NVIDIA CEO Jensen Huang

Let's do it.

Is it you? is it you? Is it you who got the truth?

Is it you? is it you? is it you? Sit me down, say it straight.

Another story on the way Who got the truth?

And Jensen, of course, is the founder and CEO of Nvidia, the company powering this whole AI explosion. At the time of recording, Nvidia is worth $1.1 trillion and is the sixth most valuable company in the entire world. And right now is a crucible moment for the company. Expectations are set high, I mean, sky high.

They have about the most impressive strategic position and lead against their competitors of any company that we've ever studied. But here's the question that everyone is wondering will Nvidia's insane prosperity continue for years to come? Is AI going to be the next trillion dollar technology wave? How sure are we of that?

And if so, can Nvidia actually maintain their ridiculous dominance as this market comes to take shape? So Jensen takes us down memory lane with stories of how they went from graphics to the data center to AI, how they survived multiple near-death experiences. He also has plenty of advice for founders, and he shared an emotional side to the founder journey toward.

And without further ado, this show is not investment advice. David and I may have investments in the companies we discuss, and this show is for informational and entertainment purposes. Only. On to Jensen, So Jensen, this is acquired. So we want to start with story time.

So we want to wind the clock all the way back to, I believe it was 1997.

You're getting ready to ship? The Riva 128, which is one of the largest graphics chips ever created in the history of computing, it is the first fully 3D-accelerated graphics pipeline for a computer. 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've got to convince developers not to use anything but those eight blend modes. Walk.

Okay, so wait, wait. First question was that the plan all along? When did you realize that it was going to work? I realized I didn't learn about it until it was too late.

We should have implemented all 32.

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 Riva 128 was was a reset of our company. Now, remember, at the time that we started the company in 1993, we were the only consumer 3D graphics company ever created. And we were focused on transforming the PC into an accelerated PC. Because at the time, Windows was really a software-rendered system, and so anyways, Riva 128 was a reset of our company, because by the time that we realized, we had gone down the wrong road, Microsoft had already rolled out DirectX. It was fundamentally incompatible with Nvidia's architecture. 3D competitors had already shown up, even though we were the first company at the time that we were founded.

So the world was a completely different place. The question about what to do as a company strategy. At that point, I would have said that we made a whole bunch of wrong decisions, but on that day, that mattered, we made a sequence of extraordinarily good decisions. And that time, 1997, was probably Nvidia's best moment. And the reason for that was our 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. We implemented a texture cache, we took the bus limit, the frame buffer limit, to as big as physics could afford. At the time, we made the biggest chip that anybody had ever imagined building. we used the fastest memories. Basically, if we built that chip, there could be nothing that could be faster.

And we also chose a cost point that is substantially higher than the highest price that we think that any of our competitors would be willing to go. If. We built it right, we accelerated everything, we implemented everything in DirectX that we knew of, and we built it as large as we possibly could. Then, obviously, nobody can build something faster than that today. In a way, you kind of do that here 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, 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. 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. 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 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 and your competitors would have caught up.

Well, not to mention we would have been out of business. Who cares exactly? So if you're going to be out of business anyways? That plan obviously wasn't the plan, the plan that companies normally go through. Which is, you know, build the chip, write the software, fix the bugs, tape out 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 Jen said, 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 and ran it on this emulator. And just sat in the lab waiting for Windows to paint. It was like 60 seconds per frame or something like that. oh, easily. I actually think that it was an hour per frame, something like that.

And so we would just sit there and watch it paint. And so, on the day that we decided to tape out, I assumed that the chip was perfect. And everything that 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 going to be 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.

No, everybody was clear, we had no shot.

Not doing it would be crazy, otherwise you might as well go home. You're going to be out of business anyways, so anything aside from that is crazy. So it seemed like a fairly logical thing.

And quite frankly, right now, as I'm describing it, you're probably thinking, Yeah, it's pretty sensible. Well, it worked, yeah, and so we take that out and went directly to production.

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 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. 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.

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, and 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.

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. And because each layer is trained one layer at a time, there's no reason why you can't make very, very deep neural networks. Okay, so now you just reason your way through, right?

Okay, so now I go back to 12 years ago and 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. Are you paying attention to the Imagenet competition every year leading up to this? Yeah, yeah. And the reason for that is because we were already working on computer vision at the time and we were trying to get Cuda to be a good computer vision system.

Or, most of the algorithms that were created for computer vision aren't a good fit for Cuda. And so we're sitting there trying to figure it out. All of a sudden, Alexnet shows up, and so that was incredibly intriguing. It's so effective that it makes you take a step back and ask yourself, Why is that happening?

So by the time that you reason your way through this, you go well. What are the kind of problems in a world where a universal function approximator can solve right? Well? We know that most of our algorithms start from principled sciences. You want to understand the causality, and from the causality, you create a simulation algorithm that allows us to scale well. For a lot of problems, we kind of don't care about the causality.

We just care about the predictability of it, like, do I really care for what reason you prefer this toothpaste? Over that, I don't really care the causality. I just want to know that this is the one you would have predicted. Do I really care that? The fundamental cause of somebody who buys a hot dog buys ketchup and mustard?

It doesn't really matter, it only matters that I can predict it. It applies to predicting movies, predicting music, It applies to predicting, quite frankly, weather. We understand 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? Yep, and so causality for a lot of problems in the world doesn't matter. We just want to emulate the system and predict the outcome, and it can be an.

And everything from obviously we're 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 it can be completely changed. And realizing that the rest of it just comes with, you know, do you have the courage to put your chips behind it?

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

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.

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 weather simulations, quantum chemistry, the list goes on, and so the number of applications of Cuda in research was very high. And so when the time came and we realized that deep learning could be really interesting. It was natural for us to go back to the researchers and find every single AI researcher on the planet. And say, How can we help you advance your work?

And that included Jan 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 Suskever there for the first time. 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, you know, blurry image of a cat, you know? But how far can it go? And so we believed in it, we believed that one. You could scale deep learning because obviously it's trained layer by layer, and you could make the 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. 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. That's where it all happened.

When OpenAI was founded in 2015, that was such an important moment. That's obvious today now. But at the time, I think most people, even people in tech, were like, What is this? Were you involved in it at all?

Because you were so connected to the researchers, to Ilya. Taking that talent out of Google and Facebook. But reseeding the research community and opening it up was such an important moment. Were you involved in it at all? I wasn't involved in the founding of it, but I knew a lot of the people there, and Elon, of course, I knew, and Peter Beal was there.

And Ilya was there, and we have some great employees today that were there in the beginning. And I knew that they needed this amazing computer that we were building. And we were building. The first version of the DGX, which 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 Open.Ai, and that was a fun day. But most of our success was aligned around in the beginning, just about helping the researchers get to the next level. I knew it wasn't very useful in its current state, but I also believed that in a few clicks, it could be really remarkable.

And that belief system came from the interactions with all these amazing researchers, and it came from just seeing the incremental progress. At first, the papers were coming out every three months, and then papers today are coming out every day, right? So you could just monitor the archive papers.

And I took an interest in learning about the progress of deep learning and to the best of my ability. Read these papers and you could just see the progress happening in real time, exponentially in real time, it even seems like within the industry.

Well, my first feeling about the language model was how.

And so my first impression of Bert was really how clever it was. And now the question is, how can you scale that? You know the first observation, 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?

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 few-step reasoning is somehow learnable from just reading things, I wouldn't be surprised. You know, 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? emergent abilities are consistent with intuitively from reasoning.

And so some of it could be predictable, but still, it's still amazing. The fact that it's sensible doesn't make it any less amazing, right? I could visualize literally the entire computer and all the modules in a self-driving car, and the fact that it's still keeping lanes makes me insanely.

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.

Exactly, yeah, yeah, when you put.

So, in our Nvidia part.

The most common way?

Researchers could suddenly get quick feedback from real user data, and, most importantly, they created a pragmatic, data-driven culture, since researchers were rewarded for driving actual product improvements. And over time, these experimentation tools gave Facebook and Google a huge edge because they really became a requirement for leading ML.

They've built a best-in-class experimentation, feature flagging and product analytics platform that's available to anyone. And surprise, surprise. A ton of AI companies are now using StatsIg to improve and deploy their models, including Anthropic. Yep, so whether you're building with AI or not.

We have some questions we want to ask you, some are cultural about Nvidia, but others are generalizable to company building broadly. And the first one that we wanted to ask is. We've heard that you have 40-plus direct reports and that this ORG chart works a lot differently than a traditional company ORG chart. Do you think there's something special about Nvidia that makes you able to have so many direct reports? Not worry about coddling or focusing on career growth of your executives. And you're like, No, you're just here to do your freaking best work and the most important thing in the world now go.

A Is that correct? and b Is there something special about Nvidia that enables that? I don't think it's.

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, 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. Have you always thought about the company this way, even from the earliest days?

Yeah, pretty much, yeah. And the reason for that is because your organization should be the architecture of the machinery of building the product, right? That's what a company is, and yet everybody's company looks exactly the same, but they all build different things. How does that make any sense?

Do you see what I'm saying? You know how you make fried chicken versus how you flip burgers versus how you make, you know, Chinese fried rice, it's 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 wire it up like a neural network. And the way that we say is that there's a phrase in the company called mission is the boss.

And so we figure out what is the mission, of what is the mission? And we go wire up the best skills and the best teams and best resources to achieve that mission. And it cuts across the entire organization in a way that doesn't make any sense. But it's looks like a little bit like a neural network, you know? And when you say mission, do you mean mission? like Nvidia's mission is, Yeah, okay.

So it's not like further accelerated computing, it's like we're shipping DGX cloud. Build Hopper, or somebody else's build a system for hopper, somebody is build Cuda for hopper, somebody's job is build Cudnn for Cuda for hopper.

Somebody's job is the mission, right, so, you know?

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 at exactly the same time, so nobody has more power than anybody else. Does it 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 earn 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 everybody knew. When we did our most recent episode in Video part three 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? and we said that for illustrative purposes.

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.

You're supposed to ask, What does it mean to me in my world? 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, what does this mean to what I'm trying to achieve? What does this mean to Nvidia?

In the age of our company and the capability of our company, you're supposed to ask yourself, What does it mean to you? And then, from that point, being informed by all these different things that we're learning, we're supposed to come up with our own strategies. What I just described is how I go about everything. You're supposed to be inspired and learn from everybody else.

The education's free. When somebody talks about a new product, you're supposed to go listen to it, you're not supposed to ignore it, you're supposed to go learn from it. It could be a competitor, it could be 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 you're supposed to come back and ask yourself, What does this mean to us? Yeah, you don't just want to imitate them, that's right, 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 social media feed recommenders to you deciding and the company deciding. We're going to go on a five-year all-in journey on data center. How did that happen?

Our journey to the data center happened, I would say, almost 17 years ago. I'm always being asked, what are the challenges that the company could see someday?

I've always felt that. The fact that Nvidia's technology is plugged into a computer and that computer has to sit next to you because it has to be connected to a monitor. That will limit our opportunity someday. Because there are only so many desktop PCs that plug a GPU into, there's only so many CRTs and, at the time, LCDs that we could possibly drive. The question is, wouldn't it be amazing if our computer doesn't have to be connected to the viewing device? That the separation of it made it possible for us to compute somewhere else. One of our engineers came and showed it to me one day. It was really capturing the frame buffer, encoding it into video and streaming it to a receiver device, separating computing from the viewing.

In many ways, that's cloud gaming. In fact, that was when we started GFN, we knew that GFN was going to be a journey that would take a long time. Because you're fighting all kinds of problems, including the speed of light, latency everywhere you look, that's right.

From listening to GFN, Geforce now, Geforce now, yeah, Geforce now, we've been working on Geforce now. That's your first cloud product.

That's right, look at GeForce now. It was Nvidia's first data center product. Our second data center product was remote Graphics, putting our GPUS in the world's enterprise data centers. Which then led us to our third product, which combined Cuda plus our GPU, which became a supercomputer, which then worked towards more and more and more. 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.

It was completely true. We're no longer limited by the physical constraints of the desktop PC sitting by your desk.

We're not limited by one GPU per person. It doesn't matter where it is anymore. That was really the great observation.

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

Even if I'm not exactly sure what and when, how do I position the company? To be near it, to be just standing near under the tree? And we can do a diving catch when the apple falls. You guys know what I'm saying, Yeah, you've got to be close enough to do the diving catch.

Rewind to 2015 and Open AI. If you hadn't been laying this groundwork in the data center, you wouldn't be powering Open AI right now. Yeah, but the idea that computing would be mostly done away from the viewing device, that the vast majority of computing would be done away from the computer itself. That insight was good. In fact, cloud computing, everything about today's computing, is about separation of that. 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.

My point is, if you could remove the obstacles everywhere else, then speed of light should be perfectly fine. You could build data centers as large as you like, and you could do amazing things. And this little, tiny device that we use as a computer, or your TV, as a computer, whatever computer, they can all instantly become amazing. And so that insight 15 years ago was a good one. So speaking of the speed of light Infiniband.

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? Well? There were several reasons for that first.

And so those characteristics are associated with Mellanox, not Nvidia, and so the day that I concluded that really, Nvidia wants to build computers of the future. And computers of the future are going to be data centers embodied in data centers, and we want to be data center oriented company. Then we really need to get into networking, and so that was one. The second thing is observation that, whereas cloud computing started in hyperscale, which is about taking commodity components a lot of users and virtualizing many users on top of one computer. AI is really about distributed computing, where one job, one training job, is orchestrated across millions of processors.

And so it's the inverse of hyperscale almost, and the way that you design a hyperscale computer. With off-the-shelf commodity. Ethernet, which is just fine for hadoop, it's just fine for search queries.

The Israel team is world-class. We have some 3,200 people there now, and it was one of the best strategic decisions I ever made. When we were researching, particularly part three of our Nvidia series, we talked to a lot of people and many people told us. The Mellanox acquisition is one of, if not the best of all time by any technology company. I think so, too, yeah, and it's so disconnected from the work that we normally do.

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.

You want to position yourself close to the opportunities, 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. What you just?

Crusoe has been ahead of the curve and is among the first cloud providers to offer Nvidia's H100s at scale. They have a very straightforward strategy. Create the best AI cloud solution for customers using the very best GPU hardware on the market that customers ask for, like Nvidia. And invest heavily in an optimized.

Ultimately, this results.

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. Your company's likely to die just because people don't actually care as much as you do about what they're building. In the later days, you actually have to be very thoughtful about competitive strategy. And I'm curious, what would be your advice? To companies that have product market fit that are starting to grow? They're in interesting growing markets, where should they look for competition?

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?

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 that doesn't change the fact that someday every car will be a software-defined car. And I think, you know, 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 the market emerges, it's very likely there aren't that many competitors shaped that way. And so we were early in PC gaming, and today Nvidia is very large in PC gaming. We reimagined what a design WorkStation would be like, and today just 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 in 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. Try to do that about a decade in advance, and so we spent about a decade in $0 billion markets.

And today, I spent a lot of time on Omniverse, and Omniverse is a classic example of a $0 billion business. There's like, 40 customers now, Amazon, BMW. Yeah, no, it's cool, it's cool.

And that network is essentially your moat, and so 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. That you're not the only company that enjoys it, but you're enjoying it with a whole bunch of other people, including me.

I'm so glad you brought this up because I wanted to ask you. In my mind, at least, 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 got to start as a technology person. When did you think about making that transition to being a platform?

Like your first graphics cards were technology, there was no Cuda, there was no platform. Yeah, what you observed is not wrong, however, inside our company, we were always a platform company.

And the reason for that is because from the very first day of our company, we had this architecture called UDA. It's the UDA of CUDa. CUDA is compute Unified device architecture. 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. 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 everyone. So was the original business plan to like...?

And DirectX was an API that made it possible for operating system to directly connect hardware, yeah, but DirectX didn't exist. When you started Nvidia, right? And that's what made your strategy run for the first couple of years, in 1993, we had DirectNvidia.

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

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, well, you were competing against Microsoft in the 90s.

I mean, that's.

Yeah.

Yeah, and so that's the Nvidia strategy. You were so militant, and I think from our research, it really was you being militant that every Nvidia chip will run. Cuda, Yeah.

If you're a computing platform, everything's got to be compatible. We are the only accelerator on the planet where every single accelerator is 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 you know? NV30 and NV35 and NV39 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.

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. Yeah, in fact, UDA goes all the way back to all of our chips today. For the record, I didn't help any of the founding CEOs that are listening. I got to tell you while you were asking that question, what lessons would I impart?

I don't know, I mean, the characteristics of successful companies and successful CEOs, I think, are fairly well described. 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 get 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, but you could do all the right smart things and still fail.

You could do a whole bunch of dumb things and I did many of them and still succeed, so obviously that's not exactly right. I think skills are 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 be one of the agents to help you succeed.

It's not enough.

I actually thought that you're starting with Revo 128 was spot on Revo 128. As I mentioned, the number of smart decisions we made, which are smart to this day, how we design chips is exactly the same to this day. Because, gosh, 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? yeah.

And so Revo 128 made a lot of great decisions in 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.

Every time that's right.

And the Nvidia philosophy was that although general purpose computing is a fabulous thing, it's going to enable software and it and everything. We felt that there were applications that wouldn't be possible or 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, the performance of Doom was really hard to achieve, even with accelerators at the time. If you didn't filter, if you didn't have to do bilinear filtering, it did a pretty good job. The problem with Doom, though, was you needed Carmack to program it. Yeah, you needed Carmack to program it.

Exactly, it was a genius piece of code, but nonetheless software renders did a really good job. And if he hadn't decided to go to OpenGL and accelerate for Quake, frankly, what would be the killer app that put us here? And so Carmack and Sweeney, both between Unreal and Quake, created the first two killer applications for consumer 3D.

And so I owe them a great deal. I want to come back real quick to you know, you said you told these stories and you're like, Well, I don't know what founders can take from 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.

Not Amazon. Well, I guess, with AWS, that's how AWS started, so I think that actually is a lesson to your point of like. That won't guarantee success by any means, right? But that'll get you hanging around a tree if the apple falls.

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

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.

When to apply human in the loop? When is it okay for a human not to be in the loop? How do you get to a point where?

Increasingly, human doesn't have to be in the loop, but human largely in the loop. Yeah, in the case of information safety, obviously, bias, false information and appreciating the rights of artists and creators. That whole area deserves a lot of attention and you've seen some of the work that we've done. Instead of scraping the internet, we partnered with Getty and Shutterstock to create a commercially fair way of applying artificial intelligence to AI.

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 test the model, validate the model before we release it on the wild again. So human is in the loop. There are a lot of different industries that have already demonstrated how to build systems that are safe and good for humanity. And obviously, the way autopilot works for a plane and two-pilot system, and air traffic control. And redundancy and diversity. And all of the basic philosophies of designing safe systems apply as well in self-driving cars. And so on, and so forth.

And so I think there's a lot of models of creating safe AI, and I think we need to apply them with respect to automation. My feeling is that, and we'll see. But it is more likely that AI is going to create more jobs in the near term. The question is, what's the definition of near term? And the reason for that is the first thing that happens with productivity is prosperity and prosperity. When the companies get more successful, they hire more people because they want to expand into more areas.

And so the question is, if you think about a company and say, okay, if we improve the productivity, 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, there's drug discovery, there are more ideas in transportation, there are more ideas in retail, there are more ideas in entertainment. That, there's more ideas in technology, so long as we believe that there are more ideas. The prosperity of the industry, which comes from improved productivity, results in hiring more people, more ideas.

Now you go back in history, we can fairly say that today's industry is larger than the world's industries 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 begat from productivity improvements. But my sense is that it's likely to generate jobs now. Obviously, net generation of jobs doesn't guarantee that any one human doesn't get fired.

Okay, I mean, that's obviously true, and it's more likely that someone will lose a job to someone else. Some other human that uses an AI, you know, and not likely to an AI, but to some other human that uses an AI. And so I think the first thing that everybody should do is learn how to use AI. So that they can augment their own productivity. And every company should augment their own productivity to be more productive, so that they can have more prosperity, hire more people.

And so I think jobs will change. my guess is that we'll actually have higher employment, we'll create more jobs, 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, this is appropriate. There's a version of something we talk about a lot on Acquired.

We call it the Moritz corollary to Moore's law, after Mike Moritz, from Sequoia Sequoia was the first investor in our company. 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? I can't, 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. And AI, your argument is basically, AI will do the same thing. Exactly, I just gave you exactly the same example that, in fact, productivity doesn't result in us doing less.

Productivity usually results in us doing more.

Everything we do will be easier, but we'll end up doing more. Yeah, because we have infinite ambition, the world has infinite ambition. And so if a company is more profitable, they tend to hire more people to do more.

Yeah, that's true. Technology is a lever.

So finally, before I gave up, as a last resort, I asked an AI chat bot, specifically Bard, to provide me a list and cite the sources of Jensen's favorite business books. And miraculously, it worked Bard found books that Jensen had called out in public forums over the past several decades. So, if you click the link in the show notes or go to Blinkist.com Slash Jensen, you can get the Blinks of all five of those books, plus a few more that Jensen's.

These books offer the mental models to adapt to a rapidly changing technology environment.

And seriously awesome, go take them up on it. We have a few lightning round questions we want to ask you, and then we have a very fun. I can't think that fast.

Okay, all right, we'll open with an easy.

I watch the TV show Okay, favorite sci-fi TV series?

Star Trek's my favorite, yeah, star.

You guys might get it. 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. Thank God you didn't, but nonetheless, it wasn't my fault.

It wasn't the Supra's fault, but it's a remarkable the one time when it wasn't the Supra's fault. 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. Nice, yeah, great car.

Thanks. Using Nvidia technology. Yeah, we're in the central computer, sweet.

I know we already talked a little bit about.

I really enjoyed Andy Grove's books, they're all really.

Grumpy but endearing. And what he?

Good job, Don. He mails it to me and I hope I've kept them. But anyways, you could tell he's a real sweetheart.

But he cares about the companies, he's a special character. Yeah, he's incredible.

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, plenty of time in the day, plenty of time to do anything to achieve things.

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 Dave are waiting. We got time. Yeah, exactly, didn't stop this from being a great job.

No, but you have to prioritize your time really carefully and don't let outlook determine that. Love that. What are you afraid of? If anything, I'm afraid of the same things today that I was 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. What point did you realize that you weren't going to have another job? that this was it? I don't change jobs.

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.

I've got great friends that 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 it went. The revolutionary impact it had on chip design and system design and computer design, in my estimation. One of the most important companies that ever came to Silicon Valley and changed everything about how computers were made.

It put me in the epicenter of some of the most important events in computer industry. It led me to meeting Chris and Curtis and Andy Bechtolsheim and John Rubenstein, and some of the most important people in the world. And Ed Frank that I was with the other day, I mean, the list goes on.

LSI logic was really important to me, and I would still be there. who knows what LSI logic would have become if I were still there, right? And so that's kind of how my mind works. Powering the AI of the world. Yeah, exactly.

I might be doing the same thing I'm doing today. I got the sense from remembering back to part one of our series on Nvidia, but until I'm fired, this is my last job. I got the sense that LSI logic might have also changed your perspective and philosophy about computing, too. The sense we got from the research was that when right out of school, when you first went to AMD, first, right?

Yeah, 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 got to do everything. And that LSI logic changed you. What LSI logic did was realize that you can express transistors and logical gates and chip functionality in high level languages. That by raising the level of abstraction and what is now called high-level design. It was coined by Harvey Jones, who's on Nvidia's board.

And I met him way back in the early days of Synopsys. 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 be a lot more productive. That logic was so sensible to me, and I was 21 years old at the time, and I wanted to pursue that vision.

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

I thought that was the best thing that ever happened to the industry and I was really happy to be part of it. And I was at ground zero. And so I saw one industry change revolutionize another industry. 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 really, really terrific. I was at the right place at the right time to see all that. That's super cool, yeah.

And it sounded like the CEO of LSI Logic put a good word in for you with Don Valentine. I didn't know how to write a business plan, which, it turns out is not actually important. No, it turns out that making a financial forecast that nobody knows is going to be right or wrong turns out not to be that important.

But the important things that a business plan probably could have teased out, I think that the 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 other skills that are involved in. Just, you know, product and positioning and pricing and go to market, and you know all that kind of stuff. But those are skills and you can learn those things easily. The stuff that is really, really hard is the essence of what I described, and I did that.

Okay, but I had no idea how to write a business plan. And I was fortunate that Wolf Corrigan was so pleased with me and the work that I did. When I was at Ellison 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. I was set up for success from that moment and got us on the ground. As long as he didn't lose the money.

I think Sequoia did okay.

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.

Yeah, 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. Yeah, 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 haven't killed you yet. No, 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. Well, in that vein, 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? I wouldn't do it.

I know, and the reason for that is really quite simple ignoring the company that we would start. First of all, I'm not exactly sure the reason why I wouldn't do it, and it goes back to why it's so hard. Is building a company? And building a video turned out to have a million times harder than I expected it to be.

Any of us expected it to be? And at that time, if we realize the pain and suffering. And just how vulnerable you, you're going to feel, and the challenges that you're going to endure, the embarrassment and the shame. And, you know, the list of all the things that that go wrong. I don't think anybody would start a company, nobody in their right mind would do it, and I think that that's kind of the 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?

I don't get the sense, though, that you're planning to retire anytime soon, though. No, no, I'm still young. You couldn't choose to say, like, whoa, this is too hard.

That's really the trick of an entrepreneur. You have to get yourself to believe that it's not that hard, because it's way harder than you think. And so if I go taking all of my knowledge now and I go back and I said, I'm going to endure that whole journey again. I think it's too much, it is just too much.

You need the unwavering support of people around you. Jim Gaethers and the Tench Coxes, and 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 need that, not kind of need that, you need that. And I'm pretty sure that almost every successful company and entrepreneurs that have gone through some difficult challenges, they had that support system around them. I can only imagine how meaningful that is.

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? Yeah, not normal, you know? You went through two, if not three 80 plus drawdowns in the public markets. To have investors who've stuck with you from day one through that must be just like so much support.

Yeah, yeah, it is incredible and you hate that any of that stuff happened and most of it is out of your control. But 80 fall, it's an extraordinary thing, no matter how you look at it. And I forget exactly. But I mean, we traded down at about a couple of two, three billion dollars in market value. For a while because of the decision we made and going into Cuda and all that work. And your belief system has to be really, really strong.

You have to really, really believe it and really, really want it, otherwise it's just too much to endure. I mean, because everybody's questioning you and employees aren't questioning you, but employees have questions, people outside are questioning you, and it's a little embarrassing.

It's like, when your stock price gets hit, it's embarrassing, no matter how you think about it, and it's hard to explain. And so there's no good answers to any of that stuff. CEOs are human, and companies are built of humans, and these challenges are hard to endure. Ben had an appropriate comment on our most recent episode on You All, where we were talking about the current situation at Nvidia.

I think you said, for any other company, this would be a precarious spot to be in, but for Nvidia....

It is the case that no chip company can ever be so big, but if you're not a chip company, then why does that apply to you? And this is the extraordinary thing about technology right now, technology is a tool and it's only so large. What's unique about our current circumstance today is that we're in the manufacturing of intelligence.

We're in the manufacturing of work world that's AI, and the world of tasks doing work, productive generative AI work, generative, intelligent work. That market size is enormous, it's measured in trillions. One way to think about that is, if you built a chip for a car, how many cars are there? and how many chips would they consume?

That's one way to think about that. However, if you built a system that, whenever needed, assisted in the driving of the car, 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.

What would it be like if we were to magically conjure up a chauffeur for everybody who has a car? And how big is that market? And obviously, obviously that's a much, much larger market and so the technology industry. What we've discovered, what Nvidia has discovered, and what some others discovered, is that. By separating ourselves from being a chip company, but building on top of a chip, and you're now an AI company, the market opportunity has grown by probably a thousand times. Don't be surprised if technology companies become much larger in the future, because what you produce is something very different.

And that's the way to think about how large can your opportunity? how large can you be? That has everything to do with the size of the opportunity.

Our huge thank you to Blinkist, Statsig and Crusoe. All the links in the show notes are available to learn more and get the exclusive offers for the acquired community from each of them. You should check out ACQ2, which is available at any podcast player, as these main acquired episodes get longer and come out. You know, once a month instead of once every couple weeks, it's a little bit more of a rarity.

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