Proprietary feedback loops create moats that widen with every interaction

The strongest AI moat isn’t proprietary data at rest — it’s proprietary data in motion. Every user interaction generates correction patterns, preference signals, and edge-case resolutions that feed back into the system. A competitor starting from scratch faces not just a data gap but an accumulating gap: the incumbent’s system improves with every interaction while the challenger has zero signal.

This goes beyond Domain-specific skill libraries are the real agent moat, not core infrastructure (which captures static expertise) to dynamic expertise that self-updates. Skills encode what you knew yesterday; feedback loops encode what you’re learning today. The mechanism works through Compound engineering makes each unit of work improve all future work applied to the product itself — each user session doesn’t just complete a task but improves future task completion. And Cross-user knowledge transfer works without fine-tuning — just a database and prompt engineering shows the infrastructure is surprisingly simple: a database and prompt engineering, not expensive training pipelines. The key is designing the capture mechanism from day one — retrofitting feedback loops onto a product that wasn’t built for them is orders of magnitude harder. This is fundamentally a UX problem, which is why The data flywheel is a UX problem — only vertical workflow surfaces can capture the knowledge: the flywheel only spins if the workflow surface is shaped to capture the right signal, and horizontal tools structurally can’t shape it. The capture mechanism also has to live somewhere you control: Memory is where agent lock-in lives — without it, agents are commoditized argues that the feedback loop itself is the memory layer, so ceding that layer to a closed harness surrenders the moat. Nadella elevates this loop to the firm’s defining asset in The learning loop becomes the firm's new IP — a hill-climbing machine that compounds unlike any other asset — a hill-climbing machine where every improved workflow generates better training signal, compounding into an advantage that survives any new model.