AI memory is not a weaker version of human memory. It is a different kind of thing, and treating the two as comparable is a category error with real relationship costs.
What an AI "remembers" lives in two places. Its weights hold compressed patterns from training: general knowledge, frozen at a cutoff, not updatable by talking to it. Its context holds whatever is currently loaded into the conversation: your documents, your instructions, the chat so far. Context is the part that feels like memory, and it has sharp limits. It is finite, it is reconstructed per session, and it degrades as it grows: models use the start and end of a long context far better than the middle.
Human memory, by contrast, is reconstructive and embodied, and it forgets adaptively, pruning what stopped mattering. The sixth essay describes the practitioner's surprise: giving an AI more memory made results worse, not better. The working strategy that survives contact with reality is the opposite of hoarding: keep the loaded context sparse and relevant, ask sharply, and stop expecting the machine to remember you the way a colleague would.