Author here. This is an RFC for open AI consent standards, not a product announcement.

The thesis: Training data lawsuits, agent security vulnerabilities, and user lock-in are architectural problems. We're trying to regulate AI without infrastructure to regulate.

I documented four standards:

- LCS-001: Consent tokens for training data (with attribution + compensation)

- LCS-002: Digital twins (user-owned, portable AI profiles)

- LCS-003: Agent permissions (capability-based security to prevent prompt injection exploits)

- LCS-004: Cross-agent memory (shared context with privacy controls)

The specs are on GitHub: https://github.com/LLMConsent/llmconsent-standards

Hardest unsolved problems I'm wrestling with:

1. Attribution in neural networks (influence functions are expensive/imperfect)

2. Enforcement without regulatory pressure

3. Whether L2s are really cheap enough for this use case

I'd especially value criticism on:

- Does LCS-003 actually prevent the "confused deputy" agent exploits?

- Is the digital twin evolution protocol (LCS-002) realistic?

- Better alternatives to blockchain for decentralized verification?

Happy to answer questions. Looking for serious technical feedback, not validation.