Even if you are the biggest critic of AI, it's hard to deny that the frontier models are quite good at the sort of stuff that you learn in school. Write a binary tree in C? Check. Implement radix sort in Python? check. An A* implementation? check.
Once upon a time, I had to struggle through these. My code wouldn't run properly because I forgot to release a variable from memory or I was off-by-one on a recursive algorithm. But the struggling is what ultimately helped me actually learn the material [2]. If I could just type out "build a hash table in C" and then shuffle a few things around to make it look like my own, I'd have never really understood the underlying work.
At the same time, LLMs are often useful, but still fail quite frequently in real world work. I'm not trusting cursor to do a database migration in production unless I myself understand and check each line of code that it writes.
Now, as a hiring manager, what am I supposed to do with new grads?
[1] which I think it might be to some extent in some companies, by making existing engineers more productive, but that's a different point
[2] to the inevitable responses that say "well I actually learn things better now because the LLM explains it to me", that's great, but what's relevant here is that a large chunk of people learn by struggling