The OSI has its own definition of open source AI: the Open Source AI Definition (OSAID 1.0). It requires model parameters, including weights, to be made available under OSI-approved terms, but does not prescribe a specific legal mechanism for doing so.
The Open Source Initiative’s Open Source AI Definition requires opening the parameters, the complete training and inference code, and enough information about the data to rebuild a substantially equivalent model.
Under the Open Source Initiative’s Open Source AI Definition, truly open-source AI requires more than downloadable weights. Users need broad rights to use, study, modify and share the system, along with access to parameters, relevant code and sufficient information about the training data and process to meaningfully modify the system. Many popular models commonly described as open source therefore fit more accurately into the broader open-weight category.
OSI released its first official draft of the OSAID in 2025. The OSAID closely tracks the OSI’s criteria for open source software.
As noted by The Open Source Initiative (OSI), a nonprofit, open source models are fully transparent. Not only can users download the models to run locally on their machines, but their full training code and training datasets (or at least data sources, if the datasets aren’t legally permitted to be copied) are also available for examination.
According to the Open Source Initiative (OSI), a truly open-source model also releases the data it was trained on, along with other information allowing those models to be studied, inspected, used, modified and freely distributed.
“We welcome Hugging Face’s work on the benefits of ‘open’ models, and we need more of it from everyone who has ever built on open source,” said Katie Steen-James, a senior U.S. policy manager at the nonprofit Open Source Initiative, which promotes open-source software.
The difference between open-source AI and weights, according to the Open Source Initiative (OSI), is that open weights usually means the trained weights are available, but not necessarily the full training data or source code. The two terms are frequently conflated. The OSI itself declared, “Every AI system in the headlines today, whether proprietary or open source, exists because researchers shared their work openly.” Without open source, there is no AI.
“Open-source” is not a marketing register. It is a standard with specific criteria. For over two decades, the Open Source Initiative has maintained the definition that governs the term in software: the freedom to use, study, modify, and share, for anyone and for any purpose. In 2024, recognizing that model releases were stretching the label past recognition, OSI extended this into the Open Source AI Definition, which requires disclosure of the complete code used in training and sufficiently detailed information about the training data for others to understand and recreate the system.
Open source is no longer just a software movement — it is the foundation the entire AI stack is built on, from CUDA to Kubernetes to the models themselves. Duane O’Brien, the new Executive Director of the Open Source Initiative (OSI), joins Alan Shimel on TechStrong TV to talk about the organization that has shepherded the Open Source Definition since 1998, why OSI-approved licenses are the Good Housekeeping Seal of the software world, and how OSI is taking on its biggest challenge yet — defining what open source actually means in the age of AI.
Our Vision on AI openness opportunities and shared language aims to provide greater clarity in the use of terminology describing AI openness. We expect this Vision to help extend the existing benefits of AI openness in science, innovation, and economic growth, as highlighted in the OECD G7 Discussion paper on The Benefits of AI Openness, while ensuring trust in technologies and access to a greater diversity of models. We welcome the valuable contribution of the Open Source Initiative, as well as other members of the community, in supporting the development of this document.
L’open source : une « Vision commune sur l’ouverture de l’IA » a été adoptée, clarifiant les principes techniques et économiques des modèles ouverts. Ce cadre, négocié avec l’Open Source Initiative et l’OCDE, vise à éviter les dérives et à favoriser l’innovation collaborative.
