[2607.17397] The unintended consequences of large language models as a labor-augmenting technology in science

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arXiv:2607.17397 (physics)

[Submitted on 19 Jul 2026]

Title:The unintended consequences of large language models as a labor-augmenting technology in science

Authors:Eamon Duede, Kevin Gross, M.J. Crockett, Carl Bergstrom<br>View a PDF of the paper titled The unintended consequences of large language models as a labor-augmenting technology in science, by Eamon Duede and 3 other authors

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Abstract:As a labor-augmenting technology, large language models (LLMs) have the potential to accelerate scientific activity across the research pipeline. But even if LLMs perform on par with human experts at selected tasks, their use will bring unintended consequences as they alter the balance of frictions and inducements that steer the allocation of research effort across projects. Here we develop a simple mathematical model to illustrate. In fields where LLMs are useful primarily as tools for discovering promising projects, researchers will become more selective about what they publish; where they facilitate the process of publishing existing data, researchers will become less selective. By allowing scientists to work more quickly, LLMs raise the opportunity cost of researcher time, creating incentives to refine papers less thoroughly before moving on. Enticing as it is to imagine that, by saving us time on mundane tasks, LLMs will provide us with more time to think deeply and develop projects completely, our results temper such hopes.

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Physics and Society (physics.soc-ph)

Cite as:<br>arXiv:2607.17397 [physics.soc-ph]

(or<br>arXiv:2607.17397v1 [physics.soc-ph] for this version)

https://doi.org/10.48550/arXiv.2607.17397

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Eamon Duede [view email]<br>[v1]<br>Sun, 19 Jul 2026 20:12:16 UTC (92 KB)

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