Finding Compounders in the Age of AI | Joseph Shaposhnik

On this episode of Excess Returns, Matt Zeigler and Bogumil Baranowski speak with Rainwater Equity ETF portfolio manager Joseph Shaposhnik about how long-term investors should think about markets in an era defined by geopolitical shocks, AI disruption, and unprecedented capital investment cycles. The conversation explores how disciplined investors can stay focused on durable businesses and long-term free cash flow rather than reacting to short-term headlines. Joseph explains how his team evaluates companies during major events, why the AI boom may create both massive disruption and opportunity, and where he believes the most attractive investment opportunities exist today.Topics covered in this episode• Why most macro headlines and geopolitical events rarely have lasting impacts on great businesses• How long-term investors should analyze conflicts and market shocks without overreacting• The defense spending supercycle and why aerospace and defense may benefit from rising geopolitical tensions• How Joseph evaluates the AI investment cycle across semiconductors, software, and hyperscalers• Why semiconductor companies may offer a lower-risk way to benefit from AI growth• The risks created by massive AI infrastructure CapEx and concentration around specific AI models• Why some software companies may face significant disruption from AI tools and LLMs• How AI could reshape business models that rely on packaging public or commoditized data• The potential rotation from the Magnificent Seven to the other 493 companies in the S&P 500• Why capital intensity may change the long-term attractiveness of some technology companies• The role of management quality and capital allocation in navigating technological disruption• Fragile vs anti-fragile business models in an AI-driven economy• Where AI may create unexpected winners across industrial and traditional industries• Why long-term investors should still prioritize durable cash flow compounding businessesTimestamps00:00 Introduction and why most headlines have limited long-term impact on businesses02:00 How experienced investors think about geopolitical shocks and market headlines04:00 Defense spending tailwinds and the aerospace and defense supercycle06:45 How investors should react when major market news breaks11:10 How Joseph evaluates the AI boom and which companies benefit most14:15 The case for opportunities outside the Magnificent Seven17:15 How rising AI CapEx is changing the economics of major tech companies21:25 Why hyperscalers face increasing concentration risk23:00 Why semiconductor suppliers may be the best positioned AI investments27:15 Why Joseph reduced exposure to software companies33:00 The importance of learning organizations and adaptive management teams37:00 AI, labor markets, and whether high-income jobs face disruption41:00 Fragile vs anti-fragile companies in the age of AI46:00 Where AI could create unexpected business winners52:00 How great management teams adapt during technological disruption57:00 How AI may accelerate entrepreneurship and innovation59:00 Why investors should remain focused on sustainable cash flow01:02:00 What the next generation of long-term compounders may look like

In this episode of Excess Returns, Matt Zeigler and Bogumil Baranowski speak with Vitaliy Katsenelson, CEO of Investment Management Associates and author of Soul in the Game. The conversation explores how value investing is evolving in a world shaped by artificial intelligence, rapidly changing economic dynamics, and historically high market valuations. Vitaliy discusses why humility and diversification are increasingly important for investors today, how to balance quality and valuation when selecting stocks, and what he has learned about selling decisions, portfolio construction, and long-term investing discipline. The discussion also moves beyond markets into deeper ideas about passion, creativity, and why investing, like art, is ultimately a creative pursuit driven by curiosity and lifelong learning.Topics covered in this episodeWhy high stock market valuations may create a headwind for future returnsThe math behind long-term stock market returns and the role of earnings growth versus valuation changesWhether the dominance of mega-cap technology companies represents a structural shift in marketsWhy AI investment could lead to both massive innovation and large amounts of wasted capitalThe importance of humility in investing during periods of rapid technological and economic changeWhy Vitaliy increased the number of stocks in his portfolio due to greater uncertaintyHow investors can think about what will not change in a rapidly evolving worldThe evolution from statistical value investing to focusing on business quality and managementWhy cheap stocks are often expensive and how narrative bias can trap value investorsThe importance of evaluating management integrity and avoiding companies with questionable leadershipHow Vitaliy thinks about selling decisions and recognizing when an investment thesis is brokenWhy many investors make their biggest mistakes by selling winners too earlyThe concept of being a value buyer but a growth holder when fundamentals improveWhy updating valuation models as businesses improve is critical to capturing long-term upsideLessons learned from great investors and the importance of surrounding yourself with thoughtful peersThe idea of building a personal operating system for investing and lifePassion, patience, and process as the three pillars of long-term investment successWhy investing is fundamentally a creative pursuit similar to art and musicThe deeper motivations behind investing and why for many great investors it is not ultimately about moneyTimestamps0:00 Vitaliy on humility and why the range of outcomes in investing is expanding2:00 The math behind long-term stock market returns4:00 Why high valuations can become a headwind for future returns6:00 Big tech growth and whether large companies now have structural advantages8:00 AI investment and the risk of massive capital misallocation10:30 Learning AI and why investors must adapt to rapid technological change14:00 Why humility leads to diversification and larger portfolios20:00 The evolution from cheap stocks to quality investing25:30 Selling discipline and recognizing when a thesis is broken34:30 Letting winners run and avoiding the mistake of selling too early42:00 Learning from other great investors and building your own framework44:30 Passion, patience, and process in investing52:00 Why great investors are motivated by more than money1:01:40 The connection between investing, creativity, and classical music

Follow Two Quants and a Financial Planner on SpotifyFollow Two Quants and a Financial Planner on AppleIn 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 episodeHow to evaluate expert opinions during major market events and filter signal from noiseAndy Constan’s framework for judging credibility based on experience and confidenceWhy charts showing markets rising after wars are often misleading data miningThe difference between believing in AI technology and believing AI stocks are good investmentsHow AI could both replace and augment human work through the task based structure of jobsRob Arnott’s definition of a market bubble using implausible growth assumptionsWhy many technology leaders ultimately fail to justify the expectations priced into their stocksThe difference between software companies whose moat is code and those with durable intangible advantagesHow brand, switching costs, distribution and network effects protect enterprise software companiesWhy AI may be one of the most disruptive technologies in history and what that means for marketsMeb Faber on the myth that the easy money has already been made in international and value stocksThe behavioral challenge of holding unpopular strategies through long periods of underperformanceRob Arnott on why small cap value could outperform large cap growth over the next decadeBen Hunt on the point in every credit cycle when lenders say no moreHow rising costs of capital can trigger boom bust credit cyclesRupert Mitchell on why global equity markets often follow government fiscal spendingThe growing role of international fiscal policy and capital flows in global market leadershipTimestamps00:00 Introduction and the idea behind the weekly Excess Returns recap show03:00 Andy Constan on how to evaluate experts and filter market commentary11:40 Why charts showing markets rising after wars can be misleading17:00 Kai Wu on AI technology versus AI investments and the future of work25:37 Rob Arnott on how to define a market bubble using valuation assumptions29:35 Kai Wu on software moats, intangible assets and enterprise software durability35:31 Rob Arnott on how disruptive AI could be for the global economy39:54 Meb Faber on why the easy money has never been made in markets43:57 Rob Arnott on small cap value versus large cap growth opportunities48:39 Ben Hunt on credit cycles and the moment lenders pull back55:56 Rupert Mitchell on fiscal spending and global equity market performance

Subscribe to the Jim Paulsen Show on SpotifySubscribe to the Jim Paulsen Show on Apple PodcastsIn 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 episodeHow geopolitical events like the Iran conflict affect markets, volatility, oil prices, and investor sentimentWhy market reactions to geopolitical shocks often fade once the situation is “vetted” by investorsThe relationship between oil prices, the US dollar, and global financial marketsWhy Paulsen remains constructive on international stocks and emerging markets despite recent volatilityWhy energy and food now represent a much smaller share of consumer spending than in past inflation cyclesThe argument that inflation fears may be overstated given structural disinflationary forces in the economyHow AI and technological innovation can destroy some jobs while simultaneously creating new economic demandWhy technological progress often lowers costs and expands markets rather than simply eliminating workThe concept that the “new economy” driven by technology investment is now large enough to influence overall GDP growthPaulsen’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 growingWhy the broader economy may resemble a recession even while headline GDP remains positiveHow the dominance of large technology companies in indexes like the S&P 500 may be masking weakness in the broader marketThe historical “toggle” between technology leadership and broader market leadership in equity marketsWhy policy conditions like the yield curve and monetary easing often drive leadership shifts toward value, small caps, and cyclical stocksWhether the Federal Reserve could begin easing policy without a traditional recessionWhy policy support may eventually broaden the bull market beyond technology stocksTimestamps0:00 Jim Paulsen on geopolitical volatility, oil prices, and market reactions2:50 How investors should think about the Iran conflict and market implications10:50 The relationship between oil prices, the US dollar, and safe-haven flows12:20 Why Paulsen likes international and emerging market stocks14:30 Why higher oil prices may not lead to sustained inflation18:40 AI disruption and the economic debate around jobs and productivity23:00 How innovation historically creates new demand and economic growth29:40 Technology is the tail wagging the economic dog33:30 Why the “new economy” is growing far faster than the rest of the economy37:00 Evidence that most of the economy may already resemble a recession41:00 Profit growth disparity between technology and the rest of the economy45:40 Why the stock market can mask weakness in the broader economy46:30 The historical leadership toggle between tech and the broader market49:00 Valuation differences between technology and other sectors50:30 How policy conditions influence market leadership55:00 Signs that leadership may already be shifting beyond tech57:00 Could the Fed ease without a traditional recession59:00 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 opportunitiesTimestamps:00:00 AI vs AI Stocks: Why Arnott Sees a Bubble00:01 Introduction to Rob Arnott and Research Affiliates02:13 The Iran Conflict and How War Impacts Markets06:41 U.S. Valuations vs International Opportunities08:50 The Extreme Spread Between Growth and Value10:00 The Small Cap Opportunity and Index Effects13:08 The Citrini AI Paper and Long-Term Technology Shifts14:09 How Technological Revolutions Destroy and Create Jobs16:00 How AI Is Already Changing Investment Research20:00 Why AI Tools Are Still Losing Money23:40 How Investors Should Think About AI Exposure25:21 Arnott’s Definition of a Market Bubble27:41 Lessons from the Dot-Com Bubble28:34 Profit Margins and Mean Reversion30:34 Technology Moats and Competitive Disruption32:12 Will Mean Reversion Still Work in Markets?36:02 The Case for International Stocks41:39 The Trifecta: A New Framework for Indexing51:15 Why Expensive Slow-Growth Companies Underperform56:25 Conglomerate Premiums and Mega Cap Tech57:00 The Long-Term Case for Value and Small Caps01:00:00 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 coveredA practical framework for analyzing geopolitical shocks, including red flags, green flags, and how to evaluate information quality during times of uncertaintyHow markets are pricing the current conflict with Iran across oil, equities, bonds, gold, and volatilityWhy historical market performance after wars may offer limited predictive value due to small sample sizesHow to think about AI from a macro perspective, including GDP growth versus GDP share and who ultimately captures the gainsThe capital markets implications of massive AI-related capex and whether equity and credit markets can fund current spending plansGrowth, inflation, and the Fed: how fiscal stimulus, wealth effects, QT, and labor market trends are shaping the current macro backdropWhy Andy has shifted away from US assets toward international markets, including the role of bond yields and global risk parityA critical look at the Trump accounts proposal and the broader issue of fiscal deficits and capital allocationThe key risks Andy is watching over the next three to six months, especially around credit markets and funding conditionsTimestamps00:00 Introduction and overview of discussion topics01:01 Framework for evaluating geopolitical shocks and information quality11:46 Market reaction to the Iran conflict and asset pricing implications23:00 Why historical war data may not be reliable for market forecasting27:03 How to analyze AI’s impact on productivity and economic growth37:00 AI capex, credit markets, and funding risks42:24 Growth, inflation, and Fed policy in the current cycle49:20 The case for international equities over US markets56:20 Trump accounts, fiscal policy, and capital allocation01:02:23 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 viralIs AI a substitute for human labor or a productivity multiplierPeople times productivity as a framework for understanding economic growthWhy we are not yet seeing major AI disruption in labor or productivity dataSoftware stocks, margin compression, and the risk to SaaS business modelsThe Jevons Paradox and whether lower costs could expand demand instead of destroy itWhy incumbents with strong intangible moats may survive AI disruptionThe difference between technological capability and real world adoption speedCompute, energy, and token costs as natural limits on AI expansionThe feedback loop argument and whether AI could cause a demand shockCreative destruction and the difficulty of forecasting new job creationAI, high income knowledge workers, and the risk to consumer spendingWealth inequality, capital versus labor, and policy responses like UBIWhy investors can be bullish on AI technology but cautious on marketsHow to think about short term disruption versus long term abundanceTimestamps:00:00 Introduction and the AI doom loop thesis02:15 Why the article triggered a market reaction06:00 People times productivity and economic growth09:00 AI and disruption in software stocks15:00 Jevons Paradox and expanding total demand19:00 AI agents, frictionless commerce, and price competition26:00 Adoption speed versus technology speed28:00 Compute constraints and natural governors on AI growth31:00 The non cyclical disruption feedback loop33:00 Creative destruction and new job formation38:00 General purpose technology and broad economic exposure44:00 Replacement versus augmentation of workers48:00 Token costs, enterprise AI spending, and labor tradeoffs51:00 High income job risk and inequality concerns

Follow Last Call on Spotify⁠⁠Follow Last Call on Apple PodcastsIn 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 marketsHow investors should think in probabilities, bull cases, base cases, and bear casesWhat options pricing and put call ratios reveal about real fear versus social media fearThe state of software stocks and whether extreme bearishness may have marked a short term bottomPrivate credit stress, rising default risks, and why every credit cycle ends when lenders say no moreAn on the ground anecdote from San Francisco illustrating how refinancing risk is playing out in real timeThe rotation from US mega caps into international stocks and why fiscal spending matters for equity marketsGold and gold miners as potential beneficiaries of global liquidity and currency shiftsWhy base rates matter when evaluating explosive AI revenue forecastsHistorical lessons from the Nifty Fifty, Japan’s bubble, the dot com era, and other periods when investors believed this time is differentPortfolio construction tools including diversification, rebalancing, and trend following in bubble environmentsTimestamps:00:00 Introduction and the AI end of times narrative02:16 Why investors are responding to fiction and what we can learn from it08:00 Brent Kochuba on options flows and software stock positioning13:00 Has extreme bearishness in software marked a bottom19:55 Ben Hunt on private credit and the boom bust cycle27:00 A San Francisco refinancing story and when lenders say no33:08 Rupert Mitchell on global markets, fiscal spending, and gold44:22 Meb Faber on base rates, bubbles, and this time is different01:00:16 How to track AI’s real world impact in corporate dataIf 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 mattersDefining risk as uncertainty of lifetime consumptionThe temporal conundrum and matching investments to time horizonsHuman capital as your most important asset and how it impacts portfolio riskThe pros and cons of a 100 percent stock allocationRethinking the 60 40 portfolio after inflation and rising ratesInternational diversification and valuation differences between US and global marketsFactor investing as a time horizon tool rather than an alpha strategyThe forward cap portfolio and skating to where the market cap puck is goingInflation protection strategies including stocks, TIPS, gold, and the permanent portfolioRisk parity and the tradeoff between diversification and returnCountercyclical rebalancing and managing behavioral riskPrivate equity, venture capital, and the illiquidity premiumDefined duration investing and asset liability matching for individual investorsThe real impact of inflation, taxes, and fees on long term returnsTimestamps:00:00 Risk as lifetime consumption and asset liability matching01:03 Introduction to Your Perfect Portfolio05:25 You are a saver, not an investor08:24 Defining risk and uncertainty of lifetime consumption10:15 The temporal conundrum and time horizons12:38 Using past performance and forecasting responsibly15:00 Human capital and portfolio construction17:12 The case for a 100 percent stock allocation19:50 Rethinking the 60 40 portfolio24:00 Adding international diversification29:43 Factor investing across time horizons35:00 The forward cap portfolio concept38:27 Inflation hedges and the permanent portfolio42:27 Risk parity explained44:49 Countercyclical rebalancing47:17 Private assets and illiquidity51:25 Defined duration strategy and Discipline Funds ETFs56:00 Real returns after inflation, taxes, and feesIf 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.

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 investorsLLMs vs agent-based AI and why the distinction matters for investment researchHow AI fits into an investor’s workflow, from due diligence to portfolio monitoringUsing AI to monitor KPIs, earnings calls, and cross-industry signals in real timeHow AI can help kill bad ideas faster and surface deal breakers earlyPrompt engineering for investors, including mindset framing, audience targeting, and output designBuilding mental models into AI systems to reflect your investment philosophyAI tech stacks for investors, including writing tools, deep research models, and browser-based AIIteration, experimentation, and standardized testing of prompts across model upgradesThe impact of AI on alpha generation, active management, and generalist vs specialist investorsOrganizational adoption strategies for investment firms considering AICustomization, agentic workflows, and what AI in investing could look like five years from nowTimestamps:00:00 How AI tools increase investor productivity01:16 Why early ChatGPT was a head fake for investors03:07 The inflection point with deep research and agentic AI05:00 LLMs vs agents explained in plain English07:01 Where AI fits inside an investment workflow09:28 Replacing manual earnings transcript work11:40 Real-time monitoring and AI alerts19:24 Using AI to kill bad investment ideas faster22:01 Trust but verify, hallucinations and safeguards25:29 Matt’s AI tech stack for investing30:00 Prompt engineering breakthroughs33:00 Standardized experimentation across new AI models36:07 Building idea generation prompts step by step40:15 Using AI as an editor and critical reviewer43:50 Does AI compress investor skill differences46:10 How funds should adopt AI internally50:40 Fear of falling behind in asset management53:05 Generalists vs specialists in an AI world55:18 AI and the pursuit of alpha57:00 Customization, agents and the future of investing01:01:10 Coding agents and building tools with AI