The quality of an answer is often orders of magnitude more important than the speed at which it's generated. Speed is important, but it is always secondary.
You wouldn't pull this in for a snappy, reactive UI driven by Python. You pull this in so that you can re-use a server-side module to perform some activity that isn't critical and so not worth re-writing in JavaScript. In another 5 years (assuming Python is still of interest and that this solution worked), it will have been refined and optimized where maybe you do reach for it by default.
Genuine question: What are the quality improvements offered by Python? Is it mostly access to the scientific community's Numpy/Scipy/Matplotlib? The language's semantics?
Most ML papers are implemented in Python, so you need it in order to be able to play with the latest and greatest toys, or you can wait for someone to port them to your favorite language, if ever.
And I would say that this is because of the language's syntax & semantics. Pythonic code looks like pseudocode, aka business logic.
There is very little boilerplate, and libraries are designed to hide what little there is.
So this means that mathematicians who hate verbosity can read and write their algorithms with little cognitive overhead.
Then you start to get the network effect where libraries are implemented or wrapped in Python, and people use those and start adding abstractions etc. The two main deep learning libraries (TensorFlow and PyTorch) are there, and those have huge gravity wells.
You wouldn't pull this in for a snappy, reactive UI driven by Python. You pull this in so that you can re-use a server-side module to perform some activity that isn't critical and so not worth re-writing in JavaScript. In another 5 years (assuming Python is still of interest and that this solution worked), it will have been refined and optimized where maybe you do reach for it by default.