Takeaways
- Use in commerce: AI-generated outputs containing third-party marks may constitute use in commerce even when the reproduction is unintentional, particularly where the AI tool competes with the trademark owner’s products. The Court left this novel question open while signaling that accidental reproduction of a mark may not automatically shield AI developers from trademark liability.
- Likelihood of confusion: Distorted or modified marks appearing on competing AI-generated outputs can support a likelihood of confusion claim at the pleading stage, even without evidence of actual confusion.
- Passing off vs. reverse passing off: Dastar does not bar “passing off” claims where AI outputs bear another’s mark; instead, it bars “reverse passing off” claims where a defendant distributes plaintiff’s work without attribution.
- Famous marks: Trademark dilution claims can survive at the pleading stage where the plaintiff pleads specific facts supporting household-name status, including search volume, customer counts, and presence in major publications.
- Training data risks: Training AI models on watermarked or trademarked content may expose them to trademark liability beyond copyright concerns to the extent models reproduce those marks in their outputs.
- Post-knowledge inaction: Awareness of trademark reproduction in AI outputs without adequate remediation may strengthen a plaintiff’s case for infringement.