That's assuming the 200 lines are logical and consistent. Many of my most frustrating LLM bugs are caused by things that look right and are even supported by lengthy comments explaining their (incorrect) reasoning.
Very far from the truth in practice, every line of code isn't as difficult/easy to review as the other.
{x{x,sum -2#x}/0 1}
def f(n):
if n <= 1:
return n
else:
return f(n-1) + f(n-2)
But it is orthogonal to the question of evaluating 2000 lines of AI code vs 200 lines of human written code. Either the human or the AI could produce idiomatic code for either language, given sufficient training data in the AI’s corpus for the language.
My guess is that the first one is much quicker to review, for a human equally fluent in both languages.
Objectives change; timeliness matters. The speed at which you deliver value is incredibly important, which is why it matters to measure your process. Deceptively dense is what I’d call software engineers who can’t accept that the process is actually generalizable to a degree and that lines of code are one of the few tangible things that can be used as a metric. Can you deliver value without lines of code?
This assumes that shorter code is faster to write. To quote Blaise Pascal, "I would have written a shorter letter, but I did not have the time."
> Can you deliver value without lines of code?
No, but you can also depreciate value when you stuff a codebase full of bloated, bug-ridden code that no man or machine can hope to understand.
“All models are wrong, some are useful”. What’s not useful is constantly bitching about how there’s no way to measure your work outside of the binary “is it done” every time process efficiency is brought up.