You helped build AI. Should you own part of it?

You may have been struggling to place AI in your worldview. Just another new tech thing like the computer or the internet. An alien species visiting earth through a digital portal. A swarm of digital ghosts summoned in the technology temples we call data centers.

This post will give you a clear picture of where AI fits in human existence. With that picture in hand, a lot of confusion clears up, and the doomer-versus-utopian arguments start to look like fights about the wrong question.

Let’s start with me.

A personal layer

Say I am an author. I have read books, articles, listened to podcasts, watched documentaries. I have a few ideas of my own. All of this sits inside me as what we might call my personal knowledge layer.

I sit in a park staring at a tree, traversing this layer until a few connections light up that seem interesting and original. I decide it’s a book.

Months later the book is finished. I publish through Amazon print-on-demand. I sell a few copies.

What happened: something moved from my non-material personal knowledge layer into the material layer we all share. From inside me, into the world.

Later I check the Amazon sales stats. 333 copies sold.

Photons leave the screen, hit my eyes, become electrical signals in my brain. I understand the number. It doesn’t matter what font, what size, what color. Recognition is the reverse direction: from the material layer back into my personal knowledge layer.

Around the turn of the century, computers learned to do this too. You show software thousands of examples of each digit, and it builds a network that can infer from a picture which number it is. This is OCR, optical character recognition.

What happened there: developers took elements from the material layer and built a mechanical version of something resembling the part of our brain that recognizes numbers. A small piece of mind, turned into machinery.

Recognizing a number is a mental activity. The software performs that activity. In our brain it’s neurons. In the computer it’s software. Two physical instruments, same mental function.

A collective layer

In 2017 a paper called “Attention is All You Need” signaled the beginning of large language models. Today they are widely used. ChatGPT, Claude, Grok.

OCR captured a tiny piece of mind. LLMs are far more ambitious. They are fed every piece of writing the developers can get their hands on. Hence the word “language” in large language model.

A useful unit of measure: a book.

A typical adult is exposed to maybe 10,000 books worth of text in a lifetime. An LLM, during training, is exposed to roughly 100 million books worth.

That much input changes what the captured thing is. Below a certain scale you have a reference work, a dictionary, an archive. Above it, you have something that can generate from inside the layer’s own logic.

What an LLM infers from this vast input is not a particular skill, like recognizing a digit. It infers the collective layer that all these expressions came from. The shared sense inside people from which writing emerges. A shared mind, of a kind. Not Jung’s collective unconscious, which was supposed to be biological and inherited. This is cultural, learned, the thing you absorb by being among other people and their works. The thing that makes a French sentence French.

Here is my point: an AI model is a physical artifact built from the inferred layer of all the humans whose expressions went into it.

This is what we traverse when we chat with an LLM.

What is new about this

Every prior tool captured one skill at a time. The Jacquard loom captured weaving. The calculator captured arithmetic. GPS captured navigation. Each of them took a particular human ability, made it explicit enough to encode, and built a machine to perform it. The skill was usually held by a class of practitioners. Society absorbed the displacement, slowly, generation by generation.

The AI revolution is structurally different. It does not capture a particular skill. It captures the general layer from which skills are exercised. Reading, writing, summarizing, comparing, explaining, framing, all of these fall inside it.

The industrial revolution mechanized one skill at a time. The AI revolution mechanizes everything at once.

That is the difference, in one sentence.

Two further things follow from this.

The scale is unprecedented. There has always been some absorption of cultural output into tools, but at the scale of 100 million books, the artifact stops behaving like a record of the layer and starts behaving like the layer.

The output is the same kind of thing as the input. The Jacquard loom did not weave master weavers. Books, articles, conversations come out of the LLM, the same kind of things that went in. This is recursive in a way no prior mechanization was.

What this clarifies

Once you see an AI model as the materialized version of the collective layer, several things that have been confusing become clear.

Training is not copying. The current legal debate about AI training data assumes that what was taken was particular works. What was actually taken was the layer those works expressed. This is why the existing vocabulary of copyright keeps misfiring. It is also why authors and artists feel something has been taken even when no specific work was reproduced. Their contribution to the layer was lifted out and put in a box.

Hallucination is not a bug in a knowledge base. The collective layer was never built to hold facts. It holds shape, association, plausibility, sense of fit. Particulars hold facts. When you query the layer directly, you get layer-shaped output: well-formed, often correct, sometimes confidently wrong. That is what the layer is like.

The technology is genuinely new. Print, recording, photography, broadcast, the internet, all expanded the material layer. They moved more particulars, faster, to more people. None of them touched the non-material layer’s substrate. The model is the first technology to operate on that layer itself. This is the actual break, and it has been hard to name because it does not look like the prior breaks.

What follows from this

Some predictions are easier to make once you see the technology in this light.

The displaced party is not a class but a stratum. Earlier mechanizations displaced specific groups: weavers, typesetters, telephone operators. Each group eventually retrained into work the previous wave had not yet captured. The AI revolution approaches every form of work that involves expression, judgment, summarization, or composition in language. There is no obvious next stratum to retrain into, because the underlying capacity for retraining is part of what is being mechanized.

The speed compresses the adjustment beyond historical precedent. The industrial revolution played out over roughly a century. The current wave is operating on a timeline of years. Generational replacement, the mechanism that absorbed prior shocks, does not work at this speed.

Concentration of power follows the structure of capture. A small number of companies hold the operative substrate of most cognitive work, trained on contributions from everyone. Historically, when private actors hold something the state cannot do without, the state finds a way to control it. Some form of public stake in frontier AI is becoming inevitable. The current dispute between Anthropic and the Department of War is an early sign of how the state is already thinking about these companies.

Meaning structures will be shaken. The industrial revolution disrupted material life and class structure, but most people’s sense of what made them human, thinking, expressing, understanding, deciding, was untouched. The AI revolution operates exactly on that ground. Religious revival, political extremism, retreat into tradition, and new meaning frameworks are all likely to appear, in different proportions in different places.

What cannot be predicted: the political form of the response, whether the technology continues improving or hits a ceiling, whether human-AI collaboration finds a stable form. Most of the doomer-versus-utopian discourse fights about these unpredictable things while taking the predictable shape for granted.

What is owed

If a model is the materialized collective layer, then the layer is not the property of the works it was inferred from. It is the property of the carriers. Every speaker of a language, every participant in a tradition, every person who wrote a letter or a comment or a help document. The layer was always collectively held.

The principle is older than the technology. When you draw from a commons, you owe the commons something back. Open source software encodes this norm in the technical world. Wikipedia runs on it. The villager who shows up to the potluck, eats three plates, and brings nothing is recognized everywhere as a freeloader.

What form the give-back should take is a separate question, and a serious one. Direct compensation for authors and artists. Universal income funded by AI-derived value. Mandatory model release after some interval. Public AI as a utility. Different mixes of these are possible, and worth arguing about.

The framing here does not settle that argument. What it does is clarify what kind of thing is being argued about. Not just labor displacement, not just intellectual property, but the materialization of something that was never anyone’s private property to begin with, and the question of what happens next.

The artifact in front of you when you chat with an LLM is the inferred collective mind of everyone whose expressions went into building it. Not a person, not a book, not a tool in the ordinary sense. Something new, with no prior object to compare it to.