When AI Gets Cheaper, Do Prices Follow? Information Delays and Algorithmic Pricing

We examine whether lower costs of running AI models lead to lower prices, and whether delays in observing competitors' prices and limits on price changes affect how much consumers benefit. We study a simulated market with two differentiated sellers, logit demand, and tabular Q learning. A factorial experiment varies the age of rival price information, how often sellers can revise prices, whether production costs fall, and differences in firm quality and costs. We evaluate policies trained on 60 paired market seeds in each of 16 conditions, for 960 training runs, and measure expected consumer surplus after learning ends. In the main setting with identical firms and falling costs, the estimated interaction between information delay and repricing frequency on consumer surplus is -0.00025 normalized units, with a 95% paired seed bootstrap interval of [-0.0402, 0.0393]. The interval includes zero, as do intervals for tests of whether the interaction changes with the cost regime or differences between firms. With the shortest information delay and the ability to revise prices every period, average price reductions equal 55.6% and 44.0% of the imposed cost reduction for the two firms. Mean consumer surplus under the learned policies is below the corresponding static grid Nash benchmark in all 16 conditions. The findings show that simulated cost savings need not be fully passed on to buyers. They do not establish collusion or describe pricing by actual AI providers.