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The Intangible Economy with Kai Wu: Daily Summaries Delivered
by Excess Returns
The Intangible Economy with Kai Wu explores the hidden forces reshaping the modern economy and their implications for investors. AI and the broader technology revolution are changing how we live, work, and create value. In each episode, Kai sits down with investors, researchers, and other experts to discuss how innovation and other intangible forces - such as brands, human capital, and network effects - are transforming markets and investment outcomes.
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- 3 weeks ago1h 8m
Rob Arnott on the Hidden Costs of Index Funds and the Real Winners From AI
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 stocksHow AI customers could capture value beyond the infrastructure buildersValuation changes versus fundamental growth in large and smaller companiesTesla, SpaceX, and the price pressures surrounding index inclusionHidden implementation costs that do not appear in an index fund’s expense ratioSelecting index members by business size rather than market valueAI financing, customer economics, and the risk of rapid hardware obsolescenceR&D, intangible assets, and what conventional book value can missCompetitive moats, leadership, and creating value for customersUsing AI in research while resisting overfitting and performance chasingResearch discussed:The Active Side of IndexingFundamental GrowthWhen 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 winners12:13 What S&P 500 investors own and pay for20:35 Why Rob Arnott would buy before index inclusion25:41 Hidden index costs and fundamental membership rules30:37 Passive investing’s embedded decisions36:40 Big Tech’s shift from asset light to capital intensive42:41 GPU depreciation and economic obsolescence47:03 R&D, intangible assets, and value investing51:04 How durable are competitive moats?59:01 Using AI in business and investment research01:05:11 Performance chasing and the backtest trap Learn more about the Excess Returns podcast network:excessreturns.coNo 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.
- Sep 15, 20261h 6m
Alex Edmans on Market Psychology, Intangible Assets and Edges That Persist
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 Marketshttps://amzn.to/3T9rdotAlex Edmans on Twitterhttps://twitter.com/aedmansTopics covered:How soccer defeats reveal the influence of investor emotions on stock pricesWhy short-term momentum and long-term reversals can coexistCostco, employee satisfaction, and the intangible value markets struggle to priceWhy publicly available research does not automatically eliminate investment opportunitiesMeasuring innovation through R&D productivity, patent citations, and patent noveltyWhat brand strength, customer reviews, and employee talent flows reveal about businessesHow AI can improve intangible investing while behavioral biases persistUpdating value investing to include human capital, brands, innovation, and network effectsSeparating financially relevant ESG factors from labels, and inclusion from demographic diversityFinding an investing edge through knowledge, endurance, and independenceTimestamps:00:00 How investor emotions move stock prices05:27 Why markets overreact and underreact11:52 Why employee satisfaction alpha persisted after publication17:46 R&D, patents, and the value of innovation21:52 Brand strength and Amazon reviews as investment signals26:13 Will AI eliminate the edge in intangible investing?30:25 Tracking talent flows and the long payoff from intangibles35:36 Finding mispriced intangibles where others cannot or will not look40:05 Why value investing needs more than accounting adjustments46:17 The End of ESG and what actually drives shareholder value52:40 Inclusion, psychological safety, and the limits of simple metrics58:41 Knowing your edge: knowledge, endurance, and independence1:04:36 Why Edmans disagrees with efficient market theoryLearn more about the Excess Returns podcast network:https://excessreturns.coNo 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.
- Aug 27, 202656 min
Dan Rasmussen on Private Markets, Intangible Moats, and Where Value Still Works
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 cloggedHow leverage and high debt costs threaten private equity returnsWhat publicly traded private equity funds reveal about true volatility and NAV discountsHow private equity shifted from old-economy buyouts into software and healthcare technologyWhy AI may have erased code as a software moat while strengthening other intangible advantagesHow ARR lending helped private credit finance software buyouts and created an obsolescence mismatchWhat AI is doing to hiring, junior roles, productivity and the composition of workWhy the AI CapEx boom may be a crowded, path-dependent overinvestment cycleWhy traditional value metrics work better in Japan than in the intangible-heavy U.S.How Tokyo Stock Exchange reforms, buybacks and dividends can unlock value in Japanese small capsHow R&D spend, specialist ownership and short interest can help quantify biotech valueTimestamps:00:00 Intro04:03 Why private equity's debt burden changes the equity math09:24 How private equity became a software momentum trade13:29 Why code may no longer be a durable software moat17:48 How private credit enabled software buyouts through ARR lending23:56 AI productivity, jobs and why displacement is slower than expected30:23 Why the AI CapEx boom may be the market's most crowded risk34:29 Rational overinvestment, leverage and the timing risk in AI38:46 Why consumers may capture more of AI's value than investors44:07 Japan's below-book-value reform and the return of old-school value51:03 Quantifying biotech value with R&D, specialist ownership and short interest55:08 Dan's non-consensus views on private markets and JapanLearn more about the Excess Returns podcast network:https://excessreturns.coNo 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.
- Jul 21, 20261h 14m
Mapping the AI Economy: Value Capture, Adoption and Moats with Azeem Azhar
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 Economyhttps://intelligence.exponentialview.co/assets/ev-state-of-ai-economy-2026.pdfWhy AI Isn't Showing Up on Your Bottom Linehttps://www.exponentialview.co/p/why-ai-isnt-showing-up-on-your-bottom-lineAzeem Azhar on Xhttps://x.com/azeemExponential Viewhttps://www.exponentialview.co/Topics CoveredThe size and growth rate of real generative AI demandHow the AI stack divides between chips, hosting, foundation models and applicationsWhy memory and energized data centers may be the key AI infrastructure bottlenecksOpen-source models, proprietary pricing and enterprise assuranceVertical integration and foundation model labs moving into applicationsHow AI value could flow to consumers rather than infrastructure providersWhy AI productivity requires workflow and organizational redesignWhat investors can learn from earnings calls, hiring and enterprise spendingForward-deployed engineers, consulting firms and vendor lock-inWhich intangible business moats strengthen or weaken as intelligence becomes abundantTimestamps00:00 The economics and sustainability of the AI boom06:34 Mapping the four layers of the AI stack10:43 Vertical integration and cross-stack competition15:31 Why memory is becoming an AI infrastructure bottleneck20:01 Open-source models versus proprietary AI24:36 Why foundation model labs are moving up and down the stack28:51 Could AI profits become consumer surplus?33:00 Why more copilots cannot create an AI-native company37:17 Job postings and the intangible investments behind AI adoption44:16 Can forward-deployed engineers transform legacy companies?49:15 Which business moats strengthen or weaken in the AI economy?54:20 Do foundation models really have network effects?59:00 Why judgment, verification and human provenance become more valuable01:04:56 The exponential gap in data centers and education01:10:06 How Azeem uses AI to deepen research and generate ideasLearn more about the Excess Returns podcast network:https://excessreturns.coNo 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.
- Jun 18, 20261h 8m
Aswath Damodaran on SpaceX, AI and the Limits of Big Market Stories
Professor Aswath Damodaran joins The Intangible Economy to break down how to value SpaceX, AI companies, intangible assets, and the future of value investing.We discuss why big markets do not automatically create big value, how AI CapEx is changing the character of major technology companies, and why the best investment stories still have to connect to the numbers.Aswath Damodaran on Xhttps://x.com/AswathDamodaranMusings on Marketshttps://aswathdamodaran.blogspot.com/Revisiting the SpaceX Valuation: A Post-Prospectus Updatehttps://aswathdamodaran.blogspot.com/2026/06/a-weeks-ago-i-assessed-value-of-spacex.htmlThe Big Market Delusion: Valuation and Investment Implicationshttps://papers.ssrn.com/sol3/papers.cfm?abstract_id=3501688Valuing Cyclical and Commodity Companieshttps://people.stern.nyu.edu/adamodar/pdfiles/papers/commodity.pdfValue Investing: Requiem, Rebirth or Reincarnation?https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3779481Topics covered:Valuing SpaceX after its IPO and why price matters even for great companiesHow Starlink, space launch, and xAI fit into SpaceX’s valuation storyWhy total addressable market can mislead investors in AI and other disruptive industriesThe problem with AI unit economics, data centers, power, water, and reinvestment needsWhy growth can destroy value when margins and returns on capital are weakHow intangible assets, R&D, future growth, and narratives should show up in valuationThe Big Market Delusion and how overconfidence drives boom and bust cyclesWhy AI CapEx is different from the dot-com boom and could create broader risksHow AI is changing the character of the Magnificent Seven and semiconductor companiesWhy value investing became rigid, ritualistic, and righteous, and how it can evolveTimestamps:00:00 Why great companies can still be bad investments01:03 Introducing Aswath Damodaran and The Intangible Economy01:49 SpaceX IPO, Starlink, xAI, and the challenge of valuing uncertainty05:31 Why Starlink became the core of SpaceX’s current revenue10:31 How Damodaran valued SpaceX across launch, connectivity, and AI14:07 Why AI’s huge market may still have difficult unit economics17:10 The tension between SpaceX competing in AI and renting data centers to competitors20:00 Why valuation should use distributions instead of false precision22:39 How stories and numbers work together in valuation26:45 Why investors confuse promises, potential, and businesses30:49 The Big Market Delusion and overconfidence in AI investing33:02 Why the AI CapEx boom is different from the dot-com bubble35:17 How AI infrastructure is changing the Magnificent Seven38:36 Nvidia, Micron, semiconductors, and the risk of peak cycle earnings41:00 Why the biggest AI market stories could be scary for society43:37 AI disruption, labor markets, and the speed of technological change46:30 Measuring which jobs and companies are most exposed to AI automation49:00 Why AI cost structure may look more like Spotify than software51:13 The unresolved business model questions for LLMs and AI agents52:29 Why traditional value investing lost its edge56:03 Passive investing, book value, and the blame game in value investing58:13 Why rigid value investing is vulnerable to AI disruption01:00:58 How value investing can adapt to intangible assets and uncertainty01:02:21 Why any company can be a good investment at the right price01:04:57 Why investing mistakes and track records are harder to judge than they look
- May 12, 20261h 16m
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 Chancellorhttps://www.edwardchancellor.com/Papers and articles discussed:Valuing AI: Extreme Bubble, New Golden Era, or Bothhttps://www.gmo.com/americas/research-library/valuing-ai-extreme-bubble-new-golden-era-or-both_viewpoints/Markets have poor scorecard for spotting AI losershttps://www.reuters.com/commentary/breakingviews/markets-have-poor-scorecard-spotting-ai-losers-2026-04-24/There’s no such thing as a good bubblehttps://www.reuters.com/commentary/breakingviews/theres-no-such-thing-good-bubble-2025-10-09/Big Booze can sweat off its multi-year hangoverhttps://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 boomWhy railway mania, autos, aircraft and the dot-com bubble offer lessons for todayWhy markets often fund major technology transitions but fail to identify the winnersThe prisoner’s dilemma driving hyperscaler AI spendingWhether AI demand can justify the supply being builtHow GPU depreciation and AI capital spending may affect reported earningsWhy hallucinations and reliability may limit the total addressable market for large language modelsThe case for looking at AI anti-bubbles instead of shorting the bubble directlyWhy China shows that strong GDP growth does not guarantee strong shareholder returnsHow intangible capital, SaaS valuations and human capital fit into capital cycle analysisWhether bubbles can be good for society while still being bad for investorsWhy the long-term interest rate cycle may have changedThe role of gold in a world of expensive stocks, rising debt and vulnerable bondsTimestamps:00:00 Edward Chancellor on capital cycles, bubbles and AI04:42 Why the railway mania became a classic overinvestment cycle09:00 Why markets fund technology booms but often miss the winners13:19 The prisoner’s dilemma behind AI spending17:30 Will AI demand justify the supply being built20:00 How capital spending can inflate profits before the bust25:08 The AI Hindenburg moment and the limits of large language models30:55 Why AI hype may exceed the proven technology35:55 Why the anti-bubble may matter more than shorting AI40:00 The energy transition bubble and the opportunity in overlooked assets45:08 China’s lesson on GDP growth and shareholder returns49:27 Big Booze, GLP-1s and the Lindy effect54:23 Can intangible capital have its own capital cycle59:54 SaaS valuations and the index creation warning signal01:04:10 Why bubbles can help society but hurt investors01:09:09 Why long-term rates may be in a new multi-decade cycle01:14:07 Why Edward Chancellor still sees a role for gold
- Mar 31, 20261h 1m
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 Futurehttps://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.pdfMeasuring the Moat: Assessing the Magnitude and Sustainability of Value Creation – https://www.morganstanley.com/im/publication/insights/articles/article_measuringthemoat.pdfOne Job: Expectations and the Role of Intangible Investments – https://www.morganstanley.com/im/publication/insights/articles/article_onejob.pdfCapitalism Without Capital: The Rise of the Intangible Economy – https://books.google.com/books/about/Capitalism_without_Capital.html?id=J3SYDwAAQBAJA Better Estimate of Internally Generated Intangible Capital – https://pubsonline.informs.org/doi/10.1287/mnsc.2022.01703Underestimating the Red Queen: Measuring Growth and Maintenance Investments – https://www.morganstanley.com/im/publication/insights/articles/article_underestimatingtheredqueen.pdfExplaining the Recent Failure of Value Investing – https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3442539Guest Links:Michael Mauboussin TwitterTopics 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 investorsTimestamps:00:00 Intro and OpenAI growth expectations vs historical base rates04:32 Why no company has ever achieved 100%+ sustained growth at scale08:47 Lessons from megaprojects and AI infrastructure buildouts13:18 Intangible assets and why outcomes now have fatter tails18:36 Why big tech is growing faster than historical precedents23:52 Where value accrues in AI and why consumers may benefit most28:21 Barriers to entry in AI including capital, talent, and scale32:47 The risk of overinvestment and historical parallels to past bubbles37:26 Game theory and competitive signaling in AI capital spending41:58 Why investment returns—not “asset light” narratives—drive value46:12 How accounting fails to capture intangible investment properly50:44 Breaking down SG&A into maintenance vs investment spending55:03 Why understanding reinvestment and ROI is the core investing skill59:18 Final thoughts on uncertainty, expectations, and base rates in AI
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About The Intangible Economy with Kai Wu
The Intangible Economy with Kai Wu explores the hidden forces reshaping the modern economy and their implications for investors. AI and the broader technology revolution are changing how we live, work, and create value. In each episode, Kai sits down with investors, researchers, and other experts to discuss how innovation and other intangible forces - such as brands, human capital, and network effects - are transforming markets and investment outcomes.
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