Maggie Welsh and Parker Hancock break down what to watch for in 2026 with respect to AI and the law. Parker provides insights on how new AI behaviors collide with long‑standing laws, why accountability falls on companies, and what in‑house teams must prioritize as AI agents start touching core systems.

Maggie Welsh and Julie Albert expose “AI washing”—overstated or misleading AI marketing—and how the FTC, SEC, and NAD are ramping up enforcement. Drawing lessons from greenwashing, they unpack real risks, common red flags, and what regulators expect. Julie shares a compliance-ready framework to make truthful, substantiated AI claims that build trust, showcase real capabilities, and avoid penalties or competitor challenges.

Maggie Welsh speaks with Nicole Spence, Brand Counsel for Generative AI Tools at IBM. They discuss data concerns, including the critical importance of data privacy, data quality, and bias mitigation. The discussion also highlights current and emerging legal frameworks around intellectual property rights.

Maggie Welsh has a conversation with Mike Silliman, discussing the synergistic relationship between AI and clean tech. He covers how AI drives both energy demand and solutions for climate change, and why AI innovation is slower in clean tech compared to other sectors. Mike also delves into challenges such as data interoperability, regulatory complexity, and venture funding.

Maggie Welsh and Joe Cahill discuss the latest report from the Copyright Office on fair use in AI model training. The report delves into the use of copyrighted works during AI development and considerations around copyright infringement and fair use. Key topics include data collection, training processes, and the complexities of licensing in AI. The nuanced legal landscape requires careful analysis of specific cases, emphasizing the importance of understanding both the technology and legal precedents.

Maggie Welsh and Julie Albert discuss the challenges of protecting trade secrets in AI. They cover the definition of trade secrets, methods for safeguarding valuable information, and the risks posed by unauthorized access and data scraping. Emphasizing the importance of understanding and securing AI tools, they provide insights on maintaining confidentiality in the evolving AI landscape.