The Peircean Theory of AI: Advancing Text Generation Through Peirce's Triadic Model, Speculative Grammar, and Methodeutics

Current large language models excel at statistical pattern recognition but struggle with interpretative reasoning and the relational dimensions of human comprehension. This study investigates how Charles Sanders Peirce’s triadic semiotic model—Sign, Object, and Interpretant—can enhance AI's interpretative capabilities beyond formal linguistics. By integrating Claudio Paolucci’s theory of machinic enunciation, the research frames generative AI as a functional, non-subjective system capable of contextually significant output. The analysis utilizes speculative grammar and methodeutics to identify gaps in current computational logic. Findings suggest that Peircean principles bridge the divide between statistical operations and social context, offering a robust theoretical framework for increasing contextual awareness and understanding the complexities of machinic meaning-making.