These are two completely different types of mistakes.

LLMs are, by now, pretty good at not making "code doesn't work" and "math doesn't work" kind of mistakes when writing code. These are also "easy" things to get good at, you learn what each part does, understand the abstractions, ensure it makes sense, and go on with your day. Unit testing helps here.

LLMs are not that good at not making "this works wrong" kind of mistakes. Maybe the code compiles and does what it has to do, but maybe it's 2 lines of code with 6 lines of comments (looking at you Claude), maybe it defines three helper functions it doesn't really need, maybe it does something "here" when it should be doing that something "there" instead, maybe it finds itself in a framework and completely disregards how the framework is supposed to do things, etc etc. These are harder things to get good at and you WILL end up with an unreadable mess if you disregard caution and let the LLM go at it.

> internships where you've worked at a real company writing real code

Though why would companies make that nontrivial investment if they can use AI instead?

Furthermore, I’d argue that learning good judgement takes around a decade of full-time dev experience. In particular, experiencing the long-term consequences of one’s design and implementation decisions. You don’t get that just by college education and a few internships.

> Shouldn't this be a pretty fundamental part of a degree? If you're in school right now for a CS or SWE related degree, I would imagine you're learning how code actually works, how the math actually works, etc.

CS and SWE are radically different subjects. Science vs Engineering. I'm sure it varies by school but many CS grads will have approximately zero exposure to engineering concepts or any of the latest and greatest frameworks.

> Shouldn't this be a pretty fundamental part of a degree? If you're in school right now for a CS or SWE related degree, I would imagine you're learning how code actually works, how the math actually works, etc.

I’m sorry to say but colleges don’t teach the kinda stuff you’re gonna use day-to-day. They’ll focus on architecture, data structures, algorithms, databases, OS internals theory that only a minuscule number of systems engineers would get to work on. They won’t teach you that in your /login endpoint, if a user is not found, you should verify the password against a pre-computed dummy hash so the response delay matches a real user account workflow to avoid timing attacks. You only learn this on the job under the supervision of a senior mentor.

Precisely my point. But unfortunately companies are rushing to get the features out. No cares if you’re learning and augmenting your own understanding or not.

I wish but at $work the push has been the opposite. I'm still allowed to write code, but interns and juniors are being told that they need to prompt all the time.

I think upper management are hoping they can replace a possibly reticent or AI skeptical "old guard" with AI native Gen Zs. In practice, the young out of school kids are much less excited about AI than the 50 year olds, so I don't know how well it will work.

Stakeholders are not technical enough to understand that so CEOs won’t do it. We are already seeing it play out. Entry-level hiring has been consistently down ever since AI got mainstream.