MuleRun CTO Shu Junliang: Model Gaps Rapidly Converging, Agent Moat Shifts to 'Speed + Data'

On April 21, during the roundtable discussion ‘Decoding Web 4.0: When AI Agents Take Over On-Chain Permissions’, MuleRun CTO Shu Junliang discussed the topic of ‘Agent Moat’. He stated that the traditional AI technology moat is being rapidly weakened, primarily due to the accelerated convergence of model capabilities and exponential improvements in development efficiency. He pointed out that the performance gap between mainstream large models is narrowing quickly, especially over the past year, where the capability gap between domestic and international models has significantly converged. Additionally, with the explosive enhancement of code generation capabilities, software development efficiency has greatly increased—features that previously took weeks or even months to complete can now be achieved in just a few days. This means that both Agent frameworks and specific functional modules can be quickly reused or replicated through open-source solutions, making the ‘functional moat’ at the product level increasingly fragile. In this context, Shu Junliang believes that the core competitiveness of Agents in the future 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 over the long term; and second, data advantages, including exclusive data resources and user accumulated data. On one hand, platforms with unique data acquisition capabilities (such as data from specific industries or regions) will form natural barriers; on the other hand, the behavioral and memory data accumulated from long-term user engagement on the platform will become key assets that are difficult to migrate, further enhancing user stickiness and product competitiveness. He concluded that as the trend of ‘equalization’ in models and technologies continues, the Agent moat is shifting from ‘technical capability’ to a comprehensive competition of ‘data assets and execution efficiency’.