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In many areas of information processing, from search optimization to information integration and trust inference, we observe a recurring pattern: meaning and structure are not imposed through design but emerge through interaction. Inference becomes a fixpoint process — a process of discovery rather than construction. This lecture reflects on a personal journey through these ideas, beginning with semantic query optimization, extending to self-organizing peer-to-peer networks and schema integration, and reputation-based trust models. Across these domains, a common thread emerges: when we let systems organize themselves, the challenge is no longer how to solve the problem, but how to ask the right questions. Today, with the rise of large-scale learning systems and agentic AI, this shift becomes even more acute. As the “how” increasingly takes care of itself through self-organization and emergent inference, the responsibility to define the “what” — to shape objectives, semantics, and purpose — remains with us. Yet, the question arises: will future systems remain bound to our goals, or will their emergent behaviors take them in unforeseen directions?