Why Artificial Intelligence Reveals the Goal of Capitalism?
Artificial Intelligence: a brief introduction
There are words that, when we pause to consider them, open up a broader space for interpretation than they appear to. End is one of them. In Spanish, it can mean goal —the direction something is oriented towards— and also termination —the conclusion of that same process—. In this ambivalence lies a conceptual richness that etymology reveals. The Greek term télos, from which our “end” springs, does not distinguish between objective and culmination: both are part of the same circular movement. Télos comes from the Indo-European root kwel- —to turn, to go around, to close a cycle—. For ancient thought, that which reaches its télos does not just end: it is realized. The end is the fulfilled goal; the goal is the natural end of the process.
Modern languages separated these meanings —goal on one hand, end on the other— because our historical imagination became linear. We believe that everything moves forward, towards more, towards better, as if every process should be prolonged indefinitely.
Perhaps that’s why we tend to view the new as if it were an expanded version of what we already know. If time is an ascending line, we assume that the future merely prolongs what has been lived: more technology, more speed, more efficiency, but nothing truly different. This idea of continuity reassures us. It places us in an imaginary position of dominance, as if what is coming is just another step on a journey we have understood for centuries. That’s why we repeat “we’ve lived this before”: not to describe reality, but to preserve a sense of familiarity in the face of the unknown. But there are moments in history when this illusion ceases to be protection and becomes danger.
Two films about the First World War show this with overwhelming clarity: Paths of Glory (Stanley Kubrick, 1957) and All Quiet on the Western Front (Lewis Milestone, 1930). Paths of Glory portrays the blindness of the French military command, convinced that the war was still what they remembered: heroic deeds, decisive advances, fields where personal courage could change destiny. All Quiet on the Western Front shows the same from the perspective of the common soldier: young men marching to the front believing they would repeat the glories of the past. What they found was no continuity at all: the war had become an industrial slaughter. In days, tens of thousands fell under machine guns capable of wiping out regiments in minutes; in hours, artillery turned villages and forests into smoking craters. On a single day of the Somme, more soldiers died than in entire months of previous conflicts, including the Napoleonic campaigns. Never had killing been so fast, so mechanical, so impersonal. The past did not return; it could not return. And those who insisted that the war “was the same as always” were profoundly mistaken: that error ended up accounting for over 17 million deaths in just four years.
The danger of believing that the new is just a repetition of the past is that one loses the ability to recognize the qualitative leap. And today, facing artificial intelligence, something similar is happening. Those who say “we’ve experienced this before with other technologies” forget that we have never before faced cognitive automation. It’s not about replacing physical force or accelerating calculations: it’s about delegating technical systems functions that were previously exclusive to the human mind. Today, approximately 65% of the world’s GDP comes from the service sector, and about 60% of global employment is based on activities whose raw material is not force, but interpretation, communication, organization, planning, analysis, or symbolic production. That is where cognitive automation introduces a radical change: it doesn’t just make what we do faster, but it can do it without requiring human presence, with minimal marginal costs and an unmatchable speed. Cognitive automation doesn’t reduce tasks: it redefines the very structure of work, shifts value towards non-human processes, and makes much of paid activity dispensable.
But that is only one of its fronts. The other —deeper— is the automation of judgment. We are not talking about technical skills, but about externalizing a function that in all human societies had remained at the core of experience: the ability to evaluate, choose, weigh, and decide. Automating judgment does not only mean that a machine decides for us; it means that the very way of deciding is transformed. Faculties that previously required attention, criterion, memory, comparison, intuition, and evaluation are now integrated into algorithmic processes designed to optimize results, not to understand them. An automated decision is no longer a human decision, even if its effects fall on humans. And when a technical system occupies that space, it doesn’t just produce answers: it defines the horizon of what is possible, determines which options we consider relevant and which vanish before they can even be thought of. Automating judgment is not only delegating decisions; it is allowing an external system to silence in advance all alternatives that will never exist for us.
We can pretend this belongs to a distant future, but it is already happening. The automation of judgment does not begin when a machine decides completely for us, but when we let it filter the world before it reaches our consciousness. Today, algorithms select what to watch, what to listen to, what to read, and what to ignore. The series we watch, the news we receive, the music we discover, or the results a search engine prioritizes are, for the most part, prior decisions of the system. Platforms, social networks, and search engines prioritize reality based on patterns we do not control; they decide what deserves attention and what can disappear without a trace. And this has a decisive effect: what attracts our attention, what we consider relevant or desirable, is part of our identity. What interests us is not superficial: it is constitutive. By delegating it, we not only give up practical functions; we give up the very process by which we become who we are. If another system decides what can interest us, it also shapes what we are capable of desiring. If it organizes the hierarchy of what is significant, it configures the limits of our inner world. Many of our preferences did not arise from our sensibility, but from recommendations accepted as our own choices. It is a silent transformation of subjectivity.
Artificial intelligence as the goal of neoliberal capitalism
To understand why artificial intelligence can become the consummation of a historical trajectory, it is first necessary to observe the movement of the system that incorporates it. Capitalism is not a set of economic rules, but a way of organizing life. Everything it touches it turns into a process: work, time, relationships, people, information. And that process has a constant direction: maximize productivity and reduce costs. Every innovation —mechanization, the factory, the assembly line, digitalization, financialization— has been integrated to advance that logic.
Neoliberalism is the most radical expression of this orientation. It extends business logic to the whole of existence: the individual becomes a performance unit; time, a resource; subjectivity, an asset; rights, costs; precariousness, an incentive. In this view, the market is not a space within society: it is the principle that organizes it. The system works best when human intervention is minimal. Inequality is no longer seen as a failure, but as a sign of efficiency.
Understanding its télos —the internal orientation that guides its evolution— involves identifying what goals it has pursued since its origins and how artificial intelligence influences its realization.
From the beginning, capitalism has sought to free itself from the limits of the human body. Mechanization replaced physical force; the assembly line annulled the variability of workers; digitalization absorbed repetitive tasks; industrial automation reduced dependence on biological rhythm and attention. Everything human that introduced fatigue, pause, or unpredictability was considered friction. AI introduces a qualitative leap because it automates the only thing that could not be automated before: cognition. Where human interpretation, decision, or coordination was once needed, a technical model can now intervene. The cognitive function begins to be replaced by an unlimited operational continuity.
To this is added another decisive goal: unlimited expansion. More production, more circulation, more accumulation. This expansion is measured not only in goods, but in the indefinite intensification of work, in the total use of available time, in the conversion of every gesture into productivity. It also manifests in the expansion of predictive models capable of anticipating decisions and transforming human uncertainty into exploitable patterns. And finally, it appears in financialization, which allows capital to grow without material production. AI amplifies these three dimensions: it multiplies relentless work, perfects prediction, and powers autonomous algorithmic markets. Growth ceases to depend on the subject: it is sustained by algorithmic capacity.
But this movement does not occur in an open or distributed space: it inherently tends to concentrate. It is not a side effect of the system, but a property inscribed in its very name. Capital comes from caput, “head”: that which is above, that which directs, that which accumulates. Capitalism, in its most elemental architecture, organizes life around centers of accumulation, not horizontal distributions. As efficiency increases, capital gathers in those who possess greater technical, financial, or informational capacity; scale becomes the decisive criterion of dominance. The system works better when power is concentrated, because concentration accelerates accumulation.
Artificial intelligence not only replicates this logic: it intensifies it like no previous technology. Algorithms learn better the more data they control, and data —the raw material of AI— is already in the hands of a small number of global actors. The quality of the model depends on its centralization: the larger the platform, the greater the precision; the vaster the flow of information, the more dominant the position obtained. AI does not democratize infrastructure: it centralizes it by its very nature. Those who control the models and the data control the entire process; those who lack access are inevitably relegated to the margins. Concentration thus ceases to be a trend to become the very form of the system, because the technology that drives it —like the capital from which it arises— improves precisely the more it concentrates.
And a final goal emerges, perhaps the deepest: to progressively reduce the role of the human being in the value chain. Every historical stage of capitalism can be read as another step in that direction: mechanization turned the body's strength into a limitation overcome by machines; the scientific organization of work replaced artisanal knowledge with standardized procedures, reducing individual initiative to a minimum margin; digitalization transformed human slowness into a delay impossible to sustain compared to the speed of information processing; and industrial automation converted human supervision and control into dispensable costs. AI completes this movement. It does not only eliminate tasks: it eliminates structural functions. In production, it plans and coordinates; in management, it analyzes and decides; in distribution, it optimizes without human intervention; in consumption, it anticipates desires; in subjectivity, it models preferences. And this last aspect is perhaps the most decisive, because it implies displacing not only work, but the very source of desire. Anticipating desires means that the system no longer waits for the consumer to choose: it leads them towards what maximizes the economic circuit's performance. Modeling preferences means that taste ceases to arise from vital experience to become the result of statistical patterns: affinities learned by the algorithm, not formed by the subject. AI defines what kind of attention we are prone to give, what sensitivity we are prone to develop, what cultural gestures we are prone to reproduce. In other words: it manages the raw material of subjectivity. And in every case it displaces the human being for the same reason that has guided capitalism for centuries: because it introduces limit, variability, uncertainty, or pause.
None of this means that the consummation of these goals has already occurred, or that it is inevitable. It means that, if the system does not change its orientation, AI is the first technology capable of precisely achieving what capitalism has pursued for centuries: to function without depending on human beings as agents of value. It does not introduce a new destiny: it reveals an old one. It does not transform the system from the outside: it perfects it from within. It automates what remained human, accelerates what was a trend, makes visible what was previously only intuition.
The end as a goal is not a prognosis, but a teleological reading: the point at which a system moves fully towards what was inscribed in it from its origins. And in that advance, AI turns human beings into what the logic of capitalism always wanted them to be: dispensable.