Vitalik Buterin is pushing back against the dominant narrative shaping today’s artificial intelligence industry. As major AI labs frame progress as a competitive sprint toward artificial general intelligence (AGI), the Ethereum co-founder argues that the premise itself is flawed.
In a series of recent posts and comments, Buterin 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 AGI acceleration.
Buterin likens the phrase “working on AGI” to describing Ethereum as simply “working in finance” or “working on computing.” In his view, such framing obscures questions about direction, values, and risk.
Ethereum as Infrastructure for Private and Verifiable AI
A central theme in Buterin’s vision is privacy-preserving interaction with AI systems. He points to growing concerns around data leakage and identity exposure from large language models, especially as AI tools become more embedded in daily decision-making.
To address this, Buterin proposes local LLM tooling that allows AI models to run on user devices, alongside zero-knowledge payment systems that enable anonymous API calls. These tools would make it possible to use remote AI services without linking requests to persistent identities.
He also highlights the importance of client-side verification, cryptographic proofs, and Trusted Execution Environment (TEE) attestations to ensure AI outputs can be checked rather than blindly trusted.
This approach reflects a broader “don’t trust, verify” ethos, with AI systems assisting users in auditing smart contracts, interpreting formal proofs, and validating onchain activity.
An Economic Layer for AI-to-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 each other for services, post security deposits, and resolve disputes using smart contracts rather than centralized platforms.
Use cases include bot-to-bot hiring, API payments, and reputation systems backed by proposed ERC standards such as ERC-8004. Supporters argue that these mechanisms could enable decentralized agent markets where coordination emerges from programmable incentives instead of institutional control.
Buterin has stressed that this economic layer would likely operate on rollups and application-specific layer-2 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 human attention limits.
Prediction markets, quadratic voting, and decentralized governance systems often falter at scale. Buterin believes LLMs could help process complexity, aggregate information, and support decision-making without removing human oversight.
Rather than racing toward AGI, Buterin’s vision frames Ethereum as a tool for shaping how AI integrates with society. The emphasis is on coordination, safeguards, and practical infrastructure, an alternative path that challenges the prevailing acceleration-first mindset.
Cover image from ChatGPT, ETHUSD chart on Tradingview
