Intellectual Property (IP) Law and Litigation
The collision between generative AI and established Intellectual Property frameworks—specifically Copyright, Patent, and Trade Secrets—represents the most active front in AI-related litigation. These frameworks were designed almost exclusively around human authorship and inventorship, creating profound legal friction.
The most immediate and widespread challenge is Training Data Liability and Copyright. Large language and generative models (LLMs/LGMs) are trained on massive datasets scraped from the public internet, which often includes billions of copyrighted works. The central legal dispute, exemplified by actions against Stability AI, Midjourney, and OpenAI, hinges on the doctrine of Fair Use in U.S. Copyright law. AI developers argue that the act of copying for training is transformative—creating a new technical tool rather than a substitute for the original work—and that the resulting model weights do not reproduce the original expressive work. Conversely, content creators assert that unauthorized mass reproduction of their work, even for intermediate purposes, constitutes infringement, particularly because the AI output often competes directly with their original material. Litigants are seeking unprecedented damages and, critically, injunctive relief that could halt the distribution or require the costly destruction of models trained on infringing data. The outcome will fundamentally redefine the scope of copyright in the digital age.
The second critical area is Authorship and Ownership of AI Output. Current U.S. and international IP law explicitly requires a human author for copyright registration. The U.S. Copyright Office has affirmed that it will register works only when a human has "creatively selected or arranged" the AI-generated elements, effectively ruling out AI as the sole legal author. This creates a significant ownership vacuum for purely autonomous AI creations, complicating commercialization and enforcement. A similar challenge exists in Patent Law, where the DABUS cases globally have affirmed the requirement for a human inventor, blocking AI systems from being formally recognized as such. The litigation challenge here is defining the necessary quantum of human input required to successfully claim ownership, a threshold that remains poorly defined for prompt engineering, finetuning, and curation.
Finally, Trade Secrets are the default protection mechanism for the AI model itself. Companies guard the model architecture, training data composition, and unique hyper-parameters as critical proprietary assets. Litigation often arises from employee poaching, where departing employees attempt to leverage knowledge of these secret ingredients for competitors, or through reverse engineering attempts. This choice of Trade Secret protection is often a strategic hedge against the potential invalidation risks and disclosure requirements of patenting, but it places a heavy reliance on contractual protections (NDAs, non-competes) and strong physical/digital security, all of which must be aggressively defended in court to maintain their legal standing. The current litigation landscape is rapidly shaping commercial norms, necessitating a shift from reactive defense to proactive data governance and licensing strategies.
