This is an interesting area for experiments. I suspect that in the longer term model optimization (knowing which bits you can leave out without affecting the functioning of the model) will become the dominant area of research just like it did with compression algorithms because effectively a model is a lossy compression scheme.

And that's good because that increases democratization of AI away from the silos that are being created.

The compression analogy is interesting. Another way of looking at it could be fine-tuning as "knowing what to leave out" - a 3B model for example tuned for a narrow task doesn't need the capacity that makes 70B good at many things.