The Industrialization of Cognition

My hypothesis is that AI is not just another productivity tool. It is becoming a new production stack. Some layers of that stack behave like productive capital, even as other layers become cheaper and more widely accessible. That changes the relationship between labor, firms, ownership, and human skill.

By productive capital, I mean an asset that can be accumulated, controlled, and used to generate output. If core layers of the AI stack become that kind of asset, then the central issue is not only what AI can do. It is who owns the systems through which AI does it.

Do we value human skill anymore?

When I read things online, I skim. Rarely do I stop and really take in what I am reading. I almost expect every single piece of text online to be gen-slop. We live in a time where the value of a word feels lower than it should. Seems like everybody and their mother has a Substack producing AI-generated thought pieces on contemporary society.

My little cousin loves drawing, painting, sculpture, and who knows what else. But she would never pursue art professionally, or otherwise, beyond a mere recreational pastime. She told me “art does not pay,” so as she thinks ahead to college applications, she “knows [she has] to major in STEM.”

Those are the words of someone who spends a significant portion of her time self-expressing through art. I wonder what her attitude tells us about other 16 year olds across the country.

Do we value craftsmanship? The intuition of a good teacher? The well-worn emotional center of a therapist’s mind after they have talked to people about their feelings for 30 years?

It seems like we disrespect skill. The forces of deindustrialization have pushed the nouveau bourgeoisie toward the idolization of professional services jobs. We used to make things; now we increasingly manipulate, package, and repackage them. Look at our financial markets. So much of modern work feels like selling, positioning, formatting, and presenting. There is an obsession with presentation itself. The pressures of contemporary competition produce anxious, superficial people.

My read is that AI intensifies this crisis because it changes the underlying mechanics of our relationship to production. It does not only change which tasks are easy or hard. It changes who controls the assets that make work possible.

I am not arguing for some romantic return to industrial labor. The point is not that factories were morally pure or that old industrial jobs are coming back. The point is that productivity can rise while the human role in production shrinks or changes. Manufacturing productivity data already shows how output per worker can become detached from the number of people directly involved in making things.¹

To understand why, it helps to start with a simple economic model.

Production functions, in plain language

A production function is a way of describing how inputs become output. For any given set of inputs, there is some calculable maximum output a firm can produce.

The mathematical model version I saw as an undergrad taking Econ 100 looked something like this:

Y = F(K, L, A)

where:

• Y = output
• K = capital
• L = labor
• A = technology/productivity

My hypothesis is that AI changes the production function because it changes all the inputs at once.

If that is right, AI will continue to influence the production function such that:

1. the relationship between inputs and outputs changes
2. the ownership and control of the means of production change
3. the institutional structure of “the economy” itself changes

In the past, when we have talked about the “means of production,” we have meant the productive assets society relies on: land, machinery, factories, logistics systems, energy, and so on. But in 2026, AI forces us to consider a new category of input that encompasses things like models, compute infrastructure, algorithmic decision-making, and autonomous coordination. Recent AI infrastructure research makes the same point from another direction: data, compute, energy, model development cost, and organizational structure are now central to how foundation models are built and controlled.²

I personally believe that today’s production function might eventually need to make the AI production stack more explicit:

Y = F(K, L, AI, D, C)

where:

• AI = model capability, orchestration, and evaluation systems
• D = proprietary data
• C = compute infrastructure

These inputs are not perfectly separate economic categories. Compute is already a form of capital, and model capability overlaps with what economists would ordinarily call technology or productivity. The point of the formula is to make the emerging AI production stack more visible.

The split is already visible. Compute, training infrastructure, and frontier-model development are capital-intensive, while applications and workflows built on top of them may become cheaper and more widely available. Stanford’s 2026 AI Index Report found that global AI compute capacity had grown 3.3 times per year since 2022, reaching 17.1 million H100-equivalents, a standardized estimate of capacity expressed as the equivalent number of Nvidia H100 chips, with Nvidia accounting for more than 60 percent of the total. At the same time, Stanford’s 2025 report found that the cost of querying a model performing at the level of GPT-3.5 fell more than 280-fold between November 2022 and October 2024.³

In between sit large datasets, foundation models, memory and context systems, evaluation layers, orchestration tools, and distribution platforms, each with different economics. This is why concentration and diffusion have to be analyzed together. When ownership of core assets is highly concentrated, their owners may gain disproportionate leverage relative to everyone else over production.

The concentration of productive assets is nothing new. Look no further than land concentration in feudalism, factory concentration in industrial capitalism, or platform concentration in today’s digital capitalism.

Does AI complement or substitute for human labor?

Classical industrial automation reduced the economy’s reliance on some forms of physical labor and helped expand the managerial class. When new technology arises, we create new jobs accordingly.

My friends love to talk about how work has seemingly become meaningless. Through spreadsheets and presentation decks, we create abstractions of value that AI can increasingly help create. Right now, specialized humans still often outperform AI when it comes to tasks of cognitive labor, coordination labor, managerial labor, creative labor, and even, sometimes, analytical labor. But AI systems keep improving.

So what happens if AI can do more and more of the tasks of work faster, cheaper, and more effectively than humans can?

In that scenario, the marginal productivity relationship between labor and capital changes.

The results may be similar to what we saw with industrial mechanization during the Industrial Revolution and beyond. Demand for workers who experience increased labor productivity as a result of the new technology might increase, while demand for workers who do not experience increased labor productivity might decrease. We might even observe so-called “superstar” effects where a small number of highly AI-leveraged individuals can outperform entire teams.

The risk I am describing is a polarized, winner-take-most market where many knowledge workers lose bargaining power.

But that does not mean human skill disappears. It means the scarce human skill may move.

When cognition becomes infrastructure

Production has always depended on some combination of labor, capital, technology, energy, and organization. But that combination changes once parts of intelligence itself become automatable: coordination, cognition, analysis, creativity, and the managerial work of turning ambiguity into action.

For some firms and forms of production, the binding constraint may increasingly move away from headcount alone. It may be compute access, energy, data, organizational agility, distribution, and more. In that world, leaner labor forces may coordinate larger productive systems while capital intensity goes up.

By capital intensity, I mean the degree to which production depends on capital assets rather than labor. If AI makes cognitive work more dependent on models, compute, data, memory, routing, and evaluation systems, then cognition itself starts to look more industrial.

It becomes easier to decompose into workflows, standardize, mediate through infrastructure, and scale through capital assets. Let’s call this the industrialization of cognition.

The new human skill stack

If cognition becomes more automatable, the scarce human skill may shift from doing every unit of cognitive labor ourselves to specifying, evaluating, governing, and improving systems that perform cognitive labor.

This is where the human role becomes more precise, not less important.

The new skill stack is not generic “AI fluency.” It is not just knowing how to use chat interfaces. It is the ability to translate messy goals into precise instructions, decompose work into bounded tasks, judge whether outputs are actually correct, notice failure patterns, structure the context a system can use, and decide where human review needs to stay in the loop.

In other words, the most valuable human work may move toward operating judgment: specification precision, evaluation, context architecture, trust boundaries, and cost judgment. These are not soft add-ons to the system. They are part of what makes an AI-supported workflow productive at all.

That is why the industrialization of cognition is not only a labor-substitution story. It is also a systems-design story. The central question is not simply whether AI can perform a task. It is who defines the task, who checks the result, who owns the context, who pays for the compute, who absorbs the error, and who gets the surplus.

The decline of firms’ transaction costs

Ronald Coase argued that firms exist because markets have transaction costs: the costs of finding people, coordinating work, negotiating agreements, monitoring performance, and enforcing expectations.⁴

If AI brings some of those costs down, including search, scheduling, contracting, knowledge retrieval, and operational monitoring, we may see firms restructure in two major, divergent ways.

One possibility is the hyper-concentrated mega-firm whose AI systems scale with data and compute. Another is the highly decentralized micro-firm that uses AI to do things that used to require larger organizations with more firepower.

I think it is likely that we will see the continuation of concentration at infrastructure layers and decentralization at application layers. These are not contradictory outcomes. AI capabilities can become cheaper and more accessible to use while the infrastructure beneath them remains difficult and expensive to own.

That split matters. A tiny team may be able to build and coordinate more than ever before, but only by renting access to infrastructure it does not own. The open question is whether today’s concentration is a temporary feature of an early market or a lasting structural condition.

AI and the composition of capital

Marx described capitalism as tending toward rising capital intensity: more production flowing through machines and infrastructure relative to labor. My concern is that AI may accelerate that tendency. If firms can produce more with smaller labor forces, labor may receive a reduced share of the income resulting from production. Labor share data is already one way economists track how much economic output accrues to workers as compensation.⁵ If AI pushes more production through capital-owned systems, the distribution question gets harder, not easier.

Nonlinear productivity growth?

A more speculative possibility is that AI may help improve the process of creating more, better AI. This could make the productive effects of AI recursive in nature and create nonlinear productivity growth. Instead of technology improving linearly through human labor alone, technology may increasingly contribute directly to its own advancement. The core argument does not depend on this happening.

Whether or not that recursive loop arrives, the ownership risk is already legible. One structural outcome might be the birth of new economic regimes, a sort of platform neo-feudalism. I mean the phrase as an analogy for dependency, not a literal return to feudalism. When a few firms own the compute, models, data, identity systems, and economic coordination layers, everybody else risks becoming a tenant, a dependent, in a proprietary ecosystem.

Long live open-source AI and public infrastructure.

The distribution question

Now comes the politics of it all.

Given that labor may have a smaller share of the income resulting from production, how does the AI-generated surplus in value get redistributed?

If this trajectory holds, ownership structures become the key political issue in our economic restructuring. That is where the state should, and must, step in to have a hand in the path our economy takes. If consumption systems detach from wages, then we may need some sort of universal income, universal job guarantee, or another structure for distributing productive surplus.

Maybe the current market structures will be preserved, and inequality will rise sharply. Maybe new public infrastructure, ownership models, or labor institutions will emerge. I do not pretend to know which path wins.

Who owns the infrastructure of cognition?

All of this leads back to the central issue: who will own the new means of production?

Speaking based on our history as a society, when a new foundational means of production emerges, whether it is agriculture, factories, electricity, or computation, societies and their economies eventually have to reorganize around that productive infrastructure. These technologies did not produce identical ownership patterns. That is precisely why the structure of the AI stack matters.

AI is forcing that question again. If cognition itself becomes industrialized, then ownership of the infrastructure of cognition becomes one of the central economic and political questions of our time.

That infrastructure is broader than the model. It includes compute, data, memory, context, routing, evaluation, trust, distribution, and the operating interfaces through which people do their work.

Our response cannot just be nostalgia or panic. The more constructive path is not full automation for its own sake. It is a human operator model with inspectable systems: clear instructions, durable memory, logs, receipts, confidence thresholds, approval gates, and review boundaries. The point is to build trust, not just capability.

If humans remain operators with real agency, those layers need to be inspectable and contestable. If they are opaque and privately controlled, we risk becoming tenants inside systems that increasingly mediate our own capacity to think, make, coordinate, and earn.

The question is not whether AI will change work. It already is. The question is whether the infrastructure of cognition will be something people can understand, shape, and contest, or something they can only rent.

Notes