Vitalik Buterin Proposes Ethereum-Based AI Safety Framework

Vitalik Buterin’s critique begins with the very language used to describe the field’s ambition. In a post on X, he argued that the phrase “work on AGI” is as vague as describing Ethereum as merely “working in finance.” This framing, he contends, reduces a complex, value-laden endeavor to a simplistic and dangerous race where the primary distinction is “that you get to be the one at the top.” By treating artificial general intelligence as a monolithic goal, Buterin believes the industry obscures more critical questions about direction, safety, and the underlying values embedded within the technology.

This philosophical stance directly counters the public positions of firms like OpenAI, whose CEO Sam Altman has expressed confidence in knowing how to build AGI and anticipates AI agents soon joining the workforce. Buterin’s alternative is not to halt progress but to guide it with explicit guardrails. His d/acc framework prioritizes building systems that “foster human freedom and empowerment” and, crucially, ensure “the world does not blow up.” This represents a foundational split in the AI community, pitting centralized control and raw capability scaling against a decentralized, verifiable, and human-centric model of development.

Moving beyond philosophy, Buterin outlined a concrete, four-part technical roadmap where Ethereum’s infrastructure is leveraged to create safer AI systems. He organized the design space into quadrants considering both infrastructure versus impact and “survive” versus “thrive” outcomes. The first quadrant focuses on tooling for trustless and private interaction. This includes promoting local LLMs that run on user devices, zero-knowledge payment systems that allow anonymous calls to AI APIs without linking identity across requests, and stronger cryptographic privacy to protect data.

A second, crucial quadrant positions Ethereum as the economic layer for AI-to-AI activity. This vision, echoed by industry builders, sees Ethereum—particularly its rollups and application-specific Layer 2 networks—becoming the default settlement layer for agentic commerce. “It’s less about ‘accelerating AGI’ and more about providing the necessary rails and guardrails for agentic commerce, trade, and investing,” said Joni Pirovich, founder and CEO of Crystal aOS. This would enable features like programmable deposits, usage-based micropayments, and on-chain dispute resolution, creating the trust layer for decentralized agent economies.

Midhun Krishna M, co-founder and CEO of LLM cost tracker TknOps.io, elaborated on the requirements, stating that such economies need “identity, reputation, and stake-weighted accountability, not just better interfaces.” The proposed technical foundation includes client-side verification of AI services and hardware attestations via Trusted Execution Environments (TEEs), ensuring users can cryptographically verify an AI’s output and behavior rather than blindly trusting a centralized provider.

The third pillar of Buterin’s framework revives the core cypherpunk ethos of “don’t trust, verify” for the AI age. It envisions local LLM assistants capable of proposing transactions, auditing smart contract code for vulnerabilities, interpreting formal verification proofs, and interacting directly with decentralized applications. This shifts control from centralized AI interfaces back to the individual user, embedding verification at the point of use. It transforms AI from a black-box oracle into a transparent tool that augments human agency within the crypto ecosystem.

Finally, the fourth quadrant targets the upgrade of collective decision-making systems. Buterin points to the potential for AI to enhance mechanisms like prediction markets and quadratic voting, creating more robust and intelligent governance models for both crypto protocols and broader societal institutions. This completes a holistic vision where AI is not a standalone, monolithic intelligence but a suite of verifiable tools integrated into a decentralized economic and governance stack, with Ethereum providing the coordination and settlement backbone.

Ultimately, Buterin’s argument is that crypto and AI must converge philosophically and technically. He laments that the two transformative technologies are too often approached from “completely separate philosophical perspectives.” His proposed synthesis aims to use crypto’s strengths—decentralization, credible neutrality, and verifiability—to solve AI’s core challenges of trust, coordination, and safety. As the race for more powerful models accelerates, Buterin’s Ethereum-based blueprint offers a competing vision for an AI future built not on who builds it fastest, but on who builds it most safely and openly.