Not CGI. Not a filter. Not footage of anything that was ever filmed. Here's what's actually happening under the hood when you type a prompt and get a video back.
Most current AI video generators are built on diffusion models. Counterintuitively, generation doesn't start from nothing and build up — it starts from pure random visual noise, the video equivalent of TV static, spread across every frame of the clip. The model then runs a series of refinement steps, and at each step, it predicts how to nudge that noisy mess slightly closer to a coherent video that matches the text prompt or reference image it was given. Repeat that refinement dozens of times — done extremely fast on specialized AI hardware — and the noise gradually resolves into a finished clip. It's the same core technique behind AI image generators like Midjourney or Gemini's image model, just extended across time as well as across a single frame's width and height.
The model doing that noise-refinement isn't guessing randomly — it learned, during training, from an enormous amount of real video, what a photorealistic movement, a specific object, or a described scene tends to look like. That training process adjusts billions of internal parameters so the model's noise-refinement predictions get progressively better at matching what real video (and the text descriptions paired with it) actually looks like. The result is a model that can produce a plausible video for a prompt it never saw during training, by combining and recombining the statistical patterns it learned from everything it did see.
Every major tool supports two starting points, and they work differently under the hood:
Keeping every frame of a clip visually consistent with every other frame — the same character, the same lighting, no random flickering or drifting details — gets computationally harder the longer the clip gets, and small errors compound across a longer sequence the same way a small measurement error compounds across a longer calculation. That's the practical reason most tools cap a single generation at somewhere between 4 and 10 seconds: it's close to the limit of what current models can keep coherent in one pass at reasonable speed and cost. A newer generation of models pushed past that in 2026 (one released that August generates up to 30 seconds in a single pass), but 4-10 seconds remains the norm across most major tools, and a longer "video" you see online is usually several separate generations edited together, not one continuous clip.
The recurring visual errors — an extra finger, jewelry that warps mid-motion, signage that renders as gibberish — aren't random bugs specific to one company's tool. They're a structural consequence of how these models work: broad, forgiving visual details (an overall pose, a general color palette, a big obvious motion like taking a bite of food) are statistically easy to get consistently right, because there's a wide range of "close enough" outputs that all look correct. Fine, rule-governed detail (an exact number of fingers in a specific pose, exact letterforms, a chain link that has to maintain physical structure as it swings) has almost no margin for statistical approximation — being close isn't the same as being right, and the model has no built-in concept of "count fingers and verify there are five." That's why the same categories of glitch show up across completely unrelated prompts and tools.
One thing this process never produces
Every diffusion step is refining the full frame — subject and background together, as one image. There's no point in that process where the model separates "the character" from "everything behind the character," so the finished clip has no built-in transparent layer or alpha channel, no matter which tool generated it. Pulling the subject out afterward is a separate step — see how AI background removal actually works, a genuinely different kind of model doing a genuinely different job.
See the Tools That Actually Do This
Related: What is generative AI? · How AI background removal works · AI video background remover
Most current AI video generators use diffusion models: the process starts with pure visual noise (like TV static, but across a whole short clip) and repeatedly refines it in small steps, each step nudging the noise slightly closer to a coherent image sequence that matches the text prompt or reference image. After enough refinement steps — often dozens, done very fast on specialized hardware — the noise resolves into a finished video clip. It's the same core technique used by AI image generators, extended across time as well as space.
Text-to-video starts the diffusion process guided only by a written description — the model has to invent every visual detail from the prompt alone. Image-to-video starts from a real reference image and generates motion and change around it, which is why it's the workflow behind most "recurring character" content: it's far easier to keep a character looking consistent across separate generations when each one starts from the same fixed reference image, rather than re-describing the character from scratch every time.
Every additional frame the model has to keep consistent with every other frame multiplies the computational cost and the chance of visible drift or errors accumulating across the clip. Most tools cap a single generation at somewhere between 4 and 10 seconds for exactly this reason. A handful of newer models can generate longer single clips (up to 30 seconds, in at least one 2026 release), but 4-10 seconds remains the norm across most major tools.
These are exactly the kinds of fine, rule-governed detail that's hardest for a statistical, pattern-based model to get precisely right and keep consistent frame to frame. Broad, forgiving details (an overall silhouette, a general color palette, big exaggerated motion) render far more reliably than small, precise, structurally exacting ones, which is why the same categories of visual glitch show up across completely unrelated tools and prompts.