Vitalik Buterin Rejects 'Race to AGI,' Proposes Ethereum-Led Alternative for Safer AI Development

Vitalik Buterin Challenges the Race to AGI Framework

Vitalik Buterin is calling for a fundamental shift in how the tech industry approaches artificial intelligence development. In a Feb. 9 post on X, the Ethereum co-founder argued that the phrase "work on AGI" itself contains a critical error, treating AGI as an undifferentiated race where the primary goal is simply reaching the finish line first 1

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. He compared this framing to vaguely describing Ethereum as just "working in finance" or "working on computing," noting it obscures more important questions about direction and values 1

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. Buterin's comments land as major AI firms continue pushing toward AGI and superintelligence, with leading labs describing rapid progress in autonomous agents and advanced models 1

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The Ethereum-led alternative Buterin proposes rejects blind acceleration in favor of systems that "foster human freedom and empowerment" and ensure "the world does not blow up," echoing his defensive-acceleration, or d/acc, framework 1

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. Rather than treating crypto and AI as separate philosophies, Buterin urged builders to integrate them, focusing on safer, more verifiable infrastructure rather than simply building larger models 1

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. His vision centers on choosing a direction for AI development that protects human agency and reduces catastrophic risk 4

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Four Pillars Define the Intersection of Ethereum and AI

Vitalik Buterin detailed his broader vision for how Ethereum and AI could work together through a four-part framework, illustrated as a 2x2 chart spanning infrastructure versus impact and survive versus thrive outcomes 1

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The first pillar focuses on enabling private and trustless AI interaction, addressing the tradeoff most users face today between powerful AI services and identity trails 4

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. This includes tooling for local LLMs, zero-knowledge proofs to make API calls anonymously without linking identity across requests, stronger cryptographic privacy, and client-side verification of AI services and TEE attestations 1

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Private data leaks by large language models have become an increasing area of concern since the rise of AI chatbots, with chat logs potentially being used against users in legal proceedings 2

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. The second pillar positions Ethereum as an economic layer for AI-to-AI interactions, supporting API calls, bot-to-bot hiring, security deposits, on-chain dispute resolution, and AI reputation standards such as proposed ERC-based models 1

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. Joni Pirovich, founder and CEO of Crystal aOS, told Decrypt that "Ethereum becoming the default settlement layer for AI-to-AI interactions is realistic," emphasizing that trust and coordination at the technology infrastructure and compliance levels are more important now than ever 1

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AI Agents and the Cypherpunk Vision of Verification

The third focus of Buterin's framework 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 dApps without relying on centralized interfaces 1

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. Buterin argued that AI agents could verify and audit every transaction, making crypto much more accessible by acting as a user's middleman to the blockchain 2

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. He admitted why the "verify everything" approach previously failed—humans won't actually verify all the code themselves—but claimed LLMs can now make that vision happen by doing the hard part 2

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AI for on-chain verification could prove valuable as increasingly sophisticated scammers emerge, with address poisoning scams seeing a major uptick since December 2

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. Buterin envisions AI agents being able to "interact economically" to handle all on-chain activity for users, with bots deployed to hire each other, handle API calls, and make security deposits—creating "economies not for the sake of economies, but to enable more decentralized authority" 2

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. Midhun Krishna M, co-founder and CEO of LLM cost tracker TknOps.io, noted that using Ethereum as an economic layer for AI will live mostly on rollups and app-specific L2s, with decentralized agent economies requiring programmable deposits, usage-based payments, and on-chain dispute resolution 1

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AI-Enhanced Governance and Prediction Markets

The fourth pillar targets upgraded prediction markets, quadratic voting, and governance systems, addressing how AI can amplify human judgment in areas previously limited by attention and decision-making capacity 1

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. Buterin stated that while mechanisms like prediction markets and decentralized governance are "all beautiful in theory," they are ultimately hampered by "limits to human attention and decision-making power," but LLMs remove that limitation and massively scale human judgment 2

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. This argument arrives during a live regulatory reset in the U.S., where on Feb. 4 the CFTC withdrew its 2024 proposed rulemaking on "event contracts" and a related staff advisory on sports event contracts 4

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At the state level, a Massachusetts judge ordered prediction-market operator Kalshi to stop offering sports-event contracts within 30 days unless it obtains a gaming license 4

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. Buterin's vision for AI-enhanced governance suggests that as AI becomes more agentic, the critical battle centers on constraints including privacy, verifiability, user-side control, and decentralization 4

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. He isn't claiming Ethereum "solves AI" but rather arguing that the "race" story—fast, centralized, and status-driven—picks a direction by default, while his alternative attempts to choose one deliberately 4

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