Python has multiple solutions for asynchronous programming - each with its own strengths and weakness. In many cases, people reach for a much more complicated solution than is actually needed forgetting the simple pool executors in the batteries-included concurrent futures library. Guidance is needed in this area so people avoid over-engineering and unnecessary complexity.

The core goal of the When of Python is to simplify the language so it is as elegant as possible and fits within our brain. The challenge is that, as Python continues to grow in popularity, the language will end up being used in almost every imaginable situation. In which case there will need to be lots of simplifying constructs specific to each situation. As Christopher Neugebauer pointed out in his Kiwi Pycon talk "The Complexity of Simplicity", there will always be complexity so the only question is where it gets handled. Either Python requires users to build complex tools out of simple components, or it provides simple constructs that fit specific situations e.g. for matrix multiplication, or for asynchronous processing. So to stay simple, the Python ecosystem will need to become more complex. Or, to put it another way, situational simplicity means more Python. Having said that, the core language can stay simple and we should keep trying to slim down and refine Common Python.