WordPress Isn’t Dead... But the Old Way of Building Websites Is

WordPress isn’t disappearing overnight, but AI-assisted development is changing how small teams build websites, landing pages, internal tools, and custom marketing systems.

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I killed my WordPress site last week.

That sentence still feels strange to write. WordPress was a big part of my career. I defended it for years, knew how to customize it, and could work my way through themes, plugins, CSS, and page builders without much trouble.

But “without much trouble” isn’t the same as “without much time.” Building a landing page still took days. Updating a site meant navigating settings, plugins, templates, and forms. A small change could turn into an afternoon of troubleshooting.

Then AI-assisted development got good enough that I rebuilt the site another way. The result looks better, takes less time to change, and gives me more control than I had with a typical WordPress setup.

So my prediction is intentionally nuanced: WordPress probably won’t vanish, but the old way of building websites is entering a slow decline. For small and midsize teams, AI-assisted development—or what many people call vibe coding—is becoming a practical alternative.

Why WordPress started feeling like a bottleneck

WordPress was revolutionary because it made publishing accessible. You didn’t need to build a website from scratch. You could install a theme, add plugins, drag elements around, and get something live.

That model still works. The problem is that every layer adds friction.

  • A page builder adds another interface to learn.
  • A form usually requires a plugin.
  • Analytics requires configuration.
  • SEO features require more settings or another plugin.
  • Custom functionality often means searching for a plugin or hiring a developer.
  • Security and performance become ongoing maintenance tasks.

I’ve been proficient with all of this. I knew how to hand-code pages, use WordPress, work in a drag-and-drop funnel builder, and make the pieces connect. It still took too long.

That was the real problem. Our marketing bottleneck wasn’t always writing the copy. AI could already help with a first draft. The bottleneck was getting the copy, images, design, forms, integrations, and pages assembled into something we could actually test.

We’d say, “Give me a few more days,” and then two weeks would pass with one page finished. Once the first page existed, duplicating it into a funnel was easy. Getting the first page live was the hard part.

AI-assisted development changes that equation. The thing that used to take weeks can often be done in a day, sometimes less.

What vibe coding actually looks like

“Vibe coding” can sound like asking a chatbot to magically build a website. That’s not really what I mean.

For me, it means describing the outcome I want in plain language, letting an AI coding tool handle much of the implementation, and then reviewing, testing, and refining the result. I’m still responsible for the direction. I still need to recognize good design, explain what feels wrong, and make decisions about security and functionality.

The process I used looked roughly like this:

  1. Start with the existing site. I gave the AI my website URL and explained that I wanted to move away from WordPress while preserving the important content and overall visual direction.
  2. Pull in the assets. The tool inspected the site, gathered the pages and images, and created a local working version.
  3. Choose a better foundation. Instead of generating every component from a blank folder, I used an open-source content management system called Payload as the base.
  4. Build locally first. The site was developed and tested on my computer before going live.
  5. Deploy through GitHub and Vercel. GitHub stores the code, while Vercel handles deployment and hosting.
  6. Iterate through conversation. When I wanted dates removed from blog posts or a layout adjusted, I described the change and let the AI make it.

The important detail is that I didn’t build everything from nothing. That would be wasteful and riskier than it needs to be.

Don’t vibe-code what open source has already solved

One of the most useful ideas I’ve encountered recently is the “third way” between vendor lock-in and building everything yourself: free and open-source software.

When I started rebuilding my site, I initially thought the AI would generate a custom content management system from scratch. Then I stopped and asked a better question: Has someone already built a reliable system for this?

The answer was yes. Payload gave me a strong foundation for content management and ecommerce, with a codebase that other developers have already worked on and examined. I could customize it instead of spending thousands of AI tokens recreating basic functions.

This principle applies far beyond websites. If you want to build a CRM, internal chat tool, project tracker, or knowledge base, first ask an AI tool to search for established open-source options. Then build on top of one that fits your needs.

That approach has several advantages:

  • You start with tested functionality instead of a blank screen.
  • You reduce the chance of forgetting important security or accessibility details.
  • You spend fewer tokens and less time generating basic code.
  • You can inspect and modify the system yourself.
  • You reduce dependence on a single commercial vendor.

Open source doesn’t eliminate risk. You still need to evaluate licensing, maintenance, security, and the quality of the project. But it’s usually a smarter starting point than asking an AI to reinvent everything.

This is similar to the broader shift I’ve written about in automation and AI agents: the advantage comes from combining capable tools with good judgment, not from blindly handing over the whole job.

The website becomes a custom marketing system

The biggest change isn’t that I can make prettier pages faster. It’s that the website can now become a system designed around how my business actually works.

For example, I can create a custom form that connects directly to my CRM. I don’t have to settle for the form builder provided by a plugin or accept a generic workflow because that’s what the software supports.

I can describe the process:

  • A visitor completes an assessment.
  • The form sends the contact to the CRM.
  • The system applies the appropriate tags.
  • The visitor receives a custom result page.
  • The team gets a notification.
  • A follow-up sequence begins.

That can all be built around the business instead of forcing the business to adapt to the limitations of a plugin.

I’m also building an importer that can take a new YouTube upload, find the transcript, turn it into a properly formatted article, place the thumbnail correctly, and publish it to the site. The point isn’t to publish more generic content. The point is to remove the repetitive implementation work between an idea and a finished asset.

That distinction matters. AI can help with the right marketing workflows, but it shouldn’t be used as an excuse to produce more material nobody needs.

Security is the reason to move carefully

I don’t recommend that every company immediately abandon its current website.

If you’re a large enterprise with hundreds of employees, sensitive customer data, and serious compliance requirements, this approach deserves extensive review before it touches production. New technology creates new failure modes. AI tools can make a bad change quickly. A database can be misconfigured. A deployment can expose something that should have remained private.

For smaller teams, the risk may be manageable, but only if you take basic precautions.

A practical security checklist

  1. Use an established foundation. Start with a maintained open-source system instead of generating critical infrastructure from scratch.
  2. Keep backups. Store your code in GitHub and maintain recoverable copies of your content and data.
  3. Separate development from production. Test changes locally or in a staging environment before pushing them live.
  4. Limit access. Don’t give an AI tool unrestricted access to every system, credential, and database unless you understand the consequences.
  5. Run a security audit. After the functionality works, ask the AI to research common vulnerabilities for your specific stack and audit the project against them.
  6. Use a second reviewer. Have a different model or a knowledgeable person inspect the code and challenge the first system’s assumptions.

That last step is especially helpful for marketers who aren’t developers. I can ask one AI tool to build something and another to red-team it. I still need to understand the recommendations, but I don’t have to rely on one system grading its own homework.

There’s also a useful form of future-proofing here. My code isn’t trapped inside one AI product. It lives in files and a repository. If I stop using one tool, I can give the same project to another. If I leave Vercel, I can deploy the repository somewhere else. The tools may change, but the underlying work remains portable.

AI content needs more human input, not less

The same shift is happening in content creation.

LinkedIn has started testing a way for users to report AI-generated content. I understand why. The platform is full of posts that sound polished but empty, along with comments that simply paraphrase whatever someone just wrote.

I’ve used AI for social posts myself. It was convenient, and sometimes the writing was sharper than my first draft. But marketers have become very good at recognizing the familiar rhythm of AI-generated content. The hooks, transitions, tidy conclusions, and vague claims all start to sound the same.

My approach has changed. I’m giving AI more of my actual thinking and less freedom to invent the substance. I might ask it to organize rough notes, clean up awkward sentences, or identify a confusing section. I don’t ask it to manufacture ninety percent of a post from ten percent of my ideas.

That’s the ratio problem. If a thirty-minute interview becomes thirty days of content, the system has to invent most of the material. That might be efficient, but it isn’t necessarily useful or authentic.

AI should help us repurpose original thinking, not replace the thinking. The human contribution needs to remain visible through experience, taste, specificity, and judgment.

This is part of why I’m skeptical when people describe AI as the death of creativity. The real danger isn’t that nobody can create anymore. It’s that people will accept generic output because producing it is easy. I explored that tension in the debate over AI and the art of marketing.

Stop chasing the biggest AI story

There’s always another major AI story to worry about: geopolitics, model competition, economic disruption, job losses, open-weight models, or some prediction about what happens ten years from now.

Some of those developments matter. But most marketers don’t need to make a decision about them this week.

The more useful question is: What is the next bottleneck holding back revenue?

Maybe the team can’t launch landing pages quickly enough. Maybe leads sit untouched in the CRM. Maybe reporting takes three days every month. Maybe customer research is scattered across documents. Maybe the sales team lacks the right content at the right stage.

Start there. Define the problem, then find out whether AI can reduce the time, cost, or effort required to solve it.

That keeps the work grounded. Instead of reacting to every headline, you’re improving the part of the business you can actually influence over the next three to six months.

For me, website development was the bottleneck. AI-assisted coding removed much of the friction between an idea and a working page. For someone else, the answer may be a custom reporting tool, a better research workflow, or an automated follow-up system.

You don’t need to build everything. You need to identify the constraint that matters and use the best available tools to relieve it.

The next practical move

WordPress isn’t dead. It remains a sensible choice for plenty of organizations, especially when stability, familiar workflows, and a large implementation ecosystem matter more than customization.

But the default assumption that every business needs a traditional WordPress site is becoming weaker. Small teams can now build custom websites and internal tools with a fraction of the old development effort.

If you want to explore the shift, don’t begin by rebuilding your entire business. Choose one contained project: a personal site, a landing page, a calculator, or a simple internal tool. Ask an AI coding assistant to inspect your current setup, recommend an open-source foundation, and explain the deployment path. Keep the original system live until the replacement is tested and backed up.

The tools will change. Codex may not be my favorite tool six months from now. Vercel may not be the best hosting option forever. That’s fine. The lasting advantage is knowing how to move from a business requirement to a working system without treating every small change like a development project.

Find the bottleneck, start with a solid foundation, keep your work portable, and let AI handle the repetitive implementation while you keep ownership of the decisions.