TheAIShift:HowWeThinkAboutTesting

The AI Shift
How We Think About Testing

AI can generate a passing test suite in minutes. That's not the same as a tested system.

Testing used to be the thing that came after building. Write the code, then prove it works. Most teams knew this was backwards in theory but lived with it in practice because the people writing code and the people reviewing it shared enough context to catch the obvious gaps.

AI changes that equation in an uncomfortable way. It will write tests, confidently and quickly, for exactly the behavior it just built. If the behavior was wrong to begin with, the tests will confirm the wrong thing. You end up with green checkmarks on a system that doesn't do what your business needs.

The shift isn't technical. It's about what you decide before you build.

When requirements are vague, AI optimizes for passing. When requirements are specific, AI can actually verify them. That means the value of a test is now determined almost entirely upstream, before a single line of code is written.

The teams discovering this aren't writing more tests. They're writing better definitions of done. Not "the booking can be cancelled" but "a booking can only be cancelled more than 24 hours before the start time, and only by the customer or an administrator, and the customer must be notified within five minutes."

That level of precision used to live in a senior developer's head. It got applied during code review, or caught in QA, or discovered by a customer at the worst possible moment.

AI doesn't have that head. It has what you wrote down.

The good news is that writing it down also makes your tests meaningful again. Precise requirements don't just guide what gets built. They make it obvious when something is wrong.