AI Writes the Code. But Who Checks What Else It Broke?

AI coding tools are getting insanely good at making fast changes.

Need a new feature? Done.

Fix a bug? Easy.

Refactor a function? A few seconds later, you’ve got a clean pull request.

But there’s a question most of us aren't asking often enough: What else did that change quietly break?

Imagine asking an AI assistant to tweak an authentication helper. The diff looks completely fine on its own. But under the hood, that exact function might also touch:

• An internal admin dashboard
• A background cron job
• Legacy integration tests
• An upstream microservice

The AI completed the exact task you gave it. But completing a task isn't the same as understanding the entire dependency graph around it.

Speed amplifies the blast radius

This isn't a brand-new problem—developers have always had to think about side effects. The difference now is speed.

When AI lets us ship code 5x faster, we can also ship unintended side effects 5x faster.

Before calling an AI-generated pull request "done," a quick sanity check goes a long way:

1. What files changed? (The obvious part)
2. What actually depends on those files?
3. Which existing behaviors could unexpectedly shift?
4. Which test suites actually need to run now?

Rethinking the workflow

While building Savyre, we realized that review shouldn't just be about inspecting the line-by-line diff. It needs an explicit **Impact Analysis phase—stepping back to look at what that change could affect across the system before merging.

AI coding shouldn't end just because the code compiles and passes a single unit test. It ends when you have real confidence that you haven't broken something three folders over.

Curious how other devs are handling this right now. Do you explicitly run impact checks on AI code, or do you just rely on normal PR reviews and CI runs?

#ai #programming #softwaredevelopment #productivity