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.

Naveen Rao × Matt Bornstein

Unconventional is pursuing physical learning circuits, arguing that analog dynamics could complement digital computing for stochastic, time-dependent workloads rather than simply extend matrix multiplication.The thesis targets an energy wall: U.S. data centers use roughly 4% of the national grid, while some estimates call for 400 additional gigawatts over the next decade.Commercial significance depends on finding a manufacturable paradigm within five years, with TSMC partnership, a potentially very large analog prototype and unresolved AGI claims creating major execution risk.

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.

Raghu Raghuram × Amin Vahdat × Jeetu Patel

AI infrastructure demand is outpacing deployment: Google’s seven- and eight-year-old TPUs remain at 100% utilization, while power, permitting, land, and supply chains may constrain trillions in committed spending for 3–5 years.Specialized silicon, distributed networking, and inference-native systems could deliver 10–100x efficiency gains, but bursty workloads and the need for full-stack co-design make utilization, architecture, and durable product differentiation central risks.

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.

Martin Casado × Raghu Raghuram × Jeetu Patel

VMware’s cycle shows how a software abstraction can disrupt an incumbent before AWS retains the virtual machine and captures developers, a constituency VMware “had no idea how to work with.”Cisco’s reset targets market in nine months, $1 billion in three to four years, and 8–10 repeatable winners by combining protected two-pizza teams with scaled distribution.AI could expand infrastructure demand 100–1,000x as agents create sustained inference workloads, but vertical integration must remain open enough to include competitors such as Microsoft Teams.

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.