NVIDIA CEO Jensen Huang

We finally sit down with the man himself: Nvidia Cofounder & CEO Jensen Huang. After three parts and seven+ hours of covering the company, we thought we knew everything but — unsurprisingly — Jensen knows more. A couple teasers: we learned that the company’s initial motivation to enter the datacenter business came from perhaps not where you’d think, and the roots of Nvidia’s platform strategy stretch back beyond CUDA all the way to the origin of the company. We also got a peek into Jensen’s mindset and calculus behind “betting the company” multiple times, and his surprising feelings about whether he’d go on the founder journey again if he could rewind time. We can’t think of any better way to tie a bow on our Nvidia series (for now). Tune in! *More Acquired:* - Get email updates https://www.acquired.fm/email and vote on future episodes! - Join the Slack http://acquired.fm/slack - Check out the latest swag in the ACQ Merch Store https://www.acquired.fm/store! Timestamps: 00:00:00 Teaser 00:00:41 Intro 00:02:54 Riva 128 00:17:27 Post-AlexNet 00:20:29 OpenAI 00:22:21 Language Models 00:24:56 Statsig 00:27:13 Direct Reports 00:32:07 Product Shipping Cycle 00:34:16 Journey to the Data Center 00:39:31 Mellanox Acquisition 00:43:41 Crusoe 00:45:45 Advice For Company Building 00:55:54 Luck & Skill 00:59:54 Job Displacement 01:06:56 Blinkist 01:08:57 Favorite Sci-Fi 01:09:33 Daily Driver 01:10:28 Favorite Business Book 01:10:55 Don Valentine 01:11:45 40 Year-Old Jensen 01:12:42 What are You Afraid of? 01:13:29 Final Job 01:19:44 Starting a Company in 2023 01:23:13 Market Drawdowns 01:27:43 Outro _Note: Acquired hosts and guests may hold assets discussed in this episode. This podcast is not investment advice, and is intended for informational and entertainment purposes only. You should do your own research and make your own independent decisions when considering any financial transactions._

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Ben Gilbert?

  I will say, David, I would love to have Nvidia's full production team every episode. It was nice not having to worry about turning the cameras on and off and making sure that nothing bad happened myself while we were recording this.

• 0:13David Rosenthal?

  Yeah, just the gear. I mean, the drives that came out of the camera.

• 0:18Ben Gilbert?

  All right, red cameras for the home studio, starting next episode.

• 0:22David Rosenthal?

  Yeah, great.

• 0:23Ben Gilbert?

  All right, let's do it.

• 0:41Ben Gilbert?

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

• 0:48David Rosenthal?

  I'm David Rosenthal.

• 0:49Ben Gilbert?

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

• 0:56David Rosenthal?

  Yeah.

• 0:57Ben Gilbert?

  After researching Nvidia for something like 500 hours over the last two years, we flew down to Nvidia headquarters to sit down with Jensen himself. And Jensen, of course, is the founder and CEO of Nvidia, the company powering this whole AI explosion. At the time of recording, Nvidia is worth $1.1 trillion and is the sixth most valuable company in the entire world. And right now is a crucible moment for the company. Expectations are set high, I mean, sky high. They have about the most impressive strategic position and lead against their competitors of any company that we've ever studied. But here's

• 1:37Ben Gilbert?

  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.

• 2:12David Rosenthal?

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

• 2:25Ben Gilbert?

  Turns out, the protagonist actually knows more.

• 2:27David Rosenthal?

  Yes.

• 2:29Ben Gilbert?

  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. Onto Jensen. So Jensen, this is acquired, so we want to start with story time. So we want to wind the clock all the way back to, I believe it was 1997. You're getting ready to ship the Riva 128, which is one of the largest graphics chips ever created in the history of computing.

• 3:12Ben Gilbert?

  It is the first fully 3D accelerated graphics pipeline for a computer. And you guys have about 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. So you're betting it all right here on the Riva 128. It comes back, and of the 32 DirectX blend modes, it supports eight of them. And you have to convince the market to buy it, and you got to convince developers not to use anything but those eight blend modes. Walk us through what that felt like.

• 3:55Speaker 02

  The other 24 weren't that important.

• 3:58David Rosenthal?

  Okay, so wait, wait. First question. Was that the plan all along? Like, when did you realize that the lead aim were going to work?

• 4:05Speaker 02

  I realized I didn't learn about it until it was too late. We should have implemented all 32. Yeah. But we built what we built, and so we had to make the best of it. That was really an extraordinary time. Remember, Riva 128 was 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 it turns out to have been completely the wrong answer. And so what Revo Run 28 was, was a reset of our company. Now, remember, at the time that we started the company in 1993, we were the only consumer 3D graphics company ever created.

• 4:50Speaker 02

  And we were focused on transforming the PC into an accelerated PC because at the time, Windows was really a software-rendered system. And so anyways, Revo 128 was a reset of our company because by the time that we realized we had gone down the wrong road, Microsoft had already rolled out DirectX. It was fundamentally incompatible with Nvidia's architecture. 30 competitors have already shown up, even though we were the first company at the time that we were founded. So the world was a completely different place. The question about what to do as a company's 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

• 5:33Speaker 02

  of extraordinarily good decisions. And that time, 1997, was probably Nvidia's best moment. And the reason for that was our backs were up against the wall. We were running out of time. We were running out of money. 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 for it. And Revo 128 is the world's first 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.

• 6:16Speaker 02

  We implemented a texture cache. We took the bus limit, the frame buffer limit, to as big as physics could afford in time. We made the biggest chip that anybody had ever imagined building. We used the fastest memories. Basically, if we built that chip... There could be nothing that could be faster. And we also chose a cost point that is substantially higher than the highest price that we think that any of our competitors would be willing to go. If we built it right, we accelerated everything, we implemented everything in DirectX that we knew of, and we built it as large as we possibly could, then obviously, nobody can build something faster than that.

• 6:59David Rosenthal?

  Today, in a way, you kind of do that here at Nvidia, too. You were a consumer products company back then, right? It was end consumers who were going to have to pay the money to buy that.

• 7:08Speaker 02

  That's right, but we observed that there was a segment of the market where people were, because at the time, the PC industry was still coming up, and it wasn't good enough. Everybody was clamoring for the next fastest thing. And so if your performance was 10 times higher this year than what was available, there's a whole, large market of enthusiasts who we believe would have gone after it. And we were absolutely right, that the PC industry had a substantially large enthusiast market that would buy the best of everything. To this day, it kind of remains true. And for certain segments of the market where the technology is never good enough, like 3D graphics, when we chose the right technology, 3D graphics is never good enough.

• 7:49Speaker 02

  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. We also made the decision to use this technology called emulation.

• 8:02David Rosenthal?

  Yeah.

• 8:03Speaker 02

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

• 8:37David Rosenthal?

  Yeah.

• 8:37Speaker 02

  And so I knew-

• 8:38David Rosenthal?

  And your competitors would have caught up.

• 8:40Speaker 02

  Well, not to mention, we would have been out of business.

• 8:43David Rosenthal?

  Who cares?

• 8:44Speaker 02

  Exactly. So if you're going to be out of business anyways, that plan obviously wasn't the plan. The plan that companies normally go through, which is, you know, build the chip, write the software, fix the bugs, tape out the new chip, so on and so forth. That method wasn't going to work. And so the question is, if we only had six months, and you get to tape out just one time, then obviously you're going to tape out a perfect chip. So I remember having a conversation with our leaders, and they said, but Jensen, how do you know it's going to be perfect? And I said, I know it's going to be perfect, because if it's not, we'll be out of business. And so let's make it perfect. We get one shot. We essentially virtually prototyped the chip by buying this emulator. And Dwight and the software team wrote our software, the entire stack,

• 9:30Speaker 02

  and ran it on this emulator, and just sat in the lab waiting for Windows to paint.

• 9:36David Rosenthal?

  It was like 60 seconds per frame or something like that.

• 9:40Speaker 02

  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 we could have tested, we tested in advance and told everybody, this is it. We're going to tape out the chip, it's going to be perfect. Well, if you're going to tape out a chip, and you know it's perfect, then what else would you do? That's actually the good question. If you knew that you hit enter, you taped out a chip, and you knew it was going to be perfect, then what else would you do? Well, the answer, obviously, go to production.

• 10:11Ben Gilbert?

  And marketing blitz.

• 10:12Speaker 02

  Yeah, yeah. And developer relations. Kick everything off, because you got a perfect chip. And so we got in our head that we have a perfect chip.

• 10:19David Rosenthal?

  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?

• 10:26Speaker 02

  No, everybody was clear, we had no shot.

• 10:31Speaker 02

  Not doing it would be crazy. Because

• 10:34David Rosenthal?

  Otherwise, you might as well go home.

• 10:35Speaker 02

  You're going to be out of business anyways. So anything aside from that is crazy. So it seemed like a fairly logical thing. And quite frankly, right now, as I'm describing it, you're probably thinking, yeah, it's pretty sensible.

• 10:46David Rosenthal?

  Well, it worked.

• 10:47Speaker 02

  Yeah. And so we take that out and went directly to production.

• 10:50Ben Gilbert?

  So is the lesson for founders out there, when you have conviction on something like the Revo 128 or CUDA, go bet the company on it. And this keeps working for you. So it seems like your lesson learned from this is, yes, keep pushing all the chips in, because so far, it's worked every time.

• 11:09Speaker 02

  No.

• 11:09Ben Gilbert?

  How do you think about that?

• 11:10Speaker 02

  No, no. When you push your chips in, I know it's going to work. Notice, we assume that we taped out a perfect chip. The reason why we taped out a perfect chip is because we emulated the whole chip before we taped it out. We developed the entire software stack. We ran QA on all the drivers and all the software. We ran all the games we had. We ran every VGA application we had. And so when you push your chips in, what you're really doing is you're, when you bet the farm, you're saying, I'm going to take everything in the future, all the risky things, and I 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, we prefetch it.

• 11:54David Rosenthal?

  We talk about this a lot. We were just talking about this on our Costco episode. You want to push your chips in when you know it's going to work.

• 12:01Ben Gilbert?

  So every time we see you make a bet the company move, you've already simulated it.

• 12:06Speaker 02

  Yeah, yeah, yeah.

• 12:07Ben Gilbert?

  Do you feel like that was the case with CUDA?

• 12:10Speaker 02

  Uh, yeah. In fact, before there was CUDA, there was CG.

• 12:15David Rosenthal?

  Right.

• 12:15Speaker 02

  And so we were already playing with the concept of how do we create an abstraction layer above our chip that is expressible in a higher level language and higher level expression? And how can we use our GPU for things like CT reconstruction, image processing? We were already down that path. And so there were some positive feedback and some intuitive positive feedback that we think 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. It is massively threaded and it is the only processor in the world that does that. And so there were a lot of characteristics about programmable shading that would suggest that CUDA has a great opportunity to succeed.

• 12:59Ben Gilbert?

  And that is true if there was a large market of machine learning practitioners who would eventually show up and want to do all this great scientific computing and accelerated computing. But at the time, when you were starting to invest what is now something like 10,000 person years in building that platform, did you ever feel like, oh, man, we might have invested ahead of the demand for machine learning since we're like a decade before the whole world is realizing it?

• 13:29Speaker 02

  I guess yes and no. You know, when we saw deep learning, when we saw AlexNet and realized its incredible effectiveness in computer vision.

• 13:40Speaker 02

  We had the good sense, if you will, to go back to first principles and ask, you know, what is it about this thing that made it so successful? When a new software technology, a new algorithm comes along, and somehow leapfrogs 30 years of computer vision work, you have to take a step back and ask yourself, but why? And fundamentally, is it scalable? And if it's scalable, what other problems can it solve? And there were several observations that we made. The first observation, of course, is that if you have a whole lot of example data, you could teach this function to make predictions. Well, what we've basically done is discovered a universal function approximator. Because the dimensionality could be as high as you want it to be. Because each layer is trained one layer at a time,

• 14:25Speaker 02

  there's no reason why you can't make very, very deep neural networks. Okay, so now you just reason your way through, right? Okay, so now I go back to 12 years ago. 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.

• 14:47David Rosenthal?

  Have you been paying attention to the ImageNet competition every year leading up to this?

• 14:50Speaker 02

  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 just take a step back and ask yourself, why is that happening? So by the time that you reason your way through this, you go, well, what are the kind of problems in a world where a universal function approximator can solve?

• 15:22David Rosenthal?

  Yeah, I'm curious on this.

• 15:23Speaker 02

  Right? Well, we know that most of our algorithms start from principled sciences. Okay, you want to understand the causality, and from the causality, you create a simulation algorithm that allows it to scale. Well, for a lot of problems, we kind of don't care about the causality. We just care about the predictability of it. Like, do I really care for what reason you prefer this toothpaste over that? I don't really care the causality. I just want to know that this is the one you would have predicted. Do I really care that the fundamental cause of somebody who buys a hot dog buys ketchup and mustard? It doesn't really matter. It only matters that I can predict it. It applies to predicting movies, predicting music.

• 16:07Speaker 02

  It applies to predicting, quite frankly, weather. We understand thermal dynamics. 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?

• 16:23Ben Gilbert?

  Yep.

• 16:23Speaker 02

  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.

• 16:30Ben Gilbert?

  And it can be an incredibly lucrative market. If you can predict what the next best performing feed item to serve into a social media feed, turns out that's a hugely valuable market.

• 16:40David Rosenthal?

  I love the examples you pulled, you know? Toothpaste, ketchup, music, movies.

• 16:45Speaker 02

  When you realize this, you realize, 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 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?

• 17:27David Rosenthal?

  So that's where we are today. And that's where Nvidia is today. But I'm curious in that, you know, there's a couple of years after AlexNet. And this is when Ben and I were getting into the technology industry and the venture industry ourselves.

• 17:40Ben Gilbert?

  I started at Microsoft in 2012.

• 17:42David Rosenthal?

  Yeah.

• 17:42Ben Gilbert?

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

• 17:49David Rosenthal?

  There were those couple of years there where...

• 17:53David Rosenthal?

  To a lot of the rest of the world, these looked like science projects.

• 17:57Speaker 02

  Yeah.

• 17:58David Rosenthal?

  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, the Netflixes, et cetera. And obviously, that led to lots of things, including OpenAI a couple of years later. But during those couple of years, when you saw just that huge economic value unlock here in Silicon Valley, how were you feeling during those times?

• 18:22Speaker 02

  The first thought was, of course, reasoning about how we should change our computing stack. The second thought is, where can we find earliest possibilities of use? If we were to go build this computer, what would people use it to do? And we were fortunate that working with the world's universities and researchers was innate in our company because we were already working on CUDA. And CUDA's early adopters were researchers because we democratized 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 seismic processing, to, you know, weather simulations, quantum chemistry, the list goes on, right?

• 19:07Speaker 02

  And so the number of applications of CUDA in research was very high. And so when the time came and we realized that deep learning could be really interesting, it was natural for us to go back to the researchers and find every single AI researcher on the planet and say, how can we help you advance your work? And that included Yann LeCun and Andrew Ng and Jeff Hinton. And that's how I met all these people. And I used to go to all the AI conferences. And that's where, you know, I met Ilya Sushkvar there for the first time.

• 19:38David Rosenthal?

  Yeah.

• 19:39Speaker 02

  And so it was really about, at that point, what are the systems that we can build and the software stacks we can build to help you be more successful, to advance the research? Because at the time, it looked like a toy. But we had confidence that even GAN, the first time I met Goodfellow, the GAN was like 32 by 32. And it was just a blurry image of a cat.

• 20:00David Rosenthal?

  Mm hmm.

• 20:00Speaker 02

  But how far can it go? And so we believed in it. We believe that, one, you could scale deep learning because obviously it's trained layer by layer. And you could make the data sets larger and you could make the models larger. And we believe that if you made that larger and larger, it would get better and better.

• 20:17David Rosenthal?

  Yep.

• 20:17Speaker 02

  Kind of sensible. And I think the discussions and the engagements with the researchers was the exact positive feedback system that we needed. I would go back to research. It was, that's where it all happened.

• 20:30David Rosenthal?

  When OpenAI was founded in 2015?

• 20:33Speaker 02

  Yeah.

• 20:34David Rosenthal?

  I mean, that was such an important moment. That's obvious today now, but at the time, I think most people, even people in tech were like, what is this?

• 20:43Speaker 02

  Yeah, yeah, yeah.

• 20:44David Rosenthal?

  Were you involved in it at all? Like, you know, because you were so connected to the researchers, to Ilya, taking that talent out of Google and Facebook, to be blunt, but reseeding the research community.

• 20:56Speaker 02

  Yeah.

• 20:56David Rosenthal?

  And opening it up was such an important moment. Were you involved in it at all?

• 21:00Speaker 02

  I wasn't involved in the founding of it, but I knew a lot of the people there. And Elon, of course, I knew. And Peter Beal was there and Ilya was there. And 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're building the first version of the DGX, which, you know, today, when you see a hopper, it's 70 pounds, 35,000 parts, 10,000 amps. But DGX, the first version that we built was used internally. And I delivered the first one to OpenAI.

• 21:34David Rosenthal?

  Yeah.

• 21:34Speaker 02

  That was a fun day. But most of our success was aligned around, in the beginning, just about helping the researchers get to the next level. I knew it wasn't very useful in its current state. But I also believe that in a few clicks, it could be really remarkable. And that belief system came from the interactions with all these amazing researchers. It came from just seeing the incremental progress. At first, the papers were coming out every three months, and then papers today are coming out every day, right? So you could just monitor the archived papers. And I took an interest in learning about the progress of deep learning and, to the best of my ability, read these papers.

• 22:16Speaker 02

  And you could just see the progress happening in real time, exponentially in real time.

• 22:21Ben Gilbert?

  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 increased the size of the models. They thought, oh, there has to be some algorithmic change that needs to happen. But once you cross that 10 billion parameter mark, and certainly once you cross the 100 billion, they just magically got much more accurate, much more useful, much more lifelike. Were you shocked by that the first time you saw a truly large language model? And do you remember that feeling?

• 22:53Speaker 02

  Well, my first feeling about the language model was how clever it was to just mask out words and make it predict the next word. It's self-supervised learning at its best. We have all this text. I know what the answer is. I'll just make you guess it. And so my first impression of BERT was really how clever it was. And now the question is, how can you scale that? The first observation, almost everything is interesting, and then try to understand intuitively why it works. And then the next step, of course, is from first principles, how would you extrapolate that?

• 23:26Ben Gilbert?

  Yep.

• 23:27Speaker 02

  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. We describe a lot of reasoning things. And so if you were to say that a few-step reasoning is somehow learnable from just reading things, I wouldn't be surprised. For a lot of us, we get our common sense and we get our reasoning ability by reading. And so 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? Immersion abilities are consistent with intuitively from reasoning.

• 24:12Speaker 02

  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.

• 24:22Ben Gilbert?

  Right.

• 24:23Speaker 02

  I could visualize, literally, the entire computer and all the modules in a self-driving car. And the fact that it's still keeping lanes makes me insanely happy. And so...

• 24:35Ben Gilbert?

  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.

• 24:51Speaker 02

  Yeah.

• 24:51David Rosenthal?

  Yeah.

• 24:51Speaker 02

  Yeah, exactly. Yeah, yeah. When you put it all together, it's still a miracle. Yeah.

• 24:56Ben Gilbert?

  All right, listeners. Now is a great time to tell you about a longtime friend of the show, Vanta. AI has scrambled the whole security picture. It used to be that you proved that you were secure once a year on audit or a static PDF, then everyone would nod and you're done. But in an AI-first world, that doesn't hold up anymore.

• 25:15David Rosenthal?

  Yep. Your risk surface changes every week now. A vendor turns on an AI feature, or someone writes in a new model without telling IT, and your posture is different than it was last week, let alone at your last audit. Vanta's own research found that around 70% of companies have this quote-unquote "shadow AI" running with no security review at all.

• 25:36Ben Gilbert?

  Right. And that's where Vanta comes in. They're the leading agentic trust platform, meaning they've built the thing that closes the gap. And the way that they close that gap is Vanta Agent. Think of it as a GRC engineer, that's Governance, Risk and Compliance, except that it's software and it doesn't sleep. It finds the issues, drafts the fixes, and cuts the time that you'd spend on vendor assessments in half.

• 25:58David Rosenthal?

  In half! Which is exactly why more than 16,000 companies today run on Vanta. Companies like Ramp, Cursor, and Snowflake all stay audit-ready and catch the risks that crop up between audits across every vendor, every AI tool, the whole environment.

• 26:16Ben Gilbert?

  And that's the real value. Trust has to be continuous now, which is why Vanta automates your security, your compliance, and the work to earn and prove trust. We're huge fans of Vanta over here, and literally hundreds of Acquired listeners have become Vanta customers at their companies over the years. So you can get $1,000 off Vanta at vanta.com/acquired. That's V-A-N-T-A dot com slash acquired for $1,000 off. And just tell them that Ben and David sent you. We have some questions we want to ask you. Some are cultural about Nvidia, but others are generalizable to company building broadly. And the first one that we wanted to ask is, we've heard that you have 40-plus direct reports and that this org chart works a lot differently

• 27:01Ben Gilbert?

  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 frickin' best work and the most important thing in the world. Now go. A, is that correct? And B, is there something special about Nvidia that enables that?

• 27:27Speaker 02

  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 the armed forces, where you have generals and colonels. 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 on top of it, there are all these different modules. And each one of these layers of modules are people. And so the architecture of the company, to me, is a computer with a computing stack

• 28:12Speaker 02

  with people managing different parts of the system. And who reports to whom, your title is not related to anywhere you are in the stack. It just happens to be who is the best at running that module on that function on that layer is in charge. And that person is the pilot in command. And so that's one characteristic.

• 28:34Ben Gilbert?

  Have you always thought about the company this way? Even from the earliest days?

• 28:38Speaker 02

  Yeah, pretty much, yeah. And the reason for that is because your organization should be the architecture of the machinery of building the product.

• 28:47Speaker 02

  Right?

• 28:47David Rosenthal?

  Yep.

• 28:47Speaker 02

  That's what a company is.

• 28:49David Rosenthal?

  Yep.

• 28:49Speaker 02

  And yet, everybody's company look exactly the same, but they all build different things. How does that make any sense? Do you see what I'm saying?

• 28:57David Rosenthal?

  Yeah.

• 28:57Speaker 02

  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? So, that's number one. In terms of information system, and how do you enable collaboration? We kind of wired up like a neural network. And the way that we say is that there's a phrase in the company called "mission is the boss."

• 29:41Speaker 02

  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 looks like a little bit like a neural network.

• 29:58David Rosenthal?

  And when you say "mission," do you mean mission like Nvidia's mission is?

• 30:02Speaker 02

  Build Hopper.

• 30:03David Rosenthal?

  Yeah, okay. So, it's not like further accelerated computing? It's like we're shipping DJX Cloud?

• 30:08Speaker 02

  Build Hopper. Or somebody else's, "build a system for Hopper." Somebody goes, "build CUDA for Hopper." Somebody's job is "build cuDNN for CUDA for Hopper." Somebody's job is the mission, right? Is, so, you know, your mission is to do something.

• 30:23Ben Gilbert?

  What are the trade-offs associated with that versus the traditional structure?

• 30:27Speaker 02

  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. In our company, the information is disseminated fairly quickly to a lot of different people. It's usually at a team level. So for example, just now, I was in our robotics meeting. And we're talking about certain things and we're making some decisions. And there are new college grads in the room. There's three vice presidents in the room. There's two 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 exactly the same time.

• 31:10Speaker 02

  So, nobody has more power than anybody else. Does that make sense? The new college grad learned at exactly the same time as the E-staff. And so, the executive staff and the leaders that work for me and myself, you earned the right to have your job based on your ability to reason through problems and helping other people succeed. And it's not because you have some privileged information that I knew the answer was 3.7 and only I knew, you know? Everybody knew.

• 31:38David Rosenthal?

  When we did our most recent episode of Media Part 3 that we just released, we sort of did this thought exercise. Especially over the last couple of years, your product shipping cycle has been very impressive, especially given the level of technology that you are working with and the difficulty of this all. We sort of said like, could you imagine Apple shipping two iPhones a year?

• 32:04Ben Gilbert?

  And we said that for illustrative purposes.

• 32:05David Rosenthal?

  For illustrative purposes, not to pick on Apple or whatever.

• 32:07Ben Gilbert?

  A large tech company shipping two flagship products or their flagship product twice per year.

• 32:11David Rosenthal?

  Yeah, or you know, two WWDCs a year.

• 32:14Speaker 02

  Yeah.

• 32:14Ben Gilbert?

  There seems to be something unique.

• 32:16David Rosenthal?

  You can't really imagine that, whereas that happens here. Are there other companies, either current or historically, that you look up to, admire, maybe took some of this inspiration from?

• 32:30Speaker 02

  In the last 30 years, I've read my fair share of business books. And as in everything you read, you're supposed to, you're supposed to, to first of all, enjoy it, right? Enjoy it, be inspired by it, but not to adopt it. That's not the whole point of these books. The whole point of these books is to share their experiences. And, and 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 capabilities of our company? And so you're supposed to ask yourself, what does it mean to you? And then from that point, being informed by all these different things that we're learning,

• 33:13Speaker 02

  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 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 it could be a competitor, it could be an adjacent industry, it could be nothing to do with us. The more we learn from what's happening out in the world, the better. But then you're supposed to come back and ask yourself, you know, what does this mean to us?

• 33:45David Rosenthal?

  Yeah, you don't just want to imitate them.

• 33:46Speaker 02

  That's right.

• 33:46David Rosenthal?

  Yeah. I love this tee up of learning, but not imitating, and learning from a wide array of sources. There's this sort of 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 and, and, you know, social media feed recommenders to you deciding and the company deciding, we're going to go on a five-year all-in journey on the data center.

• 34:20Speaker 02

  Yeah.

• 34:20David Rosenthal?

  How did that happen?

• 34:21Speaker 02

  Yeah. Our journey to the data center happened, I would say, almost 17 years ago. I'm always being asked, I mean, what are the challenges that the company could 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 opportunities someday. Because there are only so many desktop PCs that plug a GPU into. And there's only so many CRTs in the time LCDs that we could possibly drive. So the question is, wouldn't it be amazing if our computer doesn't have to be connected

• 35:04Speaker 02

  to the viewing device, that the separation of it made it possible for us to compute somewhere else? And one of our engineers came and showed it to me one day, and it was really capturing the frame buffer, encoding it into video, and streaming it to a receiver device. Separating computing from the viewing.

• 35:26Ben Gilbert?

  In many ways, that's cloud gaming.

• 35:28Speaker 02

  Yeah. In fact, that was when we started GFN. We knew that GFN was going to be a journey that would take a long time because you're fighting, you're fighting all kinds of problems, including, including the speed of light.

• 35:42Ben Gilbert?

  Latency everywhere you look.

• 35:44Speaker 02

  That's right. For listeners, GFN, GeForce Now. GeForce Now. Yeah, GeForce Now. And we've been working on GeForce Now. That all makes sense, your first cloud product. That's right. 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. 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 computing is done versus where it's enjoyed, if you can separate that, your market opportunity explodes. Yeah. And it was completely true.

• 36:26Speaker 02

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

• 36:42Ben Gilbert?

  It's a good reminder. You know, the data center segment of Nvidia's business, to me, has become synonymous with how's AI going? And that's a false equivalence. And it's interesting that you were only this ready to sort of explode in AI in the data center because you had three plus previous products where you learned how to build data center computers.

• 37:03Speaker 02

  Exactly.

• 37:04Ben Gilbert?

  Even though those markets weren't these like gigantic world-changing technology shifts the way that AI is, that's how you learned.

• 37:11Speaker 02

  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 to look around corners and and anticipate where will opportunities be someday? And even if I'm not exactly sure what and when, how do I position the company to be near it? To be just standing kind of near under the tree and we can do a diving catch when the apple falls. You guys know what I'm saying?

• 37:42David Rosenthal?

  Yeah.

• 37:42Speaker 02

  But you've got to be close enough to do the diving catch.

• 37:45David Rosenthal?

  Yeah. Rewind to 2015 and OpenAI. If you hadn't been laying this groundwork in the data center.

• 37:51Speaker 02

  Yeah.

• 37:52David Rosenthal?

  You wouldn't be powering OpenAI right now.

• 37:55Speaker 02

  Yeah, but the idea that computing will be mostly done away from the viewing device. That the vast majority of computing will be done away from the computer itself. That insight was good. In fact, cloud computing, everything about today's computing is about separation of that. And by putting it in a data center, we can overcome this latency problem. Meaning you're not going to overcome speed of light. Speed of light end to end is only 120 milliseconds or something like that. It's not that long.

• 38:23Ben Gilbert?

  From a data center to an internet user.

• 38:25Speaker 02

  Anywhere on the planet.

• 38:25Ben Gilbert?

  Yeah.

• 38:26Speaker 02

  And so we could.

• 38:26Ben Gilbert?

  Oh, I see. And literally across the planet.

• 38:28Speaker 02

  Yeah, right. So if you could solve that problem approximately, something like that. I forget the number, but it's 70 milliseconds, 100 milliseconds, but it's not that long. And so my point is if you could remove the obstacles everywhere else, then speed of light should be, you know, perfectly fine. And you could build data centers as large as you like and you could do amazing things. And and this little tiny device that we use as a computer or, you know, your TV as a computer, whatever computer, they can all instantly become amazing. And so that insight, you know, 15 years ago was a good one.

• 39:02Ben Gilbert?

  So speaking of the speed of light, Infineband.

• 39:04David Rosenthal?

  Yeah.

• 39:05Ben Gilbert?

  Like David's like begging me to go here.

• 39:07David Rosenthal?

  I was at the same time.

• 39:09Ben Gilbert?

  You totally saw that Infineband would be way more useful way sooner than anyone else realized. 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?

• 39:30Speaker 02

  Well, there are several reasons for that. First, if you want to be a data center company, building the processing chip isn't the way to do it. A data center is distinguished from a desktop computer versus a cellphone, not by the processor in it. Yep. 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 describes it, but it's the networking of it, it's the infrastructure of it, it's the, you know, how the computing is distributed, how security is provided, how networking is done, you know, so on and so forth. And so, so those characteristics are associated with Mellanox, not Nvidia.

• 40:12Speaker 02

  And so, the day that I concluded that really Nvidia wants to be, you know, build computers of the future, and computers of the future are going to be data centers, embodied in data centers, and then if we want to be data center oriented company, then we really need to get into networking. And so that was one. The second thing is, observation that whereas cloud computing started in hyperscale, which is about taking commodity components, a lot of users, and virtualizing many users on top of one computer, AI is really about distributed computing, where one job, one training job, is orchestrated across millions of processors. And so it's the inverse of hyperscale, almost.

• 40:54Speaker 02

  And the way that you design a hyperscale computer with off-the-shelf commodity Ethernet, which is just fine for Hadoop, it's just fine for search queries, it's just fine for all of those things. But not when you're sharding a model across multiple racks. Not when you're sharding a model across, right? And so, 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, you know, convinced me that Mellanox is absolutely the right company, because they're the world's leading high-performance networking company, and we've worked with them in so many different areas in high-performance computing already. Plus, I really like the people.

• 41:38Speaker 02

  The Israel team is world-class. We have some 3,200 people there now. And it was one of the best strategic decisions I've ever made.

• 41:46David Rosenthal?

  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 is one of, if not the best of all time by any technology company.

• 41:59Speaker 02

  I think so too, yeah. And it's so disconnected from the work that we normally do. It was surprising to everybody.

• 42:05Ben Gilbert?

  But framed this way, you were standing near where the action was.

• 42:10Speaker 02

  Yeah.

• 42:10Ben Gilbert?

  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.

• 42:20Speaker 02

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

• 42:41Speaker 02

  You want to position yourself close to the opportunities. Now, and so that's kind of a lot of my work is positioning the company near opportunities and having the company having the skills to monetize each one of the steps along the way so that we can be sustainable.

• 43:00Ben Gilbert?

  What you just said reminds me of a great aphorism from Buffett and Munger, which is it's better to be approximately right than exactly wrong.

• 43:08Speaker 02

  Yeah, there you go. Yeah, that's a good one. That's a good one.

• 43:11Ben Gilbert?

  To live by.

• 43:11Speaker 02

  Yeah.

• 43:12Ben Gilbert?

  All right, listeners. Now is a great time to thank our longtime friend of the show, ServiceNow. If you are running a large enterprise, AI agents are likely spread across every team and deploying them is no longer the hard part.

• 43:26David Rosenthal?

  Yeah. The hard part is knowing what permissions they have, what employees are using them for, or what decisions AI is making. AI security for an enterprise at scale is not a small concern. Like, the risks are real.

• 43:40Ben Gilbert?

  Exactly. And the challenge with AI is governing it, securing it, measuring it, and making sure that it actually delivers value. That is why ServiceNow built the AI control tower.

• 43:50David Rosenthal?

  Yep. AI Control Tower gives enterprises a single place to see, manage, govern, and optimize AI across the entire business. And it works with any AI, not just theirs. Every device on your network, every permission across every system, every AI agent visible and secure in one place.

• 44:08Ben Gilbert?

  And ServiceNow can do this because they've spent more than 20 years building the operational backbone of the enterprise. The workflows, governance, approvals, security controls, and institutional knowledge that power how work actually gets done across IT, HR, customer service, finance, and security.

• 44:24David Rosenthal?

  ServiceNow already runs more than 100 billion workflows annually and trillions of transactions for more than 85% of the Fortune 500. So when companies need a place to govern AI at enterprise scale, they're building on a platform at the center of how their business already operates.

• 44:40Ben Gilbert?

  And in a future that isn't going to be one AI, it's going to be thousands of AI agents working across every function of the company. But the question is, who's managing them all?

• 44:50David Rosenthal?

  So if you're trying to turn AI ambition into real business outcomes and make it work safely, securely, at scale, go check out servicenow.com/acquired and tell them that Ben and David sent you.

• 45:01Ben Gilbert?

  I want to move away from Nvidia, if you're okay with it, and ask you some questions since we have a lot of founders that listen to this show, sort of advice for company building. The first one is, when you're starting a startup in the earliest days, your biggest competitor is you don't make anything people want. Like, your company's likely to die-

• 45:20Speaker 02

  Non-consumption.

• 45:20Ben Gilbert?

  Just because people don't actually care as much as you do about what you're building.

• 45:24Speaker 02

  That's right.

• 45:24Ben Gilbert?

  In the later days, you actually have to be very thoughtful about competitive strategy. And I'm curious, what would be your advice to companies that have product-market fit, that are starting to grow, they're in interesting, growing markets? Where should they look for competition and how should they handle it?

• 45:41Speaker 02

  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.

• 45:54David Rosenthal?

  I've heard you or others in Nvidia, I think you used the phrase, zero billion-dollar markets.

• 45:58Speaker 02

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

• 46:12Speaker 02

  Right, because when we first got into automotive, because we believe that in the future, the car is going to be largely software. And if it's going to be largely software, a really incredible computer is necessary. And so when we positioned ourselves there, most people, I still remember, one of the CTOs told me, you know what, cars cannot tolerate the blue screen of death. I don't think anybody can tolerate that, but it doesn't change the fact that someday every car will be a software-defined car. And I think 15 years later, we're largely right. And so oftentimes, there's non-consumption, and we like to navigate our company there. And by doing that, by the time that the market emerges,

• 46:58Speaker 02

  it's very likely there aren't that many competitors shaped that way. And so we were early in PC gaming, and today Nvidia is very large in PC gaming. We reimagined what a design workstation would be like. And today, just about every workstation on the planet uses Nvidia's technology. We reimagined how supercomputing ought to be done and who should benefit from supercomputing, that we would democratize it. And look, today Nvidia's accelerated computing is 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, tried to do that about a decade in advance.

• 47:42Speaker 02

  And so we spent about a decade in zero-billion-dollar markets. And today, I spend a lot of time on Omniverse. And Omniverse is a classic example of a zero-billion-dollar business.

• 47:53Ben Gilbert?

  There's like 40 customers now, something like that?

• 47:56David Rosenthal?

  Amazon, BMW.

• 47:58Speaker 02

  Yeah, no, it's cool, it's cool.

• 47:59Ben Gilbert?

  So let's say you do get this great 10-year lead, but then other people figure it out, and you've got people nipping at your heels. What are some structural things that someone who's building a business can do to sort of stay ahead? And you can just keep your pedal to the metal and say, "We're going to outwork them, and we're going to be smarter." And like, that works to some extent, but those are tactics. What strategically can you do to sort of make sure that you can maintain that lead?

• 48:22Speaker 02

  Oftentimes, if you created the market, you ended up having what people describe as moats. Because if you build your product right... And it's enabled an entire ecosystem around you to help serve that end market. You've essentially created a platform.

• 48:40David Rosenthal?

  Uh huh.

• 48:40Speaker 02

  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 early there and you, you were mindful about helping the ecosystem succeed with you, you ended up having this network of networks and all these developers and all these customers who are, who are built around you.

• 49:03David Rosenthal?

  Yep.

• 49:03Speaker 02

  And that network is essentially your moat. And so, you know, I don't love thinking about it in the context of a moat. 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.

• 49:34David Rosenthal?

  I'm so glad you brought this up because I wanted to ask you, in my mind at least, and it sounds like in yours too. Nvidia is absolutely a platform company, of which there are very few meaningful platform companies in the world. I think it's also fair to say that when you started for the first few years, you were a technology company and not a platform company. Every example I can think of, of a company that tried to start as a platform company fails. You gotta start as a technology person. When did you think about making that transition to being a platform? Like, your first graphics cards were technology. They weren't, there was no CUDA, there was no platform.

• 50:09Speaker 02

  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.

• 50:24David Rosenthal?

  CUDA is compute unified.

• 50:26Speaker 02

  Yeah.

• 50:26David Rosenthal?

  Device architecture.

• 50:27Speaker 02

  That's right. And the reason for that is because what we've done, what we essentially did in the beginning, even though Riva 128 only had computer graphics, the architecture described accelerators of all kinds. And we would take that architecture and developers would program to it. In fact, Nvidia's first strategy, business strategy, was we were going to be a game console inside the PC.

• 50:56Speaker 02

  And a game console needs developers, which is the reason why Nvidia, a long time ago, one of our first employees was a developer relations person. And so it's the reason why we knew all the game developers and all the 3D developers and we knew everybody.

• 51:10David Rosenthal?

  So was the original business plan to like?

• 51:13Ben Gilbert?

  Sort of like to build DirectX?

• 51:14David Rosenthal?

  Yeah, compete with Nintendo and Sega as like with PCs?

• 51:18Speaker 02

  The original Nvidia architecture was called DirectNV. Direct Nvidia, yeah. And DirectX was an API that made it possible for operating system to directly connect.

• 51:31Ben Gilbert?

  Address the hardware.

• 51:31Speaker 02

  Hardware, yeah.

• 51:32David Rosenthal?

  But DirectX didn't exist when you started Nvidia, right? And that's what made your strategy run for the first couple of years.

• 51:38Speaker 02

  In 1993, we had Direct Nvidia.

• 51:40David Rosenthal?

  Yeah.

• 51:40Speaker 02

  And which in 1995 became, you know, well DirectX came out.

• 51:45Ben Gilbert?

  So this is an important lesson.

• 51:47Speaker 02

  We were always a developer-oriented company.

• 51:50Ben Gilbert?

  The initial attempt was we will get the developers to build on DirectNV and then they'll build for our chips and then we'll have a platform.

• 51:58Speaker 02

  Yeah, exactly.

• 52:00Ben Gilbert?

  What played out is Microsoft already had all these developer relationships. So you learn the lesson the hard way of like, yikes, we just got to slot into that.

• 52:07David Rosenthal?

  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.

• 52:11Speaker 02

  No, but they had a lot. They did it very differently and they did a lot of things right. We did a lot of things wrong, but having said that.

• 52:18David Rosenthal?

  You were competing against Microsoft in the 90s. I mean, that's.

• 52:20Ben Gilbert?

  It's like trying to compete against Nvidia today.

• 52:23Speaker 02

  No, it's a lot different, but I appreciate that. But we were nowhere near competing with them. If you look now, when CUDA came along and there was OpenGL, there was DirectX. But there's still another extension, if you will, and that extension is CUDA. And that CUDA extension allows a chip that got paid for running DirectX and OpenGL to create and install base for CUDA.

• 52:49David Rosenthal?

  Yeah.

• 52:49Speaker 02

  And so that's the Nvidia strategy.

• 52:51David Rosenthal?

  Is this why you were so militant? And I think from our research, it really was you being militant that every Nvidia chip will run CUDA?

• 53:00Speaker 02

  Yeah, if you're a computing platform, everything's got to be compatible.

• 53:04Speaker 02

  We are the only accelerator on the planet where every single accelerator is architecturally compatible with the others. None has ever existed. There are literally a couple of hundred million, right? 250 million, 300 million installed base of active CUDA GPUs being used in the world today. And they're all architecturally compatible. How would you have a computing platform if NV30 and NV35 and 39 and NV40, they're all different? Right? At 30 years, it's all completely compatible. And so that's the only unnegotiable rule in our company. Everything else is negotiable.

• 53:43David Rosenthal?

  I mean, and I guess CUDA was a rebirth of UDA. But understanding this now, UDA going all the way back, it really is all the way back to all the chips you've ever made.

• 53:53Speaker 02

  Yeah, yeah, yeah. In fact, UDA goes all the way back to all of our chips today.

• 53:57David Rosenthal?

  Wow.

• 53:58Speaker 02

  For the record, I didn't help any of the founding CEOs that are listening. I gotta tell you, while you were asking that question, what lessons would I impart? I don't know. I mean, the characteristics of successful companies and successful CEOs, I think, are fairly well described. 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, I have nothing but admiration and respect because I just know that it's insanely hard. And I think that everybody did many similar things. There are some good, smart things that people do. There are some dumb things that you can do.

• 54:41Speaker 02

  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 the things that you can learn along the way. But at important moments, certain circumstances have to come together. And I do think that the market has to, you know, be one of the agents

• 55:04Ben Gilbert?

  Yeah.

• 55:05Speaker 02

  to help you succeed. It's not enough, obviously, because a lot of people still fail.

• 55:09Ben Gilbert?

  Do you remember any moments in Nvidia's history where you're like, oh, we made a bunch of wrong decisions, but somehow we got saved? Because, you know, it takes the sum of all the luck and all the skill

• 55:20Speaker 02

  Yeah.

• 55:21Ben Gilbert?

  in order to succeed. Do you remember any moments where you're like?

• 55:23Speaker 02

  I actually thought that you started with REVO 128 was spot on. REVO 128, as I mentioned, the number of smart decisions we made.

• 55:33Speaker 02

  Which are smart to this day. How we design chips is exactly the same to this day. Because, gosh, you know, nobody's ever done it back then. And we pulled every trick in the book in a desperation because we had no other choice. Well, guess what? That's the way things ought to be done. And now, everybody does it that way. Right. Everybody does it. Because why should you do things twice if you can do it once? Why tape out a chip seven times if you could tape it out one time? Right? And so, the most efficient, the most cost-effective, the most competitive. Speed is technology, right? Speed is performance. Time to market is performance. All of those things apply. So why do things twice if you could do it once? Yep. And so, Revo 128 made a lot of great decisions.

• 56:18Speaker 02

  And how we spec products, how we think about market needs and lack of, and how do we judge markets? And all of this. 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...

• 56:35Ben Gilbert?

  Once you pull out all the stops and you see what you're capable of, why would you put stops in next time? Like, let's keep the stops out all the time.

• 56:42Speaker 02

  That's right.

• 56:42Ben Gilbert?

  Every time.

• 56:43Speaker 02

  That's right.

• 56:43David Rosenthal?

  Is it fair to say, though, maybe on the luck side of the equation, thinking back to 1997, that that was the moment where consumers tip to really, really valuing 3D graphical performance in games?

• 56:56Speaker 02

  Oh, yeah. So, for example, luck. Let's talk about luck. If Car Mac had decided to use acceleration, because, remember, Doom was completely software-rendered. And the Nvidia philosophy was that although general-purpose computing is a fabulous thing, and is going to enable software and IT and everything, we felt that there were applications that wouldn't be possible or would be costly if it wasn't accelerated. It should be accelerated. And 3D graphics was one of them, but it wasn't the only one. And it just happens to be the first one, and a really great one. And I still remember the first times we met John. He was quite emphatic about using CPUs and the software render was really good. I mean, quite frankly, if you look at Doom,

• 57:40Speaker 02

  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 biological filtering, it did a pretty good job.

• 57:51David Rosenthal?

  The problem with Doom, though, was you needed Carmack to program it.

• 57:54Speaker 02

  Yeah, you needed Carmack to program it, exactly. It was a genius piece of code.

• 57:59Speaker 02

  But nonetheless, software renders did a really good job. And if he hadn't decided to go to OpenGL and accelerate for Quake, frankly, what would be the killer app that put us here?

• 58:10Ben Gilbert?

  Right.

• 58:11Speaker 02

  And so Carmack and Sweeney, both between Unreal and Quake, created the first two killer applications for consumer 3D. Yeah, and so I owe them a great deal.

• 58:25David Rosenthal?

  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 actually do think, you know, if you look at all the big tech companies today, perhaps with the exception of Google, they did all start, and understanding this now about you, by addressing developers, planning to build a platform and tools for developers. You know, all of them, Apple.

• 58:47Ben Gilbert?

  Not Amazon.

• 58:48David Rosenthal?

  Well, I guess. With AWS, that's how AWS started. So I think that actually is a lesson to your point of like, "That won't guarantee success by any means."

• 58:56Speaker 02

  Right.

• 58:56David Rosenthal?

  But that'll get you hanging around a tree if the apple falls.

• 58:58Speaker 02

  Yeah. As many good ideas as we have, you don't have all the world's good ideas. And the benefit of having developers is you get to see a lot of good ideas.

• 59:08Ben Gilbert?

  Yep. Well, as we start to drift toward the end here, we spent a lot of time on the past and I want to think about the future a little bit. I'm sure you spend a lot of time on this being on the cutting edge of AI. You know, we're moving into an era where the productivity that software can accomplish when a person is using software can massively amplify the impact and the value that they're creating, which has to be amazing for humanity in the long run. In the short term, it's going to be inevitably bumpy as we sort of figure out what that means. What do you think some of the solutions are as AI gets more and more powerful and better at accelerating productivity for all the displaced jobs that are going to come from it?

• 59:50Speaker 02

  Well, first of all, we have to keep AI safe. And there's a couple of different areas of AI safety that's really important. Obviously, in robotics and self-driving car, there's a whole field of AI safety, and we've dedicated ourselves to functional safety and active safety and all kinds of different areas of safety. When to apply human in the loop? When is it okay for human not to be in the loop? You know, how do you get to a point where increasingly human doesn't have to be in the loop, but human largely in the loop? In the case of information safety, obviously bias, false information, and appreciating the rights of artists and creators. That whole area deserves a lot of attention. And you've seen some of the work that we've done.

• 1:00:35Speaker 02

  Instead of scraping the internet, we partnered with Getty and Shutterstock to create commercially fair way of applying artificial intelligence generated AI. Yep. In the area of large language models and the future of increasingly greater agency AI, clearly the answer is for as long as it's sensible, and I think it's going to be sensible for a long time, is human in the loop. The ability for an AI to self-learn and improve and change out in the wild in a digital form should be avoided. And we should collect data. We should carry the data. We should train the model. We should, you know, test the model, validate the model before we release it on the wild again. So human is in the loop. Yep.

• 1:01:20Speaker 02

  There are a lot of different industries that have already demonstrated how to build systems that are safe and good for humanity. And obviously, the way autopilot works for a plane and two-pilot system and then air traffic control and redundancy and diversity. And all of the basic philosophies of designing safe systems apply as well in self-driving cars and so on, so forth. And so I think there's a lot of models of creating safe AI. And I think we need to apply them. With respect to automation, my feeling is that, and we'll see, but it is more likely that AI is going to create more jobs in the near term. The question is, what's the definition of near term?

• 1:02:04Speaker 02

  And the reason for that is, the first thing that happens with productivity is prosperity. And prosperity, when the companies get more successful, they hire more people because they want to expand into more areas. And so the question is, if you think about a company and say, okay, if we improve the productivity, then they need fewer people. Well, that's because the company has no more ideas, but that's not true for most companies. If you become more productive and the company becomes more profitable, usually, they hire more people to expand into new areas. And so long as we believe that there are more areas to expand into, that there are more ideas in drugs, this drug discovery, there are more ideas in transportation, there

• 1:02:48Speaker 02

  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, the world's industry is 1,000 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 my sense is that it's likely to generate jobs.

• 1:03:28Speaker 02

  Now, obviously, net generation of jobs doesn't guarantee that any one human doesn't get fired, okay? I mean, that's obviously true. And it's more likely that someone will lose a job to someone else, some other human that uses an AI, you know, and not likely to an AI, but to some other human that uses an AI. And so I think the first thing that everybody should do is learn how to use AI so that they can augment their own productivity. And every company should augment their own productivity to be more productive so that they can have more prosperity, hire more people. And so I think jobs will change. My guess is that we'll actually have higher employment. We'll create more jobs.

• 1:04:11Speaker 02

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

• 1:04:35David Rosenthal?

  This is appropriate. There's a version of something we talk about a lot on acquired. We call it the Merit's corollary to Moore's law. After Mike Merit's from Sequoia.

• 1:04:46Speaker 02

  Sequoia was the first investor in our company.

• 1:04:48David Rosenthal?

  Yeah, of course, yeah. The great story behind it is that when Mike was taking over for Don Valentine 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 going to top that? You know, I can't, I can't, you know, Don's going to have us beat. We're never going to beat that. He thought about it and he realized that, well, as compute gets cheaper and it can access more areas of the economy because it gets cheaper and can get adopted more widely. Well, then the markets that we can address should get bigger.

• 1:05:21Speaker 02

  Yeah.

• 1:05:22David Rosenthal?

  And AI, your argument is basically AI will do the same thing.

• 1:05:25Speaker 02

  Exactly. I just gave you exactly the same example. That in fact, productivity doesn't result in us doing less. Productivity usually results in us doing more. Everything we do will be easier, but we'll end up doing more.

• 1:05:41David Rosenthal?

  Yep.

• 1:05:42Speaker 02

  Because we have infinite ambition, you know, the world has infinite ambition. And so if a company is more profitable, they tend to hire more people to do more.

• 1:05:50David Rosenthal?

  Yep.

• 1:05:50Speaker 02

  Yeah.

• 1:05:51Ben Gilbert?

  That's true. Technology is a lever. And the place where the idea kind of falls down is that like, that we would be satisfied.

• 1:05:59David Rosenthal?

  Yeah. Like. Humans have never ending ambition.

• 1:06:01Ben Gilbert?

  No, humans will always expand and consume more energy and attempt to pursue more ideas. That has always been true of every version of our species.

• 1:06:10David Rosenthal?

  Yeah.

• 1:06:10Ben Gilbert?

  Over time. All right, listeners. Now is a great time to talk about a new partner of ours here on Acquired, Logora, the agentic operating system that is redefining how the world's best legal teams work.

• 1:06:24David Rosenthal?

  Yep. It's sort of obvious that AI is going to completely change the legal industry. I bet most of you listening have dropped a contract into some sort of AI chatbot out there. Logora took that insight and asked the question, what if you really built something with that power from the ground up for the legal industry?

• 1:06:41Ben Gilbert?

  So the founders did exactly what great founders do, operate with obsessive customer focus. They embedded inside a massive law firm for months. They sat with the lawyers just watching how the work really gets done.

• 1:06:56David Rosenthal?

  And that's how you get features that customers love, like tabular review, where you drop in a folder of hundreds of contracts and it pulls every key term into a grid a lawyer can actually work with. Logora's bet here is interesting. Since it lets each lawyer handle more complexity, any given person can increase the quality of their work and do higher value work. And this means that the pie can grow even as each individual task takes less time.

• 1:07:20Ben Gilbert?

  And they recently launched Logora Agent, offering greater intelligence and performance. The agent lets lawyers set an objective. Then it can handle the planning and the execution and delivery of the final product. Legal teams get to maintain full control and transparency since they're still involved where judgment is required.

• 1:07:39David Rosenthal?

  And Logora works where you already work. You can use it within Microsoft Word while redlining or drafting. The early Logora numbers essentially speak for themselves. When they have a head-to-head pilot with their top competitor, they win 70% of the time. Logora now has over 100,000 lawyers on the platform from 1,200 legal teams in 50 countries. And crazily, they went from 1 million to 100 million in ARR in about 18 months.

• 1:08:09Ben Gilbert?

  Truly insane numbers. And that is the real test. Plenty of things demo well, but the question is whether a busy associate actually reaches for it during crunch time, or whether a partner trusts it before going into a conversation with a major client. If your legal team wants to check it out, whether you're a law firm or you're in-house at a company, you can learn more at logora.com/acquired and just tell them that Ben and David sent you.

• 1:08:34David Rosenthal?

  We have a few lightning round questions we want to ask you. And then we have a very fun.

• 1:08:40Speaker 02

  I can't think that fast. Okay, all right.

• 1:08:41Ben Gilbert?

  We'll open with an easy one based on all these conference rooms we see named around here. Favorite sci-fi book?

• 1:08:49Speaker 02

  I've never read a sci-fi book before.

• 1:08:51Ben Gilbert?

  No.

• 1:08:51David Rosenthal?

  Come on.

• 1:08:52Speaker 02

  Yeah, yeah.

• 1:08:53Ben Gilbert?

  What's with like the obsession with Star Trek and?

• 1:08:56Speaker 02

  I just, you know, I watched the TV show.

• 1:08:57Ben Gilbert?

  Okay, favorite sci-fi TV series?

• 1:09:02Speaker 02

  Star Trek's my favorite. Yeah, Star Trek's my favorite.

• 1:09:05Ben Gilbert?

  I saw VJr. out there on the way in. It's a good conference room name.

• 1:09:08Speaker 02

  VJr. is an excellent one, yeah.

• 1:09:09Ben Gilbert?

  Yeah.

• 1:09:10David Rosenthal?

  What car is your daily driver these days? And related question, do you still have the Supra?

• 1:09:16Speaker 02

  Oh, it's one of my favorite cars and also favorite memories. You guys might not know this, but Lori and I got engaged Christmas one year and we drove back in my brand-new Supra and we totaled it. We were this close to the end.

• 1:09:35Ben Gilbert?

  Thank God you didn't.

• 1:09:35Speaker 02

  But nonetheless, it wasn't my fault. It wasn't the Supra's fault. But it's a remark. I love that car.

• 1:09:41David Rosenthal?

  The one time when it wasn't the Supra's fault.

• 1:09:43Speaker 02

  Yeah, I love that car. I'm driven these days for security reasons and others, but I'm driven in the Mercedes EQS. It's a great car.

• 1:09:53David Rosenthal?

  Ah, nice.

• 1:09:54Speaker 02

  Yeah, great car.

• 1:09:55David Rosenthal?

  Thanks. Using Nvidia technology?

• 1:09:58Speaker 02

  Yeah, we're in the central computer.

• 1:10:04Ben Gilbert?

  Sweet. I know we already talked a little bit about business books, but one or two favorites that you've taken something from?

• 1:10:12Speaker 02

  Clay Christensen, I think, the series is the best. I mean, there's just no two ways about it. And the reason for that is because it's so intuitive and so sensible. It's approachable. But I read a whole bunch of them and I read just about all of them. I really enjoyed Andy Grove's books. They're all really good.

• 1:10:31Ben Gilbert?

  Awesome. Favorite characteristic of Don Valentine?

• 1:10:37Speaker 02

  Grumpy but enduring. And what he said to me the last time as he decided to invest in our company, he says, "If you lose my money, I'll kill you."

• 1:10:48David Rosenthal?

  Of course he did.

• 1:10:49Speaker 02

  And then, over the course of the decades, the years I followed, when something is nice written about us in Mercury News, it seems like he wrote it in a crayon. You know, he'll say, "Good job, Don." Just write over the newspaper and just, "Good job, Don," and he mails it to me. And I hope I'd kept them. But anyways, you could tell he's a real sweetheart. But he cares about the companies.

• 1:11:20David Rosenthal?

  He's a special character.

• 1:11:21Speaker 02

  Yeah, he's incredible.

• 1:11:22Ben Gilbert?

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

• 1:11:31Speaker 02

  There's plenty of time. Yeah, there's plenty of time. If you prioritize yourself properly and you make sure that you don't let Outlook be the controller of your time, there's plenty of time.

• 1:11:46David Rosenthal?

  Plenty of time in the day?

• 1:11:48Speaker 02

  To do anything.

• 1:11:49David Rosenthal?

  To achieve things?

• 1:11:50Speaker 02

  Yeah, to do anything. 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." Ben and David are waiting.

• 1:12:08Ben Gilbert?

  We have time.

• 1:12:09Speaker 02

  Yeah, exactly.

• 1:12:11David Rosenthal?

  Didn't stop this from being a great chat.

• 1:12:12Speaker 02

  No, but you have to prioritize your time really carefully. And don't let Outlook determine that.

• 1:12:18Ben Gilbert?

  Love that. What are you afraid of, if anything?

• 1:12:22Speaker 02

  I'm afraid of the same things today that I was in the very beginning of this company, which is letting the employees down. You have a lot of people who joined your company because they believe in your hopes and dreams and they've adopted it as their hopes and dreams. And you want to be right for them. You want to be successful for them. You want them to be able to build a great life as well as help you build a great company and be able to build a great career. You want them to have to enjoy all of that. And these days, I want them to be able to enjoy the things I've had the benefit of enjoying and all the great success I've enjoyed. I want them to be able to enjoy all of that. And so I think the greatest fear is that you let them down.

• 1:13:07David Rosenthal?

  At what point did you realize that you weren't going to have another job? That like this was it?

• 1:13:17Speaker 02

  I just, I don't change jobs. You know, if it wasn't because of Chris and Curtis convincing me to do Nvidia, I would still be at LSI Logic today, I'm certain of it. Wow, really? Yeah, yeah. I'm certain of it. I would keep doing what I'm doing. And at the time that I was there, I was completely dedicated and focused on helping LSI Logic be the best company it could be. And I was LSI Logic's best ambassador. I've got great friends to this day that I've known from LSI Logic. It's a company I loved then, I love dearly today. I know exactly why I went. The revolutionary impact it had on chip design and system design and computer design.

• 1:14:01Speaker 02

  In my estimation, one of the most important companies that ever came to Silicon Valley and changed everything about how computers were made. It put me in the epicenter of some of the most important events in computer industry. It led me to meeting Chris and Curtis and Andy Bechstein and John Rubenstein and, you know, some of the most important people in the world. And Frank that I was with the other day and just, I mean, the list goes on. And so LSI Logic was really important to me and I would still be there. I would, you know, who knows what LSI Logic would have become if I were still there, right? And so that's kind of how my mind works.

• 1:14:38David Rosenthal?

  Powering the AI of the world.

• 1:14:40Speaker 02

  Yeah, exactly. I mean, I might be doing the same thing I'm doing today.

• 1:14:43David Rosenthal?

  I got the sense from remembering back to part one of our series on Nvidia.

• 1:14:47Speaker 02

  But until I'm fired, this is my last job.

• 1:14:53David Rosenthal?

  I got the sense that LSI Logic might have also changed your... Perspective and philosophy about computing too. The sense I we got from the research was that when right out of school and when you first went to AMD first, right?

• 1:15:07Speaker 02

  Yeah.

• 1:15:08David Rosenthal?

  You believed that like kind of a version of that, was it the Jerry Sanders, real men have fabs? Like you need to do the whole stack, like you gotta do everything and that LSI Logic changed you.

• 1:15:18Speaker 02

  What LSI Logic did was was realize that you can express 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 Harvey Jones, who's on Nvidia's board. And I met him 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 tools. And and be a lot more productive.

• 1:16:01Speaker 02

  That logic was so sensible to me. And I was 21 years old at the time and I want to pursue that vision. Now, frankly, that that idea happened in in machine learning. It happened in software programming. I want to see it happen in digital biology so that we can we can think about biology in a much higher level language. Probably a large language model would be the the way to make it make it representable. That transition was so revolutionary. I thought that was the best thing ever happened to the industry. And I was I was really happy to be part of it and I was at ground zero. And so so I saw one industry change revolutionize another industry.

• 1:16:44Speaker 02

  And if not for LSI Logic doing the work that it did, Synopsys shortly after, then why would the computer industry be where it is today? Yeah, it's a really, really terrific. I was I was at the right place at the right time to see all that.

• 1:17:00David Rosenthal?

  That's super cool.

• 1:17:01Speaker 02

  Yeah.

• 1:17:02David Rosenthal?

  And it sounded like the CEO of LSI Logic put a good word in for you with Don Valentine.

• 1:17:08Speaker 02

  I didn't know how to write a business plan. Which it turns out is not actually important. No, no, no. It turns out that making a financial forecast that nobody knows is going to be right or wrong turns out not to be that important. But the important things that a business plan probably could have teased out, I think that the art of writing a business plan ought to be much, much shorter. And it forces you to condense, you know, what is the true problem you're trying to solve? What is the unmet need that you believe will emerge? And what is it that you're going to do that is sufficiently hard that when everybody else finds out is a good idea, they're not going to swarm it and, you know, make you obsolete? And so it has to be sufficiently hard to do. There are a whole bunch of those

• 1:17:53Speaker 02

  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 had no idea how to write the business plan. And I was fortunate that Wolf Corrigan was so pleased with me and the work that I did when I was at LSL Logic. He called up Don Valentine and told Don, you know, invest in this kid. And he's going to come your way. And so I was, you know, I was set up for success from that moment and got us on the ground, you know? As long as he didn't lose the money?

• 1:18:36Speaker 02

  I think Sequoia did okay.

• 1:18:39David Rosenthal?

  That's good.

• 1:18:39Speaker 02

  Yeah, I think we probably are one of the best investments they've ever made. Have they held through today? The VC partner is still on the board, Mark Stevens.

• 1:18:48David Rosenthal?

  Yeah, Mark, yeah.

• 1:18:48Speaker 02

  Yeah, yeah, all these years. The two founding VCs are still on the board. Sutter Hill and Sequoia? Yeah, Tench Cox and Mark Stevens. I don't think that ever happens.

• 1:18:56David Rosenthal?

  Yeah.

• 1:18:57Speaker 02

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

• 1:19:12David Rosenthal?

  They haven't killed you yet.

• 1:19:13Speaker 02

  Not yet. But they've been entertained, you know, by the company, inspired by the company and enriched by the company. And so they stayed with it. And I'm really grateful.

• 1:19:21David Rosenthal?

  Well, in that being, our final question for you. It's 2023, 30 years anniversary of the founding of Nvidia. If you were magically 30 years old again today in 2023, and you were going to Denny's with your two best friends who are the two smartest people you know. And you're talking about starting a company. What are you talking about starting?

• 1:19:43Speaker 02

  I wouldn't do it.

• 1:19:46Speaker 02

  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 a video turned out to have been a million times harder than I expected it to be, any of us expected it to be. And at that time, if we realized the pain and suffering and just how vulnerable you're gonna feel and the challenges that you're gonna endure, the embarrassment and the shame and, you know, the list of all the things that go wrong, I don't think anybody would start a company. Nobody in their right mind would do it.

• 1:20:27Speaker 02

  And I think that that's kind of the superpower of a entrepreneur. They don't know how hard it is. And they only ask themselves, how hard can it be? And to this day, I trick my brain into thinking, how hard can it be? Because you have to.

• 1:20:45Ben Gilbert?

  Still, when you wake up in the morning?

• 1:20:47Speaker 02

  Yeah, how hard can it be? Everything that we're doing, how hard can it be? Omniverse, how hard can it be? You know?

• 1:20:53David Rosenthal?

  I don't get the sense, though, that you're planning to retire anytime soon, though.

• 1:20:57Speaker 02

  No, I'm still young.

• 1:20:59David Rosenthal?

  You could choose to say like, whoa, this is too hard.

• 1:21:01Ben Gilbert?

  The trick is still working.

• 1:21:02David Rosenthal?

  You're still, the trick is still working.

• 1:21:04Speaker 02

  I'm still enjoying myself immensely and I'm adding a little bit of value. But the, that's really the trick of an entrepreneur. You have to get yourself to believe that it's not that hard. Because it's way harder than you think. And so if I go taking all of my knowledge now and I go back and I said, I'm gonna endure that whole journey again, I think it's too much. It is just too much.

• 1:21:28Ben Gilbert?

  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?

• 1:21:37Speaker 02

  I have family and friends and all the colleagues we have here. I'm surrounded by people who've been here for 30 years. Chris has been here for 30 years. Jeff Fisher's been here 30 years. Dwight's been here 30 years. Jonah and Brian have been here 25 some years and probably longer than that. And Joe Greco's been here 30 years. I'm surrounded by these people that never one time gave up. And they never one time gave up on me. And that's the entire ball of wax. And to be able to go home and have your family be fully committed to everything that you're trying to do. Thick or thin, they're proud of you and proud of the company. You kind of need that. You need the unwavering support of people around you.

• 1:22:22Speaker 02

  Jim Gaethers and the Tench Coxes and the Mark Stevens and Harvey Jones and all the early people of our company, the Bill Millers. They not one time gave up on the company and us. And you kind of, you need that. Not kind of need that, you need that. And I'm pretty sure that almost every successful company and entrepreneurs that have gone through some difficult challenges, they had that support system around them.

• 1:22:50David Rosenthal?

  I can only imagine how meaningful that, I mean, I know how meaningful that is in any company, but for you, given that, I feel like the Nvidia journey is particularly amplified on these dimensions, right? You went through two, if not three, 80% plus drawdowns in the public markets. And to have investors who've stuck with you from day one through that must be just like so much support.

• 1:23:17Speaker 02

  Yeah, yeah, it is incredible. And you hate that any of that stuff happened and most of it is out of your control. But you know, 80% fall, it's an extraordinary thing, no matter how you look at it. And I forget exactly, but I mean, we traded.

• 1:23:40Speaker 02

  Down at about a couple of two, three billion dollars in market value for a while because of the decision we made in going into CUDA and all that work. And your belief system has to be really, really strong. You know, you have to really, really believe it and really, really want it. Otherwise, it's just too much to endure. I mean, because, you know, everybody's questioning you and employees aren't questioning you, but employees have questions.

• 1:24:05Ben Gilbert?

  Right.

• 1:24:06Speaker 02

  People outside are questioning you. And it's a little embarrassing. 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's no good answers to any of that stuff. You know, CEOs are human and companies are built of humans and these challenges are hard to endure.

• 1:24:27David Rosenthal?

  Ben had an appropriate comment on our most recent episode on you all where we were talking about, you know, the current situation in Nvidia. I think he said, for any other company, this would be a precarious spot to be in. But for Nvidia.

• 1:24:41Ben Gilbert?

  This is kind of old hat.

• 1:24:42David Rosenthal?

  Yeah.

• 1:24:43Ben Gilbert?

  You guys are familiar with these large swings in amplitude.

• 1:24:46Speaker 02

  Yeah. The thing that to keep in mind is at all times, what is the market opportunity that you're engaging? And that informs your size. You know, I was told a long time ago that Nvidia can never be larger than a billion dollars. Obviously, it's an underestimation, under imagination of the size of the opportunity.

• 1:25:09Ben Gilbert?

  Yep.

• 1:25:10Speaker 02

  It is the case that no chip company can ever be so big. And so, but if you're not a chip company, then why is that applied to you? And this is the extraordinary thing about technology right now is technology is a tool and it's only so large. What's unique about our current circumstance today is that we're in the manufacturing of intelligence. We're in the manufacturing of work world. That's AI. And the world of tasks, doing work, productive, generative AI work, generative intelligent work, that market size is enormous, is measured in trillions.

• 1:25:51Speaker 02

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

• 1:26:36Speaker 02

  And so the technology industry is at the, you know, where what we've discovered, what Nvidia is discovering, what some of the discovered is that by separating ourselves from being a chip company, but building on top of a chip and you're now in the ad company, the market opportunity has grown by probably a thousand times. You know, don't be surprised if technology companies become much larger in the future because because what you produce is something very different. And that's the kind of 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.

• 1:27:15Ben Gilbert?

  Yep. Well, Jensen, thank you so much.

• 1:27:19Speaker 02

  Thank you.

• 1:27:21David Rosenthal?

  All right, listeners. Now is a great time to talk about one of our favorite companies, Statsig.

• 1:27:27Ben Gilbert?

  Yes, there is a reason why the best product teams rely on Statsig, whether they are iterating on their core product features or shipping AI-powered experiences at scale.

• 1:27:38David Rosenthal?

  Yep. In the crazy speed of today's AI world, shipping fast is just table stakes now. It's basically trivial to build and deploy your app constantly. The real advantage is how quickly you learn what changes actually created value for customers and how fast you can use that signal to guide what you ship next.

• 1:27:58Ben Gilbert?

  This is where Statsig comes in. It brings experimentation, feature flags, and product analytics into one unified system so teams can ship safely, test rigorously, and directly link what they changed to how users actually behaved.

• 1:28:10David Rosenthal?

  So if you want to make learning your competitive advantage, whether you're building new AI experiences or just evolving your existing core product, go to statsig.com/acquired to get started.

• 1:28:21Ben Gilbert?

  Ooh, David, that was awesome.

• 1:28:23David Rosenthal?

  So fun.

• 1:28:25Ben Gilbert?

  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. You should check out ACQ2, which is available at any podcast player. As these main acquired episodes get longer and come out, you know, once a month instead of once every couple of weeks, it's a little bit more of a rarity these days.

• 1:29:03David Rosenthal?

  We've been upleveling our production process, and that takes time.

• 1:29:07Ben Gilbert?

  Yes. ACQ2 has become the place to get more from David and I, and we've just got some awesome episodes coming up that we are excited about. If you want to come deeper into the acquired kitchen, become an LP, acquired.fm/lp. Once every couple of months or so, we'll be doing a call with all of you on Zoom just for LPs to get the inside scoop of what's going on in acquired land and get to know David and I a little bit better. And once a season, you'll get to help us pick a future episode. So that's acquired.fm/lp. Anyone should join the Slack, acquired.fm/slash/Slack. God, we've got a lot of things now, David.

• 1:29:40David Rosenthal?

  I know. The hamburger bar on our website is expanding.

• 1:29:43Ben Gilbert?

  Expanding. I know. That's how you know we're becoming enterprise.

• 1:29:46David Rosenthal?

  Ah.

• 1:29:46Ben Gilbert?

  We have a mega menu, a menu of menus, if you will.

• 1:29:49David Rosenthal?

  What is the acquired solution that we can sell?

• 1:29:51Ben Gilbert?

  That's true.

• 1:29:52David Rosenthal?

  We got to find that.

• 1:29:53Ben Gilbert?

  All right. With that, listeners, acquired.fm/slash/Slack to join the Slack and discuss this episode, acquired.fm/store to get some of that sweet merch that everyone is talking about. And with that, listeners, we will see you next time.

• 1:30:06David Rosenthal?

  We'll see you next time.