Seeing Like a Language Model
Dan Shipper / Chain of Thought
by Dan Shipperin Chain of ThoughtMidjourney/Every illustration.Last week I wrote that we’d be publishing a few excerpts from a book I’m writing about the worldview I’ve developed by writing, coding, and living with AI. Here’s the first piece, about the differences between the old (pre-GPT-3) worldview and the new.—Dan ShipperWhen I say the word “intelligent,” you probably think of being rational. But language models show us that this assumption is wrong.To us in the West, smarts are about being able to explicitly lay out what you know and why you know it. For us, the root of intelligence is logic and reason to ward off superstition and groupthink; it is clear and concise definitions to eradicate vague and wooly-headed thinking; it is formal theories that explain the hidden laws of the world around us—simple, falsifiable, and parsimonious yet general enough to tie together everything in the universe from atoms to asteroids.Our romantic picture of ourselves as “rational animals,” as Aristotle said, has produced everything in the modern world—rockets, trains, medicines, computers, smartphones.But this picture is incomplete. It contains a huge blind spot: It neglects the fundamental importance of intuition in all of intelligent behavior—intuition which is by nature ineffable; that is to say, not fully describable by rational thought or formal systems.How to build a thinking machineThe best way to understand what I’m talking about is to imagine trying to build a thinking machine. How would you do it?Let’s start with a task, something easy and basic that humans do every day. Maybe something like scheduling an appointment.Let’s say we’re a busy executive who gets the following appointment request:New requestFrom: Mona LeibnisHey,I’m available Monday at 3 p.m., Tuesday at 4 p.m., Friday at 6 p.m.When can you meet?We want to build a machine to intelligently schedule an appointment. How would we go about it?We’d probably start by giving our machine a couple of rules to fol...
4 Oct 2025
The drive for simple, general, abstract, universal theories has been a resounding success. It’s responsible for our progress out of darkness and superstition and into modernity. It has brought about the Western world. But it also fails when it becomes totalizing—when we allow our search for these truths to blind us to the real complexity of the world, and to alternative ways of seeing it.
What we can say about the world is not all that we know about it. We know many things that we can’t say explicitly. And the drive to say everything explicitly is doomed to failure.
But when language models see something different: Meaning emerges through contrast and relationship.
When they process text, they don't isolate words like carbon atoms under a microscope. A carbon atom in a leaf is identical to a carbon atom in a trunk, but that tells you nothing about what makes a leaf a leaf, or a trunk a trunk. Instead, language models see how words work together in the larger patterns of meaning. The word "dark" means something different when it's part of "dark chocolate" versus "dark thoughts" versus "dark matter," just as a carbon atom means something different when it's part of chlorophyll versus cellulose. What matters isn't the atomic unit, but its place in the whole: how it connects, what patterns it forms, and what larger structures it helps create.