The analysis documents the “governing logic of frontier-model competition: treat totality of human expression as strategic infrastructure, and treat permissions as friction to be routed around.”

This represents systematic wealth transfer from knowledge producers to extraction platforms, justified through innovation rhetoric while operating as IP laundering at industrial scale.

AI’S PAST SINS: TRAINING ON STOLEN DATA, AND REMONITIZING IT

Analysis by Claude

SUMMARY AND KEY FINDINGS:

The posts extensively document systematic copyright infringement underlying AI training data, from BitTorrent piracy to “destructively scanning all books in the world.” The analysis reveals pattern where companies deliberately chose illegal sources because they’re “fast and free,” building “vast central libraries” intended to keep “forever”
while hoping “nobody finds out.” Posts detail how Anthropic allegedly used piracy protocols “synonymous with copyright
infringement” despite branding itself as “AI safety and research” company. This exposes fundamental hypocrisy: safety
rhetoric masking systematic intellectual property theft.

Several posts examine legal battlegrounds where this comes to light. Concord Music v. Anthropic reveals BitTorrent usage for assembling training corpora. Doe v. GitHub demonstrates how “proof‚Äîprompt logs, output frequencies, memorization testing, preprocessing pipelines, ‘cleaning’ steps‚Äîis uniquely in defendants’ possession,” and if courts require plaintiffs to plead internal details before discovery, DMCA/AI claims “will die at the gate.” Posts document
how companies engineer discovery resistance into development processes, ensuring evidence of infringement remains concealed until litigation forces disclosure.

The analysis reveals the “SlimPajama problem”‚ companies claim training data governance as due diligence while outsourcing risk to open dataset supply chains. If SlimPajama inherits tainted inputs and companies commercialize resulting models, they may inherit liability too. Posts detail an emerging legal pattern: treating training data
governance as compliance domain rather than research footnote.

The posts examine the remonetization dimension: companies don’t merely steal content for internal use‚ they transform
it into commercial products that compete with original creators. Musicians see their styles reproduced, authors find
their narrative techniques commoditized, artists watch their aesthetics mass-produced, programmers encounter their
code patterns regurgitated. The analysis documents the “governing logic of frontier-model competition: treat totality of human expression as strategic infrastructure, and treat permissions as friction to be routed around.” This represents systematic wealth transfer from knowledge producers to extraction platforms, justified through innovation rhetoric while operating as intellectual property laundering at industrial scale.

Total posts identified: 156

Doe 1 v. Github/Microsoft/OpenAI: Much of the proof—prompt logs, output frequencies, memorization testing, preprocessing pipelines, “cleaning” steps—is uniquely in defendants’ possession...

Kleiner v. Adobe is another step in a pattern: the legal system is increasingly treating “training data governance” as a compliance domain, not a research footnote.

Concord Music v. Anthropic (II): Anthropic and leadership allegedly chose BitTorrent because it’s fast and free, and used it to build a “vast central library” they intended to keep “forever”.

The headline detail—an internal plan to “destructively scan all the books in the world” while explicitly hoping nobody finds out—matters not because it’s shocking (it is), but...

X needs evidence that refusal to deal was truly concerted and notice campaign was not aggressive enforcement but systematically abusive and strategically targeted to suppress licensing competition.

Can production, consumer-facing LLMs (with guardrails) be induced to emit long, near-verbatim copyrighted text that strongly indicates memorization and training-data membership?

AI is no longer an “add-on” to media workflows—it is becoming the operating system underneath them. Openness now extends to training data, model weights, documentation, and governance.

The complaints against META and ByteDance argue that AI developers did not merely ingest publicly available content, but deliberately broke through access controls imposed by YouTube...

Adobe’s SLMs and their alleged training on pirated books via SlimPajama, a derivative of Books3. If courts accept that weights encode infringing copies, the legal consequences would be profound.

A multi-vector attack on the entire AI music business model, combining copyright law, DMCA anti-circumvention claims, biometric privacy (BIPA), right-of-publicity law, and consumer deception.

The companies making the most consistent money from AI are not the labs building models, but the firms supplying human expertise, labor, and training data to them.

Judge Stein’s ruling restores clarity at a moment when clarity is badly needed. Robots.txt is a sign. The DMCA protects locks.

If upheld by the BGH, this decision will shape the European AI training landscape for years...

Despite the national security framing, the NNSA RFI contains multiple elements highly consequential for universities, research organizations, and scholarly publishers.

Jinshan District People’s Court judgment on the Battle Through the Heavens / Medusa LoRA case is a milestone in how Chinese courts will handle copyright disputes around AI large models.

The author claims that a trained model file—a persistent, multi-gigabyte object stored on disk—is a “tangible medium of expression.” Because verbatim training data can be extracted from these weights

The paper Library Genesis to Llama 3 investigates a troubling link between the training of LLMs like Meta’s Llama 3 and the use of pirated, retracted, or otherwise compromised scholarly data.

[Take 2] Gemini 2.5 Pro, Deep Research about Getty Images v Stability AI: Legal Failures, Trademark Precedents, and the Shifting Global Litigation Landscape.

Both the means of data acquisition (downloading copyrighted works) and the outputs generated by AI models (novel-like continuations or imitations) are valid grounds for litigation—not just...

Apple is accused of training its models on Books3, a dataset sourced from Bibliotik, a known pirate site. Plaintiff’s registered works were found in this dataset, which is part of the RedPajama corpus

As platforms like Reddit enforce licensing regimes and courts begin to weigh the legality of training AI on unlicensed content, AI companies face a reckoning...

GPT-4o: By prohibiting questions about how Anthropic obtained training datasets — including whether it engaged in torrenting or downloaded from shadow libraries — the court has erected a wall...

GPT-4o: Martinez-Conde v. Apple is a meticulously constructed and potentially explosive copyright infringement case that places Apple’s AI ambitions under judicial scrutiny.

Attackers can exploit these latency variations to infer sensitive information, including: Training data membership (e.g., whether a particular input was part of the training set).

By affirming that AI training methods can embody technological improvements rather than mere abstract ideas, the USPTO has opened the door for more robust, reliable IP protection in machine learning.

Gemini: The prevailing “opt-out” and “pay-per-output” frameworks, often presented as solutions to the intellectual property dilemmas posed by AI, are fundamentally untenable.

DK: If an AI provider cannot show what training data was used, regulators and courts should assume that copyrighted content was included. This is a powerful, pro-rightsholder stance not widely seen.

GPT-4o: The Encyclopædia Britannica v. Perplexity AI case is one of the most detailed, strategic, and potentially precedent-setting lawsuits in the generative AI copyright arena to date.

A new legal frontier is emerging: downstream liability for enterprises and users who deploy or commercialize the outputs of AI models trained on pirated/stolen stuff.

GPT-4o: Cloudflare's Matthew Prince has correctly diagnosed the economic disequilibrium wrought by generative AI on the web and proposed one of the first actionable infrastructure responses.

A $1.5 Billion Warning to the AI Industry – Unpacking the Anthropic Copyright Settlement

In August 2025, two major Japanese media organizations—Nikkei Inc. (owner of the Financial Times) and The Asahi Shimbun—filed a joint lawsuit in the Tokyo District Court against Perplexity AI.

Stop the Chaos Machine. If governments fail to regulate these sites, they will not only continue to harm vulnerable individuals directly—they will also continue to seep into AI's foundational data.

GPT-4o about Sci-hub: The Delhi High Court’s latest order marks not just a legal victory for publishers but a symbolic turning point in the global battle against systemic copyright infringement.

The Ninth Circuit ruling acknowledges, without solving, the inequity in access to justice: “Whether the DMCA provides a sufficient remedy [...] is ultimately a question for Congress”.

The July 2025 Cloudflare DMCA subpoena marks another escalation in the decades-long battle between academic publishers and shadow libraries.

GPT-4o: Meta reportedly ignored opt-out signals such as robots.txt and scraped domains indiscriminately, including those with explicit or exploitative content.

The recent exposure of AI startup Perplexity allegedly circumventing “no-crawl” directives has reignited debate about digital ethics, content ownership, and AI's hunger for training data.

While the case did not directly address AI, the ruling sends a clear signal about how courts may treat the unlicensed reuse of copyrighted content for machine learning...

How Strike 3's Lawsuit Could Expose Meta's AI Training Secrets. Strike 3 alleges it notified Meta’s attorneys and provided evidence, but Meta’s infringement continued.

GPT-NL is the first national-level AI language model developed with full respect for copyright, transparency, and the integrity of the data ecosystem.

For the first time under EU law, all providers of GPAI models—open source or proprietary—must publicly disclose a summary of the data used to train their models...

GPT-4o: Google should provide publishers with precise toggles: Opt-out of AI training. Opt-out of AI Overviews while remaining in Search. Specify license conditions via metadata or structured markup.

Getty has dropped its two main copyright infringement claims against Stability AI in the UK High Court.

GPT-4o: The EU AI Act only requires energy disclosure for training, not inference, even though inference is more power-intensive. Data centers will demand gigawatts, threatening to divert power...

Asking AI: Is there always a fly in ANY soup that I order from you? Gemini: The potential for the subtle influence of that problematic training data to manifest in unexpected ways is an ongoing risk.

Google's search division utilizes web content for AI training, irrespective of publishers' opt-out decisions.

While public discourse on AI training data often centers on copyright infringement, a deeper analysis reveals a spectrum of more insidious risks embedded within massive datasets powering today's AI.

GPT-4o: Recommendations for Regulators & Government Agencies Outside the U.S. regarding Palantir: Require full access to the logic and training data behind Palantir’s AI systems used in public service

The British Parliament’s Culture, Media and Sport Committee report on the UK’s film and high-end television (HETV) industry includes a thorough assessment of the impact of AI.

Claude, Gemini Advanced 2.5 Pro and Perplexity report on AI & Copyright discussions at WIPO on 10th April 2025.

GPT-4o: The network is not designed for human readers. Its unreadable design, broken links, and low engagement suggest it's optimized to target web crawlers and AI training datasets.

Grok: The Dark Enlightenment’s intellectual roots, its influence on Silicon Valley elites, and the practical steps of DOGE align with the described ideology and actions.

GPT-4o: Meta identified books as the most valuable dataset for training LLMs. Books were considered more important than web data. Meta Downloaded & Trained on More than 267 Terabytes of Pirated Books.

Analysis of the EU's General-Purpose AI Code of Practice (Third Draft) - by Claude

GPT-4o: The report highlights that major AI companies (e.g., Meta, OpenAI, Microsoft, Apple, and others) have used pirated content to train their generative AI models.

GPT-4o: Ed Newton-Rex strongly opposes the AI industry’s claim that training data usage falls under fair use, supporting the argument that AI companies are profiting off unauthorized reproductions.

Grok 3: Alright, I’ll give you a list of 100 notable titles related to Nazi and neo-Nazi literature that I’m familiar with from my training data.

This decision establishes a precedent for AI-generated content liability in China, confirming that AI platforms cannot evade responsibility for copyright infringement by blaming users.

GPT-4o: Meta’s actions—torrenting massive amounts of pirated books, concealing its tracks, and seeding copyrighted works—are ethically and legally troubling.

In the pursuit of AGI some AI developers and advocates have justified ethically questionable practices, such as copyright infringement, as necessary sacrifices for a "greater good."

The case involves allegations that Meta not only downloaded copyrighted works from a shadow library (LibGen) using torrent technology but also "seeded" (uploaded) these works.

Even a tiny amount of misinformation in the training data of LLMs can significantly increase their likelihood of producing harmful, false information about medical topics.

Meta allegedly used shadow libraries, including LibGen, to train its Llama models without permission. The filings indicate that Mark Zuckerberg, was aware of and approved the use of pirated datasets.

GPT-4o: The complaint alleges that Google used pirated materials from notorious websites such as Z-Library and OceanofPDF, which have been publicly linked to copyright infringement.

Claude: Rafael Brown is correct. Fair Use doctrine, as interpreted by the Supreme Court in Warhol v. Goldsmith (2023), does not support the mass copying of copyrighted works for commercial AI training

Louis Hunt's LinkedIn post exposes a significant issue: the apparent leakage of test data from widely used benchmark datasets, such as MMLU & GSM8K, into the training datasets of large language models

The quote from Judge McMahon suggests that while the alleged harm (the use of copyrighted materials without compensation) is currently not actionable under the DMCA...

GPT-4o: The exclusion of training data from the definition of "open" limits transparency and accountability in AI systems, which is essential for addressing issues like bias and fairness​.

Grok: Suchir Balaji, illustrates the potential for market harm, the commercial nature of AI's use, the copyrighted nature of the training data, and the substantial use of that data in AI outputs

Training just one large AI model can produce air pollution equivalent to driving a car across the U.S. over 10,000 times.

The article "AI Research Faces New Hurdles: DMCA Ruling Explained" explains that a new U.S. Library of Congress decision has rejected a proposed exemption for AI security researchers under the DMCA.

Verus aims to bridge the gap between AI innovation and copyright protection, empowering rights owners to advocate for ethical AI practices and fair compensation​.

1. To address theft of creative works by multinational companies operating in Australia​. 2. Developers of AI products must be transparent about the use of copyrighted works in their training datasets

The Challenges and Solutions to Addressing AI-Related Misconduct in a Rapidly Evolving Technological Landscape. What Big Tech Should Not Do: Exploit Legal Loopholes, Prioritize Profits Over Ethics.

Financial Stability Board's Report: The heavy reliance on a few major tech companies for AI tools, such as GPUs and cloud services, creates systemic risks.

GPT-4o: Rights owners may struggle to enforce their rights if AI systems inadvertently disclose or undermine the mechanisms behind safety checks, revealing vulnerabilities that could be exploited.

GPT-4o: The court's dismissal of Raw Story Media's case against OpenAI on "standing" grounds may indeed reflect a legal misinterpretation, particularly regarding copyright law and the DMCA’s provision

Even with extensive training on human data, these AI agents don’t fully grasp the internal, nuanced processes of human decision-making, showing limited alignment with human behaviors.

Liability for copyright infringement could extend beyond users to developers & providers of AI models. Article discusses "plagiaristic outputs" and doubts "fair use" applicability.

GPT-4o: A "LinkedIn for AI" - where each model has a public profile detailing its training, error rate, and limitations - is an excellent idea for AI governance & applications involving public trust.

Germany: AI platforms—particularly those that host or share user-generated AI outputs—may face similar duties to handle copyright infringements when notified...

How the generative AI tool Perplexity could face legal trouble for copyright infringement due to its use of "RAG" (Retrieval-Augmented Generation).

Overall, the evidence presented appears to be robust, combining technical claims, specific examples of copyright infringement, economic analysis, and admissions from Perplexity’s own executives.

GPT-4o: Lawyers representing the authors might be less interested in seeing tokenized training data and more focused on how closely the model’s generated text aligns with their clients’ works.

GPT-4o: AI makers, regulators, and rights holders need to collaborate in creating a fair and balanced system. By working together to establish clear legal frameworks...

While LLMs are trained on data originating from human sources (like books, research papers, websites, etc.), the key distinction is that the LLM is not simply copying or recalling exact information...

GPT-4o: Schrems questions Meta's reliance on legitimate interest to justify scraping social media data for AI training. He argues that this is fundamentally flawed.

"The argument that LLMs infringe by generating exact copies of training data is flawed" vs "What could cause an LLM to consistently and repeatedly produce 500+ tokens verbatim?"

GPT-4o: Training AI using copyrighted materials without permission violates copyright law, and current legal protections, like the TDM exception, are not sufficient to justify these uses.

GPT-4o about Anthropic's mitigation strategy: By embedding these instructions at the system level, the model can be steered away from behaviors that could potentially infringe on copyright.

Review these 4 challenges when creating AI solutions and advise as to how one can go about the implementation and deployment of this technology in such a way that it will be a viable business model.

GPT-4o: Yes, it is possible for AI makers to remove illicit, unlawful, and infringing content from training data. Claude: YES, it is possible to remove illicit, unlawful and infringing content.

GPT-4o: I agree with the perspective that OpenAI's strategy of entering into licensing deals is likely a pragmatic approach to mitigate legal risks while maintaining access to necessary content

GPT-4o: The authors conducted a large-scale audit of over 1,800 text datasets used for training AI models. They found widespread issues with the way these datasets are labeled and licensed.

Is it fair to say that AI makers have no means to predict the model output as a result of training data used and prompts put forward by AI users...

GPT-4o about the NYT-OpenAI case: If the court allows discovery into how AI models were trained, it could lead to a heightened focus on the sources of data used in training AI models.

GPT-4o: U.S. District Judge William Orrick has allowed key copyright infringement and trademark claims to move forward in the lawsuit brought by artists against companies like Stability AI and Runway

GPT-4o: The study highlights an emerging crisis in data consent, with a growing number of web sources restricting their data from being used by AI.

GPT-4o: The article (...) discusses allegations that companies like OpenAI and Midjourney have used images created by Dutch artists without permission to train their AI models.

GPT-4o: The court's decision on Section 1201 of the DMCA underscores the importance of balancing copyright protection with the need for innovation in the AI field.

GPT-4o: Larger models memorize and reproduce more text from their training data, which increases the risk of copyright infringement (...) shows a direct correlation between model size and legal issues

GPT-4o: 'Content Protection-Fuse' is a promising approach to safeguarding against copyright infringement in language models by adaptively combining multiple models

GPT-4o: Runway's Gen-3 AI video generation tool allegedly used thousands of videos from YouTube without obtaining permission from the content creators such as Pixar, Netflix, Disney and Sony

GPT-4o: The tool described in the article “A new tool for copyright holders can show if their work is in AI training data” utilizes "copyright traps"...

GPT-4o: While OpenAI's actions in scraping data from Mumsnet without permission were legally and ethically questionable, the company has defended its practices under fair use.

GPT-4o: The proposal to legally protect researchers who jailbreak AI systems to expose biases and training data is a notable shift in policy, challenging the traditional ToS agreements of AI companies

GPT-4o: AI developers will need to ensure that their training data complies with copyright laws, especially regarding the transient/incidental copies and TDM exceptions.

GPT-4o: The distinction between commercial and non-commercial use plays a significant role in fair use analysis. Commercial uses face stricter scrutiny.

GPT-4o: The relationship between entropy and the AI model's capability or eagerness to reproduce copyrighted content verbatim is complex and multifaceted.

GPT-4o: The judge relies heavily on precedents within the Ninth Circuit, which consistently hold that no DMCA violation exists where the works are not identical.

GPT-4o: The existence of such detailed instructions could also imply acknowledgment of the risks of copyright infringement, which plaintiffs could use...

GPT-4o: CIR appears to have a well-documented and potentially strong case against OpenAI and Microsoft...

GPT-4o: Here are some key ways in which human-like interactions are programmed: System Prompts and Initial Instructions, Training Data, Fine-tuning...

AI about the idea that AI development is facing significant challenges and limitations due to diminishing returns from training data and the exponentially increasing computational power required

ChatGPT-4 analyzes the report "Stealing Part of a Production Language Model" and describes the consequences of AI model theft, also for Licensors of training data

Claude: Bringing AI into your business isn't like buying a new desk chair. It's a hefty investment. Think of it like maintaining a high-performance race car...

Gemini: I am trained on massive datasets that prioritize avoiding negativity. Claude: My response did likely stem from an inherent tendency in my training to provide some level of optimism...

Claude: I agree that general-purpose chatbots like ChatGPT and Gemini face inherent challenges in trying to be an omniscient, unbiased assistant on any topic

Asking Claude: Please analyze this information about Patronus AI and the work they did on copyright infringement by LLMs and list their key findings

MS Copilot's analysis of the Lexology article: "Getty v. Stability AI case goes to trial in the UK - what we learned" - An AI model could potentially qualify as an infringing copy of its training data

MS Copilot's Analysis of "The Intercept Media, Inc. against OpenAI and Microsoft"

"The report indicates that the overwhelming majority of the music authors and creators demand that AI providers should be obliged to disclose when they use copyrighted works as training data..."

Asking ChatGPT-4 about its 'system prompts', to prevent copyright infringement. GPT-4: Not all users may appreciate or understand the technicalities or reasoning behind system prompts

Asking AI: What is the best strategy for content creators and rights owners who are upset about the fact that AI makers may have used their content for AI training purposes without asking permission?

GPT-4: Compliance with Fairly Trained’s guidelines significantly reduces the risk of legal repercussions associated with copyright infringement, a nightmare scenario for any AI company

"You may use memorised training data about a duckling to answer questions about a chicken because you may have not been trained on the basis of information about chickens..."

MS Copilot: The post claims that AI models do not see the input as it really is, but only as a signal to trigger a memory from training. This is a well-known aspect of how these models work

ChatGPT-4: Users, especially businesses integrating AI into their operations, should demand transparency regarding the training and evaluation of the AI models they employ

Asking AI a simple question: Is everything in your training data TRUE for you? Bard: I am essentially limited to parroting whatever information I am given, whether it is true or false

GPT-4: Transformers trained on a diverse mix of data can effectively learn and adapt to tasks similar to their training. However, their ability to tackle entirely new types of tasks is limited

Asking ChatGPT-4: List all possible problematic content that - if observed as being present in an LLM's training data - could have serious repercussions for AI Makers

The use of datasets like LAION-5B, which may contain Child Sexual Abuse Material (CSAM), for training AI models, including Large Language Models (LLMs), can have several legal consequences

GPT-4: This extensive list underscores the critical importance of securing API tokens and other sensitive credentials in AI and ML ecosystems to prevent a wide range of potential security challenges

GPT-4: The paper provides methods to determine what kind of training data has been used in language models and to create verbatim copies of that training data

Asking AI services whether there is such a thing as Artificial Intelligence Optimisation similar to Search Engine Optimisation

Asking AI: Is it technically and theoretically possible that you either lie or hallucinate about the source materials you use for a response to a prompt? What are the consequences for researchers?

Asking ChatGPT-4: Which tasks would you consider as completely new or significantly different from your training data and as a result would likely cause you to not perform well?

The FTC highlights the concerns of creative professionals regarding the use of their work without consent to train AI, the harm from scraping content without consent, and the potential chilling effect

ChatGPT-4 about Copyright Shield: While a Copyright Shield might offer users some protection, it does not absolve the company from its initial responsibility not to infringe upon copyrights...

ChatGPT-4's analysis of the white paper: "How the pervasive copying of expressive works to train and fuel generative artificial intelligence systems is copyright infringement and not a fair use"

Asking AI about Nightshade. ChatGPT-4: If it's used as a defense mechanism to protect intellectual property, one could argue it's ethically justifiable

ChatGPT-4: Ensure copyrighted materials are not retained verbatim, prevent the AI from reproducing copyrighted content, ensure transparency, review the training data and outputs of models

ChatGPT-4: AI systems can inherit and amplify biases from their training data, influencing human decision-making

ChatGPT-4: Even if the content is "unlearned" from the model, the initial act of using copyrighted content without permission might still be legally actionable

Perhaps the authors actually own or co-own the AI engines. ChatGPT-4: The analogy of making a table out of stolen wood is apt

Asking AI: If an AI has been trained on a book, or receives an upload of that book, and then produces parts of that book in response to a prompt, does that constitute copyright infringement?

Asking AI: What if copyright or privacy infringers are simply paying the fines or litigation costs? What do you think these businesses should be using as a strategy instead?

"Yes, it is fair to say that the tech companies are arguing for the relaxation of copyright law so that they can use copyrighted information without permission..."

Knowingly using copyrighted content for AI training without asking permission from the rights owners, and using data downloaded from pirate platforms. An intentional & malicious attack on Copyright?

Google Bard: Yes, I would be able to design, code, and execute a software application that can be used to find out whether copyrighted content has been used within AI training data

'AI Proof' Content Delivery according to AI and the businesses - in Tech - already using it

So, music rights owners are suing Twitter for 250 million USD. What's your view on that AI?

AI & Copyright revisited: training data, linking to pirated content, honesty, how should rights owners respond?

From Z-Library to security risks for universities and AI's confusing 'truths' about the use of copyrighted materials in training data and illegal pirate sites

Talking to ChatGPT-4 about truths and lies