Large language models as cognitive shortcuts: a systems-theoretic reframing beyond bullshit

Introduction: Large Language Models (LLMs) are often framed through metaphors such as "bullshit" or "stochastic parrots," emphasizing missing grounding, belief, or intention. While rhetorically powerful, these framings obscure how LLMs are used for sense-making, ideation, and communication. We reframe LLMs as Operators for General Cognitive Shortcuts (GECOS) within techno-semiotic assemblages. Methods: We develop a functional model by integrating concepts from Luhmannian systems theory, Deleuzian ontology, and minimally from Husserlian phenomenology. Using conceptual analysis as functional-comparative synthesis, we analyze human-LLM interaction without attributing agency, belief, or understanding to the model. Results: GECOS explains LLM usefulness as communicative complexity reduction: models generate connectable continuations by approximating second-order expectations ("what is expected to be expected"), enabling interactional continuity without reference to truth or intention. Via Luhmann's contingency formula, LLMs help users navigate uncertainty through procedurally plausible coherence. Discussion: The framework shifts attention from ontological debates about "understanding" to the operational role of LLMs in distributed sense-making. It also highlights risks: overreliance, emotional projection, and normative flattening when connectability substitutes for justification. Conclusion: GECOS offers a non-anthropomorphic alternative to deficit metaphors by modeling LLMs as pragmatic operators that sustain communicative momentum and enable workable continuations in complex socio-technical environments.