OpenAI's "Flip-Flop" Over Pirated Dataset Could Pierce Legal Shield, Costing Billions

OpenAI Faces Billions in Copyright Litigation as Judicial Review Cuts Through Attorney-Client Privilege

New York, NY - OpenAI, a leading artificial-intelligence developer, is confronting potential multibillion-dollar sanctions as a federal court weighs whether its actions have waived attorney-client privilege in a landmark copyright infringement case. The decision, expected to set pivotal precedents for the emerging AI sector, will decide if plaintiffs can obtain internal communications about datasets alleged to have been derived from infringing material used to train OpenAI's generative models.

The legal showdown, playing out in a Manhattan courtroom ahead of 2025-10-14, hinges on OpenAI's removal of a dataset sourced from pirated books, specifically those illicitly obtained from Library Genesis (LibGen). Plaintiffs argue this deletion was an effort to hide evidence of infringement. While OpenAI initially claimed privilege over internal discussions concerning this act, the court is now examining whether that shield was relinquished through partial disclosure. If privilege is stripped, plaintiffs could inspect a cache of internal communications-including Slack logs, emails, and memos-potentially shedding light on how the firm handled copyrighted content. Legal analysts warn that such exposure could trigger heightened statutory damages, possibly up to $150,000 per infringed work, or judicial sanctions for spoliation or bad-faith conduct. This dispute highlights a growing legal exposure for AI creators: the clash between demands for training-data transparency and the boundaries of legal privilege.

The Genesis of the Privilege Dispute

The heart of the controversy centers on OpenAI's documented removal of the LibGen dataset. At first, the company asserted that the deletion was due to "non-use," a rationale later amended by OpenAI, which said the phrasing was not meant to reveal privileged reasoning. Plaintiffs described this change as a "flip-flop," a claim the court has reportedly upheld.

Under Second Circuit precedent, selective waiver of privilege is not recognized; disclosing even a single privileged communication about a subject can waive the privilege for all related exchanges. As legal scholar Rebecca Roiphe observed, the plaintiffs' argument of "no take backs" carries considerable weight here. OpenAI's effort to offer what Roiphe termed "the most innocuous reason" for the deletion may have unintentionally opened its internal deliberations to discovery.

Escalating Stakes: Billions in Exposure

The financial stakes for OpenAI are staggering. With tens of millions of copyrighted works potentially at issue across the consolidated filings, the exposure could theoretically climb to tens of billions of dollars. This estimate relies on the statutory ceiling of $150,000 per willful infringement, a penalty scheme designed for individual violations but now applied to AI models trained on massive datasets.

The court siding with plaintiffs on the initial privilege questions means that access beyond employee communications to attorney-client correspondence is now being aggressively pursued. Such communications, if unsealed, could provide proof of willful copyright infringement, dramatically expanding OpenAI's legal liability.

The "Nuclear Option": Crime-Fraud Exception

Plaintiffs are not relying solely on waiver arguments. They have also invoked the "crime-fraud exception" to attorney-client privilege. This seldom-used doctrine holds that attorney communications are not privileged if they facilitate or conceal a crime or fraud. In this case, plaintiffs allege that OpenAI's messages might expose criminal copyright infringement or an intentional destruction of evidence.

Nathan Mammen, a partner at Snell & Wilmer, labeled this the "nuclear option," noting its rarity in copyright litigation, where it appears more frequently in patent disputes involving fraud on the United States Patent and Trademark Office (USPTO). If an attorney advised OpenAI to destroy the LibGen data rather than simply cease its use, that counsel could be implicated as a participant in the alleged infringement.

Spoliation and Its Severe Consequences

OpenAI's legal stance is especially fragile concerning spoliation-the intentional destruction of evidence. Should the court find that OpenAI deleted the LibGen dataset in anticipation of litigation, the fallout could be severe:

• Adverse-inference instructions: Juries might be told to presume the destroyed evidence was unfavorable to OpenAI.
• Willfulness presumptions: Juries could be directed to infer that OpenAI's infringement was willful.
• Monetary penalties: Fines for discovery misconduct may be imposed.
• Default judgment: In extreme cases, plaintiffs could win without a full trial.

Mammen described adverse-inference instructions as "a very powerful stick," underscoring their ability to heavily influence judicial outcomes. The central issue before the judge is not whether data was deleted- that is undisputed-but when OpenAI anticipated litigation and why deletion was chosen over preservation. Communications showing that, in early 2023, legal counsel flagged copyright risk and recommended data removal to curb exposure would constitute spoliation, whereas legitimate technical or operational reasons predating any foreseeable suit would be defensible.

Judge Ona T. Wang is expected to conduct an in camera review of the contested documents-a private examination before ruling on privilege. Legal commentators anticipate a tilt toward the plaintiffs, citing Judge Wang's recent rejection of most of OpenAI's privilege claims on related materials.

Systemic Implications for the AI Industry

The hurdles confronting OpenAI are not isolated. Firms such as Google, Meta, Anthropic, Stability AI, and Midjourney have all made training-data decisions that involved legal risk assessments and generated attorney communications discussing those risks.

Anthropic, for example, settled its author class action in August 2025 for $1.5 billion, explicitly citing "inordinate pressure" to avoid trial exposure that could have reached $1 trillion for downloads of pirated books from LibGen. David Schultz, a professor at Hamline University, noted that uncovering attorney communications would deliver an "enormous" blow to OpenAI's defense by revealing the company's "state of mind."

This scenario creates a "Catch-22" for AI developers: thorough legal diligence regarding copyright borders, fair use, and dataset sourcing produces discoverable communications that plaintiffs can later wield to prove willfulness. The more exhaustive the legal consultation, the greater the pool of evidence indicating knowledge of infringement.

The precedent set in In Re: OpenAI, Inc. Copyright Infringement Litigation will likely shape a variety of upcoming disputes:

• News-publisher lawsuits: Expected to use comparable discovery tactics to show AI firms knowingly scraped paywalled content.
• Image-creator lawsuits: Aimed at obtaining communications about dataset filtering and DMCA compliance.
• Music-rights holder suits: Targeting training data that includes copyrighted recordings.
• Code-repository actions: Scrutinizing choices to train on GPL-licensed and proprietary codebases.

Proposals for Future Regulatory Frameworks

Yvette Joy Liebesman, a professor at Saint Louis University, keenly observed that plaintiffs' attorneys will strive "to get as much information as possible to get as much money for plaintiffs as possible." To temper systemic risks and encourage coherent industry growth, several legislative and judicial reforms are under discussion:

1. Statutory clarification on AI training and fair use: Congress could specify whether training on copyrighted works amounts to infringement and outline exemption criteria.
2. Discovery limits for AI-training cases: Courts might set rules for when attorney communications about data-selection decisions become discoverable.
3. Proportional damages schemes: The current $150,000-per-work statutory award, intended for discrete infringements, may be recalibrated for AI models that interact with millions of works.
4. Safe-harbor provisions for good-faith compliance: Companies that adopt robust copyright-compliance programs-such as dataset audits, DMCA takedown procedures, and opt-out mechanisms-could receive protection from willfulness findings.

Awaiting the Precedent

In the short run, more settlements are expected across the AI sector, as firms seek to sidestep invasive discovery processes already affecting OpenAI. In the medium term, operational changes are probable, including stricter data-filtering, expanded opt-out options, and a curtailment of internal documentation of legal risk, potentially chilling legal counsel.

Over the long haul, regulatory action appears inevitable. Whether Congress or the judiciary leads the way, new frameworks that balance copyright protection with technological innovation will be essential. OpenAI's present legal battle is more than a solitary case; it is a pivotal crucible determining whether existing copyright law can accommodate AI development, or whether sweeping reform is required. The outcome of these proceedings, involving billions of dollars and the direction of an entire industry, will shape the legal terrain for AI for decades to come.

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