The AI-Native Web

For the last 25 years, the web has been optimized for one thing: human eyes.

HTML, CSS, and JavaScript were designed to tell a browser how to draw a page—where to place text, how to animate buttons, how to hide and reveal content. That model worked because the consumer of the web was always visual.

Today, a growing share of your "visitors" aren't humans at all. They're AI agents—systems like ChatGPT, Claude, Perplexity, and Apple Intelligence—that don't see layouts, colors, or interactions. They consume your site as raw text and structure, then reason over it.

And here's the problem:

When an LLM hits a modern React or Vite app, it isn't "browsing" your site. It's ingesting a massive stream of tokens that mix together:

• •Navigation menus
• •Tracking scripts
• •Layout containers
• •Hidden UI states
• •...and actual content

The web didn't change on purpose—it changed by accident.
AI showed up, and we pointed it at the wrong layer.

Modern websites are engineering marvels. They're fast, interactive, and visually rich.

They're also over-specified for machines.

A typical JavaScript-heavy page today is dominated by scaffolding:

• •Nested <div> trees
• •Utility CSS classes
• •Hydration logic
• •Analytics and experiment scripts
• •Dynamic UI state that only matters to users

For a browser, this is fine.
For an AI agent, it's overwhelming.

The result isn't that AI "fails"—it's that it makes thin, uncertain, or incorrect assumptions because the signal was buried in noise.

If the signal is hard to find, the answer will be weak.

And today, most websites are unintentionally making themselves hard to understand.

If HTML is the problem, what's the solution? The answer isn't a new standard—it's an old one: Markdown.

Markdown wasn't designed for AI. It was created in 2004 as a simple way to write formatted text. But it turns out to be almost perfectly suited for LLM consumption.

<div class="product-card">
  <div class="product-header">
    <h2 class="text-xl font-bold">
      Widget Pro
    </h2>
  </div>
  <div class="product-body">
    <p class="description">
      The best widget for teams.
    </p>
    <ul class="features-list">
      <li>Fast</li>
      <li>Secure</li>
    </ul>
  </div>
</div>

## Widget Pro

The best widget for teams.

- Fast
- Secure

When you serve Markdown to an AI agent, you're reducing the inference burden. The model can focus on understanding your content instead of parsing your layout—giving it a probabilistic advantage in reasoning over your material.

The industry has attempted to solve the AI visibility problem with several approaches. Each has significant limitations.

None of these solutions provide what AI agents actually need: clean, dynamic Markdown served at the edge, in real-time, for every page.

The web has always had a "presentation layer"—CSS, layouts, animations—that translates raw content into something humans can see and interact with.

What's been missing is a machine presentation layer: a system that translates your content into something AI agents can read and reason over.

This is the missing layer. Not a replacement for your site, but a parallel representation optimized for machine consumption.

For two decades, SEO meant one thing: rank higher in search results. Get on page one of Google, and traffic flows.

That model is fragmenting.

Today, users increasingly get answers without clicking through to websites. They ask ChatGPT. They use Perplexity. They see Google AI Overviews. The answer appears directly—and only the cited sources get credit.

AEO isn't a replacement for SEO—it's an extension. You still need traditional search visibility. But increasingly, the brands that get cited in AI answers are the ones that make their content easy for AI to understand.

Markdown is the language of AEO.

Imagine a user asks ChatGPT: "What's the best project management tool for remote teams?"

The AI will fetch information from multiple sources, process it, and synthesize an answer. Which brands get mentioned depends on:

1. 1
   Retrievability: Can the AI find and read your content?
2. 2
   Clarity: Is your value proposition clear and unambiguous?
3. 3
   Authority: Does your content signal expertise and trustworthiness?
4. 4
   Relevance: Does your content directly answer the query?

This isn't hypothetical. AI systems make these decisions billions of times a day. The brands that make themselves easy to understand are the ones that win citations—and the trust that comes with them.

The hardest part of most technical improvements is implementation. With DataJelly, there's nothing to implement.

As your site changes, DataJelly automatically updates the Markdown snapshots. New pages, updated content, removed sections—everything stays in sync without manual intervention.

We're at the beginning of a fundamental shift in how the web works.

Today, AI agents are primarily used for answering questions. But their capabilities are expanding rapidly—they can already browse, compare, and in some cases act on behalf of users.

In this environment, your website isn't just a storefront for humans—it's a machine-readable knowledge surface. The sites that are easy to understand will be the ones that get recommended, compared favorably, and chosen.

Markdown is the first step toward making your site AI-native. It's how you become a first-class citizen of the evolving web.

The best way to understand the problem is to see it firsthand. Our Visibility Test shows you exactly what AI agents receive when they fetch your pages.