I had Gemini train its own replacement for $9

This article shows how CIOs can use a large language model as a one-time labeling engine to train a much cheaper open-source model, cutting per-item inference costs from ongoing API spend to near zero after the initial training set. Strategically, it highlights a scalable pattern for AI operations: use premium foundation models to bootstrap domain-specific automation, then shift high-volume workloads to controllable internal models to reduce vendor dependence and improve cost predictability. For IT organizations, the key implication is that AI value increasingly depends on data curation, model governance, and MLOps discipline—not just model selection—because the savings only hold if the smaller model is validated and maintained against real business requirements.