How AI Generates Video

Now you understand what AI video can and can't do. But how does it actually work? What happens in the computer when you send a prompt?

The Flipbook Metaphor

Remember flipbooks? You take a notepad, draw slightly different-looking pictures on each page, and when you flip through it quickly, it looks like a movie.

That's how video works — digitally. A video isn't a continuous stream. It's individual images (frames) shown so fast in succession that your eye perceives them as motion. In cinema, that's 24 frames per second. Your brain connects these images into a story.

AI video works very similarly. The big difference is: the AI doesn't draw each image by hand. It guesses. It sees the starting state (frame 1) and the ending state (frame 30), and then it fills in the frames in between (2-29) — based on what looks natural and what movement patterns it has learned from millions of real videos.

Three Steps: From Prompt to Video

When you send a video prompt, the AI goes through three main steps:

Step 1: Prompt Understanding

The AI "reads" your text and tries to understand what you want to see. This is not trivial. "A wave crashing against rocks" is immediately clear to humans, but for an AI it's a puzzle of words that it must convert into numerical vectors.

These vectors are like mathematical descriptions: water movement, force, foam, light conditions. The AI has learned to translate certain word combinations into physical scene descriptions.

Step 2: Frame Prediction and Diffusion

This is the magical part. The AI first creates a "sketch" — a rough idea of the first and last frames of your video. Then it "thinks" the frames in between by following patterns it has learned.

This works through something called "diffusion." Imagine you drop food coloring into a glass of water. The color gradually spreads. Diffusion is how the color moves. AI video works similarly: it starts with noise and "denoise" it step by step into a coherent video.

Step 3: Consistency and Optimization

After the AI generates all frames, it checks (using machine learning) whether the frames fit together. Does the object in frame 5 look similar to frame 6? Is the lighting consistent? Is the movement smooth?

If not, the AI "adjusts." It's an iterative process — it makes multiple passes until the video is good enough.

Why Temporal Coherence Is So Hard

This is the heart of the problem. With single images (like in K03), it's simple: you generate an image, it's self-consistent. Done.

With video, everything must be consistent over time. That's exponentially harder. Think of a point on the wave: in frame 1 it's here, in frame 2 it must be a bit further, in frame 3 even further. If the AI gets this wrong — if the point jumps instead of gliding — it immediately looks unnatural.

The AI only has statistical models, no real physics simulation. It "guesses" where the point should be, based on millions of training examples. Sometimes it gets it wrong.

The Three Roles of AI Video: Multiplier, Enabler, Boundaries

Back to the concept from K01 and K02: every AI medium has three roles.

The Multiplier Role

Video generators are multipliers for efficiency and creativity. You can create videos in minutes that used to take days. You can make ten versions instead of one.

That means: more experimenting. More iterations. More chances to find something good.

The Enabler Role

Video generators enable people without equipment, without a camera, without lighting to create videos. This democratizes a profession that was once exclusive.

A designer in a small town can now create marketing videos that only big studios could before. That's empowerment.

The Boundaries Role

But there are clear limits. If you want to shoot a realistic film with humans that's physically perfect and shows subtle emotional nuance — you still need real footage. The AI can't (yet) do that.

And it's important to understand: today's limits are not tomorrow's limits. But they are real now. A good video creator with AI knows these limits and works within them.

Temporal Attention: The Secret of Movement

There's a concept in AI video called "temporal attention." It's the AI's ability to pay attention to temporal structure.

When the AI generates frame 5, it doesn't just look at frame 4 and frame 6. It looks several frames ahead and behind — to ensure the movement is consistent. It's like a human who doesn't just see the current moment, but also "feels" 1-2 seconds into the future and past.

But this attention is limited. An AI can maintain 10-frame consistency, but not 100-frame consistency. That's a current limit of the technology.

Cross-Link: Comparison with K01 (Text), K02 (Music), and K03 (Images)

Remember the theory lessons from the other clusters:

• K01-L03 (Text Theory): Text is discrete and structured. The AI can predict word by word because language has strong patterns. Long consistency is easy.
• K02-L03 (Music Theory): Music has harmony and meter rules. The AI can follow these, but subtle emotional variation is hard. Medium consistency is possible.
• K03-L03 (Image Theory): Images are static. No temporal requirements. The AI can generate very good images.
• K04-L03 (Video Theory): Video combines images + time. Time makes it exponentially harder. The AI struggles with temporal coherence.

The more dimensions (text has word order, music has time + meter, video has time + space + physics), the harder it gets for AI.

A Thought to Take Away

When you understand how AI video works — that it essentially interpolates millions of images while following statistical patterns — you also understand why it's sometimes wonderful and sometimes weird. It's not chance, not magic. It's mathematics and statistics.

And once you understand the math, you know how to work with it. You'll write better prompts. You'll know which scenes are likely to work and which won't. That's the skill of a professional.