The Hidden Risk in How Leaders Think About AI Safety

"The problem is that this approach quietly assumes something false: That you can test your way to safety. There are infinitely many possible inputs to any nontrivial AI system. No matter how large your test suite is, it is still a rounding error. At best, testing tells you what happened on a narrow slice of reality. It does not tell you what cannot happen."

"But AI systems are not stable in that way. A modern model is a large, adaptive software artifact. Small changes in input can produce qualitatively different behavior. When you test such a system, you are making a probabilistic claim. You are saying, "We did not see a failure in these cases, so we believe failures are unlikely." That belief has failed before."

Testing reveals observed AI behavior but cannot guarantee absence of failures. Formal methods can define which failures are impossible. Modern AI models have infinitely many possible inputs, making exhaustive testing infeasible. Sampling-based evaluation gives probabilistic confidence, not certainty. Small input changes can produce qualitatively different outputs, undermining stability assumptions. Leaders must treat AI safety as a risk-management and governance decision rather than merely an engineering optimization. Relying solely on larger test suites or red teams can create false confidence. Responsible management requires acknowledging limits of testing and choosing controls that reduce possible harmful behaviors.

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