In this episode of Tech Talks, Julian Dibbell is joined by partner Brian Nolan and associate Megan Fitzgerald to unpack how companies can protect AI assets and outputs using IP strategies. The conversation maps the key protectable components of AI—algorithms and code, trained models and parameters, proprietary datasets, and outputs—and evaluates the strengths and limits of trade secrets, copyrights, patents, and contracts. They highlight why trade secrets are particularly powerful for AI while probing emerging “improper means” issues like scraping, prompt injection, and ToS violations. They also survey evolving copyright law on human authorship and fair use in training, and discuss patent inventorship guidance and eligibility trends, before closing with practical contracting approaches to allocate data rights, output ownership, and IP strategy.
Show Notes:
00:02 Introduction to Protecting AI Assets and Outputs
02:00 Protectable AI Assets: Algorithms, Models, Data, Outputs
06:07 IP Toolkit: Trade Secrets, Copyright, Patents, Contracts
09:55 “Improper means” in AI: Scraping, Prompts, ToS
12:44 Using Copyright to Protect AI
16:21 Copyright: Human Authorship, Code Protection, Fair Use
21:27 Patents: Inventorship Guidance, Eligibility, Open Issues
29:58 Contracts: Data Rights, Output Ownership, Strategy
