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.

But Huang’s knowledge of business books was encyclopedic. Dwight Diercks recalled Huang arguing with another executive about how much Nvidia’s products should cost. “The guy had an MBA, but he’d never read a book about pricing,” Diercks said. “Jensen had read probably ten or fifteen.” As the argument progressed, Huang halted the discussion and asked the MBA to name his three favorite books on pricing. The guy fumbled around for a bit, unable to name a single title. Huang listed out his three favorites, then told the executive he’d resume the discussion once he’d finished them.

NINE: Cuda

“To distinguish himself, Huang had to pursue a strategy that so defied conventional business logic that ATI wouldn’t follow. He had to build an exploratory product, like a $300 entry-level scientific supercomputer that not only didn’t have competitors but also didn’t even have obvious customers. The zero-billion-dollar market, by definition, was one that only he would participate in—one that only he would even see. Huang was going to build a baseball diamond in a cornfield and wait for the players to arrive.

Early in his career, Altman observed that new CEOs only succeeded if they “refounded” the company. He did this with conviction at YC when he inherited the presidency. He created new programs, including a new fund, to expand the accelerator’s support for startups at different stages. He moved into hard technologies—those that required ambitious scientific innovation, including nuclear fusion, quantum computing, and self-driving cars. Already a prestigious name brand, YC’s sphere of influence grew from a couple hundred companies to thousands a year, turning it into a center of gravity in Silicon Valley. “The thing that I’m most proud of is we really built an empire,” Altman said after stepping down as president.

As OpenAI concentrated its bets, Altman was applying the same strategy to his other projects and investments. Over the years, as he’d shifted toward more hard-tech innovation, he had developed a belief in betting big and long on the most important projects.

“If you could wave a wand, change anything about the tech, startup, entrepreneurship ecosystem, what would you change?” his brother Jack had asked him at an event as he’d stepped down from YC. “It would be to get everyone in the ecosystem to take a much longer time horizon,” Altman had said. “This world where people start a company and plan to run it for four or five years, join a company and only plan to stay for one or two—that’s not how important shit gets done.”

Along those lines, 2021 seemed to mark a major shift in Altman’s personal investment strategy away from taking a large number of small bets toward taking a small number of really large ones. That year a startup he’d cofounded in 2019 called Tools for Humanity that had remained largely quiet saw an influx of funding and media coverage and a concerted ramp-up in its operations, as Altman directed more external attention to the company. The venture was a dedicated effort to develop a working mechanism for universal basic income, or UBI, a popular Silicon Valley idea to give everyone a regular minimum distribution of income. Altman often passionately discussed UBI as the possible antidote to a future world where AI could create mass economic fallout. At YC he had started the largest pilot in the US to study the concept, spinning out a nonprofit in the process called OpenResearch that administered the program. Over three years, OpenResearch gave a $1,000 monthly stipend to a randomly selected group of one thousand out of three thousand low-income people, with the rest getting fifty dollars a month as a control. In July 2024, OpenResearch would release its findings, showing that the unconditional cash helped people meet their basic needs, assist others, and have more economic leeway.