I can understand your complaint here, and I agree with you that people can easily go too far in the direction of 'when to implement' versus 'how to implement'—but I'm pretty sure the key is (1) Both are required, (2) it makes more sense for one of these things to precede the other.

However, I think that you’re also conflating two approaches to learning ‘context.’ One gives people an elaborate index for finding implementations when required, so that they become capable ‘solution googlers.’ This is unquestionably an inadequate approach to education. However, there is another approach to context which emphasizes principles. ‘Principles’ can be a vague word itself, but I’ll try to be more precise about it: you have understood something ‘by principles’ when you first understood a more general structure, then, in order to learn the more particular thing, you understood which parameters must be supplied to the more general structure in order to get the more particular one. E.g., you learn principles of programming languages first, then can understand new languages as parameterizations of your prior, more general knowledge. I think the context material in ADM serves as a nice guide for acquiring principles in this sense. The bit I quoted is largely significant because of their emphasis on principles.

It’s interesting that you think ADM would be the book for faking an interview. I have the exact opposite stance. If I wanted to impress a typical software person (e.g. a yet to be determined interviewer), I’d learn a lot of the details from CLRS, rather than going for ADM.

I think you're overlooking that whys and whens are still knowledge leading to understanding of a concept, and they tend to come before the hows. You want to understand why you're using the algorithm before you begin working on how to implement the algorithm. As an extreme example, consider us teaching the Quicksort and the Bogosort as pure implementation but without explanation on usage. Granted, this is unlikely to ever happen, but it serves to illustrate that this kind of knowledge is part of the understanding of any algorithm, or even further, any concept.

Now the context you're referring to regarding your "implementing ols" example seems to be largely assorted trivia to be honest. They don't know the answer, they're filling in something, and have done so through highschool and then college because something might get mercy marks. I'd like to disagree on giving that the moniker meta-knowledge, it's losely tied factoids at best.

I completely agree that deep understanding of concepts is incredibly important, but i think it's unlikely we'll see large scale change in that attitude. The way I see incentive structures for students, making that shift unconsciously is disincentivised, since moving from a cramming schedule to a more thourough approach puts you even further behind temporarily which hurts your grades. The fact that any kind of disaster or holdup could push you into the former style of learning is what I think causes the entire thing to tilt.

Whilst there are a couple more points I'd love to adress, I kinda want to question your assertion that this is a current generation thing. That is an awfully easy assertion to make, can usually be said about the asserters generation as well and all generations before that. Unless there's more concrete data to back that part up, i think it does the present generation plenty of injustice.

I think it's better to know about the existence of a problem, that it has solutions, and that the solutions can be applied to similar problems, than it is to know any given problem and solution in depth.

When you have a breadth of knowledge, you know where to look to start your implementation - or better yet, use someone else's implementation, until it's not good enough, which may never happen.

The risk with knowing a few things well is that you may be ignorant of of the existence of similar problems and know that they already have good solutions. Meta-knowledge, as you put it, is generally more likely to be useful than specific knowledge, unless you have specific knowledge about a lot of things - which is unlikely unless you've got a lot of experience.

Also, in attempt to be more objective about this divide, I've found it useful to note that there's a general tendency in intellectuals to be in one camp or the other: unifier or multiplier. I've seen this issue taken up by William James, talking about 'rationalists and empiricists' and by Freeman Dyson in an essay about 'Manchester and Athens'. At the highest heights of intellectual accomplishment, you find people who revel in particulars, and others who love only principles, each typically having an amount of disdain for the other approach. Both are necessary, though.