I don't know where PhD programs in statistics are going, but in the meantime, we can at least make the grad curriculum more useful: 0. Make gptpro/claude equivalent free for grad students; mandatory training and use of agentic workflow. Most universities still give only the 20$ version, not the most powerful one. 1. Revamp the grad programs and focus them toward "taste" and constructive criticism. I'd be ok with not having any written exams and instead having oral/take-home where students must use AI to replicate and then critique a published work to the committee's satisfaction. But here the standards have to be really higher than before for a student to "pass". This is the only thing we can still "teach". 2. Focus the grad program toward "building" instead of writing papers. A "thesis" can constitute constructing original data pipelines, assembling disparate data sources, open source of implementation of algorithms, etc. CS programs have been doing this for a while; stats has to catch up. 3. Formalize routine minimax arguments/convergence proofs via Lean, and focus on the key parts of the technical argument. In mathematics, up until recently, most graduate programs had a foreign language requirement. I wouldn't be surprised if they make Lean a language requirement in the near future.