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Generative artificial intelligence (generative AI or GenAI) is a category of AI that creates new content from inputs. That content can include text, images, video and music. The term describes a family of systems—not one product, a promise of human-like understanding, or a guarantee that every output is correct.
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.” OECD’s generative AI overview uses this concise definition. In practice, a person or another system supplies an input, and the generative system produces content in response.
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“Generative” refers to producing content. It does not, by itself, tell you how a system works internally, whether it understands its output as a person would, or how reliably it will perform a particular task.
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AI is the broader category; generative AI is one kind of AI system, distinguished by its ability to create content. Other AI applications may classify information, make predictions, or support decisions without generating new text, images, video or music.
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There is not one definition of AI that is interchangeable across every technical standard and policy. NIST’s AI glossary gathers definitions from different source documents and advises readers to consult the cited document for each definition’s context.
The OECD’s policy definition of an AI system was adopted in November 2023 and its Recommendation was further revised in May 2024. The revisions clarify that objectives may be explicit or implicit, inputs may come from humans or machines, outputs can include content, and some systems may continue evolving after deployment. This is a policy framing, not the only definition used in every technical or legal setting. The OECD explains the AI-system definition update here.
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What can generative AI do?
Generative AI can support a range of tasks, but examples of use are not endorsements for every tool or situation. The OECD identifies applications including:
- Writing and language: generating or revising text and supporting language-related work.
- Creative work: producing or assisting with content in creative industries and the arts.
- Code development: helping produce or work with software code.
- Education and search: assisting with learning activities or finding and presenting information.
- Healthcare and research: supporting work in healthcare and generating or optimizing molecular structures for pharmaceutical research.
These examples do not establish that every system is accurate, appropriate, or safe for a particular use—especially where mistakes can have serious consequences. The relevant question is how well a specific system performs on the task and under the conditions in which it will be used.
What does the label not tell you?
“Generative AI” identifies a broad class of systems; it does not specify a single capability level. A system that produces fluent text, for example, should not automatically be assumed to have dependable vision, reasoning, memory, physical manipulation, or general intelligence.
To describe capability more precisely, the OECD’s 2025 beta indicator framework 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 evidence that any one system performs all of these tasks reliably. See the OECD’s AI capabilities framework.
Limitations also depend on the kind of system and task. In a 2025 paper focused on agent systems, the OECD describes real-world multi-agent coordination as an area of ongoing research. It notes that current systems tend to rely on clearly defined objectives and can struggle with unpredictable scenarios and noisy inputs. The paper also discusses possible shortcomings in benchmark relevance and cost-effectiveness. Those observations concern agent systems; they should not be treated as a blanket description of every generative AI tool. Read the OECD’s discussion of multi-agent systems.
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How widely is generative AI being used?
In 2025, more than one-third of individuals across OECD countries used generative AI tools, according to the OECD. Its reported rates for population groups were 41.1% of employed people, 36.7% of unemployed people, and 12.5% of retired and other inactive people. These are OECD-country figures for 2025, not worldwide usage rates. The OECD’s 2025 report provides the figures and context.
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What are the main concerns?
Generative AI brings practical opportunities alongside questions that depend on how it is developed and used. The OECD identifies potential labor-market effects and ongoing copyright debates about training systems on copyrighted material. Copyright is a live policy and legal issue; it should not be treated as settled by a single universal ruling.
For organizations, the NIST Generative AI Profile, released on July 26, 2024, is a resource for identifying risks specific to generative AI and considering management actions aligned with organizational goals. It supports risk management; it does not guarantee a system’s safety or accuracy.
How should you evaluate a generative AI system?
Judge a system against the job you actually need it to do, rather than assuming the GenAI label implies broad competence. For a practical evaluation, check:
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- Task and output: Does it produce the kind of content you need?
- Task-specific evidence: Is there evidence of performance on that specific task, rather than a general claim about AI?
- Realistic conditions: How does it behave with incomplete, noisy or unpredictable inputs?
- Consequences of error: What review, safeguards or human oversight are appropriate if an output is wrong?
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