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 a## The Orthogonality of AI: Why Human and Artificial Intelligence Aren’t Racing, But Composing
the current discourse around Artificial Intelligence (AI) often frames its development as a race to surpass human intelligence.However, a more nuanced understanding suggests a different dynamic: not convergence, but *composition*. this perspective, rooted in the distinct nature of human and AI cognition, highlights the potential for a uniquely productive interplay, what’s being termed the “orthogonality dividend.”
### The Difference in How We “Think”
The fundamental difference in how humans and AI process information is illuminating. As an example, consider the past rivalry between nikola Tesla and Thomas Edison. while Edison focused on the discrete pulses of direct current (DC), counting individual units of electricity, Tesla envisioned the rotating field of alternating current (AC). He didn’t see the *effect* of electricity, but the underlying *source* – a circular motion that enabled a more efficient system. Tesla’s insight wasn’t about refining the pulses, but understanding the fundamental principle driving them.This analogy extends to human and AI cognition. The intersection isn’t in shared thought or experience, but in language – a “corridor” where both systems project patterns that occasionally align enough to facilitate communication.However,this is also where misunderstandings,even AI “hallucinations,” can originate,stemming from the projection space itself rather than the core systems.
### Orthogonal Axes, not a Shared Race
Much anxiety surrounding AI stems from the assumption of a shared axis of intelligence. But if human and AI intelligence operate along *orthogonal* (perpendicular) axes, the entire premise of a race dissolves. As explored in Psychology Today,AI isn’t attempting to catch up to or replace human intelligence; it’s simply moving in a different direction.
This difference is crucial.Human intelligence is deeply embedded in *lived time* and subjective experience.AI, conversely, operates on the architecture of complex computational systems – a geometric engine that, while powerful, lacks the grounding of human experience.
The true potential lies in harnessing this “corridor” not as a source of confusion, but as a domain for composition. A third, novel direction emerges when these perpendicular systems interact creatively. This isn’t achieved through larger models or faster processing speeds, but through purposeful human shaping of the projection space via art, design, dialog, and the request of uniquely human qualities.### The “Orthogonality dividend”
This concept is being called the “orthogonality dividend” – the idea that the very differences that make AI seem “anti-clever” are precisely what make this overlap productive. We don’t lose our cognitive identity in this interaction; we discover new ways to extend it.
Ultimately, understanding where the projections of human and AI intelligence overlap, and where they diverge, is paramount. The fundamental principles – the “circle” of Tesla’s AC and the “wave” of conventional computation – aren’t going away. Recognizing the space *between* them is where true understanding and innovation reside.