The Rise of Large Language Models and the Direction and Impact of US Federal Research Funding
Authors: Yifan Qian, Zhe Wen, Alexander C. Furnas, Yue Bai, Erzhuo Shao, Dashun Wang
Organizations: Center for Science of Science and Innovation, Northwestern University, Evanston, IL, USA · Ryan Institute on Complexity, Northwestern University, Evanston, IL, USA · Northwestern Innovation Institute, Northwestern University, Evanston, IL, USA · Kellogg School of Management, Northwestern University, Evanston, IL, USA School of Engineering, Northwestern University, Evanston, IL, USA · McCormick
Federal research funding shapes the direction, diversity, and impact of the US scientific enterprise. Large language models (LLMs) are rapidly diffusing into scientific practice, holding substantial promise while raising widespread concerns. Despite growing attention to AI use in scientific writing and evaluation, little is known about how the rise of LLMs is reshaping the public funding landscape. Here, we examine LLM involvement at key stages of the federal funding pipeline by combining two complementary data sources: confidential National Science Foundation (NSF) and National Institutes of Health (NIH) proposal submissions from two large US R1 universities, including funded, unfunded, and pending proposals, and the full population of publicly released NSF and NIH awards. We find that LLM use rises sharply beginning in 2023 and exhibits a bimodal distribution, indicating a clear split between minimal and substantive use. Across both private submissions and public awards, higher LLM involvement is consistently associated with lower semantic distinctiveness, positioning projects closer to recently funded work within the same agency. The consequences of this shift are agency-dependent. LLM use is positively associated with proposal success and higher early-stage publication output at NIH, whereas no comparable associations are observed at NSF. Notably, the productivity gains at NIH are concentrated in non-hit papers rather than the most highly cited work. Together, these findings provide large-scale evidence that the rise of LLMs is reshaping how scientific ideas are positioned, selected, and translated into publicly funded research, with implications for portfolio governance, research diversity, and the long-run impact of science.
Large language models (LLMs) have rapidly and significantly entered scientific workflows, but it remains unclear how their diffusion is associated with changes in scientists' strategies in research directions and team building. We link PubMed Central full text with OpenAlex publication and collaboration histories for 775,323 scientists and analyze CRediT contribution statements from 137,120 multi-author papers. After 2022, scientists increasingly published across more intellectually distant fields and entered fields in which they had not previously worked. These increases in interdisciplinarity and exploration were especially pronounced among established scientists and scientists from non-English-speaking low- and middle-income countries. Authors with stronger AI-writing signals were already more interdisciplinary and exploratory before the widespread adoption of LLMs, and the gap widened further after 2022 compared with authors with weaker AI-writing signals. Scientists' collaboration networks also became more interdisciplinary after 2022. Yet, among authors with stronger AI-writing signals, research interdisciplinarity was less closely tied to the disciplinary diversity of their collaborators. The division of labor within research teams also became more differentiated. Contributors on papers published after 2022 reported narrower role sets on average, coauthors shared fewer roles in common, and their role profiles became less rigid and more fluid. Software and validation roles increased, while conceptual and management roles decreased. These patterns suggest that team members are taking on more distinct responsibilities and may rely less on one another to perform research tasks. Overall, this study indicates that the LLM era coincides with a broader reorganization of scientific exploration, collaboration, and the division of labor.
Scientific language is a central infrastructure of knowledge production, but it remains unclear whether large language models (LLMs) are altering not only how scientists write, but also how scientific knowledge is communicated and accessed. Here we analyze 21.36 million scientific abstracts published between 2020 and 2024, together with historical records from major journals, to trace recent changes in the language of science. We identify a marked turning point in 2024, when scientific writing shows a sharp increase in lexical complexity alongside a decline in syntactic complexity. This shift is pervasive across disciplines and journal tiers, and is more pronounced in texts by scholars working in non-native English contexts, especially those from language backgrounds that differ more typologically from English. Controlled polishing experiments confirm that LLMs reproduce this pattern by favoring more lexically dense and syntactically compressed expression. We further show why this linguistic shift matters: it may widen the distance between scientific discourse and public-facing language, while also helping scholars in non-native English contexts navigate English-language publishing requirements. These findings suggest that LLMs may broaden participation in scientific authorship while narrowing the accessibility of scientific communication, making them a new force in the linguistic infrastructure of science.
This paper is accepted and in press for AI and Ethics. This paper includes the supplementary data file at the end of the manuscript. This study examines whether large language models (LLMs) can reliably answer scientific questions and demonstrates how easily they can be influenced by fringe scientific material. The authors modified custom LLMs to prioritise knowledge in selected fringe papers on the Fine Structure Constant and Gravitational Waves, then compared their responses with those of domain experts and standard LLMs. The altered models produced fluent, convincing answers that contradicted scientific consensus and were difficult for non-experts to detect as misleading. The results show that LLMs are vulnerable to manipulation and cannot replace expert judgment, highlighting risks for public understanding of science and the potential spread of misinformation.