Large language models are advancing beyond raw parameter scaling, adopting architectures like a mixture of experts and specialized reasoning capabilities. While these state-of-the-art models solve complex problems, they introduce a critical UX challenge: latency. Users expect instant responses, and even slight delays can feel disruptive. To address this tension, the industry can adopt strategies such as dynamic model routing, where simpler tasks are routed to faster or lightweight models, while complex tasks trigger specialized reasoning models. On-Demand Tool Execution enables models to offload computational work, allowing them to "think" in the background without blocking user interaction. By combining intelligent routing, asynchronous tools, and progressive responses, developers can leverage SOTA reasoning models without sacrificing UX, guiding the future towards hybrid systems where speed and depth coexist.