AI as Infrastructure and Capital Intensity

In this episode of Excess Returns, Professor Aswath Damodaran joins Matt Zeigler and Kai Wu for a wide-ranging conversation on valuation, portfolio construction, and how investors should think about risk, discipline, and opportunity in a market shaped by AI, market concentration, and rising uncertainty. Damodaran walks through how he builds and manages his own portfolio, why price matters more than story or quality, and how AI-driven capital spending could reshape margins and returns across the economy. The discussion blends practical investing frameworks with big-picture market insights, offering a clear look at how a valuation-driven investor navigates today’s environment.

Main topics covered
• How Aswath Damodaran builds a stock portfolio, including diversification, position sizing, and turnover
• Why investing is about buying at the right price, not buying great companies
• Using valuation frameworks to invest in young, unprofitable, and fast-growing companies
• How stories and narratives fit into valuation without replacing financial discipline
• Watchlists, patience, and waiting for price rather than chasing popular stocks
• Sell discipline, overvaluation triggers, and avoiding emotional attachment to winners
• Using probability distributions and simulations instead of single-point estimates
• How company lifecycles affect growth, margins, and capital allocation decisions
• Why many companies struggle as they age and how management quality shows up late in the lifecycle
• AI as a capital cycle and why massive AI investment may lower margins overall
• Why AI is likely to create a bubble, even if it delivers long-term economic value
• Winners and losers in the AI value chain, from infrastructure to applications
• Risks from AI infrastructure spending, debt, and cross-ownership structures
• Why private markets may not deliver better outcomes for individual investors
• How Damodaran thinks about cash, diversification, and assets uncorrelated with equities
• Reentering markets after selling and avoiding the trap of staying in cash too long
• Time horizon, legacy investing, and managing wealth across generations

Timestamps
00:00 Investing is about price, valuation, and early thoughts on AI and market risk
01:54 Personal investing philosophy and why portfolios must be investor-specific
03:00 Diversification, number of holdings, and managing downside risk
05:00 Valuation frameworks and buying companies at the right price
06:00 Stories versus numbers and avoiding the circle of competence trap
08:20 Political risk and why some sectors are hard to value
08:47 Watchlists, patience, and waiting for price to meet value
11:43 When and why to sell stocks as a value investor
12:00 Using probability distributions and simulations in valuation
15:48 Sell discipline, fund flows, and separating skill from luck
18:00 Company lifecycles, aging businesses, and management discipline
23:18 Apple, Meta, and contrasting approaches to AI investment
24:08 AI bubbles, winner-take-all dynamics, and capital cycles
27:48 Infrastructure investing, debt risk, and societal spillovers
32:20 Cross-ownership risks and AI ecosystem fragility
35:00 AI’s impact on profit margins and competition
39:41 Where AI value may accrue over time
44:38 AI tools, valuation bots, and the rise of investment scams
49:17 Private markets, alternatives, and cost structures
53:05 Cash, collectibles, and diversification beyond equities
56:33 Reentering markets after selling and avoiding market timing traps
58:35 Time horizon, legacy investing, and generational wealth