Excess Returns

In this episode of Last Call, we look back at June 2026 and break down the biggest market stories shaping investors’ outlook for the second half of the year. Matt Zeigler and Jack Forehand are joined by Andy Constan, Ben Hunt, Brent Kochuba and Eric Pachman to discuss the SpaceX IPO, AI and semiconductor cyclicality, Fed credibility, options flows, labor market quality, crack spreads and inflation risk.
Follow Last Call on Spotify⁠⁠⁠⁠⁠⁠
⁠⁠⁠⁠⁠⁠Follow Last Call on Apple Podcasts⁠
Main topics covered
Why the SpaceX IPO became the biggest market story of the month

How index flows, ETF buying and hedge fund positioning shaped SpaceX trading

Andy Constan on why future earnings growth may be oversubscribed across AI stocks

Why AI spending is benefiting semiconductors, memory and chip equipment companies

The Fab Five companies behind semiconductor capacity and why they matter

Ben Hunt on Fed credibility, market narratives, gold, the dollar and trust

Brent Kochuba on options flows, correlation risk and volatility spasms in tech stocks

Why short-term options volume may signal excess speculation in QQQ and AI stocks

How SpaceX options trading changed after the first wave of retail excitement

Eric Pachman on why headline job growth may hide weakness in wages and job quality

Why crack spreads, refining constraints and oil logistics may matter more for inflation than crude prices alone

What investors should watch next in AI, semiconductors, memory, innovation and market cycles

Timestamps
00:00 Intro
01:02 Matt and Jack introduce Last Call and the June market review
03:05 Why SpaceX dominated the month and how the IPO traded after opening
07:33 Andy Constan on Fab Five Freddy eating the semis
10:35 Why future earnings growth may be oversubscribed across the stock market
13:35 How AI compute spending flows through chips, fabs and semiconductor equipment
17:45 Are parts of the semiconductor market showing signs of an earnings bubble?
20:12 Ben Hunt on the Fed credibility chart that surprised him
23:50 Why Fed credibility, Sell America, gold and the dollar are connected
29:48 Brent Kochuba on options flows behind AI stocks, semis and SpaceX
33:36 Why semiconductor volatility may be warning of a short-term reset
38:46 What SpaceX options trading says after the initial surge
42:12 Eric Pachman on jobs, wages and what the Fed may be missing
48:24 Why crack spreads matter for oil, refining, gas prices and inflation
55:28 What to watch next in AI, semiconductors, memory demand and market cycles
59:01 Why efficiency, competition and cyclical thinking matter for AI investors
01:03:02 Matt and Jack close the episode
No information on this podcast should be construed as investment advice. Securities discussed in the podcast may be holdings of the firms of the hosts or their clients.

Warren Pies of 3Fourteen Research joins Excess Returns to break down the AI bull market, the macro risks investors should watch, and why the data still supports continued strength in semiconductors and equities. We discuss GPU demand, token usage, open source AI, Fed policy, housing weakness, oil, earnings growth, market valuations and the biggest risks to the current cycle.
Warren Pies on X
https://x.com/WarrenPies
3Fourteen Research
https://www.3fourteenresearch.com/
Caliban
https://www.3fourteenresearch.com/caliban
Main topics covered
Which bearish AI arguments actually matter for investors

Why regulatory risk may be the biggest long-term AI concern

How data center spending is crowding out housing investment

Why the Fed may struggle to cool AI-driven investment without hurting the labor market

What GPU availability says about real-time AI compute demand

Why open source AI is not yet replacing frontier models

How token pricing and OpenRouter data help measure AI usage

Why semiconductor stocks may still be in the middle of a major cycle

How semis are being valued differently than traditional cyclicals

Why Fed policy, earnings growth and market multiples are key to the second half of 2026

What oil positioning and refined product inventories say about macro risk

Why 3Fourteen remains constructive on equities despite rising overheating risk

Timestamps
00:00 Intro
01:04 Which bearish AI arguments have teeth?
04:00 Why AI regulation is the biggest long-term risk
07:03 Technology spending versus housing investment
11:03 How AI CapEx is showing up in inflation data
13:04 Why the labor market is more fragile than headline jobs data suggests
16:24 Why GPU availability is a cleaner signal than CapEx announcements
21:00 What token pricing and OpenRouter data reveal about AI demand
27:36 How 3Fourteen benchmarks frontier models against open source AI
30:00 Why the semiconductor selloff looked like a buyable dip
34:02 Are semiconductors still cyclical businesses?
38:08 Why Fed tightening could be the thing that ends the bull market
42:15 What the oil shock means now
45:47 Refined product inventories, crack spreads and energy stocks
47:18 Are earnings estimates becoming too optimistic?
50:49 Why the debasement regime still supports equities
54:05 Where to find Warren Pies and 3Fourteen Research

Ritavan joins Excess Returns to explain The System Gambit, a new framework for understanding competitive advantage, business strategy, AI disruption and long-term compounding. We discuss why traditional moat checklists can miss the real source of value, how companies can build systems competitors cannot copy, and what investors should look for when AI changes the game.
The System Gambit
https://amzn.to/4b0J32I
Main topics covered
Why the traditional moat checklist can fail investors

The three requirements for a true System Gambit

How investors can evaluate business strategy from the outside

Why code is not always the moat in the age of AI

What history can teach investors about asymmetry and leverage

Why AI adoption is not the same as AI value creation

The difference between moving fast and understanding the game

Lessons from Nokia, ASML, Amazon and Walmart

How intangible investment and J curves can hide long-term value

Why the best companies build compounding systems competitors cannot copy

How investors can identify companies changing the game rather than optimizing the old one

Timestamps
00:00 Opening preview and introduction
04:00 The three ingredients of a System Gambit
08:49 Why code is not the moat in AI software
13:00 Skanderbeg and changing the rules of the game
17:00 Good moats, good narratives and asymmetric advantage
22:31 Microscope vs telescope as a lesson for AI
28:35 AI winners, losers and high dispersion markets
32:08 Signal quality, bottlenecks and why AI adoption is not enough
36:00 Nokia, agility and the failure to build a causal model
40:15 Why understanding the game beats speed
44:00 Intangible investment, the J curve and ASML's hidden edge
49:54 The contrarian AI thesis behind The System Gambit
54:00 How to recognize a real System Gambit
58:27 Amazon, Walmart and multi-paradigm compounding
1:03:00 Prime, FBA and platform leverage
1:07:00 Walmart's answer to Amazon
1:11:06 Closing thoughts and where to find Ritavan

On this episode of the 100 Year Thinkers, Chris Mayer and Matt Zeigler discuss long-term investing, 100-baggers, AI stocks, SpaceX valuation, founder-led companies, and why the best investments often come with brutal drawdowns. We also cover his new book The Investor's Odyssey, the danger of letting labels like AI do too much work, how to think about TAM and capital allocation, and why patience may be the biggest edge for investors trying to own great businesses for decades.
⁠Subscribe to the 100 Year Thinkers on Spotify⁠⁠
⁠⁠Subscribe to the 100 Year Thinkers on Apple⁠
The Investor's Odyssey: Resisting the Sirens and Playing the Long Game⁠
https://amzn.to/44BMXeJ⁠
Main topics covered
Why SpaceX, AI and trillion-dollar IPOs are testing investor discipline

How Chris Mayer thinks about valuation after watching Google become a huge winner

Why great businesses can still be terrible investments at the wrong price

The danger of letting labels like AI, quality and TAM replace real analysis

Why many AI features may not create real customer value

What the dot-com bubble can teach investors about AI adoption and shakeouts

Why investors do not need to be early if a company is truly exceptional

How to separate AI anecdotes from real financial impact

Why capital allocation and return on invested capital matter more as companies scale

How to evaluate founder control, governance, incentives and trust

Why the best long-term stocks can still fall 50 percent or more along the way

What rational exuberance might look like for long-term investors

Timestamps
00:00 Intro: Chris Mayer on AI, SpaceX and long-term investing
04:00 SpaceX valuation vs Google and the risk of paying too much
08:01 Why labels like AI and quality can do too much work
12:05 The AI pause, the dot-com analogy and where real value may emerge
16:06 Why investors do not need to be early when a business is real
21:00 Becoming a great company versus already being mature
25:10 Thinking about TAM, market share and realistic growth expectations
29:43 Corporate governance, free float and shareholder rights
34:27 How to judge founder trust, incentives and compensation
38:57 Employee ownership, culture and building enduring companies
43:02 Investor frustration in a lopsided AI-driven market
47:02 Why even a perfect stock picker would face brutal drawdowns
52:17 The rise of trillion-dollar IPOs and the question of rational exuberance
56:29 The Investor's Odyssey and playing the long game

Ben Inker of GMO joins Excess Returns to break down whether the AI boom is an investment bubble, how it compares to 2000, 2007 and 2021, and why today’s risk may be more about earnings than valuations. We also discuss AI capital spending, market supply from IPOs, GMO’s seven-year asset class forecasts, international stocks, benchmark-free allocation and what private equity investors may be missing.
7 YEAR ASSET CLASS FORECAST
https://www.gmo.com/americas/research-library/gmo-7-year-asset-class-forecast-may-2026_gmo7yearassetclassforecast/
WHAT BARBARIANS LIKE TO TAKE PRIVATE
https://www.gmo.com/americas/research-library/part-1-what-barbarians-like-to-take-private_gmoquarterlyletter/

THE CASE FOR LIQUID ALTERNATIVES
https://www.gmo.com/americas/research-library/the-case-for-liquid-alternatives-in-todays-environment_insights/
Main topics covered
Why GMO sees the AI boom as a bubble investors may be able to navigate

The difference between easy bubbles and hard bubbles in portfolio construction

Lessons from the internet bubble, the global financial crisis and the 2021 duration bubble

Why today’s market may be an earnings bubble, not just a valuation bubble

How AI data center spending affects corporate profits before depreciation shows up

Why transformational technologies do not always reward the companies building them

The risk of circular financing, debt-funded AI spending and increasingly creative deal structures

How IPOs, share issuance and market supply can pressure stock returns

GMO’s seven-year asset class forecasts and why international stocks look more attractive than U.S. stocks

Why private equity portfolios may contain large hidden bets on small, lower-quality companies

Timestamps
00:00 AI, earnings bubbles and market supply
00:58 Why Ben Inker thinks the AI bubble may be easier to navigate
02:43 What makes a bubble easy or hard for investors
08:12 Comparing risk and return in 2000, 2007, 2021 and today
14:42 Why optimizers and real clients see risk differently
17:02 What GMO learned from managing through past bubbles
19:08 How today compares to the 2000 internet bubble
20:00 Why this may be an earnings bubble
23:34 Semiconductors, memory makers and the capital cycle
25:00 How AI CapEx compares to railroads, electricity and fiber optics
29:33 Debt, circular financing and strange AI deals
34:32 Why massive stock issuance could challenge the market
40:00 How GMO builds seven-year asset class return forecasts
41:40 Why interest rates change fair value for stocks and bonds
45:32 Why international, value and small-cap stocks look more attractive
49:06 The case for a benchmark-free portfolio
55:21 What 700 leveraged buyouts reveal about private equity
01:02:00 How public portfolios can offset private equity risks
01:03:37 Why investors need to understand what they are paid for
01:08:27 Closing thoughts