lol, I feel you, but what I'm trying to say is you often don't need to know concepts like P vs. NP for work other than an extreme baseline "is this gonna take forever if I throw more data at it?" I am not saying it's not useful, just that for lots of work it's not always super useful to know. Computer science as a field of study is much more mathy than a lot of fields of dev work. Then again, you've got other fields where it's more important. Like I'm sure folks doing 3d graphics need to know a lot more trigonometry than I do as a backend engineer.

Because: for $20 per month to the AI company, you can output poor code much much faster.

When I think about computer science as a field of science I think about things like algorithmic complexity. I believe things like what you mentioned should be taught in general education prior to university (or, like calculus, as an optional elective) and are only covered because you need to know those basics to cover the advanced things.

It's really difficult to come up with other examples of this that aren't contrived because computer programming is the only field I know of that's like this. I might compare it to architects needing to know how to use tools, but I don't think they actually cover that. Maybe a better example might be engineers needing to know how to use tools before designing machines. Either way, things like how to use tools aren't covered in those classes and they're either not taught or taught as shop classes (or maybe they are, I didn't go into those fields). Things like for loops I view as learning how to make a computer operate. Like how someone who drives a car doesn't need to know how to fix an engine but a mechanic does. But learning about computer science is more like learning about what car designers do than what mechanics do. A lot of programming work doesn't need that low level of attention to detail.

Like I said before, it doesn't hurt, but it's not super critical. A classic example is something like learning how to make a linked list. This is an early example of an assignment that starts to get into the actual computer science stuff because you start to talk about the comparisons between different data structures, like linked lists versus array lists. So in university you may be thinking "damn I'm gonna be making list implementations all the time" but you quickly learn, no, you're not. The standard library of your language already has one and it's worlds better than anything you made. Plus, 99% of the time you're gonna want an array list, at least in the types of work I do.

I hope that helps make it more clear what I'm trying to say. I'm not saying computer programming is easy or doesn't require skills.

Computer science is much more than programming. Did you cover other topics like formal logic, finite state machines, computability, crytography, machine learning etc?

That’s pretty funny.

I have been building various things with AI coding tools for a month or so now. I rate the various engines on how far I can take them before they get hopelessly lost, unable to correct their own errors. For the best tools this seems to come after about 50 to 70 iterations of asking for small feature additions or error corrections, weaker tools (like Copilot) hit these infinite loops of fixing their errors with other errors much faster.

It's a good limit, because after 2-3 hours of AI interactive development, I can then spend 4-6 hours going through the resulting code - cleaning it up and understanding how it works. I suspect if AI were taking me farther, like 100-150 iterations, it would probably take me more like 15-20 hours to unravel the various things it comes up with - kind of a point of diminishing returns.

Bottom line: think of your project in terms of microservices. AI is pretty good at microservices. As long as the individual services are each robust in their delivery of the required functions, you're in good shape.

If it ever becomes "mystery meat," it's time to recode by hand.

You need to be able to read it to understand that it’s going a little off the rails.

At least 2/3 of the time I spend with AI coding is getting it to compile without errors - that's more than a little off the rails, but it's also much more helpful when you finally do get to a working example that you can look at, instead of beating your own head against the Stack Exchange archives hoping for inspiration, let it try for you.

The problem is when people assume you can actually build an entire software/service architecture of any complexity just through vibe coding.

Welcome to CEO handling 101. It's an art, a very soft skill, and not for the faint of heart. I worked for a mid sized (50 employee) company once where I'd "speak truth to power" in our weekly meeting, get shot down rather enthusiastically by the CEO during the meeting, then after I and the rest of R&D left his office, he'd go out to production and have them start implementing all the concepts of my pitch - as his own ideas, naturally.