Software Quality | Thoughts

Software feels more buggy today. This is evident across almost every platform and is especially noticeable on major social media applications. Even iOS, long regarded for its stability, is not immune. Recently, I found myself having to force-close multiple apps just to dismiss a message pop-up that refused to close.

AI has dramatically accelerated software development. Code can be generated in seconds, features can be prototyped instantly, and teams can move faster than ever before. However, speed without sufficient understanding is risky. When AI-generated code is treated as production-ready without deep review, testing, and clear ownership, defects become almost inevitable. AI can produce convincing code, but it does not carry context, long-term responsibility, or architectural intent.

Fast shipping amplifies this problem. The pressure to release quickly often encourages teams to prioritize delivery over correctness. In this environment, AI becomes a force multiplier for speed rather than quality. Bugs slip through, edge cases are missed, and technical debt accumulates quietly until it becomes visible to users.

This is not an argument against AI. Used well, it is a powerful assistant. The issue arises when AI replaces engineering discipline instead of supporting it. Code still needs to be understood, tested, and maintained by humans who are accountable for its behavior.

If we want more reliable software, we need to slow down where it matters. AI should help us think better, not rush faster. Quality cannot be automated away, and stability cannot be an afterthought.