I'm not in this subsection of the industry, so take this with a heaping grain of salt. I'm just using my own biases here. But I get the sense that this is one

I'm not in this subsection of the industry, so take this with a heaping grain of salt. I'm just using my own biases here. But I get the sense that this is one of a number of big pieces of the puzzle of how current cutting edge models can be cut down in operational size. I have a feeling that new methods of creating models will stick around, but any deep learning model can be reduced to a less resource intensive approximation through findings such as this. And if (IF!) so a lot of AI hype is going to hit a real post-hype cycle plateau of productivity because all this stuff will become easier and cost effective to deploy to the real world.