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.

Karan Singhal × Jay Rughani

GPT-6 Luna, OpenAI’s cheapest frontier API model, beat 5.6 Sol on HealthBench Professional at 34 times lower cost.Singhal sees a path to commoditized health intelligence, but says it has not yet affected the $5.3T US spend; savings require workflow integration.Epic, UCSF, and AdventHealth offer deployment evidence, including roughly 80% lower administrative workflow costs reported by AdventHealth.

Alex Rampell × Jean-Denis Greze

Town abandoned a tax business nearing $1 million in first-year revenue as annual demand and weak word of mouth favored incumbents.Its consumer-first assistant applies cheap tokens to concrete jobs, including supplier monitoring that may save restaurants hundreds or thousands.The test is whether reusable workflows deliver recurring value before model labs replicate them.

Jad Naous × Martin Casado

AI coding has moved beyond autocomplete: Martin Casado built data-lake auto-promotion production code in 10 days versus roughly six months historically, a 10- to 20-fold gain Jad Naous says favors engineers already strong in systems.Grepr’s 90% telemetry-cost reduction offers a concrete commercial wedge, while anomaly-led diagnosis works internally and with a few customers; autonomous cluster management remains “not yet.”

Lisha Li × Nathan Lambert

Lambert estimates distillation may give Chinese labs a few months, but its value in RL-heavy post-training remains unclear.Closed labs integrate inference and lead coding, where revenue has gone from zero to $100 billion, while open-model economics fragment builders, inference firms and post-training companies.Open models may still win buyers seeking owned weights and the cheapest adequate model.

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.

David George × Kevin Mandia

AI is shifting cyberattacks from resource-constrained “sniper shots” to scalable swarms, while Armadin continuously verifies exploitable network weaknesses.Since January 2026, Armadin has found more than 90 zero-days across Fortune 500 customer environments, while Blue aims to turn a five-minute lead into autonomous controls.With technical advantages potentially copied in six months, customer execution and the Red-to-Blue feedback loop remain the operating test.

Elena Burger × Olivia Moore × Josh Elman

Consumer AI’s traffic rankings mask monetization: 29 of the 50 products leading on consumer card spending appear on neither traffic list, while the top 10% of paying users generate more than half of revenue.Personal agents are emerging, but Muse’s roughly 5 million U.S.-and-Canada downloads in about 22 days trailed Threads’ 16 million; high serving costs make lower inference costs and trusted advertising pivotal catalysts.

Erik Torenberg × Alex Atallah × Amjad Masad

Stripe’s acquisition of OpenRouter preserves its brand, roadmap, and product autonomy while pairing payments with model-independent inference infrastructure.OpenRouter’s marketplace reduces vendor lock-in, combines models, and pressures inference prices downward as enterprises evaluate task-specific performance rather than feature lists.Specialized decision models could lower model debt and police agent actions, while multi-model fusion reportedly approaches frontier quality at 40–50% of the cost; deception risk and cache reuse remain unresolved.

Seema Amble × Elena Burger × Vladimir Keil

AI procurement startups are targeting the end-to-end work hidden behind an ERP’s “$8,000” result, where 30 meetings, 500 emails and supplier counteroffers determine cost and delivery.Lio’s path from human review to $10,000, $20,000, or $100,000 negotiations highlights the production bottleneck: 70% accuracy can leave humans doing 100% of the work, while complex multimillion-dollar cases still require specialists.