Why We Can’t Define AGI Until We Understand Human General Intelligence

The hidden flaw in Artificial General Intelligence debates and the philosophical work we must do before machines can truly be called intelligent

We speak confidently about building Artificial General Intelligence, yet we cannot agree on what human general intelligence actually is. Until we confront that gap, AGI will remain less a technological breakthrough and more a mirror—reflecting our incomplete understanding of ourselves.

Executive Summary

• Artificial General Intelligence cannot be meaningfully defined until we clearly understand what constitutes Human General Intelligence.

• Today’s AGI debates rely on narrow, performance-based definitions of intelligence that overlook judgment, meaning, and moral reasoning.

• Every attempt to define AGI implicitly posits a theory of what humans believe intelligence truly is.

• Scaling machine capability does not equal expanding understanding, wisdom, or responsibility.

• The future of AI will be shaped less by technological milestones and more by how coherently humanity defines intelligence itself.

Why We Can’t Define AGI Until We Understand Human General Intelligence

For all the urgency surrounding Artificial General Intelligence, the conversation carries a quiet contradiction. We speak with growing confidence about building something called “general intelligence” in machines, yet we remain remarkably unclear about what general intelligence actually means in humans. The closer we get to powerful systems that simulate cognition, the more exposed this gap becomes. AGI, it turns out, is not merely a technical horizon. It is a philosophical mirror—one that reflects how incomplete our understanding of ourselves still is.

Every age defines intelligence in the image of its tools. The ancient world equated it with reason and rhetoric, because those were the instruments of influence. The industrial age reduced it to standardized measures because factories needed predictable performance. The digital age now equates intelligence with pattern recognition and optimization, because that is what computers do best. None of these definitions was wrong. Each was simply partial. And partial definitions, when mistaken for the whole, have a way of distorting everything built upon them.

This is the hidden assumption behind today’s AGI discourse. We talk about benchmarks, capability thresholds, emergent behaviors, and scaling laws as if they are converging on something universally understood. But they are not converging on intelligence itself. They are converging on a model of intelligence—one shaped by what machines can already do well. Performance becomes the proxy for understanding. Speed becomes the stand-in for wisdom. Breadth of task completion becomes confused with depth of meaning.

Human general intelligence, however, has never been merely about doing many things well. It is about moving meaningfully between worlds. A person can reason about physics in the morning, comfort a grieving friend in the afternoon, navigate moral ambiguity in the evening, and imagine futures that have never existed before. None of these capacities reduces cleanly to benchmarks. They involve judgment under uncertainty, responsibility without rules, creativity without templates, and reflection that can question its own premises. They are not just cognitive functions. They are expressions of being.

This is why every attempt to define AGI is, whether acknowledged or not, a theory of human intelligence. When engineers say AGI is the ability to perform any intellectual task a human can, they are quietly asserting that human intelligence is primarily task performance. When philosophers argue that AGI requires consciousness, they are asserting that awareness is central to intelligence. When ethicists insist that agency and moral responsibility must be part of the definition, they are asserting that intelligence divorced from values is incomplete. These are not technical disagreements. There are disagreements about what kind of beings we believe ourselves to be.

The persistent failure to settle on a definition of AGI is not a sign of conceptual confusion. It is a sign of unresolved anthropology. We do not yet agree on what human general intelligence actually consists of. Is it problem-solving capacity? Is it self-reflection? Is it the ability to live meaningfully in a social and moral world? Until that question stabilizes, AGI will remain a moving target—less a destination than a projection of our evolving self-image.

This matters far more than most strategic roadmaps acknowledge. Organizations are building futures around speculative timelines for AGI arrival, while neglecting the more immediate task of redesigning how humans and machines think together. Education systems rush to teach tool fluency while sidelining judgment, synthesis, and ethical reasoning—the very capacities that distinguish human intelligence when automation scales. Governance debates fixate on controlling future superintelligence while struggling to articulate what kind of intelligence we wish to cultivate in ourselves.

The danger is not that machines will become too intelligent. The danger is that we will define intelligence too narrowly, and then build systems that amplify that narrowness at a planetary scale. Intelligence without interpretive depth becomes powerful but brittle. Intelligence without moral grounding becomes efficient but directionless. Intelligence without narrative meaning becomes fast—but blind.

AGI, if it arrives in any meaningful sense, will not announce itself through a benchmark score or a product launch. It will announce itself by forcing humanity to answer questions it has long postponed. What does it mean to understand rather than merely compute? What does it mean to decide rather than optimize? What does it mean to act wisely rather than effectively? Machines may catalyze these questions, but they cannot answer them for us.

The real shift, then, is not from narrow AI to general AI. It is from unexamined intelligence to examined intelligence. The future will not belong to those who predict AGI timelines most accurately. It will belong to those who articulate the nature of intelligence most coherently—those who understand that building thinking machines is inseparable from understanding thinking humans.

Until we can describe human general intelligence in its full dimensionality—cognitive, moral, cultural, emotional, and reflective—every claim of Artificial General Intelligence will remain provisional. Impressive, perhaps transformative, but incomplete. AGI is not merely something we are trying to build. It is something we are trying to understand about ourselves.

FAQs

Q: Why isn’t current AI already considered Artificial General Intelligence? A: Because today’s AI excels at task performance and pattern recognition, but lacks the broader capacities that define human general intelligence, judgment, moral reasoning, contextual understanding, and the ability to make meaning across radically different domains.

Q: What do you mean by Human General Intelligence? A: Human General Intelligence refers to the integrated capacity to reason, imagine, judge, empathize, create, and act responsibly across uncertain and changing contexts, not merely to solve predefined problems efficiently.

Q: Isn’t AGI just a technical milestone based on capability thresholds?

A: Treating AGI as a checklist of capabilities reduces intelligence to performance. This article argues that intelligence is not only about what can be done, but about how meaning, responsibility, and judgment are exercised in doing it.

Q: Why does defining human intelligence matter for AI development?

A: Every AI system reflects an implicit model of intelligence. If we define intelligence narrowly, we will build machines that amplify narrow thinking. A richer definition leads to wiser, more aligned systems.

Q: Does this mean AGI may never arrive? 

A: It means AGI will not meaningfully arrive until humanity clarifies what it considers true intelligence. The challenge is not technological impossibility, but conceptual incompleteness.

Q: What should leaders and organizations do now instead of waiting for AGI?

A: Focus on designing human-AI systems that strengthen judgment, sense-making, and ethical decision-making today. The future advantage will belong to organizations that treat intelligence as a living system, not merely an automated function.