No Priors Ep. 130 | With OpenEvidence Founder Daniel Nadler

How does a new technology get adopted by 40% of American doctors in just 18 months? In an era where the golden age of biotechnology has also created a dark age of physician burnout, OpenEvidence found the answer by fundamentally changing how doctors access critical information. OpenEvidence founder Daniel Nadler sits down with Sarah Guo and Elad Gil to discuss how his company solved the semantic search problem in medicine. He talks about the strategy of treating doctors as consumers, striking the balance of keeping patients in the loop in medical conversations, and how technology will reshape both medicine and medical education. Plus, Daniel gives his thoughts on the roots of motivation, as well as his philosophy for recruitment. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @EvidenceOpen Chapters: 00:00 – Daniel Nadler Introduction 00:08 – OpenEvidence’s Success 01:54 – How OpenEvidence Works 06:35 – Dealing with Ambiguity 11:37 – Treating Knowledge Workers as Consumers 15:53 – Balancing Keeping Patients in the Loop 19:28 – How Technology May Shape the Future of Medicine 22:12 – How Technology Will Change Medical Education 30:40 – Examining Consumer Adoption of Preventative Health Measures 36:02 – Lessons for Other Fields 37:27 – Rationalism vs. Will 41:13 – Daniel’s Thoughts on Motivation 42:44 – Daniel’s Recruiting Philosophy 44:48 – Conclusion

Sarah GuohostDaniel NadlerguestElad Gilhost

Sep 5, 202544mWatch on YouTube ↗

EPISODE INFO

Released: September 5, 2025
Duration: 44m
Channel: No Priors
Watch on YouTube: ▶ Open ↗

EPISODE DESCRIPTION

How does a new technology get adopted by 40% of American doctors in just 18 months? In an era where the golden age of biotechnology has also created a dark age of physician burnout, OpenEvidence found the answer by fundamentally changing how doctors access critical information. OpenEvidence founder Daniel Nadler sits down with Sarah Guo and Elad Gil to discuss how his company solved the semantic search problem in medicine. He talks about the strategy of treating doctors as consumers, striking the balance of keeping patients in the loop in medical conversations, and how technology will reshape both medicine and medical education. Plus, Daniel gives his thoughts on the roots of motivation, as well as his philosophy for recruitment. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @EvidenceOpen Chapters: 00:00 – Daniel Nadler Introduction 00:08 – OpenEvidence’s Success 01:54 – How OpenEvidence Works 06:35 – Dealing with Ambiguity 11:37 – Treating Knowledge Workers as Consumers 15:53 – Balancing Keeping Patients in the Loop 19:28 – How Technology May Shape the Future of Medicine 22:12 – How Technology Will Change Medical Education 30:40 – Examining Consumer Adoption of Preventative Health Measures 36:02 – Lessons for Other Fields 37:27 – Rationalism vs. Will 41:13 – Daniel’s Thoughts on Motivation 42:44 – Daniel’s Recruiting Philosophy 44:48 – Conclusion

SPEAKERS

Sarah Guo
host
Daniel Nadler
guest
Elad Gil
host
Narrator
other

EPISODE SUMMARY

In this episode of No Priors, featuring Sarah Guo and Daniel Nadler, No Priors Ep. 130 | With OpenEvidence Founder Daniel Nadler explores aI-Powered OpenEvidence Becomes Doctors’ Operating System For Clinical Decisions Daniel Nadler explains how OpenEvidence rapidly became the dominant clinical decision support tool for U.S. physicians by treating doctors as consumer internet users and focusing narrowly on high‑stakes medical decisions. The product semantically interprets complex patient scenarios and routes doctors to precise snippets in top-tier medical literature rather than providing opaque “answers.” Nadler discusses the explosion of biomedical knowledge, why medical education must invert toward continuous learning, and how AI can act as a “curbside consult” to extend specialist-level care into under-resourced areas. He also reflects on patient access to information, cultural determinants of health, and the psychology and motivation behind building impact-driven AI products for knowledge workers.

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