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.