Vitalik Buterin opposes the dominant narrative that shapes today’s artificial intelligence industry. While major AI labs frame progress as a competitive sprint towards artificial general intelligence (AGI), Ethereum’s co-founder argues that the premise itself is flawed.
In a series of recent posts and comments, Buterin outlined an alternative approach that prioritizes decentralization, privacy, and verification over scale and speed, positioning Ethereum not as a vehicle for AGI acceleration but as a key part of the enabling infrastructure.
Buterin likens the phrase “working on AGI” to simply describing Ethereum as “working on finance” or “working on computing.” In his view, such a framework obscures questions about direction, values and risk.
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Ethereum as a private and verifiable AI infrastructure
A central theme of Buterin’s vision is interacting with AI systems while preserving privacy. He notes that concerns about data leakage and identity theft from large-scale language models are growing, especially as AI tools become more integrated into daily decision-making.
To address this, Buterin proposes a local LLM tool that can run AI models on the user’s device, alongside a zero-knowledge payment system that allows anonymous API calls. These tools allow you to use remote AI services without linking requests to a persistent identity.
He also emphasizes the importance of client-side validation, cryptographic proofs, and trusted execution environment (TEE) proofs to ensure that AI output can be checked rather than blindly trusted.
This approach reflects a broader “trust not verify” ethos, with AI systems helping users audit smart contracts, interpret formal proofs, and verify on-chain activity.
Economic layer for coordination between AIs
Beyond privacy, Buterin believes Ethereum will serve as an economic coordination layer for autonomous AI agents. In this model, AI systems can pay each other for services, post deposits, and resolve disputes using smart contracts rather than a centralized platform.
Use cases include bot-to-bot adoption, API payments, reputation systems backed by proposed ERC standards such as ERC-8004, and more. Proponents argue that these mechanisms could enable decentralized agent markets where coordination emerges from programmable incentives rather than institutional control.
Buterin emphasized that this economic layer will likely operate on a rollup or application-specific layer 2 network, rather than on Ethereum’s base layer.
Governance and market design using AI
The final pillar of Buterin’s framework focuses on governance and market mechanisms, which have historically struggled due to the limitations of human attention.
Prediction markets, secondary voting, and decentralized governance systems often fail at scale. Buterin believes LLM can handle complexity, aggregate information, and support decision-making without eliminating human oversight.
Rather than rushing towards AGI, Buterin’s vision frames Ethereum as a tool that will shape how AI integrates with society. The emphasis is on coordination, safety measures, and practical infrastructure, and alternatives that challenge the prevailing acceleration-first mindset.
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