There’s an image from basic mathematics that has been resonating with me lately. If you trace a small point on the rim of a spinning wheel and watch only its vertical position over time, the path you see a smooth, regular undulating wave that some may recall as a sine wave. But that wave is only the shadow of something that is more full and interesting. Behind it is a continuous circular motion that is rich with direction and momentum. If all you ever saw were the oscillations, you’d never truly guess the circle that produced them.
Somewhere within my own thinking about artificial intelligence I realized that this simple geometry may offer a surprisingly accurate way to understand the relationship between human thought and AI. Human cognition is the circle that is embodied, continuous, shaped by time. AI is the wave is the projection cast into the narrow slice we call language. And the place where the two appear to meet is not a shared mind, but a shared shadow.
This is what I’ve begun calling the “Corridor”. This is a thin “projection space” where two different systems overlap. It’s not a bridge, and it’s certainly not a merger. It’s simply the only dimension they can both reach. So, take a breath and let’s take a closer look.
The Human Axis
Simply put, human cognition carries the weight of continuity. Experiences don’t pass through us, they accumulate. A decision from years ago still shapes the instincts we follow today. A mistake we once made still governs the caution we bring into a room. Our memories aren’t just archived, they’re metabolized into identity.
Biology gives this process rhythm. During sleep, the brain evaluates the day’s impressions, strengthens some traces, prunes others, and weaves them into the evolving story of who we are. It’s not a storage process. It’s a negotiation, perhaps even an editorial meeting in the dark. The outcome is a sense of self that has history and consequence.
The AI Axis
I’ll argue that artificial intelligence operates on a different structure entirely. A large language model does not remember in the human sense, and it carries yesterday into today. In other words, it doesn’t inhabit time as we do. Inside LLM, ideas are represented as vectors in a mathematical space where relationships are defined by proximity and pattern.
When we ask an AI system to generate an essay or a note of sympathy, it finds a point in that space that aligns with the request and projects it into language. There’s no personal history behind the output, the model produces fluency without narrative weight.
Orthogonality and Why It Matters
In mathematics, two axes are orthogonal when movement along one tells you nothing about movement along the other. Push all you want in one direction, and you still produce zero motion in the perpendicular dimension.
Human and AI cognition sit in this kind of relationship. Human reasoning is shaped through continuity and the cost of experience. AI reasoning unfolds through high-dimensional pattern geometry. Their internal motions share no overlap, even when their outputs (on the surface) look similar.
This is the deeper structure behind the idea I’ve previously called anti-intelligence. And in this context, it reveals the risk of mistaking the quality of the projection for the nature of the source. Key insight: a fluent sentence isn’t evidence of a shared mind but of overlapping shadows.
The Corridor: Where Shadows Touch
Question: If the axes are perpendicular, how is communication even possible?
Answer: Because projection can create a shared surface.
Two systems with fundamentally different internal structures can still produce patterns that overlap when compressed into a lower dimension. Think of turbulence in an airplane. The air outside is chaotic yet our body is biological and perceptual. They never “meet” in any meaningful cognitive sense. But their projections meet in the vibration you feel in your seat. The vibration is the corridor.
Or consider an MRI scanner. Your body and the machine inhabit different worlds of living tissue on one side, electromagnetic fields on the other. But inside the scanner, billions of hydrogen nuclei begin to precess like tiny spinning tops. Their motion is circular, rotating through space. The machine never “sees” that rotation directly. It detects only the projection of that “spin” onto an orthogonal axis. That projection is the corridor, the thin slice where two incompatible systems—biology and electromagnetism—speak a common language.
Artificial Intelligence Essential Reads
Curiously, Nikola Tesla understood this dynamic intuitively. When he visualized alternating current, he didn’t see electricity as a series of discrete pulses. He saw the rotating field behind it and how that circular motion that makes AC possible. While Edison counted pulses, Tesla saw the circle spin and built the future on the source, not the shadow.
Human and AI cognition meet this way and not in shared thought or in shared experience. They meet in language and at that narrow slice where both “minds” cast patterns that sometimes align enough to produce a conversation. And the corridor is also where misunderstandings arise. Aberrancies, like hallucinations, can originate in this projection space, not in the underlying systems.
Not Convergence, but Composition
Much of the anxiety surrounding AI imagines a race along a shared axis of thought. But if the axes are orthogonal, that entire framing dissolves. AI isn’t catching up or surpassing (or replacing) human intelligence, it’s moving along a different direction.
The real frontier lies in how we use the corridor and not as a place of confusion, but as a domain of composition. A third direction becomes possible when two perpendicular systems interact creatively. That new axis won’t come from larger models or faster chips. It will come from how humans learn to work with the projection space deliberately, shaping it through art, design, dialogue, and our humanity.
I’m calling this the “orthogonality dividend.” The idea that the very differences that make human and AI thought so “anti-intelligent” also make this overlap productive. We don’t lose our cognitive identity in the corridor, we discover new ways to extend it.
Finding the Right Distance
Human intelligence carries the substance of lived time. AI carries the architecture of an incomprehensible geometric engine. They don’t share a mind, and they don’t need to. What matters is understanding how and where the projections overlap and where they don’t.
The circle isn’t going away, and neither is the wave.
And recognizing the corridor between them is a place of understanding and magic.
