You know what's wild? AI evangelists keep promising that machine learning will replace mathematicians and solve all our problems, but here's the reality check: that plane isn't flying anywhere. The classic survivorship bias illustration shows a WWII aircraft with bullet holes concentrated everywhere EXCEPT the engines and cockpit—because the planes that got hit there never made it back. The genius move was to armor the parts WITHOUT holes, not the parts WITH them. Now imagine training an AI model on this data without understanding the underlying logic. It would confidently tell you to reinforce the wings and fuselage because "that's where all the damage is!" Meanwhile, actual mathematicians and statisticians understood the invisible data—the planes that didn't return. No amount of throwing neural networks at a problem will replace understanding what you're actually measuring. So yeah, maybe we still need those pesky humans who can think about what's NOT in the dataset. Fun fact: Abraham Wald, the statistician who figured this out, was part of the Statistical Research Group during WWII, which was basically the Avengers of math nerds. They saved countless lives by actually understanding their data instead of just curve-fitting it.