From Static Weights to Living Logic: The Case for Adaptable AI Architecture

Current AI systems excel at pattern recognition but lack the biological ability to adapt their core logic in real-time, creating a fundamental bottleneck for true intelligence. By moving beyond frozen weights toward distributed and reconfigurable architectures, the industry can overcome the limitations of static models and achieve genuine environmental adaptability.