How AI Image and Video Generation Works
Type “a lighthouse at dawn, painted in watercolour” and a few seconds later an image appears that no camera captured and no painter painted. AI image generators can feel like magic, but the mechanism underneath them is understandable — and once you understand it, both their strengths and their strange mistakes make a lot more sense. Video generators use the same core idea, with one extra, very hard problem bolted on.
The core trick: learning from noise
Most modern image generators are built on a process called diffusion. It has two halves.
During training, the system studies millions of images paired with text descriptions. It practises a peculiar skill: take a clean image, gradually add random visual noise — like a television losing its signal — until only static remains, and then learn to reverse the process, removing a little noise at a time to recover the picture.
During generation, that skill runs in the direction you care about. The system starts from pure static, and, guided by your text prompt, removes noise step by step. Each step nudges the static toward something that matches your description a little better, until a coherent image emerges. You are not watching a picture being looked up; you are watching one being sculpted out of noise.
Where the prompt comes in
Your words matter because the model learned, during training, which visual patterns go with which words. That is why prompts behave the way they do:
- Specific beats vague. “A lighthouse at dawn” gives the model less to decide than “a lighthouse,” so you get fewer surprises.
- Style words work. Terms like “watercolour”, “photograph”, or “studio lighting” steer the result because the training data paired those words with recognisable looks.
- Negative prompts exist for a reason. Telling the model what to avoid works because generation is guidance, not lookup — you are steering the sculpting, and you can steer away from things as well as toward them.
Why the mistakes look the way they do
Diffusion models learn what images look like, not how the world works. That is why the classic errors are visual rather than logical: extra fingers, garbled text on signs, jewellery that melts into skin, objects that change design between two pictures of the “same” scene. The model knows text on a sign should look like lettering; it never learned to read. Newer models have steadily improved at exactly these weak points, but the pattern — plausible appearance first, underlying correctness second — is built into the approach.
Video: the same trick, plus time
A video generator faces everything above, plus one brutal extra requirement: consistency across frames. It is not enough for each frame to look good. A face must stay the same face, a jacket must keep its colour, and objects must move in ways that respect basic physics — across dozens or hundreds of frames that are all being generated, not filmed.
That is why AI video arrived after AI images, why generated clips were short for a long time, and why the tell-tale signs of AI video are temporal: details that shimmer, morph, or quietly change identity mid-shot. Progress in video models is, in large part, progress in teaching the noise-sculpting process to keep its story straight over time.
What this means when you judge a new generator
When a new image or video tool launches, you now know what to test. For images: hands, text, and whether the same subject stays consistent across several generations. For video: watch the edges of moving objects, backgrounds behind the main subject, and anything that should stay rigid. And remember what the technology is: not a camera, not a database, but a very sophisticated way of carving pictures out of static — brilliant at appearance, still learning about reality.