By SC Moatti
Leading with “we use AI” no longer sets a product apart; AI is now basic infrastructure. According to data from 576 venture-backed AI B2B companies raising over $50 million since 2025, true competitive moats today are those that AI models alone cannot replicate. Using Hamilton Helmer’s 7 Powers framework and insights from Products That Count, two moats stand out:
1. Counter-positioning: Building a business model so distinct that incumbents won’t copy it because doing so would undermine their own business economics. Examples include AI insurers who bypass traditional brokers and AI-native revenue management that competes against consulting revenue. Only 5% of companies employ this, commanding the highest valuation multiples. Key question: Would it cost an incumbent more to copy you than it would cost you to build?
2. Network economies: Value grows as more users or companies join, creating a powerful self-reinforcing cycle. Seen in platforms connecting brands, factories, advertisers, and audiences, where data accumulates and becomes harder to replicate. Also used by 5% of companies, offering strong capital efficiency.
Other assumed moats like proprietary data and switching costs are often weaker than they appear due to model erosion and high capital needs. Scale economies aren’t achievable for most startups outside major players like OpenAI.
Ultimately, the moat is structural—embedded in business model or network design—not just AI tech. Founders must define what aspect of their business would survive a better-funded competitor launching tomorrow. Those who can’t answer are merely building products, not enduring powers.
SC Moatti is Founding Managing Partner at Mighty Capital and Chair at Products That Count. She is recognized for pioneering investments and product leadership, with deep experience building award-winning products at Meta and Siebel Systems.
