NVIDIA CEO Jensen Huang
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I will say, David, I would love to have in video's full production team every episode IT was nice, nice not having to worry about turning the cameras on and off and making sure that nothing bad happened myself where we were recording this yeah.

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

right? Red cameras for the home studio starting .

next episode 2。

What's 这位。 Busy, you wait, you wait, you who got? Easy, you busy, you busy, you see me down, say, state. Welcome to this episode of acquired .

the podcast about great technology companies and the stories and playbooks .

behind them. Bert, David.

and we are your hosts, listeners, just so we don't bury the lead. This episode was insanely cool for David and night.

yeah.

After researching and video for something like five hundred hours over the last two years, we flew down to invidia headquarters to sit down with jensen himself. And Jenny, of course, is the founder and CEO of the video, the company powering this whole A I explosion at the time of recording and video is worth one point one trillion dollars and is the sixth most valuable company in the entire world.

And right now is a crucial moment for the company. Expectations are set high amid 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 and videos insane prosperity continue for years to come? Is A I 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 Jenny takes us down memory lane with stories of how they went from graphic to the data center to A I, how they survived multiple in the your death experiences. He also has plenty of advice for founders, and he shared an emotional side to the founder journey toward the end of the episode.

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

turns out the protected .

ist actually knows more. Yes.

our listeners join this lack. There is incredible discussion of everything about this company, A I, the whole ecosystem and a bunch other episodes that we've done recently going on in there right now. So that is acquired dot F M, slash slack. We would love to and without further to do the show is not investigated advice that that that I may have investments in the companies we discuss in the show is for informational and entertainment purposes only on to Johnson.

So Johnson, this is acquired. So we want to start with story time. So we want to wind the clock all way back to, I believe that was one thousand and ninety seven.

You're getting ready to ship the riva one twenty eight, 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 running 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 side on seen with the rest of the company's money you're betting IT. All right here on the river, one twenty eight IT comes back.

And of the thirty two direct explained 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 other .

twenty four and that in OK 是 妹妹 不是。 Was that the plan all a lot? Like when did you realize I realized .

I didn't learn about IT until was too late? We should have implemented all three. But, but we built, we built.

And so we had to make the best of IT. That was really an extraordinary time. Remember, river one twenty was a military envy.

One in envy. Two were based on forward textual mapping, no triangles, but curves and IT text later the curbs. And because we were rendering higher level objects, we essentially avoided using xe buffers. And we thought that that was going to be a good running ing approach and turns out to have been completely the wrong answer. And so what rivo run twenty eight was was a reset of our company.

Now remember at the time that we started the company nineteen ninety three, we were the only consumer 3d graphics company ever created。 And we we were focused on transport ming, the P C, into an accelerator PC, because at the time windows was really a software rendered system. And so anyways, river one twenty eight was a reset of our company because by the time that we realized we had gone down the wrong road, microsoft already rolled out direct text.

IT was fundamentally incompatible with nvidia's architecture. Third, competitors have already shown up. Uh, even though we were the first company at the time that we were founded.

So the world was a completely different place. The question about what to do as a company strategy at that point, I would say that we made a whole bunch of wrong decisions. But on that day that mattered, we made a sequence of extraordinary good decisions.

And that time, ninety ninety seven was probably in videos best moment. And the reason for that was our backwell against the wall we were running on the time we're running on the money, if 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 love direct access.

Now here, when I going to fight IT, let's go figure out the way to build the best thing in the world. A for IT. And even one twenty eight is the world's first, a fully accelerated hardware accelerated pipeline for the ring 3d。 And so the transformed the projection.

Every single element all the way down to the frame buffer was completely hard. Works on reading 啊。 We implement A A texture cash.

We took the bus limit, different buffer limit, to as big as as h physics could afford a time. We made the biggest chip that anybody had ever imagine building. We used the fastest memories. Basically, if we build that trip, 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 competence will be willing to go if we built IT, right? We accelerated everything. We implement everything in direct text that we knew of, and we build that as larger as we possibly could. Then obviously, nobody can build something fast than that .

today in a way you can of to do that here in video, too. You are a consumer products company back then, right? There was an consumers who are going to .

have to pay the money to was a of market where people were because at the time that the P. C. Industry was still coming up and IT wasn't good enough, everybody was climbing ing for the next fastest thing.

And so if your performance was ten times higher this year, then what was available? There's a whole large market of enthusiast who who we believe we would have gone after IT. And we were absolutely right that the P.

C. Industry had a substantially large enthusiastic market that would buy the best of everything to this day is kind of remains true. And for certain segments of the market where the technology is never good enough, laxity graphics.

When we choose the right technology, three graphics is never good enough, and we call IT back that 3d gives us sustainable technology opportunity because it's never good enough。 And so your technology can keep getting Better. We chose that.

Uh, we also made the decision to use this technology called emulation. That was a company called echoes. And on the day that I called them, they were just shutting the company down because they had no customers. And I said, hey, luck, uh, i'll buy what you have in venti and um 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've found all the bugs because we did the software, then we take down the trip again. Well, we would have been other business already.

yeah. And so I mean.

caught up well, not to mention we would .

end out of business 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 trip, write the soft, were fixed the bug, take about the new trip. So and so was that methow asn't going to work? And so the question is, if we only had six months and you get the tape out just one time, then obviously you going to take about a perfect trip.

So, so, so I remember having conversation with our leaders, and they said a chance that how do you know going be perfect? As I know, it's gonna perfect because if it's not, be out of business. And so let's make a perfect. We get one shot. We essentially virtually prototyped the chip by buying this emulator and died in sober team, wrote our software, the entire stack, and ran on this amy later, and just SAT in the lab, waiting for windows .

to paint. You know, and I painted.

I actually think that was an hour per frame, something like that. And so we resisted there and watch a pain. And so on the day that we decided to tape out, I assume that the trip was perfect. And everything that that we could have test that we tested in advance, and everybody, this is gonna tape out, the trip is is going to be perfect. Well, if you're going to take out a trip and you know it's and what else would you do that? Actually, the good question, if you knew that you hit enter, you take that a trip and you knew was going to be perfect, and what else would you do while the answer obviously go to production .

and marketing blitz, yeah, yeah.

And just to pick everything, kick everything off because you've got a perfect trip and we got in our head that we have a perfect trip.

How much of this was you and how much this was like your confounders? The rest the company in the board, was everybody telling you you are crazy?

No, everybody was clear. We had no shot. Not doing that would be crazy because .

you're gonna out doesn't anyways.

So anything aside from that is crazy. So IT seemed like a fairly logical thing. And I Frankly right now and describing IT thinking pretty simple.

Well, IT worked. yeah. And so we take that out when directly the production.

So is the lesson for founders out there when you have conviction on something like the river one twenty eight or uh kuda go back the company on IT and this keeps working for you. So IT seems like your lesson learn from this is yes, keep pushing all the chips in because so far it's work every time .

you think about no when you push your chips and um I I know it's gonna notice we assume that we tape out a perfect trip. The reason why we taped on a perfect trip is because we emulated the whole trip. Before we tape IT out, we developed the entire software stack.

We ran Q, A on all the drivers and all the software. We ran all the games we had. We ran every V, G, A application we had.

And so when you push your chips and what you really doing is you when you bet the farm, you're saying i'm going to take everything in the future, all the risky things on our pull in in advance. And that is probably the lesson to this day, everything that we can prefet ch, everything in the future that we can simulate today. Uh, we prefect.

we talk about this, just talk about this cosa de episode. You want to push your chips in when you know it's gonna.

So every time we see you make about the company move, yeah, do you feel like that was the case cut?

Uh, yeah. In fact, before that was CUDA, there was A C, G, right? And so we were already playing with the concept of how do we create an abstraction layer above our chip that is expressible in a higher level language and higher level expression and and how can we use our G P U.

For, uh, things like city reconstruction, image processing. We were already done that path. And so there were some positive feedback and some intuitive positive feedback that that we think that the general purpose computing could be possible. And if you just looked at the pipeline of a programming, al sheer IT is a processor and is a highly parallel, is a massively thread and is the only processor in the world that does. And so there are a lot of character tic about program that would suggest that coulda has a great opportunity to succeed.

And that is true if there was a large market of machine learning practitioners who would eventually show up and want to do all this great scientific computing and accelerated computing.

But at the time when you were starting to invest, what is now something like ten thousand person years in building that platform? No, did you ever feel like a man? We might have invested ahead of the demand for machine learning since were like a decade before the whole world is realizing that.

I guess yes, I know you know, when we saw deep learning, when we saw alex net and realized its incredible effectiveness and computer vision, we had a good sense, if you will, to go back the first principles and ask, you know, what is IT about this thing that made IT so successful?

When a new software technology, a new algan, comes along and somehow leap frogs thirty years of computer vision work, you have to take a step back and ask yourself, but why? And fundamentally, is is scalable. And if it's scalable, what other problems can solve? And there were several observations that we made.

The first observation, of course, is that if you have a whole lot of example data, you could teach this function to make predictions. Well, what we basically done is discovered the universal functional approximate, or because the dimensionality could be as high as you wanted to be in because each layer is trained one layer at a time. There's no reason why you can make very, very deep, uh, neural networks.

okay. So now you just reason your way through, right? okay. So now go back to twelve years ago. You can just imagine reasoning. I'm going through my head that we've discovered a universal function approach matter. In fact, we might have discovered with a couple more technologies, universal computer and you contain .

attention to the image competition. Yeah yeah. Are leading up to this. yeah.

The reason for that is because we are 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 creative for computer vision aren't a good fit for CUDA. So they're trying to figure IT out of Alice and that shows up. And so that was incredibly it's so effective that that makes you take us that back and ask yourself wise up happening.

So by time that you reason your way through this, you go, well, what are the kind of problems in a world where universal function approximate? All right? Well, we know that most of our algorithm s start from principled sciences OK you want to understand the casuality, and from the casuality you create A A simulation algorithm that allows the scale.

Well, for a lot of problems, we kind of don't care about the cause cause. We just care about the predictability of IT. Do I really care for what reason you prefer this took taste over that? I don't really care the casuality.

I just want to know that this is the one you would have protective. Do I really care that the fundamental cause of somebody who buys a high dog buys catch up and muster IT doesn't really matter IT only matters that I can predict. That he had applies to predicting movies, predicting music.

IT applies to predicting, quite Frankly, whether we understand their more 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 work. Sweet night is right? Yeah, it's a casuality for a lot of problems in the world doesn't matter. We just want to Emily the system and predict the .

outcome that can be an incredibly lucrative markets if you can predict what the next best performing feed item, just something into a social media feed.

Turns out that's a examples. Catch up exit movies, you realize.

you realize he hang I D A universal functional, approx. Matter, 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 used to talk about commerce other way designs.

And so you realize that maybe this could affect the very large part of the world's industries, almost every piece of software the world would would eventually be programmed this way. And if that's the case, then how you build a computer and how you build a trip, in fact, can be completely changed. And realizing that the rest of is just comes with, you know have the current to put your chips behind IT.

So that's where we are today um and that's where in video is today. But i'm curious and you know this couple years after alex, and this is when when and I were getting into the technology industry in the venture industry ourselves.

I started at microsoft twelve, yes. So right after alex nap, but before anyone was talking about machine learning and even the means tree engineering community.

there were those couple gears there, where, to a lot of the rest of the world, these looked like science projects. You, the technology companies here in silicon, particularly the social media companies, they were just realizing a huge economic value of goods. And obvious ly, that LED a lots of things, including open a eye a couple years later. Yeah, but during those couple years, when you saw us that huge economic value, unlike here in silicon valley, how are you feeling doing those times?

The first thought was, of course, reasoning about a how we we should change our computing stack.

The second thought is, where can we find earliest possibilities of abuse? If we were to go build this computer, what would people use IT to do? And we were fortunate that working with the worlds universities and researchers was was innate in our company because we were already working on quota and could, as early adopters were, researchers because we democratize supercomputing, you know, could that is not just use as o for A I could have used for almost all fields of science, everything from molecular dynamics to imaging, city construction to, uh uh, seismic processing to, you know, whether simulations, quantum chemistry, the list goes on, right? And the number of applications of quota in research was very high.

And so when the time came and we realized that deep learning could be really interesting, uh, IT was natural for us to go back to the researchers and find every single A I researcher on the planet and say, how can we help you advance your work and that included the milk on and Andrew angang and jeff hinton. And that's how I met all these people. And I used to go to all the eye conferences.

Ces, and that's where, you know, I met ellia succored there for the first time. Ah and so was really about at that point, where are the systems that we can build, the software stack we can build to help you be more successful to advanced the research because at the time I looked like a toy, but we had confidence that even gained the first time I met good fellowship. The again was was like thirty two by thirty two and IT was just a blurry image of a cat.

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

And we believe that if you make that larger and larger and we get Better and Better, yeah kind of sensible. And I think the discussions in the engagement with the researchers was the exact positive feedback system that we needed. I would go back to research .

that was that's where IT all happened when open a eye was founded in. Fifi was an important moment that's obvious today. Now but at the time, I think most people, even people in tech for like, what is this? Were you involved in IT at all? Like, you know, because you were so connected to the researchers, to a taking that talent out of google, facebook, to be blind, receding the research community. Yeah, 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 uh in land, of course I knew and a Peter bill was there and ella was there. And we have we have some great employees today that were there in the beginning, and I knew that they needed the amazing computer that we were building. And we were building the first version of the D.

G. X, which today, when you see a hoper. It's seven pounds, thirty five thousand parts, ten thousand ams. But D J X the first version that we built was a used internally and I delivered the first went to open the eyes that was a friday.

But most of our success was aligned around um in the beginning of 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 collects IT could be really remarkable. And that belief system came from the interactions with all these amazing researchers, and they came from in 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 and to the bus, my ability read these papers and you could just see the progress happening, you know, in real time.

exponentially in real time. IT even seems like within the industry, from some researchers we spoke with, IT seemed like no one predicted how useful language models would become when you just increase the size of the models. They thought, oh, there has to be some algorithmic change that needs to happen.

But once you cross that ten billion premier mark, and certainly once you cross the hundred billion, they just magically got much more accurate, much more useful, much more life. Like, were you shocked by that? The first time you saw a truly large language model? And do you remember that feeling?

Well, my first feeling about the language model was how clever IT was to just mask out words and and make a predict the next word. It's a self supervised learning at its best. We have all this text know.

I know what the answer is. I'll just make you guess that. And so my first impression of bird was really how clever was.

And now the question is, how can you scale that? You know, the first observation, almost everything is interesting. And then and then try to understand intuitively what IT works.

And then the next step of from first principles, how was you extrapolate that? yes. And so obviously, we knew that bird was gone to be a lot larger.

Now one of the things about these language models is it's encoding information. Isn't that right? Is compressing information. And so within the world, languages and text, there's a fair amount reasoning that's encoded in IT. And we described a lot of reasoning things.

And and so if you were to say that, uh, a few step reasoning is somehow learned from just reading things, that would would be surprised you know, for a lot of us, uh, we get our common sense and we get our our reasoning ability by reading. And so I went a mine, leon del, also learning reasoning capability. That and from reasoning ability, you have capabilities emerging abilities are consistent with intuitively from reasoning.

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

that from my first Operating systems class in college when I finally figured out all the way from programing language to the electrical engineering classes, bridged in the middle by that OS class, like, oh, I think I understand how the vanny and computer works suit to nuts.

And it's still a miracle. No, exactly. yeah. When you put IT all together is still miracle.

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We have some questions we want to ask you. Uh some are cultural about NVIDIA, but um others are generalizable to company building broadly. And the first one that we wanted to ask is, uh, we've heard that you have forty plus direct reports and that this orm chart IT works a lot differently than a traditional company or 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 friend best work and the most important thing in the world now go a, is that correct? And b, is there something special about the video enables that I .

don't think is something special. Video, I think that we had the courage to build a system like this. And video is not build like a military is not built like like the armed forces where you have in generals and coronals you, you we just we're not set up like that.

We're not set up in a command and control and information distribution system from the top down, we really built much more like a computing stack and a computing stack. The lower slayer is our architecture and then this our chip and then there's our software. And and on top of that, they're all these different modules and each one of these layers of modules are people. And so the architecture of the company to me is a computer with a computing stack with um uh people managing different part of the system and who reports to whom your titles is not related to anywhere you are in the stack just happens to be who is the best that running that module on that function on that layer IT is in charge and that person is the pilot in command. And so that's one characteristic you always .

thought about the company in this way, even from the earliest days.

Yeah, prety much. yeah. And the reason for that is because your orange zone should be the architecture of the machinery of building the product, right? Yeah, that's what the company is, yes. And yet everybody's company looking exactly the same, but they all feel different things.

How do I make any sense? Do you say, yeah you know, how you make for chicken versus how you burger versus how you make you know chinese for 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 the most companies IT all kind of looks like this.

And then do you have one group that's for a and you have another for another business. You have another for another business, and they are all kind of supposed autonomous. And so none of that stuff makes any sense to me.

That depends on what is that, 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 worried up like a neural network. And the way that we say is there is a phrase and the company commission is the boss.

And so we figure out what is the mission of what is the mission, and we go wire up the best skills and the best teams and best resources to achieve that mission. And IT cuts across the entire organization in a way that doesn't make any sense. But IT looks like a little bit .

like a neo network is in the mission, like vid mission. Ah okay. So it's not like further accelerated computing. It's like we're shopping D G X cloud.

Build harper or somebody else is build a system for harper. Somebody is build could up for harper. Somebody job is build cod N N for good up for harper. Somebody y's job is the mission right? Is so you know, your mission is to do something.

What are the trade ffs, associated with that versus the traditional structure?

The downside is the pressure on the leaders is fairly high. And the reason for that is because in a command and control system, the person who you report to have 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, is usually at a team level.

So for example, just now was I was in our robotics meeting, and we're talking about certain things, and we're making some decisions. And the new college grads in room there, three vice president in the room there, two east thousand a room. And at the moment that we decided together, we reason through some stuff.

We made a decision. Everybody heard this exactly the same time. So nobody has more power than anybody else doesn't makes sense. The new college learn that exactly the same time as the e staff. And so the executive staff and the leaders that work for me and I solve, you earn the right to have your job based on your ability, reason through problems and helping other people succeed. And and it's not because you have some privilege information that I knew the answer was three point seven and only I knew, you know everybody knew when .

we did our most recent episode in video, part three we just released, we started to this thought exercise, especially over the last couple years. Your product shipping cycle has been very impressive, especially given the level of technology that you are working with and the difficulties this all we sort of sadly, could you imagine apple shipping two iphones a year?

And we said that for illustrate .

illust purposes because .

a very tech company is in two flagship products or flagship products.

toys per year. Know two wwdc.

yeah.

there seems to be something in believe you can't really imagine that was that happens here. Are there other companies, either current or historically, that you look up to admire maybe took some of this inspiration from?

In the last thirty years, i've read my fair share of business books and as in everything you read you you're supposed to you're supposed to first more 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 show their experiences and and you you're supposed to ask, you know, what does that mean to me in my world and what does that mean to me in the context of what i'm going through? What does this mean to mean in the environment that, I mean, what does this mean to mean what i'm trying to achieve? And what does this mean to a video in the age of our company and the capability of our company? And so you always 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 strategy.

You know what I just described, this kind of how I go about everything supposed be inspired and learn from every everybody else and the education free. You know, when somebody talks about the new product, you are supposed to listen to, you support to ignore that. You are supposed to learn from IT.

And IT could be a competitor. IT could be, uh, a Jason industry. IT could be nothing to do with us. Now the more where we learn from h what's happening on the world, the Better. But then you you supposed to come back and ask yourself, you know.

what does this mean to us? That's right. Yeah, yeah. I love the tip of learning, but not imitating and learning from a wider sources. There's this sort of unbelievable third element, I think, to winning video has become today, and that's the data center. It's certainly not obviously, I can't reason from alex net and you're engagement with the research matters, you deciding and the company deciding we're going to go to five year all in journey on the data center. How did that happen?

Our journey to the data center happened always, say, almost seventeen years ago. I'm always being asked, I mean, what what are the chAllenges at the company could see some day. And and i'd always felt that the fact that in videos, s technology is plugged into a computer and that computer has to sit next to you because he has to be connected to a monitor that will limit our opportunities someday because there are only so many does top pcs that plug a GPU into, and there is only so many crs and and the time LCD that we could possibly drive. So the question is, we need to be amazing if our computer doesn't have to be connected to the viewing device that that the separation of IT are made a possible for us to compute somewhere else. And one of our engineers came a showed to me one day and IT was really capturing the frame buffer encoding IT into video and streaming IT um to A A receiver device .

separating computing from .

the in fact in fact, was when we started G F N, we knew that G F N was going to be um a journey that would take a long time because you you're fighting your fighting all kinds of problems in including the speed of light .

and late and see everywhere you look.

That's right.

Force now, yeah g force now.

And we ve been .

working on right? And our second data center product was remote graphics, putting our G P S in in the worlds enterprise data centers, which then let us to our third product, which combined CUDA plus R G P U, which became a super computer, which then work towards, you know, more, more and more. And the reason why so poor n is because the this connection between a war and videos, uh, computing is done versus where is enjoyed.

If you can separate that, your market opportunity explodes. And IT was completely true and so were no longer limited by the physical constraints of the desktop PC sitting by your desk, um you know and and we're not limited by one G P per person. And and so h IT doesn't matter where IT does anymore. And so that was really a great observation.

It's a good reminder, the data center segment of evidence business to me has become anonyme with how was A I going and that's a false equivalence. And it's interesting that you were only this ready to sort of explode in A I in the data center because you had three plus previous products where you learn how to build data set computers even though those markets weren't these like gigg antic world changing technology shifts the way the way I is. That's how you learn. Yeah.

that's right. You want to pave the way to future opportunities. You can't wait until the opportunities is sitting in front of you for you to reach of word. And so have to anticipate in our job is 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? We can do a diving catch when the apple falls, he is, say, but you gotta be close enough to do the diving catch .

one to twenty fifteen. And OpenAI, if you hadn't been laying the ground work in the data center, yeah, you wouldn't be powering over.

right? yeah. But the idea that computing will be mostly done away from the viewing device, that the vast majority 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 in a data center, we can overcome late and see problem. Meaning you're not going to be the light speed, light end to end is only twenty, twenty nine seconds or something like that is not that .

long from a data center to here.

any other planet. And so literally .

across the plant.

Yeah right. So if you could solve that problem approximately, something like get the number, but it's not that long. And so my point is if you could remove the obstacles everywhere else, then speed lives should be, you know, perfectly finding.

And you could build data centers as as large alike. And you can do the thing that, and this is little, tiny device that we use as a computer, or you, your T, V, as a computer, whatever your computer, they all, they can all instantly become amazing. And so that insight, you know, fifteen years ago, was a good one.

So speaking of the speed of light in fini band, like David, like becking me to go .

here the same.

you totally saw that in fini band would be way more useful, way sooner than anyone else. Real acquiring mellon ox, I think you uniquely saw that this was required to train large language models, and you were super aggressive in acquiring that company. Why did you see that when no one else saw that?

Well, uh, there are so many reasons for that. First um if you want to be a data center company and building the processing trippers in the way to do IT, a data center is distinguished from a test tok computer versus a cell phone not by the processor in IT that's our computer in the data center uses the same C P, U, uses the same G P S, apparently right very, very close.

And so it's not the chip is not the processing ship, but describes IT, but it's the networking of IT is the infrastructure of IT the you know how the the the computing is distributed, how security provided, how networking is done in our so on so forth. And so so those characteristics are associated with not in video. And so the day that I concluded that really in video wants to be you build computers of the future, and computers of the future will be data centres and boy, the data centers, then we then, and we want to be data and orient company, then, then we really need to get internetworking.

And so that was one. The second thing is observation that were as cloud computing started in hyper scale, which is about taking commodity components a lot of users and virtualization many users uh on top of one computer A I is really about distributed computing where one job one training job um is orchestrated across millions of processors and so is the inverse of hyper scale, almost in the way that you design a hyper scale computer with with off the shelf commode. Either net, which is just fine for hadi, is just fine for search queries, is just fine for all of those things.

When you're shouting a model, now you're in a model cross, right? And so now that observation says the type of networking you want to do is not exactly ether net. And the way that we do not working for supercomputing is really quite ideal.

And so the combination of those two ideas um convinced me that that melanie is absolutely the right the right company because they were there the worlds needing high performance working company and and we worked with them in so many different areas in a high performance computing already plus I I really like the people. Um the the the israel teams is work class. We have some three thousand two hundred people they are now. And IT was one of the best strategic decisions I ever .

made when we were researching, particularly part three of our invidia series, we talk to to a lot of people and many people told us the mellon ax acquisition is one of, if not the best of all time, any team? Yes, I think so too.

Yeah, and it's so disconnected from the work that we Normally do. IT was surprising to everybody.

but frame this way you you were standing near where the action was yeah so you could figure out as soon as that apple sort of becomes available to purchase, like, oh, l EMS are about to blow up, i'm going to need that. Everyone's gonna that. I think I know that before .

anyone else does, you want to position yourself near opportunities. You don't have to be that perfect, you know? Yes, you want to position yourself near the tree. And even if you don't catch the the apple before IT hits the ground, so long as you don't first want to pick IT up. You want to position this is close to the opportunities now. And so that kind of a lot of my work is positioning the company near opportunities, having the uh the the company having the skills to to a monetize each one of the steps along the way so that we can be sustainable.

What you just said reminds me of a great a afra m from a buffet monger, which is it's Better to be approximately right than exactly wrong.

Yeah, there you go. Ah yeah, that's a good ones, a good one. yes. Yeah.

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away from n video, if you're OK with that and ask you some questions. Since we have a lot of founders that listen to this show ort of advice for company building.

The first one is when you're starting to start up in the early liest days, your biggest competitor is ah you don't make anything people want like your company is likely to die just because people don't actually care as much as you do about what 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 half product market fit that are starting to grow? They're in interesting growing markets. Um where should they look for competition and how should they handle IT?

All there all kinds of ways to think about competition. We prefer to position ourselves in way that serves a need that usually has an emerged.

I've heard that you are others .

in the video. I think the there is no market yet, but we believe there will be one. And and usually when your position there, everybody everyone is trying to figure out why are you here.

right?

Because when we first got into automotive, because we believe that in the future, the cars can be largely software. And if this can be largely software, um a really incredible computer is necessary. And so so when we positioned ourselves there, most people and I I remember of the C T S told me, you know, White cars cannot tolerate the blue screens of death.

I don't think hey, but you can tolerate that but doesn't change the fact some day every car will be a song for define car. And I I think you know fifteen years later, we were largely right. And so often times there's nonconsumption and we like to navigate our company there.

And by doing that um by the time that you are the market emerges is very is very likely they are not that many competitors shape that way. And so we were early in P C gaming. And today h in this is very large and PC gaming. Uh we a reimagined what a what A A design war station would be like.

And today, by reward station on the planet users and video technology, we reimagine um how supercomputing out to be done and who should who should benefit from supercomputing that we would democractic ze and look today and video and actually computing is quite large region we reimagine health or would be done, and today is commercial learning and how computing we'd be done, we call A A I. And so we reimagine these kind of things, uh, try to try to do that about a decade in advance. And so we spent about a decade in zero billion on markets and today spend a lot time on on diverse. And on diverse is a, you know classic example of a little lion business.

And there's like forty customers.

Now IT was on bmw. Yes, no. Cool, cool. So let's say you do .

get .

this great ten year lead, but then other people figured out you would got people npp at your heels. What are some structural things that someone whose building a business can do to sort of stay ahead and you can just keep a pedal to the medal and say that we're going to out work and we're going to be smarter? And like that works to some extent. Those are tactics. What strategic ics can you do to sort of make sure that you can maintain that lead?

Often times if you created the market, you ended up having, you know what what people describe as motes. Because if you build your product rate and is enabled LED h an entire ecosystem around you to help serve that in market, you've essentially created a platform. Sometimes it's a it's a product base platform, sometimes as a service base platform, something a technology based platform.

But if you were you were early there and you you you are mindful about helping the ecosystem um succeeded with you. You ended up having this network of networks and all these developers and all these customers were who are built around you yeah and that network is essentially your mode. And so you know I don't love thinking about IT in the context table mode um and the reason for that is because you 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 that, but you're enjoying IT with a whole bunch about the people.

including me. I'm so good, brother. I want to ask you in my mind at least and sounds like in your two in video 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 are a technology company and not a platform company. Every example I can think of of the company to try to start this platform company fails. You got to start as a technology first. When did you think about making the transition to A T, T, like your first graphics cards technology there? No platform.

What you observe 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 have this architecture called U D.

A, is the wu da of CUDA.

And the reason for that is what we've done, what we were essential ally did in the beginning, even though we've a one twenty only had computer graphics, the architecture described accelerators of all kinds, and we would take that architecture and developers would program to IT. In fact, the video's first strategy, business strategy, was we were going to be a game console inside the PC. And a game console needs developers, which is the reason why in video, a long time ago, one of our first employees was a developer relations person. And so is the reason why we knew all the game developers and all the three developers.

and we knew we. So was the original .

business .

plan .

to like architecture .

was called direct envy, direct video. yeah. And direct x was an A P. I. That made a possible for Operating system to directly the hard, yes.

but the the, the we started in video that just stand. Nineteen ninety three.

we had to revision, and which in nineteen ninety five became you. Well, direct tex came out.

So this is an important lesson.

We were always, always a developer company.

The initial attempt was we will get the developers to build on direct envy and then they'll build for our chips and then we'll have a platform. And yeah, and I played out as microsoft are. I had all these develop relationships so you learn the less .

in the hard way like, yes, a development platform will take that.

Thank you. You know no, but they had a lot they did a very differently and and they did a lot of things. We did a lot of things wrong, but but competing and .

off in the nine, yes, no.

it's a lot different.

but appreciate that. But but we were we were nowhere near near competing with them. If you live now, when could I came along? And there was open G L, that was to our tax. Um but there's there's still another uh extension of you will and that extension is CUDA and that CUDA extension allows a trip that got paid for running direct text and open jail to create an installed base for kuda. yes.

And so that's the you are so militant and I think from my research a really was you being militant that every invidia tip will run CUDA yeah .

if you are computing platform, everything's got to be compatible. We are the only accelerator on the planet where every single accelleration, or is architecturally compatible with the others na has ever existed. There are literally a couple of hundred million, right? Two hundred and fifty million, three hundred million installed base of active.

Could A G P S being used in world today and they're all architecturally compounded? How would you have a computing platform if if you know N V thirty and M V thirty would fly, and every thirty nine and every forty there all different, right? Thirty years is all completely compatible. And so that's the only unnegotiable rule in our company. Everything else unnegotiable.

I mean, I guess kuda was a rebirth of U. D A. But understanding this now, U D A going all the way back, yeah, s, yeah, ah.

yeah. Uh, U D A goes to today. For the record, I didn't help any of the the founding CEO that that are listening.

I do you know what you are asking that question, what lessons? What are in part? Uh, I I don't know.

I mean the the charteris tics of successful companies and successful C E S, I think, are a fairly well described. There are 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 can build, uh, I I have nothing but admiration, respect, because I I just know that it's insanely hard. And I think that everybody did many similar things.

There are some good of smart things that people do. There are some dumb things that you can do, uh, but but you could do all the right smart things and still fail. You could do a whole bunch of dumb things and I did many of them and still succeed.

So obviously, that's not exactly right. You know just I think skills are are the things you can learn along the way. But an important moment.

Certain circumstances have to come together. And and I do think that, that the market has to be one of the agents yeah to help you succeed. It's not enough obviously because a lot of people still fail.

Do you remember any moments in NVIDIA history where you are like who we made a bunch of wrong decisions, but somehow we got saved because no, IT takes the sum of all the luck and all the skill in order to succeed.

Do you remember any moments I A rival in twenty eight, uh, as I mentioned, the number of smart decisions we made, which are smart to this day, how we design shifts, is exactly the same to this day because, gosh, you know, moby's ever done IT back then. And we pull every tricking the book in a desperation because we have no other choice. Well, guess what? That's the way things out to be done.

And now everybody does that that way, right? Everybody doesn't because why should you do things twice if we can do at once? Why tape out the trip seven times if you can take out one time? right? And so the most sufficient, the most cost effective, the most competitive um uh speed is technology, right? Speed performance time to market is performance.

All of those things apply. So why do things twice if we could do at once? yes.

And so everyone twenty eight made a lot of great decisions and how we speak products um how we how we think about market needs and like how do we judge markets and all of this then we make some amazing, amazingly good decisions. Yeah, we were, you know, back against the wall. We only have one more shot to do IT.

But once you pull out stops and you see we are capable of, why would you put stops in every time, stops out all the time thinking back .

to one thousand nine hundred and ninety seven that that was the moment where consumers tip to really, really valuing three graphical performance.

And again, yeah so for example, look um if if car make and hand uh decided to use acceleration because remember doing was a completely soft rendered and the NVIDIA philosophy was that although general purpose computing is is a fabulous thing, is going to enable software IT and everything. Um we felt that there were there were applications that wouldn't be possible or would be costly if IT wasn't accelerated.

IT should be excelled and three graphics was one of them. But IT wasn't the only one and IT just happens to be the first one and a really great one. And I still remember the first time we met join, he was quite and father about using CPU and the software render was really good.

I mean, quite Frankly, you look at look at doom. Uh, the performance of doom was really hard to achieve even with accelerating at the time. You know, if you didn't filter if you didn't have to do balin your filtering um I did a pretty .

good job the promoter me that was you needed comment .

programme yeah programme exactly IT was a genius p code um but nonetheless software renders that are really good job in but and if he had decided to go to open G L. And excelled accelerate for quake, uh, Frankly, you know what would be the killer APP that put us here, right? And so carmer and swing oth between the unreal and quake created the first two, a killer applications for consumer. 3， yeah and so I all them a great deal.

I want to come back very quick to said he told the stories like, well, I don't know what founders can take that. I I actually do think know if you look at all the big tech companies today, perhaps with the exception of google, all they did all start and understanding this now by you by addressing developers, planning to build a platform and tools for developers.

all of them .

apple a started. So I think that actually that will not guarantee success by any means, but that i'll get you hanging around the three the apple .

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

of good ideas yes yeah well as we we started drift toward the end here. We spend a lot of time on the past, and I want to think about the future a little bit. I'm sure you spend a lot of time on this being on the cutting edge of A I 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 in the value that there are creating, which has to be amazing for humanity in the long run. In the short term, it's gonna inevitably bumpy as we sort of figure out what that means. What do you think some of the solutions are as A I gets more or powerful and Better at accelerating credit tivy a for all the displaced jobs that are going to come from IT at .

first while we have keep A I safe. And there is a couple of different areas of A I safety that's really important.

Obviously uh in robotics and self driving car, there's a whole field of A I safety and we've dedicated ourselves to functional safety and active safety and all kinds of different areas of safety um when to apply human and loop, when is that okay for human not to be in the loop? Uh a you know how do you get to a point where where um uh increasingly human doesn't have to be in a loop, but human largely in the loop in the case of information safety, obviously bias false information and appreciating the rites of arrest and creators. That whole area uh deserves a lot of attention. And then you seen some of the work that we've done instead scraping the internet.

Um we we parted with get show stock to create commercially fairway of applying artificial intelligence to in the area of large language models and in the future of increase greater agency A I clearly the answer is as long as it's sensible, and I think it's going to be sensible for a long time, is human in loop the ability for N A I to self learn and improve and change uh out in the wild in the digital form? I should be avoided and and um we should collect data, we should Carry the data, we train a model, we should, you know test the model, validate the model before we released IT on the wild again. So humanism, the loop, there are a lot of different industries that have already demonstrated held the build systems that are safe and good for humanity and obviously the way autopilot works for for a plane and and two pilot system and an air trafic control and we done and see diversity and and all of the basic philosophy of designing safe systems um apply a as well in self driving cars and and so so four things and so I I think there's a lot of models of creating safe A I and and I think we need to apply them with respect to automation.

My feeling is that and we will see, but IT is more likely that A I is gone to create more jobs. And in the near term, the question is what's the definition of near term and the reason for that is, is um uh the first thing that that happens with productivity is prosperity and prosperity when the companies get more successful, they hired 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 they need fewer people.

Well, that's because the company has no more ideas, but that's not true for the company. Um if you become more productive and the company become more profitable, usually they had more people to expand into new areas. And so long as we believe that there more areas to expand into the, the, the, the, there are more ideas and drugs, this drug discovery, there more ideas and transportation, there are more ideas and retail, their more ideas and entertainment, that there's more ideas and 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 the world's industries, a thousand years ago. And the reason for that is because obviously, humans have a lot of ideas, and I think that there is plenty of ideas for prosperity and plenty of ideas that can be be got from productivity improvements.

But that, my sense is that is likely to generate jobs. Now obviously, the net generation of jobs doesn't guarantee that anyone human doesn't get fired OK. I mean, that's obviously true and and it's more likely that someone will lose a job to someone else, some other human that uses an A I you know, and not not likely to an A I but some other human that uses an A I.

And so I think the first thing that everybody should do is learn how to use A I. So they can productivity. And every company should argue their own productivity to be more productive so that they get more prosperity, hire more people. And so I think jobs will change. My guess is that largely have higher employment will create more jobs. I think industries will be more more productive um and many of the industries that are currently suffering from lack of lack of labor, uh work force is likely to uh use A I to get themselves off the free and and get back to growth and prosperity. So I see IT a little bit differently, but I do think that jobs will be affected um and I I encourage everybody just to learn ai.

This is appropriate as a version of something we talk about a lot and acquired. We call IT the moritz ary to morals law. After my from, yes.

of course.

the great story behind this is that when Michael was taking over for down valentine with dog, you are sitting and looking at the great returns. Looking fun. Three or four, I think, was four, maybe that had sex go.

And like, how are we ever gonna top that? No, I I can. You know that s gonna A, B, we're never gonna at that. They thought about that.

You realized that, well, as computer gets cheaper and I can access more areas of the economy because IT gets cheaper and get adopted more widely, well, then the markets that we can address should get bigger. Yeah, and your argument is basically exact. I do something .

and I just gave you exactly the same example, that in fact, productivity doesn't result in us doing less. Productivity usually result in us doing more. 嗯， everything we do will be easier, but will end up doing more.

Yeah because we have infinite ambition of the world has infinite ambition. And so so if a company is more profitable, they tend to hire more people to do more. Yeah, it's true.

Technology is a lever. And the the the place where the idea kind of false down is that like that we would be satisfied yeah like, yeah.

humans have never ending ambition.

No, humans will always expand and consumer energy and attempt to pursue more ideas that has always been true of every version of our species. Yeah over time.

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we have a few laying round questions we want to ask you. And then we and .

think of the easy one.

based on all these comfort rooms we see in name around here. Favorite size I book.

I've never read of the .

size I book before. no.

What like the obsession with star track?

And what? T, V, show favorite?

B, C, V.

A, there are on .

the way, and to get come from visors.

And excEllent.

Yeah, yeah. What car is your daily driver these days? And really, a question is, super a is one .

of my favorite cars, and also favorite. You guys might not know this, but but I lord, I got engaged um a Christmas a one year and we draw back in my my brand new super a and we total we were disco to the end.

But but .

nonetheless wasn't my foot wasn't wasn't a super sport, but but it's a market.

I ve the one time when I wasn't the super.

I love that car i'm driven these days for first curry reasons on others but um driven in the merik a yes using a video .

technology yeah has a where .

were in the the where the central computer so we I know we .

are to talk a little bit about business books, but one or two favorites that you've taken something from click .

Christians and I think has the series is the best. I mean, there's there's no no two ways about IT and and the reason for that is, is because it's so intuitive and so sensible IT, it's approached. But I read a whole bunch of them, and I read just about all of them I really enjoy. And those books, they are all really good.

awesome, favorite characteristic of don valentine.

grumpy, but enduring. And what he said to me the last time as he h decided to invest in our company, says if you lose my money, i'll kill you and uh and then uh over the course of of the decades, uh uh the years that followed, uh when something is nice reading about us in mercury news, um IT seems like he wrote in in a crayon you know good job just right right over the newspaper and just a good job dinies male italian and I hope i'd kept them on but anyways you can tell he's he's a real sweet and but he can the companies a special character yeah it's incl what is something .

that you believe today that forty year old Johnson would have pushed back on and said, no, I disagree um .

there's plenty .

of time yeah .

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

plenty of time in the day, planning of time thing to achieve something like to do.

just don't do everything, paradise, your life, make sacrifices, ices. Don't let outlook control what you do everyday. Notice I was late to our meeting and the reason for that, by the time I looked up, I oh my gosh then and dave waiting, you know, that's already we are. yeah. Yes, exactly. That didn't stop .

this from being a great time. No.

but you have to prioritize your time really carefully and don't let outlook to determine that. Love that.

What are you afraid of? If anything.

i'm afraid of the same thing today that I was I was in in the very begin of this company, which is letting the employees down. No, you have a lot of people who joined your company because they believe in your hops and dreams and and they adopted IT as their hopes and dreams and and uh you you want to be right for them. You want to be successful for them.

You want them to be able to uh, build a great life as as well as help you build a great company and be able to build a great career. You want them to have to enjoy all of that. And these days I want them to be able to enjoy the the things i've had the benefit of, enjoy and all the great success i've enjoyed. I want them to people, enjoy all that. And so I think, I think the the greatest fear is that that that you .

let them down. What point did you realize that you were gonna have another job that like this was that I just I .

don't change that. You know, if if IT wasn't because of Chris and kurtis convincing me to do do in video, I would still be a business logic today. I'm sort of IT well, yeah, really yeah, yeah.

I'm sorry that I would keep doing what i'm doing. And at the time that I was there, I was completely dedicated and focused on on helping other logic be the best company could be. And I was elsa logics best ambassador. I've got great friends to this day that i've known from from L. O logic as a company I I loved then I love dearly today. I know exactly why I went um uh the revolutionary impact of head 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 in the episode some of the most important events in computer industry and let me to meeting place and and john rubinstein and you know some of the most important people in the world and and Frank that I was with the other day and just, I mean, the list goes on and so, uh l logic was really important to me and and I would still be there I I you know who knows what else logic would have become if I were still there, right? And and so that's kind of how my mind works .

pawing I of the world .

yeah who exactly I am I might be doing the same thing .

i'm doing to the sense from yeah remember .

ing back to part one over serious on in videos but until until .

unfired this is my last I I got things that gic might have also changed perspective in philosopher about computing to the sense that we got from the research. Was that when ready of school, when you went to M. D. First right yeah you believed that like kind of a version of that was that the Terry Sanders real men have fabs like you need to do the whole stack, like you got to do everything and that L S logic changed you.

What l did was was a realized that you can express um transistors and logical gates and chip functionality in high level languages that by raising the level of abstraction and what is now call high level design, IT was coined by uh Hardy Jones, who's on an envious board.

And I had met him a way back in the early days of some options but but during that time there was this belief that you can express chip design high level languages and by doing so you could take advantage of optimizing compilers and optimization logic and and and tools um and and be a lot more productive. That logic was so sensible to me, and I was twenty one years a all the time, and I I want to pursue the ambition now, Frankly, that that idea happened and and machine learning that happened and for programing. And I want to see that happen in digital biology so that we can we can think about a biology and a much higher level language, uh, probably a large language model um would be the the way to make make a representable.

That transition was so revolutionary, I thought that was the best thing I would happen to the industry. And I was I was really happy to be part of IT. I was a grounds zero.

And so so I I saw one industry um change revolutionize another industry. And if not for L I logic doing the work that I did set options shortly after, then why would the computer industry be worried today? Yeah, IT is a really, really terrific. I was I was at the right place.

at the right time to see all that. And IT sounded like the CEO of L. S.

logic. Put a good word in for you. Ah with the valentine.

I don't know how to write a business plan.

and he turns out is not actually important.

No, no, no IT n IT. Turns out that making a financial forecast that nobody knows, uh, is going to be right, wrong. Turns out not to be done important. But the important things that a business plan probably could have teized out, I I think that the the art of writing a business plan out to be much, much shorter and have forced you to contend what what is the true problem you want to solve? What is the unmade need that you you believe will emerge and what is IT that you're gonna do that is sufficiently hard that when everybody else finds out is a good idea, then not gonna warm IT and you know make you obsolete and so that has to be sufficiently hard to do.

Um there there are home, which are those skills that are involved in just you know product and positioning and pricing and go to and you know of but those are skills and you can learn those things easily. The stuff that is really, really hard as the essence, what I described them. I did that okay but I no idea how to write a business plan and um and I was fortunate that will kordan was so pleased with me in the worth that I did when I was an else logic he called the guanine and told down you know investing this kid and um he he's gna come you away and a no I I will set up for success from that moment and .

got IT got a on ground as .

I don't lose the money no I think .

secret did OK I think we probably are one .

of the best investments they made if they .

held through today.

The VC partner is still on the board market yeah yeah yeah all these years, the two founding vcs are still on the board. And yeah tech cox and marsten s don't think that ever happens. Yeah we are singular and in that circumstance I believe they vote to value this whole time.

Uh been inspiring this whole time. Uh uh uh get great wisdom in and um a great support. But they they also were so yeah but they they've been entertained you know by the company, inspired by the company and enrich ed by the company. So they stayed with i'm really grateful.

Well in that in our final question for you, it's twenty, twenty three, thirty years, 嗯， university of the founding of NVIDIA. If you were magically thirty years old again today, twenty, twenty, thirty and you are going to Dennis with your two best friends, who are the two partial people, you know, and you're talking about starting a company, what are you talking .

about starting?

I would do IT.

I know. And the reason for that is really quite simple, ignoring the company that we would start, first of all, not exactly sure. The reason why we do IT and IT goes back to White 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 to be.

And at that time, if we realize the pain and suffering and just have vulnerable you you're gonna feel um and the chAllenges are you going to endure uh the embarrassed in the shame and you know the list of all the things that that go wrong, I don't think anybody would start company. Nobody in their right line would do that. And I think that that's kind of the the superpower of a entrepreneur. They don't know how hard is this, and they only ask themselves how hard can not be.

And to this day I I trick my brain into thinking, how hard can not be because you have to still yeah to go there? How hard can I be everything that we're doing? How hard can I be on universe? How hard can I .

be that you playing to retire?

And no, you still you .

could choose to say hard and .

i'm adding a little bit about you. But but the the that's that's really the trick of an 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, I go back and I said, i'm gonna during that whole journey again, I think it's too much. IT is just too much. Do any .

suggestions on any kind of support system or a way to get through the emotional trauma that comes with building something like this?

Have family and friends and our colleagues we have here. Uh, i'm surrounded by people who been here for thirty years, right? Chris been here for thirty years and jeff Fishers been here thirty years, do ice sphere thirty years and uh john and brian being here know twenty five, seven years and poly longer than that and you know joe greco san here thirty years.

I'm surrounded by these people that never one time gave up and they never one time gave up on me. And that's the entire blow wax, you know and to be able to go home and and have your family be fully committed to everything that you're trying to do when that there they are proud of you and proud the company and you can need that you need the unwavering ing support of people around you. Jim gathers, tch cox, yeah a harvey Jones and all the early people of our company to build Millers.

They are not one time gave up on the company. And and you can you need that. Not couldn't need that. You need that. And i'm pretty sure that almost every successful company and entrepreneurs that, that have gone through some difficult chAllenges, they they had that support system around them.

I can only imagine how meaningful that I mean, I know how meaningful that is in any company, but for you give I feel like the in video, a journey is partially amy dimension. You know, he went through two, if not three, near eighty percent plus drawdown s in the public markets. yeah. Have investors who have stuck with you? Yes, from day one through that must be so much supported.

Yeah yeah. IT is incredible. And you hate that any of that stuff happened.

And and most of you you know most of IT is is is out of your control, but you know eighty percent fall. It's an extraordinary thing. Don't 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 and market value for a while because of the decision we made in going into all that work. And your police system has to be really, really strong. You know, you have to really, really believe that and really, really want IT other.

Otherwise it's just too much to ensure, I think because you know everything questioning you and employees aren't question you, but employees of questions, right? People outside are questioning you. And uh, it's all embarrassing. And it's like, you know when your stock Price is hit is embarrassing. And no matter how you think about IT and it's hard to explain, you know and so there there's no good, good answer standing that stuff you know CEO are human and companies are built humans and these chAllenges are hard to indoor.

Then had an appropriate comment on our uh, most piece episode on you all where a uh we are talking about you know the current situation in the video. I think he said for any other company this would be A A precarious spot to be in but for video .

and this is cut all that ah yes familiar familiar with these large swings and apply .

ude yeah the thing that that to keep in mind is at all times, uh, what is the market opportunity that that you're engaging and that helped that informs your size you know I was told a long time ago that in video can never be larger than a billion dollars. Obviously, as an a estimation under under imagination of the size of the opportunity, that is the case that no chip company can ever be so big.

And so but if you're not a chip company, then then why is why is that apply to you? And this is the extraordinary thing about technology right now is technology is a tool, and it's only so large. What's what's unique about our current circumstances is that we're in the manufacturing of intelligence, where in the manufacturing of work world, thus A I and the world of tasks doing work, productive, generative, A I work generative, intelligent work, that market size is enormous as measure and trillions.

One way to think about that is if you build a chip for a car, how many cars are there and how many chips would they consume? That's one one way to think about that. However, if you if you build a uh a system and that uh whenever needed, assisted in the driving of the car, um you know what's the value of a autonomous chauffeur um every now and then and so now the market are obviously the problem becomes a much larger.

The opportunity becomes larger. Um you know what would be like if we if we were to magically conger up um a shaugh r for everybody uh who has a car? And you know how big is that market? And obviously, obviously that is a much, much larger market. And so the technology industry is that the you know where where we've discovered what NVIDIA discovered, what some of discovered is by separating ourselves from being a chip company, but but building on top of the trip and you're not in the eye company, the the market opportunity has has grown by probably a thousand times. Yeah don't be surprised of technology companies become much larger in the future because because uh what you produce uh is something very different and and that's the kind of the the uh the the way to think about you know how large can your opportunity, how large can you be that has everything to do with the size of the opportunity?

yeah. Well, jenson.

thank you so much.

Thank you who, David? That was awesome. So fun. Well.

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