We provide the first large-sample evidence of substantial anticipatory effects: firms are affected by proposed rules long before those are finalized. We construct a new data set that tracks the timeline of each rule proposal developed by federal agencies since 1995, total of 43,000 proposals. The average proposal spends more than two years in the rulemaking pipeline and only two-thirds convert into a final rule. Training a machine-learning algorithm, we derive a firm-level measure of exposure to the regulatory pipeline: the amount of rule proposals which are relevant to the firm. We find that firms with greater exposure increase overhead costs, reduce capital investments, and report lower profits, independent of their current regulatory burden and political risk. The effects increase with the expected burden of the proposals and when agencies develop rules outside of their core expertise. Calibrating a latent factor model of stock returns with our new firm-specific measure, we identify systematically important regulatory topics such as Securities, Natural Resources, and Environment. Our results are the first to document anticipatory effects based on the entire body of potential federal regulations.