TESTING — What if AI-generated code and AI-generated tests share the same blind spot? Faster validation can create confidence without adding genuinely independent evidence.
AI can now generate a feature, its unit tests, edge cases, documentation, and even a review of the implementation from the same conversational context.
That is a remarkable productivity gain.
It also creates an unusual verification problem.
If the requirement contains an ambiguity, the model may interpret it once and propagate that interpretation everywhere. The implementation follows it. The tests confirm it. The review explains why it makes sense. Several artifacts now agree, but they may be descendants of the same assumption.
Artifact diversity is not necessarily evidence diversity.
This does not make generated tests useless. They can catch regressions, forgotten branches, boundary errors, and inconsistencies extremely well. But consequential confidence may need something the generation path did not already know: an independent requirement, a domain invariant, a production example, an external policy, or another genuinely different source of judgment.
As AI makes confirmation cheaper, the valuable test may increasingly be the one capable of surprising the system that produced the code.
https://www.linkedin.com/pulse/when-test-learns-same-mistake-code-rogerio-figurelli-7hzaf