From Profit Driven Computation to Conscious Social Coordination
Hamid Akhavi
Revised and source checked edition | September 2026
Central thesis AI can make democratic economic planning more informed, adaptive, and practical, but only social ownership, constitutional rights, public transparency, and participation in power can determine whether it serves human need rather than profit or bureaucracy.
Introduction
Artificial intelligence has made a question once treated as utopian newly practical: can a complex economy coordinate production, distribution, investment, and public services according to human need rather than private profit? The answer is neither an automatic yes nor the familiar claim that a sufficiently powerful computer can replace markets and command society from above. AI can greatly expand society’s capacity to observe economic conditions, compare alternatives, anticipate shortages, and revise plans. But computation cannot decide what society ought to value, who should own its productive resources, or who has the right to make and contest collective decisions.
The central argument of A New Social Order in the Age of Consciousness is therefore more demanding than technological central planning. The contradiction between increasingly socialized production and concentrated private ownership has reached a new stage. Digital platforms, cloud infrastructure, global supply chains, automation, and AI depend on the accumulated knowledge and daily activity of millions, yet their direction and benefits remain largely controlled by a small number of corporations and owners. AI is among the most socialized productive forces humanity has created, but under capitalism it is organized primarily for accumulation, surveillance, cost cutting, market power, and profit.
A new social order would change the objective function of the economy. Its primary measure would not be the return on capital but the reliable satisfaction of material and social needs, ecological sustainability, freedom, equality, and the expansion of human capabilities. AI would serve this transformation as a public instrument of knowledge and coordination. It would inform decisions, not rule over people; widen participation, not concentrate authority; expose choices and tradeoffs, not conceal them behind a technical black box.
Capabilities and Limits of AI-Assisted Planning
AI offers several real and important capacities for social planning: forecasting demand, modeling supply chains, simulating policy alternatives, detecting anomalies, and coordinating information across sectors. Yet these capacities do not make planning democratic by themselves. Data quality, privacy, security, explainability, human oversight, and a public right to challenge decisions must all be guaranteed. Managing a firm is also not the same as democratically coordinating a society, because society decides values, priorities, and the distribution of resources—not merely operational efficiency.
Central planning, a new social order, and non-market coordination should not be treated as synonyms. Social ownership does not require every decision to be issued by one administrative center. Planning can occur at several levels: individuals and households express needs; workplace and neighborhood councils shape local priorities; sectoral institutions coordinate capacity and standards; regional bodies balance resources; and society as a whole decides long-term investment, ecological limits, and universal guarantees. The essential distinction is not between the market and the computer, but between privately concentrated economic power and social accountability—and between a system that sends information only upward to a bureaucracy and a transparent system that enables participation and continual correction.
AI simulation results should likewise not be presented as proof that an AI-planned economy will work. The AI Economist, for example, is a research environment in which reinforcement-learning agents test tax policies under simplified assumptions. Its reported improvement in the modeled equality-productivity tradeoff shows that computational experimentation may help compare policies; it is not empirical evidence that an algorithm has discovered an optimal tax system for a real country. Models illuminate consequences within their assumptions, but they do not eliminate political judgment, uncertainty, or conflict among legitimate values.
Why Capitalism Already Depends on Planning
The usual opposition between capitalism as spontaneous markets and socialism as planning obscures how modern capitalism actually functions. Large corporations plan production, inventory, logistics, labor deployment, pricing, and investment across continents. Platforms allocate visibility and opportunity through algorithms. Central banks and states use extensive data systems to stabilize finance, manage crises, procure essential goods, and direct strategic investment. The question is not whether planning exists. It is who plans, for what purpose, with what information, and under whose control.
Inside the corporation, sophisticated planning coexists with competition outside it. Yet the information produced by workers, consumers, public research, and shared infrastructure is enclosed as private property. AI intensifies this contradiction. It turns collective behavior into predictions and private revenue; it can reduce socially necessary labor, yet the resulting gains often appear as layoffs, insecurity, and higher returns to owners. A rational society would treat productivity growth as the material basis for shorter working time, better services, universal security, and greater freedom from compulsory labor.
This change cannot be accomplished by replacing corporate managers with state managers while leaving citizens passive. Bureaucratic command can reproduce opacity, privilege, and alienation in another form. The new social order must combine social ownership with participation in power: workers, communities, consumers, specialists, and elected public bodies must be able to propose, inspect, amend, approve, and evaluate plans.
What AI Can Contribute
AI can strengthen democratic planning in five practical domains. First, it can create a continuously updated social needs map. Aggregated information about housing, food, health, education, transportation, energy, care work, and environmental conditions can reveal unmet needs by locality and population without turning personal lives into commodities. Second, forecasting systems can estimate demand, capacity, bottlenecks, and vulnerability to shocks. These forecasts should be published with confidence ranges and alternative assumptions rather than presented as commands.
Third, optimization tools can compare feasible ways of meeting democratically chosen goals. A society may ask how to guarantee essential electricity while reducing emissions, how to expand housing without exhausting water supplies, or how to distribute medical resources fairly during an emergency. Multi-objective models can show tradeoffs among time, materials, energy, resilience, equality, and ecological impact. They cannot determine the moral weight of those objectives; that choice belongs to society.
Fourth, simulations can allow policies to be tested before large-scale implementation. Tax, benefit, transit, land-use, and industrial strategies can be examined under different behavioral assumptions and stress scenarios. The AI Economist is useful here as a proof of method, not as a political oracle. Likewise, research on AI-assisted allocation shows that sizeable planning problems can be computationally tractable on ordinary hardware, but computational feasibility is only one part of institutional feasibility.
Fifth, AI can support monitoring and public accountability. Open dashboards can compare promises, allocated resources, delivery times, regional disparities, emissions, working conditions, and outcomes. Anomaly detection can flag corruption, hoarding, discriminatory patterns, or deteriorating services. Every alert must remain reviewable, and affected people must have a direct path to explanation, correction, and appeal.
From Central Command to Participatory Coordination
The most defensible architecture is polycentric: coordinated across levels, but not monopolized by a single center. Workplaces would report productive capacity, input needs, safety constraints, and proposed innovations. Neighborhood and municipal councils would register needs and priorities. Sector councils would reconcile technical interdependence across energy, food, transportation, health, housing, communications, and industry. National planning institutions would address universal rights, major infrastructure, ecological ceilings, interregional balance, and long-term investment.
Information must move in both directions. Local knowledge that cannot be captured in a national database must enter through deliberation and revision. National constraints and common standards must be visible locally. AI can help translate millions of proposals into comprehensible options, identify conflicts, and calculate consequences. It must never erase minority positions or convert participation into a survey whose answers authorities can ignore.
Chile’s Project Cybersyn remains instructive precisely because its ambition was not simply to centralize every decision. It sought rapid information flows, operational feedback, and a degree of autonomy within enterprises. Its short and incomplete history does not prove the viability of contemporary democratic planning, but it demonstrates that cybernetic coordination and worker participation were being considered together decades before modern AI. Its lesson is institutional: technology should shorten the distance between knowledge and decision while preserving autonomy and feedback.
From the History of Computational Planning to Digital Participation
The discussion of AI and social planning does not begin in a historical vacuum. Over the last seventy years, governments, cities, and social movements have repeatedly tried to connect dispersed information with collective decision-making. Some experiments relied on computer networks and modeling; others on citizens’ assemblies, local councils, and participatory budgeting; newer examples bring these two paths together through digital platforms. This history proves neither that technology automatically produces democracy nor that planning is doomed to fail. What has mattered is the relationship among technical capacity, ownership, institutions of participation, and the ability to demand accountability.
Cybersyn in Chile: Rapid Feedback During a Democratic Transition
Between 1971 and the military coup of September 1973, Salvador Allende’s government developed Project Cybersyn to coordinate nationalized enterprises. Existing telex infrastructure, statistical software, an economic simulator, and an operations room were linked so managers and public institutions could identify important deviations sooner. The central innovation was not collecting every piece of data in one center. It was creating feedback loops that sent only exceptional, decision-relevant information upward while preserving a degree of autonomy for productive units.
The network’s most famous test came during the truck owners’ strike of October 1972. The telex network carried nearly two thousand daily messages about operating trucks, open routes, fuel, and raw materials, helping the government coordinate essential supplies during the crisis. The experience showed that even technology far more primitive than today’s could shorten the time between identifying a problem and taking collective action. Yet Cybersyn could not solve economic blockade, inflation, political polarization, or military violence, and it ended with the coup. Its lesson is twofold: sound technical architecture can increase resilience, but it cannot replace social organization, political legitimacy, or the defense of democratic institutions.
OGAS in the Soviet Union: Technical Capacity Without an Institutional Coalition
In the 1960s, Viktor Glushkov proposed OGAS—the Nationwide Automated Management System—to network computing centers and improve management of the Soviet economy. Technically, his idea was decades ahead of its time: a multilevel network for national data transmission, calculation, and coordination. But ministries and administrative bodies treated data and authority as sources of power and competed over budgets, ownership of the network, and control of information. The project was worn down by institutional deadlock and never realized on a nationwide scale.
This failure cannot be attributed only to the limitations of the period’s computers. OGAS shows that even an ambitious computational design can be neutralized from within when it lacks genuine participation by producers and users, clear accountability rules, and a political coalition committed to sharing information. In a new social order, an information network must make power visible and answerable, rather than merely increasing the center’s capacity to issue commands.
Porto Alegre: Participatory Budgeting and the Link Between Priorities and Allocation
Participatory budgeting in Porto Alegre, Brazil, began in 1989 and enabled residents to decide part of municipal investment through neighborhood and thematic assemblies. Participants ranked urgent needs—from sanitation and roads to schools and clinics—elected delegates, and carried decisions into the formal budget process. Especially in its early years, the mechanism increased the participation of low-income groups, redirected infrastructure toward underserved neighborhoods, and reduced dependence on patronage in the distribution of services. Its spread to thousands of local governments around the world showed that citizens can decide real tradeoffs under resource constraints when participation is tied to financial outcomes.
The weakening and eventual suspension of the process in Porto Alegre itself in 2017 is also an important warning. Participation becomes symbolic ritual when it lacks a meaningful share of the budget, comprehensible information, trackable implementation, and sustained political commitment. Future AI could calculate the cost and distributional impact of proposals and make scenarios understandable to residents, but it cannot substitute for an institutional guarantee that public decisions will be carried out.
Kerala’s People’s Planning: Building Capacity From Below
The Indian state of Kerala launched the People’s Planning Campaign in 1996 and devolved roughly 35 to 40 percent of state plan resources to local governments. Needs were raised in village and neighborhood assemblies; working groups of elected representatives, experts, staff, and social activists designed projects; and local councils decided the plans. Digital systems such as Sulekha later made the registration, evaluation, and monitoring of projects more systematic.
The decisive point in Kerala is that technology rested on fiscal decentralization, public education, volunteer technical groups, and repeated cycles of consultation. More data do not create participation unless people can interpret and contest them. The experience suggests that data literacy, equal access, facilitation, and public technical resources must be part of the planning infrastructure—just as much as AI models and computing centers.
Decidim Barcelona: Software as Public Infrastructure
Barcelona’s open-source Decidim platform was created to combine in-person and online participation. During preparation of the municipal action plan, more than ten thousand proposals and over 160,000 expressions of support were recorded; according to the project’s official reporting, 71 percent of citizen proposals were incorporated into more than 1,600 actions. The importance of the experience lies not only in participation numbers. Every proposal, endorsement, amendment, and outcome should be traceable, allowing residents to see where their input was accepted, merged, or rejected.
Decidim offers a model of technology as a digital commons: open-source code, visible rules, and adaptability by other cities. This matters for a new social order because the instruments of participation must not become another corporate monopoly or black box. Still, a good platform only reduces the cost of participation; the quality of decisions continues to depend on social representation, universal access, and a formal link between deliberation and public authority.
vTaiwan: Finding Common Ground at Scale
Since 2014, the vTaiwan process has combined face-to-face discussion with tools such as Pol.is for public consultation on digital-policy questions. Instead of amplifying the most popular or most outraged opinion, the tool reveals clusters of viewpoints and statements supported across several groups. In the UberX regulation case, more than four thousand people participated over four weeks and areas of agreement were translated into regulatory proposals. The official vTaiwan site reports that more than 28 issues have passed through the process and roughly 80 percent led to decisive government action.
This experience shows that computational tools can summarize a large dialogue, expose divisions, and identify common ground, but legitimacy does not come from the algorithm. Sampling rules, access for less-heard groups, transparency in converting opinion into policy, and the responsibility of elected officials remain essential. Experimental use of language models in newer consultations must follow the same principle: AI may help formulate and summarize, but it must not erase minority views or manufacture consensus.
Comparative Summary: Six Experiences, Four Lessons
These case studies come from very different political systems and scales, yet they reveal four common patterns. First, information acquires political value when it is connected to real authority to allocate resources or revise policy; Porto Alegre and vTaiwan demonstrate that link. Second, decentralization must be combined with multilevel coordination; Cybersyn and Kerala show that local knowledge and a society-wide view can complement one another. Third, technical capacity does not replace institutional capacity; the failure of OGAS and the decline of participatory budgeting in Porto Alegre reveal the importance of political coalitions, stable budgets, and accountability. Fourth, digital infrastructure must itself be governed democratically; Decidim reminds us that code, data, and the rules of participation must be visible, criticizable, and changeable.
In none of these examples did the machine replace politics. The more successful experiments reduced the cost of acquiring information, brought more perspectives into decision-making, and established a clearer path from participation to outcomes. The incomplete or unsuccessful cases showed that information centralization, organizational resistance, exclusion of social groups, and separation between consultation and authority can neutralize even technically impressive designs. The standard for evaluating AI in democratic planning must therefore include not only forecast accuracy, but also the expansion of participation in power, the capacity to appeal, and material improvement in life.
AI’s Future Role in Democratic Planning
AI’s future role can be understood in three layers. At the knowledge layer, systems would aggregate dispersed data while protecting privacy, reveal shortages and inequalities in near real time, and explain uncertainty in accessible language. At the deliberative layer, tools would organize thousands of proposals, place similar and conflicting arguments side by side, show the effects of each option on different regions and groups, and preserve minority views independently. At the implementation layer, models would coordinate productive capacity, energy, labor, and ecological constraints while exposing the gap between promises and results through public dashboards.
These layers are legitimate only within clear boundaries. Final decisions about goals, rights, and the distribution of sacrifices must remain human and democratic. Every algorithmic recommendation must disclose its data sources, assumptions, error range, effects on different groups, and rejected alternatives. Citizens must have rights to explanation, data correction, appeal, and a human decision. Councils and oversight bodies must also be empowered to suspend a system that discriminates, exceeds its mandate, or cannot be audited.
The practical path forward runs through limited, measurable projects—not a one-time surrender of the economy to a central model. Cities can begin with housing, transport, local energy, and preventive health planning; workplaces can assess production plans and the effects of automation with workers’ councils; and national institutions can establish data standards, universal guarantees, and regional balance. Every stage must include public reporting, independent evaluation, and reversibility. Combined with social ownership of infrastructure, political freedom, and sustained participation in power, AI can evolve from an instrument for forecasting private profit into part of collective rationality and a conscious economy.
The Democratic Objective Function
Every planning system optimizes something, even when its values remain hidden. Under capitalism, profitability disciplines most decisions. Social harms such as pollution, exhausting work, community displacement, or loss of privacy are treated as external costs unless law or resistance forces them into the calculation. In a new social order, the objectives must be explicit and contestable.
A democratic planning constitution should guarantee a floor of universal provision: nutritious food, secure housing, health care, education, energy, mobility, communication, care, and cultural participation. It should also establish ecological ceilings, labor rights, equality protections, and resilience requirements. Beyond that floor, citizens must deliberate over competing priorities. No single score can legitimately compress dignity, freedom, health, biodiversity, leisure, and equality into one supposedly neutral number.
AI should therefore produce plural options rather than a single optimum. For each major plan, the public should be able to see which goals were chosen, how they were weighted, what data were used, who benefits, who bears costs, what uncertainty remains, and what alternatives were rejected. A technically efficient plan that violates rights or entrenches domination is not socially rational.
Data as a Social Commons
Democratic planning requires extensive information, but it does not require total surveillance. The source draft sometimes assumes that more data necessarily produce better planning. That is false. Excessive collection creates risks of abuse, chilling effects, cyberattack, and bureaucratic domination. The proper principle is data sufficiency: collect the least intrusive information necessary for a defined public purpose.
Personal data should not become either corporate property or unrestricted state property. It should be governed as a protected social commons with enforceable individual rights. Planning should preferentially use aggregated statistics, privacy-preserving computation, representative sampling, and locally held data. Sensitive information should be separated from decision systems, access should be logged, retention limited, and independent institutions empowered to audit misuse.
Public ownership of key digital infrastructure is equally important. If a society depends on proprietary models, closed clouds, and corporate data pipelines, its planning sovereignty remains fragile. Open standards, interoperable systems, public-interest compute, inspectable models where feasible, and democratically governed data trusts can prevent a new technical elite from acquiring power over the social economy.
Rights Accountability and the Right to Appeal
AI may advise, forecast, and detect patterns, but no person should lose employment, housing, health care, liberty, benefits, or political rights solely because an algorithm says so. High-impact decisions require a named human and institutional authority, a comprehensible explanation, access to relevant evidence, and a timely appeal. Independent courts, a free press, labor unions, civil-society organizations, opposition parties, and elected councils are not obstacles to planning; they are safeguards against its degeneration.
Useful governance principles already exist. The NIST AI Risk Management Framework emphasizes validity, reliability, safety, security, accountability, transparency, explainability, privacy enhancement, and fairness. The OECD principles similarly connect trustworthy AI to human rights and democratic values. A new social order should go beyond voluntary guidance by constitutionalizing these protections and placing powerful planning systems under public audit.
The code, models, and data documentation used for major public decisions should be inspectable to the maximum extent compatible with privacy and security. Model changes should be versioned; forecasts compared with outcomes; errors and incidents disclosed; and systems suspended when they exceed democratically approved risk thresholds. Procurement and development teams must be separated from independent evaluation bodies to reduce conflicts of interest.
Labor and the Social Ownership of Productivity
The most immediate political test of AI is who receives its productivity gains. The International Labour Organization finds that generative AI is more likely to transform many jobs than eliminate every exposed occupation, while clerical work remains especially exposed and effects differ sharply by country and gender. This uncertainty strengthens, rather than weakens, the case for social guarantees.
In a profit-driven system, automation is introduced principally when it raises returns or managerial control. Workers experience the same technology as speedup, surveillance, de-skilling, job loss, and weakened bargaining power. In a democratically planned economy, workers and their councils should participate before deployment. They should have access to impact assessments, bargaining rights over design and use, and the power to reject systems that undermine safety or dignity.
The gains from automation should finance shorter working hours without loss of livelihood, paid retraining, expansion of socially necessary work, and a universal social floor. Care, education, ecological restoration, culture, and community life should not be devalued because markets find them insufficiently profitable. AI should release human time and creativity, not create a surplus population disciplined by insecurity.
Answering the Knowledge and Calculation Critiques
Hayek’s strongest insight is that socially useful knowledge is dispersed, contextual, and often tacit. No database fully contains what workers, users, and communities know. AI does not refute that insight. A centralized authority that treats model outputs as complete knowledge would repeat an old failure at greater speed.
The new social order answers the knowledge problem institutionally. It incorporates distributed participation, local autonomy, rapid feedback, open experimentation, and revision. Prices may remain among several information signals during transitional phases, especially where scarcity, consumer variation, or international exchange makes them useful. But prices are not sacred summaries of social value. They often conceal unequal purchasing power, monopoly, unpaid care, ecological destruction, and needs that lack profitable demand.
Planning should combine quantitative signals with rights and deliberation. Essential goods can be guaranteed independently of income. Scarce discretionary goods may use queues, lotteries, contribution rules, prices, or other mechanisms depending on the good and the phase of transition. The criterion is practical social rationality, not fidelity to one universal allocation device.
The calculation critique also assumes that markets discover possibilities through decentralized experimentation. A participatory planned economy must preserve this capacity. Workplaces, laboratories, municipalities, cooperatives, and communities should be able to propose innovations and run bounded trials. AI can compare and disseminate results, while democratic institutions decide which innovations deserve wider resources.
A Phased Transition
The new social order is a transition, not an instantaneous finished system. During the first phase, society must secure democratic freedoms, prevent economic collapse, protect essential services, open public accounts, stop capital flight and corruption, and establish worker and community participation. AI’s initial role should be modest and auditable: mapping inventories, identifying urgent shortages, coordinating logistics, detecting fraud, and publishing transparent indicators.
In the next phase, participatory institutions can develop sector plans, public data standards, social guarantees, and alternative allocation mechanisms. Pilot programs should begin in domains where goals are clear and consequences measurable, such as preventive health, renewable-energy balancing, transit scheduling, affordable housing construction, and food distribution. Pilots require public evaluation and predetermined exit rules.
Only after institutions earn trust and demonstrate competence should more complex coordination be expanded. Money, credit, interest, and market mechanisms cannot be transformed by decree without regard to production capacity, international conditions, behavioral response, and institutional maturity. The planning system must become more capable as democratic participation becomes deeper. Technical scale without political maturity would invite authoritarianism; political aspiration without operational capacity would invite scarcity and disillusionment.
A Practical Institutional Design
A credible system would include a democratically mandated social planning council; elected workplace, neighborhood, and sector councils; a professionally independent statistical service; a public AI and compute institution; an algorithmic audit authority; privacy and data-protection bodies; and independent judicial review. Their powers must be separated. The institution that builds a model should not certify its own safety, and the body that allocates resources should not control the evidence used to judge its performance.
Each major planning cycle would begin with public priorities and guaranteed rights. Technical bodies would publish feasible scenarios and resource constraints. Councils would deliberate, propose amendments, and select among alternatives. Implementation data would feed public dashboards. At fixed intervals, actual outcomes would be compared with forecasts, and citizens could trigger review when thresholds or rights were violated.
This arrangement transforms representation into continuous participation in power. Elections remain necessary, but they are not sufficient. Citizens need durable institutions through which they can shape economic decisions between elections. Transparency must include not only access to documents but the ability to understand, question, and alter the plan.
Conclusion
AI does not abolish politics. It makes the political character of economic coordination more visible. An algorithm can optimize only the goals, constraints, and data that human institutions give it. Under concentrated ownership, AI will tend to deepen concentrated power. Under bureaucratic command, it can deepen administrative domination. Under democratic social ownership, constitutional rights, pluralism, and participation, it can help society coordinate its productive forces consciously.
The strongest case for a new social order is not that AI has finally produced an infallible central planner. It is that humanity now possesses unprecedented means to make social interdependence visible and govern it deliberately. We can estimate needs in real time, compare pathways before committing resources, identify inequality and ecological damage, and correct plans more quickly. At the same time, the dangers of surveillance, automation without security, private control of shared knowledge, and algorithmic authority make the old order increasingly intolerable.
The choice is therefore not between the market and the machine. It is between rival forms of social power. The new social order in the age of consciousness seeks to place knowledge, technology, and the productive wealth created by society under society’s conscious, democratic control. AI should become neither master nor substitute for collective intelligence, but one of its most powerful instruments.
Verified References
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Akhavi, Hamid. A New Social Order in the Age of Consciousness. First English Edition, 2026. The conceptual framework used here includes socialized production, participation in power, transparent governance, an intelligent planned economy, and a phased democratic transition.
