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Generative artificial intelligence (generative AI or GenAI) is a category of AI that creates new content from inputs. The OECD gives text, images, videos and music as examples. The term describes a broad family of systems—not one product, a guarantee of human-like understanding, or a promise that every output is reliable.
What does generative AI mean?
The OECD defines generative AI as “a category of AI that can create new content such as text, images, videos and music.” Read the OECD’s definition and overview.
In practical terms, a person or another system provides an input, and the AI produces content in response. The output might be a written passage, an image, a video or music. The definition is about the system’s ability to generate content; it does not, by itself, tell you how the system works internally, how accurate it is, or whether it is suitable for a particular use.
How is generative AI different from AI?
AI is the broader category; generative AI is one kind of AI. AI systems can be designed for different objectives and tasks, and producing new content is not a defining feature of every AI system.
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Definitions also vary by context. The OECD’s policy definition of an AI system describes a system that infers from inputs how to generate outputs—such as predictions, content, recommendations or decisions—that can influence physical or virtual environments. The OECD adopted a revised definition in November 2023 and further revised its Recommendation in May 2024. Its updates clarified that objectives may be explicit or implicit, inputs can come from humans or machines, and some systems may continue evolving after deployment. This is a policy definition, not a single definition that governs every technical or legal setting. See the OECD’s explanation of its AI-system definition.
NIST’s AI glossary brings together definitions drawn from different source documents and advises readers to check the cited document for context. That is another reason to pay attention to how a definition is being used rather than assume “AI” has one interchangeable meaning in every standard or policy. Consult the NIST glossary entry for artificial intelligence.
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What can generative AI do?
Generative AI can produce different kinds of content, and systems may be built or used for particular tasks. The OECD identifies examples including:
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- Writing or assisting with code development
- Supporting creative work, including in the arts and creative industries
- Educational applications
- Healthcare applications
- Internet search
- Generating or optimizing molecular structures for pharmaceutical research
These are examples of uses, not endorsements of every tool for every purpose. A system’s capabilities and reliability depend on the task and the evidence available for that task.
What the generative AI label does not tell you
The label does not mean that a system can do every kind of work, understands content as a person does, or will produce dependable results. A useful evaluation is specific to the task: consider the intended output, evidence of performance on that task, how the system behaves with noisy or unpredictable inputs, and whether appropriate safeguards are in place.
The OECD’s 2025 beta capability-indicator framework illustrates why task-level distinctions matter. It assesses nine areas—language; social interaction; problem solving; creativity; metacognition and critical thinking; knowledge, learning and memory; vision; manipulation; and robotic intelligence—across five levels. It is a framework for describing capabilities, not proof that any one system performs all of these tasks reliably or has general, human-like intelligence. Explore the OECD’s AI capability indicators.
For a narrower example, an OECD paper on agent systems says real-world multi-agent coordination remains an area of ongoing research. It notes that current systems tend to rely on clearly defined objectives and can have limitations with unpredictable scenarios and noisy inputs. Those observations concern agent systems; they should not be generalized to every generative AI tool. Read the OECD paper on AI agents.
Where is generative AI being used?
Use varies across people and regions. In 2025, more than one-third of individuals across OECD countries used generative AI tools. The OECD reported use by 41.1% of employed people, 36.7% of unemployed people, and 12.5% of retired and other inactive people across those countries. These are OECD figures for 2025, not worldwide estimates. See the OECD’s figures on AI use.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What risks and safeguards matter?
Generative AI raises questions about labor-market effects and the use of copyrighted material in training—an ongoing policy and legal debate, not a settled universal ruling. The practical risks also depend on what a system is used for and who may be affected by its outputs.
For organizations, NIST’s Generative AI Profile is a risk-management resource released on July 26, 2024. It helps organizations identify risks specific to generative AI and consider management actions aligned with their goals. It is guidance for risk management, not a guarantee that a system will be safe or accurate. Read about NIST’s AI Risk Management Framework and Generative AI Profile.
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