The Open Source Legacy and AI’s Licensing Challenge

Open source licensing revolutionized software development, creating a thriving ecosystem built on shared innovation and collaboration. Licenses like MIT and Apache-2.0 gave developers a standard, legally robust way to share code, reducing friction and accelerating adoption.

Today, we stand at a similar inflection point with open AI models. These models, increasingly foundational to research and industry, lack an equivalent licensing standard. Existing open source software licenses weren’t designed with AI models in mind, while most model-specific licenses are either too complex, overly restrictive, or legally ambiguous.

To fully unlock the potential of open AI, we need a license purpose-built for the realities of machine learning. That’s where OpenMDW comes in.

Why AI Models Need a New License

AI models differ fundamentally from traditional software. They are:

• Composites of multiple types of components: including code, architecture, training data, weights, documentation, and evaluation protocols.
• Subject to overlapping IP regimes: such as copyright, database rights, and trade secrets, which vary across jurisdictions.
• Distributed without a consistent definition of "open": resulting in a fragmented licensing landscape.

This complexity has led to a proliferation of bespoke, incompatible licenses that often:

• Limit redistribution, reuse, or modification.
• Fail to address legal nuances unique to models.
• Create uncertainty for developers and adopters alike.

The result? Friction in open ecosystems, legal ambiguity, and a significant barrier to collaboration and innovation.

The Origins of OpenMDW

OpenMDW,  short for Open Model, Data and Weights License  was born out of the effort to implement the Model Openness Framework (MOF). The MOF is a 3-tier classification system that defines what it means for a model to be truly “open”— not just available with limitations or use restrictions, but licensed openly across its code, architecture, parameters, training data, and documentation. 

To make MOF practical, model developers needed a simple, standard license they could drop into any repository,  just like Apache-2.0 or MIT is used in software. Something purpose-built for many types of content including models, not just code.

What Makes OpenMDW Different

OpenMDW is the first truly permissive license designed from the ground up for machine learning models. Here’s what sets it apart:

Covers the Entire Model Stack

It’s designed to apply to all components of a model release:

• Model architecture
• Parameters and checkpoints
• Training and inference code
• Preprocessing and evaluation data
• Documentation (e.g., model cards, data cards)

Importantly, OpenMDW does not require inclusion of all components. It applies only to what is distributed, while remaining compatible with many other licenses that may govern certain parts of the repository. 

(OpenMDW users will of course have to continue to comply with any other third-party licenses that apply to other pre-existing materials in their repos, such as by providing license text and notices, source code where applicable, etc.)

Comprehensive and Legally Grounded

OpenMDW grants expansive permissions including under copyright, patent, database, and trade secret law, a broad legal spectrum of rights relevant to AI artifacts.

It also includes:

• A patent litigation termination clauses to deter patent assertions by users of the model’s materials
• Attribution requirements to maintain provenance and trust

Compatible with Policy and Open Source Principles

• Intended to be fully aligned with the EU AI Act’s references to “free and open-source licenses”
• Supports the Open Source Initiative (OSI) 10 principles, including free redistribution, source availability, derived works and no discrimination against persons or groups

Designed for Simplicity

• One license, one file, one place: a LICENSE file at the root of your repo
• No complex licensing matrix: no confusion for downstream users
• Easy integration into any repo: just like MIT or Apache-2.0.

Understanding the OpenMDW License

Definitions and Scope

Model Materials under OpenMDW include:

• Model architecture and trained parameters; and
• all other related materials provided under OpenMDW, which can include:
  • Preprocessing, training and inference code
  • Datasets and evaluation scripts
  • Documentation, metadata, and tools

This comprehensive scope maps directly to the Model Openness Framework (MOF), ensuring that all critical elements of a model are covered if they are included with the distribution.

The Model Materials are not intended to be a requirement of what has to be included in the distribution. It only specifies that what is included in the distribution is covered by the license, and excludes anything covered by other licenses in the distribution.

Grant of Rights

OpenMDW grants broad rights to “deal in the Model Materials without restriction,” including for example:

• Use, modify and distribute the Model Materials
• Operate under copyright, patent, database, and trade secret laws

These rights are granted free of charge, with no field-of-use restrictions,  removing ambiguity for developers and enterprises alike.

Attribution, Not Copyleft

OpenMDW imposes only minimal obligations:

• Retain the license text
• Preserve original copyright and attribution notices

There are no copyleft or share-alike conditions, meaning derivative models and integrations can remain fully permissive. This allows for maximum reuse and interoperability.

Patent Protection

To prevent misuse of the commons, OpenMDW includes a patent-litigation termination clause: if a licensee initiates offensive patent litigation over the Model Materials, their license is revoked.

This mirrors best practices in open source software and helps preserve a collaborative ecosystem.

Outputs Are Unrestricted

A major innovation: outputs generated by using a model under OpenMDW are completely free of licensing restrictions imposed by the provider of the Model Materials.

This eliminates confusion over whether generated text, images, code or predictions are encumbered  by the model provider— a common point of uncertainty in existing licenses.

How to Adopt OpenMDW

Adopting OpenMDW is straightforward:

1. Add the OpenMDW-1.0 license file to your repository: LICENSE
2. Clearly indicate that your release is under OpenMDW-1.0 in the README
3. Ensure all components of the model package are covered and disclosed, including prominently highlighting any components that are subject to other licenses

Why This Matters Now

The AI community is reaching an inflection point. Open models  from AI2’s Molmo to Mistral, and open reasoning models like DeepSeek’s R1 to multimodal agents  are reshaping what’s possible in the open. But their licensing status remains hard to characterize, since software licenses may not map cleanly onto AI models.

Some open weights models which use restrictive licenses have become gradually more permissive; but without a strong legal framework available for licensing, model producers have been forced to err towards the side of caution in designing their own licenses. 

In his recent post, Nathan Lambert of AI2 rightly notes: “One of the long standing todo items for open-source AI is better licenses”, OpenMDW helps to fill that need.

Just as Apache-2.0 and MIT became foundational licenses for open source software, OpenMDW is positioned to become the standard for open models. Its clarity, scope, and permissiveness lower barriers for developers and create certainty for companies and researchers looking to build responsibly on open foundations.

This isn’t just about legal clarity,  it’s about enabling an innovation-rich and open source AI ecosystem.

Visit openmdw.ai for more details including the FAQ.