The Rise of Large Language Models and the Direction and Impact of US Federal Research Funding
arXiv:2601.15485v3 Announce Type: replace-cross Abstract: 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 Nat
The rapid advancement and diffusion of Large Language Models (LLMs) into scientific practices necessitate an immediate examination of their impact on critical funding mechanisms.
Understanding how LLMs are reshaping federal research funding is crucial for strategic resource allocation, national scientific competitiveness, and anticipating future technological trajectories influenced by government investment.
The criteria and priorities for federal research funding are beginning to incorporate, or be influenced by, the prevalence and promise of LLMs, potentially reorienting scientific areas and methods receiving support.
- · AI research labs
- · Universities with strong AI departments
- · Companies offering AI-for-science tools
- · Policy makers engaged with emerging tech
- · Traditional research methodologies
- · Scientific fields slow to adopt AI
- · Research institutions lacking AI integration
- · Grant applicants without AI literacy
Increased federal funding allocation towards research incorporating or focused on LLM development and application.
A widening gap in research output and innovation between nations and institutions leveraging LLMs effectively and those that are not.
The development of entirely new scientific disciplines and research questions driven by the capabilities and challenges presented by advanced AI.
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