5 ms·

>A caveat: Lines of code is an imperfect measure, as it measures quantity over quality. So 8× lines of code/engineer/day in the second quarter of 2026 is almost

>A caveat: Lines of code is an imperfect measure, as it measures quantity over quality. So 8× lines of code/engineer/day in the second quarter of 2026 is almost certainly an overstatement of the true productivity gain. Nonetheless, it indicates an acceleration. At Anthropic, we don’t reward people for how many lines of code they write; rather, team members are producing more code simply because they’re using AI systems to write more code. What about the hypothesis that AI is generating more verbose code? I just see the text pretending to acknowledge "LOC != Productivity" and then using it as a metric anyway.

fooqux 5mo ago
Exactly. If AI is going to start being graded on how many LoC it generates- oh, I'm sorry, how much it "accelerates", than guess what newer models will start doing more of?
- simondotau 5mo ago
  Surely they can train AI on the signal to change as few lines as possible. Indeed, this is something I'd want to have control over when making requests. In a traditional UI, I'd imagine some kind of slider between "fewest lines" and "be bold".
  - disgruntledphd2 5mo ago
    I've been having some success asking Claude to run sloccount after each change. Seems to help a little, though it's prone to forgetting over a long session.
- verdverm 5mo ago
  I'm actually hopeful that the recursive code training will improve quality over time. I'm definitely producing higher quality code, tests, and docs. It does take attention and oversight, iteration and refinement, one cannot just let these things loose on a code base and expect good things to happen. You have to leverage them to make the good things happen.
malfist 5mo ago
One of my co-workers just asked me to review his pull request that was all AI generated. 600 files were touched, over 40k lines of code added. I'm sure he thought that was a crowning achievement, proof that AI can enable 10X developers, after all, what engineer could write 40k lines of code in a week? I declined to review it, stating that I couldn't possibly vet 40k lines of code, and wouldn't put my reputation on the line to stamp the work as good. The PR nagged me for 2 weeks from my todo list and then disappeared. I don't know if he found another dev to get an approval from, or if the PR was abandoned. But I know for sure that him and I are on two totally separate islands around the value of LLMs.
- CamperBob2 5mo ago
  I declined to review it, stating that I couldn't possibly vet 40k lines of code Gee, that sounds like a job for Claude if there ever was one.
  - malfist 5mo ago
    You're absolutely right!
  - danparsonson 5mo ago
    And how would you verify that the review was accurate?
    - sebasv_ 5mo ago
      Same way that I would trust your review to be accurate. Because the reviewer has built a reputation for correctness. Its not Claude doing the review. Its a human doing the review, but using Claude to do the reading. Its still on the human to ask the right questions to Claude.
      - kalaksi 5mo ago
        For large changes that are not straightforward and include architectural decisions, I wouldn't trust Claude enough to not read most of the code myself. I'll have to read it to be able to understand it and ask about the decisions in detail anyway. And when I start to understand it, it's not uncommon to find out that the solution can be improved and simplified in many places, and after iterating, 25-30% of code disappears. And trying to just hand-wave it to Claude, to somehow "improve it" or "simplify it", without detailed questions hasn't been very successful. It can work for some things, though.
chuckadams 5mo ago
AI generates code that mimics the existing code. If your code is terse and comment-free, then the agent’s code is too. The times I’ve seen Claude drift into a default “house style” it generated like 1 comment for every 10 LOC or so. It’s a far cry from the GPT-3 days that littered every line with the journals of Captain Obvious.
- atq2119 5mo ago
  That is definitely not my experience using Claude Code with Opus. I work in a very sparsely commented code base, and the agent produces substantially more comments than the surrounding code.
deleted 5mo ago
[deleted]
whateveracct 5mo ago
Yeah, they assume that "productivity = k * LOC" where k > 1 very flawed
keeda 5mo ago
So the more rigorous studies about AI-assisted coding productivity addressed this by keeping in place all other software development processes, including the same code review and quality standards, and only measuring throughput (PRs, LoC) before and after AI was allowed. Hence the intepretation of this 8x number depends on whether (or how much) Anthropic engineers have changed their quality standards and development processes. They don't tell us, and I am not aware of any other indications we could use to make a judgment. However, we can still do some theorycrafting! I'm convinced that to fully realize the potential of AI-assisted coding we need to revamp all the dev processes, especially how we validate code, and it would be foolish of Anthropic not to do so (unless they were conducting a rigorous study, which they don't claim to have done.) My hypothesis on the future of software validation is nothing fancy, we simply want much, much more automation for tests, observability and other bespoke verification methods than we traditionally had. But then validation code will also contribute to the LoC! My observation so far of personal as well as some "vibe-coded" open-source projects is O(LoC production code) ~= O(LoC test code). So as a SWAG the upper bound could be something like a 3 - 4x speedup, which is still remarkable. All bets are off if code quality standards are not the same.
overgard 5mo ago
I just watched copilot today turn a 8 line fix into 500 lines, so, yeah, verbosity is a big side effect
- verdverm 5mo ago
  It occurs to me this pattern might be the average code we humans have produced. We all have made those quick fixes, copy-pastas, and dirty hacks... they learned it somewhere! I also assume that some of the behavior is an artifact of their training regime.
  - TheRoque 5mo ago
    So with LLM outputting average code, and people using LLM more and more, I guess the average code will become worse over time ?
    - nielsbot 5mo ago
      Not advocating for AI code slop--but if AI coded software works correctly, maybe it doesn't matter? Except sometimes when a specialist will have to get involved. Not a perfect analogy, but most people don't write assembly these days--they have a compiler do that. Assembly still has a place, but it's a specialist task.
      - tasuki 5mo ago
        > if AI coded software works correctly, maybe it doesn't matter? The problem isn't the amount of code, it's how fitting/unfitting the abstractions are. Wrong abstractions are bugs in waiting. If there's much code with wrong abstractions, future change becomes difficult. Source: me, I've created many bad abstractions and they led to much pain...
        josephg 5mo ago
        Yeah. Its kind of strange - claude is great at some tasks, but it seems really rubbish at coming up with good abstractions a lot of the time. I've often caught it making a conceptual mistake (like "X cannot do Y") - then spending hundreds of lines working around an issue that doesn't actually exist. Its also really bad at inventing and leaning on invariants. I make rules in my code all the time - "by the time we get to path X, we know Y and Z are true.". In aggregate, these invariants make code simpler and easier to reason about. But claude doesn't do that. It just kind of - slops through and adds bespoke "just in case" workarounds all over the place. Every time I read through code its written - without fail - I find bad design / architectural choices. Maybe mythos will change this. But for now I've slowed way down on my claude code usage. You can't build a skyscraper on a foundation of mud.
snthpy 5mo ago
Just imagine the productivity gains from using LLMs to rewrite Kotlin codebases in Java!
yalok 5mo ago
Could just be more tests? :) Which is good for code quality in general and reduces support burden, but doesn’t lead directly to more features
snowwrestler 5mo ago
I don’t understand how lines of code matter at all for scary LLM core capabilities. Does the transformer architecture get better with more lines of code? My impression was that LLM training codebases were 99% resource management and only a few lines actually implement the core training algorithm, which is where 100% of the intelligence comes from. Data, not lines of code, are the constraint. After training you can adapt the intelligence in various ways, and that takes a bunch of lines of coded too. But you cant raise the intelligence ceiling again without another training run. So where is the scary recursive part?

fooqux 5mo ago

Exactly. If AI is going to start being graded on how many LoC it generates- oh, I'm sorry, how much it "accelerates", than guess what newer models will start doing more of?

simondotau 5mo ago
Surely they can train AI on the signal to change as few lines as possible. Indeed, this is something I'd want to have control over when making requests. In a traditional UI, I'd imagine some kind of slider between "fewest lines" and "be bold".
- disgruntledphd2 5mo ago
  I've been having some success asking Claude to run sloccount after each change. Seems to help a little, though it's prone to forgetting over a long session.
verdverm 5mo ago
I'm actually hopeful that the recursive code training will improve quality over time. I'm definitely producing higher quality code, tests, and docs. It does take attention and oversight, iteration and refinement, one cannot just let these things loose on a code base and expect good things to happen. You have to leverage them to make the good things happen.

malfist 5mo ago

One of my co-workers just asked me to review his pull request that was all AI generated. 600 files were touched, over 40k lines of code added. I'm sure he thought that was a crowning achievement, proof that AI can enable 10X developers, after all, what engineer could write 40k lines of code in a week? I declined to review it, stating that I couldn't possibly vet 40k lines of code, and wouldn't put my reputation on the line to stamp the work as good. The PR nagged me for 2 weeks from my todo list and then disappeared. I don't know if he found another dev to get an approval from, or if the PR was abandoned. But I know for sure that him and I are on two totally separate islands around the value of LLMs.

CamperBob2 5mo ago
I declined to review it, stating that I couldn't possibly vet 40k lines of code Gee, that sounds like a job for Claude if there ever was one.
- malfist 5mo ago
  You're absolutely right!
- danparsonson 5mo ago
  And how would you verify that the review was accurate?
  - sebasv_ 5mo ago
    Same way that I would trust your review to be accurate. Because the reviewer has built a reputation for correctness. Its not Claude doing the review. Its a human doing the review, but using Claude to do the reading. Its still on the human to ask the right questions to Claude.
    - kalaksi 5mo ago
      For large changes that are not straightforward and include architectural decisions, I wouldn't trust Claude enough to not read most of the code myself. I'll have to read it to be able to understand it and ask about the decisions in detail anyway. And when I start to understand it, it's not uncommon to find out that the solution can be improved and simplified in many places, and after iterating, 25-30% of code disappears. And trying to just hand-wave it to Claude, to somehow "improve it" or "simplify it", without detailed questions hasn't been very successful. It can work for some things, though.

chuckadams 5mo ago

AI generates code that mimics the existing code. If your code is terse and comment-free, then the agent’s code is too. The times I’ve seen Claude drift into a default “house style” it generated like 1 comment for every 10 LOC or so. It’s a far cry from the GPT-3 days that littered every line with the journals of Captain Obvious.

atq2119 5mo ago
That is definitely not my experience using Claude Code with Opus. I work in a very sparsely commented code base, and the agent produces substantially more comments than the surrounding code.

deleted 5mo ago

[deleted]

whateveracct 5mo ago

Yeah, they assume that "productivity = k * LOC" where k > 1 very flawed

keeda 5mo ago

So the more rigorous studies about AI-assisted coding productivity addressed this by keeping in place all other software development processes, including the same code review and quality standards, and only measuring throughput (PRs, LoC) before and after AI was allowed. Hence the intepretation of this 8x number depends on whether (or how much) Anthropic engineers have changed their quality standards and development processes. They don't tell us, and I am not aware of any other indications we could use to make a judgment. However, we can still do some theorycrafting! I'm convinced that to fully realize the potential of AI-assisted coding we need to revamp all the dev processes, especially how we validate code, and it would be foolish of Anthropic not to do so (unless they were conducting a rigorous study, which they don't claim to have done.) My hypothesis on the future of software validation is nothing fancy, we simply want much, much more automation for tests, observability and other bespoke verification methods than we traditionally had. But then validation code will also contribute to the LoC! My observation so far of personal as well as some "vibe-coded" open-source projects is O(LoC production code) ~= O(LoC test code). So as a SWAG the upper bound could be something like a 3 - 4x speedup, which is still remarkable. All bets are off if code quality standards are not the same.

overgard 5mo ago

I just watched copilot today turn a 8 line fix into 500 lines, so, yeah, verbosity is a big side effect

verdverm 5mo ago
It occurs to me this pattern might be the average code we humans have produced. We all have made those quick fixes, copy-pastas, and dirty hacks... they learned it somewhere! I also assume that some of the behavior is an artifact of their training regime.
- TheRoque 5mo ago
  So with LLM outputting average code, and people using LLM more and more, I guess the average code will become worse over time ?
  - nielsbot 5mo ago
    Not advocating for AI code slop--but if AI coded software works correctly, maybe it doesn't matter? Except sometimes when a specialist will have to get involved. Not a perfect analogy, but most people don't write assembly these days--they have a compiler do that. Assembly still has a place, but it's a specialist task.
    - tasuki 5mo ago
      > if AI coded software works correctly, maybe it doesn't matter? The problem isn't the amount of code, it's how fitting/unfitting the abstractions are. Wrong abstractions are bugs in waiting. If there's much code with wrong abstractions, future change becomes difficult. Source: me, I've created many bad abstractions and they led to much pain...
      - josephg 5mo ago
        Yeah. Its kind of strange - claude is great at some tasks, but it seems really rubbish at coming up with good abstractions a lot of the time. I've often caught it making a conceptual mistake (like "X cannot do Y") - then spending hundreds of lines working around an issue that doesn't actually exist. Its also really bad at inventing and leaning on invariants. I make rules in my code all the time - "by the time we get to path X, we know Y and Z are true.". In aggregate, these invariants make code simpler and easier to reason about. But claude doesn't do that. It just kind of - slops through and adds bespoke "just in case" workarounds all over the place. Every time I read through code its written - without fail - I find bad design / architectural choices. Maybe mythos will change this. But for now I've slowed way down on my claude code usage. You can't build a skyscraper on a foundation of mud.

snthpy 5mo ago

Just imagine the productivity gains from using LLMs to rewrite Kotlin codebases in Java!

yalok 5mo ago

Could just be more tests? :) Which is good for code quality in general and reduces support burden, but doesn’t lead directly to more features

snowwrestler 5mo ago

I don’t understand how lines of code matter at all for scary LLM core capabilities. Does the transformer architecture get better with more lines of code? My impression was that LLM training codebases were 99% resource management and only a few lines actually implement the core training algorithm, which is where 100% of the intelligence comes from. Data, not lines of code, are the constraint. After training you can adapt the intelligence in various ways, and that takes a bunch of lines of coded too. But you cant raise the intelligence ceiling again without another training run. So where is the scary recursive part?