AI touches all of the basic assumptions that underlie how we think about the value of postsecondary education and academic research: what students need to learn, how they learn, how institutions operate, and how universities contribute to the production and dissemination of knowledge. 

The immediate questions it raises are urgent and real. Students need AI literacy and training on work-relevant AI tools. Faculty need to rethink assignments and assessment. Institutions are trying to understand how AI improves (or complicates) operational workflows. Researchers need to understand how AI will affect methods, workflows, funding, and infrastructure. Libraries and publishers need to manage new tools for discovery, access, rights, and trust.

The harder questions are more consequential. What forms of human judgment become more valuable when routine cognitive work is automated? How can we ensure that learning still includes productive friction, unexpected feedback, and human interaction? What parts of the institutional bundle remain worth paying for? How should universities sustain independent knowledge production when the data, models, compute, and talent needed for frontier AI work increasingly sit outside the academy? And what standards of provenance, integrity, rights, and trust should govern AI-mediated discovery?

I explore these more challenging questions that point to the very heart of the mission of colleges and universities in an issue brief I published last week. Postsecondary value itself will depend on whether institutions help people do what AI cannot do alone: form purposes, make judgments, build trust, contribute to communities, and advance knowledge in service of human flourishing. But it will also depend on whether institutions can deliver that value at a price students, families, states, and society can sustain.

I invite you to engage with this brief and look forward to sharing more about how Ithaka S+R is working to help answer some of these questions in the coming months.