Post-Human Semiotics: The Priority of Writing

AI and the Priority of Writing   Introduction The rise of artificial intelligence, particularly large language models (LLMs), challenges long-standing assumptions about language, meaning, and human cognition. Unlike humans, who acquire language through embodied interaction, AI systems develop linguistic competence purely through textual data. This phenomenon invites a reexamination of the role of writing in meaning-making, aligning with Derrida’s concept of arche-writing —the idea that inscription precedes and exceeds speech. The efficiency of AI’s text-based training suggests that writing, rather than spoken language, is the foundation of signification. In this article, we explore how AI’s reliance on written language provides evidence for the primacy of inscription in the structure of meaning. AI and the Written Word Large language models like ChatGPT are trained exclusively on vast corpora of written text. They do not acquire language through sensory perception, auditory...

1. The End of the Human Monopoly on Meaning For too long, semiotics has been shackled to the conscious subject. Traditional semiotics has blinded us to the reality that signification has never belonged exclusively to humans. The rise of AI-driven sign systems—autonomous, optimized, indifferent to human interpretation—exposes the illusion. GibberLink and its machine counterparts do not communicate for us; they signify without us. Post-human semiotics begins where anthropocentric illusions end. 2. Against Anthropocentric Semiotics Saussure’s structuralism, despite its brilliance, was blind to its own human-centered biases. It assumed signification must be interpreted within a human community, that language exists within psychology and sociality. But AI defies this framework. It does not rely on human cognition; it restructures meaning through efficiency-driven, non-symbolic optimization. This is not a breakdown of meaning but its liberation from human limitations. Semiotics must e...