these challenges are hard to endure. And so... And had an appropriate comment on our West market opportunity that you're engaging. And that helped that informs your size. You know, I was told a long time ago that a video can never be larger than a billion dollars.

Obviously, it's an underestimation under imagination of the size of the opportunity. It is the case that no chip company can ever be so big. And so, but if you're not a chip company, then why is that applied to you? And this is the extraordinary thing about technology right now. Technology is a tool and it's only so large.

What's unique about our current circumstance today is 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 measuring trillions.

One way to think about that is if you build a chip for a car, how many cars are there and how many chips would they consume? That's one way to think about that. However, if you build a system that whenever needed, assisted in the driving of the car, you know, what's the value of an autonomous chauffeur every now and then? And so now that the market obviously, the problem becomes much larger, the opportunity becomes larger. What would it be like if we were to magically conjure up a chauffeur for everybody who has a car and how big is that market?

And obviously, obviously, that's a much, much larger market. And so the technology industry is that we discovered, when a video is discovered and what some of the discovered is that by separating ourselves from being a chip company, but building on top of the chip and you're not in the ad company, the market opportunity has grown by probably a thousand times. Don't be surprised if technology companies become much larger in the future because what you produce is something very different. And that's the kind of the way to think about how large can your opportunity, how large can you be? That has everything to do with the size of the opportunity.

Yep. Well, Jensen, thank you so much. Thank you. All right, listeners, now is a great time to talk about one of our favorite companies, StatSig. Yes, there is a reason why the best product teams rely on StatSig, whether they are iterating on their core product features or shipping AI-powered experiences at scale.

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It brings experimentation, feature flags, and product analytics into one unified system so teams can ship safely, test rigorously, and directly link what they changed to how users actually behaved. So if you want to make learning your competitive advantage, whether you're building new AI experiences or just evolving your existing core product, go to StatSig.com. Slash acquired to get started. Ooh, David, that was awesome. So fun.

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