AI systems do not just learn. They accumulate memory over time.

In this episode of The Unlearning Room by Forget, we talk about a problem most teams do not notice until it is too late: memory related technical debt inside AI models. As models are fine tuned, connected to new data sources, and shipped faster, they become harder to change, harder to clean, and harder to forget.

We explore why unlearning gets more expensive the longer a model runs, how forgotten data becomes embedded in weights and representations, and why designing for reversibility early matters. The episode also looks at how the Forget Protocol can be used as infrastructure to pay down memory debt and keep AI systems editable over time, not just compliant in emergencies.

This conversation is for anyone building AI systems that are meant to live longer than a demo.