I can't help but feel that people continually underestimate how bad human written code becomes over time. The exception is probably single-person passion projects or open source projects that maintain quality governance over time.
I strongly suspect most closed source code developed under commercial or internal pressure is pretty awful after a few years of development.
All LLM code has to do is suck less than existing code. And that's presuming the code quality doesn't improve as the models, the harnesses and our ways of working with them improve.
Sucky human-written code is still based on human understanding, which can change over time, be readjusted or solidified. People implement something wrong once, then update their perspective, then in the future does it right.
LLMs doesn't have this benefit. You forget to add the correct to the system prompt, and the LLM will repeat the same mistake over and over, and worse than that, their mistakes aren't based on their understanding, it's basically random guesses.
Humans, even bad coders, still seem to have some sort of architecture in mind, even if it's spaghetti, whereas LLMs (obviously) don't think more than a few steps, and never about the full scope of what they're contributing too, and on purpose too, because you want the context to be as small as possible when you work with LLMs.
With LLMs you need to thread carefully between "What does the LLM need to know?" and "Can I skip passing this to the LLM this time?" while a human you can more or less dump them everything you sit on, and let them shift it through, and they'll mostly make it out OK.
> their mistakes aren't based on their understanding, it's basically random guesses
Whilst I don't claim any true "understanding" as that is a very loaded term that doesn't mean it's just random guesses.
Anyone using recent LLM coding agents on a regular basis would probably agree that there's something going on that fits some non-athropomorphizing, non-sentience-assigning definition of "understanding"
As for the point about improvement - I think that's an orthogonal issue to the overall code quality. With regard to human codebases - there's plenty of scenarios that negate the improvement of individuals. We're comparing organizations with LLMs - not individuals with LLMs and that makes a significant difference.
> while a human you can more or less dump them everything you sit on, and let them shift it through, and they'll mostly make it out OK
i dont see why software engineers are paid so well, and are so hard to hire?
just dump a bunch of requirements on a homeless person and itll just work out
I think the real issue might be that how “good” the code is matters less than being able to form a mental model for what the human who wrote the code was “thinking”. If written by a machine, this contract is broken and we get more confused, even if our traditional methods of evaluating the code come out equal.
That doesn’t help the developers who have high standards.
Yes. But that's not the point I'm addressing.
And where do you think the LLM learned coding from?
But anyway, let the LLM verify the code to give advice on improvements but don't let it write code unverified. That's my opinion on it anyway.
I've been sent code from vendors that didn't even compile, long before llms were a thing. Most shops that aren't primarily software have really really terrible software.