Artificial general intelligence is one of those terms that appears precise until you ask three researchers to define it. The broad idea is straightforward: AGI would be an AI system with abilities that extend across a wide range of intellectual tasks rather than remaining specialized to a narrow function.
The difficulty is deciding how broad, how capable and how autonomous a system must be before the label applies.
There is no single accepted definition of AGI
Google DeepMind has described AGI as an AI system that is at least as capable as a human at most tasks, while also proposing a framework that separates generality, performance and autonomy into different dimensions.[1] OpenAI’s charter uses another formulation, defining AGI as highly autonomous systems that outperform humans at most economically valuable work.[2]
Those definitions overlap, but they are not identical. That is why confident claims that a particular model “is AGI” often say as much about the speaker’s definition as they do about the technology.
General-purpose AI is not necessarily AGI
Today’s leading models are already general-purpose in an ordinary sense. They can write, code, analyze images, answer questions, summarize documents and use tools. That breadth can make them feel general.
But general-purpose does not automatically mean human-level general intelligence. The international scientific report on advanced AI makes the distinction explicitly: present general-purpose AI can perform many tasks, while AGI is commonly used for a potential future system that equals or surpasses human performance across all or almost all cognitive tasks.[3]
Capability is only one dimension
A model might be excellent at mathematics and writing but poor at long-term planning. Another system might be less impressive in conversation but better at operating software for hours. A useful AGI definition therefore has to consider more than one benchmark.
DeepMind’s “Levels of AGI” framework proposes looking at both performance and generality, while also treating autonomy as a deployment consideration. The point is not to declare a winner. It is to create vocabulary that makes comparisons less vague.[1]
Autonomy complicates the question
Modern agent systems make the AGI debate more complicated because the model no longer has to stop after producing an answer. Agents can use tools, manage files, execute code and work through long sequences of actions.
OpenAI’s Agents API and Anthropic’s work on trustworthy agents both reflect this shift toward systems that can operate for longer periods with less step-by-step direction.[4][5]
A model that is not dramatically smarter in a narrow benchmark can still become much more economically consequential if it can reliably complete an entire workflow.
Why benchmarks alone may not settle it
AI benchmarks are useful because they give researchers measurable targets. They are also incomplete. A system can score extremely well on a test and still fail in unusual real-world situations, misunderstand instructions or struggle with tasks that require persistence and judgment.
AGI is supposed to be general. That makes it difficult to prove with a finite list of tests. Once a benchmark becomes familiar, models can also be optimized for it, making the test less informative about genuinely broad intelligence.
The definition may move as AI improves
There is another problem: humans adapt quickly to technological progress. Capabilities that once looked extraordinary can become ordinary after a few years of use. Speech recognition, computer vision, translation and fluent text generation all went through versions of this transition.
That creates a moving target. If AGI is defined partly by what feels generally intelligent to people, the psychological threshold can shift as machines normalize abilities that once seemed uniquely human.
AGI and superintelligence are not the same thing
Artificial general intelligence usually refers to breadth and human-level or better capability across many tasks. Artificial superintelligence usually refers to systems that substantially exceed human intelligence. DeepMind’s 2026 work on the transition from AGI to ASI treats those as points on a continuum rather than synonyms.[6]
The distinction matters because a broadly capable system is not necessarily superior to humans at everything.
So are we close to AGI?
There is no factual answer that can be separated from the definition being used. Frontier systems have become dramatically more general, multimodal and agentic. They also continue to make basic mistakes, require oversight and perform unevenly across domains.
The more useful question is often not whether a system has crossed a philosophical line. It is what capabilities have become reliable enough to change work, science, business or daily life.
Why the AGI debate still matters
Definitions are not just semantics. A society preparing for increasingly capable systems needs ways to describe how broad those systems are, how well they perform and how independently they can act.
Artificial general intelligence may eventually become a clear technical milestone. For now, it is better understood as a contested target with several competing definitions. The technology is moving quickly. The vocabulary is still trying to catch up.
Sources
[1] Google DeepMind, “Levels of AGI for Operationalizing Progress on the Path to AGI”
[3] International Scientific Report on the Safety of Advanced AI, Interim Report
[4] OpenAI, “Introducing the Agents API”
[5] Anthropic, “Trustworthy agents in practice”
[6] Google DeepMind, “From AGI to ASI”
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