Dylan Patel – Anthropic & OpenAI will have most of the world’s compute by 2028
Summary
Dwarkesh Patel interviews Dylan Patel of Semi Analysis on the rapid centralization of AI compute, where labs like OpenAI and Anthropic are capturing half of incremental global capacity by 2027. They discuss how these labs are shifting from venture-funded losses to massive profitability, enabling them to outbid competitors for infrastructure. The conversation covers supply chain bottlenecks, regulatory impacts on model releases, and China’s lagging but accelerating domestic chip production.
Topics discussed
Intro: Semi Analysis founder Dylan Patel and AI CapEx trends
AI Labs profitability and the shift from VC funding to revenue
Compute centralization: SpaceX, OpenAI, and Anthropic's infrastructure
The economics of compute: $6B CapEx for $100B revenue potential
Supply chain bottlenecks and arbitrage opportunities in hardware
Future compute projections: 100 gigawatts for top labs by 2028
Regulatory impacts on model release and revenue per megawatt
Value capture shifts from chipmakers to AI labs and hyperscalers
Sponsor: Using GrokBot to automate podcast editor recruitment
Revenue forecasts and the timeline for Recursive Self-Improvement
Investor pressure vs. safety: Allocating compute for training vs. inference
Global compute growth: US dominance vs. China's constrained expansion
China's semiconductor subsidies and long-term AI competitiveness
Training vs. Inference ratios and the $10 trillion CapEx forecast
Funding the AI boom: Debt issuance by hyperscalers and suppliers
Sponsor: Antithesis deterministic software testing platform
Sovereign debt crisis risks and the crowding out of other industries
Interest rate impacts and the valuation of non-AI stocks
Economic doubling and the potential default of non-AI nations
Political constraints and the possibility of slowing AI progress
Sponsor: Jane Street intern projects and financial modeling challenges
The inevitability of centralization and value capture in the AI economy
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