Large Language Publishing The Scholarly Publishing Oligopoly’s Bet on AI

Abstract

The AI hype cycle has come for scholarly publishing. This essay argues that the industry’s feverishーif mostly aspirationalーembrace of artificial intelligence should be read as the latest installment of an ongoing campaign. Led by Elsevier, commercial publishers have, for about a decade, layered a second business on top of their legacy publishing operations. That business is to mine and process scholars’ works and behavior into prediction products, sold back to universities and research agencies. This article focuses on an offshoot of the big firms’ surveillance-publishing businesses: the post-ChatGPT imperative to profit from troves of proprietary “training data,” to make new AI products andーthe essay predictsーto license academic papers and scholars’ tracked behavior to big technology companies. The article points to the potential knowledge effects of AI models in academia: Products and models are poised to serve as knowledge arbitrators, by picking winners and losers according to what they makevisible. I also cite potential knock-on effects, including incentives for publishers to roll back open access (OA) and new restrictions on researchers’ access to the open web. The article concludes with a call for a coordinated campaign of advocacy and consciousness-raising, paired with high-quality, in-depth studies of publisher data harvestingーbuilt on the premise that another scholarly-publishing world is possible. There are many good reasons to restore custody to the academy, the essay argues. The latest is to stop our work from fueling the publishers’ AI profits.

Keywords:

• academic publishing,
• artificial intelligence,
• data mining,
• prediction products,
• surveillance publishing