Examples play a critical role in the mathematician’s workflow, enabling us to explore unfamiliar mathematical landscapes, building intuition and generating conjectures along the way. Of course, there are many settings where a far greater number of examples can be generated than could be manually examined by any one person. On the other hand, recent progress in AI has resulted in pattern recognition capabilities that can capture nuanced structural features in data at huge scales. In this talk we discuss the use of AI for conjecture generation in algebraic combinatorics. We will start by describing what we have learned about the process of putting together a ‘math dataset’ aimed at machine learning. We then describe our Algebraic Combinatorics Dataset Repository where we translated open problems and foundational results into a machine learning friendly format. We then discuss a specific example where we used graph neural networks and mechanistic interpretability to re-discover several theorems which characterize mutation equivalence classes of quivers.