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.

As Sutskever rallied researchers to give him their best ideas, Brockman threw himself into the work of developing the right organizational culture. Some years later, Brockman would recount to me his thinking. To prepare, he read every book he could find on ambitious science and technology undertakings in US history: the transcontinental railroad, Thomas Edison’s light bulb, the early network of computers that would lay the groundwork for the modern Web. He absorbed them like religious texts, searching for hints and guidance on how to design his own endeavor.

My conversation with Brockman and Sutskever continued on in circles until we ran out the clock after forty-five minutes. I tried with little success to get more concrete details on what exactly they were trying to build—which by nature, they explained, they couldn’t know—and why then, if they couldn’t know, they were so confident it would be beneficial.

At one point, I tried a different approach, asking them instead to give examples of the downsides of the technology. This was a pillar of OpenAI’s founding mythology: The lab had to build good AGI before someone else built a bad one.

Through the course of my reporting, I would come to conclude something even more startling. Not even in Silicon Valley did other companies and investors move until after ChatGPT to funnel unqualified sums into scaling. That included Google and DeepMind, OpenAI’s original rival. It was specifically OpenAI, with its billionaire origins, unique ideological bent, and Altman’s singular drive, network, and fundraising talent, that created a ripe combination for its particular vision to emerge and take over. “I get the sense that Sam is the most ambitious person on the planet,” a former employee says. In other words, everything OpenAI did was the opposite of inevitable; the explosive global costs of its massive deep learning models, and the perilous race it sparked across the industry to scale such models to planetary limits, could only have ever arisen from the one place it actually did.

In her talk, Kalluri raised the idea of different axes of power. This book touches on three: knowledge, resources, and influence. As it stands now, OpenAI and its competitor empires have control of each of them: through centralizing talent, eroding open science, and sealing their models from public scrutiny, they control knowledge production; through hoarding funding, data, labor, compute, energy, and land, they control and diminish other people’s resources; through creating and reinforcing ideologies and producing wildly popular demonstrations that captivate global imagination, they command far-reaching influence. Each of these reinforces the other. Controlling knowledge production fuels influence; growing influence accumulates resources; amassing resources secures knowledge production.