The first day of AI Coding usually feels like a "10x efficiency boost"—by the second week, mass rework begins. This isn't because the models aren't powerful enough; it's because Vibe Coding (coding by vibes) skips goal definition. You tell the AI "what to do," but never tell it "what counts as done."
What this is
SDD (Spec-Driven Development) flips the script: write the spec first, then let the code become its implementation. GitHub's Spec Kit distills the flow into four steps—Spec (what to do) → Plan (how to do it) → Tasks (in what order) → Implement (build it).
A valid spec meets at least three conditions: executable (the AI knows which module to touch after reading it), verifiable (every requirement has a clear acceptance criterion), and traceable (requirement changes can locate their blast radius). "The UI should look good" and "the API should be stable" aren't specs—they can't be verified.
Industry view
Supporters argue SDD strikes at the root pain point of AI Coding—context loss and ambiguous boundaries. The three documents (proposal, design, tasks) function as a "construction blueprint" for the AI, preventing over-implementation when boundaries are fuzzy.
The dissent is equally clear. First, writing documentation has its own cost—is it too heavy for weekend projects? Second, as model capability improves, up-front specs may grow thinner; "writing thick docs" might just be a transitional solution for the current stage. Third, many failures aren't due to missing specs but to developers giving up independent thinking in front of AI.
Impact on regular people
For enterprise IT: If AI-assisted development is to upgrade from "knowing how to write prompts" to "knowing how to write specs," team workflows must be redesigned—and the roles of documentation and architecture positions may be rewritten.
For individual careers: The window in which non-programmers can build tools via Vibe Coding may be closing. Scripts and pages cobbled together through a chat window tend to collapse when extended to a second round of requirements.
For the consumer market: Direct impact is limited, but the next competitive battleground for AI coding tools may shift from "how smart the model is" to "how disciplined the collaboration workflow is."