Data moat

In a world where anyone can call GPT-4o or Claude, what stops a competitor from building the same product tomorrow? If the answer is 'our unique data,' you have a data moat. This could be proprietary transaction data, feedback loops from millions of user interactions, industry-specific datasets that aren't publicly available, or behavioral signals collected through your product over time.

Data moats often compound. The more users a product has, the more interaction data it collects, the better it can fine-tune or contextualize its AI, the better the product gets, which attracts more users. This flywheel is the AI-era equivalent of network effects. It's why some investors argue that data-rich distribution companies are better positioned than model builders, who face open-source competition from every direction.

Not all data is a moat. If your data is easily scraped, licensed, or replicated, it provides only temporary cover. True moats come from data that's inherently tied to your product, your users, or your domain relationships: data that competitors cannot buy or synthesize. Vertical AI companies with deep industry partnerships or proprietary workflows tend to accumulate the most defensible data over time.