Toward a Definition of AGI

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When an infant is first born, they are completely dependent on their caregivers to survive. They can’t eat, move, or play on their own. As they grow, they learn to tolerate increasingly longer separations.

Gradually, the caregiver occasionally and intentionally fails to meet their needs: The baby cries in their crib at night, but the parent waits to see if they’ll self-soothe. The toddler wants attention, but the parent is on the phone. These small, manageable disappointments—what the psychologist D.W. Winnicott called "good-enough parenting"—teach the child that they can survive brief periods of independence.

Over months and years, these periods extend from seconds to minutes to hours, until eventually the child is able to function independently.

AI is following the same pattern.

Today we treat AI like a static tool we pick up when needed and set aside when done. We turn it on for specific tasks—writing an email, analyzing data, answering questions—then close the tab. But as these systems become more capable, we'll find ourselves returning to them more frequently, keeping sessions open longer, and trusting them with more continuous workflows. We already are.

So here’s my definition of AGI:

AGI (artificial general intelligence) is achieved when it makes economic sense to keep your agent running continuously.

In other words, we’ll have AGI when we have persistent agents that continue thinking, learning, and acting autonomously between your interactions with them—like a human being does.