Modern AI systems are increasingly used in settings where point predictions are insufficient: scientific inference, medical risk assessment, forecasting, and decision-making require calibrated uncertainty, predictive distributions, and valid prediction sets. In this talk, I will discuss how statistical and mathematical tools can help move AI beyond point prediction. I will focus on conditional density estimation, calibration diagnostics, and conformal prediction, with emphasis on recent work incorporating epistemic uncertainty into conformal scores. I will also discuss how recent tabular foundation models can be evaluated as conditional density estimators, and why post-hoc calibration and distribution-free guarantees remain central even as predictive models become more powerful. The talk will argue that one important contribution of mathematics to AI is to provide the language, diagnostics, and guarantees needed to decide when AI predictions can be trusted.