AI has made building products easier, but it has also made lasting advantages harder to find. The signal is impossible to miss: 97% of the products nominated for this year’s Products That Count Product Awards are deeply integrated with AI. When nearly every product carries the same promise, the real question becomes much tougher: what survives when a well-funded competitor launches tomorrow with a better model?
That question sits at the center of startup strategy in the AI era. A product can use AI and still lack a durable business. As SC Moatti, founding managing partner of Mighty Capital, puts it: “If your pitch still leads with ‘we use AI,’ you’re describing infrastructure, not a business.” The strongest defenses come from advantages that a model cannot generate.
The Moat Must Survive Cheaper Building
When building is nearly free, product creation stops serving as a reliable barrier. A competitor can bring similar capabilities to market, improve the model, and challenge a startup before the original company has built enough distance. That changes what investors seek when they examine an AI business.
The real moat is the advantage many founders cannot identify: the part of the company that will survive a well-funded competitor with a better model. AI may power the product, but the defense must come from the business around it. Two structures stand out: counter-positioning and network economies.
These moats do not depend on a model staying ahead forever. They depend on a competitor facing a harder problem—copying the business without damaging its own economics, or entering a product whose value grows with every new user.
Counter-Positioning Makes Copying Too Expensive
Counter-positioning happens when a newcomer builds a business model so structurally different that the incumbent cannot copy it without destroying its own economics. The advantage does not come from having a feature the incumbent lacks. It comes from forcing the incumbent to choose between defending its existing business and adopting a model that weakens it.
Blockbuster shows how this conflict can work. The company could have matched the subscription model, but doing so would have gutted late-fee revenue, which kept its stores alive. Matching the newcomer would not have been a simple product decision; it would have attacked the economic structure supporting the incumbent.
That is the power of counter-positioning for startups. A large company may have more money, customers, and technical resources, yet copying the startup could damage the machine that made the large company successful. The moat is the conflict between the old model and the new one.
Only 5% of companies in the dataset leverage counter-positioning. Investors price that scarcity at a median enterprise value of 5.3x per dollar raised. The figures point to a clear pattern: this moat appears in few companies, but it commands a strong valuation measure when it does.
Network Economies Turn Users Into Momentum
Network economies create a different kind of defense. They arise when a product becomes more valuable to each user as more users join. Every new participant strengthens the experience for the people already inside, giving the business a growing advantage that a better model alone cannot reproduce.
LinkedIn is the textbook case of network economies. Its value comes from the network of users, not only from the software that supports it. As the network expands, the product becomes more valuable to each user, creating a structure that reaches beyond any single AI feature.
Network economies appear in only 5% of companies, but command a 4.2x multiple. That scarcity gives founders and investors another way to examine an AI startup: does each new user make the product more valuable for everyone else, or does the company simply sell access to capabilities that another model can match?
The distinction matters because AI can compress the time and cost required to build. A startup with no structural defense may gain attention, then face a competitor with a better model and deeper funding. A startup built around counter-positioning or network economies gives that competitor a harder challenge than feature replication.
What AI Startups Need To Prove Next
Vertical AI offers a strategy for startups defending themselves against big tech, but using AI in a focused market does not answer the moat question by itself. Founders still need to show what remains when models improve and building becomes nearly free.
That proof can take two forms. A business may adopt a structure that an incumbent cannot copy without gutting its own economics, or it may create a network whose value rises as more users join. Both strategies move the pitch beyond “we use AI” and toward a business advantage that models cannot generate.
As of September 11, 2026, the numbers make the opportunity plain: 5% of companies leverage counter-positioning, while network economies appear in 5% of companies. Their 5.3x median enterprise value per dollar raised and 4.2x multiple show why scarce defenses attract attention.
The AI era will keep producing more products, more models, and more competitors. The startups that endure will need more than intelligence inside the product. They will need a business structure that remains valuable when the next model arrives.
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