In AI, juxtaposition often refers to the comparison of different models or techniques to highlight their strengths and weaknesses. This can involve contrasting performance metrics, efficiency, or accuracy in various applications. By examining juxtaposed models, researchers and practitioners can better understand how to optimize algorithms and improve overall system performance.