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

arXiv: https://arxiv.org/abs/2601.15485 · HTML: https://mlsanity.com/paper/2601.15485/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 46 references that are in the map.
- First posted on arXiv 21 Jan 2026.

## Builds on (most cited first, 15 of 15)

- [Generative artificial intelligence enhances creativity but reduces the diversity of novel content](https://mlsanity.com/paper/2312.00506/) (2023)
- [Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews](https://mlsanity.com/paper/2403.07183/) (2024)
- [SciRepEval: A Multi-Format Benchmark for Scientific Document Representations](https://mlsanity.com/paper/2211.13308/) (2022)
- [Delving into LLM-assisted writing in biomedical publications through excess vocabulary](https://mlsanity.com/paper/2406.07016/) (2024)
- [The Widespread Adoption of Large Language Model-Assisted Writing Across Society](https://mlsanity.com/paper/2502.09747/) (2025)
- [Scientific production in the era of Large Language Models](https://mlsanity.com/paper/2601.13187/) (2026)
- [SciSciGPT: Advancing Human-AI Collaboration in the Science of Science](https://mlsanity.com/paper/2504.05559/) (2025)
- [Generalized Word Shift Graphs: A Method for Visualizing and Explaining Pairwise Comparisons Between Texts](https://mlsanity.com/paper/2008.02250/) (2020)
- [Early-career setback and future career impact](https://mlsanity.com/paper/1903.06958/) (2019)
- [Promotional Language and the Adoption of Innovative Ideas in Science](https://mlsanity.com/paper/2406.02798/) (2024)
- [Where there's a will there's a way: ChatGPT is used more for science in countries where it is prohibited](https://mlsanity.com/paper/2406.11583/) (2024)
- [AI-Assisted Writing Is Growing Fastest Among Less Established Scientists in Non-English-Speaking Countries](https://mlsanity.com/paper/2511.15872/) (2025)
- [Quantifying dynamics of failure across science, startups, and security](https://mlsanity.com/paper/1903.07562/) (2019)
- [Tenure and Research Trajectories](https://mlsanity.com/paper/2411.10575/) (2024)
- [Funding the Frontier: Visualizing the Broad Impact of Science and Science Funding](https://mlsanity.com/paper/2509.16323/) (2025)

## Built on by (most cited first, 1 of 1)

- [Hitting a Moving Target: Test-Time Adaptation for AI Text Detection under Continual Distribution Shift](https://mlsanity.com/paper/2606.25152/) (2026)

## Abstract (arXiv)

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.