Artificial intelligence is entering human society at an unprecedented pace. From factory floors and office desks to hospitals, schools, and everyday life, AI is becoming a foundational force in the production of knowledge, the distribution of wealth, and the organization of social relations. Public debate oscillates between hope and fear. Some anticipate a leap in productivity that will free humanity from repetitive and exhausting labor. Others warn that machines will displace workers, concentrate wealth, and ultimately undermine the social structures on which human life depends.
Both perspectives reflect genuine possibilities, yet they share a common assumption: that artificial intelligence is an autonomous force whose technological capabilities will determine the future of society. But technology has never developed in a vacuum. Who finances it, who owns it, who controls it, and whose needs it is designed to serve are not peripheral questions. They shape how technology enters society and determine who benefits from its advances, who bears its costs, and whose lives are transformed in the process.
The fundamental question, therefore, is not simply what kind of future AI will create, but who has the power to decide what that future looks like.
Every major technological transformation in modern history has contained two distinct possibilities: the expansion of human capabilities and the reorganization of power between people. The steam engine increased productive capacity without automatically eliminating workers’ poverty. Electrification and assembly-line production enabled mass manufacturing while often making labor more standardized and tightly controlled. The internet lowered barriers to communication and knowledge sharing, yet also facilitated the emergence of powerful digital platforms capable of monopolizing markets, information, and public attention. Technology expands what society can do, but it does not determine how those possibilities will be realized, nor does it guarantee that their benefits will be shared.
Artificial intelligence is no exception. It can accelerate scientific discovery, widen access to knowledge, improve medical services, and help people accomplish tasks that were previously beyond their individual capabilities. Yet the social consequences of these advances depend on the institutional and economic arrangements into which they are introduced. The same AI system might help a doctor spend less time completing paperwork and more time caring for patients. It might equally become a justification for reducing medical staff and increasing workloads. In education, AI could help teachers understand individual students’ needs, or it could be used to standardize assessment and reduce the complexity of learning to a set of measurable indicators. Technological capabilities alone cannot determine which path society takes. Those choices are made by people and institutions operating within particular structures of power and interest.
This is why the current AI boom must be understood within a broader political and economic context. Capital flows into artificial intelligence not only because the technology has genuine innovative potential, but also because it embodies enormous expectations of future growth. Under competitive pressure, companies must continually present compelling visions of the future to attract investment, sustain valuations, and secure market dominance. Model size, computing capacity, and user growth consequently become more than measures of technical progress: they become financial narratives through which uncertain future returns are translated into present economic value.
This does not mean that private investment inevitably produces a speculative bubble. Large-scale investment can sustain long-term research, build essential infrastructure, and support innovations whose benefits may take years to emerge. Many important technological advances would have been impossible without commercial enterprise.
The problem arises when expectations of future profitability become detached from demonstrable social value, and when companies must continually expand simply to justify their valuations. Under these conditions, technological competition can gradually drift away from concrete human needs. Vast resources may flow toward ever-larger and more expensive models, while persistent problems in education, healthcare, housing, and public services receive comparatively less attention because their returns are less immediate or less easily captured by private investors.
Here lies a central contradiction of the AI economy: the capacity to generate wealth is not the same as the capacity to distribute it. Higher productivity does not automatically translate into higher wages, just as lower production costs do not necessarily result in better services for ordinary people. If the gains from technological progress accrue primarily to corporations, shareholders, and those who already possess bargaining power, society may become more productive without becoming more prosperous in any broadly shared sense. The question is not merely how much wealth AI can create, but who will have the power to claim it.
The future of work illustrates this tension particularly clearly. It is misleading to frame AI as a simple contest between human workers and machines. Technology can eliminate certain jobs, create new occupations, transform existing tasks, and strengthen workers’ capabilities at the same time. The more consequential questions are whether workers can participate meaningfully in the transition, whether they can share in the gains from increased productivity, and whether they will retain the ability to secure dignified livelihoods and negotiate the conditions of their employment.
When businesses approach automation primarily as a means of reducing labor costs and weakening their dependence on workers, technological progress can be accompanied by declining job security, the erosion of skills, and increasingly intensive forms of management. Even when jobs are not eliminated, algorithmic systems can reorganize work through granular performance metrics, continuous monitoring, and automated task allocation. Workers may no longer receive every instruction directly from a human supervisor, yet find themselves increasingly dependent on algorithmic scores, rankings, and recommendations. A workplace can become more intelligent without becoming more humane.
Nor is the intelligence displayed by AI entirely detached from human labor. Behind seemingly autonomous systems lie countless tasks involving data preparation, content annotation, safety moderation, infrastructure maintenance, and other forms of work that remain largely invisible to end users. Beyond the digital interface are mines, semiconductor factories, data centers, power grids, and global supply chains. The apparent immateriality of AI rests on a substantial physical foundation. Some labor is concealed behind the polished image of intelligent products, while some environmental and social costs are transferred to other communities. The more seamless and automated a technology appears, the more important it becomes to ask who performs the work that sustains it, where its resources originate, and who bears the consequences of its material footprint.
These questions lead directly to the problem of global inequality. Advanced AI depends on highly concentrated resources, including capital, computing infrastructure, semiconductors, and specialized expertise. These resources are unevenly distributed across countries and regions. Those who control critical infrastructure can consequently exercise disproportionate influence over technical standards, commercial rules, and the international governance of AI. Meanwhile, the natural resources, data, and labor contributed by countries in the Global South may help generate technological value without ensuring that those societies share proportionately in its benefits. When AI is increasingly treated as an instrument of national competition, the space for international cooperation can narrow further.
None of this means that AI is inherently destined to reproduce existing structures of domination. The significance of these problems lies precisely in the fact that current arrangements are not inevitable. Technological infrastructure can be organized under different ownership models. Knowledge can be shared through different institutional arrangements. Public institutions can support research and applications guided by social needs rather than commercial returns alone. The trajectory of technological development is not a predetermined destiny; it emerges from countless economic decisions, institutional choices, and collective actions.
We must therefore reconsider what it means for AI to serve the public interest. Public value does not require every technological system to be developed directly by the state, nor does it demand the exclusion of commercial innovation. It means that technologies profoundly affecting the basic conditions of social life cannot be evaluated solely through market prices and investment returns. AI in education should support human development rather than merely improve administrative efficiency. AI in healthcare should improve patients’ lives rather than further marginalize those who cannot generate sufficient profit. AI in the workplace should reduce unnecessary drudgery rather than transform every minute saved into an opportunity to impose higher performance targets.
Achieving these goals requires us to move beyond technical regulation and address the distribution of wealth, labor protections, public services, and democratic participation. Rules governing corporate conduct may be necessary, but they cannot by themselves transform the distribution of technological gains. Ethical principles are equally important, yet they cannot substitute for changes in the actual relationships through which power is exercised. Workers need a meaningful voice in technological decisions that reshape their employment. People should be able to understand and challenge automated decisions that affect their rights. Public institutions need sufficient technological capacity to avoid complete dependence on a small number of commercial providers. Open-source development, public research, and international collaboration also deserve stronger institutional support, so that essential knowledge does not become entirely enclosed within the proprietary assets of a few organizations.
None of these approaches is a universal solution. Governments may lack technical expertise, and public institutions can be vulnerable to bureaucracy, conflicts of interest, and abuses of power. Open-source development can broaden participation without eliminating inequalities in computing resources. Market competition can encourage innovation without necessarily protecting fundamental rights. The challenge, therefore, is not simply to choose between the state and the market, but to establish institutions capable of limiting concentrated power, protecting rights, accommodating diverse needs, and remaining accountable to the public. Whether AI is controlled by corporations, governments, or other organizations, the same question should apply: does it give the people affected by it greater opportunities to understand, influence, and challenge the decisions that shape their lives?
This is also where a humanistic perspective becomes indispensable. We habitually measure technological progress through speed, scale, accuracy, and productivity, but rarely ask how much efficiency society actually needs, or what that efficiency should enable us to achieve. Human worth cannot be reduced to the speed at which tasks are completed. Education is more than the transmission of information; healthcare is more than the identification of disease; social life is more than a collection of processes awaiting optimization. Companionship, trust, dignity, creativity, and mutual understanding are difficult to quantify, yet they constitute much of what makes life meaningful.
If AI can take over more repetitive work, will people gain more genuine freedom over their time? If machines can generate greater wealth, will people enjoy greater security, or will they remain trapped in constant competition and fear of becoming economically redundant? If access to knowledge becomes easier, will our capacity for independent judgment deepen, or will we gradually outsource the act of thinking itself? These questions cannot be answered by technological development alone. They concern the kind of society we wish to build and the meaning we attach to human freedom.
We need not reject artificial intelligence because capital seeks profit, nor should we assume that its extraordinary potential will automatically benefit everyone. A more mature response requires us to recognize both the possibilities technology creates and the ways in which economic interests can shape those possibilities. Openness to innovation is entirely compatible with skepticism toward concentrated power. Supporting technological progress does not require abandoning the pursuit of fairness, dignity, or public accountability.
Ultimately, AI for capital and AI for society are not two fundamentally different technologies. They represent different social relationships through which the same technology can develop. In one, intelligence is primarily treated as an instrument of accumulation, while people become costs to be minimized, data to be extracted, or markets to be captured. In the other, technological development is subject to broader public deliberation, efficiency serves human flourishing, and the gains of innovation become accessible to a wider share of society. The distinction lies not simply in corporate philosophy, but in labor institutions, public policy, ownership structures, and the actual capacity of people to participate in decisions affecting their lives.
We still do not know where artificial intelligence will ultimately lead humanity. But one principle deserves to remain beyond dispute: the future should not belong exclusively to technology developers, nor should it be determined solely by financial markets and their expectations of growth. It must also belong to the people who use these systems, live with their consequences, and will shape the societies in which they operate.
Technology can help us reorganize the world, but it cannot decide for us why that world is worth organizing in the first place. Genuine progress does not mean creating machines that become increasingly powerful while requiring human beings to surrender their dignity, autonomy, and capacity for care. It means building a society in which technological advancement expands the conditions for human flourishing.
The moment we begin asking who owns technology, who benefits from it, and who has the authority to determine its purposes, the debate over artificial intelligence moves beyond speculation about the future. It becomes a discussion about the choices we can make in the present, and the kind of future we are willing to build together.
Source: toda, ncbi, washington crossing advisors, theconversation
