When I reflect on nearly two decades at the intersection of publishing and technology, it’s striking how each transformative wave—e-books, audiobooks, subscriptions—initially felt like it might upend our industry. But each also brought important opportunities and ways for publishers and authors to connect with readers. Today, artificial intelligence (AI) poses our latest and perhaps most complex inflection point yet. The questions are urgent. How will AI reshape publishing, and how should publishers and authors respond?

The reality is, AI is already using our written content. Large language models (LLMs) and other generative AI systems learn from massive amounts of text pulled from digital sources. Rights holders justifiably wonder whether this is ethical, whether it infringes on copyright, and whether it could cannibalize existing revenue streams. Optimists, like me, believe that this could be the best thing to ever happen to our industry. Books can come alive in ways never before imagined.

The debate over fair use versus infringement is playing out in courts, but practical concerns remain pressing. How can we ensure that rights holders maintain control over their work? How do we safeguard integrity and ensure fair compensation? While there’s no single answer, I believe the path forward involves implementing clear, standardized AI licensing frameworks that allow us to navigate this new landscape proactively rather than reactively.

The Case for Proactive Licensing

AI content licensing offers a constructive way forward. Rather than waiting for legal battles to shape the rules, authors and publishers can start to define them. Here are four core reasons to consider implementing AI licensing frameworks:

1. Your Content, Your Rules

Proactive licensing returns control to the creator. You gain visibility into who is using your content and how. For instance, a licensing agreement can specify that an AI model can only use your work for training but not reproduce large excerpts in its outputs. Or perhaps you decide certain sensitive materials should be excluded altogether. By negotiating these parameters, authors and publishers regain the transparency they lost when AI companies began harvesting text at scale without permission.

2. Preserve Your Message

AI models trained with properly licensed content are more likely to produce accurate representations of a work. Licensed agreements often include guidelines and best practices around attribution, summarization, and quality. This in turn helps preserve the integrity of the original content. When a passage from your book is summarized by an AI system, it is more likely to be represented correctly.

3. New Revenue Streams

AI licensing opens up a new avenue for revenue. Publishers can negotiate fees not just for training but also for other potential uses, like reference rights and transformations of content into new formats. Early deals in the news and academic publishing industries suggest that AI licensing can be quite lucrative, providing a meaningful boost to the bottom line at a time when many conventional revenue models are plateauing.

4. Future-Proofing

AI is evolving fast. In the not-so-distant future, we may see AI-generated video adaptations or interactive reading experiences. By establishing licensing agreements now, publishers position themselves to adapt quickly to whatever form AI takes next, without constantly having to revisit complicated legal negotiations. You’ll have frameworks already in place to accommodate emerging applications.

Legitimate Concerns

Even as I advocate for proactive engagement with AI licensing, I recognize the industry’s legitimate concerns:

1. Potential Market Impact

How might AI-generated derivative works affect traditional book sales? These questions are valid, and we don’t yet have definitive answers. Licensing frameworks can be designed to minimize this, but they may not eliminate the concern entirely.

2. Navigating Complex Agreements

Managing AI rights alongside traditional print, e-book, and audiobook rights adds another layer of complexity to an already intricate licensing landscape. Publishers will need to coordinate with agents, authors, and sub-rights holders to ensure that AI licenses comply with existing contractual obligations.

3. Long-Term Implications

Because AI is evolving so quickly, strategic decisions made today must account for the unknown. Publishers might wonder if they’re locking themselves into suboptimal terms, only to see more favorable standards emerge in a few years. There’s risk in any new technology, but in my experience, proactive engagement beats inaction.

Striking a Productive Balance

For publishing to thrive in the decades ahead, we need frameworks that protect creative works while allowing for innovation. This means:

• Granular Control: The ability to license content for specific, clearly defined uses, such as training, referencing, or transformations.
• Fair Compensation: Authors and publishers should be compensated for different types of AI use, from summarized references to derivative works.
• Integrity Protection: Requirements around how AI systems attribute and represent the original works can help maintain quality and credibility.
• Transparent Practices: AI companies should provide clear reports on how content is used and how the models are trained.
• Complementary Models: AI licensing should not aim to replace traditional publishing models but rather supplement them in ways that create net gains for authors, publishers, and readers alike.

A Practical Path Forward

After extensive conversations with publishers, authors, and AI companies, I’ve become convinced that standardizing AI rights is our best bet for balancing innovation and protection. At a high level, these rights can be broken down into three main categories:

1. Training Rights: This permits an AI model to “read” and learn from your content without directly reproducing it. Such an arrangement typically includes limits on how much text can appear in any AI output.
2. Reference Rights: These allow the AI to quote, summarize, or paraphrase your content with clear attribution. In practice, reference rights can be crucial for research tools or educational applications where citations are necessary.
3. Transformative Rights: This category covers entirely new AI-generated formats or derivative works based on your content. It might encompass AI-generated shows, interactive experiences, or other novel outputs. Licensing transformative rights can be the most lucrative but also the most complex, as it directly involves the creation of something “new” from your existing work.

Publishers can choose to license some or all of these rights, tailoring the agreements to align with their risk tolerance, brand values, and long-term business strategies. By separating these rights, publishers maintain control, while enabling AI companies to innovate responsibly.

Why the Time to Act Is Now

Whether publishers lean in or hold back, AI is changing how content is discovered and consumed. Those who proactively engage will be in a better position to protect and monetize their works, while also influencing the industry standards that will guide AI’s future.

Today, AI presents a new set of challenges, but also new possibilities. By working together, embracing standardized licensing, and insisting on transparency and fair compensation, we can ensure that AI works for us, not against us. In doing so, we’ll continue upholding the best traditions of publishing, preserving and rewarding creativity, while making knowledge and stories available to people around the world.

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