The real hardware story this week is buried in the papers, not the noise. RoboTTT and the neural space-time memory work actually matter—they're tackling the scaling problem that makes robot learning viable beyond the lab, while most "AI hardware" discourse is just crypto-bro speculation or regulatory theater. The poisoned pretraining data paper should scare anyone betting on foundation models as a solved problem; computational propaganda at training time is a hardware-agnostic threat that no GPU can fix. Skip the Wendy's AI clerk viral moment and the Buffett-Alphabet tea—they're financial theater—and watch instead for whether these robotics papers move from arXiv to actual systems; that's where hardware economics actually get written.