Kotaku83%

Book Database Shutters AI Service Following Outrage Over Destroyed Rare Texts 78%

By Zack Kotzer79%

8/1/2026, 1:56:06 PM

BS Summary: This article contains 28 faulty reasoning types, including Availability Heuristic, Confirmation Bias, and Hasty Generalization, with Negativity Bias as the most egregious example at 29.6% saturation with 163 hits. Analysis detected 1,448 faulty-reasoning hits from 551 analyzed words, generating a BS Score of 63.3% and a BS Rank of 78% (5,969 of 26,858 articles). This article is worse (more manipulative) than 77.80% of the article peer group.

This week, heavy readers and casual posters alike were aghast at emerging reports of rare books being mulched in bulk as a sacrifice for LLMs. 
Word traveled that book suppliers were receiving abnormally large orders, believing that AI firms sought cleaner source material while skirting copyright laws. 
At the center of the controversy was ISBNdb, a book database who not only encouraged the trend but seemed to facilitate it. 
The optics bit back, as the site now tries to distance itself from the controversy, scrubbing its own posts on the subject. 
“We’ve seen the recent coverage about a marketing landing page on our site, and we understand the concern it raised,” writes ISBNdb in an update. 
“We don’t train AI models, and we never have. 
The page was a test of market interest; no such service was ever brought to life. 
We’ve taken the page down.” 
A few days earlier, 404 Media reported about the concerning trend of suppliers suddenly hit with bulk book orders. 
With schools and libraries lean on resources, the decreased business has left them vulnerable. 
It was suspected that AI firms were making these new orders, but putting themselves in front of the crosshairs was ISBNdb, a database who seemingly introduced a service to patch AI firms through to depositories. 
“The world's best AI training data is sitting on a shelf,” read the now deleted landing page. 
“Books represent curated, peer-reviewed, domain-specific human knowledge, structured in a way no web crawl can replicate. 
Dense, edited, authoritative.” 
The outrage hit a fever pitch this week but news of the practice broke last January, when court documents exposed “Project Panama,” a program within Anthropic to scan then destroy as many books as they can get a hold of. 
Anthropic settled with authors for $1.5 billion, but the unsealed filings showed that the practice is considered legal, just bad publicity, and the company was willing to break the bank to keep it under wraps. 
Now the juice is out of the tube, and suspicious rare book orders are under intense scrutiny. 
“It benefits me financially as well as by clearing out old inventory that is otherwise unlikely to sell,” one anonymous seller told 404's Samantha Cole. 
“On the other hand, I don’t like the end-use, and I don’t like that uncommon books are being pulped.” 
Confronting the hell they’ve rendered for themselves, AI firms are struggling to find “clean” source material to train LLMs on. 
Whether it’s low quality web content, or material already generated by AI that threatens negative feedback loops, these companies are trying to siphon higher quality stuff without raising too many alarms. 
Earlier this summer A24 announced a partnership with Google to assist training DeepMind, hoping to help their media generators escape the bog of muddy brown, cursed Ghibli slop. 
As for the destruction part, it’s unlikely being done in attempts to cover their tracks or some Brianiac-style rare knowledge obsession. 
The Project Panama documents didn’t specify why they trashed the books, but it’s most likely just trying to save a buck. 
Archiving and scanning books doesn’t have to destroy the source material, but being done cheaply and quickly, it likely will. 
Ripping apart the spines for clearer scans and disposing of the crumpled heap. 
Article reasoning-pattern comparisonThis article: 23.8%Zack Kotzer: 4.7%Kotaku: 4.2%Confirmation Bias23.8%This article: 0.0%Zack Kotzer: 0.1%Kotaku: 1.3%Anchoring Bias0.0%This article: 24.3%Zack Kotzer: 4.8%Kotaku: 3.7%Availability Heuristic24.3%This article: 0.0%Zack Kotzer: 1.8%Kotaku: 1.1%Representativeness Heuristic0.0%This article: 7.3%Zack Kotzer: 0.6%Kotaku: 0.9%Hindsight Bias7.3%This article: 0.0%Zack Kotzer: 1.0%Kotaku: 1.5%Overconfidence Bias0.0%This article: 2.0%Zack Kotzer: 5.8%Kotaku: 5.1%Framing Effect2.0%This article: 0.0%Zack Kotzer: 1.2%Kotaku: 1.0%Loss Aversion0.0%This article: 0.9%Zack Kotzer: 0.7%Kotaku: 0.9%Status Quo Bias0.9%This article: 0.0%Zack Kotzer: 0.2%Kotaku: 0.4%Sunk Cost Effect0.0%This article: 0.0%Zack Kotzer: 1.1%Kotaku: 2.6%Optimism Bias0.0%This article: 16.7%Zack Kotzer: 2.8%Kotaku: 3.0%Pessimism Bias16.7%This article: 29.6%Zack Kotzer: 14.4%Kotaku: 12.7%Negativity Bias29.6%This article: 9.1%Zack Kotzer: 1.4%Kotaku: 1.7%Self-Serving Bias9.1%This article: 3.6%Zack Kotzer: 1.2%Kotaku: 1.2%Fundamental Attribution Error3.6%This article: 0.0%Zack Kotzer: 0.3%Kotaku: 0.2%Actor-Observer Bias0.0%This article: 0.0%Zack Kotzer: 2.2%Kotaku: 1.2%In-Group Bias0.0%This article: 9.6%Zack Kotzer: 1.1%Kotaku: 0.3%Out-Group Homogeneity Bias9.6%This article: 0.0%Zack Kotzer: 1.2%Kotaku: 2.9%Halo Effect0.0%This article: 0.0%Zack Kotzer: 0.2%Kotaku: 0.3%Horn Effect0.0%This article: 0.0%Zack Kotzer: 0.0%Kotaku: 0.1%Dunning-Kruger Effect0.0%This article: 10.7%Zack Kotzer: 1.6%Kotaku: 2.1%Recency Bias10.7%This article: 2.9%Zack Kotzer: 0.3%Kotaku: 0.5%Primacy Effect2.9%This article: 0.0%Zack Kotzer: 0.0%Kotaku: 0.0%Blind-Spot Bias0.0%This article: 4.0%Zack Kotzer: 2.1%Kotaku: 1.2%Ad Hominem4.0%This article: 0.0%Zack Kotzer: 0.5%Kotaku: 0.5%Straw Man0.0%This article: 2.9%Zack Kotzer: 2.7%Kotaku: 2.2%Appeal to Authority2.9%This article: 3.6%Zack Kotzer: 1.4%Kotaku: 1.9%False Dilemma3.6%This article: 8.7%Zack Kotzer: 1.6%Kotaku: 0.9%Slippery Slope8.7%This article: 0.0%Zack Kotzer: 0.0%Kotaku: 0.2%Circular Reasoning0.0%This article: 17.6%Zack Kotzer: 9.1%Kotaku: 8.4%Hasty Generalization17.6%This article: 5.1%Zack Kotzer: 0.1%Kotaku: 0.3%Red Herring5.1%This article: 0.0%Zack Kotzer: 0.2%Kotaku: 1.0%Bandwagon0.0%This article: 11.8%Zack Kotzer: 7.3%Kotaku: 5.1%Appeal to Emotion11.8%This article: 0.0%Zack Kotzer: 1.1%Kotaku: 0.8%Begging the Question0.0%This article: 6.4%Zack Kotzer: 2.2%Kotaku: 2.2%Post Hoc (False Cause)6.4%This article: 0.0%Zack Kotzer: 0.0%Kotaku: 0.2%Tu Quoque0.0%This article: 0.0%Zack Kotzer: 0.4%Kotaku: 0.6%Burden of Proof0.0%This article: 0.0%Zack Kotzer: 0.8%Kotaku: 0.1%Appeal to Nature0.0%This article: 0.0%Zack Kotzer: 0.0%Kotaku: 0.4%Composition/Division0.0%This article: 12.5%Zack Kotzer: 2.4%Kotaku: 3.7%Anecdotal12.5%This article: 0.0%Zack Kotzer: 0.1%Kotaku: 0.1%No True Scotsman0.0%This article: 12.0%Zack Kotzer: 1.6%Kotaku: 1.9%Ambiguity (Equivocation)12.0%This article: 0.0%Zack Kotzer: 0.0%Kotaku: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Zack Kotzer: 0.1%Kotaku: 0.1%Middle Ground0.0%This article: 0.0%Zack Kotzer: 0.0%Kotaku: 0.4%Personal Incredulity0.0%This article: 4.0%Zack Kotzer: 0.2%Kotaku: 0.1%Special Pleading4.0%This article: 3.8%Zack Kotzer: 0.3%Kotaku: 0.1%Genetic Fallacy3.8%This article: 2.9%Zack Kotzer: 1.3%Kotaku: 2.4%Unattributed Quote2.9%This article: 10.3%Zack Kotzer: 1.9%Kotaku: 1.6%Quote-first Misdirection10.3%This article: 12.2%Zack Kotzer: 16.3%Kotaku: 13.8%Biased Writer Voice12.2%This article: 0.0%Zack Kotzer: 1.9%Kotaku: 1.9%Indoctrination0.0%This article: 0.0%Zack Kotzer: 3.2%Kotaku: 0.5%Politically Left Leaning Bias0.0%This article: 0.0%Zack Kotzer: 0.5%Kotaku: 0.1%Politically Right Leaning Bias0.0%This article: 4.5%Zack Kotzer: 1.7%Kotaku: 2.8%Attempt to Sell a Product or S…4.5%

551 words analyzed.

Speakers

1speaker10.0%attributed speech496writer words
Selected voice

ISBNdb

100%flagged-word coverage
55 attributed words100% of attributed speech99% writer coverage
0%25.0%50.0%Attempt to Sell a Product +45.5 ptsWriter: 0.0%ISBNdb: 45.5%45.5%Unattributed Quote+29.1 ptsWriter: 0.0%ISBNdb: 29.1%29.1%Biased Writer Voice-13.5 ptsWriter: 13.5%ISBNdb: 0.0%0.0%Quote-first Misdirection-11.5 ptsWriter: 11.5%ISBNdb: 0.0%0.0%

Attribution is sentence-level. Pattern percentages are calculated only from words assigned to that voice.

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Analysis

Hover over highlighted words in the article to view the associated bias or fallacy analysis.