There Are DAOs for Biology and Medicine. Why Is Abstract Mathematics Being Left Behind?

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Decentralized science, commonly known as DeSci, promises to transform how research is funded, evaluated, and published. Yet the movement has developed unevenly.

Biology, longevity research, biotechnology, and medicine already have visible decentralized autonomous organizations. Abstract mathematics, by contrast, remains largely outside the emerging DAO economy.

This imbalance is understandable—but it is also dangerous. Mathematics is not merely another scientific discipline competing for funding. It is part of the infrastructure on which computing, artificial intelligence, cryptography, physics, engineering, economics, and even blockchain technology depend.

If DeSci wants to reshape science rather than merely finance commercially promising biotechnology, it must find a place for mathematics.

Biology and Medicine Already Have a DeSci Ecosystem

The best-known science DAOs are concentrated in biology and medicine.

VitaDAO, for example, funds longevity research and works to develop scientific discoveries into intellectual-property assets and biotechnology ventures. Molecule has also supported an ecosystem of BioDAOs focused on areas such as longevity, psychedelic research, drug repurposing, and rare diseases.

This model is attractive to investors because biomedical research can produce assets with relatively visible commercial pathways:

• a drug candidate;
• a diagnostic method;
• a patent;
• a therapeutic platform;
• a biotechnology startup;
• or a licensing agreement.

A DAO can fund early research, acquire or manage related intellectual property, and potentially receive revenue if the project succeeds.

This does not mean that biomedical research is easy or that returns are guaranteed. Drug development is expensive, uncertain, and slow. Nevertheless, investors can understand the basic economic story: fund research today, obtain a commercially valuable medical asset tomorrow.

Abstract mathematics rarely offers such a simple narrative.

Why Abstract Mathematics Is Harder to Tokenize

A mathematical theorem is not usually a commercial product.

It may be extremely important without producing immediate revenue. A new algebraic structure, proof technique, foundational language, or classification theorem may influence other fields only decades later. Its future applications may be impossible to predict at the time of discovery.

This creates several difficulties for conventional DAO funding.

1. Mathematical Value Is Difficult to Measure Quickly

In biology, a project may have observable milestones:

• a compound was synthesized;
• an experiment was completed;
• a biological effect was measured;
• or a clinical target was validated.

In pure mathematics, the central output may be a definition, theorem, proof, counterexample, or new conceptual framework. Evaluating that output can require highly specialized expertise.

A genuinely original mathematical theory may initially be understood by only a few people. The more foundational the work, the harder it may be to compare it with familiar research.

2. There May Be No Intellectual-Property Revenue

Mathematical knowledge is normally published openly. Theorems themselves are generally not treated like patentable pharmaceutical compounds.

That is good for civilization: mathematical knowledge should be reusable by everyone. But it weakens funding systems built around ownership and future licensing revenue.

A BioDAO may ask, “What intellectual property will this research create?”

A mathematics DAO must ask a different question:

What new intellectual infrastructure will this research create for humanity?

That is a harder question for investors—but arguably a more important one.

3. Mathematical Applications Often Arrive Much Later

Abstract mathematics repeatedly becomes practical long after its creation.

Number theory once appeared remote from ordinary life, yet it became fundamental to modern cryptography. Non-Euclidean geometry later became essential to general relativity. Abstract algebra supports coding theory, computer science, cryptographic protocols, and many other technologies.

The problem is that funding markets tend to discount distant and uncertain benefits. A theory that may become indispensable in 30 years is difficult to finance through mechanisms expecting a startup, token increase, patent, or exit within a few years.

4. Mathematical Research Is Commonly Misunderstood as Cheap

People often assume that mathematicians need only paper, a pencil, and time.

Mathematics may require less laboratory equipment than experimental biology, but mathematicians still need:

• income and stable living conditions;
• access to literature;
• research software and computing infrastructure;
• conference participation;
• collaborators;
• editorial and publication support;
• and, above all, uninterrupted time.

The absence of laboratory expenses does not make mathematical labor free.

Traditional Institutions Do Not Solve the Problem

It might be argued that universities and government grants already support mathematics, making a specialized DAO unnecessary. In reality, institutional funding introduces its own exclusions.

Research grants are commonly tied to universities, academic credentials, previous grants, publication records, national priorities, and established professional networks. Independent researchers may be excluded before the mathematical content of their work is seriously considered.

Even researchers inside universities are pressured to present projects in ways that committees can evaluate quickly. This can favor fashionable subjects, familiar terminology, established departments, and projects whose outcomes are easier to predict.

Research suggests that mathematical subfields do not enjoy equal status. Some areas are disproportionately represented in highly ranked departments, selective journals, and major prizes, indicating that internal prestige structures affect which work receives attention.

The result is a paradox:

Mathematics celebrates radical abstraction, but its institutions may reward conformity to existing academic categories.

A long, unconventional manuscript by an unaffiliated author can be scientifically valuable while remaining institutionally almost invisible.

The Problem Is Especially Serious for Foundational Mathematics

Foundational research often does not fit inside a narrow grant proposal.

Suppose a researcher develops a large theory that connects order theory, algebra, category-like structures, topology, and generalized actions. Evaluating it may require hundreds of pages, new terminology, and substantial preliminary study.

Such a project has several characteristics that conventional funding systems dislike:

• it is too broad for a narrowly defined grant;
• too unfamiliar for rapid peer review;
• too long for ordinary journal formats;
• too theoretical for commercial investors;
• and too difficult for general-purpose crowdfunding.

Yet precisely such work may create a new language that later simplifies many other theories.

This is relevant to research on ordered semigroup and ordered semicategory actions: an abstract subject that may provide a general framework for structures appearing across topology and algebra. Whether any particular theory ultimately succeeds must be determined through rigorous examination—not by rejecting it because its author lacks a conventional position or because the manuscript is unusually long.

A fair funding system should make deep evaluation possible.

Why DeSci Has Not Yet Corrected the Imbalance

DeSci is often described as a way to connect researchers directly with supporters, make funding more transparent, and reduce dependence on centralized grant institutions.

However, decentralization alone does not guarantee meritocracy.

A DAO can reproduce the same biases as a traditional institution:

• famous researchers attract more votes;
• popular topics attract more donations;
• visually impressive experiments outperform abstract arguments;
• commercial projects attract investors;
• and voters support work they already understand.

Blockchain governance does not automatically evaluate science correctly. It merely records and executes decisions according to specified rules.

If those rules reward popularity, the DAO becomes a transparent popularity contest.

Abstract mathematics therefore needs more than crowdfunding. It needs a system capable of identifying dependencies, evaluating expert judgments, rewarding reviewers, and financing work whose importance is not immediately obvious.

A Better Model: Funding Scientific Contribution Rather Than Intellectual Property

One possible approach is the AI Internet-Meritocracy (AIIM) proposed by World Science DAO.

AIIM is intended to allocate funding according to the evaluated usefulness and merit of contributions rather than relying solely on institutional affiliation, commercial intellectual property, or the popularity of a fundraising campaign. Its proposed model is designed to support non-discriminatory scientific grants and to make allocation decisions more transparent and auditable.

For abstract mathematics, such a system could evaluate several distinct forms of contribution:

1. Original research — definitions, conjectures, theorems, proofs, and theoretical frameworks.
2. Verification — checking proofs, identifying errors, and formally verifying results.
3. Explanation — writing accessible expositions of difficult theories.
4. Dependency mapping — determining which results depend on which earlier contributions.
5. Implementation — encoding mathematics in proof assistants or research software.
6. Application — connecting abstract theories to computing, physics, cryptography, or other disciplines.

This is important because mathematical progress is rarely the achievement of one isolated publication. It is a dependency network.

One researcher introduces a concept. Another proves a theorem about it. A third finds a counterexample. A fourth formalizes the theory. A fifth applies it to another field.

A sophisticated funding system should recognize the entire chain.

AI Can Help—but It Must Not Become the Final Authority

Artificial intelligence may reduce the cost of evaluating mathematics.

AI systems can potentially:

• summarize long manuscripts;
• compare definitions with existing literature;
• identify related results;
• check local logical consistency;
• assist with formalization;
• map citation and theorem dependencies;
• and recommend qualified human reviewers.

Formal proof systems already demonstrate that computers can help mathematicians manage increasingly complex arguments. Research on proof assistants and abstraction boundaries suggests that formal tools can improve the organization and verification of mathematical work.

However, AI should not simply issue an opaque score declaring whether a theory is valuable.

The system must remain auditable. Human experts should be able to inspect the reasoning, challenge evaluations, report errors, and compare competing assessments. AI should lower the cost of scientific review—not replace scientific accountability.

Why Supporting Abstract Mathematics Benefits Every DeSci Project

A biotechnology DAO may appear unrelated to pure mathematics. In practice, the entire DeSci ecosystem depends on mathematical work.

DAOs require:

• cryptography;
• distributed-systems theory;
• game theory;
• mechanism design;
• formal verification;
• optimization;
• statistics;
• graph theory;
• and economic modeling.

The more money and authority DAOs control, the more important rigorous mathematical foundations become.

A vulnerability in a smart contract, a flawed voting mechanism, or a poorly designed incentive structure can destroy a project. Funding foundational mathematics is therefore not charity offered by practical projects to an impractical discipline.

It is investment in the infrastructure those projects use.

DeSci Must Decide What Kind of Movement It Wants to Be

There are two possible futures for decentralized science.

In the first, DeSci becomes a new form of venture capital for biotechnology. It funds projects with recognizable intellectual property, market narratives, and investment exits. This may produce useful medical innovation, but it leaves much of science unchanged.

In the second, DeSci builds a general system for recognizing and supporting knowledge—including public goods that cannot be enclosed within patents or monetized immediately.

Abstract mathematics is a decisive test.

If decentralized science can fund only research with a visible commercial asset, it has not solved the fundamental problem of scientific funding. It has simply created a new investment mechanism.

A genuine scientific meritocracy must support discoveries before their economic value becomes obvious. It must make room for unknown researchers, unfamiliar theories, long manuscripts, negative results, verification work, and abstract ideas whose applications may belong to the future.

Abstract Mathematics Should Not Remain Outside the DAO Economy

Biology and medicine were natural starting points for DeSci. They combine urgent human needs with identifiable research assets and strong communities of patients, scientists, and investors.

But they should be the beginning—not the boundary—of decentralized science.

Abstract mathematics requires a different economic model: one based less on intellectual-property ownership and more on transparent evaluation, public benefit, scientific dependencies, and long-term contribution.

World Science DAO aims to develop such a model through AIIM. Readers who believe that important research should be evaluated by its content rather than the researcher’s status can learn more about merit-based science funding and support the development of independent scientific infrastructure.

The blockchain industry was built on mathematics. DeSci now has an opportunity to repay that debt—not merely with recognition, but with a functioning funding system for the next generation of mathematical ideas.