Shopify's ML team demonstrated compounding inference by fine-tuning a 0.8B-parameter model that outperformed GPT-5.6-sol on buyer profile tasks through three rapid training cycles in one week. The breakthrough came from reinvesting inference outputs as training data, reducing prompt costs 8x, and increasing throughput 36x across three simultaneous feedback loops. Success required task-specific quality judges, production-to-training data pipelines, rapid iteration cadence, and dynamic routing between teacher and student models.