From the Industrial Revolution to the AI Age: An Anthropology of Industrial Transformations

We usually study the steam engine and the large language model in different departments. That separation blinds us to the deep structural connections between the two greatest transformations in human history. This essay — adapted from our longer research monograph — reads them together, across epistemology, anthropology, ecology, labor, political economy and geopolitics.

1. Why think the two revolutions together?

The Industrial Revolution and the AI Revolution have traditionally belonged to different disciplines: economic history on one side, computer science on the other. But frameworks like Raymond Williams's cultural materialism and Michel Foucault's archaeology of knowledge give us the conceptual tools to see what they share (Williams, 1977; Foucault, 1969).

One warning applies to both: technological determinism must be resisted. As Langdon Winner argued in "Do Artifacts Have Politics?" (1980), technology is never independent of society — it is shaped inside social, political and economic relations. Machines don't decide our future. The arrangements we build around them do.

2. An epistemological break: how knowledge is made

Before the factory: experiential wisdom

In pre-industrial societies, knowledge was largely experiential and oral. Craft was a form of tacit knowledge — something the hand and body "knew," passed from master to apprentice (Polanyi, 1966).

The industrial turn: standardized knowledge

The Industrial Revolution changed the nature of knowledge itself. The system of interchangeable parts standardized not just components but the knowledge required to make them (Hounshell, 1984). Skill was extracted from the craftsman and embedded in the machine and the process.

The AI turn: learned, dynamic, personalized knowledge

The AI Revolution creates a new break. Knowledge is no longer solely the product of human minds — it emerges from algorithms that learn. It is dynamic and real-time rather than fixed in manuals. And it is personalized: each of us increasingly sees a different slice of the world, filtered by models (Pariser, 2011).

3. An anthropological transformation: what is a human?

The Industrial Revolution reinvented the human being. In agrarian society, a person was embedded in land, season and community. Industrial society invented the "individual" — a mobile unit of labor and consumption (Taylor, 1989).

Foucault's Discipline and Punish (1975) showed how the factory, the school and the hospital worked as institutions that trained and disciplined bodies for industrial rhythm. E. P. Thompson (1967) documented how clock-time itself had to be taught to a workforce used to task-time.

AI now forces the question again: what is a human, when machines converse, create and decide? Sherry Turkle's Alone Together (2011) explores the psychological dimension of human–machine relationships — we expect more from technology and, sometimes, less from each other.

4. The ecological dimension: our relationship with nature

The concept of the Anthropocene — the era in which humanity became a geological force — has its roots in the Industrial Revolution's fossil energy system (Crutzen & Stoermer, 2000; Malm, 2016). Climate change and biodiversity loss are its long shadow.

AI cuts both ways ecologically. It is a powerful tool for environmental monitoring and climate modeling — and, at the same time, data centers consume enormous energy and generate electronic waste. Whether AI becomes an ecological asset or liability is a design decision, not a destiny.

5. Gender and labor

The Industrial Revolution produced the doctrine of "separate spheres" — a public world of paid work and a private, domestic one. Women's labor was simultaneously exploited in the factories and rendered invisible at home (Pinchbeck, 1930).

The AI age has its own version of this problem: systems trained on historical data can reproduce the gender biases embedded in that data, and platform economies over-represent women in precarious, low-paid work. Bias is not a bug that fixes itself — it must be measured, evaluated and engineered out.

6. Political economy: from commodity fetishism to techno-feudalism

Marx's concept of commodity fetishism described how industrial capitalism hid the social relations behind products. In the AI economy, data plays a double role — it is both the means of production and the product itself (Srnicek, 2017).

Yanis Varoufakis's Techno-Feudalism thesis (2021) goes further: the big platforms no longer compete in markets so much as extract rent from the digital territories they own — a logic, he argues, that is different from capitalism itself.

7. Geopolitics: the workshop of the world, then and now

The Industrial Revolution made Britain "the workshop of the world," and industrial supremacy translated directly into military and political hegemony.

Today the same contest plays out between the United States and China over AI supremacy. Data is treated as the new oil, and compute as the new steel (Lee, 2018). For every other country — and every company — the strategic question is the same: how do you build capability without becoming purely a consumer of someone else's revolution?

8. Domains of transformation

• Healthcare — from the mechanization of medicine to personalized medicine: AI now assists diagnosis and accelerates drug discovery.
• Education — from Foucault's disciplinary spaces to learning ecosystems: personalized learning and genuinely lifelong education.
• Law — algorithmic justice, automated review and smart contracts are redrawing what legal work means.

9. Where we are now (2024–2026)

Large language models — the GPT, Claude, Gemini and Llama families and their successors — have moved AI from research labs into everyday business operations. The live debate has shifted from "can machines do this?" to "how do we deploy this reliably, safely and profitably?" Meanwhile, the AGI question — is human-level general intelligence near or far? — remains genuinely open among serious researchers.

10. Three scenarios for 2030–2050

• Techno-feudal dystopia — absolute platform control, mass surveillance, extreme inequality.
• Post-capitalist commons — open source, platform cooperatives, democratic control of key infrastructure.
• Sustainable digital society — green technology, broad social safety nets, widely shared productivity gains.

None of these is inevitable. As with the first Industrial Revolution, the outcome depends on the institutions, norms and choices we build around the technology — not on the technology alone.

11. Conclusion — and what it means for your business

There are deep continuities between the two revolutions, but the AI revolution is faster, more global and more radical. The window in which organizations adapt — or fail to — is measured in years, not generations.

That is the practical lesson we take into our consulting work: AI transformation is not primarily a technology purchase. It is an epistemological and organizational change — where your company's knowledge lives, how your people work with machines, and how value is captured. Companies that treat it that way are the ones that come out ahead.