This paper studies the impact of uncertainty on market competition. Michigan requires auto insurers to cover all expenses related to injuries from auto accidents. However, prices are unregulated as long as competition exists. Starting in the early 2000s, long-term medical costs have ballooned, leading to rapidly increasing injury coverage premiums. Detroit, for example, had an average auto insurance rate more than five times the national average in 2017. However, insurer profit also increased during this period, largely fueled by rising markups on non-injury and smaller coverages. Using publicly available data on insurance quotes and firm-level cost, I show that uncertainty played a key role in mitigating market competition. Specifically, I propose a model of insurance pricing that incorporates firm learning and risk aversion in a market with changing fundamentals. Comparing similar neighborhoods with different realizations of catastrophic claims over time, the model can explain why larger unexpected loss developments lead to higher markups ex-post, despite already exacerbated adverse selection.