AI, Mathematics, Education, and a public goods problem

[This is a guest post by Gireeja Ranade. This blog post was initially written in a different file format and converted using AI. — T.] The day after the Navier-Stokes result was announced, a student came to my office hours. They said, “Can we please talk about the Navier-Stokes result and what it means? I really need some therapy.” Two days ago another student mentioned, “I used to be really motivated, but now that all the models can do everything with just a prompt, I don’t feel like putting in any effort at all.” Every time I have such a conversation with students, I appreciate how lucky my generation has been. We didn’t have to grapple with the temptations or threats of AI when we were students. Not being able to quickly look up answers forced us to challenge ourselves, and do it together with our friends. And years of challenging ourselves somehow made us love it [1], and led us to challenging jobs across academia and industry. These jobs are simultaneously wonderful and hard. But…