The reader's edition
Empire of AI: Dreams and Nightmares in Sam Altman's OpenAI: the summary
Karen Hao is a journalist who has covered artificial intelligence for MIT Technology Review and The Wall Street Journal, and who profiled OpenAI years before ChatGPT made it famous. Empire of AI follows the company from its founding as a nonprofit in 2015, through the launch of ChatGPT, to the November 2023 crisis in which its board fired Sam Altman and then took him back days later. Hao argues that the industry OpenAI leads behaves like an empire: it lays claim to data, labour, land, energy and water that are not its own, and justifies the taking with a mission to benefit all of humanity. Drawing on interviews with around 260 people, she sets scenes inside the company beside reporting from the places that carry the costs, including data workers in Kenya and Venezuela and communities fighting data centres in Chile and Uruguay. The book ends by asking how that kind of power could be checked and spread out. Read a chapter at a time, with a printable action book at the end.
- Karen Hao
- 2025
- 4 chapters
- 6 min read
- 9 takeaways
This is HourLife's reading of Empire of AI: Dreams and Nightmares in Sam Altman's OpenAI, in our own words. It is not a substitute for the book.
Chapter 01
Before you start
The book in a minute
Tap a paragraph to highlight it. Turn pages with the arrows, the arrow keys, or a swipe.
Karen Hao is a journalist who has covered artificial intelligence for MIT Technology Review and The Wall Street Journal, and who profiled OpenAI years before ChatGPT made it famous. Empire of AI follows the company from its founding as a nonprofit in 2015, through the launch of ChatGPT, to the November 2023 crisis in which its board fired Sam Altman and then took him back days later. Hao argues that the industry OpenAI leads behaves like an empire: it lays claim to data, labour, land, energy and water that are not its own, and justifies the taking with a mission to benefit all of humanity. Drawing on interviews with around 260 people, she sets scenes inside the company beside reporting from the places that carry the costs, including data workers in Kenya and Venezuela and communities fighting data centres in Chile and Uruguay. The book ends by asking how that kind of power could be checked and spread out.
Below are the 9 ideas HourLife readers vote up most, each in our words with a note on what it asks of you, and then the steps that put them to work.
Chapter 02
What readers keep
The takeaways
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1
HourLife takeaway
The AI industry runs like an empire: it takes what is not its own and tells a story about saving the world to make the taking feel right.
Hao borrows the frame from the colonial empires of the 1800s. The resources have changed: people's writing and art, cheap labour, land, electricity and fresh water. The pattern has not. The point is not that the people involved are villains, but that the structure rewards taking.
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2
HourLife takeaway
Scale at all costs was a choice, not a law of nature.
OpenAI bet that bigger models, more data and more computing power would win, and once it moved, its rivals followed it into a race. Hao argues that this one bet, rather than anything inevitable about the technology, is what drives the hunger for data centres, chips and workers. Other ways of building AI existed, and still do.
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3
HourLife takeaway
A mission big enough can be used to justify almost anything.
OpenAI was founded to make sure artificial general intelligence benefits all of humanity. Hao reports how that mission was later used to explain secrecy, a for-profit arm and an ever faster race. She keeps asking who is counted in that 'humanity', and who is left out of it.
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4
HourLife takeaway
Behind a chatbot's polite answers are people who were paid to read the worst of the internet.
Hao reports on Kenyan contract workers, hired through the outsourcing firm Sama, who sorted violent and abusive text so OpenAI could build a content filter. They were paid a few dollars an hour, and she describes the lasting harm the work did to some of them. The safety the user sees was built on someone else's exposure.
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5
HourLife takeaway
The industry goes where labour is cheapest, and a crisis makes labour cheap.
Hao reports how Venezuela's economic collapse pushed educated people into piece-rate data labelling on online platforms. She argues that the gap between their pay and a researcher's pay reflects how the industry values people, not some objective difference in the worth of the work.
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6
HourLife takeaway
The cloud is a building, and the building needs power and water.
Data centres draw electricity and fresh water, and they are often placed where local people have little say. Hao follows residents in Chile and Uruguay who organised against planned Google data centres while their regions faced drought. The costs of AI land on a map, not in the abstract.
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7
HourLife takeaway
Hype and fear can be two halves of the same pitch.
Hao describes a field split between people who think AGI will save us and people who fear it will end us. Both camps agree it is near and enormous, and both conclusions hand more power to the few labs building it. In her telling, that split helped set up the 2023 board crisis.
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8
HourLife takeaway
Controlling what the public knows is part of controlling the empire.
A company with 'open' in its name came to share less and less about its models and training data. Hao argues that when a few firms fund most research and decide what gets published, they also shape what everyone else believes about AI. OpenAI declined to take part in her book, so she built it from hundreds of interviews and documents.
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9
HourLife takeaway
Empires are weakened from below, by people who refuse the terms.
Hao does not ask readers to wait for a wise leader. She points to artists and writers who sue or strike, communities that demand real terms before a data centre is built, and small projects such as Te Hiku Media in New Zealand, which builds language tools with its community's consent. Power spread across the supply chain is harder to concentrate.
Chapter 03
Put it to work
Your action book
Do the setup steps once, then keep the habits going. The setup changes your week; the habits keep it changed after the first good one.
The download puts this whole reading and a printable action book in one PDF: the setup checklist, then a page of real calendar days for each habit, starting on the day you choose.
Set up once
List the AI you used this week
Write down every AI tool you touched in the last seven days, what you used it for, and whether the result was worth it. Mark the uses you would happily drop.
Open your chatbot's data settings
In each AI tool you use, find the setting for whether your conversations train future models, and choose deliberately. Note what the tool says it keeps and for how long.
Find the data centres near you
Search local news and planning records for data centre projects in your region. Note what power and water they plan to use, and when the public can comment.
Choose one task you keep human
Pick one kind of work you will not hand to a model, such as a letter to a friend or your first draft of an idea. Write it down so the choice is on purpose.
Read one report on data work
Read one long investigation into the people who label data and moderate content for AI companies. Write down one thing that surprised you.
Keep it up
Ask who did the work
When a product feels like magic, name one group of people whose labour made it possible. Do it out loud once a week, in a conversation or a note.
Turn hype into a checkable claim
When you read a big AI headline, rewrite it as one claim you could check, and note who gains if you believe it. Keep a running list for a month.
Follow one local decision
Once a season, follow one local decision about power, water or land use: read the agenda, and send a comment or a question if you have one.
Chapter 04
The last word
Before you go
Pick one step from the action book and do it today. An idea from a book starts to count once it changes what one ordinary day looks like.
Every answer a machine gives you was paid for somewhere, by someone. Ask where, and ask who.
That is the end of our reading. Where to go from here, or go back to the full Empire of AI: Dreams and Nightmares in Sam Altman's OpenAI page.
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