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Stronger artificial intelligence breakthroughs hasten their own exhaustion

Beichen Lu, Honglin Bao, Qi Huang, Qingguo Meng, James A. Evans · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.27296 · cited by 0 works (OpenAlex) · open-access PDF

Russian

Прогресс в искусственном интеллекте широко предполагается масштабировать с усилиями: больше исследователей, бумаг и инвестиций должны ускорить открытие. Теории технологического роста вместо этого локализуют трудности открытия в пространстве идей. Здесь мы показываем, что ни масштаб, ни пространство недостаточно: прорывы зависят от разнообразного внимания, которое может истощить коллективная концентрация. На протяжении 36 лет компьютерной науки мы анализировали 7,2 миллиона бумаг, 73,089 обзоров коллег и 3,7 миллиона исследователей и их тематических сдвигов. Поле расширилось, в то время как его внимание резко сузилось после Трансформера. Более половины исследователей, меняющих тему, перешли в ИИ, особенно в середине карьеры, и доверие рецензентов упало на пол

Machine translation (Meta M2M100); the English original follows.

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.

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Topics: Field (mathematics), Diversity (politics), Novelty, Scale (ratio), Space (punctuation), Point (geometry)

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