Ethereum Co-Director Proposes LLM-Driven Five-Step Plan to Automate Governance
Tomasz Stańczak, co-director at the Ethereum Foundation, published a five-step blueprint to transition Ethereum governance to large language models (LLMs). The plan proposes: 1) validator operators delegating decision power to AI agents for upgrade approvals and parameter tuning; 2) EIP authors using LLMs to draft and submit proposals; 3) EIP editors employing AI review tools; 4) core developers relying on LLMs to moderate All Core Dev (ACD) meetings and vote on EIP inclusion; and 5) client teams generating fully specified, formally verified codebases from specs via AI. Stańczak argues Ethereum has an advantage because existing specs, recorded ACD calls and archived governance debates provide rich training data for LLMs. The Ethereum Foundation has already hired tooling coordinators and formed a dAI team; the proposal prioritizes real-time AI moderation, improved tooling for EIP workflows, and a cross-client team to build an AI-generated, formally verified client running in parallel with existing clients until it becomes canonical. Stańczak framed AI governance as an infrastructure upgrade to scale decision-making without replacing human oversight: validators would still accept agent recommendations and editors/authors retain final authority. Challenges cited include the technical difficulty of real-time NLP moderation and generating formally verified client code from specs alone. If executed, the plan could speed upgrades, reduce client divergence, and cement Ethereum’s lead in AI-driven blockchain governance.
Bullish
The proposal positions Ethereum to streamline and scale governance using LLMs, which could materially improve upgrade velocity, reduce client divergence, and strengthen ecosystem coordination. For traders, clearer and faster protocol upgrades reduce execution risk around forks and contentious EIPs, a factor that tends to be bullish for ETH because it lowers perceived protocol risk and supports network utility growth. Ethereum already possesses extensive governance and spec archives—useful training data that give it a first-mover advantage in AI-driven governance. Historic parallels: efforts that reduced client fragmentation (e.g., improvements after contentious upgrades) have supported market confidence and price appreciation. Short-term: news can trigger speculative buying on ETH as traders price in improved governance and future acceleration of feature rollouts; volatility may spike around implementation milestones or governance experiments. Long-term: if AI-driven governance successfully accelerates safe upgrades and yields a formally verified canonical client, fundamental network value could rise through improved reliability and developer coordination, supporting sustained demand for ETH. Risks: technical execution failure, AI-induced governance errors, or centralization concerns if agentic decision-making concentrates influence — these could negate bullish effects and produce sharp sell-offs. Overall, net effect likely bullish if implementation is cautious and transparent, but watch for governance trials and formal verification progress as key catalysts or risk points.