Artificial intelligence is fundamentally changing how software gets built.
Today you can describe to an AI tool what you want to build and, within minutes or hours, get screens, a database, APIs, business logic, and even a working application.
Many people call this new way of building software Vibe Coding.
And yes, it's impressive.
But there's something every business owner should keep in mind:
An app that works is not the same as an app that's ready for production.
A few days ago, a friend who is building an extranet for his clients asked me to review his application.
He had built it with artificial intelligence.
He told me he had taken security very seriously, and for that reason he felt confident there wouldn't be any problems.
But I decided to try something different.
I didn't start with security.
I wanted to know what happened when the application had to support a real scenario of users and data.
The Problem Showed Up in Load Testing
I prepared a load and stress test.
I generated thousands of records to simulate a real scenario and configured multiple users using the application at the same time.
This is something we can now do much faster with the help of AI.
The result was concerning.
Under a completely normal scenario, the application collapsed.
The interesting part is that when an application has little data and only one or two users, this kind of problem can go completely unnoticed.
The screens work.
The buttons work.
The forms work.
The information shows up.
Everything seems fine.
Until real users arrive.
The Problem Was in the Backend
When we reviewed the code, we found the problem.
The backend was querying the database and loading a large amount of related information—products, movements, account statements, and other records—into the server's memory.
Only after loading all that data did it apply the filters.
From a functional point of view, it appeared to work.
But from an architecture and performance point of view, it was a time bomb.
Imagine a distribution company with a few hundred products and a few thousand movements.
Most likely, nobody notices the problem during early testing.
Now imagine the company has tens or hundreds of thousands of records, and several clients start using the platform at the same time.
The situation changes completely.
The server starts consuming memory.
Queries become heavier.
Response times increase.
Users start waiting.
And eventually, the application may stop responding.
The problem wasn't in a screen.
It was in the architecture and in how the system processed data.
Code Can Work and Still Be Wrong
This is one of the main risks I see with Vibe Coding.
AI can generate code that works.
But functionality and software quality are not the same thing.
A business application needs much more than working screens.
It needs, among other things:
- Security.
- Good architecture design.
- Proper database management.
- Error handling.
- Scalability.
- Load testing.
- Stress testing.
- Proper memory management.
- Data protection.
- Access control.
- Monitoring.
- Maintainability.
- Good performance.
Many of these things aren't obvious when you simply use the application.
The Numbers Also Show It
This doesn't mean AI is bad at building software.
Quite the opposite—adoption is massive.
According to the Stack Overflow Developer Survey 2025, 84% of surveyed developers use or plan to use AI tools in their development process, and 51% of professional developers use them daily.
But there's a more interesting stat.
46% of surveyed developers distrust the accuracy of AI tools' answers, while only 33% trust them.
In other words, even the developers who use these tools know the output needs to be reviewed.
And there's more.
The same study found that 66% of developers find it frustrating to work with AI solutions that are "almost correct, but not quite", while 45% say debugging AI-generated code can take longer.
This matters because an application can pass a surface-level test and still have problems that only appear under deeper analysis.
What About Security?
Here the situation deserves even more attention.
In its 2025 GenAI Code Security Report, Veracode evaluated code generated by more than 100 language models across different programming languages.
The result:
45% of AI-generated code samples failed security tests and introduced vulnerabilities related to the OWASP Top 10.
Another interesting finding: using newer or larger models didn't automatically guarantee more secure code. In Veracode's tests, better functional code generation didn't translate into equivalent security improvements.
This leaves us with an important lesson:
AI can help you build software much faster, but it doesn't remove the need for software engineering.
Vibe Coding Isn't the Problem
Let me be very clear about this.
I don't think the problem is using AI to program.
It would be absurd to think that in our industry.
AI can dramatically speed up development.
It can help create prototypes, generate repetitive code, build interfaces, create tests, document systems, analyze errors, and accelerate many other tasks.
The problem appears when we confuse:
"AI was able to build it"
with
"The application is ready for production."
Those are two completely different things.
The Real Risk for Your Business
For a business, the problem doesn't necessarily appear when the application is built.
It can appear later.
When clients start using it.
When the amount of data grows.
When 50 users log in at once.
When the database starts growing.
When someone tries to perform an operation nobody had considered.
When an error occurs.
When someone finds a vulnerability.
Or when the server simply doesn't have enough memory to process the amount of information the application is trying to load.
At that point, we're no longer talking about programming.
We're talking about your company's reputation in front of its customers.
What Should a Business Building an App With AI Do?
If you own a business and you're building an application with AI tools, my recommendation isn't to stop using them.
My recommendation is the opposite:
Use AI, but add technical oversight.
Before launching a business application, have someone with experience review at least:
1. Architecture
Is the application designed to grow?
Does the way frontend, backend, and database communicate make sense?
2. Database
Are the queries properly built?
Are there adequate indexes?
Is it only fetching the information it needs?
3. Security
Can users only access the information they're supposed to?
Are there authentication, authorization, or data exposure issues?
4. Performance
What happens with many records?
What happens with multiple simultaneous users?
5. Load and Stress Testing
Testing that a screen works isn't enough.
You need to test what happens when the application receives a realistic load.
6. Code
Don't only ask:
"Does it work?"
Also ask:
"Is it well built?"
AI Needs Oversight, Not Just Trust
AI is democratizing software development.
That's a positive thing.
Someone who once needed months to build a prototype can now do it much faster.
But when that prototype becomes a business application, the rules change.
An application that handles customers, products, money, documents, or private information needs to go through technical controls.
Because building software is one thing.
And building software for production is something else entirely.
My friend was lucky.
We caught the problem before his clients started using the application.
And that's exactly what more companies should do.
Don't wait for the problem to appear in production.
Test before.
Review before.
Audit before.
Because in the end:
Screens we see; code we don't.
And when it comes to business software, that difference can cost a company a lot of money.
If you're building an application with AI and need someone with experience to review its architecture, security, and performance before launch, talk to a specialist. We can audit your application, run load tests, and tell you whether it's truly ready for production.
Sources:
