> There appears to have been a massive culture shift (rift?) somewhat recently - from individuals who take pride in understanding and growing what we know and what we can do with computers, to individuals who demoralize, coddle, and impede what we can do with them.

Except that none of this applies to pure functional programming. PFP is an idea -- a very interesting one -- about how to program. It neither uncovers the fundamentals of computer science nor grows what we can do with computers. There is absolutely nothing about monads that is fundamental to computation, and understanding monads does not make us understand computation better.

I could say that there appears to be a "culture shift" in which a very interesting abstraction (computation as a function) has shown some promise but has failed to yield significant results (and certainly not a revolution), and so as a marketing effort, begins to present itself as some kind of a necessary "fundamental mathematical" understanding of computer science, while it is nothing of the sort. It is an interesting reformulation where functions and monads replace the familiar concept of the continuation, and that may or may not lead to significant gains. So far, it has not.

Over-hyping research results is often the norm rather than the exception. What may be unique in this case is that rather than believing that in light of recent research, an amazing breakthrough is right around the corner, some PFP advocates seem to claim that the breakthrough is already here, although they find it hard to support their claim with any evidence. I'd say it's a combination of scientific hype and industry-method hype.

It is as worthy of study as any of the other hundreds of research ideas in CS which hold some promise but aren't yet ready for large-scale industry adoption. It is necessary only for those who are interested in exploring computer science's "theory B", and particularly programming language design (which in itself is an important but not an unusually central sub-discipline of the field).

My skepticism of PFP is specific to PFP -- certainly not to studying any of the many under-explored corners of computer-science. I think that PFP's hype/reality ratio is particularly high -- even by CS standards -- and easily rivals that of machine learning (or "AI" as some call those statistical algorithms). It is not skepticism towards study in general but towards PFP in particular, which has so far overpromised and underdelivered.

> Our coddled carpenter inevitably shrivels up in bewilderment

Requiring carpenters to spend a great deal of time to learn how to work in a completely different way, all to achieve a similar level of productivity shows, IMO, a lack of understanding of what carpenters do. It is a mathematician's duty to look at things from different perspectives out of a sense of curiosity or to fund structure. A carpenter should spend time and effort on new methods only if they increase his or her productivity commensurately. I can tell you that algorithms we require programmers to use and/or understand have a much more significant impact relative to their adoption cost.

If we compare techniques and tools to medical drugs, PFP is barely out of the lab and is very early in its human-trials phase. Treating it as a complete success, and requiring that all practitioners spend a great deal of effort learning it at this point in time, is a grievous misrepresentation of the actual achievements of the idea. It is an interesting idea, it shows some promise, and if you have time and interested in this subject, you should give it a look, that's it.