Open weights are not open source: 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.