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.

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.

In reality, the analogies to intelligence are once again anthropomorphizing and exaggerating the capabilities of the technology. While Hinton and other deep learning absolutists predicted that the shortfalls of neural networks compared with humans would go away at sufficient scale, the challenges have in fact persisted and, by many accounts, only gotten worse.

Finally, to redistribute power along our third axis, influence, we need broad-based education. The antidote to the mysticism and mirage of AI hype is to teach people about how AI works, about its strengths and shortcomings, about the systems that shape its development, about the worldviews and fallibility of the people and companies developing these technologies. As Joseph Weizenbaum, MIT professor and inventor of the ELIZA chatbot, said in the 1960s, “Once a particular program is unmasked, once its inner workings are explained in language sufficiently plain to induce understanding, its magic crumbles away.” I hope this book is just one offering to help induce understanding. It builds on the work of the many scholars, journalists, activists, and educators before me who have dedicated themselves to public education. May it be a new ground upon which many more after will rise up and build.

James didn’t worry. What greater purpose could these people find? And with so many minds applied, the pace of improvements to the machine was accelerating into another order of magnitude. He wasn’t having to shove things forward any more; the company was moving of its own accord.