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.

SIX: Jellyfish

“Unencumbered by received wisdom, TD-Gammon discovered a new approach to backgammon. It determined that human players were risking too much to establish an advantage up front and that conservative openings were better. At the same time, it would often forgo a guaranteed win in the endgame in a greedy attempt to double its score, a strategy most human players considered reckless. In the middlegame, TD-Gammon made a variety of more subtle moves that human experts understood only after deep introspection.

Dahl worked on this problem for many years. The obstacle was that he had almost no idea how his poker bot actually worked. The structure of its neural net was no easier to interpret than the nervous system of an invertebrate, and trying to tease out game-playing strategies by examining the individual weights of the grid was like trying to unravel consciousness

by looking at brain cells through a microscope. This was the criticism of neural nets and the thing that so biased the academic community against them. Once a neural net hit a training plateau—and they almost always did—there was rarely an obvious way to make it better. Classical programming was orderly and logical, but tinkering with neural nets required a different cast of mind. Dahl compared it to running a biology experiment: outcomes were unpredictable, and altering seemingly minor variables could have all manner of unanticipated results. Dahl tried everything he could think of to improve his no-limit poker bot. He fiddled with its evaluation function, he futzed with his computer’s memory, he replaced the activation trigger for the neurons, he even synthesized a simpler data universe for the bot to explore—but he never got it to play at an expert level.

Disruptive technologies, Christensen had observed, often grew out of hobbyist communities. They were developed using “bootlegged resources” in which “off-the-shelf components” were redeployed for something other than their intended purpose. They started out wonky but rapidly improved along attributes of performance that established players ignored.

But even once you had absorbed this lesson, it wasn’t easy to implement. Pursuing niche markets cost profits, making investors question your sanity. This, too, Christensen had foretold: “One of the reasons managers at established firms find it difficult to serve emerging markets is that their investors and customers tell them not to.”

That was the real secret of The Innovator’s Dilemma, which readers often missed. It was not a book about how to succeed; it was a book about how not to fail. Christensen’s book wasn’t a how-to for start-ups but a counterinsurgency manual for senior managers at stagnating firms. Thirteen years in, Huang felt that Nvidia was at risk of becoming such a firm, and it was as much paranoia as optimism that led him to pursue the mad-science market.

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.