Lesson 23 of 28 · Using AI well

What is AGI and how is it different from AI?

AGI, or artificial general intelligence, is a hypothetical AI that could learn and perform almost any intellectual task a person can, across many fields, at human level or better. Today's AI is narrow: each system is strong at some tasks and weak at others. Experts disagree on how to define or measure AGI, so treat claims that a product "is AGI" with care.

Narrow AI vs general AI

Almost all AI in use is narrow, also called weak or specialised AI: it is built for a defined range of tasks. A chess engine can beat a grandmaster but cannot fill in a tax form. A spam filter sorts email and nothing else.

Chat assistants feel more general because one system can write, summarise, translate and code. Yet their abilities are uneven: the same assistant that drafts a polished essay can miscount the letters in a word, invent a source or lose track of a long task. AGI would remove that unevenness, handling unfamiliar problems and learning new skills the way a capable person does, without being rebuilt for each one.

Why AGI is hard to define

There is no single agreed definition. Some researchers mean human-level performance on most thinking tasks. OpenAI's charter describes AGI as "highly autonomous systems that outperform humans at most economically valuable work". A 2023 paper from Google DeepMind proposed levels of AGI that rate systems on both how well and how broadly they perform.

Older ideas live on too. In 1950 Alan Turing proposed his "imitation game", now known as the Turing test: can a machine's written conversation pass for a person's? Modern chatbots often sound human, which many researchers take as a sign that fluent conversation alone is a weak test of general intelligence.

AI, AGI and ASI side by side

These terms are often mixed up in headlines:

AI (artificial intelligence): the whole field of making computers do tasks that normally need human judgement.
Narrow AI: systems built for specific tasks; this describes the AI inside everyday products.
AGI (artificial general intelligence): hypothetical AI with human-level ability across most intellectual tasks.
ASI (artificial superintelligence): hypothetical AI far beyond the best humans in nearly every field.

What AGI means for you today

AGI debates shape research priorities, safety work and regulation, so they are worth following. For choosing and using tools, though, they matter less than they seem. Marketing often borrows the language of AGI, but a tool's value lies in how well it handles your tasks now.

Judge any system by evidence on specific jobs: test it on your own examples, compare models on public benchmarks and leaderboards, and keep checking the outputs that matter. Treat both "AGI is here" and "AI is useless" as claims that need evidence.

Example

Try it: map an assistant's strengths and gaps

Try this prompt

List three kinds of task you are likely to do well and three kinds you are likely to get wrong. For each, explain why in one sentence. Then give me one short test question for each kind so I can check.

Run a few of the test questions. You will usually see the uneven profile described above: strong on some tasks, unreliable on others. That unevenness is a practical sign of narrow rather than general intelligence. The assistant's self-assessment can itself be wrong, which is one more reason to test.

Key points

AGI means hypothetical AI that can learn and do almost any intellectual task a person can.
Today's AI is narrow: strong at some tasks and unreliable at others, even within one assistant.
There is no agreed definition or test, so AGI claims depend on whose definition is used.
ASI, or superintelligence, describes AI far beyond human ability; it is also hypothetical.
For choosing tools, evidence on your own tasks matters more than AGI labels.

Try it on Mindber

Compare AI models on the leaderboard

Lesson: what is AI?

Lesson: what is agentic AI?

Glossary: reasoning model

AI fundamentals

Frequently asked questions

Does AGI exist yet?

There is no agreed answer, because there is no agreed definition or test. Some developers say their systems show early signs of general ability; many researchers point to persistent gaps such as unreliable reasoning, invented facts and difficulty learning new skills on the job. Judge any claim by evidence on specific tasks rather than by the label.

Is AGI the same as sentient or conscious AI?

No. AGI is about ability: how many kinds of task a system can learn and perform. Sentience and consciousness are about inner experience, whether a system feels anything. A system could in principle be highly capable without any experience, and science has no reliable test for machine consciousness. Most AGI definitions deliberately leave that question aside.

What is the difference between AGI and generative AI?

Generative AI is a category of real tools that create text, images, audio or code, including today's chat assistants. AGI is a hypothetical level of general ability. Some researchers see large generative models as a possible path toward AGI; others argue that new methods will be needed. Using generative AI does not mean using AGI.