It’s not advances on the underlying operation of matrix multiplication that have driven ai progress to date. It’s the layers above that; trying different neural architectures (transformers w/attention mechanisms), and also different data and training regimes (different ways of doing reinforcement learning) that are the main drivers of improved performance. Perpetual motion is a physical impossibility. Whereas Ai is already being used to improve the workflow of ai researchers, thus speeding up improvements in said research. It’s not hard to see that AI could well be spun up to continue to try new arrangements of the aforementioned levers that drive ai progress on its own.