AI coding assistants are changing the way we write software. They boost productivity, fill in boilerplate code, and make it easy to tackle bigger projects faster. But as we move faster, we’re also taking on more risks - especially when it comes to maintaining reliable business logic. That’s why having extensive tests has never been more critical.
When your app is heavy on business logic, regressions love to sneak in. You might tweak a small helper function or refactor a service layer, and suddenly a key workflow breaks in a subtle way. In a traditional workflow, code review is the safety net. As pull requests grow larger, review quality drops fast. Big PRs often slip through with a simple “LGTM.”
AI makes this problem worse in some ways. Because it accelerates coding, we feel empowered to do bigger refactors and deliver large new features more often. It’s great for velocity, but it also means our safety net - code review - gets stretched thin. That’s where strong, reliable tests are the real defense.
There’s also a deeper problem. Large language models are not decision-makers. They don’t reason about the right solution for your system. They work by pattern recognition. If you feed the same problem to an LLM ten times, you’ll often get ten different implementations. Some might be clever, some completely wrong, and most somewhere in between. They excel at finding shortcuts or “good enough” solutions instead of tackling the root cause or designing clean, maintainable architecture.
And here’s the kicker: business logic isn’t written in your codebase comments or design docs. It lives in people’s heads, in Slack threads, in old tickets, and in tribal knowledge. AI tools don’t have access to that. So while they can mimic code style or framework patterns, they can’t fully understand how your business rules fit together. That’s how seemingly harmless AI-generated updates can introduce subtle regressions that humans might not notice right away.
Extensive testing bridges that gap. It documents the system in the one language AI and humans both understand: executable truth. Tests encode how things should behave, protect against regressions, and act as living documentation. If you want to move fast with AI without breaking the foundation, every piece of critical business logic needs to be backed by solid tests.
AI helps us code faster, but testing is what keeps us coding confidently. As AI tools evolve, our discipline in writing and maintaining thorough tests might just be the difference between a team that ships faster and a team that ships chaos.