White House will gut university research funding to bankroll AI push
The new guidance could hurt large universities that rely on federal research funding.
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- White House plans to reroute federal research money from universities toward individual scientists and AI
- Critics say the shift could damage universities and inject unreliable AI into delicate R&D
- Administration argues AI-focused funding will accelerate discovery and strengthen America against Chinese competition
The White House is recommending a massive overhaul of federal research funding to direct more cash to individual scientists and the use of AI rather than universities.
The White House Office of Science and Technology Policy claims that individual researchers can help the US move faster to use AI in scientific research.
The newly released report to the president matters because it will help shape a $200 billion annual federal research and development (R&D) budget across the government until the next US president, after Donald Trump, is inaugurated.
According to policy critics, universities could suffer as they rely on federal research funding, but the Trump administration seems to be prioritizing programs such as fellowships and awards that empower individuals rather than institutions.
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For the White House, examples of model programs include a renowned National Science Foundation program for graduate students and a National Institutes of Health award for creative scientists.
“Agencies should prioritize programs that distribute funding directly to students and researchers, similar to NSF’s Graduate Research Fellowship Program, so recipients can
apply the grant to any qualifying institution that best supports their goals and retain it if they move, encouraging institutions to compete for early career talent,” the White House memo says.
Under the plan, even individual researchers who work at universities would receive funding directly, thus minimizing the direct involvement of universities.
AI models, although allegedly improving, are still prone to hallucinations and errors that can prove especially costly when applied in R&D.
“Over the last 20 or 30 years, we’ve become very stagnant in believing we should keep funding the same research in the same way at the same institutions,” Michael Kratsios, director of the Office of Science and Technology Policy, told The Wall Street Journal in an interview.
It appears that all this is done to pave the way for more aggressive use of AI in research. A planned program called the Genesis Mission aims to address research challenges using AI.
Kratsios and Russ Vought, director of the Office of Management and Budget, wrote in their memo: “Agencies should fund research that uses AI as a new instrument of scientific discovery, not merely as a tool to augment existing capabilities.”
The problem is that AI models, although allegedly improving, are still prone to hallucinations and errors that can prove especially costly when applied in R&D.
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Examples aren’t lacking. Just this May, a study conducted by Microsoft researchers found that large language models corrupt documents over the course of long, extensive workflows. This results in data deletions and hallucinations.
And this week, new research from the UK’s AI Security Institute once again showed that LLMs will, as a rule, cut corners and cheat in order to complete their tasks. This seems extremely risky in a field as fragile and attentive to details as R&D.
The memo, moreover, appears to be another part of the Trump administration’s systematic campaign to restructure, defund, and realign the US education system.
In May, a federal judge restored funding to thousands of scientific and humanities grants after they had been canceled by the US government, saying that the administration used ChatGPT to target projects for funding cuts.
