The Only 2 Moats That Actually Work In The AI Era
AI itself is no longer a durable differentiator, argues guest author SC Moatti of Mighty Capital, who says he strongest startup moats are counter-positioning and network economies.
The two moats that actually stand up in the AI era are counter-positioning and network economies. Counter-positioning means creating a business model that is so fundamentally different from competitors that it's hard to copy without hurting your own business. Examples in the AI era include AI-native revenue management systems that would hurt legacy vendors' margins if they tried to match them.
Network economies, on the other hand, are when a product becomes more valuable as more users join, creating a winner-take-all outcome. This is rare in the AI sector, showing up in only 5% of companies but commanding a 4.2x multiple. The B2B variant, where the network connects companies rather than individuals, is even more underappreciated.
While cornered resources like proprietary data and unique IP are common, they only have a 2.6x multiple, as investors are wary of them being eroded by foundation models and synthetic data. Switching costs look like a moat to many, but they often require deep enterprise entanglement, resulting in expensive sales cycles and high capital requirements.
Finally, scale economies are not the game most founders are playing, with a median multiple of only 3.2x after excluding OpenAI and Anthropic. To command a premium valuation, founders must build something underneath the AI that a model cannot replicate on its own, such as a strong business model or network architecture.
Written by urgent.news from Crunchbase News's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
