Probabilistic programming is a newer way of posing machine learning problems. As the models we want to create become more complex it will be necessary to embrace more generic tools for capturing dependencies. I wish to argue that probabilistic programming languages should be the dominant way we perform this modeling, and will demonstrate it by showing the variety of problems that can be trivially