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Empire of AI

Empire of AI

Karen Hao
2025
T2 496 pages · about 8h 16m to read Open Librarygoodreads

Critical history of OpenAI's mission drift and resource extraction.

Summary

Hao, who covered artificial intelligence for MIT Technology Review and the Wall Street Journal, wrote a reported history of OpenAI and an argument about the industry it created.

The institutional narrative traces OpenAI from its 2015 founding as a non-profit committed to developing artificial general intelligence safely and openly, through the creation of the capped-profit subsidiary in 2019, the Microsoft partnership, the shift away from publishing research, the release of ChatGPT, the board's removal of Sam Altman in November 2023 and his return days later. Hao had reported on the organisation since 2019 and had access to internal material and to current and former staff.

The argument in the title is that the leading AI companies operate on an extractive model comparable to earlier imperial enterprises: they take data produced by others without compensation, consume large quantities of energy and water in locations that bear the environmental cost, and rely on low-paid contract workers in Kenya, the Philippines and elsewhere to label data and to filter traumatic content. She reports on these workers directly. She also argues that the scaling approach, which pursues capability through ever-larger models and compute, was a choice rather than a technical necessity, and that it concentrates the field in the small number of organisations that can afford it.

Hao's reporting on labour conditions and on OpenAI's internal history is detailed and largely documented. The imperial framing is an interpretation and readers who reject it can still use the reporting. The book is critical throughout and does not present the strongest version of the case that scaling has produced genuine capability gains or that the resulting systems create value that offsets the costs she catalogues. Events in this sector also move faster than publication schedules.

Key takeaways 7

  1. OpenAI's conversion from non-profit to capped-profit structure changed who controlled the organisation while preserving the original mission statement.
  2. The decision to stop publishing research reversed the founding commitment to openness and was justified on safety grounds that also served commercial interests.
  3. Scaling models through greater compute was a strategic choice rather than a technical necessity, and it concentrated the field among organisations that could fund it.
  4. Training data was collected from creators without compensation or consent, which is the foundation of the industry's cost structure.
  5. Content moderation and data labelling depend on low-paid contract workers who absorb psychological harm the systems are designed to prevent users from seeing.
  6. Datacentre energy and water consumption falls on communities that receive little of the resulting economic benefit.
  7. The November 2023 board crisis demonstrated that formal non-profit governance could not constrain the organisation against investor and employee pressure.

Who should read it

Investors and operators in artificial intelligence who need the documented account of OpenAI's institutional history and of the sector's labour and resource inputs.

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Where it sits in the reference 3

Companies
Biographies
Business themes

Topics

Artificial IntelligenceCompany HistoriesTechnology and ComputingAI and machine learning