How Much Compute Matters for Model Leadership

Dylan and Patrick discuss scaling laws, compute spend, and why more compute can still yield meaningful performance tiers.

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My guest today is Dylan Patel. Dylan is the founder and CEO of SemiAnalysis. At SemiAnalysis Dylan tracks the semiconductor supply chain and AI infrastructure buildout with unmatched granularity—literally watching data centers get built through satellite imagery and mapping hundreds of billions in capital flows. Our conversation explores the massive industrial buildout powering AI, from the strategic chess game between OpenAI, Nvidia, and Oracle to why we're still in the first innings of post-training and reinforcement learning. Dylan explains infrastructure realities like electrician wages doubling and companies using diesel truck engines for emergency power, while making a sobering case about US-China competition and why America needs AI to succeed. We discuss his framework for where value will accrue in the stack, why traditional SaaS economics are breaking down under AI's high cost of goods sold, and which hardware bottlenecks matter most. This is one of the most comprehensive views of the physical reality underlying the AI revolution you'll hear anywhere. Please enjoy my conversation with Dylan Patel.

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Show Notes:

(00:00:00) Welcome to Invest Like the Best

(00:05:12) The AI Infrastructure Buildout

(00:08:25) Scaling AI Models and Compute Needs

(00:11:44) Reinforcement Learning and AI Training

(00:14:07) The Future of AI and Compute

(00:17:47) AI in Practical Applications

(00:22:29) The Importance of Data and Environments in AI Training

(00:29:45) Human Analogies in AI Development

(00:40:34) The Challenge of Infinite Context in AI Models