Letter Agreement Between Landlord and Tenant Did Not Extinguish GuarantyTreble Damage Award Upheld; Landlord Failed to Establish Overcharge Was Not WillfulDenying Access to Landlord Constituted Breach Entitling Landlord to PossessionTenant Entitled to Yellowstone Injunction With Respect to Taxes and Sewer Charges
New York is one of the first states to adopt laws to regulate artificial intelligence use in advertising and to strengthen post-mortem publicity rights regarding AI-generated replicas and “synthetic performers.” Given the state’s role as a bellwether for consumer-protection and advertising regulation, these new laws, combined with the state’s broader AI legislative framework, represent a shift toward transparency, consent and accountability.
State app store age verification regimes do more than reallocate responsibility between platforms and developers. They create a new data supply chain for age knowledge, one that can move COPPA questions from “do we ask age?” to “what do we do when the platform tells us?” The teams that handle this best will treat platform age signals as sensitive compliance inputs: minimize them, tightly control where they flow, and design product behavior so that minors do not trigger unnecessary collection or disclosure.
The firms leading right now chose to ask what would become possible if they managed the entire revenue lifecycle — from invoice generation to cash receipt — in one place, and what AI could actually accomplish with complete data instead of partial feeds. That is the Power of One.
A recent decision from the U.S. District Court for the Southern District of New York (SDNY), United States v. Heppner, has generated outsized commentary suggesting that the use of generative AI tools may jeopardize attorney-client privilege. A closer reading shows something far less dramatic.
The 2025 legislative cycle marked a pivotal year in U.S. privacy law, defined not only by continued nationwide expansion into AI) governance, children’s and teen privacy and online safety, as well as emerging data categories. In this article, we detail what enterprises need to be prepared for in 2026 and explain why we believe next year will be a watershed period for consumer privacy in the U.S.
Every decision to onboard a client, partner, lateral hire, contractor, consultant or expert witness carries risk. Yet despite the increasing complexity of that risk, many firms continue to rely on onboarding practices that have not kept pace with the digital world in which their clients and people operate. The result is a widening gap between how risk actually manifests today and how it is assessed at the point of onboarding.
Understanding the “not written by people” feature of AI software is the best lens for viewing AI transactions. The fact that an AI is not a legal entity, prompts the proper legal analysis of AI transactions.
A great debate seems to be rearing its head–is it better to conduct document review with traditional machine learning or with generative AI? This article examines traditional machine learning and generative AI in the context of review for production in litigation matters. While each party seeks to improve efficiency and defensibility, they differ meaningfully in workflow, transparency, human involvement, and overall impact on legal practice.
As insurance retreated from AI exposure, contracts began absorbing functions that insurance once performed. Indemnities compressed. In effect, contracts began underwriting elements of AI risk. This shift has significant consequences for lawyers drafting, negotiating and advising on AI-related agreements.
