AI and the PI: Infinite Generation, Finite Understanding

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

Generative artificial intelligence (AI) produces code and prose quickly, apparently enabling a principal investigator (PI) in a computational group to do more. Assessing this gain must also account for the laboratory’s role in training scientists who understand, maintain, and own their work. AI creates a generation–verification asymmetry: it reduces the effort of producing plausible code and prose without similarly reducing the expertise needed to evaluate them, potentially leaving groups with technical and epistemic debt while obscuring the reasoning needed for supervision. I argue that AI adoption should therefore be judged by whether it builds sustainable laboratory capacity, with assistance adjusted to task purpose and verifiability and human ownership retained for every consequential artifact.