Why AI’s favorite label is under dispute

The AI industry likes to abuse the word “open.” It appears in product releases, research papers, policy debates, and investor presentations. A company publishes model files to Hugging Face, developers run them on their own GPUs, and the release is quickly described as an “open source model.” Not necessarily. It may only be open-weight.

The difference is more than a technicality. It determines whether you can merely deploy a completed neural network or whether you can meaningfully inspect, reproduce, alter, and redistribute the system that produced it. A genuinely open source system should grant the freedom to do all of the above.

Weights are the learned numerical parameters created by training. Together with the model architecture and inference code, they allow a large language model (LLM) to function. You can download an open-weight model, self-host it, fine-tune it on internal documents, and avoid routing prompts through a proprietary API.

Open weights are publicly available. They matter because running them locally can offer greater control over data, privacy, costs, supplier API changes, and vendor lock-in. They’ve also helped build a large ecosystem of local-model runtimes, inference providers, fine-tuning tools, and specialized downstream models.

The Open Source Initiative (OSI), steward of the Open Source Definition (OSD), makes the distinction directly: “Open Weights refer to the final weights and biases of a trained neural network.” Those values determine how a model interprets prompts and produces outputs. Releasing them can let others fine-tune, adapt, or deploy the model. But the OSI adds that weights alone expose only “a fraction of the information required for full accountability.”

As James Landay, director of the Stanford Institute for Human-Centered AI (HAI), explained: “Open weights are progress. You can download the model, run it on your own machine, keep it out of someone else’s data pipeline. But you still can’t see how the thing was built, what it was trained on, or why it behaves the way it does. That’s not an open model. That’s open distribution.”

Without the training data or sufficiently detailed documentation, outsiders cannot determine which sources were used, what copyrighted or private material may have been included, how data was selected or removed, which languages and communities were underrepresented, whether benchmark data leaked into training, or what alignment and safety methods affected the model after pretraining.

Landay continued: “There’s a wide gap between open-weight AI and open source AI.” He contends that unless developers disclose training data or provide a “thoroughly documented, auditable account of it,” you can’t test, reproduce, or challenge the work in the fullest sense.

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.

Luca Antiga, CTO of Lightning AI and a prominent PyTorch contributor, has argued that OSAID’s treatment of weights leaves “a gaping hole that will make licenses less effective in determining whether OSI-licensed AI systems can be adopted in real-world contexts.”

Other open source figures have also criticized OSAID. Bruce Perens, author of the original OSD, denounced the OSAID in 2024. He later declared: “It’s not Open Source! … It’s unfortunate that the Open Source Initiative itself is now involved in Openwashing.”

He’s far from alone. Bradley Kuhn, policy fellow and hacker-in-residence at the Software Freedom Conservancy (SFC), and Red Hat Senior Commercial Counsel Richard Fontana have called for OSAID to be repealed, arguing: “The OSI acted too quickly to impose an overly ambitious policy compromise on the community. OSAID undeniably created a rift in the FOSS community; that rift seriously damaged the OSI’s reputation, authority, and influence. Meanwhile, OSAID shows no signs of having any positive policy influence on machine learning practitioners, the FOSS community, or regulators.”

OSI acknowledged when OSAID 1.0 was released in October 2024 that the definition would continue to evolve. Critics contend that its central shortcomings have yet to be resolved.

That said, the Linux Foundation’s Mike Dolan submitted the Open Model, Data, and Weights (OpenMDW) license to the OSI. The license has been around since 2025 and lists contributors from Amazon, Meta, IBM, Microsoft, and Nvidia, giving it substantial industry backing.

Conventional open source revolves around source code. LLMs are a different kettle of fish: they combine code, architecture, and numerical weights derived from training datasets that may be proprietary, copyrighted, or undisclosed. OpenMDW’s answer is to define separate terms for a model’s architecture, training data, and weights, bringing the components supplied by a licensor under one agreement.

It sounds reasonable to me, but the submission has encountered objections on OSI’s license review mailing list. As Stefano Maffulli, OSI’s former executive director, who led the organization while OSAID was being formulated, said: “I continue getting the impression that the OpenMDW review is tainted by an ideological bias: Because we don’t like big tech and AI now is big tech, then we don’t like AI; therefore, we’ll do anything to block it.”

It’s too late to bury our heads in the sand. As Stanford’s Landay put it: “Open weights answer ‘Can I run this?’ Open source answers ‘Can I trust this, improve it, and build the next thing on top of it?’ Right now almost everyone – American labs and Chinese labs alike – is answering the first question but nowhere close to the second.”

We need both. Whether OSI adopts OpenMDW is an open question. Still, OpenMDW and its supporters are at least trying to establish licensing terms that cover code, data, and weights together. Unless someone succeeds, “open AI” risks becoming an oxymoron – or merely another hollow tech marketing term. ®