Language as infrastructure for human intelligence in Large Language Models

The emergence of large language models (LLMs) has fundamentally transformed our understanding of artificial intelligence and challenged traditional conceptualizations of intelligence itself. During recent years, LLMs have demonstrated not merely imitation or mechanical combinations, but a profound internalization of linguistic frameworks that enables them to produce responses often indistinguishable from those generated by humans. This internalization compels us to reconsider the nature of intelligence and its manifestation in both human and artificial agents. In this theoretical paper, we argue that LLMs reflect not artificial but human intelligence deeply embedded in linguistic frameworks, arising from extensive training on human language datasets. We argue that language serves as a distributed intelligence infrastructure – a virtual container for many aspects of human intelligence that extends beyond the confines of individual biological brains. Intelligence, particularly in the context of language and LLMs, can be understood as an emergent phenomenon extracted from large-scale linguistic data. This theoretical framework contributes to cognitive science theories emphasizing distributed cognition and providing а novel perspective on the relationships between language and intelligence in both biological and artificial agents. Through synthesis of evidence from computational linguistics, mechanistic interpretability, and emergent capabilities research, we demonstrate that LLMs exhibit genuine human intelligence rather than mere statistical mimicry.