"AI" gets used to describe everything from a spam filter to a chatbot to the tool that made that viral video in your feed. Here's the one specific thing "generative AI" means, how these models actually learn, and where the tools on this site fit.
Most AI most people interact with day to day is discriminative, not generative — it looks at something that already exists and makes a judgment about it: is this email spam, does this photo contain a face, should this video be recommended to you next. Generative AI is a different job: instead of judging existing content, it produces new content — a paragraph of text, an image, a video clip, a voice recording — from a prompt or a starting input. ChatGPT writing an email, Midjourney drawing a picture, and Runway or Veo generating a video clip are all generative AI. A spam filter isn't, even though it's still "AI" in the broader sense.
Training a generative model means showing it an enormous number of real examples of whatever it's meant to produce — billions of sentences for a text model, huge datasets of images or video for an image or video model — and adjusting the model's internal parameters (there can be billions of them) so it gets progressively better at predicting patterns within that data: which word plausibly comes next, which pixel values plausibly belong next to which other pixel values. Critically, the model isn't storing and replaying specific training examples the way a search engine returns a stored web page. It's learning statistical patterns general enough that it can apply them to a brand-new prompt it never encountered during training — which is both the source of its usefulness (it can handle genuinely novel requests) and the source of its weirdest failures (it's always producing its best statistical guess, even when that guess is wrong).
A generative model isn't looking anything up or checking a fact — it's producing the most statistically plausible continuation of the prompt it was given, based entirely on patterns learned during training. For text models, this shows up as confidently stated incorrect information, often called "hallucination." For image and video models, it shows up as the same handful of recurring visual glitches across completely unrelated content: extra or malformed fingers, jewelry and small text that warp or come out as gibberish, fine details that don't hold up under scrutiny. These aren't random bugs specific to one tool — they're artifacts of what's structurally hard for this kind of model to get exactly right: fine, precise, rule-governed detail (an exact number of fingers, exact letterforms) is a much harder statistical target to hit consistently than a broad, forgiving one (an overall pose, a general color palette).
AI video generators (Runway, Pika, Kling, Seedance, Veo, and others) are generative AI applied to video specifically: a text description or a reference image goes in, and a short new video clip — frames the model constructed, not footage that was filmed — comes out. It's the same underlying idea as a text or image generator, just with the added complexity that every frame has to stay visually consistent with the frames around it. See our dedicated explainer on how AI video generation actually works for the mechanics, or our beginner-friendly tour of the actual tools if you just want to know what's behind the videos flooding your feed.
This is worth being precise about, because the two get conflated: AI background removal (what RemoveGifBG does) is built on a closely related family of neural-network techniques, but it's not generative in the same sense. Its job is classification, not creation — for every pixel in an image or video frame, decide whether it's part of the subject or part of the background, then keep one and discard the other. It's not inventing new pixels the way an image generator invents an entire scene from a text prompt; it's making a keep-or-discard decision about pixels that already exist. See how AI background removal actually works for the specifics, and why it's a much harder problem on video and GIFs than on a single photo.
The short version
See How AI Video Generation Works
Related: How AI background removal works · How AI videos are made (beginner's guide) · All guides
It's a category of AI models trained to produce new content — text, images, video, audio, code — rather than just classify or analyze existing content. A spam filter ("is this email spam or not") is AI but not generative. A tool that writes an email, draws an image, or generates a video from a description is generative: the output is new material the model constructs, not a label applied to something that already existed.
Training exposes the model to an enormous amount of existing example content (text, images, or video, depending on what it's meant to generate) and adjusts millions or billions of internal parameters so the model gets progressively better at predicting patterns within that data — what word plausibly follows other words, what pixel values plausibly follow other pixel values. It's not memorizing and replaying specific examples; it's learning statistical patterns general enough to apply to entirely new prompts it never saw during training.
Because the model is generating the statistically most plausible next output, not looking anything up or verifying a fact. For text, that shows up as confidently stated wrong information ("hallucination"). For images and video, it shows up as the same handful of recurring visual errors — extra fingers, warped text, jewelry that doesn't hold its shape — because those are exactly the kinds of fine, structurally precise detail that's hardest to get statistically consistent across a whole generated image or clip.
Not exactly, though it's a close cousin. AI background removal uses the same family of neural-network techniques (trained on huge amounts of example data, making pixel-level predictions) but its job is classification, not generation: for each pixel, is it foreground or background? It's not creating new pixels the way an image generator does — it's deciding what to keep from an image that already exists.