Howard Gardner spent decades trying to convince educators that the kid failing algebra might be a genius. His theory of multiple intelligences, published in 1983, proposed something radical: intelligence isn’t a single measurable thing. It’s at least eight distinct capabilities, from linguistic to spatial to interpersonal, and more recently, aspirational. Schools largely ignored him. The IQ test remained king.

Four decades later, we’re making the same mistake with machines.

The tech industry talks about artificial general intelligence as if “general intelligence” were a finish line we all agree on. OpenAI claims to be building AGI. DeepMind says it’s close. A dozen companies are racing toward the same goal. But here’s the uncomfortable question nobody pauses to answer: general intelligence measured by what standard? Defined by whom?

We’ve built a trillion-dollar industry around a word we can’t define.

The Measurement Problem

Consider what happens when you take an IQ test. You solve pattern recognition puzzles, manipulate abstract symbols, and recall vocabulary. You do this sitting still, in silence, with no social pressure and no real-world stakes. Then someone assigns you a number that supposedly captures your cognitive worth.

That number predicts certain outcomes reasonably well. But it measures a narrow slice of human capability, the slice that happens to matter for industrialized education systems and knowledge work.

What it doesn’t measure: the emotional intelligence that makes a hospice nurse effective, the kinesthetic genius of a master carpenter, the social cognition that allows a diplomat to read a room, the creative leaps that produce breakthrough art. These capabilities are real, valuable, and largely invisible to standardized testing.

When AI researchers talk about reaching human-level intelligence, they typically mean performance on benchmarks. Can the system pass the bar exam? Can it write code that compiles? These are meaningful metrics. They’re also profoundly limited.

What Machines Actually Do

Modern AI systems excel at pattern recognition across vast datasets. They predict the next token in a sequence with remarkable accuracy. They find statistical regularities that humans miss. This is genuinely impressive and genuinely useful.

But pattern recognition isn’t intelligence. It’s one component of intelligence, the way a wheel is one component of a car.

A large language model can discuss philosophy with apparent sophistication, but it has no embodied experience of the world it describes. It has never felt hunger, watched a sunset, or grieved a loss. It processes text about these experiences without the grounding that gives human language its meaning.

This isn’t a criticism. It’s a clarification. The systems we’re building are powerful tools for specific cognitive tasks. Calling them “intelligent” obscures more than it reveals.

The Biological Baseline

Human intelligence evolved under particular pressures. Our ancestors needed to track prey, identify poisonous plants, navigate social hierarchies, and teach their children. Intelligence, for us, is fundamentally about survival in physical environments filled with other minds.

This shapes everything about how we think. Human cognition is embodied, social, and emotional. We reason with metaphors drawn from bodily experience. We learn through imitation and culture. We make decisions through felt senses that integrate information too complex for conscious analysis.

None of these maps cleanly onto silicon architectures.

When we build AI systems, we’re not recreating human intelligence. We’re creating something new that shares some surface features with human cognition while differing at the substrate level. The question isn’t whether machines will become intelligent like humans. They won’t. The question is what kind of capabilities they’re developing and what that means for how we use them.

The Constellation Model

Here’s a more useful frame: intelligence as a constellation of capabilities rather than a single trait.

That constellation includes reasoning through novel problems, recognizing patterns across domains, learning from limited examples, adapting to changing environments, understanding other minds, creating genuinely new ideas, and knowing what you don’t know. Humans exhibit all of these to varying degrees. Current AI systems exhibit some powerfully and others barely at all.

GPT-4 can reason through complex problems in ways that surprise even its creators. It cannot, however, reliably know when it’s wrong. It lacks the metacognition that allows humans to feel uncertain, to seek more information before committing to an answer.

This matters enormously for deployment. A tool that’s brilliant at pattern matching but blind to its own limitations requires different guardrails than a tool with robust uncertainty estimation. Calling both “intelligent” collapses a distinction that determines whether the tool helps or harms.

Why This Matters Now

The race to AGI is premised on a category error. We’re trying to reach a destination we haven’t defined using a map we haven’t drawn.

Here’s the blunt reality: what the industry calls “artificial general intelligence” is just intelligence. We haven’t reached it yet. Current systems are sophisticated, useful, and impressive. They are not intelligent in any rigorous sense of the word. They lack the integrated, adaptive, self-aware cognition that the term implies.

And what do people call “superintelligence”? That’s sapience. Sapience involves not just processing power and pattern recognition at scale, but also wisdom, judgment, self-reflection, and an understanding of one’s own existence and purpose. The architectural configurations required for genuine sapience aren’t slightly out of reach. They aren’t on any current roadmap. We don’t even have a credible theory for how to build them.

So the industry’s terminology ladder skips two rungs. We talk about achieving AGI when we haven’t achieved intelligence. We speculate about superintelligence when sapience remains a philosophical puzzle rather than an engineering problem.

This creates real problems. Researchers optimize for benchmark performance that may not track actual capability improvements. Companies market systems as more capable than they are because “intelligent” is vague enough to cover almost anything. Policymakers struggle to regulate technologies they can’t precisely characterize.

More fundamentally, we miss opportunities to build better systems. If we stopped asking “how do we make AI more intelligent?” and started asking “which specific capabilities should this system have for this particular use?”, we’d make faster progress on problems that matter.

A medical diagnosis system doesn’t need general intelligence. It needs robust pattern recognition, calibrated uncertainty, and the ability to flag cases that require human judgment. A creative writing assistant doesn’t need to understand mortality. It needs to recognize narrative patterns and generate variations that surprise without losing coherence.

Specificity beats abstraction here. And it’s more honest.

The Path Forward

We’re not going to stop using the word “intelligence.” It’s too embedded in the conversation. But we can use it more carefully.

That means specifying which capabilities we’re discussing when we make claims about AI progress. It means acknowledging the profound differences between human and machine cognition rather than glossing over them with shared vocabulary. It means building evaluation frameworks that capture the multidimensional nature of cognitive capability.

Most importantly, it means approaching both human and artificial intelligence with appropriate humility. We don’t fully understand how human minds work. We don’t fully understand what our AI systems are doing internally. Acting as if we’ve solved problems we’ve barely articulated is a recipe for expensive surprises.

The question isn’t whether machines will become intelligent. They already are, in specific and limited ways. The question is whether we’ll develop the conceptual clarity to understand what we’re building and the wisdom to build it well.

That’s a form of intelligence we still need to develop ourselves.

---

If you find this content valuable, please share it with your network.

Follow me for daily insights.

Schedule a free call to start your AI Transformation.

Book me to speak at your next event.

Chris Hood is an AI strategist and author of the #1 Amazon Best Seller Infailible and Customer Transformation, and has been recognized as one of the Top 40 Global Gurus for Customer Experience. His latest book, Unmapping Customer Journeys, will be published in April 2026.