Yeah I think Rust's place in this ecosystem is a C/C++ replacement. All the Python and R data science packages call into native code to do the heavy lifting--th

Yeah I think Rust's place in this ecosystem is a C/C++ replacement. All the Python and R data science packages call into native code to do the heavy lifting--the linear algebra, gradient descent etc. The next Tensorflow or PyTorch type framework could be implemented in Rust instead of C++. But it shouldn't matter to the end users--they would still use the Python or R bindings so they can have an interactive REPL environment to do their analysis in. Julia is interesting because it's performant enough to implement these things directly--Julia's machine learning frameworks are written in pure Julia (with the exception of the calls to CUDA libraries for GPU) and can achieve near-native performance, so solving the "two language problem" is part of its value proposition. I do wonder how valuable it is for the whole stack to be written in one language, and whether that will blur the lines between the software engineers who implement data science packages and the data scientists who use them.