Hallucination as an Inevitable Byproduct of Intelligence in Large Language Models

Hallucination in Large Language Models (LLMs) is often treated as a correctable flaw—an artifact to be minimized through dataset curation, scaling, or architectural refinement. This paper advances a different thesis: hallucination is not an incidental failure but an unavoidable structural property of generative intelligence itself. We ground this claim in information theory and cognitive science, arguing that intelligence in LLMs emerges through generalization, a process fundamentally rooted in lossy compression of vast and heterogeneous training data. Lossy compression necessitates reconstructive inference, where missing or ambiguous details are filled in from learned priors. While this mechanism supports creativity, abstraction, and adaptability, it also unavoidably produces outputs that diverge from verifiable facts, labeled as hallucination.To substantiate this argument, we integrate theoretical modeling with a synthesis of recent empirical findings on LLM hallucination rates, scaling laws, and retrieval-augmented architectures. We analyze cases from both open-domain and high-stakes applications, highlighting contexts where hallucination is functionally beneficial and where it presents critical risks. We further examine parallels with human memory errors, particularly reconstructive recall, to situate LLM behavior within broader frameworks of intelligent systems.From this perspective, attempts to eliminate hallucination may dismantle the very mechanisms enabling generalization and intelligence-like behavior. We conclude by outlining implications for AI safety research, policy design for national-scale sovereign AI initiatives, and deployment strategies in domains such as law, medicine, and national security, where balancing creativity with factual reliability is paramount.