Essays

A draft essay on the missing half of the science funding system. In a taxi, the driver rates you and you rate the driver. Both sides know that bad behavior leaves a trail. That mutuality is the accountability.

This is now the structure of any normal relationship. Patients rate doctors. Employees rate employers on Glassdoor. Buyers and sellers rate each other on every marketplace. In a working market, both sides carry a public reputation built by the other.

Science funding is the exception.

Funders rate scientists in granular detail. Every paper, every grant, every collaboration, every line of a CV follows a researcher for life. Scientists do not rate funders at all. No platform. No rating. No public information about how a specific funder works with its applicants: how quickly it makes decisions, how fairly it reviews, whether it pays on time, how its program officers behave, whether the actual selection criteria match the stated ones, whether it values scientific novelty.

The relationship is one-directional. The side with the money owes nothing to the other side.

Two roles, not a feudal hierarchy

Scientists create what did not exist before them. New theories. New methods. New medicines. Knowledge that changes the life of humanity. This is real work, and it has real value.

Funders make this work possible. Their work matters too. But the money is not more important than the work it funds. The people with the money are not above the people doing the work. Each role has its own purpose. Neither role stands over the other.

Today's system says otherwise. Scientists are evaluated continuously. Funders are evaluated never. One side is on probation. The other sits in the judge's seat.

This is wrong. Mutual evaluation is not just a feedback mechanism. It is the recognition that both sides do real work, with real responsibility, and both must answer for how they do it.

The shape of the asymmetry

A funder evaluating a researcher sees the full picture: publication history, citations, prior grants, recommendations from colleagues, informal calls between program officers and previous collaborators.

A researcher evaluating a funder sees the funder's own website, a list of past awardees if it is public, and rumors from the senior whisper-network. Funders have no citation index. No reference letter. There is not even a reliable way to find out whether the last cohort of grantees was paid on time.

Senior scientists carry tacit knowledge about which funders are reasonable and which are bureaucratic disasters. That knowledge is closed off from the people who need it most: early-career researchers, international applicants, scientists outside the dominant institutional networks. They learn it one wasted application at a time.

What scientists would say if asked

Talk to working researchers in several countries. The list of complaints repeats:

Reviews arriving months, sometimes a year, after the promised deadline. Program officers quietly narrowing the goals of an active grant. Application criteria stated on the website that don't match the criteria actually used. Multi-month payment delays after an award. Reviewers who clearly haven't read the proposal. Mid-grant changes to reporting, eligibility, or budget rules, applied retroactively. Terms that quietly disadvantage international, independent, or non-institutional applicants. Ideological filtering presented as scientific. Some are individual incidents that any large system occasionally produces. Others are durable institutional patterns, well known inside the field and invisible to anyone entering it. The funder's website gives the funder's own preferred description of itself. Nothing else exists.

Why this won't be fixed from inside

A scientist who leaves a public review risks their next application. A funder asking for feedback will get polite answers from those still hoping for a grant, and silence from everyone else. A scientific society that tried to maintain such a rating consists of people dependent on the same funders.

Everyone inside the system has an interest in not breaking it. That is the structural blockage.

The solution must come from outside. And it must protect the individual scientist with anonymity, not with public accusation.

Hypothesis and experiment

Hypothesis. Systematic collection of anonymous feedback from researchers produces a signal that distinguishes between funders. And most importantly: these differences over time correlate not only with the quality of the applications a funder attracts, but also with the quality of the questions that funder poses to the field. The question a funder asks the field — that is where the real difference is. A well-posed question is half the answer. This is a still-underrated axis, and it can only be evaluated through the eyes of scientists who have worked with that question.

Methodology. We survey every researcher who has had contact with a funder or has consciously avoided one. Five groups:

Those who applied and received the grant. Those who applied and did not receive it. Those currently working under an awarded grant. Those who considered an application, began writing it, but did not submit. Those who decided from the start not to engage with that funder at all. The last group is the most important and the most invisible. Scientists often refuse to work with a funder long before submission. They heard from colleagues, saw the funder's past behavior, read the terms, and closed the tab. No one sees these people — not the funder, not the statistics, not the rankings of past winners. And it is precisely they who reveal how much of the field a funder is losing without knowing it.

The questionnaire is structured, with falsifiable parameters. The exact set is a separate task, and one to be solved in the field, not from a desk.

The results will be of obvious interest to those who give money to the funders themselves.

Fund management wants to act safely. We, on our side, will venture a cautious hypothesis: that scientists would actually like more novelty from their funders.

Publication. Aggregate scores are published openly, with sample sizes shown. The funder has the right of reply.

What we get immediately. The experiment is informative from day one. After the first hundred responses, patterns are already visible: which funders keep their deadlines, which change rules retroactively, which can pose meaningful questions and which cannot. This does not need to be waited for.

What we get in two or three years. Enough data to test the correlation between researchers' assessments and independent metrics: the share of repeat applications from strong scientists, access for early-career and non-institutional groups, the quality of publications produced under awarded grants. If the assessments turn out to be noise — the hypothesis is refuted, the infrastructure is not needed. If they correlate — for the first time we have a science of effectiveness for the means used in science itself.

We can and would like to take on this task on our own platform — Eternal Search.

Why now

Such a system did not exist anywhere before. Not for taxis, not for restaurants, not for doctors. The habit of rating the other side of the market simply was not there. Ten years ago, "rate your funder" would have sounded like an idea from another planet. Today it is the norm of everything except science.

What has changed is also the structure of science itself. A single person with the right tools and a public reputation can do real work. That is the new reality, and the grant system is built for the old one. It still sees only institutions and the people attached to them. Everyone else, it does not see at all.

Mikhail Batin

I am a clinical endocrinologist by training and an applied scientist by daily practice. I run a small team that builds AI pipelines for medical research and clinical decision support. Over the past two years, I have helped researchers at multiple institutions develop grant applications, advised a longevity nonprofit on its funding strategy, and watched — closely and repeatedly — what happens when scientifically promising teams collide with the modern grant system. The pattern is consistent enough that I no longer think of it as bad luck.

The bottleneck I want to describe is this: science funding is allocated to institutions on the basis of one-shot proposals reviewed at one moment in time, with no built-in mechanism to validate execution before the bulk of the money is committed, and no continuous measurement of scientific quality once it is. The unit being funded is the institution, even though the work is done by a team. The signal being evaluated is a written document, even though the variable that predicts success is the team's ability to operate. And the evaluation is one-shot, even though good science is a long, iterative process that can quietly drift into bad science years before the final paper is submitted.

I think this is a structural problem rather than a problem of bad reviewers or insufficient money, and I think a small, well-designed pilot could test a different architecture.

The team-shaped hole When a major grant is awarded, the formal recipient is the institution. The institution takes overhead, provides administrative scaffolding, and assumes nominal accountability. But the actual work is done by a principal investigator and the people they recruit. If that team is operationally weak — if it cannot scope, plan, communicate, course-correct, or absorb a setback — the project will underdeliver regardless of the institution's name on the letterhead. Every working scientist knows this. The funding system mostly does not.

This matters because the proposal-review model evaluates scientific vision (sometimes well) and team capability (almost never directly). A stellar idea written by a team that cannot ship will fail. A modest idea executed by a team that can ship and iterate often becomes excellent. Reviewers can guess at execution capability from the CV, but they cannot test it. And because the first tranche of money is also the last consequential decision point, there is no way to update on observed performance without the political and bureaucratic cost of clawing back a multi-year grant.

The result is a high-variance, low-feedback allocation system. Successful PIs accumulate funding partly because of past success rather than ongoing performance. Promising independent or non-traditionally-positioned scientists — a population that is now substantial — are hard to fund because there is no entry-level rung small enough to risk on them. Excellent operators without an institutional pedigree fall through the cracks; pedigreed operators who have stopped operating well keep being renewed.

A two-stage pilot I propose testing the following architecture:

Stage 1 — Concept grants. A pool of capital is divided into a large number of small grants, in the range of $10,000 to $30,000 each. The applicant is a team, not an institution. The application is short: a hypothesis, a falsifiable prediction, a six-month plan, and a concrete deliverable — a preliminary dataset, a working prototype, a registered protocol, a replication, a methodological audit. The size of the grant is deliberately small enough that it forces operational discipline. The team has to scope, prioritize, and produce something defensible with limited resources. That constraint is the point. Stage 1 is a behavioral test as much as a scientific one. We observe whether the team can plan, distribute money sensibly, communicate, and ship.

Stage 2 — Scaled grants. Teams that produce credible Stage 1 deliverables become eligible for substantially larger awards — the kind of money that today is allocated in a single proposal cycle. The Stage 2 application is a full research program, but now the panel has something the conventional system never has: observed evidence of how this specific team operates with someone else's money on a real deliverable. Funding is awarded with frequent (quarterly or semi-annual) structured reporting and clear, pre-specified conditions under which it can be paused or redirected.

The Stage 1 pool needs to be large enough to fund hundreds of teams per cycle, because the value of the design comes from the breadth of the funnel. This is the inverse of the current system, where the funnel is narrow at the top and the consequences of misallocation are absorbed in multi-year increments.

Importantly, a Stage 1 grant is not contingent on confirming the team's hypothesis. Well-designed experiments are informative under failure, and a team that runs a clean negative result is more fundable than a team that produces a glossy non-result. The Stage 1 evaluation is about scientific rigor and operational capability, not outcome bias.

What role for AI There is a version of this proposal that is just "use AI to review grants faster," and that is not what I am proposing. Faster bad review is still bad review. The role I see for AI is structural, not procedural — it makes a different funding architecture administratively cheap enough to actually try.

Concretely: in the systems I build, the language model is a component, not a decider. The structural spine is deterministic code; the model is invoked for narrowly scoped language tasks — parsing a free-text application into structured fields, scoring a section against a published rubric, drafting a summary from data the script has already prepared — and its outputs are logged, checked, and where consequential, surfaced to a human. The model never holds the control flow. It is an instrument for translating between text and structured representation, not an authority on what should happen next.

Within that constraint, two functions seem realistic on a near-term horizon:

First, matching at scale. A platform that ingests team-level applications and matches them to topically aligned funders, donors, and reviewer panels makes a Stage 1 system with hundreds of micro-grants per cycle logistically feasible. This is plumbing, not judgment.

Second, continuous quality scoring. What I would call a theory-quality pipeline is something specific: a structured system that assesses ongoing work, against a published rubric, on dimensions like internal consistency of reasoning, falsifiability of predictions, appropriate statistical handling, and engagement with the prior literature actually cited. Such a pipeline is imperfect today but is benchmarkable — it can be calibrated against expert-rated cases, its disagreements with experts are inspectable, and it produces a longitudinal signal that the current peer-review-then-silence model does not produce at all. It is not a judge. It is an instrument, closer to a quality metric than to a reviewer. If sustained low scores correlate with weak downstream output, that is a signal worth acting on; if they don't, the instrument is recalibrated or retired.

Both functions are enabling rather than substituting. They are the reason a structural change that would have been administratively prohibitive a decade ago is plausible now.

The pilot Concretely, what I am proposing can be tested with the following experiment.

Set aside a defined pool — illustratively, $2–5M — for a single Stage 1 cycle of 100–200 micro-grants. Recipients are teams. Applications are evaluated by a hybrid pipeline: AI-assisted triage and rubric scoring, with a small expert panel making final selections. Six months later, deliverables are evaluated by the same panel plus an independent set of blinded domain reviewers. A pre-registered subset of high-performing teams advances to Stage 2 on terms comparable to a conventional grant, while a matched control of conventionally-awarded grants of similar topical scope is tracked alongside for comparison.

Pre-registered outcomes:

Per-dollar quality of Stage 1 deliverables, scored by blinded experts against a published rubric. Stage 2 performance of teams selected via the two-stage path versus matched conventional awards, at 12 and 24 months. Demographic, geographic, and institutional composition of Stage 1 awardees versus conventional grant recipients in the same area — does the funnel reach scientists the existing system structurally misses? Calibration of the quality-scoring pipeline against blinded expert ratings, including a transparent error analysis of where it agrees and disagrees with humans. The experiment is informative under any outcome. If the two-stage path produces no improvement over conventional review, the simpler model is vindicated and we save further effort. If it produces improvement on even one of the four axes — particularly the third — the case for restructuring at least one rung of science funding becomes empirically grounded rather than rhetorically argued.

Why now Three things have changed in the last few years that make this pilot tractable in a way it was not before.

Language models have crossed a threshold where structured rubric scoring of scientific text is reproducible enough to be benchmarked against expert ratings. The output is not yet trustworthy as a sole judge, but it is good enough to serve as triage and as a calibrated longitudinal instrument — and crucially, its errors are now legible enough to audit. Online infrastructure can match thousands of small applications to topically appropriate reviewers and funders at marginal cost, which is what makes hundreds of micro-grants per cycle administratively realistic. And there is now a real population of working scientists — independent, industry-adjacent, internationally mobile, often holding strong credentials but not embedded in the institutions current funding flows through — who do excellent work and would respond to a system whose first rung is small, fast, and team-shaped.

I am not writing this as a critique of any particular funder. I am writing it as a working scientist who has watched the gap between what scientists actually need and what the system actually delivers, and who believes the gap is closable with a specific, runnable experiment that is informative under any result.

A pilot of this shape would cost a small fraction of a single conventional R01 cycle. The downside is bounded. The upside, if even a modest version of the hypothesis is correct, is a different and better-calibrated way to allocate the next generation of science funding.