Tarek Ziadé

Why Open Source Is Fundamental in AI (Essay)

2025-12-18

Artificial intelligence is becoming a foundational layer of modern software. It is no longer confined to research labs, but embedded directly in everyday tools and user experiences.

As AI moves closer to users, openness becomes a question of power. Who can inspect these systems? Who can adapt them? And who ultimately controls how they evolve?

The web offers a useful reference point. Open source software and open standards turned the World Wide Web into shared infrastructure rather than a proprietary stack owned by a single company or government, even if many tried to enclose parts of it. That openness was not accidental. It shaped who could participate, compete, and be held accountable.

What Open Source AI Enables

Open source AI is often reduced to code availability. In practice, and as the Open Source Initiative (OSI) emphasizes in its Open Source AI Definition, it is about concrete freedoms.

An open source AI system can be used, studied, modified, and shared. Studying means inspecting behavior, limits, and failure modes. Modifying means adapting models to new domains, languages, or constraints. Sharing means deploying systems without being locked to a single vendor or API.

These freedoms must apply not only to code, but also to models, weights, and the tooling required to run them. Without that access, reuse is brittle and understanding remains shallow.

Open source enables verification, reproducibility, and portability. It allows systems to be audited, adapted, and redeployed independently. In a field defined by cost, scale, and complexity, these are not luxuries. They are prerequisites for agency.

Open access does not eliminate power imbalances. Compute, data, and expertise still matter. But it preserves the possibility of independent action, which is often the difference between participation and dependency.

Open Standards and Shared Infrastructure

Open source alone is not enough. Open standards define shared interfaces that allow independently built systems to work together.

The web proved this model at global scale. By separating interfaces from implementations, standards enabled competition without fragmentation. In AI, standards around model formats, inference interfaces, evaluation, and data documentation can lower switching costs and prevent ecosystems from hardening into silos controlled by a few gatekeepers.

Without standards, “openness” risks collapsing into a collection of incompatible artifacts, each tied to its own platform or service.

Looking Ahead

Some infrastructure works best when treated as a common good. The web’s resilience came from the fact that no single actor owned its foundations.

AI is on track to become similar infrastructure. The question is not whether it will be powerful, but whether it will be governable.

If core models, datasets, and interfaces are only accessible through proprietary APIs and cloud platforms, then “AI adoption” will mostly mean dependency. Choice will be limited to pricing tiers, usage caps, and terms of service.

Not everything should be owned and monetized by a small number of companies. Projects like Mozilla’s Common Voice show that shared assets can be built and maintained in the open, at meaningful scale.

Shared infrastructure also depends on shared spaces. Platforms like Hugging Face play a critical role by enabling collaboration around models, datasets, and tools, and by lowering the barrier to participation in open AI ecosystems.

Open source and open standards are not about nostalgia or ideology. They are about keeping the option to walk away. To inspect. To fork. To rebuild.

Once that option is gone, it is rarely recovered.

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