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Reposted by @yifanqian.bsky.social

Digital Science @digital-science.com · 28/08/2026

Is AI-assisted writing having an impact on research funding? Using data from Dimensions, this study published in @pnas.org looks into how U.S. federal agencies are funding research in the age of LLMs. Led by @dashunwang.bsky.social & @yifanqian.bsky.social doi.org/10.1073/pnas.2601439123

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Proceedings of the National Academy of Sciences @pnas.org · 20/08/2026

A study of private grant proposal submissions and publicly released awards from NSF and NIH finds a sharp rise in LLM use since 2023. At NIH—but not at NSF—proposals created with LLMs are more likely to be funded and to yield more publications. In PNAS: ow.ly/5zHM50ZBWoP

$Corpus-level estimates of the fraction of LLM-modified sentences in private and public NSF and NIH grants from 2021 to 2025, computed using rolling three-month windows (points).$

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Michael Hochberg @mkhochb.bsky.social · 13/08/2026

New in PNAS: proposals with heavier estimated LLM involvement were less distinctive and more likely to be funded at NIH: www.pnas.org/doi/10.1073/.... Human incentives can create selection pressures, while AI may amplify alignment with prevailing norms: www.pnas.org/doi/10.1073/...

pnas.org

The rise of large language models and the direction and impact of US federal research funding | PNAS

Federal research funding shapes the direction, diversity, and impact of the US scientific enterprise. Large language models (LLMs) are rapidly diff...

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Nathalie J Arhel @arhelnathalie.bsky.social · 14/08/2026

Grant proposals written with the help of AI simply recycle ideas from previously funded projects. By using generative AI, we are gradually collapsing the diversity and semantic distinctiveness of our research. Be original, don't use AI. www.pnas.org/doi/10.1073/...

pnas.org

The rise of large language models and the direction and impact of US federal research funding | PNAS

Federal research funding shapes the direction, diversity, and impact of the US scientific enterprise. Large language models (LLMs) are rapidly diff...

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Zander Furnas @alexanderfurnas.com · 13/08/2026

Really excited about this paper I played a small role on. Fantastic work by my colleagues!

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Nicholas Weller @nichweller.bsky.social · 13/08/2026

Nice thread and interesting analysis here. Someone on Bluesky (I can't find it yet) wrote how AI might make the failings of academic grants and publishing more obvious. Pushing grant applications closer to recently funded work certainly suggests AI use might make merit review more conservative.

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yifanqian.bsky.social @yifanqian.bsky.social · 13/08/2026

How is generative AI shaping the public funding landscape? Check out our latest paper in @pnas.org: The Rise of Large Language Models and the Direction and Impact of US Federal Research Funding: www.pnas.org/doi/10.1073/... 1/n

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Zander Furnas @alexanderfurnas.com · 22/04/2026

I've got a paper "Bipartisan-cited science is rare, unevenly distributed, and disproportionately influential" published today at @pnas.org with @dashunwang.bsky.social. www.pnas.org/doi/10.1073/...

Bipartisan-cited science is rare, unevenly distributed, and disproportionately influential
Alexander C. Furnas https://orcid.org/0000-0001-8006-7798 and Dashun Wang

Abstract
This study offers a systematic analysis of scientific papers cited in both Republican and Democratic policy documents. Using data from Overton and Dimensions, we examine congressional reports, hearings, and think tank publications. We find that bipartisan citations, while rare, highlight papers of exceptional scientific influence. Policy documents citing these papers also receive more citations, amplifying their policy impact. Yet, bipartisan-cited science is unevenly distributed—concentrated in monetary policy and healthcare, but notably absent in climate, inequality, and race and gender. These results show that bipartisan engagement, though limited, marks a uniquely influential core of science in both research and policy.

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Ian Hutchins @bihutchins.bsky.social · 06/02/2026

“Higher LLM involvement predicts greater publication output, though concentrated in non-hit papers… One possible interpretation is that NIH funding and review norms more strongly reward incremental, executable projects” arxiv.org/html/2601.15...

arxiv.org

The Rise of Large Language Models and the Direction and Impact of US Federal Research Funding

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