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Excess Returns

Excess Returns is dedicated to making you a better long-term investor and making complex investing topics understandable. Join Jack Forehand, Justin Carbonneau and Matt Zeigler as they sit down with some of the most interesting names in finance to discuss topics like macroeconomics, value investing, factor investing, and more. Subscribe to learn along with us.

Latest Episodes:

He Quantified 200 Years of Disruption | Kai Wu on Separating Software Survivors from Value Traps Jun 02, 2026

Show notes

Kai Wu of Sparkline Capital joins Excess Returns to break down his latest research on AI disruption, software stocks, value traps, and intangible moats. We discuss why software valuations have collapsed, why traditional value investing can fail during technological disruption, and how investors can separate potential AI winners from companies whose business models may be permanently impaired.

AI Disruption: Moats and Value Traps
https://www.sparklinecapital.com/post/ai-disruption

Kai Wu on X
https://x.com/ckaiwu

Sparkline Capital
https://www.sparklinecapital.com/

Topics Covered:

Why software stocks are trading at a historically unusual discount to the market
How AI disruption can create both real opportunities and dangerous value traps
Why Blockbuster, Borders, RadioShack and newspapers offer lessons for today’s software selloff
How patent data and natural language processing can measure technological disruption
Why disruption has helped explain the poor performance of traditional value investing
Why value investing may still work in sectors insulated from technological change
How intangible assets like brand, human capital, intellectual property and network effects can protect companies
Why Walmart and The New York Times survived disruption while other incumbents did not
How David Teece’s complementary assets framework applies to AI, software and moats
Why AI adoption and intangible value together may help identify software survivors
Why high dispersion in disruption-scare stocks creates a potential opportunity for stock pickers

Timestamps:
00:00 Software stocks now trade at a historic discount
04:26 What makes a cheap stock a value trap
08:25 Measuring disruption using patents, filings and natural language processing
13:23 Is AI the biggest disruptive wave in history?
14:55 Why disruption keeps stacking on retailers
17:10 How technological change disrupted traditional value investing
21:20 Why value investors need to know when not to apply old metrics
25:06 Why more of the market is exposed to innovation than ever before
27:07 What Walmart and The New York Times teach about surviving disruption
32:40 The four intangible moats that can protect companies
35:02 Why intangible value works better in disrupted industries
38:50 Apple, Amazon, Macy’s and the difference between disruptors and value traps
42:58 Applying intangible value to beaten-down software stocks
47:05 Why AI adoption alone is not enough
48:23 How AI could improve margins for surviving software companies
50:09 Which industries are adopting AI fastest
52:14 The software sweet spot: AI adoption plus intangible moats
53:53 Why disruption-scare stocks have extreme return dispersion
57:40 What happens when intangible value is applied to high-disruption stocks
01:01:42 Why “code is not the moat” for many software companies

The Three Cracks in the AI Trade | Ben Hunt, Brent Kochuba and Aahan Menon on What Could Derail the Market's Biggest Bet May 30, 2026

Show notes

In this episode of Last Call, we break down one of the most confusing market backdrops in years: AI-driven earnings optimism, rising oil and inflation risk, stretched options positioning, and the market impact of a potential SpaceX IPO. Jack Forehand and Matt Zeigler are joined by Aahan Menon, Ben Hunt, and Brent Kochuba to examine what macro data, political narratives, options flows, and index mechanics are saying about where markets could go next.

Follow Last Call on Spotify⁠⁠⁠⁠⁠

⁠⁠⁠⁠⁠Follow Last Call on Apple Podcasts⁠

Topics Covered:

Why markets are looking through war, oil shocks and valuation concerns
How earnings estimates are driving sector performance in the AI trade
Aahan Menon on growth, inflation, oil prices and macro regime signals
Why demand destruction from higher energy prices can take longer than investors expect
What a rising growth and rising inflation regime can mean for stocks, commodities and bonds
Ben Hunt on World War AI and the collision between AI market optimism and political backlash
Why opposition to AI data centers could become a major market and election issue
Brent Kochuba on call buying, implied volatility and signs of options market froth
Why CORE 1M and skew signals may be warning of a downside spasm
How the SpaceX IPO could affect index flows, active managers and mega-cap stocks

Timestamps:
00:00 Intro: AI, inflation and options risk in one market
05:40 Earnings estimates, AI optimism and why fundamentals still matter
10:31 Aahan Menon on a difficult macro backdrop
15:29 Why energy shocks and demand destruction take time
20:24 Why inflation can persist even if the oil shock eases
24:47 Ben Hunt on World War AI and the AI resource build-out
30:00 AI CapEx as the pillar holding up market optimism
34:00 The political backlash against AI data centers
38:00 Why data center opposition matters for markets
42:09 Why price action can distort the AI narrative
47:48 CORE 1M, stretched call prices and downside spasm risk
52:00 Why Nasdaq options are priced for upside crashes
56:11 Index rules, human judgment and the SpaceX IPO
01:00:34 The free float problem and rebalancing pressure
01:05:22 Space data centers, valuation and the size of the AI opportunity

Cheap Is a Warning, Not a Thesis | Adam Parker on What This Market Is Really Pricing May 28, 2026

Show notes

Adam Parker returns to Excess Returns to explain why the market may be trading more on future fundamentals than investors think, how AI is reshaping stock selection, and why traditional valuation signals may be less useful than they once were.

We discuss AI revenue exposure, software vs. semiconductors, Mag Seven positioning, gross margins, estimate achievability, spinoffs, and Adam’s highest-conviction contrarian sector idea.

Adam Parker on X
https://x.com/Adam_Parker_Tri

Trivariate Research
https://trivariateresearch.com/

Trivector Research
https://www.trivectorresearch.com

Topics covered:

Why “sell in May” and other calendar-based market rules often lack statistical support
Why Adam thinks the stock market leads the economy, not the other way around
How to think about whether today’s AI market is a bubble
Why the market may be trading on 2030 or 2031 fundamentals
When investors may start demanding returns on AI capital spending
Why AI could create new jobs rather than simply destroy existing ones
How large AI-related IPOs like SpaceX could affect index mechanics and portfolio flows
Why gross margin expansion is one of Adam’s most important stock selection factors
Why Adam remains cautious on software and prefers semiconductors over software
How valuation, quality, and other traditional factors may have changed since COVID
Why estimate achievability and incremental margins matter more than simple beats and misses
How to think about the Mag Seven, Nvidia, and market concentration
Why spinoffs may become more important in an AI-driven market
Why healthcare is Adam’s highest-conviction contrarian sector idea

Timestamps:

00:00 Why the market may be trading on future fundamentals
04:37 Is today’s stock market an AI bubble?
08:45 When AI capex needs to show real returns
13:00 How trillion-dollar IPOs could reshape index mechanics
19:00 Why gross margin expansion is such a powerful factor
23:00 Why software companies face AI-driven margin pressure
27:21 Where AI semiconductor exposure goes next
31:54 Why valuation does not work for stock picking
35:03 What has changed in markets since COVID
39:22 Estimate achievability and incremental margins
43:06 How to think about the Mag Seven and Nvidia
47:55 Why healthcare could be the biggest AI opportunity

He Built the Fund He'd Hold 30 Years | Eric Crittenden on What Investors Pick When Labels Come Off May 26, 2026

Show notes

Eric Crittenden joins Matt Zeigler and Jason Buck for a deep dive into trend following and managed futures.

They discuss why systematic macro trend investing works, how risk transfer creates a return premium, and how trend can fit inside a diversified all-weather portfolio.

Standpoint Funds

https://www.standpointfunds.com/

Topics covered:

Why trend following can struggle during fast reversals and thrive after regime shifts
How systematic investors manage whipsaws, drawdowns, and emotional pressure
The trade-offs between short-term, medium-term, and long-term trend signals
Why Eric prefers simple, durable systems over complex models and constant tinkering
When it makes sense to remove a futures market from a systematic portfolio
Why trend following may earn a risk transfer premium from hedgers and commercial users
How copper producers, options markets, and insurance help explain trend following returns
Why rising interest rates and short bond positions can benefit managed futures
How trend following can pair with global equities in an all-weather portfolio
Why smoothing a trend strategy can reduce its value when investors need convexity most
The behavioral challenge of holding diversifiers that look wrong at the wrong time
Why investors and advisors often want alternatives but struggle to stick with them

Timestamps:

00:00 Why trend following opportunities appear under pressure

04:39 Pro-growth positioning before the whipsaw

09:32 Short-term vs long-term trend signals

13:46 The danger of tinkering with systematic strategies

18:43 What actually changes in a durable process

23:27 Rising rates, short bonds, and collateral yield

28:00 Copper hedging and why trend followers buy rising prices

32:00 Options, insurance, and risk transfer through time

36:28 Regime shifts and supply-demand imbalances

41:00 What investors choose when asset classes are anonymized

45:11 Building a portfolio for 30-year terminal wealth

50:06 Why portfolio construction is different than judging individual strategies

56:15 Why trend following and value investing require faith

01:00:42 Reducing errors vs chasing highlight-reel winners

01:05:36 Where to follow Eric and Standpoint

Cliff Asness on Bubbles, Private Equity and His Research Greatest Hits May 23, 2026

Show notes

Cliff Asness returns to Excess Returns for a greatest hits tour through some of his most important and entertaining investing ideas.

We discuss bubble logic, today’s AI market comparisons, why volatility still matters as a risk measure, private equity “volatility laundering,” international diversification, market timing myths, pulling the goalie, and how machine learning is changing quantitative investing.

Cliff Asness on X
https://x.com/CliffordAsness

AQR Capital Management
https://www.aqr.com/

Papers Discussed

Bubble Logic: Or, How to Learn to Stop Worrying and Love the Bull
https://www.aqr.com/Insights/Research/Working-Paper/Bubble-Logic-Or-How-to-Learn-to-Stop-Worrying-and-Love-the-Bull

Rubble Logic: What Did We Learn From the Great Stock Market Bubble?
https://www.aqr.com/Insights/Research/Journal-Article/Rubble-Logic

My Top 10 Peeves
https://www.aqr.com/-/media/AQR/Documents/Insights/Journal-Article/My-Top-10-Peeves.pdf

Volatility Laundering
https://www.aqr.com/Insights/Perspectives/Volatility-Laundering

I Did Not Predict What Is Going on in Privates
https://www.aqr.com/Insights/Perspectives/I-Did-Not-Predict-What-is-Going-on-in-Privates

(So) What If You Miss the Market's N Best Days?
https://www.aqr.com/Insights/Perspectives/So-What-If-You-Miss-the-Markets-N-Best-Days

International Diversification Works (Eventually)
https://www.aqr.com/Insights/Research/Journal-Article/International-Diversification-Works-Eventually

International Diversification - Still Not Crazy after All These Years
https://www.aqr.com/Insights/Research/Journal-Article/International-Diversification-Still-Not-Crazy-after-All-These-Years

Perhaps the Most Important Essay I Will Ever Co Author
https://www.aqr.com/Insights/Perspectives/Perhaps-the-Most-Important-Essay-I-Will-Ever-Co-Author

Main topics covered:

How the dot-com bubble created its own internal logic
Why Dow 36,000 and Cisco message boards captured bubble thinking
What investors learned, and failed to learn, from the tech bubble
How today’s AI market compares with the dot-com era
Why long periods of underperformance make even good strategies hard to stick with
Why Cliff still defends volatility as a useful risk measure
Why “cash on the sidelines” is a misleading market narrative
How private equity smoothing can make risk look lower than it really is
Why the private markets debate is not a short-term prediction
Why the “missing the best 10 days” argument against market timing is incomplete
Why international diversification can still matter after decades of US outperformance
What pulling the goalie can teach investors about risk, incentives and career risk
How machine learning changes quant investing without eliminating economic intuition

Timestamps:

00:00 Why certainty is dangerous in investing
04:58 Why Bubble Logic never became a book
10:18 Cisco, Yahoo message boards and bubble psychology
14:16 Rubble Logic and the lessons investors failed to learn
18:04 What today’s AI market has in common with the dot-com bubble
22:23 Why the long run can lie to investors
26:02 Volatility, permanent loss of capital and real risk control
30:19 Why there is no cash on the sidelines
34:00 Private equity, smoothing and volatility laundering
39:47 Why Cliff did not call the private markets downturn
43:19 The flaw in the missing the best 10 days argument
49:00 Why international diversification still works eventually
53:35 Why crashes are global but lost decades are local
57:30 Pulling the goalie and asymmetric risk
01:01:00 Why coaches and investors avoid optimal decisions
01:07:36 Machine learning, overfitting and economic intuition
01:10:50 Leverage, short selling and derivatives in quant portfolios
01:16:26 Where to follow Cliff Asness

He Studied Every Bear Market Since 1929 | Ben Carlson on How the Worst Starting Point Still Made 8% May 21, 2026

Show notes

Ben Carlson joins Excess Returns to discuss his new book Risk and Reward and the biggest lessons investors can learn from market history. We cover how to think about risk, inflation, market timing, bear markets, lost decades, diversification, compounding and why surviving volatility is the key to building long-term wealth.

Ben's Book
https://amzn.to/4dFHsQz

Ben Carlson on X
https://x.com/awealthofcs

Ben's Blog
https://awealthofcommonsense.com/

Main topics covered:

Why risk is hard to define and always involves trade-offs
How vivid risks like sharks and headlines distort investor decision-making
Why doing nothing can be one of the hardest parts of investing
How inflation should be viewed through personal finance, human capital and long-term investing
Why stocks can be an inflation hedge even if they struggle during inflation spikes
Why waiting for the market coast to clear often fails
What the world’s worst market timer teaches about saving and staying invested
How loss aversion shapes investor behavior
What the Great Depression, bear markets and 30-year returns teach about long-term investing
Why there is no perfect portfolio and the best strategy is one you can actually stick with

Timestamps:

00:00 Ben Carlson on why risk and reward are attached

06:35 Doing nothing, action bias and better investing behavior

11:51 Inflation psychology and lessons from the 1970s

16:55 Why stocks can hedge inflation over the long run

21:07 Why waiting for the coast to clear is a market timing trap

26:30 Time horizons, loss aversion and portfolio behavior

31:49 Government rescue, left-tail risk and unintended consequences

35:54 Recessionary vs non-recessionary bear markets

42:09 Why the stock market and economy can diverge

47:24 Why compounding is about holding, not trading

51:37 Starting valuations, lost decades and future returns

55:40 Risk, reward and the biggest lesson for investors

Is AI Still in 1995? Gene Munster and Doug Clinton on the Next Phase of the AI Boom May 19, 2026

Show notes

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 on AI, Bubbles and Why Mean Reversion Lives On May 16, 2026

Show notes

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

He Studied the Financial System for Decades | Marc Rubinstein on Where the Real Risk Is May 15, 2026

Show notes

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

Lessons from Investing Through Bubble Regimes with Andy Constan May 14, 2026

Show notes

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

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