# Generative AI

Generative AI refers to machine learning systems that create new content such as text, images, audio, video or code, rather than only classifying or analyzing existing data.

Generative AI is the branch of [artificial intelligence](https://www.wikiprompt.org/wiki/artificial-intelligence) concerned with producing new content: text, images, audio, video, code or 3D assets. Where a classifier maps an input to a label, a generative model learns the distribution of its training data well enough to sample new examples from it. The term became mainstream during the [AI boom](https://www.wikiprompt.org/wiki/ai-boom) that followed the [launch of ChatGPT](https://www.wikiprompt.org/wiki/chatgpt-launch) in November 2022, and by 2024 it was shorthand for an entire product category and investment wave.

## Technical foundations

Modern generative AI rests on a small set of model families. [Autoregressive models](https://www.wikiprompt.org/wiki/autoregressive-model) such as [large language models](https://www.wikiprompt.org/wiki/large-language-model) generate sequences one [token](https://www.wikiprompt.org/wiki/tokenization) at a time and power chat assistants and code generation. [Diffusion models](https://www.wikiprompt.org/wiki/diffusion-model) dominate image and video synthesis, learning to reverse a gradual noising process. Earlier approaches include the [generative adversarial network](https://www.wikiprompt.org/wiki/generative-adversarial-network) (2014), which drove the first wave of photorealistic synthesis and [deepfakes](https://www.wikiprompt.org/wiki/deepfake), and the [variational autoencoder](https://www.wikiprompt.org/wiki/autoencoder). Nearly all current systems are built on the [transformer](https://www.wikiprompt.org/wiki/transformer) architecture and trained with [self-supervised learning](https://www.wikiprompt.org/wiki/self-supervised-learning) over web-scale [training data](https://www.wikiprompt.org/wiki/training-data).

## Modalities

Text generation is the most widely deployed form, through assistants like [ChatGPT](https://www.wikiprompt.org/wiki/chatgpt), [Claude](https://www.wikiprompt.org/wiki/claude) and [Gemini](https://www.wikiprompt.org/wiki/gemini). [Text-to-image](https://www.wikiprompt.org/wiki/text-to-image) systems such as [Stable Diffusion](https://www.wikiprompt.org/wiki/stable-diffusion) and [Midjourney](https://www.wikiprompt.org/wiki/midjourney) brought generation to visual art in 2022; [text-to-video](https://www.wikiprompt.org/wiki/text-to-video) followed from 2024 with Sora, Veo and Kling. Other active modalities include [speech synthesis](https://www.wikiprompt.org/wiki/text-to-speech), [music generation](https://www.wikiprompt.org/wiki/music-generation) and [3D generation](https://www.wikiprompt.org/wiki/text-to-3d).

## Impact and debate

Generative AI reshaped software development through [AI coding assistants](https://www.wikiprompt.org/wiki/ai-coding-assistant), marketing, design and education, while raising disputes over [copyright](https://www.wikiprompt.org/wiki/ai-copyright) in training data, job displacement in creative industries, misinformation and low-quality synthetic content at scale, sometimes called [AI slop](https://www.wikiprompt.org/wiki/ai-slop). Critics such as the authors of the [stochastic parrot](https://www.wikiprompt.org/wiki/stochastic-parrot) paper question whether fluent generation reflects understanding. Proponents argue the technology augments rather than replaces human creativity, a debate that remains unresolved.

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Source: https://www.wikiprompt.org/wiki/generative-ai
License: CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/)
Last updated: 2026-09-02T22:00:26.424529+00:00