AI lab revenue is 10x'ing YoY while compute capacity only 3x's due to hard constraints (Moore's Law slowing, EUV bottlenecks, wafer saturation), forcing compute prices up as labs bid aggres…

Frontier labs (Google, Anthropic) are paying 2x spot prices ($900M/month for 110K GPUs) to neocloud providers like SpaceX for guaranteed capacity, efficiency, and security — signaling a str…

As AI models become more capable, they can monetize compute far more effectively, driving up compute prices despite 3x annual supply growth. The marginal value of compute could reach $250K/…

As compute costs rise, labs with more token-efficient models can charge large premiums because weaker models burn more expensive compute for same results, creating winner-take-most dynamics…

When compute becomes scarce and expensive ($20/hr for H100), labs with more token-efficient models gain compounding advantage — they burn fewer tokens per task, effectively 'creating comput…

Unlike the Simon-Ehrlich bet on metals where innovation increased supply, compute supply is far less elastic and substitutable, suggesting current scarcity may persist rather than being sol…

The 3x annual compute growth relies on three components (Moore's Law 1.4x, new fabs 1.2x, wafer reallocation 1.8x) all hitting physical limits by 2025-2030, making supply unable to absorb d…

The 3x annual compute growth decomposes to: 1.4x from Moore's Law (slowing, miracle to sustain), 1.2x from new fabs (bottlenecked by ASML EUV output through 2030+), 1.8x from wafer realloca…

Model training's one-time fixed cost amortized across infinite users creates extreme economies of scale unlike human labor, driving 10x revenue growth on 3x compute growth and concentrating…