We often say AI’s mistakes are "by design," but they’re really not. AI wasn’t built to fail in these specific ways—its errors emerge as a byproduct of how it learns.

But what if we actively use them as a tool instead of just tolerating AI’s weird mistakes or trying to eliminate them?

Here are some unexpected but potentially valuable use cases where treating AI mistakes as a form of bias—rather than just failure—could lead to new insights and innovations.