Counter-positioning, network economies: the only AI moats that pay

SC Moatti's analysis draws on Crunchbase data covering 576 AI B2B companies that raised $50 million-plus rounds since the start of 2025, cross-referenced with Hamilton Helmer's 7 Powers framework and insights from Products that Count's 600,000-plus product leader community. The starting observation is that 97% of products nominated for this year's Products That Count Product Awards are deeply integrated with AI—evidence, in Moatti's view, that AI itself has stopped functioning as a differentiator.

The data separates the powers that pay from those that don't. Counter-positioning shows up in only 5% of companies but commands a median enterprise value of 5.3x per dollar raised. Network economies also appear in just 5% yet command a 4.2x multiple. By contrast, cornered resources like proprietary data and unique IP are far more common at 44% but carry the worst multiple at 2.6x. Switching costs are the most crowded power at 37% with a 4x multiple, but the capital required to reach that stickiness is roughly 10x higher than network economies. Scale economies, excluding OpenAI and Anthropic, see their median multiple collapse from 6.1x to 3.2x, with 88% of the category's capital belonging to those two companies.

The stakes for founders come down to a single diagnostic question Moatti poses: what about your business would survive a competitor who starts today with more capital and a better model? If the answer is a structural feature of the business model or network architecture rather than model quality or data volume, the company is likely to command the 4x to 5x multiples the data associates with these two powers.