AI Coding Tools Are Changing How Websites and Apps Are Built

A Surprisingly Good Start

Recently, I tried one of those AI coding tools to build a web app. The experience was surprisingly good at first. I asked the AI to create a client-side tool using JavaScript, and it generated something that looked clean, modern, and easy to understand. The code structure did not feel messy or confusing like I expected.

It actually looked similar to a normal professional web project.

When the AI Started Ignoring Instructions

After some time, things started getting strange.

I repeatedly told the AI to avoid certain libraries because I already had my own solution working. Even after multiple instructions, it kept trying to use different tools and frameworks. It felt like the AI got stuck in a loop where it ignored my directions and continued doing things its own way.

This is one of the biggest limitations of AI coding tools right now. They are powerful, but they still make assumptions instead of always following instructions exactly.

What Is “AI Coding” or “Vibe Coding”?

The idea behind AI coding tools is simple:

You describe what you want in normal language, and the AI generates the code for you.

For example, you can say:

• Build me a personal website
• Add these pages
• Use this design style
• Create a login form
• Add animations

The AI then creates the project files automatically. After that, you upload or deploy those files online and suddenly you have a working website or app.

This process makes software development much faster, especially for beginners and small projects.

Why AI Coding Tools Feel Powerful

One of the biggest advantages is that you do not always need to know programming languages deeply.

Instead of writing code manually, you explain your ideas in English and let the AI handle most of the technical work.

This makes it possible to:

• Build websites quickly
• Create simple tools and automations
• Test ideas faster
• Generate layouts and UI designs
• Create prototypes in minutes

For many people, this removes a huge barrier to starting projects.

The Problem: AI Still Makes Technical Decisions

Even though AI tools are helpful, they are not completely automatic.

If you simply ask for “a website,” the AI may decide on its own to use:

• A static site generator
• A JavaScript framework
• A CMS with databases
• Extra libraries and dependencies

Sometimes these choices are good. Sometimes they are unnecessary.

If you do not understand the technical side at all, you may not even realize what kind of system the AI is building for you.

That is why some technical understanding still matters.

Why Technical Knowledge Still Helps

Knowing basic development concepts helps you guide the AI more effectively.

For example, you can specify:

• Which framework to use
• SEO requirements
• Deployment methods
• Hosting setup
• Performance optimizations
• File structure
• Security practices

Without guidance, AI tools usually choose whatever seems most common or easiest.

That works for simple projects, but larger projects need more control and planning.

Testing Is Still Important

AI can generate code quickly, but that does not mean everything works perfectly.

Some problems are obvious because you get error messages. Others are subtle and harder to notice.

Modern AI testing tools can now interact with websites visually like humans do.

Instead of writing complicated browser automation scripts, you can say things like:

“Accept the cookie banner and download the newest files.”

The AI then opens the browser, clicks buttons, navigates pages, and performs actions automatically.

This makes testing and automation much easier than before.

AI Is Great for Small Projects and Experiments

For prototypes and experiments, AI coding tools are incredibly useful.

You can create:

• Websites
• Dashboards
• Internal tools
• Automation scripts
• Test environments
• Small apps

What used to take hours can now take minutes.

A lot of repetitive setup work becomes almost effortless.

The Biggest Weakness: Long-Term Maintainability

One major issue is that AI often keeps adding layers of code instead of restructuring things properly.

This can lead to:

• Duplicate functions
• Unnecessary complexity
• Messy architecture
• Inconsistent coding styles
• Difficult maintenance later

Experienced developers usually think about long-term scalability and clean architecture. AI tools are still weak in this area.

That is why production software still requires human oversight.

How AI Is Changing Programming

Another interesting shift is how coding itself feels.

Developers are spending less time thinking about:

• Syntax
• Parameters
• APIs
• Boilerplate code

Instead, they focus more on:

• Behavior
• Features
• Outcomes
• User experience
• System design

The AI handles much of the implementation detail while humans focus on higher-level decisions.

Developers Are Still Important

AI coding tools do not make developers useless.

They simply change where human value matters most.

Humans are still needed for:

• Architecture decisions
• Planning
• Debugging complex issues
• Security
• Scalability
• Product thinking
• Long-term maintenance
• Edge cases

AI is very good at speeding up repetitive work, but reliable systems still require human judgment.

Final Thoughts

AI coding tools are already extremely powerful for:

• Prototypes
• Personal projects
• Experiments
• Quick websites
• Repetitive workflows

They can dramatically speed up development and make software creation more accessible.

However, they still need guidance, testing, oversight, and technical understanding if you want reliable and maintainable systems.

Right now, the best results come from combining AI speed with human experience and decision-making.