Federal budget cuts threaten to decimate America’s AI superiority—and other countries are watching
Federal research investment is an essential ingredient for American dominance in the global AI ecosystem. The astonishing breakthroughs of recent years rely upon a symbiotic balance where federal funding spreads high-risk, high-reward seeds and private-sector funding nurtures the most robust developments. Together, they help American AI thrive while sustaining our nation’s economic and strategic interests. However, the recent and upcoming cuts to federal research spending pose a grave threat to this delicate balance, endangering our country’s leadership in a sector projected to be worth $15.7 trillion by 2030, according to PricewaterhouseCoopers.
The blend of federal and corporate investment has historically produced AI results that thrive in global competition. The United States Joint Economic Committee reveals that federally funded research yields annual returns of 25-40%. Top-quartile venture capital funds achieve returns of 15-27%, according to an analysis by Seraf Investor.
Total annual federal research and development spending on AI is well under $4 billion—far less than the typical revenue of one medium-sized tech company. That modest sum has delivered immense value. Without long-term, foundational, and high-risk federal research investments, the seeds of innovation cannot take root. This dynamic combination not only fosters technological breakthroughs but also nurtures a workforce that is well-versed in the latest scientific advancements, ready to cultivate new developments in various sectors.
The proposed dramatic budget and personnel reductions to the National Science Foundation (NSF), National Institutes of Health (NIH), Department of Energy (DOE), and other federal granting agencies threaten to dismantle this ecosystem and decimate America’s AI superiority. We can see this effect directly by looking at historical examples and thinking about what we would have lost if the federal/private symbiosis had been broken.
ChatGPT and generative AI. Generative AI, which includes applications like ChatGPT and DALL-E, sprouted from NSF-funded fundamental university research in deep learning, computer vision, and natural language processing. Now, these technologies stand to contribute up to $4.4 trillion annually, according to projections from McKinsey & Company.
AlphaFold. AlphaFold, developed by DeepMind, is another testament to the power of federally funded research. By determining protein structures, AlphaFold enables a new generation of targeted pharmaceuticals that will transform health care. This breakthrough would not have been possible without decades of federal-funded research not only in AI and computing but also in fundamental biological research, yielding the Protein Data Bank and other important data sources, which AlphaFold requires to function. The NIH has been a major contributor, investing approximately $3.3 billion in human genetics and genomics research in 2019 alone. This research sowed the ground for the commercial genomics sector, which has directly and indirectly contributed nearly $1 trillion to the U.S. economy since 1988. That is approximately $8 of growth added to the economy for every $1 of federal seeding. Furthermore, according to Vantage Market Research, the computational biology market, which includes technologies like AlphaFold, was valued at $4.14 billion in 2021 and is projected to reach $10.82 billion by 2028, reflecting a compound annual growth rate of 18.1%. This rapid growth is also driving significant job expansion, and, according to Zippia, the field is expected to grow by 17% between 2018 and 2028 because of increasing demand for AI-driven life sciences research and bioinformatics applications.
Self-driving cars. In 2005, the Defense Advanced Research Projects Agency (DARPA) established the DARPA Grand Challenge for autonomous vehicles. This federal investment fertilized the advancements in autonomous driving technology while exemplifying the rich fruit that publicly funded research achieves. Today, the autonomous vehicle industry is projected to generate $300 billion to $400 billion in revenue by 2035, according to McKinsey & Company. Modern autonomous vehicles use computer vision tools—initially developed through university research into neural networks and computational infrastructure—that not only aid self-driving cars but also play critical roles in medical diagnoses, facial recognition, and agricultural monitoring.
Hardware and computing power. Federal funding for high-performance computing, through agencies like the DOE, NSF, and DARPA, has grown the infrastructure that enables AI acceleration, with over $1 billion to seed academic research in parallel computing and AI hardware since the 1980s, according to Strategic Computing: DARPA and the Quest for Machine Intelligence, 1983–1993. These investments have created core technologies for AIdriven computing essential to companies like NVIDIA, AWS, and Google Cloud, with the AI chip market projected to exceed $200 billion by 2030. These investments in distributed cloud computing and parallel processing not only support AI, but also weather predictions, quantitative finance, and airplane design.
The case for sustained federal AI investment
America’s leadership in the global AI and computing ecosystem is at risk; competitors are quickly gaining ground as they aggressively ramp up government-funded research programs. The U.S. must accelerate research advancements in AI and computing infrastructure to ensure our research institutions remain globally competitive and our AI and computing ecosystem continues to thrive. This research cannot take place in isolation; coupling AI and computing with advances in the natural, mathematical, and social sciences and engineering is essential to training workers and catalyzing economic advancement across the technology, agriculture, and health-care sectors. Federal investments in AI are not discretionary expenditures—they are economic imperatives. They fuel innovation, create high-value jobs, and secure America’s leadership in global technology markets. Without sustained commitment, the U.S. risks falling behind in the next wave of AI-driven transformation, which will be dominated by efforts in nations that recognize AI as the staple crop of future economic power.
Rebecca Willett is Worah Family Professor of Statistics and Computer Science in the Wallman Society of Fellows and Director of AI in the Data Science Institute, University of Chicago. Henry Hoffmann is Liew Family Chair of the Department of Computer Science, University of Chicago
This article originally appeared on Fortune.com.
