Background and Core Challenges
Decentralized governance in DAOs and blockchain systems often struggles with limited human attention and domain expertise. Traditional delegated voting mechanisms tend to centralize decision-making power among a few representatives, failing to address the complexity and volume of modern governance needs.
Personal Governance Agents
Deploying user-specific large language models to automatically participate in governance decisions based on individual preferences. These agents would request user input only for high-stakes or ambiguous proposals, lowering the barrier to participation and improving efficiency.
Public Conversation Agents
AI models can summarize, analyze, and structure community discussions into digestible insights. This approach enhances information accessibility while preserving user privacy, enabling more effective collective decision-making.
Incentivized Suggestion Markets
Combining prediction market principles with AI-powered evaluation systems to reward high-quality proposals and arguments. This mechanism encourages meaningful contributions and improves the overall quality of governance outcomes.
Privacy-Preserving Governance
For decisions involving sensitive data, technologies like Trusted Execution Environments (TEE) or secure multi-party computation can enable governance processes where data remains private but conclusions remain verifiable and actionable.