Why AI Is Changing How We Build Websites

I is reducing the effort required to move from an idea to a functioning website. But easier production does not guarantee a better product. Judgment, originality and an understanding of users are becoming more valuable, not less.

AI is changing the process of building websites from manual production to direction and refinement.

For most of the internet’s history, building a good website required a fairly predictable combination of skills.

Someone planned the structure. A designer created the initial layouts. A copywriter produced the content. A developer turned the designs into code. Other specialists might then handle hosting, analytics, search engine optimisation, accessibility, security and ongoing maintenance.

Even a relatively simple website could involve several people and weeks of work.

Artificial intelligence is beginning to compress that entire process.

AI can help someone move from a rough idea to a visual concept, generate working code, produce draft content, identify technical problems, create images, suggest improvements and test alternative versions—all within the same working session.

This change is happening alongside a broader acceleration across the digital world. As I explored in why the internet is changing faster than ever, new platforms, changing search behaviour and increasingly capable AI systems are forcing online businesses to adapt more quickly than before.

That does not mean expertise is becoming irrelevant. It means the parts of website creation that require expertise are changing.

The most valuable skill is gradually moving away from manually producing every individual component. It is becoming the ability to define the right problem, direct the available tools, recognise weak output and turn a fast prototype into something people genuinely want to use.

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“The biggest change AI has made is not simply faster coding. It has dramatically shortened the distance between having an idea and finding out whether that idea is any good.”

A brief history of how websites have evolved

AI-assisted development makes more sense when viewed as the latest stage in a much longer evolution.

Every major era of the web has reduced a different barrier.

Early HTML made publishing possible. Content management systems made it accessible to non-developers. Responsive frameworks simplified mobile design. Cloud platforms reduced infrastructure work. No-code tools removed some of the need for manual programming.

AI now reduces the effort required to move from an idea to an implementation.

The technologies have changed enormously, but the direction has remained surprisingly consistent: each generation makes it easier for more people to build and publish online.

AI is simply pushing that process much further.

Website creation is moving from production to direction

Traditional website development is largely a production process.

Every page, component and function has to be deliberately created. Even when templates and frameworks are used, someone still needs to configure, write and assemble the individual elements.

AI-assisted development is increasingly a process of direction and refinement.

Instead of manually producing everything, a website creator can describe an intended outcome:

• Build a simple dashboard for tracking marketing campaigns.
• Make this page feel more trustworthy and less corporate.
• Turn this spreadsheet into a searchable directory.
• Add a tool that recommends a service based on the visitor’s answers.
• Create three alternative versions of this landing page.
• Identify why the mobile layout is breaking.
• Rewrite this component so it can be reused across multiple pages.
• Add authentication and save each user’s results.

The AI can then produce a starting point that the creator inspects, tests and improves.

This does not eliminate development work. In many cases, it changes the sequence of that work.

The creator starts with a generated implementation and works backwards, checking whether the architecture, interface, code and assumptions are sound.

GitHub’s research into AI-assisted software development suggests that coding tools are already becoming part of normal development workflows. The most important benefit is not merely generating more code. It is reducing the time developers spend on repetitive tasks so that they can concentrate on architecture, testing and problem-solving.

The cost of producing the first version is falling.

As that happens, judgment becomes more important than production speed.

The bottleneck is no longer always development

For years, a new website idea could be delayed by practical questions:

• Who will design it?
• Who will develop it?
• How much will the first version cost?
• How long will it take?
• Which platform should we use?
• What happens when something breaks?
• Do we need to hire a specialist before testing the idea?

Those questions still matter, particularly for large or business-critical projects. However, they no longer have to prevent someone from testing the basic concept.

A founder can generate a functional prototype before hiring a full development team. A marketer can create a working internal tool without waiting for a place in an engineering schedule. A developer can test several possible architectures before committing to one.

This changes the economics of experimentation.

Previously, you often had to believe strongly in an idea before investing in its development. Now it is increasingly possible to build enough of the idea to test that belief.

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“A few years ago, launching a new web project could mean committing a meaningful amount of time before knowing whether anyone wanted it. AI makes it possible to test more ideas with less initial risk.”

This is especially significant for smaller businesses.

A small team will not necessarily match a large organisation in infrastructure, data or distribution. It can, however, use AI to reduce the amount of routine production required to get something in front of users.

The result is not that every small company suddenly becomes successful. It is that more of them can reach the point at which customers—not development costs—decide whether an idea survives.

Websites are becoming tools rather than collections of pages

Many websites still follow a model that has existed for decades:

1. The company publishes information.
2. The visitor reads it.
3. The visitor fills in a form, makes a purchase or leaves.

AI creates the opportunity for a more active relationship.

A website can ask questions, interpret answers, search a knowledge base, adapt recommendations, summarise complex information or help the visitor complete a task.

Instead of publishing another static article about choosing a service, a business could create an interactive selector that recommends the most appropriate option.

Instead of providing a long support library, it could allow visitors to ask questions in natural language.

Instead of forcing every visitor through the same navigation, it could provide different routes based on what the person is trying to accomplish.

This is one of the most important changes AI is bringing to website creation: websites are increasingly expected to do something useful, not merely display information.

• Product recommendation assistants
• Interactive eligibility checkers
• Research and comparison tools
• Natural-language site search
• Document analysers
• Personalised learning tools
• Account-management assistants
• Automated reporting dashboards
• Customer-support agents
• Content transformation tools
• Service recommendation engines
• Interactive calculators

Modern development platforms are already being structured around these uses. The Vercel AI SDK, for example, supports streaming responses, structured outputs, multiple model providers and tool calling.

These are not merely features for creating chatbots. They are building blocks for applications that can access information, interact with systems and complete controlled actions.

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“I believe the future belongs to websites that solve problems. A static page can still be useful, but the most valuable websites will increasingly help visitors make decisions or complete work.”

Natural language is becoming a development interface

Website builders have long attempted to make development more visual.

Drag-and-drop editors allowed people to create layouts without writing CSS. Content management systems separated content from code. Component libraries made it possible to reuse interface elements.

AI introduces another interface: ordinary language.

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“Reduce the visual weight of the header and make the main action more obvious.”

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“Create a comparison table using the data already on this page.”

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“Make the mobile navigation accessible from a keyboard.”

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“Explain why this database request is slow.”

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“Turn this page into a multi-step tool.”

This is a substantial shift because it allows people to work at a higher level of abstraction.

They do not always need to know the exact property, function or file responsible for a change before requesting it. The AI can help identify the relevant implementation.

However, natural-language development has an important weakness: a plausible response can still be wrong.

AI might misunderstand the existing architecture, introduce an unnecessary dependency, create inaccessible markup or solve a visible symptom without addressing the underlying problem.

The user therefore needs enough understanding to evaluate the result.

The interface may become easier, but responsibility does not disappear.

Developers are becoming reviewers, architects and problem definers

The argument that AI will either replace developers completely or leave development unchanged is too simplistic.

The more likely outcome is that the composition of development work changes.

• System architecture
• Code review
• Security
• Data modelling
• Performance
• Integrations
• Automated testing
• Edge cases
• Product decisions
• User experience
• Monitoring
• Reliability
• Cost control

Someone still needs to understand how the pieces fit together.

A generated feature might work during a demonstration but fail under real traffic. It might expose sensitive information, behave unpredictably, create an accessibility barrier or incur far higher infrastructure costs than expected.

AI can accelerate the production of code. It cannot guarantee that a system is appropriate for the organisation deploying it.

This distinction is important when considering what AI can and cannot replace. AI is particularly effective at accelerating repeatable production work. It remains much less reliable when a decision requires accountability, original judgment, contextual understanding or a genuine relationship with another person.

This is why experienced developers may become more productive rather than simply less necessary. They can use AI to remove repetitive work while applying their knowledge to a larger number of consequential decisions.

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“AI can write code, but it cannot take responsibility for the business using that code. Someone still needs to understand the users, the risks and the consequences of getting it wrong.”

Design is becoming faster—and more disposable

AI can generate page structures, visual directions, illustrations, interface copy and alternative layouts rapidly.

That makes early-stage design less precious.

A team no longer needs to spend several days refining its first idea before seeing any alternatives. It can produce several directions, compare them and discard weak concepts much earlier.

This can improve design, but only when the increased speed is used to explore.

There is also a risk that everyone uses similar prompts, models and component libraries, producing websites that are technically polished but visually indistinguishable.

• The same oversized headings
• The same gradients
• The same rounded cards
• The same three-column feature sections
• The same vague claims
• The same animated dashboard mock-ups
• The same generic AI illustrations
• The same interchangeable landing-page language

When execution becomes easier, originality becomes harder to fake.

Designers will therefore remain important—not simply because they can produce layouts, but because they can create a coherent visual language, recognise clichés and connect design decisions to human behaviour.

AI can generate possibilities. It cannot guarantee taste.

Content creation is easier, but ordinary content is worth less

AI has removed much of the mechanical difficulty involved in producing website copy.

• Service-page drafts
• Product descriptions
• Frequently asked questions
• Metadata
• Support documentation
• Blog outlines
• Onboarding messages
• Interface labels
• Email sequences
• Localised content
• Structured data suggestions
• Alternative calls to action

The immediate effect is an enormous increase in supply.

When almost any business can generate acceptable copy quickly, acceptable copy stops being a meaningful competitive advantage.

The differentiators become experience, evidence, examples, original data and a clear point of view.

Google’s guidance on AI features and website content reinforces the importance of creating useful, non-commodity material rather than producing large amounts of content that add little beyond information already available elsewhere.

• Crawlable pages
• Clear internal linking
• Helpful visible text
• Accurate structured data
• Strong page experience
• Original information
• Reliable sources
• Clear authorship

This increases the value of content that contains something only its creator can credibly provide.

• First-hand experience
• Original experiments
• Proprietary data
• Customer insights
• Detailed case studies
• Contrarian but defensible opinions
• Photographs or videos of real work
• Transparent failures
• Demonstrations of a genuine process

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“AI has made it easier than ever to publish content, but it has also made generic content easier to ignore. The websites that stand out will contain knowledge, evidence or experience that cannot be reproduced from a simple prompt.”

Search is becoming more fragmented

For many website owners, visibility historically meant ranking in Google.

That remains important, but discovery is becoming more distributed.

• Traditional search results
• Google AI Overviews
• AI Mode
• ChatGPT
• Claude
• Gemini
• Perplexity
• YouTube
• TikTok
• Reddit
• Industry newsletters
• Specialist marketplaces
• AI agents acting on the user’s behalf

This changes how websites should be planned.

A website still needs to serve human visitors, but its information must also be sufficiently clear, credible and accessible for search systems and AI tools to understand.

That does not mean filling pages with artificial phrases designed for language models.

It means publishing information that is:

• Specific
• Well organised
• Internally consistent
• Supported by evidence
• Clearly attributed
• Easy to crawl
• Available in text
• Regularly maintained

The websites most likely to remain visible are not those that mechanically mention the greatest number of keywords. They are the ones that become useful and trustworthy sources.

Personalisation will move beyond changing someone’s name

Website personalisation is not new.

Ecommerce stores recommend products. Advertising platforms change landing pages. Streaming services tailor their home screens. Email tools insert names and company details.

AI allows personalisation to become more conversational and situational.

• The visitor’s stated objective
• Their level of experience
• The size of their organisation
• Previous interactions
• Their location
• Their existing tools
• Their preferred format
• The problem they are trying to solve

A first-time visitor and an experienced customer may receive fundamentally different journeys through the same underlying service.

This can make websites more useful, but it also introduces questions involving consent, privacy, explainability and data storage.

Personalisation should help the visitor. It should not become an excuse for unnecessary surveillance.

The strongest implementations will probably be those in which visitors knowingly provide information because the benefit is obvious.

Website stacks likely to become more popular in 2027

There will not be one winning website stack in 2027.

Different projects have different requirements, and established platforms will not disappear simply because newer tools are fashionable.

However, several combinations are becoming attractive because they support fast deployment, managed infrastructure and AI functionality without requiring a large operations team.

The following table represents likely directions, not guaranteed outcomes.

Astro’s approach is particularly interesting for content-focused sites because it allows static content and dynamic components to coexist. Its explanation of server islands shows how a mostly static page can contain selected personalised or dynamic sections without forcing the entire page to behave like a complex application.

For application development, Next.js and similar full-stack frameworks are likely to remain attractive because they bring interface and server-side functionality into one ecosystem.

Supabase and other managed Postgres platforms are also appealing because they combine relational databases, authentication, storage and AI-oriented capabilities. Supabase’s documentation for pgvector explains how embeddings can be stored and searched alongside conventional application data.

The wider pattern is more important than any one framework.

• Managed infrastructure
• Type-safe development
• Reusable components
• Server-side and edge rendering
• Model-independent AI layers
• Integrated authentication
• Relational and vector retrieval
• Continuous deployment
• Smaller operational teams
• Natural-language development interfaces

The best stack is still the simplest one that reliably solves the problem.

Performance will remain important

AI does not remove the basic expectations people have of websites.

Visitors still want pages to load quickly, remain stable and respond immediately.

In fact, AI functionality can make performance more difficult.

A website may now need to load an interactive interface, retrieve data, stream a model response, call an external service and update the page dynamically. If implemented poorly, this produces an experience that feels slower and less predictable than a traditional website.

Google’s Core Web Vitals guidance continues to focus on loading performance, responsiveness and visual stability.

Real-user measurement will therefore become increasingly important.

• Largest Contentful Paint
• Interaction to Next Paint
• Cumulative Layout Shift
• Error rates
• AI response latency
• Failed tool calls
• Abandoned interactions
• Mobile performance
• Model costs
• Infrastructure costs

AI should make a website more useful, not simply more complicated.

Security becomes more complex when websites can take action

A traditional chatbot that only produces text creates one category of risk.

An AI agent that can read customer records, change bookings, issue refunds, edit content or access internal tools creates a much larger one.

• Prompt injection
• Excessive permissions
• Sensitive data exposure
• Untrusted model output
• Insecure integrations
• Supply-chain vulnerabilities
• Unexpected tool usage
• Cost-based attacks
• Inadequate logging
• Overreliance on automated decisions

The OWASP GenAI Security Project documents risks affecting large-language-model applications, including prompt manipulation, insecure output handling, sensitive-information disclosure and excessive agency.

These are not concerns reserved for the largest technology companies.

A small business that connects an AI assistant to email, customer data or financial systems needs to decide exactly what the assistant may read and what it may change.

Permissions should be narrow. Important actions should be logged. High-consequence decisions should require confirmation.

The ability to build an agent quickly does not mean it is safe to give that agent unrestricted access.

Accessibility cannot be delegated entirely to AI

AI tools can identify some accessibility problems and suggest improvements.

They can help add labels, review contrast, explain semantic HTML and generate testing scenarios.

They can also create inaccessible interfaces confidently.

A generated component might rely on colour alone, omit keyboard navigation, use inappropriate ARIA attributes or create interactions that are difficult for assistive technologies to interpret.

Accessibility therefore needs to remain part of the development process rather than a final prompt asking the AI to “make everything accessible.”

The MDN accessibility resources provide a useful foundation for understanding semantic HTML, keyboard accessibility and assistive technologies.

Human testing and specialist review will still be necessary for important products.

AI can assist with accessibility. It should not become an excuse to avoid responsibility for it.

AI makes continuous experimentation practical

One of AI’s most immediate advantages is the speed at which alternatives can be created.

• Headlines
• Calls to action
• Page structures
• Onboarding questions
• Pricing explanations
• Product descriptions
• Support content
• Navigation labels
• Recommendation logic
• Follow-up messages

This makes continuous experimentation available to smaller organisations.

However, producing variations is not the same as learning.

The team still needs a clear hypothesis, reliable measurement and enough data to interpret the result.

Otherwise, AI merely creates more versions of the same uncertainty.

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“The real advantage is not that AI can generate ten landing pages. It is that it allows us to test an assumption sooner. The value comes from what we learn, not from the number of versions produced.”

The risks of building too quickly

Speed is valuable, but it can hide poor decisions.

AI-assisted development makes it possible to build an impressive demonstration before answering basic questions:

• Does anyone need this?
• Is the data reliable?
• Can the business afford to operate it?
• What happens when the model is wrong?
• Who is accountable for its actions?
• Is a simpler non-AI solution better?
• Can the system be maintained?
• Are users comfortable sharing the required information?

There is a danger that companies add AI because it creates the appearance of innovation.

A conventional search box may be more reliable than a generative assistant. A well-designed form may be faster than a conversation. A clear article may answer the question better than a chatbot.

The objective should not be to maximise the amount of AI in a website.

It should be to improve the user’s outcome.

My predictions for website development beyond 2027

1. Most serious websites will contain an AI-assisted interaction

This will not always take the form of a visible chatbot. AI may operate behind search, recommendations, support, personalisation, reporting or content management.

2. Natural-language editing will become standard

Website owners will increasingly request changes by describing outcomes. Visual editors and code editors will continue to exist, but natural language will become another normal control layer.

3. Model choice will become less visible

Developers will increasingly use gateways or abstraction layers that allow models to be changed based on cost, speed, availability or task suitability.

4. AI agents will connect websites to business operations

Websites will not merely answer questions. They will check systems, update records and complete approved actions.

5. One-day prototypes will become ordinary

Creating a convincing first version in a day will stop being remarkable. Turning that prototype into a secure, reliable and profitable product will remain difficult.

6. Original experience will become more valuable

As generic content becomes abundant, first-hand knowledge, reputation, communities and proprietary information will matter more.

7. Small teams will operate larger portfolios of products

AI will allow experienced people to manage more websites and tools without expanding their teams at the same rate.

8. Websites will adapt more dynamically to intent

Instead of making every visitor follow the same journey, sites will increasingly understand what someone is trying to achieve and present an appropriate route.

9. Search, browsing and task completion will begin to merge

A person may ask an AI system to research options, compare providers and complete an action without manually visiting every page.

10. Human judgment will become the premium skill

When code, copy and designs can all be generated, the ability to determine what is accurate, appropriate and worthwhile becomes more valuable.

What website owners should do now

There is no need to rebuild an entire website simply to claim that it uses AI.

A more sensible approach is to identify one part of the customer journey where AI could create a measurable improvement.

1. Make a large resource library easier to search.
2. Help visitors choose between several services.
3. Convert an internal spreadsheet into a useful customer tool.
4. Create a better support experience.
5. Personalise onboarding based on a few explicit answers.
6. Automate a repetitive website-management task.
7. Help users understand complex information.
8. Produce structured summaries from uploaded documents.

Start with the problem, not the technology.

Then measure whether the AI-assisted version is actually faster, clearer or more useful than the existing process.

• Clear information architecture
• Strong original content
• Fast page delivery
• Accessible interfaces
• Accurate analytics
• Secure permissions
• Reliable first-party data
• Good documentation
• Consistent branding
• Genuine customer understanding

AI amplifies what is already there.

It can help a focused business move faster. It can also help a confused business create more confusion.

From FrontPage to AI

My own experience of building websites has followed much of this wider evolution.

I began during an era when creating a website meant manually assembling pages in Microsoft FrontPage, experimenting with layouts and learning by putting things online.

Today, I can use AI as part of the planning, coding, writing, testing and experimentation process.

I described that personal journey in From FrontPage to AI.

The tools have changed almost beyond recognition, but the underlying motivation remains familiar: take an idea and turn it into something useful on the internet.

AI makes that process faster. It does not decide which ideas deserve to exist.

Frequently asked questions

Will AI replace web developers?

AI is likely to reduce the amount of routine coding developers perform, but it does not remove the need for architecture, testing, security, product judgment and accountability. The role is changing more than it is disappearing.

Can AI build a complete website?

AI can generate layouts, code, content and functionality, especially for prototypes and relatively simple projects. A production website still needs human review for reliability, accessibility, security, performance and business suitability.

What is the best AI web development stack?

There is no universal best stack. The right choice depends on the project. Next.js, Astro, managed Postgres platforms and model-independent AI SDKs are becoming popular because they support fast deployment and smaller operational teams.

Should every website have an AI chatbot?

No. AI should solve a real user problem. In many cases, better search, clearer navigation, a useful calculator or a well-designed form will be more effective than a chatbot.

How will AI change SEO?

Search visibility is becoming more fragmented across traditional results, AI summaries and conversational assistants. Clear structure, original expertise, crawlable text, accurate information and strong technical foundations remain important.

Final thoughts

After more than a decade of building and running online businesses, I see AI as one of the largest changes to website creation since content management systems made publishing accessible to people who were not developers.

It is reducing the effort required to create the first version of almost anything.

Code can be generated. Designs can be explored. Content can be drafted. Problems can be diagnosed. Interactive features can be added much earlier in the life of a project.

But easier production does not guarantee a better website.

• Which problems are worth solving?
• What do users actually need?
• What should a business be trusted to do?
• Which information is accurate?
• What experience feels clear and credible?
• When is AI genuinely helpful?
• When is a simpler solution better?

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“AI is not removing the need for website creators. It is changing what makes them valuable. The ability to produce code is becoming more accessible, but judgment, originality, responsibility and an understanding of users remain difficult to automate.”

The future will not necessarily belong to the website with the most AI features.

It will belong to the businesses that use AI deliberately—building faster where speed helps, retaining human control where judgment matters and creating experiences that solve real problems better than what came before.