The semiotic landscape of visual media is becoming more complex as artificial intelligence (AI) becomes more prevalent in producing visual content.To fully understand the complex relationship of AI-generated visual content with our models of visual cognition, we should analyze them in the context of a framework that considers meta-seeing, our ability to reflect on one's own perceptual and cognitive processes, which were shaped by thousands of years of the development of our biological and perceptual systems and are influenced by technological progress.Navigating the intricacies of an AI-generated environment requires our ability not only to engage in meta-seeing but also to understand meta-seeing as related to metacognition, the awareness and understanding of one's own thought processes. This approach would enable us to thoroughly analyze our confidence in visual information while also evaluating the concealed power dynamics of images. It would also enable us to recognize the multimodal character of our visual experience, as well as our dependence on visual abduction and the utilization of multiple models of visual cognition to access and analyze the world around us.Furthermore, tracing the intertextual and intervisual relationships that shape AI visuals allows us to situate them within the broader context of our visual cognition and cultural heritage. This research contributes to the ongoing project (CERS), which investigates the interaction between AI and art and culture production, by providing a semiotic framework for understanding and navigating the complexities of the AI-generated visual landscape.