I don't think getting stuck in a local optimum is as important as the speed with which you get there.

Techniques for finding a global optimum of a numerical function are expensive, time-consuming, and often find worse solutions before they find the best one. Do this in a startup, and your local-optimum-seeking competition will crush you.

Seems to me that thinking there is a better solution that your users don't know about (which led you to the local optimum) is exactly the lack of humility that Paul was warning against.