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Prakash Pawar
Prakash Pawar

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Not Everything You See Is an AI

#ai #llm #website #product

How many “AI-powered” websites are just well-engineered scrapers

Over the last few years, “AI-powered” has quietly become the most overused label in tech.

If a product:

  • works on complex websites,
  • handles JavaScript-heavy pages,
  • or produces clean, structured output,

it’s very often described as AI.

But here’s a reality check that many engineers already know:

Most of these products are not powered by AI at all.

They are powered by code.
Good, old-fashioned, well-engineered code.

This article explains why so many products look like AI, how they actually work, and how the same behavior can be replicated without using any machine learning.

The Illusion of Intelligence

Let’s start with a simple experiment.

If you run:

curl https://music.youtube.com
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You’ll get a mostly empty HTML shell.

No playlists.
No songs.
No meaningful content.

So when a website claims it can “read YouTube Music”, “understand Instagram pages”, or “extract content from any site”, the natural assumption is:

“There must be AI involved.”

In most cases, there isn’t.

Why Traditional Scraping Appears to Fail

Modern websites are fundamentally different from older server-rendered pages.

Most of them are:

  • Single Page Applications (React / Vue / Angular)
  • Hydrated entirely on the client
  • Loaded via background API calls
  • Rendered progressively as the user scrolls

Tools like curl or requests fail because they:

  • fetch only source HTML
  • do not execute JavaScript
  • do not trigger lazy loading

A real browser, however, does all of that automatically.

What’s Actually Happening Behind the Scenes

Many products branded as “AI website readers” follow a pipeline like this:

Incoming URL
→ Headless browser (Chromium)
→ Execute JavaScript
→ Wait for network to settle
→ Scroll the page
→ Capture rendered DOM
→ Remove UI noise (menus, scripts, ads)
→ Convert HTML into Markdown / text
→ Return response
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Every step here is deterministic.

There is:

  • no model training
  • no prediction
  • no reasoning
  • no inference

Just a browser executing code exactly the way it was designed to.

A Common Pattern You’ll See in “AI” Products

You may have noticed products with names like:

“AI Web Reader”
“AI Content Extractor”
“AI Website Analyzer”

Let’s take a hypothetical example — “SmartReader AI”.

From the outside, it:

  • accepts a URL
  • works on complex websites
  • returns clean Markdown or JSON

Under the hood, it:

  • launches a headless browser
  • scrolls the page
  • extracts the DOM
  • applies deterministic cleanup rules

The AI part, if present at all, might only be used later—for summarization or formatting.

The core functionality works perfectly without AI.

Why This Feels Like AI to Users

This illusion comes from three factors:

1. JavaScript execution

Once JavaScript runs, all backend APIs have already returned data.
The browser simply assembles it into the DOM.

2. Content normalization

Navigation bars, ads, and UI chrome are removed, leaving only the “useful” content.

3. Clean output formats

Markdown and structured text feel intentional and intelligent.

But none of these require machine learning.

Rebuilding the Same System Using Only Scrapers

You can replicate the same behavior using standard tools.

Step 1: Render the page

Use a headless browser like Playwright or Puppeteer to load the site exactly like a real user.

This unlocks:

  • dynamic data
  • lazy-loaded sections
  • client-side API responses

Step 2: Scroll programmatically

Many pages load content only on scroll.

A simple scroll-and-wait loop is enough.

Step 3: Capture the DOM

Once rendering stabilizes, extract the final HTML.

At this point, everything visible to the user already exists in the DOM.

Step 4: Extract main content

Use deterministic tools such as:

  • Mozilla Readability
  • DOM heuristics
  • Tag-based filtering

This removes:

  • headers
  • sidebars
  • menus
  • scripts

Step 5: Convert formats

Transform the cleaned HTML into:

  • Markdown
  • JSON
  • plain text

The output looks “smart” because it’s curated—not because it’s intelligent.

Why AI Is Often Unnecessary at This Stage

Scraping and rendering are deterministic problems.

AI systems are probabilistic.

If the data:

  • already exists in the DOM
  • has a consistent structure
  • is visually rendered

then introducing AI usually adds:

  • cost
  • latency
  • operational complexity
  • uncertainty

For extraction tasks, engineering is usually the better tool.

Where AI Actually Makes Sense

AI becomes valuable after the data is extracted, not before.

Good use cases include:

  • summarizing long articles
  • clustering related content
  • semantic search
  • question answering across documents

In short:

AI helps you understand content — not fetch it.

The Engineering Reality

Many so-called “AI-powered” products are better described as:

Browser automation platforms with a clean UX.

That’s not a criticism.

It’s a reminder that:

  • not everything impressive is AI
  • fundamentals still matter
  • browsers are incredibly powerful execution engines

Final Thoughts

The next time you see a product that:

  • works on JavaScript-heavy websites
  • extracts clean content
  • feels magically intelligent

ask a simple question:

Is this AI — or just a browser running code really well?

Often, the answer is the latter.

And sometimes, the smartest systems are the ones that don’t pretend to be intelligent at all.

If you have any questions or want to discuss this further, feel free to leave a comment or
Tweet me.
Thanks for reading.

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