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.

But IBM failed to commercialize Tesauro’s project—why would a vendor of business servers sell commercial backgammon software to a few hundred customers? Why, indeed.

This adorable nook in the marketplace was filled in 1994 by the Norwegian researcher Fredrik Dahl. Dahl was an unusual man who enjoyed backgammon, chess, simulated tank battles, jiu-jitsu, and foraging in the woods for edible fungi. He worked for Norway’s defense establishment, where he simulated outcomes from a hypothetical Soviet invasion. His work drew inspiration from the 1983 movie WarGames, starring Matthew Broderick. In that movie, an AI attempts to start a nuclear war.

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.

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.

“Catanzaro was convinced the solution was to redesign the microchip anew. He cofounded the UC-Berkeley Parallel Computing Lab in the mid-2000s, along with several colleagues. There, Catanzaro made a list of existing parallel applications. The business problem, he could see, was that even for the supposedly hungriest customers, the demand for computing power was capped: once you sold an oil prospector a supercomputer, you saturated demand for years. What you needed, Catanzaro figured, was an application that was so hungry for computation that it could never be satisfied. You needed another application like 3D graphics that demanded more computer power once its initial needs were fulfilled. Eventually, Catanzaro deduced what had to be parallel computing’s killer app. “The answer to that was AI,” Catanzaro said. “I came to AI from the bottom up. I came from a circuits perspective. I felt it was just inevitable that AI was the most important computational workload.