Beichen Lu | Searcharxiv

arXiv2026

Stronger artificial intelligence breakthroughs hasten their own exhaustion

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.

cs.AI↗

arXiv2026

Persistence Paradox in Dynamic Science: Evidence from the Deep Learning Revolution

Persistence is often regarded as a virtue in science. In this paper, however, we challenge this conventional view by highlighting its contextual nature, particularly how persistence can become a liability during paradigm shifts. We focus on the deep learning revolution catalyzed by AlexNet in 2012. Analyzing the 20-year career trajectories of more than 5,000 scientists active in top machine learning venues during the preceding decade, we examine how their research focus and output evolved. We first uncover a dynamic period in which leading venues increasingly prioritized cutting-edge deep learning developments, displacing traditional statistical learning methods. Scientists responded to these changes in markedly different ways: those who were previously successful or affiliated with established teams adapted more slowly. Such persistence is positively associated with productivity but, after 2012, negatively associated with scientific impact. Most researchers, and the largest share of the field's output, cluster in a band of moderate persistence, pointing to a trade-off between output and impact, as well as to institutional frictions that make larger departures costly. These conclusions are robust to alternative identification strategies and to competing explanations such as topic popularity premiums and survivorship bias. Taken together, our macro- and micro-level findings suggest that, in this case, a paradigm shift creates an opportunity structure by devaluing the very expertise that conferred incumbents' advantage in the first place.

cs.DL↗