Karen Hao

When longtime AI expert and journalist Karen Hao first began covering OpenAI in 2019, she thought they were the good guys. Founded as a nonprofit with safety enshrined as its core mission, it was meant, its leader Sam Altman told us, to act as a check against more purely market forces.

But the core truth of this massively disruptive sector is that it requires an unprecedented amount of proprietary resources: the ‘compute’ power of scarce high-end chips, the sheer volume of data that needs to be amassed at scale, the humans on the ground ‘cleaning it up’ for sweatshop wages throughout the Global South, and a truly alarming spike in the need for energy and water underlying everything. We have entered a new, ominous age of empire with OpenAI setting a breakneck pace, as a small group of the most valuable companies in human history try to chase it down.

In exhilarating prose and with unparalleled access to those closest to Sam Altman, Hao recounts the meteoric rise of OpenAI and shows us the sinister impact that this industry is having on society.

Empire of AI

Frank Huang

Altman framed the proposal to employees carefully: OpenAI's initial commitment to avoid profit motives was made in the spirit of preventing the lab from compromising on its mission. But given that the lab's success required capital the nonprofit couldn't raise, clinging onto the original structure now held a greater risks of endangering the mission. In the end, most people agreed, though some reluctantly, that the LP was the best way forward.

"The thing that's interesting about OpenAI and Deep Mind and Google Brain are doing is the scale of their ambition," wrote Scott to Nadella and Gates in mid-June, referring to Google's AI research division, "and how that ambition is driving everything from datacenter design to compute silicon to networks and distributed systems architectures to numerical optimizers, compiler, programming frameworks, and the high level abstractions that model developers have at their disposal."

But Hooker and many other researchers, including Deborah Raji, disagree with the compute-threshold approach for regulating models. While scale 

 lead to more advanced capabilities, the inverse is not true: Advanced capabilities do not require scale. A deep learning model trained only on high-quality biological data, for example, can be a very powerful generator of biological recipes at a very small scale. Through distillation, one of the techniques that OpenAI referenced in its 2021 research roadmap, large models can also be transformed into small models with similar capabilities. Scaling models doesn’t guarantee advancements in certain capabilities either; that depends once again on what’s in the model’s training data as well as which type of neural network is being trained. In the end, not all models are built on Transformers. Compute is thus not much correlated with risk at all, says Hooker, let alone with specific kinds of risks, such as the ones laid out in the white paper.

Annie's story also complicates the grand narrative that Sam and other OpenAI executives have painted of AI ushering in a world of abundance. Altman has said that he expects AI to end poverty. Brockman has repeated, through his stories about his friend and his wife, Anna, that AGI will dramatically improve healthcare. Sutskever has said that it will lead to wildly effective, dirt-cheap psychotherapy. And yet, against the reality of the lives of the workers in Kenya, activists, in Chile, and Altman's own sister's experience bearing the brunt of all these problems, those dreams ring hollow.

Karolina

"Digital" technologies do not just exist digitally. The "cloud" does not in fact take the ethereal form its name invokes. To train and serve up AI models requires tangible, physical data centers. And to train and run the kinds of generative AI models that OpenAI pioneered requires more and larger data centers than ever before.

Those computers emanate a unseemly amount of heat, like a laboring laptop a million times over. To keep them from overheating, the buildings also have massive cooling systems-large fans, air conditioners, or systems that evaporate water to cool down the servers. The equipment all together creates a cacophony of humming, whirring, and crackling that can—especially in underdeveloped communities—be heard for miles, twenty-four hours a day, creating a relentless and body-warping source of noise pollution.

It’s very clear that the AI industry today is rooted in a colonial ideology. It imposes its worldview and its technology on the rest of the world.

To lose a language is a global tragedy; it's also a personal one. To be severed from your inheritance and forced to preserve someone else's, or risk being beaten, is to establish, in one of the rawest ways possible, a clear hierarchy between whose history, whose culture, whose knowledge deserves to be passed down and whose is so insignificant it deserves to be erased. Large language models accelerate language loss.

"Data is the last frontier of colonization," Mahelona told me: The empires of old seized land from Indigenous communities and then forced them to buy it back, with new restrictive terms and services, if thew wanted to regain ownership. “AI is just a land grab all over again. Bịg Tech likes to collect your data more or less for free—to build whatever they want to, whatever their endgame is—and then turn it around and sell it back to you as a service."

What I reject is the dangerous notion that broad benefit from AI can only be derived from—indeed, will ever emerge from—a vision for the technology that requires the complete capitulation of our privacy, our agency, and our worth, including the value of our labor and art, toward an ultimately imperial centralization project.

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