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.

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.

The Stanford AI Index reveals a "jagged frontier" where advanced models achieve superhuman performance on complex tasks like the International Mathematical Olympiad, yet fail at simple, common-sense activities like reading an analog clock. This highlights their lack of real-world grounding and the need for more holistic "world models."

Large Language Models learn the structure and language of mathematical solutions from vast text data. This allows them to generate convincing explanations and steps, but they don't perform actual calculations. Their "fluency" in math-like text is different from a calculator's logical execution, leading to confident but incorrect answers.

Advanced AI models exhibit profound cognitive dissonance, mastering complex, abstract tasks while failing at simple, intuitive ones. An Anthropic team member notes Claude solves PhD-level math but can't grasp basic spatial concepts like "left vs. right" or navigating around an object in a game, highlighting the alien nature of their intelligence.

AI shows uneven progress in mathematics. While it can solve complex geometry problems from the International Math Olympiad (IMO) almost instantly, it struggles with combinatorics, which requires more playful, puzzle-like creativity. This highlights the 'spiky frontier' of AI capabilities, where proficiency in one domain doesn't guarantee it in another, closely related one.

Today's AI systems mirror Douglas Hofstadter's prophetic concept of a 'smart, stupid' machine. They exhibit high competence in complex domains like coding or writing essays but can make surprising, nonsensical errors, revealing a significant gap between their surface performance and genuine understanding.

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.

Current AI models exhibit "jagged intelligence," performing at a PhD level on some tasks but failing at simple ones. Google DeepMind's CEO identifies this inconsistency and lack of reliability as a primary barrier to achieving true, general-purpose AGI.