We create decentralized AI infrastructure and builder community
Infrastructure for Web3 AI, decentralized AI governance, and bridge between Web2 and Web3 for AI.
Venture DAO for AI, support for diverse AI financing models, and AI value chains.
Grow a global community of Web3 AI, incentives, and collective efforts to deliver growth and value.
Decentralized governance of ML artifacts (e.g., data, models, codes, applications) and life-cycle management.
Community wisdom and collective efforts for value co-creation in Web3 AI.
AI Fi for Web3 and dApps.
Deep integration of ML workflows with tokenomics.
WhitePaper
Technical Overview Paper
The decision-making process of AI DAO is transparent to its community. Participants of AI DAO can contribute their areas of expertise and work together as a community to create better AI models and applications as collective efforts. The DAO-based approach ensures that the participants will have a vested interest in the AI DAO. Contributors are incentivized and rewarded with design principles of tokenomics. As a result, they will be more engaged with the community. The main focus of AI DAO is creating and growing a community of contributors and builders who are well-versed in AI and Web3 to engage in the collaborative development of AI.
AI DAO is a platform for AIFi. It provides an environment for experimenting with finance innovation and token-based models to fund and support AI projects. It offers a more agile and transparent DAO-based process for AI finance. One benefit of AI DAO is that it lowers the barrier for small AI teams. A benefit of AI DAO protocol is that it codifies value creation by using smart contracts. A team can use convertible tokens to develop AI technologies and validate new ideas instead of spending resources on overheads required for forming and managing a traditional company. When the time is ready, equity shares can be issued to the token holders.
Ownership of AL/ML assets (data, models, and code). Web3 expands the concept of digital ownership to a new level with decentralized, permanent data storage managed by decentralized governance mechanisms like data owned by DAOs.
A hybrid environment manages the ML workflow process with both on-chain and off-chain components, which brings benefits such as transparency, accountability, and auditability to ML workflow.
AI DAO governs data acquisition and model ownership. Contributions are incentivized to improve the process and the outcome of data acquisition and model training.
Designed and Developed by Team AiDao