Stronger artificial intelligence breakthroughs hasten their own exhaustion

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

DOI: 10.48550/arXiv.2610.27296 · open-access PDF

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 Transforme

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