AI has a deep understanding of how this code works

Original Article ↗ Hacker News Discussion ↗

Context of the PR

  • A large PR (~13–22k LOC) added DWARF debugging support to OCaml, mostly generated by LLMs.
  • The submitter openly described prompting Claude/ChatGPT and having them also write the explanations, copyright analysis, and even markdown planning files.
  • The work appears influenced by an existing DWARF implementation in a forked compiler, which was also pointed at the AI as reference material.

Maintainers’ Concerns and Project Process

  • Core complaint: a massive, first-time PR with no prior proposal, design discussion, or buy‑in, in an area where others are already working carefully in smaller, reviewable steps.
  • Maintainers emphasized:
    • Too big for the small core team to safely review.
    • Insufficient tests for the amount and centrality of code.
    • Design issues (DWARF library tightly coupled into the compiler, long‑term tech debt).
  • Several commenters stressed that such a PR would be unacceptable even if written entirely by a human.

AI-Generated Code: Quality, Accountability, and Review Burden

  • Many maintainers report AI code is harder to review than human code: it looks polished, but signals of author competence and intent are missing.
  • Accountability problem: there is no evolving contributor behind the code, just one‑off artifacts; each PR might be disconnected from the last.
  • Reviewers reject the idea that their role is to deeply vet code that the submitter themselves doesn’t fully understand.

Copyright and Provenance Issues

  • Multiple files in the PR named another developer as author; the submitter’s answer (“AI decided, I didn’t question it”) became emblematic of the entire episode.
  • Commenters see this as a red flag about provenance and as evidence that LLMs can silently “adapt” or copy from nearby codebases.
  • Some argue accepting code with unknown origins is legally risky and socially corrosive, even if licenses are technically compatible.

Open Source Culture, Spam, and Platform Choices

  • Maintainers describe a growing wave of AI‑generated, “drive‑by” PRs from contributors seeking résumé material or attention.
  • Brandolini’s law is invoked: it takes orders of magnitude more effort to refute AI slop than to produce it.
  • Proposed responses:
    • Stricter contribution guidelines, explicit AI policies, and pre‑discussion requirements.
    • Rejecting AI PRs outright, or at least massive ones.
    • Moving away from GitHub or adding friction (self‑hosted repos, email patches, requiring local accounts) to filter out low‑investment contributors.
    • Encouraging AI enthusiasts to maintain their own forks or greenfield projects instead of offloading maintenance onto existing teams.

Views on “Good” Uses of AI

  • Some accept LLMs as personal tools: generating one‑off features for private forks, experiments, or non‑critical code, provided the user owns and understands the result.
  • Many draw a hard line at merging large AI‑generated features into mature, shared codebases without thorough human design, ownership, and review.

Reaction to Maintainers’ Conduct

  • Commenters widely praise the OCaml maintainers’ patience, clarity, and emotional maturity in handling the situation.
  • There is debate over whether such politeness scales, or whether harsher, more “Torvalds‑like” responses will become necessary as AI‑driven spam increases.