The A.I. Boom and the Spectre of 1929 (www.newyorker.com)

A new analysis linking today’s A.I.–driven market euphoria to the 1929 crash argues that familiar bubble dynamics are resurfacing: exuberant narratives about transformative technology, rising prices divorced from conventional valuation metrics, and expanding leverage and creative financing. Financial leaders including Goldman’s David Solomon and JPMorgan’s Jamie Dimon have warned of an eventual drawdown, while the Bank of England flags U.S. valuations—by the cyclically adjusted P/E (CAPE) measure—as “comparable to the peak of the dot‑com bubble.” Critics counter that forward‑looking earnings expectations keep valuations less stretched, but the debate underscores heavy concentration in Big Tech and the gap so far between A.I. spending and realized returns. The piece draws lessons from Andrew Ross Sorkin’s new book 1929 and economic history: crashes often follow phases of overconfidence, weakened lending standards, and outright fraud (“the bezzle”), and modern markets add speed through algorithmic and online trading. Concrete contemporary risks include large, nontraditional credit expansion in private lending, potentially circular corporate transactions (cited in comparisons to dot‑com-era deals), and headline moves like Nvidia’s announced up-to-$100B investment in OpenAI. The implication for the AI/ML community: technological promise can fuel systemic financial risk; prudent modeling of adoption, realistic growth assumptions, and attention to funding structures matter as much as algorithmic innovation.