# GTM kit

## Title options
1. Beyond Amnesia: When Memory Tries Too Hard
2. The Over-Remembering Machine
3. The Real Problem With AI Memory Is Not Forgetting
4. AI Memory Has a Restraint Problem
5. Why Persistent Memory in LLMs So Often Feels Wrong
6. The Next Problem in AI Memory Is Overreach, Not Amnesia

## Subhead options
1. Persistent memory in LLMs often fails not by forgetting, but by intruding. The real frontier is memory governance: what gets written, retrieved, surfaced, and left unsaid.
2. The hardest AI memory problem is no longer storage. It is deciding what should be retrieved, what should be surfaced, and what should be left unsaid.
3. The systems that win will not be the ones that remember the most. They will be the ones that know when not to bring memory back.

## TL;DR
AI memory is usually framed as a forgetting problem: context windows are too small, sessions reset, continuity breaks. But persistent memory introduces a second failure mode that can be just as damaging: overreach. A stale interest gets written into memory, retrieved too easily, and surfaced too aggressively until the system starts performing continuity instead of helping with the current task. This series argues that the real design problem is not just storage. It is the full governance stack around writing, classifying, retrieving, surfacing, suppressing, and retiring memory over time.

## Social copy
### Short
The next hard problem in AI memory is not just forgetting.
It is **overreach**: storing too much, retrieving too eagerly, and surfacing memory when it no longer helps.

### Long
Most AI memory discussions still orbit around forgetting. But persistent memory creates another failure mode: the system keeps bringing back old context that is technically true, vaguely relevant, and still wrong to mention. That is not a storage problem. It is a governance problem. I wrote a 4-part series on how this happens, why the obvious fixes only solve part of it, what `openclaw-mem` gets right, and what the field still has not solved.
