After a few weeks programming the emulator, Kirk realized he had a second, tacit role at Nvidia: curbing the technical ambitions of cofounder Curtis Priem. Kirk had invented the quadratic-mapping technique used in the NV1, but when he arrived at Nvidia, he advised the company to abandon it. “It was just an idea I had,” Kirk said. “I have lots of ideas.” But this only made Priem promote quadratic mapping more aggressively. Priem was a purist who dismissed technical compromises as spineless concessions to the money guys. “The way Curtis thinks is for the end point,” Malachowsky said. “But he doesn’t really have it in his makeup to, like, stay in business.”

Kirk soon realized that the abstruse question of whether or not to use quadratic mapping was a proxy for the more interesting question of who was actually in charge at Nvidia.

By 2012, the situation was becoming dire. Nvidia’s stock price had not appreciated in more than a decade, and although revenues and employment at the company had grown considerably, profits remained flat. Huang was bringing supercomputing to the masses, but the masses didn’t want it.

Disruptive technologies, Christensen had observed, often grew out of hobbyist communities. They were developed using “bootlegged resources” in which “off-the-shelf components” were redeployed for something other than their intended purpose. They started out wonky but rapidly improved along attributes of performance that established players ignored.

But even once you had absorbed this lesson, it wasn’t easy to implement. Pursuing niche markets cost profits, making investors question your sanity. This, too, Christensen had foretold: “One of the reasons managers at established firms find it difficult to serve emerging markets is that their investors and customers tell them not to.”

That was the real secret of The Innovator’s Dilemma, which readers often missed. It was not a book about how to succeed; it was a book about how not to fail. Christensen’s book wasn’t a how-to for start-ups but a counterinsurgency manual for senior managers at stagnating firms. Thirteen years in, Huang felt that Nvidia was at risk of becoming such a firm, and it was as much paranoia as optimism that led him to pursue the mad-science market.

SEVENTEEN: Money

“Like many firms, Nvidia allowed employees to purchase stock at a discount to market prices. What set Nvidia’s program apart was that employees were allowed to purchase stock at a discount to the lowest price at any point in the last two years. These purchases were capped at a certain dollar amount, but as the stock went vertical, the program basically turned into free money, and those who maxed out their contributions each year made the trade of a lifetime. With the windfall extending deep into middle management, some newer employees expressed concerns that the nouveau-riche veterans were entering a state of “semiretirement.” Executives disputed this characterization. Jeff Fisher, who ran the company’s gaming side, had been among the first thirty employees. “Many of us are financial volunteers at this point,” he said, “but we believe in the mission.”

The lure of developing this revolutionary technology offered purpose beyond what money could buy. This was especially true of the old guard, who’d spent years explaining to baffled peers why they were working for a gaming company and who constantly had to correct the pronunciation of the firm’s name. AI had not been a consideration for these veterans, and they were as surprised to be working on it as anybody. “There was no way me, or anybody else, could have dreamed at the time that this stuff that science fiction writers might come up with has become a reality,” said Jay Puri, Nvidia’s head of sales, who started work at the company in 2005. The value of Puri’s shares exceeded $700 million by 2024, but he felt that the interesting work at Nvidia was only beginning. “Maybe I’m biased, but I think it really is the most important technology company of our time,” he said.

Two fringe strains of computer science, starved of investment, hated—no, detested—by industry and researchers alike had somehow unified to form a thriving, sprawling entity now careering toward sentience. “I just thought, there is no way that Nvidia is this lucky,” Aarts said. “There’s no way that deep learning just fits this perfectly because Nvidia has never put any effort into it!”

Huang called it “luck, founded by vision.”

For Dally, it was Huang’s tireless work ethic that made Nvidia succeed. Even Dally, who left no spare second in his day, could not quite believe the superhuman efforts of his boss. “The rest of us are just here to reduce the bandwidth demands on Jensen,” Dally said. “I mean, when does he sleep?” Diercks agreed: “His hobbies are work, email, and work.”

Plenty of people worked long hours, though. Jens Horstmann attributed Huang’s success to his adaptability. “I’ve often asked myself, how is it that we started in the same cubicle, you know, with a similar IQ, both working equally hard,” Horstmann said. “How is it that this person not only built this amazing company, but also a network around him of people that—that would just die for him if needed?” Huang, Horstmann believed, had changed himself many times. He recalled Huang at LSI, pushing the simulation software to its outer limits. “Now, he’s still doing the same thing, but what he’s engineering is himself. He was not born as a great CEO; he was not destined to be one. He transformed himself into one, just by abstracting! Just by problem-solving the inputs and outputs of what a good CEO should be.