Why Artificial Intelligence Means the End of Capitalism · Part I: The End as Goal
Artificial Intelligence: a brief introduction
There are words that, when we pause to consider them, open up a wider space for interpretation than they seem to at first glance. End is one of them. In Spanish, it can mean goal—the direction something is headed—and also termination—the closure of that very process. In that ambivalence lies a conceptual richness that etymology reveals. The Greek term télos, from which our “end” derives, makes no distinction between objective and culmination: both are part of the same circular movement. Télos comes from the Proto-Indo-European root kwel-—to turn, to revolve, to close a cycle. For ancient thinking, that which reaches its télos doesn't just finish: it fulfills itself. The end is the accomplished goal; the goal is the natural outcome of the process.
Modern languages separated these meanings—goal on one side, end on the other—because our historical imagination became linear. We believe everything moves forward, toward more, toward better, as though every process must extend endlessly.
Perhaps that’s why we tend to view the new as merely an expanded version of what we already know. If time is an ascending line, we assume the future only stretches out what we have lived: more technology, more speed, more efficiency, but nothing truly different. That idea of continuity reassures us. It situates us in an imaginary place of control, as if what’s coming were just one more step on a path we’ve understood for centuries. That’s why we repeat “we’ve been through 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 stops being protection and becomes danger.
Two films about World War I show this with overwhelming clarity: Paths of Glory (Stanley Kubrick, 1957) and All Quiet on the Western Front (Lewis Milestone, 1930). In Paths of Glory, we see the blindness of the French military elite, convinced that war was still what they remembered: heroic deeds, decisive advances, battlefields where personal courage could change fate. All Quiet on the Western Front shows the same from the foot soldier’s perspective: youths marching to the front thinking they would repeat the glories of the past. What they encountered was not a continuation at all: war had become 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 one day at the Somme, more soldiers died than in entire months of previous conflicts, including the Napoleonic campaigns. Never before had killing been so fast, so mechanical, so impersonal. The past did not return; it could not return. And those who insisted that war “was the same as ever” were profoundly mistaken: that error ended up costing more than 17 million lives in just four years.
The danger in believing that the new is just a repetition of the past is losing the ability to recognize a qualitative leap. And today, with artificial intelligence, something similar is happening. Those who say “we’ve seen this before with other technologies” forget that we have never before faced cognitive automation. It’s not about replacing physical strength or speeding up calculations: it’s about delegating to technical systems functions that were previously exclusive to the human mind. Today, approximately 65% of global GDP comes from the service sector, and around 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 do what we did faster; it can do it without human presence, with minimal marginal costs and at an impossible-to-rival speed. Cognitive automation does not reduce tasks: it redefines the very structure of work, shifts value to non-human processes, and makes much remunerated activity dispensable.
But that is only one of its fronts. The other—deeper—is the automation of judgment. We’re not speaking of technical skills, but of externalizing a function that in all human societies had remained at the core of experience: the ability to assess, 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, discernment, memory, comparison, intuition, and valuation are now integrated into algorithmic processes designed to optimize outcomes, not to understand them. An automated decision is no longer a human decision, even though its effects fall on humans. And when a technical system occupies that space, it does not just produce answers: it defines the horizon of the possible, determining what options we consider relevant and which fade away before they’re even thought of. To automate judgment is not only to delegate decisions; it is to allow an external system to silently preempt all alternatives that will never come into being for us.
We may pretend this belongs to a distant future, but it’s 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 awareness. Today algorithms choose what to watch, what to listen to, what to read, and what to ignore. The shows we watch, the news we receive, the music we discover or the search engine results we see are, in the majority, prior decisions by the system. Platforms, social networks, and search engines rank reality according to patterns we don’t control; they decide what deserves attention and what can vanish 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 do not just yield practical functions; we yield the very process by which we become who we are. If another system decides what we might be interested in, it also shapes what we are capable of desiring. If it organizes the hierarchy of significance, it configures the boundaries of our inner world. Many of our preferences did not originate from our sensitivities but from recommendations accepted as if they were 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, we should first 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 is turned into process: work, time, relationships, people, information. And that process has a constant direction: maximizing productivity and reducing costs. Every innovation—mechanization, the factory, the assembly line, digitalization, financialization—has been integrated to advance this logic.
Neoliberalism is the most radical expression of this orientation. It extends the logic of business to the whole of existence: the individual becomes a performance unit; time, a resource; subjectivity, an asset; rights, costs; precarity, an incentive. In this view, the market is not a space within society: it is the organizing principle. The system works best when human intervention is minimal. Inequality is no longer seen as a flaw, but as a sign of efficiency.
Understanding its télos—the internal orientation that guides its evolution—means identifying what goals it has pursued since its origins and how artificial intelligence influences their realization.
From the start, capitalism has sought to free itself from the limits of the human body. Mechanization replaced physical strength; the assembly line annulled worker variability; digitalization absorbed repetitive tasks; industrial automation reduced dependence on biological rhythm and attention. Everything human that introduced fatigue, pause, or unpredictability was seen as friction. AI introduces a qualitative leap because it automates the one thing that could not be automated: cognition. Where interpretation, decision-making, or human coordination were needed before, now a technical model can intervene. The cognitive function begins to be replaced by unlimited operational continuity.
Added to this is 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, the total use of available time, the conversion of every act into productivity. It is also reflected in the expansion of predictive models capable of anticipating decisions and turning human uncertainty into exploitable patterns. And finally, it appears in financialization, allowing capital to grow without material production. AI amplifies these three dimensions: it multiplies work without rest, perfects prediction, and powers autonomous algorithmic markets. Growth no longer depends on the subject: algorithmic capacity sustains it.
But this movement does not occur in an open or distributed space: it tends, inherently, to concentrate. This is not a side effect but a property inscribed in its very name. Capital comes from caput, “head”: what is above, what leads, what accumulates. Capitalism, in its most basic architecture, organizes life around centers of accumulation, not horizontal distributions. As efficiency increases, capital clusters in those with greatest technical, financial, or informational capacity; scale becomes the decisive criterion of dominance. The system works best when power is concentrated, because concentration accelerates accumulation.
Artificial intelligence does not just replicate this logic: it intensifies it as no previous technology ever has. Algorithms learn better the more data they control, and the data—the raw material of AI—are already in the hands of a small number of global actors. The quality of the model depends on centralization: the larger the platform, the greater the precision; the more vast the information flow, the more dominant the position. AI does not democratize infrastructure: it centralizes it by its very nature. Those who control the models and data control the entire process; those without access are inevitably relegated to the margins. Concentration thus ceases to be a trend to become the system’s very form because the technology that drives it—like the capital from which it springs—improves precisely as it concentrates.
And a final goal emerges—perhaps the deepest of all: progressively reducing the role of the human being in the value chain. Each historical stage of capitalism can be seen as one more step in this direction: mechanization turned bodily power into a limitation overcome by machines; scientific management replaced artisanal knowledge with standardized procedures, reducing individual initiative to a bare minimum; digitalization turned human slowness into an insurmountable disadvantage compared to computing speeds; and industrial automation rendered human supervision and control dispensable costs. AI completes this movement. It does not just 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 shapes preferences. And this last aspect is perhaps the most decisive, because it means displacing not just labor, but the very source of desire. Anticipating desires means the system no longer waits for the consumer to choose: it leads them towards what maximizes economic circuit performance. Shaping preferences means that tastes no longer arise from life experience, but become the outcome of statistical patterns: affinities learned by algorithms, not formed by the subject. AI defines what kinds of attention we are likely to give, what sensitivities we are likely to develop, what cultural gestures we are likely to reproduce. In other words: it manages the raw material of subjectivity. And in each case it displaces the human being for the same reason that has driven capitalism for centuries: because humans introduce limits, variability, uncertainty, or pause.
None of this means that the realization of these goals has already happened, or that it is inevitable. It means that if the system does not change its orientation, AI is the first technology capable of precisely accomplishing what capitalism has pursued for centuries: operating without relying on humans as agents of value. It does not introduce a new destiny: it reveals an old one. It does not transform the system from outside: it perfects it from within. It automates what remained human, accelerates what was already a trend, and makes visible what was once only intuition.
The end as goal is not a prediction but a teleological reading: the point at which a system fully advances toward what had been inscribed in it from the beginning. And in that advance, AI turns humans into what capitalism’s logic always wanted them to be: dispensable.