The Diary of a CEOKaren Hao: Why 'AGI' is a slogan, not a destination
Through race-or-die narratives, AI leaders extract resources and legitimacy; hidden labor and worsened conditions sit beneath automation gains.
At a glance
WHAT IT’S REALLY ABOUT
AI empires, OpenAI turmoil, and harms behind the AGI myth
- Karen Hao frames Big AI as “empires” that extract data, exploit global labor, and shape public narratives to consolidate power while claiming a benevolent mission.
- She argues “AGI” is an elastic, audience-dependent concept used to sell products, raise capital, and deter regulation, rather than a coherent scientific target.
- Hao recounts reported inside dynamics at OpenAI—including the board’s brief firing of Sam Altman—portraying a leadership culture of instability, internal distrust, and contested safety priorities.
- The conversation explores real-world externalities of AI scale: job ladder erosion via automation and data-annotation work, plus energy/water/air-quality harms from data centers disproportionately sited in vulnerable communities.
- Hao advocates breaking up concentrated AI power and redirecting innovation toward “bicycle not rocket” AI—more targeted, efficient systems—while encouraging public resistance through regulation, lawsuits, local organizing, and withholding data/adoption.
IDEAS WORTH REMEMBERING
5 ideas“AGI” is treated as a flexible slogan, not a stable technical destination.
Hao argues there is no consensus definition of human intelligence, enabling AI leaders to redefine AGI depending on the audience—Congress (societal cures), consumers (assistant), Microsoft (revenue target), or the public (economic outperformance).
The AI race narrative can function as strategic persuasion (“China will win”), not neutral forecasting.
She claims leaders amplify existential stakes to justify centralizing control and accelerating investment, turning fear and utopian promises into leverage for capital, adoption, and regulatory deference.
OpenAI’s leadership crisis is presented as a governance failure under extreme stakes.
Hao reports that concerns about Altman’s “instability,” inconsistent disclosures, and internal chaos contributed to the board’s decision to fire him quickly—then backlash from employees and Microsoft enabled his rapid return.
AI’s “automation benefits” can be downstream of hidden labor and worsening work conditions.
She highlights data-annotation and RLHF-style workflows as essential to model performance, describing an emerging underclass of precarious workers—including laid-off professionals—training systems that may later displace more jobs.
Job displacement is driven by both model capability and executive choice.
Hao notes firms may replace workers because AI is “good enough” and cheaper, or use AI as justification for downsizing; she cites Klarna’s shrinkage via attrition alongside heavier AI use as an example of nuanced pathways.
WORDS WORTH SAVING
5 quotes“They profit enormously off of this myth.”
— Karen Hao
“We need to break up the empires of AI.”
— Karen Hao
“If most of the climate scientists in the world were bankrolled by fossil fuel companies, do you think we would get an accurate picture of the climate crisis?”
— Karen Hao
“It’s not that these technologies don’t have utility, it’s that the production of these technologies right now is exacting a lot of harm on people.”
— Karen Hao
“Let’s not make it go flawlessly if we don’t agree with what they are doing.”
— Karen Hao
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