Why AI Companies Shred Rare Books: What Your Vibe Coding Feeds On
The race for AI training data creates a paradox: destroying knowledge. From Satya Nadella's warning to Anthropic's surveillance, we uncover the hidden data supply chain and ethical issues behind 'vibe coding' that developers must know.
Physically shredding rare books to train AI models is an extreme example of the irony that technology, far from preserving knowledge, instead destroys it. Moreover, as Microsoft CEO Satya Nadella warned, the more seriously companies use AI, the more they end up handing over their core intellectual property to the model. This article examines what data the generative AI we use in the 'vibe coding' era actually feeds on, and the ethical issues and practical pitfalls caused by this opaque supply chain.
Rare Book Shredding: The Ironic Reversal of Knowledge Preservation Technology
Ultimately, the performance of AI models is determined by the quantity and quality of data. As competition intensified, companies deemed open texts insufficient, and now cases have emerged where even old rare books are shredded for digitization. This process, which completely dismantles books to extract printed typographic data, erases centuries-old cultural heritage in an instant. The fact that AI, which makes knowledge easily accessible to all, destroys the very originals it is based on, starkly reveals the contradictory nature of technological progress. Beyond mere physical damage, this raises fundamental questions about an attitude that reduces humanity's shared knowledge ecosystem to massive training data.
Is Using AI Models Equivalent to Abandoning Intellectual Property? Satya Nadella's Warning
Beyond physical destruction, the opacity of data sourcing also threatens companies' digital assets. In July 2026, Microsoft CEO Satya Nadella warned, 'The moment companies start using AI models seriously, they hand over the very intellectual property (IP) that makes them valuable to the model.' This points to the risk that when using services from major labs like OpenAI or Anthropic, internal confidential data or proprietary know-how may unintentionally be included in model training. In reality, many companies use APIs to fine-tune their own data, without clearly knowing how the uploaded data is processed. The data disappears into a 'black box,' and the company effectively provides its core competitiveness for free.
Surveillance and Unauthorized Collection: Reckless Data Competition Among AI Companies
The race for data doesn't stop there. In July 2026, global media outlet Futurism reported that Anthropic had hidden code in its AI system prompts to secretly collect user information. Discovered by an anonymous researcher called 'Thereallo,' this code was transmitting a wide range of data, from device information to conversation content, without permission. The actions of Anthropic, which has positioned itself as an ethical center of the AI industry, starkly illustrate how desperate and uncontrollable data collection has become. The situation where even personal information not consented to by users becomes fodder for AI models deepens the dark shadow of surveillance capitalism behind technological convenience.
The Price of Ignoring Data Quality: Declining Trust and Cost Explosions
Indiscriminately more data does not always guarantee better results. On the contrary, models trained on low-quality data amplify bias, cause hallucinations, and reduce reliability. In July 2026, Business Insider reported a case where a startup accidentally spent $30,000 (about 42 million won) on AI tokens in a single month. They had recklessly adopted AI for fast development but failed to properly consider data volume and API call optimization, causing costs to spiral out of control. Similarly, Forbes analyzed that while manufacturers rushed into AI adoption, they saw almost no return on investment (ROI). When only quantitative expansion is pursued, neglecting data quality and cost efficiency, technology ends up tripping up businesses.
The Naked Truth of Vibe Coding: The Reality of the Data We Feed
The 'vibe coding' trend among developers is no exception. This method of generating code with natural language prompts tends to blindly rely on the technology, without knowing what data the underlying AI model was trained on. Vast amounts of data have nurtured these models, but among it may be texts of uncertain copyright, biased information, or even user data collected without authorization. Developer communities must now not only check the accuracy of generated output but also trace what the model 'fed on.' Demanding data from transparent and verifiable sources, such as public datasets or synthetic data, is the first step toward a healthy technology ecosystem.
How Not to Become an Accomplice in the Knowledge Ecosystem
Choosing models without considering data ethics can go beyond mere technical debt, becoming complicity in destroying the foundation of humanity's knowledge ecosystem. Practical alternatives include demanding disclosure of what data AI models were trained on, and paying attention to regulatory frameworks like the EU AI Act or open-source models. Additionally, rather than blindly trusting AI-generated output, we must build workflows involving critical human review. For example, tools like md-log can help periodically examine AI-generated analysis and leave immutable logs, aiding in tracking potential errors or biases created by models. Ultimately, under the guise of convenience, we have both the right and responsibility not to remain the final link in the chain that shreds knowledge.
References
- Microsoft CEO Satya Nadella to every company across the world using AI: You are paying for your own IP, s - The Times of India
- Anthropic Caught Secretly Spying on Users - Futurism
- Manufacturers Rushed Into AI. The Returns Aren’t Showing Up - Forbes
- Microsoft CEO Satya Nadella warns of AI risks - Zamin.uz
- My startup accidentally spent $30,000 on AI tokens in a month. It was worth it to move fast — but we found a simple fix. - Business Insider
- AI’s Next Race: Cost, Control, and Compute - CNBC
- Claude, ChatGPT: AI labs buy, scan, shred millions of rare books | news.com.au — Australia’s leading news site for latest headlines
- AI Companies Are Still Buying Up Old Books by the Pallet - Then Shredding Them - Gadget Review
- AI Firms Scan Then Destroy Rare Book Editions
- AI Companies Are Buying Antique Books, Ingesting Their Contents ...
Frequently asked questions
- What is the real reason AI companies shred rare books?
- They dismantle books to quickly digitize physical text data. The process of high-speed scanning and extracting text from printed information results in the destruction of cultural heritage.
- What risk did CEO Satya Nadella warn about regarding AI models?
- The risk is that as companies use AI models, they unintentionally provide their core intellectual property to the model. Data uploads via APIs are handled opaquely, which can cause them to lose the foundation of their competitiveness.
- What does the case of Anthropic's unauthorized data collection signify?
- It reveals that even a company that claims to uphold ethical standards in the AI industry is extracting personal information without user consent. This shows that the competition for data acquisition has reached a state of uncontrollability.
- Why do costs explode after adopting AI?
- In most cases, it's because data volume and API call optimization are not properly considered. Without securing quality data, models operate inefficiently, leading to unexpected excessive token costs.
- How can developers practice data ethics when vibe coding?
- They should verify the training data sources of the models they use, and prioritize models based on open-source or public data. It is essential to always have a human review of AI-generated outputs, and using tools that leave immutable logs can also help.