When Productivity Turns Against Its Purpose

There is something deeply unsettling about the current AI boom.

The technology is often presented as a form of advanced productive power: a new general-purpose technology capable of increasing output, reducing costs, accelerating research, and making knowledge more accessible. In principle, this should be good news. If machines can perform more cognitive work, society should be able to produce more with less effort. People might work fewer hours, receive better services, and spend more time on relationships, education, creativity, and civic life.

But that is not the direction in which the AI economy is currently moving.

The same technology that promises abundance is being built through large-scale extraction. It consumes books, conversations, code, images, attention, electricity, water, and human labour. It may increase productivity while weakening the communities that produced the knowledge it depends on. It may create immense wealth while making ordinary workers more replaceable. It may offer new creative tools while pushing culture toward imitation and sameness.

The central problem is not that AI is intelligent. The problem is that the economic system tends to treat everything it can use as a resource, including people.

People are becoming inputs

The principle that “people are ends, not merely means” is usually associated with Kant. It does not mean that human beings can never participate in an exchange or help one another achieve practical goals. Work, cooperation, and trade all involve using one another’s abilities.

The important word is “merely”. A person must not be reduced to an instrument whose only value is what can be extracted from them.

Much of the AI economy is built around exactly this reduction.

A person’s writing becomes training data. Their photographs become examples for a vision model. Their code becomes a pattern for generation. Their conversations become behavioural signals. Their attention becomes a product metric. Their creative style becomes a feature that a company can imitate and sell back to the market.

The person disappears behind the dataset.

This is not automatically unethical. Knowledge has always circulated. People learn from books, teachers, colleagues, and public conversations. The issue is not whether learning from human work is allowed in every circumstance. The issue is whether the people whose work creates the value have knowledge, consent, control, or a meaningful share in the resulting benefits.

When a company turns millions of human contributions into a private model, the transaction is rarely symmetrical. The contributors supplied the material. The company owns the infrastructure, the model, and the commercial relationship with the user. The public helped produce the culture; the firm captures the resulting capability.

That is a familiar pattern in capitalism, but AI makes it faster and less visible.

Cultural heritage treated as raw material

The treatment of books offers a clear example of this tension. In some large-scale digitisation processes, books may be dismantled or cut apart so that their pages can be scanned more quickly. From a narrow engineering perspective, this may appear efficient. From a cultural perspective, it can be destructive.

A book is not only a sequence of words. Its paper, binding, typography, marginalia, edition, and physical history may all matter. For rare books and local publications, the physical object may contain evidence that a plain text file cannot preserve.

The danger is that a culture of optimisation sees only what can be extracted. The book becomes a container of tokens. The archive becomes a data source. The past becomes fuel for a product.

Digitisation is valuable, and much historical material should be preserved in digital form. But preservation cannot be defined only as successful text extraction. A technology company should not be allowed to decide that an irreplaceable cultural object is disposable simply because a faster scanning process produces a more useful dataset.

The same principle applies beyond books. Human culture is full of things whose value cannot be represented by their informational content alone. A letter is not merely text. A song is not merely audio. A community is not merely a database of posts.

The emptying of public knowledge communities

The extraction of online communities raises a different but related concern.

Platforms such as Reddit and Stack Overflow did not become valuable merely because they contained answers. They became valuable because people asked questions, shared experience, challenged one another, and corrected mistakes. The knowledge was maintained through relationships and feedback.

A model can absorb the visible result of that process without carrying the social conditions that produced it.

This creates a troubling economic loop:

People contribute knowledge to public communities. AI companies extract that knowledge. Users move from the communities to private AI interfaces. The communities lose traffic and motivation. Fewer people contribute new knowledge. The models inherit a poorer information environment.

The problem is not simply that a company collected publicly accessible text. Public availability does not settle every question of fairness. A post written to help a community is not necessarily a gift to every future commercial system. The contributor may have accepted one social context, not unlimited commercial reuse.

There is also a difference between taking a snapshot of knowledge and sustaining knowledge production. A company can train on years of technical answers, but it does not automatically inherit the future maintenance work: the updates, corrections, new edge cases, and practical experience that keep those answers useful.

If the AI industry takes from communities without helping to maintain them, it risks consuming the very ecosystem on which it depends.

A more legitimate system would give contributors meaningful choices, provide clear attribution, direct traffic back to original sources, and return part of the value to the communities that produced it. Data should not be treated as an oil field that companies can drain once and abandon.

The environmental cost of artificial abundance

AI is also changing the meaning of efficiency.

The industry often speaks as if larger models and greater compute are self-evidently desirable. Yet AI systems require electricity, cooling, water, chips, land, data centres, and supply chains. These costs do not disappear because the final product is digital.

A user may see a cheap answer in a chat window. They do not necessarily see the electricity required to run the system, the water used to cool the infrastructure, the environmental cost of manufacturing the hardware, or the waste generated when equipment is replaced.

This creates a familiar economic arrangement: the benefits are privatised while many costs are distributed across society.

The question is not whether AI should consume resources. Every useful technology consumes resources. The question is whether companies are required to disclose and bear the costs of that consumption, or whether society is expected to subsidise private growth in the name of innovation.

Nor does every task require the largest available model. A simple search, classification task, or formatting operation may not need vast computational resources. If the industry treats maximal scale as the default solution to every problem, it may turn technical ambition into environmental waste.

An AI economy that cannot distinguish between useful computation and prestige computation is not necessarily advanced. It may simply be expensive.

Productivity without prosperity

The most important economic paradox is that AI can increase productivity without reducing poverty.

This should not be surprising. Productivity describes how much an economy can produce with a given amount of labour and resources. It does not decide who owns the machines, who receives the additional income, or what happens to the people whose work is displaced.

Suppose AI allows one worker to produce what previously required five workers. Several outcomes are possible. The workers could work fewer hours while maintaining their income. The company could lower prices and expand access. The additional profit could fund better public services. Or the company could dismiss four workers, increase the remaining worker’s workload, and transfer most of the gains to shareholders.

The technology does not choose among these outcomes. Institutions do.

This is why advanced productive forces do not automatically solve poverty. Poverty is not caused only by an inability to produce enough goods and services. It is also shaped by ownership, bargaining power, housing costs, healthcare, education, taxation, and access to social protection.

AI may make some services cheaper. It may give small teams capabilities that were once available only to large organisations. It may help people in poorer regions access tools that were previously expensive. These are real possibilities.

But AI can also reduce wages in writing, translation, customer service, design, and entry-level programming. It can transfer training costs from employers to individuals. It can make work more precarious while increasing expectations of productivity. It can create a society that is richer in aggregate but less secure for the people who do not own the systems producing that wealth.

A society may have more output and more billionaires while ordinary people still cannot afford housing. There is no contradiction in that. Production and distribution are different questions.

The erosion of human creativity

The final concern is innovation itself.

AI can help people explore ideas, test alternatives, and overcome the blank page. Yet the commercial use of AI may also push culture toward statistical sameness. When millions of people rely on similar models, prompts, and evaluation standards, their writing, design, code, and products can begin to resemble one another.

The danger is not that every AI-generated work will be bad. Much of it will be competent. That may be more dangerous in some ways. A flood of competent, inexpensive, similar material can crowd out work that is slower, stranger, or more personal.

Innovation does not come only from recombining existing patterns. It also comes from paying attention to reality, encountering failure, and asking questions that do not yet have a profitable answer. Human creativity is shaped by bodies, places, relationships, and histories. It develops through experience that cannot be completely separated from the person who lived it.

If AI becomes a substitute for reading, observing, practising, and thinking, people may become better at producing answers and worse at discovering problems. We may gain speed while losing direction.

The creative risk is therefore not simply that AI will replace artists. It is that a system optimised for efficiency will devalue the conditions under which genuine originality develops.

Technology needs a political purpose

None of this requires rejecting AI.

AI can be a useful tool when it expands human agency. It can help a researcher handle more material, help a disabled person communicate, help a small business compete, and help a worker escape repetitive tasks.

But the same system becomes dangerous when it treats human beings as expendable inputs, public knowledge as free fuel, and environmental resources as invisible subsidies.

The decisive questions are political and ethical:

• Who owns the models and the infrastructure?
• Who can refuse data collection?
• Who receives the benefits of automation?
• Who bears the risk of displacement?
• Who is allowed to appeal when an automated decision harms them?
• Who decides which forms of human work remain valuable?
• What happens to the communities that AI depends on?

A technology can be advanced while the society using it remains unjust. Increased productive power creates possibilities; it does not create a moral order.

The promise of AI should not be measured only by how much content a model can generate or how many workers a company can replace. It should be measured by whether people gain more security, more freedom, more time, and more control over their lives.

The purpose of productivity is not to produce more powerful systems for their own sake. It is to improve human life.

If AI makes companies richer while making people more disposable, communities weaker, and the environment poorer, then it is not solving the problem of scarcity. It is reorganising scarcity around a new centre of power.

The question facing the AI age is therefore not whether machines will become more human.

It is whether a society obsessed with making machines more capable will remember that human beings were never supposed to become their raw material.