On the other hand, a lot of the most popular Python libraries became effectively standard and lots of people agglomerated around them instead of creating compet

On the other hand, a lot of the most popular Python libraries became effectively standard and lots of people agglomerated around them instead of creating competing standards because it would be hard for any smaller team to replicate even their base functionality. Creating a numpy, scipy, sklearn, pandas, tensorflow or pytorch would involve lots of optimized C/C++ code and python wrappers, which is a considerably high barrier. If anyone could make an equally high performance 80% functionality/use cases coverage version of those libraries using high level python in maybe a month, would that cause a fragmentation in a way that we would get a lot of 80% libraries (with different 80%) instead of one 95% library that is supported everywhere?