When software was new, "open source" meant transparency, cooperation, and freedom. Coders had access to source code, could modify it, and redistribute it. But with the age of AI, the term open source is becoming hazy—and contentious.
The AI models, and especially large language models (LLMs), are highly advanced and computationally costly to train. Others claim they are open source but offer only pieces of their models—e.g., weights without training data or code without documentation. This put a demand on terms such as "open-weight" or "partially open" that don't fully represent the whole essence of the original open source.
Efforts like Meta's LLaMA and Mistral have brought the debate further by publishing powerful models with less restriction, but even those stop short of complete openness. Licenses enter into this picture very much—most so-called "open" models contain usage limitations that ban commercial deployment or derivative work.
Real open source AI would mean everything available: model design, training code, datasets, weights, and a license permitting reuse and adaptation. However, in practice, competitive pressures, safety concerns, and compute cost render such openness exceptional.
In this new era, "open source" all too often means "open enough to experiment with, but not to challenge." This evolution undermines the values of the open source movement, raising ethical and pragmatic concerns about transparency, control, and accessibility.
As AI becomes more central to society, the debate over what open source truly means—and who gets to define it—will only intensify. If we’re not careful, the term “open” could become little more than a marketing label.
Real openness in AI must go beyond sharing code—it must reflect a commitment to transparency, reproducibility, and equitable access to innovation.
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