Generative AI models for text, images, and video share a common underlying idea: learn statistical patterns from enormous amounts of existing content well enough to generate new content that follows those patterns. The specific mechanism differs meaningfully across media, though, and those differences explain why each behaves the way it does.
Text generation: predicting the next piece, repeatedly
We've covered this in depth in how LLMs actually work, but the short version is this: text generation works by repeatedly predicting a plausible next token given everything generated so far, building up a full response through many small prediction steps.
Image generation: starting from noise and refining toward a result
Most current image generation models use a diffusion-based approach. The process starts with random visual noise and refines it step by step toward an image that matches the text description, guided by what the model learned about how real images matching similar descriptions look. It's a fundamentally different process from text's sequential token prediction: closer to sculpting a result out of noise than writing it out piece by piece.
Video generation: extending image generation across time
Video generation builds on similar techniques to image generation but adds the challenge of staying coherent across many frames. Characters, objects, and motion have to stay consistent and make physical sense from one frame to the next, rather than one static image looking plausible in isolation. This temporal consistency is what makes video generation meaningfully harder than single-image generation, not just "more images."
What all three share
- Training on enormous amounts of existing content, learning statistical patterns well enough to generate new content that follows them, rather than retrieving stored examples directly.
- No inherent understanding of truth or accuracy. The underlying mechanism is pattern-based generation, not fact verification, so generated content in any of these media can look plausible and still be wrong or inconsistent with reality.
- Sensitivity to how the request is specified. A clearer, more specific prompt generally produces a result closer to what's intended, across text, image, and video alike.
Why the differences matter practically
Text generation errors tend to be factual or logical: a wrong claim, a broken piece of reasoning. Image and video generation errors tend to be visual or physical: an anatomically implausible hand, an object that changes appearance between frames. A person would immediately recognize these as wrong, but they don't map onto "a false statement" the way a text hallucination does. Evaluating output quality requires different criteria for each medium, not one general standard for "is the AI output good?"
How we approach this
We evaluate and deploy text, image, and video generation with an understanding of each medium's specific generative mechanism and failure modes, rather than treating "generative AI" as one undifferentiated capability with a single quality standard across all three.