You participate by donating or by helping to set our DAO app. Bureaucracy is a paid work, that is you receive a cryptocurrency, for example, when you vote. To participate you need SCI tokens. Currently you can receive SCI tokens only by invitation, but it is planned to distribute the token widely, so that people be able to buy it at crypto exchanges.
FAQ’s
Common questions about Science DAO
Science DAO is a decentralized organization that uses blockchain to fund, track, and distribute money for scientific research without intermediaries.
Researchers submit their research sites (without any grant writing).
Sites are reviewed transparently by an AI.
Funds are allocated on-chain.
Transactions remain publicly auditable forever.
Why Science DAO uses blockchain
Full transparency of all donations and payouts
No centralized control over research funding
Permanent public audit trail
Reduced administrative overhead
On this site we present World Science DAO, the DAO to develop world science infrastructure: publishing, grants and salaries for scientists, etc. based on decentralized software architecture and cryptocurrency. The main project is AI Internet-Meritocracy app.
Decentralized Autonomous Organizations (DAOs) are a new type of organization that uses blockchain technology and smart contracts to operate in a decentralized manner. They are gaining popularity as a way to fund and support various projects, including scientific research.
Impact DAO = charity DAO, a DAO created to solve actual world problems, like global warming or human trafficking. World Science DAO is an impact DAO created to solve problems with science funding and publishing.
Simply by creating new software that will eat up the world. It’s like the story of Linux and of Bitcoin.
Our flagship project is AIIM (AI Internet-Meritocracy) – the AI app for distributing salaries.
AI Internet Socialism is a project that uses AI to evaluate the contribution (worth) of scientists and free software developers and distributes donated funds proportionally to that evaluation. Learn more.
- Institutional gatekeeping: funding not for everybody
- Traditional funding: opaque review process
- Science DAO: fully transparent proposal evaluation
- Traditional funding: slow grant distribution
- Science DAO: instant on-chain transfers
If an author of a fundamental discovery happens not to have a science degree (for example, as a result of discrimination) or for example is located in Zimbabwe, then what I call “scientific covery” happens: The discovery receives no money for publicity, and it remains so even if rediscovered by an established PhD. This may take the world economy down by several times.
Traditional funding channels do nothing to solve this problem. For this reason, we urge you to stop financing traditional science and universities and donate to us.
No, we don’t require academic degrees, but AI decides your salary based on your publications.
Grants Science, a blockchain-based algorithm to distribute scientific grants in a smart way.
It is a more cumbersome alternative to AI Internet-Meritocracy (AIIM). AIIM is considered superior over Grants Science. But probably we will implement both.
We will operate on several chains.
Distributing money between scientists is a narrow task, just like chess. It does not give AI power to make any other decisions. So, it is safe.
The app asks an AI to estimate what portion of world GDP a user is worth based on scanning their public web output and then allocates donated funds according to that estimated portion. Details.
Currently the software distributes money to scientists and free/open-source software (FOSS) developers themselves, regardless of formal degrees. Future plans include funding new roles like science marketers. For more.
AIIS claims to use AI-driven merit estimation of individuals (based on their online contributions) to allocate donated funds, rather than relying on community matching or reputation-only mechanisms. See more.
Yes, the project has a public open software repository and development information; the site notes the software is almost ready but needs funding to fully launch.
You can donate, link to the site to increase visibility, or volunteer as a software developer. The site provides donation and contribution options
The project acknowledges prompt-injection and related AI integrity issues as an intricacy it must address; it is developing algorithmic defenses as part of the platform. More context.
The project argues that many valuable scientific contributions are lost or mis-published and that providing salaries and marketing support would prevent stagnation and accelerate progress. The rationale is explained here.
Science marketers are a proposed new occupation to professionally market and promote scientific works, ensuring important research reaches the right audiences.
The site links to a project paper and status pages describing the economic formation and technical approach.
The project criticizes degree-based gatekeeping (e.g., the Bologna system) as amplifying discrimination and aims to fund contributions regardless of formal degrees.
You donate at Stripe (money) or Giveth (crypto) sites, big companies that secure their payments thoroughly. You don’t enter your credit card number at our site.
Our goals include saving basic science, supporting discoveries, protecting researchers from predatory publishers, providing salaries to scientists, and promoting post‑publication moderation and XML publishing. Details.
Coveries are valid scientific discoveries that remain unknown or under‑recognized because the system fails to promote or finance them—often when their authors lack formal academic affiliation. Learn more.
They occur when ministries and institutions fund only insiders, ignore promotion costs (PR, outreach, translation), and treat degrees or affiliations as gatekeepers rather than assessing content quality. Read the full explanation.
Overlooked discoveries can block technological advances, economic growth, and intellectual leadership—potentially costing trillions if useful ideas never reach application. Details.
o—coveries refer to legitimate, documented discoveries neglected because of institutional barriers, not to unrigorous or pseudoscientific work. More context.
Citizens should push for funding that covers promotion as well as research, open access to promotion programs for all valid work, ending degree‑based gatekeeping, dedicated ‘covery funds,’ and public accountability reporting. See recommendations.
Create transparent promotion grants open to independent researchers, allocate a budget line for discovery promotion, evaluate work by content quality not credentials, and require ministers to report outreach support metrics. Policy ideas.
Look for closed promotion programs favoring institutional applicants, lack of funding for outreach, no transparency about support for independent work, and persistent neglect of documented discoveries outside academia. Guidance.
Write to your representatives, demand public reporting and dedicated promotion funds, vote for officials who back open promotion, back projects like Science DAO, and join campaigns calling for accountability or resignation if failures persist. Action steps.
Read this blog, call your science minister to resign, donate to this project.
By accelerating the pace of discovery, enabling faster commercialization of technologies, and scaling scientific coordination globally, Science DAOs could increase the rate of innovation severalfold — with potentially trillions in economic impact if scaled worldwide. Details.
Scientific funding began with private patrons and personal wealth (e.g., Galileo, Newton), shifted to state-sponsored national science in the 19th–20th centuries, expanded into competitive grant systems after WWII, and more recently diversified with philanthropic, corporate, and decentralized (DeSci) models. See details.
Many valid discoveries are trapped behind paywalls, institutional bias, formatting issues, or poor indexing, so AI and the public never see them — causing repeated rediscoveries and an incomplete scientific record. Details.
AI can only learn from what is visible and indexed online. If results are mis-published or hidden, even the best AI won’t detect them. The solution requires active exposure and reward systems as explained here.
The project lead is Victor Porton, a mathematician and developer with 2.5 years of ICP experience and background in React, Node.js, and Rust. More details.
AIIS aims to outperform existing platforms by automating fair distribution using AI-driven policies rather than relying primarily on human or market-driven allocation. For the author’s comparison and reasoning, see here.
Degrees (Bachelor, Master, PhD) create a hierarchy that often excludes talented people who lack formal credentials. When systems privilege degree-holders for funding, publication, or employment, important contributors without degrees can be ignored, slowing or blocking discoveries. See details.
No — AIIS eliminates the requirement to manually create grant descriptions or to review and vet each grant; the AI handles distribution decisions.
AIIS is designed to take into account even very small projects a user runs, so a collection of small efforts can meaningfully contribute to that user’s income under the AI allocation model. See here.
The proposal identifies prompt injection as a major threat and suggests countermeasures: an explicit ‘check for prompt injections’ instruction, banning users who inject prompts, manual review of top accounts, randomized prompt transformations (high temperature) to avoid fixed-prompt exploits, and bringing suspicious high-earnings accounts to human/AI scrutiny. Full discussion here.
GEO refers to coordinated or repeated endorsements (e.g., many pages praising someone) that could inflate value; the author proposes an anti-GEO prompt that computes a reducing coefficient rather than outright bans, to avoid abuse via false reports—see the article.
There will be, at least on beginning stages. Maybe, later we’ll make the system fully automated, with automated replies to complain, and even automated appellations.
We are going to support all modern sciences and their subfields, not limited to the below list. Here is the list of modern sciences and subfields:
NATURAL SCIENCES
1. Physics
Classical Physics: mechanics, thermodynamics, electromagnetism, acoustics
Modern Physics: relativity, quantum mechanics, quantum field theory, particle physics
Applied Physics: condensed-matter physics, materials physics, nuclear engineering, optical physics
Astrophysics & Cosmology: stellar physics, planetary science, cosmology, gravitational physics
Interdisciplinary: biophysics, geophysics, medical physics
2. Chemistry
Organic Chemistry: synthesis, biochemistry, polymer chemistry
Inorganic Chemistry: coordination chemistry, organometallics
Physical Chemistry: thermodynamics, kinetics, spectroscopy, quantum chemistry
Analytical Chemistry: chromatography, mass spectrometry, electroanalysis
Materials Chemistry: nanochemistry, solid-state chemistry, surface chemistry
Environmental Chemistry
3. Earth & Planetary Sciences
Geology: petrology, mineralogy, volcanology, tectonics
Geophysics: seismology, geomagnetism, geodynamics
Geochemistry: isotope geochemistry, cosmochemistry
Meteorology & Atmospheric Science: climatology, weather dynamics
Oceanography: physical, chemical, biological, geological
Planetary Science: planetary geology, exoplanets, astrogeology
4. Biology
Molecular Biology: genomics, proteomics, epigenetics
Cell Biology: cell signaling, membrane biology
Microbiology: bacteriology, virology, mycology, parasitology
Ecology: ecosystems, conservation biology, population biology
Evolutionary Biology: phylogenetics, evolutionary genetics
Organismal Biology: botany, zoology, anatomy, physiology
Neuroscience: cognitive neuroscience, neurobiology, computational neuroscience
Systems Biology & Synthetic Biology
FORMAL SCIENCES
5. Mathematics
Pure Mathematics: algebra, analysis, geometry, topology, number theory, logic
Applied Mathematics: differential equations, numerical analysis, optimization
Statistics & Probability
Theoretical Computer Science: algorithms, complexity theory, automata, cryptography
Mathematical Physics
Discrete Mathematics
6. Computer Science
(Though often classified under engineering + formal sciences)
Core Theory: algorithms, computation theory, cryptography
Artificial Intelligence: ML, NLP, computer vision, reinforcement learning
Systems: operating systems, distributed systems, networks
Software Engineering: programming languages, compilers, formal verification
Data Science: data mining, big data, information retrieval
Human-Computer Interaction
Cybersecurity
SOCIAL SCIENCES
7. Psychology
Cognitive, clinical, developmental, social, neuropsychology, behavioral
8. Sociology
Social theory, social networks, stratification, demography, economic sociology
9. Economics
Microeconomics, macroeconomics, econometrics, behavioral economics, development economics, finance
10. Political Science
Comparative politics, international relations, political theory, public policy, security studies
11. Anthropology
Cultural anthropology, biological anthropology, archaeology, linguistic anthropology
12. Human Geography
Urban studies, population geography, economic geography, GIScience
HEALTH & LIFE SCIENCES
13. Medicine & Clinical Sciences
Internal medicine, surgery, cardiology, oncology, neurology
Genetics, immunology, pathology
Epidemiology, public health
Pharmacology
14. Veterinary Science
15. Nursing & Allied Health
ENGINEERING & APPLIED SCIENCES
16. Engineering (General)
Mechanical Engineering: thermofluids, robotics, acoustics
Electrical Engineering: circuits, signal processing, power engineering
Computer Engineering
Civil Engineering: structural, geotechnical, transport, environmental
Chemical Engineering: process engineering, biochemical engineering
Materials Engineering
Aerospace Engineering
Nuclear Engineering
Industrial & Systems Engineering
17. Environmental & Sustainability Sciences
Environmental science
Climate science
Conservation science
Sustainable engineering
Renewable energy systems
INTERDISCIPLINARY / EMERGING SCIENCES
18. Data, Information, and Complexity Sciences
Data science
Complexity science
Network science
Information theory
19. Cognitive & Brain Sciences
Cognitive science
Computational neuroscience
AI + neuroscience hybrids
20. Life-Environmental Interface
Biogeochemistry
Earth system science
Astrobiology
21. Technology & Society
Science and Technology Studies (STS)
Ethics of AI
Digital sociology
22. Space Sciences
Space engineering
Astrobiology
Planetary science
Deep-space communication
HUMANITIES (often included in broad academic taxonomies)
23. Philosophy
Logic, epistemology, ethics, metaphysics, philosophy of science
24. History
World history, history of science, economic history
25. Linguistics
Phonetics, syntax, semantics, historical linguistics, computational linguistics
26. Arts & Cultural Studies
Literature, media studies, art history
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