Academic publishers are turning to AI licensing deals as a new revenue source amid fears of US research funding cuts.

Informa’s Taylor & Francis signed a US$10 million deal with Microsoft last year.

This agreement allows access to part of its library to train large language models.

Other publishers, including John Wiley & Sons and Bloomsbury Publishing, have also partnered with AI firms to monetize their academic content.

These deals significantly boosted publisher earnings, with Informa reporting US$75 million in 2024 from AI-related data access.

Licensing agreements may help offset expected losses from US federal research budget cuts that could reduce subscription revenue.

While some publishers reject licensing deals, concerns remain over author compensation, content control, and the need for stronger IP protections.

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🔗 Source: Bloomberg

🧠 Food for thought

1️⃣ The birth of a publisher revenue lifeline amid academic funding pressure

Academic publishers’ AI licensing deals represent a significant new revenue stream during a challenging period for traditional funding models.

Taylor & Francis’s non-recurring revenue from AI licensing reached $75 million in 2024, accelerating their growth from 3% to 15% year-over-year according to Berenberg analysis 1.

These deals provide crucial financial buffer against potential US research funding cuts, which could create a £33 million ($45 million) revenue shortfall for Taylor & Francis if the proposed 43% National Institutes of Health budget reduction materializes 1.

This trend is similar to developments in news media, where the New York Times recently secured an AI licensing agreement with Amazon, signaling a shift toward monetizing content archives through AI partnerships 2.

Publishers are positioning these deals as complementary revenue streams rather than replacements for core subscription models, enabling them to navigate the transition to an AI-influenced publishing landscape.

2️⃣ Author compensation emerges as a flashpoint in AI licensing agreements

The distribution of revenue from AI licensing deals has created significant tension between publishers and authors, highlighting unresolved questions about intellectual property rights in the AI era.

According to the Authors Guild, academic authors are receiving minimal compensation—in one case just $97 for a book used in LLM training by Taylor & Francis—raising concerns about fair value distribution 1.

The disparity between publisher revenues and author compensation is stark, with Microsoft offering HarperCollins $5,000 per title (split 50/50 with authors) while publishers like Taylor & Francis secure multi-million dollar deals 1.

This compensation gap reflects broader challenges in valuing intellectual property for AI training, with the Authors Guild advocating that authors should receive 75-85% of AI licensing revenue, significantly more than current practices 1.

The situation highlights how traditional publishing contracts never anticipated AI training use cases, creating contractual gray areas that publishers and authors are now navigating without established industry standards.

3️⃣ The dual strategy: integrating AI while selling content to train AI

Academic publishers are pursuing a two-pronged approach to AI, licensing content externally while implementing AI tools internally to transform their core operations.

Publishers are increasingly adopting AI for manuscript screening, peer review enhancement, and plagiarism detection, with approximately 54% of publishers now using AI for content enhancement and workflow automation 3.

These internal AI implementations can reduce editing time by up to 30% while enhancing quality control, addressing longstanding efficiency challenges in academic publishing 3.

The dual approach allows publishers to monetize their existing content libraries while simultaneously leveraging AI to address industry pain points, particularly the persistent shortage of qualified peer reviewers that has historically slowed publishing timelines 1.

In 2024, academic publishing saw over 4,600 paper retractions, driving publishers to invest in AI tools that can better detect fraudulent research and enhance the integrity of their publications 1.

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