Kleiner Perkins

Kleiner Perkins is one of the oldest and most consequential venture firms in Silicon Valley, operating out of Menlo Park since 1972 and shaped over the decades by the people who funded Genentech, Amazon, Google, and a long line of category-defining companies. The current iteration is leaner than its dot-com peak and visibly focused on early-stage technology, with a partner group that has rebuilt the firm around a small set of conviction bets per year rather than a sprawling portfolio. Healthcare is one of the firm’s recurring themes, and AI in healthcare is now a clear priority on the back of OpenEvidence and similar wagers in the clinical software stack.

The healthcare thesis blends two long-running positions: AI and machine learning applied to clinical workflows, and the broader category of healthcare software platforms that can become enduring infrastructure. The OpenEvidence investment, an AI-driven clinical knowledge platform now used by a sizable share of US physicians, captures the firm’s preference for software that earns daily use from clinicians and compounds proprietary distribution over time. Adjacent areas of interest include AI for medical documentation, decision support, payor and provider operations, and tools that change how research evidence reaches the point of care.

Kleiner Perkins typically invests at Seed, Series A, and Series B, with checks ranging from roughly five to twenty-five million dollars and the ability to follow on heavily into winners. Geographic focus is North America, with a strong Bay Area concentration and selective bets elsewhere. The firm leads or co-leads more often than not and is comfortable taking board seats and concentrated positions.

The partner group is small by design. Mamoon Hamid and Ilya Fushman lead much of the consumer and enterprise software practice, Mood Rowghani and Annie Case work on the digital health and consumer health sides, Bucky Moore covers infrastructure and increasingly AI, and Wen Hsieh leads the hard tech and life sciences practice. Founders engaging on a healthcare AI deal should expect to work with whichever partner most closely matches the company’s primary buyer, and to encounter strong opinions about pricing, hiring, and go-to-market sequencing.

The approach is hands-on in a way that has not really gone out of style at the firm. Portfolio companies get help with executive recruiting through a dedicated talent team, introductions across the broader Kleiner Perkins network of operators and customers, and partner-level engagement on board and strategy questions. Founders should be prepared for direct feedback and an expectation that the company moves quickly on hiring and product. The firm is a strong fit for ambitious technical founders building horizontal AI products with healthcare as an early or primary market, and a tougher fit for narrowly clinical companies that depend on slow regulatory cycles, where the firm’s pacing and ownership expectations can feel mismatched. For teams that match the model, the brand, network, and capital depth still translate into real downstream advantages.