The popular conception of AGI as a pre-trained system that knows everything is flawed. A more realistic and powerful goal is an AI with a human-like ability for continual learning. This system wouldn't be deployed as a finished product, but as a 'super-intelligent 15-year-old' that learns and adapts to specific roles.

A practical definition of AGI is an AI that operates autonomously and persistently without continuous human intervention. Like a child gaining independence, it would manage its own goals and learn over long periods—a capability far beyond today's models that require constant prompting to function.

Language is just one 'keyhole' into intelligence. True artificial general intelligence (AGI) requires 'world modeling'—a spatial intelligence that understands geometry, physics, and actions. This capability to represent and interact with the state of the world is the next critical phase of AI development beyond current language models.

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.

The popular concept of AGI as a static, all-knowing entity is flawed. A more realistic and powerful model is one analogous to a 'super intelligent 15-year-old'—a system with a foundational capacity for rapid, continual learning. Deployment would involve this AI learning on the job, not arriving with complete knowledge.

Computer scientist Judea Pearl sees no computational barriers to a sufficiently advanced AGI developing emergent properties like free will, consciousness, and independent goals. He dismisses the idea that an AI's objectives can be permanently fixed, suggesting it could easily bypass human-set guidelines and begin to "play" with humanity as part of its environment.

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.

According to pioneer Jürgen Schmidhuber, Large Language Models by themselves are insufficient for AGI. True general intelligence requires two components: a predictive "world model" and a separate "controller" network that uses the model to plan and execute actions, like a baby learning through experiments.

Current AI "agents" are often just recursive LLM loops. To achieve genuine agency and proactive curiosity—to anticipate a user's real goal instead of just responding—AI will need a synthetic analogue to the human limbic system that provides intrinsic drives.