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Technological progress has deep implications on science and society. We use AI-based content analysis method to trace and visualize these trends. Methodologically, we use emerging digital methods for history (of science) and show how distant reading can be used to detect trends, and how AI-based close reading, for instance via RAG systems, can assist close reading. We use document classification, topic modelling, and then argue that conceptual maps are particularly suitable as distant reading visualisation method for network research, both for social networks but also as semantic networks visualising trends and shifts in associations across time, or between stakeholders. We then show how the source texts can be efficiently searched, with methods ranging from corpus concordancing to RAG approaches. We give two case studies to focus on social implications: first, based on a historical data source corpora , we show how Scholasticism increasingly gave way to Empiricism, particularly in medicine. Second, we investigate the fundamental changes that AI has on science and society today. Using social media sources, we focus on contested sciences, especially technological singularity, where the belief in AI and innovation lead to over-optimistic ideology but also fears of extinction, or job loss and poverty.