Memory is what turns a generic assistant into something that feels like it knows you. It is also where a product can quietly overstep. The useful question is not how much you can store. It is what is worth keeping to serve the person, and what is safer to let go.
Appropriate flow, not total recall
The cleanest way to think about the line comes from the philosopher Helen Nissenbaum, whose framework of contextual integrity holds that privacy is the appropriate flow of information, not its absence. Models do not hold that line on their own. The ConfAIde benchmark found that leading models reveal information in ways people judge inappropriate a large share of the time, even when told to be careful.
Legible, and in the user's hands
The labs have converged on giving users the controls. OpenAI's memory ships with ways to see, edit, and turn off what it keeps. Anthropic scopes memory per project and offers an incognito mode that saves nothing. The research system MemGPT shows the architecture that makes this possible, explicit tiers a system pages in and out rather than one opaque store. The pieces below go into where memory earns trust and where it loses it.
Sources and further reading