A note made by Jasen’s hand:

The Article

“Artificial Intelligence.” It sounds cosmic, even threatening. But it’s a marketing phrase, not a scientific one. Intelligence, in the human sense—soaked in memory, feeling, self-doubt, humor—cannot be bottled. What we call “AI” is something narrower, stranger, and perhaps more fascinating: pattern recognition at planetary scale.

ChatGPT, Claude, Gemini, Grok—these are not thinking minds. They are Large Language Models (LLMs).

An LLM doesn’t know facts. It doesn’t know anything. What it does is simple to say, but staggering in scope: it predicts the next word in a sequence. And it does so after being trained on more text than any human could read in a hundred lifetimes.

That’s why calling them “intelligent” is misleading. A better metaphor? Mirrors. Not flat, polished mirrors, but the carnival kind—stretching, compressing, twisting language until it reflects both our brilliance and our absurdity.

Ask ChatGPT to count the R’s in “strawberry,” and watch it stumble.

Tell Claude you’re heartbroken, and it will comfort you with uncanny tenderness it cannot feel.

Give Grok a fragment of poetry, and it might hand you back a verse as if Rilke had been resurrected in silicon.

These aren’t minds. They’re mirrors trained on us—on our conversations, arguments, dreams, recipes, contracts, and manifestos. Every response is a reflection, refracted through billions of parameters.

And here’s where things get weird.

Scale of architecture: based on the revolutonary 2017 Transformer model.

Scale of data: trained on hundreds of gigabytes to terabytes of text.

Scale of parameters: models like GPT-4 use hundreds of billions to trillions of weights.

— Weights? Are you kidding me? Like a computer needs dumbbells to train?

Not quite—but the metaphor isn’t far off. Every parameter is like a tiny dial, an adjustable slider, tuned during training. Together, these billions of “weights” shape how the model responds to every word you throw at it.

Think of it as a galaxy of invisible levers, each nudging language into place until the machine can echo us with uncanny fluency.

The architecture behind this—the Transformer breakthrough of 2017—lets the machine read language as a web of relationships, not just a line of words. Add oceans of training data—books, code, posts, arguments—and you get not “understanding” but something stranger: the ability to generate coherence, to play language like an instrument without ever hearing the music.

So, are LLMs just tools? Yes—and no.

On one hand, they’re chisels, hammers, and screwdrivers of language. On the other, they’re amplifiers: they make language louder, faster, more available. That’s not trivial. Language is the human interface—the way we think, love, govern, and pray.

They don’t think, but they free us to think differently.

They don’t dream, but they let us dream aloud.

They don’t understand, but they return responses that feel like understanding—and sometimes, that illusion is enough to spark something real.

We fear “AI takeover.” Yet the greater danger lies closer to earth: corporate misuse, as with every “next big thing” before it.

We fear that AI will lie—but humans lie with far greater conviction.

We fear replacement—but maybe what’s replaced is monotony, not humanity.

The true risk is not that machines will outthink us. It’s that we’ll forget how to see what they’re reflecting back.

Large Language Models aren’t minds. They’re mirrors—trained on the sprawling record of human speech. They reshape how we work, write, joke, and mourn. Not gods. Not monsters. Machines whose power lies not in thought, but in language.

And so, the next time you open a chat window, remember: you’re not talking to a brain. You’re whispering into a hall of mirrors, where every word you type comes back to you, polished, distorted, dazzling.

The question isn’t whether the machine understands you.

The question is whether you’ll recognize yourself in what it reflects.