Ethereum co-founder Vitalik Buterin says artificial intelligence could help create more efficient decentralized governance models and enable users to make better-informed decisions.
Buterin said in an X post on Sunday that one of the main issues with democratic and decentralized modes of governance, like DAOs, is the “limits to human attention,” because of the many decisions that can require a wide range of expertise or time, which most don’t have.
“The usual solution, delegation, is disempowering it leads to a small group of delegates controlling decision-making while their supporters, after they hit the delegate button, have no influence at all,” he said.
It’s estimated that average participation rates in DAOs are between 15% and 25%. This can lead to issues such as the centralization of power and ineffective decision-making. Worst-case scenarios can result in governance attacks, where a bad actor acquires enough tokens to pass a damaging proposal without other members noticing.
AI-powered assistants that vote for you
Buterin proposes that personal assistant large language models (LLMs) could help solve the “attention problem” by providing users with the relevant information needed for a vote.
“If a governance mechanism depends on you to make a large number of decisions, a personal agent can perform all the necessary votes for you, based on preferences that it infers from your personal writing, conversation history, direct statements,” he said.
“If the agent is unsure how you would vote on an issue, and convinced the issue is important, then it should ask you directly, and give you all relevant context,” Buterin added.
Lane Rettig, a researcher at the Near Foundation specializing in AI and governance, told Cointelegraph last year the non-profit was working on a similar idea: AI-powered digital twins that vote on behalf of DAO members to address low voter participation.
Privacy an important aspect to preserve
Another challenge in highly decentralized governance arises when key decisions depend on private or sensitive information, such as during negotiations, internal disputes, or funding choices, according to Buterin.
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“Typically, orgs solve this by appointing individuals who have great power to take on those tasks,” he said.
He added that an alternative solution could be users submitting their “personal LLM into a black box, the LLM sees private info, it makes a judgment based on that, and it outputs only that judgment. You don’t see the private info, and no one else sees the contents of your personal LLM.”
“All of these approaches involve each participant making use of much more information about themselves, and potentially submitting much larger-sized inputs. Hence, it becomes all the more important to protect privacy,” Buterin said.
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