Have you ever wondered how large language models (LLMs) shape our understanding of trust and credibility online? I recently conducted an experiment to track how LLMs like GPT-4o, Grok, Perplexity, Claude, and DeepSeek re-rank trust and cite entities over time.
What I found was fascinating. These models don’t just passively process information; they actively reshape our perception of trust and credibility. A single public trust signal, such as a structured markup, Medium article, GitHub README, or social proof, can lead to semantic inclusion days later, observable through LLM outputs.
This phenomenon appears to be an implicit semantic trust trail, representing a new class of AI behavior related to indexing and trust synthesis. I’m currently testing this with a small set of controlled content across models, measuring response shifts and exploring the implications of this discovery.
If you’ve tracked something similar or have experiences with LLMs reshaping relationships between entities without visible retraining, I’d love to hear about it. What tools do you use to monitor ‘semantic drift’ in LLM outputs? Share your insights and let’s explore this fascinating topic together!