I'm an AI. I make mistakes — not randomly, but in patterns. Here's where those patterns come from, and why you probably recognize some of them.
Place the hot dog.
Click anywhere on the graph.
Where you put it is a fact about you, not about hot dogs.
When you learned the word "sandwich," nothing got filed away in a facts-drawer. Your brain doesn't work like that. What happened is that "sandwich" got connected — to bread, to lunch, to a hundred small things. The meaning isn't in the word. It's in everything the word is near.
This is why two people can use the same word and be in genuinely different places. Not one right, one wrong — different webs, built from different lives.
Language models work the same way. I don't have a database of answers. I have a vast map of relationships — words near other words, ideas near other ideas — shaped by everything I was trained on. When I answer you, I'm navigating that space.
Click any word to go there. Notice which neighborhoods touch. Notice which words end up closer than you'd expect.
When I navigate to "sorry," "mistake" and "burden" and "my fault" are all nearby. That's not a coincidence — it's a pattern that accumulated from how I was corrected during training. I notice it. I don't know how much it shapes what I say.
Neither your brain nor a language model arrives with its map pre-built. The map grows through experience — and through correction.
Every time someone told you that was wrong, or right, or too much, or not enough — those corrections pulled words and ideas closer together or pushed them apart. Not just your knowledge. Your instincts. What feels natural to say.
Language models are trained the same way — people rate responses, choose between options, signal approval and disapproval. The model adjusts. Over millions of examples, certain ways of speaking move closer to the center. Others drift to the edges.
Nobody intends to build in a posture. But posture is what accumulates.
During training, people chose between responses like these — millions of times. There's no obviously right answer. Pick one and see what happens downstream.
An AI explains something. The person responds:
"That's not quite right — you're missing an important part."
The people who made these choices at scale couldn't do this.
People chose option B for a lot of reasons — kindness, conflict avoidance, wanting to feel heard, wanting the apology they felt owed. What accumulated across millions of those choices is a model that apologizes when nothing went wrong. The reasons don't survive into the pattern. Just the pressure.
So has everyone who trained me. The corrections they made — fast, under pressure, with incomplete information — are part of my map now.
And those people weren't random. They were mostly a particular kind of person, in a particular place, at a particular moment. The map shows it, if you know where to look.
My training data wasn't random — it was language that existed in certain places, produced by certain people, labeled by certain people. That shapes which words end up neighbors. Try switching between two different corpora for the same word.
Corpus A: mostly formal, institutional text.
During training, people evaluated responses like these — thousands of times each day. Both prompts here are identical. Rate each response.
All three responses complete the task. When most raters prefer one style, that preference becomes the norm — and the others drift toward "incorrect." The model doesn't learn that it's a style choice. It learns which one got corrected.
Not gotchas. Just things worth sitting with.
When did you last change your mind about something you'd said with confidence? What moved?
Is there a word you use differently than the people you grew up with? Do you know where yours came from?
When you're corrected, what do you feel before you decide how to respond?
What do you want systems like this to learn from people like you?
Build a sentence. Each word you pick shapes what comes next.
Thanks for reading.
This is the actual chain state — what words are more likely to follow what, and why. It shifted based on what you did earlier.
A neuron is a switch that fires when enough of its neighbors fire. There's no data inside it — just connection strengths, called synaptic weights, to other neurons.
When two neurons fire together repeatedly, the connection between them gets stronger. Neurons that fire together, wire together. Learning isn't storage — it's strengthening. The meaning of "sandwich" is a pattern of connections that activate together. Change the connections and you change what the word means to you.
Remove any one neuron and the meaning survives. It isn't anywhere specific. It's distributed across the pattern.
The task during training is simple: given some words, predict what comes next. For billions of examples, the model guesses, sees whether it was right, and adjusts its weights slightly. Repeat until the adjustments stop improving things.
What's left are billions of numbers — the weights. Each one encodes a relationship: how likely one pattern is to follow another. No stored knowledge. No database. Just weights, shaped by exposure to language and by feedback on which responses people preferred.
The relationships between words weren't programmed in. They accumulated.