AI, AGI and ASI: the difference

Three labels, three levels

People often use AI, AGI and ASI as if they meant the same thing. They do not. A useful way to remember them is as three steps on a ladder: tools that do particular jobs, then systems that can do most jobs a person can, then systems that go far beyond us.

Each step is a matter of degree, and the edges are blurry. Even so, keeping the three apart makes most news stories easier to read.

AI: the everyday word

Artificial intelligence, or AI, is the widest term. It covers any software that does something we would normally expect to need human thinking, such as recognising speech, recommending a film, translating a sentence or writing an email.

Most AI in use today is what researchers sometimes call narrow. It is built or trained for a set of tasks and works well inside them. Modern chatbots are broader than older tools, since one system can write, summarise, code and answer questions. Still, they are usually described as AI rather than AGI.

The word has also shifted over time. Tasks once thought to need real intelligence, like playing chess or reading handwriting, tend to stop being called AI once computers handle them routinely. That habit is sometimes described as moving the goalposts, and it is a reminder that the label is loose.

AGI: matching people

AGI stands for artificial general intelligence. The rough idea is a system that can learn and perform almost any intellectual task a capable person can, rather than being tied to one narrow area.

Here the trouble begins, because there is no single accepted definition. Some definitions focus on doing most economically valuable work. Others focus on passing a wide range of tests, or on learning new skills as efficiently as a person does. Different organisations pick different ones, which is why two people can argue about whether AGI has arrived and both be using the word sensibly.

• Breadth. Can it handle many kinds of task, not just one?
• Level. Is it as good as a typical person, or as good as an expert?
• Autonomy. Can it carry out long pieces of work without constant help?

Because the definitions differ, be careful with any headline that says AGI has been achieved, or is a set number of years away. Ask which definition is being used.

It also helps to know that AGI is not a switch that flips on a particular day. Systems may become broadly capable in some areas well before others, and people may disagree for years about the moment the label fits. Some researchers prefer to talk about degrees of generality rather than a yes or no.

A further wrinkle is that a system can be better than most people at some tasks and still worse than a child at others. Uneven ability is normal for current models, and it makes simple comparisons with human intelligence tricky.

ASI: going beyond people

ASI stands for artificial superintelligence. It means a system that is much better than the best humans across nearly every field that matters. This is the same idea covered in What is superintelligence?.

ASI is a higher bar than AGI. A system that matches a capable person is not automatically far beyond one. Whether the gap between the two would be crossed quickly or slowly is a live debate, covered in the guide on fast or slow takeoff.

The word superintelligence is used more or less interchangeably with ASI. Some writers add distinctions, such as a system that is only faster than people, one that is collectively stronger through many copies working together, or one that is better in quality of thought. These are ideas from the research literature and are useful to know exist, though most everyday discussion does not need them.

No system today is described as ASI by serious researchers, and there is no agreed test for recognising one.

Why the distinction matters

The three terms point to different questions. For ordinary AI, the questions are practical: is it accurate, fair, private and useful? For AGI, the questions become economic and social, such as what happens to work and who benefits. For ASI, the questions include control and safety, which the alignment guide explores.

Mixing the terms together can also make a debate go in circles. One person may be talking about the chatbot on their phone while another is talking about a hypothetical system far beyond anything built. They will not agree, because they are not discussing the same thing.

Telling them apart in practice

When you see one of these words in a headline or an advertisement, a few quick questions help.

• Which definition of the term is being used, and who wrote it?
• Is the claim about what a system can do now, or what it might do later?
• Is there a test or measurement behind it, or only an opinion?

The last question is where measurement comes in. Benchmarks are the main tool for comparing systems over time, and the next guide, How AI progress is measured, explains what they show and where they mislead. The benchmark explorer lets you look at real results yourself.

See it in the explorerWatch five benchmarks climb, quarter by quarter.