A year ago, “vibe coding” was a joke term. Today it describes something a lot of people genuinely do: open an AI coding tool, describe what they want in plain English or even Roman Urdu, and keep nudging the result until it works. No syntax, no framework decisions, sometimes no code reading at all.
For business owners this raises a fair question. If a manager can build an internal tool over a weekend, why pay a software house for months of work? The honest answer is that vibe coding is excellent at some things and quietly dangerous at others, and the line between them is not where most people expect.
What vibe coding actually is
The term covers a style of building where the person steering the AI mostly judges the result rather than the code. You describe a feature, run it, see whether it behaves, and describe the fix. The code itself is treated as an implementation detail you may never open.
That is different from an experienced developer using an AI assistant. A developer reads what the tool produces, questions the database design, notices the missing permission check, and rejects the shortcut that will hurt later. Same tools, very different outcome.
Where it works well
- Prototypes and demos. Showing investors or your own team what a product could feel like, in days instead of weeks.
- Internal one person tools. A calculator, a report formatter, a script that renames files. If it breaks, one person is inconvenienced.
- Validating an idea before a real build. Clicking through a rough version often changes the requirements more than any meeting would.
- Landing pages and simple static sites where there is no login, no payments and no stored customer data.
In all of these, the cost of a hidden mistake is small and the speed is genuinely valuable.
Where it breaks
The problems appear the moment real users, real money or real data arrive. They are rarely visible in a demo, which is exactly why they are dangerous.
Security that looks fine but is not
An AI will happily build a login screen that works. Whether it also stops one customer from reading another customer’s orders by changing a number in the URL is a separate question the person steering may never think to ask. Access control, input validation and secret handling are the areas where generated code most often “works” while being unsafe.
Data models that cannot grow
Early decisions about how data is stored decide how painful every later feature will be. A generated app often stores things in whatever shape made the current screen easiest. Six months later, adding multiple branches or a second currency means rewriting half the system.
No one understands the code
If nobody on your side has read the code, nobody can fix it at 2 am when payments stop. Each new prompt also tends to patch rather than restructure, so the codebase becomes layers of fixes on fixes. Eventually the AI itself starts breaking one thing while fixing another.
Performance under real load
A tool that feels instant with ten test records can crawl with fifty thousand. Missing indexes, loading entire tables into memory and repeated database calls inside loops are common in generated code and invisible until the data grows.
A practical way for businesses to use it
- Use it to discover requirements, not to ship production. Let your operations person build the rough version of the tool they want. That prototype is a far better brief than a written document.
- Draw a hard line at customer data and money. Anything that stores personal information, handles payments or integrates with accounts needs an engineer who reads every line.
- Get a code review before anything goes live. An experienced developer can usually tell within a few hours whether a generated app is a sound starting point or a rebuild.
- Keep ownership clear. Store the code in your own repository, not only inside a tool account that belongs to one employee.
What this means for hiring developers
AI has not removed the need for engineers. It has changed what good engineers spend time on. Less typing of routine code, more design, review, security and integration work. When you evaluate a development partner, ask how they use AI tools and, more importantly, how they check what those tools produce. A team that cannot explain its review process is a risk whether or not it uses AI.
We wrote more about this shift in how AI coding assistants change a software team, and about choosing the right foundations in choosing a tech stack for a startup.
Signs your vibe coded tool needs a professional rebuild
- More than a handful of people now depend on it daily.
- It stores customer names, phone numbers, CNICs or payment details.
- Every new change breaks something that used to work.
- Nobody can explain how a particular feature works under the hood.
- It slows down noticeably as data accumulates.
None of these mean the prototype was a waste. A working prototype shortens a professional build considerably, because the hardest part of most projects is agreeing on what to build.
A realistic scenario: from prototype to production
The operations manager at a courier company in Karachi is frustrated with a spreadsheet used to track rider cash collections. Over two weekends, using an AI coding tool, she builds a small web app: riders enter collections, the office sees daily totals, and a report shows who still owes cash.
Within a month, forty riders use it daily. Then problems appear. A rider discovers he can see other riders’ entries by changing a number in the address bar. Totals occasionally double when someone clicks save twice on a slow connection. The app slows noticeably as records grow. Nobody knows how to add branch level reporting without breaking something.
The company asks a development team to review it. Their assessment:
- The screens and workflow are excellent, because the manager understood the real process deeply.
- The database structure needs redesign for branches and auditing.
- Access control is missing and must be built properly.
- Duplicate submissions need server side protection.
- Performance issues come from loading all records on every page.
The team rebuilds the backend properly while keeping the manager’s screen designs and workflow almost unchanged. The rebuild takes a fraction of the time a project starting from a written brief would have taken, because the prototype had already answered most design questions. See courier and delivery management systems.
A risk based guide to what you can vibe code
| Risk level | Examples | Recommended approach |
|---|---|---|
| Low | Personal productivity scripts, internal calculators, mockups, one off data cleanup | Vibe coding is fine, keep backups |
| Medium | Internal tools used by a team, dashboards without personal data | Prototype freely, then get a developer review before wide use |
| High | Anything with logins, customer data, payments, integrations with core systems | Use prototypes only as specifications, build with professional engineering |
| Critical | Financial systems, health records, systems regulators oversee | Professional development with security testing and formal review |
What a professional review checks
When a vibe coded app is handed to engineers, a structured review typically covers authentication and authorisation, input validation and injection risks, how secrets and credentials are stored, database design and data integrity, error handling and logging, performance with realistic data volumes, dependency health and licences, backup and recovery, and whether the code can be maintained by others. The output is usually a short report classifying the app as ready with minor fixes, needing targeted hardening, or best rebuilt using the prototype as the design reference. See software project warning signs.
Tips for non developers who vibe code
- Describe the process, not just screens. Explain who does what, in what order, and what can go wrong.
- Ask the AI to explain its choices about data storage and security, even if you cannot judge every detail.
- Use version control or regular backups so a bad change can be undone.
- Test with fake data only, never real customer information.
- Write down every rule the app must follow. This becomes invaluable documentation.
- Stop and get help once other people start depending on it.
Frequently asked questions
Is vibe coding the same as no code tools?
Not quite. No code tools give you fixed building blocks inside a platform. Vibe coding generates real source code from descriptions, which is more flexible but also means someone must eventually understand and maintain that code.
Can a vibe coded prototype be turned into a real product?
Sometimes. If the data model and structure are reasonable, engineers can harden it. If not, the prototype still has value as a precise, clickable specification for a clean rebuild.
Which parts should never be vibe coded?
Authentication, payments, permission checks and anything storing customer personal data should always be written or thoroughly reviewed by an experienced developer. See data protection for apps in Pakistan.
Will vibe coding replace software companies?
It changes what software companies spend time on. Prototyping becomes faster and cheaper, while architecture, security, integration and long term maintenance remain professional work.
The bottom line
Vibe coding is a genuine productivity shift, and businesses that ignore it will move slower than those that use it well. The mistake is treating a convincing demo as a finished product. Use it to explore, to prototype and to brief your developers clearly. Then let engineers turn the idea into software that is secure, maintainable and ready for real customers.
If you have a prototype and want an honest assessment of whether it can be hardened or should be rebuilt, talk to our team. Our custom software development work often starts exactly there.