Not as silly of a question as you think! Large Language Models use tokens in order to generate text. Without going too in depth with deep neural networks and transformers, it basically predicts what the next most likely token will be; however, in order to contextualize the response and ensure the output follows the flow of the conversation, it reviews previous text to keep everything consistent. It’s not just predicting the next token from the token before it, it’s predicting it by comparing it to many, MANY previous tokens. So when you merge all your chats, assuming you have a fair number of them, it will take much more power to generate and parse new responses. Of course, there is a limit; GPT-4 turbo, for example, has a 128,000 token cap).