Against this backdrop, OpenAI began the first phase of its project in late 2021. Under the code names PBJ1, PBJ2, PBJ3, and PBJ4, it thrust teams of Sama workers into more traumatic content-moderation work, for on average between $1.46 and $3.74 an hour. Workers had no idea for whom or why they were doing the project, kept in the dark under the nondisclosure terms of the contract, common in the data annotation industry. What they did know was what was in front of them: the hundreds of thousands of grotesque text-based descriptions that they needed to read and sort into categories of severity. Was it violence or extremely graphic violence, harassment or hate speech, child sexual abuse or bestiality?

Gradually, the work broke many of the workers, the impacts radiating beyond each individual to the people who depended on them in their communities. Only after the release of ChatGPT would they begin to grasp what exactly they had paid for with their peace of mind.

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.

Fuentes taught me two truths that I would see reflected again and again among other workers, who would similarly come to this work amid economic devastation. The first was that even if she wanted to abandon the platform, there was little chance she could. Her story—as a refugee, as a child of intergenerational instability, as someone suffering chronic illness—was tragically ordinary among these workers. Poverty doesn’t just manifest as a lack of money or material wealth, the workers taught me. It seeps into every dimension of a worker’s life and accrues debts across it: erratic sleep, poor health, diminishing self-esteem, and, most fundamentally, little agency and control.

When I wrote the story of Okinyi and the other three Kenyan workers for The Wall Street Journal, OpenAI sought to distance itself from the responsibility of the toll its project exacted. It was Sama that had followed inadequate procedures to protect their workers, OpenAI leadership said; with Sama’s pristine reputation before then, OpenAI couldn’t have known that the workers were struggling.

But the consistency of workers’ experiences across space and time shows that the labor exploitation underpinning the AI industry is systemic. Labor rights scholars and advocates say that that exploitation begins with the AI companies at the top. They take advantage of the outsourcing model in part precisely to keep their dirtiest work out of their own sight and out of sight of customers, and to distance themselves from responsibility while incentivizing the middlemen to outbid one another for contracts by skimping on paying livable wages. Mercy Mutemi, a lawyer who represented Okinyi and his fellow workers in a fight to pass better digital labor protections in Kenya, told me the result is that workers are squeezed twice—once each to pad the profit margins of the middleman and the AI company.

From there Miceli embarked on a new research project to put their philosophy into practice. She created the Data Workers’ Inquiry and invited data workers from around the world to formulate their own research questions about the data-annotation industry and how to make it better. Regardless of where they lived, she paid them a standard researcher’s salary in Germany, where she is based, to reflect the value of the work they did: twenty-five euros an hour.

“There’s always this false logic around data work: What is the minimum that we can pay these people? That comes from a colonialist logic: You choose a place that allows you to do the most with the cheapest budget and where you can really steal from people, steal resources at low cost,” Miceli says. “The question is why are these companies paying two dollars an hour if the work is making them billions or trillions in revenue? Why don’t we look at how much these companies can pay instead of how much less these workers can take?”