The real AI hardware story this cycle is Hyperscale Data's Michigan expansion—while everyone fixates on model weights and training tricks, actual compute capacity constraints are the binding problem. The papers on RL without ground truth and probability-correctness alignment are intellectually interesting but solving mostly academic problems; what matters is whether we can *run* these models efficiently at scale, which is why llama.cpp's rapid iteration cycle (five releases in quick succession) actually signals more competitive pressure in inference optimization than any single research breakthrough. Palantir's valuation spike matters only if it translates to actual hardware-adjacent infrastructure deals; right now it's just financial theater. The "Sol model for government" leak is worth watching—if OpenAI is carving out a separate product for regulated sectors, that's infrastructure thinking, not just model release strategy.