Summary
Progress in AI is often thought to get faster with more researchers and papers, but this study shows that focusing too much attention on big breakthroughs can actually deplete the variety of ideas needed for new discoveries. The authors analyzed decades of computer science research and found that after major advances like Transformers, more people concentrate on AI, especially mid-career researchers, but reviewers become less confident about new work. Each strong breakthrough raises the field's capacity for ideas but also exhausts them faster, making it harder for the next discovery to happen. Similar patterns were found in biology, suggesting this is a broader phenomenon about how attention affects innovation.
What this means in practice
- •For ai research managers: Design research funding and hiring strategies that protect diverse topic attention to prevent exhaustion after major AI breakthroughs.
- •For academic journal editors: Adjust peer review policies to avoid reinforcing attention concentration and support wider acceptance of diverse AI research topics.
Authors
Beichen Lu, Honglin Bao, Qi Huang, Qingguo Meng, James A. Evans
Abstract
Progress in artificial intelligence is widely assumed to scale with effort: more researchers, papers and investment should accelerate discovery. Theories of technological growth instead locate the difficulty of discovery in the space of ideas. Here we show that neither scale nor space is enough: breakthroughs depend on diverse attention, which collective concentration can deplete. Across 36 years of computer science, we analyze 7.2 million papers, 73,089 peer reviews and 3.7 million researchers and their topic shifts. The field expanded while its attention narrowed sharply after the Transformer. Over half of topic-shifting researchers moved into AI, especially mid-career ones, and reviewer confidence fell by a full point on a 5-point scale. Each successive breakthrough, from AlexNet to large language models, raised the field's carrying capacity for ideas but was followed by steeper exhaustion of those ideas. Bigger breakthroughs capture attention more completely, depleting the diversity their successors may draw upon. Structural biology shows the same cycle after cryo-EM and AlphaFold. Simulations show how well-intentioned and widely used policies such as reciprocal reviewing empower prolific authors and further narrow what the field accepts. We call for institutions that protect diverse attention in artificial intelligence rather than reward concentration on the winning paradigm.