Scientific Visibility Is a Coordination Problem — An Experimental Approach
Important scientific and technical work can remain obscure because it is difficult to discover, difficult to evaluate, badly indexed, poorly presented, or disconnected from the people who could use it. Science DAO treats this as a coordination problem rather than claiming to have a unique or proven solution.
Our working hypothesis
We are testing whether a combination of AI-assisted evaluation, public evidence, transparent funding records, and better science communication can help useful work receive more attention and support. This is an experimental hypothesis.
What AI can and cannot do
AI can help summarize, classify, compare, and retrieve work. It can also make serious mistakes. It cannot turn an unsupported claim into a validated result, and it cannot substitute for domain expertise where expert verification is required.
Why visibility matters
Research that cannot be found is less likely to be read, criticized, replicated, cited, funded, or used. Better metadata, open publication, structured evidence, and responsible science communication can reduce this problem.
Why visibility is not enough
Visibility can amplify both good and bad work. For that reason, Science DAO’s approach should connect discoverability to evidence: source documents, code, datasets, preregistrations, reproducible procedures, known limitations, and independent reviews.
Current status
AIIM is an experimental funding system in beta. Its evaluations are heuristic judgments rather than validated measurements of scientific value or causal impact. On-chain payment records are available, while parts of the current implementation and governance remain off-chain or administratively controlled.
Correction regarding Navier–Stokes
A previous claimed solution of the Navier–Stokes problem by Victor Porton was found to be incorrect. Science DAO does not treat that proof attempt as a valid result. This correction does not by itself resolve the status of separate mathematical work such as Discontinuous Analysis, which must be evaluated on its own definitions, proofs, publication record, and independent review.
What would count as evidence of progress?
- Preregistered evaluations and adversarial tests
- Public datasets and reproducible procedures where appropriate
- Independent reviews, including critical reviews
- Documented failures and corrections
- Evidence that allocation decisions are stable, fair enough for their stated purpose, and resistant to manipulation
See Testing & Evidence and Independent Review.
👉 Help fund the next public test of AIIM.
Help Test a New Way to Fund Science
AI Internet-Meritocracy (AIIM) is an operational beta designed to allocate available donations to researchers and open-source developers using AI-assisted evaluation of documented contributions. Payment transactions are already recorded on-chain.
The next major evidence milestone is a proposed five-month public adversarial test of the allocation model, with $1,000 distributed to eligible funding recipients. Donations help pay for the development, infrastructure, reviewer and participant recruitment, outreach, and operating work needed to reach and evaluate that milestone.
You do not need to assume AIIM is already proven to support the project. Your donation helps turn the proposal into evidence: what works, what fails, and what should change.
Support the next testing milestone → Read the test proposal →
Independent review
Researchers and technical reviewers: independent criticism is welcome, including negative conclusions. Review AIIM’s assumptions, governance, failure modes, and testing plan →
Research status: AIIM remains experimental. AI-generated evaluations can contain factual errors or biases, and decentralized governance and non-custodial components remain under development. That uncertainty is why public testing, auditability, and external criticism are central to the project.