The requirement for 100% accuracy is close, but not quite correct. Even in many highly critical human endeavors, there are many errors. The key to success is

The requirement for 100% accuracy is close, but not quite correct. Even in many highly critical human endeavors, there are many errors. The key to success is not absolute error-free perfection, it is no critical errors in components that are severe enough to kill the project. Every rocket launch has some issues, but the successful ones have issues where it doesn't explode or land in the wrong orbit. In the spreadsheet example, dropping a critical zero will cause damage akin to the rocket explosion. But dropping an "O" in a label field is utterly trivial. Humans understand the distinction, constantly make such judgements and focus on the critical areas in their moment-to-moment work and embed it in their work processes. These constant criticality judgements are not just binary, but refined scaled, and serve to apply resources where needed. The AI systems do not have such a judgement layer, and apply the same degree of inaccuracy to every part of their domain. So, absolute 100% accuracy is required, as errors are no less likely in the critical components.