Generative artificial intelligence increasingly mediates how individuals interpret information, perform cognitive tasks, and participate in economic and political life. While such systems promise efficiency and expanded access to knowledge, their societal effects are unevenly distributed. This article develops the concept of societal bifurcation to explain an emerging structural divergence between a cognitively resilient minority, capable of integrating AI reflectively, and a cognitively dependent majority, whose reliance on automated interpretation reduces interpretative autonomy. Drawing on contemporary empirical evidence from cognitive science, labour research, and human–AI interaction studies, the article shows how unstructured AI use diminishes metacognitive monitoring and inflates confidence, while labour-market restructuring amplifies differences in adaptability and resilience. These cognitive and economic dynamics interact with an increasingly fragile democratic information environment shaped by synthetic communication and declining epistemic trust. The article argues that these processes form a self-reinforcing sociotechnical mechanism through which cognitive dependency, economic inequality, and democratic vulnerability become mutually constitutive. By conceptualising societal bifurcation as a distinct analytical framework, the article contributes to sociological and science and technology studies debates on inequality, agency, and governance in AI-mediated societies, while highlighting the importance of sustaining interpretative autonomy in the age of generative AI.
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Michael Gerlich
Societies
Business School Lausanne
SBS Swiss Business School
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Michael Gerlich (Thu,) studied this question.
www.synapsesocial.com/papers/69a287b00a974eb0d3c0392e — DOI: https://doi.org/10.3390/soc16030082