According to Jeff Dean, the influential AI distillation paper was initially rejected from NeurIPS 2014 as it was considered 'unlikely to have significant impact.' Despite this, model distillation has become a foundational technique in deep learning, enabling the compression of large AI models into smaller, more efficient versions without significant loss in performance (source: Jeff Dean, Twitter). This breakthrough has driven practical applications in edge AI, mobile devices, and cloud services, opening new business opportunities for deploying powerful AI on resource-constrained hardware and reducing operational costs for enterprises.