My original comment certainly reads that way, but my intent was really to point out that it doesn't make sense to privilege the Turing machine model in the study of computation. I wrote more about why in this comment: https://news.ycombinator.com/item?id=27334163
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.
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.
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.
Sometimes that could mean using the lambda calculus, particularly in study of language theory and type systems. Other times that could mean some sort of black box model, such as when proving lower bounds for solving problems using specific operations (see e.g. the sorting lower bound). Yet other times, like when establishing the ground-zero of some new variety of computational hardness, I can't think of many more suitable models to cut up into pieces and embed into the substrate of some other problem than those based upon Turing machines.