BlockBeats News, April 21st - During the "Decoding Web 4.0: When AI Agent Takes Over On-Chain Authority" roundtable discussion, MuleRun CTO Steven Hu elaborated on the "Agent Moat" topic, stating that the traditional concept of an AI technology moat is rapidly weakening. The core reason lies in the accelerating convergence of model capabilities and the exponential improvement in development efficiency.

He pointed out that the performance gap between mainstream large models is rapidly narrowing, especially over the past year, where the gap in model capabilities both domestically and internationally has significantly converged. At the same time, with the explosive improvement in coding capability, software development efficiency has greatly increased—features that used to take weeks or even months to complete can now be implemented within days. This means that whether it is the Agent framework or specific functional modules, they can be quickly reused or replicated through open-source solutions, making the product-level "feature moat" increasingly fragile.

In this context, Steven believes that the future core competitiveness of Agents will mainly be reflected in two aspects: first, the ability for continuous high-frequency iteration, meaning whether the team can maintain a leading product update speed in the long term; and second, data advantage, including exclusive data resources and user data accumulation. On the one hand, platforms with unique data acquisition capabilities (such as industry-specific or regional data) will form a natural barrier. On the other hand, the behavioral and memory data accumulated by users on the platform through long-term use will also become critical assets that are challenging to migrate, further enhancing user stickiness and product competitiveness.

He concluded that in the trend of the gradual "equalization" of models and technologies, the Agent moat is transitioning from "technical capability" to the comprehensive competition of "data assets and execution efficiency."