Of people who use these tools, only a very small percentage of them also build said tools. Which makes sense. In industry, companies want to build meaningful pr

Of people who use these tools, only a very small percentage of them also build said tools. Which makes sense. In industry, companies want to build meaningful products and data sets that provide value which can be exchanged for money. That means delivering some majority of the value in the shortest amount of time possible. For that reason, engineering teams have little room for R&D, including the kind you're hinting at. Scikit, for example, has largely academic contributors (http://scikit-learn.org/stable/about.html http://scikit-learn.org/stable/about.html). Not surprising. Talents of pedantry, proof-writing, and pushing the boundaries of theory probably lies in academia as opposed to industry. Industry rewards shipping code (useful oversimplification) and academia rewards novel theory (yet another oversimplification). Solving problems requires understanding what solutions exist (and whether they can be used, must be built upon, could be used in ensemble, etc). Choosing among those solutions requires understanding (to some varying degree) why and how the solution solves the problem. Choosing the correct out-of-the-box solution is not trivial. One very real danger here is unwittingly lying with statistics. Which is arguably worse than wittingly doing so.