Speed is not the same as readiness
AI-generated code can be useful when it is reviewed, tested and shaped by experienced product and engineering judgement. Problems appear when a prototype becomes business-critical without controls.
Common issues include unclear data boundaries, weak authentication, copied patterns, brittle integrations and missing deployment discipline.
What to test
Founders and investors should inspect code ownership, security basics, dependency risk, test coverage, hosting, data protection and the ability of a human team to maintain the system after launch.
Ask for a review before an AI-built MVP becomes the production foundation.