That is true for the LLMs you have access to now. Now imagine if the LLM had been trained on your entire code base. And not just the code, but the entire commit history, commit messages and also all of your external design docs. And code and docs from all relevant projects. That LLM would not be new to the problem every prompt. Basically, imagine that you fine-tuned an LLM for your specific project. You will eventually have access to such an LLM.
Conceptually, there is no difference between fine-tuning the LLM for being a law expert of specific country and fine-tuning the LLM for being an expert for given codebase. Former is already happening and is public. Latter is not yet public but I believe it is happening.
Reason why big co are pursuing generic LLMs is because they serve as a foundation for basically any other derivative and domain-specific work.
Consider that you're coding a Linux device driver. If you ask for help from an LLM that has never seen the Linux kernel code, has never seen a Linux device driver and has never seen all of the documentation from the Linux kernel, you're going to need to provide all of this as context. And that's both going to be onerous on you, and it might not be feasible. But if the LLM has already seen all of that during training, you don't need to provide it as context. Your context may be as simple as "I am coding a Linux device driver" and show it some of your code.
Of course, because I am not new to the problem, whereas an LLM is new to it every new prompt. I am not really trying to find a fair comparison because I believe humans have an unfair advantage in this instance, and am trying to make that point, rather than compare like for like abilities. I think we'll find even with all the context clues from MCPs and history etc. they might still fail to have the insight to recall the right data into the context, but that's just a feeling I have from working with Claude Code for a while. Because I instruct it to do those things, like look through git log, check the documentation etc, and it sometimes finds a path through to an insight but it's just as likely to get lost.
I alluded to it somewhere else but my experience with massive context windows so far has just been that it distracts the LLM. We are usually guiding it down a path with each new prompt and have a specific subset of information to give it, and so pumping the context full of unrelated code at the start seems to derail it from that path. That's anecdotal, though I encourage you to try messing around with it.
As always, there's a good chance I will eat my hat some day.