Vibe Coding Workflow Fully Deconstructed: Six Stages of AI-Assisted Development and Team Collaboration Architecture

I. A Technical Reading of a Cultural Turning Point

In February 2025, Andrej Karpathy — OpenAI co-founder and former Tesla AI Director — introduced a concept on social media that sparked widespread discussion: vibe coding^[1]. He described a development mode where the developer relies entirely on AI: describing requirements to AI via voice, accepting all output without reading code diffs, pasting error messages directly to AI for fixes, and continuing to push forward even after the code has exceeded personal comprehension — candidly admitting that this approach "works surprisingly well" for experimental weekend projects.

This statement warrants serious analysis not only because of who said it, but because it precisely reveals a fundamental shift already underway: when AI's code generation capabilities reach a certain level, the developer's role shifts from "writing code" to "describing intent and verifying results." In his classic 1987 paper^[2], Brooks distinguished between the essential complexity and accidental complexity of software; vibe coding is essentially the extreme form of delegating all accidental complexity to AI.

But a vast chasm exists between personal weekend projects and enterprise production systems. This article systematically deconstructs the workflow Karpathy described, identifies six key operational stages, analyzes the applicability boundaries of each stage in enterprise contexts, and proposes a four-layer team collaboration architecture to help technical decision-makers find the balance between speed and quality that suits their organization.

II. Deconstructing the Six Operational Stages of AI-Assisted Development

Stage One: The Paradigm Shift in Intent Expression

The first characteristic of vibe coding is that developers express requirements through natural language — or even voice — rather than code syntax. Developers use voice input tools to describe desired functionality directly to AI, rather than writing code by hand.

This reflects a deeper shift: the developer's cognitive resources are reallocated from "how to implement" to "what to implement." In traditional development, even senior engineers spend considerable effort on syntax details, API lookups, and framework configuration; in AI-assisted workflows, this cognitive load is transferred to the AI. McKinsey's research^[3] confirmed this effect: AI's acceleration effect is most significant for documentation writing and boilerplate code, precisely because the cognitive load of these tasks primarily stems from accidental complexity.

Enterprise implications: When "describing requirements" replaces "writing code" as the core operation, the clarity and precision of requirements become the new productivity bottleneck. Team capability building needs to expand from programming language proficiency to problem definition and requirement articulation skills.

Stage Two: Result-Oriented Abstract Instructions

The second characteristic is result-oriented instruction delivery — developers specify "what to do" rather than "how to do it." For example, asking AI to "reduce the sidebar spacing by half" rather than manually finding CSS properties and modifying values. The abstraction level of operations is elevated from implementation details to functional descriptions.

This lowers the entry barrier for specific technology stacks, enabling domain experts who may not be proficient in a particular framework's syntax to participate in software development. But it simultaneously blurs the line between "understanding the system" and "using the system" — in the long term, teams must ensure that someone truly understands the implementation logic and technical constraints of the system.

Stage Three: Full Acceptance — The Deliberate Abandonment of Review

The third stage is the most controversial: fully accepting AI output without reading code diffs. This maximizes development speed but completely removes the human quality gate.

Research from MIT Sloan Management Review in 2025^[4] provided quantified consequences of this approach: large-scale code analysis showed that AI-assisted development led to an eightfold increase in duplicate code blocks and a twofold increase in code churn rate. When developers skip review, these quality issues accumulate continuously as technical debt.

Enterprise implications: This is the most critical divergence point between personal experimentation and enterprise engineering. In enterprise contexts, code review is not only a quality control mechanism but also a core process for knowledge transfer and architectural consistency maintenance. BCG's research^[5] found that 50% of CTOs cannot quantify AI's actual impact on engineering performance — skipping review further exacerbates this measurement problem.

Stage Four: Error-Driven Iterative Correction

In vibe coding, debugging is simplified to: encountering an error, providing the error message directly to AI, and having AI diagnose and fix it on its own. This creates a rapid iterative cycle of "execute, error, feed, fix."

This pattern is impressively efficient when handling surface-level errors (syntax errors, type mismatches, missing dependencies). But it is fundamentally symptom-oriented rather than root-cause-oriented — AI fixes the error message, but does not necessarily resolve the deeper design issue that caused it.

Enterprise implications: Automatic correction of surface errors is worth adopting at all levels, as it can dramatically save debugging time. But for errors involving business logic, data consistency, or security, teams still need systematic root cause analysis rather than relying solely on AI's symptomatic fixes.

Stage Five: Code Scale Beyond Individual Comprehension

As AI continuously generates code, the overall codebase quickly exceeds any individual's comprehension range. This is tolerable in personal projects but carries fundamental significance in team environments.

Conway's Law^[6] tells us that a system's architecture reflects the communication structure of the organization that designed it. When no team member fully understands the system, the organization has effectively lost control over the system architecture. Research from Harvard Business School^[7] further reveals that entry-level developer job postings are already 4.7 percentage points below trend — meaning fewer people will build their understanding of the full system from the ground up in the future.

Enterprise implications: Teams need to establish mechanisms for "distributed understanding": not requiring everyone to understand everything, but ensuring that every critical module of the system has at least one member with deep comprehension. This requires deliberate knowledge allocation strategies rather than relying on naturally formed cognitive distribution.

Stage Six: Stochastic Debugging Strategies

The final stage occurs when AI cannot fix a particular issue, and the developer responds by asking AI to make various exploratory modifications until the problem disappears. This is a non-deterministic debugging strategy — substituting search space exploration for systematic problem analysis.

For UI-level issues (style conflicts, layout shifts), this strategy has low cost and is frequently effective. But for business logic errors, data processing issues, or security vulnerabilities, random modifications may introduce new, more insidious defects.

Enterprise implications: Establish clear tiered strategies — UI and presentation layer issues may be addressed through rapid iterative fixes, but issues involving data, security, or core business logic must undergo structured analysis and verification processes.

III. Applicability Boundaries: The Spectrum from Weekend Projects to Enterprise Systems

Karpathy himself explicitly noted that vibe coding is better suited for experimental, disposable projects. This self-qualification is highly significant — it implies a core insight: the six stages described above are not an all-or-nothing package, and enterprises can selectively adopt them.

Data from McKinsey's tracking of over 600 organizations^[8] provides an important reference: top-performing organizations achieved 16–30% productivity gains and 31–45% quality improvements, but their common characteristic was simultaneous reform of processes, roles, and ways of working. Successful AI adoption is not a binary choice between "full acceptance" and "full rejection," but rather establishing a layered decision framework for different types of work.

This is precisely the model we propose next.

IV. Layered Team Collaboration Architecture: The Four-Layer Model

Based on the analysis of the six stages above and our practical experience, we propose the following four-layer AI collaboration architecture. Each layer corresponds to a different type of software work, defining varying degrees of AI autonomy and human intervention density.

Layer One: AI Direct Execution Layer

Scope: Prototyping, internal tools, one-off scripts, experimental feature validation

This is the operational mode closest to vibe coding. AI enjoys maximum autonomy, with developers primarily responsible for intent expression and result verification, and code review simplified from line-by-line inspection to function-level acceptance. This layer emphasizes speed and exploration, suitable for work with low failure costs that does not enter production. Nearly all six operational stages are applicable at this layer.

Layer Two: Human-AI Collaboration Layer

Scope: Feature development, standard component implementation, feature extensions to existing systems

AI handles code generation while developers handle review and correction. Similar to a pair programming work model, but with AI playing the role of a junior partner. Most day-to-day feature development belongs to this layer. Developers need the ability to read and evaluate AI output — MIT Sloan's research^[4] reminds us that this review step is the critical defense line against technical debt accumulation. Stages one, two, and four are applicable at this layer, but stage three (skipping review) is explicitly excluded.

Layer Three: Architecture Guardian Layer

Scope: System design, API contract definitions, data model design, cross-service integration

Humans lead, with AI providing assistance only on implementation details. Architecture decisions — including service boundary delineation, data flow design, and API versioning strategy — are entirely the responsibility of senior engineers and architects. BCG's research^[9] found that outdated system architectures severely undermine the effectiveness of AI tools, inversely validating that human judgment at the architectural level is irreplaceable. Only stage one (natural language expression) is applicable at this layer; the automation level of all other stages should be strictly limited.

Layer Four: Quality Governance Layer

Scope: Security reviews, compliance verification, performance benchmarking, pre-launch approval

Human judgment serves as the final decision authority, with AI acting as an auxiliary tool for detection and analysis. Security scans and compliance checks can leverage AI to improve coverage and detection efficiency, but final risk assessments and launch decisions must be made by personnel with professional judgment. The core principle of this layer is: AI provides information, humans make decisions.

V. A New Interpretation of Conway's Law and Role Evolution

Conway's Law, proposed in 1968^[6], states that a system's architecture mirrors the communication structure of the organization that designed it. In the AI era, this law gains an important corollary: how teams delineate AI's autonomy boundaries will directly map to the software system's quality characteristics.

If a team adopts the Layer One operating mode for all work, the resulting system will lack coherent architectural logic, technical debt will be distributed chaotically, and maintenance costs will be unpredictable. Conversely, if all work is overly conservatively restricted to Layers Three and Four, the team cannot fully capitalize on the efficiency gains that AI delivers. The key lies in precise layered judgment.

For specific roles, this implies the following evolutionary directions:

• Junior developers see their value center shift from "writing code" to "collaborating efficiently with AI and learning to review AI output," primarily working at Layers One and Two, but requiring guidance and supervision from senior members
• Senior developers become the core reviewers of AI output, establishing quality defense lines between Layers Two and Three, while also being responsible for structuring team experience into knowledge documents that AI can consume
• Architects see their role expand from "designing systems" to "designing AI's constraint boundaries" — defining which decisions AI can make autonomously and which must go through human approval
• Technical leads become cross-layer coordinators, dynamically assigning tasks to appropriate collaboration layers based on work type and risk level

The Stanford AI Index 2025^[10] documented the rapid improvement of AI capabilities in software engineering benchmarks — SWE-bench solving rates surged from 4.4% to 71.7% within a year. It is foreseeable that the boundaries of each layer will continue to adjust as AI capabilities evolve, but the fundamental principle remains unchanged: the closer the decision is to core business logic and system architecture, the higher the density of human judgment required.

VI. Conclusion: Precise Layering, Not Wholesale Acceptance or Rejection

Karpathy's vibe coding is not a concept to be adopted or rejected wholesale. It is a prism that decomposes AI-assisted development operating modes into a discernible spectrum — from fully automated to fully human-controlled. What enterprise technical decision-makers need to do is not choose a single fixed point on this spectrum, but select different positions for different types of work.

Teams that can precisely match the appropriate density of human-AI collaboration for each type of work will simultaneously capture both AI's speed dividend and the quality assurance of engineering discipline. This requires not better AI tools, but clearer organizational judgment — and this, precisely, is part of what Brooks called "essential complexity" nearly four decades ago.

If your team is planning an AI-assisted development adoption strategy or looking to establish a layered collaboration framework suited to your organization, we welcome a deep discussion.