OpenAI’s Alleged Deception in NYT Copyright Suit Threatens Its Legal Defense

OpenAI faces the prospect of court sanctions in its bitter copyright battle with The New York Times and other news organizations. The latest accusations paint a picture of deliberate obstruction. The company supposedly claimed for more than two years it could not search its training data or user logs. Then it admitted otherwise. Billions of ChatGPT conversation records? Hidden or deleted. In violation of court orders.

But first. The stakes. This isn’t some minor discovery spat. It’s a direct challenge to how OpenAI built its empire. The New York Times sued OpenAI and Microsoft in late 2023. It alleged the companies trained their models on millions of the paper’s articles without permission. Outputs from ChatGPT often reproduced Times content nearly verbatim. That undermined subscriptions. It stole value.

Evidence of Concealment Emerges

Depositions changed everything. According to a motion filed Thursday by the Times, the Daily News, Chicago Tribune, Denver Post and others, OpenAI executives initially told the court and plaintiffs that searching its massive training corpus for specific news articles was impossible. Too big. Too unstructured. A technical dead end. The outlets needed that search to prove infringement. OpenAI said no.

Then came the reversal. Later in the case, OpenAI admitted it could search the data. It had built tools to do exactly that. The plaintiffs’ motion, reported by Ars Technica, claims this shift came only after prolonged resistance. OpenAI had faked the inability. Or at least misrepresented it.

And the logs. Oh, the logs. The motion alleges OpenAI hid billions of them. A searchable database of 78 million chat conversations surfaced in depositions. The New York Times and its allies wanted these records. They would show whether ChatGPT was spitting out their journalism in response to user queries. Proof of output infringement. OpenAI balked. It cited user privacy. It warned of enormous burden to de-identify the data. It fought hard against producing them.

Yet the company kept the data. For a while. Then it deleted swaths of it. Despite a court preservation order. The plaintiffs say this was no accident. “Serious sanctions are especially appropriate here because OpenAI’s conduct, including the violation of the Court’s preservation Order, was knowing and intentional,” the motion stated, as quoted in the New York Daily News.

One sample tells the tale. OpenAI produced a 20 million log sample. It then applied AI to redact it. Nineteen billion redactions followed. So many that the court itself questioned the process. The sample became skewed. Useless for the plaintiffs’ purposes. OpenAI’s approach, the motion argues, turned discovery into a farce.

But why would a company with OpenAI’s resources and talent play these games? The answer lies in the case’s core. If the Times can show its articles sit in the training data and that ChatGPT reproduces them, fair use arguments weaken. OpenAI has long claimed training on publicly available web text counts as fair use. The outputs matter too. If the model simply parrots copyrighted news, the defense crumbles.

So. Hide the evidence. Claim technical impossibility. Delete the logs after the preservation order. That seems to be the allegation. OpenAI has signaled the deleted data cannot be retrieved. Convenient.

The company pushed back in earlier statements. In a 2025 blog post responding to the Times’ data demands, OpenAI COO Brad Lightcap called the lawsuit “baseless.” He argued the plaintiffs sought to force indefinite retention of consumer ChatGPT and API data on mere speculation. “The New York Times continues to demand that OpenAI keep a specific set of user data from April-September 2025,” Lightcap wrote, per OpenAI’s own response page.

Yet the new motion suggests those arguments masked deeper problems. OpenAI had the search capability all along. It just didn’t want to use it against itself.

News organizations piled on quickly. The Denver Post, Chicago Tribune and others joined the call for sanctions. Their reporting echoed the core claims. OpenAI destroyed evidence. It hid its ability to locate stolen news stories in training data and in ChatGPT responses. X posts from outlets like Variety and Ars Technica amplified the news within hours of the filing. Public sentiment on the platform turned sharply critical. One post from @tldrnewsletter summed it up: the Times and publishers accuse OpenAI of lying about searchability and hiding billions of chat logs.

This fight didn’t appear from nowhere. The Times has pursued aggressive tactics before. It updated its terms of service years ago to block AI training on its content. It sued after evidence mounted that GPT models regurgitated its articles. OpenAI countered that the Times hired a hacker to provoke rare regurgitation by exploiting bugs. “The truth, which will come out in the course of this case, is that the Times paid someone to hack OpenAI’s products,” OpenAI said in a 2024 filing, according to Legal Dive.

Both sides have thrown heavy punches. But the latest accusations strike at the heart of judicial process. Spoliation of evidence. Misrepresentation to the court. If proven, sanctions could include adverse inferences. The judge might simply assume the deleted logs would have shown infringement. Or worse. Case dismissal for OpenAI on certain claims. Monetary penalties. The company could face a fatal blow in this particular lawsuit.

And the timing. AI companies scramble for training data. The New York Times reported in 2024 that tech giants including OpenAI cut corners, ignored policies and discussed skirting copyright law to feed their models. Exhaustion of reputable English-language text forced desperate measures. That story, from The New York Times, laid bare the pressure.

Publishers have fought back. Many blocked crawlers. Some charge for data access. Reddit and Stack Overflow did exactly that. The Data Provenance Initiative found a dramatic drop in available content for AI datasets. Paywalls rose. Robots.txt rules tightened. OpenAI itself has said it honors opt-outs. But trust erodes when accusations like these surface.

OpenAI’s position remains that training constitutes fair use. It points to transformative nature of its models. It argues outputs rarely reproduce source material exactly. Yet the Times provided dozens of examples where ChatGPT did just that. The company claimed those examples resulted from adversarial prompting. Not normal use.

Now the discovery fight threatens to overshadow the merits. If OpenAI cannot produce clean evidence. If the court believes it actively hid or destroyed relevant records. The fair use argument may never get a full hearing. Judges don’t like games with evidence. Especially not from sophisticated parties who preach transparency in AI development.

So what happens next? The federal judge will review the motion. OpenAI will respond. It may offer technical explanations. It may blame overzealous redaction on privacy obligations. It may argue the plaintiffs overstate the search capability’s practicality at scale. Depositions will be dissected. The 19 billion redactions will draw scrutiny.

But the damage to OpenAI’s reputation grows. Industry insiders already whisper about aggressive tactics in data acquisition. This episode adds fuel. Other publishers watch closely. More suits could follow if sanctions hit hard. Microsoft, as co-defendant, feels the heat too. Its investment in OpenAI ties their fates.

The broader AI industry faces a reckoning on data. Clean, licensed datasets cost money. They slow development. Yet scraping everything and hoping for fair use looks increasingly risky. Courts may draw clearer lines. Congress could step in with new rules. For now, this case tests the limits.

OpenAI built its lead on speed and scale. That required data. Lots of it. The question is whether it played by the rules in obtaining and defending that data. The plaintiffs say no. They say OpenAI lied. It hid. It deleted. And now it may pay the price.

Short answer. The motion lands like a bombshell. Long explanation. It reveals deep tensions between rapid AI progress and intellectual property rights. Between corporate secrecy and court transparency. Between what companies say publicly about ethics and what they do privately to protect their models. The outcome could reshape how the entire sector approaches training data. No one wins if trust collapses completely. But someone will lose in court.