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Science DAO uses blockchain for decentralized, transparent funding for anyone who published some research or software.
- Full transparency of all donations and payouts: Anyone, anytime can look up the data.
- No centralized control over research funding: The decisions are taken solely by AI and human voters for moderation.
- A durable on-chain record of specified transactions and governance actions, subject to the properties and continued accessibility of the chosen blockchain: All monetary transactions and the record of the AI reasons are stored permanently,
- Reduced administrative overhead: It is decided by AI, not people. People however, can vote to cancel an AI decision, to avoid scams. No grant committee, no ministry of science, no universities in between of researchers and funding.
Support Independent Science
Our flagship product is AI Internet-Meritocracy - an app, that unlike universities distributes money directly to researchers and open source developers, without traditional bureaucracy.
AIIM’s dependency-aware allocation model is currently being tested. Support the next testing milestone.
Supporting independent science is not only a matter of fairness to researchers whose expertise and work are often underfunded. It is also essential for addressing systemic failures in scientific publishing that delay discoveries and leave important results unnoticed. In science and software, even one missing component can prevent an entire system from working.
Help valuable research and open-source infrastructure move forward. Please make a donation to support independent scientists and free software developers.
Dislclaimer
Experimental-system notice: AI Internet-Meritocracy is an experimental funding system. Its AI-generated evaluations are heuristic judgments based on available public or connected-account evidence; they are not validated measurements of a person’s causal economic or scientific impact. The current beta uses custodial and administrative components. Decentralized governance, non-custodial wallets, and complete on-chain auditability remain under development. Evaluations may contain factual errors or biases and should be interpreted together with audit logs, appeals, human oversight, and published test results.
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