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 subsequent 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.

翻译：联邦研究基金塑造了美国科学事业的方向、多样性与影响力。大型语言模型正快速渗透至科研实践领域，在展现巨大潜力的同时引发了广泛担忧。尽管人工智能在科学写作与评估中的应用日益受到关注，但关于LLM如何重塑公共资助格局的研究尚不充分。本研究通过整合两个互补数据源，考察了LLM在联邦资助关键环节的参与情况：其一是来自美国两所大型R1大学的美国国家科学基金会与国家卫生研究院保密项目提案（涵盖已资助、未资助及待审提案），其二是NSF与NIH已公开的全部资助项目数据。研究发现，LLM使用率自2023年起急剧上升，并呈现双峰分布特征，表明轻微使用与实质性使用存在明显分野。在保密提案与公开资助项目中，较高的LLM参与度始终与较低的语义独特性相关，使得项目内容更接近同一机构近期资助的研究方向。这种转变的影响具有机构差异性：在NIH体系中，LLM使用与提案成功率及后续论文产出量呈正相关；而在NSF体系中未观察到类似关联。值得注意的是，NIH体系中的生产力提升主要集中于非热点论文而非高被引研究。综上，这些发现提供了大规模证据，表明LLM的兴起正在重塑科学思想的定位、筛选及向公共资助研究的转化过程，对科研组合治理、研究多样性及科学长期发展具有深远影响。