AI Product Moat Research Note

AI Product Moat Research Note

The moat framework tests whether an advantage survives model substitution, price compression, platform copying, employee departure, and a customer export request.

Potential sources of durability include owned distribution, workflow ownership, lawful exclusive feedback, accumulated configuration or state, trust and approval, ecosystem complements, switching burden tied to real value, scale economics, and operational execution.

Each candidate needs an asset, evidence, compounding mechanism, copying path, dependency, and failure condition. "Proprietary data" is incomplete unless the product has the right to use it, the data improves a measured outcome, the improvement compounds, and a rival cannot obtain a substitute. "Integration" is incomplete unless repeated use creates durable workflow ownership or costly replacement.

Trust can be an advantage when it is backed by controls, audit evidence, reliability history, incident response, contractual accountability, and domain approval. A generic safety claim is not a moat.

Switching costs should not be praised automatically. Pain created by lock-in can attract regulation and customer resistance. The stronger form is accumulated value that a customer willingly renews and can still export under fair terms.

The output should be a defensibility statement that names the strongest evidence and the easiest credible attack.