T. Rowe Price technology portfolio manager Dom Rizzo joins Jack Forehand and Kai Wu to break down the AI investment cycle, hyperscaler capital spending, semiconductor demand, and why the recent tech selloff may look more like 1998 than the end of the boom. They discuss AI return on investment, OpenAI and Anthropic, open versus closed models, financing the data center buildout, the future of software, labor productivity, and how to construct a global technology portfolio.
Topics covered
Why Dom sees similarities between the 2026 semiconductor correction and the 1998 selloff
Why hyperscaler AI CapEx could accelerate from already historic levels
What cloud revenue growth and operating margins say about AI return on invested capital
Why end-user productivity is the key test for sustainable AI demand
Open-weight models versus frontier labs and where AI economic value may accrue
Why chips, memory, logic semiconductors, TSMC and ASML sit at critical points in the AI value chain
How equity, debt and operating cash flow could finance the next stage of the data center buildout
Why semiconductors remain cyclical even in a structurally capital-intensive AI boom
Why AI agents could turn traditional enterprise software into data pipes
AI productivity, labor displacement and the case for faster GDP growth
How Dom thinks about technology portfolio construction, risk factors and global stock selection
Full Transcript: Dom Rizzo on the AI Capital Cycle and Where Value Accrues
Jack: Welcome to Excess Returns. I’m Jack Forehand, joined by the better half of our hosting team today, Kai Wu of Sparkline Capital. And today we’re really lucky to have Dom Rizzo on. Dom’s the portfolio manager of T. Rowe Price’s Global Technology Equity Strategy and the firm’s technology ETF, and we are gonna talk about the thing everybody’s talking …
Timestamps
00:00 AI, the tech correction and the 1998 comparison
04:07 Why the AI capital spending cycle may only be halfway
12:33 The real test for AI demand: end-user ROI
17:00 Why frontier models may capture most of the economic value
21:23 Where the biggest AI moats and profit pools could emerge
28:12 Financing the AI buildout with equity and debt
36:03 Are semiconductors in a supercycle or still cyclical?
41:43 What AI agents mean for traditional software companies
46:03 AI productivity versus labor displacement
51:01 Building a portfolio for a technology revolution
56:06 Global tech opportunities and Dom’s stock-picking framework
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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.
