This chapter proposes the concept of techno-anaesthetics to critically examine how visual representations of artificial intelligence—especially in stock imagery and generative AI outputs—contribute to the depoliticisation of public discourse. It argues that such images do not merely illustrate AI, but actively shape its cultural imaginary by reproducing aesthetic tropes that convey neutrality, inevitability, and benign control. Drawing on visual grammar, semiotics, and the philosophy of technology (particularly Simondon’s notion of techno-aesthetics), the chapter shows how these representations operate as “screens” that obscure rather than reveal complexity. The analysis extends from legacy stock photography to AI-generated imagery, highlighting a semiotic feedback loop in which generative models re-ingest the very clichés they were trained on. The result is a symbolic regime that numbs critical engagement and consolidates power around those who control platforms, datasets, and algorithms. The chapter calls for a politicized ethics of AI communication—one that foregrounds aesthetic dissensus and symbolic struggle as conditions for democratic participation in technoscientific futures.

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Techno-Anaesthetics: The Politics of AI Imagery from Stock Images to Generative Models

  • Alberto Romele

摘要

This chapter proposes the concept of techno-anaesthetics to critically examine how visual representations of artificial intelligence—especially in stock imagery and generative AI outputs—contribute to the depoliticisation of public discourse. It argues that such images do not merely illustrate AI, but actively shape its cultural imaginary by reproducing aesthetic tropes that convey neutrality, inevitability, and benign control. Drawing on visual grammar, semiotics, and the philosophy of technology (particularly Simondon’s notion of techno-aesthetics), the chapter shows how these representations operate as “screens” that obscure rather than reveal complexity. The analysis extends from legacy stock photography to AI-generated imagery, highlighting a semiotic feedback loop in which generative models re-ingest the very clichés they were trained on. The result is a symbolic regime that numbs critical engagement and consolidates power around those who control platforms, datasets, and algorithms. The chapter calls for a politicized ethics of AI communication—one that foregrounds aesthetic dissensus and symbolic struggle as conditions for democratic participation in technoscientific futures.