The Man Who Built NVIDIA (with Stephen Witt)

He arrived in America as a child with no English. He was mistakenly sent to a school for juvenile delinquents. He faced rampant prejudice--yet Jensen Huang, the under-the-radar CEO of NVIDIA, became a catalyzing figure behind the AI revolution and built the most valuable company in the world. Listen as journalist Stephen Witt speaks with EconTalk's Russ Roberts about how Jensen pivoted from manufacturing processing units for video games to leveraging their capacity into astonishing computing power and speed. They analyze why Huang bet so heavily on AI when no one else did, and why NVIDIA processors enjoyed almost unrivalled market dominance for so long. They also explore Huang's unique way of thinking and problem-solving—as well as his temperamental leadership style.

• 0:02Speaker 00

  Welcome to Econ Talk, Conversations for the Curious, part of the Library of Economics and Liberty. I'm your host, Russ Roberts of Shalem College in Jerusalem and Stanford University's Hoover Institution. Go to EconTalk.org where you can subscribe, comment on this episode, and find links to other information related to today's conversation. You'll also find our archives with every episode we've done going back to 2006. Our email address is [email protected]. We'd love to hear from you. Today is March 5, 2026, and my guest is author Stephen Witt. His latest book and the

• 0:43Speaker 00

  subject of today's conversation is "The Thinking Machine: Jensen Wang, Nvidia, and the World's Most Coveted Microchip." Stephen, welcome to Econ Talk.

• 0:54Speaker 01

  Well, thank you so much for having me.

• 0:57Speaker 00

  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, 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 them, 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

• 1:42Speaker 00

  his beginning? He comes to the United States under unusual circumstances as a young, as a kid.

• 1:48Speaker 01

  Yep. Jensen 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. There were tanks in the street. And his parents said, "Let's get the kids out of here." 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 Anita Baptist Institute in rural Kentucky in the United States,

• 2:31Speaker 01

  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 grounds are littered with cigarette butts and all the kids have like, they're basically criminals. And Jensen's first night there, he's 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,

• 3:11Speaker 01

  almost exclusively white. And at this time, actually, you know, the Vietnam War is still going on. So a lot of racism against Asians. Jensen's called us as 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 even a leader. And then sort of when his parents returned a couple of 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.

• 3:57Speaker 01

  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.

• 4:34Speaker 00

  And at one point, This is an undergrad at Oregon State. He ends up getting, I think, a master's at Stanford. Is that correct?

• 4:44Speaker 01

  Mm hmm.

• 4:45Speaker 00

  And at some point, he forms his own company. Is it 1993? Is that the right date?

• 4:53Speaker 01

  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.

• 5:05Speaker 00

  Then it's

• 5:05Speaker 01

  The quality. Yeah. Open all the time. It's open 24 hours.

• 5:08Speaker 00

  Very popular. 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 from where they, where had they been before? And why did they break off? What was the, what was the vision that that company was going to fulfill? Because it's quite surprising.

• 5:30Speaker 01

  Jensen's co-founders, Chris Malakowski and Curtis Primm, had been at Sun Microsystems. And Jensen had been at LSI Logic. And they knew each other, actually, because Sun was LSI's customer. 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.

• 6:12Speaker 01

  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 to see what was going on. It 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

• 6:53Speaker 01

  mostly, you know, gunfights and car chases and gore and zombies. But they roll out this 3D graphics chip. Initially, it's a flop. They almost go out of business, actually, the three of them. Nvidia almost went out of 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 was a big hit in 1996 or 1997, kind of the first three-dimensional shooting game.

• 7:23Speaker 00

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

• 8:07Speaker 00

  very small number zero. And so nobody really wanted to take a chance on it, but 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?

• 8:31Speaker 01

  Um, you have to be a little careful with Jensen, who has a man, a tendency to retcon the past to fit the story.

• 8:41Speaker 00

  Yeah, sure.

• 8:42Speaker 01

  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 Myst, 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, 1995, trying to do the same thing. And in fact, Nvidia 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 for him. Because when you're in last place, you can do anything you want. There's no real risk to it.

• 9:27Speaker 01

  You're going to go out of business anyway. So Jensen through this, it's true. So Jensen threw 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.

• 10:03Speaker 00

  Yeah. That's a nice story. It's a good story.

• 10:06Speaker 01

  This is one thing I noticed with Jensen, 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.

• 10:19Speaker 00

  Well, I was, 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 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.

• 10:46Speaker 01

  I'll tell you what I think the lesson of that early video game era is.

• 10:51Speaker 01

  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 call 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 for them. Jensen knows how to win in a knife fight.

• 11:31Speaker 01

  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 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.

• 12:16Speaker 01

  He, he destroyed our company without mercy, without pity. And, um, you know, that's not the story he's going to tell. But that's what happened.

• 12:25Speaker 00

  For sure. Why did they call it Nvidia?

• 12:29Speaker 01

  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, 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. Uh, that's where it comes from. And, you know, I would say they have done that. They have made their competitors green with envy.

• 13:05Speaker 00

  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 four 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, three point six and someone like that.

• 13:29Speaker 01

  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 Poor's 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.

• 13:48Speaker 00

  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, I think soon we will say created the modern world, which is a frightening, 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 poach some engineers from them, but there's still lots of competition.

• 14:26Speaker 00

  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 called artificial intelligence and that they might be able to contribute to it.

• 14:57Speaker 01

  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 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.

• 15:30Speaker 01

  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 parallel compute approach is much harder to program.

• 16:14Speaker 01

  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's 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 CUDA. It was free. It was open source. It wasn't open source, but it was free.

• 16:59Speaker 01

  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 Jensen started to do all this outreach to scientists during Phase 2. 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.

• 17:35Speaker 01

  So by 2008 or 2009, this program is up and running. They've got, you know, a few, let's say 100,000 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 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 homebrewed supercomputer built out of graphics cards?

• 18:20Speaker 01

  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 1,100 pages long. Of those 1,100 pages, about 16 total are devoted to neural nets.

• 19:05Speaker 01

  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.

• 19:19Speaker 01

  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 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 homebrew 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.

• 20:01Speaker 01

  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, not commodity, but cheap hardware that you could just buy at 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 to, say, 2012 or 2013, Nvidia's stock goes nowhere. And in fact, Jensen was not well regarded.

• 20:43Speaker 01

  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 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.

• 21:28Speaker 01

  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 galaxy made of money, it turns out. You know, I guess we'll get into that. I mean, one of the...

• 21:54Speaker 00

  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 it was around 2020. And 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

• 22:38Speaker 00

  Nvidia as 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 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 made some people think it won't last for long, and some people think it's just getting

• 23:20Speaker 00

  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.

• 23:53Speaker 01

  They built the modern 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 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

• 24:38Speaker 01

  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 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 gamers, they could put, you know, run this on an Nvidia chip, that's the best platform for this particular game. And all the gamers would go out and spend $1,000 on Nvidia hardware. And then they did the same for scientists.

• 25:23Speaker 01

  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 had, they 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 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 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 backwater

• 26:04Speaker 01

  because that meant 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 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 the computer, the smarter it gets.

• 26:35Speaker 01

  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 ChatGPT.

• 27:20Speaker 01

  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, this is like the best thing that can ever happen because it 100 or even 1,000 x's the demand for their microchips. And so this is the point at which Nvidia, which had actually been doing well already

• 28:04Speaker 01

  based on the computer vision results, starts to really rocket 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 data goes in and intelligence comes out.

• 28:24Speaker 00

  But why is it that in these server farms, this...

• 28:29Speaker 00

  This ugly anonymous unbranded thing in a 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?

• 28:54Speaker 01

  Yeah. So why can't we just use an AMD chip? Well, we can.

• 28:58Speaker 00

  A what chip? An AMD?

• 28:59Speaker 01

  AMD is one of Nvidia's rivals. 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 their software in particular is just not as good as Nvidia's 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, Nvidia was really the only game in town.

• 29:41Speaker 01

  I mean, this is the genius of the $0 billion market. Who's going to be crazy enough to spend 10? This is how much it costs. $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, 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 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 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.

• 30:26Speaker 01

  Jensen is, you know, he won the knife fight. He won 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 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.

• 31:07Speaker 01

  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 they're five or 10 years behind. The problems they face today are the problems that Nvidia solved five to 10 years ago.

• 31:31Speaker 00

  You you talk about the influence of Clayton Christensen on Jensen Wang's thinking, and it's a 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 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.

• 32:14Speaker 00

  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 will. And this is really the, I think, the great. Success of capitalism in the last 100 years, the pace of innovation, if anything, has quickened. It 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 not true.

• 32:58Speaker 00

  They will make payroll 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.

• 33:10Speaker 01

  Yeah, the motto for a long time was Nvidia is 30 days from going out of business. In the early days, that was actually literally true a couple of times.

• 33:18Speaker 00

  Yeah. But talk about Clayton Christensen's book, The Innovator's Dilemma, and what you and Wang, Jensen Wang.

• 33:29Speaker 01

  Okay, I'd love to hear your take on it too, because I'm obsessed with Christensen. So to begin with, I think 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.

• 34:14Speaker 01

  And it doesn't make a lot of money. You can't sell the dirt bike. 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 10x 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 think you know what happened with Honda. They came to dominate the US dirt bike market.

• 34:56Speaker 01

  They leveraged that expertise to build a compact car and they raided 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 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 counterinsurgency manual for decision makers at established firms to avoid getting raided by these low-cost players. And there's a line explicitly in the innovator's dilemma that I highlighted because I was so shocked by it.

• 35:39Speaker 01

  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.

• 36:23Speaker 01

  Jensen used to assign the innovator's 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 retcon the past to fit his current operating philosophy.

• 37:07Speaker 01

  Also, many of his advice, according to his exactly, is just kind of internally contradictory. 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 koans. 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 differ on Christensen? I should say I'm obsessed with Christensen.

• 37:43Speaker 00

  So I have a small, I mean, what I've 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, Koi Filanesser 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

• 38:28Speaker 00

  their life. I'll try to get your picture, want a link to it. But it was a computing crucial, important computing tool for engineers when they didn't have calculators. So what should worry Koi Filanesser? Well, the normal thing you'd worry about is your competitive, your competitor's 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.

• 39:13Speaker 00

  Dents 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.

• 39:55Speaker 00

  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 flat-footed.

• 40:09Speaker 01

  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 innovator's dilemma, it's even why you can read the book, absorb its lessons, and still actually fail.

• 40:26Speaker 00

  Sure.

• 40:27Speaker 01

  When you want to 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.

• 40:45Speaker 00

  Sure.

• 40:46Speaker 01

  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 are 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 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, this science project that won't benefit them directly. So it's hard, your investors and customers don't like it.

• 41:29Speaker 01

  And this was, I think, Kristensen's, to me, his most profound insight.

• 41:33Speaker 00

  Yeah.

• 41:34Speaker 01

  That's why it's hard.

• 41:36Speaker 01

  Actually, when he interviewed managers at top firms in the 80s, he was like, 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 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 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 mid 2000s, because 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

• 42:19Speaker 01

  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. And since Jensen's success is that he was able to ignore his investors, right? It's not easy to do.

• 42:52Speaker 00

  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 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

• 43:34Speaker 00

  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, I'm supernormal. He doesn't say he's normal. He's supernormal, which is 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, as a strategist for a company that has become at the heart of, of again, the modern world?

• 44:07Speaker 01

  Well, well, uh, 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 needs security. Like his face is everywhere. He's incredibly famous in Asia. So it depends on where you are in the world. Um, you know, having said that, uh, what makes Jensen different? Well, first of all, I know whenever, 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.

• 44:51Speaker 01

  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? 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 don't know, taking trombone lessons this week, I don't play the trombone. I assume you don't either, maybe you do, 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 trombone for 12 hours a day while we were doing eight at best. And by the end of those, and he also learns faster.

• 45:34Speaker 01

  So by the end of those six months, Jensen would be the best trombone player among us. Uh, 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 domain expert. Um, 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, guys, you're 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.

• 46:16Speaker 01

  So he can use his intelligence to repurpose his company and even himself to the task at hand. You know, he thinks like an engineer. It's all inputs and outputs. So I'll give you the most recent example. Everyone in 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.

• 47:00Speaker 01

  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 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, you know, him lifting sales restrictions on China, not putting tariffs on Taiwanese products, or allowing me to get a lot of H1B visas for my workers in Silicon Valley. All of which are sort of against what you would think Trump would want, right?

• 47:44Speaker 01

  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's ever faced before. He's 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.

• 48:11Speaker 01

  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 wizened 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 berates employees too. That part, you know, it's controversial. He can be a really rough leader. I mean, he

• 48:54Speaker 01

  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 out of you. And so Jensen's just doing the same thing within his company. One guy said, you know, he's not the only S and P 500 CEO to 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 question whether this is necessarily a broadly repeatable management lesson or cork of Jensen's personnel.

• 49:31Speaker 00

  Reminded me of the portrait of Steve Jobs 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 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 Isaacson'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, who says the following,

• 50:12Speaker 00

  "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."

• 50:22Speaker 00

  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 extraordinariness is his success. There's something incredibly seductive about the way he's transformed the personal lives of his employees through the rise in the 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

• 51:03Speaker 00

  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-

• 51:16Speaker 01

  Yeah, I mean, it's a combination of religious leaders do often use this approach, especially like cult leaders, right? 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 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

• 51:56Speaker 01

  real. You know, so 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 going to miss it, you are going to hear about it vocally from the editor.

• 52:24Speaker 01

  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, it 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.

• 52:46Speaker 01

  In the war, the captain, lieutenant screaming at the soldiers. You know, it is, it is an 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 kind of almost human. It's inevitable when you're under stress, under tight pressure to deliver this stuff. As Nvidia has grown and Jensen succeeded, I think it has gotten a little self indulgent.

• 53:11Speaker 00

  But it's a style that's gone out of fashion, you know, to some extent. When I think about Bobby Knight, you mentioned basketball, he's 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.

• 53:33Speaker 01

  I think, I think they have, you know, I think a Bobby Knight couldn't exist today.

• 53:37Speaker 00

  Yeah.

• 53:37Speaker 01

  Greg Popovich yells a lot. JB Bickerstaff yells quite a lot. This is still the case. I don't think they're throwing, 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.

• 54:02Speaker 01

  It's not Google.

• 54:05Speaker 01

  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 Jensen inculcates that desperation, that sense of fear and that sense of almost near panic every day.

• 54:41Speaker 00

  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 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.

• 55:26Speaker 00

  And I've always thought the United States, 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 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 war via proxies. And so, you know, standing up for Taiwan is a way the United States shows China is not going to 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 conquer Taiwan.

• 56:10Speaker 00

  But talk about why this company, again, I think most people have never heard of it. Why is it important?

• 56:16Speaker 01

  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 possess Taiwan.

• 56:57Speaker 01

  You know, in terms of like geographic importance, it would be similar to like the Saudi oil fields. That's basically the similar contribution. You imagine the Saudi oil fields went offline for a few years. That's what would happen 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 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, just like a warship,

• 57:40Speaker 01

  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 signal to China.

• 57:52Speaker 00

  Yeah.

• 57:53Speaker 01

  You know, we're going to 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 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

• 58:36Speaker 01

  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.

• 58:45Speaker 00

  And 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 and other ways, to distract the United States and and have a a make it challenging for the United States to both be involved in the Middle East and, say, in in Taiwan. But we'll link to those articles. I want to close with a and your point about sinking that worship 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

• 59:26Speaker 00

  you it affects you personally. But it was fascinating to me to read Jensen Huang'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 going to ruin the world, they're going to 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 believe that it always has. I believe it is likely it will turn out well in the future, although there are

• 1:00:09Speaker 00

  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 have 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 is going to steal all the jobs, make humans redundant, and we're going just going to 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, because we're going to be dominated by machines. And he doesn't believe that. Of course, he has a terrible emotional ability to believe that

• 1:00:48Speaker 00

  because he has become an extraordinarily wealthy man worth over $100 billion by embracing an AI future. I just want your own thoughts as we as we continue to ride this wave.

• 1:01:03Speaker 01

  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, animals, or kind of biological entities emerge. And then they destroy everything that's there. And they rebuild the world in their own image.

• 1:01:45Speaker 01

  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 remade the Earth in 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.

• 1:02:29Speaker 01

  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 talked 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 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 Asia and the Bering Strait and these big cats, these predators came over, they killed almost everything that was

• 1:03:10Speaker 01

  existing in North America at that time. And again, when humans came, they killed multiple categories of large animals, 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-perpetuating organisms with their own will and their own desire to survive, I think, will determine whether or not we live in the flourishing utopian world of economics or the more dangerous world of biology.

• 1:03:43Speaker 00

  My guest today has been Stephen Witt. His book is The Thinking Machine. Stephen, thanks for being part of Econ Talk.

• 1:03:50Speaker 01

  Thank you so much, Russ.

• 1:03:58Speaker 00

  This is Econ Talk, part of the Library of Economics and Liberty. For more Econ Talk, go to EconTalk.org, where you can also comment on today's podcast and find links and readings related to today's conversation. The sound engineer for Econ Talk is Rich Goyette. I'm your host, Russ Roberts. Thanks for listening. Talk to you on Monday.