Modern scholarly publishing depends on an extraordinary amount of unpaid work.
Researchers conduct studies, write papers, review manuscripts, edit journals, sit on editorial boards and mentor the next generation of scholars. Most of this activity receives little or no direct financial reward but, collectively, it advances knowledge, improves research quality and strengthens the global research community.
For decades, this arrangement has worked because there has been an unwritten gentleman’s agreement across the research community. Researchers contribute their expertise, knowing that others will do the same. Although no one is paid for much of this work, everyone benefits from a stronger and more trustworthy scholarly ecosystem.
Artificial intelligence may be changing that equation.
Beyond Supporting Scholarship
When researchers volunteer their time to review a manuscript or make an editorial decision, they generally assume they are helping another researcher and, by extension, the wider academic community. Increasingly, however, that same work may have a value that extends far beyond scholarship.
Research papers are already recognised as valuable sources of information for AI systems. Where access is lawful, they can help AI answer scientific questions, summarise literature and explain complex concepts.
Most researchers would probably welcome that as the purpose of publishing research is to share knowledge as widely as possible. But there is another, perhaps, more interesting question.
It Is About More Than Published Papers
Behind every published article sits a vast collection of intellectual work that is rarely seen outside of the publication process.
Reviewer reports explain why paper are accepted or rejected. Editors weigh competing opinions before reaching difficult decisions. Authors respond to detailed critiques, clarifying methods, strengthening evidence, refining their conclusions and adjusting their writing style.
Taken together, this material may be one of academia’s most valuable, yet least appreciated, intellectual assets. It does not simply contain knowledge, but expert judgement and human thought processes. It shows how experienced researchers evaluate evidence, identify weaknesses, construct persuasive arguments and improve the quality of scientific work. In effect, it exposes the thinking behind scholarship rather than simply its outcomes. Large language models are no longer learning only from published conclusions. They may also be learning from the reasoning, judgement and critique that produced them.
Expertise Has Economic Value
If AI systems learn from this material, they are not simply learning scientific facts. They are learning how experts think.
That distinction matters because expertise has enormous commercial value. The world’s leading AI companies are investing billions of dollars to build systems that increasingly perform tasks requiring human judgement. If researchers’ expertise contributes to improving those systems, an important question follows. Are researchers still volunteering their time solely for the benefit of scholarship, or are they also becoming part of an unpaid workforce helping to create commercially valuable AI products?
Is This Different from the Past?
Some readers may argue that nothing has changed. Knowledge has always been cumulative, with researchers building on previous discoveries without having to pay the original authors for their intellectual property.
Traditional scholarship largely recycled value back into the academic community. Today’s AI ecosystem includes some of the world’s most valuable commercial organisations. If academic expertise is contributing to products that generate significant commercial returns, should researchers have greater visibility over how their work is being used?
The Question Is About Transparency
This is not an argument against artificial intelligence. AI is already transforming research for the better. It helps researchers search the literature, analyse data, improve writing, translate documents and explore new ideas. Used responsibly, these tools have enormous potential to accelerate scientific discovery.
The issue is not whether AI should benefit from scholarly knowledge. The issue is whether researchers fully appreciate how their knowledge and expertise is being used.
Transparency has become a cornerstone of responsible publishing. We now expect declarations of funding, conflicts of interest, author contributions, data availability and, increasingly, AI use.
Several publishers have already announced partnerships with AI companies or updated their policies to address the use of scholarly content in developing AI systems. As these relationships evolve, transparency becomes increasingly important.
If publishers license content for AI development, or if scholarly material contributes to commercial AI systems, researchers may simply want to know. They may have no objection whatsoever.
Others, however, may conclude that the existing arrangements are inappropriate. They may argue that researchers should either be compensated for this additional value or, at the very least, be given sufficient information to decide whether they wish to contribute their expertise.
Has the Social Contract Changed?
Peer review has traditionally been viewed as academic service. Researchers review papers because previous researchers reviewed theirs. It is a system built on reciprocity rather than payment. But reciprocity becomes more complicated if the outputs of that work acquire value beyond the scholarly community.
Reviewer reports, editorial decisions, revision letters and other forms of expert judgement may now contribute to technologies that extend well beyond academic publishing. If that is happening, perhaps it is time to ask whether the traditional social contract still reflects the reality of modern scholarship.
A Debate Worth Having
This is unlikely to be the last ethical question raised by artificial intelligence in scholarly publishing. For decades, researchers have willingly donated their expertise because they believed they were strengthening scholarship. If that same expertise is now helping to build commercial AI systems, perhaps the question is no longer whether researchers are training AI. Perhaps the better question is whether researchers have quietly become an unpaid workforce for one of the fastest-growing commercial sectors in the world.
Perhaps researchers are doing exactly what they have always done: advancing knowledge for the benefit of society. Or perhaps they have quietly become one of the world’s largest unpaid workforces for the commercial AI industry.
I don’t know the answer, but I do think it is a conversation worth having. I’d be interested to hear your thoughts.
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About the Author
Professor Graham Kendall is Vice-Chancellor of GlobalNxt University, Malaysia, and an Emeritus Professor at the University of Nottingham. Over the past 25 years, he has published more than 300 peer-reviewed papers and has served as Editor-in-Chief and Associate Editor of several international journals. Through Publishing with Integrity, he explores the ethical, governance and practical challenges facing scholarly publishing, encouraging greater transparency, integrity and informed debate across the global research community.