Empire of AI: Dreams and Nightmares in Sam Altman's OpenAI

by Karen Hao

In 2012, the two moved from Wellington back to the town where Jones was born, Kaitāia, in Aotearoa’s northern reaches. Jones became CEO of Te Hiku Media, a public radio station that broadcasts in te reo, part of a broader network of media and other organizations engaged in te reo’s revitalization. In his new role, Jones identified an opportunity. Over its twenty-odd years of broadcasting, Te Hiku had amassed a wealth of archival audio of people speaking te reo, including a recording of his own grandmother Raiha Moeroa, born in the late nineteenth century, whose accent had yet to be distorted by the influences of the colonizers’ English.

Jones and Mahelona were determined to carry out the project only if they could guarantee three things—consent, reciprocity, and the Māori people’s sovereignty—at every stage of development. This meant that even before embarking on the project, they would get permission from the Māori community and their elders, asking them if the endeavor was even something they wanted; to collect the training data, they would seek contributions only from people who fully understood what the data would be used for and were willing to participate; to maximize the model’s benefit, they would listen to the community for what kinds of language-learning resources would be most helpful; and once they had the resources, they would also buy their own on-site Nvidia GPUs and servers to train their models without a dependency on any tech giant’s cloud. Most crucially, Te Hiku would create a process by which the data it collected would continue to be a resource for future benefit but never be co-opted for projects that the community didn’t consent to, that could exploit and harm them, or otherwise infringe on their rights. Based on the Māori principle of kaitiakitanga, or guardianship, the data would stay under Te Hiku’s stewardship rather than be posted freely online; Te Hiku would then license it only to organizations that respected Māori values and intended to use it for projects that the community agreed to and found helpful. “Data is the last frontier of colonization,” Mahelona told me: The empi...

But it is yet another lesson to be drawn from Te Hiku’s experience that the three hundred ten hours still proved sufficient for developing the very first te reo speech-recognition model with 86 percent accuracy. Where OpenAI seeks to develop singular massive AI models that will do anything, a quest that necessarily hoovers up as much data as possible, Te Hiku simply sought to create a small, specialized model that excels at one thing.

Te Hiku isn’t the only organization pursuing new paths for AI development. Through the course of my reporting for this book, I was repeatedly inspired by the many organizations and movements around the world that have blossomed to resist the empires of AI, assert their rights to self-determination, and envision a new way forward. After Timnit Gebru was ousted from Google, she founded a nonprofit in December 2021 to continue her research. She named it DAIR, the Distributed AI Research Institute—“distributed” to defy centralization.

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?”

A continent away, Okinyi is also organizing. In May 2023, a little over a year after OpenAI’s contract with Sama abruptly ended, he became an organizer of the Kenya-based African Content Moderators Union, which seeks to fight for better wages and better treatment of African workers who perform the internet’s worst labor. Half a year later, after going public about his OpenAI experience through my article in The Wall Street Journal, he also started a nonprofit of his own called Techworker Community Africa, TCA, with one of his former Sama colleagues Richard Mathenge. In August 2024, as we caught up, Okinyi envisioned building TCA into a resource both for the African AI data worker community and for international groups and policymakers seeking to support them. He had been organizing online conferences and in-school assemblies to teach workers and students, especially women, about their labor and data privacy rights and the inner workings of the AI industry. He was seeking funding to open a training center for upskilling people. He had met with US representatives who came to visit Nairobi to better understand the experience of workers serving American tech companies. He was fielding various requests from global organizations, including Equidem, a human and labor rights organization focused on supporting workers in the Global South, and the Oxford Internet Institute’s Fairwork project.

He’s been speaking with MOSACAT in Chile and reaching out to as many other communities as possible that are also resisting the tech industry’s exploitation and extractivism. By connecting their movements across borders, by sharing information and resistance strategies with one another, he sees a path to building more collective power that can pressure and evolve the industry toward something better. “We need to fight on a global level,” he says.

In her 2019 talk at NeurIPS, during the Queer in AI workshop, Ria Kalluri, an AI researcher at Stanford, proposed an incisive alternative to the question of how to ensure AI does “good.” Goodness, benefit to humanity—these terms will always be in the eye of the beholder. Rather, we should ask how AI shifts power: Does it consolidate or redistribute that power? To put it in the frame of this book, does it continue to fortify the empire, or does it begin to wrest us back toward democracy?

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.

Redistributing knowledge will also need policies that require companies to relinquish key details about the training data and technical specifications of their models and supercomputers.

“If you’re using a car, if you are buying an appliance, you have an Energy Star rating,” says Sasha Luccioni at Hugging Face. “But AI is so integrated into our society, so widely used in products, and we don’t have any information about the sustainability of these systems.”

The Hollywood strikes, which successfully secured writers and actors protections against certain uses of AI, illustrated the critical role that unions will play in resisting the devaluing of human labor, the depression of wages, and the consolidation of money away from workers in the hands of AI companies.

Janet Guyon, my editor at Quartz, who was the first person who knew what she was talking about to tell me she believed I could make a great journalist. To Gideon, then the editor in chief of MIT Technology Review, who made a crazy bet to give me my first full-time job in journalism, to cover artificial intelligence, no less, putting me on a yearslong journey I could have never imagined.