When Daniel Nadler and Zachary Ziegler launched OpenEvidence three years ago, the platform's trajectory toward becoming the fastest-growing AI tool in American clinical practice wasn't obvious. Today it is the dominant reality: the startup just hit a $12 billion valuation, doubling in a matter of months, and now supports an estimated 18 million clinical consultations a month across more than 740,000 US physicians — roughly 45% of the country's doctors.

Unlike general-purpose AI tools, OpenEvidence was purpose-built for medicine from the outset. The platform sifts through millions of peer-reviewed studies and verified clinical content to surface precise, evidence-based answers to complex clinical questions within seconds. Rather than relying on a single all-purpose model, OpenEvidence routes queries to specialized "expert" AI models focused on specific domains like oncology, radiology, and neurology — mimicking, in software, the structure of a hospital referring a case to the right specialist.

Nadler isn't new to building AI companies at scale. Raised in Toronto and trained at Harvard, he previously founded Kensho Technologies, an AI analytics startup that S&P Global acquired in 2018 for roughly $550 million. With OpenEvidence, he put significant personal capital behind the bet, investing $10 million of his own money and retaining a majority ownership stake of around 58%. That conviction has paid off considerably: recent estimates put Nadler's net worth at approximately $7.6 billion, more than double what it was just months earlier. Co-founder and CTO Zachary Ziegler, whose background is in natural language processing research at Harvard, built much of the technical backbone underpinning the platform.

The business model is notably restrained for a company at this scale. OpenEvidence runs on an ad-supported structure where pharmaceutical and medical device companies buy targeted, brief video ads tied to clinical search queries — but the founders have deliberately avoided monetizing more aggressively, wary of compromising the physician experience that drives adoption in the first place. The approach has still proven commercially viable: the company surpassed $100 million in annualized revenue in 2025. The recent $250 million Series D, led by Thrive Capital and DST Global, brought total funding to approximately $700 million.

What OpenEvidence's rise suggests about the broader AI-in-healthcare moment is more structural than technological. The company's leadership has framed its approach as assembling many specialized, narrow AI systems rather than leaning on one general-purpose model — a design choice that mirrors how genuinely complex medical decision-making actually works. As AI becomes more embedded in clinical settings, OpenEvidence's growth points to a particular lesson: success in this space isn't just about model capability. It's about earning the sustained trust of professionals whose decisions carry real consequences, one accurate answer at a time.