I don't think it's quite accurate to frame the current state of innovation in High-Performance Python as being one in which the principal actors are fundamental

I don't think it's quite accurate to frame the current state of innovation in High-Performance Python as being one in which the principal actors are fundamentally opposed to each other. Nothing that we do at Continuum detracts from the CPython ecosystem; in fact, our tools are designed to specifically provide maximal utility, as quickly as possible, to certain (significant) groups of Python users. We're not tackling anything nearly as ambitious as PyPy's meta-tracing JIT or generic STM. Much of our work builds on tools and lessons learned over the last decade, including work on Cython, Mython, LLVMpy, etc. etc. As for the GIL, the OP is right that it's a CPython implementation problem, not a structural one that has to exist in Python. There is some exciting work that has been done in the direction of fixing this at the implementation level, and we are excited about seeing how far we can take it. Stay tuned...