compute-moats

MIT CSAIL: 80-90% of Frontier AI Performance Is Just Compute

The study's headline finding confirms what everyone suspects: scale drives frontier performance. The buried finding inverts it: individual labs produce models with 40x compute efficiency variance, meaning they can't reliably reproduce their own results. If the frontier is a spending race and the spending doesn't produce consistent outcomes, the moat thesis weakens from both directions. The entire analysis is also blind to where differentiation actually moved: post-training alignment, tool use, and inference-time compute are now the layers where product quality diverges, and none of them show up in a pre-training scaling regression.