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.

The product was known as a “graphics accelerator,” and at least thirty-five competitors were trying to build one. Huang worried there was no space for a thirty-sixth. The leading expert in computer graphics was Jon Peddie, who had written several textbooks on the topic. Huang had reached out to Peddie to get a sense of the market, and the two soon became friends, with Huang calling incessantly, asking questions late into the night. Peddie advised Huang that the space was too crowded and that many of the best engineers were already working for other start-ups. “I told him not to do it,” Peddie said. “That was the best advice he never took.

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.

This was a little surprising, for while working at Nvidia was stimulating, it was never exactly fun; the corporate culture that Huang fostered was closer to Microsoft than Google, closer to IBM than Apple. But years earlier, Chiu, the Taiwanese physicist, had told Huang that he’d allowed him to do his “life’s work.” The phrase had stuck with Huang, and now he wanted to offer that same opportunity to his staff. “We want NVIDIA to be a place where people can build their careers over their lifetime,” the company wrote in its annual report. “Our employees tend to come and stay.”

The appeal lay in what Nvidia allowed you to achieve. It was not a secret that Huang pushed people hard. Thus, he attracted determined workaholics seeking to establish legacies as inventors. In the same way that a bestselling author didn’t stop writing, even many wealthy Nvidia engineers kept showing up to work each day to attack difficult technical problems. Those engineers collectively held more than fifteen thousand patents, but there was always something left to build.

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.