NVIDIA CEO Jensen Huang

Ben Gilbert 0:00 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.

David Rosenthal 0:13 Yeah, just the gear. I mean, the drives that came out of the camera.

Ben Gilbert 0:18 All right, uh, Red cameras for the home studio starting next episode.

David Rosenthal 0:22 Yeah, great.

Ben Gilbert 0:23 All right, let's do it.

Speaker 0:24 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 me down, say it straight. Another story on the way. Who got the truth?

Ben Gilbert 0:41 Welcome to this episode of Acquired, the podcast about great technology companies and the stories and playbooks behind them. I'm Ben Gilbert.

David Rosenthal 0:48 I'm David Rosenthal.

Ben Gilbert 0:49 And we are your hosts. Listeners, just so we don't bury the lead, this episode was insanely cool for David and I.

David Rosenthal 0:56 Yeah.

Ben Gilbert 0:58 After researching NVIDIA for something like five hundred 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 one point one trillion dollars 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.

David Rosenthal 2:12 Yeah, I got new perspective on the company and on him as a founder and a leader just from doing this, despite, [chuckles] you know, we thought we knew everything before we came in advance, and, uh, it turned out we didn't.

Ben Gilbert 2:25 Turns out the protagonist actually knows more. [chuckles]

David Rosenthal 2:27 Yes. [chuckles]

Ben Gilbert 2:29 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 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.

Ben Gilbert 2:55 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 nineteen ninety-seven. 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.

Jensen Huang 3:18 Yeah.

Ben Gilbert 3:18 And you guys have about-

David Rosenthal 3:21 And you're running out of money

Ben Gilbert 3:21 ... 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 sight unseen with the rest of the company's money.

Jensen Huang 3:36 Yeah.

Ben Gilbert 3:36 So you're betting it all right here on the Riva 128.

Jensen Huang 3:39 Yeah.

Ben Gilbert 3:39 It comes back, and of the thirty-two 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 felt like.

Jensen Huang 3:55 The other twenty-four weren't that important. [laughing]

David Rosenthal 3:58 [laughing] Okay, so wait, wait. First question: Was that the plan all along? Like, when, when did you realize that only eight were gonna work?

Jensen Huang 4:04 We should-- I realized... I didn't learn about it until it was too late. We should have implemented all thirty-two, yeah.

David Rosenthal 4:10 [chuckles]

Jensen Huang 4:10 But, but it-- 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 NV3. NV1 and NV2 were based on forward texture mapping, no triangles, but curves, and it tessellated 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 Riva 128 was, was a reset of our company. Now, remember, at the time that we started the company in nineteen ninety-three, we were the only consumer 3D graphics company ever created, and we, 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 DirectX. It was fundamentally incompatible with NVIDIA's architecture. Thirty competitors have already shown up, uh, even though we were the first company at the time that we've-- 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 backs were up against the wall. We were running out of time, we're running out of money, and 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, DirectX 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 Riva 128 is the world's first, uh, fully accelerated, hardware-accelerated pipeline for rendering 3D. And so-... the transform, the projection, every single element, all the way down to the frame buffer, was completely hardware accelerated. Uh, we implemented a, a, 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 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 implement everything i- in DirectX that we knew of, and we built it as large as we possibly could, then obviously nobody can build something faster than that.

David Rosenthal 6:59 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 gonna have to pay the money to buy this.

Jensen Huang 7:08 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 ten times higher this year than what was available, there's a whole large market of enthusiasts who, who we believe would, 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, this 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. We chose that. Uh, we also made the decision to use this technology called emulation.

David Rosenthal 8:03 Yeah.

Jensen Huang 8:03 There was a, 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, uh, I'll buy what you have in inventory, and, uh, you know, uh, 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, 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.

David Rosenthal 8:37 Yeah.

Jensen Huang 8:38 And so I knew-

David Rosenthal 8:38 And your competitors would have caught up.

Jensen Huang 8:40 Well, not to mention, we would have been out of business. [laughing]

David Rosenthal 8:43 Exactly. [laughing] Who cares?

Jensen Huang 8:43 So- [laughing] exactly. And so if you're gonna 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 the new chip, so on and so forth, that method wasn't gonna 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 gonna tape out a perfect chip.

David Rosenthal 9:09 [laughing]

Jensen Huang 9:09 So I, so, so I remember having a conversation with our leaders, and they said: "But Jensen, how do you know it's gonna be perfect?" And I said: "I know it's gonna be perfect, because if it's not, we'll be out of business, and so let's make it perfect." [laughing]

David Rosenthal 9:20 [laughing]

Jensen Huang 9:20 "We get one shot." We essentially virtually prototyped 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 what-

David Rosenthal 9:37 It was like-

Jensen Huang 9:37 It painted a thousand-

David Rosenthal 9:38 - sixty seconds for a frame or something like that.

Jensen Huang 9:39 Oh, easily. I actually think that it was an hour per frame, something like that. And so we would just sit there and watch it 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 in advance and told everybody, "This is it. We're gonna tape out the chip. It's gonna be perfect." Well, if you're gonna tape out a chip and you know it's perfect, then what else would you do? That's actually the good question.

David Rosenthal 10:02 Hmm.

Jensen Huang 10:03 If you knew that you hit enter, you taped out a chip, and you knew it was gonna be perfect, then what else would you do? Well, the answer, obviously: go to production.

Ben Gilbert 10:11 And marketing blitz.

Jensen Huang 10:12 Yeah, yeah.

David Rosenthal 10:13 Yeah.

Ben Gilbert 10:13 And developer relations and-

Jensen Huang 10:13 Just start-- kick everything off. Kick everything off, because you got a perfect chip, and so we got it in our head that we have a perfect chip.

David Rosenthal 10:20 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?

Jensen Huang 10:26 No, everybody was clear we had no shot. [laughing]

Ben Gilbert 10:29 [laughing]

David Rosenthal 10:29 [laughing]

Jensen Huang 10:29 There's-- uh, the, the, not doing it would be crazy, because otherwise-

David Rosenthal 10:34 Otherwise, you might as well go home.

Jensen Huang 10:35 Yeah, you're gonna 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, as I'm describing it, every- you're probably thinking, "Yeah, it's pretty sensible."

David Rosenthal 10:46 Well, it worked.

Jensen Huang 10:47 Yeah. And so we taped that out and went directly to production.

Ben Gilbert 10:50 So is the lesson for founders out there, when you have conviction on something, like the Riva one twenty-eight or, uh, 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-

Jensen Huang 11:08 [laughing] yeah

Ben Gilbert 11:08 ... it's worked every time.

Jensen Huang 11:09 No. [laughing]

Ben Gilbert 11:10 How do you think about that?

Jensen Huang 11:10 No, no. When you push your chips in, um, I, I know it's gonna 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 gonna take everything in the future, all the risky things, and I'm gonna 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 prefetch it.

David Rosenthal 11:54 Uh, we talk about this a lot. We were just talking about this on our Costco episode. You wanna push your chips in when you know it's gonna work.

Ben Gilbert 12:02 So every time we see you make-

Jensen Huang 12:03 Exactly

Ben Gilbert 12:03 ... a bet the company move-

Jensen Huang 12:04 Yeah

Ben Gilbert 12:05 ... y- you've already simulated it. You know.

Jensen Huang 12:07 Yeah. Yeah, yeah, yeah.

Ben Gilbert 12:07 Do you feel like that was the case with CUDA?

Jensen Huang 12:10 ... Uh, yeah. In fact, before there was CUDA, there was, uh, CG.

David Rosenthal 12:15 Right.

Jensen Huang 12:15 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, and how can we use our GPU 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 shading that would suggest that CUDA has a great opportunity to succeed.

Ben Gilbert 12:59 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 ten thousand person years-

Jensen Huang 13:17 Yeah

Ben Gilbert 13:17 -in building that platform-

Jensen Huang 13:19 Yeah.

Ben Gilbert 13:19 -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?"

Jensen Huang 13:29 I guess yes and no. You know, when we saw deep learning, when we saw AlexNet and realized its 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 or new algorithm comes along and somehow leapfrogs thirty 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. Uh, 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've 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, uh, neural networks. Okay, so now you just reason your way through, right? Uh, okay, so now I go back to twelve years ago, uh, you could 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 teach.

David Rosenthal 14:47 Had you been paying attention to the ImageNet competition-

Jensen Huang 14:49 Yeah, yeah

David Rosenthal 14:49 -the year leading up to this?

Jensen Huang 14:50 Yeah, yeah. And the reason for that 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 algorithms that were created for computer vision aren't a good fit for CUDA. And so we're sitting there trying to figure it out, all of a sudden, AlexNet 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, uh, can solve?"

David Rosenthal 15:22 Yeah, I'm curious on this.

Jensen Huang 15:22 Right? Well, we know that most of our algorithms, uh, start from principled sciences. Okay, you want to understand the causality, and from the causality, you create a, 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.

David Rosenthal 15:51 Mm-hmm.

Jensen Huang 15:51 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 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 a sweater or not. Isn't that right?

David Rosenthal 16:23 Yep.

Jensen Huang 16:23 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.

Ben Gilbert 16:30 And it can be an incredibly lucrative market. So if you can predict what the next best-performing, uh, feed item-

Jensen Huang 16:36 Yeah [chuckles]

Ben Gilbert 16:37 -to serve into a social media feed, turns out that's a hugely valuable market.

David Rosenthal 16:40 This is where I was going to go with that. I love the examples you pulled, you know-

Jensen Huang 16:42 Yeah

David Rosenthal 16:42 ... toothpaste, ketchup-

Jensen Huang 16:44 Exactly

David Rosenthal 16:44 ... music, movies.

Jensen Huang 16:45 And 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 are 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. And realizing that, the rest of it is just comes with, you know, do you have the courage to put your chips behind it?

David Rosenthal 17:27 So that's where we are today, um, and that's where NVIDIA is today.

Jensen Huang 17:31 Mm-hmm.

David Rosenthal 17:32 But I'm curious in the... You know, those couple of years after AlexNet-

Jensen Huang 17:35 Mm-hmm

David Rosenthal 17:35 ... and this is when Ben and I were getting into the technology industry and the venture industry ourselves.

Ben Gilbert 17:40 I started at Microsoft in twenty twelve.

Jensen Huang 17:42 Yeah.

David Rosenthal 17:42 Yeah.

Ben Gilbert 17:42 So right after AlexNet, but before anyone was talking about machine learning, and even the mainstream engineering community.

David Rosenthal 17:49 There were those couple of years there where, to a lot of the rest of the world, these looked like science projects.

Jensen Huang 17:57 Yeah.

David Rosenthal 17:58 The technology companies here in Silicon Valley, particularly the social media companies, they were just realizing huge economic value out of this, the Googles, the Facebooks-

Jensen Huang 18:08 Yeah

David Rosenthal 18:08 ... the Netflixes, et cetera.

Jensen Huang 18:10 Yeah.

David Rosenthal 18:10 And obviously, that led to lots of things, including OpenAI a couple of years later.

Jensen Huang 18:13 Yeah.

David Rosenthal 18:13 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?

Jensen Huang 18:22 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 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 democratized 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, uh, 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 researchers and find every single AI researcher on the planet and say: "How can we help you advance your work?" And that included Yann LeCun and Andrew Ng and Geoff 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 Ilya Sutskever there for the first time.

David Rosenthal 19:38 Yeah.

Jensen Huang 19:39 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, it looked like a toy, but we had confidence that even GAN, the first time I met Goodfellow, the GAN was- it was like thirty-two by thirty-two, and it was just a, you know, blurry image of a cat, you know?

David Rosenthal 20:00 Mm-hmm.

Jensen Huang 20:01 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 layer by layer, and you could make the datasets 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.

David Rosenthal 20:17 Yep.

Jensen Huang 20:17 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

Jensen Huang 20:29 it all happened.

David Rosenthal 20:30 When OpenAI was founded in twenty-

Jensen Huang 20:32 Yeah. Fifteen? Yeah.

David Rosenthal 20:34 I mean, that was such a, an important moment. That's obvious today now, but at the time, I, I think most people, even people in tech, were like-

Jensen Huang 20:43 Yeah

David Rosenthal 20:43 ... "What is this?" [chuckles]

Jensen Huang 20:43 Yeah, yeah, yeah.

David Rosenthal 20:44 Were, were you involved in it at all? Like, you know, because you were-

Jensen Huang 20:47 Yeah

David Rosenthal 20:47 -so connected to the researchers-

Jensen Huang 20:48 Yeah

David Rosenthal 20:48 -to Ilya, taking that talent out of Google and Facebook, to be blunt-

Jensen Huang 20:53 Yeah

David Rosenthal 20:53 -but re seeding the research community-

Jensen Huang 20:56 Yeah

David Rosenthal 20:56 -and opening it up, um, was such an important moment. Were you involved in it at all?

Jensen Huang 21:00 I wasn't involved in the founding of it, but I knew, uh, a lot of the people there, and, um, uh, Elon, of course, uh, I knew, and, uh, uh, Peter Thiel was there, and Ilya was there, and, uh, we have, 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 were building the first version of the DGX, which, you know, today when you see a Hopper, it's seventy pounds, thirty-five thousand parts, ten thousand amps. But DGX, the first version that we built, was, uh, used internally, and I delivered the first one to OpenAI-

David Rosenthal 21:34 Yeah

Jensen Huang 21:34 ... and that, that was a fun day. But most of our success was aligned around, um, in the beginning, uh, just about helping the researchers get to the next level. I knew it wasn't very useful in its current state, but I also believed 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, 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.

Ben Gilbert 22:21 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 ten billion parameter mark, and certainly once you cross the hundred 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?

Jensen Huang 22:54 Well, my first feeling about the language model was how clever it was to just mask out words and, and, uh, 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 know, the first observation on 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?

Ben Gilbert 23:25 Yep.

Jensen Huang 23:26 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, few-step reasoning is somehow learnable from just reading things, I wouldn't be surprised. Uh, you know, for a, 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?

Ben Gilbert 24:08 Mm-hmm.

Jensen Huang 24:08 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 b- modules in a self-driving car, and the fact that it's still keeping lanes makes me insanely happy. And so [laughing] -

Ben Gilbert 24:35 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 Neumann computer works, soup to nuts, and it's still a miracle.

Jensen Huang 24:51 Yeah.

David Rosenthal 24:51 Yeah.

Jensen Huang 24:52 Yeah, yeah. Exactly. Yeah, yeah. When you put it all together, it's still a miracle. Yeah.

Ben Gilbert 24:56 Now is a great time to talk about one of our favorite companies, Statsig, and we have some tech history for you.

David Rosenthal 25:02 Yes. So in our NVIDIA Part Three episode, we talked about how the AI research teams at Google and Facebook drove incredible business outcomes with cutting-edge 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.

Ben Gilbert 25:30 Yeah, the most common way to deploy new models was through experimentation, A/B 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 metrics. Great experimentation tools transformed the machine learning development process. They de-risked 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, and most importantly, they created a pragmatic, data-driven 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.

David Rosenthal 26:14 Yep. So now you're probably thinking, "Well, that's great for Facebook and Google, but my team can't build out our own internal experimentation platform." Well, you don't have to, thanks to Statsig. So Statsig 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 Statsig to improve and deploy their models, including OpenAI and Anthropic.

Ben Gilbert 26:47 Yep, so whether you're building with AI or not, Statsig 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, simple pricing for enterprises, and no seat-based fees. If you're in the Acquired community, there's a special offer. You get five million free events a month and white-glove onboarding support. So visit statsig.com/acquired and get started on your data-driven journey.

Ben Gilbert 27:13 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 forty-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 freaking best work, and the most important thing in the world, now go?"

Jensen Huang 27:50 Mm-hmm.

Ben Gilbert 27:51 A, is that correct, and B, is there something special about NVIDIA that enables that?

Jensen Huang 27:56 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 built like a military... It's not built like a- like the armed forces, where you have, you know, generals and colonels, and 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. 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 and 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 is the best at running that module on that function, on that layer, i- is in charge, and that person is the pilot in command. And so that's one characteristic. And, um-

Ben Gilbert 29:03 Have you always thought about the company this way?

David Rosenthal 29:06 Even from the earliest days?

Jensen Huang 29:07 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?

David Rosenthal 29:16 Yep.

Jensen Huang 29:17 That's what a company is.

David Rosenthal 29:18 Yep.

Jensen Huang 29:19 And yet everybody's company look exactly the same, but they all-

David Rosenthal 29:22 [laughing]

Jensen Huang 29:22 ... build different things. How does that make any sense?

David Rosenthal 29:25 No.

Jensen Huang 29:25 Do you see what I'm saying?

David Rosenthal 29:26 Yeah.

Jensen Huang 29:26 You know, how you make fried chicken versus how you flip burgers versus how you make, you know, 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 go build it? That's-- so that's number one. In terms of information system and how do you enable collaboration, we kind of wire it 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 neural network-

Ben Gilbert 30:27 Mm.

Jensen Huang 30:27 You know, if you draw one out.

David Rosenthal 30:28 And when you say mission, do you mean mission like-

30:30 ... NVIDIA's mission is-

Jensen Huang 30:31 Build Hopper.

David Rosenthal 30:32 Yeah, okay. So it's not like further accelerated computing-

Jensen Huang 30:36 No

David Rosenthal 30:36 - it's like worshiping DGX cloud.

Jensen Huang 30:38 Build Hopper, or somebody else's, uh, build a system for Hopper. Somebody is, uh, build 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.

Ben Gilbert 30:52 What are the trade-offs associated with that versus the traditional structure?

Jensen Huang 30:56 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 than you are.

Ben Gilbert 31:13 Mm.

Jensen Huang 31:13 In our company, the information is disseminated fairly quickly to a lot of different people, and it's usually at a team level. So, for example, just now, I was in, 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 E-staffs in the room, and at the moment that we decided together, we reasoned through some stuff, we made a decision, everybody heard it exac- exactly the same time. So nobody has more power than anybody else.

Ben Gilbert 31:42 Mm.

Jensen Huang 31:43 Does that make sense? The new college grad learned at the exact same, same time as the E-staff. And so the, the executive staff and the, and the leaders that, that work for me and myself, you earn the right to have your job based on your ability to reason through problems and helping other people succeed. And, and it's not because you have some privileged information that I knew the answer was three point seven, and only I knew, you know?

Ben Gilbert 32:06 Right.

Jensen Huang 32:06 Everybody

Jensen Huang 32:07 knew.

David Rosenthal 32:08 When we did our most recent episode, NVIDIA Part Three, that we, we just released, we sort of did this thought exercise, um, especially over the last couple of years-

Jensen Huang 32:17 Mm-hmm.

David Rosenthal 32:18 -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? [chuckles]

Ben Gilbert 32:33 And we said that for illustrative purposes.

David Rosenthal 32:34 For illustrative purposes, not to pick on Apple or whatever-

Ben Gilbert 32:36 A large tech company-

David Rosenthal 32:37 A large tech company

Ben Gilbert 32:37 ... shipping two flagship products-

David Rosenthal 32:39 Yeah

Ben Gilbert 32:39 -or their flagship product twice per year.

David Rosenthal 32:41 Yeah, or, you know, two WWDCs a year.

Jensen Huang 32:43 Yeah.

Ben Gilbert 32:44 There seems to be something unique-

David Rosenthal 32:45 You can't, like, you can't really imagine that, whereas-

Jensen Huang 32:47 Yeah

David Rosenthal 32:47 -that happens here. Are there other companies, either current or historically-

Jensen Huang 32:54 Mm-hmm

David Rosenthal 32:54 -that you look up to, admire, maybe took some of this inspiration from?

Jensen Huang 32:59 In the last thirty 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, uh, 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, 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 and what I'm trying to achieve, and what does this mean to NVIDIA 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?

David Rosenthal 33:38 Mm-hmm.

Jensen Huang 33:39 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 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 to it. You're not supposed to ignore it. You're supposed to go learn from it, and, uh, it could be a competitor, it could be a adjacent industry, it could be nothing to do with us. Uh, the more we're-- we learn from, uh, what's happening out in the world, uh, the better. But then you're, you're supposed to come back and ask yourself, you know, "What does this mean to us?"

David Rosenthal 34:14 Yeah, you don't just want to imitate them.

Jensen Huang 34:15 That's right.

David Rosenthal 34:16 Yeah.

Jensen Huang 34:16 Yeah.

David Rosenthal 34:16 [chuckles] I love this tee-up 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 AlexNet and your engagement with the research community-

Jensen Huang 34:39 Uh-huh

David Rosenthal 34:40 ... uh, and, and-

Jensen Huang 34:41 Uh-huh

David Rosenthal 34:41 -you know, social media feed recommenders-

Jensen Huang 34:42 How I got here

David Rosenthal 34:43 -to-

Jensen Huang 34:43 Yeah, yeah

David Rosenthal 34:43 -you deciding, and the company deciding-

Jensen Huang 34:45 Yeah, all in

David Rosenthal 34:46 -we're gonna go on a five-year, all-in journey-

Jensen Huang 34:48 Yeah

David Rosenthal 34:48 -on the data center.

Jensen Huang 34:49 Yeah, yeah.

David Rosenthal 34:50 How did that happen?

Jensen Huang 34:51 Yeah. Our journey to the data center happened, I would say, almost seventeen years ago. I'm always being asked, I mean, what, what are the challenges that the company could 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, and 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 showed it to me one day, and it was really capturing the frame buffer, encoding it into video, and streaming it, um, to a, a receiver device-

David Rosenthal 35:52 Mm

Jensen Huang 35:53 ... separating computing from the viewing.

Ben Gilbert 35:55 In many ways, that's cloud gaming.

David Rosenthal 35:57 Cloud gaming. [chuckles]

Ben Gilbert 35:57 Like eighteen years early. [chuckles]

Jensen Huang 36:00 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're, you're fighting, you're fighting all kinds of problems, including, including the speed of light. And-

Ben Gilbert 36:12 Latency everywhere you look.

Jensen Huang 36:13 That's right.

David Rosenthal 36:14 For listeners, GFN, GeForce Now.

Jensen Huang 36:15 GeForce Now.

David Rosenthal 36:16 Yeah.

Jensen Huang 36:16 Yeah, GeForce Now. And, and we've been working on GeForce Now-

David Rosenthal 36:18 It all makes sense, your first cloud product.

Jensen Huang 36:20 That's right. Now-- And, and look at, look at GeForce Now was NVIDIA's first data center product, and our second data center product was Remote Graphics, putting our GPUs in, in the world's enterprise data centers-

David Rosenthal 36:32 Hmm

Jensen Huang 36:32 ... 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.

David Rosenthal 36:53 Yeah. Yeah.

Jensen Huang 36:54 And it was completely true.

David Rosenthal 36:55 Mm.

Jensen Huang 36:55 And so we're no longer limited by the physical constraints of the desktop PC sitting by your desk, um, you know, and, and we're not limited by one GPU per person. And, and so, uh, it doesn't matter where it is anymore, and so that was really the great observation.

Ben Gilbert 37:11 It's a good reminder. I-- you know, the data center segment of NVIDIA's business, to me, has become synonymous with h-h-how is AI going?

Jensen Huang 37:19 Mm-hmm.

Ben Gilbert 37:19 And that's a false equivalence, and it's interesting that you were only this ready to sort of explode in AI in the data center because you had three-plus previous products where you learned how to build data center computers.

Jensen Huang 37:32 Exactly, yeah.

Ben Gilbert 37:33 Even though those markets weren't these, like, gigantic, world-changing technology shifts the way that-

Jensen Huang 37:39 Yeah

Ben Gilbert 37:39 ... AI is-

Jensen Huang 37:39 Yeah

Ben Gilbert 37:39 -that's how you learned.

Jensen Huang 37:40 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. You know, our job as CEOs is to look around corners and, and to 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. [laughing]

Ben Gilbert 38:10 [laughing]

David Rosenthal 38:10 [laughing]

Jensen Huang 38:10 You guys know what I'm saying?

Ben Gilbert 38:11 Yeah.

David Rosenthal 38:12 Yeah.

Jensen Huang 38:12 But you've got to be close enough to do the diving catch.

David Rosenthal 38:14 Yeah. Rewind to twenty fifteen and OpenAI-

Jensen Huang 38:17 Yeah

David Rosenthal 38:18 ... if you hadn't been laying this groundwork in the data center-

Jensen Huang 38:20 Yeah

David Rosenthal 38:21 ... y-you wouldn't be powering OpenAI right now.

Jensen Huang 38:24 Yeah, but the idea that computing would be mostly done away from the viewing device, that the vast majority of computing would be done away from the computer itself, that insight was good.

David Rosenthal 38:37 Mm-hmm.

Jensen Huang 38:37 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 a hundred and twenty milliseconds or something like that. It's not that long.

Ben Gilbert 38:52 From a data center to a internet user.

Jensen Huang 38:54 Anywhere on the planet.

Ben Gilbert 38:55 Yeah.

Jensen Huang 38:55 Yeah. And so we could-

Ben Gilbert 38:56 Oh, I see.

Jensen Huang 38:56 If we-

Ben Gilbert 38:56 End to end, literally across the planet.

Jensen Huang 38:58 Yeah, right. So if you could solve that problem, approximately something like that, I, I forget the number, but it's, it's seventy milliseconds, a hundred 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-

Ben Gilbert 39:14 Hmm

Jensen Huang 39:14 ... perfectly fine, and, and you could build data centers as, as large you like, and you could do amazing things, and, and, uh, this little tiny device that we use as a computer or, you know, your TV as a computer, whatever computer, they all c- they can all instantly become amazing.

Ben Gilbert 39:28 Yeah.

Jensen Huang 39:28 And so that insight, you know, fifteen years ago was a good

Jensen Huang 39:31 one.

Ben Gilbert 39:31 So speaking of the speed of light, InfiniBand.

David Rosenthal 39:34 Yeah. [laugh]

Ben Gilbert 39:34 [chuckles] Like, D-David's, like, begging me to go here.

Jensen Huang 39:36 Uh-huh.

Ben Gilbert 39:37 Can feel it.

David Rosenthal 39:37 I was at the same time.

Ben Gilbert 39:39 You totally saw that InfiniBand would be way more useful, way sooner than anyone else realized.

Jensen Huang 39:45 Yeah.

Ben Gilbert 39:45 Acquiring Mellanox, 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?

Jensen Huang 39:59 Well, uh, there are several reasons for that. First, um, if you want to be a data center company building 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.

Ben Gilbert 40:14 Yeah.

Jensen Huang 40:15 A desktop computer in a 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 des- 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 and so forth. And so, so it-- those characteristics a-are associated with Mellanox, 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 we-- then if we want to be a data center-oriented company, then, 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 u-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-

Ben Gilbert 41:33 Yeah

Jensen Huang 41:33 ... for all of those things, it's-

Ben Gilbert 41:34 But not when you're sharding a model across multiple racks.

Jensen Huang 41:36 Not when you're sharding a model across, right. And so, uh, that observation says that the type of networking you want to do is 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, uh, you know, convinced me that, that Mellanox is, is absolutely the right, the right company because they were-- they're the world's leading high-performance networking company, and, and we've worked with them in so many different areas in, in, uh, high-performance computing already. Plus, I, I really like the people. Um, uh, the, the, the Israel team is world-class. Uh, we have some three thousand two hundred people there now, and it was one of the best strategic decisions I've ever made.

David Rosenthal 42:15 When we were researching, particularly part three of our NVIDIA series, we talked to a lot of people, and many people told us the Mellanox acquisition-

Jensen Huang 42:24 Uh-huh

David Rosenthal 42:24 ... is one of, if not the best of all time-

Jensen Huang 42:27 Yeah

David Rosenthal 42:27 -by any technology company.

Jensen Huang 42:28 Yeah. I think so too, yeah.

David Rosenthal 42:30 Yeah.

Jensen Huang 42:30 And it's so disconnected from the work that we normally do. It was surprising to everybody. I-

Ben Gilbert 42:35 But framed this way, you were, you were standing near where the action was-

Jensen Huang 42:39 Yeah

Ben Gilbert 42:39 ... 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?"

Jensen Huang 42:50 Yeah. You want to position yourself near opportunities. You don't have to be that perfect, you know?

Ben Gilbert 42:56 [chuckles]

Jensen Huang 42:57 You, you, 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. [laughing]

Ben Gilbert 43:09 [laughing] Oh, my-

Jensen Huang 43:10 You just, you wanna 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, uh, having the, the, uh, the, the company having the skills to, to, um, monetize each one of the steps along the way so that we can be sustainable.

Ben Gilbert 43:29 What you just said reminds me of a great, uh, aphorism from, uh, Buffett and Munger, which is, "It's better to be approximately right than exactly wrong." [chuckles]

Jensen Huang 43:37 [chuckles] Yeah. There you go. Yeah-

Ben Gilbert 43:38 It's a-

Jensen Huang 43:38 ... That's a good one.

Ben Gilbert 43:39 It's a good one-

Jensen Huang 43:39 That's a good one

Ben Gilbert 43:40 ... to live by.

Jensen Huang 43:40 Yeah.

Ben Gilbert 43:42 All right, listeners, we are here to tell you about a company that literally couldn't be more perfect for this episode, Crusoe.

David Rosenthal 43:48 Yes, Crusoe, as you know by now, is a cloud provider built specifically for AI workloads and powered by clean energy, and NVIDIA is a major partner of Crusoe. Their data centers are filled with A100s 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. Crusoe has been ahead of the curve and is among the first cloud providers to offer 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.

Ben Gilbert 44:33 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 3,200 gigabit InfiniBand and leveraging Crusoe's network-attached block storage solution. And because their cloud is run on wasted, stranded, or clean energy, they can provide significantly better performance per dollar than traditional cloud providers.

David Rosenthal 44:58 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, Crusoe is actually reducing more emissions than they would generate. In fact, in 2022, Crusoe captured over four billion cubic feet of gas, which led to the avoidance of approximately five hundred thousand metric tons of CO2 emissions. That's equivalent to taking about a hundred and sixty thousand cars off the road.

Ben Gilbert 45:30 Amazing. If you, your company, or your portfolio companies could use lower cost and more performant infrastructure for your AI workloads, go to crusoecloud.com/acquired. That's C-R-U-S-O-E cloud.com/acquired, or click the link in the show notes.

Ben Gilbert 45:46 I wanna 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-

Jensen Huang 45:52 Mm

Ben Gilbert 45:52 ... sort of advice for company building.

Jensen Huang 45:54 Mm.

Ben Gilbert 45:55 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-

Jensen Huang 46:05 Non-consumption

Ben Gilbert 46:06 ... just because people don't actually care as much as you do about what you're building.

Jensen Huang 46:08 That's right. Yeah.

Ben Gilbert 46:09 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, you know, have product market fit, that are starting to grow, they're in interesting growing markets? Um, where should they look f- for competition, and how should they handle it?

Jensen Huang 46:27 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.

David Rosenthal 46:40 I've heard, uh, you or others in NVIDIA, I think, use the phrase zero billion dollar markets.

Jensen Huang 46:43 Yeah, that's exactly right.

David Rosenthal 46:44 Yeah.

Jensen Huang 46:44 You know, 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?" [chuckles] Right? Because when we first got into automotive, because we believe that in the future, the car is gonna be largely software, and if it's gonna be largely software, um, a, a really incredible computer is necessary. And so, so when we positioned ourselves there, most people... I, I, I still remember one, one of the, one of the CTOs told me, "You know what? Cars cannot tolerate the blue screen of death." And I said, "Well, I don't think anybody can tolerate that, but- [laughing]

Ben Gilbert 47:21 [laughing]

Jensen Huang 47:21 ... but that, that doesn't change the fact that someday, every car will be a software-defined car." And I, I think, you know, uh, fifteen years later, we're, we're, we're, we're largely right. And so oftentimes there's non-consumption, and we like to navigate our company there. And by doing that, um, by the time that you, uh, that the market emerges, it- it's very, it's very likely there aren't that many competitors shaped that way.

Ben Gilbert 47:47 Mm-hmm.

Jensen Huang 47:47 And so we were early in PC gaming, and today, uh, NVIDIA is very large in PC gaming. Uh, we, uh, reimagined what a, what a, uh, design workstation would be like, and today, just about every workstation on the planet uses NVIDIA's technology. Uh, we re- reimagined, um, how supercomputing ought to be done and who should, who should benefit from supercomputing, that we would democratize it, and look, today, NVIDIA's in, in accelerated computing is, is, um, quite large. And we reimagined how software would be done, and today it's called machine learning, and how computing would be done, we call it AI. And so we reimagined these kind of things, uh, try to, try to do that about a decade in advance. And so we spent about a decade in zero billion dollar markets.

Ben Gilbert 48:31 Mm-hmm.

Jensen Huang 48:31 And today, I spend a lot of time on Omniverse, and Omniverse is a, you know, classic example of a zero billion dollar [chuckles] business. And-

Ben Gilbert 48:39 There's, like, forty customers now, something like that. [laughing]

David Rosenthal 48:41 Yeah. [chuckles] Like Amazon, BMW.

Jensen Huang 48:43 Yeah.

David Rosenthal 48:43 Yeah.

Jensen Huang 48:43 No, it's cool. It's cool.

Ben Gilbert 48:45 So let's say you do get this great ten-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 stay ahead? And you can just keep your pedal to the metal and say, "We're gonna outwork them, and we're gonna 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?

Jensen Huang 49:07 ... oftentimes, if you created the market, you ended up having, you know, what, what people describe as moats. 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.

David Rosenthal 49:25 Mm-hmm. Yeah.

Jensen Huang 49:26 Sometimes it's a, it's a product-based platform, sometimes it's a service-based platform, sometimes it's a technology-based platform. But if you were, 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.

David Rosenthal 49:48 Yep.

Jensen Huang 49:49 And that network is essentially your moat. 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 company that enjoys it, but you're enjoying it with a whole bunch of other people, including me.

David Rosenthal 50:20 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-

Jensen Huang 50:25 Mm-hmm.

David Rosenthal 50:25 - Nvidia is absolutely a platform company.

Jensen Huang 50:28 Mm-hmm.

David Rosenthal 50:28 Uh, 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.

Jensen Huang 50:40 Mm, mm.

David Rosenthal 50:41 Every example I can think of of a company that tried to start as a platform company-

Jensen Huang 50:44 Mm-hmm

David Rosenthal 50:44 - fails. [chuckles] You gotta start as a technology first.

Jensen Huang 50:47 Mm-hmm.

David Rosenthal 50:48 When did you think about making that transition-

Jensen Huang 50:49 Yeah

David Rosenthal 50:49 - to being a platform? Like, your first graphics cards were-

Jensen Huang 50:52 Yeah

David Rosenthal 50:52 - technology. They weren't-- There was no CUDA. There was no platform.

Jensen Huang 50:54 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.

David Rosenthal 51:09 CUDA is Compute Unified-

Jensen Huang 51:11 Yeah

David Rosenthal 51:11 - Device Architecture?

Jensen Huang 51:12 Right. Right. 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 Riva 128 only had computer graphics, the architecture described accelerators of all kinds.

David Rosenthal 51:26 Hmm.

Jensen Huang 51:27 And we would take that architecture, 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-

David Rosenthal 51:44 [chuckles]

Jensen Huang 51:44 - Nvidia, a long time ago-

David Rosenthal 51:46 Hmm

Jensen Huang 51:46 ... 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-

David Rosenthal 51:55 Wow!

Jensen Huang 51:55 - we knew everybody.

David Rosenthal 51:55 So wait, so was the original business plan to, like-

Ben Gilbert 51:58 Sort of, like, to build DirectX?

David Rosenthal 51:59 Yeah, compete with-

Jensen Huang 52:01 In, in fact-

David Rosenthal 52:01 - Nintendo and Sega-

Jensen Huang 52:02 In fact

David Rosenthal 52:02 - as like, with PCs?

Jensen Huang 52:04 In fact, the original Nvidia architecture was called DirectNV. [laughing]

Ben Gilbert 52:08 [laughing]

David Rosenthal 52:08 [laughing]

Jensen Huang 52:08 Direct Nvidia. Yeah.

David Rosenthal 52:10 Huh.

Jensen Huang 52:10 And DirectX was an API that made it possible for operating system to directly-

Ben Gilbert 52:16 Address the hardware

Jensen Huang 52:16 ... connect.

David Rosenthal 52:16 Yeah.

Jensen Huang 52:17 Hardware. Yeah.

David Rosenthal 52:18 But DirectX-

Jensen Huang 52:18 Acquired Direct-

David Rosenthal 52:18 - didn't exist when you started Nvidia, right? And that's what-

Jensen Huang 52:21 And-

David Rosenthal 52:21 - made your strategy run for the first couple years.

Jensen Huang 52:23 In nineteen ninety-three-

David Rosenthal 52:23 Yeah

Jensen Huang 52:24 ... we had Direct Nvidia.

David Rosenthal 52:25 Yeah.

Ben Gilbert 52:25 Huh!

Jensen Huang 52:26 And which in nineteen ninety-five became, you know, well, DirectX came out. [chuckles]

Ben Gilbert 52:31 So this is an important lesson. You-

Jensen Huang 52:33 Yeah. We were always, always a developer-oriented company.

Ben Gilbert 52:36 Right.

Jensen Huang 52:36 Yeah.

Ben Gilbert 52:36 The initial attempt was, we will get the developers to build f- on DirectNV, and then they'll build for our chips, and then we'll have a platform. And-

Jensen Huang 52:44 Yeah, exactly

Ben Gilbert 52:45 ... what played out is Microsoft already had all these developer relationships-

Jensen Huang 52:48 Exactly.

Ben Gilbert 52:49 - so you learned the lesson the hard way of like-

Jensen Huang 52:50 Yeah

Ben Gilbert 52:50 ... yikes, we just got to slot into that.

David Rosenthal 52:51 I mean, 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," [chuckles] you know?

Jensen Huang 52:56 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 that-

David Rosenthal 53:03 You were competing against Microsoft in the nineties. I mean, that's, uh-

Jensen Huang 53:05 Yeah. [chuckles]

Ben Gilbert 53:05 It's like competing against Nvidia today.

David Rosenthal 53:07 Yeah. [laughing]

Jensen Huang 53:08 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 and there was OpenGL, there was DirectX, um, but there's, there's still another, uh, extension, if you will, and that extension is CUDA.

David Rosenthal 53:23 Yep.

Jensen Huang 53:23 And that CUDA extension allows a chip that got paid for running DirectX and OpenGL to create an install base for CUDA.

David Rosenthal 53:34 Yeah. Yeah.

Jensen Huang 53:35 And so that's the Nvidia strategy-

David Rosenthal 53:36 And is this why you are so militant? And I think from our research, it really was you being militant, that every Nvidia chip will run CUDA.

Jensen Huang 53:45 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 co-- arch- architecturally compatible with the others. None has ever existed. There are literally a couple of hundred million, right, two hundred and fifty million, three hundred million installed base of active CUDA GPUs being used in the world today, and they're all architecturally compatible. How would you have a computing platform if, if, you know, NV30 and NV35 and or thirty-nine and NV40, they're all different?

David Rosenthal 54:19 Mm-hmm.

Jensen Huang 54:20 Right? At thirty years, it's all completely compatible. And so-

David Rosenthal 54:24 [laughing]

Jensen Huang 54:24 - that's the only unnegotiable rule in our company. Everything else is negotiable.

David Rosenthal 54:28 [laughing] I mean, and I guess, uh-

Jensen Huang 54:31 [chuckles]

David Rosenthal 54:31 - CUDA was a rebirth of UDA, but understanding this now, UDA going all the way back-

Jensen Huang 54:36 Yeah

David Rosenthal 54:36 ... it really is all the way back to all the chips you've ever made.

Jensen Huang 54:38 Yeah, yeah, yeah. And, and in fact, it, uh, UDA goes all the way back to all of our chips today.

David Rosenthal 54:42 Wow!

Jensen Huang 54:44 For the record, I didn't help any of the, the founding CEOs that, that are listening. I gotta tell you, you know, while you were asking that question, what, what lessons would I impart? Um, I, I, I don't know. I mean, the, the characteristics of successful companies and successful CEOs, I think are, are fairly well described.

David Rosenthal 55:00 Mm-hmm.

Jensen Huang 55:00 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, 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, um, but, 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. You know, just I think skills are, are the things that you can learn along the way, but at important moments, certain circumstances have to come together, and, and I do think that, that the market has to, you know, be one of the agents-

Ben Gilbert 55:49 Yeah

Jensen Huang 55:50 -to help you succeed. Uh, it's not enough, obviously, because a lot of people

Jensen Huang 55:54 still fail.

Ben Gilbert 55:55 Do you remember any moments in NVIDIA's history where you're like: "Ooh, 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 the skill-

Jensen Huang 56:05 Yeah

Ben Gilbert 56:06 ... in order to succeed. Do you remember any moments where you're like-

Jensen Huang 56:08 I actually thought that you starting with Riva, Riva one twenty was de-- spot on. Uh, Riva one twenty-eight, uh, uh, as I mentioned, the, 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.

Ben Gilbert 56:38 Right.

Jensen Huang 56:39 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 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 is performance. All of those things apply, so why do things twice if you could do it once?

Ben Gilbert 57:00 Yeah.

Jensen Huang 57:01 And so Riva one twenty-eight made a lot of great decisions in 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 this. 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-

Ben Gilbert 57:21 Once you pull out all the stops and you see what you're capable of, why would you put stops in next time?

Jensen Huang 57:25 Exactly.

Ben Gilbert 57:25 Like, let's keep stops out all the time-

Jensen Huang 57:27 That's right

Ben Gilbert 57:27 ... every time.

Jensen Huang 57:28 That's right.

David Rosenthal 57:28 Is it fair to say, though, maybe on the luck side of the equation, thinking back to nineteen ninety-seven, that that was the moment where consumers tipped to really, really valuing three D graphical performance in games?

Jensen Huang 57:41 Oh, yeah. So for example, luck. Let's, let's talk about luck. Um, i- if, uh, Carmack hadn't, hadn't, um, uh, decided to use acceleration, because remember, Doom was completely software-rendered, and the NVIDIA philosophy was that although general purpose computing is a, is a fabulous thing, and it's going to enable software and IT and everything, um, we felt that there were, there were applications that wouldn't be possible or would be costly if it wasn't accelerated. It should be accelerated. And three D 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 times we met John, he was quite emphatic about using CPUs, and, and his 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 bilinear filtering, um, uh, it did a pretty good job.

David Rosenthal 58:36 The problem with Doom, though, was you needed Carmack to program it. [chuckles]

Jensen Huang 58:39 Yeah, you needed Carmack 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, and if he hadn't decided to go to OpenGL and accelerate, uh, accelerate for Quake, uh, frankly, you know, what would be the killer app that put us here?

Ben Gilbert 58:56 Right.

Jensen Huang 58:56 And so Carmack and Sweeney, both between, uh, Unreal and Quake, created the first two, uh, killer applications for, for consumer three D. Yeah, and so, uh, I, I owe, owe them a great deal.

David Rosenthal 59:10 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 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-

Jensen Huang 59:26 Mm-hmm

David Rosenthal 59:26 ... planning to build a platform-

Jensen Huang 59:28 Mm-hmm

David Rosenthal 59:28 ... and tools for developers.

Jensen Huang 59:29 Mm-hmm.

David Rosenthal 59:30 Um, you know, all of them.

Jensen Huang 59:32 Mm-hmm.

David Rosenthal 59:32 Apple-

Ben Gilbert 59:32 Not Amazon.

David Rosenthal 59:33 Well, I guess-

Ben Gilbert 59:35 That's how AWS started, though.

David Rosenthal 59:36 With AWS, that's how AWS started. So I think that actually is a lesson to your point of like-

Jensen Huang 59:39 Yeah

David Rosenthal 59:39 ... that won't guarantee success by any means.

Jensen Huang 59:41 Right.

David Rosenthal 59:41 But that'll get you hanging around the tree if the apple falls.

Jensen Huang 59:44 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 good ideas.

Ben Gilbert 59:53 Yep.

Jensen Huang 59:54 Yeah.

Ben Gilbert 59:55 Well, as we, we start to drift toward the end here, we've spent a lot of time on the past-

Jensen Huang 1:00:00 Mm-hmm

Ben Gilbert 1:00:00 ... 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 at accelerating productivity, uh, for all the displaced jobs that are going to come from it?

Jensen Huang 1:00:35 Well, first of all, we have to keep AI safe, and there's a couple of different areas of AI safety, um, 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 a human not to be in the loop? Uh, 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?

Ben Gilbert 1:01:05 Yeah.

Jensen Huang 1:01:06 In the case of 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 Shutterstock to-... create commercially fair way of applying artificial intelligence, generative AI.

David Rosenthal 1:01:30 Yep.

Jensen Huang 1:01:30 In the area of, uh, 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 gonna be sensible for a long time, is human in the loop. The ability for an AI to self-learn and improve and change, uh, out in the wild, uh, i- in a digital form, uh, should be avoided. And, and, um, uh, we should collect data, we should curate the data, we should train the model, we should, you know, test the model, validate the model before we release it out in the wild again, so human is in the loop.

David Rosenthal 1:02:05 Yep.

Jensen Huang 1:02:06 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 self-driving cars and, and so on and 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, uh, and we'll see, but it is more likely that AI is gonna 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 productivity 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 productivity, then they need, they need fewer people," well, that's because the company has no more ideas, but that's not true-

David Rosenthal 1:03:14 [laughing] Yeah.

Jensen Huang 1:03:14 -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 are 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, 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 what-- the, the world's industry 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 begat from productivity improvements. But then my sense is that it's likely to generate jobs. Now, obviously, the 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, uh, will lose a job to someone else, some 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 can have more prosperity, hire more people. And so I think jobs will change. My guess is that we'll actually have higher employment. We'll 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, uh, workforce, is likely to, uh, use AI to get themselves off their 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.

David Rosenthal 1:05:20 This is, uh, appropriate. There's a version of, um, something we talk about a lot on Acquired. We call it the, uh, Moritz Corollary to Moore's Law-

Jensen Huang 1:05:27 Uh-huh.

David Rosenthal 1:05:28 -after Mike Moritz from, uh-

Jensen Huang 1:05:29 Uh-huh. Yeah, I know Mike

David Rosenthal 1:05:29 ... from, uh, Sequoia.

Jensen Huang 1:05:30 Very well, yeah.

David Rosenthal 1:05:30 Um, and, uh-

Jensen Huang 1:05:31 Sequoia was w- the first investor in our company.

David Rosenthal 1:05:34 Yeah, of course. Yeah.

Jensen Huang 1:05:35 Yeah.

David Rosenthal 1:05:35 The great story behind it is that, uh, when Mike was taking over for Don Valentine with-

Jensen Huang 1:05:39 Mm-hmm

David Rosenthal 1:05:40 ... with Doug, he was sitting and looking at Sequoia's returns, and he was looking at fund three or four, I think it was four maybe, that had Cisco in it, and he was like: "How are we ever gonna top that? You know, I can't, I can't-- You know, Don's gonna have us beat. We're never gonna beat that." And 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 a- get adopted more widely, well, then the markets that we can address should get bigger.

Jensen Huang 1:06:07 Yeah.

David Rosenthal 1:06:07 And AI, your argument is basically-

Jensen Huang 1:06:09 Exactly the same.

David Rosenthal 1:06:09 AI will do the same thing.

Jensen Huang 1:06:10 Exactly.

David Rosenthal 1:06:11 This cycle will continue.

Jensen Huang 1:06:11 I, 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.

David Rosenthal 1:06:22 Hmm.

Jensen Huang 1:06:23 Everything we do will be easier, but we'll end up doing more.

David Rosenthal 1:06:26 Yeah.

Jensen Huang 1:06:27 Because we have infinite ambition. You know, the, the, the world has infinite ambition, and so, so if a company is more profitable, they tend to hire more people to do more.

David Rosenthal 1:06:35 Yep.

Jensen Huang 1:06:36 Yeah.

Ben Gilbert 1:06:36 That's true. Technology is a lever, and the, the, the place where the idea kind of falls down is that, like, th- that we would be satisfied.

David Rosenthal 1:06:44 Yeah.

Ben Gilbert 1:06:44 Like, uh-

David Rosenthal 1:06:45 Yeah, humans have never-ending ambition.

Ben Gilbert 1:06:47 No, h- 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-

David Rosenthal 1:06:55 Yeah

Ben Gilbert 1:06:55 ... over time.

David Rosenthal 1:06:57 Now is a great time to share something new from our friends at Blinkist and Go1 that is very appropriate to this episode.

Ben Gilbert 1:07:04 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 Blinkist 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 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.

David Rosenthal 1:07:48 ... Yes, and we also have an offer from Blinkist and Go1 that goes beyond personal learning. Blinkist 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.

Ben Gilbert 1:08:06 And just like all other episodes, Blinkist is giving Acquired listeners an exclusive fifty percent discount on all premium content. This gives you key insights from 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 includes curated reading lists and progress tracking features, all overseen by a dedicated customer success manager to help your team flourish as you grow.

David Rosenthal 1:08:32 Yes. So to claim the whole free collection, unlock the fifty percent discount, and explore Blinkist's enterprise solution, simply visit blinkist.com/jensen and use the promo code Jensen. [chuckles] 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 thanks to them, and seriously, this offer is pretty awesome. Go take them up on it.

David Rosenthal 1:08:57 We have a few lightning round questions-

Jensen Huang 1:09:00 Oh, dear.

David Rosenthal 1:09:00 -we want to ask you.

Jensen Huang 1:09:00 Oh, dear.

David Rosenthal 1:09:00 And then we have, uh, [chuckles] -

Jensen Huang 1:09:02 Oh, dear.

David Rosenthal 1:09:02 And then we have a very fun-

Jensen Huang 1:09:03 I can't think that fast. Okay.

Ben Gilbert 1:09:04 We'll, we'll-

Jensen Huang 1:09:05 All right

Ben Gilbert 1:09:05 ... we'll open with an easy one based on all these, uh, conference rooms we see-

David Rosenthal 1:09:07 Yeah

Ben Gilbert 1:09:07 ... named around here.

Jensen Huang 1:09:08 Uh-huh.

Ben Gilbert 1:09:09 Favorite sci-fi book?

Jensen Huang 1:09:12 I've never read a sci-fi book before.

David Rosenthal 1:09:14 No!

Ben Gilbert 1:09:14 Come on.

Jensen Huang 1:09:15 Yeah. Yeah, yeah.

Ben Gilbert 1:09:16 What's with, like-

David Rosenthal 1:09:16 You're missing out

Ben Gilbert 1:09:17 ... the obsession with Star Trek and, like-

Jensen Huang 1:09:19 Well, just, you know, I watched the TV show. Yeah. [chuckles]

Ben Gilbert 1:09:21 Okay.

David Rosenthal 1:09:21 Favorite sci-fi TV series?

Ben Gilbert 1:09:24 Favorite sci-fi TV show.

Jensen Huang 1:09:24 Uh, oh, Star Trek's my favorite.

Ben Gilbert 1:09:26 Yeah.

Jensen Huang 1:09:26 Yeah, Star Trek's my favorite. [chuckles]

Ben Gilbert 1:09:28 I saw V'ger out there on the way in.

Jensen Huang 1:09:29 [chuckles]

Ben Gilbert 1:09:30 It's a good, it's a good conference room name.

Jensen Huang 1:09:31 V'ger is an excellent one, yeah.

Ben Gilbert 1:09:32 Yeah.

David Rosenthal 1:09:33 What car is your daily driver these days? And related question: do you still-

Jensen Huang 1:09:37 These days

David Rosenthal 1:09:37 ... have the Supra?

Jensen Huang 1:09:39 Oh, [chuckles] it 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, uh, one year, and we drove back in my, my brand-new Supra, and we totaled it. We were this close to the end.

Ben Gilbert 1:09:58 Thank God you didn't.

Jensen Huang 1:09:58 Yeah, but, but nonetheless, uh, it was my fault. It wasn't, wasn't the Supra's fault, but, but, uh, it, it's a remarkable [chuckles] I, I love that car.

David Rosenthal 1:10:04 The one time when it wasn't the Supra's fault. [chuckles]

Jensen Huang 1:10:06 [chuckles] 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, uh, Mercedes EQS. It's a great car.

David Rosenthal 1:10:16 Ah, nice!

Jensen Huang 1:10:17 Yeah. Yeah, great car.

Ben Gilbert 1:10:18 Nice.

Jensen Huang 1:10:18 Yep.

David Rosenthal 1:10:19 Using NVIDIA technology?

Jensen Huang 1:10:21 Yeah, it has... Yeah, we're in, we're in the, in the, uh, uh, the, the, uh, we're the central computer. Yep.

Ben Gilbert 1:10:27 Sweet.

Ben Gilbert 1:10:28 I know we already talked a little bit about business books, but one or two favorites that you've taken something from.

Jensen Huang 1:10:35 Clay 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 be- 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 Andy, Andy Grove's books. They're all really good.

Ben Gilbert 1:10:53 Awesome.

Ben Gilbert 1:10:55 Favorite characteristic of Don Valentine?

Jensen Huang 1:11:01 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." [laughing]

David Rosenthal 1:11:11 [chuckles] Of course he did.

Jensen Huang 1:11:12 And, uh, and then, uh, over the course of, of the decades, uh, uh, the years that 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, Don," you know-

David Rosenthal 1:11:28 [chuckles]

Jensen Huang 1:11:28 ... just right, right over the newspaper, and just, "Good job, Don," and he mails it to me. And, and, uh, I, I hope I'd kept them, but anyways, uh, you, you could tell he's a, he's a real sweetheart, and, and, um, uh... but, but, uh, he cares about the companies.

Ben Gilbert 1:11:43 I bet.

David Rosenthal 1:11:43 He's a special character.

Jensen Huang 1:11:44 Yeah, he's incredible.

Ben Gilbert 1:11:45 What is something that you believe today, that 40-year-old Jensen would have pushed back on and said, "No, I disagree"?

Jensen Huang 1:11:54 Um, there's plenty of time.

Ben Gilbert 1:11:56 Hmm.

Jensen Huang 1:11:57 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.

David Rosenthal 1:12:09 Plenty of time in the day, plenty of time-

Jensen Huang 1:12:12 To do anything

David Rosenthal 1:12:12 ... to achieve things? Like-

Jensen Huang 1:12:13 Yeah, to do anything

David Rosenthal 1:12:14 ... just in-

Jensen Huang 1:12:15 Just don't do everything. Prioritize your life. Make sacrifices. Don't let Outlook 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 Dave are waiting," you know? It's already-

Ben Gilbert 1:12:31 We had time.

Jensen Huang 1:12:32 Yeah.

David Rosenthal 1:12:32 Yeah.

Jensen Huang 1:12:32 Exactly. And so-

David Rosenthal 1:12:34 Didn't stop this from being a great chat.

Jensen Huang 1:12:35 No, but you have to prioritize your time really carefully, and don't let Outlook de- determine that.

Ben Gilbert 1:12:41 Love that.

Ben Gilbert 1:12:42 What are you afraid of, if anything?

Jensen Huang 1:12:45 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, for them. You 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 able to enjoy all of that, and so, so I think, I think the, uh, the greatest fear is that, uh, that, uh, you let them down.

David Rosenthal 1:13:30 At what point did you realize that you weren't gonna have another job, that, like, this was it?

Jensen Huang 1:13:40 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 today, I'm certain of it.... Wow! Yeah. 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 d- dedicated and focused on, on helping LSI Logic be the best company it 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 LSI Logic. Uh, it's a company I, I loved, uh, then, I love dearly today. I know exactly why it 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 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 Bechtolsheim 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, 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, now-

David Rosenthal 1:15:01 Powering the AI of the world.

Jensen Huang 1:15:03 Yeah, exactly.

David Rosenthal 1:15:03 [chuckles]

Jensen Huang 1:15:04 I mean, I, I might be doing the same thing I'm doing today.

David Rosenthal 1:15:05 I got the sense from-

Jensen Huang 1:15:07 Yeah

David Rosenthal 1:15:07 ... remembering back to part one of our, uh, series on NVIDIA-

Jensen Huang 1:15:10 But until, until I'm fired, [laughing] I'm gonna-- This is, this is my last job.

David Rosenthal 1:15:15 This was- [clapping]

Jensen Huang 1:15:15 This is it.

David Rosenthal 1:15:15 I love it.

Jensen Huang 1:15:16 Yeah, yeah.

David Rosenthal 1:15:16 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-

Jensen Huang 1:15:25 Mm-hmm

David Rosenthal 1:15:25 ... was that when, right out of school and when you first went to AMD first, right?

Jensen Huang 1:15:30 Yeah.

David Rosenthal 1:15:31 You believed that, like, kind of a version of that, was it the Jerry Sanders, "Real men have fabs?"

Jensen Huang 1:15:37 Mm-hmm.

David Rosenthal 1:15:37 Like, you, you need to do the whole stack.

Jensen Huang 1:15:38 Mm-hmm.

David Rosenthal 1:15:38 Like, you gotta do everything, and that LSI Logic changed you.

Jensen Huang 1:15:41 What LSI Logic did was, was, uh, realized that you can express, um, transistors and logical gates and chip functionality in high-level languages. That by raising the level of abstraction in what is now called high-level design, it was coined by, uh, Harvey Jones, who's on, on N-NVIDIA's board, and I met, met him, uh, way back in the early days of Synopsys. 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 twenty-one years old at the time, and I, I wanted to pursue that vision. Now, frankly, that, that idea happened in, in, um, uh, machine learning, it happened in, you know, software programming, and 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 that ever happened to the industry, and I was p- 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 i- if not for LSI Logic doing the work that it did, uh, Synopsys shortly after, then why would the computer 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.

David Rosenthal 1:17:23 That's super cool.

Jensen Huang 1:17:24 Yeah.

David Rosenthal 1:17:25 And it sounded like the CEO of LSI Logic, uh, put a good word in for you-

Jensen Huang 1:17:29 Yeah

David Rosenthal 1:17:30 ... with Don Valentine, too. [chuckles]

Jensen Huang 1:17:31 I, I didn't know how to write a business plan, and- Which it turns out is not actually important. No, no, no. [laughing] It turn- it turns out that making a financial forecast that nobody knows, uh, is gonna 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, the art of writing a business plan ought to be much, much shorter, and it forces you to 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? Mm-hmm. And what is it that you're gonna do that is sufficiently hard, that when everybody else finds out it's a good idea, they, they're not gonna swarm it and, you know, make you obsolete? And so it has to be sufficiently hard to do. Um, uh, 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 of what I described, and I did that okay, but I, I had no idea how to write a business plan. And, um, uh, and, and I was fortunate that Wolf Corrigan was so pleased with me and the work that I did when I was at LSI Logic. He called up Don Valentine and, and told Don, "You know, invest in this kid, and, um, uh, he's gonna come your way." And, and, uh, uh, so, so I was, you know, I was, I was set up for success from that moment and got it, got us off the ground. Yeah. As long as he didn't lose the money. [laughing] No, I, I think Sequoia did okay. [laughing]

David Rosenthal 1:19:02 Yeah. [laughing] That's good.

Jensen Huang 1:19:02 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, uh, is still on the board, Mark Stevens.

David Rosenthal 1:19:11 Yeah, Mark. Yeah.

Jensen Huang 1:19:12 Yeah.

David Rosenthal 1:19:12 Yeah.

Jensen Huang 1:19:12 Yeah, yeah. All these years. The two founding VCs are still on the board. Sutter Hill and Sequoia? Yeah, Tench Coxe and Mark Stevens.

David Rosenthal 1:19:18 Wow!

Jensen Huang 1:19:18 I don't think that ever happens.

David Rosenthal 1:19:19 Yeah.

Jensen Huang 1:19:20 Amazing. 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, uh, great support. Uh, but they, they also, uh, were-

David Rosenthal 1:19:34 They haven't killed you yet. [laughing]

Jensen Huang 1:19:35 ... No, not yet. But they've, 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... and I'm, I'm really grateful.

David Rosenthal 1:19:44 Well, and that being our final question for you, it's twenty twenty-three-

Jensen Huang 1:19:48 Mm.

David Rosenthal 1:19:49 Thirty years-

Jensen Huang 1:19:50 Mm

David Rosenthal 1:19:50 ... anniversary of the founding of NVIDIA. If you were-... magically thirty years old again today-

Jensen Huang 1:19:56 Mm.

David Rosenthal 1:19:56 - in twenty twenty-three, 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?

Jensen Huang 1:20:06 I wouldn't do it.

David Rosenthal 1:20:07 [laughing]

Jensen Huang 1:20:09 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 building a company and building NVIDIA 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 how vulnerable you, you're gonna feel, um, and the challenges that you're gonna 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 trick my brain into thinking, "How hard can it be?" Because you have to-

David Rosenthal 1:21:08 Still-

Jensen Huang 1:21:09 Yeah

David Rosenthal 1:21:09 ... when you wake up in the morning?

Jensen Huang 1:21:10 Yep. How hard can it be? Everything that we're doing, how hard can it be? Omniverse, how hard can it be? You know, in terms of-

David Rosenthal 1:21:16 I don't get the sense, though, that you're, um, planning to retire anytime soon, though. [chuckles]

Jensen Huang 1:21:20 No.

David Rosenthal 1:21:20 Like, you're still-

Jensen Huang 1:21:21 I'm still young. I'm still young. [chuckles]

David Rosenthal 1:21:22 You, you could choose to say, like, "Whoa, this is too hard."

Ben Gilbert 1:21:24 The trick is still working.

David Rosenthal 1:21:25 You're still-

Jensen Huang 1:21:26 Yeah

David Rosenthal 1:21:26 ... the trick is still working. [chuckles]

Jensen Huang 1:21:26 No, I, I'm, 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.

David Rosenthal 1:21:36 Mm.

Jensen Huang 1:21:36 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 gonna endure that whole journey again," I think it's too much. It is just too much.

Ben Gilbert 1:21:51 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?

Jensen Huang 1:22:00 Uh, family and friends and, and all the colleagues we have here. Uh, I'm surrounded by people who've been here for thirty years, right? Chris has been here for thirty years, and, uh, Jeff Fisher's been here thirty years. Dwight's been here thirty years, and, uh, Jonah and Brian have been here, you know, twenty-five-some years, and, uh, probably longer than that. And, you know, Joe Greco's been here thirty 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, 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 Mor-- you know, the, the Tench Coxes, the Mark Stevens, and, you know, Harvey Jones and all the, the early people of our company, the Bill Millers, they, uh, uh, not one time gave up on the company and us. And, and you kind of-- you need that. Uh, not kind of need that, you need that.

Ben Gilbert 1:23:02 Yeah.

Jensen Huang 1:23:02 And I'm pretty sure that almost every successful company and entrepreneurs that, that have gone through some difficult challenges, they, they had that support system around them.

David Rosenthal 1:23:13 I can only imagine how meaningful that... I mean, I know how meaningful that is in any company, but for you, [chuckles] given that, I feel like the NVIDIA journey is, um-

Jensen Huang 1:23:23 Not normal

David Rosenthal 1:23:23 ... particularly amplified- [chuckles]

Ben Gilbert 1:23:25 Yeah

David Rosenthal 1:23:25 ... on these dimensions, right?

Jensen Huang 1:23:26 Yeah, not normal.

David Rosenthal 1:23:27 And like, you know, you went through two, two, if not three-

Ben Gilbert 1:23:31 Near-death experiences

David Rosenthal 1:23:31 ... eighty-plus drawdowns in the public markets.

Jensen Huang 1:23:33 Yeah.

David Rosenthal 1:23:34 To have investors who've stuck with you-

Jensen Huang 1:23:36 Yeah

David Rosenthal 1:23:36 ... from day one through that-

Jensen Huang 1:23:37 Yeah

David Rosenthal 1:23:37 ... must be just, like, so much support.

Jensen Huang 1:23:40 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, is out of your control. But, you know, eighty percent 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-

David Rosenthal 1:24:06 Yeah

Jensen Huang 1:24:06 ... 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. Other- otherwise, it's just too much to endure. I mean, because, you know, everybody's questioning you, and employees aren't questioning you, but employees have questions.

Ben Gilbert 1:24:28 Right.

Jensen Huang 1:24:29 Um, people outside are questioning you, and, uh, it's a little embarrassing. Uh, it's like, you know, 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 built of humans, and, and, uh, these challenges are hard to endure. And so-

David Rosenthal 1:24:51 Ben had an appropriate comment on our, uh, most recent episode on you all, where, uh, uh, we were talking about, you know, the current situation at NVIDIA, and I think he said, "For any other company, this would be a, in a precarious spot to be in. But for NVIDIA... [chuckles] "

Ben Gilbert 1:25:04 Then this is kind of old hat.

David Rosenthal 1:25:05 Yeah. [chuckles]

Jensen Huang 1:25:05 Yes.

Ben Gilbert 1:25:06 You know, you guys are-

Jensen Huang 1:25:07 Nothing

Ben Gilbert 1:25:07 ... familiar, familiar with these large swings in amplitude.

Jensen Huang 1:25:10 Yeah. The thing that, that to keep in mind is, at all times, uh, w- 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.

Ben Gilbert 1:25:32 Yep.

Jensen Huang 1:25:33 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 does that apply to you?

Ben Gilbert 1:25:43 Yeah.

Jensen Huang 1:25:43 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 cir- 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 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, 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, uh, obviously, the problem becomes much larger, the opportunity becomes larger. Um, you know, what would it be like if we, if we were to, to magically conjure up, um, a chauffeur for everybody, uh, who has a car? And, you know, how big is that market?

Ben Gilbert 1:26:55 Yeah.

Jensen Huang 1:26:55 And obviously, obviously, 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 it 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 much larger in the future because, because, uh, what you produce, uh, i- is something very different. And, and that, that's the kind of the, the, uh, uh, the, 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.

Ben Gilbert 1:27:38 Yep. Well, Jensen, thank you so much.

Jensen Huang 1:27:42 Thank you.

Ben Gilbert 1:27:43 Ooh, David, that was awesome.

David Rosenthal 1:27:45 So fun.

Ben Gilbert 1:27:47 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 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.fm/email. Our huge thank you to Blinkist, Statsig, and Crusoe. All the links in the show notes are available to learn more and get the exclusive offers for the Acquired community from each of them. You should check out ACQ2, 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, uh, once every couple of weeks, it's a little bit more of a rarity these days.

David Rosenthal 1:28:37 We've been upleveling our production process, and that takes time.

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David Rosenthal 1:29:13 I know. The hamburger bar on our website is expanding.

Ben Gilbert 1:29:16 Expanding. I know. That's how you know we're becoming enterprise.

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David Rosenthal 1:29:22 What is the Acquired solution that we can sell?

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David Rosenthal 1:29:39 We'll see you next time.

Speaker 1:29:40 [singing] Who got the truth? Is it you? Is it you? Is it you? Who got the truth now? Uh. [upbeat music]