An LLM can be thought of as a curve fitting function where the query is a set of points and the output is the curve that runs through those points with the minimum of error.
You can increase a fit by increasing the number of variables in the fit function. In statistics this is can lead to overfitting, which will lead to an imprecise model, but in an LLM overfitting is a good thing, because the modeled domain in specific to the set of prompts.
But that comes with a cost, compute power and latency. I suspect what's happening is they're turning down the fit quality to save money as demand increases. Most people aren't paying for AI access. As a side effect it's also lowering the hype factor that was attracting unwanted regulatory attention.