Ethereum co-founder Vitalik Buterin proposed a technical overhaul of decentralized autonomous organizations (DAOs), calling for the use of personal artificial intelligence agents to privately cast votes on behalf of users and help scale digital governance.
The plan, announced on social media platform X a month after Buterin criticized DAOs for moving into low participation and centralization of power, aims to move users away from delegating votes to large token holders.
Instead, individuals would deploy their own AI model, trained on their past messages and stated values, to vote on the thousands of decisions DAOs face.
“There are many thousands of decisions to be made involving many domains of expertise, and most people do not have the time or skills to be an expert in one, let alone all of them.” Buterin wrote. “So what can we do? We use personalized LLMs to solve the attention problem.”
First is content confidentiality, which ensures that sensitive data remains confidential. AI agents will operate in secure environments such as multi-party computation (MPC) or trusted execution environments (TEEs), enabling them to process private data without leaking it to the public blockchain.
Second is participant anonymity. Buterin called for the use of ZKPs (zero-knowledge proofs), a cryptographic tool that allows users to prove they are eligible to vote without revealing their wallet address or how they voted.
This protects against coercion, bribery and whaling, where smaller voters mimic the decisions of large token holders.
These AI stewards will automate routine participation in governance, flagging only key issues for human review.
To filter out low-quality or spam proposals, an emerging problem as generative AI floods open forums, Buterin suggests launching prediction markets. In these, agents could bet on the likelihood that proposals would be accepted.
Good bets would provide payouts, incentives for valuable contributions, while penalizing noise.
Buterin also called for privacy protection tools such as multiparty computing and trusted execution environments that would allow AI agents to assess sensitive data, such as job applications or legal disputes, without exposing it on a public blockchain.
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