Aswath Damodaran on SpaceX, AI and the Limits of Big Market Stories

Rob Arnott, founder of Research Affiliates and chairman of Syzygy Asset Management, examines S&P 500 concentration and who could benefit most from AI. Owning today’s market leaders is not necessarily the same as owning tomorrow’s biggest investment winners. In this episode of The Intangible Economy, Rob joins Kai to explore how index construction shapes what investors own, why index additions can create hidden costs, and why he would consider buying a richly valued IPO before index funds have to buy it. The conversation also covers AI’s shift toward capital-intensive business models, GPU depreciation, intangible assets, and the importance of testing investment ideas rather than chasing impressive backtests. Topics covered: Why Rob Arnott sees a real AI revolution alongside risks in the prices of its leading stocks How AI customers could capture value beyond the infrastructure builders Valuation changes versus fundamental growth in large and smaller companies Tesla, SpaceX, and the price pressures surrounding index inclusion Hidden implementation costs that do not appear in an index fund’s expense ratio Selecting index members by business size rather than market value AI financing, customer economics, and the risk of rapid hardware obsolescence R&D, intangible assets, and what conventional book value can miss Competitive moats, leadership, and creating value for customers Using AI in research while resisting overfitting and performance chasing Research discussed: The Active Side of Indexing Fundamental Growth When Will AI Be Both Powerful and Profitable? Learn more about Syzygy Asset Management. Timestamps: 00:00 Rob Arnott on index concentration and AI’s future winners 12:13 What S&P 500 investors own and pay for 20:35 Why Rob Arnott would buy before index inclusion 25:41 Hidden index costs and fundamental membership rules 30:37 Passive investing’s embedded decisions 36:40 Big Tech’s shift from asset light to capital intensive 42:41 GPU depreciation and economic obsolescence 47:03 R&D, intangible assets, and value investing 51:04 How durable are competitive moats? 59:01 Using AI in business and investment research 01:05:11 Performance chasing and the backtest trap Learn more about the Excess Returns podcast network: excessreturns.co No information discussed in this podcast should be construed as investment advice. Securities discussed may be held by the hosts and guests, their firms or their clients.

Alex Edmans joins Kai Wu to explore behavioral finance, market inefficiencies, and why investors overlook intangible assets such as employee satisfaction, innovation, and brand strength. Drawing on his book The Madness of Markets, the London Business School finance professor explains why markets overreact to headlines, underreact to long-term value, and leave opportunities for patient, independent investors. The Madness of Markets https://amzn.to/3T9rdot Alex Edmans on Twitter https://twitter.com/aedmans Topics covered: How soccer defeats reveal the influence of investor emotions on stock prices

Dan Rasmussen, founder and managing partner of Verdad Advisers and author of The Humble Investor, joins Kai Wu to examine the unraveling of private equity, the rise of private credit, and how AI is reshaping software, labor, and the economics of technology investing. They also explore the massive AI CapEx boom, why value investing has struggled in the intangible-heavy U.S. market, the unusual opportunity in Japanese small caps, and how investors can quantify intangible value in biotech. Topics covered: Why private equity became a consensus trade and why exits are now clogged

Azeem Azhar joins Kai Wu to break down the real economics of the AI boom, including the $110 billion demand base, where profits may accrue across chips, hosting, foundation models and applications, and whether spending can translate into enterprise productivity. They discuss AI infrastructure bottlenecks, open-source competition, vertical integration, organizational redesign, software moats, human judgment and the signals investors can use to identify companies turning AI adoption into durable competitive advantage. The State of the AI Economy https://intelligence.exponentialview.co/assets/ev-state-of-ai-economy-2026.pdf Why AI Isn't Showing Up on Your Bottom Line https://www.exponentialview.co/p/why-ai-isnt-showing-up-on-your-bottom-line Azeem Azhar on X https://x.com/azeem Exponential View https://www.exponentialview.co/ Topics Covered The size and growth rate of real generative AI demand

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 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