I think the idea or methodology is something along the line of one starts with the local models then when they hit a wall then continue with their current results in a frontier model, thus the other half is those last bits one could not compute locally.
If you are only using chatbots for code gen then for sure this is a reasonable approach. If you're trying to learn something then your best bet is to not have the lowest jpg quality version of human knowledge be your teacher.
We could probably get the same results by offloading 20% of obviously smaller / simpler / well defined tasks to smaller models and keep the 80% for ones that benefit from the big iron. Not the 80/20 they were referring to, but still it's something.
I find myself wasting time on smaller models or wasting money on frontier models. I only have so much of either.