**AI is giving scientists more ideas than they can test**

> A new report from Google, Google DeepMind and M.I.T. finds that researchers are using AI—a lot. It’s just not helping as much in the lab September 18, 2026 3 min read Add Us On Google Add SciAm AI is giving scientists more ideas than they can test A new report from Google, Google DeepMind and M.I.T. finds that researchers are using AI—a lot. It’s just not helping as much in the lab By Peter Hall edited by Eric Sullivan Artificial intelligence has proven more useful to some scientific fields than others. Marilyn Sargent © 2023 The Regents of the University of California, Lawrence Berkeley National Laboratory 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.  On supporting science journalism If you&\#x27;re enjoying this article, consider supporting our award-winning journalism by subscribing . By purchasing a subscription you are helping to ensure the future of impactful stories about the discoveries and ideas shaping our world today. Amanda Montañez; Source: “AI in Science: Early Insights,” by Mihai Codreanu et al. Released online September 2026 ( data ) “Eight-nine percent of those who save time using AI spend more than a 10th of the time that they’re saving actually checking AI outputs,” says Mihai Codreanu , a research economist at Google and co-lead author of the report. “Forty-six percent spend more than a quarter.” Codreanu says the time spent checking whether responses and predictions are correct affects which types of work AI can do—and how useful it can be in different fields. “There is a lot of heterogeneity,” he says. A mathematical proof can be checked against formal rules, for instance, but a predicted protein function has to be built in a lab and tested in a living system. The people building and studying these systems point to different causes for th...

**来源信息**
- **来源**：Hacker News：AI 热帖
- **分类**：行业
- **发布时间**：2026-09-20 18:43（北京时间）
- **原文**：[打开原文](https://www.scientificamerican.com/article/why-ai-is-speeding-up-scientific-research-but-not-lab-experiments)