Beyond the Code: Defining Open Source AI

The tech industry is currently witnessing a linguistic land grab. As AI floods the ecosystem, the definition of "Open Source" is being stretched to its breaking point. While traditional Open Source Software relies on deterministic code and transparent licenses, the non-linear, opaque nature of Large Language Models challenges these old paradigms. Without a rigorous redefinition of openness, we risk a future of black-box innovation.

To bridge this gap, we will start by establishing a baseline understanding of AI mechanics. With this core understanding established, we will deconstruct the AI stack to understand why traditional software licenses fail. We’ll move beyond the surface level to explore:

• The Pull Request Paradox: Why modifying a model’s behavior is fundamentally different from a standard pull request and what that means for community contribution.
• Sovereignty vs. Access: A deep dive into the "Openness Spectrum", comparing different Open Source AI standards, including the Open Source AI Definition by the Open Source Initiative.
• The Anatomy of Openwashing: How providers use the "Open" label to gain community trust while gatekeeping the curation of training data and the compute recipes essential for true Open Source AI.

Attendees will walk away with a comprehensive framework for evaluating AI "openness", a technical understanding of why "open weights" does not equal "open source", and the vocabulary needed to advocate for true digital sovereignty in their communities or organizations.