Beyond the Vibe: When AI-Assisted Development Needs Structure

There is a moment in most AI-assisted projects when momentum stalls. The prototype worked. The demo impressed. The early screens came together in minutes rather than days. And then, somewhere around the third iteration or the second team member, things begin to unravel. Changes cascade unpredictably. Previous decisions become opaque. The system that felt so alive in exploration becomes brittle under sustained development.

This is not a failure of technique. It is a structural limitation of how many of us currently work with AI coding tools.

The Shape of the Problem

Vibe Coding—a term introduced by Andrej Karpathy in early 2025—describes an approach where developers guide large language models through natural language, accepting generated code without deep review, iterating through conversation rather than comprehension. The appeal is obvious: accessibility, speed, the collapse of traditional barriers between idea and implementation. For solo projects, internal tools, or weekend experiments, it works remarkably well.

The difficulties emerge at specific thresholds. When a second person needs to understand the system. When requirements shift and previous decisions must be traced. When something breaks in production and the debugging instinct finds nothing to hold onto. As one practitioner observed, the codebase becomes "a delicate house of cards, and nobody seemed to notice." The generated code may run, but running is not the same as working—not when the system must persist, scale, or be maintained by people who did not write the original prompts.

The pattern is consistent across reports from teams who have scaled vibe-coded projects: duplicated logic scattered across files, inconsistent naming, architecture that exists implicitly in conversation history rather than explicitly in structure. The speed that felt liberating in week one becomes technical debt in month three. Intent disappears into ephemeral prompts. Decisions lose their rationale.

Specification as Response

Spec-Driven Development has emerged as a complementary response to these limitations. The core idea is straightforward: make intent explicit before—or alongside—generation. Instead of treating prompts as disposable inputs, treat specifications as durable, shared artifacts. The specification becomes the source of truth; code becomes its expression.

GitHub's Spec Kit, Amazon's Kiro, and various practitioner frameworks share a common structure: define what the system should do in natural language, elaborate that definition into a technical plan, break the plan into discrete tasks, then execute. The AI generates code against a contract rather than from improvisation. Changes propagate through the specification first, then into implementation.

This is not a return to waterfall documentation. Specifications remain living artifacts, version-controlled and editable. The difference from vibe coding lies in where decisions accumulate. In prompt-driven work, decisions dissolve into conversation history. In spec-driven work, decisions persist in structured form—reviewable, shareable, traceable.

Two Modes, Not Competing Ideologies

The distinction between vibe coding and spec-driven development is less about ideology than about appropriate context. Both modes serve legitimate purposes; the question is when to switch between them.

Vibe coding excels at exploration: validating concepts, testing assumptions, building quick proofs. The overhead of specification is unnecessary when the goal is learning rather than delivery. Disposable experiments should feel disposable.

Spec-driven work becomes necessary when systems must persist beyond their creator, when teams must coordinate, when accountability and reproducibility matter. The switch often happens gradually—a project that began as exploration accrues enough value that it needs to survive its own origin. At that point, implicit intent needs to become explicit.

Where Product Creators Come In

For digital designers and product managers, this shift carries significant implications. The traditional artefacts of product work—requirements documents, user stories, interface specifications—are not obsolete. They are, if anything, more consequential than before. When AI can generate implementation rapidly, the quality of input specifications determines the quality of output.

This reframes the designer's contribution. The work is less about producing screens or flows and more about framing problems clearly: translating user needs, business constraints, and system behaviours into structured descriptions that AI can act upon reliably. Specification becomes a creative act—one connected increasingly to design tools, shared system definitions, and emerging interfaces like MCP protocols that link AI agents to external context.

The hypothesis worth examining: in an environment where implementation is increasingly automated, the human contribution shifts toward orchestration. Designers shape systems rather than individual components. They define intent, establish constraints, validate outcomes. The skills that transfer naturally—systems thinking, user empathy, cross-functional communication—become more valuable. The skills that may not transfer—pixel-level execution, detailed interaction design—become candidates for augmentation or delegation.

A Working Hypothesis

Whether this shift is realistic in daily work remains an open question. Most product teams today still operate within traditional software development workflows. AI-assisted methods—whether vibe-driven or spec-driven—remain emergent practices, adopted unevenly and often experimentally. The tools are evolving rapidly; the practices around them are still stabilising.

But if we assume that AI-assisted development continues to mature and spread, a hypothesis emerges:

Vibe coding and spec-driven development may come to serve different purposes in the product creation cycle. Vibe coding for discovery, prototyping, and learning—contexts where speed matters more than durability. Spec-driven development for delivery, implementation, and long-term robustness—contexts where reproducibility and shared understanding matter more than velocity.

If this holds, the challenge for product creators would be recognising when to operate in each mode, and developing the judgment to switch between them. The prototype that succeeds through vibes would eventually need the structure that specifications provide. The specification that guides implementation would occasionally need the exploratory freedom that vibes allow.

The role of the digital designer, in this framing, would not be to choose one mode over the other but to orchestrate their appropriate use—knowing when to explore, when to formalise, and how to translate between the two. That translation, potentially, is where consequential product decisions will increasingly be made.

This remains a hypothesis, not a conclusion. But it is one worth observing closely.