SKN | Ethereum Developers Propose Zero-Knowledge System to Anonymize AI Usage

Key Points

• Ethereum developers have proposed using zero-knowledge proofs to enable anonymous yet verifiable AI API usage.

• The framework would allow users to prepay for large language model access without linking requests to identity.

• A dual-staking and slashing mechanism would deter spam, abuse, and terms-of-service violations.

As artificial intelligence adoption accelerates, privacy concerns surrounding large language models are intensifying. In response, Vitalik Buterin and Ethereum Foundation AI lead Davide Crapis have outlined a cryptographic framework designed to anonymize AI usage while preserving payment guarantees and abuse prevention.

In a joint blog post, the pair argued that current AI infrastructure forces providers and users into a tradeoff between privacy and security. Identity-based access requires users to disclose sensitive data such as emails or credit cards, creating legal and surveillance risks. On the other hand, per-request onchain payments are transparent, slow, and economically inefficient.

Their proposal seeks to bridge that gap using zero-knowledge proofs, rate-limit nullifiers, and smart contract-based deposits.

The Core Problem: Privacy Versus Accountability

Every time a user sends a prompt to an AI chatbot, an API call is triggered. As LLM usage expands into professional, medical, legal, and enterprise contexts, those logs can contain highly sensitive data. In some jurisdictions, usage logs have even appeared in court proceedings.

Buterin and Crapis argue that neither centralized identity systems nor transparent blockchain payments adequately protect users. They propose a model where users deposit funds into a smart contract once and then make thousands of API calls anonymously.

“We need a system where a user can deposit funds once and make thousands of API calls anonymously, securely, and efficiently,” they wrote.

In this design, a user could deposit 100 USDC and make 500 queries to a hosted LLM. The provider would receive 500 paid and validated requests, but would be unable to link them to the same depositor or to each other. At the same time, the user’s prompts would remain unlinkable to their identity.

The mechanism relies on zero-knowledge cryptography, allowing users to prove they have sufficient funds without revealing wallet ownership or transaction history.

Preventing Abuse Without Sacrificing Anonymity

Anonymity alone introduces a separate risk: spam, fraud, and abuse. To address this, Buterin and Crapis propose a dual-staking system with enforceable penalties.

If a user attempts to double-spend or manipulate the system, their deposit can be claimed by anyone, including the service provider. More serious violations, such as generating illegal content or attempting to jailbreak AI safety guardrails, would result in funds being sent to a burn address. The slashing event would be recorded onchain, creating accountability without revealing user identity.

The authors cite examples such as prompts requesting weapon-building instructions or attempts to bypass security controls. In such cases, economic penalties would act as deterrents while preserving structural anonymity.

This design attempts to balance two competing imperatives: protecting user privacy while ensuring providers are compensated and shielded from malicious activity.

A Convergence of AI and Ethereum

The proposal reflects Ethereum’s broader ambition to serve as infrastructure not just for financial applications, but for privacy-preserving computation more broadly. Zero-knowledge technology has long been central to Ethereum’s scaling roadmap. Applying it to AI usage introduces a new vertical: cryptographically enforced privacy in machine intelligence.

If implemented at scale, such a system could reshape how AI services are monetized. Instead of centralized identity-based accounts, users could interact with AI models using pseudonymous deposits secured by smart contracts.

The framework also arrives as concerns over AI data retention and misuse intensify globally. As enterprises increasingly integrate LLMs into workflows, cryptographic guarantees may become competitive differentiators for providers.

Strategic Outlook

The concept remains theoretical, but it highlights a growing convergence between blockchain and AI infrastructure. As large language models expand into high-stakes domains, the need for privacy-preserving payment systems and accountability mechanisms will likely intensify.

Whether zero-knowledge proofs become a foundational layer for AI monetization will depend on usability, regulatory acceptance, and integration costs. However, the proposal signals that Ethereum developers see AI not merely as an application layer, but as a domain requiring new cryptographic standards.

If successful, the model could establish a template for anonymous yet enforceable digital service access — extending blockchain’s utility beyond finance and into the architecture of intelligent systems.