While LLMs are designed to generalize from their training data, rather than simply memorizing it, overfitting occurs in niche areas. There's be plenty of niche areas and LLMs of today merely repeat training data, like with the NYT case. Larger datasets and better algorithms will help to an extent, but you'll always have niche topics where overfitting happens. The intent has always been to generalize well, however, it may not be feasible to do so in the long tail of the internet. How should copyright law address this?
It's not just Twitter. Most popular APIs tend to regulate and constrain use as they grow popular. This has dissuaded developers, and certainly startups from building products on top of APIs. VCs do not like companies depending on third party APIs either, due to the fact that they may be arm twisted by the API service.
Twitter's attitude towards developers is potentially impacting the uptake of libraries like Fabric, despite having nothing to do with their API. What Twitter may have a shot at, as a result of this effort is to get usage of their libraries like Fabric on par standing ground with libraries and products like Parse from Facebook.
The PEP acknowledges the existence of high-end statistics libraries. It also notes that the alternative to such libraries are DIY implementations - which are often incorrect in their implementation.
The PEP proposes adding simple, but correct support for statistics.
Apart from high-end libraries being an overkill and DIY implementations being incorrect, the PEP also cites resistance to third party software in corporate environments. This problem is more social than technical though, and I'm not sure what weight must be attached to it
What a positive way to give back! Kudos!