Reasoning quality decouples from model size — edge AI and inference economics in play

Winners: edge-AI chipmakers and on-device assistant vendors — reasoning-class quality at 1.3B parameters moves flagship AI features onto phones and laptops. Inference-cost-sensitive SaaS gets margin relief.

Pressured: API providers whose moat is owning the biggest model. GPU cloud inference demand softens at the margin if quality-per-FLOP keeps improving faster than usage grows.

Signals: watch whether major labs ship recursive-depth variants within two quarters; track open-source adoption of the released weights; the key technical question is whether the result replicates above 7B parameters — a positive replication at 30B would be a regime change for inference economics.

Difficulty to commercialize: 4/10. Drop-in compatible with standard transformer stacks and the code is already public, but production wins require retraining from scratch, and latency-sensitive applications need careful recursion budgets.