On the Thursday a week before Thanksgiving, after Gebru had submitted the paper to the conference, she received a calendar invite without explanation to meet Megan Kacholia, Google Research’s VP of engineering, over a video call less than three hours later. The meeting lasted only thirty minutes, and Kacholia cut to the point: Gebru needed to retract the paper.

The request was a dramatic aberration from the way Google and the rest of the industry handled research. Like many labs at other companies, Google Brain had until then largely conducted itself as an academic operation and given researchers wide latitude to pursue the questions they wanted to. At times, the company reviewed papers to ensure they didn’t expose sensitive IP or customer data. But researchers like Gebru had never known the company to block or retract a paper simply for shedding light on inconvenient truths. That Google was even willing to pull this move, some researchers would later reflect, was not only because of the new competitive pressure from OpenAI but also because of the work OpenAI had done to legitimize withholding research after GPT-2.

For more than a year, the protests continued, picking up a second wave after Google fired Meg Mitchell less than three months later. Google said she had violated multiple codes of conduct; Mitchell had been downloading her emails and files related to Gebru’s ouster. Several Google employees, including Bengio, resigned; at least one conference and several researchers rejected Google’s sponsorship money. The company sought to stem the unending tide of criticism with the formation of a new center of expertise on responsible AI and public commitments to diversity. “This was a painful moment for the company,” a Google spokesperson said. “It reinforced how important it was that Google continue its work on responsible AI and learn from the experience.”

But what seemed to bother Dean the most was how other people had misread Strubell’s research to make Google look significantly worse. The “Stochastic Parrots” paper, Dean argued, risked exacerbating this issue. Because Gebru did have access to Google’s internal numbers and was citing Strubell’s external estimate anyway, it could appear as if Strubell’s calculations were an accurate reflection of the company’s emissions. To Dean, this justified his and other senior executives’ criticisms of Gebru’s paper: If Gebru had wanted to cite Strubell, she should have chosen an estimate that was not Google’s Evolved Transformer; if Gebru had wanted to cite the Evolved Transformer, she should have sought internal Google numbers.

Some researchers found this logic frustratingly inadequate. Google had never made those internal numbers public previously, even in response to Strubell’s original paper; now it was blaming Gebru for its own lack of transparency while also refusing to let her cite publicly available estimates based on legitimate assumptions. Never mind that the company had unceremoniously forced out Gebru before she’d even had a chance to consult internal numbers and revise her paper. The only possible outcome of this catch-22 was censorship of critical accountability research.

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.

The prior month, in September 2023, Raji had found herself the singular academic, with financial ties neither to the industry nor Doomer community, testifying to Congress next to Altman, Musk, Nadella, Gates, Zuckerberg, Pichai, and Jack Clark, among other tech executives. They were all present for the very first of Senator Chuck Schumer’s AI Insight Forums, among the hottest and most consequential series of policy convenings that year to set in motion AI legislation. As her fellow witnesses spouted spectacular, unbacked claimsn about the promises and perils of AI, peppered with well-timed references to beating China that straightened the backs of attending senators, what shocked Raji the most was how much many in the audience appeared to buy into everything.

It dawned on her that the people sitting next to her, and their massive policy teams, had monopolized the message in Washington for so long that many policymakers now viewed it as gospel. A Schumer spokesperson would later note in the press that the senator was personally consulting with Altman and other OpenAI executives as he moved closer to regulation. “That for me was a huge realization,” Raji says. “Wow, we need more people just debunking—just looking at what people are saying and being like, ‘Actually, reality is more complicated.’”