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The post describes a personal preference for books published in 2022 or earlier, rooted in the assumption that older titles reflect human labor—typing, editing, and proofreading—that feels more credible than modern AI-assisted publishing. The author is not rejecting LLMs in general and admits they use them daily for coding, but questions whether effortless creation should change how readers assign trust. Commenters largely mirror that skepticism, especially around reference books and technical content, arguing that AI-generated work often appears polished at a surface level but lacks depth, verification, and curation. Others note this bias now extends to online posts as well, with people using publication date as a proxy for authenticity. Several commenters challenge the premise directly, arguing bad writing and misinformation predated 2022 and that novelty should be judged by substance and sources, not age alone. The discussion also highlights detection tools’ limits, platform economics that reward low-cost saturation, and the spread of poor AI content on marketplaces. Across the thread, participants propose either stricter norms for publication quality, better fact-based workflows, or long-term adaptation where readers rely on trusted filters and recommendations.
Commenters report seeing abundant low-quality AI-generated nonfiction on major platforms and describe a preference for older, human-authored materials as a defensive heuristic against spam, shallow prose, and unverifiable claims. Some share examples of dubious AI-book catalogs, fake-seeming authorships, and forum posts whose dates may be manipulated as SEO tactics. A recurring caution is that AI text can feel formulaic and emotionally flat, with instant polish masking weak reasoning. Others push back, arguing that any era has “bad books,” that humans invented and consumed derivative content long before modern models, and that quality control in publishing and curation will remain the key differentiator. A middle ground appears: selective AI assistance for language mechanics is acceptable, but full drafting/repackaging is rejected in some circles. The thread also suggests practical responses, including more explicit standards, better sourcing and fact-checking pipelines, and stronger community taste-making over algorithmic defaults.