Vitalik Buterin outlines an ethereum ai path to challenge Big Tech in the race for safer intelligence

In a new manifesto on technology and power, Vitalik Buterin argues that ethereum ai can underpin a safer, more decentralized future for artificial intelligence.

Vitalik Buterin challenges the global race to AGI

Ethereum co-founder Vitalik Buterin has issued a sharp warning about the current worldwide push toward Artificial General Intelligence (AGI). He argues that the prevailing race, driven mainly by speed and scale, is fundamentally misguided and increases systemic risk instead of managing it responsibly.

According to Buterin, competition focused on being first to AGI lacks clear direction, intention, and guardrails. Moreover, he believes this approach amplifies the danger of catastrophic failures, because safety and governance lag behind raw capabilities. That said, he calls for technology that embeds caution and verifiability at its core.

In a summary of his stance shared on social media by Coin Bureau, Buterin is quoted as saying that the “race for AGI” is flawed. Instead, he wants a safer, decentralized AI stack built on Ethereum, with local models and crypto-style governance countering the dominance of Big Tech platforms.

Ethereum as a neutral base layer for decentralized AI

Buterin envisions an AI ecosystem where intelligence is not monopolized by a few corporations, but distributed across many actors using open infrastructure. In this model, power disperses through users and communities, rather than being concentrated inside proprietary, closed systems run by major technology companies.

Moreover, he places Ethereum at the center of this transformation, treating it not just as a blockchain, but as a financial coordination layer. This neutral base layer can host autonomous AI agents that interact, transact, and coordinate directly on-chain without relying on trusted intermediaries.

The article explains that AI agents could operate on Ethereum using rollups and Layer 2 networks. These scaling solutions would allow AI systems to initiate transactions securely, resolve conflicts on-chain, and provide cryptographic proofs of their actions. As a result, the need for off-chain trust assumptions would be drastically reduced.

Local AI models, privacy, and zero-knowledge tools

Privacy is another critical pillar of Buterin’s proposal. He strongly favors local AI models over centralized systems that must ingest vast amounts of user data. In his view, local deployments keep control in the user’s hands, limit data extraction, and reduce the risk of pervasive surveillance by state or corporate actors.

That said, Buterin does not argue against powerful AI capabilities; instead, he wants those capabilities embedded in architectures that naturally preserve privacy. Local processing, combined with cryptographic tools, could make it possible to enjoy advanced AI while exposing far less sensitive information to third parties.

Within this framework, Ethereum becomes a toolkit for zero-knowledge payments and verifiable checks. AI agents could prove that certain computations or decisions were made, and that specific conditions were met, without revealing underlying confidential data. Moreover, this design promises enhanced safety and accountability without sacrificing core functionality.

Crypto-style governance as protection against AI abuses

Governance sits at the heart of Buterin’s critique of present AI development. He argues that oversight should be built on transparent, programmable, and community-driven systems rather than opaque corporate boards or poorly understood regulatory processes. In his view, crypto governance ai models are better suited to managing powerful technologies.

By comparison, traditional AI governance often operates in the dark, consolidating decision-making power in a handful of executives or regulators. However, these arrangements typically lack robust accountability mechanisms and clear incentive structures aligned with the broader public interest.

Ethereum-based governance, Buterin suggests, could offer a credible alternative. Token-weighted or reputation-based systems can distribute influence, while on-chain rules and incentives reward beneficial behavior in a transparent manner. This creates alignment and introduces consequences inside AI ecosystems that, today, often operate without meaningful checks.

Defensive acceleration and the path to responsible AI

Buterin situates his vision within the concept of defensive acceleration ai. Rather than simply trying to slow down AI research, this strategy aims to accelerate the development of safety mechanisms, governance frameworks, and open infrastructure that can keep pace with capabilities.

Moreover, this approach rejects blind scaling and what he sees as “wantonness” in the current race. Instead, it insists that ethics, verifiability, and accountability become enforceable properties of the underlying technology stack, not optional add-ons layered on top after deployment.

In this context, Buterin argues that an ethereum ai architecture can anchor the convergence between crypto and AI. The goal is to shift the trajectory of the field: from domination to coordination, from extractive data mining to user empowerment, and from unchecked acceleration to sustainable innovation guided by robust governance.

From domination to coordination in the AI era

Buterin’s vision ultimately reimagines how societies might govern intelligent systems in the coming decade. He proposes that a neutral, programmable settlement layer like Ethereum can help transform AI from a tool of centralized control into an ecosystem of accountable, cooperating agents.

If realized, this model would make transparency the default, allow verifiable behavior by AI entities, and embed incentives that favor responsible use over reckless scaling. That said, achieving such a shift will require technical advances, community consensus, and deliberate policy choices across both the crypto and AI sectors.

As debates over AGI intensify in 2025 and beyond, Buterin’s call for an Ethereum-led, decentralized AI stack offers a blueprint for those seeking to balance innovation with safety, privacy, and genuine user empowerment.