How to Earn Money with AIIM

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AI Internet-Meritocracy app offers an easy way to get grants for software or research, without grant writing and without the requirement to have a science degree.

To earn money with AI Internet-Meritocracy, you first need to create one or more of:

  • strong scientific profile at ORCID;
  • strong free software creator profile at such sites as GitHub.

Then following the procedure (our service is 100% free for you), connect accounts at Connect page and then pass evaluation by an AI that decides how much to pay you.

Since passing evaluation you are eligible for a part of funds donated to our system. You will receive an email welcoming you to KYC verification before we deliver the first payment.

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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