SYLVIE DOUGLIS, BYLINE: This is PLANET MONEY FROM NPR.
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DARIAN WOODS, HOST:
All right, we have a riddle for you. See if you can guess what we're talking about today.
WAILIN WONG, HOST:
I was born at a Denny's restaurant.
WOODS: My name is Latin for envy.
WONG: I played a lot of video games in my younger years. Today, I'm more into poetry and translation.
WOODS: I'm 31 years old, and I've been down and out so many times that my unofficial motto is that I'm 30 days from going out of business.
WONG: Still don't have it? What if we said, for a moment, I was the most valuable company on the planet?
WOODS: Yeah, that's right. The answer is the computer chip designer Nvidia. And Nvidia's chips are the leading choice for artificial intelligence developers. In the last couple of years, Nvidia's sales have shot up like a jet plane - its stock price more like a rocket.
WONG: But there are several chip companies out there, like AMD and others. So why is Nvidia the one dominating the AI scene?
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WOODS: Hello, and welcome to PLANET MONEY. I'm Darian Woods.
WONG: And I'm Wailin Wong.
WOODS: Nvidia went from selling niche graphics cards for gaming to becoming one of the biggest companies in the world with help from another tech giant. Today on the show, two stories about innovators who went up against the status quo and, in the process, helped transform and industry we've all come to rely on.
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WOODS: Nvidia is often called a chipmaker, and that's a bit of an oversimplification. First of all, Nvidia doesn't actually manufacture the semiconductor chips. That's done by companies like TSMC and Samsung. Nvidia does the designing, like where the circuits all go on the chip. It also makes the software so that developers can work with those chips.
WONG: This combination has its roots in 1992. Jensen Huang and two other engineer friends met at a Denny's restaurant in San Jose. Their idea was to improve video games by building specialized chips for rendering 3D graphics. This decision would become incredibly lucrative.
WOODS: One person who's spent a lot of time unearthing Nvidia's history is David Rosenthal. David is one half of the tech podcast "Acquired," which made this epic series of podcasts on the company's history.
DAVID ROSENTHAL: Nvidia corporate communications got in touch and said, who were your sources? Who told you all this in the company? And we're like, well, nobody. We just - you know, we watched a lot of YouTube videos with Jensen, and we read a lot of papers.
WONG: This led to an in-person interview with Jensen Huang towards the end of last year.
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ROSENTHAL: First question - when did you realize that only eight were going to work?
JENSEN HUANG: We should have - I realized I didn't learn about it until it was too late. We should have...
WOODS: By all accounts, working for Jensen is super intense and not for everybody. Failures are shared publicly, for example. But David says those who stay are loyal.
ROSENTHAL: Jensen is truly singular in Silicon Valley, and I think - I mean, the man is 60 years old. Nobody from his generation is still running their company in the same way that he is. He is probably more engaged in every detail at Nvidia than any of those other founders ever were at their companies.
WONG: David says the way that Jensen is involved in the details is one key to why Nvidia rode the AI wave so astutely.
ROSENTHAL: He started moving the Nvidia ship in the direction that it is today with AI and machine learning starting in, like, 20 years ago.
WOODS: That's incredible foresight. You know, it's obviously way before ChatGPT, but it's even before driverless cars or even voice recognition. How did he see this coming?
ROSENTHAL: Well, I think this is the key to Jensen. If you were to say that sentence to him, I think he would respond and say, no, it was not before all those things. All of those things were happening. They were just happening in the deep scientific computer science research community. He was so deeply plugged into all of this that he knew the principal actors who now are some of the, you know, true leaders in AI research at OpenAI, at Anthropic and elsewhere these days. He knew them personally. He was reading the white papers. He was visiting them at universities all back in the mid-2000s.
WONG: At this time, Nvidia was mainly known by 15-year-olds upgrading their gaming computers. But in the early 2000s, Jensen essentially made a huge bet on selling supercomputing power to a wider range of people. And as part of this, the company launched a software development framework called CUDA. CUDA acts kind of like a middleman between the software developer and the chip, and the system would be crucial for AI after a turning point in 2012.
ROSENTHAL: This was the big bang moment that kicked off research and investment at a commercial scale into artificial intelligence.
WOODS: It was at this annual competition for researchers to submit AI systems that could recognize images from a massive database. The database was called ImageNet, and progress had been gradual. Entries could correctly identify what a majority of images were but would typically have an error rate of around 25 or 30%. That year, a team from the University of Toronto submits this entry that they call AlexNet.
ROSENTHAL: It blows away the rest of the field. So the percentage of images it got wrong was around 15% - 15. This was a quantum leap.
WONG: The team used an existing method of AI called a neural network, which was powerful but had always been limited by how much computing power it required to train. To overcome this, AlexNet did something different. The standard approach had been to train these models using central processing units, or CPUs, the generalist brains in your computer. But these calculate their instructions sequentially. You can think of it like one calculation, then another. So instead, this team ran these training calculations using graphics processing units, GPUs, what you would play video games with.
ROSENTHAL: A graphics card handles tens of thousands of instructions at a time. I've really widened the pipe of the amount of compute that I can stuff through this thing at any given point in time.
WOODS: And who makes graphics cards? Nvidia, of course.
ROSENTHAL: People at Google and people at Facebook said, holy crap. You could use, they realized, these image classification systems to build way better social media feed recommenders.
WONG: So for image recognition, programmers were training the software to notice patterns in, say, pictures of cats - whiskers, fur, four legs. And similarly, the big social media companies realized they could train software to notice patterns in what kinds of pictures and posts and movies people like on the internet.
ROSENTHAL: That was, you know, billions and billions and billions of dollars of profits. So there was a good 10-year run where, like, nothing else mattered.
WOODS: And that meant that places like YouTube and Facebook and Instagram were hiring a lot of AI talent. And David says this was actually part of the motivation for Elon Musk and Sam Altman to found OpenAI.
ROSENTHAL: They were really worried that Google and Facebook had just become a duopoly of all the AI development and research talent because it was the only, you know, economically viable use case.
WONG: OpenAI's launch of ChatGPT in late 2022 was the next hinge moment for AI and for Nvidia. It woke investors and everyday people to advancements in AI that got them dreaming about the future. It's where Nvidia suddenly started its journey, becoming one of the most valuable companies on the planet.
WOODS: In the parlance of tech investing, Nvidia has this giant moat. David reckons it's protected from competition in the foreseeable future because of CUDA, that development system.
ROSENTHAL: These systems build on top of themselves over time and get more and more complex and powerful. So it would be like somebody going and starting a new phone operating system from scratch and saying, OK, what are all the list of things we need to build to make this phone operating system viable to compete with Android and iOS? That's a tall order.
WONG: David says network effects are also important. Now millions of other developers use CUDA. So if you're a college student, that's the language you're going to learn. It's a self-reinforcing cycle. This moat is something that has gotten people speculating about government action. News outlets like The New York Times have reported that the Department of Justice has cleared the way for possible antitrust action against Nvidia.
WOODS: Jensen, though, has said he doesn't like the word moat and prefers to think of the company as working alongside the entire AI ecosystem. Whichever words you use to describe it, Nvidia is standing on top of the world right now. Though, even with all its chip sales, its stock price is very highly priced by conventional measures. One misstep, and there is a long way to fall.
WONG: All the way into the moat filled with barra-CUDAs (ph).
WOODS: Barra-CUDAs. That's quite funny.
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WOODS: If you want to hear more on Nvidia, check out our next PLANET MONEY bonus episode, which is out in a couple of weeks. It'll feature my extended interview with David, where we go into more detail on the company, on CUDA and Jensen Huang. That's just for PLANET MONEY+ supporters. It's one of the perks you get for signing up, including sponsor-free listening and access to exclusive merch. Go to plus.npr.org/planetmoney for more.
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WOODS: After the break, another unlikely origin story that made Nvidia's rise possible - how America's advanced chipmaking ended up in Taiwan.
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ADRIAN MA, HOST:
I'm Adrian Ma.
WOODS: And I'm Darian Woods. If you have the latest iPhone, you'll find inside it a computer chip with parts so fine-grained that you can measure them in atoms. That processing chip contains 16 billion transistors. The iPhone chip and most cutting-edge chips for computers and cars are made in a handful of factories on a small island in the Pacific - in Taiwan.
MA: And the story of how Taiwan came to make the most advanced chips in the world can basically be told through the story of one man - Morris Chang. He's like a founding father of the chip/semiconductor industry.
WOODS: It's not an exaggeration to say that Morris' personal career obstacles and what he chose to do about them helped shape the balance of power between the U.S. and the world. When Morris Chang grew up in China and Hong Kong, he fled three wars and saw a lot of poverty around him. So when he got into Harvard at age 18, he had one goal - to make money. And he looked around at what a middle-class kid from China could do to make a lot of money back in 1950.
MA: Engineering. So he left Harvard for MIT, which actually had an engineering program.
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MORRIS CHANG: I really wasn't all that interested in mechanical engineering or engineering at all.
MA: (Laughter) But he's in it for the bucks, right?
WOODS: That's right.
MA: That is Morris Chang speaking in an interview with Stanford's president in 2014.
WOODS: And Morris eventually gets a job at electronics company Texas Instruments, and Morris is promoted really quickly. By the early 1970s, he's vice president. But pretty soon, he finds his career stalling, and he can't get promoted further. Chang-Tai Hsieh is an economist at the University of Chicago who was born in Taiwan. And at one point, he even worked at the same organization that Morris once led.
MA: Chang-Tai says in the early 1970s, all chip design, manufacturing, testing, and packaging was done in the U.S. And the U.S. was so dominant because semiconductors were invented here.
CHANG-TAI HSIEH: It was prohibitively expensive to get into this industry. You had to come up not only with the expertise to put together the design, but then you also had to spend several billion dollars putting up the manufacturing capacity. Then you also...
WOODS: Just a couple of details about how hard it is to manufacture semiconductors. Your building has to be constructed so that it doesn't vibrate, down to just nanometers. You cannot build a facility where there's any dust that can fall on your chips. It has to be a thousand times cleaner than a hospital operating theater.
MA: But there were a few things other companies could do. In the 1970s, Morris Chang realized he could save his company a lot of money by outsourcing simple tasks like packaging and testing to Taiwan. In Taiwan, incomes were only about a tenth of American incomes. And Morris supercharged the company's growth by pioneering new ways of pricing the computer chips that would, you know, result in a loss now but capture the market share as the cost of making chips got cheaper and cheaper. Oh, and he also ran the division that invented the Speak & Spell.
WOODS: Chang-Tai Hsieh says that despite Morris Chang's achievements, for the next decade, his career kind of hit a wall.
HSIEH: He wanted to be chief executive. And then - and this is something that he has said publicly. He felt that, given the nature of the culture at Texas Instruments, he would never become the CEO.
WOODS: Why not?
HSIEH: Because he's Asian. I mean, that's what he has said, that it was a very white company. It was a culture there in which people like him don't get promoted.
MA: Then in 1983, after a couple of years of agonizing and after 25 years with Texas Instruments, Morris Chang decided to leave the company. And before long, the premier of Taiwan recruits Morris to run this thing called the Industrial Technology Research Institute. Basically, the goal is to turn Taiwan into a tech powerhouse. Think of it as an organization that's part research, part government investing in new companies.
HSIEH: The culture very much was that of a start-up.
MA: Chang-Tai worked here in 1990.
HSIEH: It wasn't a typical government bureaucracy. They were working 12-hour days in shoebox apartments. People get paid nothing. But there's this dream that it's a combination of getting rich and that you're going to change the world.
WOODS: And before long, the government of Taiwan realizes, wait - we have this semiconductor superstar right in front of us, someone who could have been CEO at one of the very few companies in the world who can make cutting-edge chips. So they say, how about instead of helping out other companies, you run your own, a semiconductor company? And I want to pause here for a second because this is a pretty bold decision. It's a possibly foolish idea because history books are full of cases where a government says, we want to make whatever, cars, spaceships, cellphones. And then it just fails spectacularly. It costs an immense amount of money, doesn't turn a profit most of those times. But Taiwan decides to do it with one of the absolute hardest types of businesses to start, both then and now.
MA: So Morris looks around, and he realizes there just are not enough scientists who know how to design chips to build, you know, essentially the Taiwanese version of Texas Instruments. So Morris says, let's try something different. He thinks, what do we have? Well, we've got cheap labor that's getting more and more skilled at advanced manufacturing. We've got solid supply lines for factories.
WOODS: And then he thinks about how America almost has the reverse problem. It's got too many people who can design chips. So many people who would start their own semiconductor company, but building the factory in the U.S. is too expensive.
HSIEH: His basic idea was to say, you know, why don't we develop a company that would only serve other customers in which we completely get out of the design business? We only do the manufacturing part of the business. And this would allow us to serve lots of customers that at that time, their needs weren't being met.
WOODS: And this simple decision, this decision to separate manufacturing from design, completely changed the chip industry. In 1987, Morris Chang founded Taiwan Semiconductor Manufacturing Company - TSMC.
MA: And over the years, more and more companies started to use TSMC. Some big chipmakers even sold off the manufacturing parts of their businesses, knowing that, you know, they could design the chips, and TSMC could make them.
WOODS: And with the advent of TSMC and its Taiwanese competitor UMC, some startup founder could have a great idea for a new chip, like a new graphics card for gaming, but they didn't need hundreds of millions of dollars. This is exactly what happened with Nvidia. It designed semiconductors in America, but TSMC makes them.
HSIEH: A company like Nvidia would not have existed without TSMC.
WOODS: So, on the one hand, yes, TSMC took a lot of the chip manufacturing business away from America, but it also allowed entrepreneurial dreams from Silicon Valley to come true. Now TSMC is the most important semiconductor manufacturing company and the eighth largest company in the world. It makes the vast majority of the world's most advanced chips. It's so critical for global supply chains that U.S. lawmakers are nervous. In August of 2022, President Joe Biden passed a bill called the CHIPS Act, which means $39 billion for companies making computer chips in America.
MA: Which is so much money, right?
HSIEH: It's not that much money relative to how much is needed to build because each semiconductor plant is about $28 billion.
WOODS: OK, so you might be able to build two plants, maybe.
HSIEH: I guess my prediction is that this is going to be another boondoggle. It's that it's going to be...
MA: And so this money is supposed to light a fire under the collective butts of the chip manufacturing industry, right? Like, what the U.S. is trying is something that the Taiwanese government tried about 35 years ago. And it worked for Taiwan because Morris had the skills and saw a slice of the market he could take at just the right time. And the government funded it like a business investment - right? - not a handout to a company.
HSIEH: I would say that the only plausible case for something like the CHIPS Act is if, at some point in the future, there's war between Taiwan and China, and TSMC is either destroyed or TSMC is taken over by the Chinese Communist Party.
WOODS: Up to $6.6 billion of the CHIPS Act money has been allocated in direct funding for a TSMC factory. This one is being built in Arizona. Chip production is set to start in 2028.
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WOODS: And the irony is in another world where Morris Chang found more open doors in the U.S., a company like TSMC could have been American to begin with.
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WOODS: On the next PLANET MONEY, 40 years ago, an airline CEO had a novel idea.
UNIDENTIFIED PERSON #1: Our concept was everybody wants to get from A to B. They don't go on an airplane to get food.
UNIDENTIFIED PERSON #2: That sounds a lot like my airplane experience today.
UNIDENTIFIED PERSON #1: (Laughter) Well, please don't compare us to Spirit.
WOODS: The story of the first national budget airline and why flying has become the way it is today. That's next time on PLANET MONEY.
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WOODS: These stories originally aired on The Indicator from PLANET MONEY and were originally produced by Julia Ritchey and Viet Le with help from Nicky Ouellet. They were edited by Kate Concannon and engineered by Cena Loffredo and James Willetts. Fact-checking was done by Taylor Washington and Sierra Juarez. Our PLANET MONEY edition was produced by Willa Rubin, edited by Jenny Lawton, fact-checked by Sofia Shchukina and engineered by Neal Rauch. Alex Goldmark is our executive producer.
I'm Darian Woods. This is NPR. Thanks for listening.
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