Excess Returns

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

Follow Last Call on Spotify⁠
⁠Follow Last Call on Apple Podcasts
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.

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

Subscribe to Two Quants and a Financial Planner on Spotify
Subscribe to Two Quants and a Financial Planner on Apple

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