Excess Returns

AI is moving from hype to real enterprise adoption, and Gene Munster and Doug Clinton join Excess Returns to explain what that means for investors, technology stocks, energy demand, jobs and the next phase of the AI trade. We discuss why AI may still be early in its bubble cycle, how frontier models like GPT, Claude, Gemini and Grok compare, why AI-powered investing is becoming more practical, and where the biggest second-order opportunities may emerge.
Gene Munster on X
https://x.com/munster_gene
Doug Clinton on X
https://x.com/dougclinton
Deepwater Asset Management
https://www.deepwatermgmt.com/
Intelligent Alpha
https://www.intelligentalpha.co/
Main topics covered:
• Why Doug Clinton still thinks AI could become a bigger bubble than dot-com
• How Claude Code, Codex and frontier AI models are changing enterprise productivity
• The job disruption risk for knowledge workers and why AI adoption may become a survival skill
• Why the AI model race may not be winner-take-all
• How Intelligent Alpha uses large language models to evaluate stocks and earnings expectations
• Why GPT, Claude and DeepSeek perform differently across investing tasks
• The AI infrastructure boom and why energy may be one of the most underappreciated bottlenecks
• Hyperscaler CapEx, data centers and the investment case for continued AI spending
• How major AI IPOs like SpaceX, Anthropic and OpenAI could affect public markets
• Why space, orbital data centers and zero-gravity manufacturing could become real investment themes
Timestamps:
00:00 AI, electricity and intelligence
04:33 Why new AI models changed the semiconductor trade
09:14 What AI means for knowledge worker jobs
14:03 Codex, Claude Code and Google’s AI challenge
18:50 OpenAI, Apple and the model capacity race
23:03 How many frontier AI models can survive?
27:18 Intelligent Alpha’s AI earnings benchmark
31:34 Why AI investors avoid emotional bias
35:33 Where to invest in the AI stack
39:00 Why AI energy demand is still underappreciated
43:43 How markets are judging hyperscaler AI spending
48:00 The investment opportunity in space
52:20 Final thoughts and closing

Jeremy Grantham joins Excess Returns to discuss The Making of a Permabear, mean reversion, market bubbles, AI, the Magnificent 7, and the long-term lessons investors can take from his career at GMO. We cover why he rejects the simple “permabear” label, how he thinks about valuation and bubbles, why AI may be both transformative and dangerous for investors, and why long-term thinking is so hard but so essential.
The Making of a Permabear: The Perils of Long-term Investing in a Short-term World
https://groveatlantic.com/book/the-making-of-a-permabear/
GMO
https://www.gmo.com/americas/
Grantham Foundation
https://granthamfoundation.org/
Topics covered:
Why Jeremy Grantham thinks the “permabear” label misses the point

The difference between being generally bearish and making a true “abandon ship” call

Mean reversion, valuation cycles, and why history still matters for investors

Why monopoly power helped reshape U.S. profit margins and market concentration

How AI could turn today’s monopoly winners into brutal competitors

Why new technology often becomes a cost of doing business rather than a permanent profit boost

How Grantham defines bubbles using two-sigma market events

Lessons from Japan, the dot-com bubble, the housing bubble, and the 2021 speculative peak

Why institutional investors struggle to stick with value strategies during bubbles

The role of purpose, climate risk, toxicity, and long-term thinking in Grantham’s later career

The one lesson Grantham would teach ordinary investors about pessimism, realism, and time horizons

Timestamps:
00:00 Jeremy Grantham on unpleasant news and long-term investing
04:18 Reinvesting when terrified in 2009
08:43 Why Grantham told investors to abandon ship in 2008
10:28 Mean reversion and why history matters
14:00 Monopoly power, the Mag 7, and rising market concentration
17:14 Why AI is important but impossible to forecast
20:21 AI as a cost of doing business
21:24 From monopoly profits to brutal AI competition
24:05 How investors should think about valuation mean reversion
27:00 Why high returns on capital should eventually attract competition
29:47 How Grantham defines a market bubble
33:00 Japan’s extreme bubble and GMO’s zero weight decision
34:19 The dot-com bubble and the pain of being early
38:00 Grantham’s bubble warning signal in 2021
41:35 Whether today’s market is showing classic bubble behavior
43:00 QuantumScape, meme stocks, and speculative excess
46:35 How ChatGPT interrupted the 2022 bear market
49:12 Investor behavior and the cost of underperforming in a bubble
55:00 Purpose, philanthropy, climate risk, and useful work
01:01:03 The one lesson Grantham would teach average investors

Marc Rubinstein joins Excess Returns to explain what private credit, bank earnings, insurance balance sheets, fintech growth, and arbitrage firms reveal about the modern financial system. The conversation covers why private credit risks may not be systemic in the traditional banking-crisis sense, but still matter for investors because of redemption gates, hidden leverage, opaque structures, incentive conflicts, and correlations that can spike when markets are under stress.
Marc Rubinstein on X
https://x.com/MarcRuby
Net Interest
https://www.netinterest.co/
In this episode, we discuss:
Why the Fed says private credit redemption risks are limited and manageable

What Blue Owl’s redemption gates reveal about private credit liquidity

How post-2008 bank regulation pushed risk into private credit, hedge funds, trading firms, and exchanges

Why banks and private credit firms are both competitors and collaborators

The “layer cake” of leverage connecting banks, private credit, and borrowers

How HSBC’s loss tied to Atlas and MFS highlights hidden credit risks

Why insurance companies have become increasingly tied to private credit

Why rapid growth can be dangerous in financial businesses

What bank earnings show about the gap between weak consumer confidence and resilient spending

Why post-mortem reports from SVB, Credit Suisse, and other failures reveal what investors could not see in real time

How Revolut became one of the most interesting fintech stories in global banking

Why Marc calls this a potential golden age of arbitrage

What Jane Street, public BDC discounts, private asset valuations, and geopolitical fragmentation tell us about market structure

Why investors may still be too anchored to the 2008 banking playbook

Where Marc sees risk and opportunity in financials, banks, Europe, and non-bank financial institutions

Timestamps:
00:00 Private credit, hidden risks, and correlation spikes
05:03 Why Blue Owl became a private credit warning sign
10:20 How private credit grew after the 2008 financial crisis
15:30 Banks and private credit as financial “frenemies”
19:44 HSBC, Atlas, MFS, and the layer cake of leverage
24:11 Apollo, Athene, insurance assets, and private credit incentives
29:20 Why higher rates have not broken more of the financial system
33:40 Bank earnings, consumer confidence, and resilient spending
37:20 Why “I don’t know” can be a powerful signal from bank CEOs
41:46 Revolut and the ambition to build a truly global bank
47:38 Why growth can be dangerous in finance
52:19 Private assets, public BDC discounts, and arbitrage opportunities
56:34 What investors misunderstand about banks today
59:31 How Marc would think about financials as a long-short investor

First Principles with Andy Constan launches with a deep dive into market bubbles, AI, semiconductor stocks, and the financial conditions that can turn powerful technological change into a dangerous investment regime. Andy explains how bubbles form, why they are almost impossible to time, how today’s AI boom compares to past episodes like 1987, the dot-com bubble, housing, and the bond bubble, and what investors should watch as expectations, financing, and FOMO build.
Andy Constan on X
https://x.com/dampedspring
Damped Spring Advisors
https://dampedspring.com/
Topics covered:
Why bubbles are easy to identify in hindsight but nearly impossible to define in real time

The difference between an expensive market and a true bubble regime

How new technologies, easy money, regulation, and exogenous shocks can create bubble conditions

Why AI may rhyme with the internet boom without being an exact repeat

The role of ChatGPT, Microsoft’s OpenAI investment, and semiconductor earnings expectations

What the 1987 crash, Japan, housing, bonds, and dot-com bubble can teach investors today

Why human nature, FOMO, and “keeping up with the Joneses” make bubbles so powerful

How the late-1990s Fed response to Long-Term Capital Management helped fuel the final phase of the tech bubble

Why tech’s current size in the economy and market may limit how far the AI boom can grow

How AI capex, hyperscaler spending, buybacks, debt issuance, and IPO supply could determine what happens next

Timestamps:
00:00 Intro and the challenge of identifying bubbles
04:32 Expensive markets vs true bubble regimes
09:57 The five bubble episodes Andy compares to today
14:35 Root conditions, escalation events, and the peaking phase
19:20 Why the 1987 crash may also have been a bubble
24:25 The late-1990s setup and the Netscape Navigator moment
28:00 Crisis analogs, easy financial conditions, and today’s AI parallels
32:20 Long-Term Capital Management and rocket fuel for the tech bubble
36:11 Why tech’s market share matters more today than in the 1990s
43:18 Policy mistakes, subsidies, and how governments feed bubbles
47:42 Semiconductor earnings expectations and valuation risk
53:45 The AI capex chain and where the money has to come from
58:42 IPOs, corporate debt, and the financing risk behind the AI boom
01:02:27 What investors should do differently in a bubble regime

Edward Chancellor joins Kai Wu on the latest episode of the Intangible Economy 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.
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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