Spec Is Not the Cure — Unless It’s Discovered Through Discussion

Over the past year, three terms have dominated conversations around AI coding: Spec / Plan / Design Document There’s a growing belief that if a model can first generate a comprehensive spec, and an agent can then execute against it, complex tasks can be automated end-to-end. It sounds reasonable. In practice, it rarely works that way. The problem isn’t that specs are unimportant. The problem is that we’re generating them in the wrong way. That’s precisely the gap CodeFlicker is designed to address. 1. We’ve Misunderstood What a Spec Actually Is In most AI IDEs, a “spec” typically means: A document generated before coding A description of implementation steps or architecture A one-shot artifact that drives downstream execution This mindset is inherited from traditional software engineering. But in AI-native workflows, this definition breaks down. The real value of a spec is not the “plan” itself. It’s whether the spec encodes sufficient context. A spec is not a plan. A spec is an explic