> Are you really saying that you can keep more of the code in your head than AI with a one million token context window or that you can read and understand various markdown files in a repo faster?
Of course. A million tokens is 50,000 lines of code. That's nothing.
It's not that you memorize a million lines of code. It's that you develop a solid mental model of how those lines of code are organized, how they work together, what the important gotchas are, what hurt performance in the past. You know the system well. Then when you are interacting with something, you can generally make predictions about the best way to make a change to that system, and you know in which modules to look when diagnosing a problem, so you can work your way around the codebase efficiently.
Most large mature projects are millions of lines of code. Chrome, Photoshop, databases like oracle or postgres.
But the LLM with that million token context window has that 50,000 line memory and then its general training data. That's it.
So your "value" as a developer who is using AI is to bring that mental model with you so you can supervise the AI and guide it to do surgery on that million line codebase with it's puny 50,000 line knowledge of your codebase.
But when you stop coding, you begin to lose your mental model. So, yes, there is a short term burst of productivity as your mental model fades but you are still good enough to guide the LLM. But after a while -- and depending on how deeply that code has been burned into your mind, it could be 6 months or a couple of years -- you will no longer be able to effectively supervise the LLM. You'll be like that noob that wanders into a big codebase they don't understand anymore. Then you wont be able to supervise the LLM.
You have to keep coding in a codebase to maintain your understanding of it. It really is just like having the LLM do your homework for you. Use the LLM to help you learn, to help you do your work, but keep doing the exercises yourself, so that your mental model remains fresh.