Daniel Nadler, CEO and co-founder of OpenEvidence, an AI search tool for doctors, doubled his net price to $7.6 billion after his startup raised $12 billion in additional capital.
Mauricio Candela for Forbes
TThree years ago, Daniel Nadler has launched OpenEvidence, an AI-based search tool that helps physicians quickly and simply get answers to complicated clinical questions. Now the startup’s latest $250 million fundraising has doubled its value to $12 billion and made Nadler twice as wealthy. Forbes estimates the Miami-based AI founder now has a net price of $7.6 billion, up greater than 100% from his net price of $3.6 billion at the tip of October.
OpenEvidence has quickly turn out to be considered one of the most popular AI startups within the healthcare sector. Today, roughly 740,000 physicians – roughly 45% of physicians within the United States – use the search engine to go looking hundreds of thousands of peer-reviewed research publications in leading medical journals and find useful (sometimes life-saving) information in seconds fairly than hours or days. Last month, doctors turned to the software for about 18 million clinical consultations, Nadler said. “We have become the standard operating system for physicians,” he said Forbes.
According to Nadler, the startup has exceeded an annual sales rate of $100 million in 2025 and has been earning money from promoting, though the vast majority of the promoting inventory has not yet been activated. The startup could make about $1 billion in revenue if it sold every thing, he said, but Nadler doesn’t plan to accomplish that in favor of prioritizing user experience — as Google did in its early days. Pharmaceutical and medical device corporations should buy 5-second video ads within the app and the ads shall be shown based on the sort of search query entered by the doctor.
Thanks to its head start and rapid adoption by medical professionals across the country, Nadler is not too nervous about competing products like OpenAI’s ChatGPT catching up. The popular chatbot doesn’t have a dedicated tool for doctors, which hasn’t stopped them from using it anyway. OpenAI recently launched a dedicated product that enables consumers to ask ChatGPT health and wellness inquiries to ChatGPT.
Thrive Capital, the famed backer of other fast-growing AI stars like Cursor and OpenAI, and DST Global led the Series D round, bringing the startup’s total funding to $700 million. OpenEvidence still has lots of of hundreds of thousands of dollars within the bank, Nadler said, and the capital infusion was less crucial than to capitalize on enterprise capital enthusiasm to back proven players in AI. “There really are the same dozen funds tracking the same half-dozen companies,” he said. “It’s a duplication of the same company.”
In 2018, Nadler, a Toronto-raised Harvard graduate, sold his first startup, an AI-powered data analytics company called Kensho Technologies, to S&P for $700 million. Thanks to his 20 percent stake, he pocketed $140 million and used a few of the money to purchase up $30 million price of Nvidia shares in 2019. In 2024, he sold it for about $100 million. “Good trade,” he said. “Sold too early to be honest.” He also invested money in his second company, OpenEvidence, investing $10 million of his own money within 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 valuation of $3.5 billion. That’s also when he bought a 38 million dollars Beachfront penthouse in Miami, where he spends most of his time. Since then, his net price has grown according to the corporate’s valuation, reaching $3.6 billion in October before rising again after its latest fundraising. His co-founder Zachary Ziegler owns a 7.3% stake in the corporate, price $875 million.
OpenEvidence continues to coach and improve search models that may accurately discover information relevant to a medical query. Nadler plans to make use of the funds to create an “orchestra” of smaller models that focus on providing answers to a particular area within the broader field of medication. The idea is to coach a series of models who’re medical specialists in areas reminiscent of oncology, radiology and neurology. Each specialist model is trained using data from real-world scenarios reminiscent of clinical consultations to capture how doctors think through complex cases. Every time a physician queries the search engine, a central model analyzes it and internally forwards the query to the suitable expert model to get the very best possible answer. “If you think about it, it functions just like a human hospital,” he said.
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