Excess Returns

This episode of Excess Returns features GMO’s Tom Hancock on how to think about AI as an investment opportunity and what truly defines “quality” in today’s market. The conversation breaks down the AI value chain, challenges common assumptions about where value will accrue, and ties it all back to building durable portfolios in a rapidly changing technological landscape.
Tom walks through his “Hype vs High Conviction” framework, explaining why identifying the right layer of the AI ecosystem may matter more than simply betting on the theme itself, and why balance sheets, durability, and capital allocation remain critical even in the most exciting growth environments.
Hype vs High Convictionhttps://www.gmo.com/americas/research-library/hype-vs-high-conviction_insights/
Topics Covered:
Why AI may be the most important investment decision today

The four-layer AI stack: applications, LLMs, hyperscalers, and infrastructure

Why investors confuse secular trends with investable opportunities

Following the money through the AI value chain

The hidden risks of investing lower in the stack

Why today’s tech leaders differ from the dot-com era

Growth vs maintenance capex and what it means for AI economics

Why software may be more resilient than markets think

How GMO defines “quality” and why it matters in volatile markets

Portfolio construction: where GMO is investing (and avoiding) in AI

Timestamps:
00:00 Intro and framing the AI investment debate
00:00:55 Tom Hancock background and focus on quality investing
00:02:00 What investors are getting wrong about AI
00:03:23 Breaking down the four layers of the AI ecosystem
00:06:45 Applications vs infrastructure: where value may accrue
00:08:45 Why predicting AI winners is still difficult
00:11:00 Following the cash flows through the AI stack
00:13:00 Why AI funding is more stable than past tech bubbles
00:16:00 Big Tech strategy differences and capital allocation decisions
00:17:34 Are today’s tech companies higher quality than in 1999?
00:19:00 Growth vs maintenance capex and implications for Nvidia and others
00:22:00 Depreciation, chip lifecycles, and hidden risks in capex assumptions
00:24:00 Capital intensity vs quality: when heavy investment is a feature
00:27:00 Why incumbents may benefit most from AI
00:28:30 Risks in the LLM layer and potential commoditization
00:30:10 Software disruption fears: overdone or justified?
00:34:06 Defining “quality” in investing
00:36:00 Balance sheets vs return on capital
00:38:32 Why GMO sold Oracle and the risks of leverage
00:40:18 What happens if AI spending slows down
00:41:35 Where the biggest risks are in the AI stack
00:44:26 Where GMO is positioned vs the S&P 500
00:48:00 How new ideas enter a quality portfolio
00:51:00 Sell discipline and portfolio turnover
00:53:00 International vs US quality investing

This episode of Excess Returns features Jim Paulsen breaking down the current macro environment through a series of powerful indicators, including oil, interest rates, consumer behavior, and market sentiment. The discussion explores whether today’s environment signals a slowing economy—or the early stages of a new bull market hidden beneath the surface.

Subscribe to the Jim Paulsen Show on Spotify⁠

⁠Subscribe to the Jim Paulsen Show on Apple Podcasts

Jim walks through a wide range of charts and frameworks, from the Walmart vs. luxury retail signal to private credit stress, productivity trends, and policy uncertainty, offering a data-driven perspective on where markets and the economy may be headed next.
Paulsen Perspectives Substack
https://paulsenperspectives.substack.com
Topics Covered
Why the recent oil spike hasn’t impacted inflation and interest rates as expected

Slowing economic growth vs. recession risk and what the Fed might do next

The Walmart vs luxury retail indicator and what it signals about the economy

Private credit risks and how they differ from traditional credit crises

Why many indicators point to a new bull market rather than a bear

The role of sentiment, volatility, and uncertainty in driving market returns

Market rotation from mega-cap “new era” stocks to broader market leadership

Corporate profits divergence and the opportunity in the rest of the economy

Liquidity, cash levels, and positioning as potential fuel for markets

Productivity trends and whether AI-driven gains are real or overstated

Timestamps
00:00 Intro and current macro backdrop
01:05 Oil spike and limited impact on yields and inflation
04:45 Growth outlook and why recession may still be avoided
07:10 Fed policy and the stagflation question
10:15 Walmart vs luxury retail indicator explained
13:40 Private credit stress vs traditional credit cycles
17:00 Why this isn’t 2008 and how balance sheets differ
19:50 Private credit risks and market spillover effects
22:15 Bear market fears vs signs of a new bull
23:45 Consumer confidence and its impact on returns
25:05 Oil spikes historically as buy signals
26:15 VIX, volatility, and market bottoms
27:05 Yield curve steepening and market implications
28:05 Sentiment indicators and what they really reflect
30:00 Market rotation and broadening beyond mega caps
32:45 Passing the baton from tech to broader markets
35:15 Corporate profits divergence and future potential
37:00 Policy uncertainty and why it can be bullish
42:05 Liquidity, cash levels, and risk allocation
43:20 Options positioning and put-call signals
44:05 Gold vs commodities and risk appetite
45:10 Consumer credit contraction and market signals
46:20 Polymarket recession probabilities as sentiment
47:30 Economic sentiment collapse and contrarian signals
48:10 Interest rate expectations and positioning
49:05 Unemployment trends and historical market bottoms
50:25 Productivity trends and AI impact on the economy

This episode of Excess Returns features Tony Wang of T. Rowe Price discussing how investors can identify “inevitabilities” in technology and position portfolios to benefit from long-term innovation trends. The conversation explores AI, semiconductors, and the evolving investment landscape, while also breaking down Tony’s portfolio construction process and how he navigates cycles, valuation, and disruption risk.
Tony explains why AI is fundamentally changing the cost of intelligence, how agentic systems could reshape software and labor markets, and why the current AI buildout may differ from past tech cycles. The discussion also dives into where we are in the AI cycle, how to think about the Mag 7, and what investors may be missing across the tech stack.
T. Rowe Price Science and Technology Fund
https://www.troweprice.com/financial-intermediary/us/en/investments/mutual-funds/us-products/science-and-technology-fund.htmlTopics Covered
What it means to invest in “inevitabilities” and separating signal from noise in markets

Why AI and compute demand represent a structural shift similar to past tech waves

The rise of agentic AI and how it could transform software and productivity

Whether AI is underappreciated or already priced into markets

The “multiple moons” idea and why AI may not be a winner-take-all market

How AI could reshape the labor market, productivity, and economic growth

The AI CapEx debate and why this cycle may differ from the dot-com buildout

Where we are in the AI cycle: training vs inferencing and deployment phase

The impact of AI on software companies and the innovator’s dilemma

How semiconductors, memory, and infrastructure remain key bottlenecks

The changing nature of the Mag 7 and capital intensity in AI

Tony’s portfolio construction framework across compounders, emerging tech, and value

How he generates ideas using S-curve adoption and economic bottlenecks

Position sizing, risk management, and balancing growth with drawdown control

Sell discipline: valuation, fundamentals, and market signals

Timestamps
00:00 Introduction and Tony Wang overview
01:05 Investing in inevitabilities and long-term thinking
03:00 Differentiating inevitability from hype and consensus
04:45 AI inevitability and the rise of agentic systems
07:00 Cost of intelligence and productivity implications
08:00 Real-world examples of AI adoption (customer service, agents)
09:00 Is AI underappreciated by markets?
11:15 AI as a “space race with multiple moons”
13:30 AI as the dominant driver of markets today
15:00 AI’s impact on jobs, productivity, and the economy
18:30 Creativity, judgment, and the future of work
20:45 Physical AI and robotics opportunity set
22:30 AI CapEx debate vs the dot-com era
25:30 Semiconductors vs software in the AI stack
28:15 AI disruption risk for software companies
31:00 Cyclicality in semiconductors and how AI changes it
33:30 The evolving role of the Mag 7 in AI
36:30 Competition, startups, and AI democratization
38:00 Where we are in the AI cycle today
40:00 Idea generation and S-curve adoption framework
42:30 Case study: memory and AI bottlenecks
44:45 Example position: optical networking and infrastructure
46:40 Portfolio construction and position sizing
49:00 Sell discipline and managing valuation risk

This episode explores the growing signs of a shift beneath the surface of the market, as technical indicators point to weakening momentum in equities and a potential change in leadership. Katie Stockton joins the show to break down what recent signals in the S&P 500, oil, gold, and sector rotation are telling us about where markets may be headed next.
We cover the implications of a new monthly MACD sell signal, the importance of market breadth and leadership, and how investors can interpret shifting trends across asset classes using a disciplined technical framework.

More on Katie's Strategies
https://www.fairleadstrategies.com/

Topics Covered:
Why a new monthly MACD sell signal may signal a longer, choppier market phase

The difference between fast corrections and slow grind bear phases

Key S&P 500 support levels and what a breakdown could mean for downside risk

How technical indicators help filter noise in headline-driven markets

The breakout in crude oil and what it signals about a potential new cycle

Whether sharp price moves are sustainable or likely to reverse

Understanding overbought and oversold conditions across different timeframes

Why mega-cap weakness is critical to overall market direction

The shift from growth to value and what it means for investors

Sector rotation trends and where leadership is emerging in 2025

What gold’s recent run and emerging weakness signal for safe haven assets

How a systematic, technical approach can help manage drawdowns and re-entry timing

Timestamps:
00:00 Intro
04:18 S&P 500 momentum deterioration and MACD sell signal
08:09 Key support levels and downside scenarios for equities
12:53 Crude oil breakout and implications for a new cycle
16:01 What overbought and oversold really mean in practice
20:04 Mega-cap weakness and shifting market leadership
24:41 Concentration risk in investor portfolios
27:52 Value vs growth rotation and cycle dynamics
32:13 Market breadth and confirmation signals
36:19 Moving averages, death cross, and trend interpretation
39:56 Inside the TAC ETF and sector rotation strategy
44:04 Gold trends and why consolidation may be next
47:00 Key signals to watch going forward

In this inaugural episode of our new show, The Intangible Economy with Kai Wu, we explore how AI, intangible assets, and unprecedented capital investment are reshaping the future of markets. Michael Mauboussin joins Kai to break down why today’s AI expectations may be historically unmatched—and what that means for investors trying to assess risk, returns, and who ultimately captures value.
Subscribe on Spotify
Subscribe on Apple

The conversation moves from base rates and AI growth expectations to competitive dynamics, capital cycles, and the fundamental shift toward intangible-driven business models that are changing how we think about valuation, moats, and market structure.
Papers and Resources Discussed:
Bayes and Base Rates: How History Can Guide Our Assessment of the Future
https://www.morganstanley.com/im/en-us/institutional-investor/insights/consilient-observer/bayes-and-base-rates.html
The Impact of Intangibles on Base Rates
https://www.morganstanley.com/im/publication/insights/articles/article_theimpactofintangiblesonbaserates.pdf
Measuring the Moat: Assessing the Magnitude and Sustainability of Value Creation
https://www.morganstanley.com/im/publication/insights/articles/article_measuringthemoat.pdf
One Job: Expectations and the Role of Intangible Investments
https://www.morganstanley.com/im/publication/insights/articles/article_onejob.pdf
Capitalism Without Capital: The Rise of the Intangible Economy
https://books.google.com/books/about/Capitalism_without_Capital.html?id=J3SYDwAAQBAJ
A Better Estimate of Internally Generated Intangible Capital
https://pubsonline.informs.org/doi/10.1287/mnsc.2022.01703
Underestimating the Red Queen: Measuring Growth and Maintenance Investments
https://www.morganstanley.com/im/publication/insights/articles/article_underestimatingtheredqueen.pdf
Explaining the Recent Failure of Value Investing
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3442539
Guest Links:
Michael Mauboussin Twitter
Topics Covered:
Why OpenAI’s projected growth would be unprecedented in market history

How base rates provide a reality check on AI expectations

The role of diffusion models and adoption curves in forecasting technology

Why massive capital investment in AI may follow past boom-bust cycles

Lessons from large-scale infrastructure projects and why timelines break

How intangible assets change the distribution of business outcomes

The rise of “fat tails” and why more companies now massively win or fail

Who captures value in AI across the stack from chips to applications

Why competition may drive AI profits toward consumers, not producers

How accounting distorts intangible investment and misleads investors

Timestamps:
00:00 Intro and OpenAI growth expectations vs historical base rates
04:32 Why no company has ever achieved 100%+ sustained growth at scale
08:47 Lessons from megaprojects and AI infrastructure buildouts
13:18 Intangible assets and why outcomes now have fatter tails
18:36 Why big tech is growing faster than historical precedents
23:52 Where value accrues in AI and why consumers may benefit most
28:21 Barriers to entry in AI including capital, talent, and scale
32:47 The risk of overinvestment and historical parallels to past bubbles
37:26 Game theory and competitive signaling in AI capital spending
41:58 Why investment returns—not “asset light” narratives—drive value
46:12 How accounting fails to capture intangible investment properly
50:44 Breaking down SG&A into maintenance vs investment spending
55:03 Why understanding reinvestment and ROI is the core investing skill
59:18 Final thoughts on uncertainty, expectations, and base rates in AI