The first AMS AI conference took place in 1998 and included 47 presentations with many earth science topics similar to those presented today: precipitation predictions, satellite retrieval and pattern recognition, climate classification and prediction, image processing, decision aids and natural language systems. Looking back at the abstracts and presentations of past AMS AI conferences allows to track the popularity of different AI/ML methods and how they were implemented in the earth sciences. While Neural Networks were the most popular technique in 1998, mentioned in 49% of all abstracts, this percentage drops to 27% in 2008 and 10% in 2019. The implementation of other AI techniques for earth science problems varied through the conferences and included fuzzy logic, tree-based methods, genetic algorithms and support vector machines. The field continues to evolve and by the 2019 AMS AI conference, deep learning was by far the method of choice with 36% of all abstracts, far ahead of any other AI method. We are expecting this trend to continue in parallel with an increase in the popularity of AI/ML as a method to better predict and gather a deeper understanding of a wide variety of complex and nonlinear processes in the earth sciences.