On March 13, the Ethereum Foundation board released the 'EF Mandate' document, explicitly establishing CROPS as the strategic cornerstone for 以太坊. This acronym encapsulates five critical pillars: Censorship Resistance, Capture Resistance, Open Source, Privacy, and Security. The mandate clarifies that while performance optimization and cost reduction remain relevant, they are secondary to maintaining user self-sovereignty and resisting extraction attempts. Data compiled by Woofun AI shows that this strategic pivot aims to distinguish 以太坊 from competitors by focusing on fundamental capabilities rather than short-term metrics like transaction speed or fee structures. The long-term vision prioritizes a robust infrastructure where protocol-level enhancements, such as client diversity and validator decentralization, directly support these core values.

The operational scope of CROPS extends beyond the protocol layer into application and user experience domains. At the application level, the framework demands that wallets, RPC services, browsers, and signing interfaces minimize reliance on centralized intermediaries.

Concurrently, user experience design must evolve to provide clearer signing mechanisms and verifiable interactions, ensuring security does not depend solely on user comprehension of complex transactions. Vitalik Buterin recently emphasized that the Ethereum Foundation must shrink in scale to focus exclusively on these high-impact areas, acknowledging that limited resources cannot cover the entire ecosystem. Instead, investments are directed toward tasks crucial for CROPS realization that other entities cannot reliably undertake, such as the Ethereum Audit Subsidy program designed to lower barriers for security audits.

In a significant expansion of this framework, Vitalik Buterin integrated CROPS into the discourse on artificial intelligence on May 28. He highlighted the release of DeepSeek V4, a 2-bit quantized model capable of running on systems with approximately 90 GB of video memory. Performance metrics indicate operation speeds of around 35 tok/s on Apple hardware and about 7 tok/s on AMD hardware. Woofun AI notes that true 'CROPS AI' must support diverse hardware platforms rather than merely adopting a decentralized label. The convergence of CROPS 以太坊 and CROPS AI is evident in the potential for zero-knowledge proofs to enable paid remote large language model calls and private 以太坊 RPC reads, creating a synergistic environment for enhanced security.

This integration reframes the relationship between users and digital agents. Historically, applications maintained clear boundaries where users interacted with specific tools for transfers, transactions, or information retrieval. The advent of AI agents blurs these lines, allowing natural language commands to execute complex DeFi strategies, manage assets, or find optimal cross-chain paths.

However, this convenience introduces critical risks regarding what transactions an AI signs on behalf of the user and what privacy data it exposes. If AI operates entirely on centralized cloud services, sensitive information including asset details, transaction intentions, and identity preferences could be aggregated by a few service providers. Woofun AI analysis suggests that reliance on opaque APIs and unverifiable reasoning processes in this context significantly increases the difficulty for users to understand the trade-offs they are making.

The CROPS AI framework addresses these vulnerabilities by demanding that AI systems be resistant to censorship, open, transparent, and secure. Ideally, such systems should run locally or minimize dependence on centralized clouds to reduce information leak risks. This ensures users retain ultimate control over their operations, a necessity that becomes more acute as AI interacts closer to user assets. The overlap between the CROPS 以太坊 access layer and CROPS AI is substantial; both seek to solve the fundamental problem of how users can leverage powerful digital tools without surrendering privacy, identity, and decision-making power to centralized intermediaries. Whether checking balances via RPC services or invoking remote AI models, the risk of data exposure remains a shared challenge.

Initiatives like ZK-powered paid remote LLM calls and private 以太坊 RPC reads represent the practical intersection of these two domains. They aim to construct a more private, verifiable, and trust-reducing on-chain access layer alongside an open, localized AI execution environment. This dual approach creates new pathways for digital interaction, fundamentally altering the role of the wallet layer in the Web3 ecosystem. As users begin to express on-chain needs through natural language, wallets will transition from simple signing tools to comprehensive control centers for digital activities. They will be tasked with verifying DApp reliability, simulating transaction consequences, and monitoring AI agent data access, directly shaping the development of Web3 interaction experiences over the coming decade.

Despite current market conditions potentially dampening interest in conceptual frameworks, the CROPS principles address technical factors crucial for long-term trends during economic downturns. The framework unifies the challenges faced by 以太坊 and AI under a common question: can users retain control over their lives and data as digital systems become increasingly powerful? Security and privacy cannot be retroactively applied; they must be foundational. In an era where AI accelerates its dominance over the digital world, the commitment to being understandable, verifiable, private, and secure likely constitutes the true foundation for 以太坊's continued success and relevance.