Scientists who adopt AI gain productivity and visibility: On average, they publish three times as many papers, receive nearly five times as many citations, and become team leaders a year or two earlier than those who do not. But when those papers are mapped in a high-dimensional “knowledge space,” AI-heavy research occupies a smaller intellectual footprint, clusters more tightly around popular, data-rich problems, and generates weaker networks of follow-on engagement between studies. The pattern held across decades of AI development, spanning early machine learning, the rise of deep learning, and the current wave of generative AI. “If anything,” Evans notes, “it’s intensifying.” […] Aside from recent publishing distortions, Evans’s analysis suggests that AI is largely automating the most tractable parts of science rather than expanding its frontiers.