From Concept of Humanity to Economics of Social Systems and Architecture of Collective Intelligence

Preview of My New Book: “From the BCM Model to Hybrid HCAI – Part III: Growth and Prosperity for All Through Federated Neurosymbolic Hybrid HCAI!” Author: Friedrich R. Schieck, published in May 2026

ABSTRACT

This article develops a comprehensive political economy of social systems that integrates David Hume’s realist view of human nature with issues of modern organizational culture, technological transformation, AI governance, and collective intelligence. It begins with the premise that human behavior can never be understood in isolation. People do not act in a vacuum, but within concrete institutional, cultural, and economic orders. They respond to incentives, costs, benefits, status consequences, belonging, power relations, property rights, information flows, sanctions, and rewards. Culture thus appears not primarily as a system of officially formulated values, but as the actually effective architecture of behavior within a social system.

Culture as the Economic Architecture of Behavior – At the heart of this is the distinction between professed culture and culture that actually takes effect. Organizations, states, universities, government agencies, or digital platforms may officially espouse values such as openness, truth, innovation, responsibility, or excellence. What matters, however, is whether their actual decision-making, reward, and sanction systems also uphold these values. Where dissent is punished, an open culture cannot emerge. Where risk is sanctioned, a culture of innovation does not emerge. Where responsibility is invoked but liability is diffusely distributed, a culture of accountability does not emerge. Culture is thus understood as the invisible infrastructure of allocation. It distributes attention, credibility, resources, responsibility, influence, and recognition. Economically, culture alters the relative costs of behavior: it makes certain actions cheap and others expensive. Silence can be rational when dissent jeopardizes careers. Conformity can seem sensible when conformity is rewarded. Avoiding innovation can become the dominant strategy when mistakes are severely punished and successes are scarcely rewarded. The article thus demonstrates that many social systems later lament precisely the behavior that they themselves generated through their own architecture.

Hume’s View of Human Nature as the Foundation of Social System Analysis – David Hume’s insight that human beings must be understood not as abstract rational beings but as beings who actually act forms the anthropological foundation of this article. People act based on motives, passions, habits, interests, fears, the need for recognition, and social bonds. Reason can organize, compare, and justify, yet the energy for action often stems from sources that are not purely rational. This perspective is applied to social systems. People defend status because status means access to resources, credibility, and influence. They defend worldviews because these provide orientation and security of investment. They defend budgets, responsibilities, and group loyalties because these secure power, protection, and future scope for action. Behavior thus appears not merely as individual moral failure, but as the result of institutional embeddedness. Anyone who wants to understand why people remain silent, obstruct, resist, or avoid responsibility must examine what visible and invisible benefits are associated with this behavior.

Innovation, Resistance, and Institutional Inertia – A central theme of this article is the question of why new ideas often encounter resistance in social systems. New ideas are rarely judged solely on whether they are true, efficient, or useful. They alter responsibilities, threaten established hierarchies, shift budgets, devalue old skills, challenge worldviews, and undermine existing claims to profit or returns. Innovation is therefore always also a conflict over distribution. Historical examples such as John Harrison and the Board of Longitude, Newton and Leibniz, Tesla, Edison, and Westinghouse, Koch and Pasteur, as well as the debates between Heisenberg, Schrödinger, Einstein, and Bohr, demonstrate that knowledge and technical progress never emerge in neutral spaces. They encounter authorities, professional cultures, national affiliations, institutional interpretive authority, market interests, and existing positions of power. The article interprets these examples not as mere anecdotes, but as reflections of social systems. They demonstrate that truth and better solutions do not automatically prevail, but require fair review processes, epistemic openness, and institutional learning capacity.

The Adaptation Gap as a Productivity Problem – This article describes the adaptation gap as the disconnect between technological dynamics and organizational reality. New technologies do not automatically generate productive outcomes. They require complementary changes in processes, skills, organizational structures, data architectures, leadership models, standards, and business models. If these changes do not occur, the result is more digital tools but not more productivity; more data but not more judgment; more automation but not more accountability. Thus, technology is understood as dependent on its institutional embedding. In a learning-oriented and accountable organization, AI can enhance human capabilities, reduce search costs, and enable better decisions. The same AI, however, can reinforce mistrust, increase diffusion of responsibility, reproduce poor data, and accelerate the concentration of power within a defensive, hierarchical, and opaque organization. The marginal productivity of a technology therefore depends not solely on its technical capabilities, but on the quality of the culture and governance in which it is embedded.

AI, AGI, and the Political Economy of Collective Intelligence – This perspective becomes particularly clear in the discussion surrounding artificial intelligence and AGI. The article critiques the view of AGI as an isolated machine capability. Modern AI systems rely on human knowledge, language, culture, interaction, feedback, usage data, and social infrastructures. When platforms translate these collective inputs into models and monetize them privately, there is a risk of privatizing collective intelligence. The central question is therefore not only when AI will become generally intelligent, but what architecture of collective intelligence emerges through humans, machines, data, rules, property rights, platforms, and governance. Who controls models, data, computing power, and interfaces? Who bears responsibility? Who is allowed to object? Who benefits from productivity gains? Who gains access to the infrastructures? And who is excluded? AI thus emerges as a subject of political economy because it reorganizes knowledge processing, interpretive authority, labor, markets, property, and distribution.

The Architecture of Responsible Social Systems – The guiding principle for responsible collective intelligence is encapsulated in the following statement: “Subsymbolics scales. Symbolics regulates. Humans decide. Federation distributes. Governance is accountable.” This principle is interpreted as a general cultural and economic model of social systems that extends beyond the AI debate. Subsymbolics refers to perception, pattern recognition, and the processing of high levels of complexity. Symbolism stands for explicit rules, roles, concepts, and procedures. Humans stand for normative judgment and responsibility. Federation stands for the distribution of knowledge, power, and decision-making autonomy. Governance stands for accountability, correction, liability, and legitimate changeability. A social system, therefore, does not become intelligent simply by having individual intelligent actors or powerful technologies. It becomes intelligent when it processes relevant information in a timely manner, allows for dissent, corrects errors, integrates decentralized knowledge, controls power, and makes decisions accountable. Collective intelligence arises from the right combination of perception, meaning, decision-making, distribution, and responsibility.

Conclusion – The article concludes that the crucial question regarding culture is not what values a social system professes, but what behaviors its architecture actually produces. Human behavior is not merely a matter of psychology, but also a consequence of architecture. People defend status, worldviews, vested interests, budgets, careers, interpretive authority, and group loyalties because social systems link these assets to tangible benefits. Good culture does not deny these forces, but rather designs institutions in such a way that truth, learning, responsibility, and the common good become more likely than silence, conformity, and the safeguarding of returns and power in a rent-seeking economy. The future of social and socio-technical systems is therefore not determined solely by what humans or AI can achieve. What matters is how people, technology, institutions, markets, property regimes, information flows, and governance are interconnected. A just, adaptive, and federated architecture of collective intelligence must productively embed human limitations, responsibly harness technical capabilities, and prevent the dominance of power concentration, diffusion of responsibility, or bureaucratic rigidity. In doing so, the article combines Hume’s view of human nature with modern AI governance, political economy, cultural theory, and institutional architecture into a theory of accountable collective intelligence.

Keywords: Human-Centered AI; Hybrid HCAI; neuro-symbolic AI; federated AI; artificial intelligence; AGI; collective intelligence; organizational culture; adaptation gap; AI governance; platform power; concentration of power; symbolic regulation; subsymbolic scaling; human judgment; federated responsibility; common good; digital platforms; sociotechnical systems.

Introduction: The Fundamental Economic Question of Every Culture
People Do Not Act in a Vacuum
Culture as the Economic Architecture of Behavior
The Adaptation Gap as a Productivity and Cultural Problem
Two Perspectives on the Same Fundamental Question
Why People Reject New Ideas
Status as Intangible Capital
Worldviews as the Cognitive Infrastructure of Economic Action
Vested Interests, Budgets, and Profits
Interpretive Authority as Economic Power
Group Loyalty and the Economics of Belonging
Richard Vetter and the Institutional Inertia of Markets
From Organization to Collective Intelligence
The Five Functional Logics as an Economic Cultural Model of Social Systems
AGI, Platform Power, and the Political Economy of Collective Intelligence
On Good and Bad Cultures
Historical Examples as Economic Mirrors of Social Systems
From Hume to Federated Hybrid HCAI: An Integrated Interpretation
Open Fundamental Questions of a Political Economy of Collective Intelligence
Conclusion: Culture as a Responsible Economy of Collective Intelligence

1. INTRODUCTION: THE FUNDAMENTAL ECONOMIC QUESTION OF EVERY CULTURE

The question of why people behave the way they do leads us first to anthropology, psychology, and cultural theory. But it also leads us deep into economics. For human behavior never arises in a vacuum. People act within the context of scarcity, expectations, power relations, incentive structures, property regimes, information asymmetries, and distributional conflicts. They react not only to reasons, but to costs and benefits; not only to truth, but to status consequences; not only to moral insight, but to career opportunities, budgetary impacts, belonging, sanctions, and rewards. Anyone who wants to understand culture must therefore also understand its economic grammar.

In this sense, culture is not merely a system of shared values, rituals, or self-descriptions. Culture is the actually effective order of behavior within a social system. It determines which information circulates and which is withheld; which risks are taken and which are avoided; which ideas receive capital, attention, and legitimacy; which actors can secure their positions; and which forms of loyalty, conformity, criticism, innovation, or responsibility are rewarded. Culture is thus an invisible infrastructure of allocation. It distributes attention, credibility, resources, responsibility, influence, and recognition.

The classic question about culture is:

What values shape a social system?

The key economic question is:

Which incentives, property rights, information flows, positions of power, liability frameworks, and distributional effects actually lead to which behaviors?

A company, a government agency, a university, a nation, a religious community, an academic school, a professional association, or a digital platform may officially espouse values such as openness, truth, innovation, excellence, or responsibility. What matters, however, is whether the system actually reflects these values in its real-world reward, decision-making, and sanction systems. Where openness is claimed but dissent is punished, an open culture does not emerge. Where innovation is demanded but risk is penalized, a culture of innovation does not emerge. Where responsibility is invoked but accountability is diffused, a culture of responsibility does not emerge.

This perspective is in the spirit of David Hume’s *A Treatise of Human Nature*. Hume did not wish to understand human beings as abstract rational beings, but rather as beings who actually act. He was interested not only in how people ought to think, but in how they actually judge, believe, feel, hope, fear, err, adapt, defend themselves, and navigate social relationships. His famous thesis that reason is the “slave of the passions” can be read as a warning for social systems: arguments alone rarely move people. Reason can organize, justify, compare, and calculate. The energy for action, however, stems from motives, interests, emotions, habits, the need for recognition, fears, and bonds.

This insight is fundamental to a political economy of social systems. People are not simply rational utility-maximizers in the narrow sense of the term, nor are they merely morally fallible beings.

They act within institutional contexts in which certain behaviors become expected, advantageous, or risky.
They defend status because status means access to resources.
They defend worldviews because worldviews provide orientation and investment security.
They defend budgets because budgets secure room for maneuver.
They defend areas of responsibility because areas of responsibility generate power, visibility, and future bargaining positions.
They defend group loyalty because belonging offers protection.

Anyone who wants to understand human behavior in social systems must therefore ask what visible and invisible benefits are associated with that behavior.

Here, Hume aligns with the approach of a federated neuro-symbolic hybrid HCAI. While Hume provides the anthropological foundation, the architectural perspective shifts the debate on artificial intelligence, organizations, and culture from a mere question of performance to an institutional and economic one. What matters is not merely what humans or machines are capable of achieving. What is decisive is under which architectural, institutional, and economic conditions this performance is generated, distributed, controlled, legitimized, and accounted for. A capability alone does not yet generate prosperity. A model alone does not yet generate social intelligence. A technology alone does not yet generate equitable distribution. Only institutional embedding determines whether capabilities result in productive, accountable, and public-good-oriented value creation.

The formula “Subsymbolism scales. Symbolism regulates. Humans decide. Federation distributes. Governance is accountable” can therefore be interpreted, beyond the AI debate, as an economic and cultural yardstick for social systems. Subsymbolism stands for the ability to perceive complexity and recognize patterns. Symbolism stands for explicit rules, concepts, roles, and procedures. Humans represent normative judgment and responsibility. Federation represents the distribution of knowledge, power, and decision-making autonomy. Governance represents the meta-order that enables accountability, correction, liability, and legitimate change. A social system does not become intelligent simply because it has individual intelligent actors. It becomes intelligent when its architecture connects these elements in such a way that knowledge can be translated into better decisions, responsibility into sustainable action, and technological capability into broad prosperity.

2. PEOPLE DO NOT ACT IN A VACUUM

No one acts in isolation. Every person is embedded in roles, expectations, routines, power dynamics, communication channels, and reward systems. An employee may remain silent not because they have nothing to say, but because they have learned that speaking out is dangerous in their organization. A manager may not block a new idea because they are incapable, but because that idea challenges their existing strategy, their authority, their budget, or their career. A scientist may not oppose a new theory out of stupidity, but because it threatens their worldview, their school of thought, their life’s work, or their institutional standing. A government agency may not delay an innovation out of malice, but because its rules, liability frameworks, and review procedures were designed for old technologies. Behavior is therefore always both individual and systemic. People act out of personal motives, but these motives are shaped by social systems. A system can reward or punish courage. It can promote truth or make it dangerous. It can treat mistakes as learning opportunities or as flaws. It can examine or reject outsider ideas. It can make power visible or disguise it as a practical necessity. It can clarify responsibility or distribute it in such a way that, in a crisis, no one is responsible.

In economic terms, culture alters the relative costs of different behaviors. It makes certain actions cheap and others expensive. Remaining silent can be cheap if speaking out jeopardizes career prospects. Conformity can seem rational if it is rewarded. Avoiding innovation can be sensible when mistakes are heavily penalized, but successful innovations are scarcely rewarded. Avoiding responsibility can become the dominant strategy when liability is individual, but profits are reaped collectively or hierarchically. In poorly designed systems, defensive behavior is therefore not simply a moral failure. It is often a rational adaptation to misguided incentives. This is a crucial point. Many organizations later lament precisely the behavior they themselves created. They lament a lack of courage, even though their promotion logic rewards risk avoidance. They lament silo thinking, even though budgets, target systems, and reporting lines privilege silo success. They lament a lack of accountability, even though decision-making processes diffuse responsibility. They lament a lack of innovation, even though they treat deviation as a disruption. From an economic perspective, this is not a paradox, but a consequence of institutional misincentives.

Culture is thus a prerequisite for production. It determines whether knowledge is translated into decisions or remains trapped in silos. It determines whether decentralized information is utilized or ignored. It determines whether people report problems early on or tactically conceal them. It determines whether collective intelligence emerges or collective paralysis sets in. Those who treat culture merely as a soft factor overlook its hard economic nature. Culture determines transaction costs, coordination capabilities, the speed of innovation, the cost of errors, the level of trust, and ultimately productivity.

3. CULTURE AS THE ECONOMIC ARCHITECTURE OF BEHAVIOR

Every culture within a social system is an architecture of behavior. It shapes what people perceive, what they say, what they keep to themselves, what they risk, what they avoid, and what guides their decisions. This architecture can be consciously designed or unconsciously perpetuated. It can be open, adaptable, and productive. But it can also become defensive, opaque, profit-driven, and hostile to innovation. In many social systems, there is an official culture and an actual culture. Officially, the message might be: “We promote innovation.” In reality, however, those who take no risks are rewarded. Officially, it is said: “We value open communication.” In reality, employees learn that criticism of powerful figures is unwelcome. Officially, it is said: “We make objective decisions.” In reality, decisions are driven by status, networks, budgets, political considerations, and informal dependencies. Officially, it is said: “We take responsibility.” In reality, decisions are fragmented to the point where no one is clearly accountable.

The crucial question, therefore, is not: What values does a system profess? But rather: What behaviors does it actually generate? This question is more precise from an economic perspective because it focuses on observable effects. It does not ask about intentions, but about incentive effects. It does not ask about guiding principles, but about the actual allocation of attention, power, resources, and risks. Culture acts like an invisible infrastructure of transaction costs. In an open, trusting, and adaptive culture, dissent, information exchange, error correction, and cooperation are comparatively low-cost. People need to expend less energy on hedging, tactics, and self-protection. They can report problems sooner, assess risks more realistically, and share knowledge more quickly. In a defensive culture, by contrast, the costs of truth rise. Anyone who voices a problem may jeopardize their position. Anyone who admits a mistake may lose face. Anyone who introduces a new idea may threaten existing interests. The system then expends a significant portion of its energy not on creating value, but on securing positions.

This makes culture a factor in productivity. It influences how quickly organizations learn, how effectively they leverage decentralized knowledge, how efficiently they manage conflicts, how reliably they identify risks, and how fairly they distribute rewards. A poor culture increases internal transaction costs. It makes communication costly, truth risky, innovation unlikely, and accountability unclear. A good culture lowers these costs. It fosters trust, connectivity, adaptability, and productive forms of conflict. This perspective is particularly important because modern value creation is increasingly knowledge-based, digital, networked, and collaborative. In industrial systems, rigid hierarchies and standardized processes could be productive for a long time, as long as the environment was stable and tasks were clearly definable. In complex knowledge economies, however, value creation depends more heavily on whether information can be processed quickly, credibly, and in a context-sensitive manner. There culture becomes the core economic infrastructure.

4. THE ADAPTATION GAP AS A PRODUCTIVITY AND CULTURAL PROBLEM

The adaptation gap refers to the disconnect between technological dynamics and organizational reality. The faster the environment, technology, markets, regulations, and societal expectations change, the greater the pressure on traditional social systems. If their roles, processes, information flows, decision-making pathways, and accountability structures fail to evolve accordingly, structural overload ensues. This problem is particularly evident in the productivity debate. New technologies rarely generate immediate economic benefits.

They require complementary investments: new processes, new skills, new organizational structures, new data architectures, new leadership models, new standards, and often new business models as well. If these complementary changes do not materialize, the technology remains below its potential. There are more digital tools, but not more productivity. More data, but not more discernment. More speed, but not more direction. More automation, but not more responsibility.

This applies not only to digitalization, but to culture in general. A social system can adopt new technologies and yet remain trapped in old power structures, siloed thinking, and communication barriers. In that case, digitalization becomes superficial rather than transformative. AI can even exacerbate existing inefficiencies. It automates not intelligence, but old routines. It does not increase accountability, but obscures it. It does not enhance learning capacity, but creates new dependencies on systems whose assumptions, data, and decision-making logic are not understood. Economically, this means: The marginal productivity of a technology depends on the quality of its institutional embedding. AI embedded in a learning-capable, responsible, and federated organization can expand human capabilities, reduce search costs, consolidate knowledge, and enable better decisions.

The same AI, in a defensive, hierarchical, and non-transparent organization, can reinforce mistrust, increase diffusion of responsibility, reproduce poor data, and accelerate the concentration of power. The adaptation gap therefore explains why people often adopt a defensive stance during change processes. When a system is too slow, too contradictory, or too unclear, people protect themselves. They withhold information, secure their areas of responsibility, avoid accountability, follow formal rules rather than common sense, and seek security in group loyalty. This behavior is not merely a psychological phenomenon. It is economically rational when the institutional architecture punishes openness and rewards caution.

5. TWO PERSPECTIVES ON THE SAME FUNDAMENTAL ISSUE

Hume helps us understand human beings. The architectural perspective of federated neuro-symbolic hybrid HCAI helps us understand social and socio-technical systems. These two perspectives go hand in hand, because any realistic theory of social systems requires both a realistic view of human nature and an institutional theory of embeddedness. Hume shows that humans are not neutral machines of reason. They act based on motives, passions, habits, sympathies, fears, interests, and social bonds. Hybrid HCAI shows that this is precisely why every complex system needs an architecture that consciously organizes responsibility, rules, distribution, feedback, and governance. Since humans do not automatically act objectively, it is not enough to simply provide them with information. One must shape the conditions under which information is heard, examined, accounted for, and translated into action. This question becomes particularly visible in the AI debate.

AI systems generate predictions, content, recommendations, or decisions. They influence information markets, labor markets, business models, administration, education, research, and the political public sphere. Therefore, they must not be judged solely on the basis of performance. Their institutional embedding is decisive.

Who controls the data?
Who owns the models?
Who sets the standards?
Who bears liability?
Who benefits from productivity gains?
Who can object?
Who can challenge decisions?
Who is excluded from value creation?

These questions show that AI is not merely a technical matter. It is part of a political economy of knowledge processing. It changes who owns information, who prepares decisions, who replaces or supplements labor, who controls markets, who has access to infrastructure, and who can reap the resulting gains in productivity or economic rents (rent-seeking economy). The technical capabilities of a model are therefore only one side of the debate. The other side is the institutional order within which this capability operates.

Applied to social systems, this means: A good culture does not replace human responsibility with procedures, hierarchy, or technology. It strengthens responsibility by making information accessible, clarifying rules, defining roles, distributing power, organizing incentives, and institutionalizing correction. Such a culture recognizes that people have interests. It does not moralize about this fact, but rather designs institutions so that individual motives align as often as possible with collective learning capacity, productivity, and responsibility.

6. WHY PEOPLE RESIST NEW IDEAS

New ideas are rarely judged solely on whether they are true, efficient, or useful. They are also judged on what changes they bring to the social system. A new idea can reveal that previous assumptions were wrong. It can challenge existing responsibilities. It can shift budgets. It can undermine established experts. It can bypass hierarchies. It can threaten business models. It can raise the question of why a problem wasn’t solved sooner.

That is why new ideas generate resistance. This resistance is not always irrational. Sometimes objections are justified. New solutions may be immature, risky, expensive, poorly integrated, or have greater social consequences than their proponents realize. But in social systems, factual and non-factual motives become intertwined. The protection of quality can be linked to the protection of status. Safety arguments can identify real risks, but they can also delay innovation. Methodological rigor can secure knowledge, but it can also serve as a means of exclusion. Regulatory caution can protect the common good, but it can also reinforce barriers to market entry.

From an economic perspective, innovation is always also a conflict of distribution. It creates winners and losers. It alters scarcity. It devalues old skills and creates new ones. It shifts bargaining power. It destroys existing rent-seeking gains and opens up new ones. That is why resistance to innovation is not merely a problem of misguided insight, but often a problem of rational defense of interests. With every innovation, therefore, two questions must be asked. The first is: What objective reasons speak for or against this idea? The second is: What economic, institutional, and cultural interests are at play in the background? Only when both questions are asked can one distinguish between legitimate quality assessment and disguised protection of vested interests. A healthy culture must take objections seriously without being naive about the interests at stake.

7. STATUS AS INTANGIBLE ASSETS

People defend their status because status means recognition, influence, and security. From an economic perspective, status is a form of intangible capital. It provides access to networks, credibility, resources, attention, and the authority to interpret. Anyone who has been regarded for years within a social system as an expert, decision-maker, professor, executive, master craftsman, civil servant, founder, or thought leader possesses more than just symbolic prestige. They hold a position that generates real advantages. New ideas challenge status orders, even if they do not intend to. When an outsider finds a better solution, the question arises as to why the established experts did not find it. When a young employee identifies a weakness, the question arises as to why leadership overlooked it. When a practitioner outperforms an academic solution, it disrupts not just a procedure but a hierarchy. Defending one’s status is therefore not merely a matter of vanity. It is a defense of access, influence, credibility, and future returns.

The conflict between John Harrison and the Board of Longitude is a prime example of this. Harrison was not an academic astronomer, but a craftsman-turned-watchmaker and engineer. His chronometers offered a technical solution to the problem of determining longitude at sea. Yet recognition of this solution was not merely a matter of technical functionality. It touched upon the interpretive authority of astronomical elites, the legitimacy of artisanal expertise, and the institutional question of who was permitted to decide on truth, verification, and prize money. Harrison’s case shows that a good idea in social systems must do more than just work. It must also withstand status hierarchies, professional cultures, and institutional self-preservation.

In economic terms: it must not only demonstrate technical efficiency but also overcome the social barriers to entry that protect established players. An adaptive culture must therefore develop processes that evaluate ideas for quality without distorting them through status filters. It must recognize that epistemic justice and economic productivity are interconnected. If good ideas are systematically blocked because their originators do not fit into the hierarchy, the system loses its capacity for innovation.

8. WORLDVIEWS AS THE COGNITIVE INFRASTRUCTURE OF ECONOMIC ACTIVITY

People defend worldviews because they provide guidance. They tell us what is possible, what matters, who is credible, and which explanations are acceptable. When a worldview is threatened, it triggers not only intellectual resistance but also emotional defensiveness. A worldview is not merely an opinion. It is a cognitive infrastructure that structures perception, expectation, and decision-making. The debates between Heisenberg, Schrödinger, Einstein, and Bohr over quantum mechanics illustrate this particularly clearly. It was not just about equations and experiments. It was about conceptions of reality, causality, measurability, and the laws of nature. Einstein’s skepticism toward certain interpretations of quantum mechanics was not an expression of a lack of intelligence, but rather an expression of a deep philosophical unease with a worldview that placed greater emphasis on probability and measurement conditions. Such worldviews exist within organizations as well.

Some companies believe that control is more important than trust.
Some government agencies believe that compliance with rules is more important than impact.
Some academic circles believe that only certain methods are legitimate.
Some political groups believe that only their own side can represent the truth.

Such worldviews shape what is considered reasonable in the first place. Economically, worldviews are relevant because they structure the formation of expectations. Expectations influence investments, risk-taking, innovation decisions, and market behavior. A company that views digitalization solely as a cost factor invests differently than a company that sees it as the infrastructure for new value creation.

An administration that views citizens primarily as a risk will establish different procedures than one that sees them as co-producers of public impact. A society that views AI as a replacement for humans will create different institutions than one that sees AI as an extension of human judgment. From Hume’s perspective, it is understandable why worldviews do not simply disappear through better arguments. People are attached not only to statements, but to orders of meaning. Reason can shed light on existing attachments, but it can also defend them. That is why every cultural and economic transformation is simultaneously a struggle over expectations, narratives, and legitimacy.

9. ASSETS, BUDGETS, AND PROFITS

Social systems create vested interests. This refers not only to material privileges, but also to responsibilities, procedures, budgets, titles, networks, market shares, norms, and the right to interpret. Those who benefit from the status quo rarely have a spontaneous interest in changing it. Entitlements have often developed historically, are institutionally safeguarded, and are culturally legitimized. Precisely for this reason, they do not appear to their holders as privileges, but as the norm. Economically, many entitlements can be understood as rents in a rent-seeking economy.

Rent-seeking economy refers to an economic system in which income is generated primarily through the exploitation of scarcity or external revenues, rather than through productive labor or investment. In other words, rents are returns that do not stem primarily from the creation of new value, but from protected positions, access restrictions, regulation, standards, property rights, network effects, or information advantages. Innovation threatens such rents because it resolves old scarcity issues, allows new entrants, devalues existing interfaces, or redistributes power.

That is why conflicts over innovation are often conflicts over distribution. A new technology can deliver better results and yet be opposed because it devalues existing business models. A new process can be more efficient and yet be blocked because it challenges departments or leadership roles. A new scientific theory can be convincing and yet generate resistance because it affects academic chairs, research programs, or national prestige hierarchies.

The conflict between Nikola Tesla, Thomas Edison, and George Westinghouse can be interpreted in exactly this way. The so-called “War of the Currents” was not merely a technical conflict between direct current and alternating current. It was a battle over infrastructure, patents, investments, standards, network effects, and public perception. Whoever controlled the technical standard controlled future revenue streams. The technical issue was inextricably linked to a question of market share and power.

This draws a direct parallel to the current state of AI. Today’s debates about AI are not merely technical in nature. They concern ownership, infrastructure, platform power, data access, computing capacity, standards, and future revenue streams. The question is not simply whether AI is powerful. The question is who controls the foundational models, data centers, data streams, interfaces, and distribution channels. In a digital economy, infrastructure ownership can translate into market power, and market power can in turn lead to returns or rents, which then translate into political influence.

This makes AI a central issue in political economy. If a small number of actors control the fundamental infrastructures of collective intelligence, there is a risk of a new rent-seeking economy emerging. The productivity of many people, organizations, and knowledge communities would then be converted into platform rents for a few infrastructure owners. A just architecture of collective intelligence must address this risk through institutional measures.

10. POWER OF INTERPREDATION AS ECONOMIC POWER

In every social system, there are individuals or institutions that interpret reality. In academia, these include professional associations, journals, peer reviewers, academic chairs, and schools of thought. In business, they include executive boards, strategic planning teams, financial control departments, expert panels, and informal centers of power. In states, these are ministries, courts, government agencies, the media, and political parties. In digital platform economies, it is increasingly the operators of the infrastructure, algorithms, and models.

Interpretive authority means: one does not necessarily possess the truth, but one has influence over what is considered true, relevant, reputable, or legitimate. Economically, interpretive authority is valuable because it directs resources. Whoever determines what is considered a problem influences where money, time, attention, and political energy are directed. Whoever determines which method is considered credible influences careers and research programs. Whoever determines which data is visible influences decisions. Whoever determines which answers a platform provides influences perception and behavior.

The conflict between Newton and Leibniz illustrates how knowledge, priority, and interpretive authority are intertwined. At stake were mathematics, but also fame, national scientific prestige, and the question of who could lay claim to a fundamental discovery. The conflict between Robert Koch and Louis Pasteur reveals a similar interplay of science, rivalry, national identity, and institutional competition. Knowledge was not merely a contribution to the truth, but also a means of national and institutional self-assertion. In the AI economy, interpretive authority is shifting to a new level. When AI systems generate answers, organize information, recommend content, and allocate attention, platforms become epistemic infrastructures.

They influence not only markets but also the perception of reality. This is economically explosive because interpretive authority and value creation coincide. Whoever owns the infrastructure that structures knowledge can simultaneously monetize the resulting returns.

This very theme emerges in my critique of AGI (From BCM to Federated Neuro-Symbolic Hybrid HCAI). When AGI is described as the output of isolated machines, it can obscure the fact that such systems rely on collective human knowledge, interactions, data, corrections, and social infrastructures. The platform then appears to be the source of intelligence, even though it, to a significant extent, condenses, formalizes, and monetizes societal intelligence. The authority to interpret no longer lies with the society that generates knowledge, but with the platform that incorporates that knowledge into its infrastructure.

The core economic question is therefore:

Who owns collective intelligence?
Who is allowed to translate it into models, platforms, and business models?
Who controls access?
Who receives the profits?
And what kind of governance prevents socially generated knowledge from being converted into private interpretive and capital power?

11. GROUP LOYALTY AND THE ECONOMICS OF BELONGING

People don’t just believe arguments. They believe people, groups, and institutions they trust. A sense of belonging often carries more weight than the truth. Information coming from one’s own group is evaluated more favorably. Information coming from outside is viewed with greater skepticism. The same statement can be assessed completely differently depending on who makes it. Group loyalty can be understood not only in psychological terms but also in economic terms. Groups build trust, reduce coordination costs, and enable cooperation in the face of uncertainty. Those who belong to a group gain access to information, protection, reputation, and opportunities. Group loyalty is therefore a social resource. But it also has its downsides. It can shut out outside knowledge, cover up mistakes, reinforce silos, and block collective learning.

This explains why conflicts between scientific schools, nations, companies, or professional groups can become so intense. Robert Koch and Louis Pasteur represented not only competing research achievements but also national scientific cultures. Knowledge was seen not only as a contribution to science but also as an achievement of a nation, a school, or an institutional camp. Truth was translated into hierarchies of belonging. Group loyalty is powerful in companies as well. Departments defend their areas of responsibility. Locations defend their importance. Professional groups defend their methods. Management levels defend their own language. IT, line departments, controlling, sales, production, and legal each develop their own frameworks of understanding.

What appears to be progress to one group may seem like a loss of control to another. What efficiency means to corporate leadership may mean devaluation to employees. What standardization means to IT may mean a loss of contextual knowledge to business units. That is why every complex social system needs translation architectures. It is not enough to simply provide information. Information must be embedded in roles, rules, decision-making processes, and responsibilities in such a way that groups do not automatically go on the defensive. This is precisely where the BCM concept comes into play: communication, role clarification, feedback, and distributed responsibility are not soft byproducts, but hard prerequisites for collective agency. They lower coordination costs, reduce mistrust, and enable productive cooperation across group boundaries.

12. RICHARD VETTER AND THE INSTITUTIONAL INERTIA OF MARKETS

The example of Richard Vetter and condensing boiler technology shows that innovation often fails not because of the technical idea itself, but because of how it fits into existing standards, testing agencies, market structures, installation routines, and established practices. A new technical solution must do more than just work. It must be approved, understood, insured, sold, installed, financed, and institutionally accepted. Innovation is therefore never merely invention. It is always also about diffusion, standardization, financing, qualification, and building trust. This is where the ambivalence of governance becomes apparent. Testing agencies, standards, and approval procedures are necessary to ensure safety, quality, and reliability. At the same time, they can act as barriers to innovation if they cling too strongly to old technical paradigms or are indirectly used by established players to stabilize existing market positions. Then the system no longer protects just safety, but also the past. Economically, the issues at stake are market entry costs, path dependence, and standard-setting. Existing technologies are not merely technically available.

Around them emerge supply chains, qualifications, testing procedures, business models, maintenance routines, insurance logic, and customer expectations. A new technology must compete against this entire structure. Even if it is more efficient in the long term, it can cause high transition costs in the short term. These transition costs are often borne by actors other than those who benefit in the long term. This is precisely where blockages arise. This tension is central to the culture of any social system. Stability is necessary. But too much stability leads to stagnation. Rules are necessary. But rules without the capacity to learn become a barrier. Governance is necessary. But governance without the capacity for correction becomes a form of power for the status quo. Good governance must therefore function in two ways: it must limit risks while simultaneously ensuring the capacity to learn. It must establish standards without sacralizing path dependence. It must protect quality without making market entry impossible for better solutions.

13. THE ORGANIZATION FOR COLLECTIVE INTELLIGENCE

Every social system possesses a form of collective intelligence—or collective stupidity. A social system is intelligent when relevant information reaches the right people in a timely manner, when dissent is possible, when mistakes are corrected, when different perspectives are productively combined, and when decisions can be held accountable. A social system is unintelligent when information is withheld, when hierarchy replaces truth, when mistakes are covered up, when group loyalty prevents criticism, when power masquerades as objectivity, and when no one is held accountable anymore.

In economic terms, collective intelligence can be understood as a system’s ability to translate decentralized knowledge into better decisions and productive value creation. This ability does not depend solely on individual competencies. It depends on information architecture, incentive structures, property rights, governance, trust, and the capacity to manage conflict. A system can have many intelligent people and still act foolishly if knowledge does not circulate, dissent is not permitted, or responsibility is diffuse. Conversely, a well-designed system can combine limited individual capabilities in such a way that collective learning capacity emerges.

This turns the question “Why do people behave the way they do?” into a question of architecture.

Which structures give rise to which behaviors?
Which communication channels enable or hinder learning?
Which roles foster accountability or diffuse responsibility?
Which incentive systems promote cooperation or opportunism?
Which ownership and power structures determine who benefits from knowledge?
Which rules lower the costs of truth?
Which institutions prevent capital rents from crowding out productive innovation?

These questions show that collective intelligence does not simply arise from more data, more experts, or more technology. It arises from the right combination of perception, meaning, decision-making, distribution, and responsibility. This is precisely where the question of culture intersects with the architectural question of hybrid HCAI. An organization, an administration, a platform, or a society does not become more intelligent simply because it possesses more information. It becomes more intelligent when it can translate information into responsible decisions.

14. THE FIVE FUNCTIONAL LOGICS AS AN ECONOMIC CULTURAL MODEL OF SOCIAL SYSTEMS

The formula “Subsymbolics scales. Symbolics regulates. Humans decide. Federation distributes. Governance is accountable” can be interpreted as a general model of accountable social systems. It describes not only a technical architecture but also the economic logic underlying the functioning of collective intelligence.

In AI systems, “subsymbolics scales” means: neural networks, pattern recognition, forecasting, and statistical processing of large data sets. Applied to social systems, this level represents sensing. A social system must perceive what is happening. It must gather feedback, evaluate data, recognize moods, identify risks, process market signals, and notice changes in its environment. Without this ability, it acts blindly. But perception alone is not enough. Data does not tell us what is right on its own. Forecasts do not provide values. Statistical correlations do not replace responsibility. A system that only scales but does not understand makes decisions quickly, but not necessarily well.

“Symbolism governs” means: Explicit rules, roles, concepts, processes, norms, and decision-making logics are necessary to prevent behavior from becoming arbitrary. A social system needs such symbolic structures because they create predictability, accountability, and understanding. Economically, they reduce transaction costs. They enable cooperation under uncertainty because actors know which rules apply, who is responsible, and how conflicts are handled. But rules must not become rigid. They must remain verifiable, explainable, and correctable. A culture is not good simply because it has many rules. It is good when its rules enable responsibility rather than hiding it.

“Humans make the decisions” means that the human level remains the normative center. People must define objectives, weigh values, assess exceptions, and take responsibility. Neither organizations nor algorithms should distribute responsibility in such a way that, in the end, no one is accountable. This is especially true in the age of AI. When decisions are delegated to technical systems, human judgment must not be rendered meaningless. AI can support, synthesize, simulate, warn, and make suggestions. But it must not be misused to relieve responsibility. A decision does not become accountable simply because a model recommended it.

Decentralized federation means: knowledge, data, responsibilities, and decision-making autonomy must not be unnecessarily centralized. Federation protects against the concentration of power and epistemic monopolization. In social systems, this is crucial because central authorities rarely have access to all local information. People on the ground know things that those at the top do not. Users recognize problems that developers do not see. Departments possess contextual knowledge that can be lost in centralized models. Federation does not mean chaos. It means orderly distribution. Economically, it leverages decentralized knowledge and prevents a central authority from becoming a bottleneck for collective intelligence.

Responsible governance means that a social system requires a meta-order that defines who is authorized to make decisions, who exercises oversight, who makes corrections, who is held accountable, who intervenes, and how rules can be changed. Without governance, the distribution of responsibilities leads to a diffusion of accountability. With poor governance, centralization becomes domination. With good governance, a system becomes capable of learning, accountable, and capable of correction. Governance is therefore not merely bureaucracy. It is the institutional condition that allows freedom, innovation, and responsibility to coexist. Together, these five functional logics form an economic cultural model. Sub-symbolism provides perceptual capacity. Symbolism provides coordination capacity. Human decision-making provides normative orientation. Federation provides distribution and resilience. Governance provides legitimacy, accountability, and correction. If any of these levels is missing, the system becomes one-sided. It can become blind, arbitrary, dehumanized, centralistic, or irresponsible.

15. AGI, PLATFORM POWER; AND THE POLITICAL ECONOMY OF COLLECTIVE INTELLIGENCE

My article extends this cultural question into the digital present. As AI systems increasingly permeate human communication, knowledge work, and decision-making processes, new social systems emerge. Platforms are not merely technical tools. They are cultural, institutional, and economic orders. They determine what information becomes visible, what interactions are possible, what data is collected, what contributions are utilized, and who benefits from the resulting value creation.

That is why the AGI debate is also a debate about culture and distribution. When AGI is portrayed as autonomous machine intelligence, the human, social, and institutional contributions that came before it disappear from view. The system appears “intelligent,” even though it is built on collective human intelligence. The platform appears as the source of performance, even though it condenses the knowledge, language, interaction, and feedback of many people. Economically, this is a question of appropriation.

Modern AI systems rely on human knowledge, language, culture, interaction, correction, feedback, and usage data. Millions of people contribute directly or indirectly to the performance of such systems. However, the economic benefits are often skimmed off by a few platforms. This creates the risk that AI will become an infrastructure for the privatization of collective intelligence.

Socially generated knowledge is translated into private models; these models are monetized through platforms, and the resulting profits and rents flow to a small number of infrastructure owners.

The central question is therefore not just: When will AI become generally intelligent?

But rather:

What architecture of collective intelligence is emerging here?
Whom does it serve?
Who controls it?
Who benefits from it?
Who bears responsibility?
Who is allowed to change the rules?
Who can object?
Who is excluded?
Who gains access to data, models, computing power, and interfaces?
Who can build their own systems?
Who remains dependent?

This shifts the AGI debate from machine ontology to institutional ontology. Intelligence is then not merely a property of an isolated technical system, but a property of properly organized relationships between people, machines, rules, data, contexts, property rights, feedback, and governance. Such a perspective shifts the economic debate. The focus is no longer solely on model size, benchmarks, and autonomy, but on complementarity, distribution, infrastructure access, public goods, platform power, and democratic control. The economic danger lies in the fact that AI generates productivity but does not distribute prosperity widely. Technology can increase overall economic output while still concentrating wealth, power, and control. It can complement or devalue work. It can create new opportunities or deepen dependencies. That is why it is not enough to speak of “AI for all.” We must specify the frameworks for ownership, competition, taxation, the labor market, and infrastructure that are necessary for productivity gains to be widely disseminated.

16. GOOD AND BAD CULTURES

A toxic culture can be recognized by the fact that responsibility becomes unclear. Decisions are made, but no one wants to take credit for them. Mistakes happen, but no one is held accountable. Rules are in place, but no one can explain why. Data is used, but no one takes responsibility for its consequences. Power is exercised, but presented as an unavoidable constraint. In such cultures, people behave defensively. They document everything to protect themselves, avoid risks, speak in clichés, seek cover, and follow the hierarchy, even when they have doubts. They protect their group, their budget, their role, and their career. The system then produces behavior that it subsequently condemns as morally wrong. Such a culture cannot be improved through appeals. You cannot simply ask people to be braver, more open, or more innovative if the architecture of the system punishes courage, openness, and innovation. Cultural change therefore always entails architectural change. It means changing incentives, information flows, decision-making authority, liability structures, positions of power, and distribution mechanisms.

A healthy culture within a social system recognizes that people have interests, fears, and loyalties. It therefore incorporates processes that make these biases visible and manageable. It separates the person from the argument. It evaluates ideas based on quality, not on the status of the person who proposed them. It allows dissent without treating it as disloyalty. It protects outsider perspectives. It makes power dynamics visible. It creates feedback loops. It enables correction without humiliation. It assigns responsibility in a way that keeps it manageable, clear, and fair. Such a culture is not conflict-free. On the contrary: it allows for productive conflict. It knows that learning is not possible without dissent. It also knows that harmony is often merely the surface of hidden fear. A good culture therefore does not mean tranquility, but rather sustainable forms of disagreement. Economically, productive disagreement is a mechanism for error reduction and knowledge integration. It prevents bad decisions from remaining unchallenged out of fear, loyalty, or hierarchy. There is a deep connection here to Hybrid-HCAI. A responsible AI architecture must not aim to replace human judgment. It must enhance human judgment. Similarly, a responsible organizational culture must not aim to suppress conflict. It must structure conflict in such a way that insight, accountability, and shared learning become possible. A good culture is thus a responsible economy of collective correctability.

17. HISTORICAL EXAMPLES AS ECONOMIC REFLECTIONS OF SOCIAL SYSTEMS

These historical examples—Hume, Newton, and Leibniz; Harrison, Tesla, and Edison; Koch and Pasteur; Heisenberg, Schrödinger, Einstein, and Bohr; Richard Vetter—are not merely anecdotes from the history of science and technology. They are reflections of social systems. They show that new discoveries do not emerge in a vacuum. They encounter authorities, institutions, interests, worldviews, markets, testing procedures, and cultural expectations. They show that truth does not automatically prevail simply because it is true. It must navigate social structures. And these structures can either foster or block it. The key point is this: In each of these cases, the issues at stake went beyond mere technical matters. They also involved status, recognition, interpretive authority, belonging, economic interests, and institutional self-assertion.

Harrison had to do more than simply demonstrate that his clocks worked. He had to stand up to the institutional authority of an established system of testing and interpretation.
Tesla, Edison, and Westinghouse were not merely disputing technical superiority, but also infrastructure, standards, patents, and future revenue streams.
Newton and Leibniz were not merely fighting over mathematical priority, but also over fame, national scientific honor, and historical interpretation.
Koch and Pasteur represented not only competing experiments, but national scientific cultures and institutional rivalry.
The debates surrounding quantum mechanics were not merely mathematical disputes, but battles over a worldview.

That is what makes these examples so valuable for the present day. After all, today’s conflicts over AI, digitalization, energy, climate, health, education, or public administration follow similar patterns. Here, too, the issue is not merely which solution is objectively better. It is about which power dynamics, business models, worldviews, and cultural self-images are affected by that solution. Those who block climate innovation may be defending not only doubts but also assets. Those who regulate or deregulate AI are not acting solely out of concerns for security or freedom but are influencing market structures.

Those who delay administrative digitization may be protecting not only quality but also existing jurisdictions. Those who resist educational innovation may be defending not only standards but also professional identities. Historical examples therefore teach us not only humility toward pioneers. They teach us about institutional design. It is not enough to honor outsiders after the fact. We must design systems in such a way that today’s outsiders are not systematically blocked. We must create processes in which quality can prevail over status, in which objections are made transparent, in which power interests are visible, and in which new solutions are given a fair chance to be evaluated.

18. FROM HUME TO THE FOSTERED HYBRID HCAI: AN INTEGRATED INTERPRETATION

The combination of Hume, BCM, and federated neuro-symbolic hybrid HCAI can be formulated as an integrated theory of social systems. Hume explains why people do not act in a purely rational manner. BCM demonstrates that organizations cannot manage complexity through centralization alone, but rather through communication, role clarification, feedback, and adaptive self-regulation. Hybrid HCAI demonstrates the architecture required for human, symbolic, and machine intelligence to interact responsibly.

The adaptation gap demonstrates why technical innovation does not generate sustainable productivity without cultural and institutional renewal. The critique of AGI demonstrates why intelligence must not be misunderstood as isolated machine performance, but rather as a socio-technical and political-economic order of collective intelligence. This leads to a common insight: The quality of a social system does not depend solely on the abilities of its individual members or tools. It depends on the architecture within which these abilities are integrated.

A system can have many intelligent people and still act foolishly. It can have a lot of data and still make the wrong decisions. It can use modern AI and still lose accountability. It can achieve high efficiency and yet undermine human dignity. Conversely, a well-designed system can combine limited individual capabilities in such a way that collective intelligence emerges.

The question “Why do people behave the way they do?” therefore leads to a deeper question: What kind of structure produces what kind of behavior?

When people defend their status, one must ask how status is distributed and threatened within the system.
When people remain silent, one must ask what the consequences of dissent are.
When people block innovation, one must ask whose vested interests are being affected.
When people prioritize group loyalty over truth, we must ask how belonging is organized.
When people avoid responsibility, we must ask whether responsibility is assigned clearly, fairly, and equitably.
When AI systems concentrate power, we must ask who owns the infrastructure, data, models, and interfaces.

These questions make it clear: Behavior is not just a matter of psychology. Behavior is also a consequence of architecture. And architecture is not just a matter of technology. Architecture is a combination of institutions, rules, property rights, power relations, information flows, incentive systems, and cultural expectations.

19. OPEN FUNDAMENTAL QUESTIONS IN A POLITICAL ECONOMY OF COLLECTIVE INTELLIGENCE

From this perspective, several fundamental questions arise. They concern not only AI or AGI, but the architecture of social systems as a whole. The central question is: How must people, rules, institutions, power, technology, and responsibility interact in order to foster collective intelligence—rather than collective paralysis, concentration of power, or diffusion of responsibility?

The first question concerns our view of human nature. Do our organizations, institutions, and AI architectures operate on the basis of a realistic view of human nature? Hume suggests that humans should not be understood primarily as rational utility-maximizers or neutral seekers of truth. They act based on habits, passions, interests, fears, the need for recognition, and social bonds. Many modern systems nevertheless act as if people make purely objective decisions as soon as they have enough information. This is obviously not true. People defend status, worldviews, vested interests, budgets, careers, affiliations, interpretive authority, and group loyalty. The open question is therefore: How must social systems be designed if we start not from the ideal rational human being, but from the actual human being?

The second question concerns cultural diagnosis. How can we reliably determine whether a social system rewards truth, learning, and responsibility—or conformity, silence, and the preservation of power? Many systems claim to value openness, innovation, and the ability to learn. In reality, however, they often reward conformity, adherence to hierarchy, and risk avoidance. The problem is that the official culture and the culture that actually prevails are at odds with one another. A robust diagnosis would not need to examine mission statements, but rather behavioral patterns: Who is allowed to disagree? Who is heard? Who loses face? Who is allowed to admit mistakes? Who benefits from the old ways? Who bears responsibility for the new? Who gets to decide what counts as objective?

The third question concerns status. How can we prevent status from supplanting truth? New ideas are judged not only on their merit, but also on the status of their originator. Outsiders, practitioners, young people, women, minorities, or individuals outside established networks are often scrutinized more rigorously than those belonging to the recognized mainstream. This creates a fundamental problem for knowledge and productivity. A social system must be able to evaluate ideas in such a way that rank, background, titles, institutional affiliation, or interpretive authority do not determine the truth.

The fourth question concerns vested interests. How do we distinguish legitimate criticism from the defense of power? New ideas often encounter objections. Some objections are objectively justified. Others serve to delay, fend off, or defend existing interests. The challenge at hand is to develop processes that reveal the interests behind an objection. A healthy culture would ask: Does this argument actually protect quality, safety, and accountability—or does it primarily protect status, budget, market share, and old responsibilities?

The fifth question concerns interpretive authority. Who gets to decide what is considered true, relevant, or legitimate? In every social system, there are entities that interpret reality: professors, board members, government agencies, standards bodies, platform operators, the media, professional associations, courts, AI models, or algorithmic recommendation systems. The problem is exacerbated by AI because digital platforms not only convey information but increasingly structure knowledge, generate answers, and direct attention. The open question is: How can we prevent interpretive authority from being monopolized within opaque technical or institutional infrastructures?

The sixth question concerns responsibility. Who makes the decisions—and who is liable? Modern social and socio-technical systems lead to a diffusion of responsibility. Decisions arise through processes, committees, algorithms, platforms, rules, models, and interfaces. In the end, it is often unclear who is actually responsible. If an AI system makes a recommendation, a person follows it, an organization has set the framework, a provider has trained the model, and a regulatory authority has authorized its use—who then bears responsibility? The formula “People decide. Governance is accountable.” sets a standard, but it must be translated into concrete liability, audit, escalation, and intervention mechanisms.

The seventh question concerns governance. How can governance become adaptive rather than bureaucratic? Governance is necessary, but it can itself become a problem. It is meant to ensure accountability, but it can stifle innovation. It is meant to limit risks, but it can protect vested interests. It is meant to establish rules, but it can create new complexities. Too little governance leads to abuse of power, lack of transparency, and diffusion of responsibility. Too much rigid governance leads to bureaucracy, stifling of innovation, and an inability to adapt. The open question is therefore: What does recursive governance look like—governance that can review and change its own rules without becoming arbitrary?

The eighth question concerns federation. How can power be distributed without losing the ability to act? Federation is intended to prevent knowledge, data, models, and responsibilities from being monopolized by a few central authorities. It safeguards plurality, contextual knowledge, and local sovereignty. But federation also creates problems: How do you prevent fragmentation? How do you ensure common standards? How do you coordinate local autonomy with overarching responsibility? How do you prevent federation from becoming an excuse for incompetence? A good federated architecture must distribute power while remaining binding, efficient, and accountable.

The ninth question concerns AGI itself. Is AGI a property of an isolated technical system—or the result of an organized human-machine institution? The classic AGI debate asks when a machine will match or surpass human cognitive abilities across many domains. The perspective developed here shifts this question. What matters is not only what a model can do, but what form of social intelligence emerges through people, machines, rules, data, platforms, and institutions. Perhaps, therefore, we need less a machine-centered concept of AGI and more an institutional concept of collective intelligence.

The tenth question concerns ownership. Who owns collective intelligence? Modern AI systems are based on human knowledge, language, culture, interaction, correction, feedback, and usage data. Millions of people contribute directly or indirectly to the performance of such systems. However, the economic benefits are often reaped by just a few platforms. The open question is: How can users, authors, knowledge communities, organizations, and societies be fairly included in the value creation that results from their contributions?

The eleventh question concerns distribution. Does AI create prosperity for all or wealth for the few? Technology does not automatically lead to equitable distribution. AI can boost productivity while still concentrating profits among a small number of infrastructure owners. It can complement or devalue work. It can create new opportunities or deepen dependencies. The unresolved question is: What kind of property, competition, tax, labor market, and infrastructure frameworks does an AI economy need to ensure that productivity gains are widely distributed?

The twelfth question concerns complementarity. Does AI complement humans or replace them? Hybrid HCAI is based on complementarity: AI should enhance human capabilities, not supplant human responsibility. In practice, however, AI systems are often used for cost reduction, automation, and standardization. The open question is: How can AI be designed to strengthen human judgment, creativity, responsibility, and expertise rather than undermine them?

The thirteenth question concerns quality. How does a system become better—rather than worse—as the number of users increases? Many digital systems scale technically, but not qualitatively. More users do not necessarily mean better collective intelligence, but rather more noise, manipulation, polarization, data garbage, concentration of power, or algorithmic bias. An architecture of collective intelligence must combine feedback, reputation, rule-based systems, contextual knowledge, human review, machine scaling, and governance in such a way that growing participation enhances quality.

The fourteenth question concerns information flows. How does the right information reach the right people at the right time? Many poor decisions are not made because no one knew anything, but because the information did not reach the right people, was not taken seriously, or could not be voiced. A good information architecture must reliably link perception, significance, responsibility, and decision-making.

The fifteenth question concerns power. How can we prevent AI from reinforcing existing power structures? Those who possess more data, capital, computing power, talent, and market access can build better systems, attract more users, collect more data, and become even more powerful. Without competition policy, open standards, public infrastructure, data access, interoperability, and clear rights, there is a risk of a new form of digital rent-seeking or feudal structure (rent-seeking capitalism).

The sixteenth question concerns democracy. How can collective intelligence remain subject to democratic control? As AI systems increasingly filter information, prepare decisions, structure communication, and organize value creation, they become infrastructures that are relevant to democracy. Pure state control can be inefficient or open to political abuse. Pure private control can be opaque, monopolistic, and at odds with the public good. Pure self-regulation is often insufficient. What is needed are hybrid models comprising public standards, independent audits, federated infrastructures, user rights, transparency obligations, forms of participation, and clear liability rules.

The seventeenth question concerns dignity. In what areas should humans not be replaced? Not all automation is problematic. But there are areas where human presence, empathy, responsibility, and recognition are indispensable: caregiving, the justice system, education, personnel decisions, critical administrative decisions, medical borderline cases, and the democratic decision-making process. The line between meaningful support and impermissible disenfranchisement must be defined institutionally, not merely technically.

All of these questions lead to one central overarching question: What must a just, adaptive, federated, and accountable architecture of collective intelligence look like that productively combines human limitations, technical capabilities, and institutional responsibility? This question goes beyond traditional organizational development, traditional AI ethics, and traditional AGI debates. It connects Hume’s view of human nature with modern AI governance, political economy, institutional architecture, and cultural theory.

20. CONCLUSION: CULTURE AS A RESPONSIBLE ECONOMY OF COLLECTIVE INTELLIGENCE

The question “Why do people behave the way they do?” is a fundamental question in every culture and social system. From an economic perspective, it concerns the incentives, information flows, property rights, power structures, accountability mechanisms, and distributional effects that shape behavior. Hume helps us understand that people do not act as purely rational beings. They are shaped by motives, passions, habits, interests, fears, needs for recognition, and social bonds. The perspective of the federated neuro-symbolic hybrid HCAI expands this insight to include the architectural question: How must social and socio-technical systems be designed so that these human forces act productively rather than destructively? People rarely act purely objectively. They defend status, worldviews, vested interests, budgets, careers, national or organizational affiliations, interpretive authority, and group loyalty.

These forces do not disappear through digitalization. Nor do they disappear through AI. On the contrary: AI can amplify, obscure, or reorganize them. It can concentrate power, diffuse responsibility, automate interpretive authority, and privatize collective intelligence. But it can also expand human capabilities, make knowledge more accessible, connect decentralized expertise, enable better decisions, and distribute productivity gains more widely. Which of these possibilities becomes reality is determined not by technology alone, but by its institutional and economic architecture. That is why the future is not determined solely by what AI can do. It is determined by the architecture in which AI, people, rules, institutions, markets, and property regimes are interconnected.

A good culture—whether in business, academia, government, society, or digital platforms—must therefore do more than just ensure efficiency. It must combine awareness, adherence to rules, human decision-making, federated distribution, and responsible governance. It must allow for dissent, make power visible, clarify responsibility, enable learning, and prevent status or vested interests from replacing truth.

In this sense, the architectural formula from my article “From BCM to Federated Neuro-Symbolic Hybrid HCAI” can be interpreted as a general cultural and economic principle: Sub-symbolics scales. Symbolics regulates. Humans decide. Federation distributes. Governance is accountable.

Or, to put it more generally: A social system becomes intelligent when it perceives complexity, makes rules explicit, upholds human responsibility, distributes knowledge and responsibilities, and ensures that its own decisions are verifiable, correctable, and accountable.

The real cultural question is therefore not just: Which solution is objectively correct?

But rather:

What cultural, institutional, and economic forces are preventing the right solution from being identified, accepted, fairly applied, and responsibly implemented?

Only by asking this second question can we understand why people act the way they do—and how social systems can be designed so that human limitations, technical capabilities, and institutional responsibility come together to form a more equitable form of collective intelligence.

The open challenge of our time is to design social and socio-technical systems in such a way that human limitations do not become an obstacle, technical capabilities do not lead to a concentration of power, and institutional responsibility does not become bogged down in bureaucracy.

Good architecture need not deny human passions, interests, and loyalties. It must embed them in such a way that truth, learning, responsibility, and the common good become more likely than the defense of status, the preservation of vested interests, the safeguarding of privileges, and group loyalty.

This is precisely where the transition from Hume to BCM, hybrid HCAI, and a federated architecture of collective intelligence lies.

METHODOLOGY AND ACKNOWLEDGMENTS

ChatGPT was used to assist in drafting individual sections of text. This tool helped with spelling, grammar checking, restructuring sentences, and improving clarity. The generated content was critically reviewed and revised by the author, who is responsible for the final version. The actual ideas, arguments, and interpretations in this document are the author’s own.

In this context, I noticed that ChatGPT either crashed or provided contradictory and illogical answers to complex questions. Only after an extensive dialogue with ChatGPT did I receive coherent results. In other words: Through our dialogue, ChatGPT learned from me to understand and reproduce causal and logical relationships.

Since I assume that other ChatGPT users have had similar experiences, I would like to thank all the authors mentioned and those unknown who, consciously or unconsciously, shared their knowledge with ChatGPT!

The translation into English was done automatically using DeepL.

SOURCES AND FURTHER READING

Acemoglu, Daron / Johnson, Simon: Power and Progress. Our Thousand-Year Struggle over Technology and Prosperity, New York 2023.
David Hume: A Treatise of Human Nature. Project Gutenberg edition. Of particular relevance are Hume’s introduction, which introduces the concept of a “science of man,” and Book II on the role of passions and reason.
Hume, David: A Treatise of Human Nature, 1739/1740; in particular Book II on the role of passions, will, and reason. A modern edition of the text documents the famous formulation that reason is the “slave of the Passion.”
Stanford Encyclopedia of Philosophy: “Hume on the Emotions” and “Hume’s Moral Philosophy” on the role of passions, motivation, and reason in Hume.
Erik Brynjolfsson / Daniel Rock / Chad Syverson: “Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics,” NBER Working Paper No. 24001, 2017. Among other things, the authors discuss implementation delays, measurement problems, redistribution, and false expectations as reasons for the productivity paradox.
European Union: Regulation (EU) 2024/1689, Artificial Intelligence Act. The AI Act was adopted in 2024 and entered into force on August 1, 2024; its obligations apply on a phased basis. ISO/IEC 42001:2023: Information technology — Artificial intelligence — Management system. Die Norm beschreibt Anforderungen an Einrichtung, Umsetzung, Aufrechterhaltung und kontinuierliche Verbesserung eines AI Management System in Organisationen.
Marcus, Gary: public criticism of LLMs as a sufficient path to AGI; summarized, for example, in Axios 2025.
Gary Marcus: “Is AGI the right goal for AI?”, October 16, 2025. Marcus discusses AGI as the cognitive versatility and competence of a well-educated adult and simultaneously criticizes the equating of today’s LLM performance with robust general intelligence.
OECD: Explanatory Memorandum on the Updated OECD Definition of an AI System, OECD Artificial Intelligence Papers, No. 8, 2024. The OECD explains the updated definition of an AI system as a machine-based system that generates outputs such as predictions, content, recommendations, or decisions and can influence physical or virtual environments.
Royal Museums Greenwich: “Longitude found – the story of Harrison’s timekeepers.” Presentation of John Harrison’s chronometers and their significance for the problem of longitude.
Friedrich Reinhard Schieck: From BCM to Federated Neuro-Symbolic Hybrid HCAI – What is AGI – and under what architectural, institutional, and economic conditions does artificial intelligence create growth and prosperity for all? Published 04/2026. In particular, the architectural formula “Subsymbolics scales. Symbolics governs. Humans decide. Federation distributes. Governance is accountable.”
Schieck, Friedrich Reinhard: “From the BCM Model to Hybrid HCAI – Part I: The Story of an Idea Whose Time Has Come,” Journal of Strategic Innovation and Sustainability, 20(4), 2025.
Shneiderman, Ben: Human-Centered AI, Oxford University Press, 2022.
Time: “The Real History Behind The Current War,” on the conflict between Edison, Tesla, and Westinghouse.
On the Koch-Pasteur rivalry: historical overviews of the rivalry between Robert Koch and Louis Pasteur, particularly in the context of national competition, language barriers, and scientific debate.
S. Department of Energy: “The War of the Currents: AC vs. DC Power.” Overview of the roles of Tesla, Edison, and Westinghouse in the War of the Currents.
Ricardo Hausmann / Andrés Velasco: “The Real Question About the AI Future,” Project Syndicate, April 8, 2026. The authors shift the debate from mere speculation about a bubble to the question of what kind of global economy would need to emerge for today’s AI valuations to appear plausible.

eMail: friedrich@schieck.org; fs@bcmconsult.com;
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REFERENCES

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Project Syndicate – The Real Question About the AI Future / Ricardo Hausmann & Andrés Velasco ask what kind of world economy would vindicate today’s market valuations of a handful of US firms.
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org – A Treatise of Human Nature / No quality of human nature is more remarkable, both in itself and in its consequences, than that propensity we have to sympathize with others.Read more
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eu – Regulation (EU) 2024/1689 – EUR-Lex / JavaScript is disabled. In order to continue, we need to verify that you’re not a robot. This requires JavaScript. Enable JavaScript and then reload the page.
eu – EU – 2024/1689 – EN – EUR-Lex / JavaScript is disabled. In order to continue, we need to verify that you’re not a robot. This requires JavaScript. Enable JavaScript and then reload the page.
eu – AI Act enters into force – European Commission / 1 Aug 2024 — The AI Act introduces a uniform framework across all EU countries, based on a forward-looking definition of AI and a risk-based approach:.Read more
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org – Explanatory memorandum on the updated OECD definition … / This document contains clarifications to the definition of an AI system contained in the 2019 OECD. Recommendation on AI (the “AI Principles”) (OECD, 2019[1]) …Read more
org – OECD: The Organisation for Economic Co-operation and … / Working with over 100 countries, the OECD is a global policy forum that promotes policies to improve the economic and social well-being of people around the …
org.in – Power and progress : our thousand-year struggle over … / If there are continuing problems, talented entrepreneurs and scientists will invent solutions—more-capable robots, human-level artificial intelligence, and …Read more
project-syndicate.org – Andrés Velasco / 26 Jan 2018 — Apr 8, 2026 Ricardo Hausmann & Andrés Velasco ask what kind of world economy would vindicate today’s market valuations of a handful of US firms.Read more
project-syndicate.org – Ricardo Hausmann / 1 May 2014 — Apr 8, 2026 Ricardo Hausmann & Andrés Velasco ask what kind of world economy would vindicate today’s market valuations of a handful of US firms.Read more
project-syndicate.org – Reinventing Economics by Robert J. Shiller / 14 Sept 2009 — The failure of economists’ models to forecast the current crisis will mark the beginning of their overhaul, which will happen as economists’ …
project-syndicate.org – Wird AGI tatsächlich die „letzte Erfindung” sein? / 19 Mar 2026 — The Real Question About the AI Future. Apr 8, 2026 Ricardo Hausmann & Andrés Velasco. When Fools Go to War. Apr 3, 2026 Federico Fubini …Read more
edu – Daron Acemoglu | MIT Economics / Daron Acemoglu is an Institute Professor at MIT, Faculty Co-Director of the James M. and Cathleen D. Stone Center on Inequality and Shaping the Future of Work.Read more
edu – AI AND THE MODERN PRODUCTIVITY PARADOX / Our new research takes a close examination of recent patterns in aggregate productivity growth for a better understanding of the apparent contradictions. AI, …Read more
edu – POWER and PROGRESS – MIT Stone Center on Inequality … / Acemoglu and Johnson give an incisive analysis of the economics of labor and technology, along with a trenchant critique of the ‘techno-optimism’ of corporate …Read more
com – 🌿 David Hume: Reason is Slave to the Passions / Hume argued that reason doesn’t drive our actions, it serves our emotions. We don’t start with facts and build towards feelings. We start with …Read more
com – Ai and the Economics of Technology as described by Paul … / Bankruptcy risk peaks in 2023–2024 (median ~60%) when companies invest heavily ahead of revenue, drops to near zero by 2026–2028 as R1 kicks in, …Read more
com – The Real Question About the AI Future | Emilio Planas / Ricardo Hausmann and Andrés Velasco examine how AI could reshape global economics as value concentrates in firms controlling models, chips …
com – OECD – OCDE / The Organisation for Economic Co-operation and Development (OECD) is an international organisation that works to build better policies for better lives.Read more
eu – The Act Texts | EU Artificial Intelligence Act / These webpages for the Artificial Intelligence Act (Regulation (EU) 2024/1689), Official Journal version of 13 June 2024 are linked below. Please select …Read more
eu – EU Artificial Intelligence Act | Up-to-date developments and … / The AI Act is a European regulation on artificial intelligence (AI) – the first comprehensive regulation on AI by a major regulator anywhere.Read more
eu – High-level summary of the AI Act / On 18 July 2025, the European Commission published draft Guidelines clarifying key provisions of the EU AI Act applicable to General Purpose AI (GPAI) models.Read more
eu – Article 6: Classification Rules for High-Risk AI Systems / The EU Commission will provide guidelines and examples of high-risk and non-high-risk AI systems. They can also add or remove conditions for high-risk …Read more
com – ISO/IEC 42001:2023 Artificial Intelligence Management … / ISO/IEC 42001 specifies the requirements and provides guidance for establishing, implementing, maintaining, and continually improving an AI management system …Read more
com – Daron Acemoglu / The colonial origins of comparative development: An empirical investigation D Acemoglu, S Johnson, JA Robinson American economic review 91 (5), 1369-1401, 2001Read more
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com – Power and Progress: Our Thousand-Year Struggle Over … / A bold new interpretation of why technology benefits the elites – and how we must reshape the path of innovation to create true shared prosperity.Read more
de – Human-Centered AI : Shneiderman, Ben / Publisher, Oxford University Press ; Publication date, 10 Feb. 2022 ; Language, ‎English ; Print length, 400 pages ; ISBN-10, 0192845292.
de – Daron Acemoglu: Bücher / Warum Nationen scheitern: Die Ursprünge von Macht, Wohlstand und Armut | Ausgezeichnet mit dem Wirtschaftsnobelpreis 2024 … Macht und Fortschritt: Unser 1000- …Read more
com – Comments – Is AGI the right goal for AI? – by Gary Marcus / 16 Oct 2025 — So not only is AGI not a meaningful goal, it is not even a meaningful concept until we fully understand how conscious biological intelligence …Read more
com – Comments – How Should We Think About the Economics of AI? / 22 Mar 2025 — The promise of Ai, human hubris and greed, the recommissioning of oil/gas/coal/nuke plants and the ever increasing impacts of climate change.Read more
com – AI & Tech Roundup: Ricardo Hausmann and Andrés Velasco … / AI & Tech Roundup: Ricardo Hausmann and Andrés Velasco on the real question about the AI future, Shashi Tharoor on whether India can become the …Read more
com – Treatise of Human Nature Book II: The Passions / by D Hume · 2017 · Cited by 13 — Treatise II. David Hume iii: The will and the direct passions before it. You can see this at work in the order that is always observed in historical …Read more
net – (PDF) Regulation 2024/1689 of the Eur. Parl. & Council of … / In Spring 2024, it adopted a landmark regulation titled the Artificial Intelligence Act. The Act entered into force on August 1, 2024, and most of its …Read more
net – (PDF) Power and Progress: Our 1000-Year Struggle Over … / 28 Mar 2026 — … Technology and Prosperity by Daron Acemoglu and Simon Johnson. … progress does not automatically result in prosperity for all. This is …Read more
org – Explanatory memorandum on the updated OECD definition … / Downloadable! In November 2023, OECD member countries approved a revised version of the Organisation’s definition of an AI system.
org – Human-Centered AI, by Ben Shneiderman. Oxford / by J Killoran · 2024 · Cited by 1 — Human-Centered AI, by Ben Shneiderman. Oxford: Oxford University Press, 2022. 305 pp. Author & abstract; Download; Related works & more; Corrections. Author.
org – Artificial Intelligence and the Modern Productivity Paradox / by E Brynjolfsson · 2018 · Cited by 2414 — We describe four potential explanations for this clash of expectations and statistics: false hopes, mismeasurement, redistribution, and implementation lags.R…
com – The economy is changing. Don’t forget who fears it most. / 1 Mar 2026 — Don’t forget who fears it most. Artificial intelligence doomer narratives reflect personal concerns, not economic reality. March 1, 2026.Read more
com – Will AI really take over our jobs? The FT’s Martin Wolf and … / The FT’s Martin Wolf and Nobel Prize-winning economist Paul Krugman ask if advances in artificial intelligence will reshape the working world as we know it.
com – This morning with Economist Paul Krugman for our new … / In this video, I break down key lessons from economist Paul Krugman on how policy choices shape real life—jobs, wages, prices, and who actually …Read more
com – OECD (@the_oecd) · Paris / 147K followers · 2.5K+ posts · Official account of the Organisation for Economic Co-operation and Development | #BetterPoliciesforBetterLives.
com – Daron (@oficialdaron) • Instagram photos and videos / Artista solo 🎙️ O CEMITÉRIO, assista agora · Daron, uma aula de rock pesado no Red Star Studio em Pinheiros #heavymetal # · um pouco do que foi o @ …Read more
org – ISO/IEC 42001:2023(en), Information technology / The AI management system provides requirements specific to managing the issues and risks arising from using AI in an organization. This common approach …Read more
com – Key Issue 3: Risk-Based Approach – EU AI Act / The EU AI Act introduces a proportionate risk-based approach to AI regulation, which imposes a gradual scheme of requirements and obligations depending on the …Read more
com – DARON – Französisch-Deutsch Übersetzung / Übersetzung Französisch-Deutsch für DARON im PONS Online-Wörterbuch nachschlagen! Gratis Vokabeltrainer, Verbtabellen, Aussprachefunktion.
com – What did David Hume mean when he said that “reason is a … / 8 Jun 2011 — Reason is the slave of the passions in the sense that practical reason alone cannot give rise to moral motivation.Read more
com – ISO/IEC 42001: a new standard for AI governance / ISO/IEC 42001:2023 is the latest standard for an artificial intelligence management system (AIMS), offering a structured framework for AI governance. It helps …Read more
com – „Power and Progress“ von Simon Johnson … / Power and Progress. Our Thousand-Year Struggle Over Technology and Prosperity Winners of the 2024 Nobel Prize for Economics. Simon Johnson und Daron Acemoglu.Read more
ai – Updated OECD Definition of an AI system / This document contains proposed clarifications to the definition of an AI system contained in the 2019 OECD Recommendation on AI (the AI Principles) to support …Read more
blog – Book review: “Power and Progress” – by Noah Smith / 21 Feb 2024 — In which Daron Acemoglu and Simon Johnson fail to convince me that innovation needs to be steered away from automation.
artificial-intelligence-act.com – Artificial Intelligence Act, Regulation (EU) 2024/1689 – Links / 6 February 2025 – Commission Guidelines on the definition of an artificial intelligence system established by Regulation (EU) 2024/1689 (AI Act). The AI Act …Read more
gsc-co.com – BS ISO/IEC 42001:2023 / The AI management system provides requirements specific to managing the issues and risks arising from using Al in an organization. This common approach …Read more
com – Power and Progress: Our Thousand‐Year Struggle over … / 27 Mar 2024 — Power and Progress: Our Thousand-Year Struggle over Technology and Prosperity, Daron Acemoglu and Simon Johnson, Basic Books, London, 2023.Read more
org – Human-centered AI | Proceedings of the 5th Workshop on … / Human-centered AI: ensuring human control while increasing automation. Author: Ben Shneiderman.
com – “A Treatise of Human Nature” by David Hume / Written in 1739-1740, Hume dives into human emotions, reason and morality, declaring humans are weird, driven by passions, not reason, with …Read more
com – 2025 was supposed to be the Year of the A.I. Agent. … / Gary Marcus, a reputable AI sceptic with strong background in AI have just made a bet with Mils Brundage, an intependent AI researcher who …Read more
com – Project – Ricardo Hausmann & Andrés Velasco ask what … / Ricardo Hausmann & Andrés Velasco ask what kind of world economy would vindicate today’s market valuations of a handful of US firms …
com – In 2026 expect the economic, financial and social … / Artificial intelligence (AI) is set to have a ripple effect through the global economy, impacting 40% of jobs in emerging markets, and 26% of …Read more
com – Power and Progress: Our Thousand-Year Struggle Over … / 16 May 2023 — Kindle $17.99. Rate this book. Power and Progress: Our Thousand-Year Struggle Over Technology and Prosperity. Daron Acemoğlu, Simon Johnson.Read more
org – HUME’S TREATISE / by P Russell · 2007 — 63 Hume’s general aim is to provide an account of the scope and limits of human reason, and the way in which it relates to the role of the passions in human …Read more
ai / ISO/IEC 42001:2023 Artificial Intelligence Management … / ISO/IEC 42001:2023 represents the world’s first international standard specifically designed for AI governance, providing organizations with a comprehensive …Read more
com – OECD publishes explanatory memorandum on AI system … / 5 Mar 2024 — The memorandum describes AI systems as machine-based systems that infer outputs like predictions and decisions, varying in autonomy and adaptiveness.Read more
org – Brynjolfsson, E., Rock, D. and Syverson, C. (2017) Artificial … / The aim of this study was to develop an adequate mathematical model for long-term forecasting of technological progress and economic growth in the digital age …Read more
org – Regulation 2024/1689 of the Eur. Parl. & Council of June … / by NA Smuha · 2025 · Cited by 60 — The AI Act is a product safety legislation at heart: it treats AI systems like products that need to be made “safe” through a set of harmonized European rules.Read m…
org – Human-Centered AI, by Ben Shneiderman. Oxford / by J Killoran · 2024 · Cited by 1 — Ben Shneiderman presents a compelling argument for why the future of artificial intelligence (AI) design should be human-centric.
org – Hume on Reason and Passion (Chapter 11) / Very few passages in Hume are better known or more often quoted than his declaration that “reason is, and ought only to be the slave of the passions.Read more
com – dj-daron / DJ DaRon ist ein Newcomer DJ aus Linz, geboren in Leipzig (German). Durch seine Mutter und seiner damaligen Arbeit im Linzer Millennium war er immer schon …Read more
com – Human-Centered AI by Ben Shneiderman | PDF / Human-Centered AI (Ben Shneiderman) (Z-lib.org) – Free download as PDF File (.pdf), Text File (.txt) or read online for free.
com – On the origin of Hume’s philosophy in the passions / by K Martin · 2021 · Cited by 8 — Hume famously, albeit ambiguously, claims that “reason is, and ought only to be a slave of the passions” (2.3.3.4). But philosophy’s legitimacy as the quest for …Re…
com – Rebuilding the pyramid: The AI Act’s risk-based approach … / by GG Gasiola · 2025 · Cited by 4 — The recently enacted EU AI Act (AIA) is based on a “clearly defined risk-based approach.” This regulation employs a complex normative structure, comprising …Read…
com – Artificial Intelligence and the Modern Productivity Paradox: A … / Systems using artificial intelligence match or surpass human-level performance in more and more domains, leveraging rapid advances in other technologies and …Read more
com – Risk-based approach to EU AI act: benefits and challenges … / by R Justo-Hanani · 2026 · Cited by 2 — This article examines co-regulation, a hybrid regulatory model where public and private sectors collaborate to manage AI risks within a risk-based framework.Rea…
org – Unpacking the EU AI Act: Key Concepts and What They … / 5 Feb 2025 — The EU AI Act came into force on August 1, 2024. It is a groundbreaking legal framework for artificial intelligence (AI) in Europe.Read more
io – Dr. Ben Goertzel’s Conversation with Dr. Gary Marcus at … / 4 Sept 2025 — They also debate the feasibility of achieving AGI by 2029, where Gary is more skeptical, due to the complexity and current limitations of AI …Read more
de – Human-Centered AI – Ben Shneiderman / 20 Mar 2025 — A HUMAN-CENTERED APPROACH TO ARTIFICIAL INTELLIGENCE WILL ENSURE HUMAN CONTROL OVER POWERFUL AND HELPFUL FUTURE MOBILE DEVICES AND SERVICES.
com – The Race for AGI: Why 2025 Might Be the Year Everything … / Some AI experts, like Gary Marcus, have long argued that current large language models alone are “not the royal road to AGI” — they excel at …Read more
eu – The concept of ‘AI system’ under the new AI Act / 11 Dec 2024 — 23 In contrast, OECD, Explanatory memorandum on the updated OECD definition of an AI system, 2024, p 9, where it is emphasised that systems of …Read more
com – ISO 42001 Certification for AI Management Systems / ISO 42001 certification services support responsible AI governance with transparency and compliance. Partner with TÜV SÜD and get certified today.
ai-act-law.eu – AI Act as a neatly arranged website – Legal Text / The AI Act was published in the Official Journal of the European Union on 12 July 2024. It enters into force 20 days after its publication on 1 August 2024.Read more
com – Gary Marcus: a sceptical take on AI in 2025 / 15 Jan 2025 — In this episode, Alok Jha, The Economist’s science and technology editor, interviews Gary Marcus, one of modern AI’s most energetic critics.Read more
de – Daron Acemoglu / Daron Acemoglu, geboren 1967 in Istanbul, ist Professor für Wirtschaftswissenschaften am renommierten Massachussetts Institute of Technology (MIT).Read more
de – OECD | BMZ / Sie hat ihren Sitz in Paris. Derzeit sind in der OECDOrganisation für wirtschaftliche Zusammenarbeit und Entwicklung, englisch: Organisation for Economic Co- …Read more
com – ISO/IEC 42001 Certification – Artificial Intelligence (AI) … / ISO/IEC 42001 provides a certifiable AI management system (AIMS) framework in which AI systems can be developed and deployed as part of an AI assurance …Read more
de – Explanatory memorandum on the updated OECD definition … / In November 2023, OECD member countries approved a revised version of the Organisation’s definition of an AI system. This document contains proposed …Read more
com – Gary Marcus says LLMs are a “dress rehearsal” for AGI / Marcus said AGI is the real goal — not today’s flawed language models.
com – Valuing AI: Extreme Bubble, New Golden Era, or Both / 13 Jan 2026 — ” In 1998, the economist Paul Krugman predicted that the internet’s economic impact would be “no greater than the fax machine.” To be fair …Read more
edu – Human-Centered Artificial Intelligence / Book. Ben Shneiderman’s book on Human-Centered AI, was published by Oxford University Press in February 2022. It greatly expands on the contents of the earlier …
ai – ISO/IEC 42001:2023 (AIMS) — AI Management System Guide / ISO/IEC 42001:2023 is an AI management system (AIMS) standard. It provides requirements for how an organization governs AI across its lifecycle — leadership …Read more
org – Hume on the Passions / by S BUCKLE · 2012 · Cited by 28 — Treatise of Human Nature , ed. D. F. Norton and M. J. Norton (Oxford: Clarendon Press, 2007), 2 Vols; Volume 1 (Text). Annette …Read more
org – Daron Acemoglu – Interview / Daron Acemoglu, economic sciences laureate 2024, speaks about the root causes of persistent poverty among the poorest nations and how to build the types of …Read more
go.cr – What is AI_ Can you make a clear distin…tween AI and non … / The OECD just released an explanatory memorandum on its updated definition of an AI system. In this blog post, we explain the main points. Input, including data.Read more
org – Artificial Intelligence and the US Economy: An Accounting … / 16 Jan 2026 — Nobel Prize Paul Krugman seemed to agree that the “AI boom is driving most, possibly all, of the economy’s recent growth”. J.P. Morgan said that …Read more
ai – Regulation (EU) 2024/1689 — Artificial Intelligence Act … / 6 Jan 2026 — It entered into force in August 2024, establishes prohibited AI practices, specific obligations for high‑risk systems and general‑purpose AI …Read more
com – Daron Acemoglu – Nobel Perspectives … / Daron Acemoglu is a Turkish-American economist best known for his influential research on institutions, political economy, and economic inequality. Born in …Read more
edu – No. 101: Truly Risk-Based Regulation of Artificial Intelligence … / 21 Oct 2024 — This paper argues that risk-based regulation is indeed the right approach to AI regulation. At the same time, however, the paper shows that important …Read more
schweitzer-online.de – Human-Centered AI | Ben Shneiderman | 2022 / The remarkable progress in algorithms for machine and deep learning have opened the doors to new opportunities, and some dark possibilities.
ferner-alsdorf.de – Erläuterndes OECD-Papier zur aktualisierten … / 27 Nov 2023 — Das Papier „Explanatory Memorandum on the Updated OECD Definition of an AI System“, veröffentlicht im März 2024 … Definition

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