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.

David George × Sarah Wang × Alex Immerman × Santiago Rodriguez

AI’s rally is being driven by earnings rather than multiple expansion, with the S&P 500 below 20 times earnings while growing about 15%.Capex is projected to rise from $416 billion in 2025 to about $780 billion in 2026 and exceed $1 trillion annually from 2027; enterprise adoption remains concentrated in pilots, with inference-cost declines and workflow deployment as catalysts.

Anish Acharya × David Pawlan

AI assistants may win by recovering “free money”—HSA reimbursements, fare credits, or 50% lower water bills—not by abstract productivity gains.Proactivity can be a moat when agents act invisibly, but one unauthorized insurance switch could destroy trust.At roughly $20 per user per day for ambitious browser-driven services, economics may require narrow agents priced at $200-$300—or eventually $1,000—a month.

Jen Kha × David George × Aram Verdiyan

AI is making capital itself compound advantage, pushing venture’s power law beyond prior tech cycles as compute improves products and inference demand remains unlimited.Only 20 of 3,000 US VC firms produced consistent 3x net returns over two decades, supporting concentrated LP portfolios and 5–10%+ late-stage sizing in category-defining companies.Meanwhile, traction is harder to parse and pre-ChatGPT software LBOs face repricing, leaving diffusion, liquidity, and power access as key risks and catalysts.

David George × Gavin Baker

AI demand accelerated through July and August, with usage concentrated among possibly sub-10 million heavy users and no leader identifying a worsening quantitative metric.Nebius suggests nine-to-ten-month paybacks on $50B-per-gigawatt builds, supported by 50-60% customer prepayments; undersupply through ’28 could lift token prices, while shifting capacity to training could cut lab revenue from $480B to $120B.

Erik Torenberg × Benedict Evans

Agentic coding has crossed unmistakable product-market fit, with customers pulling products from vendors’ hands.Foundation models appear headed toward commodity economics absent durable differentiation, while today’s token scarcity meets $1 trillion–$2 trillion of capex and “100x, 200x” efficiency gains.Cheaper development should create more software, but the unresolved question is which SaaS incumbents survive and whether models capture infrastructure-like returns.

David George × David Clark

OpenAI and Anthropic now add more monthly revenue than Meta, Google, or Microsoft despite AI diffusion below 5%, making “in the token path” George’s rule for application winners.Five frontier labs could lower token prices, but Chinese models reportedly offer 80% of frontier capability at 10% of the cost; scarce capacity lasts through early 2029, with algorithmic breakthroughs the key reversal risk.

David Haber × Lloyd Blankfein

Finance is adopting AI aggressively, but near-zero error tolerance requires proven and experimental systems to run in parallel—sometimes 50 runs with perfection on the last 49—raising costs before efficiency gains.Opaque software executing 70,000 transactions could scale hidden errors and leverage, making testable reliability, regulation, and public legitimacy key adoption constraints for OpenAI, Anthropic, and other systemically important AI companies.

David Haber × Owen Jennings

Block says Opus 4.6 and Codex 5.3 broke the link between headcount and output, prompting a workforce reduction of slightly more than 40% concentrated in development rather than compliance or sales.Small squads now supervise abundant machine labor, with meetings down 70-80% and Money Bot reduced from roughly 15 people to four plus $2,000 of tokens, while distribution, regulation, network effects, hardware, and proprietary economic data define the emerging moat.