3 VCs and 0 funds investing in AI Applications
Managing Director
General Catalyst
Check: No personal equity check-size range is published. Customer Value financings are bespoke, performance-linked capital structures and disclosed company packages should not be generalized into a personal minimum or maximum.
Pranav invests through a capital-allocation lens: separate the predictable customer-acquisition engine of a product-market-fit company from its higher-risk product and operating company, then finance each with capital matched to its risk and return. He favors companies with clean integrated data, durable cohort economics, strong lifetime value, scalable go-to-market systems, and founders thoughtful enough to optimize long-term enterprise value rather than headline EBITDA.
Partner, Head of Investment Team
Craft Ventures
Check: No personal check-size range is published. Craft invests from early-stage venture through growth, and Michael's disclosed financings span several stages; those total round sizes are not personal or standard check sizes.
Michael invests across venture and growth with a focus on cybersecurity, infrastructure, and AI applications. His operating framework prizes durable revenue, efficient growth, healthy unit economics, precise ideal-customer selection, strong retention, and repeatable go-to-market systems. His recent portfolio work concentrates on autonomous security, machine and agent identity, developer platforms, data infrastructure, and applied AI with clear production usage.
Partner
Khosla Ventures
Check: 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.