Excess Returns

Kai Wu of Sparkline Capital shows how to uncover hidden AI exposure, overlapping ETF holdings, and unintended factor bets in your portfolio. Owning several funds can still leave you dependent on the same companies and the same AI spending cycle.
In this episode of Excess Returns, Kai joins Matt Zeigler and Justin Carbonneau for a walkthrough of Sparkline Analytics. Using examples from the S&P 500, Nasdaq, emerging markets, and active funds, they examine what investors actually own beneath the fund labels.
Kai explains his distinction between AI infrastructure, early adopters, and laggards, then demonstrates how factor analysis, intangible asset data, and a portfolio builder can help investors ask better questions about diversification and manager performance.
Explore Sparkline Analytics:
https://app.sparklinecapital.com/
Topics covered:
How AI infrastructure exposure can overlap across the S&P 500, Nasdaq, and thematic AI funds
Why Kai Wu distinguishes AI early adopters from infrastructure builders and laggards
How rising capital expenditures can change the economics of formerly asset-light businesses
Why a fund's number of holdings can overstate its diversification
How semiconductor exposure can connect emerging markets to the same AI trade
The trade-off between reducing AI infrastructure exposure and increasing exposure to laggards
What ARKK illustrates about separating stock selection from factor headwinds
Why dividend, value, and low-volatility funds can have similar underlying exposures
How patents, R&D, and human capital reveal assets traditional value metrics can miss
Using fund comparisons and portfolio look-through tools to identify overlapping positions
Chapters:
00:00 Sparkline Analytics and hidden AI exposure in the S&P 500
08:05 AI adopters, laggards, and multinational businesses
12:47 How index exposure changes as the AI cycle evolves
20:41 Does adding an AI fund diversify your portfolio?
25:44 Capital spending and effective portfolio concentration
30:14 Capital intensity and AI exposure beyond Big Tech
35:44 Diversifying without retreating into AI laggards
40:48 Screening funds and looking inside ARKK
46:11 Separating stock-picking alpha from factor exposure
52:35 Dividend, value, and low-volatility fund overlap
57:52 Intangible value and a fund-diligence checklist
01:05:39 Building a portfolio and looking through its holdings
01:10:39 Where to find Sparkline Analytics
Learn more about the Excess Returns podcast network:
https://excessreturns.co
No information discussed in this podcast should be construed as investment advice. Securities discussed may be held by the hosts and guests, their firms or their clients.

Daniel Crosby explains why investors can lose money in winning funds and how to build a portfolio they can actually hold. Picking the right investment is only part of the challenge; staying with it through discomfort is where behavior can overwhelm the plan.

In this episode of Excess Returns, psychologist and Orion Chief Behavioral Officer Daniel Crosby joins Matt Zeigler to discuss the new paperback edition of The Behavioral Investor. Daniel shares the story of a fund that returned over 18% annually for a decade while its average investor lost money, then explains how safety, stability, and success portfolios can help align investment decisions with human psychology. They also explore gambling apps, the limits of risk questionnaires, social media feedback loops, and the human side of financial advice in an AI-driven world.

Topics covered:
- Why a fund's returns can look very different from the returns its investors earn
- Building safety, stability, and success buckets around different financial needs
- The differences between risk tolerance, risk capacity, and risk composure
- Why knowing what to do does not guarantee doing it during a drawdown
- Daniel Crosby's case for rules, diversification, and meaningful benchmark differences
- How sports betting and prediction markets blur the distinction between investing and gambling
- Why recent market experience can create unrealistic expectations about future returns
- Education, environment, and encouragement as tools for changing investor behavior
- How algorithms reinforce market biases and why excitement can signal a blind spot
- Where AI can free financial advisors to spend more time on human relationships
The Behavioral Investor - paperback edition:https://link.amazon/B00hgkHGG
Daniel Crosby on X:
https://x.com/danielcrosby

Orion Behavioral Finance:
https://orion.com/behavioral-finance

Research discussed:
The heat of the moment: the effect of sexual arousal on sexual decision making
https://onlinelibrary.wiley.com/doi/abs/10.1002/bdm.501

Chapters:
00:00 Daniel Crosby and The Behavioral Investor
04:51 Why winning funds can have losing investors
11:46 Risk tolerance, capacity, and composure
17:38 Stability portfolios and rules-based stock selection
23:06 Investing, gambling, and prediction markets
27:27 Recency bias and unrealistic return expectations
31:30 Addictive apps and changing investor behavior
35:48 Investor distractions and the media diet
40:51 Investment excitement, humility, and AI
45:00 What music reveals about consistency and experimentation
49:29 Where to find Daniel Crosby and the book

Learn more about the Excess Returns podcast network:
https://excessreturns.co

No information discussed in this podcast should be construed as investment advice. Securities discussed may be held by the hosts and guests, their firms or their clients.

Ted Mortonson of Baird explains why conviction in the AI boom doesn't mean buying every dip in tech stocks.

He joins Jack Forehand and Justin Carbonneau to explain what experienced technology investors watch before putting money back to work—and why he still sees AI as the most powerful technology cycle of his career.

The discussion moves from lessons of the dot-com bust to the companies Ted believes can capture AI's profits. They explore the importance of free cash flow, the financing demands of the data center buildout, and how large IPOs could force portfolio managers to sell existing holdings. Ted also shares his views on Apple, Meta, Nvidia, Tesla, and SpaceX, before closing with lessons from his service as a naval aviator.

Topics covered:
- Why Ted Mortonson's institutional clients aren't rushing to buy the semiconductor dip
- What past tech cycles teach about taking profits, preserving capital, and waiting for selling to settle
- Why Ted Mortonson sees a stronger long-term AI opportunity than the dot-com cycle
- How geopolitics, energy costs, regulation, and investor sentiment shape technology investing
- Why free cash flow, engineering resources, and manufacturing scale matter for AI beneficiaries
- Financing the infrastructure buildout before enterprise adoption delivers returns
- How large IPOs and circular financing can create risks for existing shareholders
- Ted Mortonson's concerns about Apple and his outlook for Meta, Tesla, and SpaceX
- Optical networking, advanced chip architectures, and high-bandwidth memory
- Navy lessons on decisions under uncertainty, teamwork, and integrity

Chapters:
00:00 Ted Mortonson on the risks facing tech investors
05:42 Market sentiment and why his clients aren't buying yet
12:00 Dot-com lessons and the danger of buying every dip
16:17 Why Ted sees a different AI cycle
23:06 Which companies could capture AI's profits?
27:38 Regulation and financing the AI buildout
35:10 Can the market absorb massive AI IPOs?
39:30 Circular financing and the Tesla-SpaceX thesis
46:06 Apple, Meta, and competing AI strategies
54:19 SpaceX, advanced chips, and memory technology
58:26 Lessons from serving as a naval aviator
01:03:35 Closing thoughts

Ted Mortonson at Baird:
https://www.rwbaird.com/corporations-and-institutions/institutional-equities-research/team/ted-mortonson/

Learn more about the Excess Returns podcast network:
https://excessreturns.co

No information discussed in this podcast should be construed as investment advice. Securities discussed may be held by the hosts and guests, their firms or their clients.

Brian Portnoy of Shaping Wealth explores why experienced investors build very different portfolios—and what that means for your own money.
In How I Invest My Money Volume Two, 25 new contributors explain how they invest their personal wealth. Brian joins Matt Zeigler to discuss what those stories reveal about the gap between an optimal portfolio on paper and one you can actually live with.
From large cash reserves and paid-off mortgages to investments in their own businesses, their choices reflect family needs, career risk, and priorities that a model can struggle to capture.
The conversation also explores why low-cost index funds don't settle every investing question, how financial professionals think about bonds and alternatives, and what it means to compound relationships, knowledge, and opportunities alongside money.
How I Invest My Money Volume Two:
https://link.amazon/B0aVpNlfS
Topics covered:
Why knowing how markets work doesn't produce one right portfolio for everyone
What Ben Hunt and other contributors reveal about investing beyond a brokerage account
Connecting personal goals to asset allocation and risk tolerance
Why business ownership and household needs change the role of stocks and bonds
Why financial professionals may deliberately hold substantial cash reserves
The difference between spreadsheet optimization and a portfolio you can stick with
Paying off a mortgage, feeling secure, and accepting the opportunity cost
The complexity and illiquidity that can come with alternative investments
Matt Zeigler's approach to compounding human, intellectual, social, and financial capital
Brian Portnoy's experience editing the book with his son Zach and sharing opportunities across generations
Chapters:
00:00 How finance professionals invest their own money
05:35 Why investing extends beyond index funds
12:20 Connecting financial goals to portfolio choices
18:32 Why investors choose different stock and bond allocations
25:05 Cash reserves, career risk, and peace of mind
34:55 Paying off a mortgage: security versus opportunity cost
40:11 Alternatives, complexity, and the investment industry
44:40 Matt Zeigler on investing in more than money
52:22 Brian Portnoy on family, opportunity, and working with his son
Shaping Wealth:
https://www.shapingwealth.com
Follow Brian Portnoy on X:
https://x.com/bportnoy_
Learn more about the Excess Returns podcast network:
https://excessreturns.co
No information discussed in this podcast should be construed as investment advice. Securities discussed may be held by the hosts and guests, their firms or their clients.

Rob Arnott, founder of Research Affiliates and chairman of Syzygy Asset Management, examines S&P 500 concentration and who could benefit most from AI. Owning today’s market leaders is not necessarily the same as owning tomorrow’s biggest investment winners.
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In this episode of The Intangible Economy, Rob joins Kai Wu to explore how index construction shapes what investors own, why index additions can create hidden costs, and why he would consider buying a richly valued IPO before index funds have to buy it. The conversation also covers AI’s shift toward capital-intensive business models, GPU depreciation, intangible assets, and the importance of testing investment ideas rather than chasing impressive backtests.
Topics covered:
Why Rob Arnott sees a real AI revolution alongside risks in the prices of its leading stocks
How AI customers could capture value beyond the infrastructure builders
Valuation changes versus fundamental growth in large and smaller companies
Tesla, SpaceX, and the price pressures surrounding index inclusion
Hidden implementation costs that do not appear in an index fund’s expense ratio
Selecting index members by business size rather than market value
AI financing, customer economics, and the risk of rapid hardware obsolescence
R&D, intangible assets, and what conventional book value can miss
Competitive moats, leadership, and creating value for customers
Using AI in research while resisting overfitting and performance chasing
Research discussed:
⁠The Active Side of Indexing⁠
⁠Fundamental Growth⁠
Learn more about ⁠Syzygy Asset Management⁠.
Timestamps:
00:00 Rob Arnott on index concentration and AI’s future winners
12:13 What S&P 500 investors own and pay for
20:35 Why Rob Arnott would buy before index inclusion
25:41 Hidden index costs and fundamental membership rules30:37 Passive investing’s embedded decisions
36:40 Big Tech’s shift from asset light to capital intensive
42:41 GPU depreciation and economic obsolescence
47:03 R&D, intangible assets, and value investing
51:04 How durable are competitive moats?
59:01 Using AI in business and investment research
01:05:11 Performance chasing and the backtest trap
Learn more about the Excess Returns podcast network:⁠excessreturns.co⁠
No information discussed in this podcast should be construed as investment advice. Securities discussed may be held by the hosts and guests, their firms or their clients.