AGI (Artificial General Intelligence)
A hypothetical system with broad human-level capability across domains — undefined enough that people can argue about whether it's arrived.
Reviewed July 11, 2026Stable
When not to use it
- In technical planning. It's not a specification and it doesn't inform any decision you'll make this year.
- As a reason to ignore present harms. Systems deployed today affect people today, whatever arrives later.
- As a claim about a product. "A step toward AGI" is unfalsifiable and usually means the benchmarks moved.
Reach for something else instead
- Specific capability claims — "it does X at Y accuracy on Z" is testable and therefore means something.
- Task-level evaluation for anything you're building. Your problem is your problem.
- Levels or continuum framings if you must discuss general capability — at least they're operationalisable.
This entry is part of a longer guide: What is artificial intelligence?
Read more on the blog
- The secret language that never was, and the escape that didThe famous stories about AI going rogue are mostly false. The verified incidents are less dramatic and more concerning, and the difference between them is the whole subject.
- Will AI take my job? What the evidence showsThe frightening headline numbers and the reassuring ones are both real, because they measure different things. AI acts on tasks, not jobs, and a job is a bundle of tasks. That single distinction explains why the studies appear to contradict each other and what the evidence actually supports.
- Superintelligence: the empirical record is zeroSixty years after the intelligence explosion was described, no system has demonstrated sustained open-ended self-improvement. The public forecasts come from five people with the same financial interest.
- What can AI do, and what can't it? A predictive mapAny list of what AI can do is out of date before you finish reading it. What does not go out of date is the set of task properties that predict whether AI will be good at something, and they have almost nothing to do with how hard the task feels to a person.
Further reading
- Morris et al. (2023), Levels of AGI: Operationalizing Progress on the Path to AGI — an attempt to make the term measurable, and a fair account of why it's hard.
- Bubeck et al. (2023), Sparks of Artificial General Intelligence — the most cited argument that something changed, and worth reading with its critics.
- Chollet (2019), On the Measure of Intelligence — the case that current benchmarks measure skill, not intelligence, and a proposed alternative.
Primary sources, listed so you can check the claims on this page rather than take them on trust.
Where people go wrong
- Reasoning from impressive performance on one task to expected performance on another. Human abilities correlate; a model's don't.
- Treating disagreement about timelines as a factual dispute. It's largely a definitional one.
- Assuming the definitional vagueness is accidental. Plenty of people have reasons to define it where it suits them.
At a glance
FieldFoundations
Core ideabroad human-level capability
Statushypothetical, undefined
Track recordseventy years of optimistic predictions
DifficultyBeginner
Often compared with
AGI vs. narrow AI — hypothetical breadth vs. the systems that actually do things.
Where this sits
1 concept come first. Understanding it opens up 2 more.
1Levelsteps in
1Needs firstconcepts
2Opens up1% of 310
1Areastays here
Learn these firstArtificial Intelligence
Computed from the prerequisite graph, not assigned. How this works