Stronger artificial intelligence breakthroughs hasten their own exhaustion

The paper argues that progress in AI is not simply a function of scale or the breadth of ideas, but depends on diverse attention, which can be depleted by collective focus on a single paradigm. Analyzing 36 years of computer‑science literature, the authors find that after the Transformer, the field’s attention narrowed sharply, with many researchers shifting into AI and reviewer confidence dropping. Each major breakthrough— from AlexNet to large language models—raises the field’s capacity for ideas but also accelerates the exhaustion of those ideas, a cycle mirrored in structural biology after cryo‑EM and AlphaFold. The study warns that policies like reciprocal reviewing may unintentionally concentrate attention on prolific authors, urging institutions to safeguard diverse attention rather than reward concentration on a single winning paradigm.