The commoditization of AI foundation models has created a paradox: AI capabilities are more accessible than ever, yet the companies with proprietary data are pulling further ahead.
Here is why. When every startup can call GPT-5 or Claude via API, the model itself is not a moat. The moat is in the data you use to fine-tune, the feedback loops you create with users, and the domain-specific datasets you accumulate over time.
I look for three types of data moats in startups: exhaust data (data generated as a byproduct of your core service that improves with every customer), network data (data that becomes more valuable as more participants join), and regulatory data (data you are uniquely positioned to collect due to compliance requirements or licensing).
If your AI startup cannot articulate which of these three data moats you are building, you are building a feature, not a company. And features get absorbed by platforms.