This is just a thought experiment.

I don't mean to be treading on feet but I'm noticing this more and more in the debates around AI. Imagine if there are developers out there that could have done this task in 30 mins without AI.

The level of performanace of AI solutions is heavily related to the experience level of the developer and of the problem space being tackled - as this thread points out.

Unfortunately the marketing around AI ignores this and makes every developer not using AI for coding seem like a dinosauer, even though they might well be faster in solving their particular problems.

AI is moving problem solving skills from coding to writing the correct prompts and teaching AI to do the right thing - which, again, is subjective, since the "right thing" for one developer isn't the "right thing" for the another developer. "Right thing" being the correct solution, the understandable solution, the fastest solution, etc depending on the needs of the developer using the AI.

IMHO, the thirty minute developer would still save 10 minutes by vibe coding. That marketing's not wrong.

Spelling out exactly what you want and checking/fixing what you receive is still faster than typing out the code. Moreover, nobody's job involves nothing but brainiac coding, day after day. You have to clean up and lay foundations, whatever level you are at.

There are two ways to do this. One is to one-shot or maybe few-shot a solution. Maybe this works. Maybe it doesn't. Sometimes it works if you copy a solution from [Product 1] to [Product 2] and say "Fix this."

The other is to look at the non-working solution you get, read through it, and think "Oh, I didn't know about that framework/system/product/library, that's neat" and then do some combination of further research and more hand-holding to get to something that does work.

This is useful, more or less, no matter what your level.

It's also good for explaining core industry tooling you've maybe never used before. If you're new to Postgres/NoSQL/AWS/Docker/SwiftUI/whatever it can talk you through it and give you an instant bootcamp with entry-level examples and decent solutions.

And for providing fixes for widely known bugs and issues in products that may not be widely known to you (yet.)

IME ChatGPT5 is pretty solid with most science/tech up to undergrad. It gets hallucinatory past that, and it's still flattering, which is annoying, but you can tell it to cut that out.

Generally you can use it as a dumb offshore developer, or as an infinitely patient private tutor.

That latter option is very useful. The first, not always.

> The level of performanace of AI solutions is heavily related to the experience level of the developer and of the problem space being tackled - as this thread points out. > > Unfortunately the marketing around AI ignores this and makes every developer not using AI for coding seem like a dinosauer, even though they might well be faster in solving their particular problems.

You're not necessarily wrong, but I think it's worth noting that very few developers are only ever coding deep in their one domain that they're good at. There's just too many things to be deeply good at everything. For example, it's common that infra and CI tasks are stuff that most developers haven't learned by heart, because you don't tend to touch them very often.

Claude shines here — I've made a lot more useful GitHub Actions jobs recently, because while I could automate something, if I know I'm going to have to look up API docs (especially multiple APIs I'm not super familiar with) then I tend to figure that the automation will lose out the trade-off between doing the task (see https://xkcd.com/1205/). Claude being able to hash out those rapidly, and in a way that's easily verifiable that it's doing the right thing, has changed that arithmetic for me substantially.