Open source AI models have closed the performance gap with frontier closed models to just 3–5 points in key benchmarks—making them functionally equivalent for most enterprise tasks. This convergence, accelerated by Chinese model distillation and Google’s Gemma series, enables local execution on consumer hardware and powers always-on agentic systems. Cost advantages are dramatic: open alternatives are over 25x cheaper than closed APIs, slashing expenses for high-volume workloads like summarization and content creation. Enterprises are now adopting an AI workflow triage strategy—matching task sensitivity, regulatory requirements, and cost constraints to the right model type: local open models for private or autonomous workflows, cheap open APIs for scalable low-stakes tasks, and premium closed models only where legal indemnification and IP protection are non-negotiable. The future points toward specialized, task-optimized LLMs—both local and cloud-based—with innovation increasingly distributed across global open ecosystems rather than centralized at frontier labs.