Vibe Coding vs Spec-Driven Development: When Each Fails

Vibe coding — using AI coding tools (Cursor, Claude Code, GitHub Copilot) to write code from natural language descriptions without formal specifications — ships features in days that would have taken weeks in 2023. It also produces codebases that become incoherent around 90 days in. Spec-driven development — writing formal specifications before a line of code is written — produces coherent codebases and ships features in months. The question is not which approach is correct. It is what each approach fails at and how to get the benefits of both without the costs of either.

What vibe coding actually is

Vibe coding is a term coined by Andrej Karpathy in February 2025 to describe the practice of directing AI coding tools with high-level descriptions rather than specifications. Instead of writing a detailed spec for an authentication system, you tell Cursor "add Google OAuth" and review the output. The AI makes the implementation decisions — which library, which data model, which API shape.

This is genuinely fast. Teams in early-stage startups and solo developers have shipped full SaaS products in days using Cursor, Claude Code, and Windsurf. The feedback from the community is consistent: vibe coding removes the blank page problem for code the same way AI PRD generators remove it for documentation.

The problem surfaces at scale and over time.

When vibe coding fails

Failure mode 1: The architectural drift problem

Each vibe coding session starts with no memory of previous sessions (unless you explicitly provide context). Session 1 might create users with a userId field. Session 12 might use user_id. Session 23 might use id prefixed with "usr_". The codebase accumulates inconsistency at the rate of sessions. At 90 days of daily coding, you have a data model that makes no internal sense.

The fix is not to stop using AI coding tools. The fix is to maintain an architecture document that every session reads as context. This is the architecture blueprint that Scriptonia generates from PRDs — a single source of truth for data models, API contracts, and component boundaries that any AI coding session can use to make locally-consistent, globally-coherent decisions.

Failure mode 2: The scope expansion problem

Vibe coding is too frictionless. A developer who can implement any feature in an hour will implement features that were never planned. This is not a failure of the tool — it is a feature that becomes a liability without product constraints. Without a scope document, the product grows in every direction simultaneously. Users find 12 features that each work at 70% quality instead of 3 features that work at 95% quality.

Failure mode 3: The debugging abyss

Code written by AI is readable but not always debuggable by the AI that wrote it — especially when the bug involves the interaction of code written across multiple sessions with different contextual assumptions. Engineering teams report that post-vibe-coding debugging sessions take 3–5× longer than debugging equivalent hand-written code, because the AI makes assumptions in each session that are not documented anywhere.

When spec-driven development fails

Failure mode 1: Specs that are immediately wrong

Formal specifications written before any prototype exists are based on assumptions about user behavior, technical constraints, and market conditions. All three of these assumptions are wrong to some degree. The longer the spec cycle before coding begins, the more wrong the spec will be by the time code ships. This is the classic waterfall failure: a perfect document describing a product nobody needs.

Failure mode 2: Specs that nobody reads

The best-written 50-page specification becomes a historical artifact within two sprints. The codebase diverges from the spec; the spec diverges from reality; developers stop consulting it. Maintaining a specification that stays synchronized with a living codebase is a full-time job that most teams don't have budget for.

Failure mode 3: Specification paralysis

Teams new to formal specifications often produce PRDs and design documents for three months before writing a line of code. By the time they start coding, the market has moved, the team has had three strategy meetings, and the spec has been revised so many times that no one has high confidence in it.

The hybrid approach that works

The right approach is a lightweight specification layer — not a formal spec, not zero documentation — that gives AI coding tools the consistent context they need to make coherent decisions, without the overhead of a full waterfall specification process.

The minimum viable specification for a vibe coding team has three components:

Component 1: A data model document

A living document that defines every entity in the system, its fields, types, and relationships. Updated whenever a new entity is added. Pasted as context in every AI coding session that touches data. One page maximum. This single document prevents 80% of the architectural drift problem.

Component 2: A scope boundary document

A one-page document that defines what the product does and what it explicitly does not do. Updated weekly. Any feature that falls outside the scope boundary gets a deliberate decision before implementation — not an impulse build. This prevents scope expansion.

Component 3: An agent-ready specification for each feature

Before a new feature is coded, write a 200–400 word specification that includes: what the feature does, the specific user story, the data it creates or reads, the API endpoint it requires, and the success criteria. This is the "prompt requirements document" — the minimum specification that an AI coding tool needs to produce coherent, architecture-consistent code on the first attempt.

This hybrid approach — vibe coding with a lightweight spec layer — produces the speed benefits of vibe coding without the 90-day wall. The spec is not waterfall planning; it is the context document that makes AI coding tools reliable.

The emerging category: AI-native specification tools

The tooling category emerging around this problem is AI-native specification — tools that generate the spec layer automatically from rough inputs, rather than requiring teams to write formal documentation. Scriptonia is in this category: paste a Slack thread or voice note, get a PRD with user stories and acceptance criteria, get an architecture blueprint with data models and API endpoints. The spec is generated from the idea rather than written from scratch.

The practical implication: a team using Cursor for coding and Scriptonia for specification can get the speed benefits of vibe coding with the consistency benefits of spec-driven development. Generation time for a feature spec: under 90 seconds. Time saved versus writing the spec manually: 2–4 hours. Architectural coherence maintained at 6 months: substantially higher than pure vibe coding.