Dylan Patel

Dylan Patel × Christopher Gannatti

Memory has flipped from AI’s biggest loser to its biggest winner, with capacity growing 20-30% annually while demand doubles for the next three years.Prices are already up 4x, with another 2x, 3x possible before margins reach 85-90% and eventually cycle back toward the 70s or lower.

Dylan Patel × Dwarkesh Patel

AI scaling’s binding constraint is migrating to ASML, whose EUV capacity could cap annual AI-chip output at roughly 200GW by 2030.Supply scarcity is supporting H100 contracts near $2.40/hour against a $1.40 all-in build cost, while memory absorbs roughly 30% of Big Tech’s 2026 CapEx and new fabs arrive only in late 2027/2028.The central timeline risk is geopolitical: fast progress favors the US, but slower returns could let China’s verticalized supply chain scale past the West, especially if Taiwan is lost.

Patrick O'Shaughnessy × Dylan Patel

The OpenAI–Nvidia arrangement shifts enormous balance-sheet risk through gigawatt-scale commitments, while token-cost declines and scaling economics keep demand for compute tied to continued model improvement.Google and Meta emerge as favored platform positions, but neoclouds and AI software face contract, debt, hardware-obsolescence, and gross-margin risks as China accelerates its semiconductor and data-center buildout.

Erik Torenberg × Dylan Patel × Sarah Wang × Guido Appenzeller

Nvidia’s $5 billion Intel investment, following SoftBank’s $2 billion and the U.S. government’s $10 billion, could lower Intel’s cost of capital and redraw PC and data-center competition, though Patel says Intel still needs roughly $50 billion.Huawei has credible 7 nm designs and ambitious custom-HBM products, but HBM3 yields, etch capacity, and domestic volume remain unresolved as Nvidia’s upside depends on $450–500 billion of hyperscaler capex rather than further share gains.