Anyone who has mentored as part of a junior engineer internship program AND has attempted to use current gen ai tooling will notice the parallels immediately. There are key differences though that are worth highlighting.

The main difference is that with the current batch of genai tools, the AI's context resets after use, whereas a (good) intern truly learns from prior behavior.

Additionally, as you point out, the language and frameworks need to be part of the training set since AI isn't really "learning" it's just prepolulating a context window for its pre-existing knowledge (token prediction), so ymmv depending on hidden variables from the secret (to you, the consumers) training data and weights. I use Ruby primarily these days, which is solidly in the "boring tech" camp and most AIs fail to produce useful output that isn't rails boilerplate.

If I did all my IC contributions via directed intern commits I'd leave the industry out of frustration. Using only AI outputs for producing code changes would be akin to torture (personally.)

Edit: To clarify I'm not against AI use, I'm just stating that with the current generation of tools it is a pretty lackluster experience when it comes to net new code generation. It excells at one off throwaway scripts and making large tedious redactors less drudgerly. I wouldn't pivot to it being my primary method of code generation until some of the more blatant productiviy losses are addressed.