Sarah Tishler was recently quoted in an Ars Technica article about the litigation between xAI and OpenAI.
he article, titled “Musk has no proof OpenAI stole xAI trade secrets, judge rules, tossing lawsuit,” addresses a recent decision by Judge Rita Lin of the Northern District of California granting OpenAI’s motion to dismiss xAI’s First Amended Complaint. The article, incorporates extensive commentary from Sarah about the high-profile lawsuit:
Commercial litigator Sarah Tishler told Ars that the order “boils down to a fundamental concept in trade secret law: hiring from a competitor is not the same as stealing trade secrets from one.”
“Under the Defend Trade Secrets Act, xAI has to show that OpenAI actually received and used the alleged trade secrets, not just that it hired employees who may have taken them,” Tishler said. “Suspicious timing, aggressive recruiting, and even downloaded files are not enough on their own.”
Tishler suggested that the ruling will likely be welcomed by AI firms eager to secure the best talent without incurring legal risks from their hiring practices.
“In the AI industry, where talent moves fast and the competitive stakes are enormous, this ruling reaffirms that suspicion is not enough,” Tishler said. “You have to show the stolen information actually made it into the competitor’s hands and was put to use.”
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All xAI has claimed is that OpenAI induced former employees to share secrets, and so far, nothing backs that claim, Lin said. Tishler noted that the court also rejected an xAI theory that “OpenAI should be responsible for what its new hires did before they arrived” for “the same reason: without evidence that OpenAI directed the theft or actually put the stolen information to use, you cannot hold the company liable.”
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Further weakening xAI’s arguments, if Li indeed shared confidential information during his presentation while interviewing for OpenAI, xAI has alleged no facts suggesting that OpenAI was aware Li was sharing xAI trade secrets, Lin wrote.
This “makes it very hard to argue OpenAI ever used anything he allegedly took,” Tishler told Ars.
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Tishler has been closely monitoring xAI’s trade secret legal battles. In October, she noted that Li is in a particularly prickly position, facing pressure in civil litigation from Musk to turn over data that could be used against him in the Federal Bureau of Investigation’s criminal investigation into Musk’s allegations. As Tishler explained:
“The practical reality is stark: Li faces a choice between protecting himself in the criminal action with his silence, and the civil consequences of doing so. Refuse to answer, and xAI could argue adverse inferences; answer, and the responses could feed the criminal case.”
Ultimately, the FBI is trying to prove that Li stole information that qualified as a trade secret and intended to use it for OpenAI’s benefit, while knowing that it would harm xAI. If they succeed, “xAI would suddenly have a government-backed record that its trade secrets were stolen,” Tishler wrote.
If xAI were so armed and able to keep the OpenAI lawsuit alive, the central question in the lawsuit that Lin dismissed today would shift, Tishler suggested, from “was there a theft?” to “what did OpenAI know, and when did it know it?”
The February 25, 2026, article was written by Ashley Belanger, a Senior Policy Reporter for Ars Technica.
Sarah Tishler is a partner at Beck Reed Riden LLP. She is a commercial litigator whose practice focuses on complex business disputes in state and federal courts, including intellectual property, breach of contract, and fraud claims. Her experience also includes regulatory investigations, international arbitration, securities litigation, shareholder litigation, class-action litigation, and pre-transactional advising.
eck Reed Riden LLP is among the leading authorities in trade secret, noncompete, and unfair competition law, and our experience handling these matters is backed by our extensive employment law and business litigation experience. Our hand-picked team combines attorneys with complementary expertise and practical experience.
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