Why Your Vibe-Coded App Will Break at Scale

Let me be clear upfront: AI code generation is genuinely impressive. You can go from idea to working prototype in an afternoon. That is not a small thing. For validating concepts, building internal tools, or just figuring out if an idea has legs, vibe coding is a legitimate superpower.

But here is the part nobody talks about on Twitter: the prototype that wowed your team in a demo is architecturally incapable of surviving contact with real users at real scale. Not because AI is bad at coding. Because prototypes and production software are fundamentally different things, and AI tools are optimised for the former.

No Error Handling (or the Wrong Kind)

AI-generated code tends to follow the “happy path.” It handles what happens when everything works perfectly. It almost never handles what happens when things go sideways.

Your prototype fetches data from an API? Great. What happens when that API is down? What happens when it returns a 500? What happens when it returns valid JSON but with unexpected null values in fields you assumed would always be populated?

In a typical vibe-coded app, the answer is: the whole thing crashes. Or worse, it silently swallows the error and shows stale data without telling anyone.

At 10 users, nobody notices. At 1,000 users, your support inbox catches fire.

Authentication That Isn’t

AI tools love to generate a basic login flow. Username, password, maybe even a JWT token. Looks complete in the demo. But scratch the surface and you will find tokens that never expire, no refresh token rotation, passwords stored in ways that would make a security engineer weep, and session management that amounts to “store the token in localStorage and hope for the best.”

Real authentication means rate limiting on login attempts, proper token lifecycle management, role-based access control, secure password hashing with salts, and audit logs. None of that appears in a vibe-coded prototype because none of it is needed for the demo.

Hardcoded Everything

This one is universal. AI-generated code is riddled with hardcoded values. API URLs pointing to localhost. Database connection strings embedded in component files. Business logic constants scattered across dozens of files with no central configuration.

I have seen vibe-coded apps where the Stripe API key was hardcoded directly into a React component. Not an environment variable. Not a config file. Literally sitting in JSX, ready to be committed to a public GitHub repo.

No Rate Limiting or Resource Management

Your prototype has an endpoint that queries a database. A single user clicks a button, it runs the query, returns results. Works fine.

Now imagine 200 users hitting that endpoint simultaneously. Or one malicious user hitting it 10,000 times in a minute. Without rate limiting, connection pooling, query optimisation, and caching, your database falls over. And when the database falls over, everything falls over.

AI tools do not think about concurrent users. They build for a single request-response cycle in isolation. The concept of thundering herds, connection exhaustion, or memory leaks from unclosed connections simply does not exist in the vibe-coded world.

Spaghetti State Management

State management is where vibe-coded apps get truly creative, and not in a good way. You end up with a tangled web where the same piece of data lives in three different places, gets updated in two of them, and the third one is stale half the time.

A typical pattern: user data stored in a React context, duplicated in local component state, with a separate copy cached in the browser. Update one and the others are out of sync until the user refreshes the page.

At prototype scale, this causes minor visual glitches. At production scale, it causes real business problems. Orders that show different totals on different screens. Inventory counts that are wrong. Customer records that contradict themselves.

No Tests, No Safety Net

AI-generated code almost never comes with tests. This is arguably the single biggest scaling risk, because it means every change you make to fix one of the problems listed above might break something else, and you will not know until a customer tells you.

Without tests, your codebase is a house of cards. Every bug fix is a gamble. Every feature addition is a prayer. The bigger the app gets, the more terrifying every deployment becomes.

The Scaling Cliff

All of these problems share a common trait: they are invisible at small scale. Your app works perfectly for 5 users. It works okay for 50. At 500, things start getting weird. At 5,000, it falls apart completely.

This is the scaling cliff, and it is not a gradual slope. It is a sudden drop. One day your app is fine. The next day you get featured on a podcast, traffic triples, and everything breaks at once.

This Is Not an AI Problem. It Is an Expectations Problem.

The issue is not that AI generates bad code. The issue is that we are asking prototyping tools to produce production software, and those are two very different things.

A prototype needs to demonstrate functionality. Production software needs to handle failure, manage resources, secure data, scale horizontally, and be maintainable by a team over years. These are not features you bolt on later. They are architectural decisions that shape the entire codebase.

The smartest approach is to use AI tools for what they are genuinely brilliant at: rapid prototyping and concept validation. Then, once you have proven the idea works, invest in building it properly.

What “Building It Properly” Actually Means

You do not need to throw away your prototype. The prototype told you what to build and how users interact with it. That is incredibly valuable information that would have cost you months to gather the traditional way.

What you need is someone who takes that validated concept and builds it with proper error handling, real authentication, configuration management, resource limits, clean state management, and comprehensive tests. Someone who has built production software before and knows where the landmines are.

The prototype got you from zero to one. Getting from one to one hundred is a different discipline entirely.