The widespread adoption of next-generation sequencing technology in molecular pathology has enabled us to interrogate the genome as never before. The huge quantities of data generated by sequencing, the enormous complexity of human and microbial genetics, and the need for fast answers demand increasing use of automation as we diagnose disease and guide patient treatment. Much of this automation is based on tools that fall under umbrellas that have come to be known as machine learning and artificial intelligence (AI). In this review, we outline some of the broad ideas that underpin these complex computational methods. We discuss the roles of pathologists and data scientists in creating new tools and factors to keep in mind when adopting these systems for use in molecular pathology. We give special attention to regulatory and professional society guidance for validating them in individual institutions and to possible sources of bias. Finally, we briefly discuss ongoing efforts in computer science that may dramatically impact AI in the future.