The U.S. biomedical innovation system is world-leading, built by a century of federal investment that peaked last year at $50 billion. That foundation has been thrown into uncertainty by shifts in policy under the second Trump Administration aimed to reduce spend, shift research priorities, and divest from universities. While these changes have appeared to many to come on suddenly, they reflect long-growing concerns with the U.S. scientific system—initiated by issues with trust and reproducibility, barriers to accessing knowledge, and structural limitations on creative risk-taking.
The scientific community is actively seeking additional funders to step in and fill the anticipated funding gap, which may be as big as $20B annually. New pledges are starting to be made. At the state level, California is advancing a bill to fund general scientific research and Texas is advancing a bill to fund Alzheimer’s research. Both of these states have a history of scientific funding, including through the California Institute for Regenerative Medicine and the Texas Cancer Prevention and Research Institute of Texas (CPRIT). Corporate pledges are also happening—Recursion Pharmaceuticals has funded a pre-seed accelerator designed to close gaps in SBIR funding—and groups outside of the US are launching opportunities for funding or training that are aimed at US-based scientists. Other potential sources of funding include philanthropies, family offices, regional governments, private investors, disease foundations, and crowd-funding. If contributions from these groups rise to meet the anticipated $20B funding gap, the system will shift fundamentally—from one organized around a central funder to one sustained by a kaleidoscope of funders. This transition will open-up new and interesting opportunities.
A shift in how science is funded will be accompanied by a shift in how science is practiced. The university, which stands as the current institutional model for science, has been honed over centuries to support the discovery of new knowledge. Over the past 75 years, the US universities have evolved in lockstep with federal science funding—both shaped around the structural frame of an individual lab run by a single investigator and centered on a specific line of inquiry. This mutual reinforcement has created a feedback loop: universities structure themselves to win grants and grants are structured to support the kinds of work that universities are best equipped to host. In many cases, this alignment runs so deep that universities place limits on accepting non-federal funding, particularly when that funding requires different administrative handling. While this structure continues to excel at discovery research, it has struggled to support other modes of science—particularly those that seek to integrate data science practices like machine learning and the increasingly ubiquitous artificial intelligence. These approaches are certainly in use within universities, but they rely on infrastructure that has been bolted onto an organizational model originally built for analog-era research.
At the same time that federal funding has started to pull back from universities, there has been a rise in the creation of independent research institutes. Non-federal funders who are interested in projects that vary from NIH-funded research in scale, duration, and outputs are increasingly choosing to pull scientists out of the university and instead place them into smaller purpose-built institutes. This allows them to be laser-focused on their research mission without the constraints of existing institutions.
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