AI boosts publication output more at non-elite institutions: Evidence from AlphaFold
AlphaFold, an AI system for highly accurate protein structure prediction, has the potential to lower barriers in structural biology research. We hypothesize that its release has created greater opportunities for scientists at non-elite institutions. To test this, we employ a difference-in-differences strategy, comparing the publication output of 3434 structural biologists with that of 3014 other (non-structural) biologists. Scientists are categorized as being at elite or non-elite institutions based on whether their affiliations are among the top 50 institutions ranked by their h-index. Our findings suggest that AlphaFold-like models are associated with a larger increase in the publication output of structural biologists at non-elite institutions. However, this increase is largely concentrated in lower-tier journals. These results demonstrate how AI is reshaping competitive dynamics and altering the landscape of scientific progress.
Authors
- Tianxing Pan (ORCID: https://orcid.org/0000-0002-6698-8387)
- Jiang Li (ORCID: https://orcid.org/0000-0001-5769-8647)
Institutions
- Nanjing University (CN)
Publication Details
- Journal
- Journal of Informetrics
- Published
- 2026-09-18
- DOI
- https://doi.org/10.1016/j.joi.2026.101859
- Primary Topic
- scientometrics and bibliometrics research
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Major Program of National Fund of Philosophy and Social Science of China