That's really the TL;DR I also got from the computational linguistic courses I attended.
There's probably the Pareto principle at works. Having no solution is worse than having an 80% solution that works well enough when the 100% solution is much harder to achieve (and some of the problems not even humans would be able to solve properly).
Recently I wrote a web-extension for Firefox that displays funny "Deep thought" quotes.
I wanted to analyse the quote text and fetch relevant images to animate in the background of the quote text. After reading several NLP tutorials, guess what I did as a first PoC - Pick the 3 longest words in a quote text and run an image search with those 3 words.
6 lines of plain javascript code that can be run anywhere almost instantly https://github.com/TheCodeArtist/deep-thought-tabs/blob/mast...
I get relevant images in the search results 99/100 times. The quirks of searching often result in the image adding to the funny-ness of the "Deep Thought" on display.
Its so effective that i ended up publishing the "Deep Thought Tabs" web-extension with this approach itself: https://addons.mozilla.org/en-US/android/addon/deep-thought-...
Later I tried using the nlp-compromise js library to identify "topics" of interest within a quote text - typically nouns, verbs, and adjectives. Comparing the results with my "3-longest-words" approach, I found that the longest words were anyways almost always the "topic" words that NLP identified for any given quote text.
Back in games we'd do all sorts of tricks in networking to make it look like things were happening(sound effects, decals, etc) in response to local events until we could have the server provide the definitive call on some game state.
Most players thought we had a much higher fidelity sim then we actually did. It's a pretty common technique across a lot of games. You can get away with quite a bit by being smart about what you "fake" and what you actually make work end-to-end.
So, in summary, you point is not wrong. But it's no reason for bashing computational linguistics. It is common across many disciplines to use not-yet-perfect solutions as long as you don't know how to do better.
That said, I don't fully agree with the notion that "hacking a solution" is the suggested way of doing things. Computational linguistics is a pretty wild field with a lot of sub-disciplines. In a lot of those, the state of the art consists of quite sophisticated approaches that are the result of years of research. Take speech recognition, for instance. Currently, deep learning approaches take the cake, but there is also a plethora of insights that have been gained from improving the traditional methods over decades.
I think, a more nuanced point of view is called for here.
It's surprising how often you can get very far with imperfect solutions. ELIZA is the classic example. A simple program with very little code could convince people that they were talking to another human or at least machine with an understanding of their feelings.
ELIZA was coded completely by humans. Of course, nowadays we have more sophisticated ways of doing that. We can throw a few topic tagged example sentences with connected replies at a computer and it will mostly reply with the right answers to similar sentences. This is only possible because computational linguistics provided the foundation for that.
Still many solution are hacky to this day but that is because computational linguistics is more concerned about interaction with imperfect humans than most of the other disciplines in computer science.