Excess Returns

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In this new weekly Excess Returns recap, Jack Forehand and Matt Zeigler highlight the most important investing insights from recent conversations across the Excess Returns podcast network. Drawing on discussions with Andy Constan, Rob Arnott, Kai Wu, Ben Hunt, Rupert Mitchell, Meb Faber and others, the episode connects ideas across macro, markets, AI, credit cycles and valuation. The conversation focuses on timeless investing principles investors can apply today, including how to evaluate expert opinions, how AI may reshape markets and jobs, what defines a true market bubble, why international stocks may be benefiting from global fiscal spending, and why the best opportunities in markets often come after long periods of underperformance.
Topics covered in this episode
How to evaluate expert opinions during major market events and filter signal from noise

Andy Constan’s framework for judging credibility based on experience and confidence

Why charts showing markets rising after wars are often misleading data mining

The difference between believing in AI technology and believing AI stocks are good investments

How AI could both replace and augment human work through the task based structure of jobs

Rob Arnott’s definition of a market bubble using implausible growth assumptions

Why many technology leaders ultimately fail to justify the expectations priced into their stocks

The difference between software companies whose moat is code and those with durable intangible advantages

How brand, switching costs, distribution and network effects protect enterprise software companies

Why AI may be one of the most disruptive technologies in history and what that means for markets

Meb Faber on the myth that the easy money has already been made in international and value stocks

The behavioral challenge of holding unpopular strategies through long periods of underperformance

Rob Arnott on why small cap value could outperform large cap growth over the next decade

Ben Hunt on the point in every credit cycle when lenders say no more

How rising costs of capital can trigger boom bust credit cycles

Rupert Mitchell on why global equity markets often follow government fiscal spending

The growing role of international fiscal policy and capital flows in global market leadership

Timestamps
00:00 Introduction and the idea behind the weekly Excess Returns recap show
03:00 Andy Constan on how to evaluate experts and filter market commentary
11:40 Why charts showing markets rising after wars can be misleading
17:00 Kai Wu on AI technology versus AI investments and the future of work
25:37 Rob Arnott on how to define a market bubble using valuation assumptions
29:35 Kai Wu on software moats, intangible assets and enterprise software durability
35:31 Rob Arnott on how disruptive AI could be for the global economy
39:54 Meb Faber on why the easy money has never been made in markets
43:57 Rob Arnott on small cap value versus large cap growth opportunities
48:39 Ben Hunt on credit cycles and the moment lenders pull back
55:56 Rupert Mitchell on fiscal spending and global equity market performance

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In this episode of the Jim Paulsen Show, Jim joins Jack Forehand and Justin Carbonneau to break down the macro forces shaping today’s markets and economy. Jim explains why the economy may be far weaker than headline GDP numbers suggest, how technology and AI investment are masking weakness in the broader economy, and why leadership in the stock market may be shifting. The conversation also explores the market implications of geopolitical conflict, the relationship between policy and market leadership, and how investors should think about AI’s long-term economic impact.
Topics covered in this episode
How geopolitical events like the Iran conflict affect markets, volatility, oil prices, and investor sentiment

Why market reactions to geopolitical shocks often fade once the situation is “vetted” by investors

The relationship between oil prices, the US dollar, and global financial markets

Why Paulsen remains constructive on international stocks and emerging markets despite recent volatility

Why energy and food now represent a much smaller share of consumer spending than in past inflation cycles

The argument that inflation fears may be overstated given structural disinflationary forces in the economy

How AI and technological innovation can destroy some jobs while simultaneously creating new economic demand

Why technological progress often lowers costs and expands markets rather than simply eliminating work

The concept that the “new economy” driven by technology investment is now large enough to influence overall GDP growth

Paulsen’s analysis showing that roughly 11 percent of the economy tied to new-era investment is growing rapidly while the remaining 89 percent is barely growing

Why the broader economy may resemble a recession even while headline GDP remains positive

How the dominance of large technology companies in indexes like the S&P 500 may be masking weakness in the broader market

The historical “toggle” between technology leadership and broader market leadership in equity markets

Why policy conditions like the yield curve and monetary easing often drive leadership shifts toward value, small caps, and cyclical stocks

Whether the Federal Reserve could begin easing policy without a traditional recession

Why policy support may eventually broaden the bull market beyond technology stocks

Timestamps
0:00 Jim Paulsen on geopolitical volatility, oil prices, and market reactions
2:50 How investors should think about the Iran conflict and market implications
10:50 The relationship between oil prices, the US dollar, and safe-haven flows
12:20 Why Paulsen likes international and emerging market stocks
14:30 Why higher oil prices may not lead to sustained inflation
18:40 AI disruption and the economic debate around jobs and productivity
23:00 How innovation historically creates new demand and economic growth
29:40 Technology is the tail wagging the economic dog
33:30 Why the “new economy” is growing far faster than the rest of the economy
37:00 Evidence that most of the economy may already resemble a recession
41:00 Profit growth disparity between technology and the rest of the economy
45:40 Why the stock market can mask weakness in the broader economy
46:30 The historical leadership toggle between tech and the broader market
49:00 Valuation differences between technology and other sectors
50:30 How policy conditions influence market leadership
55:00 Signs that leadership may already be shifting beyond tech
57:00 Could the Fed ease without a traditional recession
59:00 What a policy shift could mean for the next phase of the bull market

Rob Arnott returns to Excess Returns to discuss the biggest questions facing investors today, including the impact of geopolitical conflict, the valuation gap between U.S. and international markets, the long-term investment implications of artificial intelligence, and why extreme spreads between growth and value may present major opportunities. Arnott, founder of Research Affiliates and pioneer of fundamental indexing, explains why AI itself is not necessarily a bubble but many AI stocks may be priced for implausible growth. He also discusses why small cap and value stocks may offer some of the most compelling long-term opportunities in decades, how market narratives drive valuations, and why diversification beyond the U.S. could be critical for investors. Throughout the conversation, Arnott draws on decades of market history to explain how bubbles form, why profit margins tend to mean revert, and how investors should think about positioning portfolios for the next market cycle.
Topics covered in this episode:
• Why Rob Arnott believes AI is real but many AI stocks may be in a bubble
• How market narratives can push valuations far beyond fundamentals
• Why U.S. stocks trade at roughly twice the valuation multiples of international markets
• The widening valuation gap between growth and value stocks
• Why small cap stocks may be one of the most attractive opportunities today
• The massive capital spending required to build the AI ecosystem
• How technological revolutions historically destroy jobs but create new opportunities
• Why investors should learn to use AI tools to remain competitive
• The definition of a market bubble based on implausible growth expectations
• Lessons from the dot-com bubble and the history of dominant technology companies
• Why profit margins tend to mean revert over time
• The long-term outlook for international stocks and diversification
• How fundamental indexing works and why it can create rebalancing alpha
• The concept of the “Trifecta” approach combining value, core indexing, and growth
• The risks of conglomerate premiums and the diversification discount
• Why the largest companies in the market rarely remain dominant over long periods
• How investors should think about balancing growth exposure with cheaper opportunities
Timestamps:
00:00 AI vs AI Stocks: Why Arnott Sees a Bubble
00:01 Introduction to Rob Arnott and Research Affiliates
02:13 The Iran Conflict and How War Impacts Markets
06:41 U.S. Valuations vs International Opportunities
08:50 The Extreme Spread Between Growth and Value
10:00 The Small Cap Opportunity and Index Effects
13:08 The Citrini AI Paper and Long-Term Technology Shifts
14:09 How Technological Revolutions Destroy and Create Jobs
16:00 How AI Is Already Changing Investment Research
20:00 Why AI Tools Are Still Losing Money
23:40 How Investors Should Think About AI Exposure
25:21 Arnott’s Definition of a Market Bubble
27:41 Lessons from the Dot-Com Bubble
28:34 Profit Margins and Mean Reversion
30:34 Technology Moats and Competitive Disruption
32:12 Will Mean Reversion Still Work in Markets?
36:02 The Case for International Stocks
41:39 The Trifecta: A New Framework for Indexing
51:15 Why Expensive Slow-Growth Companies Underperform
56:25 Conglomerate Premiums and Mega Cap Tech
57:00 The Long-Term Case for Value and Small Caps
01:00:00 Why Market Leaders Rarely Stay on Top

In this episode of Excess Returns, we welcome back Andy Constan of Damped Spring Advisors for a wide-ranging discussion on geopolitical risk, AI and productivity, capital flows, credit markets, fiscal policy, and the shift from US to international equities. Andy walks through the framework he uses to evaluate uncertainty, from wars and geopolitical shocks to the long-term implications of artificial intelligence, and explains why capital markets and funding conditions may matter more than bold narratives. We also explore growth, inflation, Fed policy, and the structural case for global diversification in today’s macro environment.
Main topics covered
A practical framework for analyzing geopolitical shocks, including red flags, green flags, and how to evaluate information quality during times of uncertainty

How markets are pricing the current conflict with Iran across oil, equities, bonds, gold, and volatility

Why historical market performance after wars may offer limited predictive value due to small sample sizes

How to think about AI from a macro perspective, including GDP growth versus GDP share and who ultimately captures the gains

The capital markets implications of massive AI-related capex and whether equity and credit markets can fund current spending plans

Growth, inflation, and the Fed: how fiscal stimulus, wealth effects, QT, and labor market trends are shaping the current macro backdrop

Why Andy has shifted away from US assets toward international markets, including the role of bond yields and global risk parity

A critical look at the Trump accounts proposal and the broader issue of fiscal deficits and capital allocation

The key risks Andy is watching over the next three to six months, especially around credit markets and funding conditions

Timestamps
00:00 Introduction and overview of discussion topics
01:01 Framework for evaluating geopolitical shocks and information quality
11:46 Market reaction to the Iran conflict and asset pricing implications
23:00 Why historical war data may not be reliable for market forecasting
27:03 How to analyze AI’s impact on productivity and economic growth
37:00 AI capex, credit markets, and funding risks
42:24 Growth, inflation, and Fed policy in the current cycle
49:20 The case for international equities over US markets
56:20 Trump accounts, fiscal policy, and capital allocation
01:02:23 What Andy is watching most closely in the months ahead

In this episode, Jack Forehand and Kai Wu break down the viral “AI doom loop” article that sparked debate across Wall Street, Silicon Valley, and even the Federal Reserve. They walk through the core thesis that artificial intelligence could trigger a non-cyclical economic disruption, separating signal from noise and exploring what it could mean for software stocks, labor markets, productivity, wealth inequality, and long-term investing. Rather than reacting emotionally, they analyze the mechanics step by step, asking whether AI is more likely to replace workers or amplify them, how fast adoption can realistically happen, and what investors should be watching right now.
Main topics covered:
The core thesis behind the AI doom loop scenario and why it went viral

Is AI a substitute for human labor or a productivity multiplier

People times productivity as a framework for understanding economic growth

Why we are not yet seeing major AI disruption in labor or productivity data

Software stocks, margin compression, and the risk to SaaS business models

The Jevons Paradox and whether lower costs could expand demand instead of destroy it

Why incumbents with strong intangible moats may survive AI disruption

The difference between technological capability and real world adoption speed

Compute, energy, and token costs as natural limits on AI expansion

The feedback loop argument and whether AI could cause a demand shock

Creative destruction and the difficulty of forecasting new job creation

AI, high income knowledge workers, and the risk to consumer spending

Wealth inequality, capital versus labor, and policy responses like UBI

Why investors can be bullish on AI technology but cautious on markets

How to think about short term disruption versus long term abundance

Timestamps:
00:00 Introduction and the AI doom loop thesis
02:15 Why the article triggered a market reaction
06:00 People times productivity and economic growth
09:00 AI and disruption in software stocks
15:00 Jevons Paradox and expanding total demand
19:00 AI agents, frictionless commerce, and price competition
26:00 Adoption speed versus technology speed
28:00 Compute constraints and natural governors on AI growth
31:00 The non cyclical disruption feedback loop
33:00 Creative destruction and new job formation
38:00 General purpose technology and broad economic exposure
44:00 Replacement versus augmentation of workers
48:00 Token costs, enterprise AI spending, and labor tradeoffs
51:00 High income job risk and inequality concerns