The Man Who Built NVIDIA (with Stephen Witt)

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- Today is March 5th, 2026 and my guest is author, Stephen Whitt, his latest book and the subject of today's conversation is the thinking machine, chants and wang in video and the world's most coveted microchip. Stephen, welcome to econtalk.

- Oh, thank you so much for having me. - So this is really an extraordinary book. It's a history indirectly of, I'd say, the last 30 years or so of the digital age.

It's an incredible portrait of a visionary and his company.

“I think some of my listeners and viewers”

will know, well, have heard of in video, but won't know much about it other than perhaps that it's the most valuable company and the world measured by market capitalization. Most of them I don't think we'll know who chants and wang is

and your book is a wonderful introduction to both wang and in video. Let's start by going to the beginning. Chants and wang, the founder and CEO, what is his beginning?

He comes to the United States under unusual circumstances as a young as a kid. - Yeah, chants and was born in Taiwan in 1963. He moved to Thailand when he was about five years old. His father was an engineer who worked at a petroleum company.

And then he came to the US when he was about 10. In 1973, there was like a coup d'etat in Thailand and it was violent over tanks in the street. And his parents said, let's get the kids out of here. So he sent Jensen and his older brother ahead to the US.

They were planning to move to the US, but Jensen showed up alone to live with his uncle. His uncle didn't know what to do with these kids. And so he looked for a boarding school that would take him. And he found the Anita Baptist Institute

in rural Kentucky in the United States, which I think he may have thought was a prestigious preparatory academy. But the boys show up there, sight unseen, two foreigners barely speaking English.

And they realized they've arrived at a reform school for juvenile delinquents. The grounds are littered with cigarette butts and all the kids are basically criminals. And Jensen's first night there,

he's put with a 17-year-old roommate who's regionally been stabbed in a knife fight. So the kids are carrying switch blades. They're pretty poor backgrounds for the most part, mostly the children of tobacco farmers

or coal miners almost exclusively white. And at this time actually, the Vietnam War are still going on. But a lot of racism against Jensen's called, also as a racial slurs, it's a very difficult environment. But amazingly, Jensen thrives here.

He does well.

He's a good student, he's always been a good student.

But he actually becomes one of the most popular students in the school and even a leader. And then sort of one his parents returned a couple years later and he was back to Portland. He's had this kind of unusual experience

of being cast into basically a knife fight and surviving, right? And this kind of sticks with him.

“He would later say this is one of the most important things”

that ever happens to him. But still, despite surviving this kind of juvenile delinquent academy, he's still a nerd. He's still front of the class top grade, top test scores, ends up majoring in electrical engineering

and gets a job in 1983 in Silicon Valley on the Silicon side, you know, designing microchips, not software, but electrical engineering. And so really starts building computers from the transistor up, from the circuit board up.

And has done so continuously ever since ultimately revolutionizing

what the computer can be. And at one point, this is an undergrad at Oregon State. He ends up getting, I think a master is at Stanford, is that correct? And at some point, he forms his own company.

Is it 1993, is that the right day? 1993, the booth at Denny's Diner. If you're not familiar with the United States, Denny's is, you know, it's, no more for the adequacy

Yeah, open all the time. So he forms this company in 1993 with two other folks. And what's the goal of the company? Why did they break off for where they,

“where had they been before and why did they break off?”

What was the vision that that company was going to fulfill? Because it's quite surprising. Jensen's co-founders, Chris Malakowski, and Curtis Supreme, had been at Sun Microsystems. And Jensen had been an LSI logic.

And they knew each other actually because Sun was LSI's customer. So, so, Jensen was essentially a sales guy and Chris and Curtis were buying stuff. Now, that's a little demeaning to Jensen. Yeah, technically he worked at sales,

but he still had this master's in electrical engineering. And in fact, Curtis and Chris were his most sophisticated and technically demanding customers. What they wanted to do was design a microchip that could work as a three-dimensional graphics controller

for video games. Readers of a particular age will remember the Nintendo 64, which came out around this time. And moved us from side scrolling video games where we kind of moved across a static map

to ones that were rendered of three dimensions with almost like a camera in real time. And we could move our point of view around to see what was going on. There was a radical upgrade.

Basically what we're doing is we're drawing points in space

and then painting in the textures in them to make these kind of blocky, polygonal figures and have them move around. So, it's really actually a pretty tough math and physics problem. You wouldn't necessarily know that from what the game designers used

to build with it, which is mostly gunfights and car chases and gore and zombies. But they roll out this 3D graphics chip. Initially, it's a flop. They almost got a business. Actually, the three of them in video, almost mounted business twice in the early days.

But they managed to stabilize and find a niche market for this 3D graphics controller, especially for the video game Quake, which is a big hit in 1986 or 1986.

I mean, kind of the first 3-dimensional shooting game.

The strange part about this, of course, is they're going to end up changing the world in a rather extraordinary ways. But at the time, they were trying to convince their companies before they founded their own company,

that there was money to be made in video games. Which was in 1993 or '92 when they were having that argument before they founded in video, an absurd argument. They couldn't get anybody to fund them because the projected size of that market and I've heard

Jensen Wang talk about this in a talkie game. I think it's Stanford. The size of that market was estimated at zero, which is a small market, very small number zero. And so no one really wanted to take a chance on it,

but they believed it. Why did they leave their comfortable jobs? And at this point, I think Jensen Wang had just become a husband and maybe a father, he had to be thinking about the future. Why did he feel that was a risk we're taking in 1993?

“You have to be a little careful with Jensen.”

Who has a tendency to retcon the past to fit the story? Yes, sure. It was much larger than zero. There were 30 or 40 companies attempting to do exactly this thing. The market, they will tell you, Jensen will tell you,

well, there's no market, that was not true. It was obvious post-dume post-mist that the PC gaming market was going to be big money. The size of the market was going to be the challenge there was that everyone saw that. I think there were 40 plus companies by 1994 and 1995 trying to do the same thing.

And in fact, in video it was all the way at the back of the line.

They were basically in last place and as Jensen has described it,

this was actually an opportunity because when you're in last place, you can do anything you want, there's no real risk to it. You're going to go out of business anyway. So Jensen through this, it's true. So Jensen through this huge Hail Mary to design his chips in a new way,

using a simulator rather than build a prototype. And this allowed them to skip about six months of work and actually arrive first to the market with what was basically even by his own admission an inferior knockoff product, but it came out faster. And that was enough to keep the company alive in those early days.

“I think the zero billion market came later.”

I think that Jensen did not identify that till later. But he wants a story. Yeah, that's a nice story.

It's something I noticed with Jensen interviewing him a lot. He's got great stories. He's got great anecdotes. The details of those anecdotes tend to shift around a bit over time.

“So as a journalist, you have to be a little careful with them.”

But I find that story interesting because the theme of it is, you know, for some people anyway, and maybe he eludes to this as, you know, trust your intuition, don't listen to the so-called experts. And of course, that suffers from survivor bias. He survived.

He took a leap that worked out, up many, many other people, other potential founders or actual founders, took a leap for a market that nobody believed in and didn't make it. But, you know, I'll probably what I think less than of that early video game there I is. The survivor, but yes, they're survivor bias, but who gets to be the survivor?

Jensen and his team would go into their whiteboard in their office. And they would list out all of their competitors. And they would list out who the best engineers were working at each of their competitors. And then they would come up with strategic plans to post that specific, best engineer and get them to come work at a video.

They call it brain extraction. And once they did, once they extracted out of their company's brain, they would typically collapse very quickly because they no longer had their best person working from them. Jensen knows how to win in a knife fight. And the other guys did it.

Jensen had that ruthless killer instinct that you sometimes need in business. And he really made that the culture of Nvidia. And the other guys really came from the gaming space. They were wearing flip-flops to the office, you know, it was fun for them. They didn't think like killers.

Jensen thought like killer. And of those 29, 30, 40 companies that were out there by 2000,

there was basically just one left Nvidia.

So he won like Battle Royale of the 3D graphics controller market. If you talk to people who were experiencing the other side of that, they were like, God, he was just ruthless. I mean, he was just a shark. He destroyed our company without mercy, without pity.

And, you know, that's not the story he's going to tell. Well, that's what happened for sure. Why did they call it Nvidia? Yeah, they wanted to make their competitors green with envy. They wanted people to envy them.

And originally, they called the company Invision. But this turned out to be the manufacturer of environmentally friendly toilet paper. So they went back to the kind of drawing board.

“I think Chris, one of the Curtis, one of the co-founders,”

had a Latin dictionary. And Nvidia is the Latin word for envy. And so they called it Nvidia. That's where it comes from. And, you know, I would say they have done that.

They have made their competitors green with envy. And just a fast forward to the present. Just to give people who don't follow this closely. They are the number one, as I mentioned, a market cap company in the world.

There were over four trillion, trillion with a T,

trillion dollars, Apple is second.

The last, I looked about a week ago. And there are three point, something three point six. And some other thing. Well, not just that. But in fact, they recently hit the highest single concentration of any stock

in the S&P 500, since standard impores started keeping track. So in real terms, inflation adjusted not only the most valuable company made the world, they're, by some measures, the most valuable company in history. Yeah.

So let's talk a little bit about how they got there. So they start off. And then we're going to talk about some of the personal issues. And how the creativity of Nvidia created on,

“I think soon we will say, created the modern world,”

which is a priting weird thing to say. But I think it might be true. It's certainly the theme of your book. So they're in the gaming world. And somewhere along the line, and they're pretty good.

But as you say, they have a lot of competitors. They do post some engineers from them. But there's still lots of competition. But at some point, they realize that they can use their engineering ability to create chips that will facilitate other things,

besides car chases and killing monsters. And what's that transition like? And then bring us up to 2013 when they realize that there's this new thing, we can call artificial intelligence, and that they might be able to contribute to it.

I think, you know, we could divide the Nvidia story into three phases.

The first is the first, I would say, eight years or so,

as they go from spitballing in a diner to actually

And all of that is just the success and rise of their gaming product.

“But also the organic growth of the video game market, which was huge.”

Around 2001, they had started to notice that these graphics chips didn't work like normal computer chips. They were much more what is called arithmetic dense.

So what that means, basically it's parallel computing or accelerated computing,

the microchip will pulse with each clock cycle. On a classic Intel CPU, about 3% of the microchip lights up with each cycle. So about 3% of the silicon is actually active, these pulse. For an Nvidia chip, it was more like 30 or 40%. So they were doing a much greater volume of calculations per second, per tick.

And you might say, well, I don't all chips work that way.

“Well, the answer is that the parallel compute approach is much harder to program.”

But when you do it right, it's much more dense and much faster. And what started to happen was that scientists noticed this, quantum physicists, people doing medical imaging, people with needs for very high, kind of high power, high performance computing. And they actually started to hack the video game circuits, the video game of us programming,

just to get to these circuits. Jensen saw this and he was like, well, they shouldn't be hacking our video game stuff. I will build them a platform. I will build them software so that they can do science on these graphics processing units, on these GPUs. And that was a platform called Kuda. It was free.

“It was open source and wasn't open source, but it was free.”

It was an open platform just for downloading it and you would do it and then you could do science on GPUs. You could use them to do like medical imaging, quantum computing, all of the stuff. And so Jensen started to do all this outreach to scientists during phase two. Now, they made a huge list of potential applications for parallel computing, whether forecasting, oil prospecting, all of these potential customers and way down at the bottom

of the list. And this tiny little use case that they barely considered was something called computer vision. So by 2008 or 2009, this program is up and running. They've got, you know, a few, not let's say 100,000 downloads are so per year, but it's not really a success.

And it's extremely resource intensive. And this is the zero billion dollar market.

This is Jensen inventing this platform from scratch, basically, and losing money to do so. Now, you might ask, who is this for? Right, really? Well, it's not actually for mainstream research scientists, because those guys are well funded. Those guys can afford time on a classical super computer, who needs a Jerry rigged home brews, super computer built out of graphics cards. Well, it's a marginalized scientist. It's a scientist without a lot of research money.

It's a mad scientist, basically. And it's the very fringes of science at this time in the computer vision world. The very smallest most kind of meager customer, where these guys doing this form of AI, and it was an unpopular form of AI that did something called a neural net, which simulated the firing of the neurons in the human brain. I have the 2011 artificial intelligence textbook by Russell and Norvig on my shelf. It's about 1100 pages long.

Of those 1100 pages, about 16 total are devoted to neural nets. And that was the state of the art in 2011. This was a dead technology. Nobody believed in neural nets. And these guys were fringe mad scientists working on the absolute limits of computer science. But Jensen had built them this tool. And around 2010, 2011 and 2012, the mad scientists get a hold of two GPUs, two Nvidia graphics cards, total cost about $1,000. And they used

CUDA this platform to Jerry Riggs, a supercomputer. They started simulating the neurons of the human brain on this kind of like tiny homeburo supercomputer and they usher in a scientific revolution. Because as it turns out, the thing that the neural nets were missing was just firepower. They were just missing computing power. And when you unite these two technologies, you have an extraordinary

breakthrough that was known as Alex Net. We're suddenly computers which had struggled to label

images this computer vision program. Suddenly computers can see and they can identify images

not commodity, but cheap hardware. You can just buy it best buy. You can have a revolution in computer

“science. You don't need a computer. You don't need a supercomputer. And this inaugurates the third phase”

of Nvidia, which is the AI phase. Now, up until this time in the video, it had actually been struggling. If you look at the period from 2001 to say 2012 or 2013, Nvidia stock goes nowhere. And in fact, Jensen was not well regarded. There were activist investors taking positions in Nvidia, demanding change. It had a stagnant company. They were going to have to reform the board. And the reason was the super computing effort this platform Wall Street did not believe in

it. It was the zero billion dollar market. Jensen was spending a billion dollars plus and research

money a year to pursue what looked like these marginal weirdos. Weird customers. Weird ideas, right? I can't express to you how fringe these AI guys were. They were not popular. Even in the AI community, they weren't popular. And AI itself was viewed as a career graveyard at that time. I mean, you went in AI because you wanted to be a research academic. You didn't start a company. The amount of total venture capital investment in 2010 into AI was closer to zero than any other

meaningful number. But they had this breakthrough. And it started to build the modern world in integrating phase three of Nvidia, which basically was a rocket ship to plan it money. You know, it was just a galaxy made of money. It turns out. I guess we'll get into that. I mean, one of the, I'm going to give you the incorrect interpretation. I'll let you correct it. Before I got very far in your book, I looked at Nvidia's market cap. Some number of years ago,

something before 20, it was maybe around 2020. I may have the numbers wrong, but it doesn't matter. It was worth at that time. Remember, this is in the middle of this phase you're talking about.

“It was worth something like $300 billion. And I think they were the 15th most valuable”

company in the world, which is no small feat. And so you could, you could describe Nvidia's success as the following. And this is not, I want you to refute this. But this was my impression as I started to read more about Nvidia's a newcomer and just starting your book. I thought, well, they start. It's a company that starts with this graphics program that's used for gaming. And they get lucky because it turns out there's a big demand for this that was unexpected

in things outside of games and they profited from that. And so when AI came along, which was lucky for them, the demands are computing were so intense. They had the best chip. So they fell into this extraordinary frenzy of VC investment that we're in the middle of right now. And it made some people think it won't last for a long and some people think it's just getting started. And that's beyond the scope of this conversation. But obviously Nvidia's market

capitalization, the value of the company is reflects this incredible surge in artificial intelligence

work. And after I read your book, I realized that's down the right way to look at it. And so for starters, they weren't particularly profitable in advance of this revolution. But it wasn't just luck. Obviously, there's some luck involved, but they helped create it explained. They built the bottom world. I mostly reject the narrative that they got lucky. I mean, yeah, they didn't identify AI as their big customers when they were building Kuda. But they were very deliberately trying to

“unlock new branches of science. I mean, that's why you do this, right? Maybe you can't predict”

an advance exactly which new branch of science that you're going to unlock. But they were certainly trying to unlock some kind of scientific revolution with the understanding that when that happens, now you can build a whole platform around that. And you can build a whole ecosystem. You know, it wasn't a charity. From the start, they were engineering what they called vendor lock into the parallel computing platform, into the Kuda system. Once you learned how to do science

on a GPU, you were basically locked into this relatively expensive, actually quite expensive, hardware upgrade ladder, basically forever. And I had long been true of the gamers.

You know, Nvidia had always been good at going into the video game companies. And even like

having a guy kind of embedded in the video game company, helping the game developers optimize their game for Nvidia hardware. So that when it came out, all the gamer, they could put,

And all the gamers were glad and spent $1,000 on Nvidia hardware. And then they did the same for

“scientists. And as the AI kind of revolution started, all of the science grew, the entire ecosystem”

grew around this particular chip right down to the guts of the machine. They struggled a bit, actually, getting scientists to use Kuda sometimes because they would already have their own programs. And the scientists would be like, "What do you mean? I'm going to refactor a million lines of code to do weather forecasting on a GPU?" I'm not going to do that. It's going to take forever. It's going to take years. I want to do that. But with AI, there was nothing to refactor. There was

nothing to rewrite. It was being built from scratch around this platform, right? It was good, actually, that it was kind of a backwater because that maybe they could just rebuild everything and build it for the first time that is from scratch. And so that turned out to be enormously profitable. Now, along the way, two things happen that really turbocharged into the one is, and this came as a shock to a lot of people. AI is a brute force problem. It's basically linear.

The more computing power you throw at the computer, the smarter it gets. And the demand for AI

is functionally unlimited. Why would you not want something more intelligent? You're never satisfied.

You always want a more intelligent system. So the smarter the computer gets, kind of counterintuitively, actually increases the amount as new potential applications are unlocked. The second thing that really helped in video was in 2017 at Google, they introduced a new deep learning or AI kind of architecture, a new blueprint for AI called a transformer, which was basically a funnel that took massive massive amounts of data and distilled intelligence from it. The best known transformer model is the

generalized pre-trained transformer or GPT chat GPT. That's where that comes from. Now, this is great for Nvidia that this works because it really turns AI into like heavy industry, basically. Giant bonds called data centers, giant warehouses that have to be full of Nvidia equipment running 24/7 around the clock to distill intelligence to distill insights from massive, massive, massive amounts of data. It's almost like an oil refinery or something. It's like

“this big, heavy industry project. And for Nvidia, this is like the best thing that can ever happen”

because it's a hundred or even thousand exes that demand for their microchips. And so this is the point at which Nvidia, which had actually been doing well already, based on the computer vision results, starts to really rock it from being, you know, $5, $10 billion market capitalization to

$500 billion and then ultimately $5 trillion. Jensen calls these data centers AI factories

where our data goes in and intelligence comes out. Why is it that in these server farms, this ugly anonymous, unbranded thing in an relatively deserted part of, say, America? Why is it, quote, full of Nvidia equipment? What's the alternative? If Nvidia disappeared today, what would be, why would, why is, what would replace it if anything, and why is it not as good? Yeah, so why can't we just use an AMD chip? What we can't a what chip an AMD AMD is going to

“have to do is right. Yeah, why can't we just use an AMD chip? You can, but then you have to go back”

into the guts of your AI code and rewrite a whole bunch of code, refactor a whole bunch of code. And people who have worked with AMD chips just will straight up tell you they're not as good. And they're software in particular is just not as good as Nvidia software. It's harder to get those chips to do what you want. Now, the risk competition today, Google has something called the tensor processing unit, the TPU and a lot of AI developers are now also using that. But at least for a long time in

video was really the only game in town. I mean, this is the genius of the zero billion dollar market. Who's going to be crazy enough to spend 10, this is how much it cost, $10 billion building a science platform that a handful of people are going to use. I mean, early in the days of CUDA, they wanted to use it for medical tomography, just like cancer imaging. And Jensen built this giant contraption

that cost a couple million dollars and it had two customers total, two doctors used it at first.

So it doesn't seem to make sense. But the logic is the kind of a genius of it is that if you

crazy enough to follow you and you're the only person around and you're alone to enjoy the benefits. Jensen is, you know, he won the knife fight. He won the the 30 to 40 person competition.

But it's scarred him and he never wanted to do it again. And for the rest of his career, he would always

steer away from knife fights. He would always steer away from these kind of battle royale marketplaces with 30 companies in it into weird kind of niche applications that that looked small and didn't apparently seem profitable at least at first. But if you had the vision to think,

“well, what would this industry look like if I gave them a million times more computing power?”

How might it grow than you can be alone to do it? You know, the profits that Nvidia has earned, their gross margin is so high that it's like, it's like throwing chum in the water. Like sharks come after you. It just creates a feeding frenzy. And so today, there actually is a lot of competition. But there are five or ten years behind. The problems they face today are the problems that Nvidia solved five to ten years ago. And you, you talk about the influence of Clayton

Kirstenson on Jensen Wang's thinking. And it's my take on that the innovator's dilemma is a little bit different than the one that you attribute to Jensen Wang. It's also not the one that he accepts. He accepts neither of my interpretation or yours as to what he learned from that book. But I just want to make the point that one of the things that's obvious that you stress and this is true of many, many successful companies, they're very aware that success can be very

“fleeting. And there's no resting on your laurels. You have to innovate and you have to, in many ways,”

create your own competition. You have to create products that might cannibalize your own existing products because if you don't someone else, well, and this is really the, I think the great success of capitalism in the last hundred years, the the pace of innovation of anything has

as quick and doesn't always show up in the data. But Jensen Wang's attitude and the culture of

Nvidia is clearly, we might not make payroll at the end of the week. And I've heard that from other successful companies, it's kind of a fake mantra. It's, it's not true. You, they will make payroll next month, say. But if you pretend that you might not, you're more likely to make it a year from now and fibers should out and so on. You're the model for a long time was Nvidia's 30 days from going out of business.

Now in the early days, that's literally true. But talk about Clayton Christians's book, the inventor of the innovator of the Lamai and what you and Wang, Jensen Wang. Okay, I'd like to hear your take on it because I'm obsessed with Christensen.

“So to begin with, I think the, you know, this is where we get the term disruption. This is sort of”

a Clayton coin this term. Now that term has grown meaningless through our use. It's become a buzzword. But if you read the source material, disruptive companies were not necessarily high tech. In fact, one of the canonical examples from the book was a Honda Motors, like, like a dirt bike that Honda introduced into the United States market in the 1960s, early 1960s.

Now, this was a low margin product for a limited number of customers. Basically, they were selling

dirt bikes to offroading enthusiasts. That's not a big market, right? You know, on its own. And it doesn't make a lot of money. You can't sell the dirt bike for your customers. Don't have that much money. And GM looked at the dirt bike market and they were selling catalogs. And they said, "Well, why would we move into a low margin product with a limited customer base?" Like, that doesn't make any sense. We're just going to sell catalogs to businessmen

and make 10x per unit than we wouldn't miss selling a dirt bike. If we went into this business actually, our profit margins would go down. And we would have to draw capital away from our best customers to serve our most major customers. So we're not going to do that. Well, I think you know what happened with Honda. They came to dominate the U.S. dirt bike market. They leveraged that expertise to build a compact car and they rated the automotive industry

from below. And ultimately, they were huge threat to GM. And so, Christensen's conclusion from all from this and many, many similar experiences in corporate world was that this was a chronic air that managers made. And this is kind of the secret of the innovators dilemma I would say. It's not really a manual for startups on how to succeed.

getting rated by these low-cost players. And there's a line explicitly in the innovators dilemma

that I highlight because I was so shocked by it. But basically, the line of paraphrasing, but it's

roughly, there are times when it is correct to ignore high-argent opportunities and pursue low-margin ones. And there are times when it is correct to ignore large customer bases and pursue small, niche customer bases. And then he makes the point that is especially true when the innovation seems to be not high-end-putted high-tech, but basically, boot-legged solutions to existing problems. All of that is the Nvidia story, right? All of that is the story of using video game cards,

boot-legging them for a different purpose. All of that is a low-margin niche customer that Nvidia pursuit. Jensen used to assign the innovators dilemma to all of his executives and he actually hired Christensen as a consultant for some point. Now, knowing all that, when I asked Jensen about

“Christensen, he was like, "Yeah, I mean, you have to read that book and it was all of his lessons,”

but there's much, much more to it than that." And there's even certain ways in which Christensen was wrong. And when I asked him what those were, he refused to tell me. So he knows something we don't. And I think it shows in their market capitalization. And perhaps someday, he will write his own business book and his own business philosophy. But again, I would be a little careful about it because, as I said at the beginning, Jensen does have a tendency to wreck on the past to fit his current

operating philosophy. Also, mentorship is advice according to music, actually, is just kind of internally contradictory, even in the same sentence. One person compared it to like, if you wrote a book, it would like be a book of Zen Cowans, like these kind of like, single sentence statements that are profound, but take some unpacking. I think it's often that way in business. I think it's often the case that there's kind of your lowest margin zero billion in customer can be your best customer.

“I mean, that would be one of, one of Jensen's Cowans, how is that true? And you have to think”

through it to see how it might be true. What's your take, though? How's your take different on Christians? I should say obsessed with Christians. So I have a small, I mean, what I've learned from them is a small thing, and it's not, it's orthogonal to what you just said. It's just fascinating to me,

which is I always understood the lesson of that book to be that you're real, often you're real

competitor is not someone in your industry. It's a variation on the way you described the GM thing. It's like, well, a motorcycle is not a competitor for a car, it's a different thing. But an example, my favorite example is the slide roll. So the slide roll, quite full of NASA is the dominant, I think, or one of the dominant firms of the slide roll manufacturing. And I'm sure, most of my listeners have never, and viewers have never seen a slide roll in their life. I'll try to get your picture

want to link to it. But it was a computing crucial, important computing tool for engineers when they didn't have calculators. So what should worry, quite full of NASA? Well, the normal thing it worry about is your competitive, your competitor's slide roll manufacturing company, that they might make it out of something different, or there's might become more precise, or the

reader, the little thing that helps you see where the answer is, might get more illuminated.

We can think of a thousand ways, you can improve the slide roll. But that's not what happened. A thing came along, called a calculator that's not just a better slide roll that dense your market share, it eliminates you, it turns you into a footnote. And the idea that innovation comes from the unexpected and in particular, not from your own industry, which is the GM example, also GM Honda example, is a fantastic example. Again, of the power of competition and how

creative it is to change the landscape of both the companies, obviously, but more importantly, the customer. So the customer gets an extraordinarily better experience with a calculator with the Honda Civic relative to its price, and for the chip that gives you a much more vibrant video game, and then ultimately a much more effective AI research tool, which is not what it was

“supposed to be doing, and your caught flatfooted. Yeah, I mean, I think that's right. I mean,”

the other thing that makes it tough is, and this is really the hard part about the individuals dilemma. It's even why you can read the book, absorb its lessons, and still actually fail. Sure.

into a low margin business that is not going to promise returns anytime soon, your investors will start screaming at you. When you want to pivot, if you're selling a high margin product, sure, and you take the money that you earn from that and plow it into it, what is essentially experimental, low margin product, without a lot of customers, you will hear about it from Wall Street, and you will hear that you are an idiot.

And in fact, Wall Street did not like what Nvidia was doing. Jensen really had to fight not just competitors, but his own investors and even often his own customers to do this, right, because the cash flow that's going into the scientific computing market, that's coming from the video gamers. You're having to charge the gamers more to do this science project that won't benefit them directly. So, it's hard, your investors and customers don't like it. And this was,

“I think, Christensen, to me, is most profound answer. Yeah, that's why it's hard.”

Actually, when he interviewed managers at top firms in the 80s, he was actually a lot of these guys understood this already. They actually saw the problem already, but they were bound, they couldn't convince managers and investors to go along. And this is actually what, what, cratered Intel. If you go in the Intel and talk to people, they actually many of them will tell you, I mean, who knows, maybe they're covering their own bots, but they will tell you they saw it

coming. And in fact, Intel did have its own parallel computing GPU initiative in the mid 2000s, because they saw what was coming within video. They saw the value of the platform that Jensen was building. But Intel had huge profits and was one of the largest companies on the planet, and to pursue this market would mean lowering Intel's profit margins, which investors just

“don't like it. They just look at those numbers. And they're like, I just don't like it.”

Sorry, your profit margins will down. Sound the stock. And you get calls about it in the conference call. And if you can't explain or articulate to a stable group of investors, why you're doing this,

why you're spending $10 billion per suing a market that has fewer than $1 billion in revenue each year,

it's very, very, very hard to do. And it's, it's that, it's a success. It's that he was able to ignore his investors, right? It's not easy to do. Let's turn to the secret sauce to the extent there is one of what makes Jensen wearing a successful CEO. He is not nearly as well known outside of Silicon Valley as many, many, many other legendary innovators and leaders. You talk about his temper, the way he burrates, employees often, his unbelievable pursuit of perfection, his work ethic

is off the charts. And yet, when you talk to him, you, he's a very powerful section, a passage at the end of the book when you confront him with the dangers of AI, which we'll come back to. He says, you know, I'm just normal. I'm super normal. He doesn't say normal. He's super normal,

which is sort of an oxymoron. And then you say, I've never met anyone like you.

And I'd like you to expand on that. How is he different in terms as a manager, as a, as a, as a

“strategist for a company that has become at the heart of, again, the modern world?”

Well, well, two things. First, it's true that Jensen is not nearly as well known as some other Silicon Valley figures. Until you go to China, until you go to Asia, he is as popular in Asia as Steve Jobs was at the peak of Apple. He is a household name. Everyone knows who he is. He's a celebrity. People follow him down the street. He's security. His face is everywhere. He's incredibly famous in Asia. So, depends on where you are in the world. You know, having said that,

what makes Jensen different? Well, first of all, no one ever, it's funny how rare people

will just come out and say this, but Jensen is just smarter than almost anyone. His IQ is through the roof. His ability to absorb synthesize and use new information is almost, it's uncanny, how fast he can do that. When he was a kid, he started playing table tennis. He was like 15 years old. He had no background in the sport. And within six months, he was nationally ranked. Right. Now, a lot of people can do that. And that is true. And almost any field that he enters.

You know, if we all started, I don't know, taking Trompon lessons this week. I don't play the Trompon. I assume you don't either. Maybe probably not. And we did that for six months. And we all put in the same amount of effort. By the end of those six months, well, first of all, Jensen would have practiced the Trompon for 12 hours a day while we were doing

months, Jensen would be the best Trompon player among us. And there's, I'm not just saying that there's

multiple times in his career where that basically that exact thing has happened. He had to rapidly

learn some new field. And within a few months is actually a domain expert. So, that's very hard to do. I mean, I asked Morris Chang the CEO of TSMC, the big Taiwanese manufacturer of microchips, what made Jensen different. And Morris Chang is 92 years old now. So, he doesn't have that much time to talk about stuff. He's waited his hand to be ready. He's like, guys, he's just smarter than everyone else. I mean, that was just just takeaway. He's just smarter than everyone else.

“I think on top of that he is very adaptable. So, he can use his intelligence to repurpose his company”

and even himself to the task at hand. He thinks like an engineer. It's all inputs and outputs.

So, I'll give you the most recent example. Everyone at Silicon Valley is trying to get Donald Trump

to do what they desire, what they want. Some of them are successful, some of them are not so successful. The most successful so far has been Jensen. He has appeared in public with Trump seven times in the past year. He has gotten everything he needs from the Trump administration. Now, Jensen is not a political creature by nature. He has not historically had any involvement with politics at all. In fact, I said that on my book, I published it and then instantly he pivoted and

proved me wrong by befriending Donald Trump. But what Jensen's going to do is he's going to approach Donald Trump just like he would any other problem as an engineering problem. He's going to study the inputs and outputs of Donald Trump. He's going to say, "When I give this input to Trump, this happens. When I give this one this happens." When I modify my inputs just enough, I get what I want as an output from Trump, which might be him lifting sales restrictions on China, not putting

tariffs on Taiwanese products or allowing me to get a lot of H1B visas from my workers in hand, in Silicon Valley. All of which are against what you would think Trump would want. Trump would seem to want to limit the sale of microchips to China, would seem to want to put tariffs on Taiwan, and would seem to want to stop visas in the United States, but Jensen gets all those three things from. And this is not a problem that Jensen has ever faced before. He just learned how to do it

faster and better than any other Silicon Valley, exactly. So he has this remarkable adaptability

“that I think he can really change himself to the moment in hand. I think the other thing that Jensen”

now has, that none of the other Silicon Valley guys have, is 30 years in the chair. He is a wizard elder of Silicon Valley. He has been in the CEO spot for 30 straight years. He is the single longest serving CEO in the entire S&P 500 tech sector. And so I think that means that he has seen it all. Now, and he can use his accumulated intelligence, his work ethic, his adaptability, and his wisdom to succeed. Now, as you say, he beriates employees too. That part, you know,

it's controversial. He can be a really rough leader. He screams to people. He screamed at me. You know, his point of view is, you know, some people are like, well, listen, you look at like a great sports coach, great military general. They're not trying to be your best friend. And sometimes they will yell at you to get the best out of you. And so Jensen's just doing the same thing with his company.

“One guy said, you know, he's not the only S&P 500 CEO to scream his employees. Maybe that's”

all true. But when I witnessed it firsthand, I must say it did seem a little self-indulgent. I question, but this is necessarily a broadly repeatable management lesson or a court of Jensen's person now. Remind of me of the portrait of Steve Jobs and Walter Isaacson's book a book that I did not read for a long time because every review of it said the same thing. It is, you know, Steve Jobs is a jerk. That was the punchline of that book. He berates his employees.

He's subnoxious, film the blank. That is not the lesson of Walter Isaac's book after I read. The lesson is is that even though he would often berate his employees, they followed him.

They were devoted to him. And there's an amazing line in your book from an interview

did with one of his employees, who says the following. Jensen is not an easy person to get along with all the time. I've been afraid of Jensen sometimes, but I also know that he loves me. Close quote. Now, if you get employed to feel that way, I'm not sure it's a good idea or not, but it tells you you're dealing with somebody quite extraordinary. Now, some of that extraordinary

personal lives of his employees through the Ryzen stock price. So I understand he's going to get a lot of

“loyalty just for that reason alone, but I think it's more complicated than that. I think there's a”

certain, I hate to use this word, Messiah like complex that both employees have and leaders have at times, both in business or in politics, where the facts aren't really what's important. It's a feeling of connection that is somewhat irrational or non-rational, and it's clear to me. It's a combination of religious leaders, doom often used this approach, especially with coal leaders. You both love the guy and you're afraid of him. And what this does is it means that you're

so eager to please the guy and you're so, you're so scared of displeasing him that you basically

organize your life around the principles that he tells you of what to do. And Jensen very much has that. I say in the book he's like a prophet. He's like a prophet. It's true. He makes predictions about the future. The difference is everything he says comes true. And when the things that he says comes true, everyone in the room gets to add a zero to their net worth. So you follow this prophet, you have done well. He has led you to the promised land for real.

“You know, I think that's part of it. You know, I think the other thing is this is a demanding”

industry. It's hardware industry. Things have to be on time. They have to be on deadline. You know,

I have worked in a newsroom for a lot of my life. If you have a hard deadline to get something in it,

it looks like you're going to miss it. You were going to hear about it vocally from the editor. And the editor is under tremendous pressure to get the thing out on time. And in video, this deadlines are tight. The schedules are tight. They can't delay the product. Must come out, it must come out on time. And that can lead to screaming. You know, you watch the basketball game called just screaming at the players. Often. In the war, the captain lieutenant is screaming at the

soldiers. You know, it isn't actually effective way to lead people. It's unpleasant. I mean, I have been screamed at for being late on deadline. On the pleasant experience. And, you know, often it's just quite a room was humid. It's inevitable when you're under stress, under tight pressure to deliver this stuff. As a video has grown in Jensen's exceeded,

“I think it has gotten a little self-indulgent. But it's a style that's gone out of fashion,”

you know, to some extent. You know, I think about Bobby Knight, you mentioned basketball, is this screamer, throws chairs, does, you know, did all kinds of things. Those kind of approaches have generally been softened. But maybe not in the tech world, but it's a little bit different. I think they have, you know, I think Bobby Knight couldn't exist today. Yeah. Yeah. Great pop of which yells a lot. J.B. Bickers staff yells quite a lot. This is still the case.

I don't think they're throwing, no, they're choking players anymore. That Bobby Knight was a little scream. But even in his day, he was viewed as very extreme. And it probably wouldn't be tolerated now. But, you know, it's still there. I think for a lot of these guys. And especially, you know, this is the thing about Nvidia. It's not Google. There's not a ball pit in the office. There's not a rock climbing gym in the office. This kind of touch your feel, your fun, creative,

software, stuff. You know, that's not the vibe at Nvidia. The vibe is, we need the microchip yesterday. It's late. It's late. We're late turning it in. And our competitors are going to catch us and destroy us. If we don't produce this thing on deadline, on time to the best of our capability. There's a sense that you're just constantly constantly falling behind. And J.B. It's an ankle case. That desperation. That's a fear. And that sense of almost neurpanic every day.

Let's talk about TSMC, the Taiwan Semiconductor Manufacturing Company. Right now, the United States is fighting a war against Iran alongside Israel. And a couple of commentators have suggested, and I'm sure there are others, that this has to do with China. And when you start that way, you think, well, come on, what does this have to do with China? But I don't want to go into the Iran war right now. But what I do

want to point out and what I learned from your book, which surprised me, is the importance of Taiwan.

China has eyed Taiwan for a long time. And I've always thought the United States, I'm naive

person sometimes about geopolitical things when I was younger. I thought, well, the United States

China used to be the Soviet Union. And so sometimes each of these countries would be fighting essentially a war via proxies. And so standing up for Taiwan is the way the United States shows China is not going to be pushed around. But your book points out that the Taiwan semi-conducting manufacturing company is not a small thing. And it's not a small thing if it were taken over by China. It's not clear they could take it over if they say conquered Taiwan. But talk about why

“this company, again, I think most people have never heard of it. Why is it important?”

They build all the world's most advanced microchips in Taiwan. It is a global manufacturing choke point. When they had to shut down their facilities for a little while during COVID,

the entire world economy ground to a hall. Basically, you couldn't get a new car because the

microchips that you needed for the car weren't being produced on the line in Taiwan. So there's vitally important that this place stays open. And that's on purpose. Morris Chang built that to build what he called the silicon shield surrounding Taiwan, where if China invaded, they would cause the world economy to crash because it made it more painful for China to possess Taiwan. In terms of geographic importance, it would be similar to the Saudi oil fields. That's

basically the similar contribution. You imagine the Saudi oil fields went off lines for a few

“years. That's what would happen if Taiwan went off line. And it probably would go online even”

if China did manage to seize Taiwan, they probably would just blow up the factory to be honest with you. It's not clear that China would ever come into possession of this. But it would cause the world economy to crash. You mentioned your ramp. And I won't go too much into it. But I saw

yesterday that for the first time since 1945, the US had used the torpedo to sink a washing

label conflict. First time since 1945. Now perhaps the US has some kind of limited strategic or tactical goal and sinking that boat. But to me, it's a signal. It's a signal to China. Yeah, yeah, we're going to sink your boats and we can do it. So if China wants to evade Taiwan, you know, we talk about this a lot, but people don't discuss the details. It would be the largest amphibious invasion in human history. It would make detail look small. I mean, they would need

four or five times the number of boats and people to get across the channel to Taiwan. And it's a longer trip to. And patrolling that trip in very short order, there's going to be a bunch of autonomous submarines with the capability of sinking the transport boats. So to me, this boat sinking is, I don't think it achieves any obvious strategic or tactical goal inside of Iran right now. I think

“it's a signal. It's just my opinion, but but as I look at this, that's what it looks like to me.”

In the claim of these commentators and we'll put links up to it because we don't have time to go into it. But the claim of these commentators is that China has been pushing its influence in Iran, both with weapons in other ways, to distract the United States and have a trip make it challenging for the United States to both be involved in the Middle East and say in Taiwan. But we'll link to those articles. I want to close with a and your point about sinking that

warship is a perfect example that that argument. I want to close with the risks of AI to humanity. We're pretty aware of the risks of AI to journalism, which you write about in the book explicitly because you affects you personally. But it was fascinating to me to read Jensen Wang's defense of AI research and innovation as being no different. And many people have argued as many of them are my friends, by the way, economics have argued. So different than

any other tool. We had electricity. We had the printing press. We had the computer. Everybody said they're going to ruin the world. They're going to take all the jobs. They won't be any use for

human beings anymore. The steam engine, et cetera, et cetera. And yet it always turns out well.

And I believe that it always has. I believe it is likely it will turn out well in the future. Although there are other things I worry about with AI besides how many jobs there are. But that's the kind of but many people were just about that. And in the conversation you have with Wang at the end of the book and your last interview with him in the writing of the book he dismisses it really with the discussed partly because he's heard it so many times that AI is going to steal all the jobs.

Make human remains redundant. And we're going to just get to be in the AI museum. The human being

going to be dominated by machines. And he doesn't believe that. Of course, he has a terrible emotional ability to believe that because he has become an extraordinarily wealthy man worth over $100

million dollars by embracing an AI future. I just want to your own thoughts as we continue to

ride this wave. Two ways to think about this. One is the economic approach. And if we use the classic tools of economics to analyze AI, it is fantastic. Things look great. This is another tool that humans get to use to improve global productivity, cure disease, accomplish all sorts of new things make our lives better. Full stop. That's the end of that. It's just better. The other way to think about it

is the biological way. And what happens in biology is new systems or animals or kind of biological

entities emerge. And then they destroy everything that's there. And they rebuild the world in their

“own image. And that's what happened when the human brain came online. The ecosystem of our planet”

has been transformed. There's more animals in captivity that we use for farming on Earth's surface by a factor of 100 than there are wild animals. Now, we we made the Earth an our image. What we wanted it to be is the neural net a productive economic tool or is it the next phase of biology? I think where you land on that question informs your sensibility about what's to come. For Jensen and company, it is not, it may be biologically inspired. In fact, it is bio mimicry.

“That's what it is. But it's not in and of itself biology. And therefore, it's an economic”

tool that we can use to enrich ourselves. Some of the other pioneers I talk to see it more like a biological revolution. And those can be very dangerous. Life on Earth has undergone multiple

times where new organisms emerged. There was no oxygen on Earth two billion years ago.

And then algae showed up and they oxygenated the Earth. Sounds great for us, but it killed almost everything alive. You know, when the land bridge was connected with with Asia and the bearing straight and these big cats, these predators came over, they killed almost everything that was existed in North America at that time. And again, when humans came, they killed multiple categories of large animals. It wiped them off the map. A biological revolution can be dangerous if you're on

the wrong side of it. And these systems are inspired by biology. Whether or not they can evolve into kind of self-propeachuating organisms where they're on well and their own desire to survive,

“I think, well determine whether or not we live in the flourishing utopian world of economics”

or the more dangerous world of biology. I guess today has been Stephen Witt. His book is The Thinking Machine. Stephen Thanks for being part of E-Kontalk. Thank you so much, Ross. This is E-Kontalk, part of the Library of Economics and Liberty. For more E-Kontalk, go to E-Kontalk.org where you can also comment on today's podcast and find links and readings related to today's conversation. The sound engineer for E-Kontalk is Rich Koyette. I'm your host, Russ Roberts. Thanks for listening.

Talk to you on Monday.