Partner
Khosla VenturesCheck size: No personal check range is published. Khosla finances early technical experiments through its Seed Fund and larger early-to-late opportunities through its Main Fund; historical firm commentary described many seed investments around $1M–$2M. These figures are not Rajesh's personal authority.
Rajesh backs bold founders reinventing how the world powers, builds, and heals itself. His two decades in corporate venture, startups, and network systems inform an industrial-scale lens across renewables, decarbonization, advanced manufacturing, critical minerals, compute infrastructure, and the convergence of those fields with AI. The core question is whether a technical advance can survive scale-up and reshape the economics of a foundational system.
Bold technical founders taking on essential physical systems, large global markets, differentiated science or engineering, and a credible scale path. His global portfolio experience suggests particular sensitivity to manufacturing ecosystems, strategic partners, capital intensity, deployment geography, and how AI can improve industrial or compute systems rather than decorate them.
Khosla's published criteria imply weak fit for incremental green claims, projects with no proprietary advantage, technologies that cannot reach commercial scale or cost parity, small markets, capital plans without staged risk removal, and teams that omit supply chain, manufacturing, infrastructure, or deployment realities.
Quantify the incumbent system and the improvement: energy, materials, throughput, yield, cost, carbon, reliability, or health outcome. Explain scientific proof, scale-up path, manufacturing and supply chain, critical inputs, partners, permitting or certification, capital needs, and adoption economics. Break financing into explicit risk-removal milestones and show why the end market is enormous enough to justify physical-world complexity.
Classify the core system being changed, quantify its global economic and resource burden, and show the technical mechanism by which the startup delivers a step-function improvement.
Stage proof from lab result to pilot, yield and reliability, supply chain, first commercial deployment, repeatable plant or manufacturing design, and financeable global rollout.
Identify scarce minerals, equipment, energy, compute, suppliers, permitting, and strategic partners; model how each constraint behaves at 10x and 100x deployment before underwriting scale.
Require AI to improve a measurable physical outcome—yield, uptime, energy, speed, quality, autonomy, or discovery—and verify the data and control loop survives real operating conditions.
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Rajesh previously led Applied Ventures, Applied Materials' venture arm, managing 85 startups and two deep-tech funds in Korea and Taiwan. Earlier he worked with startups at Third Point Ventures and Bell Labs, where he helped develop and deploy next-generation network systems globally. He holds an HBS MBA, completed University of Maryland graduate studies, and earned an IIT Madras engineering degree with the President of India Medal for overall excellence.