Lines of code got a better publicist

Original Article ↗ Hacker News Discussion ↗

Return of Lines-of-Code Metrics

  • Commenters note a resurgence of LoC as a success metric in the AI era, despite long-standing criticism.
  • OpenAI’s “million lines of code by agents” blog post is cited as emblematic: heavy emphasis on LoC with little clarity on product value.
  • Comparisons to Linux, Chrome, and large monorepos highlight that huge LoC counts are not inherently impressive or desirable.

AI Slop, Technical Debt, and Maintainability

  • “AI slop” is contrasted with traditional “technical debt” and seen as more concrete: large volumes of code few people understand, often far larger than necessary for the purpose.
  • Some argue every line of code is a liability; LoC should be spoken of as a “cost” rather than an asset.
  • Concerns that LLM-written codebases have recognizable patterns: plausible code, thin or fake tests, unclear abstractions.

Productivity and Bottlenecks

  • Many say “writing code is no longer the bottleneck”; real constraints are deciding what to build, code review, testing, and organizational process.
  • Individual developers often feel faster, especially for tests, boilerplate, and legacy-code comprehension, but team‑ or company‑level gains are modest due to unchanged downstream bottlenecks.
  • There’s debate over claims like “1M LOC per engineer per month”; some see it as about automated porting, others as unrealistic and easily gamed.

Metrics, Gaming, and Goodhart’s Law

  • Examples: lines-of-code-based performance reviews, PR-count-driven evaluations, code-coverage quotas gamed by generated junk.
  • Commenters repeatedly invoke Goodhart’s Law: when LoC, PRs, or “% code written by AI” become targets, they cease to measure real productivity.

Adoption Pressure and Cultural Split

  • Some insist every engineer must use AI daily to remain employable and competitive; others reject this as hype or “gun-to-the-head” adoption.
  • Mixed field reports: nontechnical or junior users initially impressed, but often disappointed when using AI for tasks they know well.
  • HN itself is perceived by different participants as both “AI fanboy” and “AI hate” territory, reflecting a polarized community.

Safety-Critical and “Real” Products

  • Safety‑critical and embedded sectors are described as largely not adopting LLM codegen; one cited study finds major guideline non‑compliance.
  • A broader contrast is drawn between sectors that must ship working, profitable products and those seen as focused on “faux productivity.”