If AI is helping people code better, why aren't products getting better?

Hacker News Discussion ↗

AI coding assistants like Copilot and ChatGPT are widely seen as speeding up routine programming tasks, especially for novices and rapid prototyping, but many developers report little or no improvement in the quality of finished software products. Commenters argue that product quality depends more on factors like UX, product management, incentives, and long-term maintainability than on how quickly code is written, and that AI-generated code can even increase technical debt if used carelessly. Several voices note we’re still early in the adoption curve, so any real gains may show up later—if organizations choose to invest productivity gains into polish and better features rather than just more output or cost-cutting.

Speed vs. Quality of Code

  • Many argue AI tools mostly make coding faster, not better.
  • Gains are largest for novices and in boilerplate, simple scripts, and glue code.
  • Experienced devs see “advanced autocomplete”: useful, but not transformative.
  • Some expect quality to decrease as less-skilled devs ship more code they don’t fully understand.

Code Quality vs. Product Quality

  • Strong consensus: better code ≠ better product.
  • Product quality is driven by UX, research, feature choices, and iteration cycles, not just implementation.
  • AI can produce “perfect code for a bad feature”; it doesn’t fix bad product decisions.

Who Benefits and How

  • AI is praised for:
    • Rapid prototypes/MVPs and throwaway apps.
    • Exploring unknown stacks, frameworks, and libraries.
    • Tedious transformations, config files, tests, and small automation scripts.
  • It behaves like a broad but shallow junior dev: decent at idioms, weak at deep, domain-specific problems.

Maintainability, Bugs, and Tech Debt

  • Concern that AI-generated code will be average, verbose, inconsistent, and harder to maintain.
  • Debugging vague, real-world bugs in complex systems remains hard; AI helps little there.
  • Fear that companies will replace senior devs with juniors + AI, increasing long‑term tech debt and bugginess.

Incentives, Enshittification, and Where Gains Go

  • Multiple comments: business incentives prioritize profit and feature throughput over quality.
  • Any productivity gains are often converted into “more features” or cost cuts, not polish.
  • Better tools historically lead to more software and complexity, not necessarily nicer products.

Timing, Adoption, and Unclear Effects

  • AI coding tools have only been widely used for ~1–2 years; many existing products predate them.
  • Some expect noticeable improvements in 3–5 years as new codebases started with AI mature.
  • Others see no clear evidence yet of large productivity or quality gains and suspect hype.

Overall Sentiment

  • Broad agreement: AI is already a useful coding aid and prototyping tool.
  • Disagreement on whether it “codes better,” and little belief that it has yet made mainstream products meaningfully better.