AI assistants: The convenient crutch for new developers or a recipe for disaster

Here's my concern with this. Is that the AI is good at generating "mostly correct" code, that will have subtle errors, so less skilled coders just take the output of whatever the LLM spits and assume it's correct, later it turns out there is a bug. The person who submitted the code doesn't understand the code, because they didn't bother learning it just that it was mostly correct. Instead of discovering and fixing the source of the error they just go in and discover where the error is output and slap some more AI generated code over it to squash the bug.

Now you may be saying "what's the big deal with that? That happens all the time all over the place."

The issue is that the biggest thing AI brings is it allows you to generate lots of code, very fast. So we have more and more code, that is less and less understood by any human being.

Eventually no one has any idea what the code is doing in the first place, and what ends up happening is the Cult Mechanicus of Warhammer 40k we become tech priests running around performing arbitrary and inane rituals to appease "the machine spirit" because no one anywhere has any idea how the dang program actually works.

Like we've all heard horror stories of the Oracle DB code base, imagine if that started to become common. I mean people talk about how AI helps them generate code faster, but to be honest I find most issues are the result of too much code, rather than too little.

My point is I grow concerned about the ability of AI to generate code and the effect that is going to have on increasing the amount of code while reducing understanding.

I think their is a fair amount of Javascript and before that PHP floating around indicates we should not be making it easier for idiots to write software.

Was this article AI written?

This is exactly the same format of argument that convinced me as a learner to use notepad++ for years, which I now believe to be a garbage idea. IDEs provide so much important contextual information - a copilot program to show you different techniques would be helpful in a similar vein.

Caveat: chatGPT is a chat program, that it can produce working code in places is a coincidence, it would not be appropriate for this use

I think there's a secret third thing, domain specific fundamentals/boilerplate.

I've had good results with code generation for minimal proof-of concept things, like 'show me code for [maze construction algorithm]' or 'visualize this platonic solid in mathplotlib, with numeric labels on every vertex' or 'fetch data from https://hacker-news.firebaseio.com/v0/item/34741898.json and build a pandas dataframe from it'.

I've come to prefer it to visiting Stack Overflow when I need to get up to speed on a new thing, so that I don't have to read people's petty ego trips or irrelevant answers. I also find it responsive when I give carefully detailed prompts to develop an algorithm, as it saves me a lot of typing mistakes and syntactical screwups, eg mixing up dimensions in 3d or flipping [x:] and [:x]. Debugging sometimes feels faster than on my own code, because I didn't mentally commit to the error and create a blind spot for myself.

It's much easier to get good results by specifying incremental (across multiple responses) or stepwise instructions in pseudocode, rather than big bang problem statements. Although the latter occasionally kick out magically-working complete programs, they're much more likely to yield catapults: https://www.youtube.com/watch?v=5aCgSwmm5Ho

Let's hope chatgpt&co will not end up like no-code which overpromised for decades and didn't deliver. But surely I'm only having a negative outburst here. ;)