AI Broke the Internet—And Now It’s Feeding on the Mess

The internet’s never been messier, and it’s AI’s fault. According to a sharp analysis from The Atlantic, the web is drowning in sloppy, repetitive, and often useless content—and ironically, AI is both the creator and the consumer of this mess.

Let’s break down what’s going wrong, why it’s accelerating, and what it means for the future of information online.

The Jankification of the Web

Here’s what’s happening:

• AI-generated content floods the internet: Blogspam, affiliate reviews, SEO bait, “summary” pages—all spun up in seconds by bots.
• Other AIs read that garbage as source material: Language models are now trained and retrained on content that was itself created by AI.
• The quality spirals: Each generation of AI eats a more degraded version of the web than the one before. It’s like making photocopies of photocopies—until the text becomes noise.

This cycle is called model collapse—where AI systems trained on AI content lose originality, truth, and utility.

How AI Is Poisoning Its Own Well

• Repetitive Answers
  Language models give predictable, dull responses—often just regurgitating widely circulated content.
• Hallucinated Sources
  When AI reads from other AI-generated junk, it increasingly invents facts or distorts meaning.
• Broken Links and Dead Ends
  Users end up clicking AI-generated links leading to AI-written nonsense—sometimes with fake citations and unreadable grammar.

Why the Internet Is Becoming “Janky”

• SEO Arms Race
  Everyone is racing to rank high on Google. That means more content, more keywords, more fluff—and less substance. AI makes it faster and cheaper to flood the game.
• Decline of Original Content
  Journalism, expert blogs, and human storytelling struggle to compete. Their stuff gets scraped, summarized, and buried.
• AI Models Cannibalize the Web
  The same AI-generated garbage is scraped and used to train the next AI. The result? A degraded loop that amplifies nonsense and filters out nuance.

What Can Be Done?

1. Watermark AI Content
   Flag AI-written pages so future models can avoid using them as training data.
2. Promote Human-Created Work
   Search engines and platforms must prioritize verified, original sources—especially in science, news, and education.
3. Redesign Training Pipelines
   Model makers need better filters to detect and exclude low-quality, synthetic web content from training datasets.
4. Transparency from AI Developers
   Companies should disclose what data sources they train on—and when those sources include previous AI generations.

3 FAQs

1. What’s “model collapse” and why does it matter?
Model collapse happens when AI systems are trained mostly on AI-generated data. Over time, this creates echo chambers of low-quality output—losing originality, creativity, and factual accuracy.

2. How can I tell if content was written by AI?
Look for repetitive phrasing, bland language, unnatural transitions, or missing citations. AI-written content often sounds “off”—vaguely helpful, but hollow.

3. Will the internet ever go back to being human-driven?
It’s unlikely to go back, but it can be rebalanced. If we reward human insight and verify sources, we can still build a web that’s both AI-enhanced and human-led.

We wanted smarter tools. We got smarter spam. Unless we rein in how AI uses and recycles the web, we’re heading for an internet that sounds confident—but says nothing.

Sources The Atlantic