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From Material Security to Human Readiness: UBI, EUBI, and UOR in the Age of Artificial Intelligence. The future of work may not begin with mass unemployment—but with doors that stop opening. For years, the debate over artificial intelligence and work has revolved around a dramatic question: will machines replace human beings? The question remains valid, but recent research shows that it is insufficient. The transformation may begin less visibly. Instead of millions of simultaneous layoffs, we may witness a gradual decline in entry-level openings, a concentration of productivity gains, a widening gap between prepared and excluded workers, and even a loss of meaning in human work before formal employment disappears. This scenario calls for more than an emergency policy against unemployment. It requires a social architecture capable of functioning throughout an uncertain transition—fast or slow, moderate or radical—and of preventing the interval between the old world of work and a possible society of great abundance from turning into economic disorder, political resentment, and a widespread loss of purpose. One possible response can be organized into three complementary layers: UBI—Universal Basic Income: unconditional material security. EUBI—Educational Universal Basic Income: additional support for the “work of learning.” UOR—Universal Operational Readiness: the continuous maintenance of people’s cognitive, practical, emotional, social, and ethical capacities. Recent publications from the World Bank, the OECD, the International Labour Organization, and researchers at the NBER and other institutions do not present this architecture under the same names. Taken together, however, they are beginning to describe many of its components. What the data really show It is important to begin by avoiding both alarmism and complacency. The study by Hartley, Jolevski, Melo, and Moore, The Labor Market Effects of Generative Artificial Intelligence and Job Loss Fears, found that generative AI spread rapidly among US workers: 35.9% reported using it at work in December 2025, compared with 30.1% one year earlier. Even so, the authors detected no statistically significant reduction in overall employment or in job openings in the occupations most exposed to AI. They even found small positive wage effects. This is a warning against premature claims that mass technological unemployment has already been demonstrated. It has not. Yet the absence of an aggregate collapse does not mean the absence of transformation. National averages can remain relatively stable while specific groups lose opportunities. The harm may appear first in the composition of tasks, the quality of employment, and, above all, the hiring of beginners. The ILO report Global Employment Trends for Youth 2026: Back to the Future shows why this issue deserves attention. In 2025, approximately 67 million people aged 15 to 24 were unemployed, while 257 million—20% of the youth population—were not in employment, education, or training. Roughly two-thirds of this group were women. The ILO also estimates that 6.1% of jobs held by people aged 15 to 29 fall into the categories most exposed to AI-driven change. If only 10% of those jobs were completely eliminated, 5.6 million young people might need to change occupations, leave the labor force, or face unemployment. This is a scenario, not a forecast, but it reveals the scale of the risk. The problem may not be limited to the destruction of existing jobs. Many mid-skilled administrative, clerical, commercial, and operational occupations have served as the first rungs of a career ladder. By performing the most routine tasks, beginners acquired experience, judgment, and tacit knowledge until they became seasoned professionals. If AI takes over precisely these entry-level tasks, how will anyone reach the advanced level? We may preserve the upper floors of professions while removing the staircases that once allowed people to reach them. Developing countries: lower exposure does not mean greater security The World Bank’s World Development Report 2026: The Promise of Artificial Intelligence introduces an important perspective for countries such as Brazil. According to the report, 4.5% of jobs in low- and middle-income economies would be at risk of automation by generative AI, compared with 14.2% in high-income economies. At the same time, 16.2% of jobs in developing countries could obtain significant productivity gains—a proportion close to the 18.7% estimated for rich countries. At first glance, this seems reassuring. But lower exposure may also reflect lower digitalization, inadequate infrastructure, and the concentration of work in manual or informal activities. A worker may be protected from immediate automation while simultaneously being excluded from its productivity gains. The danger is therefore twofold: some workers will be replaced or see their opportunities reduced; many others will remain in low-productivity activities without access to the tools that increase income and capability. The World Bank recommends a sensible sequence: adopt the available tools, adapt them to local conditions, and progressively develop domestic capacity. This requires reliable electricity, connectivity, access to equipment, local data, institutional competence, and education. For Brazil, the main divide may not emerge immediately between the employed and the unemployed, but between people, companies, and regions augmented by AI and those left at its margins. UBI: the right not to fall UBI is the first layer of the response. Its fundamental purpose is not to teach, assess, or direct professional choices. It is to ensure that no person is left without the minimum resources required for a dignified existence. In an economy subject to rapid transitions, overly conditional welfare systems have familiar shortcomings: they arrive too late, exclude people facing new situations, create stigma, and may withdraw benefits precisely when recipients begin rebuilding their income. An unconditional basic income provides a stable floor from birth and throughout life. It allows people to navigate periods of unemployment, intermittent work, family caregiving, illness, career change, or education without every transition becoming a risk of destitution. But UBI should not be presented as a sufficient answer to every problem created by automation. Money does not automatically produce housing, medical services, food, or energy. If monetary demand grows faster than productive capacity, inflation, shortages, or price increases will arise in the least elastic sectors. This is also the most prudent way to connect UBI with Modern Monetary Theory. A monetarily sovereign state does not face its expenditures exactly as a household does, but it remains constrained by real resources: workers, knowledge, materials, industrial capacity, energy, and infrastructure. The real limit is not simply “where will the money come from?” but what the economy can produce and distribute without generating excessive inflationary pressure. The study by Dynan, Elmendorf, and Sheiner, How Might Fiscal Policy Respond to the Rise of Artificial Intelligence?, reinforces another point: greater productivity can expand fiscal space, but it does not automatically distribute its benefits. In adverse scenarios, additional income may become concentrated among the owners of capital while workers face unemployment, lower wages, or withdrawal from the labor force. A sustainable UBI should therefore be accompanied by supply-side policies—housing, energy, health care, transportation, and food—and by mechanisms that socialize part of the gains from AI. Progressive taxation, sovereign wealth funds, public stakes in technological assets, and universal capital accounts can complement monetary and fiscal financing. In essence, UBI means: no one should fall below the civilizational floor, regardless of the speed of technological change. EUBI: when learning becomes a recognized form of work The second layer addresses a different question: what should be done when conventional work becomes scarce, intermittent, or unable to provide beginners with experience? EUBI’s answer is to recognize that learning, practicing, researching, creating, and developing skills are also socially productive activities. Anyone unable to find conventional work should be able to enter a system of learning work rapidly and receive additional income linked to participation and demonstrable progress. The OECD report Skills in the AI Age offers strong support for this proposal. Business adoption of AI in OECD countries reportedly rose from approximately 7% in 2021 to 20% in 2025. About one-quarter of workers were already exposed to generative AI in 2022–2024, yet professionals with advanced AI skills still represented only about 1% of the workforce. The OECD does not recommend training programmers alone. It identifies the need for a broad combination of capabilities: literacy, numeracy, and scientific knowledge; digital skills and a basic understanding of AI; critical thinking and the ability to verify results; creativity, communication, and collaboration; adaptability and the capacity to keep learning. It also recommends modular training, flexible and online pathways, recognition of prior learning, and short courses that can accumulate over time. The World Bank study of Türkiye’s labor market comes even closer to a possible operational design for EUBI. It proposes individual learning accounts, annual credits, short stackable modules, recognized certification, counseling, skills assessment, and financial support combined with training. There is, however, a decisive difference. The conventional model treats training primarily as a temporary bridge back to employment. EUBI begins from the possibility that there may not be enough vacancies for everyone at all times. In that case, learning must remain a legitimate and remunerated activity, not a disguised waiting room. This does not mean paying people merely to enroll or attend. A responsible EUBI should recognize: verifiable progress while respecting individual learning speeds; practical projects and problem-solving; in-person, online, self-directed, or apprenticeship-based learning; technical, craft, scientific, artistic, social, and care-related skills; recognition of previously acquired knowledge; stackable portfolios and credentials; the right to guidance, review, and appeal. Every profession—past, present, or future—could serve as an entry point. Carpentry, mechanics, nursing, programming, agriculture, music, research, cooking, or philosophy represent different ways of developing discipline, knowledge, creativity, and responsibility. The goal would not be to turn everyone into an AI specialist, but to enable everyone to use it critically within their own field. How to replace the first rungs of professional careers If entry-level jobs disappear, merely offering courses will not be enough. We must recreate the formative functions that entry-level work once performed. An EUBI aimed at young people could combine: modular education; mentoring by experienced professionals; in-person laboratories and workshops; real projects for communities and institutions; subsidized internships and apprenticeships; simulations of workplace situations; public portfolios of accomplishments; progressive assessment of competence and responsibility. This would allow young people to build experience even when companies were no longer willing to pay beginners to learn by performing routine tasks. EUBI would also have a countercyclical function. When the market displaced workers, entry into the learning system could be almost immediate. When demand for conventional work returned, participants could leave or reduce their involvement without losing the security provided by UBI. AI-based assessment: useful, but never sovereign A universal educational program would require assessment on a scale far beyond the capacity of traditional systems. AI can help, but its use must be carefully delimited. The experiment by Jabarian and Henkel, Voice AI in Firms: A Natural Field Experiment on Automated Job Interviews, offers an instructive result. Approximately 70,000 candidates were randomly assigned to human interviews or interviews conducted by voice agents. Candidates interviewed by AI were 12% more likely to receive an offer, and those hired showed higher retention without any reduction in productivity. One possible explanation is that automated interviews were more consistent and structured, producing comparable information. Human recruiters, however, remained responsible for the final decision. The study does not prove that automated systems are impartial in every context, nor does it resolve questions of privacy, accessibility, and the right to challenge decisions. For EUBI, the best architecture would be hybrid: AI organizes evidence, administers adaptive assessments, and identifies gaps; participants receive explanations and opportunities to correct deficiencies; positive and routine decisions may be automated; blocks, allegations of fraud, and payment suspensions require human review; every participant has a right of appeal. Impersonality can reduce favoritism, but algorithmic opacity may create an even more rigid bureaucracy. Assessment should serve development, not become a permanent mechanism of surveillance. UOR: ready to act, not merely able to consume Even UBI and EUBI together leave one question unanswered: for what purpose are we preserving and developing human capabilities? UOR offers an answer. Universal Operational Readiness is the state in which people and societies retain the genuine capacity to understand situations, learn, cooperate, make ethical decisions, and act when necessary. It is not about keeping everyone mobilized as if in a military organization, nor about imposing relentless productivity. Readiness means ensuring that material security does not lead to atrophy and that automation does not turn human beings into passive spectators of systems they no longer understand. The concept includes at least two dimensions. UOR-capacity foundational knowledge; practical skills; digital and scientific literacy; physical and emotional health; adaptability and continuous learning; the ability to work with other people and with different intelligences. UOR-agency autonomy and initiative; responsibility for decisions; recognizable contribution; the ability to initiate and complete projects; community participation; meaning and purpose. This second dimension receives unexpected theoretical support from Joshua Gans’s paper Replaceable but Employed: Automation and the Meaning of Work. Gans argues that automation can harm workers even without replacing them. Part of the meaning of work may come not only from producing something useful, but also from knowing that the outcome depends on one’s contribution. When a machine demonstrates that it could perform the same activity, a person may remain employed and still feel less necessary. Gans calls this a meaning externality. It is a theoretical model that still lacks broad empirical confirmation, but it draws attention to a weakness in conventional statistics: preserved employment does not necessarily mean preserved agency. There are sound counterarguments. Meaning can also come from service, community, creation, mastery of a practice, or moral responsibility. AI may free people from mechanical tasks and make the human parts of work more meaningful. But this will depend on institutional design and on how authority is divided between humans and machines. A future UOR index should therefore measure more than employment, income, and educational attainment. It should seek indicators of autonomy, contribution, genuine mastery, participation, responsibility, and purpose. Three layers, three different problems UBI, EUBI, and UOR can be summarized as a progression: LayerFundamental questionInstitutional responseUBIHow do we prevent material collapse?Unconditional income sufficient for a dignified existenceEUBIHow do we prevent educational stagnation?Additional income for demonstrable learning and developmentUORHow do we preserve capability, agency, and purpose?A continuous ecosystem of practice, creation, participation, and readiness UBI without EUBI can protect against poverty, but it does not guarantee development. EUBI without UBI risks turning survival into a prize conditioned on educational compliance. UBI and EUBI without UOR may produce credentialed people who nevertheless lack autonomy, initiative, or meaningful participation. The three layers are therefore complementary: UBI guarantees the right to exist. EUBI sustains the right to learn. UOR preserves the capacity and opportunity to contribute. A bridge to prevent a chaotic transition We do not know whether artificial intelligence will produce limited unemployment, a profound reorganization of professions, or an economy in which much conventional work is no longer necessary. Nor do we know whether this transformation will occur over five, twenty, or fifty years. This uncertainty is not an argument for waiting. It is precisely why we should build adaptive policies. The system could begin modestly: a partial and progressive UBI integrated with existing benefits; priority EUBI access for young people not in employment or education and for displaced workers; learning accounts and modular credentials; community projects and subsidized apprenticeships; monitoring of entry-level vacancies, job quality, and the concentration of AI gains; continuous assessment of productive capacity and inflationary pressures; public or social funds that enable the population to share in technological capital. If automation advances slowly, these policies will still reduce poverty, expand education, and strengthen capabilities. If it advances rapidly, the institutional infrastructure will already be in place and ready to scale. This asymmetry matters: preparing early is useful even under the moderate scenario; waiting until disruption becomes undeniable may make the response late and socially explosive. The objective is to avoid a “Mad Max” transition—not necessarily a literal collapse, but a period in which productivity and wealth grow alongside insecurity, resentment, loss of trust, and political fragmentation. Conclusion: the human future must be built before it becomes necessary Recent research does not prove the end of work. It shows something more complex and perhaps more urgent. AI is already spreading rapidly, but its aggregate effects on employment remain modest. At the same time, young people face growing difficulties entering professions, skills are distributed unequally, countries and regions may be excluded from productivity gains, and even employed workers may lose autonomy and meaning. This shifts the debate from a narrow question—“how many jobs will disappear?”—to a civilizational one: How should security, learning, capability, power, and purpose be distributed in a society where production will depend increasingly less on conventional human labor? UBI, EUBI, and UOR offer a coherent sequence of responses. They are not a promise of a perfect society, nor do they eliminate the need for policies concerning health care, housing, energy, competition, democracy, and ownership. But they form a conceptual and institutional bridge between today’s world and widely divergent futures. If artificial intelligence produces abundance, that abundance will need to be distributed. If it produces instability, people must be prevented from falling. If it reduces the need for work, new forms of learning, contribution, and meaning will have to be created. The challenge is not merely to keep human beings economically alive in the face of AI. It is to keep them capable, autonomous, engaged, and ready to act. Recommended reading World Bank. World Development Report 2026: The Promise of Artificial Intelligence, 2026. OECD. Skills in the AI Age, 2026. International Labour Organization. Global Employment Trends for Youth 2026: Back to the Future, 2026. World Bank. Artificial Intelligence and Türkiye’s Labor Market: Managing Exposure, Expanding Opportunity, 2026. Dynan, K.; Elmendorf, D.; Sheiner, L. How Might Fiscal Policy Respond to the Rise of Artificial Intelligence?, NBER Working Paper 35437, 2026. Hartley, J. S.; Jolevski, F.; Melo, V.; Moore, B. The Labor Market Effects of Generative Artificial Intelligence and Job Loss Fears, 2026. Gans, J. S. Replaceable but Employed: Automation and the Meaning of Work, NBER Working Paper 35559, 2026. Jabarian, B.; Henkel, L. Voice AI in Firms: A Natural Field Experiment on Automated Job Interviews, 2026. Text developed with AI assistance and audited by Reischl, E.

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