This sounds like magical thinking.

For one, there are objectively detrimental ways to organize code: tight coupling, lots of mutable shared state, etc. No matter who or what reads or writes the code, such code is more error-prone, and more brittle to handle.

Then, abstractions are tools to lower the cognitive load. Good abstractions reduce the total amount of code written, allow to reason about the code in terms of these abstractions, and do not leak in the area of their applicability. Say Sequence, or Future, or, well, function are examples of good abstractions. No matter what kind of cognitive process handles the code, it benefits from having to keep a smaller amount of context per task.

"Code structure does not matter, LLMs will handle it" sounds a bit like "Computer architectures don't matter, the Turing Machine is proved to be able to handle anything computable at all". No, these things matter if you care about resource consumption (aka cost) at the very least.

> For one, there are objectively detrimental ways to organize code: tight coupling, lots of mutable shared state, etc. No matter who or what reads or writes the code, such code is more error-prone, and more brittle to handle.

Guess what, AIs don't like that as well because it makes harder for them to achieve the goal. So with minimal guidance, which at this point could probably be provided by AI as well, the output of AI agent is not that.

Yes LLMs aren't very good at architecture. I suspect because the average project online has pretty bad architecture. The training set is poisoned.

It's kind of bittersweet for me because I was dreaming of becoming a software architect when I graduated university and the role started disappearing so I never actually became one!

But the upside of this is that now LLMs suck at software architecture... Maybe companies will bring back the software architect role?

The training set has been totally poisoned from the architecture PoV. I don't think LLMs (as they are) will be able to learn software architecture now because the more time passes, the more poorly architected slop gets added online and finds its way into the training set.

Good software architecture tends to be additive, as opposed to subtractive. You start with a clean slate then build up from there.

It's almost impossible to start with a complete mess of spaghetti code and end up with a clean architecture... Spaghetti code abstractions tend to mislead you and lead you astray... It's like; understanding spaghetti code tends to soil your understanding of the problem domain. You start to think of everything in terms of terrible leaky abstraction and can't think of the problem clearly.

It's hard even for humans to look at a problem through fresh eyes; it's likely even harder for LLMs to do it. For example, if you use a word in a prompt, the LLM tends to try to incorporate that word into the solution... So if the AI sees a bunch of leaky abstractions in the code; it will tend to try to work with them as opposed to removing them and finding better abstractions. I see this all the time with hacks; if the code is full of hacks, then an LLM tends to produce hacks all the time and it's almost impossible to make it address root causes... Also hacks tend to beget more hacks.