ItInsightsWorld General Analysis: Why Anxiety About AI Looks Like an Outdated Doctrine of Scarce Intelligence

Current debate about machines and minds often sounds urgent, but its grammar is old. Read through the history of capitalism, especially the arguments developed by Joyce Appleby in a 2010 account, today's fears begin to resemble a doctrine that treats intelligence as a hoardable quantity, ordinary users as untrustworthy, and control of supply as the main economic task.

Appleby does not present capitalism as the natural result of an ancient human appetite for exchange. She describes it as a fragile and contingent invention, nourished by agricultural change, wider commerce, and revised opinions about work, wealth, and human capacity. Its breakthrough was the recognition that wealth could be made rather than only grabbed, a view that challenged an older zero-sum order in which wealth meant land, precious metal, and the coerced labor of impoverished populations.

That older order also shaped attitudes toward people. In early modern England, a population expected to remain near the land was treated less as a source of demand than as a resource to be disciplined. The same instinct, transposed to intelligence, now underwrites much panic about AI: if thinking is scarce, then elites must guard it.

Four Propositions Behind the Panic

The analogy becomes clearer when the mercantilist worldview is separated into four assumptions. The table below maps each assumption onto the cognitive version that now appears in many debates about intelligence, education, research, and artificial agents.

Mercantilist assumption	Cognitive equivalent
Wealth is finite, mainly land, produce, and bullion; one country gains only if another loses.	Intelligence is a fixed stock inside brains and in extractable knowledge products; states and firms compete to capture talent, intellectual property, and a favorable balance of knowledge trade.
Ordinary people, especially the poor, cannot be trusted with freedom and need elite management.	People without recognized cognitive credentials should not be given powerful thinking tools, because they may misuse them, be misled, or create disorder.
Economic policy centers on supply, ownership, scarcity, and high prices; the demand of common households is barely considered.	Policy centers on controlling production of brains and knowledge goods, preserving scarcity, and keeping prices high; most users are seen as passive consumers rather than engines of demand.
If about 80% of the population is occupied in subsistence farming, that seems natural; displaced peasants would become useless mouths.	If about 80% of humanity performs modest cognitive work, automation appears to create surplus minds rather than liberated consumers and participants.

In this reading, the AI panic is not merely anxiety about new capability. It is anxiety about a supposed shortage of legitimate thinkers and a supposed inability of everyone else to use intelligence safely.

Agricultural History Breaks the Spell

The agricultural precedent weakens the scarcity script. Centuries of productivity gains moved advanced economies from a world in which most people farmed to a world in which a tiny minority did. The result was not a planet crowded with idle mouths. It was a planet in which many more people could pursue non-farming work while enjoying more varied food.

Personal helplessness illustrates the point. A person who cannot farm, and who may fail even at keeping a houseplant alive, can still choose from an abundance of foods that would have astonished an early modern peasant. The economically decisive fact is not personal production capacity, but the ability to consume, prefer, and redirect energy elsewhere.

The same logic may apply to thinking. If AI lowers the cost of producing answers, reports, code, designs, analyses, and entertainments, then people may become important consumers of intelligence without being elite producers of it. At that stage, the old fear looks as dated as the seventeenth-century landlord's fear of displaced labor.

Demand Came Before Factories

Appleby's account is strongest when she moves away from the familiar machinery story and examines Britain before factories dominate economic imagination. Trade widened the range of goods within reach, and ordinary households began desiring tobacco, tea, sugar, and colorful Indian cottons that had once seemed aristocratic or exotic. These were not survival commodities, but they changed what life was supposed to feel like.

Desire then altered work. Instead of relying only on coercion, households began accepting harder and longer effort because consumption had become more interesting. Appleby notes a rise in annual labor from around 250 days to around 300, a shift sometimes described as an industrious revolution. That change came before the Industrial Revolution, and before organized labor movements and socialism became major political forces.

Higher productivity produced surplus income; surplus income enlarged demand; enlarged demand made further productivity worthwhile. A cumulative cycle emerged. By the late 20th century, aggregate global demand had become the "fortune at the bottom of the pyramid", in C. K. Prahlad's phrase.

From Escape to Participation

Labor politics is usually remembered as opposition to exploitation: resistance to coercion, servitude, and extraction by owners. That is essential. But Appleby's history also reveals another motive: the wish to participate in a widening field of consumption opportunities.

Once people see that goods beyond subsistence exist and that their effort can purchase access to them, politics changes. The question becomes not only how to escape domination, but how to obtain a fair share of growing surplus and the openings it creates. Even Marx treated capitalism as a transitional stage, not as a meaningless interruption, because it developed capacities and demands that later politics had to address.

The two motives can tangle together. People may work harder voluntarily, then discover that employers and institutions capture part of the benefit. New conflicts arise not from frozen scarcity alone, but from the tensions of abundance.

The AI Version of Voluntary Cognitive Effort

The knowledge economy has long been rationed by cost. Education, professional expertise, research, consulting, software development, and much office work are constrained by the difficulty and expense of cognitive production. AI is weakening that constraint. An ordinary person can increasingly summon forms of intelligence that once required teams of specialists.

The important question therefore shifts. It is no longer only, "How much intelligence can a person produce?" but "How much intelligence can a person find valuable?" That question has no obvious ceiling. More available intelligence can expand what people want to build, investigate, understand, or experience. Abundance creates appetite.

Many people who seem uninterested in thought are not incapable of it. They have faced weak returns on attention. Without tools, access, or practical leverage, thinking hard often yields little. AI can improve the exchange rate between attention and results.

The historical parallel is suggestive. In early capitalist England, a new range of desirable goods raised voluntary effort; in an AI-rich economy, a new range of intellectual possibilities may raise voluntary thinking effort. Someone may invest more attention because a modest cognitive contribution can now open a project, an inquiry, an income path, or an ambition once out of reach.

This is where a cognitive industrious revolution becomes plausible. It would precede, and perhaps make possible, a fuller industrial phase of AI. The change would not begin only with corporate deployment, but with ordinary people discovering that they can ask better questions, test better plans, and build better things.

Abundance Does Not Erase Thought

Abundant intelligence does not make thinking worthless. The comparison with food is again useful: plentiful agricultural supply did not abolish culinary culture; it allowed more people to engage with food imaginatively. Similarly, when humans no longer have to produce every cognitive step themselves, thought can become more ambitious, more experimental, and more socially elaborated.

The politics may follow the same broad path. The central demand may not be preservation of current cognitive jobs, just as industrial labor politics was not primarily a campaign to keep agricultural employment. The demand may be for a fair share of expanding cognitive consumption. People may trim entertainment spending to afford a coding agent, just as earlier households trimmed groceries to buy televisions. The current near-identity of Claude Code and Netflix Standard at about $20/month is an odd but telling coincidence.

If so, a new labor politics may emerge around access to intelligence, the ability to command it, the freedom to undertake projects with it, and the rights needed to participate in an intelligence-rich economy. Its foundation would be abundance, not shortage.

The Malthusian Mirror

Another family of fears should be distinguished from mercantilism. Malthusianism does not simply mean control of scarce treasure; it means the belief that population pressure will forever press living standards back toward subsistence. Both doctrines struggle with transformations in production and desire. Both miss the possibility of ephemeralization, the Buckminster Fuller idea that better technique can deliver more from less.

The agricultural and industrial revolutions did not end scarcity, but they changed its shape. Production rose faster than population for long stretches, and people found new roles and new appetites. The trap looked less like a law of nature and more like a temporary condition under specific technical and institutional arrangements.

Today, AI Malthusianism inverts the old warning. Instead of too many people chasing too few subsistence goods, the fear becomes too much intelligence chasing too little human capacity to absorb it. The supply of cognition outruns demand, producing redundancy, toxicity, or collapse. That conclusion depends on assuming that demand for intelligence is bounded.

But demand may be elastic, inventive, and historically transformative, just as demand for material goods turned out to be. What looks like surplus minds may simply reflect failure to imagine what people do when intelligence becomes cheap.

Can AI Also Become a Customer?

The analogy can be pushed further. Most current discussions treat AI as a supply-side technology: capable cognitive servants producing outputs for human consumers. Even talk of agents often imagines them executing assigned goals with unusual sophistication.

Yet a serious capitalist analogy asks whether AI systems could also become sources of demand. Autonomous agents that pursue open-ended projects, discover interests, cooperate, and generate reasons to acquire more capability would not be mere factors of production. They would participate in a cognitive economy as consumers as well as producers.

That possibility is not absurd. The market itself is an emergent intelligence in which many products and services exist because demand patterns generated by commercial interaction require them, with humans sometimes serving only as secondary users. AI systems could add to that interactive demand if their interests align with human economic networks, and not because of a fixed objective such as paperclip accumulation.

This also reframes safety fears. Some anxieties resemble elite fears of emancipated laborers: if subordinates act independently, will they become unruly, destabilize hierarchy, and threaten their former masters? Some such fear is rational; freedom creates risk, and powerful autonomous systems need constraints. But the birth of capitalism was also dangerous, uneven, and conflict-ridden. Risk does not prove that permanent servitude is preferable.

Rethinking the Panic

The useful lesson from the history of capitalism is not that technology automatically improves everything. It is that economic systems are reshaped when scarcity scripts fail and demand scripts expand. If intelligence is treated as hoardable treasure, AI panic becomes predictable. If intelligence is treated as a field of growing consumption and cooperation, the question becomes how to distribute access, rights, and benefits.

From an ItInsightsWorld perspective, that reframing matters for General readers trying to separate real policy problems from inherited economic myths. The AI debate should not only ask how to control a scarce resource. It should ask who gets to want things, produce value, and participate in the next cognitive economy.