Stronger artificial intelligence breakthroughs hasten their own exhaustion
Authors: Beichen Lu, Honglin Bao, Qi Huang, Qingguo Meng, James A. Evans
Organizations: School of Public Policy and Management, Tsinghua University, Beijing, China. · Knowledge Lab, University of Chicago, Chicago, IL, USA. · Data Science Institute, University of Chicago, Chicago, IL, USA. · Department of Sociology, University of Chicago, Chicago, IL, USA. · Santa Fe Institute, Santa Fe, NM, USA.
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.
Figures & tables
Figure 1: Attention concentrates. a , b , Yearly Gini coefficients of research attention ( a ) and citations ( b ) hold flat then break upward in 2017. c , AI’s share of computer-science publications increases between 2017 and 2025, while systems and theory contract. d , Author migration probability into AI peaks at age 31.8 and the 82nd yearly publication-rank percentile. e , Across 73,089 peer reviews from 2017–2025, reviewer confidence declines from 3.9 to 2.9 on a 0–5 scale (left). Toxic comments increased after 2017, were briefly tempered by the NeurIPS move to OpenReview, and then rebounded (right). Lines in a , b , d , and e are fitted using a three-year rolling average.
Figure 2: Cyclical exhaustion and policy backfire. a , Each unique noun in the stable corpus is treated as an idea; ideas are born, die after three years of disuse, and may reactivate. b , The yearly share of newly born ideas rises sharply at AlexNet and the Transformer and then declines. c , Breakthroughs not only birth ideas; they bury them with a lag. d , Citation share for the original AlexNet and Transformer papers peaks and declines for both. Lines in b – d are fitted using a three-year rolling average. e , Active-idea populations fit logistic carrying-capacity curves across three successive cycles (solid lines), with the dashed tails marking observed deviation below each modeled ceiling. The drop is modest after AlexNet, severe after the Transformer, and emerges before the new ceiling under LLMs. The cycle is compressing. f , Existing policies may not address this: greater reciprocity strength ρ (high-submission reviewers being asked to review even more) drives stronger convergence toward the tastes of high-submission established researchers.
Appendix figures & tables6 assets
Supplementary material from the paper’s appendix.
Appendix
Figure 1: Robust results on attention concentration. Gini coefficient under different TF-IDF keyword extraction parameters. Panels A, B, and C correspond to k=5 , k=10 , and k=15 , respectively. Lines are fitted using a three-year rolling average.
Figure 2: Robust results on idea representation. Robustness of idea lifecycle metrics across idea representation methods. Conclusions are robust.
Figure 3: Illustration of the four-stage spiral of technological revolution. Across 36 years of AI development including three major technological advances (AlexNet, the Transformer, and LLMs), we repeatedly observe the same pattern, a four-stage spiral of technological revolution : initiation , where a new paradigm has just emerged; growth , where the S-curve reaches its steepest slope and new ideas proliferate; saturation , where the idea population calcifies and may even deteriorate; and finally breakthrough , a largely unpredictable transformation that lifts the carrying capacity and restarts the cycle.
Figure 4: Cyclical exhaustion in structural biology. Left: Recognition of cryo-EM came later than the peak in new ideas. AlphaFold produced a second, smaller peak. Right: Active ideas follow the same growth pattern as in computer science: each breakthrough raises the carrying capacity of idea space, which is then repeatedly exhausted.
(1)
(2)
Logit
Logit
Age
0.025***
0.073***
(0.000)
(0.001)
Age 2
-0.001***
(0.000)
Prior Status
0.265***
1.083***
Appendix
Table 1: Determinants of author migration to AI (using yearly publication percentile to represent prior status)
(1)
(2)
Logit
Logit
Age
0.021***
0.056***
(0.000)
(0.001)
Age 2
-0.001***
(0.000)
Prior Status
0.395***
0.490***
Appendix
Table 2: Determinants of author migration to AI (using yearly citation percentile to represent prior status)
Independent researcher · HSBC Business School, Peking University, Shenzhen City, Guangdong 518055, China · Artificial Intelligence Research Institute, Shenzhen University of Advanced Technology, Shenzhen, China
Computational Social Science and Humanities, Barcelona Supercomputing Center, BSC-CNS, Barcelona, Spain · Max Planck Institute for Demographic Research, Rostock, Germany