Multinational enterprises (MNEs) develop their networks of foreign affiliates gradually over time. Instead of exploring all profitable opportunities immediately, they first establish themselves in their home countries and then enter new markets stepwise. We argue that this behavior is driven by uncertainty concerning a firm’s success in new markets. After entry, the firm collects information which is used to update its beliefs about its performance at a market. As conditions in different markets are correlated, the information gathered in one of them can also be used to update beliefs elsewhere – with the degree of correlation depending on issues such as the geographical or cultural distance between markets. This correlated learning may render it optimal to enter markets sequentially – an investment in market A is only followed by entry in market B if the firm was sufficiently successful in A. The prediction that firms start their expansion in markets that are closer to their home base and then proceed step by step is supported by our empirical analysis, which features the universe of foreign affiliates held by German multinationals. Based on a rich set of benchmark estimates and sensitivity checks, we identify correlated learning across markets beyond alternative explanations as a key driver of gradualism in the genesis of multinational foreign affiliate networks.