We Scanned 131 AI-Built Websites, and the 'AI Look' Wasn't the Biggest Tell — SiteBlob<br>People think AI-built websites are obvious.

They imagine the same gradient hero, the same rounded cards, the same vague startup copy, the same oversized headline, and the same polished-but-empty SaaS sections.

That is the popular theory of the "AI website look."

But in this pilot SiteBlob audit, that was not the strongest tell.

The more useful signal was not the screenshot.

It was what the browser received, rendered, and exposed.

SiteBlob scanned 131 publicly accessible websites attributed to five AI-builder families and compared them with 10 human-led modern open-source control sites .

The first surprise was that ordinary quality did not separate the groups very well.

The average conventional quality score was 45.0 for the confirmed AI-built websites and 43.6 for the controls. The empirical AUC for conventional quality was 0.539 , which means ordinary quality was a weak separator in this pilot cohort.

The second surprise was even simpler.

The visible "AI look" barely separated the groups at all.

Visual template tropes appeared in 50.4 percent of confirmed AI-built websites and 50.0 percent of the controls.

Almost identical.

So if your AI website audit starts and ends with gradients, cards, motion, and generic SaaS sections, you may be looking at the least useful layer.

The stronger difference appeared beneath the interface.

The short version

This was an exploratory browser-rendered audit, not a population study.

The goal was not to build a magic detector that proves whether an unknown website was made with AI.

The goal was narrower and more practical:

When we compare attributed AI-built websites with human-led controls, which observable website patterns actually differ?

The answer was not ordinary visual polish.

It was structural residue.

By structural residue , we mean observable traces in the delivered website that may reflect how the site was assembled. These traces can appear in implementation, layout, metadata, copy, accessibility, browser behavior, mobile behavior, and repeated production patterns.

SiteBlob groups many of these traces into a secondary composite called S24 .

S24 is not a normal quality score. It is not just asking whether a site looks nice or loads correctly. It looks across several signal families, including HTML and CSS structure, embedded styling, visible-template cues, layout regularity, symmetry, stylometric patterns in copy, metadata behavior, trust-surface depth, accessibility baselines, runtime behavior, and other production-pattern signals.

That is why the screenshot was not enough.

A site can look modern and still carry residue from a builder, generator, export process, or rushed AI-assisted workflow.

What was scanned

The audit compared 131 successfully scanned websites attributed to five AI-builder families with 10 human-led modern open-source controls .

The unit of observation was the website.

SiteBlob scanned public pages and looked at what the browser could actually observe after the site was delivered.

That included:

public HTML

CSS structure

metadata

rendered DOM

runtime behavior

mobile layout behavior

accessibility and semantic baselines

site structure

visible template cues

copy and stylometric patterns

trust-surface signals

Attribution was established before scanning and independently from the measured outcomes.

That matters because the audit was not trying to guess authorship from the same signals it later measured. It was comparing already attributed groups to see which patterns were actually useful.

The visual story was weaker than expected

A lot of people expect AI-built sites to fail the eye test.

Sometimes they do.

But that was not the strongest pattern in this pilot audit.

Conventional website quality overlapped substantially between confirmed AI-built websites and controls. The AI-built group averaged 45.0 . The controls averaged 43.6 .

That is not the kind of gap that supports a simple story like "AI sites are obviously worse."

The common visual tropes were even less useful.

Visual template tropes appeared in 50.4 percent of the confirmed AI-built websites and 50.0 percent of the controls.

In plain English: the visible AI-looking layer was basically a coin flip in this cohort.

That does not mean visual clichés are fake. It means they are not enough.

Gradient backgrounds, rounded cards, generic hero sections, and polished SaaS layouts are now common across the web. Human designers use them. Templates use them. No-code tools use them. AI builders use them too.

So the visible look can be memorable without being very diagnostic.

Figure: In this pilot cohort, conventional quality scores overlapped substantially, while the secondary full S24 composite showed stronger observed separation.<br>The stronger tell was structural residue

The clearer difference appeared below the screenshot layer.

One recurring bundle stood...