# Generative AI

Generative AI is a subfield of artificial intelligence that uses generative models to create new content such as text, images, audio, video, or code, based on patterns learned from training 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), [Gemini](https://www.wikiprompt.org/wiki/gemini), [Microsoft Copilot](https://www.wikiprompt.org/wiki/microsoft-copilot), [DeepSeek](https://www.wikiprompt.org/wiki/deepseek), [Doubao](https://www.wikiprompt.org/wiki/doubao), [Grok](https://www.wikiprompt.org/wiki/grok), [Kimi](https://www.wikiprompt.org/wiki/kimi) and [Qwen](https://www.wikiprompt.org/wiki/qwen). [Text-to-image](https://www.wikiprompt.org/wiki/text-to-image) systems such as [DALL-E](https://www.wikiprompt.org/wiki/dall-e), [Firefly](https://www.wikiprompt.org/wiki/firefly), [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 LTX. 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.

## Recent developments

By mid-2025, companies were increasingly abandoning generative AI pilot projects due to difficulties with integration, data quality and unmet returns, leading analysts at Gartner and The Economist to characterize the period as entering the Gartner hype cycle's "trough of disillusionment" phase. In an October 2025 survey by the Federal Reserve, one in four U.S. workers reported using generative AI at work during the previous month. A 2024 survey by the Just So Soul social media app reported that 18% of respondents born after 2000 used generative AI "almost every day", and that over 60% of respondents like or love AI-generated content (AIGC), while less than 3% dislike or hate it. In a 2024 survey by Ipsos, Asia-Pacific countries were significantly more optimistic than Western societies about generative AI, with 68% of Asia-Pacific respondents believing AI had a positive impact on the world, compared to 57% globally. A UN report indicated that Chinese entities filed over 38,000 generative AI patents from 2014 to 2023, more than any other country.

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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: 2026-10-10T19:14:46.079408+00:00