Dylan Patel

Dwarkesh Patel × Dylan Patel

Anthropic and OpenAI could receive 40%–50% of new compute next year, potentially controlling most usable FLOPs by end-2028 as GB300, TPU v7 and Trainium 3 improve performance per watt 3–5×.Anthropic’s revenue has reached as high as $50M/MW, it started turning profitable in Q2, and compute repricing, regulation and export controls leave margins, financing and centralization as key risks.

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.

Dylan Patel × Erin Price-Wright × Guido Appenzeller × Erik Torenberg

GPT-5’s router is an economic release, directing simple queries to mini models while reserving “ungodly amounts of compute” for transactions OpenAI could monetize through agentic commerce.Flat-rate subscriptions face heavy-user losses, while custom silicon threatens Nvidia mainly if demand stays concentrated among hyperscalers; powered sites, grid equipment, and Intel’s capital needs remain near-term constraints on broader deployment.

Sarah Guo × Dylan Patel

OpenAI’s expected compact, reasoning-heavy open model could push intelligence pricing below the frontier, while inference differentiation shifts from kernels toward orchestration, networking, reliability, and physical infrastructure.Roughly 200 neoclouds face consolidation as GPU economics diverge, while NVIDIA’s hardware, networking, and software moat remains difficult to replicate; bottlenecks in power, packaging, labor, and export policy are the next constraints to monitor.

Lex Fridman × Dylan Patel × Nathan Lambert

DeepSeek’s V3 and R1 reset the AI cost curve through reinforcement learning, open weights, mixture-of-experts routing, and MLA, not a mythical $5 million frontier model.With roughly 37 billion of 600-plus billion parameters active per token and custom H800 scheduling, efficiency may expand inference demand, while export controls, TSMC, power, and cooling remain decisive constraints.