  Large Language Models (LLMs) have emerged as transformative forces in the
realm of natural language processing, wielding the power to generate human-like
text. However, despite their potential for content creation, they carry the
risk of eroding our Semantic Capital (SC) - the collective knowledge within our
digital ecosystem - thereby posing diverse social epistemic challenges. This
paper explores the evolution, capabilities, and limitations of these models,
while highlighting ethical concerns they raise. The study contribution is
two-fold: first, it is acknowledged that, withstanding the challenges of
tracking and controlling LLM impacts, it is necessary to reconsider our
interaction with these AI technologies and the narratives that form public
perception of them. It is argued that before achieving this goal, it is
essential to confront a potential deontological tipping point in an increasing
AI-driven infosphere. This goes beyond just adhering to AI ethical norms or
regulations and requires understanding the spectrum of social epistemic risks
LLMs might bring to our collective SC. Secondly, building on Luciano Floridi's
taxonomy for SC risks, those are mapped within the functionality and
constraints of LLMs. By this outlook, we aim to protect and enrich our SC while
fostering a collaborative environment between humans and AI that augments human
intelligence rather than replacing it.
