No, I don't think we are talking about the same kind of complexity here. Complexity Theory is exactly the opposite of real world pragmatism. It is about the asymptotic behaviors of algorithms and the resources need to solve certain class of problems such as Traveling Salesman or Min-Set-Cover.
Actually, that is exactly what I am talking about. The implication was that problems like that do show up in "real-life" coding, and while brute-force solutions for those problems work for very small n, you need to recognize the fact that those problems do not have reasonable solutions in general, and you should know how to approximate the solutions (in the technical sense of "approximation"), and furthermore know enough complexity theory to know the situations where even an approximation will not have a reasonable solution.
For example, I believe that the problem of creating optimal schedules for high school students is NP-Complete (optimal in the sense that it will satisfy all of the students' preferences for electives, and all of the teachers' preferences for the classes they want to teach).
Yes, I admit that the example will never involve petabytes of data. That doesn't invalidate my original point though.
