More than a decade after the introduction of the FAIR Principles and after billions of euros invested in research data infrastructure, a fundamental question remains: What value has FAIR actually delivered to researchers? In this talk, Sebastian Hellmann and Jan Forberg from the Institute for Applied Informatics (InfAI) take a pragmatic perspective and examine the microeconomics of FAIR adoption. Researchers constantly balance effort, cost, and administrative burden against the practical benefits they receive. We revisit lessons learned from the past decade and discuss which practices and tooling genuinely reduce friction while increasing the value of research data. Building on this analysis, we present the design philosophy behind the open-source tools Databus and MOSS, developed in the DBpedia ecosystem. These tools enable organizations to build distributed data catalogs with collaborative metadata annotations and tailored search capabilities, aiming to provide tangible value to researchers while keeping the effort required for FAIR data management as low as possible. In addition, both systems are designed to be increasingly operable and maintainable by AI agents, enabling automated metadata curation, catalog management, and dataset integration workflows. The presentation is aimed at research infrastructure administrators, data stewards, and platform maintainers who are responsible for implementing FAIR systems that genuinely support researchers rather than adding additional complexity.