Piracy Suit Against Meta Tests Corporate Responsibility in AI Development

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Friday, Oct 31, 2025 7:14 am ET2min read
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- Meta faces lawsuit over alleged AI training with pirated porn content, denying claims as baseless.

- Strike 3 alleges 2,400 adult film downloads via hidden IP addresses to develop AI video tools since 2018.

- Meta argues downloads were likely personal use, citing low annual rates and no evidence linking to AI models.

- Case joins broader copyright lawsuits against AI firms, with OpenAI facing similar claims over ChatGPT training data.

- Meta's AI spending surged to $71B in 2025, straining finances as legal costs and operational risks rise.

Meta Faces Legal Challenge Over Alleged AI Training with Pirated Porn Content

Meta Platforms Inc. is seeking to dismiss a lawsuit from Strike 3 Holdings LLC, a Miami-based adult film distributor, which accuses the tech giant of illegally downloading thousands of pornographic videos to train artificial intelligence models. The company has called the allegations "nonsensical and unsupported," arguing that the claims rest on "guesswork and innuendo" rather than concrete evidence of AI training, according to

. The suit, filed in July, alleges that used corporate and hidden IP addresses to torrent nearly 2,400 adult films since 2018 as part of a broader effort to develop multimodal AI systems, as detailed in an Ars Technica article.

Strike 3's complaint claims that Meta's downloads, including those from a "stealth network" of 2,500 untraceable IP addresses, were part of a coordinated effort to build an unannounced adult version of its AI-powered video generation tool, as Ars Technica reported. Meta's motion to dismiss, however, counters that the scale and pattern of the alleged activity—averaging just 22 downloads per year across 47 IP addresses—defies the "concerted effort" required for AI training, according to the same Ars Technica coverage. The company also argues that its terms of service explicitly prohibit generating adult content, making the claim implausible, a point Ars Technica notes.

Meta further contends that the downloads were likely for personal use by employees, contractors, or third parties, rather than corporate AI development. The motion highlights that many of the downloads occurred years before Meta's AI research initiatives began in earnest and that no evidence ties the activity to specific employees or models, as Ars Technica observed. "Monitoring every file downloaded by any person using Meta's global network would be an extraordinarily complex and invasive undertaking," the filing states, dismissing the notion that the company should be held responsible for unverified torrenting by unknown individuals, a line also reported by Ars Technica.

The case has drawn attention as part of a broader wave of copyright litigation targeting AI companies. In a related development, a New York federal judge recently denied OpenAI's motion to dismiss a lawsuit from authors who claim their works were used to train ChatGPT, according to

. The ruling allows claims that AI-generated text infringes copyrights to proceed, complicating the legal landscape for tech firms relying on large language models.

Meta's financials also reflect the growing costs of AI development. The company reported mixed third-quarter earnings, with operating margins slipping to 40% from 43% in 2024 due to a 28% rise in R&D expenses, according to

. Meta has repeatedly raised its 2025 AI-related spending guidance, now projecting $71 billion in expenditures, and expects capital outlays to grow further in 2026, the Barron's piece notes. These costs have strained Meta's balance sheet, with debt and liabilities surpassing cash reserves in recent quarters, as reported by Barron's.

The outcome of the Strike 3 case could have significant implications for AI training practices. If Meta prevails, it may reinforce the argument that corporations are not liable for unverified user activity on their networks, as discussed in

. Conversely, a ruling against Meta could force tech companies to adopt stricter monitoring protocols, increasing operational costs and potentially stifling innovation.

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