We Keep Inheriting AI Websites That Need to Be Rebuilt

In the past month, Alopex has inherited three recently built websites that need to be substantially—or completely—rebuilt. Each organization paid less than $2,000 for its website. Based on the actual requirements, each project should probably have cost somewhere between $6,000 and $8,000.

I understand why the offers were attractive. If someone says they can produce what appears to be the same website for a quarter of the price, hiring them does not feel irresponsible. It feels efficient. The problem is that these organizations did not purchase smaller or simpler versions of professionally built websites. They purchased websites whose real costs had been deferred.

Those costs are now reappearing as slow performance, unstable features, confusing architecture, nonsensical content and systems that are exceptionally difficult to modify without breaking something else. When we explain that rebuilding may be more economical than repairing, an understandable question follows: if the website was apparently so simple to build, why can’t an experienced development team simply fix it?

The easiest analogy I can offer is a motorcycle engine installed inside a car. The engine may start. The vehicle may move. It may even look convincing during a demonstration. When the owner eventually brings it to a mechanic, however, the mechanic cannot service it as though it were an ordinary car. The problem is not that the mechanic lacks skill. The problem is that the machine no longer follows the architecture, expectations or service procedures of the thing it claims to be.

Alopex is qualified to repair these websites. In some cases, doing so would simply waste the client’s money.

Now, before we get into the meat of the argument, I want to answer this basic question:

Are AI-built websites bad?

Not necessarily. AI can help experienced developers build websites faster, but a website generated primarily through AI is only as reliable as the people reviewing the architecture, content, security, accessibility and performance. The risk isn't using AI. It's publishing work nobody involved can independently evaluate.

The Websites Looked Finished

None of these clients knowingly purchased something that looked broken. Modern AI tools can generate attractive layouts, animations, forms, databases and plausible marketing copy at extraordinary speed. A website can look polished during a presentation long before it is reliable enough to represent a real organization.

Once we began looking beneath the visible design, we found inconsistent components, unexplained behavior, poor performance and sections that were extremely difficult to change safely. We also found an astonishing amount of leftover placeholder copy. Some of it did not make sense. Some of it made claims that had clearly never been verified. There was enough of it to make one conclusion unavoidable: nobody had carefully read every word before publishing the websites.

That is not an obscure software-engineering problem. Reading and verifying the content is one of the most basic responsibilities in a professional website project. Our own website design process includes discovery, auditing, writing, content capture, design, development and continued iteration because the finished website depends on all of those disciplines working together. Generating a set of pages is only one part of the job.

In another inherited website, we discovered that visitors were being sent to an overseas online casino. The client did not know the link existed and had no practical way to remove it.

I want to be precise about what that example proves. We cannot say with certainty that AI created the casino link. It could have originated in generated code, a copied component, a compromised dependency, a later intrusion or another failure entirely. Anyone claiming to know its exact origin without investigating the system would be guessing. What concerns me is that an organization’s public website contained a link it had never approved, while neither the client nor its vendor appeared to have meaningful control over the system.

A Visible Website Is Not a Finished Website

Clients naturally evaluate what they can see: the layout, colors, copy, animations and apparent features. Professional development includes much more than that visible layer.

A production website requires decisions about architecture, accessibility, security, performance, responsive behavior, search visibility, analytics, integrations, content ownership, testing, backups and long-term maintenance. Forms must handle information correctly. Tracking needs to measure real outcomes. Content needs to be accurate. Another qualified developer should be able to inherit the system without conducting a digital archaeological excavation first.

These responsibilities are measurable. Google’s Core Web Vitals evaluate loading performance, interactivity and visual stability using real user-centered data. The Web Content Accessibility Guidelines contain testable criteria for how people perceive, navigate and interact with websites across devices and assistive technologies. The OWASP guidance on secure code review specifically emphasizes human analysis for vulnerabilities that automated tools often miss, including business logic and context-specific security problems.

A screenshot cannot establish that any of this work happened. Neither can a smooth presentation on the device used to build the website.

Alopex approaches professional website development as a collaboration between designers, content specialists and full-stack developers. Search performance is considered while the content and architecture are being developed—not sprinkled on after launch. Accessibility and responsive behavior are tested. Analytics are integrated. The website is built so the client can continue using it after the original project is complete.

That difference explains much of the gap between a website sold for less than $2,000 and one properly scoped at $6,000–$8,000. The client may not have received an extraordinary discount on professional development. They may have received the visible portion of the project while research, writing, testing, optimization and maintainability were omitted.

Cheap Websites Defer Their Costs

The missing work eventually returns in another form. The organization pays through poor search visibility, lost leads, slow performance, incorrect content, difficult updates and development time spent understanding decisions that were never documented.

Software research has made the economics of late discovery clear for decades. A NIST study on inadequate software testing found that more than half of software defects were not identified until downstream in the development process, creating substantial economic costs. The study predates modern generative AI, but its central lesson remains relevant: problems become more expensive after they have been allowed to settle into a finished system.

Generative AI can accelerate that process in both directions. It can help an experienced team test ideas, investigate issues and complete routine work faster. It can also allow someone to produce a much larger and more complicated system before encountering the limits of their own understanding.

A 2025 randomized controlled study by Model Evaluation & Threat Research examined experienced developers completing real tasks in mature open-source projects they already knew. With the AI tools available during the study, the developers took 19% longer when AI was permitted—even though they believed it had made them faster. The researchers were careful not to claim that AI always reduces productivity, and the technology continues to change rapidly. The result is still a useful warning: the feeling of moving quickly is not proof that the total work has become faster or better.

Google’s 2024 DORA research on generative AI in software development found a similar tension. Greater AI adoption was associated with modestly lower delivery throughput and meaningfully lower delivery stability. The research does not prove that AI caused every decline, but it reinforces something professional developers already understand: generating more code is not the same as delivering more reliable software.

That is particularly relevant when someone sells a website based on how quickly the first version appeared. Speed during generation may be real, but it says very little about the time required to review, test, correct, secure and maintain the result.

Alopex Uses AI Too

This is not an argument against AI-assisted development. Alopex uses AI, and we have increasingly adopted best practices governing where it can accelerate work and where human control is essential.

AI can help with repetitive development tasks, documentation, debugging, analysis and quality assurance. It can support our developers while they investigate unfamiliar systems. It can help us identify inconsistencies and test more possibilities in less time. Used correctly, it allows experienced people to spend more time on architecture, user experience and decisions that require judgment.

The danger appears when AI is allowed to build entire modules without anyone involved truly understanding what it produced.

Microsoft Research’s study of vibe coding reached a more useful conclusion than the usual argument that AI-generated code is either wonderful or terrible. The researchers found that programming expertise does not disappear when AI writes more of the code. Expertise shifts toward managing context, evaluating output, debugging failures and deciding when to stop prompting and intervene directly.

A qualified developer using AI is operating a power tool. The developer understands the larger system, recognizes suspicious output, tests the result and remains accountable for what goes live. Someone whose only technical skill is prompting can request a form, animation or integration but may have no independent way to determine whether it is secure, accessible or maintainable. If the only available response to a failure is another prompt, the AI is not being supervised in any meaningful sense.

At Alopex, AI may help us move faster, but it does not relieve us of responsibility. A human still needs to understand the architecture, review the content, evaluate performance and know how to repair the website without blindly generating another layer of code.

The Competence Gap Is Harder for Clients to See

Before generative AI, people with limited development experience usually encountered the limits of their knowledge fairly early. They could install a template, modify a simple page or work within the boundaries of an established platform. Building something substantially more complicated required learning the underlying technology or hiring someone who understood it.

AI has moved that boundary. Someone with very little technical knowledge can now produce an impressive demonstration, sophisticated animation or complicated interaction. The result may look advanced enough to sell before anyone determines whether its creator understands it.

Experienced developers using AI responsibly and inexperienced vendors relying almost entirely on AI can both produce an attractive homepage. The difference often becomes visible only when the website needs to change, performs poorly or begins behaving in ways nobody expected. At that stage, a qualified development team may technically be able to repair it while still concluding that repair is the least responsible financial recommendation.

This is one reason Alopex offers website support, management and technical triage. We regularly inherit websites built by other teams, reverse-engineer how they function and determine whether they can be stabilized. Sometimes the answer is a straightforward repair. Sometimes the architecture is so unreliable that continuing to add labor would cost more than rebuilding the system correctly.

That judgment is not gatekeeping. It is part of protecting the client from paying repeatedly for the same underlying failure.

Questions to Ask Before Buying a Website

I would not ask a prospective developer whether they use AI. Most capable development teams now use it somewhere, and refusing modern tools is no more proof of quality than adopting them enthusiastically.

Instead, ask who is making the technical decisions and who remains responsible for the finished product:

• Who reviews and tests AI-generated code?
• Who has read and verified every public-facing claim?
• How are security, accessibility and performance evaluated?
• What happens when the website needs to change?
• Can another qualified developer inherit the system?
• What access, documentation and ownership will we receive?
• When AI produces something wrong, how will the developer recognize it?

A qualified vendor should welcome these questions and explain the system without hiding behind jargon or the tool used to generate it.

You should also ask to see evidence of completed work. Alopex publishes examples through our website and marketing case studies, including systems that connect websites to inventory platforms, improve conversion paths and remain maintainable after launch. A portfolio should demonstrate more than attractive screenshots; it should show that the work continued producing value once real users, real content and real business requirements entered the system.

If you already own a recently built website and suspect something is wrong, Alopex can perform a professional website audit. We can generally determine quickly whether you are dealing with a manageable defect, accumulated technical debt or a system that would cost more to repair than rebuild. Our technical SEO services can also identify code, content and performance issues that prevent an otherwise attractive website from being found or producing results.

AI should help qualified developers produce better work, test more thoroughly and explore solutions faster. What it cannot do is make professional responsibility, technical judgment or basic human review optional.