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 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.