Research Institutions as a Kolkhoz: Could AIIM Make Science More Efficient?

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Hypothesis: AI Internet-Meritocracy (AIIM) could produce more valuable research per dollar than institution-centered funding by reducing bureaucracy, strengthening individual recognition, and giving researchers greater control over their work. Its potential advantages concern three distinct outcomes: money usage, the quantity of discoveries, and the quality of discoveries. None has yet been established by a comparative evaluation of AIIM.

A useful analogy is the difference between working in a kolkhoz, a Soviet collective farm, and cultivating a farm one owns. In the first model, the worker operates within a collective administrative structure. In the second, the person doing the work has greater control over decisions and a more direct stake in the results.

Applied to science, the question is whether researchers should depend primarily on institutional permission or receive support attached to their own demonstrated contributions.

The kolkhoz analogy: who controls the scientific harvest?

Many existing research institutions combine productive work with centralized control over employment, budgets, facilities, and research priorities. Researchers may need approval from department heads, grant committees, or project leaders before they can pursue an idea. Their continued support can depend partly on institutional priorities and their ability to navigate funding procedures.

This resembles the organizational problem suggested by the kolkhoz analogy: the person who understands the work does not necessarily control the resources needed to do it. MIT’s Soviet history course materials distinguish the kolkhoz, or collective farm, from the sovkhoz, or state farm.

The comparison has limits. Modern universities are not Soviet collective farms, and the analogy does not equate academic employment with forced collectivization. Institutions differ enormously: some offer substantial autonomy, while others impose tight project controls. Scientists also already receive individual credit and sometimes share commercial returns.

The relevant comparison concerns decision rights, administrative dependence, and the connection between contributions and rewards.

AIIM as an owned farm: autonomy without enclosing knowledge

AI Internet-Meritocracy proposes allocating donated funds to scientists and free and open-source software developers using AI assessments of their published contributions. Its stated model allows participation without a degree or conventional grant proposal. A beta exists, but its economic-impact estimates are heuristic and have not been validated as measurements of causal economic contribution.

The owned-farm analogy captures the intended relationship: researchers develop their own body of work, retain identifiable credit, and receive support associated with their contributions rather than solely through an institutional position.

Here, “ownership” means greater control over one’s research direction and a personal stake in recognition. It does not require exclusive ownership of scientific knowledge. Open results can remain available to everyone while their creators receive individual support.

Unlike a farmer selling a harvest, an AIIM participant does not receive a market price or a guaranteed return. Payments depend on available donations and the allocation system. Consequently, the analogy suggests a mechanism to investigate; it does not establish AIIM’s efficiency.

Where AIIM could be more efficient

Dimension	Expected advantage under the hypothesis	Why it could occur	What would demonstrate it
Money usage	More useful research activity per dollar	Less repeated grant writing and allocation administration; direct support for overlooked contributors	Lower total cost per independently validated contribution, including evaluation and infrastructure costs
Quantity of discoveries	More distinct, valid findings at the same budget	More research time, broader participation, and fewer interruptions caused by funding procedures	More independently validated findings per dollar over comparable periods
Quality of discoveries	Greater reliability, originality, and downstream usefulness	Greater research autonomy and recognition of substantive contributions, if evaluation rewards these qualities	Independent assessments, replication, verified proofs, and demonstrated reuse over time

1. Money usage: less effort obtaining permission to work

Research funding consumes resources before research begins. Applicants prepare proposals, colleagues review them, and institutions administer the process.

An observational study published in BMJ Open estimated that preparing 3,727 applications for Australia’s 2012 NHMRC Project Grant round consumed approximately 550 researcher working years, with an estimated salary cost of AU$66 million. This concerns one funding scheme and one year; it is not a worldwide estimate. Proposal preparation can also clarify research plans, so its entire cost should not be classified as waste. Herbert et al., “On the time spent preparing grant proposals”.

AIIM’s proposed advantage is to reuse evidence of completed work rather than require repeated competitive proposals. If that reduces administrative effort without degrading funding decisions, more money and researcher time could support investigation itself.

The farm analogy is straightforward: time spent securing permission to cultivate is time unavailable for cultivation.

However, AI evaluation, appeals, fraud prevention, payment administration, and human oversight all cost money. Laboratories, equipment, technicians, and data services remain necessary. Direct payments do not eliminate those costs, and shared facilities can be cheaper than individual provision.

The efficiency test is the full cost of producing valuable research, not simply the percentage paid directly to researchers.

2. Quantity of discoveries: more time and more people investigating

AIIM could increase discovery output through three mechanisms.

First, researchers could redirect time from applications and reporting toward proofs, experiments, software, and data analysis. Second, contribution-based eligibility could support capable people excluded by institutional hiring or credential requirements. Third, sufficiently dependable funding could reduce interruptions that force productive researchers to abandon unfinished work.

An independent farmer can adapt cultivation to local conditions. Similarly, a researcher with control over their agenda can follow an unexpected result without first obtaining a new project authorization.

These mechanisms do not require assuming that scientists are lazy or motivated only by money. Support can make it possible for already motivated people to spend more time on research.

The prediction concerns distinct, valid discoveries, not publication counts. More papers can reflect fragmented reporting, duplication, or unreliable claims. Software, datasets, and replication studies should also be measured as valuable outputs in their own categories.

The quantity advantage would weaken if donations were too small or unpredictable to sustain work, or if assessing existing contributions favored established researchers so strongly that newcomers could not get started.

3. Quality of discoveries: room for originality and lasting usefulness

Autonomy could help researchers pursue important questions whose value is difficult to explain in a short proposal. Recognition tied to a person’s substantive contribution could also encourage careful documentation, reusable software, and work that remains useful beyond a funding cycle—provided the evaluation system recognizes these qualities.

Relevant evidence comes from Azoulay, Graff Zivin, and Manso’s study in The RAND Journal of Economics. They found that Howard Hughes Medical Institute investigators produced high-impact papers at a higher rate than a comparison group of similarly accomplished NIH-funded scientists, with research changes suggesting greater exploration. The HHMI funding model combined tolerance of early failure with long-term rewards. “Incentives and Creativity: Evidence from the Academic Life Sciences”.

This supports the plausibility of the autonomy mechanism. It is not evidence that AIIM works: HHMI is itself an institution, and publication impact does not fully measure scientific quality.

AIIM would need to distinguish visibility from value. If its assessments reward fashionable topics, persuasive presentation, or citation volume without checking substance, participants could optimize for scores rather than discovery. Rapidly fluctuating rewards could also discourage the long investigations that the model hopes to enable.

Better scientific incentives require reliable evaluation and enough stability to tolerate an uncertain harvest.

How to test the efficiency hypothesis

A credible comparison should follow similarly situated researchers in the same fields, with comparable funding and access to facilities. Where feasible, eligible volunteers could be randomly assigned to AIIM-style support or a conventional funding process.

The study should preregister three outcomes: total cost per validated contribution; the number of distinct validated findings; and independent assessments of reliability, originality, and usefulness. Reviewers should be blinded to funding source where practical. Evaluation costs, unsuccessful research, and externally subsidized infrastructure must be counted in both groups.

AIIM’s own scores cannot serve as the principal proof of its success. Early studies can measure administrative savings and allocation reliability; discovery quality requires longer follow-up, especially in fundamental research.

From institutional dependence to researcher autonomy

The strongest version of the hypothesis is that AIIM could outperform heavily bureaucratic funding arrangements where administrative dependence suppresses productive individual initiative. The initial opportunity may be greatest in fields with relatively modest equipment needs, such as mathematics and some software research.

Large experimental programs will still need coordinated teams and shared infrastructure. Researchers supported through AIIM could collaborate with institutions and use shared facilities while retaining greater independence in their funding.

The owned-farm ideal is responsibility combined with freedom to cultivate. Applied to science, it offers a clear proposition: attach support more closely to the people creating knowledge, give