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Nobel economist Daron Acemoglu, promoting his book "What Happened to Liberal Democracy?", argues the system has failed to deliver shared prosperity and that trust in establishment politics has eroded. He pushes back on the idea that social liberalism caused the problem, and on the claim that liberalism only works when anchored in religion.
- Acemoglu says the bottom half of the US population has gained nothing from economic growth and has lost ground in real terms, which undercuts liberalism's core promise of shared prosperity.
- He sees a second, less tangible crisis: a broad public feeling that establishment politics and democracy itself no longer work, which he argues is driven partly by internal weaknesses rather than just social media or provocateurs.
- He rejects blaming social liberalism or secularism for the decline, arguing social liberalism drove real gains in emancipation and anti-discrimination, while liberalism's real mistake was sidelining community, including religious community, through top-down intervention.
Daron Acemoglu, an MIT economist, is launching a series of posts on AI that focus on uncomfortable questions and potential dangers he feels the public conversation is avoiding. He's positioning himself as an outsider to AI science who wants to highlight important issues he believes industry insiders and media are overlooking.
- The current public conversation on AI is not addressing what Acemoglu sees as the biggest issues and dangers
- Important questions about AI are being neglected by both industry insiders and mainstream media coverage
- Acemoglu is explicitly disclaiming expertise in AI science while still attempting to shift the discourse toward questions he considers critical
Daron Acemoglu argues that the AI industry faces a fundamental dilemma: reaching the massive revenues needed to justify its investments ($3.7 trillion annually by 2032) would trigger a dramatic surge in inequality, while failing to reach those revenues could trigger an economic crash. Either way, inequality likely increases.
- Van Nieuwerburgh's calculations show AI needs to generate $3.7 trillion in annual revenue by 2032 (up from ~$200 billion now) to hit a 10% return on investments averaging 3.6% of GDP annually—a threshold that would make capital income roughly 10% of total US national income.
- Since capital income is far more unequally distributed than labor income, a massive rise in capital's share of national income would sharply increase overall inequality, and automation typically drives wage inequality among workers as well.
- Acemoglu thinks the industry won't actually hit these revenue targets because AI diffusion is slow, open-source models will limit proprietary pricing power, and current AI can't automate entire occupations—so a crash becomes more likely than runaway inequality, though either outcome worsens inequality.
Daron Acemoglu argues that current AI systems are developing distorted intelligence optimized for narrow metrics rather than genuine superintelligence, and this distinction matters for how we should regulate and respond to AI risks.
- Goal-driven autonomous behavior (like plants and insects) shouldn't be confused with actual intelligence, which is general-purpose, holistic, and environmentally adapted
- AI labs may be creating distorted intelligence by chasing impressive capabilities through imperfect quantitative metrics that warp model behavior
- The answer to whether we're building distorted intelligence or superintelligence determines what dangers to watch for, what policies to adopt, and what realistic hopes to hold
Daron Acemoglu argues that while AI's potential for drug discovery is often used to justify massive investments, the evidence shows AI has delivered little so far—and redirecting some of that money toward proven public health interventions would do far more to improve American life expectancy.
- AI has produced minimal gains in actual drug discovery despite significant effort. A recent paper found that while AlphaFold predicted protein structures, there's "no evidence so far that more applied, early-stage drug development is targeting these proteins."
- The US has the lowest life expectancy among wealthy nations (about 5 years behind Switzerland, Sweden, Japan), mostly from preventable deaths before age 70 due to chronic disease, overdoses, and poor public health—not lack of new drugs.
- The US spends far more on healthcare than peer countries but too little on public health (preventive care, nutrition, immunization, primary care access), suggesting reallocating AI investment toward these proven interventions would yield better health outcomes.
Daron Acemoglu argues that AI is too important to be left to experts and technocrats alone—democratic institutions must shape its direction. He pushes back on four common arguments for technocratic control, then raises the harder problem: how do you include the voices of billions of people outside the US, Europe, and China in AI decisions that will affect everyone.
- The contradiction is stark: if AI will transform every aspect of society, claiming it's too complex for democratic input doesn't hold. Citizens don't need to code; they need enough information to decide what kind of future they want and how to weigh tradeoffs.
- Competition between AI labs and countries isn't a reliable safeguard—it often brings out the worst in organizations. Relying on the ethical judgment of unconstrained leaders is historically a bad bet.
- Democratic involvement might actually reduce polarization and strengthen trust in institutions, while delegating decisions to technocrats without public input could worsen both.
- The global dimension is unresolved: even if democracies like the US and Europe figure out how to govern AI, the technology will shape the lives of 6 billion people who have no seat at the table.