Capability overhang
When an AI system can do more than people have yet realised, or been able to draw out of it.
A capability overhang exists when a model or the available hardware is able to do more than is currently being achieved. The extra ability is there, but people have not yet found the right way to ask for it, or the right method to release it.
For example, a model might turn out to solve harder problems when it is prompted differently, given tools, or allowed to think for longer. Months after release, users find abilities that the builders did not test for. Better methods can then produce a sudden jump in what existing systems seem able to do.
The term matters in two ways. For safety testing, it means a clean evaluation may understate what a model could do later, so testers try hard to draw out its full ability. For pace of progress, some argue that abundant hardware combined with a later software advance could cause faster change than expected, which is a concern for those who prefer slow, gradual development.
The concept is used in different ways by different writers, and it is difficult to measure, because an ability no one has found is by definition hard to detect. Some researchers think it is a real effect, others doubt it is large.