A common misinterpretation of these results was that all NP-complete problems are hard, no chance for good algorithms. On this basis some such problems generated much hope in cryptography: the adversary would be helpless. Karp and others noticed that this was naive. While worst instances of NP-complete problems defeat our algorithms, such instances may be extremely rare. In fact, fast on average algorithms were found for a great many NP-complete problems. If all NP problems are easy on average, the P=?NP question becomes quite academic. Even if exponentially hard instances exist, those we could ever find might all be easy. Some problems (like factoring) seem hard for typical instances, but nothing is proven at all to support this (crucial, e.g., for cryptography) belief. These issues turned out to be subtle and it was not clear how a theory could distinguish intrinsically hard on average problems. [Levin 86], [Venkatesan, Levin STOC-88], [Impagliazzo, Levin, FOCS-90] proposed such a theory with first average case intractability results. Random (under uniform distribution) instances of some problems are now known to be as hard as random instances of any NP-problem under any samplable distribution. A number of subsequent papers extended this area substantially. See expositions in [Johnson 84], [Gurevich 85] and at http://www.uncg.edu/mat/avg.html.