The pivot penalty in research
Open accessAbstract
Scientists and inventors set the direction of their work amid evolving questions, opportunities and challenges, yet the understanding of pivots between research areas and their outcomes remains limited1–5. Theories of creative search highlight the potential benefits of exploration but also emphasize difficulties in moving beyond one’s expertise6–14. Here we introduce a measurement framework to quantify how far researchers move from their existing work, and apply it to millions of papers and patents. We find a pervasive ‘pivot penalty’, in which the impact of new research steeply declines the further a researcher moves from their previous work. The pivot penalty applies nearly universally across science and patenting, and has been growing in magnitude over the past five decades. Larger pivots further exhibit weak engagement with established mixtures of prior knowledge, lower publication success rates and less market impact. Unexpected shocks to the research landscape, which may push researchers away from existing areas or pull them into new ones, further demonstrate substantial pivot penalties, including in the context of the COVID-19 pandemic. The pivot penalty generalizes across fields, career stage, productivity, collaboration and funding contexts, highlighting both the breadth and depth of the adaptive challenge. Overall, the findings point to large and increasing challenges in effectively adapting to new opportunities and threats, with implications for individual researchers, research organizations, science policy and the capacity of science and society as a whole to confront emergent demands. An analysis of millions of scientific papers and patents reveals a ‘pivot penalty’ when researchers shift direction, with the impact of studies decreasing rapidly the further they move from their previous work.
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By dramatically reducing the cost of drafting, synthesizing, and revising complex text, LLMs may help scientists articulate ideas more clearly, navigate the growing burden of knowledge, and explore unfamiliar domains more efficiently.
“By dramatically reducing the cost of drafting, synthesizing, and revising complex text, LLMs may help scientists articulate ideas more clearly, navigate the growing burden of knowledge, and explore unfamiliar domains more efficiently (28, 29).”
From The rise of large language models and the direction and impact of US federal research funding (2026) · cites this paper as [29]
The Discussion (page unavailable) describes a growing difficulty in moving beyond established expertise, but does not address whether LLMs help scientists overcome it. Searches for LLMs and for help with drafting, synthesis, and revision found no passage supporting the attributed claim. (LLM, conf=0.76)
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Wang, B., Jin, C., Song, C., Bjelland, J., Uzzi, B., & Wang, D. (2026) · Advanced Science · doi:10.1002/advs.78029
Self-citationLemaitre, J. C., Popelka, D., Ribotta, B., Westlake, H. E., Chakrabarti, S., Xiaoxue, L., Hanson, M. A., Jiang, H., Cara, F. D., Kurant, E., et al. (2026) · eLife · doi:10.7554/elife.108403.2
- Authors
- Hill, R., Yin, Y., Stein, C., Wang, X., Wang, D., & Jones, B. F.
- Affiliations
- Northwestern University (US)
- University of California, Berkeley (US)
- Corresponding author
- Wang, Dashun
- Jones, Benjamin F.
- Venue
- Nature
- Published
- 2025-05-28
- Volume
- 642
- Issue
- 8069
- Pages
- 999-1006
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC12222024
- OpenAlex
- W4410818492
- Cited by
- 41
- References
- 76
- Citations by year
- 2026: 25 · 2025: 15 · 2024: 1
- FWCI
- 25.58
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Nature (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Funders
- National Science Foundation (2404035)
- Alfred P. Sloan Foundation (G-2019-12485)
- Yale University
- Brigham Young University
- Northwestern University
- Georgetown University
- University College London
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › scientometrics and bibliometrics research
- Social Sciences › Decision Sciences › Information Systems and Management › Research, Science, and Academia
- Social Sciences › Decision Sciences › Information Systems and Management › Interdisciplinary Research and Collaboration
- Keywords
- creative search, scientific impact, publication success, patent impact, interdisciplinary research, COVID-19 pandemic research, science policy
- MeSH terms
- COVID-19, Cooperative Behavior, Efficiency, Humans, Patents as Topic, Research, Research Personnel
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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