When Content Signals Lose Credibility: Signalling Equilibria on Human-Centred Platforms in the LLM Era

On platforms where readers value personal voice and human-authored expression, fluent writing used to be a credible sign that a post reflected real human effort. Large language models have made such writing cheap to produce, so readers can no longer reliably infer whether a post is human-authored from surface quality alone. We develop a signalling framework to study how the spread of large language models (LLMs) can reshape content valuation on online platforms that focus on personal voice, interpersonal resonance, and the credibility of human-authored expression. Using Reddit data from a narrative, emotion-oriented community, we recover reduced-form sign patterns that discipline the post-LLM regime analysed in the model. We show that, in the model, asymmetric technological change can erode the credibility of conventional writing cues and move equilibrium from separating to semi-separating and, in some parameter regions, to pooling or collapse on this class of platforms. We then use agent-based simulations to compare four platform interventions. The simulations indicate that selectively increasing the cost borne by content producers whose output is more heavily dependent on AI generation provides the strongest benchmark performance in the model, while labelling with appeal offers a more attractive practical balance among the more implementable interventions.