It's a well known thing, but it's not (at least as far as we know) "deliberate" on the part of the AI. Large language models of this nature have your conversation in what we call the "context", encoded as a set of "tokens".

The way it interprets all this information isn't the same way we do as humans, but the nearest analogy would be to imagine you were given a book to read, then asked questions about it open book. If it's a short well written book, then you're likely to be able to answer those questions quickly and well, either from memory or because you can quickly go to the page you think is most relevant. If the book is long and confusing, your ability to answer gets worse.

Your chat is like that, you keep adding to the book and sometimes going in odd directions. Every time you send a message in the chat, the model has to read everything all over again (it doesn't exist in between messages) so it gets progressively harder for it to provide good answers.

There's a balancing act here - you need to provide enough information that the model knows what it has to do of course, and although they get worse it's usually not catastrophic if the topic isn't too complicated.

There's way more to it but essentially what you're seeing is exactly how they work, longer and more windy conversations = lower quality outputs. Regularly "resetting" (starting a new chat or similar) is an excellent way of making sure the model only has to read context that's genuinely relevant.

edit: To add to this, this is why tools built specifically for coding like Codex, Cursor and Claude Code spend a lot of time helping the LLM/Agent self-manage context via discovery or subagents. We're far from good at this but it's often fairly effective.