The growing use of generative AI in consumer decision-making introduces both facilitation and constraints on choice autonomy. This paper investigates the impact of AI-driven recommendations on consumer decision processes, specifically how differing architectures of generative AI platforms influence consumer choices. Grounded in consumer choice and autonomy theories, we conduct an exploratory analysis comparing different generative AIs platforms. The findings reveal distinct parameters of “choice architectures, “ and two main generative AI axes. Information-oriented generative AIs offer functionalities as practical info, visual elements, websites links and user ratings to enhance the user decision efficiency. Consultation-oriented generative AIs, in contrast, provide more decisional guidance but with limited data sourcing transparency, encouraging more supportive but potentially directive interactions. This study suggests a first conceptualisation of generative AI choice architecture grounded on these axes. It underscores the need for further research into the implications of AI recommendation systems on consumer autonomy.