Daniel Nadler, CEO and cofounder of OpenEvidence, an AI search tool for doctors, doubled his net worth to $7.6 billion after his startup raised additional capital at a $12 billion valuation.
Mauricio Candela for Forbes
Three years ago, Daniel Nadler launched OpenEvidence, an AI-based search tool that helps doctors get answers to complicated clinical questions quickly and easily. Now, the startup’s latest $250 million fundraise, which doubled its valuation to $12 billion, has made Nadler twice as rich. Forbes estimates that the Miami-based AI founder is now worth $7.6 billion, up more than 100% from his $3.6 billion net worth in late October.
OpenEvidence has quickly become one of the hottest AI startups in the healthcare sector. Today some 740,000 physicians — about 45% of doctors in the United States — use the search engine to scan through millions of peer-reviewed research publications across top medical journals and find useful (sometimes lifesaving) information in seconds instead of hours or days. Last month, physicians turned to the software in some 18 million clinical consultations, Nadler said. “We’ve become the default operating system for doctors,” he told Forbes.
The startup crossed $100 million in annualized run rate in 2025, making money from advertising even though most of the ad inventory hasn’t been unlocked yet, Nadler claims. The startup could make about $1 billion in revenue if it sold all of it, he said, but Nadler isn’t planning to do so in order to prioritize the user experience— much like Google did in its early days. Pharmaceutical and medical device companies can buy 5-second video ads on the app and ads are delivered based on the kind of search query entered by the physician.
Thanks to a head start and rapid adoption from medical professionals across the country, Nadler isn’t too worried about competitive products like OpenAI’s ChatGPT catching up. The popular chatbot doesn’t have a specific tool for doctors, which hasn’t stopped them from using it anyway. OpenAI recently launched a dedicated product for consumers to ask ChatGPT health and wellness related questions to ChatGPT.
Thrive Capital, the famous backer of other fast growing AI stars like Cursor and OpenAI, and DST Global led the Series D round, which brings the startup’s total funding to $700 million. OpenEvidence still has hundreds of millions of dollars in the bank, Nadler said, and the capital injection was less of a need and more to capitalize on the excitement in venture capital to back proven players in AI. “You really have the same dozen funds chasing the same half dozen companies,” he said. “It’s doubling down on the same company.”
In 2018, Nadler, a Harvard alum who grew up in Toronto, sold his first startup, an AI-powered data analytics firm called Kensho Technologies, to S&P for $700 million. Thanks to his 20% stake, he pocketed $140 million and used some of the cash to buy up $30 million in Nvidia stock in 2019. He sold them for about $100 million in 2024. “Good trade,” he said. “Sold too early to be honest.” He also poured cash into his second venture, OpenEvidence, investing $10 million of his own money into the startup. It was a wise bet on himself. Today, he still owns about 58% of OpenEvidence.
Nadler first became a billionaire in July 2025, when OpenEvidence reached a $3.5 billion valuation. That’s also when he bought a $38 million beachfront penthouse in Miami, where he spends most of his time. Since then his net worth has grown alongside the company’s valuation, reaching $3.6 billion in October before climbing after the latest fundraise. His cofounder Zachary Ziegler owns a 7.3% stake in the company that’s worth $875 million.
OpenEvidence continues to train and improve search models that can accurately spot information that’s relevant to a medical query. Nadler plans to use the funds to build an “orchestra” of smaller models that specialize in answers related to a specific domain within the broader field of medicine. The idea is to train a series of models that are medical specialists in areas like oncology, radiology and neurology. Each specialist model is trained on data collected from real world scenarios like clinical consultations to capture how doctors think through complex cases. Each time a doctor queries the search engine, a central model will analyze it and internally route the question to the appropriate expert model for the best possible answer. “If you think about it, it works exactly like a human hospital,” he said.
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