Abstract: This article examines Nested Learning (NL), a novel framework that reconceptualizes neural networks as hierarchical systems of interconnected optimization problems operating at multiple temporal scales. Drawing from neuroscientific principles of memory consolidation and Google Research's recent theoretical work, we explore how NL addresses fundamental limitations in current deep learning systems—particularly their static nature after deployment and inability to continually acquire new capabilities. The framework reveals that existing architectures like Transformers and optimizers such as Adam are special cases of nested associative memory systems, each compressing information within distinct "context flows." We analyze NL's implications for organizational AI strategy, examining three core innovations: deep optimizers with enhanced memory architectures, self-modifying sequence models, and continuum memory systems. Through practitioner-oriented analysis of experimental results and architectural patterns, we demonstrate how NL principles enable more adaptive, efficient, and cognitively plausible AI systems. This synthesis connects theoretical advances to practical deployment considerations for enterprises navigating the evolving landscape of foundation models and continuous learning requirements.