Artificial general intelligence (AGI) is a proposed capability threshold: an AI system that can match or exceed human performance on all or almost all cognitive tasks. Today’s AI includes both systems built for narrow tasks and general-purpose systems that can handle a much wider range of activities. A broad task range is not, by itself, evidence that a system meets the much higher AGI threshold.
What does artificial general intelligence mean?
The International AI Safety Report’s 2024 interim report describes AGI as a potential future system that matches or exceeds human performance on all or almost all cognitive tasks. It also cautions that there is no universally precise definition of the term. That makes AGI a disputed capability concept, not a settled product category with one agreed finish line. Read the report’s definition and discussion.
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The term is often used for a system with broad cognitive abilities, but “broad” alone does not establish human-level performance across nearly all kinds of thinking. Claims about AGI therefore need to specify what definition and evidence they rely on.
How AGI differs from narrow and general-purpose AI
| Term | Working meaning | What it does not establish |
|---|---|---|
| Narrow AI | An AI system specialized for one task or a few similar tasks, such as a particular kind of classification or recommendation. | Specialization does not mean a system is weak or unimportant in its intended use. |
| General-purpose AI | A model that can perform, or be adapted to perform, a wide variety of tasks. The 2024 interim report also applies the term to systems built on such models or derived from them. | A wide task range does not show that performance is at human level across almost all cognitive tasks. |
| AGI | A proposed future system that matches or exceeds human performance on all or almost all cognitive tasks. | There is no universally agreed precise definition or single decisive test established by the sources cited here. |
General-purpose AI is thus a weaker description than AGI. It describes range and adaptability, not necessarily the depth or consistency of performance across that range. A system also need not be multimodal to count as general-purpose under the interim report’s terminology: “general-purpose,” “multimodal,” and “AGI” describe different properties.
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How to assess claims that a system is approaching AGI
A useful framework separates capability into breadth, depth, and autonomy. Morris and coauthors proposed these dimensions to make progress claims more operational than a simple “AGI or not” label. See their levels-of-AGI framework.
Breadth: how many different tasks?
Ask whether a system handles meaningfully different kinds of tasks and whether it can transfer what it has learned to unfamiliar tasks. A system that performs many related variations of one task may have less generality than one that succeeds across unrelated domains.
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Depth: how well does it perform?
Compare results with an appropriate human reference group and look at performance across the full set of tasks being claimed. Strong performance on a selected task or benchmark is evidence about that task; it does not establish broad competence at human level.
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Consider whether the system can complete work without step-by-step human instruction. Autonomy is related to, but distinct from, breadth and performance. Morris and coauthors include it because capability and the degree of independent operation can affect deployment risks.
Why benchmarks cannot certify AGI
Benchmarks provide evidence about the tasks they test, not a universal AGI certificate. The International AI Safety Report’s interim edition notes that benchmarks may poorly represent real-world tasks, that meaningful generalization is disputed, and that high scores can sometimes reflect memorized patterns rather than robust transfer.
ARC-AGI-2 is one example of a benchmark designed to examine abstract reasoning and problem solving more granularly. Its publisher says it uses first-party human testing and tasks intended to limit memorization and brute-force search. Those design goals can make it useful evidence about the capabilities it targets, but its score does not measure every dimension of AGI. Read the ARC-AGI-2 benchmark description.
When evaluating a benchmark claim, check the benchmark and version, which tasks were tested, the comparison group, and the date. Then ask what the result does—and does not—support about performance on unfamiliar, real-world tasks.
Are today’s AI systems AGI?
Modern general-purpose systems can perform across a broad range of activities, and benchmark performance has advanced rapidly. But performance varies by task and benchmark, and benchmark results do not settle whether a system can operate at human level across nearly all cognitive tasks. Broad capability is evidence of progress toward generality, not a verdict that AGI has been reached.
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Because the threshold itself lacks a universally agreed definition, a categorical claim that a current system is or is not AGI should name the definition being used and the evidence behind it. Terms such as “agentic” or “multimodal” do not answer that question on their own: they describe other aspects of a system, not whether it meets the proposed cross-task performance threshold.
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