Good points. Also looking at your original example I noticed that not only humans can explain regularities they expect in many different ways (also correcting along the way), they can basically ask LLM to base the result on a reference. In your example you provided a template with an img tag and brackets having different attributes patterns. But one can also just ask for a html-style tag. As I did with the "Please create a regex for extracting image file names when in a text a html-style tag img is met" (not posting it here, but "src" is clearly visible in the answer). So the "knowledge" from other domains is applied to the regex creation.
It doesn't matter how fast I get at regex, I still won't be able to type any but the shortest (<5 characters) patterns out quicker than an LLM can. They are typing assistants that can make really good guesses about my vaguely worded intent.
As for learning deficit: I am learning so much more thanks to heavy use of LLMs!
Prior to LLMs the idea of using a 100 line PostgreSQL query with embedded regex to answer a mild curiosity about my use of alt text would have finished at the idea stage: that's not a high value enough problem for me to invest more than a couple of minutes, so I would not have done it at all.