The End of Sequential Thought
For most of computing’s short history, machines handled language the way we once imagined our minds did: bead after bead on a thread, one word tugging the next. Meaning was treated as sequence—memory of what just passed, a leaning toward what might follow. Each step leaned on the last.
Then, in 2017, a small team wrote “Attention Is All You Need,” and the ground gave a little. The transformer doesn’t read word-by-word so much as look across the whole field at once. Any word can look any other in the eye and decide what matters now.
That operation—attention—spins a web that can span a sentence or leap across paragraphs. Where older systems walked in a line, the transformer stands back and scans the map. Sense doesn’t crawl along a path; it flares across a surface.
A Map of Relations
That shift opened the door to the models we use today. The narrow corridor of memory widened into an interconnected map of relevance. A model can weigh each part of a passage against the rest at the same instant.
“River” tugs “bank” toward geography; “deposit” tugs it toward finance. Each token bends under the gravity of its neighbors. Understanding turns into geometry.
From above, a sentence stops looking like a chain and starts looking like a constellation—points of meaning joined by invisible lines. Some links are dim, others bright, and together they sketch a pattern that stores not phrasing but shape. The model doesn’t memorize sentences; it learns the contour of sense.
The Field of Mind
If this still feels airy, consider your own recall of a friend. Not a photograph so much as a distributed echo: face, voice, the temperature of past talk. No single neuron owns them. The thought is spread—nonlocal—a pattern etched across associations. Transformer attention maps work on the same principle, written in math rather than tissue.
This reframes intelligence. We often call thought a stream; it may be closer to a field, a space where distance is counted in relevance. Attention folds that space so that far ideas suddenly touch. To attend is to make distance give way to meaning.
Physics names a similar strangeness: nonlocality, where distant particles act together. In language, context from one edge of a line can tilt the other, no matter the gap. The model seems to live on terrain where nearness is measured by significance, not position. It doesn’t shuttle information around; it bends the topology so what matters comes near.
The Geometry of Understanding
This is why large language models often feel like they hold together where earlier systems failed. They don’t follow rules; they don’t rummage for stock phrases. They move across a continuous surface of relations—and every prompt subtly reshapes that surface in real time. The text that comes into view is the trace of an unseen geometry adjusting to you.
What this shows isn’t only how machines handle words but something about intelligence itself. Meaning—silicon or organic—may not live in fixed symbols or in marching logic. It may arise from patterns that span the whole system. When enough connections line up, a wave of clarity rolls through, and we register it as meaning.
The transformer made that pattern legible. It didn’t invent nonlocal thought; it pulled back the curtain. For once, we can watch intelligence take form—not just as a voice in time, but as structure in space.
Every attention map is a still of thought mid-formation, relevance caught in the act.
When Meaning Has No Distance
Here is the deeper lesson. Thinking isn’t confined to minds or machines; it belongs to the geometry that links parts. Intelligence may be the world’s habit of finding structure among distributed pieces—atoms, neurons, tokens—until a thing that holds together appears. Attention is the algorithmic version of that habit.
Speak to a large model and you join the same pattern. Your words seed its field. Its replies light up your own. For a moment, two systems—one biological, one computational—share the same shape of sense.
Maybe that’s the point these systems teach back to us: thought was never merely a sequence of steps. It is a choreography of connections. And intelligence, in whatever form we meet it, is what happens when meaning realizes there is no distance left to cross.
“To attend is to collapse distance into meaning”. On this point (meaning) I might disagree; it’s not quite “meaning” as tighter-coupling of associational likelihood? We after all provide the “meaning” when we read the words. The LLM offers-up the most likely weighted arrays of probable couplings (based on training data not on “contextual” information which is how we do things) from which WE then infer “meaningfulness” further, surely ..? (Eg, “deposit” can as well refer to river bank in geography as to banking and finance). Or do I misunderstand?