This source presents a trajectory for generative AI (GenAI) in consumer research, outlining three stages: democratization, where GenAI expands accessibility for consumers and researchers; the average trap, where GenAI's predictive nature leads to generic outcomes; and model collapse, where GenAI increasingly learns from its own output, losing connection to real-world human behavior. The authors identify data and model challenges inherent in each stage, such as embedding real-world biases during democratization and producing unfaithful outputs in the average trap and model collapse. Finally, the text proposes consumer research opportunities to counter these challenges, suggesting strategies like focusing on marginalized consumers, fine-tuning models, engineering responses, and preserving human agency to realign AI with human-centric consumption.