The difference is that with software before the rise of giant machine-learning models, the software is derived entirely from human-understandable source, and often a full software stack can be built on a single computer, as anyone who ever ran Gentoo knows. While the source of any non-trivial software stack is too large for any single human to understand in full, a single human can understand any part of it. That's not true of model weights. And my understanding is that even the smallest open-weights models that are remotely in the same class as LLMs require large computing clusters with powerful GPUs to train. That puts training a model, or retraining one from its true source materials (the training data and processes), in an entirely different class than rebuilding Gentoo, even with GNOME, Firefox, LibreOffice, etc., even if the training data and processes are available, which they're usually not. So, we should treat these models as a different kind of thing than the tools that we developers have worked with until now.