We vibe code. We are not going to pretend otherwise. When a client needs a working prototype by Friday, or we want to test whether an idea has legs before committing weeks of development time, AI assisted coding is genuinely one of the best tools available. You describe what you want, the model writes it, you tweak and iterate. In the right context, it is brilliant.
But here is the thing we have learned from building production software for startups and SMEs: vibe coding is a starting point, not a finish line. The code that gets you from zero to demo is rarely the code that should be handling real user data, processing payments or running unsupervised in production. The gap between "it works on my machine" and "it works reliably for thousands of users" is where most of the real engineering lives.
What Is Vibe Coding?
The term was coined by Andrej Karpathy in early 2025. The idea is simple: instead of writing code line by line, you describe what you want to an AI coding tool, accept its suggestions, and steer the output through conversation rather than keystrokes. You focus on the what, and the AI handles the how.
In practice, this means you can go from a blank editor to a working application in hours rather than weeks. Need a dashboard that pulls data from an API and displays it in charts? Describe it. Need a form that validates input and saves to a database? Describe it. The AI generates the code, you run it, and if something looks wrong you describe the fix.
For speed of iteration, nothing else comes close. We use tools like Cursor, Claude and GitHub Copilot daily. They are part of how we work, and they make us significantly faster at shipping software.
Why It Works So Well for Prototypes
Vibe coding excels in situations where speed matters more than polish. When you are validating a business idea, exploring a technical approach, or building an internal tool that only your team will use, the trade offs are entirely reasonable. You get a working thing quickly. You can show it to users, gather feedback, and decide whether to invest further.
We have used vibe coding to build client demos in a single afternoon that would have taken a week with traditional development. For early stage founders who need to test assumptions before committing budget, this is transformative. You can see your idea working before you have spent thousands on custom development.
The problem is not vibe coding itself. The problem is when people treat vibe coded software as production ready without understanding what that actually requires.
The Security Problem
AI generated code has a consistent blind spot: security. Not because the models are incapable of writing secure code, but because they optimise for the thing you asked for, and most people do not ask for security. They ask for features.
Here are some of the issues we regularly find when reviewing vibe coded applications:
- Missing input validation. The AI builds a form that accepts user input and passes it straight to a database query. No sanitisation, no parameterised queries, no protection against SQL injection or cross site scripting.
- Exposed API keys. Secrets hardcoded into frontend code or committed to version control. The AI puts the key where it works, which is often in the client side JavaScript where anyone can read it.
- Broken authentication. Login systems that check credentials but do not properly manage sessions, tokens or permissions. Routes that should be protected are left open because nobody asked the AI to lock them down.
- Overly permissive database rules. Row level security disabled or misconfigured because the prototype "just needed to work." Every user can read and write every other user's data.
- No rate limiting. APIs that accept unlimited requests with no throttling, making them trivial to abuse or accidentally overwhelm.
None of these are theoretical. They are things we have seen in real codebases that were days away from going live with paying customers.
The Bug Problem
Security is the most dangerous category, but it is not the only one. Vibe coded software tends to have a particular flavour of bugs that stem from the AI's approach to problem solving.
AI models generate code that satisfies the happy path. They build for the scenario where everything goes right: the network is fast, the data is clean, the user follows the expected flow. But real software lives in a world of edge cases. What happens when the API returns an error? When the user submits the form twice? When the database connection drops mid transaction? When someone pastes emoji into a field that expects a number?
Vibe coded applications tend to fail silently in these situations. The error is swallowed, the data is corrupted quietly, or the user sees a blank screen with no explanation. These bugs are particularly nasty because they do not show up during development. They appear weeks later when a real user does something unexpected, and by that point the damage may already be done.
There is also the problem of code you do not understand. When an AI writes your application, you may not fully grasp how it works under the hood. This is fine during prototyping, but it becomes a serious liability when something breaks in production and you need to diagnose and fix it quickly.
The Technical Debt Problem
AI generated code tends to be verbose and repetitive. The model does not refactor. It does not notice that three components share the same logic and extract it into a shared utility. It does not think about the overall architecture of your application. It solves the immediate problem you described, and it does so by writing new code rather than improving existing code.
Over time, this creates a codebase that is harder to maintain, harder to extend and harder for other developers to work with. Each new feature adds more code rather than building on clean abstractions. Dependencies accumulate without review. Configuration is scattered across files. The application works, but changing anything feels risky because you cannot predict what will break.
This is manageable in a prototype you plan to rewrite. It is a serious problem in software you plan to build on for years.
How We Bridge the Gap
At Solve Studio, we use vibe coding as a tool within a broader engineering process. It accelerates the parts of development where speed matters most, and we apply proper engineering discipline to the parts where reliability matters most.
In practice, this looks like a two phase approach:
Phase one: vibe code the prototype. Move fast. Get the core idea working. Show it to users. Test assumptions. Do not worry about perfect code structure or comprehensive error handling at this stage. The goal is to learn whether the product is worth building properly.
Phase two: harden for production. Once you have validated the idea, go through the codebase systematically. This is where experienced developers earn their keep. We review every route, every database query, every authentication flow. We add input validation, error handling, logging and monitoring. We restructure the code so it can be maintained and extended. We write tests for the critical paths. We set up proper deployment pipelines with staging environments.
This is not about rewriting everything from scratch. Much of the vibe coded logic is perfectly fine. The structure, the data model, the user flows: these are often solid. What needs attention is the plumbing: the security layer, the error boundaries, the edge cases, the operational concerns that separate a demo from a product.
What to Check Before Going Live
If you have vibe coded an application and you are thinking about putting it in front of real users, here is a practical checklist of what to review:
- Authentication and authorisation. Can users only access their own data? Are admin routes properly protected? Are sessions managed securely?
- Input validation. Is every form field, query parameter and API input validated on the server side? Never trust the client.
- Secrets management. Are API keys, database credentials and tokens stored in environment variables, not in the code? Are they excluded from version control?
- Error handling. Does the application handle failures gracefully? Do users see helpful messages? Are errors logged so you can diagnose problems?
- Database security. Are queries parameterised? Is row level security configured correctly? Are backups running?
- HTTPS and headers. Is the application served over HTTPS? Are security headers configured (CORS, CSP, HSTS)?
- Rate limiting. Are public endpoints protected against abuse?
- Data privacy. Are you handling personal data in compliance with GDPR? Do you have appropriate consent mechanisms and data retention policies?
This is not an exhaustive list, but it covers the areas where vibe coded applications most commonly fall short.
Vibe Coding Is Not the Problem
We want to be clear: vibe coding is not bad practice. It is a genuinely useful development approach that has made software development faster and more accessible. The founders and teams we work with are right to use it. We use it ourselves, every day.
The risk comes from skipping the step between "it works" and "it is ready." That step requires experience, attention to detail, and an understanding of the ways software fails in the real world. It is not glamorous work, but it is the work that prevents your application from leaking user data, corrupting records, or falling over under load.
If you have built something with AI tools and you are ready to take it to production, or if you have an idea you want to prototype quickly and then build properly, we would like to help. We speak both languages: the fast, iterative language of vibe coding, and the careful, methodical language of production engineering. The best products use both.
Frequently Asked Questions
What is vibe coding?
Vibe coding is a style of software development where you describe what you want in plain language and an AI tool writes the code for you. You guide the direction, accept or reject suggestions, and iterate quickly without writing every line by hand. The term was coined by Andrej Karpathy in early 2025.
Is vibe coding safe for production applications?
Not without review. Vibe coded software often contains security vulnerabilities, missing input validation, hardcoded secrets and poorly structured error handling. It works well for prototypes and internal tools, but production applications need a proper security audit and code review before going live.
What are the biggest risks of vibe coding?
The main risks are security vulnerabilities such as SQL injection and exposed API keys, subtle logic bugs that only appear under real world conditions, poor error handling that fails silently, and accumulation of technical debt that makes the codebase difficult to maintain or extend.
Can I use vibe coding to build an MVP?
Yes, and it is one of the best use cases for vibe coding. It lets you validate ideas quickly and cheaply. The key is to treat the vibe coded version as a prototype and invest in a proper review and hardening process before launching to real users with real data.
About the Author
James Pates is the founder of Solve Studio, an AI automation consultancy based in Brighton and London. He builds custom automations, MVPs and web applications for startups and SMEs across the UK.