This position paper discusses the profound impact of Large Language Models (LLMs) on semantic change, emphasizing the need for comprehensive monitoring and visualization techniques. Building on linguistic concepts, we examine the interdependency between mental and language models, highlighting how LLMs and human cognition mutually influence each other within societal contexts. We introduce three primary theories to conceptualize such influences: (T1) Recontextualization, (T2) Standardization, and (T3) Semantic Dementia, illustrating how LLMs drive, standardize, and potentially degrade language semantics. Our subsequent review categorizes methods for visualizing semantic change into frequency-based, embedding-based, and context-based techniques, being first in assessing their effectiveness in capturing linguistic evolution: Embedding-based methods are highlighted as crucial for a detailed semantic analysis, reflecting both broad trends and specific linguistic changes. We underscore the need for novel visualization tools to explain LLM-induced semantic changes, ensuring the preservation of linguistic diversity and mitigating biases, while providing essential insights for the research on semantic change visualization and the dynamic nature of language evolution in the times of LLMs.