Vitalik Buterin Tests Multilayer Privacy Setup for AI Health Queries

The system relies on Alibaba’s Qwen3.8-Flash-Next model running locally to act as a privacy filter. Rather than sending raw personal data to frontier AI models, the local agent constructs sanitized queries that retain only the information necessary for reasoning. This process attempts to strip away identifying writing patterns and specific context before a request leaves the user's device.

To manage the remaining metadata, Buterin integrated zkAPI, an Ethereum Foundation project that decouples payment identity from individual API calls, alongside Tor to mask the user’s IP address. Despite the architectural design, performance hurdles persist. Buterin reported that Tor latency is currently 10 to 100 times higher than optimal, and his local model achieves only 20–30 tokens per second, falling short of his 100 tokens per second target for a seamless experience. As development continues, the primary challenge remains balancing data obfuscation with the utility of the remote model’s feedback.