If you understand there are multiple models from multiple providers, some of those models are better at certain things than others, and how you can get those models to complete your tasks, you are in the top 1% (probably less) of LLM users.
This is almost surely wrong but my point was about GPT5 level models in general not GPT5 specifically...
At the same time, more capable models are also a lot more expensive to train.
The key point is that the relationship between all these magnitudes is not linear, so the economics of the whole thing start to look wobbly.
Soon we will probably arrive at a point where these huge training runs must stop, because the performance improvement does not match the huge cost increase, and because the resulting model would be so expensive to run that the market for it would be too small.
Passing in docs usually helps, but I've had some incredibly aggravating experiences where a model just absolutely cannot accept their "mental mode" is incorrect and that they need to forget the tens of thousands of lines of out of date example code they've ingested during training. IMO it's an under-discussed aspect of the current effectiveness of LLM development thanks to the training arms race.
I recently had to fight Gemini to accept that a library (a Google developed AI library for JS, somewhat ironically) had just released a major version update with a lot of API changes that invalidated 99% of the docs and example code online. And boy was there a lot of old code floating around thanks to the vast amounts of SEO blog spam for anything AI adjacent.
I think you overestimate the amount of code turnover in 6-12 months...
I think we're a lot more likely to get to the limit of power and compute available for training a bigger model before we get to the point where improvement stops.