A recent Science article frames large AI models as cultural technologies on par with the printing press [1]. Building on that perspective, this paper introduces the Networked Two-Way Communication Channels (NTCC) framework as a practical tool for user interface (UI) design and AI alignment. NTCC conceptualizes each human-AI or UI encounter as a two-way Shannon channel, where six entropy terms are logged in nested, dynamically expandable nodes. This creates a networkable semantic index that reveals hidden uncertainty, or entropy misalignment, without requiring subjective user surveys. By combining domain-specific semantics with quantitative metrics, NTCC enables designers to visualize information flow, diagnose miscommunication, and iteratively reduce interface noise. We present the updated data structure, a lightweight toolset, and a summary of sample case studies illustrating how NTCC can support both today’s interface design and tomorrow’s AI agents in aligning with human meaning.