AI's Trap: Settling for Boilerplate over Elegant Code (dayvster.com)

An opinion-driven piece warns that AI-assisted coding has subtly raised developers’ tolerance for boilerplate and inelegant solutions. Using Picasso’s distillation metaphor, the author argues that as code generation tools (Copilot, ChatGPT, Grok) become better at producing working output, teams increasingly offload ownership to AI: prompt, iterate until “it works,” then commit dozens of autogenerated files and let PR bots or linters handle review. That shortcut hides brittle, surface-level fixes that don’t reflect deep problem understanding, lead to recurring QA tickets, and disincentivize later refactoring. This trend matters because ugly, repetitive code carries real technical costs: higher cognitive load, slower onboarding, harder maintenance, more time to fix bugs, and thinner institutional knowledge. AI will reliably solve the explicit task but won’t learn from past mistakes, infer hidden requirements, or distill elegant abstractions—so relying on it fosters a maintenance spiral that demands more tooling (and more AI) to manage increasingly noisy codebases. The author’s prescription is cultural: treat AI as an aid, not the author; keep aesthetic and engineering standards; refactor; and preserve developer ownership so solutions remain readable, performant, and resilient.