AGI
Artificial general intelligence: a hypothetical AI that matches human ability across a wide range of tasks. Definitions vary, which is why the practical question is which capability threshold changed a decision.
AGI stands for artificial general intelligence: AI that can perform at roughly human level across a broad range of cognitive tasks, instead of excelling at one narrow job. There is no single agreed definition or test, and different organizations draw the line in different places.
Because the term is contested, "Is this AGI?" is rarely a useful question inside an organization. It invites an argument about labels. The more productive question is which capability threshold has actually changed enough to change one of our decisions, such as what to automate, what to build, what to stop doing.
A longer view
The question is older than it looks. In 1959 the Turkish mathematician Cahit Arf asked whether a machine can think, and he placed the threshold in adaptation rather than in the number of problems solved. Modern benchmarks for LLMs measure performance on fixed tasks and say less about that kind of generality.
For leaders, the sensible approach is to judge a particular system by what it does under specific conditions, with specific authority, failure modes and costs.
Read more in The wrong questions about AI right now.
Related terms
Intibak kabiliyeti
Turkish for the ability to adapt. In Cahit Arf's 1959 lecture on thinking machines, it is the threshold that matters: not how many problems a machine solves, but whether it can adapt to new ones.
Benchmark
A standardised test used to compare AI models. Useful as evidence about someone else's system, but only trustworthy for you when it describes the exact serving route, harness and conditions you will run.
LLM
Large language model: an AI model trained on very large amounts of text to predict and generate language, which can be used to answer, summarise, write, reason about and act on text.