General Partner at Andreessen Horowitz (a16z)
Check size: $1M-$100M+ (leads a16z's $1.25B+ infrastructure fund)
Leads a16z's infrastructure practice and is one of Silicon Valley's most technically credible VCs, having built VMware NSX and pioneered software-defined networking. Invests 'from markets in' rather than 'from companies out' — he maps massive market opportunities first, then finds founders who can serve them. Believes AI is analogous to 1996 of the internet boom with years of growth still ahead. Sees the AI coding market alone as a potential $3 trillion opportunity. Vocal advocate for open source in AI — co-authored a piece in The Economist with Ion Stoica arguing open source is critical to AI's future. Deeply concerned about Chinese dominance in open-source AI models (claims ~80% of his portfolio companies use Chinese open-source models). Thinks AI value will ultimately accrue at the application layer more than the model layer, drawing parallels to how SaaS-era startups captured more value at the application layer than infrastructure. Believes AI is fundamentally changing SaaS pricing from per-seat to outcome-based models. Champions spatial intelligence as AI's next frontier (deeply involved with World Labs / Fei-Fei Li).
Lead with the technical architecture — he is a builder who respects deep technical credibility. Show him the system design, not the pitch deck. If you are building infrastructure, explain why developers will love it and show community adoption metrics. He thinks in terms of developer adoption curves and ecosystem effects. Frame your opportunity in terms of market size first ('markets in'), then show why your team can capture it. If you are in AI, address defensibility explicitly — he has written extensively about value accrual in the AI stack and is skeptical of pure API wrappers. Show cost reduction potential in orders of magnitude, not percentages. If open source, articulate the monetization path clearly. He personally codes with Cursor nightly, so he can evaluate developer tools at a practitioner level — do not fake technical depth.
Technical founders building infrastructure that other companies build on top of. Open source projects with strong community adoption and clear monetization paths. AI companies with genuine technical moats, not just API wrappers. Developer tools that fundamentally change workflows. Companies where AI creates 4-5 orders of magnitude cost reduction over incumbent approaches. Spatial intelligence and world models. Software-defined approaches to traditionally hardware problems (inspired by his SDN background).
Companies where the 'AI' is just calling someone else's API. Infrastructure without developer love. Closed-source tools competing against strong open source alternatives without clear differentiation. Consensus deals where every fund is piling in without independent technical conviction. Companies without a clear path from model capability to defensible product.
Companies should evaluate cloud vs. on-prem based on workload maturity and cost trajectory.
“This feels like 1996.”
— The Generalist podcast (October 2025)
“If you subtract out the dollars invested, it's the fastest-growing company we've ever seen.”
— Fortune (March 2026)
“There's nothing in the Cursor numbers that would suggest there's anything but total success right now.”
— Fortune (March 2026)
DAOs challenge Coase's theory by reducing coordination costs below what any firm can achieve.
Cloud infrastructure spending is the next largest cost center after headcount for most tech companies, and repatriation is a real option.
Most AI companies that claim data moats actually have defensibility problems.
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