In recent years, the computational social choice community has increasingly studied the topic of proportional representation. This topic is particularly relevant for political elections, with many countries basing their voting systems on this principle, especially in Europe. However, the ideas behind proportional representation are also relevant in many other domains, including applications in artificial intelligence. We discuss a model of sequential decision making with proportional rules, and how it can be used for three applications: for merging the outputs of several large language models, for improving reinforcement learning from human feedback (RLHF), and for virtual democracy.