AI coding tools (Cursor, Bolt, Replit, and product-native tools) can rapidly clone basic SaaS feature sets in under an hour for single-digit dollars. For PLG/bottom-up SaaS companies whose adoption funnel depends on SMB users trialling before buying, this creates a structural threat: users can build "good enough" alternatives cheaply, bypassing the traditional adoption funnel. The defensible moat against this is enterprise-grade security, compliance, deep ecosystem integrations, and proprietary customer data/workflow context that cannot be replicated in a rapid prototype. Companies with primarily SMB motion and without deep enterprise moats face permanent channel impairment, not a cyclical dip.
When analyzing PLG/bottom-up SaaS companies, assess AI commoditization risk to the SMB funnel as a distinct structural risk factor. Companies with >50% of revenue from SMB/PLG motion and without deep enterprise moats (regulatory compliance, proprietary data integration, mission-critical workflows) warrant a higher structural risk discount than their historical volatility would suggest. Conversely, companies that have successfully monetized AI capabilities within their own products — converting the disruption into a product advantage — may turn the threat into an accelerant. Screen for: (1) % revenue from SMB vs enterprise, (2) depth of enterprise switching costs, (3) whether the company is building AI-native features that create new differentiation vs. what an AI prototype can replicate.