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Deep learning isn't magic, and it won't work at all on the specific problems you want it to work on, but it is capable of more than you know when it comes to problems where the manifold hypothesis applies (and that's a lot of problems)
This is a bit like watching a bunch of people trying to make steam-powered airplanes and rockets work in the year 1800 (it won't) while largely missing the world-changing potential of applying steam power to trains and large industrial machinery
"Meh, it's just a factory, boring... but look at my cool model rocket!" No, the boring factory is going to change the face of the world, and meanwhile your GPT model rocket won't scale past the toy stage
Not a steampunk believer I see 
That said yes, a lot of it seems to be connected to problems not really being filtered that way on whether deep learning should be applied due to its performance on certain seemingly similar tasks.
I don't know about airplanes or rockets but they did manage to pull off steam-powered submarines and may I say they did spectacularly wellhttps://youtu.be/IZS0RpOgdfQ
True ☑︎ We are often deluded by taking moonshots when in fact we need to be working on things that move the needle
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