In this episode of Excess Returns, we dive deep into one of the most pressing investing debates today: how to think about valuations, profit margins, and artificial intelligence in a market that feels both expensive and transformative. Sam Ro joins Matt Zeigler and Kai Wu for a wide-ranging conversation that explores whether traditional valuation tools still matter, how AI is reshaping corporate economics, and why history suggests investors should be cautious about bubble narratives even when enthusiasm runs high. From profit margins and capital intensity to the future of the Magnificent Seven, this episode focuses on how long-term investors can frame uncertainty without relying on false precision or short-term market calls.
Timestamps
00:00 Valuations, bubbles, and why timing markets is so hard
01:41 Do valuations still matter for investors
05:58 S&P 500 valuation levels versus history
09:30 Profit margins and why mean reversion has not shown up yet
14:39 Household finances, pricing power, and consumer resilience
15:47 AI, productivity, and the limits of forecasting economic impact
19:15 Valuations adjusted for structurally higher profit margins
21:15 Tech multiples, growth expectations, and PEG ratios
24:07 Are we in an AI bubble and why that question may not help
29:14 Lessons from past bubbles and irrational exuberance
30:14 How transformative AI could be compared to past innovations
35:20 Massive AI capital spending and the risk of overbuild
39:42 Who captures value in AI: builders versus users
46:39 Revenue per worker and productivity trends
48:00 Dispersion inside the Magnificent Seven
51:34 Big tech shifting from asset-light to asset-heavy models
59:53 Turnover among top companies over time
01:01:10 Why Wall Street price targets miss the point
01:04:30 Presidential cycles and market returns
01:06:28 Fund manager surveys and why popular risks are often lagging indicators
Topics covered
How investors should think about valuations over long time horizons
Why elevated profit margins may be more structural than cyclical
The role of AI in productivity, earnings, and competitive dynamics
Bubble psychology and lessons from the dot-com era
Capital intensity, overinvestment, and the risk of write-downs
Why AI infrastructure builders may not capture most of the value
What dispersion within the Magnificent Seven signals for markets
Why broad diversification still matters in a rapidly changing market
