Nearly three months into President Donald Trump’s term, the future of American AI leadership is in jeopardy. Basically any generative-AI product you have used or heard of—ChatGPT, Claude, AlphaFold, Sora—depends on academic work or was built by university-trained researchers in the industry, and frequently both. (View Highlight)
“I don’t think anybody would seriously claim that these [AI breakthroughs] could have been done if the research universities in the U.S. didn’t exist at the same scale,” Rayid Ghani, a machine-learning researcher at Carnegie Mellon University, told me. But Trump and the Department of Government Efficiency have frozen, canceled, or otherwise slowed billions of dollars in grants and fired hundreds of staff from the federal agencies that have funded the nation’s pioneering academic research for decades, including the National Institutes of Health and the NSF. The administration has halted or threatened to withhold billions of dollars from premier research universities that it has accused of anti-Semitism or unwanted DEI initiatives. Graduate students are being detained by immigration agents. Universities, in turn, are issuing hiring freezes, reducing offers to graduate students, and canceling research projects. (View Highlight)
utwardly, Trump has positioned himself as a champion of AI. During his first week in office, he signed an executive order intended to “sustain and enhance America’s dominance in AI” and proudly announced the Stargate Project, a private venture he called “the largest AI infrastructure project, by far, in history.” He has been clear that he wants to make it as easy as possible for companies to build and deploy AI models as they wish. Trump has consulted and associated himself with leaders in the tech industry, including Elon Musk, Sam Altman, and Larry Ellison, who have in turn showered the president with praise. But generative AI is not just an industry—it is a technology dependent on progressive innovations. Despite his bravado, Trump is rapidly eroding the engine of scientific innovation in America, and thus the capacity for AI to continue to advance. (View Highlight)
In a statement, White House Assistant Press Secretary Taylor Rogers wrote that the administration’s actions are in service of building up the economy, fighting China, and combatting “divisive DEI programs” at the nation’s universities. “While Joe Biden sat back and let China make gains in the AI space, President Trump is restoring America’s global dominance by imposing tariffs on China—which has ripped us off for far too long,” Rogers wrote. (As my colleague Damon Beres wrote earlier this week, tariffs may only hurt American technology businesses.) (View Highlight)
Despite Trump’s aims, the United States now risks losing ground to Canada, Europe, and, indeed, China in the race for AI and other technological innovation. In a Nature poll of American scientists last month, 75 percent of respondents—some 1,200 researchers—said they were considering leaving the country. New scientific and technological developments may occur elsewhere, slow down, or simply stop altogether. (View Highlight)
“Curiosity-driven research is what allows us to explore directio (View Highlight)
“All of these innovations, whether it’s the transformer or GPT or something else like that, were built on top of smaller little breakthroughs that happened earlier on,” Mark Riedl, a computer scientist at the Georgia Institute of Technology, told me. Needing to show investors progress each fiscal quarter, then a source of revenue within a few years, limits what topics scientists can pursue; meanwhile, federal grants allow them to explore high-risk, long-term ideas and hypotheses that may not present obvious paths to commercialization. The largest tech companies, such as Google, can fund exploratory research but without the same breadth of subjects or tolerance for failure—and these giants are the exception, not the norm. (View Highlight)
The AI industry has turned previous, foundational research into impressive AI breakthroughs, pushing language- and image-generating models to impressive heights. But these companies wish to stretch beyond chatbots, and their AI labs can’t run without graduate students. “In the U.S., we don’t make Ph.D.s without federal funding,” Riedl said. From 2018 to 2022, the government supported nearly 50billioninuniversityprojectsrelatedtoAI,whichatthesametimereceivedroughly14 billion in non-federal awards, according to research led by Julia Lane, a labor economist at NYU. (View Highlight)
“The way in which innovation has occurred as a result of federal investment is investments in people,” Lane told me. And perhaps as important as federal investment is federal immigration policy: The majority of top AI companies in the U.S. have at least one immigrant founder, and the majority of full-time graduate students in key AI-related fields are international, according to a 2023 analysis. Trump’s detainment and deportation of a number of immigrants, including students, have cast doubt on the ability—and desire—of foreign-born or -trained researchers to work in the United States. (View Highlight)
If the pool of talented AI researchers shrinks, only the true AI behemoths will be able to pay them; as the pool of federal science grants dwindles, those same firms will likely further steer research in the directions that are most profitable to them. Without open academic research, the AI oligopoly will only further cement itself. (View Highlight)
“Part of what has built the United States into a real juggernaut of research and innovation is the fact that people have shared research,” Alondra Nelson, a professor at the Institute for Advanced Study who previously served as the acting director of the White House Office of Science and Technology Policy, told me. OpenAI, Anthropic, and Google share limited research, code, or training data sets, and almost nothing about their most advanced models—making it difficult to check products against executives’ grandiose claims (View Highlight)
More troublingly, progress in AI—and really any technology or science—depends on collaboration among people and pollination of ideas. These firms could plow ahead with the same massive, expensive, and energy-intensive models that may not be able to do what they promise. Fewer and fewer start-ups and academics will be able to challenge them or propose alternative approaches; these firms will benefit from fewer and fewer graduate students with outside perspectives and expertise to spark new breakthroughs. (View Highlight)
