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%).

Hassabis argues AGI isn't just about solving existing problems. True AGI must demonstrate the capacity for breakthrough creativity, like Einstein developing a new theory of physics or Picasso creating a new art genre. This sets a much higher bar than current systems.

Google DeepMind's Demis Hassabis includes physical embodiment in his 5-10 year AGI timeline, while Anthropic's Dario Amadei focuses on Nobel-level cognitive tasks in a 1-2 year timeline. This distinction is critical for understanding their predictions.

Moving away from abstract definitions, Sequoia Capital's Pat Grady and Sonia Huang propose a functional definition of AGI: the ability to figure things out. This involves combining baseline knowledge (pre-training) with reasoning and the capacity to iterate over long horizons to solve a problem without a predefined script, as seen in emerging coding agents.

Google DeepMind CEO Demis Hassabis argues that today's large models are insufficient for AGI. He believes progress requires reintroducing algorithmic techniques from systems like AlphaGo, specifically planning and search, to enable more robust reasoning and problem-solving capabilities beyond simple pattern matching.

When pressed on a timeline for AGI, Sundar Pichai argues the specific date is a distraction. He believes the critical factor is the accelerating rate of progress, stating society must prepare now for increasingly powerful systems, regardless of when they meet the definition of AGI.

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.

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.

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.