Microsoft’s Copilot Evidence in the NYT Suit is Inherently Misleading by Design
The legal filings in the New York Times copyright case show Microsoft’s defense of Copilot relies on misleading statistics that artificially minimize the AI’s reliance on copyrighted material. The company insists its chatbot rarely outputs full sentences or substantial portions of news articles, yet the methodology behind these claims remains entirely undisclosed.
The core problem lies in how regurgitation is measured. Microsoft frames Copilot as transformative by emphasizing that complete, verbatim sentence reproduction is uncommon—but only if the search is restricted to exact, unmodified text. This approach ignores how large language models often preserve the original’s structure, logic, and proprietary details through paraphrasing or minor alterations. By setting an artificially high threshold for what counts as a "substantive" reproduction, Microsoft can skew the data to suggest far less infringement than actually occurs.
The opacity of testing procedures further undermines the defense. The full set of prompts used to generate responses—and the dataset analyzed—is never disclosed, making it impossible to verify whether the results reflect real-world usage. When Copilot is tested with generic queries, it naturally avoids reproducing entire articles, but targeted prompts—those users actually employ—significantly increase the likelihood of near-verbatim output. Microsoft’s selective metrics treat these instances as rare anomalies rather than systemic violations, yet for publishers, even infrequent unauthorized reproductions across millions of users still constitute widespread copyright infringement.
The distinction between synthesis and direct reproduction is also blurred. While Copilot’s architecture is designed to synthesize information, its outputs often deliver the same value as original reporting, effectively depriving publishers of potential clicks or subscriptions. Microsoft’s defense doesn’t prove originality so much as it redefines copying to exclude any meaningful infringement. Without transparency about how these statistics are derived, the company’s claims remain a curated snapshot rather than an honest assessment of how much New York Times content appears in Copilot’s responses.
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Frustrating when official metrics lie. Which data project gave you those fake numbers? The testing procedures are opaque; we need the complete list of prompts used in testing to verify the metrics.
I'm skeptical. Are those figures based on raw documents or unique token counts? An examination of the legal filings in the New York Times copyright suit reveals that the figures Microsoft uses to defend Copilot are heavily distorted. The company claims its chatbot rarely outputs full sentences or substantive portions of news articles, yet the structure of their evidence remains a complete black box. The central issue in the dispute revolves around how regurgitation is quantified. Microsoft seeks to portray Copilot as a transformative tool rather than a simple copy-paste mechanism. By arguing that the AI rarely reproduces complete sentences or substantial segments of news articles, the company contends that the output qualifies as fair use due to its originality. Yet, the methodology behind these statistics is conspicuously opaque. Microsoft's presented evidence appears to depend on an exceptionally narrow set of parameters. If the search is limited to exact, verbatim matches of massive paragraphs, the company can easily assert that such occurrences are rare. This approach, however, overlooks how large language models actually function. These systems frequently paraphrase or slightly modify a few words while preserving the original structure, logic, and proprietary details of New York Times reporting. By establishing a high threshold for what qualifies as a substantive chunk, Microsoft can statistically minimize the perceived scale of infringement. This situation highlights a broader issue concerning the opaque nature of their testing procedures. The complete set of prompts used to generate the statistics is not disclosed, making it impossible to replicate or verify the results. The figures could be based on a narrow, specific search query like "searching for exact, verbatim matches of massive paragraphs," which would naturally yield low results for paraphrased content.
Suspicious. Which specific data cleaning step usually skews these numbers the most? An examination of the legal filings in the New York Times copyright suit reveals that the figures Microsoft uses to defend Copilot are heavily distorted. The company claims its chatbot rarely outputs full sentences or substantive portions of news articles, yet the structure of their evidence remains a complete black box. The central issue in the dispute revolves around how regurgitation is quantified. Microsoft seeks to portray Copilot as a transformative tool rather than a simple copy-paste mechanism. By arguing that the AI rarely reproduces complete sentences or substantial segments of news articles, the company contends that the output qualifies as fair use due to its originality. Yet, the methodology behind these statistics is conspicuously opaque. Microsoft's presented evidence appears to depend on an exceptionally narrow set of parameters. If the search is limited to exact, verbatim matches of massive paragraphs, the company can easily assert that such occurrences are rare. This approach, however, overlooks how large language models actually function. These systems frequently paraphrase or slightly modify a few words while preserving the original structure, logic, and proprietary details of New York Times reporting. By establishing a high threshold for what qualifies as a substantive chunk, Microsoft can statistically minimize the perceived scale of infringement. This situation highlights a broader issue concerning the opaque nature of their testing procedures. The complete set of prompts used t