AI has expanded the middle and lower parts of the book market much more than the top.

2025-11-28 · Source: Pascal’s Substack · Field: Media & Entertainment — Publishing & Journalism, Content Creation & Production, Artificial Intelligence & Machine Learning · Depth: Fundamental Awareness, medium

Summary

A recent NBER working paper, "AI AND THE QUANTITY AND QUALITY OF CREATIVE PRODUCTS: HAVE LLMS BOOSTED CREATION OF VALUABLE BOOKS?", reveals that generative AI has dramatically increased the number of new book releases on Amazon, nearly tripling between 2022 and late 2025, particularly in practical nonfiction categories like travel and self-help. While this surge has expanded consumer surplus by about seven percent in 2025, the average quality of new books has declined, with AI-detected content receiving fewer ratings, worse sales ranks, and lower star ratings. AI has primarily expanded the middle and lower segments of the book market, enabling less experienced authors to publish, rather than displacing incumbent authors or significantly boosting the top tier of quality books. This shift creates an "abundance problem" for publishing, moving the bottleneck from production to discovery, verification, and trust.

Key takeaway

For publishers and educational institutions navigating the "age of infinite publishability," your strategic focus must shift from content production to becoming a trusted institution. Invest in strong editorial selection, author credibility, peer review, and robust discovery systems to differentiate high-quality content. Do not measure success by the sheer volume of AI-generated resources, but by verifiable learning outcomes, accuracy, and user trust, as the market increasingly punishes low-effort AI output.

Key insights

AI lowers content creation costs, increasing output but decreasing average quality, shifting value to trust and curation.

Principles

• Abundance shifts value to curation.
• Trust becomes expensive when production is cheap.
• Quality cannot be reduced to consumer appeal.

In practice

• Implement robust content verification systems.
• Prioritize editorial judgment and provenance.
• Focus on learning outcomes, not just content volume.

Topics

• Generative AI
• Book Market Expansion
• Content Quality
• Publishing Industry
• Informational Pollution

Best for: CTO, VP of Engineering/Data, Executive, Director of AI/ML, Policy Maker, AI Ethicist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Pascal’s Substack.