⚖️ AI Copyright Case

Federal Intervention in AI Copyright Litigation

The Department of Justice under the Trump administration submitted a 20-page Statement of Interest in the U.S. District Court for the Southern District of New York, intervening in the copyright infringement lawsuit filed by The New York Times against OpenAI and Microsoft. Representing the first formal executive-branch intervention in ongoing AI copyright litigation, the brief argues that training large language models (LLMs) on copyrighted public texts generally qualifies as "fair use" under federal copyright law. Citing national security imperatives and economic competitiveness grounded in executive orders, the government asserts that ingesting published text to train generative neural networks is "extraordinarily transformative" and does not serve as a market substitute for the underlying source material. Although non-binding on the presiding judge, this official position formally aligns executive policy with the foundational training methods deployed across the artificial intelligence sector.

Analysis for Startup Founders

For technology founders building or fine-tuning generative models, this Statement of Interest offers a pivotal indicator of shifting legal risks surrounding data ingest workflows. While a federal agency's amicus filing does not carry the precedent-setting weight of a judicial ruling, it provides substantial policy backing for the defense that raw pre-training on publicly available data constitutes fair use. However, founders must carefully distinguish between general data ingestion and unlawful data acquisition. As demonstrated in recent rulings—such as the action against Anthropic—courts remain willing to penalize companies that obtain training sets via illicit "shadow libraries" or pirate repositories, regardless of whether the eventual AI training itself is considered transformative. Furthermore, the threat of claims regarding verbatim text output (memorization) remains active if models output substantial portions of copyrighted material to end users without transformation.

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