AI-coding tools have elicited mixed reactions among software developers, with some praising their productivity boost and others questioning their value, particularly in enterprise environments where adoption is inconsistent. Key challenges include providing adequate context for large language model agents, bridging the learning gap for effective tool usage, and preserving tribal knowledge within companies. Spec-driven development is proposed as a paradigm shift, emphasizing detailed, version-controlled specifications that serve as comprehensive context for AI agents, treating them as foundational elements like code. The concept of runbooks is introduced to capture and share AI prompting knowledge, allowing for collaborative, multiplayer environments that enhance developer collaboration and continuity in AI-assisted projects. By preserving prompts and utilizing runbooks, teams can maintain a living repository of AI execution patterns, bridging the gap between product specifications and code, and turning AI coding into a collective endeavor.