Artificial intelligence is helping scientists do something just as important as running experiments: secure more grant funding.
A recent paper, “Funding the Frontier: Visualizing the Broad Impact of Science and Science Funding,” suggests that AI-assisted grant writing is gaining momentum — and may already be delivering results. In an analysis of thousands of proposals submitted between 2021 and 2025 to the National Institutes of Health and the National Science Foundation, researchers found that proposals showing signs of AI assistance were associated with a 4 percent higher acceptance rate.
Two of the study’s authors, Dashun Wang and Yifan Qian, computational social scientists at Northwestern University, examined submissions from two large U.S. universities. They sorted proposals into two groups: those likely generated with AI support and those without apparent AI fingerprints. The difference in success rates was modest but notable.
In a highly competitive funding environment, a 4 percent boost is not trivial. It hints at a shift in how researchers are approaching one of the most contested aspects of academic life.
Winning a grant requires more than good science. Researchers must show a broader impact, how their work will influence society beyond academic journals. That may be with patents, public policy, clinical treatments or economic growth. But proving those connections has traditionally been difficult.
According to the authors, most previous analysis linked funding with scientific publications, while leaving out those broader impacts. In other words, the system has been measuring papers, not effects beyond publication.
The researchers built a machine-learning platform called Funding the Frontier (FtF), which connects 7 million research grants to 140 million scientific publications, 160 million patents, 10.9 million policy documents, 800,000 clinical trials and 5.8 million news articles, all tied together through 1.8 billion citation links.
Combining that scale of information would be impossible even for an army of humans working around the clock for a lifetime. The authors describe it as “the largest and most comprehensive data aggregation of science funding and its downstream impacts.”
By analyzing these interconnected datasets, the platform shows how research funding travels outward — into industry, legislation, medicine and media.
The system uses machine learning models trained on historical grants and text data to identify patterns. According to the authors, those models can produce “forecasted insights into which research directions are likely to deliver high societal impact.”
That predictive element is where the implications for grant writing become clear.
If AI can show which types of projects tend to lead to patents or policy influence, researchers can use similar tools to frame proposals more effectively. Instead of guessing how to articulate impact, they can draw on data from millions of past grants and billions of citation links.
The paper argues that “the ability to evaluate the multidimensional impacts of funding on society is critical to ensure science policies and investments align with social needs.” Funding agencies are increasingly focused on that alignment. They want measurable returns. They want evidence that taxpayer dollars matter, and AI makes those connections visible.
For a researcher drafting a proposal, that could mean using AI tools to identify how similar work has translated into real-world outcomes. It could mean refining language to emphasize societal benefits that data shows funders value. It could mean anticipating where a line of research might lead — and explaining that pathway clearly.
But there are risks. Systems trained on historical data may favor established fields or reinforce existing funding patterns. Breakthrough discoveries do not always follow predictable paths.
Still, the direction is hard to ignore. Science funding is becoming more data-driven, and AI is central to that shift.
Funding the Frontier demonstrates that when millions of grants and billions of citations are analyzed together, science looks less like a collection of isolated studies and more like a network that stretches into patents, policies, clinical trials and headlines.
