Episode
What Past Capital Cycles Can Teach Us About AI with Edward Chancellor
- Published
- May 12, 2026
- Duration seconds
- 4589
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Summary
Edward Chancellor joins Kai Wu to discuss what financial history and capital cycle theory can teach investors about today’s AI boom. They explore why transformative technologies can still produce terrible investor returns, how overinvestment develops, where anti-bubbles may be forming, and what past episodes like the railway mania, the dot-com bubble, China’s investment boom and the post-2008 interest rate regime suggest about the risks and opportunities today. Guest links: Edward Chancellor https://www.edwardchancellor.com/ Papers and articles discussed: Valuing AI: Extreme Bubble, New Golden Era, or Both https://www.gmo.com/americas/research-library/valuing-ai-extreme-bubble-new-golden-era-or-both_viewpoints/ Markets have poor scorecard for spotting AI losers https://www.reuters.com/commentary/breakingviews/markets-have-poor-scorecard-spotting-ai-losers-2026-04-24/ There’s no such thing as a good bubble https://www.reuters.com/commentary/breakingviews/theres-no-such-thing-good-bubble-2025-10-09/ Big Booze can sweat off its multi-year hangover https://www.reuters.com/commentary/breakingviews/big-booze-can-sweat-off-its-multi-year-hangover-2025-07-10/ Topics covered: How capital cycle theory applies to the AI data center boom Why railway mania, autos, aircraft and the dot-com bubble offer lessons for today Why markets often fund major technology transitions but fail to identify the winners The prisoner’s dilemma driving hyperscaler AI spending Whether AI demand can justify the supply being built How GPU depreciation and AI capital spending may affect reported earnings Why hallucinations and reliability may limit the total addressable market for large language models The case for looking at AI anti-bubbles instead of shorting the bubble directly Why China shows that str…