Betting on AI: Jensen Huang and NVIDIA’s Rise to the Top

- Today is March 5th, 2026, and my guest is author Stephen Witt, his latest book. And the subject of today's conversation is The Thinking Machine, Jensen Wang, Nvidia, 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, 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, will have heard of Nvidia, but won't know much about it other than perhaps that it's the most valuable company in the world measured by market capitalization. Most of 'em, I don't think will know who Jensen Wang is. And your book is a wonderful introduction to both Wang and Nvidia. Let's start by going to the beginning. Jensen 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. Jensen was born in Taiwan in 1963. He moved to Thailand when he was about five years old. His, well, 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 a, like a coup de ta in Thailand, and it was violent. There were tanks in the street, and his parents said, let's get the kids outta 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, the one Baptist Institute in, 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 realize they've arrived at a reform school for juvenile delinquents. The the grounds are littered with cigarette butts and all the kids have, like, they're basically criminals. And Jenssen's, first night there he is put with a 17-year-old roommate who's recently been stabbed in a knife fight. So the kids are carrying switchblades. They're, 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, you know, the, the Vietnam War still going on. So a lot of racism against Asians. Jensen is called sorts of racial slurs. It's a very difficult environment. But amazingly, Jensen thrives here. He does well. He is 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 even a leader. And then sort of when his parents return 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, 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, I mean, despite surviving this kind of juvenile delinquent academy, he's still a nerd. You know, he's still front of the class. Top, top grades, 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, and has done so continuously ever since, ultimately revolutionizing what the computer can be.

- And at one point, he's an undergrad at Oregon State. He ends up getting, I think, a master's at Stanford. Is that correct? And at some point he forms his own company. Is it 1993? Is that the right

- Case? 1993. The booth at Denny's Diner, if you're not familiar with the United States, Denny's is, is, you know, it's known more for the adequacy of its food then it's the quality. It's, yeah, it's open all the time. It's open 24 hours. So very

- Popular.

- Yeah.

- So he forms this company in 1993 with two, 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, what was the vision that that company was gonna fulfill? 'cause it's quite surprising.

- Jens Jenssen's, co-founders, Chris Malakowsky and Curtis Preem had been at Sun Microsystems, and Jensen had been at, at LSI Logic, and they knew each other actually, because son was, Ellis i'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 in 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 in three dimensions with almost like a camera in real time. And we could move our point of view around, 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, it's really a, actually a pretty tough math and physics problem. You wouldn't necessarily know that from what the game designers use to build with it, which is mostly, you know, gunfights and car chases and gore and zombies. But they roll out this 3D graphics chip initially, it, it's a flop. They almost go outta business. Actually, the three of them, Nvidia almost went out of business twice in early, early days. But they managed to stabilize and find a niche market for this 3D graphics controller, especially for the video game quake, which was a big hit in 1996 or 1997, kind of the first three-dimensional shooting game.

- The strange part about this, of course, is they're going to end up changing the world Yeah. In rather extraordinary ways. But at the time there, they were trying to convince their companies before they founded their own company, that, that there was money to be made in video games. Yes. Which was in 1993 or 92, when they were having that argument before they founded Nvidia, 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 talk he gave, 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, they believed in 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 worth taking in 1993?

- You know, you have to be a little careful with Jet Jensen, who has a man a, a tendency to reccon the past to fit the story that he wants to tell. Sure, 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, oh, there's no market. That was not true. It was obvious post doom, post mist, that the PC gaming market was going to be big money. The size of the market was gonna be The challenge there was that everyone saw that. I think there were 40 plus companies by 1994 and 1985 trying to do the same thing. And in fact, a video was all the way at the back of the line. They were basically in last place. And as Jetson has described it, this was actually an opportunity for him, because when you're in last place, you can do anything you want. There's no real risk to it. You're gonna outta business anyways. So Jensen threw this, it's true. So Jensen threw this huge Hail Mary to design his chips in a new way, using a, 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 a good story. It's

- The one thing I noticed with Jetson interviewing him a lot, you know, 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.

- Well, I always, I find that story interesting because, you know, the theme of it is, you know, trust, for some people anyway, and maybe he alludes to this, is, you know, trust your intuition. Don't, don't listen to the so-called experts. And of course that suffers from survivor bias. He, he, he survived. He took a leap that worked out at 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 anyway,

- I'll tell you what, I think the lesson of that early video game era is the survivor. But yes, there's survivor bias, but who gets to be the survivor? Jensen and his team would go into their, 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, come up with strategic plans to poach that specific best engineer and get them to come work at Nvidia. They called it brain extraction. And once they did, once they extracted that other 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 didn't. Jensen had that, that ruthless killer instinct that you sometimes need in business. And he, he, 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, they didn't think like Killers. Jensen thought like a killer. And of those 29, 30, 40 companies that, that were out there by 2000, there was basically just one left Nvidia. So he won like the battle royale of the 3D graphics controller market. If you talk to people who experienced the other side of that, they were like, God, he was just ruthless. I mean, he was just a shark. He, he destroyed our company without mercy, without pity. And you know, that's not the story he's gonna tell. Yeah. But 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 Envision, but this turned out to be the manufacturer of, of environmentally friendly toilet paper. So they went back to the d the, the kind of drawing board. I think Chris, one of the, or 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, it, I would say they have done that. They have made their competitors green with envy.

- And just to fast forward to the present, just to give people who don't follow this closely, they are the number one, as I mentioned, market cap company in the world. They're worth over 4 trillion trillion with a T trillion dollars. Apple is second, the last I looked about a week ago. And they're at three point something, 3.6 and something like that.

- Well, not just that, but in fact, they recently hit the highest single concentration of any stock in the s and p 500 since standard and Porous started keeping track. So in real terms, inflation adjusted not only the most valuable company in 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 gonna talk about some of the personal issues and how the creativity of Nvidia created, I think soon, we'll, we will say created the modern world, which is a frightening, it's a weird thing to say, but I think it might be true. It's certainly the theme of your book. Yeah. 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, they, they do poach some engineers from them, but there's still lots of 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, and killing monsters. And what's that transition like? And, and then bring us up to 2013 when they realize that there's this new thing called artificial intelligence and that they might be able to contribute to it.

- I, 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 joining the s and p 500 in 2001. And all of that is just a 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 arithmetically 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. Each 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, why don't all chips work that way? Well, the answer is that the, 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 powered, high performance computing. And they actually started to hack the video game circuits, the video game 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, a platform called Kuda. It was free, it was open source. It 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 this stuff. And so Gensy started to do all this outreach to scientists during phase two. Now they made a huge list of potential applications for parallel computing, weather forecasting, oil prospecting, all of these potential customers. And way down at the bottom of the list in 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, let's say a hundred thousand downloads or 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, 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 supercomputer who needs a jerry rigged home brew supercomputer 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 at the very fringes of science at this time in the computer vision world, the very smallest, most kind of meager customer were 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 20 10, 20 11 and 2012, the mad scientists get a hold of two GPUs, two Nvidia graphics cards, total cost about a thousand dollars. And they use Cuda, this platform to Jerry rig a supercomputer. They start simulating the neurons of the human brain on this super, on this kind of like tiny home brew super 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 AlexNet, where suddenly computers, which had struggled to label images, this computer vision program, suddenly computers can see and they can identify images correctly with an unprecedented level of accuracy on basically the cheapest commodity. No, not commodity, but cheap hardware that 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, Nvidia had actually been struggling. If you look at the period from 2001, say 2012 or 2013, NVIDIA's 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 drafting to reform the board. And the reason was this supercomputing effort, this platform, wall Street did not believe in it. It was the zero billion dollar market. Jensen was spending a billion dollars plus in research money a year to pursue what looked like these marginal weirdos, weird customers and weird ideas. Right? I, I can't express to you how fringe these AI guys were. They 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 inaugurating phase three of Nvidia, which basically was, you know, a rocket ship to planet money. You know, it was just a, a galaxy made of money as it turns out. You know, I guess we'll get into that.

- I mean, one of the, I'm gonna give you the incorrect interpretation. I'll let you correct it. Before I got very far in your book, I, I looked at NVIDIA's market cap some number of years ago, something before 20. It was, maybe it was around 2020. And I may have the numbers wrong, but it doesn't matter. It, it was worth at that time, remember, this is in the middle of this phase you're talking about 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 as a, a newcomer and just starting your book. I thought, well, they, 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 on 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 may, some people think it won't last for 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, in artificial intelligence work. And after I read your book, I realized that's not 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. Explain, no,

- They built the, they built the modern world. I, I mostly rejected a narrative that they got lucky. I mean, yeah, they didn't identify AI as their big customers when they were building Cuda, 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 in 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 Cuda system. Once you learned how to do science on a GPU, you were basically locked into this relatively expensive, actually quite expensive hard hardware upgrade ladder, basically forever. And it 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 go gamer, they could put you knowlet on an Nvidia chip that's the best platform for this particular game. And all the gamers would go out and spend a thousand dollars on, on Nvidia hardware. And then they did the same for scientists 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 had, they'd struggled a bit actually getting scientists to use Cuda sometimes because they would already have their own programs. And the scientists would be like, what do you mean I'm gonna re re refactor a million lines of code to do weather forecasting on A GPU. I'm not gonna do that. It's gonna take forever, it's gonna take years. I don't 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, a backwater because that meant they could just rebuild everything and build it for the first time. That is from, from scratch. And so that turned out to be enormously profitable. Now, along the way, two things happened that really turbocharged Nvidia. 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 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 demand as new potential applications are unlocked. The second thing that really helped Nvidia 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 barns 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 is like the best thing that can ever happen because it, it it a hundred or even a thousand xs of 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 rocket from being, you know, five, $10 billion market capitalization to 500 billion. And then ultimately 5 trillion Jensen calls these data centers AI factories where intelligent, where, sorry, data goes in and, and intelligence comes out.

- Why is it that in these server farms, this, this ugly anonymous unbranded thing in a relatively deserted part of, 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, and why is it not as good?

- Yeah, so why can't we just use an A MD chip? Well, we can

- A a what chip? An A MD oh A

- MD is is

- One of a co

- Yeah, Why can't we just use an A MD 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 a MD chips just will straight up tell you they're not as good. And their software in particular is just not as good as Nvidia software. It's harder to get those chips to do what you want. Now there is 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 dollars market. Who's gonna be crazy enough to spend 10? This is how much it costs $10 billion building a science platform that a handful of people are gonna use. I mean, early in the days of Cuda, they wanted to use it for medical demo tomography, which is like, you know, cancer imaging. And Jensen built this giant contraption that cost a couple million dollars and it had two customers total. Two, two doctors used it at first. So it doesn't seem to make sense. But the, the logic is the kind of a genius of it is that if you can get that to work and you can unlock new uses, then when it does succeed, no one else has been 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 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 you would, 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 then you can be alone to do it. You know, the profits that Nvidia has earned, their, 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 they're five or 10 years behind. The problems they face today are the problems that Nvidia solved five to 10 years ago.

- And you, you talk about the influence of Clayton Christensen on Jensen Wang's thinking, and it's my take on that the Innovator's dilemma is, is a little bit different than the one that you attribute to, to Jensen Wang. It's also not the one that he accepts. He accepts neither my interpretation or yours for as to what he learned from that book. But I, I just wanna 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, you have to create products that might cannibalize your own existing products. Because if you don't, someone else will. And this is really the, I think the great success of capitalism in the, in the last a hundred years. The, the pace of innovation, if anything has, has quickened 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, a fake mantra. It's, it's not true. You, you would, they will make payroll it next month say, but if you pretend that you might not, you are more likely to make it a year from now and five years from now and so on.

- Yeah. The, the motto for a long time was NVIDIA's 30 days from going out of business in the early days. That was actually literally true a couple times.

- Yeah. But, but talk about Clayton Christensen's book, the in the Inventor's Dilemma. The Innovator's Dilemma and what you and, and Wang Jensen Wang.

- Okay. I'd love to hear your take on it too, 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 Clayton coined this term. Now that term has grown meaningless through overuse. 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 motorcycle, 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 off-roading 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 Cadillacs 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 gonna sell Cadillacs to businessmen and make 10 x per unit, then we wouldn't be 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 meager customers. So we're not gonna do that. Well, I, I think you know what happened with Honda? They, they came to dominate the US dirt bike market. They leveraged that expertise to build a compact car and they rated the automotive industry from below, and ultimately they were a huge threat to gm. And so Christensen's conclusion from all from this and many, many similar experiences in the corporate world was that this was a chronic error that managers made. And this is kind of the, the secret of the innovator's dilemma, I would say it's not really a manual for startups on how to succeed. It's actually a, a counterinsurgency manual for decision makers at established firms to avoid getting rated by these low cost players. And there's a line explicitly in the innovator's dilemma that I, I highlighted 'cause I was so shocked by it. But basically the line, I'm paraphrasing, but it's roughly, there are times when it is correct to ignore high margin 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 input and high tech, but basically bootlegged solutions to existing problems. All of that is the Nvidia story, right? All of that is the story of using video game cards, bootlegging them for a different purpose. All of that is a low margin niche customer that Nvidia pursued. Jensen used to assign the invi 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 absorb 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, his own business philosophy. But again, I would be a little careful about it because as I said it kind of at the beginning, Jensen does have a tendency to erect con the past to, to fit his con his current operating philosophy. Also, many of his advice according to his executives is kind of internally contradictory. Even, even like in the same sentence, one person compared it to, like, if he wrote a book, it would like be a book of zen koans. Like these kind of like, you know, 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, that there's kind of your lowest margin zero billion customer can be your best customer. I mean, that would be one of, one of Jensen's Coan. How is that true? And you have to think through it to see how it might be true. What's your take though? How does your take differ on Christensen? I should say? I'm obsessed with Christensen,

- So I have a small, I mean, what I learned from him is a small thing and it's, it's not, it's orthogonal to what, 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 your real competitor is not someone in your industry. It's a variation on, on the, the way you described the GM thing. It's like, well a motorcycle is not a competitor for a for a car. It's a different thing. But an example, my favorite example is the slide rule. So the slide rule ser is the dominant, I think, or one of the dominant firms of slide rule manufacturing. And I'm sure most of my listeners have never, and viewers have never seen a slide rule in their life. We'll try to get your picture one I link to it. But it was a computing crucial important computing tool for engineers when they didn't have calculators. So what should worry coil and ser Well, the normal thing you'd worry about is your competitive, your competitor slide rule manufacturing company that they might make it out of something different or theirs might become more precise or the reader, the 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 a slide rule, but that's not what happened. A thing came along called a calculator that's not just a better slide rule that dents your market share, it eliminates you, it turns you into a, 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, 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, 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 you're caught flatfooted.

- Yeah, I mean I think that's right. The, I mean, the other thing that makes it tough is, and this is really the hard part about the innovators dilemma. It's even why you can read the book, absorb its lessons and still actually fail. Sure. When you wanna go, especially if you're a publicly traded company 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 or if you're selling a high margin product,

- Sure.

- And you take the money that you earn from that and plow it into what is essentially an experimental low margin product without a lot of customers, you will hear about it from Wall Street and you will hear that you were an idiot. And in fact, wall Street did not like what Nvidia was doing. Jensen really had to fight not just his competitors, but his own investors and even often his own customers to do this, right? Because the, the, the cash flow that's going into the, the scientific computing market that's coming from the video gamers, you're having to charge the gamers more to do this, 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's to me his most profound insight. Yeah, yeah. That's why it's hard Actually, when he interviewed managers at top firms in the eighties, he was like, actually a lot of these guys understood this already. They actually saw the problem already, but they were bound, they, they couldn't convince managers and investors to go along. And this is actually what, what cratered intel. If you go into Intel and talk to people, they actually, many of them will tell you, I mean, who knows, maybe they're, they're covering their own butts, but they will tell you they saw it coming. And in fact, Intel did have its own parallel computing GPU initiative in the, in the mid two thousands. 'cause they saw what was coming with Nvidia. 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 went down. I'm selling 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 pursuing a market that has fewer than $1 billion in revenue each year, it's very, very, very hard to do in a, in a sense, Jensen's success is that he was able to ignore his investors. Right. It's not easy to do.

- Let, let's turn to the secret sauce, to the extent there is one of what makes Jensen Wang a successful CEO. He is not nearly as well known outside of Silicon Valley as many, many other legendary innovators and leaders. You talk about his temper, the, the way he berates employees, often his unbelievable pursuit of perfection, his work ethic is off the charts. And yet when you talk to him, you, there'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 he's normal, he's super normal, but just sort of an oxymoron. And then you say, I've never met anyone like you. And I'm, I'd like you to expand on that. How is he different in terms as a manager, as a hu as a, as a, as a strategist for a company that has become at the heart of, 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's a household name. Everyone knows who he is. He's a celebrity. People follow him down the street. He needs security. Like his face is everywhere. He's incredibly famous in Asia. So it depends on where you are in the world. You know, having said that, what makes Jensen different? Well, first of all, and 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 the sport, and within six months he was nationally ranked. Right? Not a lot of people can, can do that. And that is true in almost any field that he enters. You know, if we all started, I dunno, taking trombone lessons this week, I don't play the trombone. I assume you don't either. Maybe you 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've practiced the trombone for 12 hours a day while we were doing a at best. And by the end of those, and he also learns faster. So by the end of those six months, Jensen would be the best trombone 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's had to rapidly learn some new field and within a few months is, is actually a, 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, 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 just waved his hand at me right away. He's like, guy, he's just smarter than everyone else. I mean, that was just his 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 sim set, his company, and even himself to the task at hand. You know, he's, he thinks like an engineer. It's all inputs and outputs. So I'll give you the most recent example. Everyone on Silicon Valley is trying to get Donald Trump to do what, 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 in my book. I published it and then instantly he pivoted and proved me wrong by befriending Donald Trump. But what Jenssen's gonna do is he's gonna approach Donald Trump just like he would any other problem as an engineering problem. He's gonna study the inputs and outputs of Donald Trump. He's gonna 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, you know, him lifting sales restrictions on China, not putting tariffs on Taiwanese products, or allowing me to get a lot of H one B visas from my workers in, in, in, in, in Silicon Valley. All of which are sort of against what you would think Trump would want, right? Trump would seem to wanna limit the sale of microchips to China, would seem to want to put tariffs on, on Taiwan and would seem to wanna stop visas in the United States. But Jensen gets all those three things from, and this is not a problem that Jess ever faced before. He just learned how to do it faster and better than any other Silicon Valley exec. So he has this remarkable adaptability that I think he really can change himself to the moment at 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 whined 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 and 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 beres employees too. That part, you know, it's controversial. He can be a really rough leader. I mean, he screams at 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, a great military general. They're not trying to be your best friend. And sometimes they will yell at you to get the best outta you. And so Jenssen's just doing the same thing within his company. And one guy said, you know, he's not the only s and p 500 CEO just scream at his employees. Maybe that's all true. But when I witnessed it firsthand, I must say it did seem a little self-indulgent. I I question whether this is necessarily a broadly repeatable management lesson or a cork of, of Jenssen's personality

- Reminded me of the portrait of Steve Jobs in, in 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 it is, you know, Steve Jobs is a jerk. That was the punchline of that book. He berates his employees, he's obnoxious, fill in the blank. That is not the lesson of Walter Isaac's book after I read it. 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, one of his employees, you 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 can get employee 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 extraness is his success. There's, there's something incredibly seductive about the way he's transformed the personal lives of his employees through the rise in stock price. So I understand he's going to get a lot of loyalty just for that reason loan. But I think it's more complicated than that. I think there's a, a certain, I I hate to use this word, messiah, like complex both employee 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.

- Yeah, I mean it's a combination of religious leaders do often use this approach. Yeah. Especially like cult leaders, right? Yeah. 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 know, 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 Jenson very much has that, I say it in the book, he's like a prophet. He's like a prophet. And 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, we've done well. He has led you to the promised Land for real. You know? So I, I think that's part of it. You know, I think the other thing is this is a demanding industry. It's a 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 and it looks like you're gonna miss it, you are going to hear about it vocally from the editor. And the editor is under tremendous pressure to get the thing out on time. At Nvidia, the 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 coaches screaming at the players, often you in the war, the the captain, the lieutenant's screaming at the soldiers. You know, it, it is a, it is an actually effective way to lead people. It's unpleasant. I mean, I've been screamed at for being late on on, yeah, it's on the pleasant experience and you know, often it's just kind of almost human. It's inevitable when you're under stress, under type pressure to deliver this stuff. As NVIDIA's grown and Jensen succeeded, 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, who's this a 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 where it's a little bit, it's a little bit different,

- I Think. I think they have, you know, I think a Bobby Knight couldn't exist today. Yeah. Greg Popovich yells a lot. Well, JB Bickerstaff yells quite a lot and it's still, still the case. I don't think they're throwing, they're choking players anymore. That Bobby Knight was a little extreme, 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 is 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 gonna 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 Jensen inculcates that desperation, that sense of fear and that sense of almost near panic 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's, 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 wanna point out, and what I learned from your book, which surprised me, is the importance of Taiwan. China has IED Taiwan for a long time, and I've always thought the united, I'm a naive person sometimes about geopolitical things. When I was younger, I thought, well, the United States is defending Taiwan because one, they, they are locked in a somewhat global power struggle against China. It used to be the Soviet Union. And so sometimes each of these countries would be fighting essentially a, a war via proxies. And so, you know, standing up for Taiwan is a way the United States shows China is not gonna be pushed around. But your book points out that the Taiwan semiconductor 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, 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 of 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 halt. 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 it'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, to possess Taiwan. You know, in terms of like geographic importance, it would be similar to like the Saudi oil fields. That's basically the, the similar contribution you can imagine. The Saudi oil fields went offline for, for, for a few years. That's what would happen if, if Taiwan went offline 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 the possession of this, but it would, it would cause the world economy to crash. You know, you mentioned Iran, and I won't go too much into it, but I saw yesterday that for the first time since 1945, the US had used a torpedo to sink a warship na naval conflict. First time since 1945. Now perhaps the US has some kind of limited strategic or tactical goal in sinking that boat. But to me it's a signal, it's a single to China.

- Yeah,

- Yeah. We're gonna sink your boats and we can do it. So if China wants to invade Taiwan, you know, we talk about this a lot, but people don't discuss the details. It would be the largest amphibious invasion in, in human history. It would make D-Day 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 too. And patrolling that trip in very short order, there's gonna be a bunch of autonomous submarines with the capability of, 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 it, as I look at this, that's what it looks like to me.

- And the claim of these commentators, and we'll put links up to it 'cause 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 and other ways to distract the United States and, and have a, a cha make it challenging for the United States to both be involved in the Middle East and say in, in Taiwan. But we, we'll link to those articles. I wanna close with a and your point about sinking that worship is a perfect example. That that argument, I, I wanna close with the risks of AI to humanity. We we're pretty aware of the risks of AI to journalism, which you write about in the book explicitly because you, it affects you personally. But it was fascinating to me to read Jensen Wang's defense of AI research and, and innovation as being no different. And many people have argued this, many of them are, my friends, by the way, in economics have argued it's no different than any other tool. We had electricity, we had the printing press, we had the computer. Everybody said they're gonna ruin world. They're gonna take all the jobs. There 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, 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 are just about that. And in the conversation you had with Wang at the end of the book, in your last interview with him in the writing of the book, he dismisses it really with disgust, partly because he's heard it so many times that AI's gonna steal all the jobs, make human re humans redundant. And we're gonna just gonna be in the AI museum, the human being as sort of a, the way we might look at a Neanderthal, at a museum of natural history. 'cause we're gonna 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 a hundred billion dollars be by embracing an AI future. I just want your own thoughts as we, 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. And 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, or kind of biological entities emerge and then they destroy everything that's there and they rebuild the world on 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 a hundred than there are wild animals. Now, we remade the earth on 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 biomimicry. 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. The life on Earth has undergone multiple times where new organisms emerged. There was no oxygen on earth 2 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 Strait and these big cats, these predators came over. They, they killed almost everything that was existing in North America at that time. And again, when humans came, they killed multiple categories of large animals. It wiped 'em 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 perpetuating organisms with their own will and their own desire to survive, I think will determine whether or not we live in the, the flourishing, utopian world of economics or the more dangerous world of biology.

- My guest today has been Stephen Witt. His book is The Thinking Machine. Stephen, thanks for being part of EconTalk. Thank you so much, Ross.