The a16z Show

Andy Jassy × Ben Horowitz × Erik Torenberg

Amazon expects about $220 billion of 2026 CapEx, yet AWS capacity is sold out for 2026, largely for 2027, and partly for 2028; AWS AI has exceeded a $25 billion revenue run rate.Data centers require investment roughly two years before monetization, pressuring free cash flow; broader enterprise inference adoption also hinges on costs Jassy still calls an order of magnitude too high.

Raghu Raghuram × Matt Garman

AWS is underwriting durable AI demand with $220 billion of 2026 capex while reserving scarce accelerators for startups as well as frontier labs.Positive-ROI production workloads and single-digit maximum customer concentration temper demand risk, while AWS plans to buy 2 million NVIDIA GPUs over the next couple of years.Agentic workloads may strengthen AWS’s architectural edge, but power, memory, chips and enterprise evaluation remain constraints to monitor.

Gavin Baker × David George

Gavin Baker argues AI does not resemble 2000: NVIDIA trades near 40 times trailing earnings versus Cisco’s 150–180 times, GPUs are fully utilized, and major buyers’ ROIC has risen roughly 10 points.The open risk is whether returns persist through Blackwell spending, while Google’s TPU competition, incumbent distribution, and outcome-based pricing could reshape infrastructure, SaaS margins, and AI monetization.

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.

Steph Smith × Martin Casado × Steven Sinofsky

DeepSeek R1’s permissive MIT-like license and released reasoning traces enable broad adoption and distillation into smaller models, shifting strategic advantage toward distribution and “AGI in your pocket.”Model value may migrate from benchmark leadership to stateful workflows as competitors catch up, while scale-out expands endpoints without eliminating hyperscale compute.The clearest policy signal is that chip and open-source restrictions did not prevent capable Chinese research, making faster domestic innovation and permissionless diffusion the unresolved US advantage.