The main finding of complex systems research is that there can be a disconnect between the local behavior of the interacting elements of a complex system and regularities that are observed in the global behavior of that system, making it virtually impossible to derive the global behavior from the local rules. It is arguable that intelligent systems must involve some amount of complexity, and so the global behavior of AI systems would therefore not be expected to have an analytic relation to their constituent mechanisms. This has serious implications for the methodology of AI. This paper suggests that AI researchers move toward a more empirical research paradigm, referred to as “theoretical psychology,” in which systematic experimentation is used to discover how the putative local mechanisms of intelligence relate to their global performance. There are reasons to expect that this new approach may allow AI to escape from a trap that has dogged it for much of its history: on the few previous occasions that something similar has been tried, the results were both impressive and quick to arrive.