Blog · Review Essay · May 2026

Empire of AI and the Mission That Became an Empire

Karen Hao's Empire of AI is a reported history of OpenAI and a broader argument about the political economy of artificial intelligence. Its most useful lesson is that a technology company can speak in the language of public benefit while building the material shape of an empire: data, compute, labor, energy, secrecy, and belief organized around a future only a few institutions claim authority to define.

The Book

Empire of AI: Dreams and Nightmares in Sam Altman's OpenAI was published by Penguin Press on May 20, 2025. Penguin Random House lists the hardcover at 496 pages, with a paperback scheduled for May 19, 2026. The publisher describes the book as an account of OpenAI, ChatGPT, Sam Altman, the AI arms race, and the global costs of data, compute, labor, energy, and water. Kirkus gives the same publication date, ISBN, page count, and publisher, and frames the book as a pointed account of AI regulation and harm reduction.

Hao is not approaching the subject from outside the field. Penguin's author note identifies her as an award-winning AI journalist, formerly a Wall Street Journal reporter and MIT Technology Review senior editor for AI. SIAM News places the book in continuity with Hao's 2020 MIT Technology Review investigation of OpenAI, which examined how competitive pressure eroded the organization's early promise of openness.

The book is partly a company history: the nonprofit origin story, the rise of Sam Altman, the 2019 capped-profit structure, the Microsoft relationship, ChatGPT, secrecy around frontier models, and the November 2023 board crisis. But the stronger reading is not merely biographical. Hao uses OpenAI as the cockpit view of a larger system: a small set of firms racing to accumulate capital, chips, data, electricity, political access, and cultural permission under the sign of AGI.

Mission as Governing Myth

The central tension in Empire of AI is the gap between mission language and institutional behavior. OpenAI began with a public-benefit story: advanced AI would be developed for humanity rather than captured by narrow commercial incentives. Hao's account follows how that story became less a constraint than a source of authorization.

This is the book's most important contribution to thinking about AI institutions. A mission can discipline an organization, but it can also sanctify expansion. If the institution believes it is racing on behalf of everyone, then ordinary checks can start to look like irresponsible delay. Openness can be traded for safety. Safety can be traded for speed. Public benefit can be routed through private infrastructure. The more sacred the end state becomes, the easier it is to rationalize the means.

That dynamic is not unique to OpenAI. It is a pattern of technological politics. A future good is projected with such force that present costs become collateral: worker injury, local water stress, public opacity, weak consent, copyright conflict, energy demand, and the narrowing of democratic choice. The book asks readers to judge AI not by the promised destination alone, but by the institutions the promise is already building.

Extraction Behind the Interface

Hao's empire frame works because it pulls attention away from the chat window and toward the supply chain. Large AI systems are not weightless minds. They require data pipelines, annotation and moderation labor, electricity, water, chips, minerals, cloud contracts, model deployment, and legal arrangements that determine who benefits and who absorbs risk.

SIAM News summarizes the book's reporting on workers in Colombia and Kenya who annotated or filtered difficult material for AI systems under poor conditions, and on environmental burdens connected to mines and data centers in Chile, Uruguay, and elsewhere. Kirkus likewise notes Hao's attention to data centers, developing-world data gaps, low-wage AI labor, and the imbalance between booster promises and present harms.

This makes the book especially useful against a common interface illusion. The more fluent the system becomes, the easier it is to forget the human and material world underneath it. A model that answers instantly can make extraction feel like intelligence. Hao reverses the camera: before calling the interface magical, count the people and places made invisible by the magic trick.

Secrecy and Concentrated Authority

The other major thread is secrecy. SIAM News contrasts OpenAI's original public commitment to publishing research with later model releases that withhold core details about architecture, training compute, dataset construction, and training methods. Hao's critique is not that every dangerous capability should be published carelessly. It is that secrecy shifts authority toward a small set of corporate actors while the public is asked to accept their claims about safety, capability, and necessity.

That matters because frontier AI is not only a product category. It is becoming infrastructure for education, search, work, software, creative production, public services, medicine, warfare, and personal companionship. When a closed institution supplies the cognitive layer for many other institutions, opacity becomes political power. It decides who can audit, contest, replicate, regulate, or refuse the system.

The November 2023 OpenAI board crisis becomes important for this reason. In Hao's telling, and in the Atlantic excerpt adapted from the book, the leadership struggle exposed how much of the AI future was being shaped by a tiny circle of executives, investors, scientists, employees, and platform partners. The drama was not gossip on the side of the real story. It was the governance story.

The AI-Age Reading

The AI age has a distinctive belief problem. It does not only produce tools. It produces grand narratives about intelligence, progress, salvation, national competition, existential risk, abundance, and inevitability. Those narratives organize money and attention. They tell governments what counts as strategic. They tell workers what kind of displacement is acceptable. They tell users that adoption is participation in the future.

Empire of AI is strongest when it treats AGI as an ideology as much as a technical objective. The term can name a research ambition, but it can also serve as a flexible permission structure. If AGI is near, then scale becomes urgent. If scale is urgent, then capital concentration looks necessary. If concentration is necessary, then secrecy, energy demand, labor outsourcing, and weak public oversight can be reframed as temporary costs on the way to universal benefit.

The danger is not simply that the prediction is wrong. The danger is that the prediction changes the world before it is tested. Data centers get built. Labor markets are reorganized. Artists and writers are scraped or substituted. Schools and companies restructure around model access. States subsidize infrastructure. A speculative future becomes a present administrative fact.

Where the Book Needs Care

The book should not be read as proof that all AI research is inherently imperial, that all frontier labs are identical, or that every technical advance is a scam. That flattening would weaken Hao's sharper point. The question is institutional form: who controls the system, what resources it consumes, what stories legitimate it, what publics can inspect it, and who has standing when the costs arrive.

It also needs to be read with attention to genre. Empire of AI is reported, critical, and argumentative. It foregrounds harms and power. Readers who want a technical account of model architecture, benchmark progress, or beneficial deployments will need companion sources. That is not a defect so much as a boundary. Hao is writing the political history of an industry that often prefers to be evaluated only by demos and future promises.

The fairest use of the book is not as a verdict that AI should stop. It is as a test for whether AI institutions deserve public trust. Do they disclose enough for democratic scrutiny? Do they protect workers and communities rather than externalizing harm? Do they permit refusal and appeal? Do they separate safety claims from market positioning? Do they let the public inspect the mission, or only applaud it?

The Site Reading

The book belongs in this catalog because it shows how a belief system can become infrastructure. The dream of beneficial superintelligence does not remain in manifestos, blog posts, board decks, and launch-stage rhetoric. It becomes contracts, data centers, labor markets, secrecy norms, procurement dependencies, and legal arrangements. A story about intelligence becomes a system for arranging the world around those who claim to be building it.

That is the recurring pattern to watch. The interface gives the user a helpful answer; the institution behind the interface asks for more data, more compute, more energy, more trust, more legal room, and more patience. The public is invited to judge the answer while the machinery that produced it recedes from view.

Hao's best warning is therefore practical. Do not evaluate AI only at the point of use. Follow the system backward into labor and infrastructure, sideways into governance and ownership, and forward into the futures it uses to excuse the present. The question is not whether the machine sounds intelligent. The question is what kind of world has to be built so that it can keep speaking.

Sources

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