But as Numba has shown, it&#x27;s achievable to write a CPython-to-LLVM-IR compiler for subsets of the language, then add NumPy-awareness, and add multiple targets like GPUs, and achieve better-than-PyPy speed ups jitting pure Python (CPython) code.<p>I think it&#x27;s an interesting question as to whether PyPy &quot;is the future&quot; or rather something more like Numba, or even a whole ecosystem of specialized variants of Numba that may be tweaked or tuned to take advantage of different hardware optimizations, work better for different domain problems (e.g. image processing vs. network programming).<p>In a lot of cases where PyPy improves over some standard CPython code, it&#x27;s exactly the sort of code you really wouldn&#x27;t care about optimizing in the context of a larger application. In any sort of scientific application, the pure CPython parts of it are usually small and insignificant compared to what&#x27;s written in NumPy&#x2F;SciPy&#x2F;pandas and science libraries written on top of those, and Numba can be seen as the first &quot;specializing compiler&quot; for this sort of application -- with a lot of room for people to write other kinds of specializing compilers that compile different genres of Python code for domain-specific purposes, the way that Numba compiles generally scientific Python for numerical computing purposes.