The Meaning Problem in AI: From Language Generation to Meaning Governance

Contemporary generative AI systems have achieved significant advances in linguistic and conversational coherence, enabling real-time communication across languages, cultures, and contexts. However, these systems remain fundamentally limited by a critical structural gap: they generate language without governing meaning. As a result, outputs that are syntactically correct and contextually plausible may still fail to preserve intent, align with context, or accurately represent meaning.
This paper adopts an interdisciplinary approach, integrating insights from artificial intelligence and communication theory to examine the limitations of current AI-mediated communication. It identifies the absence of pre-output meaning evaluation as a primary driver of misalignment, hallucination, communicative failure, and related phenomena such as AI sycophancy.
In response, this work introduces meaning governance, defined as a system’s capacity to evaluate, preserve, and regulate meaning prior to the generation of output. The paper argues for a shift from language generation toward meaning-aware communication systems that incorporate contextual alignment, uncertainty evaluation, and response control.
As AI becomes increasingly embedded in communication infrastructures, preserving meaning becomes essential to maintaining trust, coordination, and communicative reliability at scale. The future of AI communication will be defined not by linguistic generation alone, but by the ability to govern meaning under conditions of uncertainty and complexity.

Artificial Intelligence (AI), Large Language Models (LLMs), Meaning Governance, AI Communication, Relational Intelligence

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