For McClintock, money and resources became the means to do her research, rather than her research being a means to make money. Every person in our study made this flip at some point in their lives, from seeing money principally as the output of their efforts (“I work to make money”) to seeing money as an input to fuel their efforts (“I need money to do what I am encoded for”).

Beijing understands social media sites, like Facebook or TikTok, primarily as freewheeling platforms of expression. They bring little gain in economic productivity while creating huge potential for political unrest. Meanwhile, the Chinese leadership looks more longingly at places like Germany, a country that hasn’t developed digital giants but is firmly grounded in manufacturing industries.

In the United States, physics and mathematics PhDs hardly have a chance to consider working in their field before a tech giant or hedge fund picks them up at the sidelines of a conference, flashes them with a humongous pay package, and folds these eager minds into their glamorous embrace. Senior government advisors have more or less stated that Beijing intends to block these temptations. Yao Yang, a dean at Peking University, has remarked with satisfaction that salaries have fallen in the financial industry after regulators imposed a salary cap of $400,000 on the financial sector. Its idea, Yao said, is “to reduce the attractiveness of finance and increase the development of manufacturing.

At a time when freewheeling corporate research was still permitted, Microsoft anthropologist Mary L. Gray and computational social scientist Siddharth Suri were among the first to show the world the plight of workers like those in Kenya who build their livelihoods around an essential piece of the AI supply chain.

In 2019, they published their book Ghost Work, based on five years of extensive fieldwork, revealing a hidden web of piecemeal labor and digital exploitation that propped up Silicon Valley. Tech giants and unicorns were building their extravagant valuations not just with engineers paid six-figure salaries in trendy offices. Essential, too, were workers, often in the Global South, being paid pennies to carefully annotate reams of data. Take self-driving cars. A self-driving car needs to drive in the correct lane, respond to erratic driving behavior, and pause at a safe distance for schoolchildren crossing the road. To do this, the software system controlling the car uses an amalgamation of several deep learning models, including those dedicated to recognizing objects on the road: lane markings, road signs, traffic lights, vehicles, trees, pedestrians.

At the time, Schmidt’s prediction made me wonder whether the second time around would still be coincidence or whether data-annotation firms would make a playbook out of what had worked there. Scouting workers in crisis could become a surefire way to continue driving down the costs of the labor that serves as the lifeblood of the AI industry. Looking back several years later, that’s exactly what happened—and what has become one of the most stunning parallels between empires of old and empires of AI. One of the defining features that drives an empire’s rapid accumulation of wealth is its ability to pay very little or nothing at all to reap the economic benefits of a broad base of human labor.

These days Fuentes works on five data-annotation platforms at the same time to make a little more than the minimum wage in Colombia, around $335 a month. Each task pays on average between one and five pennies; she still forces herself to wake up when tasks arrive in the middle of the night. “We are ghosts to society, and I dare say we are cheap, disposable labor for the companies we have served for years without guarantees or protection,” she wrote for her project. Since the Data Workers’ Inquiry, she has continued to speak about these experiences in online talks and webinars in the hopes of applying pressure on companies and policymakers to enforce better worker treatment.