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Back in the 1950s, a Russian linguist named Roman Jakobson walked into a Harvard classroom and found economic equations on the blackboard. Instead of erasing them, he said, “I’ll teach with this.”

Why? Because he understood something profound: language works like an economy. Words relate to other words the same way supply relates to demand.

Fast forward seventy years. We built machines that prove Jakobson right.

The literary theory nobody read

In the 1980s, professors with unpronounceable names wrote dense books about how language is a system of signs pointing to other signs. How meaning doesn’t come from the “real world” but from the web of relationships between words themselves.

Everyone thought this was academic nonsense.

Turns out, it was a blueprint for ChatGPT.

What we got wrong about AI

We keep asking: “Is it intelligent? Does it understand?”

Wrong questions.

Better question: “How does it create?”

Because here’s what’s actually happening inside these machines: They’re mapping the statistical relationships between every word and every other word in human culture. They’re building a heat map of how language actually works.

Not how we think it should work. How it does work.

The poetry problem

A Large Language Model doesn’t write poems. It writes poetry.

What’s the difference?

Poetry is the potential that lives in language itself—the way words want to dance together, the patterns that emerge when you map meaning mathematically.

A poem is what happens when a human takes that potential and shapes it with intention.

The machine gives us the raw material. We make the art.

Why this matters

Two groups are having the wrong argument:

The AI boosters think we’re building digital brains. The AI critics think we’re destroying human authenticity.

Both are missing the point.

We’re not building intelligence. We’re building culture machines. Tools that can compress and reconstruct the patterns of human expression.

That’s not a bug. It’s the feature.

The real opportunity

Instead of fearing these machines or anthropomorphizing them, we could learn to read them.

They’re showing us something we’ve never seen before: a statistical map of human culture. The ideological patterns that shape how we think and write and argue.

Want to understand how conspiracy theories spread? Ask the machine to write about mathematics and watch it drift toward culture war talking points.

Want to see how certain ideas cluster together in our collective imagination? Feed it a prompt and trace the semantic pathways it follows.

What comes next

We need a new kind of literacy. Not just reading and writing, but understanding how these culture machines work. How they compress meaning. How they generate new combinations from old patterns.

We need to become rhetoricians again. Students of how language shapes reality.

Because these machines aren’t replacing human creativity.

They’re revealing how human creativity actually works.

The future belongs to those who can read the poetry in the machine.

Based on a post by Henry Farrell

ChatGPT advises women to ask for lower salaries, finds new study

July 11, 2025Business

A new study has found that large language models (LLMs) such as ChatGPT consistently advise women to ask for lower salaries than men, even when both have identical qualifications. The research was led by Ivan Yamshchikov, a professor of AI and robotics at the Technical University of Würzburg-Schweinfurt (THWS) in Germany. Yamshchikov, who also founded Pleias — a French–German startup building ethically trained language models for regulated industries — worked with his team to test five popular LLMs, including ChatGPT. They prompted each model with user profiles that differed only by gender but included the same education, experience, and job…