Then he would talk to people. As Bill explained it one time at a Google management seminar: “I have a little more time than Larry does to do some of that stuff. I have a little more time than Sundar does to do some of that stuff, so, you know, I’ll say to Sundar, Do you want me to meet with so-and-so? Sure. And here’s what I’m going to tell ’em. You okay with that? Yeah. Great. Perfect, and, you know, that helps a little bit in moving the thing along. Let’s get it moving.

The Thinking Machine - Stephen Witt

Introduction:

“This is the story of how a niche vendor of video game hardware became the most valuable company in the world. It is the story of a stubborn entrepreneur who pushed his radical vision for computing for thirty years, in the process becoming one of the wealthiest men alive. It is the story of a revolution in silicon and the small group of renegade engineers who defied Wall Street to make it happen. And it is the story of the birth of an awesome and terrifying new category of artificial intelligence, whose long-term implications for the human species cannot be known. At the center of this story is a propulsive, mercurial, brilliant, and extraordinarily dedicated man. His name is Jensen Huang, and his thirty-two-year tenure is the longest of any technology CEO in the S&P 500.

Huang is a visionary inventor whose familiarity with the inner workings of electronic circuitry approaches a kind of intimacy. He reasons from first principles about what microchips can do today, then gambles with great conviction on what they will do tomorrow.

In pushing Shoquist, Huang was employing a scheduling technique he called the “speed of light.” He drilled this management concept into his employees with the fervor of religious doctrine—almost everyone at Nvidia I talked to referenced the “speed of light” at least once. “Speed of light” did not mean, as one might assume, to move quickly. Instead, Huang encouraged managers to identify the absolute fastest that something could conceivably be accomplished, given an unlimited budget, and assuming that every single thing went right. (For example, traveling from New York to London at the “speed of light” would involve perfect weather, zero traffic, and a supersonic plane.) Managers could then work backward from this unachievable constant to realistic but still impressive delivery times. “It sounds hard, but it really takes the pressure off of you,” Shoquist told me. “Once you understand the physical limits of what is possible, you understand the competition can’t go any faster either.

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.

Still, if Bengio, Hinton, and Sutskever had been sidelined by capital, the points they made remained valid. They had seen better than anyone the potential of what AI technology could be, and they had the academic credentials to prove it. If they were worried now, I felt it was worth listening to. “Right now there are ninety-nine very smart people trying to make AI better and one very smart person trying to figure out how to stop it taking over,” Hinton said.