*Consumer research, *Innovation management, *Data analysis, *Consumption (Economics), Generative artificial intelligence, Machine learning, Prejudices, Data distribution

We develop a novel generative AI (GenAI) trajectory, “democratization-average trap-model collapse,” to identify data and model challenges posed by GenAI, from which we project the GenAI future of consumer research. This trajectory consists of three key phenomena: democratization broadens consumer participation, the average trap produces generic responses, and model collapse occurs when GenAI outputs lose human sensibilities. Data and model challenges arise as democratization enhances data representation while also embedding real-world biases. The average trap, caused by next-token prediction models, leads to generic outputs that lack individuality. Additionally, model collapse occurs when GenAI increasingly learns from its own outputs, amplifying machine bias and diverging from human behavior. To address these challenges, researchers can leverage democratization to study marginalized consumers and prioritize humancentered research over purely data-driven methods. The average trap can be mitigated by fine-tuning models with task-specific and marginalized consumption data while engineering responses for uniqueness. Preventing model collapse requires integrating human–machine hybrid data and applying theories of mind to realign AI with human-centric consumption. Finally, we outline three future research directions: preserving data distribution tails to support consumption democratization, countering the average trap in next-token prediction, and reversing the trajectory from democratization to model collapse. [ABSTRACT FROM AUTHOR]

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