Excess Returns

First Principles with Andy Constan launches with a deep dive into market bubbles, AI, semiconductor stocks, and the financial conditions that can turn powerful technological change into a dangerous investment regime. Andy explains how bubbles form, why they are almost impossible to time, how today’s AI boom compares to past episodes like 1987, the dot-com bubble, housing, and the bond bubble, and what investors should watch as expectations, financing, and FOMO build.
Andy Constan on X
https://x.com/dampedspring
Damped Spring Advisors
https://dampedspring.com/
Topics covered:
Why bubbles are easy to identify in hindsight but nearly impossible to define in real time

The difference between an expensive market and a true bubble regime

How new technologies, easy money, regulation, and exogenous shocks can create bubble conditions

Why AI may rhyme with the internet boom without being an exact repeat

The role of ChatGPT, Microsoft’s OpenAI investment, and semiconductor earnings expectations

What the 1987 crash, Japan, housing, bonds, and dot-com bubble can teach investors today

Why human nature, FOMO, and “keeping up with the Joneses” make bubbles so powerful

How the late-1990s Fed response to Long-Term Capital Management helped fuel the final phase of the tech bubble

Why tech’s current size in the economy and market may limit how far the AI boom can grow

How AI capex, hyperscaler spending, buybacks, debt issuance, and IPO supply could determine what happens next

Timestamps:
00:00 Intro and the challenge of identifying bubbles
04:32 Expensive markets vs true bubble regimes
09:57 The five bubble episodes Andy compares to today
14:35 Root conditions, escalation events, and the peaking phase
19:20 Why the 1987 crash may also have been a bubble
24:25 The late-1990s setup and the Netscape Navigator moment
28:00 Crisis analogs, easy financial conditions, and today’s AI parallels
32:20 Long-Term Capital Management and rocket fuel for the tech bubble
36:11 Why tech’s market share matters more today than in the 1990s
43:18 Policy mistakes, subsidies, and how governments feed bubbles
47:42 Semiconductor earnings expectations and valuation risk
53:45 The AI capex chain and where the money has to come from
58:42 IPOs, corporate debt, and the financing risk behind the AI boom
01:02:27 What investors should do differently in a bubble regime

Edward Chancellor joins Kai Wu on the latest episode of the Intangible Economy to discuss what financial history and capital cycle theory can teach investors about today’s AI boom. They explore why transformative technologies can still produce terrible investor returns, how overinvestment develops, where anti-bubbles may be forming, and what past episodes like the railway mania, the dot-com bubble, China’s investment boom and the post-2008 interest rate regime suggest about the risks and opportunities today.
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Topics covered:
How capital cycle theory applies to the AI data center boom

Why railway mania, autos, aircraft and the dot-com bubble offer lessons for today

Why markets often fund major technology transitions but fail to identify the winners

The prisoner’s dilemma driving hyperscaler AI spending

Whether AI demand can justify the supply being built

How GPU depreciation and AI capital spending may affect reported earnings

Why hallucinations and reliability may limit the total addressable market for large language models

The case for looking at AI anti-bubbles instead of shorting the bubble directly

Why China shows that strong GDP growth does not guarantee strong shareholder returns

How intangible capital, SaaS valuations and human capital fit into capital cycle analysis

Whether bubbles can be good for society while still being bad for investors

Why the long-term interest rate cycle may have changed

The role of gold in a world of expensive stocks, rising debt and vulnerable bonds

Timestamps:
00:00 Edward Chancellor on capital cycles, bubbles and AI
04:42 Why the railway mania became a classic overinvestment cycle
09:00 Why markets fund technology booms but often miss the winners
13:19 The prisoner’s dilemma behind AI spending
17:30 Will AI demand justify the supply being built
20:00 How capital spending can inflate profits before the bust
25:08 The AI Hindenburg moment and the limits of large language models
30:55 Why AI hype may exceed the proven technology
35:55 Why the anti-bubble may matter more than shorting AI
40:00 The energy transition bubble and the opportunity in overlooked assets
45:08 China’s lesson on GDP growth and shareholder returns
49:27 Big Booze, GLP-1s and the Lindy effect
54:23 Can intangible capital have its own capital cycle
59:54 SaaS valuations and the index creation warning signal
01:04:10 Why bubbles can help society but hurt investors
01:09:09 Why long-term rates may be in a new multi-decade cycle
01:14:07 Why Edward Chancellor still sees a role for gold

Brent Kochuba of SpotGamma joins Jack Forehand for the May 2026 OPEX Effect to break down what options positioning is saying after a massive AI and semiconductor-led market rally. They discuss SPX call volume, zero DTE options, dealer gamma, VIX expiration, NVIDIA earnings, oil risk, AI CapEx, and why options flows may help explain both the market’s recent melt-up and the potential for a volatility shift after OPEX.
Guest Links
Brent Kochuba on X
https://x.com/spotgamma
SpotGamma
https://spotgamma.com/
Topics Covered
Why the market has ignored oil shocks and geopolitical risk while AI earnings dominate investor attention

How AI CapEx, semiconductors and mega-cap tech have driven a powerful melt-up in stocks

Why options volume and zero DTE trading are increasingly important for all investors

How dealer hedging, delta and gamma can affect stock market moves

Why options expiration can create short-term turning points in markets and volatility

What the May OPEX setup says about call-heavy positioning in the S&P 500

Why single-stock options activity in NVIDIA, Tesla, Apple, Amazon and AI-related names matters

How record SPX call volume is being driven by short-dated options flows

Why Brent is watching VIX expiration, NVIDIA earnings and May 19 to May 20 for volatility expansion

What oil, VIX, correlation and dispersion are signaling about market risk

Timestamps
00:00 Intro: SPX call volume, call-heavy positioning and transient options flows
00:57 Are we in melt-up mode?
05:29 AI, UFOs and how fast market narratives are changing
09:00 Why options flows matter more for everyday investors
13:39 Could SpaceX become the next huge options market?
16:00 How dealer hedging, delta and gamma move through the market
20:44 Why OPEX can become a turning point for stocks and volatility
23:22 Why May OPEX is so call heavy
28:07 The market rally into May expiration
33:00 AI rebranding, meme behavior and downside headline risk
36:07 Reviewing last month’s oil and volatility setup
40:17 How the war flipped market leadership back to tech
44:13 Dealer gamma support in the S&P 500
49:19 Single-stock gamma in NVIDIA, Tesla, Apple and Amazon
51:06 Record SPX call volume and the role of zero DTE
54:55 Semiconductor, AI and memory call volume
57:50 From bearish positioning to peak-bull dispersion
59:22 Oil, the S&P 500 and changing correlations
01:03:06 COR1M, dispersion risk and when Brent considers hedging
01:04:57 Brent’s key takeaways for May OPEX and volatility expansion

Elena Khoziaeva, Co-Chief Investment Officer and Portfolio Manager at Bridgeway Capital Management, joins Excess Returns to discuss factor investing, small caps, value investing, market concentration, intangibles, passive investing, market neutral strategies, and the role of AI in quantitative investment research.
We cover how Bridgeway combines disciplined quantitative models with human judgment, why the S&P 500 may be less diversified than investors think, and how investors can think about diversification when mega-cap growth stocks dominate market returns.
Bridgeway Capital Management
https://bridgeway.com/
I Know What You Did Last Summer
https://bridgeway.com/perspectives/i-know-what-you-did-last-summer/
How Many Stocks Are Effectively in the S&P 500?
https://bridgeway.com/perspectives/how-many-stocks-are-effectively-in-the-sp500/
Topics Covered
Why quantitative investing still needs human judgment and skepticism

The difference between smart beta and true multi-factor portfolio construction

How Bridgeway combines value, quality, sentiment and risk controls

Why the size premium may depend on how small-cap stocks are defined

Why recently fallen large caps and IPOs can distort small-cap research

How the small-cap universe has changed as companies stay private longer

How intangible assets affect traditional value and quality metrics

Why value can work in bursts and why timing factor rotations is so difficult

How concentrated the S&P 500 has become using the HHI framework

Why passive investing may create opportunities for active small-cap managers

How market neutral strategies can help investors manage equity market volatility

How AI can help with data, text analysis and trading without replacing investment judgment

Timestamps
00:00 Why fewer than 50 stocks are driving S&P 500 returns
01:04 Bridgeway’s evidence-based investing approach
02:59 Why quantitative models need human judgment
07:52 Smart beta vs multi-factor investing
11:32 How Bridgeway builds multi-factor portfolios
16:08 Rethinking the size premium
20:31 Has the small-cap universe gotten worse?
23:49 How intangibles change value investing
28:05 Does value still work?
30:09 Why value returns can be episodic
33:11 Why factor investors need patience
35:22 How concentrated is the S&P 500?
40:29 Factor strategies as portfolio diversifiers
41:41 Passive investing and market structure
44:27 Managing volatility with market neutral strategies
49:40 How systematic managers update their models
55:02 How Bridgeway is using AI
01:00:03 Elena’s biggest lesson for investors

This episode of our new showThe 100 Year Thinkers brings together Chris Mayer and Ian Cassel for a deep discussion on long-term stock picking, microcap investing, business quality, AI disruption, management teams, and the behavioral skills that separate great investors from great analysts.
They explore why the edge in investing may increasingly come from judgment, presence, relationships, patience, and the ability to hold the right businesses through uncertainty.
Subscribe to the 100 Year Thinkers on Spotify⁠⁠
⁠⁠Subscribe to the 100 Year Thinkers on Apple
Topics Covered
Why being present with management teams may still be an investor edge in the age of AI

How microcap investing differs from small-cap, mid-cap and large-cap investing

Why talking to management can build conviction but also create bias

How Chris Mayer thinks about vertical market software, mission-critical systems and AI disruption

Why AI may become table stakes rather than a durable competitive advantage

How small companies can use AI to improve workflows, sales, inventory and productivity

Why many microcaps have short shelf lives and rarely become true long-term compounders

The role of intelligent fanatics, owner-operators and repeat winners in great investments

Why management transitions can create powerful microcap opportunities

The difference between being a great analyst and being a great investor

Why execution, position sizing, selling losers and holding winners matter more than hit rate

How Matt and Bogumil apply the lessons to AI, business quality and the limits of small business scalability

Timestamps
00:49 Introducing Chris Mayer, Ian Cassel and 100 Year Thinkers
04:59 Ian Cassel’s first management meeting and XM Satellite Radio
09:00 Why management meetings deepen understanding but can also mislead
14:32 Chris Mayer on the real edge in long-term investing
18:40 Mission-critical software, systems of record and AI disruption
22:45 How microcap companies are using AI in real businesses
27:02 AI as table stakes and when disruption creates opportunity
31:29 Why most microcaps have short shelf lives35:51 Finding Tom Brady before the market knows he is Tom Brady
40:53 Why owner-operators and intelligent fanatics matter
45:03 Second-in-command leaders, repeat winners and chips on shoulders
49:27 Analyst vs investor and the missing skills of stock picking
54:00 Using data to identify investor strengths, weaknesses and decision errors
58:14 Position sizing and letting small positions earn the right to grow
01:03:00 Peter Lynch, stocks as businesses and learning to think like an owner
01:07:00 AI, human judgment and the limits of automation
01:11:00 Why not every small business can become the next Facebook
01:15:00 Where to follow Bogumil and the 100 Year Thinkers series