The riveting investigative account of Nvidia, the tech company that has exploded in value for its artificial intelligence computing hardware, and Jensen Huang, Nvidia’s charismatic, uncompromising CEO In March 2024, following the revelation that ChatGPT had trained on Nvidia’s microchips, and twenty-one years after its founding in a Denny’s restaurant, Nvidia became the third most-valuable corporation on Earth. In Essential to Nvidia’s meteoric success is its visionary CEO Jensen Huang, who more than a decade ago, on the basis of a few promising scientific results, bet his entire company on AI. Through unprecedented access to Huang, his friends, his investors, and his employees, Witt documents for the first time the company’s epic rise and its iconoclastic CEO, who emerges as a compelling, single-minded, and ferocious leader, and now one of Silicon Valley’s most influential figures. <The Thinking Machine< is the story of how Nvidia evolved from selling cheap, aftermarket circuit boards to hundred-million-dollar room-sized supercomputers. It is the story of a determined entrepreneur who defied Wall Street to push his radical vision for computing, in the process becoming one of the wealthiest men alive. It is about a revolution in computer architecture, and the small group of renegade engineers who made it happen. And it’s the story of our awesome and terrifying AI future, which Huang has billed as the “next industrial revolution,” as a new kind of microchip unlocks hyper-realistic avatars, autonomous robots, self-driving cars, and new movies, art, and books, generated on command.

Autorentext

Stephen Witt

Leseprobe
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 prob…