I'm tired of fixing customers' AI generated code

Original Article ↗ Hacker News Discussion ↗

Scope of the problem: AI‑generated client code

  • Many API users now arrive with code written by LLMs that:
    • Call non‑existent endpoints or fields hallucinated by the model.
    • Ignore rate limits, error handling, and basic HTTP concepts.
  • This creates expectation that the API provider will debug or even design the entire app for free.
  • Several commenters note this isn’t fundamentally new (stack‑overflow copy/paste, script kiddies), but AI massively increases the volume and speed of low‑quality code.

Causes: low skills + “just make a thing” mindset

  • People want to build products, not learn programming; AI appears to let them skip fundamentals.
  • Some see this as an extension of junior devs with buzzwords but little depth.
  • Others argue self‑teaching takes substantial time; a “week to learn programming” is unrealistic for most.
  • There’s concern that reliance on AI erodes understanding and makes debugging much harder.

Proposed coping strategies for API providers

  • Improve self‑service:
    • Strong docs, OpenAPI specs, generated SDKs, minimal “hello world” examples in popular languages.
    • Public FAQs and support articles targeting common AI‑induced mistakes.
  • Change support model:
    • Explicitly limit free support; add paid tiers, “developer”/enterprise support, or “no support” positioning.
    • Fire or filter high‑maintenance customers; set clear boundaries early.
    • Build a community forum so other users can help for free.
  • Offload work:
    • Partner with consultants or freelancers specializing in fixing AI‑generated apps.
    • Some see a “gold mine” market in rescuing these projects.

“Use AI to fix AI” ideas

  • Suggestions include:
    • A support bot/agent grounded strictly in the API docs to detect invalid endpoints/fields and propose corrections.
    • LLMs to triage, auto‑respond, or prioritize tickets.
    • Using hallucinated endpoints as signals for missing or confusing API design (“hallucination‑based API design”).
  • Skeptics note that:
    • LLMs also hallucinate and can get stuck in loops without a knowledgeable human guiding them.
    • Scale of low‑quality code/content may become unmanageable.

Broader views on AI and coding

  • Many see AI as:
    • Good for speeding up typing, boilerplate, and simple bug hunting.
    • Dangerous when used as a substitute for understanding or for full app design.
  • Review burden rises: it’s harder to vet large, incoherent AI‑written patches; some fear “AI reviewing AI” is misguided.
  • Others think, as with ATMs and cloud, AI will create new jobs: cleaning up, hardening, and extending AI‑generated prototypes.