The United States is increasingly organizing its artificial intelligence strategy around a concept it cannot clearly define, cannot reliably measure and may never achieve in the singular, decisive form imagined.

That concept is Artificial General Intelligence, or AGI.

In Washington and Silicon Valley, AGI has become the policy anchor and rhetorical North Star. Lawmakers invoke it to justify massive investments. Tech executives tie timelines to presidential terms or national dominance. Analysts warn that the first country to reach it will shape the global order. The language is urgent: a race, a finish line, a winner-take-all victory.

There is only one problem: no one agrees on what AGI actually is.

Moving target

Ask ten AI researchers for a definition, and you will likely get ten different answers. Some describe human-level performance across all cognitive tasks. Others frame it economically — the automation of the most valuable human labor. Still others emphasize autonomy, continuous self-improvement or the capacity for original scientific discovery.

These are not interchangeable. A system that excels at writing code, generating essays or solving benchmarks is not the same as one that can redesign its own architecture, conduct groundbreaking research or reliably operate in open, unpredictable environments.

Yet public debate and policy routinely collapse these distinctions into a single, shifting target. As observers have long noted, AGI often seems to mean "whatever the next system cannot yet do."

'Situated' intelligence

Even leading figures acknowledge the issue. OpenAI's Sam Altman has at times called AGI "not a super useful term" because definitions vary so widely. The goalposts keep moving, making any strategy built around hitting them inherently unstable.

The confusion runs deeper than semantics. AGI rests on an implicit and rarely examined assumption: that intelligence is a unitary capability that can be reproduced in a single system, and that it would closely resemble human cognition.

This is a category error.

A bird and an airplane both fly, but they do so through entirely different mechanisms. The similarity is in the outcome, not the underlying process. Today's AI systems are like airplanes: they perform tasks that resemble human cognition — reasoning, diagnosing, optimizing, creating — through statistical pattern matching on vast amounts of data, not through experience, intention, emotion or embodied understanding.

Human intelligence is "situated." It emerges from bodies, cultures, social relationships, context and lived reality. AI simulates tone without feeling it, reproduces patterns without inhabiting them, and generates language without genuine intention. This gap is not a temporary shortfall awaiting more scale. It is structural.