AI

“Not Invented Here” (NIH) syndrome is that voice in your head that says, “we can build that ourselves,” every time someone suggests using an external solution. It's the organizational equivalent of refusing to ask for directions because you're pretty sure you can figure out the route—even though your phone has GPS and traffic data that would get you there faster. For decades, this syndrome has led engineering teams down expensive rabbit holes: rebuilding open-source libraries, creating internal project management tools instead of using Jira or Linear, building custom authentication systems instead of integrating with Auth0. The drivers are always the same: technical pride, desire for control, and the belief that your requirements are uniquely special.

Every product team now has access to the same AI-powered development tools that can scaffold components, suggest workflows, and generate entire features from prompts. The question isn't whether you can create fast—it's whether you can create right. Speed is commoditized. Strategy isn't.

Everyone's talking about AI making software development faster, but here's the thing nobody mentions in those breathless AI productivity reports: speed only helps if you know where you're going. You can ship features twice as fast, but if you're building the wrong features, you're just getting to failure faster. You can iterate on designs in half the time, but if you don't know what's broken about the current experience, you're just polishing problems more efficiently. The real bottleneck isn't development velocity—it's decision velocity. And that comes down to metrics.