Dylan Patel

Jordan Nanos × Dylan Patel × Jordan Schneider

NVIDIA’s leadership shock has turned alleged chip diversion into an export enforcement crisis, though evidence that Jensen Huang knew remains circumstantial.Patel says U.S.-controlled GPU rentals could preserve a remote cutoff over Chinese compute while earning providers millions per megawatt; Schneider questions whether U.S. frontier labs would gain more value from scarce capacity.The panel supports MATCH restrictions to constrain China’s chipmaking scale, while NVIDIA’s outlook depends on executive retention and Rubin roadmap execution.

Austin Lyons × Stacy Rasgon × Dylan Patel

Financing costs and shortages in substrates, memory, and clean-room capacity could constrain AI infrastructure supply through 2028, while on-site generation may ease power limits.Construction exceeds $60 billion per gigawatt, shifting cost negotiations; Nvidia’s pricing power depends on performance gains outpacing higher costs.Patel expects Nvidia’s CoWoS and HBM share to fall, even as earnings at least double next year; custom silicon and enterprise token demand remain key signals.

Alex Kantrowitz × Dylan Patel × Jordan Nanos

SemiAnalysis estimates next-year US AI infrastructure spending near $2 trillion, or 5–6% of a $30-trillion-plus economy, including capacity for overseas inference demand.Hyperscaler capex grew 116% year over year in Q3 2026, but demand exceeds supply; returns depend on six-year GPU and 15-year data-center lives, while neo-cloud security and internal model use remain risks.

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.

Patrick O'Shaughnessy × Dylan Patel

SemiAnalysis’s Claude Code spend has reached a $7M annual run rate against $25M of salaries, while Anthropic’s revenue growth implies a 72% gross-margin floor and pricing power that still may not clear capacity constraints.Mythos’s reported L4-to-L6 leap, selective cybersecurity access at 5–10x token cost, and sold-out DRAM, GPUs, CPUs, and fab equipment make compute supply and deployment breadth the key catalysts to monitor.

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.