[throw46365]

> I don't think this is indicative of people who don't know what they're doing. I think this is indicative of people using "AI" tools to help with programming at all.

I think using AI tools to write production code is probably indicative of people who don't really know what they are doing.

The best way not to have subtle bugs is to think deeply about your code, not subcontract it out -- whether that is to people far away who both cannot afford to think as deeply about your code and aren't as invested in it, or to an AI that is often right and doesn't know the difference between correct and incorrect.

It's just a profound abrogation of good development principles to behave this way. And where is the benefit in doing this repeatedly? You're just going to end up with a codebase nobody really owns on a cognitive level.

At least when you look at a StackOverflow answer you see the discussion around it from other real people offering critiques!

ETA in advance: and yes, I understand all the comparison points about using third party libraries, and all the left-pad stuff (don't get me started on NPM). But the point stands: the best way not to have bugs is to own your code. To my mind, anyone who is using ChatGPT in this way -- to write whole pieces of business logic, not just to get inspiration -- is failing at their one job. If it's to be yours, it has to come from the brain of someone who is yours too. This is an embarrassing and damaging admission and there is no way around it.

ETA (2): code review, as a practice, only works when you and the people who wrote the code have a shared understanding of the context and the goal of the code and are roughly equally invested in getting code through review. Because all the niche cases are illuminated by those discussions and avoided in advance. The less time you've spent on this preamble, the less effective the code review will be. It's a matter of trust and culture as much as it's a matter of comparing requirements with finished code.

[beachy]

> And where is the benefit in doing this repeatedly? You're just going to end up with a codebase nobody really owns on a cognitive level.

You could say the same about the output of a compiler. No one owns that at a cognitive level. They own it at a higher level - the source code.

Same thing here. You own the output of the AI at a cognitive level, because you own the prompts that created it.

[l33t7332273]

>No one owns that at a cognitive level

Notwithstanding the fact that compilers did not fall out of the sky and very much have people that own them at the cognitive level, I think this is still a different situation.

With a compiler you can expect a more or less one to one translation between source code and the operation of the resulting binary with some optimizations. When some compiler optimization causes undesired behavior, this too is a very difficult problem to solve.

Intentionally 10xing this type of problem by introducing a fuzzy translation between human language and source code then 1000xing it by repeating it all over the codebase just seems like a bad decision.

[throw46365]

> You could say the same about the output of a compiler.

Except, for starters, that you're not using the LLM to replace a compiler.

You're using it to replace a teammate.

[theamk]

Yes, and when compilers fail, it's a very complex problem to solve, that usually requires many hours from experienced dev. Luckily,

(1) Compilers are reproducible (or at least repeatable), so you can share your problem with other, and they can help.

(2) For common languages, there are multiple compilers and multiple optimization options, which (and that's _very important_) produce identically-behaving programs - so you can try compiling same program with different settings, and if they differ, you know compiler is bad.

(3) The compilers are very reliable, and bugs when compiler succeeds, but generates invalid code are even rarer - in many years of my career, I've only seen a handful of them.

Compare to LLMs, which are non-reproducible, each one is giving a different answer (and that's by design) and finally have huge appear-to-succeed-but-produce-bad-output error rate, with value way more than 1%. If you had a compiler that bad, you'd throw it away in disgust and write in assembly language.

[throwaway2037]

    > I think using AI tools to write production code is probably indicative of people who don't really know what they are doing.

People said the same to me for using Microsoft IntelliSense 20 years ago. AI tools for programming are absolutely the future.

[throw46365]

But not the now, quite obviously.

Colour me cynical but I don't feel like pretending the future is here only to have to have to fix its blind incompetence.