Context gives AI its sense of meaning. It’s what separates a random reply from a useful one. But it’s also the hardest thing to preserve. Every time a model runs, it starts almost from scratch. It only knows what fits inside the prompt window. Everything else; past messages, files, goals, has to be reintroduced.
This is where things break down. The more context you try to feed in, the more expensive and slower each call becomes. There’s a limit to how much a model can “see” at once. Once you go beyond that limit, information gets cut off, summarized, or forgotten.
The illusion of understanding fades as soon as the AI can’t connect the current moment with the earlier ones.
People expect something closer to human continuity. They want to pick up where they left off. They want the AI to remember the project’s direction, tone, and past decisions. But that kind of awareness is heavy. It demands storage, retrieval, and constant updates to stay relevant. And each of those costs compute.
Most existing tools use tricks to work around this. They store text embeddings, retrieve snippets, or condense entire conversations into short summaries. These methods work to a point, but they lose structure. Summaries flatten the relationships between ideas. Embeddings can surface keywords but miss the subtle logic that links them.
The real problem is that models don’t have a continuous sense of time or self. They don’t remember, they only infer. Each call is a guess at what the user meant based on a frozen snapshot of data.
So how can a system stay aware without constantly reprocessing the same context?
How can it maintain a living memory that grows with the user, instead of a static summary that fades over time? That’s the real challenge of building anything that thinks with you, not just for you.
- Sam