The term AGI is often used without a clear definition, leading to unproductive debates. A better approach is to define it functionally. Either AGI is achieved when AI's impact fundamentally transforms society, or it should be viewed as a spectrum of increasing generality, not an all-or-nothing milestone.

A consortium including leaders from Google and DeepMind has defined AGI as matching the cognitive versatility of a "well-educated adult" across 10 domains. This new framework moves beyond abstract debate, showing a concrete 30-point leap in AGI score from GPT-4 (27%) to a projected GPT-5 (57%).

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.

Mustafa Suleiman offers clear definitions: AGI is human parity on most tasks. Superintelligence exceeds human performance and discovers new knowledge. The Singularity is the sci-fi point where a superintelligence can recursively self-improve. This clarifies the ladder of AI progression beyond generic terms.

The definition of AGI is a moving goalpost. Scott Wu argues that today's AI meets the standards that would have been considered AGI a decade ago. As technology automates tasks, human work simply moves to a higher level of abstraction, making percentage-based definitions of AGI flawed.

Sundar Pichai shares his working definition of Artificial General Intelligence (AGI), developed with DeepMind CEO Demis Hassabis. He describes it as a system that can comprehensively perform a wide range of tasks, including cognitive ones, in a way that is comparable to human ability.

The debate over AGI is reframed: we have already achieved AI that is better than humans at over 50% of individual skills. The bottleneck is not technological capability but the massive cost and effort required to implement and integrate these systems fully, similar to how we have sustainable energy tech but haven't fully transitioned.

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.

Shane Legg proposes "Minimal AGI" is achieved when an AI can perform the cognitive tasks a typical person can. It's not about matching Einstein, but about no longer failing at tasks we'd expect an average human to complete. This sets a more concrete and achievable initial benchmark for the field.