When Everyone Writes Well: The Economics of Writing with AI

Writing produces two things: the manuscript published now and the capability its author will hold next year. Artificial intelligence can raise the first while lowering the second, and which happens depends on the task the author delegates. The relevant comparison is between workflows rather than between using and refusing AI. This article separates writing into its tasks and applies a standard tool of economics to each, synthesising existing models into five predictions. Cheaper drafting lets an author test more versions of an argument, and the largest gains should go to researchers writing in a second language, whose language costs are unrelated to their evidence. Manuscript studies, including new evidence from economics working papers, fit this pattern, although they measure prose and output rather than argument quality. Polishing prose with a model while delegating the first formulation of a problem can leave a better text and a weaker author, and it makes the generated draft an anchor and a default. Cheap fluency makes polished prose a weaker guide to the underlying research. The evidence is uneven, and I note where it is thin. As models improve, the bottleneck moves, and so does the division of labour between author and machine.