The real hardware story is hiding in the software releases. llama.cpp's relentless weekly iterations (b10076, b10075, b10069, b10068) matter more than any $450M semiconductor funding round because they're proving efficient inference on consumer hardware is outpacing the "build bigger chips" narrative—what actually changes how AI gets deployed. Japan's FRONTia and the parade of hyperscaler infrastructure deals are noise; watch instead whether these optimized inference frameworks crack the latency/power constraints that make edge AI viable, because *that's* where hardware economics actually shift. The papers on visual representations and embodied control are the real technical ammunition for the next hardware cycle, not the venture theatre masquerading as progress.