Venture 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 Hari's check size.
Hari invests where technology can advance intelligence, security, infrastructure, and health. His founder/operator experience gives him a practical lens on regulated healthcare distribution, enterprise partnerships, product, and scaling. Within Khosla's broader approach, this means early, bold experiments aimed at large technology-disruptable markets, with a proprietary advantage and a low-cost plan to retire the most important technical and commercial risks.
State the large problem, why a technology or business-model discontinuity makes it solvable now, and the proprietary advantage. Identify the highest technical, regulatory, distribution, and adoption risks; define the cheapest milestones that retire them; and show why the team can build an enduring category leader. For health or enterprise AI, be exact about workflow integration, buyer, measurable outcomes, and implementation—not just model capability.
Entrepreneurs applying differentiated technology to consequential problems in AI, security, infrastructure, or health. His operator history and Khosla's published criteria point toward strong founder commitment, a genuine unfair advantage, a very large market, and a concrete plan to eliminate the riskiest assumption cheaply and quickly.
No personal pass list is public. Khosla explicitly rejects growth capital, project finance, real estate and ordinary small businesses without differentiated technology; niche markets with limited upside; copycat plans; public-company equity raises; and founders optimizing for a quick sale rather than a category-defining company.
Map the venture's core advance to greater intelligence, stronger cyber or infrastructure security, or improved health, then test whether the technical edge produces a meaningful and measurable real-world outcome.
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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