This paper defines and examines GPT inflation: the systemic oversupply of AI-generated content and automated interactions relative to human attention and verification capacity. When the marginal cost of producing text, images, and conversations collapses, the marginal informational value and credibility of each message decline. The effect is an attention-economy analogue to monetary inflation, more output “buys” less attention and trust. This research develop the concept, distinguish it from misinformation and ordinary information overload, and trace the mechanisms that drive it: elastic supply enabled by generative models, slow-to-scale human attention and fact-checking, fragile detection, feedback loops in training data, and rebound effects in energy and water use. The paper then translate these dynamics into practical risks for platforms, organizations, educators, researchers, and citizens, and propose plain safeguards that target the imbalance: provenance by default, calibrated friction against flood behaviors, small-N human checkpoints for consequential communications, and metrics that measure verification and trust, not only throughput. The paper closes with a research agenda to quantify GPTinflation in the wild and to evaluate interventions that restore the signal-to-noise ratio without discarding the real productivity gains of generative tools.