AI models excel only at tasks they are specifically trained on, leading to a fragmented skill set rather than a universal intelligence. This "jaggedness," driven by the distribution of training data, will persist even as models become more powerful, challenging the notion of a smooth path to general superintelligence.

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.

Framing AGI as reaching human-level intelligence is a limiting concept. Unconstrained by biology, AI will rapidly surpass the best human experts in every field. The focus should be on harnessing this superhuman capability, not just achieving parity.

Instead of a single, generalizable AI, we are creating 'Functional AGI'—a collection of specialized AIs layered together. This system will feel like AGI to users but lacks true cross-domain reasoning, as progress in one area (like coding) doesn't translate to others (like history).

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.

Hinton dismisses the concept of AGI as a singular moment when AI becomes equal to humans. He argues intelligence is 'jagged'—AI is already superhuman in domains like general knowledge but subhuman in others. There won't be a moment of perfect parity across all tasks.

Human intelligence is fundamentally shaped by tight constraints: limited lifespan, brain size, and slow communication. AI systems are free from these limits—they can train on millennia of data and scale compute as needed. This core difference ensures AI will evolve into a form of intelligence that is powerful but alien to our own.

The next leap in AI will come from integrating general-purpose reasoning models with specialized models for domains like biology or robotics. This fusion, creating a "single unified intelligence" across modalities, is the base case for achieving superintelligence.

Defining AGI as 'human-equivalent' is too limiting because human intelligence is capped by biology (e.g., an IQ of ~160). The truly transformative moment is when AI systems surpass these biological limits, providing access to problem-solving capabilities that are fundamentally greater than any human's.