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

The week's most clarifying number wasn't a revenue figure or a benchmark score: it was 40x, the compute efficiency variance MIT CSAIL found within individual labs producing frontier models, meaning a single developer can't reliably reproduce its own results even when it controls the spending. That internal inconsistency quietly dissolves the moat thesis from both directions: if the frontier is a spending race and the spending doesn't produce consistent outcomes, neither scale nor safety restrictions reliably compound into durable advantage. That framing lands harder alongside Ramp's transaction data, where the more expensive, supply-constrained product is growing fastest precisely because product differentiation has become so hard to verify that buyers are using price as a trust proxy. And it reframes the Morningstar moat downgrades: if 37 application-layer moats narrowed because AI compresses the cost of performing expertise, the labs producing the underlying models face the same compression one layer down. Pre-training scale is now a commodity floor, not a ceiling; the differentiation that actually moves enterprise purchasing decisions has migrated to post-training alignment and inference-time compute, layers that don't appear in any scaling regression.