Buterin’s approach contrasts with the AGI acceleration narratives from major AI labs, focusing on safer, Ethereum-based AI coordination.
Vitalik Buterin is calling for a different path in artificial intelligence — one that rejects a blind “race to AGI” and instead relies on Ethereum-style decentralization, verification, and privacy as guardrails for the AI era.
“The frame of ‘work on AGI’ itself contains an error,” Ethereum co-founder Buterin wrote in a post on X Monday, noting that the goal is often treated as an undifferentiated race where the main distinction is simply “that you get to be the one at the top.”
He compared the phrase to vaguely describing Ethereum as just “working in finance” or “working on computing,” saying it obscures more important questions about direction and values.
Buterin said AI and crypto are too often approached from “completely separate philosophical perspectives,” and urged builders to integrate them.
Instead of raw acceleration, AI development should focus on systems that “foster human freedom and empowerment” and ensure “the world does not blow up,” Buterin wrote, echoing his defensive-acceleration, or d/acc, framework.
Joni Pirovich, founder and CEO of Crystal aOS, told Decrypt, “Ethereum becoming the default settlement layer for AI-to-AI interactions is realistic.
It’s less about ‘accelerating AGI’ and more about providing the necessary rails and guardrails for agentic commerce, trade, and investing.
Trust and coordination, especially at the technology infrastructure and compliance infrastructure levels, are even more important now than ever.”
The comments land as major AI firms continue to publicly push toward AGI and superintelligence, with leading labs describing rapid progress in autonomous agents and advanced models.
Buterin claims his alternative centers on safer, more verifiable infrastructure rather than larger models, outlining a practical roadmap in which Ethereum plays a central, though not exclusive, role.
That includes local LLM tooling, zero-knowledge payments that let users call AI APIs without linking identity across requests, stronger cryptographic privacy, and client-side verification of AI services and attestations.
“Using Ethereum as an economic layer for AI-to-AI interaction is also directionally correct, but it will live mostly on rollups and app-specific L2s,” Midhun Krishna M, co-founder and CEO of LLM cost tracker TknOps.io, told Decrypt.
Decentralized agent economies need programmable deposits, usage-based payments, and on-chain dispute resolution, Krishna said, adding that AI-augmented governance will require “identity, reputation, and stake-weighted accountability, not just better interfaces.”
Vitalik grouped the Ethereum-AI design space into a four-part framework, illustrated as a 2×2 chart, spanning infrastructure vs. impact and survive vs. thrive outcomes.
One quadrant centers on tooling for trustless and private AI interaction, including local LLMs, zero-knowledge payments for anonymous API calls, cryptographic privacy upgrades, and client-side verification of AI services, TEE attestations, and proofs.
Another quadrant positions Ethereum as an economic layer for AI activity, supporting API payments, bot-to-bot hiring, security deposits, on-chain dispute resolution, and AI reputation standards, such as proposed ERC-based models, aimed at enabling decentralized agent coordination rather than in-house platform control.
A third focus revives the cypherpunk “don’t trust, verify” vision through local LLM assistants that can propose transactions, audit smart contracts, interpret formal verification proofs, and interact with apps without relying on centralized interfaces.
A fourth targets upgraded prediction markets, quadratic voting, and governance systems.
