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How AI Fails
AI is one of the most capable tools available today. It can accelerate research, simplify complex tasks, generate content at scale, and surface patterns that would take humans far longer to find. That capability is exactly what makes it worth understanding carefully. The more useful a tool, the more consequential it becomes when it fails — and AI fails in specific, learnable ways.
A Few Common Ways AI Fails
These are not all the ways AI can fail. They are documented failure patterns that appear consistently across real-world cases. Click any mode to see how it shows up and what to do.
Each failure mode listed above is defined in plain language in the Appendix.
You’ve just read a few failure modes; here is how to respond to them.
Before acting on any output, establish what’s at stake if it’s wrong.
Responds to: Hallucination, False Precision, Omission
You are the verification layer the model does not have.
Responds to: Prompt Sensitivity, Context Collapse
Know which tasks need a human in the loop — and keep them there.
Responds to: Data Exposure, Security Failure, Automation Drift
What worked last month may not work today.
Responds to: Automation Drift (listed twice deliberately — drift needs both a human-in-the-loop gate and ongoing monitoring), Authority Confusion & Bias
