Monetisation Models for Academic Creators: A Mixed-Methods Study of 997 Doctorate-Signed Newsletters and 92 Public Discussion Threads

Scholars who publish for the public increasingly try to earn from that work, and generative AI tools promise to cut the hours it takes. This study proposes a mixed-methods framework for describing how academic creators monetise their expertise and how AI tools could change the economics, and applies it to public evidence. A quantitative strand classified the latest posts of 174,656 active newsletters on a large publishing platform, 997 of them signed by authors with a doctoral or professorial title; a qualitative strand coded 92 public question threads with 357 answers in which academics discussed earning from their expertise. One doctorate-signed newsletter in five mentioned an offer of its own, more often than other newsletters in the same topic category (odds ratio 1.47), usually through a single model such as books, courses or paid membership, whereas sponsorship and donations were rare. AI was discussed by 31% of the doctorate-signed newsletters, but only 0.6% described using it in their own work, and naming AI tools was not associated with more offers or more frequent posting. The threads raised employer policy, ethics and tax as the main concerns, and AI appeared in only one of them. A labelled planning simulation suggests that AI drafting mainly saves hours rather than raising income, and pays off most for creators with little time. A protocol for the survey and interviews, with their sample sizes, is proposed.