In this chapter, we introduce knowledge sharing in Q&A communities, uncovering how hierarchy, often invisible yet deeply influential, shapes who speaks, who is heard, and who remains silent. But in today’s AI-driven landscape, these power dynamics are not just social; they are also computational. Machine learning algorithms increasingly influence whose answers are promoted, which voices are amplified, and how information circulates. What hidden forces, both human and algorithmic, govern participation and visibility? As we peel back these layers, we explore how AI can either reinforce existing hierarchies or help dismantle them by identifying underrepresented expertise and enabling more equitable knowledge flow. Can we design strategies, aided by intelligent systems, to break down these barriers and foster genuine participation? Or will entrenched power structures, now encoded in data and algorithms, remain too resistant to change? The answers lie ahead, though the path to them remains layered in both human and machine uncertainty.