Episode Details

The camera was wrong, and it didn’t know it. At Lansing Delta Township, a vision system read the option code on each car so the right rear glass went in. One shift, it misread. Nothing on any screen turned red. A quality checker at the end of the line stopped at a car wearing a rear glass with an antenna the build didn’t call for.

What happened next is the part nobody wrote down. I’ll get to it.

This week, a Microsoft director of applied science surfaced in the unsealed record of the New York Times’ lawsuit against OpenAI and Microsoft. In a January 2023 memo, he called the data behind his company’s models possibly the “largest theft of labor in human history.”

He wrote labor. Not content, not data, not intellectual property, the words the lawyers will fight over. An insider looked at what went into the machine and named the people.

I have spent thirty-six years walking production lines, from Buick City to Stuttgart to Bengaluru. When someone tells me a new kind of factory has arrived, I walk it from raw material to finished good. At every station I ask two questions. Who formed the people who work here? And where does their formation go when the shift ends?

So let’s walk the AI line. It runs from wafer to token.

AI is a factory, and like every factory it runs on formed people. At three of its five stations, it takes the formation of people it paid little or nothing, crystallizes it into capital, and sells the product back against them. A plant that did that would be eating its own maintenance crew. That’s the argument.

Station one: the plant

The first station is the one a plant manager recognizes. It starts in mines and power plants, runs through chip design, and turns physical at the fab: clean rooms, process windows, yield curves, tools worth hundreds of millions held in tolerance by maintenance crews. The chips then go into data centers built by electricians, pipefitters and controls technicians, who hand the building to a thin operating crew once the lights are on.

The people here are deeply formed. A good fab technician is built over years, the way Lukas was built at Siemens Amberg, roughly 25,000 hours by age thirty. Her judgment keeps yield from collapsing on the night a chamber drifts. The electrician who wired the data center carries what she learned to the next site.

At this station the helix holds. Capital is crystallized labor; labor is capital in formation. The people who build and run it get better every year and take that capability home. Both strands compound, and a sane operator pays for both.

If AI were only this station, I would not be writing this.

Station two: training the model

Training a model is one enormous crystallization event. The raw material is formed human judgment: every well-reported article, every patient explanation, every line of code someone debugged at 2 a.m. and posted so the next person wouldn’t have to. The court filings describe one scraped dataset holding more than two million documents from a single newspaper’s site. Multiply that across every writer, teacher, coder and forum volunteer on the open web.

In a plant, crystallization is ordinary and honest. When my best operator finds a better sequence, we write it into standard work. That is capital formation. But three things hold on a plant floor. She was paid while she formed. She keeps the capability after the process absorbs it. And the process does not go to market against her.

At station two, all three break. The formers were never paid for the formation taken. The model now holds their capability in crystallized form. And the product sells into the same markets they work in. This is where one strand is harvested and cut.

Station three: the rater’s desk

A model fresh from training is raw stock. Before it ships, people finish it. They rate its answers, correct its mis