Excess Returns

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

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In this episode of Last Call, Jack Forehand and Matt Zeigler look past the headlines to unpack what really moved markets this month. From the viral AI end of times scenario that sparked responses from Citadel, Fed Governor Waller, and Jeremy Siegel, to the growing stress in private credit and the rotation out of US mega cap stocks, this is a different kind of market wrap. Instead of recapping what the S and P 500 did, we explore what investors are actually doing with their money, how narratives shape positioning, and what the data says about whether this time is different.
Featuring Brent Kochuba of SpotGamma, Ben Hunt of Epsilon Theory, Rupert Mitchell of Blind Squirrel Macro, and Meb Faber of The Idea Farm, this episode dives into AI, software stocks, options flows, credit cycles, global equity markets, gold, and the power of base rates in investing.
Main topics covered:
The viral AI bear case scenario and why a fictional narrative moved real markets

How investors should think in probabilities, bull cases, base cases, and bear cases

What options pricing and put call ratios reveal about real fear versus social media fear

The state of software stocks and whether extreme bearishness may have marked a short term bottom

Private credit stress, rising default risks, and why every credit cycle ends when lenders say no more

An on the ground anecdote from San Francisco illustrating how refinancing risk is playing out in real time

The rotation from US mega caps into international stocks and why fiscal spending matters for equity markets

Gold and gold miners as potential beneficiaries of global liquidity and currency shifts

Why base rates matter when evaluating explosive AI revenue forecasts

Historical lessons from the Nifty Fifty, Japan’s bubble, the dot com era, and other periods when investors believed this time is different

Portfolio construction tools including diversification, rebalancing, and trend following in bubble environments

Timestamps:
00:00 Introduction and the AI end of times narrative
02:16 Why investors are responding to fiction and what we can learn from it
08:00 Brent Kochuba on options flows and software stock positioning
13:00 Has extreme bearishness in software marked a bottom
19:55 Ben Hunt on private credit and the boom bust cycle
27:00 A San Francisco refinancing story and when lenders say no
33:08 Rupert Mitchell on global markets, fiscal spending, and gold
44:22 Meb Faber on base rates, bubbles, and this time is different
01:00:16 How to track AI’s real world impact in corporate data
If you enjoy deep dives into investing, AI, market structure, credit cycles, global equities, and evidence based portfolio construction, be sure to subscribe to Excess Returns for more conversations like this.

In this episode of Excess Returns, we sit down with Cullen Roche to discuss his new book Your Perfect Portfolio and the deeper principles behind building a portfolio that actually fits your life. Rather than starting with asset allocation models or return forecasts, Cullen reframes investing around risk, time horizons, and lifetime consumption. We explore how to think about stocks, bonds, factor investing, international diversification, private assets, inflation hedges, and more through the lens of financial planning and asset liability matching. This is a practical, wide ranging conversation about portfolio construction, behavioral risk, and how investors can align their investments with real world goals.
Main topics covered:
Why you are a saver, not an investor, and why that distinction matters

Defining risk as uncertainty of lifetime consumption

The temporal conundrum and matching investments to time horizons

Human capital as your most important asset and how it impacts portfolio risk

The pros and cons of a 100 percent stock allocation

Rethinking the 60 40 portfolio after inflation and rising rates

International diversification and valuation differences between US and global markets

Factor investing as a time horizon tool rather than an alpha strategy

The forward cap portfolio and skating to where the market cap puck is going

Inflation protection strategies including stocks, TIPS, gold, and the permanent portfolio

Risk parity and the tradeoff between diversification and return

Countercyclical rebalancing and managing behavioral risk

Private equity, venture capital, and the illiquidity premium

Defined duration investing and asset liability matching for individual investors

The real impact of inflation, taxes, and fees on long term returns

Timestamps:
00:00 Risk as lifetime consumption and asset liability matching
01:03 Introduction to Your Perfect Portfolio
05:25 You are a saver, not an investor
08:24 Defining risk and uncertainty of lifetime consumption
10:15 The temporal conundrum and time horizons
12:38 Using past performance and forecasting responsibly
15:00 Human capital and portfolio construction
17:12 The case for a 100 percent stock allocation
19:50 Rethinking the 60 40 portfolio
24:00 Adding international diversification
29:43 Factor investing across time horizons
35:00 The forward cap portfolio concept
38:27 Inflation hedges and the permanent portfolio
42:27 Risk parity explained
44:49 Countercyclical rebalancing
47:17 Private assets and illiquidity
51:25 Defined duration strategy and Discipline Funds ETFs
56:00 Real returns after inflation, taxes, and fees
If you are interested in portfolio construction, asset allocation, financial planning, factor investing, inflation protection, or building a long term investment strategy that matches your goals, this conversation offers a thoughtful framework for thinking differently about risk and returns.