/>iFor many reasons, existing open source licenses are not a good fit for AI. Simply put, AI involves more than just software and most open source licenses are designed primarily for software. Much work has been done by many groups to assess the open source license requirements for AI. For example, the OSI has published its version of an AI open source definition – The Open Source AI Definition – 1.0. Recently, the Linux Foundation published a draft of the Open Model Definition and Weight (OpenMDW) License.
The OpenMDW License is a permissive license specifically designed for use with machine‑learning models and their related artifacts, collectively referred to as “Model Materials.” “Model Materials” include machine‑learning models (including architecture and parameters) along with all related artifacts—such as datasets, documentation, preprocessing and inference code, evaluation assets, and supporting tools—provided in the distribution. This inclusive definition purports to align with the OSI’s Open Source Definition and the Model Openness Framework, covering code, data, weights, metadata, and documentation without mandating that every component be released. The Model Openness Framework is a three-tiered ranked classification system that rates machine learning models based on their completeness and openness, following open science principles.
The OpenMDW License is a permissive license, akin to the Apache or MIT license. It grants a royalty free, unrestricted license to use, modify, distribute, and otherwise “deal in” the Model Materials under all applicable intellectual‑property regimes—including copyright, patent, database, and trade‑secret rights. This broad grant is designed to eliminate ambiguity around the legal permissions needed to work with AI assets.
The primary substantive compliance obligation imposed by OpenMDW is preservation of the license itself. Any redistribution of Model Materials must include (1) a copy of the OpenMDW Agreement and (2) all original copyright and origin notices. Compliance is as easy as placing a single LICENSE file at the root of the repository. There are no copyleft or share‑alike requirements, ensuring that derivative works and integrations remain as unconstrained as possible.
There is however a patent‑litigation‑termination clause. If a licensee initiates litigation alleging that the Model Materials infringe their patents—except as a defensive response to a suit first brought against them—all rights granted to that licensee under the OpenMDW are terminated. This provision serves to discourage aggressive patent actions that could undermine open collaboration.
Any outputs generated by using the Model Materials are free of license restrictions or obligations. The license also disclaims all warranties and liabilities “to the greatest extent permissible under applicable law,” placing responsibility for due diligence and rights clearance squarely on the licensee.
We all know that AI will be transformative, but we do not yet know all the ways in which it will be so. One of the transformations that AI will undoubtedly drive is a redefinition of what it means to be “open source” and the type of open source AI licenses. As a leader of my firm’s Open Source Team and its AI Team, the intersection of these areas is near and dear to my heart. While many lawyers and developers may not yet have focused on this, it will be a HUGE issue. If you have not yet done so, now is a good time to start.
One of the core issues is that traditionally, under an open source license, the source code is made available so others can copy, inspect, modify and redistribute software based thereon. With AI, the code alone is often not enough to accomplish those purposes. In many cases, other things are or may be necessary such as the training data, model weights and other non-code aspects that are important to AI. This issue is significant in many ways. So much so that, as mentioned above, the Open Source Initiative, stewards of the Open Source definition, developed the Open Source AI Definition 1.0 to REDEFINE the meaning of open source in the context of AI. To learn more about these issues, check out the OSI Deep Dive initiative here.
