Tools like Cursor, Bolt, v0, and Lovable have radically changed development speed. In 2025 and 2026 we have seen founders with little technical knowledge launch working prototypes in days or even hours. It is a genuine shift.
The problem is what happens next.
The Prototype Works. The Product Does Not.
There is an enormous difference between a prototype that works in a demo and a product that can scale with real users. Most AI-generated vibe coding code has the following characteristics:
- No tests: no unit tests, no integration tests, no end-to-end tests. If you change something, you do not know what broke until a user sees it.
- No observability: no structured logging, no tracing, no metrics. When something fails in production, you have no information to diagnose it.
- No security: environment variables in the code, potential SQL injection, simplified authentication, CSRF ignored. AI does not prioritize security — it prioritizes making things work.
- No error handling: the happy path is covered, edge cases are not. A user with unexpected data can break entire flows.
- Architecture that does not scale: giant components, business logic mixed with presentation logic, no layer separation. Adding a new feature becomes exponentially expensive.
The "I'll Fix It Later" Myth
This is the most dangerous pattern: launching with the vibe coding prototype, getting first users, and telling yourself that "when we have more traction, we'll refactor."
The problem is that technical debt compounds. Every new feature you add on top of a fragile foundation makes it more fragile. Every bug you patch without understanding the root cause creates three new bugs. And when you finally reach the point where the foundation is unsustainable, you have real users you cannot interrupt and code you cannot touch without it breaking.
Investors know this too. In Series A technical due diligences, uncurated vibe coding code is a significant red flag.
The 5 Problems Blocking the Path to Production
1. Absence of Tests
Without tests, you cannot refactor safely. Without refactoring, technical debt accumulates. Without resolving technical debt, development velocity collapses.
The solution is not to write tests for everything retroactively (not cost-effective). It is to add tests strategically: first for critical business flows, then for components that change most frequently.
2. Hardcoded Secrets and Configuration
AI code frequently includes API keys, connection strings, and configuration directly in the code or in files that get committed to the repository.
The solution is to systematize secret management with tools like Doppler, AWS Secrets Manager, or simply well-managed environment variables with an updated .env.example and validation on startup.
3. Lack of Separation of Concerns
Vibe coding components often mix presentation logic, business logic, and data access. This makes the code impossible to test and very difficult to modify.
Progressive refactoring toward a layered architecture (presentation → domain → infrastructure) is the path forward. It does not need to be done all at once.
4. No CI/CD
Without a continuous integration pipeline, there is no automated quality control. Any push can break production.
A basic CI/CD with GitHub Actions takes 2–4 hours to set up and provides enormous protection: lint, tests, build, and automated deployment.
5. No Error Monitoring
Without tools like Sentry, Datadog, or similar, errors in production are invisible until a user complains. By the time that happens, the problem has been occurring for a while.
The Progressive Transformation
The good news is that you do not need to rewrite everything. Transforming a vibe coding prototype into a production product can be done progressively, preserving functionality throughout.
The correct order is:
- Technical audit: understand what exists, what risk it implies, and what to prioritize.
- Resolve critical security issues: secrets, authentication, unsanitized inputs.
- Add observability: logging, error tracking, basic metrics.
- Configure CI/CD: lint, basic tests (even if few), automated build.
- Add tests incrementally: starting with the most critical business flows.
- Refactor architecture: layer separation, modularization, technical debt cleanup.
This process, done rigorously, transforms a prototype into a foundation on which a real product can be built.
At Divolut we specialize in exactly this. If you have a working AI-built product and want to know if it is ready to scale, request a free technical audit.