A new project aims to build a shared, open source AI training dataset that anyone can contribute to, much like an open source software project. It aims to make training data more transparent than that of many open-weight models that have recently taken the industry by storm. CentOS and Rocky Linux founder Gregory Kurtzer is behind the effort, dubbed Open Weights, Artifacts, Licenses, Data, Origins (OpenWALDO), and it's funded by CIQ, his AI infrastructure company, which also sponsors Rocky Linux. Kurtzer described the effort as trying to bring the open-source ethos to AI model design, which has yet to be truly open – even downloadable open-weight models still have closed-source training data that is unknown to users, alongside other limitations that make them less than truly open source. “I’ve spent my career watching open source turn users into builders, competitors into collaborators, and shared problems into common infrastructure that operates at massive scale,” Kurtzer said in the announcement. “OpenWALDO brings that proven model to AI. Let’s work together, build its foundation in the open, and collaboratively take AI to the next level.” CIQ, which authored the announcement, argues that open-weight models keep that foundation a secret because of where it comes from: Copyrighted data, responses distilled from other models, user-generated content that may not have been given in a truly open manner, and the like. “There is often no way to know what data trained a given model, under what license, or with what consent,” CIQ said, adding that hidden training data content could taint models, putting customer software stacks at risk. In addition to that, there’s the simple fact that, when everyone is training their AI models in secret, a lot of duplicate work is happening that wastes lots of time and computing resources. A single, shared set of public training data, the OpenWALDO team argues, would not only make training more efficient across the industry, but also mean that every improvement to the dataset could benefit future models trained on it. “A lab or company can take the corpus and its bill of materials as a verified baseline, add its own proprietary data, build, and ship, with a clear, auditable line back to its sources,” CIQ explained. With prices steep and ROI still largely absent, open AI models (not to be confused with OpenAI models) have risen to prominence in the AI zeitgeist lately. Models out of the home of open-weight AI, China, are closing in on the capabilities of closed-source frontier lab models like ChatGPT and Claude, leaving many businesses wondering why they ought to pay through the nose for AI services they don’t own, can’t truly control, and have no visibility into. Some frontier labs have warned that open-weight models pose security and misuse risks. Kurtzer argues that open source software faced similar concerns. “Open source has won this argument before,” he said, pointing to similar arguments made about open code, namely that it’s insecure, impossible to trust, and the like. “Linux didn't win by being certified safe. It won by being inspectable, forkable, and community validated.” “AI is missing that same property, and OpenWALDO is how we build it,” Kurtzer said. Turning to open-source training datasets is a big ask for an industry already so far down the closed training data path, of course, and only time will tell if OpenWALDO is a revolution or another obscure OSS project that gets minimal attention from the AI community. So far, the OpenWALDO dataset contains 167.3 billion reference tokens pulled from things like government records, open-source academic papers, mailing lists, and public domain literature - a drop in the bucket next to the tens of trillions of tokens used to train frontier AI models and their open-weight counterparts. We asked if anyone has trained a model on the OpenWALDO set yet, but CIQ didn’t respond. Those interested in contributing to, or making use of, OpenWALDO can find more on the project’s website (linked above) and its GitHub page. ®
