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.

FIVE: Going Parallel

“Kirk and Huang each combined a commanding breadth of technical expertise with a talent for low cunning. As Nvidia succeeded, many of the other graphics start-ups failed. Huang, sensing opportunity, created a master list of competitors on the whiteboard in his office. Then, in consultation with Kirk, he identified the two or three best engineers at each company and began strategizing on how to poach them.

Kirk recalled showing off the Riva 128 to a competitor at a trade show. When the engineer saw what it could do, he gave up on the spot. “I hired him within a few days—and that killed that company, right?” Kirk said. “Because, you know, I removed its brain.” Kirk, mild and professorial, had a predator’s instincts. “We had all the geniuses from all the other start-ups, and as we were successful in overtaking more and more of these little companies, the remaining companies had a harder and harder time staying alive.

Of more than a hundred former and current Nvidia employees I spoke with for this book, almost all had a tender story about Huang to relate. One employee—the same one whom Huang had humiliated in front of dozens of people, asking for a full refund of his salary—told me that when he was later diagnosed with a serious medical issue, Huang offered to pay in full, out of pocket, for his treatment. When Ben Garlick decided to leave Nvidia for a start-up, he was startled to receive an impassioned, personal plea from Huang to stay.

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.

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.