TL;DR: In AI-first products, a moat is proprietary data others can’t access—the stuff that feeds your model and improves it over time. The real moat isn’t static data; it’s the pipeline that continuously generates and refines it.
The phrase often goes: “A data moat is having access to data others don’t.” But that’s half the story. Raw data alone is worthless. The moat is operational: the closed loop where usage generates better data, which trains a better model, which attracts more users, who generate more data. Competitors might replicate your features, but they can’t replicate your data stream.
The strongest data moats are those where the product itself is the data collection mechanism. Recurring interactions, user behavior, corrections—all feeding back into the system. (cheating-is-all-you-need, moats)