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Artificial general intelligence (AGI) usually means AI able to perform a broad range of intellectual work, rather than being built for one narrow task. But there is no universally accepted definition, test, or arrival date. OpenAI defines AGI in terms of autonomous systems outperforming humans at most economically valuable work; Google DeepMind instead offers a framework for comparing performance, generality, and autonomy. Those are different ways to describe the goal, not a shared pass-or-fail standard.
What is artificial general intelligence?
AGI is a label for AI with broad capabilities across many kinds of intellectual work. The idea is broader than a system designed for a particular task, but the term remains unsettled: researchers and organizations do not all use the same definition or require the same evidence.
One organizational definition
OpenAI’s Charter defines AGI as “highly autonomous systems that outperform humans at most economically valuable work.” That definition emphasizes both independence and performance in economically valuable work; it is OpenAI’s institutional definition, not a consensus accepted by the whole field.
A framework for comparing capabilities
Google DeepMind’s 2024 “Levels of AGI” paper proposes a common language for comparing systems and describing progress. Rather than setting one universal threshold for AGI, its framework considers performance, generality, and autonomy. It also points to the difficulty of creating benchmarks that reliably measure capability and behavior across levels. The framework is a proposal, not a universally adopted standard.
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How is AGI different from today’s AI?
The useful distinction is not simply “smart” versus “not smart.” A system can perform impressively on some tasks without demonstrating broad, reliable ability across many types of work or the autonomy specified by a particular definition. Evaluating an AGI claim therefore means asking what the system can do, how widely its abilities transfer, and how independently it can pursue tasks.
| Dimension | What it asks | Why it matters |
|---|---|---|
| Capability depth | How well does the system perform a given task? | A strong result on one task does not by itself establish broad capability. |
| Generality or breadth | How widely do capabilities transfer across different kinds of tasks? | A system may be capable in one area but not general across areas. |
| Autonomy | How independently can the system pursue tasks or work? | Some definitions, including OpenAI’s, make autonomy part of the threshold. |
| Measurement | Do benchmarks capture robust performance rather than a narrow demonstration? | Benchmark design is challenging, and a result depends on what the test actually measures. |
| Risk governance | Which capability thresholds prompt evaluation, safeguards, or deployment limits? | Safety decisions concern not just what a system can do, but how its capabilities are assessed and managed. |
The first four dimensions reflect Google DeepMind’s framework and its discussion of measurement. Risk governance is reflected in OpenAI’s stated safety approach and Preparedness Framework; neither organization’s framework should be treated as a field-wide standard.
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Do we already have AGI?
The sources covered here do not establish a shared test or consensus determination that current systems meet AGI criteria. General-purpose AI systems may show impressive capabilities, but whether any system qualifies depends on the definition chosen, the benchmarks used, and whether autonomy is required. A confident yes-or-no answer without stating those criteria would imply more agreement than exists.
When will AGI arrive?
No specific arrival year is established. OpenAI’s Charter says, “The timeline to AGI remains uncertain.” Google DeepMind’s 2026 report says that human-level AGI has become a concrete next-decade target for many large AI organizations. That describes an institutional target, not a guarantee, proof of a consensus forecast, or evidence that current systems meet AGI criteria.
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The path beyond AGI is uncertain too. Google DeepMind discusses multiple possible pathways from AGI to artificial superintelligence (ASI), and cautions against assuming that progress will take the form of one dramatic step-change. A target or scenario should not be confused with a demonstrated capability or a reliable prediction.
What could go wrong?
Risks depend on a system’s capabilities and how it is used. OpenAI’s Preparedness Framework, version 2, dated April 15, 2025, tracks three capability areas that could contribute to severe harm:
- Biological and chemical capabilities: the framework tracks capabilities in these areas as a potential source of risk.
- Cybersecurity capabilities: it tracks cyber-related capabilities that could pose risks.
- AI self-improvement capabilities: it tracks the ability of AI systems to contribute to improving AI capabilities.
These are categories in OpenAI’s framework, not an exhaustive list of AI risks or an industry-wide consensus. The framework describes threat models and measurable capability thresholds to guide evaluation and safeguards before deploying very capable models.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How could AGI be kept safe?
Safety is an ongoing process of evaluating capabilities, deciding what risks they create, and applying safeguards. OpenAI describes its approach as involving staged capability progress, testing, risk mitigation, and meaningful human ability to intervene. These are OpenAI’s stated practices and principles; they are not a guarantee that a system will be safe.
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Evaluate capabilities before deployment
OpenAI’s Preparedness Framework describes using capability thresholds and evaluations to identify when a model could create severe-harm risks, then building safeguards before deployment. The practical value of a threshold depends on the evaluation: tests must meaningfully measure the capability and the risk under consideration.
Preserve human intervention
OpenAI’s safety page emphasizes the ability for people to intervene and deactivate capabilities, including when systems operate through devices or networks of agents. This is a stated safety principle, not proof that intervention will always be possible or sufficient.
Share responsibility
OpenAI frames safety as a collective effort involving industry, academia, government, and the public. Because AGI has no single agreed definition and its long-range development is uncertain, safety work and capability evaluation need to evolve alongside systems rather than wait for a universally agreed milestone.
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