In the AI industry, the division of tasks among models is becoming increasingly specialized. The recently introduced Jev model has captured the spotlight due to its unique capability of outputting only probability judgments. This innovative approach eliminates the necessity for text generation, enabling the model to perform judgment tasks with exceptional cost-efficiency and speed. Its versatility shines through in various applications, such as optimizing large model contexts, automating operations on mobile devices, real-time database filtering and sorting, making game operation decisions, and data cleaning. In doing so, it revolutionizes traditional paradigms for large model applications.

Drawing inspiration from Jevons' Paradox, the Jev model's cost-effective nature has unlocked a plethora of edge scenarios. This has propelled the transformation of AI infrastructure from a 'centralized control' model to a 'distributed division of labor' approach. By segregating judgment and generation tasks, it allows large models to concentrate more on front-end expression. Consequently, this has given rise to a myriad of new intelligent application scenarios, reshaping the landscape of AI possibilities.