AI Can Build a Website. Can It Build the Right One?

A few years ago, building a custom website could require an entire team: a business analyst, UX architect, designer, illustrator, frontend and backend developers, QA engineer, art director, CTO and DevOps engineer. Today, you can describe a website in a prompt and get something running in minutes.

AI can write the copy, generate layouts, produce images, create React components, connect APIs, fix bugs, build a database and even deploy the project.

So the obvious question is whether companies still need agencies to build websites at all. For some projects, probably not.

If you need a simple landing page, an internal tool, an early prototype or a temporary website to validate an idea, AI can already take you surprisingly far. The barrier between having an idea and seeing something functional in a browser has become dramatically lower.

But creating something that works in a browser and building a website that works reliably for a business are still two very different tasks.

AI has already changed how agencies work

AI has already changed a large part of everyday agency production. Tasks that used to take hours of manual work can now be explored, drafted or completed much faster. At Cuberto, we see this across almost every discipline. Designers can explore more directions before committing to one. Developers can prototype functionality, investigate unfamiliar errors and generate repetitive code faster. Content, research and documentation can also be processed much more efficiently.

The time saved at the production stage does not remove the need for expertise. It gives the team more room to evaluate the result, understand what should change and make sure each decision still works as part of the larger product.

Getting a website running is no longer the difficult part

AI has significantly lowered the technical barrier to creating websites.

Current tools can generate layouts, navigation, forms, animations, responsive interfaces and reasonably functional frontend code from a relatively simple description. Someone with limited knowledge of HTML, CSS or JavaScript can now get much further than would have been possible only a few years ago.

For a simple website, that may genuinely be enough. The difficulty starts when the project leaves the ideal conditions of a demo and enters production.

A form works locally but stops sending submissions after deployment. The desktop version looks fine, while the navigation breaks on certain mobile devices. A CMS that seemed convenient at first becomes difficult to maintain once the content grows. A third-party script delays rendering. Analytics starts firing duplicate events. Marketing adds several tracking services and performance suddenly drops. Search engines index pages that were never meant to appear in search. An external API changes its behavior.

None of this is unusual once a website reaches production. You can keep feeding each problem back to an AI assistant, and sometimes it will find the right fix immediately. In other cases, it will suggest several plausible fixes without understanding the underlying reason the problem exists.

This is where a person without technical experience can easily end up in a loop: one fix creates another issue, and the next prompt adds another workaround. Eventually the project becomes difficult to understand even for the person who originally generated it.

At that point, you still need someone who can look at the system as a whole. And finding experienced developers willing to take responsibility for a collection of isolated fixes inside somebody else's AI-generated project may be harder and more expensive than involving an experienced developer earlier in the project.

A good website is a system

This is one of the things that tends to disappear from discussions about AI website builders.

A website is not a collection of independently generated screens. Navigation affects content. Content affects layout. Layout decisions affect responsive behavior. Motion affects performance. The way the CMS is structured affects frontend architecture. Analytics and marketing tools affect loading speed and privacy. Hosting affects response times and reliability. SEO decisions may influence routing, content structure and how an existing website is migrated.

These relationships are not difficult because each individual problem is impossible to solve. They are difficult because changing one part of the system often changes several others.

An experienced team understands those dependencies before they become visible problems.

If we design a motion-heavy hero, we are already thinking about what happens on mobile and slower internet. If the client needs several content types in the CMS, development decisions start before the final interfaces are finished. If the website already receives search traffic, redesigning the page structure cannot be treated separately from migration and indexing.

This is also why evaluating digital work only by how it looks is becoming increasingly misleading.

A polished interface is easier to produce than ever

AI has made one part of design much more accessible: producing something that looks finished.

Layouts, illustrations, typography combinations and complete visual directions can now be generated very quickly. A presentation can look convincing even when relatively little work has gone into understanding the product behind it.

For clients, this makes appearance alone a much less useful way to judge design quality.

The harder questions come after the first impression. Does the hierarchy still work with real content? Can users understand where to go next? The interface also needs to hold together across mobile and desktop, empty and error states, motion, accessibility and future product growth.

We use AI in our own design process to explore ideas faster, but choosing the direction still depends on the product, audience and business goal. A concept may look strong and still be wrong for the project.

The visual layer still matters. It just tells you less about the quality of the work behind it than it used to.

Coding is becoming easier. Engineering is not.

AI can generate code extremely quickly, but speed should not be confused with engineering quality.

In practice, we treat current coding assistants more like extremely fast junior developers than autonomous senior engineers.

They can build components, write utility functions, connect APIs, explain unfamiliar libraries and solve many isolated technical problems. In the hands of an experienced developer, this can save a huge amount of time. The problem begins when generated code is treated as finished code rather than a first implementation that still needs review.

AI rarely has enough context to understand why previous architectural decisions were made, which shortcuts are acceptable and which ones may create problems six months later. When asked to fix an issue, it may solve the immediate problem by adding another condition, another abstraction or another workaround instead of recognizing that the underlying structure needs to be reconsidered.

It can also refactor individual pieces of code, but refactoring a real product is rarely just about rewriting one function more elegantly. It requires understanding how different parts of the system depend on each other, what can safely change and what should remain stable.

For senior developers, more of the work moves toward reviewing generated solutions, controlling architecture, removing unnecessary complexity and deciding when an AI-generated implementation is safe to keep and when it should be rewritten.

Can you build a website yourself with AI?

Yes. For many types of projects, it now makes sense to try.

If you are building a personal website, a simple landing page, an internal tool or an early product prototype, hiring a full agency may be unnecessary. AI gives founders and small teams an opportunity to test ideas that previously would have required a meaningful design and development budget.

The point where professional involvement becomes more important is when the website itself becomes important to the business.

If it needs to consistently generate qualified leads, support paid marketing, handle large amounts of content, integrate with business systems, work across markets, maintain strong performance or represent a company where digital perception matters, the task is no longer simply to create pages.

At that level, AI remains useful, but it becomes part of the team's toolkit rather than a replacement for the team.

Where AI fits in the process

AI has already made website production faster, and we expect that to continue.
For simple projects, it can remove the need for a traditional agency altogether. For larger projects, it helps experienced teams spend less time on repetitive production and more time on UX, architecture, performance, testing and the business problem behind the website.

For larger projects, someone still needs to own the final result. AI can generate a solution, but the team has to understand how it fits into the rest of the system, decide whether it is ready to ship and fix it when it is not.

For us, that is where AI is most useful today: it makes an experienced team faster without removing the need for that experience.

Frequently asked questions

Can AI build a complete website from scratch?

Yes. AI can generate layouts, copy, images, frontend and backend code and even help deploy a website. For landing pages, prototypes, personal websites and internal tools, this may already be enough.

The limitations become more noticeable when a project needs complex integrations, a scalable CMS, strong performance or long-term maintenance.

Do I still need a web developer if I use AI?

Not for every project. AI can take a non-developer much further than before.

For production websites, experienced developers are still important for architecture, code review, security, integrations and problems that affect several parts of the system at once.

Should the same agency handle UX and UI?

Usually yes. UX decisions determine structure, hierarchy and behavior, while UI determines how those decisions are communicated through the interface. Separating them between unrelated teams often creates unnecessary handoffs.

Can AI replace a web design agency?

For a simple website, sometimes.

For a business-critical website, the work usually extends beyond generating pages. UX, conversion, positioning, content structure, technical architecture, performance and implementation still need to work together.

Is AI-generated code production-ready?

It can be, but it should not be assumed.

AI-generated code works well for many isolated tasks, but larger projects require review because architecture, dependencies, security and existing conventions matter just as much as whether the code runs.

What types of websites are best suited to AI website builders?

Landing pages, personal sites, prototypes, early startup websites and internal tools are obvious candidates.

AI-only development becomes more difficult when a project requires custom business logic, complex integrations, large content structures, advanced motion or several teams maintaining the product over time.

When should I hire an agency instead of building with AI?

Look at the role of the website rather than the number of pages.

If it needs to generate qualified leads, support paid acquisition, explain a complex product, work across markets or integrate with business systems, an experienced team becomes much more valuable.

How are agencies using AI today?

We use AI across research, content, visual exploration, prototyping, development, documentation and debugging.

The main benefit is speed. It reduces repetitive production and gives the team more time for UX, architecture, interaction, performance, QA and the decisions that have a bigger impact on the final product.