Excess Returns

This episode features Jim Grant of Grant’s Interest Rate Observer on inflation, war, monetary policy, and the long arc of credit cycles. Grant explains why inflation is ultimately driven by monetary debasement and why war, fiscal policy, and central bank actions may be setting the stage for a more persistent inflationary regime than markets expect.
We explore how today’s environment compares to past inflationary periods, the hidden risks in credit markets and public debt, and what history teaches us about AI investment booms, oil shocks, and monetary disruption. Grant also discusses trust in financial systems, the role of gold, and why markets are always harder in real time than they appear in hindsight.

Grant’s Interest Rate Observer
https://www.grantspub.com/
Topics Covered:
Why war is inherently inflationary and how it strains the productive economy

The difference between measured economic stability and underlying systemic risks

How inflation shifted from a wartime phenomenon to a permanent feature of modern monetary policy

The Fed’s 2% inflation target as a structural form of currency debasement

Lessons from the 1970s inflation and oil shocks vs. today’s environment

Why inflation is a ratchet that erodes purchasing power over time

The importance of trust in credit markets and growing risks in private credit structures

Public debt, Treasury market dynamics, and early signs of strain in government financing

Historical parallels between AI investment and past technological booms like the internet

The role of gold as a hedge against (and investment in) monetary instability

The durability of the US dollar despite long-term structural concerns

Why investing is always difficult in the present—even when it looks obvious in hindsight

Timestamps:
00:00 Intro and Jim Grant on the true causes of inflation
04:04 Why war drives sustained inflation and current geopolitical risks
08:00 Historical perspective on inflation before the 1970s
12:00 Oil shocks, Volcker, and lessons from past inflation cycles
16:00 Why inflation never reverses and purchasing power declines
20:00 Trust in markets and the foundation of credit systems
24:00 Private credit risks and the modern credit cycle
28:00 Public debt, Treasury markets, and fiscal sustainability concerns
32:00 Treasury auctions, yields, and early warning signs in bonds
35:25 AI capex boom and lessons from past technological bubbles
38:17 Air conditioning, internet bubbles, and delayed economic payoffs
40:00 The Fed, Treasury, and hidden financial interdependence
44:14 Asset allocation, gold, and monetary disruption
48:44 The dollar’s strength and global dominance
53:41 Why investing is always difficult in real time
59:00 Advice on markets, newsletters, and enduring uncertainty

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This episode of The Opex Effect breaks down why markets have remained surprisingly resilient despite geopolitical chaos, an oil shock, and extreme headline risk. Brent Kochuba joins Jack Forehand to analyze what’s really driving the market beneath the surface—from options flows and gamma positioning to the collapse in volatility and what it signals for the next move.
They explore how the options market is shaping price action in ways most investors miss, why the VIX collapsed despite elevated risk, and what positioning tells us about the path forward as we head into earnings and the next major options expiration.
Topics covered:
Why markets have stayed near highs despite war, oil spikes, and macro uncertainty

The “taco trade” and why investors expect bad news to reverse quickly

How options flows and dealer hedging are influencing stock prices

Why call options are historically cheap heading into earnings

The mechanics of gamma, delta hedging, and market maker positioning

Why options expiration (OpEx) can act as a turning point for markets

The divergence between oil prices and equity volatility

What the collapse in the VIX reveals about investor positioning

The role of zero-DTE options in reinforcing short-term market ranges

Key resistance levels forming from call selling and what they mean for upside

Timestamps:
00:00 Why markets aren’t reacting to geopolitical chaos
04:18 The “taco trade” and shifting market expectations
07:30 How options flows influence stock market movements
11:10 Why OpEx can drive market turning points
13:05 Volatility compression and the gamma-volatility relationship
15:30 How large options positioning shapes market behavior
18:05 Why positioning has shifted toward calls
20:00 Why this OpEx may be less impactful than prior ones
22:00 Market positioning into earnings and key drivers ahead
24:10 Using gamma maps to identify support and resistance
27:00 Revisiting the JP Morgan collar trade and March lows
30:00 Correlation spikes and the oil-volatility relationship
33:00 Why oil has stopped driving equity volatility
34:30 The breakdown between oil and VIX correlation
36:00 Why volatility may reprice higher after OpEx
37:05 The oil curve and expectations for a short-term shock
39:40 One of the largest VIX collapses ever
41:00 How options positioning drove the volatility unwind
43:00 Why selling volatility has become a dominant strategy
45:00 The feedback loop between rising markets and falling volatility
For more information on SpotGamma and Brent’s work:
https://spotgamma.com
Follow Brent on Twitter:
https://twitter.com/spotgamma

This episode of Excess Returns features GMO’s Tom Hancock on how to think about AI as an investment opportunity and what truly defines “quality” in today’s market. The conversation breaks down the AI value chain, challenges common assumptions about where value will accrue, and ties it all back to building durable portfolios in a rapidly changing technological landscape.
Tom walks through his “Hype vs High Conviction” framework, explaining why identifying the right layer of the AI ecosystem may matter more than simply betting on the theme itself, and why balance sheets, durability, and capital allocation remain critical even in the most exciting growth environments.
Hype vs High Conviction
https://www.gmo.com/americas/research-library/hype-vs-high-conviction_insights/
Topics Covered:
Why AI may be the most important investment decision today

The four-layer AI stack: applications, LLMs, hyperscalers, and infrastructure

Why investors confuse secular trends with investable opportunities

Following the money through the AI value chain

The hidden risks of investing lower in the stack

Why today’s tech leaders differ from the dot-com era

Growth vs maintenance capex and what it means for AI economics

Why software may be more resilient than markets think

How GMO defines “quality” and why it matters in volatile markets

Portfolio construction: where GMO is investing (and avoiding) in AI

Timestamps:
00:00 Intro and framing the AI investment debate
00:00:55 Tom Hancock background and focus on quality investing
00:02:00 What investors are getting wrong about AI
00:03:23 Breaking down the four layers of the AI ecosystem
00:06:45 Applications vs infrastructure: where value may accrue
00:08:45 Why predicting AI winners is still difficult
00:11:00 Following the cash flows through the AI stack
00:13:00 Why AI funding is more stable than past tech bubbles
00:16:00 Big Tech strategy differences and capital allocation decisions
00:17:34 Are today’s tech companies higher quality than in 1999?
00:19:00 Growth vs maintenance capex and implications for Nvidia and others
00:22:00 Depreciation, chip lifecycles, and hidden risks in capex assumptions
00:24:00 Capital intensity vs quality: when heavy investment is a feature
00:27:00 Why incumbents may benefit most from AI
00:28:30 Risks in the LLM layer and potential commoditization
00:30:10 Software disruption fears: overdone or justified?
00:34:06 Defining “quality” in investing
00:36:00 Balance sheets vs return on capital
00:38:32 Why GMO sold Oracle and the risks of leverage
00:40:18 What happens if AI spending slows down
00:41:35 Where the biggest risks are in the AI stack
00:44:26 Where GMO is positioned vs the S&P 500
00:48:00 How new ideas enter a quality portfolio
00:51:00 Sell discipline and portfolio turnover
00:53:00 International vs US quality investing

This episode of Excess Returns features Jim Paulsen breaking down the current macro environment through a series of powerful indicators, including oil, interest rates, consumer behavior, and market sentiment. The discussion explores whether today’s environment signals a slowing economy—or the early stages of a new bull market hidden beneath the surface.

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Jim walks through a wide range of charts and frameworks, from the Walmart vs. luxury retail signal to private credit stress, productivity trends, and policy uncertainty, offering a data-driven perspective on where markets and the economy may be headed next.
Paulsen Perspectives Substack
https://paulsenperspectives.substack.com
Topics Covered
Why the recent oil spike hasn’t impacted inflation and interest rates as expected

Slowing economic growth vs. recession risk and what the Fed might do next

The Walmart vs luxury retail indicator and what it signals about the economy

Private credit risks and how they differ from traditional credit crises

Why many indicators point to a new bull market rather than a bear

The role of sentiment, volatility, and uncertainty in driving market returns

Market rotation from mega-cap “new era” stocks to broader market leadership

Corporate profits divergence and the opportunity in the rest of the economy

Liquidity, cash levels, and positioning as potential fuel for markets

Productivity trends and whether AI-driven gains are real or overstated

Timestamps
00:00 Intro and current macro backdrop
01:05 Oil spike and limited impact on yields and inflation
04:45 Growth outlook and why recession may still be avoided
07:10 Fed policy and the stagflation question
10:15 Walmart vs luxury retail indicator explained
13:40 Private credit stress vs traditional credit cycles
17:00 Why this isn’t 2008 and how balance sheets differ
19:50 Private credit risks and market spillover effects
22:15 Bear market fears vs signs of a new bull
23:45 Consumer confidence and its impact on returns
25:05 Oil spikes historically as buy signals
26:15 VIX, volatility, and market bottoms
27:05 Yield curve steepening and market implications
28:05 Sentiment indicators and what they really reflect
30:00 Market rotation and broadening beyond mega caps
32:45 Passing the baton from tech to broader markets
35:15 Corporate profits divergence and future potential
37:00 Policy uncertainty and why it can be bullish
42:05 Liquidity, cash levels, and risk allocation
43:20 Options positioning and put-call signals
44:05 Gold vs commodities and risk appetite
45:10 Consumer credit contraction and market signals
46:20 Polymarket recession probabilities as sentiment
47:30 Economic sentiment collapse and contrarian signals
48:10 Interest rate expectations and positioning
49:05 Unemployment trends and historical market bottoms
50:25 Productivity trends and AI impact on the economy

This episode of Excess Returns features Tony Wang of T. Rowe Price discussing how investors can identify “inevitabilities” in technology and position portfolios to benefit from long-term innovation trends. The conversation explores AI, semiconductors, and the evolving investment landscape, while also breaking down Tony’s portfolio construction process and how he navigates cycles, valuation, and disruption risk.
Tony explains why AI is fundamentally changing the cost of intelligence, how agentic systems could reshape software and labor markets, and why the current AI buildout may differ from past tech cycles. The discussion also dives into where we are in the AI cycle, how to think about the Mag 7, and what investors may be missing across the tech stack.
T. Rowe Price Science and Technology Fund
https://www.troweprice.com/financial-intermediary/us/en/investments/mutual-funds/us-products/science-and-technology-fund.htmlTopics Covered
What it means to invest in “inevitabilities” and separating signal from noise in markets

Why AI and compute demand represent a structural shift similar to past tech waves

The rise of agentic AI and how it could transform software and productivity

Whether AI is underappreciated or already priced into markets

The “multiple moons” idea and why AI may not be a winner-take-all market

How AI could reshape the labor market, productivity, and economic growth

The AI CapEx debate and why this cycle may differ from the dot-com buildout

Where we are in the AI cycle: training vs inferencing and deployment phase

The impact of AI on software companies and the innovator’s dilemma

How semiconductors, memory, and infrastructure remain key bottlenecks

The changing nature of the Mag 7 and capital intensity in AI

Tony’s portfolio construction framework across compounders, emerging tech, and value

How he generates ideas using S-curve adoption and economic bottlenecks

Position sizing, risk management, and balancing growth with drawdown control

Sell discipline: valuation, fundamentals, and market signals

Timestamps
00:00 Introduction and Tony Wang overview
01:05 Investing in inevitabilities and long-term thinking
03:00 Differentiating inevitability from hype and consensus
04:45 AI inevitability and the rise of agentic systems
07:00 Cost of intelligence and productivity implications
08:00 Real-world examples of AI adoption (customer service, agents)
09:00 Is AI underappreciated by markets?
11:15 AI as a “space race with multiple moons”
13:30 AI as the dominant driver of markets today
15:00 AI’s impact on jobs, productivity, and the economy
18:30 Creativity, judgment, and the future of work
20:45 Physical AI and robotics opportunity set
22:30 AI CapEx debate vs the dot-com era
25:30 Semiconductors vs software in the AI stack
28:15 AI disruption risk for software companies
31:00 Cyclicality in semiconductors and how AI changes it
33:30 The evolving role of the Mag 7 in AI
36:30 Competition, startups, and AI democratization
38:00 Where we are in the AI cycle today
40:00 Idea generation and S-curve adoption framework
42:30 Case study: memory and AI bottlenecks
44:45 Example position: optical networking and infrastructure
46:40 Portfolio construction and position sizing
49:00 Sell discipline and managing valuation risk