7 ways AI is negatively affecting the book industry in 2026

A human first look at how AI in publishing can damage creativity, trust, and the future of storytelling. Then a reminder of why human made books still matter.

Introduction

AI is reducing human creativity and is hurting our environment at the same time.

In addition to taking away from young creatives, research shows that AI consumes water and energy in large amounts in data centers, leading to a reduction in environmental health.

Fortunately, many in the book writing, editing, and publishing industry are not turning to AI as fast as other industries. The ethics and morals of the current book making industry have helped preserve the book making process as we know it.

So today, I’ll talk about 7 different ways AI negatively affects the book making industry, and then a few ways we can stop the introduction of AI into this beloved industry.

What are 7 reasons that AI negatively affects the book making industry?

1. Authors’ work can be copied for AI training without consent, credit, or payment

Books that are used for LLM (Large Language Model) training are often included without permission, credit, or consent of the authors. This allows LLM’s to push out more and more content that is similar to or uses parts of the book, which can hurt authors and their ability to protect their work.

Sources

• Harvard Law School: Key issues in writers’ case against OpenAI
• Authors Guild: Class action suit against OpenAI

2. AI makes it easier to flood storefronts with low quality or scam books, hurting discoverability for real authors

The speed of AI writing tools makes it easier to publish large volumes of low quality, misleading, or scam books. That kind of flooding can bury real authors, making discoverability harder and weakening trust in what readers see on storefronts.

Sources

• Reuters: ChatGPT launches boom in AI written e books on Amazon
• Wired: Scammy AI generated book rewrites are flooding Amazon

3. Working creatives in the book pipeline can lose paid opportunities

As publishers experiment with automation, some writing, editing, translation, narration, and illustration tasks can be shifted away from paid professionals. Even when quality is lower, the pressure to cut costs can reduce opportunities for working creatives across the book pipeline.

Sources

• World Economic Forum: The Future of Jobs Report 2023
• PEN America: The human spark that drives creativity must be safeguarded

4. AI can introduce errors, hallucinations, and fabricated “facts,” increasing editorial risk and weakening trust

Large Language Models can produce confident sounding errors, including fabricated details and citations. In nonfiction especially, this increases editorial risk and can weaken reader trust if AI is used for research, summarization, or drafting without careful verification.

Sources

• Stanford HAI: Legal mistakes with large language models are pervasive
• MIT Sloan Teaching and Learning Technologies: Addressing AI hallucinations and bias

5. AI encourages imitation and derivative writing rather than original storytelling

Generative AI systems learn patterns from existing text and can reproduce styles and structures that feel familiar. That can encourage imitation and copycat content at scale, which pressures originality and makes it easier for derivative work to spread.

Sources

• U.S. Copyright Office: Part 3 report on generative AI training
• Wired: AI generated imitations and rewrites affecting authors

6. AI can homogenize writing and reduce creative diversity across stories

When many writers and publishers rely on the same tools, the output can start to sound similar over time. Recommendation and ranking systems can also reinforce what already performs well, which can narrow what gets promoted and reduce creative diversity.

Sources

• Harvard Business Review: How generative AI could disrupt creative work
• ACM: Measuring commonality in recommendation of cultural products

7. Legal uncertainty and policy fights create instability for authors and publishers

Copyright and AI policy are evolving quickly, with major lawsuits and new government analysis still unfolding. That uncertainty makes long term planning harder for authors and publishers, and it increases the legal risk around training data, licensing, and distribution.

Sources

• U.S. Congress CRS: Generative Artificial Intelligence and Copyright Law
• The Guardian: Meta wins AI copyright lawsuit as US judge rules against authors

Conclusion

The growing use of AI in publishing raises concerns that go far beyond convenience or speed. When authors’ work is used without consent, when low-quality books flood storefronts, and when creative labor is quietly replaced, the principles the book making industry was built on began to fade. Add in fabricated information, imitation over originality, homogenized writing, and unresolved legal uncertainty, and the downsides of generative AI become more and more clear in these times of uncertainty.

These changes affect everyone, whether it be authors, readers, editors, or anybody else involved in this process. Books thrive on originality, story telling, and lived experience. Artificial intelligence prioritizes efficiency and scalability over thorough work and creativity, and this is easy to see through the results Learning Language Models produce.

That is why many readers are seeking out books and creators who still work the old-fashioned way. Independent authors, small presses, and traditionally crafted works that rely on human storytelling rather than content automation are becoming more important than ever. Supporting human-made stories is one way to support creativity, in a society that increasingly does not.

The Nova Series exists within that space. Written by a young, independent creator Nuru Kyubwa, Nova is built on human imagination. It represents the kind of storytelling that values creativity over shortcuts and voice over automation.