The Intangible Economy with Kai Wu

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

• Why short-term momentum and long-term reversals can coexist

• Costco, employee satisfaction, and the intangible value markets struggle to price

• Why publicly available research does not automatically eliminate investment opportunities

• Measuring innovation through R&D productivity, patent citations, and patent novelty

• What brand strength, customer reviews, and employee talent flows reveal about businesses

• How AI can improve intangible investing while behavioral biases persist

• Updating value investing to include human capital, brands, innovation, and network effects

• Separating financially relevant ESG factors from labels, and inclusion from demographic diversity

• Finding an investing edge through knowledge, endurance, and independence

Timestamps:

00:00 How investor emotions move stock prices
05:27 Why markets overreact and underreact
11:52 Why employee satisfaction alpha persisted after publication
17:46 R&D, patents, and the value of innovation
21:52 Brand strength and Amazon reviews as investment signals
26:13 Will AI eliminate the edge in intangible investing?
30:25 Tracking talent flows and the long payoff from intangibles
35:36 Finding mispriced intangibles where others cannot or will not look
40:05 Why value investing needs more than accounting adjustments
46:17 The End of ESG and what actually drives shareholder value
52:40 Inclusion, psychological safety, and the limits of simple metrics
58:41 Knowing your edge: knowledge, endurance, and independence
1:04:36 Why Edmans disagrees with efficient market theory

Learn more about the Excess Returns podcast network:

https://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.

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

• How leverage and high debt costs threaten private equity returns

• What publicly traded private equity funds reveal about true volatility and NAV discounts

• How private equity shifted from old-economy buyouts into software and healthcare technology

• Why AI may have erased code as a software moat while strengthening other intangible advantages

• How ARR lending helped private credit finance software buyouts and created an obsolescence mismatch

• What AI is doing to hiring, junior roles, productivity and the composition of work

• Why the AI CapEx boom may be a crowded, path-dependent overinvestment cycle

• Why 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 caps

• How R&D spend, specialist ownership and short interest can help quantify biotech value

Timestamps:
00:00 Intro
04:03 Why private equity's debt burden changes the equity math
09:24 How private equity became a software momentum trade
13:29 Why code may no longer be a durable software moat
17:48 How private credit enabled software buyouts through ARR lending
23:56 AI productivity, jobs and why displacement is slower than expected
30:23 Why the AI CapEx boom may be the market's most crowded risk
34:29 Rational overinvestment, leverage and the timing risk in AI
38:46 Why consumers may capture more of AI's value than investors
44:07 Japan's below-book-value reform and the return of old-school value
51:03 Quantifying biotech value with R&D, specialist ownership and short interest
55:08 Dan's non-consensus views on private markets and Japan

Learn more about the Excess Returns podcast network:

https://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.

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

• How the AI stack divides between chips, hosting, foundation models and applications

• Why memory and energized data centers may be the key AI infrastructure bottlenecks

• Open-source models, proprietary pricing and enterprise assurance

• Vertical integration and foundation model labs moving into applications

• How AI value could flow to consumers rather than infrastructure providers

• Why AI productivity requires workflow and organizational redesign

• What investors can learn from earnings calls, hiring and enterprise spending

• Forward-deployed engineers, consulting firms and vendor lock-in

• Which intangible business moats strengthen or weaken as intelligence becomes abundant

Timestamps

00:00 The economics and sustainability of the AI boom
06:34 Mapping the four layers of the AI stack
10:43 Vertical integration and cross-stack competition
15:31 Why memory is becoming an AI infrastructure bottleneck
20:01 Open-source models versus proprietary AI
24:36 Why foundation model labs are moving up and down the stack
28:51 Could AI profits become consumer surplus?
33:00 Why more copilots cannot create an AI-native company
37:17 Job postings and the intangible investments behind AI adoption
44:16 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 valuable
01:04:56 The exponential gap in data centers and education
01:10:06 How Azeem uses AI to deepen research and generate ideas

Learn more about the Excess Returns podcast network:
https://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.

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 X
https://x.com/AswathDamodaran

Musings on Markets
https://aswathdamodaran.blogspot.com/

Revisiting the SpaceX Valuation: A Post-Prospectus Update
https://aswathdamodaran.blogspot.com/2026/06/a-weeks-ago-i-assessed-value-of-spacex.html

The Big Market Delusion: Valuation and Investment Implications
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3501688

Valuing Cyclical and Commodity Companies
https://people.stern.nyu.edu/adamodar/pdfiles/papers/commodity.pdf

Value Investing: Requiem, Rebirth or Reincarnation?
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3779481

Topics covered:

• Valuing SpaceX after its IPO and why price matters even for great companies

• How Starlink, space launch, and xAI fit into SpaceX’s valuation story

• Why total addressable market can mislead investors in AI and other disruptive industries

• The problem with AI unit economics, data centers, power, water, and reinvestment needs

• Why growth can destroy value when margins and returns on capital are weak

• How intangible assets, R&D, future growth, and narratives should show up in valuation

• The Big Market Delusion and how overconfidence drives boom and bust cycles

• Why AI CapEx is different from the dot-com boom and could create broader risks

• How AI is changing the character of the Magnificent Seven and semiconductor companies

• Why value investing became rigid, ritualistic, and righteous, and how it can evolve

Timestamps:

00:00 Why great companies can still be bad investments
01:03 Introducing Aswath Damodaran and The Intangible Economy
01:49 SpaceX IPO, Starlink, xAI, and the challenge of valuing uncertainty
05:31 Why Starlink became the core of SpaceX’s current revenue
10:31 How Damodaran valued SpaceX across launch, connectivity, and AI
14:07 Why AI’s huge market may still have difficult unit economics
17:10 The tension between SpaceX competing in AI and renting data centers to competitors
20:00 Why valuation should use distributions instead of false precision
22:39 How stories and numbers work together in valuation
26:45 Why investors confuse promises, potential, and businesses
30:49 The Big Market Delusion and overconfidence in AI investing
33:02 Why the AI CapEx boom is different from the dot-com bubble
35:17 How AI infrastructure is changing the Magnificent Seven
38:36 Nvidia, Micron, semiconductors, and the risk of peak cycle earnings
41:00 Why the biggest AI market stories could be scary for society
43:37 AI disruption, labor markets, and the speed of technological change
46:30 Measuring which jobs and companies are most exposed to AI automation
49:00 Why AI cost structure may look more like Spotify than software
51:13 The unresolved business model questions for LLMs and AI agents
52:29 Why traditional value investing lost its edge
56:03 Passive investing, book value, and the blame game in value investing
58:13 Why rigid value investing is vulnerable to AI disruption
01:00:58 How value investing can adapt to intangible assets and uncertainty
01:02:21 Why any company can be a good investment at the right price
01:04:57 Why investing mistakes and track records are harder to judge than they look

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

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