[TuringTest]

Well, computing science studies programming paradigms; so defining them and analyzing what makes them suitable for what purposes is pretty much within its scope.

As I said above, it may very well be that the best usage for Turing machines is using them in mathematical proofs; where the efficiency of the computation is not a concern.

[fnrslvr]

Really the best usage of all the computation models we're discussing here is using them in mathematical reasoning. If you're looking to "create real working programs," then a better basis is probably going to be some combination of actual industry-grade programming languages and actual CPU architectures.

This response might come off as a little facetious, but seriously, I think the idea of "founding" industrial computing languages/platforms upon theoretical research models of computation misunderstands the relationship between theory and practice. There is a relationship for sure, the research on these models usually does want to translate into real-world implications somehow, but your functional programming language is not the literal lambda calculus.

[TuringTest]

Certainly practical programming languages are not a one-to-one implementation of a theoretical model, but these models do create families of related languages that keep a close relationship and are separated from languages based on a different model.

Each time a new theoretical model is created to represent a particular programming problem, entirely new languages are created to ease the practical approaches of building systems for the underlying problem.

And it is worth keeping track of which models are good for which problems. So no, theoretical models are not good just for doing math with them, also for guiding practical usage.