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

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In this episode of The Opex Effect, Jack and Brent break down the growing impact of options markets on stocks, volatility, and sector rotation. While the major indexes appear calm, massive moves beneath the surface tell a very different story. From software stocks and AI disruption to gold, silver, bonds, and the Nasdaq, they analyze how dealer hedging flows, gamma positioning, implied volatility, and options expiration cycles may be shaping market behavior more than headlines suggest. If you want to understand why markets can feel wildly volatile yet go nowhere, and how options positioning can influence short term price action, this episode provides a deep dive into the mechanics driving today’s market environment.

Main Topics Covered

• Why the market feels like the wildest calm market of all time

• Massive single stock volatility versus muted index performance

• Software stock weakness, AI disruption, and the so called SaaS apocalypse

• The surge in options volume and the rise of zero DTE in major stocks

• How dealer hedging, delta, gamma, and volatility flows impact equities

• The historical tendency for markets to flip direction after options expiration

• Realized volatility versus intraday volatility and what is being hidden

• Beneath the surface rotation into value, small caps, energy, and defense

• Gold and silver volatility spikes and what options volume signaled at the top

• Rising demand for puts and what skew is telling us about downside risk

• Correlation spikes, VIX behavior, and the risk of a volatility expansion

• How positioning can create rapid market spasms in single stocks like Nvidia and Tesla

• Why this environment may represent a staging area for a larger move

Timestamps

00:00 Violently going nowhere and hidden volatility
01:01 The wildest calm market of all time
04:00 Introduction to The Opex Effect and options driven flows
05:29 The growth of options trading and zero DTE impact
11:00 Dealer hedging, delta, and how options move stocks
13:42 Why options expiration can trigger regime changes
16:22 Intraday volatility versus close to close volatility
20:18 Extreme rotation beneath the surface
21:00 Measuring expiration size with the lobster claw rating
25:00 Single stock positioning and March expiration risk
27:35 Core one month correlation warning signals
33:00 Rising put demand and what skew reveals
36:45 Asset rotation in bonds, gold, bitcoin, and tech
43:06 Correlation spikes and crash risk setup
46:40 The quickening of volatility and single stock spasms

In this episode of Excess Returns, we sit down with Neil Howe, author of The Fourth Turning Is Here and co-creator of the Fourth Turning generational framework, along with Ben Hunt of Epsilon Theory, to discuss where we are in the current cycle and what it means for markets, inflation, AI, capital flows, and America’s long-term economic outlook. From the debasement trade and rising gold prices to global capital crowding out and the structural forces shaping productivity and growth, this conversation connects generational theory with real-world investing decisions. If you’re thinking about inflation, deficits, AI capital spending, global diversification, or how to position defensively and offensively in a shifting macro regime, this discussion provides a powerful framework for navigating what may be a historic transition period.

Topics Covered

• The Fourth Turning framework and where we are in the current crisis cycle

• Why inflation is not a problem but a policy solution in major crises

• The collapse in US national savings and long-term deficit risks

• Capital flows, the debasement trade, and the future of the US dollar

• Gold, commodities, and real assets in a regime shift

• Global diversification and opportunities outside the United States

• AI capital spending, productivity gains, and the risk of overinvestment

• Crowding out effects from government deficits and AI hyper scaling

• Trust, geopolitics, and the long-term implications for global markets

• Healthcare, demographics, and structural investment themes

• Defensive and offensive positioning in a Fourth Turning environment

Timestamps

00:00 Inflation as a solution and the generational crisis framework
04:00 Explaining the Fourth Turning and historical crisis cycles
12:55 Narratives, generational archetypes, and market behavior
22:24 Is the Fourth Turning pessimistic or optimistic
34:00 Inflation, gold, and the debasement trade
40:00 Global capital flows and the reversal of US inflows
50:00 AI capital spending and the K shaped capital markets
55:09 Crowding out, deficits, and slow growth risks
01:02:23 Defensive and offensive investment positioning
01:09:31 Final thoughts on diversification, gold, and financials

In this episode of Excess Returns, we sit down with Pete Hecht of AQR to break down portable alpha, capital efficient portfolio construction, and how investors can combine equity beta with truly diversifying sources of alpha. We cover how portable alpha works in practice, how it solves the funding problem for alternative strategies, and why implementation details like leverage, liquidity, and financing costs matter more than most investors realize. If you’re interested in diversification, long short investing, managed futures, equity market neutral strategies, or improving total returns without giving up equity exposure, this discussion provides a practical and detailed framework.

Main Topics Covered

• What portable alpha actually is and how it differs from traditional stock bond alternative portfolios

• How portable alpha combines equity beta exposure with unconstrained long short alpha

• The funding problem with alternatives and how portable alpha solves it

• Turnkey implementation versus separating alpha managers and beta overlays

• The role of equity market neutral, managed futures, and multi strategy approaches

• Why private equity and private credit are poor candidates for portable alpha

• Long short leverage versus long only leverage and how to think about risk

• Target volatility, risk models, and stress testing leveraged portfolios

• Financing costs in futures markets and how higher interest rates affect strategies

• How to evaluate portable alpha using excess returns, tracking error, and tail risk

• Tax aware implementation and after tax returns

• Why mutual funds are not obsolete for active long short strategies

• The importance of asking whether a view is already priced into valuations

Timestamps

00:00 Why you cannot eat a risk adjusted return
02:12 Defining portable alpha and the problem it solves
03:55 Portable alpha versus traditional balanced portfolios
06:54 The funding problem with diversifying alternatives
09:00 How portable alpha works in practice
13:05 What types of alpha strategies work best
16:35 Managed futures and crisis alpha
19:49 Simplicity versus complexity in implementation
21:46 Why private equity and private credit do not work in portable alpha
24:15 Understanding leverage and risk management
29:18 Target volatility and portfolio construction
34:52 Stress testing and lessons from COVID and 2022
35:01 Risks and financing costs of portable alpha
38:50 Interest rates and leveraged strategies
39:07 Identifying hidden beta and volatility laundering
46:08 Introducing AQR Fusion Funds
50:25 Evaluating performance versus the benchmark
53:17 Tax efficiency in long short mutual funds
57:29 Is your view already priced in

In this episode of Excess Returns, Kai Wu of Sparkline Capital returns to discuss his latest research on AI adoption, ROI, and what it all means for investors.

Building on his prior work on the AI CapEx boom, Kai tackles the trillion dollar question at the center of today’s market: Is AI generating real, measurable economic returns across the broader economy, or are we still in an infrastructure-driven bubble?

Using a systematic analysis of earnings calls, patent data, and adoption trends, Kai lays out a framework for identifying which companies are truly benefiting from artificial intelligence and how investors can position portfolios accordingly.

Find the Full Paper Here:
https://etf.sparklinecapital.com/

Main topics covered:

• Satya Nadella’s AI bubble framework and why broad economic diffusion matters

• The AI adoption S-curve and where we are in the technology diffusion cycle

• A new AI ROI taxonomy based on earnings call analysis and quantified economic gains

• Real-world AI productivity, revenue, and cost-saving examples across industries

• Infrastructure vs early adopters vs laggards and how companies were categorized

• AI-driven outperformance and excess returns across different adopter groups

• Valuation dispersion between AI infrastructure stocks and AI early adopters

• The risk of overcapacity and lessons from railroads and the dot-com telecom boom

• Competition among large language models and the durability of AI moats

• S&P 500 exposure to AI infrastructure and hidden concentration risk

• The case for AI early adopters as a middle ground between growth and value

• Intangible value investing and the concept of AI yield

Timestamps:

00:00:00 The trillion dollar question and what “real ROI” means
00:03:19 Nadella’s bubble framework: diffusion vs a narrow CapEx trade
00:06:08 The classic tech diffusion S-curve and where AI is on it
00:32:25 Why infrastructure is being rewarded even if the ROI story is different
00:33:04 The key chart: adoption vs valuation shows “basically no relationship”
00:38:00 Why early adopters and laggards should separate
00:38:26 The “25% ROI” example and how it could show up later in fundamentals
00:39:03 Railroads and fiber: builders go bankrupt, users capture the value
00:39:45 Telecom index fell 95% and never recovered (dot-com bust parallel)
00:40:00 The application layer captures profits; infrastructure becomes a utility
00:41:00 The punchline: transformative tech, but builders can still be bad investments
00:42:57 Overcapacity question: where are we on the line?
00:43:17 The buildout: another $5 trillion of data centers “or whatever the number is”
00:44:00 If there’s no ROI, companies cancel orders
00:45:01 Moat and LLM competition discussion begins
00:49:00 The big one: adding infrastructure names gets the S&P to 46% AI infrastructure
00:50:00 “Alternative indices” swing you to laggard risk
00:51:00 The “false choice” and the “middle ground” framing (early adopters)

In this episode of Excess Returns, we sit down with Bloomberg Opinion columnist Nir Kaissar for a wide-ranging conversation on markets, AI, interest rates, private credit, small caps, and the risks investors may be underestimating. Nir shares his unexpected predictions for 2026, challenges the consensus on Fed rate cuts, explains why high profitability may be putting a floor under valuations, and offers a thoughtful framework for thinking about AI, concentration risk, and the future of public versus private markets. This is a deep dive into today’s most important investing debates, grounded in history and focused on what may come next.

Topics Covered

• Nir’s unexpected predictions for 2026 and why mass adoption of autonomous vehicles may arrive faster than investors expect

• Why the consensus on lower interest rates in 2026 may be wrong and what the two year Treasury yield is signaling

• The impact of tariffs, affordability pressures, and corporate margins on inflation

• Why high corporate profitability may support elevated stock market valuations even if returns slow

• The role of earnings growth in driving S&P 500 returns and why 2015 to 2024 may not repeat

• Is AI more like 1995 or 1999 in the internet cycle and what that means for long term investors

• The convergence of big tech companies around AI and the risks of a more zero sum competitive landscape

• Why companies staying private longer could hurt retail investors and distort public market indices

• Concentration risk in the S&P 500 and what it means for long term portfolio construction

• Opportunities and risks in small cap stocks, including the importance of quality screens

• The growth of private credit markets and the hidden risks investors may not see

• Why Treasuries may still be the cleanest shirt in the laundry during a crisis

• Lessons from 20 years of running strategies and what Nir has changed his mind about

Timestamps

00:00 Nir’s 2026 predictions and the rise of Waymo
05:00 Interest rates, Trump, and the outlook for Fed policy
08:40 Tariffs, inflation, and corporate margins
12:00 Valuations, profitability, and future S&P 500 returns
16:00 AI compared to the internet era and long term investing lessons
19:00 Public versus private markets and regulatory concerns
32:00 Concentration risk and the Magnificent Seven
39:00 Small caps, quality screens, and value opportunities
47:00 Private credit risks and default cycles
54:30 Nir’s investment philosophy and 20 year lessons