Recent probabilistic models of natural language interpretation pay insufficient attention to what a speaker might say, mistakenly focusing instead on what is probable in the world. As a result, these models produce incorrect results, require data that is not realistically obtainable, and entail the solution to problems that intractable but irrelevant to the task of natural language interpretation. In contrast, a proper understanding of the nature of discourse allows us to so simplify any probabilistic elements so as to trivialize their role. The challenger for natural language interpretation, then, is not to perfect a probabilistic account, but to develop a plausible account of discourse. Such an account needs to focus on the nature of the contents speakers convey, and on cooperative principles of communication.