In this episode of Excess Returns, we sit down with Adrian Helfert of Westwood to discuss how investors should be thinking about portfolio construction in a market shaped by artificial intelligence, high levels of concentration, shifting interest rate dynamics, and evolving economic signals. The conversation covers how AI-driven capital spending is changing return profiles across markets, why traditional investing rules are breaking down, and how investors can balance growth, income, and risk in an uncertain environment. Adrian shares his framework for understanding return drivers, his views on market concentration and valuation, and how to think about diversification, macro risk, and income generation going forward.

Main topics covered
• How Westwood frames portfolio construction around capital appreciation, income, and event-driven returns
• Why AI spending is both a major opportunity and a growing existential risk for large companies
• The sustainability of market concentration and what it means for future returns
• Whether higher interest rates really hurt growth stocks the way investors expect
• How massive data center and AI capital expenditures could translate into productivity gains
• The case for market broadening beyond the Magnificent Seven
• Why traditional recession indicators have failed in recent cycles
• How inflation, labor markets, and Federal Reserve policy interact today
• Rethinking the classic 60/40 portfolio and the role of private markets
• Using covered calls and active income strategies to manage risk and generate yield

Timestamps
00:00 Introduction and near-term opportunities versus long-term risk
02:40 Capital appreciation, income, and event-driven investing framework
06:30 Have markets structurally changed to support higher returns
09:30 Intangible assets, AI, and margin expansion
10:20 The scale of AI and data center capital spending
13:00 Productivity gains and return on investment from AI
16:00 AI as both opportunity and risk for companies
19:30 Market concentration and diversification concerns
23:30 Will market leadership eventually broaden
25:30 Growth stocks, duration, and interest rates
29:30 International diversification and global investing
33:30 Why recession indicators have failed
39:00 Inflation outlook and Federal Reserve policy
46:00 Rethinking the 60/40 portfolio
53:00 Enhanced income strategies and covered calls
59:00 One investing belief most peers disagree with

In this special episode, Adam Butler and Ben Hunt join Matt Zeigler to unpack one of the most charged debates in markets and economics today: whether our official statistics still reflect lived reality. Building on Mike Green’s work and Adam Butler’s essay The Bureau of Missing Children, the conversation moves beyond the technical definition of poverty to a deeper idea of economic precarity, the growing gap between what we measure and what people actually experience. Together, they explore debt, housing, childcare, labor mobility, AI, and the erosion of meaning in economic language, while wrestling with what policy, community, and human-centered solutions might look like in a world that increasingly feels unstable.

Main topics covered

• Why the debate should focus on precarity rather than poverty

• The disconnect between inflation statistics and lived experience

• How debt, housing, childcare, and education drive economic insecurity

• The idea of a participation budget for modern family formation

• Why labor mobility has broken down since the financial crisis

• How asset prices and credit intensify risk for households

• The role of grandparents and off-balance-sheet support in the economy

• Darwin’s wedge, positional goods, and rising costs of everyday life

• The impact of AI, technocracy, and anti-human incentives

• Centralized versus decentralized solutions to today’s economic challenges

• What it means to carry the fire and preserve human-centered values

Timestamps
00:00 Introduction and the emotional roots of the precarity debate
02:00 Poverty versus precarity and what we are really measuring
06:30 Technocrats, narratives, and the limits of economic statistics
09:00 Personal experiences with precarity and debt
15:00 The Bureau of Missing Children and family formation economics
21:00 Modeling household income and participation budgets
25:50 Rising costs of childcare, housing, and everyday life
33:00 Darwin’s wedge and positional competition
36:45 Debt, housing, and labor immobility
40:00 Grandparents, unpaid care, and off-balance-sheet subsidies
46:30 How today differs from 40 or 50 years ago
49:40 Labor mobility as a lost engine of opportunity
55:00 Policy paths, mission-driven economics, and decentralization
01:11:00 Visionary leadership versus bottom-up solutions
01:15:50 Carrying the fire and preserving meaning
01:17:30 Where to follow Adam Butler and Ben Hunt

In this episode of Excess Returns, we sit down with David Wright, Head of Quantitative Investing at Pictet Asset Management, for a deep and practical conversation about how artificial intelligence and machine learning are actually being used in real-world investment strategies. Rather than focusing on hype or black-box promises, David walks through how systematic investors combine human judgment, economic intuition, and machine learning models to forecast stock returns, construct portfolios, and manage risk. The discussion covers what AI can and cannot do in investing today, how machine learning differs from traditional factor models and large language models like ChatGPT, and why interpretability and robustness still matter. This episode is a must-watch for investors interested in quantitative investing, AI-driven ETFs, and the future of systematic portfolio construction.

Main topics covered:

• What artificial intelligence and machine learning really mean in an investing context

• How machine learning models are trained to forecast relative stock returns

• The role of features, signals, and decision trees in quantitative investing

• Key differences between machine learning models and large language models like ChatGPT

• Why interpretability and stability matter more than hype in AI investing

• How human judgment and machine learning complement each other in portfolio management

• Data selection, feature engineering, and the trade-offs between traditional and alternative data

• Overfitting, data mining concerns, and how professional investors build guardrails

• Time horizons, rebalancing frequency, and transaction cost considerations

• How AI-driven strategies are implemented in diversified portfolios and ETFs

• The future of AI in investing and what it means for investors

Timestamps:
00:00 Introduction and overview of AI and machine learning in investing
03:00 Defining artificial intelligence vs machine learning in finance
05:00 How machine learning models are trained using financial data
07:00 Machine learning vs ChatGPT and large language models for stock selection
09:45 Decision trees and how machine learning makes forecasts
12:00 Choosing data inputs: traditional data vs alternative data
14:40 The role of economic intuition and explainability in quant models
18:00 Time horizons and why machine learning works better at shorter horizons
22:00 Can machine learning improve traditional factor investing
24:00 Data mining, overfitting, and model robustness
26:00 What humans do better than AI and where machines excel
30:00 Feature importance, conditioning effects, and model structure
32:00 Model retraining, stability, and long-term persistence
36:00 The future of automation and human oversight in investing
40:00 Why ChatGPT-style models struggle with portfolio construction
45:00 Portfolio construction, diversification, and ETF implementation
51:00 Rebalancing, transaction costs, and practical execution
56:00 Surprising insights from machine learning models
59:00 Closing lessons on investing and avoiding overtrading