If ML has solved the mathematical induction problem, then what is left to do? Much, as it turns out. We next describe the frontier of deduction problems that have been largely unaddressed by contemporary ML in the chemical sciences, but we also predict a long afterlife for the mathematical induction problem. For example, work will continue to reduce data requirements (e.g., using transfer learning ), to be more intelligent in how data is acquired (e.g., using active learning ), training models for new properties as datasets become available (e.g., predicting reaction properties), and towards making ML models better approximate the causality and interpretability associated with models derived by humans (e.g., using advanced forms of symbolic regression ). These activities will be impactful and present their own intellectual challenges, but in aggregate they will not represent a fundamental departure from the already successful, if not yet fully complete, campaign to solve the mathematical induction problem.