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.
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Elias Torres argues that revenue is not the ultimate validator of a product. He has seen founders with $50 million in revenue who are "delusional" that their product truly works or is sticky. This time, he is prioritizing user obsession and product stickiness over early monetization to avoid this trap.
When evaluating a startup, don't accept analogous trends as proof of demand. For example, Drift's pitch deck used consumer messaging growth to justify B2B marketing software. A better approach is to find direct evidence of business users already struggling with the specific project the product addresses.
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.
The biggest initial hurdle for a new product isn't getting the first dollar of revenue; it's crossing the chasm from a user trying the product once to becoming a truly engaged, repeat user. This "penny gap of engagement" is the most critical early milestone to overcome for long-term success.
Sales are a vanity metric for product-market fit. The real test is having ~25 customers who have successfully implemented your product and achieved the specific ROI promised during the sales process. If you don't have this, you have a product problem, not a go-to-market problem.
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.
Dynamic Signal generated millions in ARR, but analysis revealed customers treated the product like a one-off media buy, not a recurring software subscription. The high revenue hid an unsustainable, services-based model with low lifetime value.
Beyond outright fraud, startups often misrepresent financial health in subtle ways. Common examples include classifying trial revenue as ARR or recognizing contracts that have "out for convenience" clauses. These gray-area distinctions can drastically inflate a company's perceived stability and mislead investors.
