We introduce an information bundling model that addresses two important but relatively unstudied issues in real mar- kets for information goods: automated customization of con- tent based on categories, and competition among content providers. Using this model, we explore the strategies that sellers (or automated agents acting on their behalf) might use to set both price and bundle composition, and the mar- ket dynamics that might ensue from such strategy choices. The model incorporates different categories of information, explicitly accounts for finite production and consumption costs, and allows for possibly heterogeneous valuations by consumers. First, we determine the optimal bundle compo- sition and price for a monopolist as a function of the seller's production costs and the consumers' preferences and con- sumption costs. For finite costs, finite-sized bundles are op- timal. Then, we use game-theoretic analysis and simulation to explore the behavior of the market when there are multi- ple content providers. We find that, if consumer preferences are homogeneous, sellers choose to offer the same bundle that a monopolist would choose, but that competition forces sellers to offer the bundles at cost. For heterogeneous pref- erences, positive profits are possible, but there appears not to be a pure strategy Nash equilibrium. This is manifested as a never-ending cycle of prices and bundle choices when sellers employ a myopic best-response algorithm.