In many cases, you want to be careful about using JIT'd platforms. Many people forget that the memory requirements skyrocket. When you want to use a language on many different platforms, including embedded, you start to see how having a JIT interpreter as your reference platform can be disadvantageous. CPython isn't exactly slow either, especially when you consider many 'intensive' modules are written directly in C.
Pypy should stay as it is, an experiment that can be used for people who require more performance for certain workloads. Of course, having part of your language written in C for CPython can hurt sometimes, when you can't easily use the functionality on other interpreters.
almost agree, except for the 'experiment' part. i don't want it to be an experiment, i want it to be a production-ready interpreter that i can use instead of cpython with minimal effort when i know i can trade memory for speed.
Pypy should stay as it is, an experiment that can be used for people who require more performance for certain workloads. Of course, having part of your language written in C for CPython can hurt sometimes, when you can't easily use the functionality on other interpreters.