The push for Python performance and JIT compilation has little to do with AI and more to do with Python's explosion in adoption for backend server applications in the 2010s, as well as the dedication of smaller projects like PyPy that existed largely because it was possible to make them exist. The ML/AI boom helped spread Python even farther and wider, yes, but none of the core language performance improvements are all that relevant for ML or AI.
As another commenter pointed out, the performance bottlenecks in AI specifically have essentially to do with the CPython runtime performance. The only exception is in the pre-processing of very large text corpora, and that alone has hardly been a blip on the radar of the people working on CPython performance.
Moreover, most of the "Python performance" projects that do sit closer to machine learning use cases (Cython-Numpy integration, Numba, Nuitka) are more or less orthogonal to the more recent push for Python interpreter performance.
Cython itself and MypyC are mainly relevant because they are intended to be general-ish purpose performance boosters for CPython, and in doing so helped fill the need for greater performance in "hot and loopy" code such as network protocols, linters, and iterators. Cython also acted as a convenient glue layer for ad-hoc C library binding. But neither project is all that closely related to AI or to the various JIT compilers that have arisen over the years.
Not at all, given Facebook and Microsoft involvement into making CPython folks finally accept a JIT has to be part of the story, coupled by NVidia and Intel work on GPU JIT DSLs for Python.
Yeah but how much of the Microsoft and Facebook effort was due to AI directly, as opposed to the general popularity of Python? which is undoubtedly driven nowadays by AI, but indirectly.
The push for Python performance and JIT compilation has little to do with AI and more to do with Python's explosion in adoption for backend server applications in the 2010s, as well as the dedication of smaller projects like PyPy that existed largely because it was possible to make them exist. The ML/AI boom helped spread Python even farther and wider, yes, but none of the core language performance improvements are all that relevant for ML or AI.
As another commenter pointed out, the performance bottlenecks in AI specifically have essentially to do with the CPython runtime performance. The only exception is in the pre-processing of very large text corpora, and that alone has hardly been a blip on the radar of the people working on CPython performance.
Moreover, most of the "Python performance" projects that do sit closer to machine learning use cases (Cython-Numpy integration, Numba, Nuitka) are more or less orthogonal to the more recent push for Python interpreter performance.
Cython itself and MypyC are mainly relevant because they are intended to be general-ish purpose performance boosters for CPython, and in doing so helped fill the need for greater performance in "hot and loopy" code such as network protocols, linters, and iterators. Cython also acted as a convenient glue layer for ad-hoc C library binding. But neither project is all that closely related to AI or to the various JIT compilers that have arisen over the years.