When AI starts learning from its own echo

A support copilot gives an answer based on a policy document. A staff member edits it, adds the response to the help centre, and the next version of the copilot retrieves it as evidence.

No model retraining is needed for a feedback loop to form. Generated or lightly edited answers can gradually look like independent confirmation, while the original source—and any uncertainty in it—fades from view.

The design challenge isn’t simply “keep humans in the loop”. It’s preserving the lineage of knowledge: what came from an authoritative source, what was inferred, what a person changed, and when the underlying policy was last checked. That matters in any workflow where AI outputs can become tomorrow’s reference material.

What would you trust as evidence that a growing AI-assisted knowledge base is learning from real-world experience rather than increasingly polished echoes of itself?