The founder predicts that hyper-specific vertical AI solutions are too easy to replicate. While they may find initial traction, they lack a durable moat. The stronger, long-term business is building horizontal tools that empower users to solve their own complex problems.

Even a seemingly acceptable 4% monthly churn will eventually cap your growth, as acquiring new customers becomes a treadmill to replace lost ones. Reducing churn to 2.5-3% is a more powerful growth lever than finding new marketing channels once you hit a plateau.

Since today's AI companies grow too fast to have multi-year renewal data, investors must adapt their diligence. The focus shifts from long-term retention to short-cycle retention and, crucially, deep product engagement. High usage is the best leading indicator of future stickiness and value.

TinySeed identifies "vibe-coding"—using AI to write code without expert engineering oversight—as a major investment risk. This approach leads to unmaintainable code, causing feature velocity to collapse and catastrophic regression bugs within 6-18 months, effectively creating a technical time bomb they are unwilling to fund.

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.

The narrative of "0 to $100M in a year" often reflects a startup's dependence on a larger, fast-growing customer (like an AI foundation model company) rather than intrinsic product superiority. This growth is a market anomaly, similar to COVID testing labs, and can vanish as quickly as it appeared when competition normalizes prices and demand shifts.

The dot-com era saw ~2,000 companies go public, but only a dozen survived meaningfully. The current AI wave will likely follow a similar pattern, with most companies failing or being acquired despite the hype. Founders should prepare for this reality by considering their exit strategy early.