Artificial intelligence (AI) makes it easy to create, remix, and distribute content at scale, and that speed is a significant part of its value. It is also where intellectual property (IP) risk can creep in. That risk is not limited to the end user generating an AI output. It can also extend to the companies that build the tool, host it, integrate it into other products, or deploy it for customers.
A useful legal reference point is MGM Studios Inc. v. Grokster, Ltd., 545 U.S. 913 (2005), a seminal case on secondary liability. Grokster distributed peer-to-peer software with lawful uses, but the case turned on whether the company encouraged infringement. The Supreme Court focused on inducement, finding that even if a product can be used for legal purposes, a company can still face secondary liability if its messaging, product choices, or business model appear designed to drive impermissible infringement.
That idea carries over to AI models today, which can be general purpose, but disputes often turn on what the product is steering users to do. Then, once credible warning signs appear, attention shifts to how the company responds.
If you are assessing how an AI secondary liability claim might be framed, consider these questions.
- What are we encouraging, even indirectly? Marketing copy, tutorials, example prompts, and default workflows can read like a “how-to” guide. If templates aim for near replicas of branded characters, a plaintiff may argue the product is being sold with infringement in mind.
- Can we tell a strong lawful-use story? “Substantial non-infringing use” matters most when it is real and central to the product. A tool used primarily for internal drafting, meeting summaries, and transforming a company’s own materials is easier to defend than a tool whose primary intended workflow is rewriting paywalled articles.
- What do we know, and when did we know it? Credible notices, repeated complaints, and internal metrics that point to obvious infringement patterns can make a lack of knowledge argument hard to sustain. After a certain point, inaction can start to be perceived as a decision in itself.
- How much control do we have, and are we monetizing the risk? If you can supervise use through accounts, moderation, or termination rights, and you profit directly from high-volume usage, claimants may argue you had both the ability to intervene and a financial incentive not to.
To maintain the most defensible posture, companies should maintain documented, repeatable governance across the AI lifecycle, including training data traceability, policies for customer fine-tuning on third party content, monitoring for output patterns that suggest replication, and a clear process for handling repeat users who push high-risk requests. Product features, contract language, and marketing materials should also be aligned so your claims about the tool match what it actually does. The goal is to be able to show that you anticipated foreseeable risks, made reasonable design and operational choices to mitigate them, and improved based on what you observed in production.