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I think the author is missing a key distinction. Before, lines of code was (mis)used to try to measure individual developer productivity. And there was the col

I think the author is missing a key distinction. Before, lines of code was (mis)used to try to measure individual developer productivity. And there was the collective realization that this fails, because good refactoring can reduce LoC, a better design may use less lines, etc. But LoC never went away, for example, for estimating the overall level of complexity of a project. There's generally a valid distinction between an app that has 1K, 10K, 100K, or 1M lines of code. Now, the author is describing LoC as a metric for determining the proportion of AI-generated code in a codebase. And just like estimating overall project complexity, there doesn't seem to be anything inherently problematic about this. It seems good to understand whether 5% or 50% of your code is written using AI, because that has gigantic implications for how the project is managed, particularly from a quality perspective. Yes, as the author explains, if the AI code is more repetitive and needs refactoring, then the AI proportion will seem overly high in terms of how much functionality the AI proportion contributes. But at the same time, it's entirely accurate in terms of how this is possibly a larger surface for bugs, exploits, etc. And when the author talks about big tech companies bragging about the high percentage of LoC being generated with AI... who cares? It's obviously just for press. I would assume (hope) that code review practices haven't changed inside of Microsoft or Google. The point is, I don't see these numbers as being "targets" in the way that LoC once were for individual developer productivity... there's more just a description of how useful these tools are becoming, and a vanity metric for companies signaling to investors that they're using new tools efficiently.

o_nate 8mo ago
I don't think the author is missing this distinction. It seems that you agree with him in his main point which is that companies bragging about LOCs generated by AI should be ignored by right-thinking people. It's just you buried that substantive agreement at the end of your "rebuttal".
jihadjihad 8mo ago
> It seems good to understand whether 5% or 50% of your code is written using AI, because that has gigantic implications for how the project is managed, particularly from a quality perspective. I'd say you're operating on a higher plane of thought than the majority in this industry right now. Because the majority view roughly appears to be "Need bigger number!", with very little thought, let alone deep thought, employed towards the whys or wherefores thereof.
- readthenotes1 8mo ago
  A higher plane of thought would be "was AI able to remove 5% or 50% of the code while keeping or adding functionality and not diminishing clarity, consistency, and correctness"
Kerrick 8mo ago
> the overall level of complexity of a project The overall level of complexity of a project is not an "up means good" kind of measure. If you can achieve the same amount of functionality, obtain the same user experience, and have the same reliability with less complexity, you should. Accidental complexity, as defined by Brooks in No Silver Bullet, should be minimized.
- saidnooneever 8mo ago
  a bit of a nit but accidental complexity is still complexity, so even if that 1M lines could reduce to 2 kines its still way more complex to maintain and patch than a codebase thats properly minimized and say 10k lines. (even though this sounds unreasonable i dont doubt it happen..)
- mehagar 8mo ago
  So the question is, are these AI tools primarily creating inherent complexity, or is it a significant amount of accidental complexity? And if AI tools are writing all of the code, does it even matter anymore?
  - orderone_ai 8mo ago
    It absolutely does matter. LLMs still have to consumer context and process complexity. The more LoC, the more complexity, the more errors you have and the higher your LLM bills. That's even in the AI maximalist, vibe-code only use case. The reality is that AI will have an easier time working in a well-designed, human-written codebase than one generated by AI, and the problem of AI code output turning into AI coding inputs resulting in the AI choking and on itself and making more errors tends to get worse over time, with human oversight being the key tool to prevent this.
- crazygringo 8mo ago
  > The overall level of complexity of a project is not an "up means good" kind of measure. I never said it was. To the contrary, it's more of an indication of how much more complex large refactorings might be, how complex it might be to add a new feature that will wind up touching a lot of parts, or how long a security audit might take. The point is, it's important to measure things. Not as a "target", but simply so you can make more informed decisions.
mehagar 8mo ago
If tech companies want to show they have a high percentage of LoC being generated by AI, it's likely they are going to encourage developers to use AI to further increase these numbers, at which point is does become a measure of productivity.
VirusNewbie 8mo ago
> would assume (hope) that code review practices haven't changed inside of Microsoft or Google. Google engineer perspective: I'm actually thinking code reviews are one of the lowest hanging fruits for AI here. We have AI reviewers now in addition to the required human reviews, but it can do anything from be overly defensive at times to finding out variables are inconsistently named (helpful) to sometimes finding a pretty big footgun that might have otherwise been missed. Even if it's not better than a huamn reviwer, the faster turnaround time for some small % of potential bugs is a big productivity boost.
- aix1 8mo ago
  > the faster turnaround time for some small % of potential bugs is a big productivity boost For a balanced perspective, one should also account for the cost of triaging and disregarding AI findinds that aren't useful or actionable.
temp84858696945 8mo ago
One key point of insight is that companies are counting single line AI autocomplete as an AI generated line of code, pumping up these metrics quite significantly.

o_nate 8mo ago

I don't think the author is missing this distinction. It seems that you agree with him in his main point which is that companies bragging about LOCs generated by AI should be ignored by right-thinking people. It's just you buried that substantive agreement at the end of your "rebuttal".

jihadjihad 8mo ago

> It seems good to understand whether 5% or 50% of your code is written using AI, because that has gigantic implications for how the project is managed, particularly from a quality perspective. I'd say you're operating on a higher plane of thought than the majority in this industry right now. Because the majority view roughly appears to be "Need bigger number!", with very little thought, let alone deep thought, employed towards the whys or wherefores thereof.

readthenotes1 8mo ago
A higher plane of thought would be "was AI able to remove 5% or 50% of the code while keeping or adding functionality and not diminishing clarity, consistency, and correctness"

Kerrick 8mo ago

> the overall level of complexity of a project The overall level of complexity of a project is not an "up means good" kind of measure. If you can achieve the same amount of functionality, obtain the same user experience, and have the same reliability with less complexity, you should. Accidental complexity, as defined by Brooks in No Silver Bullet, should be minimized.

saidnooneever 8mo ago
a bit of a nit but accidental complexity is still complexity, so even if that 1M lines could reduce to 2 kines its still way more complex to maintain and patch than a codebase thats properly minimized and say 10k lines. (even though this sounds unreasonable i dont doubt it happen..)
mehagar 8mo ago
So the question is, are these AI tools primarily creating inherent complexity, or is it a significant amount of accidental complexity? And if AI tools are writing all of the code, does it even matter anymore?
- orderone_ai 8mo ago
  It absolutely does matter. LLMs still have to consumer context and process complexity. The more LoC, the more complexity, the more errors you have and the higher your LLM bills. That's even in the AI maximalist, vibe-code only use case. The reality is that AI will have an easier time working in a well-designed, human-written codebase than one generated by AI, and the problem of AI code output turning into AI coding inputs resulting in the AI choking and on itself and making more errors tends to get worse over time, with human oversight being the key tool to prevent this.
crazygringo 8mo ago
> The overall level of complexity of a project is not an "up means good" kind of measure. I never said it was. To the contrary, it's more of an indication of how much more complex large refactorings might be, how complex it might be to add a new feature that will wind up touching a lot of parts, or how long a security audit might take. The point is, it's important to measure things. Not as a "target", but simply so you can make more informed decisions.

mehagar 8mo ago

If tech companies want to show they have a high percentage of LoC being generated by AI, it's likely they are going to encourage developers to use AI to further increase these numbers, at which point is does become a measure of productivity.

VirusNewbie 8mo ago

> would assume (hope) that code review practices haven't changed inside of Microsoft or Google. Google engineer perspective: I'm actually thinking code reviews are one of the lowest hanging fruits for AI here. We have AI reviewers now in addition to the required human reviews, but it can do anything from be overly defensive at times to finding out variables are inconsistently named (helpful) to sometimes finding a pretty big footgun that might have otherwise been missed. Even if it's not better than a huamn reviwer, the faster turnaround time for some small % of potential bugs is a big productivity boost.

aix1 8mo ago
> the faster turnaround time for some small % of potential bugs is a big productivity boost For a balanced perspective, one should also account for the cost of triaging and disregarding AI findinds that aren't useful or actionable.

temp84858696945 8mo ago

One key point of insight is that companies are counting single line AI autocomplete as an AI generated line of code, pumping up these metrics quite significantly.