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 had managed to stay ahead of his competitors so far, but his asset-light “merchant” business was essentially just a collection of engineers sitting around a Silicon Valley office park. If those engineers weren’t constantly developing new, difficult-to-replicate technology,

manufacturers in Asia would start knocking off his chips, and Nvidia would cease to exist. “If we don’t reinvent computer graphics, if we don’t reinvent ourselves, and we don’t open the canvas for the things that we can do on this processor, we will be commoditized out of existence,” Huang later said. Not to gamble was the biggest risk of all.

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.

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.

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.