The traditional rivalry between open-source and closed AI models is becoming increasingly obsolete as the industry shifts toward new paradigms, according to industry analysts Dan Whitenack and Chris Benson. Speaking on the Practical AI podcast, the pair argue that the longstanding "model wars" that once dominated AI discourse are fading in importance as physical AI, edge computing, and specialized hardware implementations take center stage. The conversation suggests that the distinction between open and closed models matters far less than it did in previous years, as practical deployment considerations trump ideological preferences. The broader transformation underway involves the emergence of agentic systems—autonomous AI agents that can perform complex workflows—and AI-driven infrastructure that prioritizes integration and performance over model provenance. Rather than choosing between open models like LLaMA and proprietary alternatives, organizations are increasingly focused on how AI systems work within their broader technical ecosystems. This shift reflects a maturation of the AI market where practical considerations around deployment, efficiency, and integration have overtaken debates about model accessibility and openness.