Partner at Sequoia Capital
Check size: $500K-$50M+ (stage dependent)
The defining voice of Sequoia's AI thesis and arguably the most influential AI investor-thinker in Silicon Valley. Co-authors a series of landmark essays with Pat Grady that have become the canonical frameworks for understanding generative AI's evolution. Core conviction: the application layer — not foundation models — is where enduring value accrues. Foundation models are commoditizing rapidly (only five scaled players remain: Microsoft/OpenAI, Amazon/Anthropic, Google, Meta, xAI), so the real opportunity is in vertical AI applications that solve end-to-end human problems. Believes we have entered the 'Age of Abundance' where AI makes once-scarce labor available everywhere at near-zero cost, transforming the addressable market from software ($1T) to services ($10T+). In this world, 'taste' — the human judgment to decide what to build and how — becomes the scarcest resource. Her January 2026 essay declares AGI is functionally here in the form of long-horizon agents that can sustain multi-step work, correct errors, and persist toward goals autonomously. Sequoia has deployed roughly $150M into foundation models but over $1.5B into application-layer companies, reflecting a 10:1 bet on applications over infrastructure.
Frame your company within her published frameworks — reference the specific Act you believe your company exemplifies. Show you are building from the customer-back (Act Two), not technology-out (Act One). Demonstrate why your product is AGI-native, not just ML-enhanced. If you are building a vertical agent, explain your end-to-end workflow, your data moat, and your RL/synthetic data training approach. Show retention metrics and daily engagement, not just signups. She is deeply analytical (Princeton economics, Goldman, TPG) — come with data, a clear thesis about 'why now,' and a view on how you capture value as foundation models commoditize. Reference the Age of Abundance framing: show how your company makes previously expensive services accessible at near-zero marginal cost. Do not AI-wash your deck — she and her co-investors see through it immediately. Small team leveraging AI tools effectively is a positive signal.
Founders building AGI-native companies where AI agents function as autonomous colleagues, not assistants. Products that solve problems end-to-end from the customer back (not technology-out demos). Companies riding the Age of Abundance — making previously expensive professional services (legal, medical, financial) accessible to everyone at near-zero marginal cost. Small, agile teams that use AI to compete at the scale of legacy enterprises. Vertical AI with deep domain expertise and proprietary data moats (company-specific knowledge graphs, embedded workflows).
AI wrappers without technical depth or defensibility. Companies where AI is a feature bolted on, not the core architecture. Products competing purely on model quality without application-layer differentiation. 'AI-washed' pitch decks — adding an AI slide without genuine AI-native architecture. Lightweight novelty apps that demonstrate cool technology but lack retention and daily-active-user engagement. Companies that cannot articulate why they win in a world where foundation models are commoditized.
“We're entering the Age of Abundance — where AI makes once-scarce labor available everywhere at near-zero cost.”
— AI Ascent 2025, May 2025
“The application layer is where value finally comes together.”
— AI Ascent 2025, May 2025
“Coding has reached screaming product-market fit.”
— AI Ascent 2025, referencing Cursor's trajectory from zero to $500M ARR in under 18 months
Act 1 came from the technology-out — foundation models as a new hammer generating a wave of novelty apps. Act 2 must come from the customer-back, solving real human problems end-to-end. The biggest challenge is not finding use cases but proving lasting value — retention, not novelty. Invoked Amara's Law: we overestimate technology in the short run and underestimate it in the long run. As foundation models commoditize, the real value shifts to the application layer (product, UX, workflows). Published alongside the V3 generative AI market map.
Introduced the generative AI market map and thesis. Argued that a new class of AI had emerged that creates rather than merely analyzes — shifting the marginal cost of creation toward zero. Every industry requiring original human work (coding, design, law, marketing, gaming) is up for reinvention. Outlined four waves of development from transformer breakthroughs to killer app emergence. Became the foundational reference document for the entire generative AI ecosystem.
AI is progressing from 'thinking fast' (rapid pre-trained pattern matching, System 1) to 'thinking slow' (deliberate reasoning at inference time, System 2). The reasoning layer — inspired by AlphaGo-style approaches — endows AI with problem-solving that goes beyond pattern recognition. This enables 'service-as-a-software,' where the addressable market expands from the $1T software market to the $10T+ services market. Foundation layer has stabilized to five scaled players. Reasoning models are strong on logic-proximate domains (coding, math, science) but still developing on open-ended domains (writing, strategy).
Identified a massive gap between the revenue expectations implied by the AI infrastructure build-out (projected from NVIDIA's data center revenue run rate) and actual revenue growth in the AI ecosystem. The AI industry needs to generate $600 billion annually in revenue to justify current hardware spending levels — raising the question of whether this is sustainable or a bubble.
Declares that AGI is functionally here. Long-horizon agents — AI systems that can sustain multi-step work, correct their own errors, and persist toward goals autonomously — are the realization of AGI. Coding agents are the first proof point. One litmus test: can you hire an agent? In 2023-2024 AI apps were chatbots; in 2026-2027 they will be doers that feel like colleagues. Usage shifts from a few queries per day to all-day, every-day autonomous work. Human roles shift from executor to manager of AI teams.
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