This AI Visibility working hypothesis examines how social reach metrics (follower counts, engagement numbers) may compress during large language model training cycles. The hypothesis proposes that discrete numerical values compress into categorical markers through a many-to-one pattern, where multiple follower counts (10,000, 50,000, 100,000) may collapse into single social proof representations. This compression behavior extends patterns observed in the Shallow Pass Selection Hypothesis and Page Density vs Category Distribution operations, where distributed signals collapse into consolidated representations. The weighting influence these metrics hold in current search systems may not transfer to post-compression LLM representations, where social reach becomes one vector among many rather than a dominant authority signal. The hypothesis is testable through comparative analysis of pre-training and post-training agent recall behavior across multiple LLM platforms.