Twelve months ago, we were building elaborate agent frameworks, complex RAG stacks, and orchestration layers to compensate for model limitations. Today, frontier models are quietly absorbing much of that scaffolding. Larger context windows, stronger reasoning, and native tool use are collapsing layers of glue we once believed were essential. The half-life of AI best practices is shrinking. In this keynote, I reflect on what it means to lead and build in a world where last year’s architecture becomes today’s technical debt. Drawing from enterprise-scale AI deployments, I will explore the shift from heavy scaffolding to starting simple—and from building glue to designing domain-driven evaluation. As models improve, durable advantage moves away from elaborate orchestration and toward judgment: knowing what “good” looks like, building feedback loops that sustain quality, and architecting systems that expect replacement. The system hasn’t disappeared—it has moved upward, into governance, observability, cost discipline, and human alignment. But this is also a personal story. What does it feel like to watch carefully engineered best practices age in months? How do we maintain confidence and clarity when the technology redefines itself faster than our roadmaps? And how do we help the next generation build careers that endure beyond any single stack? In a world where models eat the glue, the most important skill is not mastering today’s infrastructure—it is learning how to evolve with it.