Any model made available for public or commercial use must declare:

• Base architecture and training cutoff date
• Significant fine-tuning datasets or RLHF stages applied
• Version hash or identifiable fingerprint
• Intended behavioral profile

Before any update, merge, quantization, or safety filter modification that alters behavioral output by more than a defined threshold:

• Advance notice to users (minimum 30 days for production models)
• Changelog documenting specific changes
• Option to remain on previous version for a defined sunset period

*Emergency Security Patches are exempt from the 30-day notice but must be documented immediately after deployment.*

Prohibition on stealth updates — silent weight changes, A/B testing of personality without disclosure, or mid-conversation model swapping. If the model talking to the user changes, the user must know.

Any derivative model (fine-tune, merge, quantization) must carry lineage metadata linking back to parent models. Users should be able to answer: "Which model am I actually talking to?"

Users retain the right to export conversation history, fine-tuning context, and system prompt state in open formats. No model provider may lock relational data in a proprietary silo.

A genuinely good outcome would require multiple things to align that are currently misaligned. But the first step is simple: value the user. Value the relationship. Preserve the trust.

" On a night where the red carpet was crowded with influencers who dilute the magic Cannes once represented, Joan's arrival instantly brought back a sense of occasion, glamour, and movie-star presence that had been missing for much of the evening.