The article is here; the Introduction:
The era of generative artificial intelligence ("Generative AI") has begun, whether we want it to or not. As this Article explains, we also now have new methods for creating, losing, disseminating, and even leaking trade secrets as a result. Indeed, from ingesting trade secrets in its training data to sharing trade secrets in response to queries, Generative AI opens new challenges to trade secrecy even while it adds to an information ecosystem that thrives on knowledge dissemination. This Article is the first to examine this new and immediate challenge and its trade secrets implications. It is written not only to frame the discussion about Generative AI and trade secrecy, but also the impact of Generative AI on information control and flows more broadly, for future analysis.
Emerging from the debates in technology and academic circles about solving problems through massive computing power and automated decision-making, algorithmic discrimination, and privacy, Generative AI is now approaching the forefront of the basic questions of what it means to be human. Unusually, we can trace the day that this happened to November 30, 2022, the day that a hitherto largely unknown company, OpenAI, unilaterally decided to release its Generative AI, ChatGPT, for public consumption and use.
Generative AI is defined as
a set of algorithms, capable of generating seemingly new, realistic content—such as text, images, or audio—from the training data. The most powerful generative AI algorithms are built on top of foundation models that are trained on a vast quantity of unlabeled data in a self-supervised way to identify underlying patterns for a wide range of tasks.
Beyond the grand philosophical questions, AI also raises fundamental questions as to intellectual property law and information flows, because Generative AI creates. As Dan Burk explains in his recent article,
