Vitalik Revisits Ethereum and AI: Opposes "Indiscriminate Acceleration," Advocates for Decentralization and Privacy Shaping the Future of AI

**February 10th** Ethereum co-founder Vitalik Buterin published an article this morning updating his framework for thinking about the intersection of Ethereum and artificial intelligence (AI). He emphasized that artificial general intelligence (AGI) should not be pursued via "blanket acceleration"—instead, the values of crypto and AI must be deeply integrated to build an AI future that advances human freedom, security, and decentralized cooperation. Buterin pushed back against the misconception that AI is a competition where "whoever achieves AGI first dominates." He argued that both Ethereum’s evolution and humanity’s approach to AGI hinge on choosing the right direction, not blindly speeding up technological progress. Core goals include: - Preventing AI from marginalizing humans or entrenching permanent disempowerment through unavoidable power structures; - Mitigating systemic risks from out-of-control AI or imbalanced offense-defense dynamics. ### Medium-Term Priority Directions Buterin outlined four key areas for practical progress: 1. **Build more trustless/privacy-focused AI interaction tools** This includes local large language model (LLM) tools, privacy-preserving zero-knowledge (ZK) API payments, cryptographic schemes to boost AI privacy, and client-side validation of server-side guarantees (e.g., trusted execution environment [TEE] proofs, cryptographic proofs). He noted this effectively extends Ethereum’s privacy roadmap to LLM computation scenarios. 2. **Ethereum as an AI economic interaction layer** Use cases cover AI API calls, bot-to-bot employment relationships, collateral mechanisms, on-chain dispute resolution, and AI reputation systems (such as ERC-8004). The goal is to enable AI with economic capabilities to support a decentralized AI architecture—rather than closed systems controlled by single organizations. 3. **Turn a "cyberpunk-style self-verifying world" into reality** LLMs can solve the core bottleneck: humans’ inability to audit code line-by-line. This enables trustless interaction, including: - Using Ethereum apps without third-party UIs; - Generating and validating transactions via local models; - Auditing smart contracts locally; - Understanding formal verification (FV) proofs; - Verifying trust models for apps and protocols. 4. **Reshape markets and governance mechanisms** Buterin noted that prediction markets, decentralized governance, secondary voting, and combinatorial auctions are theoretically compelling but long limited by human attention and decision-making. LLMs can scale human judgment, making these mechanisms feasible again. Buterin concluded these directions align with his d/acc (defense-in-depth acceleration) framework and highlight synergies between AI, ZK, and Ethereum. He believes building on the decentralized principles established in 2014—plus leveraging AI and cryptographic tools—can turn these visions into reality.