This paper describes an initial exploration into large learning systems, i.e., systems that learn a large number of rules. Given the well-known utility problem in learning systems, efficiency questions are a major concern. But the questions are much broader than just efficiency, e.g., will the effectiveness of the learned rules change with scale? This investigation uses a single problem-solving and learning system, Dispatcher-Soar, to begin to get answers to these questions. Dispatcher-Soar has currently learned 10,112 new productions, on top of an initial system of 1,819 productions, so its total size is 11,931 productions. This represents one of the largest production systems in existence, and by far the largest number of rules ever learned by an AI system. This paper presents a variety of data from our experiments with Dispatcher-Soar and raises important questions for large learning systems.