I once worked on a massive codebase that had survived multiple acquisitions, renames and mergers over a 20 year period. By the time I left it had finally passed into the hands of a Fortune 500 global company.
You would often find code that matched an API call you required that was last updated in the mid-2000s, but there was a good chance that it was not the most recent code for that task, but still existed as it was needed for some bespoke function a single client used.
There could also be similar API calls with no documentation, and you had to pick the one that returned the data fields that you wanted.
Many didn’t code (much) before.
That being said, a context length problem could be potentially be solved but it will take a bit of time, I think Llama4 had 10M context length (not sure if anyone tried prompting it with that much data to see how effective it really is)
Like I don't memorize the last 20 commits, but I know generally the direction things are going by reading those commits at some point
And even if you juiced up a context length of an LLM to astronomical numbers AND made it somehow better at parsing and understanding its context, it will not always repeat said capabilities in other codebases (see for example o3 supposedly being the top of most benchmarks but it will still fumble a simple variation mother-is-a-surgeon puzzle).
I am not saying its impossible for a company to figure this out, but it will be incredibly hard.