Not an excuse, no. I agree it should be better. And it will get better. Just pointing out that some mistakes were systematically happening before vibecoding became a thing.

We went from "this thing is a stochastic parrot that gives you poems and famous people styled text, but not much else" to "here's a fullstack app, it may have some security issues but otherwise it mainly works" in 2.5 years. People expect perfection, and move the goalposts. Give it a second. Learn what it can do today, adapt, prepare for what it can do tomorrow.

No one is moving the goalposts. There are a ton of people and companies trying to replace large swathes of workers with AI. So it's very reasonable to point out ways in which the AI's output does not measure up to that of those workers.

I thought the idea was that AI would make us collectively better off, not flood the zone with technical debt as if thousands of newly minted CS/bootcamp graduates were unleashed without any supervision.

LLMs are still stochastic parrots, though highly impressive and occasionally useful ones. LLMs are not going to solve problems like "what is the correct security model for this application given this use case".

AI might get there at some point, but it won't be solely based on LLMs.

> "what is the correct security model for this application given this use case".

Frankly I've seen LLMs answer better than people trained in security theatre so be very careful where you draw the line.

If you're trying to say they struggle with what they've not seen before. Yes, provided that what is new isn't within the phase space they've been trained over. Remember there's no photographs of cats riding dinosaurs but SD models can generate them.

Saying that they aren't worse than an incompetent human isn't a ringing endorsement.

LLMs are not meant to be infallible it's meant to be faster.

Repeat after me, token prediction is not intelligence.