Partner at Khosla Ventures
Check size: Not publicly disclosed. Khosla Ventures uses a Seed Fund for experiments and a Main Fund for early-to-later rounds, including financings above $10M in total round size; round size is not the firm's or Jon's check size.
Jon focuses on how machine learning reshapes the full enterprise stack: core infrastructure, developer tools, and end-user applications. His experience as an early Palantir engineer, infrastructure founder, Docker enterprise leader, growth product executive, core-ML engineering head, and Meta VR/ML leader creates a technical-operator lens on platform adoption and scale. He pairs that with Khosla's preference for proprietary advantages, large markets, fast risk retirement, and category creation.
Explain where in the ML stack the company sits, why that layer captures durable value, and what proprietary technology, data, workflow integration, or distribution prevents commoditization. Bring technical depth, production evidence, unit economics, security and reliability tradeoffs, and a fast plan to retire the key risk. Show how the product becomes a category-defining platform rather than a thin feature or undifferentiated model wrapper.
Technically exceptional founders building differentiated ML infrastructure, developer tools, or applications that can redefine a large market. Khosla's shared lens emphasizes an unfair advantage, short learning cycles, explicit technical risk, strong economic benefit, and the ambition to build a category rather than copy an incumbent.
No personal pass list is public. Khosla explicitly rejects copycat plans, niche markets with limited upside, ordinary small businesses without technical or business-model innovation, growth/project-finance use cases, and teams aiming primarily for a quick acquisition.
Locate the product across model, infrastructure, developer-tool, workflow, and application layers; identify the proprietary technology, data, integration, or distribution that keeps value from collapsing into an adjacent layer.
A clearly labeled firm framework: identify the key technical and market risks, choose the least expensive experiments that can invalidate them, and use seed capital to reach decisive proof rather than manufacture long-range forecasts.
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