AI Search 101
What is strong and narrow AI?
Narrow AI performs specific tasks within a defined domain. Strong AI, also called artificial general intelligence, would match human capability across any intellectual task. Every system in existence is narrow. Strong AI remains hypothetical, and the distinction is what most confused claims about AI depend on.
The basics
| Narrow AI | Also called weak AI or applied AI |
| Strong AI | Also called artificial general intelligence, AGI |
| Currently exists | Narrow only |
| Classic test | The Turing test |
| Related idea | The technological singularity |
What makes a system narrow?
Not its capability, but the boundary of that capability.
A chess engine plays chess better than any human and cannot do anything else. That is obviously narrow. A large language model writes, translates, summarises, codes and reasons about a huge range of subjects, which makes it look broad, and it is still narrow. Its domain is text prediction. Everything it does is a form of that one operation, and outside it the system has no capability at all.
The test is not how many things a system appears to do. It is whether the competence transfers. A person who learns to drive gains something applicable to cycling, to spatial reasoning, to teaching someone else. Narrow systems do not carry competence across in that way, because they have no model of the world to carry it in.
What would strong AI require?
Several things nobody knows how to build.
Transfer. Applying what was learned in one domain to a genuinely different one, without retraining.
Learning from little. People generalise from a handful of examples. Current systems need vast quantities.
Common sense. A working model of how physical and social reality behaves, used automatically rather than recited.
Goals of its own. Deciding what is worth doing rather than executing what was asked.
Whether scaling current approaches produces these properties or whether something architecturally different is required is the central open question in the field, and serious researchers disagree.
Why does the distinction matter commercially?
Because almost every overclaim and every misplaced fear lives in the gap between the two.
Vendors describe narrow tools in language that implies general capability, and buyers form expectations that no system can meet. Meanwhile discussion of AI risk often slides between two very different concerns: what narrow systems do badly today, and what a general system might do in principle. The first is a procurement and governance problem with practical answers. The second is a research question.
The useful discipline in any evaluation is to ask what the system's domain actually is, and what happens at its edge. That question deflates most marketing and clarifies most decisions.
How the distinction is used in marketing
Production. Setting realistic expectations internally about what a tool will and will not do, which is what determines whether an adoption programme succeeds.
Analysis. Evaluating vendor claims. A tool described as understanding a business is a narrow system with a good demo.
Distribution. The assistants describing companies to buyers are narrow systems. They are extremely good at producing plausible descriptions and have no way to verify one, which is precisely why what they read matters so much.
How this impacts your business
A language model does not look a company up in a register. It composes a description from everything it read during training and whatever it retrieves at the moment of the question. Trade press, directories, forums, competitor comparison pages, and somewhere in that mix the company's own website too.
So the description a buyer sees is assembled, not published. No company controls it, but every company supplies a share of the material it is built from.
The traffic impact is still small, roughly one percent of sessions for most companies. The influence is not small, because the reader is often a buyer forming a first impression of what a company does and who it serves, before any page gets a click.
Checking takes ten seconds. Type the company name into ChatGPT and read the answer.
Frequently asked questions
Is ChatGPT narrow or strong AI?
Narrow. Its breadth of subject matter is not the same as breadth of capability, and its domain is language.
What is the difference between weak AI and narrow AI?
None in ordinary use; they are interchangeable terms. Weak is the older philosophical term, narrow the more common technical one.
When will strong AI arrive?
Nobody knows. Published estimates from credible researchers range from years to many decades to never, and the spread reflects genuine uncertainty rather than a consensus with outliers.
Is superintelligence the same as strong AI?
No. Strong AI means human-level general capability. Superintelligence means substantially beyond it, and is a further hypothetical step.
Does narrow mean limited?
Not in performance. Narrow systems routinely exceed human ability inside their domain. Narrow describes the boundary, not the level.
Related definitions
- What is artificial intelligence?
- What is the Turing test?
- What is the technological singularity?
- What is agentic AI?
- AI Search 101
Written by Lari Numminen, Generate More. Also available in Finnish. Updated 31 August 2026.