Daron Acemoglu (@DAcemogluMIT) on X
Short summary
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.
Full summary
Daron Acemoglu raises a stark math problem: AI companies need to generate roughly $3.7 trillion in annual revenue by 2032 to justify their current investment levels, assuming a standard 10% return. That's about 10% of current US national income, and it'll stay around that share even as the economy grows. The catch is that most of this revenue flows to capital owners, not workers. Since capital income is already concentrated among the wealthy—and capital's share of national income has hit an all-time high of 47%—funneling another massive chunk of revenue to capital would turbocharge inequality significantly.
The inequality problem doesn't stop there. Acemoglu's research with Pascual Restrepo shows that when automation increases capital's share of income, wage inequality among workers tends to rise too. So you'd get a double hit: wealth concentrating at the top, and wages becoming more unequal at the bottom. This creates a genuine dilemma with no good exit. If AI companies actually hit those $3.7 trillion revenue targets, inequality explodes. If they don't reach those numbers, the industry faces a crash that could destroy jobs and output.
Acemoglu thinks the industry probably won't hit those revenue targets anyway. AI adoption has been slower than hype suggests, competition from open-source models is undercutting prices, and AI still can't fully automate entire jobs—it's more of a productivity tool than a replacement. But even if a dramatic crash gets avoided, he expects AI diffusion to push inequality up between capital and labor and within labor itself. The real question lurking underneath all this: can democracy actually function at the extreme inequality levels we're heading toward?