Definition
Artificial general intelligence is a contested term for AI with broad, transferable competence across many cognitive tasks rather than capability limited to one task or domain. Many definitions compare that breadth with human intelligence, but they disagree about which people, tasks, environments, autonomy levels, and performance thresholds count.
AGI is therefore a research aim and a claim that needs an operational definition, not a capability that can be established by the label alone. A system might answer questions across many subjects yet fail at long-horizon action, learning a new task, transferring knowledge between settings, or remaining reliable under unfamiliar conditions. Another definition may not require all of those properties.
Origin and adoption of the term
Mark Avrum Gubrud used "artificial general intelligence" in a 1997 paper about advanced automation and international security. It is one of the earliest documented uses available in the supporting record, but it does not prove that he was the first person to use the phrase.
By the 2006 Artificial General Intelligence Research Institute workshop, Pei Wang and Ben Goertzel described AGI as a label adopted by researchers who wanted to distinguish general machine intelligence from the specialized systems that dominated mainstream AI. They also said it was not a precisely defined technical term. That warning still applies.
Why it is contested
There is no agreed threshold for how broad, transferable, autonomous, or human-like a system must be before it qualifies as AGI. Definitions also choose different human comparison groups, task sets, tools, learning requirements, and reliability standards. Two claims can therefore use the same label for materially different capabilities. This glossary treats AGI as incomplete unless the speaker supplies an operational threshold.
Distinguish it from nearby terms
- Narrow AI performs within a bounded task or domain, even when it performs there better than people.
- A foundation model can support many downstream tasks without meeting a stated AGI threshold for transfer, autonomy, robustness, or breadth.
- Artificial superintelligence usually means performance beyond humans across broad domains. AGI does not always imply that stronger claim.
- Human-level AI is also incomplete unless the comparison population, tasks, tools, time, and error tolerance are specified.
Operational significance
AGI forecasts, product promises, and safety arguments often use different thresholds while appearing to discuss the same milestone. Before attaching a date, risk estimate, or governance trigger to AGI, write down the evaluated domains, transfer conditions, autonomy, learning requirements, robustness tests, and comparison baseline.
Check your understanding
A model passes exams in medicine, law, and mathematics but cannot reliably complete unfamiliar multi-step work without a person repairing its plan. Whether that is AGI cannot be answered until the definition states how breadth and autonomy are weighted.