Vitalik Buterin opposes the dominant discourse that shapes the artificial intelligence sector today. While major AI labs view progress as a competitive sprint toward artificial general intelligence (AGI), Ethereum’s co-founder says the principle itself is flawed.
In a series of recent posts and comments, Buterin has outlined a different approach, one that prioritizes decentralization, privacy, and verification over scale and speed, with Ethereum positioned as a key piece of enabling infrastructure rather than a vehicle for accelerating AGI.
Buterin compares the phrase “working on AGI” to describing Ethereum as simply “working in finance” or “working on IT.” According to him, such a framework obscures questions of orientation, values and risk.
ETH's price trends to the downside on the daily chart. Source: ETHUSD on Tradingview
Ethereum as an infrastructure for private and verifiable AI
A central theme of Buterin’s vision is privacy-respecting interaction with AI systems. It highlights growing concerns about data leaks and exposure of identities from large language models, especially as AI tools are increasingly integrated into everyday decision-making.
To solve this problem, Buterin offers local LLM tools that allow AI models to run on users’ devices, as well as zero-knowledge payment systems that allow anonymous API calls. These tools would allow AI services to be used remotely without linking queries to persistent identities.
It also highlights the importance of client-side verification, cryptographic proofs, and Trusted Execution Environment (TEE) attestations to ensure that AI results can be verified rather than blindly trusted.
This approach reflects a broader “don’t trust, verify” philosophy, with AI systems helping users audit smart contracts, interpret formal proofs, and validate on-chain activity.
An economic layer for AI-AI coordination
Beyond privacy, Buterin sees Ethereum playing a role as an economic coordination layer for autonomous AI agents. In this model, AI systems could pay themselves for services, pay security deposits, and resolve disputes using smart contracts rather than centralized platforms.
Use cases include bot-to-bot hiring, API payments, and reputation systems supported by proposed ERC standards such as ERC-8004. Proponents argue that these mechanisms could enable decentralized agent markets where coordination emerges from programmable incentives rather than institutional control.
Buterin pointed out that this economic layer would likely run on application-specific layer-2 stacks and networks, rather than Ethereum’s base layer.
AI-assisted governance and market design
The final pillar of Buterin’s framework focuses on governance and market mechanisms that have historically struggled due to the limits of human attention.
Prediction markets, quadratic voting, and decentralized governance systems often fail at scale. Buterin believes LLMs could help address complexity, aggregate information and support decision-making without removing human oversight.
Rather than rushing toward AGI, Buterin’s vision presents Ethereum as a tool to shape how AI integrates into society. The emphasis is on coordination, safeguards and practical infrastructure, an alternative path that challenges the dominant acceleration-focused mentality.
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