Watch AI Model Vaporize into the Clouds

What you’re watching isn’t random — it’s a real phenomenon called **model collapse** (or generative drift).

AI image models don’t actually “see” like we do. They learn patterns from massive datasets of real images — things like faces, textures, lighting, proportions. When you generate an image, the model is basically sampling from a probability map of what *should* look real.

But here’s where it gets interesting:

When you take an AI-generated image and feed it back into the model again (and again), you’re no longer sampling from real-world data — you’re sampling from **its own imperfect output**.

Each pass introduces tiny errors:

* Slight distortions in facial symmetry

* Misinterpreted textures

* Blurred or exaggerated features

Individually, these errors are small. But over multiple generations, they **compound**.

This is called **error accumulation**.

At a basic level, it’s like making a copy of a copy of a copy — eventually, the quality breaks down.

At a deeper level, it’s a **distribution shift**:

The model was trained on real images, but now it’s being forced to interpret synthetic ones. It starts drifting away from reality because it’s operating outside its original data distribution.

The result?

* Faces become warped

* Features get exaggerated

* The image enters the “uncanny valley”

* Eventually, it turns into complete visual chaos

That’s what you’re seeing here — not randomness, but **structured degradation over time**.

In AI research, this is part of a broader concern: if models are increasingly trained on AI-generated data, they can slowly lose fidelity to reality altogether.

So what looks like a glitch…

is actually a glimpse into how AI systems break when they start feeding on themselves.