How open is your opensource project - the varying degrees of freedom

The term "open source" often implies freedom, transparency, and collaboration, but in reality, openness exists on a spectrum. Some projects are fully community-driven, while others are controlled by a select group of maintainers. Some software is source-available but with restrictive licenses, while others embrace permissive models. And in the world of AI and machine learning, the definition of "open" is even more complex—where does true openness lie when models release weights but keep training data closed?

This session explores the many dimensions of open-source freedom, breaking it down into key areas such as:

• Governance models – Who truly controls decision-making?
• Licensing and restrictions – From permissive (MIT, Apache) to copyleft (GPL) to source-available licenses (BSL, RAIL).
• Open contribution vs. open maintenance – Are contributions from the community welcomed or just visible?
• AI and "Open" Models – Open weights, open data, and why the debate around AI openness is heating up.
• Corporate-backed vs. grassroots open-source – The power dynamics shaping the ecosystem.

As open source becomes increasingly commercialized, understanding these nuances is critical for developers, maintainers, and businesses alike. This session will provide a framework for evaluating openness, share real-world examples, and challenge the audience to rethink what "open" really means in the modern tech landscape.

Whether you're an open-source enthusiast, project maintainer, AI researcher, or policy maker, this talk will equip you with insights to navigate the evolving definitions of open source and make informed choices about the software you build, use, and contribute to.