Personally I think this is the root cause of most sloppy AI code. If you just look at the code that was generated, and you don't think "I would've come up with that", then probably the code is wrong.
I'm not sure how to feel about any of this. On the one hand it clearly shows yet again how gullible people are. I wonder if senior engineers (those who can actually solve novel problems) value in the job market will go up as a result of this? Or will the market be saturated with so much AI-enabled waste that it brings the entire fields salary down as a whole? I feel bad for the end consumer who has to tolerate lower and lower quality products year over year, as the general software engineering practice seemingly burns to the ground, and becomes a chinese sweatshop churning out counterfeit sneakers.
Interestingly though I don't get it to write code. It's no good at the language I write in, so useless there.
As a "tutor" though it's been really useful. I'm asking a lot of (probably simple) questions, and the answers are "right enough". Occasionally I'm not sure why something is failing, and it's usually helpful there too.
So, less brain-rot and more "helpful senior who helps me along".
[1] the work I'm doing is related to SQL, which I've used here and there before, but not to the depth or degree I am now. I don't need it to write SQL, but rather to answer more general questions and to compare SQL databases, discuss effeciency and so on.
I've particularly enjoyed converting terse documentation into a .md file and feeding into the LLMs context window, then using the LLM to "query" the underlying document.
Where it's always fallen short for me is code generation, and frankly that feature just doesn't interest me.
And if there is any thinking involved, it's not in trying to figure out the code, but trying to figure out why the AI can't figure out the code. It's a subtle difference, but results in a huge change in quality.