There’s a line I’ve always loved: “The map is not the territory.” Alfred Korzybski wrote it in 1933 as a warning that our descriptions of reality are never the thing itself. Maps guide us, but they aren’t the ground we traverse. Lately, that line feels more relevant than ever. Because for the first time in history, we’ve built something that lives entirely inside the map. Artificial intelligence, especially the large language models shaping our era, doesn’t walk through the territory of experience. It moves through a hyperdimensional matrix of tokens linked to probabilities. Yes, it’s fluent, astonishingly so, yet blind to the world those words describe. I call this anti-intelligence: the performance of understanding without the consciousness of experience. It’s a term I’ve used before, but here it takes on new weight. AI doesn’t lie or misbehave. It simply operates outside the bounds of reality.

Human cognition has always been a negotiation or even battle between imagination and experience. We build models and then we test them. We get things wrong, learn, and rejigger against the facts of the real world. Our intelligence lives in that loop between abstraction and embodiment. AI has no loop. It never leaves the page. When a model falters because of a stray phrase—say, when the simple addition of “cats sleep for most of their lives” triples its error rate.  Now, let’s be clear, that’s not confusion, it’s exposure. The system doesn’t know which parts of language belong to meaning and which don’t. It reads everything as pattern. That’s the curious mirage of AI. It’s the words without the world.  Or should I say map?

Korzybski famous and timeless quote was about humans, not machines. He warned that when we mistake a symbol for the thing it represents, we drift toward ambiguity, if not fiction. What’s unsettling now is that we’ve mechanized that ambiguity in the context of AI. We’ve built a technological architecture that embodies it with an odd perfection. And because AI speaks so persuasively, we start to believe it. A generated paragraph about empathy can feel like empathy itself. And a simulated diagnosis can feel like understanding. The danger isn’t deception, it’s equivalence. So, remember, the algorithm doesn’t lie, it just neither knows nor cares.

So, if AI lives in the map, then we remain the territory. The goal isn’t to merge the two but to hold them in tension. That distance—between representation and reality—is where depth arises. I’ve called this parallax cognition: when two distinct forms of knowing observe the same problem from different vantage points. The difference creates critical dimensionality. Consider AlphaFold, the AI that predicted protein structures. It recognized patterns invisible to us, but the discovery only mattered once human scientists interpreted what those patterns meant in biological terms. That’s parallax in action. AI sees the map and we walk the ground. Together, but distinct, we generate insight neither could reach alone.

There’s a fair question that’s often raised: If it works, does it matter how? For translation, maybe not, for navigation, perhaps less. But in meaning-dense domains like medicine, ethics, and fine art, how it works is the difference between simulation and understanding. AI’s competence can mask its detachment and the map can be dazzling enough that we forget it isn’t the journey.

Anti-intelligence isn’t a flaw, it’s the logical endpoint of symbol-based reasoning. It represents the perfection of the map and the potential elimination of the territory. Korzybski’s century-old warning is resonant today.  Once our abstractions become too beautiful, we start living inside them. AI has given us the most complete map humanity has ever drawn. The challenge is to stay grounded and to make sure the map still serves our earth beneath it.