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Saved February 14, 2026
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This article examines why many AI startups struggle with long-term growth and defensibility. It argues that current AI products often rely on short-term viral growth strategies instead of building sustainable value. The author highlights the absence of new market opportunities and the complexities of achieving product/market fit in consumer networks.
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SuperMe has been developing an AI-native network business for over a year, raising a critical question: why aren’t more companies pursuing this path? The assumption is that AI should create new consumer network products, which would attract entrepreneurs. However, the market reveals that building new network businesses requires patience, favorable conditions, and a unique alignment of factors that are currently lacking.
The author contrasts “magic tricks” with “moats” in the context of product development during platform shifts. Magic tricks are flashy and viral but lack long-term sustainability; they often use generic technology, have low user retention, and are easily replicated by competitors. Examples include early viral apps during the mobile boom, which ultimately did not endure. In contrast, enduring companies like Instagram and Uber were built with defensibility in mind from the start. The current AI landscape mirrors this pattern, where rapid growth obscures weaknesses until the novelty wears off.
Venture capitalists are drawn to fast growth, making it easy to overlook the long-term viability of these AI companies. They operate on a portfolio model, where backing several high-growth startups increases the chances of finding a few that succeed sustainably. Most investors today focus on subscription metrics rather than the more complex network density, which affects funding decisions.
Alex Zhu's analogy of building a new consumer network product being akin to creating a new country highlights the need for "new land." Unlike mobile technology, which expanded access and created new distribution channels, AI has yet to unlock similar opportunities, forcing founders to compete for user attention within existing products. The author introduces the concept of the “three-body problem” in network product/market fit, emphasizing the need for alignment among target audience, network size, and value proposition. Misdiagnosing issues can lead to misguided strategies, complicating the path to building a defensible network. Until AI creates new opportunities or alternative strategies for density emerge, many AI products will rely on temporary viral success rather than lasting value.
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