I've seen this phenomenon as well. I've noticed a few factors that I hypothesize contribute to this:
1) Making Computer Science programs in universities overly simple. When I was in college, I tutored students in CS. When I started college, the CS 101 course was taught in Java, and when I finished, it was being taught in Python. There was an immense gap between the understanding of the Python-first vs. Java-first students.
The Java-first students understood fundamental concepts like arrays, passing parameters by reference vs. by value, "OOP" concepts, and other common paradigms in languages. The Python-first students would use "lists" and "dictionaries" for many problems without understanding that those structures impacted the time complexity of their solutions, or that they used memory allocation, reallocation, hashing, etc. Python is great for hacking together a non-fault-tolerant program that does X as quickly as possible, but I saw it damage the way the students thought about computing.
It made the students think it ought to be easy. It made them think that they should be able to write, in nearly plain English, what they wanted the computer to do, and that it ought to do it. It made them think they should be able to intuitively be able to code without spending any amount of time learning the craft, and that they could do anything an advanced programmer could do by typing a few lines of simple code. They were less willing to accept that sometimes problems are hard, sometimes they would have to think, and sometimes they would have to write more than a for loop to solve problems. The Java students' problem sets covered implementation of data structures and complex applications, while the Python students struggled to put together even the implementation.
For a humorous and broader explanation of this subject, see James Mickens' excellent article The Night Watch [0]. For example: "That being said, if you find yourself drinking a martini and writing programs in garbage-collected, object-oriented Esperanto, be aware that the only reason that the Esperanto runtime works is because there are systems people who have exchanged any hope of losing their virginity for the exciting opportunity to think about hex numbers and their relationships with the operating system, the hardware, and ancient blood rituals that Bjarne Stroustrup performed at Stonehenge."
2) Bad documentation, and bad users of documentation. I dislike documentation that neglects how the tool works and instead creates a cookbook that its users can copy verbatim. It has similar effects to what I described above. Developers that use this type of documentation find themselves helpless when they encounter a problem they've not memorized the solution to. I think it also creates a learned helplessness wherein users cannot question the model the tool uses or think outside that model to solve complex problems. I prefer documentation that teaches the ethos of the subject in question so that I can understand better and imagine new solutions.
I've also heard the complaint that Git's documentation is awful; some say that it is only useful if you know already know where what you are looking for is, and useless if not. In other words, it lacks an "apropos" (aka "man -k") equivalent. This is my gripe with "wiki" style documentation. It is scattered and unsearchable and has no cohesion. We as developers need to do a better job creating documentation that is searchable, coherent, and useful to new and experienced users alike. Various sections of documentation should have ample cross-references to help users understand the connection between parts of the system to help them form a mental model of how tooling works.
[0]: https://www.usenix.org/system/files/1311_05-08_mickens.pdf