Robert Sternberg, the present-day guru of intelligence, writes that the major factor in whether people achieve expertise “is not some fixed prior ability, but purposeful engagement.” Or, as his forerunner Binet recognized, it’s not always the people who start out the smartest who end up the smartest.

Of course, Huang would work hard anyway. It is in his nature. If there is a theme to his life, it is amplification; he has executed on the same simple precepts of diligence, courage, and mastery of fundamentals again and again and again, to greater and greater effect. I was surprised to learn how much of the man he later became was present in the immigrant child arriving unaccompanied by his parents in the United States in 1973 to an environment so unconducive to flourishing that it seems a miracle he survived it. To understand Huang fully, we begin not at Denny’s restaurant, nor in the giant cathedrals of technology he later commissioned, but at this tiny rural school.

Coxe and Stevens agreed that it was Huang, specifically, rather than Nvidia’s proposal that attracted their attention. “The reason we backed these dudes is because they were world-class computer scientists,” Coxe said. “The average CEO will try to listen to the customer, but in computing, that’s a big mistake, because customers just don’t know what’s possible. They just don’t know what can be done!” Coxe observed that Intel and Microsoft had later struggled under more conventional management: “Jensen, from the beginning, was a world-class engineer who could see what was possible.

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.

Firms that attempted to replicate GPT with in-house data often produced shambolic “knowledge engines” that were little better than toys. These AIs supplemented the standard large-language-model training set with emails, mission statements, patent applications, legal memoranda, and other exciting selections from the internal corporate syllabus. As the buzz percolated through middle management, executives at marketing, media, and health-care firms launched ambitious initiatives, sometimes openly telling staff that many employees would be laid off once the neural nets were working. But much of what was produced was vaporware: late, expensive, and barely functional. Many users felt that AI technology simply

wasn’t ready.