Here is an observation. Tensorflow and most other “Python” deep learning frameworks are actually written in C++. Python is used just as a scripting language to “glue” various moving pieces together. Any other scripting language could be used instead, with the corresponding (relatively thin) bridge. Of course, the current momentum is on the Python side. If the past history on innovation and rise/fall of languages teaches us anything, the lesson we can learn is that it does not really matter who came first to the field and it does not even matter who is the leader now.
Kotlin is, without doubt, considerably more productive for any project of non-trivial size due to its static types and emphasis on toolability. Even at 10K+ Python LOCs you start to feel pains of a dynamic language. Python works nicely in slide-ware and in small code snippets of the kind you can put into iPython notebooks, where you can actually execute the code on your data and then enjoy code completion and integrated help on the actual, dynamically resolved object instances. As soon as you start writing the actual non-tirival code, abstracting it into modules, etc, it all starts to fail utterly – code completion and help becomes useless for any non-trivial framework even in state-of-the-art Python IDEs like PyCharm.
The first player in ML field that will realise that Python is roadblock to further scale will reap the benefits. All we can do in Kotlin team is to make sure that when this realisation comes, Kotlin is in good shape to serve as a viable alternative to be considered.