To be clear, AI can drive scientific breakthroughs. My concern is about their magnitude and frequency. Has AI really shown enough potential to justify such a massive shift in talent, training, time, and money away from existing research directions and towards a single paradigm?
Every field of science is experiencing AI differently, so we should be cautious about making generalizations. I’m convinced, however, that some of the lessons from my experience are broadly applicable across science:
- AI adoption is exploding among scientists less because it benefits science and more because it benefits the scientists themselves.
- Because AI researchers almost never publish negative results, AI-for-science is experiencing survivorship bias.
- The positive results that get published tend to be overly optimistic about AI’s potential.
As a result, I’ve come to believe that AI has generally been less successful and revolutionary in science than it appears to be.
Ultimately, I don’t know whether AI will reverse the decades-long trend of declining scientific productivity and stagnating (or even decelerating) rates of scientific progress. I don’t think anyone does. But barring major (and in my opinion unlikely) breakthroughs in advanced AI, I expect AI to be much more a normal tool of incremental, uneven scientific progress than a revolutionary one. {read}