Appearance and Reality in the Age of Automated Imagogenesis

Abstract

This essay analyzes the transformation of our relationship with appearance and reality, driven by the transition from manual and mechanical techniques to a regime of automated imagogenesis. Building on an initial analysis (2015), the research is updated to propose a taxonomy of automated operations on appearance—unveiling (dis-covering), capturing (covering), and synthesizing (en-covering)—linking each to analogous journalistic practices. The concept of iconomorphosis is introduced: the technological metamorphosis of the image, now accelerated by advances in Generative Artificial Intelligence and text-to-image models. It is argued that this automation, rather than creating a crisis of evidence, reveals the insufficiency of a purely instrumental rationality based on technical verification and compels us to confront the primacy of the social practices of seeing.

Using the interpretative disputes surrounding the Large Hadron Collider (LHC) in 2015, the bombing of barges in the Caribbean by the Trump Administration in 2025, and the false information about vaccines during the coronavirus pandemic (2021) as case studies, it is demonstrated that social practices are more decisive than technologies themselves in the construction of meaning. A classification of images based on the number of interpretative layers they activate is proposed, suggesting that contemporary visual literacy should focus on the analysis of these disputes. It is concluded that the proliferation of synthetic images (Deepfakes) acts as a catalyst, forcing a transition towards a social rationality where trust resides not in the image, but in the source. Contemporary literacy, therefore, consists not in learning to detect what is false, but in analyzing the disputed interpretative layers and consciously reconstructing social trust. The fundamental question shifts from “is this real?” to “whom do we trust to construct reality?”.