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I got fooled by AI-for-science hype—here's what it taught me

understandingai.org · 3,543 words · saved by 1 readers

I’m excited to publish this guest post by Nick McGreivy, a physicist who last year earned a PhD from Princeton. Nick used to be optimistic that AI could accelerate physics research. But when he tried to apply AI techniques to real physics problems the results were disappointing. I’ve written before about the Princeton School of AI Safety, which holds that the impact of AI is likely to be similar to that of past general-purpose technologies such as electricity, integrated circuits, and the Internet. I think of this piece from Nick as being in that same intellectual tradition. —Timothy B. Lee In 2018, as a second-year PhD student at Princeton studying plasma physics, I decided to switch my research focus to machine learning. I didn’t yet have a specific research project in mind, but I thought I could make a bigger impact by using AI to accelerate physics research. (I was also, quite frankly, motivated by the high salaries in AI.) I eventually chose to study what AI pioneer Yann LeCun lat

I got fooled by AI-for-science hype—here's what it taught me I used AI in my plasma physics research and it didn’t go the way I expected. Nick McGreivy May 19, 2025 405 67 80 Share I’m excited to publish this guest post by Nick McGreivy , a physicist who last year earned a PhD from Princeton. Nick used to be optimistic that AI could accelerate physics research. But when he tried to apply AI techniques to real physics problems the results were disappointing. I’ve written before about the Princeton School of AI Safety, which holds that the impact of AI is likely to be similar to that of past gen

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