USA – MASSACHUSETTS Trends and Developments Contributed by: Paul A. Hughes, Evan S. Kipperman, Daniela Badiola Spanos, Katherine “Katie” Rubino, Tamia Simonis and Mark W. Heaphy, Wiggin and Dana LLP
or that produce effects in the EU, wherever the entity may be based. IP Strategy In 2025, Boston was ranked as a state with one of the highest AI ecosystems throughout the continental United States (see here ). As companies across every sector accelerate their adoption of AI tools, long- standing assumptions about patentability and inven - torship are being tested. This shift is prompting organ - isations to rethink traditional intellectual property (IP) strategies and adapt them to a world where human and machine contributions increasingly intertwine. A foundational principle of US patent law is that only a natural person can be named as an inventor. The US Patent and Trademark Office (USPTO)’s recent Inventorship Guidance for AI‑Assisted Inventions reaffirms this requirement, even as AI systems play a growing role in generating inventive concepts. US copyright law follows a similar rule: human authorship is required. For patent applicants, inventions that incorporate AI-generated inputs or outputs raise new questions about obviousness, enablement and disclosure at the USPTO. Inventors must now determine how much detail to provide about their AI models, such as training data and model parameters, to demonstrate human conception and reduction to practice. The line between human insight and machine-generated out - put is becoming more complex to document. AI systems also rely heavily on large, often sensitive datasets, including genomic sequences, compound libraries, biomarker repositories and user-generated information. As AI models are increasingly trained on combinations of proprietary, licensed and open‑source data, companies must confront difficult questions about data ownership, rights to AI-derived insights, public disclosure of sensitive information into open source models, and how to protect valuable datasets themselves. These issues are especially pressing in life sciences, where data assets can be as valuable as the inventions they enable. The rapid integration of AI into research and develop - ment is creating a heightened need for cross-function -
al collaboration. Legal, scientific and technical teams must work together to document human contributions to AI‑assisted inventions, negotiate licensing agree - ments that address data and model usage, and ensure compliance with evolving global standards. While AI is accelerating innovation at an unprec - edented pace, it is also destabilising traditional IP structures. Companies that wish to safeguard their competitive advantage must proactively adapt their IP strategies to the realities of AI‑enabled discovery and development. Sourcing and Technology Transactions The exponential proliferation of AI and deep machine learning across nearly every category of sourcing, technology and managed services is rapidly reshap - ing legal and operational approaches to deal making. AI-driven technologies are a material component of nearly every services transaction today. This new par - adigm has necessitated a rethinking of market norms and established approaches to key contractual terms, including IP ownership, service level agreements, per - formance warranties, indemnification provisions, and limitations on the parties’ liability to one another. IP ownership Under most legal systems, including US copyright law, a work must be authored by a human to qualify for copyright protection. When a provider uses generative AI to produce code, designs, reports or other deliv - erables, the protectability of those outputs and who owns them becomes legally uncertain. As a result, customers increasingly demand explicit contractual terms that address AI-generated outputs separately from human-authored work. These terms often take a “belt-and-suspenders” approach: a work-for-hire designation where possible, a broad assignment of rights (including a waiver of moral rights where permit - ted), and a fallback perpetual licence in case neither of those is effective. Providers frequently rely on proprietary or third-par - ty AI systems, including large language models and machine learning, to improve productivity and efficien - cy. This creates several negotiation pressure points. Providers typically argue that their models, training data, algorithms and fine-tuned configurations are
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