AI agents, not just data, will accelerate science

The essay pivots away from a seductive narrative: that AI, embodied by AlphaFold's 2024 Nobel Prize, will simply accelerate all of science at digital speed. AlphaFold's achievement was real and rare, but it rested on an exceptional foundation—the Protein Data Bank, built through 53 years of international cooperation and roughly $21 billion in experimental work. The author argues this is not a replicable template. Most of experimental science does not operate under such controlled, standardized conditions. Cell lines drift, chemicals contain trace impurities, lab humidity varies. Assembling datasets consistent enough, accurate enough, precise enough, and scalable enough to train modern neural networks in biology or chemistry would require new measurement approaches that do not yet exist and will not be ready for decades.

Instead, the essay advances AI agents as the transformative tool: systems that combine reasoning with access to multiple tools (digital or physical) and can iterate under uncertainty, just as working scientists do. Unlike AlphaFold, which applies brute force to a narrow, well-defined problem, agents are generalists. They do not invent a new way to do science; they digitally model how humans actually do it—combining docking calculations, running binding assays, weighing the strengths and failures of each method, and revising conclusions as evidence arrives. Google's AI Co-Scientist, announced in May, reached a correct conclusion about antibiotic resistance in hours that took Imperial College researchers a decade of wet-lab work to derive independently.