artificial general intelligence
In plain wordsAI that could learn and do almost any mental task a capable adult can, rather than excelling at one narrow job. Experts disagree on the exact bar and on when, or whether, it will arrive.
An AI system whose competence is broad rather than task-specific: able to learn and perform most cognitive tasks that humans can, carry skills over to unfamiliar problems, and do so at least at the level of a skilled adult. No agreed test exists, and definitions differ in what they measure. Capability definitions assess breadth and depth of performance, as in Google DeepMind's levels of AGIlevels of AGI, n.: A scheme from Google DeepMind researchers for grading AI by how well it performs and how broad its skills are, from emerging to superhuman, much as self-driving cars are graded by levels. (2023) or a 2025 proposal by Dan Hendrycks and colleagues that scores systems against the Cattell–Horn–Carroll model of human cognitive abilities. Economic definitions use work: the OpenAIOpenAI, prop. n.: The American company that makes ChatGPT and the GPT models. It began in 2015 as a nonprofit research lab and is now a for-profit company overseen by a nonprofit foundation. Charter (2018) speaks of 'highly autonomous systems that outperform humans at most economically valuable work'. Formal definitions measure goal achievement across environments (universal intelligenceuniversal intelligence, n.: A mathematical definition of intelligence as an agent's average ability to achieve goals across every possible environment, with simpler environments counting more.); others stress efficient acquisition of new skills (the premise of ARC-AGIARC-AGI, prop. n.: Puzzles made of colored grids — and, since 2026, small video-game-like worlds — that people solve easily but AI long found hard, designed to test whether a machine can learn a new skill from just a few examples.) or the ability to automate AI research itself. Because today's language modelslarge language model, n.: A computer program trained on enormous amounts of text to predict the words that come next. At vast scale, that simple skill lets it chat, answer questions, translate, summarize and write code. are superhuman at some tasks and weak at others (jagged intelligencejagged intelligence, n.: The uneven pattern of AI abilities: today's systems can solve very hard problems yet fail at some a child could manage, so their skills form a spiky profile rather than a smooth one.), it is disputed whether they are early AGI, as the 2023 Sparks of AGISparks of AGI, prop. n.: A 2023 Microsoft research paper arguing that an early version of GPT-4 showed the first glimmers of general, humanlike intelligence. Its claim provoked sharp debate about how to judge AI abilities. paper suggested, or lack essentials such as continual learningcontinual learning, n.: The ability of an AI to keep learning new things over time from a stream of experience, without forgetting what it already knew and without being retrained from scratch. and reliable long-horizon agency.
Every lab says it is building AGI, but no two of them define it the same way.
Origin
Earliest known use: Mark Gubrud's 1997 paper 'Nanotechnology and International Security', on the military implications of advanced technologies. Shane Legg proposed the term to Ben Goertzel in 2002 as the title of a book (edited by Goertzel and Cassio Pennachin, published 2007) on returning to AI's original goal; an annual AGI conference followed from 2008. It went mainstream as OpenAIOpenAI, prop. n.: The American company that makes ChatGPT and the GPT models. It began in 2015 as a nonprofit research lab and is now a for-profit company overseen by a nonprofit foundation. and Google DeepMindGoogle DeepMind, prop. n.: Google's main artificial intelligence laboratory, formed in 2023 by joining DeepMind and Google Brain. It builds the Gemini models and scientific AI systems such as AlphaFold. made it their declared goal.
Usage note
Often interchangeable with human-level AIhuman-level AI, n.: AI that can match an ordinary adult across the whole range of thinking tasks, such as talking, learning, reasoning and planning. An older, more intuitive name for the goal now usually called general AI.; distinct from strong AIstrong AI, n.: Originally, the claim that a computer running the right program would literally have a mind and understand, not just imitate understanding. Today it often simply means AI as capable as humans across the board., a philosophical thesis about genuine minds. Some researchers, including Yann LeCunYann LeCun, prop. n.: A French-American computer scientist who pioneered the neural networks that recognize images, led Meta's AI research for over a decade, and argues that chatbots alone will not reach human-level AI., reject the term as ill-defined. Under Microsoft's October 2025 agreement with OpenAI, an OpenAI declaration of AGI was to be verified by an independent expert panel; an amendment of 27 April 2026 made OpenAI's payments to Microsoft 'independent of OpenAI's technology progress', which commentators read as ending the AGI clause.
Related
- superintelligencesuperintelligence, n.: A mind, usually imagined as an AI, that would leave even the cleverest people far behind in nearly every kind of thinking, not just in one game or skill. None is known to exist.
- human-level AIhuman-level AI, n.: AI that can match an ordinary adult across the whole range of thinking tasks, such as talking, learning, reasoning and planning. An older, more intuitive name for the goal now usually called general AI.
- levels of AGIlevels of AGI, n.: A scheme from Google DeepMind researchers for grading AI by how well it performs and how broad its skills are, from emerging to superhuman, much as self-driving cars are graded by levels.
- transformative AItransformative AI, n.: AI that changes the world as deeply as farming or the Industrial Revolution did. It is defined by its effects on society and the economy, not by how humanlike or clever it is.
- general-purpose AIgeneral-purpose AI, n.: AI that can do a wide range of different tasks, like writing, coding, translating and analyzing images, rather than the one job it was built for. Today's chatbots are the main example.
- AI timelinesAI timelines, n.: Forecasts of when AI will reach major milestones, such as matching humans at most work. People with short timelines expect this within years; others expect decades, or never.
- universal intelligenceuniversal intelligence, n.: A mathematical definition of intelligence as an agent's average ability to achieve goals across every possible environment, with simpler environments counting more.
- Sparks of AGISparks of AGI, prop. n.: A 2023 Microsoft research paper arguing that an early version of GPT-4 showed the first glimmers of general, humanlike intelligence. Its claim provoked sharp debate about how to judge AI abilities.
- narrow AInarrow AI, n.: AI built to do one specific job, or a small set of related jobs, such as playing chess or spotting spam, without being able to turn its skill to unrelated problems.
- strong AIstrong AI, n.: Originally, the claim that a computer running the right program would literally have a mind and understand, not just imitate understanding. Today it often simply means AI as capable as humans across the board.
Sources
- Gubrud, M. A. (1997). Nanotechnology and International Security. Fifth Foresight Conference on Molecular Nanotechnology.
- Goertzel, B. & Pennachin, C. (eds.) (2007). Artificial General Intelligence. Springer.
- OpenAI (2025). The next chapter of the Microsoft–OpenAI partnership. 28 October 2025. openai.com/index/next-chapter-of-microsoft-openai-partnership
Mentioned in
ARC-AGIARC-AGI, prop. n.: Puzzles made of colored grids — and, since 2026, small video-game-like worlds — that people solve easily but AI long found hard, designed to test whether a machine can learn a new skill from just a few examples., artificial intelligenceartificial intelligence, n.: Computer systems that do things we would normally credit to a thinking mind, such as holding a conversation, reading an X-ray or planning a route, and the field of research that builds them., Demis HassabisDemis Hassabis, prop. n.: A British AI researcher and former chess prodigy who co-founded DeepMind, led the teams behind AlphaGo and AlphaFold, and shared the 2024 Nobel Prize in Chemistry., generative AIgenerative AI, n.: AI that produces something new on request, such as an essay, a picture, a song or a piece of code, having absorbed from enormous numbers of existing examples what such things typically look like., Humanity's Last ExamHumanity's Last Exam, prop. n.: A test of 2,500 extremely hard questions with definite answers, written by about a thousand experts worldwide and billed as the last academic exam AI would need; at launch, top models scored under 10%., intelligenceintelligence, n.: The ability to learn, understand, reason and solve problems — especially new ones — in order to reach goals. Experts disagree about how to define it precisely, for people and even more for machines., OpenAIOpenAI, prop. n.: The American company that makes ChatGPT and the GPT models. It began in 2015 as a nonprofit research lab and is now a for-profit company overseen by a nonprofit foundation., superintelligencesuperintelligence, n.: A mind, usually imagined as an AI, that would leave even the cleverest people far behind in nearly every kind of thinking, not just in one game or skill. None is known to exist., Turing testTuring test, n.: Alan Turing's proposed test of machine intelligence: a judge holds typed conversations with a hidden person and a hidden computer, and the computer passes if the judge cannot consistently pick it out., AI effectAI effect, n.: The habit of deciding that once a computer can do something — play chess, recognize speech, translate — it no longer counts as real intelligence, so the label keeps moving on to tasks machines cannot manage yet., AI hypeAI hype, n.: Exaggerated claims about what AI can do or will soon do, made to attract money, attention or customers, which can mislead the public and policymakers., AI timelinesAI timelines, n.: Forecasts of when AI will reach major milestones, such as matching humans at most work. People with short timelines expect this within years; others expect decades, or never., AI-completeAI-complete, adj.: Describes a problem believed to be so hard that solving it would require general, human-level intelligence, such as flawless translation or truly understanding a story., artificial superintelligenceartificial superintelligence, n.: Superintelligence in machine form: AI that would outthink the best humans at nearly every intellectual task. Often shortened to ASI and pictured as the stage after human-level AI., country of geniuses in a datacentercountry of geniuses in a datacenter, phr.: A phrase for future AI so capable that one data center could hold the equivalent of millions of Nobel-level experts, working faster than people on any problem., DeepMindDeepMind, prop. n.: A London artificial intelligence company, bought by Google in 2014, famous for programs that mastered the game of Go and predicted the shapes of proteins. In 2023 it merged into Google DeepMind., general-purpose AIgeneral-purpose AI, n.: AI that can do a wide range of different tasks, like writing, coding, translating and analyzing images, rather than the one job it was built for. Today's chatbots are the main example., human-level AIhuman-level AI, n.: AI that can match an ordinary adult across the whole range of thinking tasks, such as talking, learning, reasoning and planning. An older, more intuitive name for the goal now usually called general AI., jagged intelligencejagged intelligence, n.: The uneven pattern of AI abilities: today's systems can solve very hard problems yet fail at some a child could manage, so their skills form a spiky profile rather than a smooth one., Liang WenfengLiang Wenfeng, prop. n.: A Chinese entrepreneur who founded DeepSeek, whose low-cost, openly released AI models shocked global markets in January 2025., narrow AInarrow AI, n.: AI built to do one specific job, or a small set of related jobs, such as playing chess or spotting spam, without being able to turn its skill to unrelated problems., Peter NorvigPeter Norvig, prop. n.: An American computer scientist who co-wrote the standard AI textbook, led research at Google, and helped show that simple methods trained on huge amounts of data often beat clever hand-built ones., scaling hypothesisscaling hypothesis, n.: The bet that today's AI methods, simply made bigger with more computing power and data, will keep getting smarter — possibly all the way to human-level intelligence and beyond — without needing fundamentally new ideas., Shane LeggShane Legg, prop. n.: A New Zealand-born AI researcher who co-founded DeepMind and helped make 'artificial general intelligence' a mainstream term and goal.