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.

More than a decade ago, Pierre Azoulay made exactly this point in a short but influential Nature piece, arguing that “it is time to turn the scientific method on ourselves.” His argument was simple: if scientists insist on evidence and experimentation in their own work, they should apply the same standards to the meta-issues of grantmaking, peer review, career structures, and research institutions themselves.

Patrick Collison and Tyler Cowen make a similar argument in We Need a New Science of Progress.

Even in 2026, this idea is still far too salient. We argue about whether to fund people or projects, whether peer review is too conservative, whether younger scientists are underfunded, whether longer grants would help, or whether AI will make science more productive, but we have yet to develop enough solid empirical evidence on any of these points.

To be sure, not everything can be optimized by a tidy randomized trial. Indeed, we are fans of Lant Pritchett’s critique of RCTs in development economics as focusing too much on trivial-but-measurable issues rather than broader macroeconomic questions that were much more important. At the same time, many important choices in science funding and organization are testable, and we should test them.

We want a scientific ecosystem that is more productive, creative, and better at finding real discoveries even in areas that are unpopular. To be clear, there is almost certainly no such thing as “one right way” to fund science. Instead, we need to learn which approaches work best, in which settings, and for whom. In its ideal form, our scientific system would look more like an enormous garden — one that includes everything from tomatoes to basil to zucchini to okra — than like a field of identical corn stalks. And like a good garden, it would require deliberate cultivation and pruning (i.e., the willingness to pull things that aren’t working).

If even three or four of these experiments are actually run in the next few years via new “metascience offices” at federal agencies or through a potential lab or fund studying Inquiry itself, we would be pleased.

We are actively looking for bright minds to develop these lines of inquiry with us: if this is you, reach out to us at aish@analoguegroup.org and stuartbuck@goodscienceproject.org.

Funding Mechanisms and Selection

1. Person vs. Project Funding

Traditional project grants ask scientists to predict, years in advance, what they will discover and exactly how they will do it. That is odd, since at the frontier of scientific research, the whole point is that you can’t predict what will happen in 5 years.

A routine suggestion is that agencies should fund the person rather than the project – i.e., pick smart, talented people, give them significant grants, and then get out of their way. Such an approach would give more room for creativity and switching directions in response to new evidence, while reducing the scientist’s need to spend endless time chasing new grants.

There’s a lot to like about that idea, and programs like the HHMI Investigator Program and the Wellcome Trust Senior Investigator Awards already take this approach. A famous paper by Pierre Azoulay and his colleagues found that HHMI’s grantees “produced high-impact papers at a much higher rate” than similar NIH-funded scientists. That said, Azoulay once told me [Stuart] that he thought the real secret of HHMI grants might be that they last 7 years with near-automatic renewal, which would give scientists more leeway to tackle huge problems that aren’t reducible to the timeline of a typical NIH grant.

A randomized trial at NIH could compare person-based and project-based funding directly. It could track the degree of novelty in future publications, the rate of breakthrough discoveries, the number of genuine failures due to tackling a huge problem, and the later careers of junior lab members.

2. Blinded Peer Review

Does anonymizing a researcher’s identity and institutional affiliation reduce bias and/or increase funding for unconventional or early-career applicants?

Consider which approach would have been more likely to fund Albert Einstein in 1904, when he was a patent clerk with no academic track record, or Katalin Karikó in the 1990s, when her CV looked mediocre by American standards. Blinded review might have given these ideas a fair hearing on their merits. Unblinded review, on the other hand, might have screened out both of them before anyone read the proposal.

Peer review could be influenced by prestige, reputation, career stage, gender, race, and institutional affiliation. An experiment could randomly assign proposals to blinded or unblinded review, then compare who gets funded, what kinds of projects are supported, and which system better predicts later research impact.

Worth reiterating: this idea is exactly the opposite of “fund the person, not the project.” Blinded peer review amounts to “fund the project, not the person.” Perhaps we need both approaches as part of a broader portfolio.

3. Golden Ticket System and Peer Review Variance

Consensus-based peer review often filters out exactly the kinds of proposals that might lead to breakthroughs. After all, highly original ideas can produce polarized reactions. A few reviewers may see major promise, while others see fatal flaws. Averaging the scores tends to make those proposals look exactly like the most mediocre proposals.

A “golden ticket” system would let each reviewer champion one proposal per cycle, even if others disagree. A looser version would track the variance in reviewer scores and preferentially fund proposals that receive both very high and very low ratings.

An experiment could test whether golden-ticket or high-variance proposals are actually riskier and more innovative than consensus-approved proposals, whether they fail more often, and whether their successes are more important when they land.

Indeed, this sort of policy might actually grow more effective the more widely it is adopted. If the theoretical justification is correct, we should see:

• The lowest effect when analyzing existing data (everyone was applying under the preexisting criteria, so the proposals are as homogeneous as ever);

• The next lowest effect when used in a small pilot experiment (the program may not be widely known yet);

• A stronger effect when used in a large RCT (although researchers might still pull their punches because they fear being in the control group);

• The strongest effect when a new policy is rolled out on a wide basis to great fanfare, in a way that attracts new applicants and encourages everyone to submit different proposals than before.

In other words, even a large RCT might still underestimate the possible effect due to system-wide behavioral changes.

NSF is currently running an experiment along these lines, although the results will be kept private. That is itself worth commenting on: it is ironic that a metascience experiment by a science funding agency would be withheld from the scientific community. Other agencies, such as NIH, could engage in a larger experiment on a more public basis.

4. Partial Randomization of Grants

Research on peer review reliability suggests that panels often disagree sharply about which borderline proposals should be accepted. That implies that many grant decisions near the cutoff are already partly arbitrary.

Fang and Casadevall made the case for lottery-based funding in a 2016 mBio paper: if the review process cannot reliably distinguish similar applications, a lottery for the “gray zone” may be more honest than pretending that tiny score differences reflect any actual difference. And it might be more efficient too.

An experiment could assign borderline proposals to lottery-based or standard allocation, and then compare their later research impact, as well as whether scientists feel like the process was fair. At a minimum, we think the process might be more efficient in terms of wasted time by both proposers and peer reviewers.

5. Distributed Peer Review

Should grant applicants be required to review one another’s proposals? The UK Metascience Unit has piloted this approach, and has suggested that distributed review may have many advantages over traditional peer review (including eliminating the need to spend time recruiting outside reviewers who are often late or non-respondent, eliminates the need for burdensome panels, solicits reviews from people who are highly motivated to be familiar with the funding call, and more).

This approach could speed up decisions, streamline the process, and give applicants more insight into how proposals are judged. As for conflicts of interest, the UK team divided proposals into two large groups at random, and required applicants in one group to rate the other group (i.e., only those applications not in direct competition with their own). Still, this new idea could result in lower quality reviews and/or more homogeneity.

An experiment could compare distributed review with the standard panel review on speed, quality, fairness, and reliability.

6. Program Officer Discretion

Would giving program officers more authority to overrule peer review lead to more breakthrough science?

Program officers often have deep knowledge of their fields and long-term views of where they are headed. They may spot opportunities that panels miss, especially when novel work looks too risky or strange, which is precisely where breakthrough ideas might be lurking.

DARPA is frequently cited as a model where program managers have unusual discretion, and DARPA’s track record — from the internet to GPS to foundational work on mRNA vaccines — is often invoked to argue for more discretion elsewhere. But how much of DARPA’s success is actually attributable to manager discretion, as opposed to its unique institutional position and its ability to sell research results to a large “customer,” is itself an open question.

An organization the size of NIH could experiment with giving certain program officers the discretion to bypass peer review, and then see whether their funded projects show more promise over time. To be sure, as with other interventions discussed above, the true effect of such a policy might be seen in the long run, as new program officers are recruited and as scholars are incentivized to submit ideas that they previously would have left in the file drawer.

7. Size of Grants

What is the optimal size of a scientific grant? This question gets surprisingly little attention given how much money is at stake. NIH R01 grants typically provide around $400,000 per year in direct costs. Is that the right number, or would science be better served by larger and fewer grants?

There are potential tradeoffs at any particular size of the average grant. Smaller grants would arguably spread resources more widely, fund a wider range of investigators, and possibly even increase the diversity of ideas. But smaller grants might force scientists to operate on a shoestring or to cobble together multiple small awards just to keep a lab running, which would increase the potential administrative burden plus the time spent grant-writing. Larger grants might give scientists more freedom to pursue longer-term questions without constantly begging for the next dollar.

An experiment could randomly vary the size of awards within the same program, holding total dollars constant, and then track the number and quality of publications, the novelty of the work, the number of researchers trained, and the cost per meaningful discovery.

8. Concentration of Grants: Should There Be Limits?

Should there be a cap on how many grants one scientist can hold at the same time?

Right now, there is no hard limit at NIH or most other agencies. Some scientists hold four, five, or even more concurrent R01-equivalent awards. That raises an obvious question: is the 5th grant to a single PI producing more value than a 1st grant to someone who currently has no funding at all?

The argument for concentration is that some scientists are simply more productive than others, and giving them more resources is just rewarding excellence. That could well be the case, but the case against concentration is also seemingly persuasive. After all, there are diminishing marginal returns in most areas of human life. Moreover, after a lab grows to a certain point, the PI is mostly not doing the science, but instead is acting as a manager of other scientists. That might be highly productive in some instances, but it might be the case that the post-docs or graduate students who are doing most of the real lab work would do equally well (or even better!) if more empowered to follow their own ideas.

There is also a portfolio theory argument here. If the goal is to maximize the probability of at least one breakthrough across the entire system, you may want more independent bets rather than fewer large ones. Five PIs with one grant may offer more “shots on goal” than one PI with five grants (emphasis on “may”: given the degree of groupthink and conformity in science, it is also quite possible that five PIs may just end up doing variations of the same thing).

The NIH has occasionally discussed grant caps, and the idea always generates fierce opposition from the scientists who would be affected (which is to say, the most powerful scientists in any given field).

In any event, the question is empirical, not political. An experiment could compare outcomes under different regimes — say, a maximum of three concurrent R01s in one arm, versus no cap in another — and track total scientific output, the diversity of funded ideas, and whether the cap pushes talented investigators to mentor more independent scientists rather than running everything themselves.

Side note: an NIH-wide policy makes little sense, in that a computational biologist working with public datasets needs a very different budget than someone running a wet lab with expensive reagents and a dozen postdocs.

9. Length of Grants

Most scientific grants last three to five years. Given review times and the need for resubmission, scientists often start planning renewals long before the current award ends. That can push them toward safer work with quicker payoffs.

As already discussed above, the well-known study by Pierre Azoulay and colleagues comparing HHMI investigators to NIH-funded researchers found that HHMI’s “person not project” model seemed to produce more impact. But one plausible hypothesis is that the real driver of HHMI’s success was simply the length of the grants: HHMI made seven-year awards with near-automatic renewal. That timeline gave researchers breathing room to tackle difficult problems where a breakthrough might take five years, in contrast to the NIH timeline where scientists need to stick to problems that will generate plenty of publishable results in the first three years. Pierre Azoulay even told me that he suspected that the time frame — seven years plus near-automatic renewal — was actually the key factor, rather than “person not project” per se.

If longer grants are the real driver, that would be one of the most practical policy levers available. Funders would not need to redesign their review systems, change their evaluation criteria, or adopt new metrics. They would just need to write bigger checks less often. A randomized experiment could compare shorter and longer grants, and then track the kinds of problems scientists choose, publication patterns, failure rates, and eventual impact.

10. Team Design: Size, Stability, and Turnover

What kinds of teams produce the best science? Research by Wu, Wang, and Evans (Nature, 2019) suggests that small teams are more likely to disrupt existing paradigms, while large teams are better at consolidating and extending established knowledge. A closely related question is whether it is better to fund a new assistant professor launching an independent lab or to add another person to a very large and already successful group. Funding the new investigator might increase the diversity of ideas and approaches, whereas funding the established lab may produce faster short-term outputs like papers and patents.

There’s another version of the question here: what is the impact of team/lab size not just on the science but on the people themselves? For example, if you’re a graduate student joining a lab with 30 people, you’re almost certainly signing up to work with someone who is well-known, and who will be able to further your career. At the same time, you could end up competing with many other trainees for the PI’s attention, and you may end up working on a small piece of a large project that was created long before you arrived. On the other hand, if you join a much smaller lab, you will likely get more face time with your advisor, more latitude to define your own project, and less internal competition.

We don’t actually know which environment (large lab vs. small lab) produces better scientists in the long run. An experiment (likely at NIH) could vary the likelihood of grant acceptance based on lab size, and then could track outcomes like scientific productivity of the labs as a whole, as well as individual-level outcomes as to the graduate students and post-docs that emerge from the labs. Whatever the outcome, this information would be useful both to funders and to thousands of students making a consequential decision with essentially no evidence to guide them.

11. Interdisciplinary Team Formation

Many important discoveries happen at the boundaries between fields. But interdisciplinary collaboration often amounts to the equivalent of stitching together an elephant’s trunk, a goat’s face, a tiger’s body, and a horse’s tail, and then expecting it to be a functional animal.

Simply putting people from different backgrounds together is not enough. The Human Genome Project succeeded as an interdisciplinary effort because it required biologists, computer scientists, and ethicists to work toward a shared concrete goal with clear milestones. By contrast, many university “interdisciplinary institutes” have become collections of disciplinary silos that happen to share a budget line. Goodhart’s Law takes effect: people aim to optimize for the measurement of interdisciplinarity as opposed to optimizing actual scientific work. Successful interdisciplinary work may require shared physical space, longer timelines, structured time for building common understanding, or individuals who can bridge multiple fields.

One design could compare different models for assembling and supporting interdisciplinary teams, including architectural design as to where offices are located and institutional choices as to which conferences people are funded to attend. There may be other ideas as to how best to create true interdisciplinary collaboration, and we should be exploring them all.

12. Training and Mentorship Methods

What kinds of scientific training actually work best? The usual model is apprenticeship: junior scientists learn by working closely with senior mentors. In the best cases, that model can result in high-power labs that produce a string of future Nobel Prize winners. But as far too many people know, direct apprenticeship can involve unhealthy power dynamics, PIs that take too much credit for other people’s work and ideas, and worse.

There is also a larger question about whether our best scientists are spending their time well. Consider an analogy from basketball. Steph Curry is the greatest shooter in NBA history, but he still works constantly with his shooting coach Bruce Fraser to find new ways to improve. There is no equivalent in science, i.e., where a Lasker winner works with a daily coach as to how to become even more creative and productive.

Why isn’t it possible to have the same sort of expert trainer in science — someone whose entire career is dedicated to finding new ways to help expert scientists get even better at management, creative thinking, and the like?

Instead, we do the opposite. We constantly “promote” the best scientists to be lead investigators in their own labs, where they then end up spending most of their time on fundraising, hiring, training and teaching, dealing with internal bureaucracy, and more, rather than on science!

We would never “promote” world-class athletes at the height of their careers to spend 90% of their time coaching other people and dealing with sports agents rather than actually playing. Instead, we let the top athletes focus on their sport while giving them external help from strength coaches, shooting coaches, and more.

An experiment here could compare different training structures and mentorship models over the long term, tracking productivity, career advancement, mental health, and eventual impact.

13. Early vs. Late Career Funding

Many of the greatest scientific breakthroughs in history have come from people in the early to mid-20s, whether Newton’s calculus, Einstein’s relativity, or Watson co-authoring the paper on DNA structure.

But in today’s academic system, there is almost nowhere that a genius at age 23 would be allowed to thrive. NIH data show that the average age at first R01 grant has risen from about 36 in 1980 to about 43 today. That is seven additional years of career spent in subordinate positions, often doing work that advances someone else’s agenda.

There are small-scale programs that attempt to provide funding to early-career investigators, but these seem insufficient. We should experiment with larger-scale efforts to steer more funding to younger investigators across all areas of science.

14. Graduate Student and Post-Doc Independence

Outside of some fellowship programs, graduate students and post-docs [which we will call “trainees” for short] are usually funded through their PI’s grants. That means that the trainee’s financial survival depends on keeping the PI happy.

This power dynamic can be unhealthy. Anecdotally, we have heard of many cases where a post-doc from another country (e.g., China or India) is forced to p-hack or even worse, because failing to produce a “good result” for a demanding PI might jeopardize their immigration status! More broadly than that, any trainee who disagrees with their advisor’s scientific direction, or who wants to pursue a side project, or who has a legitimate complaint about how they’re being treated, has to weigh those concerns against the fact that the PI controls their paycheck and ultimately their future career.

What if all trainees were funded independently through fellowships or training grants that they hold personally, rather than through their advisor’s research grants? The advisor would still provide mentorship and lab space, but the financial relationship would be different. A student with independent funding might be more empowered to try new ideas without worrying about whether the PI will cut them loose, since they can vote with their feet. And PIs would have to earn their trainees’ continued respect.

An experiment could randomly assign trainees to either PI-funded or independently-funded tracks within the same departments, and then compare their research productivity, the novelty of their work, their willingness to switch topics or labs, their completion rates, their mental health, and their long-term career outcomes. If independently funded trainees produce more original work and are more likely to stay in science, that would be a good case for restructuring how we fund graduate training and post-docs.

Incentives and Scientific Culture

15. Negative Results and Failure

Failure and null results are an inherent part of science. After all, if the results of an experiment could be perfectly predicted ahead of time, we would have already made that scientific discovery and the experiment wouldn’t be new science. But null and negative results are often hidden in file drawers because they are less exciting and less likely to be accepted for publication. As a result, other scientists can waste time on dead ends, and ideas can look much more certain than they really are.

The NIH Director is interested in requiring the publication of null results, as are people at the White House.

But therein lies a potentially interesting experiment. What if NIH created different disclosure requirements: some grants might require negative-results reporting, while other grants would just be business-as-usual.

The experiment could then track whether the requirement improves reproducibility, and if so, by how much. Over the long term, it would be interesting to see the effect on scientific culture and on the rate of discovery.

16. Red-Teaming Scientific Consensus

When a field is dominated by one theoretical framework or approach, should science agencies deliberately fund one or more “red teams,” i.e., serious critics of the current paradigm?

Scientific consensus can often turn into groupthink, whereas we know that many scientific breakthroughs were initially dismissed, ignored, or even ridiculed.

Red-teaming means deliberately supporting researchers who challenge the currently dominant ideas. This may be especially useful in fields where progress has stalled despite large investments over decades, or where the dominant theory has not been tested as rigorously as people assume. The Templeton Foundation has funded some work along these lines in physics, supporting challenges to certain interpretations of quantum mechanics.

An experiment could identify fields with an especially strong consensus around a view that has yet to be unambiguously successful in the real world, fund red teams in some but not others, and compare whether that type of funding leads to new insights and potentially breakthroughs.

Commercialization and Translation

17. Optimal Commercialization Pathways

What is the best path from discovery to market?

The standard university route runs through tech transfer offices, a system created largely by the Bayh-Dole Act of 1980, which gave universities the right to patent federally-funded inventions. But that system is often criticized as slow, bureaucratic, and ineffective, and it might be a far better idea to give professors/inventors the presumptive right to file for patents and negotiate with venture capital directly, with the university perhaps getting a small automatic fee.

The best pathway probably depends on the technology, the market, and the institution. We could imagine several experiments that would assign similar discoveries to different commercialization routes, and compare the time to market, financial returns, researcher satisfaction, and eventual social impact.

Methodological Rigor

18. Replication Requirements

Replication is an inherent part of the scientific process, yet too many scientists refuse to engage in direct replication because it is less likely to be funded or published. We have a collective action problem here, and large funders like NIH or NSF could solve it, because everyone cares about funding.

The Reproducibility Project: Psychology (2015) attempted to replicate 100 published studies and found that only about 36 percent produced statistically significant results in the replication, compared with 97 percent in the originals. The Reproducibility Project: Cancer Biology found similar results; in the cases where they even attempted a replication experiment (often impossible due to the lack of details and a lack of cooperation from the original labs), the effects in the replication experiments were only 1/6th the size of the original publications!

That said, we can’t replicate everything–that would be incredibly inefficient. How does this turn into an experiment? An experiment could vary replication requirements across similar research areas and compare both the reliability of published findings and the pace of genuine discovery.

19. Preregistration Impact

Does preregistration improve credibility, or does it get in the way of exploration? The theory behind preregistration is that it will reduce p-hacking, HARKing, and other questionable practices. But it may also fit some fields much better than others, and in some settings it may add bureaucracy without much gain.

In some fields (clinical trials), preregistration is already standard practice and is even legally mandated for trials submitted to the FDA. That said, we could do experiments in other fields, such as preclinical research. An experiment could require preregistration for some grants but not others, and then compare reproducibility, false positives, and the rate of useful exploratory findings. We should also study the model of registered reports, although a funding agency would need to collaborate with journals so as to set up a system in which papers were accepted based on the research design rather than the results.

Artificial Intelligence

Caveat: AI is a fast-moving field, and all of the experiments below would make sense if, and only if, we could deploy them quickly and get feedback on results within a year (or hopefully 6 months).

20. AI for Detecting Scientific Fraud and Errors at Scale

How successfully could AI screen the scientific literature for fraud, errors, and questionable practices at a scale humans cannot match? Today, most error detection relies on whistleblowers along with a few intrepid and dedicated investigators like Elisabeth Bik. But new AI tools could help scan thousands of papers for image manipulation, impossible numbers/data, or suspicious statistical patterns.

As an experiment, we could randomly assign different fields or sub-fields to be examined by different tools that might detect fraud or questionable research practices. We would then check for the downstream impacts on practices in those fields (e.g., what some would call data hygiene and similar issues). At the same time, we would want to look for possible downsides, such as a drag on creativity or exploration. As well, we would want to check for possible gaming (i.e., maybe people who are more worried about being caught p-hacking or cheating will just up their game by finding more creative ways to cheat).

21. AI Impact on Scientific Diversity

Will widespread AI use make science more diverse or more uniform? If most scientists use similar AI tools, research may start to converge more and more on the same methods, questions, and frameworks. A recent research paper by James Evans and his colleagues showed that scientists using AI published more papers but with a narrower range of topics and engagement with colleagues. On the other hand, AI tools could (at least in theory) help scientists borrow methods and ideas from distant fields.

This is more naturally studied as an observational or quasi-experimental design than as a true randomized experiment, since researchers cannot easily be prevented from using whatever tools they choose. We could compare fields with different patterns of AI adoption and track whether their agendas and topics become narrower over time.

22. AI-Driven Experimental Design

Can AI systems design better experiments than human scientists? This possibility might be especially promising in areas like materials science, drug discovery, and synthetic biology.

But AI-designed experimentation might miss out on tacit knowledge, real-world constraints, or unusual observations that a human would notice. A metascience experiment could partner with AI firms to look at human and AI workflows; we would want to look at results like novelty and surprisal (i.e., does a proposed experiment draw on a wider literature and propose more novel ideas to test)?

23. AI as Assistant for Peer Review

Peer reviewers for federal agencies are usually overworked and behind schedule (as has been the case for me on many occasions!). AI could help, if allowed. That is, AI tools could help peer reviewers by checking basic statistics, determining the novelty of a proposal, and evaluating a scientist’s prior work and track record. That sort of assistance could free human peer reviewers to focus on larger issues, such as the significance of the ideas at hand and the theoretical advances being proposed.

That said, AI-assisted review could lead to hidden bias against certain people or ideas, as well as reducing human attention in ways that undermine the process.

We could launch an experiment here by varying the level of AI assistance across peer reviewers, while looking at any number of outcomes (speed, quality, bias, novelty, and user acceptance).

24. AI-Assisted Grant Writing and Proposal Inflation

One of the most immediate uses of AI in science is likely to be grant-writing. That could save time, but it could also make proposals more formulaic and harder to distinguish. Moreover, federal agencies like NIH have already moved to ban the use of AI (“Applications that are either substantially developed by AI or containing sections substantially developed by AI are not considered the original ideas of applicants and will not be considered by NIH.”).

An experiment could randomly provide some applicants with access to approved AI tools and training, while other applicants would be forbidden from using AI. Reviewers could also be randomized as to whether they know that AI assistance was permitted. The study could then measure proposal quality and novelty, funding rates, and ultimately, the downstream research that gets funded.

25. Autonomous Lab Platforms vs. Conventional Lab Workflows

Do partially or fully autonomous labs produce better science than conventional labs?

There is a great deal of interest (and hype!) around self-driving labs and automated experimentation. But we don’t actually know whether these systems and companies will improve the quality of science and the follow-on innovation, rather than just increasing the volume of science being produced. Science isn’t measured by the pound, after all.

A metascience experiment could compare projects conducted through conventional lab workflows versus more automated or partially autonomous systems. We would look at the cost, the time spent, reproducibility, and the rate of genuinely surprising findings.

Scientific Communication and Publication

26. Adversarial Collaboration

Some scientific disputes go on for years because each side runs studies with its own favorite methods and theories, but dismisses the other side’s work. Adversarial collaboration is a concept developed by Daniel Kahneman, who described it in a 2003 American Psychologist article and later in Thinking, Fast and Slow. The idea is to force both sides to agree in advance on the design and analysis of an experiment, while committing to accept the results no matter how they turn out.

One of the best-known examples is the adversarial collaboration between Kahneman himself and Gary Klein on expert intuition, which produced a joint 2009 paper. The two researchers originally held different views about when expert judgment could be trusted. By working together, they were able to identify the specific conditions under which each view was correct, a result that neither would have reached alone.

A funding agency like NIH could create an adversarial collaboration program, fund such efforts in some fields but not others, and then compare whether scientific disputes are resolved faster and whether the process generates insights that neither side saw in advance.

27. Open Science Mandates: How Much Openness Is Optimal?

How much openness is actually best? As much as I have argued for open science for the past 15 years, mandatory openness could also create perverse incentives. That is, scientists might hold results back until they have extracted every paper they can, or they might avoid collecting expensive data if their competitors can free-ride immediately.

The NIH’s 2023 Data Management and Sharing Policy is the largest-scale openness mandate in US science. It is still too early to assess its effects, which is precisely why experimental variation in open science requirements would be valuable.

A metascience experiment could randomly assign different openness rules within the same grant program, such as immediate sharing, delayed sharing, data-only sharing, or sharing on request. It could then compare the levels of reuse and reproducibility over time, as well as the cost. (Not all data sharing is worth paying for it forever, and we should be measuring the cost of preserving all data versus the benefits as in how often it is reused).

Research Environment and Institutions

28. Geographic Concentration vs. Distribution of Funding

Is it better to concentrate funding in elite institutions and places, or spread it more widely around the country? Right now, research funding tends to be heavily concentrated in a small number of geographic clusters on the coastal US, with exceptions like Houston, Austin, Chicago, etc. Some think that this concentration of funding makes some sense. Perhaps there are agglomeration effects arising from dense talent networks and serendipitous contact from people who run into each other more often.conven

That said, some of the best ideas in research come from unexpected places, like Katalin Kariko in the 1990s. Perhaps it is the case that federal funding should be spread around the nation more broadly in an attempt to elicit more innovative ideas from places and people that wouldn’t have seemed obvious in advance.

A metascience experiment could direct supplementary funding to researchers outside major hubs and then compare their productivity and impact with similar researchers at elite institutions.

29. The Optimal Scientific Conference

Scientists spend a lot of time and money on conferences. The assumed benefits seem to be obvious: scientists get to hear the latest work in their field, and to have a more serendipitous exchange of new ideas. But we have no idea how much any of that actually matters.

A metascience experiment could fund a bunch of small workshops, virtual meetings, and unconferences, across many fields, and then look for outcomes such as the extent of new collaborations and the rate of idea generation.

30. The Sabbatical

The idea of a sabbatical for researchers rests on the idea that any scientist needs time for deep work, rather than being distracted by the constant demands of teaching classes and committee involvement. That intuition is widespread, but we don’t know actually know how much difference it makes (if any) to let scientists have sabbaticals.

A metascience experiment could randomly offer some mid-career researchers a fully funded semester or year with no teaching, administrative work, or grant-writing obligations. We would then compare their creativity, well-being, and career satisfaction with a control group. For the first time, we would have rigorous evidence about one of the most widespread academic practices.

Alternative Funding Models

31. Prizes versus Grants

Grants fund inputs, while prizes fund outputs. Quite the opposite approach! Prizes seem to work best when the problem is well-defined and success is measurable. A 2007 Harvard Business School study of InnoCentive (now part of Wazoku), an online prize platform for scientific and technical problems, found that problems were more likely to be solved by people from outside the problem’s home discipline.

But as yet there is little systematic evidence that prizes work better than grants. A metascience experiment could take a set of well-defined scientific problems and randomly assign some to be funded through grants and others through prize competitions. We would then compare the quality and speed of solutions, the diversity of applicants and solutions, and the total cost of the project.

32. Forecasting Tournaments for Scientific Proposals

Grant review at major funding agencies usually asks peer reviewers to come up with overall scores and narrative judgments, but rarely are they asked for clear predictions of whether a grant will meet its milestones. (Caveat: I have served as a peer reviewer for NSF, and their particular program did ask us all to estimate probabilities as to grant outcomes.)

Philip Tetlock’s work on superforecasting — including the IARPA-funded Good Judgment Project and his 2015 book Superforecasting — showed that some people are better calibrated than others when making predictions about uncertain events. Not only that, this skill is measurable and trainable.

The same principle could be applied to science funding. Reviewers and program officers could be asked to estimate the probability that a proposal will lead to a major paper, a useful tool, a replicable result, or a successful translation.

An experiment could then compare standard peer review versus explicit forecasting. Do the reviewers asked to make forecasts end up making better judgments? And how do we calibrate reviewer judgments over time? These are all unanswered questions in metascience.

Again, if even three or four of these experiments are actually run in the next few years via new “metascience offices” at federal agencies or through a potential lab or fund studying Inquiry itself, we would be pleased.

We are actively looking for bright minds to develop these lines of inquiry with us: if this is you, reach out to us at aish@analoguegroup.org and stuartbuck@goodscienceproject.org.

Thanks to Tom Kalil, Kris Gulati, and Misha Teplitsky for feedback on this essay.

Expeditions is an experimental fund organized around one slippery quality: interestingness. We are pursuing the core argument of Kenneth Stanley and Joel Lehman’s Why Greatness Cannot Be Planned: the most consequential discoveries are rarely reached by marching toward a predetermined objective. We back independent researchers, amateur inventors, and others chasing ideas that feel as though they should exist, even when their purpose isn’t yet legible.

Since soft-launching last year, a few things have evolved:

• We have a new website for the fund: expeditions.earth.

• We now back teams, in addition to individuals.

• Every Expedition commits to a publishable artifact as part of our acceptance criteria. This could be a report, an essay, a public good, or anything else that feels right.

This season, we ask: what does it take to be a sovereign, cooperative human inside complex systems? How do we ground ourselves in our bodies and experience, and reach for tools both fringe and rigorous?

The six Expeditions below inquire from completely different directions. We’re pleased to introduce them.

Material Farming — Filipe Natalio

Keywords: materials science, plant biology, sustainability, biomanufacturing, agriculture

Everyone learns that plants need sunlight. Material Farming bets that’s just one mode of their being. Plants once fed on sugars and other nutrients for millions of years, when Earth was hospitable to photosynthesis. If they still remember how to metabolize sugars, then, in theory, we could promote certain properties in plants by feeding them purposefully tailored diets.

If it works, we could grow food where sunlight is scarce and grow materials with properties baked in — like cotton that’s already blue and needs no dye — without resorting to toxic chemicals or genetic engineering.

The Space Between — Cameron Boehmer

Keywords: rouleaux, blood coagulation, microclots, structured water, Long COVID, grounding, infrared light, EMF, independent research, microscopy

When red blood cells stick together, they don’t flow into our narrowest capillaries, and our tissues suffer oxygen and nutrient deprivation. The Space Between is an independent investigation into blood coagulation and the subtle ways modern living disrupts the flow on which every cell depends. Led by a self-funded researcher and former software engineer, this expedition explores how cheap, everyday interventions — grounding and earthing, sauna, infrared light, and reducing EMF exposure — might prevent blood cell clumping. It is a deliberate attempt to investigate fringe territory with discipline rather than dismissal, treating unfashionable questions with the rigor they deserve.

The implications reach further than they first appear: Long COVID, estimated to affect 300–400 million people worldwide, the harmful effects of air travel on the body, sleep quality, inflammation, and vitality in general. The work will be published as a study in two versions — one scientific, one written for a lay audience — so that the findings are useful to anyone, not only to specialists.

Live Longer World — Aastha Simes

Keywords: longevity, public health, evidence synthesis, accessibility, preventive care

Everyone wants to be healthier; almost no one has time to parse through the snake-oil in longevity. Live Longer World does that work for you, metabolizing vast amounts of health research and filtering what’s real from what’s a hoax.

The reframe is what makes it powerful: instead of pharmaceuticals and risky interventions, Aastha starts with your environment: light, air, water, EMF, toxins. The result is a set of interventions that are practical, accessible, and testable, shared with genuine respect for readers’ time and attention.

Health is a fundamental leverage point: when people feel better, they show up better everywhere else. The protocols discussed help us live closer to how we evolved without giving up the comforts of the modern world. Aastha also interviews scientists at the frontiers of longevity and biology. The Live Longer World podcast runs in the spirit of open inquiry and is unafraid to challenge conventional health knowledge.

Conditions of We — Stephen Le

Keywords: trust, coordination, game theory, Elinor Ostrom, cooperation, anthropology, mediation, field research

Finding collaborators has never been easier, yet most initiatives still fall apart — not for lack of talent, but for lack of trust. Conditions of We investigates exactly when trust emerges, holds, and breaks down.

Stephen treats trust as something built, with specific and often expensive parts: not a vague feeling but a set of conditions you can name, design for, and repair. If it works, the payoff is large. As capital concentrates in fewer hands, the ability to coordinate may be the biggest wedge left for everyone else, and better tools for trust could unlock cooperation at every scale, from two people to whole institutions.

Homunculus — Jad Rabhi & Loriann Razafimandimby

Keywords: organic intelligence, synthetic biological intelligence, bio-interfaces, predictive processing, neuroscience, cybernetics, living systems

Artificial intelligence is cool, but there’s something even better: communicating with organic intelligence directly. Homunculus is an expedition into interfaces with living systems — the messy, adaptive intelligence that biology has been refining for billions of years. Rather than simulating intelligence in silicon, this work asks how we might listen to and converse with the intelligence already present in living tissue.

Niche Design — Itay Dreyfus

Keywords: design research, cultural anthropology, publishing, pseudoanonymity, identity, zines, long tail

Some products are quietly perfect and never go mainstream — and never want to. Niche Design is a zine about that long tail: the passion-driven corners of design the market overlooks.

For 2026, Itay turns to pseudoanonymity as a vehicle for culture-making. For the first time, “anon” accounts are being treated as legitimate voices, marking a shift from identity as something fixed to something you create and deploy — a future glimpsed today in obscure forums.

The artifact is the second volume of Niche Design: a self-funded study of design culture, now extended into pseudoanonymity as an evolving form of authorship.

Expeditions is run by Karol Majewski, Program Director for Expeditions under The Analogue Group.

If you’d like to be a patron of interesting work, please reach out to us at expeditions@analoguegroup.org.

We’re delighted to share that SovietRxiv (sovietrxiv.org), an open-access archive that translates Soviet-era scientific literature into English, has launched. The platform opens with more than 15,000 full-text translations spanning physics, mathematics, engineering, and other fields: material that has been largely unreadable to English-speaking researchers since it was first published.

The launch corpus includes roughly 15,000 full-text translations retained alongside their translations. Every paper is paired with its original PDF, an English translation, an abstract, and similarity search built on document embeddings, so a researcher can move from a reference to its underlying source in a few steps. SovietRxiv is a sibling to ChinaRxiv, part of a broader effort to make neglected scientific literatures legible across languages.

SovietRxiv is supported by the Revival Fund, an initiative of Analogue dedicated to excavating, validating, and reviving neglected or forgotten research lineages. “Nestled in these archives are threads that have stayed unpulled in the Western scientific corpus, for all sorts of reasons,” says Seconds. “This work can reopen doors that had closed to time.”

Still, millions of pages of Soviet-era science remain available online but unread in English. SovietRxiv’s next phase focuses on expanding the corpus in response to what researchers most want translated, and raising support to fund that work. A “chat with the corpus” feature, which lets users query the archive in natural language, is currently in prototype.

About SovietRxiv SovietRxiv is an open-access archive that translates Soviet-era scientific preprints and journal articles into English, retaining the original-language PDFs alongside each translation. It is built by Seconds and supported by the Revival Fund at Analogue. Learn more at sovietrxiv.org.

About the Revival Fund The Revival Fund excavates, validates, and revives neglected or forgotten research lineages. It is an initiative of Analogue, a philanthropic R&D platform that helps new fields emerge before traditional institutions know how to fund or support them. Learn more at therevivalfund.com and analoguegroup.org.

Want to support this work? Let’s get in touch: wendi@analoguegroup.org and aish@analoguegroup.org

In our selection process, we focused on three main principles:

• The applicants’ rigor of methodology (do they know how to carry out the work to best revive the research, and do they have the relevant experience)

• The research’s maturity for revival (does the idea or research speak to a particular stream of work today that could benefit from an added perspective or experimentation)

• The applicants’ clarity on where the work should go to flourish beyond Revival (do they know the most effective audience, and are we able to help)

As a small, nimble entity in formation, we hope to bypass path dependencies of the larger funding bodies in recognizing and supporting ideas illegible or overlooked elsewhere.

In that spirit, we are pleased to announce our inaugural cohort of grantees and their projects.

Subscribe now

Aiden Sagerman (History of Science, Harvard) is reviving diagrammatic reasoning:

Through the early twentieth century, topology relied on a distinctly visual language, geometric intuition captured by diagrams, but by mid-century it had shifted to algebraic logic. Sagerman is charting that transition by zooming in on Samuel Eilenberg and Saunders Mac Lane’s invention of category theory between 1942 and 1945 and the origin of the commutative diagram and their role in the invention of category. He believes that examining how visual reasoning was essential to twentieth-century mathematical theory-building offers a concrete way to understand the loose notion of “creativity” that LLMs struggle with.

Nora N. Khan (critic and curator) is resurfacing epistemic virtues:

Metascience discourse today is largely about how to improve science funding, research, and publication. Khan is after the variables underneath it, the environmental and organizational conditions that facilitate breakthrough inquiry. Her work will focus on recovering the epistemological models of forgotten collectives (the Detroit Geographic Expedition and Institute, Gene Youngblood’s Intermedia columns, the KUNCI Study Forum, the Living Midnight Narrative Outfit) as “epistemic holding environments” that held a generative space for the uncertainty and ambiguity of visionary knowledge production.

Iris Long and Yanlin Lu (curator; Princeton) are reconstructing somatic science:

In 1981, Qian Xuesen, the father of Chinese rocketry, coined “somatic science” (renti kexue), framing the body as a cybernetic “open complex giant system” with multiple “functional states” and even “extraordinary functions.” In the 80s and 90s, this became a serious national effort under the banner of an “Eastern Scientific Revolution,” one which was later dismissed by critics as the greatest pseudoscience movement in contemporary China. Long and Lu are relooking at the archive of this anomalous bodily phenomena research, and inviting five artists to reenact the experiments. They believe its vocabulary of “functional states” is significantly analogous with current work on interoception and embodied cognition.

Daniel Burger (EightSix Science) is reviving brain-in-vat research:

From the 1920s to 1971, Soviet and American physiologists pursued isolated brain perfusion – keeping a brain alive and functional outside the body. Experiments included Sergei Brukhonenko’s “autojektor” which sustained isolated dog heads and Robert White’s cooled primate brains in the 1960s and 70s. Over time, shifting funding priorities, an absence of immediate downstream applications, and a technical dead end led the program to collapse. Burger is restarting it now that organ perfusion is a mature commercial industry and post-mortem perfusion work has shown new confidence in brain tissue resilience. He is working towards a Stage 1 proof-of-concept of a controlled “brain-in-vat.”

Jenn Leung and Chloe Loewith (Lifefabs; Future Impact Group) are rebuilding synthetic intelligence:

There is still no standard for encoding information to living neurons, and no agreed way to measure what a brain-on-a-chip is “doing” or whether it learns. Leung and Loewith, early collaborators with Cortical Labs (whose CL1 runs on roughly 800,000 lab-grown human neurons, and which once taught a neuron culture to play Pong), are reviving the bottom-up tradition of artificial life (Alife) to build reproducible benchmarking standards at the API layer. They are using multiplayer games as the interface and training the neuronal culture as game agents inside them.

Abhinav Singh (Harvard) is reviving molecular inference:

In the 2000s researchers showed that statistical algorithms could be run as chemical reaction networks physically built from DNA strand displacement (chemistry as a computational substrate). However, this “molecular inference” gained only slow progress because real molecules leak, cross-react, and behave differently than simulations predict. Singh wants to make DNA computing an engineering discipline: a reproducible simulation-build-test pipeline that defines “failure predictors” for leakage and crosstalk, and widens the robust operating range with GPU-scale sequence optimization.

Seconds_0 is reintroducing Soviet research:

As English became the globalized language of twentieth-century science, Russian journal papers were marginalized and their results forgotten. Seconds, who built and runs Chinarxiv.org (22,700+ machine-translated Chinese papers with full text, search, and a free public API) is adapting that infrastructure to Soviet research: automated OCR, technical Russian, mathematical equations and diagrams, and PDF generation. The grant funds the R&D and we are internally curating the first journals to surface their “alpha” papers with help from Ulkar Aghayeva and Soviet history experts.

Outside of this inaugural cohort, the Putnam Papers is our other internally incubated project which follows the life and work of forgotten mid-century American physicist, Peter Putnam. By sourcing a set of obscure scientific papers and interviews, we aim to reconstruct and re-contextualize the ideas of an influential (to his more famous contemporaries) thinker through a set of commissioned essays – the first of which is being written by Kartik Tiwari, a PhD student at the University of Bonn working towards doctorates in both physics and philosophy. His work will look at Putnam as an intellectual link between Arthur Eddington (best known for providing the first empirical proofs for the theory of relativity) and John Archibald Wheeler (Putnam’s research advisor and a giant of 20th century physics).

Please get in touch with Hiya Jain [hiya@analoguegroup.org] if you would like to learn more about the project or pitch a research essay.

We extend our gratitude to Peter Wang, the Noticing Foundation, and the Good Science Project whose support made the fund possible; if you’d like to get involved as a patron, please get in touch with Wendi Yan [wendi@analoguegroup.org] and Aishwarya Khanduja [aish@analoguegroup.org]

The Resonant Computing Lab, an Analogue project, is launching initiatives to materialize the principles of the Resonant Computing Manifesto.

The Resonant Computing Lab is working to cultivate a world where technology makes us more human rather than more absent. The Lab is launching three main initiatives:

• Gardens — small groups of 3–10 people tending a single question over a season until something real grows: a framework, a paper, a prototype. The first are forming around Portable Memory and EQ-based evaluation frameworks for AI. Propose a Garden.

• Gatherings — community-hosted dinners, talks, walks, and work nights held anywhere in the world, on the premise that resonance is easier to feel in a room than to describe. Submit a Gathering.

• Resonant Projects — recognition for under-the-radar work that already makes us more human, marked by the Resonance Badge, with small grants in some cases. Nominations are open through June 21.

Across all three, the Lab scouts and supports builders who move with humility and curiosity. Momentum is already visible in the manifesto community: a recent resonance-themed demo night, hosted with interface.click drew 25+ builders.

About the Resonant Computing Lab

The Resonant Computing Lab is an Analogue initiative supporting tools and technologies that improve how we think, perceive, and world-build. It is the institutional vehicle for the Resonant Computing Manifesto. The Lab is directed by Liz Dorman and Carson Levine.

resonantcomputinglab.com

Funding inquiries: carson@analoguegroup.org · liz@analoguegroup.org · aish@analoguegroup.org

The Analogue Group wishes to congratulate our collaborator Seconds for their recent publication in Reason. They are working with the Research Revival Fund to translate Soviet-era academic literature into English, bringing a corpus of pre-1980s scientific work finally into the English-language scientific record. Their ChinaRxiv.org project has done the same for Chinese research preprints, at scale.

In their piece for Reason, they use the Sylvain Lesné Alzheimer’s fraud case to illustrate how institutions meant to safeguard scientific integrity — peer review, journal editorial boards, university compliance — are structurally incapable of catching fraud, because the incentives reward publication over truth. They propose open infrastructure as a counterweight: making the full body of human research accessible, translatable, and replicable by independent researchers. We support their work in advocating for open, distributed science.

Read the full article: How a Scientific Cartel Protects Fraudsters and Rakes in Billions of Taxpayer Dollars.

Editor’s note (June 2026): Since publication, the Research Revival Fund has been renamed the Revival Fund.

Resonant Computing Lab is now open. It provides grants, retroactive awards, and commissioned work to independent builders, researchers, and “gardeners” who treat technology as something that shapes environments, which in turn shape us.

The lab offers hands-on resources for builders to create private, dedicated, plural, adaptable, and prosocial software. It operates across what it calls the pace layers: from fast‑moving commerce and fashion down to governance and nature; curating a portfolio of self‑initiated, scouted, and nominated initiatives.

Visit The Resonant Computing Lab at resonantcomputinglab.com.

The Analogue Group is happy to announce the launch of a new, experimental grant program dedicated to reviving neglected, abandoned, or marginalized research across all fields. The fund supports researchers and thinkers who are working to recover, clarify, and re‑activate promising lines of inquiry that have been prematurely dismissed, buried, or otherwise lost.

Research Revival provides grants of 4,000–10,000 USD, with some projects eligible for up to 25,000 USD, to support work such as archival recovery, translation, replication, experimental follow‑up, and creative or speculative forms grounded in rigorous inquiry.

Examples might include rediscovering forgotten Soviet biology, reassessing buried social‑science findings, or reinvestigating abandoned medical or experimental work.

The program, directed by Wendi Yan, is open worldwide to both academic and independent researchers, with no formal credential requirements.

Submit your quest at researchrevival.org.

Editor’s note (June 2026): Since publication, the Research Revival Fund has been renamed the Revival Fund.

Second Renaissance, a project we soft-launched earlier this year, was officially announced today. It’s a living archive of scenius — from Bell Labs to Pixar to Black Mountain College. Kudos for Tasneem Nabi (@TasneemNabi) for making this happen!

Visit Second Renaissance at secondrenaissance.now.

Analogue has been highlighted as a leading example of “crazy philanthropy”: bold, unconventional funding that breaks from traditional institutional constraints.

In The Case for Crazy Philanthropy, the publication argues that modern science funding has become overly bureaucratic, with philanthropy increasingly funneling resources to established universities rather than supporting outsider talent and novel ideas.

Philanthropy can have a much higher impact if it doesn’t just piggyback on existing institutions and ideas. Instead of giving more money to universities that are already sitting on hundreds of billions of dollars in endowments, why not give money to the Analogue Group, which tries to find the next Katalin Karikó, or to Convergent Research, which funds an entirely new type of research organization?

In summer 2025, federal funding to UCLA was abruptly suspended mid-cycle, creating an immediate crisis: the university’s Institute for Pure & Applied Mathematics (IPAM) lost access to grants already allocated for ongoing research, including those supporting mathematician Terence Tao’s summer salary and his students’ stipends. The suspension was tied to campus issues unrelated to research merit—but the timing was devastating. Tao, already having deferred previously released NSF funds to support graduate students, suddenly had no resources for payroll that week.

The Analogue Group recognized the gap and moved the same day. On August 12, 2025, we announced a $50,000 donation to The UCLA Foundation to bridge the immediate funding crisis and ensure Tao’s work and his team could continue uninterrupted. It was the kind of responsive, rapid support that exists in the gap between institutional failure and research collapse—and it’s precisely the kind of decision The Analogue Group was designed to make.