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：联邦科研资助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（LLMs）正快速渗透至科研实践中，既展现出巨大潜力，也引发了广泛关切。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LLMs的兴起如何重塑公共资助格局仍鲜为人知。本研究通过融合两个互补数据集，考察LLMs在联邦资助流程关键环节中的参与情况：其一为来自美国两所大型R1研究型大学的国家科学基金会（NSF）与国家卫生研究院（NIH）保密提案（涵盖获资助、未获资助及待审提案），其二为面向公众公开的NSF与NIH全部资助项目。我们发现，自2023年起LLM使用量急剧上升，并呈现双峰分布，表明存在少量使用与大量使用之间的明确分界。无论处于私人提案还是公共资助阶段，较高的LLM使用水平始终与较低的语义独特性相关，使项目更趋近于同一机构近期资助的研究。这一转变的后果因机构而异。在NIH，LLM使用与提案成功率和后续出版物产出量呈正相关，而NSF中未观察到此类关联。值得注意的是，NIH的生产力提升主要集中于非高被引论文，而非最具影响力的卓越成果。综合而言，这些发现提供了大规模证据，表明LLMs的兴起正在重塑科学创意的定位、筛选及转化为公共资助研究的路径，对投资组合治理、科研多样性及科学长期影响力具有深远影响。