I can’t tell from this if you’re encouraging the Python community to drop compatibility and move forward with PyPy (this is very unlikely to happen since the core developers remain committed to CPython as the implementation of Python), or if you’re exhorting us (the Julia community) to reconsider some compatibility thing.
On the subject of compatibility and how language design choices make it hard for some languages to run fast, it seems to be generally very poorly understood just how hard the designs of languages like Python and R are for really high performance implementations. If you’re interested, here are two talks on the subject:
Armin Ronacher (one of the creators of PyPy), “How Python was Shaped by leaky Internals”:
Jan Vitek, “Making R run Fast”:
This talk contains the following key quotation by Jan:
I haven’t seen a language that is as hard to optimize as R. Somebody should give the R designer that particular prize as probably the hardest language to optimize… Compared to R, JavaScript is a beauty of elegance and good design.
The title of this talk is largely aspirational (they haven’t made R run much faster), although Jan’s research lab has a variety of projects trying to improve R’s performance.