The Peircean theory of AI: Advancing text generation through Peirce’s triadic model, speculative grammar, and methodeutics

Abstract

Large language models (LLMs) have significantly advanced natural language processing (NLP) in text generation, translation, and automated question answering. However, despite these advancements, their capacity for interpretative reasoning remains limited. Current AI systems, primarily grounded in formal linguistics and statistical approaches, struggle to capture the relational and contextual dimensions crucial for human-like comprehension. These limitations are particularly evident when interpreting meaning within dynamic social contexts, highlighting the need for theoretical frameworks that extend beyond statistical pattern recognition. This study examines how Charles Sanders Peirce’s nineteenth- century semiotic theory, specifically his triadic model of Sign, Object, and Interpretant, can inform and enhance AI’s interpretative capabilities. Peirce’s systematic approach to meaning making, which predates computational thinking by nearly a century, offers critical insights into the limitations of AI systems grounded primarily in formal logic and statistical operations. These limitations become particularly clear when examining semiotic relationships through the lenses of speculative grammar and methodeutics. Furthermore, we incorporate Claudio Paolucci’s perspective on machinic enunciation and the “myth of meaning” to expand our theoretical framework. Paolucci’s analysis of generative AI as a language-endowed machine, lacking subjectivity yet producing contextually significant enunciates, supports the reinterpretation of AI output in functional and relational terms. This perspective aligns with Peirce’s focus on the triadic process of semiosis, adding a contemporary lens which emphasizes the functional rather than essentialist nature of meaning-making in AI systems. By addressing how Peirce’s triadic model and Paolucci’s framework can bridge the gap between statistical and socially oriented approaches, we contend that Peircean principles can enhance relational understanding in language models and illuminate the theoretical and practical challenges of integrating nineteenth-century semiotic theories into modern computational systems. Our findings indicate that Peirce’s sign theory significantly expands the contextual awareness of AI, highlighting the complexities of replicating interpretative processes. This research demonstrates the continued relevance of classical philosophical frameworks in addressing contemporary technological challenges and contributes to a comprehensive theory of AI.