Intro

In this episode of Excess Returns, Gene Munster and Doug Clinton of Deepwater Asset Management join Justin and Jack to explore the technological, economic, and investing implications of AI. They discuss why they believe we’re still in the early stages of a multi-year bull market driven by AI, how the technology is reshaping jobs and productivity, and what it means for investors. The conversation also covers how companies like Nvidia, Apple, Tesla, and Meta fit into this AI cycle, the energy demands of AI, and the future of AI-driven investing through Intelligent Alpha and its GPT ETF.

Topics covered:
• Why Gene and Doug believe AI represents a once-in-a-generation wealth creation opportunity
• How AI may impact corporate profitability and hiring trends
• The political and social dynamics slowing AI adoption
• Doug’s “detective, people-pleaser, and tastemaker” framework for future human jobs
• How Intelligent Alpha uses large language models to manage portfolios
• The advantages of AI-driven investment models over humans
• Economic and market implications of an AI productivity boom
• The hardware-data-application structure of technological cycles
• The role of energy, especially nuclear and solar, in supporting AI growth
• The competitive race among model providers like OpenAI, Google, and Meta
• Apple’s long-term AI positioning and potential comeback
• Tesla’s valuation, autonomy vision, and the future of robotics
• The inevitability and function of bubbles in breakthrough technologies
• The rise of private markets and retail investor access to innovation
• Future frontiers in quantum computing and biotechnology

Timestamps:
00:00 Introduction and Deepwater’s AI thesis
03:00 Why AI marks a multi-year bull market opportunity
08:00 Political reality and limits of AI deployment
11:00 The future of human work: detectives, people-pleasers, tastemakers
16:00 Inside Intelligent Alpha and the GPT ETF
19:00 Why AI can outperform human managers
25:00 How AI affects productivity, margins, and employment
26:00 Hardware, data, and application cycle in AI
28:00 The energy constraint: nuclear, gas, and solar
29:30 The model race: OpenAI, Google, Meta
34:00 Apple’s role and long-term AI potential
39:30 Tesla, autonomy, and long-term disruption
44:00 Are bubbles necessary for technological revolutions?
49:00 Private vs. public investing in innovation
51:00 Beyond AI: quantum computing and life extension technologies
54:45 Closing thoughts