Vibe Coding Reality Check: What We Find Inside AI-Built MVPs

The Call We Get Every Few Weeks

A founder shipped their MVP in a weekend. Cursor, Bolt, v0, some combination of all three. The demo looked clean. The investor meeting went well. Then a real user signed up, clicked something unexpected, and the whole thing fell apart.

That is when they call us.

We are not here to criticize AI coding tools. They are genuinely useful and they have lowered the barrier to getting something in front of users faster than ever before. But there is a gap between a working demo and a production-ready product, and that gap is where founders are quietly burning money.

Here is what we actually find when we open those codebases.

Problem 1: Security That Would Embarrass a Junior Developer

The most common issue we see is exposed credentials. API keys, database connection strings, Stripe secret keys sitting directly in the source code or in a .env file that got committed to a public GitHub repo.

AI tools generate working code. They do not always generate safe code. They will write whatever gets the app running, and if you do not know to check, you will not notice.

We audited one fintech MVP where the Stripe secret key was hardcoded into three separate files. The founder had shared the GitHub link in a Discord server to get feedback. The repo was public for six days.

Fixing this after the fact means rotating every credential, auditing every service that credential touched, and rebuilding the authentication layer. That is a week of work minimum.

Building it right the first time takes one afternoon.

Problem 2: No Input Validation Anywhere

AI tools generate happy-path code. They build what you described. They do not anticipate what a malicious user, a confused user, or an automated bot will send to your API.

We have seen text fields that accept unlimited input and crash the server. We have seen file upload endpoints with no type or size validation. We have seen numeric fields that accept strings and throw unhandled exceptions that expose stack traces to the browser.

Stack traces are a gift to anyone trying to map your system. They tell an attacker exactly what framework you are using, what version, and sometimes the file path structure of your server.

One founder we worked with had a lead generation tool that had been live for three weeks. It had already been scraped 40,000 times by bots because there was no rate limiting and no CAPTCHA on the submission endpoint. Their email list was worthless before they even launched.

Problem 3: Architecture That Cannot Be Extended

This is the expensive one.

AI tools write code that works for the specific thing you asked. They do not think about what you will need in three months. Business logic ends up scattered. A pricing rule lives in the frontend component. The same database query gets written six times across six files. Authentication checks appear in some routes and not others.

When you go to add a feature, you cannot. Not cleanly. Every change requires understanding a tangle of interdependent code that was never designed to be read by a human.

We rebuilt a SaaS product for a founder who had spent four months trying to add a team accounts feature to their vibe-coded MVP. Their original developer had tried for two months and failed. The codebase had no concept of multi-tenancy anywhere in its structure. Adding it meant touching 200 files.

We rebuilt from scratch in six weeks and shipped the team feature on day one of relaunch.

Problem 4: Databases Built for Demos, Not Load

No indexes. No foreign key constraints. Tables named data, stuff, temp_fix_this_later. Queries that do full table scans on every page load.

This is invisible at 10 users. At 500 users, page loads slow to eight seconds. At 2,000 users, the database locks up entirely.

We have seen this kill momentum at exactly the wrong moment, right when a founder's product is starting to get traction. The product that could have grown gets pulled offline for emergency repairs instead.

What It Actually Costs to Fix vs. Build Right

Here is the honest comparison.

A production-ready MVP built properly takes four to eight weeks depending on complexity. It costs more upfront than a vibe-coded weekend build.

Fixing a vibe-coded codebase that has been in production for three months typically takes longer than building from scratch, because every fix requires understanding broken architecture before changing it. Founders often spend 60 to 70 percent of the rebuild budget just on the audit and cleanup phase before a single new line of code gets written.

The founders who come to us with the most expensive problems are not the ones who built badly on purpose. They are the ones who did not know what questions to ask before they shipped.

What You Can Do Before You Ship

If your MVP was built with AI tools and you are about to share it with users or investors, run through this checklist before you go live.

Search your entire codebase for the words secret, key, password, and token. Make sure none of those values are hardcoded strings.

Try submitting empty forms, oversized files, and unexpected characters in every input field. See what breaks.

Ask your developer or a technical advisor to explain the data model to you. If they cannot explain it clearly in ten minutes, it is not well designed.

Check your database for indexes on any column you filter or sort by.

None of this requires a full audit. It requires thirty minutes and a willingness to find problems before your users do.

Building fast is not the mistake. Shipping without knowing what you built is.