Note: This essay on the future of American science was originally commissioned by the magazine Liberal Currents from Aishwarya Khanduja of Analogue Group and Stuart Buck of Good Science Project. It appeared in a collection called The Reconstruction Papers; the hard copy can be ordered here.
In 2025, the Trump administration in 2025 tried a . . . different approach to science than we had seen before. It took the unprecedented step of canceling (not just refusing to renew) thousands of ongoing grants, including clinical trials. It fired staffers at science agencies like NIH and NSF, and proposed massive budget cuts on the order of 40 percent to 50 percent or more. It engaged in culture-war battles with many American universities, using the threat of withholding grants for everything from cancer research to cosmology research (which had never been done before). And via the Department of Government Efficiency, it paradoxically imposed new rules that made everything more inefficient than anyone could have imagined (such as one that required advance approval when spending more than $1 on a federal credit card)1.
A future administration will have some obvious choices. But if it merely tries to return to the status quo, we will have wasted an opportunity to rethink the relationship between the US government and science that has persisted since the post-World War II era.
Most scientists privately (and sometimes publicly) agree that the pre-Trump scientific ecosystem was not entirely without flaws. As our friend Tom Kalil, a science-policy veteran, is fond of saying, it’s not as if scientists thought that the system was heaven on earth as of December 2024. Long before Trump, the system was too bureaucratic and slow, too conservative in how it selected projects via peer review, and lacking in enough opportunities to exercise creativity.
To fix these problems, we need to reconsider the institutional design principles we use to allocate billions in public dollars every year.
Since the post-WWII explosion in science funding, we have designed scientific institutions and funding from the top down. That is, we have created large bureaucracies at NIH, NSF, the Department of Energy’s National Laboratories, and more, all within a broad framework (credited to Vannevar Bush’s Endless Frontier) about the relationship among government, universities, and science.
Those were acts of institutional design that, consciously or not, reflected a very particular and time-bound set of assumptions about governance, incentives, careers, and much more.
When we think about institutional design, we often turn to James Scott’s Seeing Like a State. Scott’s basic argument is that modern states often try to make complex adaptive systems more “legible” to centralized planners: easier to measure, classify, standardize, and control. But in doing so, governments can unwittingly undermine the local knowledge and organic diversity that made those systems work in the first place.
Perhaps his most famous example is the ill-fated attempt to make forestry more “scientific.” Professional foresters disliked messy, diverse forests with multiple species, irregular spacing, and all the apparent inefficiencies of nature. Instead, they tried planting neat checkerboard patterns of the same species of tree.
On paper, this looked more scientific and rational. It was easier for bureaucrats to count and administer. But over time, according to Scott, these “rational” forests often performed worse. They were more vulnerable to disease, pests, soil depletion, and other failures. What looked irrational from the perspective of the planner turned out to be functional from the perspective of the living system.
American science policy, we would argue, has frequently made a parallel mistake.
Over time, we have built a research system that is increasingly organized around legibility to bureaucracies. Scientific projects must be described in advance, broken into specific aims and deliverables, and evaluated through standardized procedures that make them easier for large bureaucracies to process.
Institutions, likewise, are forced into a strict set of administratively familiar forms (“institutional isomorphism,”2 as sociologists would say). The university PI lab funded by project grants is legible, as is the top-down national initiative with an inspiring name (such as a “Cancer Moonshot”3 or a “National Plan to Address Alzheimer’s Disease”4).
But legibility is not the same thing as vitality. It’s the paradox of progress5: Attempting to manage and control future progress via top-down mechanisms can actually inhibit such progress from occurring.
Outside of certain narrow aims, science is not engineering. In other words, science is not a machine whose outputs can be reliably maximized by Soviet-style central planners if only the metrics are precise enough. In an ideal state, science would be closer to an ecosystem: a mostly emergent process in which real progress often comes from diversity of methods and institutions, strange side paths, local knowledge, and forms of exploration that may look inefficient or irrelevant at the time.
That is why the right metaphor for science policy is not top-down industrial-style planning. It is gardening6.
Gardeners cannot predict the exact path of every root, or force every living thing into an identical shape. Instead, they create the conditions under which plants can flourish. That means tilling the soil, providing water and fertilizer, pulling weeds, protecting fragile growth from the weather and from invasive pests, and introducing variety as a way of maintaining soil quality. Good gardeners try to take a strong hand in producing their desired outcomes, but they do so with humility about the fact that, ultimately, they cannot control all of the complex organisms and ecosystems in play.
We should think of science policy in much the same way. The state should absolutely fund science and build institutions around it, and yes, there should be space for top-down initiatives at DARPA, ARPA-H, NASA, and elsewhere.
But in most cases, the state should act less like a central planner dictating how to do science and more like a gardener cultivating a rich and diverse ecosystem. That means:
Supporting many kinds of organizations rather than just one dominant form.
Funding shared infrastructure, tools, datasets, and public goods that allow unexpected work to emerge.
Making room for originality and heterogeneity rather than demanding that everything be justified by the same bureaucratic norms and practices.
Paying attention to whether the ecosystem is healthy overall, not just whether a handful of programs can be given an impressive title and a press release.
Indeed, we need to be wary of the recurring temptation of top-down “moonshot” initiatives that politicians seem to love. Some such efforts are worthwhile, especially when a problem is well-specified and when a top-down national effort actually makes sense. But in science policy, the moonshot model is overused. Cancer and neurodegeneration are not engineering problems like the Apollo missions or the Manhattan Project. As has often been the case in medicine, the greatest breakthroughs progress may emerge in places that nobody predicted.
For too long, we have leaned too heavily on seeing science like a state. It is time to learn how to see it like a gardener.
What was broken
The postwar research system did many things extraordinarily well. The United States experienced decades of scientific and technological progress, from the internet to self-driving cars to miracle drugs for cancer (e.g., Gleevec7 for a particular type of leukemia).
That said, for the past few decades, many observers have been pointing to deep structural problems in the way we fund and perform science.
First, grant writing and report writing are too time-consuming. Highly trained scientists spend countless hours8 writing proposals, tailoring their goals to what they think reviewers would want, revising applications that are rejected in 90 percent of cases.
We should not want our scientific talent wasting their efforts navigating complex bureaucracies rather than advancing our knowledge, any more than we would want Steph Curry or Taylor Swift to spend half their time on compliance work rather than playing basketball or making music.
Second, the funding system is structurally conservative and risk-averse. This is because panel-based peer review was never designed to identify the most transformative ideas. It is reasonably good at filtering out bad or unserious proposals. It is much worse at selecting projects that have tremendous potential but seem irrelevant, risky, premature, or hard to explain. By definition, no true scientific breakthrough would have been the consensus view of the entire field five years before it was made.
Third, there is the problem of organizational monoculture. To be clear, the federal government does not only fund university labs. It also funds the Department of Energy’s national labs, NASA centers and affiliated institutions like the Jet Propulsion Laboratory, large clinical-trial networks at places like the MD Anderson Cancer Center, major scientific collaborations like the Whole Earth Telescope, and other structures that do not fit the simple PI-grant template.
But the most typical grant in the American research ecosystem—especially in biomedicine9 and much of basic science—is the university lab led by a principal investigator (PI), staffed largely by graduate students and postdocs, and funded through project-specific grants.
That form is suitable for some kinds of scientific work. But it is not the only type of science that needs funding. Long-term data collection, scientific infrastructure, replication, systems engineering, and interdisciplinary projects are often unsuitable for the standard lab model. Again, it’s not that these items never get funded; it’s that they should be a much higher priority than they are currently.
A fourth problem is how we treat the scientific workforce. The system relies largely on trainees who do much of the day-to-day work while chasing an increasingly narrow set of stable jobs. To put it bluntly, we typically fund several times as many postdocs in biomedicine as there are academic jobs in that field. We are trapping thousands of people in low-paying jobs throughout their thirties while dangling the prospect of an academic position that will never arise.
Many capable researchers eventually leave not because they lack talent but rather because they realize that the bargain on offer is a pyramid scheme.
None of that started with Trump. And while it may be impolite to say so, the way that the status quo makes use of many thousands of graduate students and postdocs primarily serves the interests of established scientists, not the public. A system can be highly dysfunctional while still being comfortable for the handful of people and institutions at the top.
AI could change the bottlenecks of science, and therefore the institutions we need
Much of the current discussion treats AI as an immensely promising tool to be inserted into the old scientific machine. That’s true, but it misses a broader point.
The more important question is how AI could change the location of scientific bottlenecks through lowering transaction costs (as per the economist Ronald Coase) and streamlining previously tedious tasks.
In some domains, literature review, coding, hypothesis generation, and parts of experimental design are already becoming cheaper and quicker. If generating plausible ideas becomes easier, then adjudicating which ones are actually true becomes more important. That is, if AI can help produce a huge flood of hypotheses, then the value of reliable experiments, gold-standard datasets, replication studies, and organizations dedicated to all the above rises accordingly. After all, the real world is still far too complicated for any conceivable AI system to predict in full.
This could well matter as to how organizations are structured. How so? An institutional form that made sense for a lone researcher with an idea and some graduate students may not be what makes sense when what we need is a better way of integrating experiments and data with AI, automation, and replication.
So the right response to AI is not merely to sprinkle such tools into existing labs and hope for the best. We should ask which organizations are best suited to scientific work when some activities become much cheaper and others become much more valuable.
The answer to that question likely points toward a more pluralistic ecosystem.
Different kinds of science need different organizational forms
Another oddity about our current system is that it treats different kinds of scientific work as if they were basically the same thing.
They are not.
Some science is discovery-oriented, i.e., it is driven by curiosity, insight, and the ability to notice what others missed.
Some science is platform-oriented, i.e., building tools, datasets, atlases, instruments, and software that make many future discoveries possible.
Some science is about validation, with the goal being replication and quality control. In a healthier ecosystem, replication centers would be present everywhere, rather than being viewed as side quests.
Some science is translational, i.e., focused on trying to connect research to real users, institutions, hospitals, schools, and the like.
And some science is mission-oriented, i.e., a sustained and coordinated effort aimed at an ambitious, identifiable goal. The Apollo program, disease-focused clinical networks, and DARPA (and its imitators) all fit this mold better than the usual investigator-initiated grant.
In all of these cases, there is no reason to think that the organizational or bureaucratic form should be the same.
A reform agenda should explore a larger design space
For decades, the science-policy discussion has been rooted in an oddly stunted imagination about institutions. Folks can picture a university lab, or a national lab, or DARPA, or a corporate R&D group (with an obligatory mention of Bell Labs and Xerox PARC). And in the past few years, they may mention Focused Research Organizations (FROs). That is often about as far as the institutional imagination goes.
But the design space for research organizations is enormous.
These organizations can vary along many dimensions, including:
whether they are oriented toward discovery, validation, or platform-building;
whether projects are expected to produce results in two years or ten;
whether funding goes to people, projects, milestones, or missions;
whether the main personnel are trainees or permanent staff;
how much internal hierarchy there is;
whether research agendas are chosen by committees, program managers, users in the field, or some hybrid;
what their fundraising/revenue strategy is;
how long the organization has existed;
whether the organization is aimed at publications, technical benchmarks, some public mission, or actual end users.
FROs vary two of those factors (focus and time), and quite successfully: There is a set of narrow and definable scientific/engineering problems that demand an intense focus over five years. And the genius of FROs is that by defining these problems, and giving a name to a new institutional form, they became more fundable and legible.
But there are many types of problems that go unaddressed in our current system. Likewise, there are many more dimensions or factors that we could attend to within various scientific organizations in order to solve them. The possibilities here are wide, limited only by our imagination. Institutional isomorphism would be an issue even if we lived in a time of stability and stasis, but it is an especially bad idea in the age of AI.
The most vital structural reform is straightforward in principle, even if it’s politically difficult: The federal government should stop treating universities as the default home of publicly funded research.
To be sure, universities are among the major achievements of human civilization, and they remain excellent at many things: training, disciplinary depth, curiosity-driven inquiry, and the long cultivation of scholars.
At the same time, it is unlikely that the German research university model adopted in the US in the late 1800s to early 1900s is the single best way for most scientific research to be organized and performed. While universities have been integral to the post-WWII ecosystem, they should be treated as one potential grantee among many, not as the presumptive destination for most research dollars. Institutions should change as society, the economy, and technology continue to evolve.
That means expanding support for other kinds of institutions (which can include new types of centers affiliated with universities, of course—universities themselves can evolve too).
The FRO model makes sense for initiatives like building shared tools, developing field-wide datasets, and solving coordination problems that are too large for an ordinary lab but not yet attractive to industry.
Another approach that makes sense is the permanently staffed research institute outside the university system. Places like Janelia and the Allen Institute have shown what can happen when an institution is designed around long-term staff, internal collaboration, and organizational mission rather than the endless turnover of trainees and the incentives of individual grant-seeking researchers.
Yet another possibility is a hybrid public-interest lab that is a cross between a contractor, a nonprofit institute, and an R&D shop, that is, an organization that can combine government work, philanthropic support, and independent technical exploration. An older example is BBN, which was instrumental in creating the original internet. We can imagine new versions of such an organization in many scientific fields (see Eric Gilliam’s work on this).
And then there is an entire class of institutions that we will increasingly need: scientific commons institutions. Their job would be to maintain public goods for the broader ecosystem, such as datasets, automation platforms, and negative-results repositories.
In other ways, we must broaden how the government supports research. Our funding agencies mostly (albeit not always) structure much “science funding” around giving money to a project while letting the grantee assemble whatever else they need. But many research teams/labs require bundled capabilities such as access to expensive compute or to lab automation.
Federal agencies should therefore do more to experiment with larger funding packages that include support for centralized resources. Imagine a team working on protein design. Instead of receiving money alone, it might earn access to a national scientific cloud, credits for shared wet-lab automation, and support from research software engineers.
This would lower barriers to entry for new organizations and make it easier for small, high-quality teams to compete with large incumbents, rather than requiring every lab to reinvent the wheel.
We also need selection mechanisms beyond the standard model of peer review. Peer review became so dominant over the past sixty years that almost everyone started to believe it was inherent to the scientific process. That is not the case. Peer review may seem obvious, but most of the highly significant scientific breakthroughs in history happened before peer review became common. This is no surprise, because consensus committees are definitionally going to be bad at funding ideas outside the existing consensus.
A healthy scientific ecosystem would make greater use of alternative ways of deciding what to fund. The DARPA model is one such alternative: program managers with expertise, substantial budgets, and the authority to assemble portfolios without waiting for committee consensus.
But that should not be the only alternative. Lottery-based systems10 for proposals above a quality threshold could be more efficient than pretending we know how to tell apart proposals that are a tenth of a point apart. Long-term “people, not projects” funding can allow the best researchers11 to change direction when they figure out a new approach. Prizes and advance market commitments can work quite well when the government has a concrete public goal in mind.
As noted above, validation and replication will become even more critical. Given what we have seen to date, one consequence of AI will be a dramatic increase in the volume of scientific publications and claims, which may just mean more noise for both humans and AI models to sift through. We should create more institutions whose primary job is to test, replicate, and resolve uncertainty.
The workforce model also must be rebuilt from the ground up. Right now we take highly capable people, route them into decade-long training pipelines, rely on them for the daily labor of science, pay them very poorly relative to their skills and age, and then make most of them compete for a tiny number of jobs.
A more rational system would provide serious career pathways for research scientists, software developers, and other people whose main job is not to become a PI but to make the scientific enterprise work. CMU Robotics showed in the late 1980s12 that it was possible to build a world-class research environment with stable roles for technical staff and builders, not just for faculty stars. More institutions should copy that lesson.
Again, none of this suggests that universities are ineffective institutions, let alone that we oppose them. It merely means that they are not the one-and-only solution for every type of scientific work. Public policy should say this explicitly: Some forms of research belong in universities, some in national labs, some in nonprofit institutes, some in start-ups or hybrids, some in public mission labs, and some in shared infrastructure organizations. Instead of trying to squeeze (nearly) every research topic into the typical university grant, we should be asking,“What team and organization is best suited to this task?”
Openness should be far more prevalent
Publicly funded science should be open by default, with data, code, and everything else made readily available in usable, machine-readable form.
This matters for all the familiar reasons, such as reproducibility and fact-checking. But it matters even more in an AI era, because scientific progress increasingly depends on these kinds of shared materials and infrastructure, and it doesn’t work if researchers dump half-documented files into obscure repositories. The goal is to create a more usable scientific commons that humans and AI can access: high-quality datasets, interoperable infrastructure, and institutions that keep these resources alive over time.
None of that is free. That kind of openness requires organization, staffing, and money, which is why it belongs in the discussion of institutional design, not just in the discussion of norms. One of the biggest successes in AI-driven science (AlphaFold) happened only because of open databases13 of well-curated data like the Protein DataBank. We need to replicate that model many times over.
Our scientific success depends on democratic state capacity
Science policy typically crops up in national debate (if at all) as a matter of budgets, grants, and national initiatives like a “cancer moonshot.” But at a deeper level, it is about whether a democratic government can still create effective institutions.
The postwar generation did not inherit NIH, NSF, DARPA, and the national labs from the heavens. It built them from scratch by making decisions about how researchers would be funded, where they would work, and how public dollars would be used.
We have the same responsibility now. We shouldn’t merely try to tinker with institutions and funding streams that are optimized for legibility to centralized bureaucracies. That is the temptation James Scott described: seeing a living system only in the simplified terms that make it easiest for the state to monitor and administer.
Science is not a plantation of identical trees growing in neat rows for the convenience of foresters. It is a living ecosystem: diverse, unpredictable, and more fruitful than any Soviet-style planner could have specified in advance.
The question, then, is not whether to reverse the chaos of 2025. The question is whether we can do more than that, whether we admit that the old system had already become too narrow and rigid, whether we can come up with experiments in organizational form, and whether we can build an ecosystem that is more pluralistic for the era of AI.
The task is not to see science like a state. It is to see it like a gardener.
Aishwarya Khanduja is the founder of Analogue Group, an R&D platform for the emergence of new fields. She previously was a biotech founder and operator.
Stuart Buck is the executive director of the Good Science Project, a nonprofit focused on improving science. He led a major grantmaking initiative on the same topic while at Arnold Ventures.
Hannah Natanson, Emily Davies, and Dan Lamothe, “DOGE’s $1 Spending Card Limit Touches Everything From Military Research to Trash Pickup,” The Washington Post, March 9, 2025, https://www.washingtonpost.com/politics/2025/03/09/doge-government-credit-card-limits/
Paul DiMaggio and Walter W. Powell, “The Iron Cage Revisited: Institutional Isomorphism and Collective Rationality in Organizational Fields (Translated by G. Yudin),” Journal of Economic Sociology Vol. 11, no. 1 (1983): 34-56, https://doi.org/10.17323/1726-3247-2010-1-34-56
“The Cancer Moonshot,” Cancer.gov, April 29, 2025, https://www.cancer.gov/research/progress/moonshot-cancer-initiative
“National Plan to Address Alzheimer’s Disease,” ASPE, November 23, 2015, https://aspe.hhs.gov/national-plan-address-alzheimers-disease
Aishwarya Khanduja, “The Paradox of Progress: Trying to Predict Impact Often Prevents the Highest Impact,” Substack.com, the Good Science Project, December 13, 2024, https://goodscience.substack.com/p/the-paradox-of-progress-trying-to
Alex Komoroske, “The Magic of Acorns,” Medium, February 22, 2024, https://medium.com/@komorama/the-magic-of-acorns-a3c91204d5f9
Leslie Pray, “Gleevec: The Breakthrough in Cancer Treatment,” Nature.com, 2008, https://www.nature.com/scitable/topicpage/gleevec-the-breakthrough-in-cancer-treatment-565/
“Faculty Workload Survey,” Federal Demonstration Partnership, September 4, 2025, https://thefdp.org/demonstrations-resources/faculty-workload-survey/
Stuart Buck, “Texas Gas Stations, NIH-Sponsored Post-Docs, and the Foolishness of Artificially Subsidizing Demand,” Substack.com, the Good Science Project, January 1, 2024, https://goodscience.substack.com/p/texas-gas-stations-nih-sponsored
Dalmeet Singh Chawla, “They Wanted Research Funding, So They Entered the Lottery,” The New York Times, February 14, 2020, https://www.nytimes.com/2020/02/14/science/research-funding-lotteries.html
Pierre Azoulay, Joshua S. Graff Zivin, and Gustavo Manso. “Incentives and Creativity: Evidence From the Academic Life Sciences,” The RAND Journal of Economics Vol. 42, no. 3 (September 2011): 527-54, https://doi.org/10.1111/j.1756-2171.2011.00140.x
Eric Gilliam, “A Scrappy Complement to FROs: Building More BBNs,” Substack.com, the Good Science Project, October 16, 2024, https://goodscience.substack.com/p/a-scrappy-complement-to-fros-building
Ewen Callaway, “The Huge Protein Database That Spawned AlphaFold and Biology’s AI Revolution,” Nature Vol. 634, no. 8036 (October 31, 2024): 1028-29, https://doi.org/10.1038/d41586-024-03423-0
A taxonomy would be really useful. Where you started (discovery, platform, validation, translational, mission goal) is right: begin with purpose. Then the rest of the defining attributes can be added to draw out additional distinctions. How they are funded, located, timeframe, etc.
Absent a taxonomy, everything has to be described through a combination of what something is, what something seems to be, and what it isn't. So for example our funding/support model is FRO-like, but for community-defined problems, agnostic on structure (so, not an independent and time-bound FRO, but FRO-like in its active coordination model), and BBN-like in the mix of contractor, non-profit and R&D shop.
I expect that if you can prod a discussion around taxonomy -- and we start fitting the various models that exist to support research into it -- that we'd find a lot of convergent evolution. After all, we are all facing similar environmental pressures.
This rules, obviously. Have y’all created like a “taxonomy of organizational forms” for the distribution of kinds of science? Such a thing I think would be insanely compelling, both because of the generative potential and by implication to visualize how big a slice of the status quo is mechanically suboptimal for engendering the outcomes we want to enact.