Behind the enthusiasm sits a real problem: academic labs are so starved for compute that a pre-training run now has to be stitched together from scraps across clusters. The commentator finds it telling that this kind of improvisation earns praise from someone who actively funds academic AI through Laude — the endorsement is as much about the compute divide as the engineering feat. On our view, patchwork runs like this won't produce frontier models, but they matter for keeping independent research alive while a handful of labs monopolize large-scale training. Expect more of it: as compute concentrates, cobbled-together distributed training becomes a survival skill rather than a stunt.