The narrative of tech enthusiasts dropping AI tools like Cursor is misleading. While early adopters chase the newest thing, enterprise diffusion is slow and sticky. Cursor's jump to $2B ARR demonstrates that the majority of the market is just beginning to adopt these tools, making the online chatter irrelevant to business momentum.

Contrary to assumptions about user stickiness, consumers of AI models will quickly switch to a better-performing or cheaper alternative. The 22% drop in ChatGPT usage after new Gemini models were released demonstrates that brand loyalty is low when model performance is the key value proposition.

The true indicator of Product-Market Fit isn't how fast you can sign up new users, but how effectively you can retain them. High growth with high churn is a false signal that leads to a plateau, not compounding growth.

A key trend TinySeed observes among AI-focused applicants is extremely high churn, often 10-20% per month. Even with rapid top-line growth, this level is deemed "catastrophic," indicating many new AI products struggle with defensibility and long-term customer value, making them risky investments despite the hype.

Lin warns that much of today's AI revenue is 'experimental,' where customers test solutions without long-term commitment. He calls annualizing this pilot revenue 'a joke.' He advises founders to prioritize slower, high-quality, high-retention revenue over fast, low-quality growth that will eventually churn.

The current AI hype cycle can create misleading top-of-funnel metrics. The only companies that will survive are those demonstrating strong, above-benchmark user and revenue retention. It has become the ultimate litmus test for whether a product provides real, lasting value beyond the initial curiosity.

While individual AI companies see slightly lower retention than SaaS, Stripe's data reveals customers often churn from one provider directly to a competitor, and sometimes switch back. This indicates the problem being solved is highly valued, and the churn reflects a rapidly evolving, competitive market, not a lack of product-market fit for the category itself.

While impressive, hypergrowth from zero to $100M+ ARR can be a red flag. The mechanics enabling such speed, like low-friction monthly subscriptions, often correlate with low switching costs, weak product depth, and poor long-term retention, resembling consumer apps more than enterprise SaaS.

Unlike previous tech cycles where early revenue was a strong signal, the current AI hype creates significant "experimental demand." Companies will try, pay for, and even renew products that don't fully work. Investors must look beyond revenue to assess true product-market fit.