AI intelligence shouldn't be measured with a single metric like IQ. AIs exhibit "jagged intelligence," being superhuman in specific domains (e.g., mastering 200 languages) while simultaneously lacking basic capabilities like long-term planning, making them fundamentally unlike human minds.

Greg Brockman describes the imminent arrival of AGI not as a singular event where AI becomes uniformly superhuman, but as a 'jagged' reality. The AI will be superhuman at most intellectual computer-based tasks while still struggling with some basic tasks a human can do, making a clear definition difficult.

The argument that AI models have uneven ('jagged') capabilities is a weak safety guarantee. Geoffrey Irving notes that as models improve, even their weakest performance areas will likely exceed top human abilities, making the overall system superhumanly capable despite internal inconsistencies.

Progress towards AGI is not a smooth climb. Models exhibit "spikiness"—they can perform at a world-class level on one narrow domain but degrade to a "bad high school student" with slight perturbations. This non-intuitive generalization makes their capabilities uneven and unpredictable.

AI's capabilities are highly uneven. Models are already superhuman in specific domains like speaking 150 languages or possessing encyclopedic knowledge. However, they still fail at tasks typical humans find easy, such as continual learning or nuanced visual reasoning like understanding perspective in a photo.

Demis Hassabis explains that current AI models have 'jagged intelligence'—performing at a PhD level on some tasks but failing at high-school level logic on others. He identifies this lack of consistency as a primary obstacle to achieving true Artificial General Intelligence (AGI).

A practical definition of AGI is its capacity to function as a 'drop-in remote worker,' fully substituting for a human on long-horizon tasks. Today's AI, despite genius-level abilities in narrow domains, fails this test because it cannot reliably string together multiple tasks over extended periods, highlighting the 'jagged frontier' of its abilities.

Frontier AI models exhibit 'jagged' capabilities, excelling at highly complex tasks like theoretical physics while failing at basic ones like counting objects. This inconsistent, non-human-like performance profile is a primary reason for polarized public and expert opinions on AI's actual utility.

AI models exhibit a "jaggedness" where capabilities are not uniform. They perform at expert levels on verifiable, RL-tuned tasks but remain basic on subjective, unoptimized ones (like humor). This suggests intelligence isn't generalizing smoothly across all domains.