Ask any mathematician where artificial intelligence stands in their field, and you’ll hear it moved into the office down the hall, knocked off a few open problems and is elbowing in on the credit. Ask a biologist or chemist, and you’ll hear it’s still circling the parking lot. People have spent years expecting AI to change how new medicines are discovered, for example. So why hasn’t it?
In recent months, AI companies have turned to generating proofs, including results that have prompted accusations that they scooped academics, to put notches in their models’ belts and showcase scientific capability. Meanwhile the U.S. government has repeatedly stressed, including in a National Science Foundation (NSF) memo last week, that AI will “transform the questions researchers can answer” and the way science is done, highlighting it as a research priority. But those advances haven’t translated into anything like the same acceleration for drug discovery or experimental biological research.
A report out this week from Google, Google DeepMind and the Massachusetts Institute of Technology’s MIT FutureTech quantifies some of the gaps. The researchers set out to measure how AI is changing the economics of science in a variety of fields and, in the process, pointed to places where it still stalls. The study combines a survey of 637 scientists with analyses of 15 million Gemini conversations and an inventory of more than 2,600 specialized models. (The scientists were recruited through specialist panels, so frequent AI users may have been overrepresented.) About 44 percent of those scientists said that, over the past two years, their main research bottleneck had shifted downstream, toward later stages such as physical experimentation and data collection. Forty-one percent said their backlog of untested hypotheses had grown.
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