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Key Takeaways Testing shows what happened; formal methods define what failures are impossible.
AI safety is a leadership risk decision, not an engineering optimization problem.
I have spent a lot of time watching smart teams do the wrong thing for very rational reasons. When leaders talk about AI risk, the conversation usually collapses into testing, with bigger eval suites, red teams and synthetic data.
The instinct is understandable. Testing feels concrete. You can point to dashboards. You can say you ran 10,000 cases instead of 1,000, and it looks like progress.
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. For leaders, that distinction is everything.
This is a decision about how you manage risk in systems you’re responsible for and that are already reshaping how capital, attention and value flow, even if you do not fully understand them yet.
Sampling feels like control, but it’s not
Executives are used to operating in environments where sampling works. Instead of interviewing every customer, a leader might talk to a handful and infer broader patterns.
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