Open Weights Are Good. Open Source Is Better.

Understanding the Difference Between Open Weights and Open Source AI

The growing interest in open-weight AI models has sparked important conversations about accessibility, innovation, and the future of AI development. However, it is essential to clarify the distinction between open-weight models and fully Open Source AI, as the two are not equivalent in terms of the freedoms they provide.

Open-weight models release the model's weights, allowing users to adjust some aspects of the model's behavior. This provides greater flexibility compared to closed models, where users are locked into a single provider's infrastructure and fees. Users can run open-weight models locally or collaborate with third-party providers, and they can even fine-tune the model with their own data. These capabilities offer more control and customization than closed models, but they do not fully align with the principles of Open Source software.

True Open Source AI, as defined by the Open Source Initiative, goes beyond open weights by also releasing the code used to train the model and either the training data or a detailed account of how the data was built. This level of transparency enables users to fully exercise the four software freedoms: the freedom to use, study, modify, and share the model without needing permission from the rights holder.

These freedoms are critical for ensuring trust, verification, and innovation in AI systems. For example, researchers working with the Open Source AI model Olmo were able to study how the model learns by analyzing the full training dataset and model checkpoints. This kind of research is only possible when all components of the model are available for inspection and modification.

As AI becomes more integrated into daily life and cybersecurity threats increase, the ability to study and verify AI models becomes even more important. Open-weight models, while more flexible than closed models, still limit the ability to fully understand and modify the model. This is where Open Source AI provides a more robust foundation for innovation, collaboration, and trust in AI systems.

The current focus on openness in AI is a positive development, but it is important to recognize that Open Source AI represents the next step in this evolution. While open-weight models offer benefits over closed models, the full potential of AI innovation lies in the Open Source approach, which aligns with the core tenets of the Open Source movement.

Source: Open Source Initiative. Analysis based on publicly available information; no hands-on testing is claimed.