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From Work Product to Exhibits -- The Emerging Discoverability of AI Prompt
A recent ruling from a federal magistrate judge in Connecticut signals that courts may increasingly treat certain uses of generative AI as part of an expert's methodology, and therefore discoverable, rather than merely a research aid that can remain hidden.
In climate litigation brought against Shell, Magistrate Judge Thomas Farrish ordered production of AI prompts used by a testifying expert, Dr. Naomi Oreskes, who relied on ChatGPT to help identify and to sort through a large collection of documents in the case. The court reasoned that because the AI-assisted process informed how the expert selected and reviewed relevant materials underlying her opinions, the prompts were relevant to understanding and evaluating the reliability of the expert's methodology. The court noted that in expert discovery, an opposing party is entitled to examine not only an expert's conclusions, but also the processes used to reach them, including AI tools that may have influenced the expert's review of the evidence. The environmental group that retained Dr. Oreskes is fighting the order, asking the District Judge Monday to halt the disclosure.
A Video Game Lawsuit Shows How Discovery into AI Chat Logs is Changing the Game
A recent Delaware Court of Chancery opinion arising from the acquisition of a video game studio carries an important lesson far beyond M&A litigation: AI chatbot exchanges are now entering the discovery record and affecting case outcomes.
In Fortis Advisors LLC v. Krafton, Inc., the dispute was a familiar one: whether, years after an acquisition, former stockholders of the acquired company were entitled to an additional earnout payment based on post-closing performance. What makes the case unique—at least for now—is the evidence the Court found especially persuasive: the buyer CEO’s chatbot prompts and the AI-generated responses.
For litigators across all practice areas and the clients they counsel, the takeaway is immediate: AI chat logs have entered the e-discovery stream and may be consequential evidence. That development will not stay confined to nine-figure Delaware corporate disputes. It is coming to civil litigation broadly, and lawyers and their clients need to be thinking about it now.
Blockchain, AI Training Data, and Protecting Intellectual Property in the Next Deal
Ali Dhanani
The intersection of blockchain technology and AI training data disputes continue to generate significant market attention. The following offers practical perspective for companies seeking to protect and monetize their intellectual property in AI-related transactions, licensing, and investment. Copyright disputes have been prominent in the Courts concerning whether a company’s data has been used to train institutional and foundational AI models.
AI on Trial: Morgan v. V2X Draws New Lines on Work Product Protection and Confidentiality
As artificial intelligence tools become ubiquitous in litigation, federal courts are increasingly confronting novel questions about how longstanding doctrines of work product protection and confidentiality apply when parties route litigation materials through public AI platforms. In the span of just seven weeks, three federal courts have issued notable decisions at this intersection: Warner v. Gilbarco, Inc. (E.D. Mich. Feb. 10, 2026), United States v. Heppner (S.D.N.Y. Feb. 17, 2026), and Morgan v. V2X, Inc. (D. Colo. Mar. 30, 2026). Together, these decisions begin to define an emerging (yet still unsettled) framework governing AI use in discovery, with significant practical consequences for litigants, attorneys, and in-house legal teams.
AI Regulatory Update for Energy: New Timelines in the EU, New Standards in the U.S.
Matthew R. Baker, Samir A. Bhavsar
In our March 2026 alert, we wrote that the EU AI Act's August 2, 2026, compliance deadline made structured AI governance an urgent priority for energy companies operating in or serving the EU market. In the two months since, the regulatory landscape has shifted on both sides of the Atlantic. The EU has reached a political agreement to push the high-risk compliance deadline back by more than a year. At nearly the same time, the U.S. National Institute of Standards and Technology (NIST) launched a parallel effort to develop a Trustworthy AI in Critical Infrastructure Profile that will, in time, set expectations for how AI is deployed across U.S. energy operations.
The pace of AI regulation has not slowed. It has shifted in form and focus. For energy executives, the practical question is no longer whether to build a structured AI governance program but how to design one that responds to two regimes operating on different mechanisms while pointed at the same underlying concerns.
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