Harvard CMSA summit: recommendations for the maths PhD in the age of AI

Your AI and this story

Key facts

• Summit: Sept 17–18, 2026 at Harvard CMSA, 20 Garden Street, closed and invitation-only; organisers Dan Freed, Joe Harris, Mark Kisin and Melanie Matchett Wood (CMSA event page)
• Report published Sept 25 as a guest post by Bryna Kra and Rachel Ward on Terence Tao’s blog, with an 8-page PDF; called a first draft to be refined ‘over the coming months’; feedback by blog comment or email to CMSA
• Top recommendations: (1) new training in collaboration, formulating questions, interdisciplinary work, AI tools and formalization; (2) communication skills as an explicit degree expectation; (3) assessment by in-person written or oral exams, not homework; (4) AI tools available to all graduate students, ‘with use never mandatory’; (5) regular advisor–student discussions about AI use
• (6) Three ethical rules: responsibility for the correctness and understanding of mathematics under your name, ‘always disclose AI use’, and explicit agreement from all involved parties before entering material into an AI system; (7) rigorous in-person assessment of research by multiple faculty at least annually; (8) a thesis need not record the first proof of a statement; (9) no PhD awarded primarily on the dissertation text; a rigorous defence requires ‘complete mastery’
• Graded list of AI uses, from likely helpful to likely harmful to learning: figures, literature search, language clean-up, proofreading, introductions to new fields, coding, self-refereeing, autoformalization, deep exploration, brainstorming and, last, proof generation. Principle: ‘AI use should accelerate understanding, not bypass understanding’
• Privacy: even with training opt-outs, privacy with commercial systems cannot be guaranteed; locally run open-weight models are named as the exception
• The views ‘represent broad agreement but are not necessarily unanimously held’ (report)
• Same week on Tao’s blog: Jess Werk’s guest post ‘Cognitive Sanctuaries’ (Sept 27) argues for department structures that protect slow thinking for PhD students