The temporal gap between macroeconomic news release and order flow response in U.S. equity markets has compressed sharply since 2023. We advance the Sentiment Conductivity Hypothesis, which posits that Large Language Models embedded in trading algorithms accelerate the translation of sentiment in unstructured public information into quote updates and liquidity provision. This letter introduces the LLM Reaction Index (LRI), a descriptive measure of news-to-quote lag compression comparing a pre-LLM benchmark period (2018 to 2019) with a post-LLM period (2023 to 2024). Using 50 matched macroeconomic news events affecting U.S. equity ETFs, we find median lag compression for CPI releases from 450 ms to 120 ms. After accounting for estimated infrastructure speed-ups, the remaining 230 to 280 ms compression likely reflects multiple mechanisms, including non-LLM algorithmic improvement