The bottleneck was never data or algorithms: building a learning utility for AI-enabled learning health systems

Although envisioned nearly two decades ago, the learning health system (LHS) remains largely unrealized at scale. The limiting factor is not data or algorithms but rather the failure to build, the technical, human, and cultural infrastructure needed to operationalize continuous learning. We propose the learning utility: the full institutional stack that turns data and AI into continuous, bidirectional, measurable learning at the speed of care. If data is the fuel and AI is now a new generation technology, learning is the electricity and the learning utility is the grid. The utility’s core mechanism is bidirectional learning flow between evidence and practice: evidence informs practice, while practice continuously generates data that refines evidence. Today both directions run at project speed, not utility speed. Building the utility therefore requires a three-layer infrastructure stack: Foundation (data/technology, heavily invested), Machinery (feedback loops and monitoring, the largest gap), and Enabling Environment (governance, trust, and alignment, largely absent). We argue for a measurable “GDP (Gross Domestic Product) of learning” as the alignment mechanism across stakeholders, operationalized through a learning compact that distributes obligations and supports across the ecosystem. The recommendation: invest differently, not necessarily more, starting with the translator workforce that bridges the three layers.