Jason Cohen founded WP Engine and scaled it past $100M ARR. He writes long-form essays at A Smart Bear on product strategy, founder decision-making, and the structural forces that shape SaaS outcomes. His writing is known for challenging frameworks that feel rigorous but rely on unmeasurable inputs, including RICE-style confidence scoring, TAM estimates, and other prioritization heuristics that impose false precision on genuinely uncertain futures.
Operating themes
- Frameworks that require unmeasurable inputs are worse than nothing. Cohen's recurring argument is that a decision tool is only as good as the data entering it. When an input like "confidence" cannot be defined or calibrated, a framework built on it produces decisions that feel principled but are effectively arbitrary.
- Genuine uncertainty demands uncertainty-native tools. His alternative frameworks (always-true features, asymmetric bets, SLC quick discovery) are designed for the condition he actually observes in product work: Knightian uncertainty, where the distribution of outcomes is not just unknown but unknowable in advance.
- Scale as the source of evidence. Cohen draws on building WP Engine across many product cycles and team configurations. His arguments are grounded in repeated observation rather than theoretical modeling.