Every model has a physical limit to how much it can hold in active memory at once. When production workloads hit that limit, they hit the context wall, and the failure is silent. An AI model's context window is the amount of text it can hold and reason over at a given time. Every token in the window has to be held in graphics processing unit (GPU) memory the model can actively attend to. GPU memory is the most expensive and constrained tier in the serving stack, and is small compared to how much text a real conversation, document, or workflow generates. As a session gets longer, the limit steadily approaches.
When hit, the model begins to drop earlier context to make room, drifts off track, or starts giving answers that don't line up with something it was told 3 steps ago. Despite its imposing imagery, the context wall isn't a hard error but rather a quiet degradation. The model sounds just as confident with half the context as with all of it. Demos are short by design and never run long enough to hit the context wall. In production, it shows up constantly, and in ways that are easy to miss until a customer notices. Even within the window, not all context gets equal treatment.
Research on long-context models has found models are consistently better at using information at the very start or end of the context than information buried in the middle, with accuracy dropping by as much as 30% or more for facts placed in the middle of a long context. So even a request that technically fits inside the window doesn't guarantee equal treatment of everything in it. Any 1 of these is a quiet, hard-to-diagnose failure. Most systems don't throw an error when context gets truncated.
The model answers anyway, using whatever's left, and nothing in the response tells you it was working with a partial picture. It's tempting to wait for bigger context windows. Bigger windows push the wall further out, but the cost scales with it. More memory per request, fewer concurrent users, higher price per conversation. At any point in a long-running session, the model is actively working with recent context and not all context is equally active. Earlier turns, old tool outputs, and documents that were relevant 10 steps ago are sitting in GPU memory at full cost.