Because very often the best solution does not win, which is why we are still using x86(_64), Unix, etc.

Python is a good example of worse is better. A functional language could provide more elegant abstractions. But for most people the ML APIs that Python provides are good enough. By taking a pragmatic approach, Python was faster to capture some niches, including machine learning and data science. Now we are at a point that even if another language had better abstractions and had similar library support, it would face an uphill battle because of inertia. Why would someone invest in learning another language for machine learning when Python is good enough?

To me it seems that only a language that has both a good model development story and a good deployment story stands a chance to compete with Python, since deployment (especially on mobile/embedded devices) is a weak point of the Python ML ecosystem.