Artificial superintelligence (ASI) is a hypothetical level of AI capability at which an artificial intellect would substantially exceed the strongest human cognitive abilities across nearly all fields. It is a description of broad capability—not proof that an AI is conscious, has a human-like body, or has already been built.
What does artificial superintelligence mean?
Nick Bostrom defines a superintelligence as “an intellect that is much smarter than the best human brains in practically every field, including scientific creativity, general wisdom and social skills.” His definition emphasizes breadth: the system would excel across many kinds of demanding cognitive work, rather than merely outperforming people at one specialized task. Bostrom, “How Long Before Superintelligence?”
Authors do not always use the terms identically, but “artificial superintelligence” generally applies the idea of superintelligence to an AI system. The key claim is about how capable the system would be, not how it was built or whether it thinks or feels like a person.
How is ASI different from AGI?
In common usage, artificial general intelligence (AGI) refers to AI at roughly human-level capability across a broad range of tasks; ASI refers to a level beyond that. These labels describe a continuum rather than a universally agreed set of measured thresholds. A system’s success on a single benchmark or specialized task does not, by itself, establish that it is generally superintelligent.
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| Term | Usual meaning | What the label does not establish |
|---|---|---|
| Narrow or task-specific AI | AI designed or demonstrated to perform particular tasks. | Broad ability to transfer skill across unrelated domains. |
| AGI | A proposed human-level point of general capability. | A single agreed definition or test for human-level generality. |
| ASI | A hypothetical level of capability beyond human-level AGI, across a broad range of cognitive domains. | That such a system exists, is conscious, or has reached a measurable threshold accepted by everyone. |
Google DeepMind’s June 2026 report treats the path from human-level AGI to artificial general superintelligence as a transition along a continuum. It describes the endpoint intuitively as more intelligent and cognitively capable than large organizations of humans, while examining possible routes rather than reporting that ASI has been achieved. Google DeepMind, “From AGI to ASI”
How would superintelligence be judged?
There is no universal operational threshold or definitive benchmark for ASI established in the cited sources. A meaningful claim would need to address several dimensions, not just a striking result in one test:
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- Breadth: Does the system perform strongly across many cognitive domains, or only a narrow set of tasks?
- Depth: How does it compare with the strongest human individuals and collective human expertise?
- Generalization: Can it adapt what it knows to unfamiliar tasks and different kinds of work?
- Evidence: Is the claim supported by repeatable demonstrations, or is it a definition, forecast, or hypothetical scenario?
Because definitions and evaluation methods differ, no single benchmark should be treated as a conclusive ASI test. The term is most useful as a broad capability concept, with any specific claim assessed on its evidence and scope.
Does ASI have to be conscious or human-like?
No. Bostrom’s definition concerns intellectual capability and leaves both subjective experience and implementation open. An ASI, as defined by capability, would not have to be sentient, conscious, embodied, or built on any particular hardware. Those are separate questions, not requirements built into the term.
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Does ASI exist today?
ASI is hypothetical, not a verified present-day system. Google DeepMind’s 2026 report discusses possible movement from AGI toward ASI; IBM likewise describes artificial superintelligence as hypothetical. Neither framing establishes that ASI has been attained. IBM, “What Is Artificial Superintelligence?”
What paths from AGI to ASI have been proposed?
Google DeepMind’s June 2026 report discusses four possible routes. These are pathways under consideration, not predictions that any one route will occur:
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- Scaling AGI: Extending existing approaches as capability and resources grow.
- AI paradigm shifts: Developing different methods that could change how capable systems are built.
- Recursive improvement: Systems contributing to the improvement of later systems, potentially accelerating development.
- Multi-agent collectives: Coordinating large groups of AI agents so their combined work produces higher capability.
The report stresses uncertainty: technical bottlenecks and other frictions could have small or substantial effects. It says continued acceleration cannot be ruled out, not that acceleration or ASI is certain. Google DeepMind, “From AGI to ASI”
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why do safety and governance come up in discussions of ASI?
If future AI systems become much more capable than humans, questions arise about how to align their behavior with human intentions and how institutions should oversee their development. These are concerns about possible future systems, not part of the definition of ASI itself.
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OpenAI’s 2023 governance essay argues for coordination and discusses possible international oversight, while explicitly describing the technical ability to make superintelligence safe as an open research question. In its 2023 Superalignment article, OpenAI also argues that current alignment methods relying on human supervision may not scale to systems much smarter than humans. These are OpenAI’s dated assessments and positions, not settled proof of a particular future outcome. OpenAI, “Governance of superintelligence”; OpenAI, “Introducing Superalignment”
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