Open weights (vs open source)

Plain English. Releasing a model's trained parameters — the weights — for anyone to download, run and fine-tune. That is not open source, though it is routinely called that: the training data, training code and recipe usually stay private, so you can operate and modify the artefact but not reproduce or audit how it was made. Licences vary too; some "open" releases carry commercial restrictions.

Why it moves money. Open weights turn the model itself into a commodity input. Once a near-frontier model is a free download, the price a closed API can charge is capped by the cost of running the open alternative — and the releasing lab is betting on a different prize: becoming the base layer an ecosystem builds on, with the distribution, talent and standards position that brings. It is a margin-destroying weapon for the releaser's competitors and a moat of a different shape for the releaser.

What to watch. Derivative counts and share of tokens actually served, which measure whether a release became infrastructure — and the licence terms, which decide who may build on it commercially.

From the signals. Tim O'Reilly's argument that the open source question is about architecture, not weights. Hugging Face's review: Chinese labs set the open-weights size ceiling in almost every month of 2026. Qwen is now the base layer: 151,448 derivative repositories, 2.6 times Meta's footprint.