Soar is an attempt to realize a set of hypothesis on the nature of general intelligence within a single system. One central hypothesis is that chunking, Soar's simple experience-based learning mechanism, can form the basis for a general learning mechanism. It is already well established that the addition of chunks improves the performance in Soar a great deal, when viewed in terms of subproblems required and number of steps within a subproblem. But this high level view does not take into account potential offsetting costs that arise from various computational effects. This paper is an investigation into the computational effect of expensive chunks. These chunks add significantly to the time per step by being individually expensive. We decompose the causes of expensive chunks into three components and identify the features of the task environment that give rise to them. We then discuss the implications of the existence of expensive chunks for a complete implementation of Soar.