The Intangible Economy with Kai Wu

What Past Capital Cycles Can Teach Us About AI with Edward Chancellor

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/ [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/ [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/ [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/ [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/ [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 strong GDP growth does not guarantee strong shareholder returns How intangible capital, SaaS valuations and human capital fit into capital cycle analysis Whether bubbles can be good for society while still being bad for investors Why the long-term interest rate cycle may have changed The role of gold in a world of expensive stocks, rising debt and vulnerable bonds Timestamps: 00:00 Edward Chancellor on capital cycles, bubbles and AI 04:42 Why the railway mania became a classic overinvestment cycle 09:00 Why markets fund technology booms but often miss the winners 13:19 The prisoner’s dilemma behind AI spending 17:30 Will AI demand justify the supply being built 20:00 How capital spending can inflate profits before the bust 25:08 The AI Hindenburg moment and the limits of large language models 30:55 Why AI hype may exceed the proven technology 35:55 Why the anti-bubble may matter more than shorting AI 40:00 The energy transition bubble and the opportunity in overlooked assets 45:08 China’s lesson on GDP growth and shareholder returns 49:27 Big Booze, GLP-1s and the Lindy effect 54:23 Can intangible capital have its own capital cycle 59:54 SaaS valuations and the index creation warning signal 01:04:10 Why bubbles can help society but hurt investors 01:09:09 Why long-term rates may be in a new multi-decade cycle 01:14:07 Why Edward Chancellor still sees a role for gold

Michael Mauboussin: Base Rates, AI Adoption, and Investing in the Intangible Economy

This episode of The Intangible Economy explores how AI, intangible assets, and unprecedented capital investment are reshaping the future of markets. Michael Mauboussin joins Kai Wu to break down why today’s AI expectations may be historically unmatched—and what that means for investors trying to assess risk, returns, and who ultimately captures value. The conversation moves from base rates and AI growth expectations to competitive dynamics, capital cycles, and the fundamental shift toward intangible-driven business models that are changing how we think about valuation, moats, and market structure. Papers and Resources Discussed: Bayes and Base Rates: How History Can Guide Our Assessment of the Future https://www.morganstanley.com/im/en-us/institutional-investor/insights/consilient-observer/bayes-and-base-rates.htmlThe Impact of Intangibles on Base Rates – https://www.morganstanley.com/im/publication/insights/articles/article_theimpactofintangiblesonbaserates.pdf Measuring the Moat: Assessing the Magnitude and Sustainability of Value Creation – https://www.morganstanley.com/im/publication/insights/articles/article_measuringthemoat.pdf One Job: Expectations and the Role of Intangible Investments – https://www.morganstanley.com/im/publication/insights/articles/article_onejob.pdf Capitalism Without Capital: The Rise of the Intangible Economy – https://books.google.com/books/about/Capitalism_without_Capital.html?id=J3SYDwAAQBAJ A Better Estimate of Internally Generated Intangible Capital – https://pubsonline.informs.org/doi/10.1287/mnsc.2022.01703 Underestimating the Red Queen: Measuring Growth and Maintenance Investments – https://www.morganstanley.com/im/publication/insights/articles/article_underestimatingtheredqueen.pdf Explaining the Recent Failure of Value Investing – https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3442539 Guest Links: Michael Mauboussin Twitter Topics Covered: -Why OpenAI’s projected growth would be unprecedented in market history - How base rates provide a reality check on AI expectations - The role of diffusion models and adoption curves in forecasting technology - Why massive capital investment in AI may follow past boom-bust cycles - Lessons from large-scale infrastructure projects and why timelines break - How intangible assets change the distribution of business outcomes - The rise of “fat tails” and why more companies now massively win or fail - Who captures value in AI across the stack from chips to applications - Why competition may drive AI profits toward consumers, not producers - How accounting distorts intangible investment and misleads investors Timestamps: 00:00 Intro and OpenAI growth expectations vs historical base rates 04:32 Why no company has ever achieved 100%+ sustained growth at scale 08:47 Lessons from megaprojects and AI infrastructure buildouts 13:18 Intangible assets and why outcomes now have fatter tails 18:36 Why big tech is growing faster than historical precedents 23:52 Where value accrues in AI and why consumers may benefit most 28:21 Barriers to entry in AI including capital, talent, and scale 32:47 The risk of overinvestment and historical parallels to past bubbles 37:26 Game theory and competitive signaling in AI capital spending 41:58 Why investment returns—not “asset light” narratives—drive value 46:12 How accounting fails to capture intangible investment properly 50:44 Breaking down SG&A into maintenance vs investment spending 55:03 Why understanding reinvestment and ROI is the core investing skill 59:18 Final thoughts on uncertainty, expectations, and base rates in AI