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Introduction
This is the story of how a niche vendor of video game hardware became the most valuable company in the world. It is the story of a stubborn entrepreneur who pushed his radical vision for computing for thirty years, in the process becoming one of the wealthiest men alive. It is the story of a revolution in silicon and the small group of renegade engineers who defied Wall Street to make it happen. And it is the story of the birth of an awesome and terrifying new category of artificial intelligence, whose long-term implications for the human species cannot be known.
At the center of this story is a propulsive, mercurial, brilliant, and extraordinarily dedicated man. His name is Jensen Huang, and his thirty-two-year tenure is the longest of any technology CEO in the S& P 500. Huang is a visionary inventor whose familiarity with the inner workings of electronic circuitry approaches a kind of intimacy. He reasons from first principles about what microchips can do today, then gambles with great conviction on what they will do tomorrow. He does not always win, but when he does, he wins big: his early, all‑in bet on AI was one of the best investments in Silicon Valley history. Huang’s company, Nvidia, is today worth more than $3 trillion, rivaling both Apple and Microsoft in value.
In person, Huang is charming, funny, self-deprecating, and frequently self-contradictory. He keeps up a semicomic deadpan patter at all times. We met in 2023 for breakfast at a Denny’s diner, his favorite restaurant chain. Huang had developed the business plan for Nvidia at this same restaurant thirty years earlier; chatting with our waitress, he ordered seven items, including a Super Bird sandwich and a chicken-fried steak. “You know, I used to be a dishwasher here,” he told her. “But I worked hard! Like, really hard. So I got to be a busboy.”
Huang, born in Taiwan, immigrated to the United States when he was ten. Denny’s was the crucible of his assimilation—working there as a teenager, he ate through the entire menu. Still, he told me, he maintains an outsider’s perspective. “You’re always an immigrant,” he said. “I’m always Chinese.” He cofounded Nvidia (pronounced IN‑vidia, not NUH-vidia) in 1993 when he was thirty, first targeting the nascent market for high-end video game graphics. His products were popular; his customers liked to build their own PCs, sometimes buying transparent housing to showcase their Nvidia hardware.
In the late 1990s, seeking to better render the Quake series of games, Nvidia made a subtle change to the circuit architecture of its processors, allowing them to solve more than one problem at a time. This approach, known as “parallel computing,” was a radical gamble. “The success rate of parallel computing was zero percent before we came along,” Huang said, rattling off a list of forgotten start-ups. “Literally zero. Everyone who tried to make it into a business had failed.” Huang ignored this dismal record, pursuing his unconventional vision in open defiance of Wall Street for more than a decade. He looked for customers besides gamers, ones who needed a lot of computing power—weather forecasters, radiologists, deep-water oil prospectors, that sort of thing. During this time, Nvidia’s stock price floundered, and he had to fend off corporate raiders to retain his job.
Huang stuck with this bet, losing money on it for years, until in 2012 a group of dissident academics in Toronto purchased two consumer video game cards to train an exotic kind of artificial intelligence called a neural network. At the time, neural networks, which mimic the structure of biological brains, were deeply out of favor, and most researchers considered them obsolete toys. But when Huang saw how fast neural networks trained on his parallel-computing platform, he staked his entire company on the unexpected symbiosis. Huang now needed two underdog technologies to work—two technologies that had always failed the test of the marketplace in the past.
When this audacious corporate parlay hit, Nvidia increased in value several hundred times. In the past decade, the company has evolved from selling $200 gaming accessories to shipping multimillion-dollar supercomputing equipment that can fill the floor of a building. Working with pioneers like OpenAI, Nvidia has sped up deep-learning applications more than a thousand times in the last ten years. All major artificial-intelligence applications—Midjourney, ChatGPT, Copilot, all of it—were developed on Nvidia machines. It is this unprecedented increase in computing power that has made the modern AI boom possible.
With a near-monopoly on the hardware, Huang is arguably the most powerful person in AI. Certainly, he’s made more money from it than anyone else. In the strike‑it‑rich tradition, he most closely resembles California’s first millionaire, Samuel Brannan, the celebrated vendor of prospecting supplies who lived in San Francisco in 1849. Except rather than shovels, Huang sells $30,000 AI‑training chips that contain one hundred billion transistors. The wait time to purchase his latest hardware is currently more than a year, and on the Chinese black market, his chips sell for double the price.
Huang doesn’t think like a businessman. He thinks like an engineer, breaking down difficult concepts into simple principles, then leveraging those principles to great effect. “I do everything I can not to go out of business,” he said at breakfast. “I do everything I can not to fail.” Huang believes that with AI, the basic architecture of digital computing, little changed since it was introduced by IBM in the early 1960s, is being reconceptualized. “Deep learning is not an algorithm,” he said. “Deep learning is a method. It’s a new way of developing software.”
This new software has incredible powers. It can speak like a human, write a college essay, solve a tricky math problem, provide an expert medical diagnosis, and cohost a podcast. It scales with the amount of computing power available to it and never seems to plateau. The evening before our breakfast, I’d watched a video in which a robot, running this new kind of software, stared at its hands in seeming recognition, then sorted a collection of colored blocks. The video had given me chills; the obsolescence of my species seemed near. Huang, rolling a pancake around a sausage with his fingers, dismissed my concerns. “I know how it works, so there’s nothing there,” he said. “It’s no different than how microwaves work.” I pressed Huang—an autonomous robot surely presents risks that a microwave oven does not. He responded that he has never worried about the technology, not once. “All it’s doing is processing data,” he said. “There are so many other things to worry about.”
Where this will lead is anyone’s guess; many technologists now worry that AI’s capabilities pose a direct threat to the survival of the human species. (Among these “doomers” are the Toronto scientists who first implemented AI on Huang’s platform.) Huang dismisses such pessimism. For him, AI is a pure force for progress, and he has declared that it is spurring a new industrial revolution. He doesn’t permit much disagreement on this topic, and his force of personality can be intimidating. (“Interacting with Jensen is like sticking your finger in the electrical socket,” one of his executives said.) Huang’s employees worship him—I believe they would follow him out of the window of a skyscraper if he saw a market opportunity there.
In May 2023, hundreds of industry leaders endorsed a statement that equated the risk of runaway AI with that of nuclear war. Huang didn’t sign it. Some economists have observed that the Industrial Revolution led to a relative decline in the global population of horses and have wondered if AI might do the same to humans. “Horses have limited career options,” Huang said. “For example, horses can’t type.” As he finished eating, I expressed my concerns that, someday soon, I
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