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.

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.

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 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.

Alex Rampell × Erik Torenberg × Mike Cannon-Brookes

AI is repricing SaaS before proving universal impairment, with Zendesk-like seat models exposed to agent substitution while Workday’s employee-based pricing and Adobe’s middle position may be underappreciated.Atlassian’s three great quarters, accumulated process knowledge, Teamwork Graph and extensibility strategy could make core systems stickier, but value depends on fair pricing and product design that earns trust as agents enter workflows.

Jen Kha × David George

AI-native companies grow more than 2.5x faster, with top performers reaching 693% year-over-year growth and $500,000-$1 million of ARR per employee.Engagement and operating evidence includes Navan handling 50% of travel interactions with AI and expanding gross margins 20 percentage points, while enterprise change management remains the key execution risk.

David Haber × David Solomon × Ben Horowitz

David Solomon sees one of the strongest macro setups in his “40-odd years” in markets, combining fiscal expansion, rate cuts, deregulation and an unprecedented capital-investment supercycle.Confidence has shifted M&A from “no” to “maybe,” potentially producing the biggest M&A year in history, while Goldman’s $1.9 trillion balance sheet and $500 billion deposit base frame the scale-and-funding challenge; FTC uncertainty and geopolitical risk remain key variables.

Jen Kha × David George

AI infrastructure is becoming a utility layer: big-tech capex annualizes near $400 billion, model-access costs fell more than 99% in two years, and ChatGPT reached 365 billion searches in two years.Energy is likely the next five-year bottleneck, followed by cooling, while application durability depends on 90% or more retention, easy acquisition, and workflow depth rather than model access alone.