Vanity metrics like total revenue can be misleading. A startup might acquire many low-priced, low-usage customers without solving a core problem. Deep, consistent user engagement statistics are a much stronger indicator of genuine, 'found' demand than top-line numbers alone.

When evaluating AI companies, focus on customer love (gross retention) and efficient acquisition over gross margins. High margins are less critical initially, as the 99%+ decline in model input costs suggests a clear path to future profitability if the core product is sticky.

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.

Metrics like product utilization, ROI, or customer happiness (NPS) are often correlated with retention but don't cause it. Focusing on these proxies wastes energy. Instead, identify the one specific event (e.g., a team sending 2,000 Slack messages) that causally leads to non-churn.

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.

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 pipeline is important, the real signal of a successful AI-driven business is the depth of customer engagement. Are customers expanding beyond their initial use case? Are developers integrating your tool into core workflows? Are communities actively discussing you? These leading indicators show a stronger foundation than top-of-funnel metrics alone.

Don't jump directly to optimizing for high-level business outcomes like retention. Instead, sequence your North Star metric. First, focus the team on driving foundational user engagement. Only after establishing that behavior should you shift the primary metric to a direct business impact like revenue or retention.

Because AI products improve so rapidly, it's crucial to proactively bring lapsed users back. A user who tried the product a year ago has no idea how much better it is today. Marketing pushes around major version launches (e.g., v3.0) can create a step-change in weekly active users.