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This question does not come from a developer working on any of these languages. I am a data scientist working *in* these languages. But I'd like to see some clarity how these ecosystems will transition from Intel to Apple Silicon. Intel has specifically built tools for Python lately. R became much more efficient with Revolution (now Microsoft) bundling Intel's Math Kernel Library (and more) into R. R can also be much faster on the Mac with the Accelerate framework (esp. BLAS and LAPACK from veclib, though these are not the officially supported default for the Mac build). As we are investing into these platforms (both Apple hardware and our own codebase, not to mention human capital), it would be great to get more advance guidance on what performance we can expect on what front. Data scientists are more than just pro consumers needing an Adobe update for the new architecture (though for Matlab or Stata, the situation is similar), but less than full-blown developers who will use Swift anyway. Converters from coremltools can save some models (say, scikit-learn under Python) to use in apps. Does this promise any further optimization and support for Python on Apple Silicon?