What Counts As Open-Source?

When Meta Platforms’ chief artificial intelligence scientist Yann LeCun last month posted on LinkedIn about Meta’s strategy of releasing its large language models for free, some commenters praised its approach, saying it was “reshaping industry collaborations.” Others disagreed with LeCun’s description of the strategy as “open-source.”

“They should absolutely get credit for open model NOT open source—calling something open source without being open source is really misrepresenting the open source movement,” one commenter wrote. “It's a shame open-source in this context is just marketing spin for data laundering,” another said. 

A third suggested that Meta refer to LLMs such as its Llama model as “open-weight” instead of “open-source,” because the company shares model weights, or the settings that determine how a model responds to queries, but not information like training data.

This may seem like an argument over semantics. But whether some models—including those from Meta, France’s Mistral and Germany’s Aleph Alpha—are truly open-source has become a recurring debate among people who work on AI, particularly academic researchers.