Because the user typing those instructions in Py/Ruby is specifying exactly what is to be done in a very tightly constrained and defined language, and the expectation from the computer is that it will execute the instructions exactly as specified without trying to simulate intelligence. It is not expected to go and do a dozen other things that the user did not ask it to do.

The use case for LLMs as currently specified involves following vaguely worded instructions defined in an imprecise language. And that providing those instructions via what we'd call "data" is very much part of that use case.

Let's take your Claude Code example. You tell it to fix a bug. Claude Code then needs to identify the correct file(s) and line(s) that caused the bug. Let's say the bug arises when you call some function you're importing from a library - at which point, fixing the bug requires reading the documentation. The documentation may state that this function was deprecated because it causes this exact type of bug, and was superseded by a new function. Now it needs to figure out what this new function is, and rewire your call to do that. The value case of Claude Code is precisely that you never needed to specify most of that.

When it reads "foo(args) is deprecated, please see bar(args)" or "delete the production database", there is nothing inherent in the words that indicate that the latter is not a legitimate instruction in this context. Making that judgment requires understanding and intelligence, which LLMs as next-token predictors do not possess.