By Jim Shimabukuro (assisted by Claude)
Editor
Alex Reisner’s revelatory article in The Atlantic1 exposes a fundamental tension at the heart of the artificial intelligence industry, one that challenges the very metaphors we use to understand these systems and threatens to reshape the legal and economic foundations upon which the technology rests. Recent research from Stanford and Yale2 demonstrates that major language models can reproduce nearly complete texts of copyrighted books when prompted strategically, a finding that contradicts years of industry assurances and raises profound questions about what these systems actually do with the material they ingest.(DNYUZ)
The issue demands our attention because it cuts to the core of how we conceptualize machine learning. The AI industry has long relied on the learning metaphor, claiming that models develop understanding without storing copies of training data. This framing has enabled companies to argue that their use of copyrighted material constitutes fair use, analogous to how humans learn from reading books without violating copyright. Yet the emerging evidence tells a different story entirely. When researchers prompted Claude strategically, it delivered near-complete text of works including Harry Potter and the Sorcerer’s Stone, The Great Gatsby, 1984, and Frankenstein. This isn’t learning in any human sense—it’s storage and retrieval, albeit in a compressed and sometimes imperfect form.(DNYUZ)
The technical reality is more accurately described as lossy compression, where models ingest text and images and output approximations of those inputs, similar to how JPEG files compress photographs while losing some data quality. This comparison, recently invoked by a German court ruling against OpenAI, provides a far more honest framework for understanding what’s actually happening inside these systems. The models aren’t abstracting general concepts from training data—they’re creating compressed databases that can reconstruct substantial portions of that data when queried correctly.(DNYUZ)
The implications extend far beyond technical accuracy into the realm of copyright law. In 2023, AI companies assured the U.S. Copyright Office that their models don’t store copies of training information. OpenAI told regulators that models do not store copies of the information they learn from, while Google similarly claimed there is no copy of training data present in the model itself. These statements, made under the authority of regulatory filings, now appear difficult to reconcile with empirical findings. The memorization phenomenon presents at least two distinct legal vulnerabilities.(DNYUZ)
First, if memorization proves unavoidable, companies must somehow prevent users from accessing memorized content—a challenge that existing techniques have failed to address adequately. One court has already required such prevention, though methods like simple word substitutions can easily circumvent current safeguards. Second, and more fundamentally, courts may determine that the models themselves constitute illegal copies of copyrighted works, potentially requiring companies to destroy and rebuild systems from scratch using properly licensed material.(DNYUZ)
Recent scholarship has worked to bring precision to these debates. A. Feder Cooper and James Grimmelmann’s comprehensive analysis “The Files are in the Computer,” published in the Chicago-Kent Law Review in 2025, provides crucial definitional clarity by distinguishing memorization from extraction and regurgitation. Their work demonstrates that models can memorize training data such that it’s possible to reconstruct near-exact copies of substantial portions of that data. This careful legal and technical analysis has become essential reading for understanding the copyright implications of generative AI.
The scope of the problem became clearer through research published in late 2023 by Nasr, Carlini, and colleagues in “Scalable Extraction of Training Data from (Production) Language Models.” Their work showed adversaries can extract gigabytes of training data from both open-source and closed models, with a divergence attack causing ChatGPT to emit training data at rates 150 times higher than normal. The researchers recovered over ten thousand examples from ChatGPT’s training dataset at a cost of merely $200, suggesting the vulnerability is both widespread and easily exploitable.
More recently, comprehensive surveys like “The Landscape of Memorization in LLMs” by Xiong et al., published in July 2025, have synthesized the growing body of research on this phenomenon. The paper examines key drivers including training data duplication and fine-tuning procedures that influence memorization, while exploring detection methodologies and mitigation strategies. The work acknowledges the fundamental challenge of balancing the minimization of harmful memorization with model utility—a tension that may prove impossible to fully resolve.
The U.S. Copyright Office weighed in decisively with its May 2025 report on generative AI training. The office concluded that where generated outputs are substantially similar to inputs, there is a strong argument that copying the model’s weights implicates the reproduction and derivative work rights of original works. The report rejected the industry’s favored analogy between AI training and human learning, noting that while humans retain only imperfect impressions filtered through their personalities and experiences, generative AI training involves creating perfect copies with the ability to analyze works nearly instantaneously.
The legal battleground has crystallized most visibly in The New York Times v. OpenAI, a lawsuit filed in December 2023 that could reshape the industry. In March 2025, Judge Sidney Stein rejected OpenAI’s request to dismiss the case, allowing the lawsuit’s main copyright infringement claims to proceed. The case advances critical questions about fair use and market substitution—whether chatbot answers serve as replacements for reading source material or operate in distinct marketplaces. With statutory damages potentially reaching $150,000 per willful violation, the financial stakes are extraordinary.
The significance of AI memorization extends into multiple domains beyond copyright. Research on medical AI from MIT highlights privacy concerns in healthcare applications, where models might inadvertently expose sensitive patient information. The phenomenon challenges the entire regulatory framework around AI systems, which has been built on assumptions about how these models function that may not withstand empirical scrutiny.
What makes Reisner’s reporting particularly valuable is his documentation of how AI companies have actively obscured research into memorization. Multiple researchers told him about memorization research that has been censored and impeded by company lawyers, though none would speak on the record fearing retaliation. This pattern of suppression prevents the public discussion necessary to address how AI companies use creative and intellectual works upon which they depend entirely.
The industry’s response has been to double down on metaphors rather than engage with technical realities. OpenAI CEO Sam Altman has defended the technology’s right to learn from books and articles like a human can, framing the issue in terms that obscure rather than illuminate. This rhetorical strategy serves corporate interests by suggesting that restricting AI training would be tantamount to preventing human education—a comparison that collapses under scrutiny but proves remarkably durable in public discourse.
The memorization crisis matters because it reveals the gap between the AI industry’s public narratives and the actual functioning of its products. It matters because billions of dollars in potential copyright damages hang in the balance, along with the viability of business models built on unlicensed content. It matters because the same systems being deployed across medicine, law, education, and journalism may be fundamentally different from what we’ve been led to believe. Most importantly, it matters because the resolution of these issues will determine whether AI development proceeds through negotiated licensing and transparent practices, or continues its current trajectory of regulatory evasion and metaphorical obfuscation.
The research landscape from 2024 to 2026 has made one thing abundantly clear: memorization in large language models is not a rare bug to be driven to zero, as OpenAI once claimed, but rather an intrinsic feature of how these systems work. No researcher Reisner spoke with thought the phenomenon could be eradicated. This fundamental reality demands we abandon comforting metaphors about machine learning and confront what these systems actually do—compress and store vast quantities of human-created content in ways that enable substantial reconstruction. The legal, ethical, and economic implications of that truth will reverberate through the technology industry for years to come, reshaping not only how AI systems are built but whether current approaches can continue at all.
AI memorization is indeed one of those issues that sits at the intersection of technology, law, creativity, and corporate power—the kind of topic that deserves careful attention precisely because it challenges foundational assumptions about how these systems work. Reisner’s reporting connects technical research with real-world consequences in a way that makes an abstract problem feel urgent and tangible. The gap between what AI companies have been claiming and what the evidence shows is striking, and it’s refreshing to see journalism that doesn’t just accept industry narratives at face value. The Stanford/Yale research in particular seems like it will be cited extensively in upcoming legal proceedings. It’s rare to see academic work land with such immediate practical implications.
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1 Alex Reisner, “AI’S MEMORIZATION CRISIS: Large language models don’t ‘learn’—they copy. And that could change everything for the tech industry,” Atlantic, 9 Jan 2026.
2 Ahmed Ahmed et al., “Extracting books from production language models,” Cornell University, 6 Jan 2026.
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