What we lost the last time code got cheap

LLMs as Code Readers and Explainers

• Many commenters use LLMs primarily to read code: summarizing repos, explaining unfamiliar modules, or visualizing structure (HTML/SVG diagrams, even videos).
• They’re seen as especially useful for onboarding to large codebases and quickly locating relevant files/lines.
• Some constrain prompts (e.g., ignore domain naming, focus on data flow and math/state transitions) to reduce hallucinations and abstraction bloat.
• Several argue LLMs now make “understanding the codebase” less of a bottleneck than the article suggests.

Quality and Reliability of AI-Generated Code

• Experiences diverge sharply:
  • Some report code “almost never” works on first try and needs several iterations, especially for non-trivial tasks.
  • Others say recent models often produce working code on first run, especially with clear specs.
• Recommended mitigations: red/green TDD, having agents run tests and linters, and giving explicit goals so the model iterates until tests pass.

Documentation, Intent, and Comments

• Broad agreement that “why” and “why not” decisions are more valuable than “what this line does.”
• Mixed experiences: some see over-documentation with irrelevant change history in comments; others find insightful rationales, especially on bug fixes.
• One major concern: agents rarely capture architectural/product decisions unless explicitly instructed. Proposed solutions:
  • decisions.md / ADRs, design docs in-repo, richer commit messages.
  • PRs centered on plan/spec files, with code as a derived artifact.
• Some tools default to minimal comments, prompting users to override that behavior.

Impact on Skills, Engagement, and Responsibility

• Worry that relying on LLMs to read/write code can erode individual understanding; critics liken it to “letting your brain atrophy.”
• Counterpoint: humans already wrote poor code without full context; tools can be force multipliers if used thoughtfully.
• Real-world anecdotes: seniors skipping requirements and blaming agents; AI-generated proposals violating org policies. Some managers respond with clear expectations and, if needed, performance processes.
• Suggestions for keeping engineers engaged: involve them in design and intent-setting, framing them as “implementers of functionality” rather than just “writers of code.”

Attitudes Toward AI-Generated Text vs Code

• Recurrent thread: suspicion of “LLM-sounding” prose in the article and elsewhere, and frustration with derivative, unedited AI text.
• Others warn against a “witch hunt” where any punchy or formulaic style is labeled AI, including genuine human writing.
• Noted double standard: many accept AI-authored code but show strong contempt for AI-authored essays and blog posts, even though low-cost generation in both domains shifts the burden onto readers/reviewers.