Excess Returns

In this episode of Excess Returns, we sit down with Matt Russell of Business Breakdowns to explore how AI is actually being used in investing today. We go beyond the hype and break down practical use cases for AI in portfolio management, stock research, due diligence, monitoring, and idea generation. From deep research models and agentic AI to prompt engineering and workflow design, this conversation walks through how professional investors can use AI tools to increase productivity, improve decision-making, and reduce blind spots without losing their edge. If you are an asset manager, analyst, allocator, or DIY investor wondering how AI will impact investing and stock picking, this episode offers a clear, practical roadmap.
Main topics covered:
The evolution from early large language models to deep research and agentic AI for investors

LLMs vs agent-based AI and why the distinction matters for investment research

How AI fits into an investor’s workflow, from due diligence to portfolio monitoring

Using AI to monitor KPIs, earnings calls, and cross-industry signals in real time

How AI can help kill bad ideas faster and surface deal breakers early

Prompt engineering for investors, including mindset framing, audience targeting, and output design

Building mental models into AI systems to reflect your investment philosophy

AI tech stacks for investors, including writing tools, deep research models, and browser-based AI

Iteration, experimentation, and standardized testing of prompts across model upgrades

The impact of AI on alpha generation, active management, and generalist vs specialist investors

Organizational adoption strategies for investment firms considering AI

Customization, agentic workflows, and what AI in investing could look like five years from now

Timestamps:
00:00 How AI tools increase investor productivity
01:16 Why early ChatGPT was a head fake for investors
03:07 The inflection point with deep research and agentic AI
05:00 LLMs vs agents explained in plain English
07:01 Where AI fits inside an investment workflow
09:28 Replacing manual earnings transcript work
11:40 Real-time monitoring and AI alerts
19:24 Using AI to kill bad investment ideas faster
22:01 Trust but verify, hallucinations and safeguards
25:29 Matt’s AI tech stack for investing
30:00 Prompt engineering breakthroughs
33:00 Standardized experimentation across new AI models
36:07 Building idea generation prompts step by step
40:15 Using AI as an editor and critical reviewer
43:50 Does AI compress investor skill differences
46:10 How funds should adopt AI internally
50:40 Fear of falling behind in asset management
53:05 Generalists vs specialists in an AI world
55:18 AI and the pursuit of alpha
57:00 Customization, agents and the future of investing
01:01:10 Coding agents and building tools with AI

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In this episode, we explore one of the most important but overlooked questions in investing: what is the purpose of your portfolio? Through a series of powerful clips and reflections from Aswath Damodaran, Meb Faber, Ben Hunt, Cullen Roche, Corey Hoffstein, Daniel Crosby, Larry Swedroe, and Wes Gray, we examine how goals like financial freedom, funded contentment, liability driven investing, retirement planning, and multi generational wealth shape the way we invest. This conversation goes beyond beating the market and focuses on preserving and growing wealth, reducing financial stress, aligning money with meaning, and defining what a life well lived truly looks like.
Topics covered include:
Why the end game of investing matters more than beating the market

Preserving and growing wealth vs trying to get rich

Freedom as the ultimate goal of financial independence

Funded contentment and what it means to live a life well lived

Liability driven investing and matching assets to future needs

The difference between getting rich and staying rich

Needs vs desires and understanding marginal utility of wealth

Retirement planning and redefining success beyond a number

Multi generational wealth and thinking beyond your own lifetime

The psychological impact of growing up with or without money

Financial freedom, stress reduction, and peace of mind

Tactical financial goals vs long term purpose driven investing

Education, legacy, and investing in the next generation

Why once you win the game you may not need to keep playing

Timestamps:
00:00 Aswath Damodaran on preserving and growing wealth
10:04 Meb Faber on freedom, contentment, and the hedonic treadmill
22:36 Ben Hunt on funded contentment and finding your pack
28:23 Cullen Roche on risk as uncertainty of consumption
33:25 Corey Hoffstein on liability driven investing and not worrying about money
41:50 Daniel Crosby on financial freedom and living life on your own terms
47:33 Larry Swedroe on needs vs desires and staying rich
55:54 Wes Gray on big blue arrows, tactical goals, and peace of mind

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In this episode of the 100 Year Thinkers, Matt Zeigler and Bogumil Baranowski continue their conversation with Robert Hagstrom and Chris Mayer, diving deeper into general semantics and what it means for investors navigating AI enthusiasm, market volatility, benchmark obsession, and the gamification of markets. From Warren Buffett’s cathedral versus casino metaphor to the risks hiding in so-called “safe” consumer staples stocks, this discussion explores how language, expectations, and mistaken certainty shape investment decisions. If you want to think more clearly about markets, technology, valuation, and your own reactions as an investor, this episode offers a powerful mental framework.
Topics Covered
What general semantics is and how language influences how investors think

IFD disease idealism frustration demoralization and how unrealistic expectations impact markets

AI hype, capital spending, and the prisoner’s dilemma facing major tech companies

Warren Buffett’s cathedral versus casino metaphor and what it means for investors today

Why beating the S and P 500 may not be the right benchmark for success

The gamification of markets, retail trading growth, and the shift from long-term investing to speculation

Terminal value risk in software stocks amid AI disruption

Why low volatility “warm fuzzy” stocks like consumer staples may be more dangerous than they appear

Expectations investing, confidence versus overconfidence, and avoiding mistaken certainty

The map is not the territory and how to avoid confusing models with reality

Everything is connected to everything else markets as biological systems rather than mechanical systems

Delayed gratification, compounding, and why wealth is built later in the investment journey

Timestamps
00:00 Cathedral versus casino capitalism and the market metaphor
02:00 What is general semantics and why it matters for investors
03:00 IFD disease unrealistic expectations and AI hype
06:40 Outperformance, Bill Miller, and unrealistic return expectations
09:00 Are market benchmarks the right way to measure success
12:00 What if stock market indexes did not exist
14:00 Public versus private markets and myopic loss aversion
18:40 Compounding, volatility, and delayed gratification
21:00 AI valuations, strategic capital spending, and economic returns
24:20 The AI adoption cycle frustration and demoralization
30:40 The man in overalls story and delaying reactions
33:30 Warren Buffett cathedral versus casino metaphor revisited
35:00 Gamification of markets passive flows and species shift in investing
39:00 When to sit still versus when to act in volatile markets
43:00 Mistaken certainty and the biggest risks in today’s market
45:00 The hidden risk in consumer staples and low volatility stocks
47:20 Expectations investing confidence versus overconfidence
49:40 Everything is connected markets as living systems
53:00 What success really means beyond beating an index
56:20 The map is not the territory final lessons for investors

In this episode of Excess Returns, Jason Hsu returns for a wide-ranging conversation on China’s economy, the global AI race, emerging markets, factor investing, and what the next phase of globalization could mean for U.S. investors. We explore how China’s fiercely competitive domestic capitalism contrasts with common Western narratives, why AI could reshape professional services the way globalization reshaped manufacturing, and how investors should think about portfolio allocation in a shifting G2 world.
This discussion covers China manufacturing dominance, Chinese EV competition, U.S. vs. China AI strategy, emerging markets investing, factor investing in inefficient markets, and how machine learning is changing quantitative portfolio management.
Main topics covered
Why U.S. investors misunderstand China’s economic system and the role of competition inside its domestic market

How China became the world’s manufacturing powerhouse and what that means for tariffs and trade wars

The Chinese government’s role as a venture-style capital allocator rather than a central planner

The real estate reset in China and the shift toward technology, AI, and advanced manufacturing

AI as the next wave of globalization and its impact on professional services and labor markets

Whether the U.S. vs. China AI competition is truly winner-take-all

Capital expenditure intensity in the U.S. vs. capital efficiency and open-source innovation in China

U.S. exceptionalism, G2 geopolitics, and portfolio diversification beyond a U.S.-centric allocation

Why emerging markets ex-China may differ from China tech exposure

The case for separating China from emerging markets in asset allocation

The concept of China as an alpha reservoir due to retail-driven market inefficiencies

Why traditional value and factor strategies have struggled in the U.S. but still work in China

How machine learning and AI are changing quantitative investing and factor construction

The launch of CNQQ and accessing large-cap China technology exposure

Timestamps
00:00 China as the world’s factory and the role of fierce internal competition
01:02 Why U.S. investors misunderstand China’s economy
03:48 Is China capitalist despite the Communist Party label
05:33 The government as a VC-style investor rather than central planner
07:45 China EV competition and manufacturing dominance
09:23 Tariffs, trade leverage, and manufacturing monopoly dynamics
12:18 China’s bear market and valuation opportunity
13:59 The real estate reset and shift toward productive capital
16:00 AI as the next wave of globalization
18:01 Labor force participation and economic disruption from AI
19:46 Jobs that may survive in an AI-dominated world
22:00 Is U.S. vs. China AI a winner-take-all battle
24:13 Chip restrictions and long-term innovation incentives
26:54 Capital efficiency in China vs. heavy AI capex in the U.S.
29:27 Rebalancing away from U.S.-centric portfolios
31:18 The end of U.S. exceptionalism and the move toward a G2 world
34:00 How endowments approach U.S., developed, and emerging markets
36:35 CNQQ and accessing China large-cap technology
40:45 China as the great alpha reservoir
45:49 The future of factor investing in efficient vs. inefficient markets
49:06 Machine learning, factor decay, and next-generation quant strategies
55:17 Can AI replace active portfolio managers
If you enjoy deep conversations on global markets, AI investing, China technology, emerging markets, and quantitative strategies, make sure to subscribe to Excess Returns for more interviews with leading investors and thinkers.

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In this episode of Click Beta, Matt Zeigler sits down with Cameron Dawson of NewEdge Wealth and Dave Nadig of ETF.com for a wide-ranging conversation on markets, macro data, positioning, tokenization, AI productivity, and the narratives driving investor behavior. The discussion dives into consensus forecasts, the K-shaped economy, international equity performance, dollar positioning, AI capex, and whether the biggest market moves are driven by fundamentals or liquidity shifts. Along the way, they explore tokenization in financial markets, stablecoins, Fed balance sheet dynamics, and how AI is quietly reshaping productivity for small businesses and individuals. This episode is a deep dive into stock market trends, economic data distortions, asset allocation shifts, and the structural forces shaping the investing landscape in 2026.
Main topics covered:
• Why consensus forecasts are average and why that creates risks for investors
• Cyclical reacceleration narrative versus liquidity-driven market rotation
• The K-shaped economy and distortions in US jobs data
• Healthcare hiring versus cyclical employment weakness
• AI capex spending and who actually benefits
• Energy, industrials, and staples outperformance versus tech concentration
• International equities versus US stocks and valuation percentiles
• US dollar positioning extremes and contrarian signals
• Positioning versus narrative and where market surprises hide
• Tokenization, decentralized finance, and DTCC proposals
• Stablecoins, collateral efficiency, and capital reuse in markets
• Fed balance sheet, leverage ratios, and financial system risk
• AI productivity gains in small and mid-sized businesses
• The future of work, automation, and economic dispersion
Timestamps:
00:00 Cameron on cyclical reacceleration and market expectations
03:00 Consensus forecasts and average return assumptions
06:00 K-shaped economy and distorted jobs data
10:00 AI capex and disconnect between perception and reality
12:30 Liquidity shifts and market rotation beyond mega caps
14:00 International equity valuations and performance gap
16:50 Dollar positioning and contrarian signals
18:20 Positioning versus narrative in stock performance
20:00 Tokenization and ETF market plumbing
22:00 Stablecoins and capital efficiency
24:00 Atomic settlement versus traditional clearing
27:00 Fed balance sheet and leverage ratio debate
30:00 Recessions, market resets, and social impact
39:00 Cultural distribution, media fragmentation, and market narratives
47:00 AI productivity, small business impact, and economic implications
For more episodes from the Excess Returns network, including macro investing, asset allocation, ETFs, and AI-driven market insights, visit excessreturnspod.com