TL;DR: Built a working prototype with AI tools? That's genuinely valuable but taking a prototype to production requires addressing data models, security, testing, architecture and development processes that AI can't handle alone (at least not yet). Here's what you need to know before scaling.
We've written previously about vibe coding and the tools that make it possible. There's no denying it: AI-assisted development has been genuinely transformative. Non-technical founders can now create working prototypes that would have required a development team just two years ago. These prototypes can be feature rich, polished and ready to onboard customers... however there's a "But" coming...
But here's the uncomfortable truth we're seeing in January 2026: the vibe can only take you so far.
We're increasingly being approached by founders who've successfully vibe coded their way to a working prototype. They've validated their idea, got early users excited, maybe even collected some revenue. Now they want to scale the product. They want to add new features. They want to build a proper business.
And that's when they hit the wall. Taking the next leap isn't possible with AI (at least not yet) and they need experienced technical leadership to move forward and they need to invest in this appropriately.
The prototype paradox
There's a peculiar paradox at the heart of vibe coding success. The very thing that makes it brilliant for prototyping – rapid iteration without deep technical understanding – is precisely what makes it dangerous for production systems.
When you're prototyping, speed is everything. You're testing assumptions, getting feedback, pivoting quickly. It genuinely doesn't matter if your database schema is a bit wonky or your authentication flow has edge cases. You're exploring, not building to last.
But production is different. Production means real users who are paying real money and using real data. Production means handling payments securely. Production means scaling and releasing without falling over. Production means maintaining and extending code for years, not weeks.
The mental model shift required is significant. You're no longer asking "does this work?" but rather "will this still work when thousands of users are hammering it simultaneously?" and "can we modify this in six months without rewriting everything?".
Can you ship features without the service falling over or customers having data loss? The vibe can't help us at this point.
Five areas where vibe coded prototypes typically fall short
Based on our experience reviewing and rescuing vibe coded projects, here are the common issues we see.
1. Data models that don't scale
This is probably the most common problem we encounter. AI is remarkably good at generating code that works for your immediate use case. It's less good at anticipating how your data requirements will evolve.
We recently reviewed a vibe coded project where the founder had built an impressive marketplace app. It worked beautifully for their 50 beta users. But the data model had fundamental issues:
- User preferences were stored as a JSON blob rather than properly normalised tables
- There was no concept of soft deletes, making it impossible to maintain audit trails
- The relationships between entities were flat when they needed to be hierarchical
- Critical fields that would later need to be searchable weren't indexed
None of these issues prevented the app from functioning. But they would have made scaling to 5,000 users a nightmare and adding planned features practically impossible without a significant rewrite.
An experienced developer spots these issues instinctively because they've seen what happens when data models don't scale. They ask questions like "what happens when a user has 10,000 of these?" and "how will we query this when we add the filtering feature?" These questions simply don't occur to someone (or some AI) focused purely on making the current feature work.
2. Security that isn't
This isn't about GDPR compliance or cookie banners. This is about fundamental security – protecting your users' personal data and potentially commercially sensitive information from attackers or even other users of the platform.
Vibe coded applications often have security vulnerabilities that aren't immediately apparent. The code works. It does what it's supposed to do. But it also does things it shouldn't do if someone sends the wrong kind of request.
Common issues we see:
- Insufficient input validation: AI-generated code often trusts user input far too readily. SQL injection, XSS attacks, and similar vulnerabilities can slip through.
- Broken authentication flows: The happy path works fine. But what about edge cases? Can users access other users' data by manipulating IDs in URLs? Are password reset flows actually secure?
- Exposed sensitive data: API endpoints that return more information than they should. Debug information left in production. Secrets committed to version control.
- Missing rate limiting: Nothing stopping someone from hammering your API endpoints, whether for credential stuffing attacks or simply to rack up your infrastructure bill.
- Poor or untested authorisation: Role-based authorisation should scope access to resources in your project and it's an area AI is particularly poor at having a 'mental model' of.
The scary thing about security vulnerabilities is that your application will work perfectly until it doesn't. And when it doesn't, you're potentially facing data breaches, regulatory issues and destroyed user trust you've worked hard to establish.
3. Tests that test nothing useful
AI can write tests that pass. But do those tests actually verify the behaviour you need?
Tests are important in software to give us confidence our product is robust and repeatable. I don't think a single vibe coded project that's come across my desk has been tested at all but in my experience with using AI to write tests they can be quite poor anyway.
I've seen AI-generated tests that pass and that green tick of a passed test gives you confidence. But that confidence is misplaced because the test isn't actually verifying the complex behaviours that matter in production. I've seen AI write a test that passes but the expectation is wrong or it plainly avoids making the method call it should be testing.
We call this "checkbox testing" – tests that exist to tick a box rather than to catch bugs. They provide false confidence, which might be worse than no tests at all.
4. Architecture that backs you into corners
Software architecture is about trade-offs. Every decision opens some doors and closes others. Good architecture keeps the right doors open – the ones you'll need as your product evolves.
Vibe coded projects often end up with architectures that work fine initially but create serious constraints later:
- Over-abstraction where you need simplicity: AI sometimes generates enterprise-grade abstractions for straightforward problems, creating maintenance nightmares.
- Third-party dependencies baked in too deeply: What seems like a helpful integration becomes a liability when you need to switch providers or the service changes its API.
- No separation of concerns: Business logic mixed with presentation logic mixed with data access logic, making changes risky and unpredictable.
The challenge is that architectural problems often don't manifest until you're several months into development. By then, fixing them means significant refactoring – essentially throwing away some of the vibe coded work and starting again.
5. No processes for sustainable development
This one often surprises non-technical founders. You've got working code. Why do you need processes?
Because production isn't a destination – it's an ongoing journey. You'll need to:
- Release updates safely: How do you deploy changes without breaking things for existing users? How do you roll back if something goes wrong?
- Test before shipping: What's your UAT process? How do you verify that new features work with existing functionality?
- Handle incidents: When (not if) something breaks at 3am, how do you respond? How do you diagnose issues quickly?
- Manage environments: You can't test in production. But vibe coded projects often don't have proper staging environments.
- Scale your team: When you hire your first developer, how do they understand the codebase? What's documented? What are the conventions?
These processes might seem like bureaucratic overhead, but they're actually what allows you to move fast safely. Without them, every change is a gamble.
The good news: your prototype isn't worthless
If you've vibe coded your way to a working prototype, you haven't wasted your time. Far from it.
You've done something incredibly valuable: you've validated that there's actually demand for what you're building. You've learned what features users actually want. You've probably pivoted a few times based on feedback. You've made the expensive mistakes cheap.
This is genuinely the ideal position to be in. You're not asking a development team to build something based on assumptions and guesswork. You're showing them something that works and saying "make this production ready."
That's a much clearer brief. It's a much lower risk engagement. And frankly, it's more interesting work for developers because the product direction is already proven.
What "production ready" actually means
So what does the journey from prototype to production actually involve? Here's what it typically looks like when a development team takes over:
Technical audit and gap analysis
First, we need to understand what we're working with. This means reviewing the existing code, understanding its structure, and identifying the gaps between prototype and production quality.
This isn't about criticising the vibe coded work. It's about being honest about what needs to change. Some code might be reusable. Some might need refactoring. Some might need replacing entirely.
Data model redesign (if needed)
If the data model has fundamental issues – and it often does – this needs addressing early. Migrating data is always possible, but it's much easier before you have tens of thousands of users.
Security hardening
A proper security review, implementing authentication correctly, adding appropriate authorization checks, validating inputs, handling errors safely, and ensuring sensitive data is properly protected.
Test suite development
Writing tests that actually test the behaviours that matter. Not just happy path tests, but edge cases, error conditions, and integration tests that verify components work together correctly.
Infrastructure and deployment
Setting up proper environments (development, staging, production), configuring CI/CD pipelines, implementing monitoring and alerting, and establishing deployment processes that allow you to ship confidently.
Documentation and knowledge transfer
Ensuring that the codebase is understandable, conventions are documented, and future developers (whether internal or external) can work effectively with the system.
When to make the transition
How do you know when it's time to take your MVP from prototype to production? Here are some signals:
- You're ready to take payments: The moment money is involved, security and reliability become non-negotiable.
- You're handling sensitive data: Personal information, health data, financial data, commercially sensitive data – these require proper protection.
- You're getting real traction: If users are depending on your product, downtime and bugs become genuinely costly.
- You're planning to raise funding: Investors will want to know you have a technical foundation that can scale.
- You're hiring: New team members need a codebase they can work with effectively.
The transition doesn't have to be all or nothing. Some founders work with a development team to production-harden the most critical parts first – typically authentication, payments, and core data handling – while continuing to iterate on features themselves.
Making the transition successfully
If you're ready to take your vibe coded prototype to production, here are some suggestions for working with a development team:
Be honest about the codebase's origins. Developers would much rather know upfront that they're working with AI-generated code. It sets appropriate expectations and allows for proper planning.
Share your product knowledge. You understand your users better than anyone. That context is invaluable for making the right technical decisions.
Stay involved in prioritisation. The development team will likely identify many things that could be improved. Help them understand what matters most for your business goals.
Plan for ongoing collaboration. Production ready isn't a one-time achievement. You'll need ongoing development support to maintain, extend, and scale your product.
Ready to go from prototype to production?
At Add Jam, we've helped numerous founders make the transition from vibe coded prototype to production ready product. We understand both worlds – we're enthusiastic users of AI tools ourselves, but we also bring over a decade of experience building scalable, secure applications.
If you've got a working prototype and you're ready to take it to the next level, we'd love to chat. We offer a Code Review service that can give you a clear picture of where your prototype stands and what it would take to make it production ready.
Alternatively, book a free 30-minute consultation to discuss your specific situation. No obligation – just honest advice about whether you're ready for the transition and what that journey might look like.
Your vibe got you this far. Now let's build something that lasts.
