Academic Publishers Turn to AI Deals as US Research Funding Faces Cuts

With US research budgets on the chopping block, academic publishers are scrambling to offset losses by striking lucrative AI licensing agreements. The move highlights how traditional knowledge gatekeepers are adapting—or exploiting—the AI gold rush.

Behind the scenes: Major publishers are quietly monetizing decades of peer-reviewed research by licensing content to AI firms hungry for training data. Critics argue this creates yet another paywall in the knowledge economy.

The cynical take: Nothing solves a funding crisis like selling your crown jewels to tech giants—until the next budget crisis hits. Academia''s new motto: ''Publish or license, but preferably both.''

TLDR;

• Academic publishers are turning to AI licensing as U.S. research funding faces steep cuts.
• Deals with major tech firms are helping publishers generate millions from their archives.
• Authors are raising concerns over inadequate compensation and transparency.
• AI tools are also being adopted internally to improve publishing efficiency.

Academic publishers are increasingly turning to artificial intelligence as both a revenue stream and a strategic tool, amid growing uncertainty over future U.S. research budgets.

With looming federal cuts threatening traditional funding sources, publishers are striking licensing deals with tech companies to allow access to their content for training large language models, Bloomberg reported Tuesday. These agreements are emerging as a financial buffer in an industry historically reliant on institutional subscriptions.

Notably, Taylor & Francis, a division of Informa, is among the first to capitalize in a big way, having secured a $10 million deal with Microsoft last year. That arrangement granted Microsoft limited access to academic material for AI training, a move that ultimately contributed to a reported $75 million in non-recurring revenue for the publisher in 2024. The growth helped accelerate Informa’s performance significantly, jumping from modest single-digit expansion to a 15% increase year-over-year.

U.S. Funding Cuts Drive Urgency in Monetizing Archives

The drive toward AI licensing is not just opportunistic as it is increasingly a matter of financial survival. Proposed cuts to the U.S. National Institutes of Health alone could result in a 43% reduction in funding, slashing tens of millions in revenue from publisher budgets. Taylor & Francis faces a potential $45 million shortfall if the budget changes materialize. Other players such as Wiley and Bloomsbury Publishing are also signing deals, highlighting a shift toward monetizing content archives in the face of an evolving fiscal landscape.

This strategy mirrors moves seen in mainstream media, where companies like the New York Times have inked similar agreements. Publishers are careful to position these deals as supplemental rather than replacements for Core revenue models. Yet it is clear they offer a vital cushion as the industry adapts to the dual pressures of AI disruption and budgetary tightening.

Authors Sound Alarm Over Unequal Rewards

While publishers enjoy a windfall, authors are increasingly voicing frustration over how these deals are being structured and disclosed. Critics point to glaring disparities between publisher profits and author compensation. In one case, an academic author reportedly received just $97 for a book included in an AI training dataset. Meanwhile, publishers secure seven- and eight-figure licensing packages.

Groups like the Authors Guild argue that many legacy contracts did not anticipate the use of written works for AI training. This legal grey area has left many writers sidelined from negotiations and denied a share of the proceeds. Some authors advocate for receiving a significant majority of AI-related revenues, between 75 and 85 percent, arguing that their intellectual contributions are being undervalued in this rapidly developing market.

AI Becomes Both Product and Process in Publishing

Beyond external deals, publishers are also incorporating AI into their internal operations. Tools for manuscript screening, plagiarism detection, and peer review assistance are becoming widespread. Currently, over half of publishers now use AI in some FORM to streamline editorial workflows and enhance content quality. These innovations are helping to reduce delays in the publication process and address long-standing issues such as reviewer shortages and editorial backlogs.

AI is also playing a role in maintaining research integrity. In 2024 alone, more than 4,600 academic papers were retracted, prompting increased investment in AI tools capable of identifying questionable or fraudulent research. This dual approach of selling access to content for AI training while embedding AI in editorial processes, is becoming the new standard for an industry under pressure to evolve quickly.