“Rebuilding” Statistics in the Age of AI: Culture, Infrastructure, and Training

This perspective article distills themes from the 2024 JSM town hall, Statistics in the Age of AI, where panelists discussed how the field should evolve as foundation models, large-scale empirical modeling, and data-intensive infrastructures reshape science and society. Rather than a transcript or comprehensive survey, the article offers a forward-looking perspective grounded in the town hall discussion and the authors’ reflections. We organize the discussion around five recurring questions: (i) disciplinary culture and incentives, (ii) the role of data curation and annotation, (iii) engagement with modern empirical modeling, (iv) training for large-scale AI applications, and (v) partnerships with key AI stakeholders. We emphasize that statistics already leads in areas such as uncertainty quantification, study design, evaluation, and inference, while arguing that some parts of the field could engage more deeply with data engineering, cloud-scale computation, and end-to-end AI systems. We conclude with pragmatic recommendations for training, incentives, and collaboration so that statisticians can help shape the data-centric future.