# The Inevitability No One Sees | $11 Billion Tech Manager on What Investors Miss About AI

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Text version: https://stenobird.com/podcast/excess-returns-902655/the-inevitability-no-one-sees-11-billion-tech-manager-on-what-investors-miss-about-ai.md
Podcast: [Excess Returns](https://stenobird.com/podcast/excess-returns-902655)
Published: 2026-04-06T13:24:55+00:00
Episode link: https://podcasters.spotify.com/pod/show/excess-returns/episodes/The-Inevitability-No-One-Sees--11-Billion-Tech-Manager-on-What-Investors-Miss-About-AI-e3hggb5
Audio file: https://traffic.megaphone.fm/APO3830350236.mp3
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Duration seconds: 3755

## Resource

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.html Topics 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 c…

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