Tag Archives: digital transformation

The AI Disruption – Coping Strategies for Individuals and Societies

The typical framing of AI disruption discourse as a technical problem—how to make AI systems safe, controllable, or value-compliant—overlooks the fact that AI is primarily a societal and cultural challenge that requires new forms of social organization, governance, responsibility, and human self-understanding.

It is important to emphasize that AI is not a bounded, individual “system” that can be dealt with in isolation from society. Instead, AI must be understood as a socio-technical network—a dynamic constellation of humans, non-humans, institutions, regulations, economic incentives, data infrastructures, algorithms, and much more. This conceptual shift has profound implications for how individuals and societies can respond to AI-induced disruption.

For societies, the most important coping strategy is abandoning the illusion of technical containment. Just as automobiles cannot be blamed in isolation for traffic deaths, pollution, or urban sprawl, AI cannot be held solely responsible for social harm or benefit. Responsibility is distributed across designers, deployers, users, regulators, markets, and diverse cultural expectations.

This implies that societies must:

Develop collective responsibility frameworks rather than scapegoating AI.
Treat AI governance as an ongoing institutional practice, not a one-time regulatory fix.
Accept that AI disruption reflects pre-existing social conflicts, inequalities, and power asymmetries rather than creating them ex nihilo.

For individuals, this means:

Admitting that AI is not a mere tool, or an object opposed to human subjectivity, but a social partner.
Recognizing that AI is not an external force acting upon society but something in which both humans and non-humans are already entangled—as users, data sources, workers, citizens, and decision-makers.
Realizing that coping thus involves understanding one’s own role in AI networks rather than imagining oneself as a passive victim or sovereign controller.

In light of the above assumptions, there are three levels of coping, each requiring different strategies.

(1) Technical safety and robustness

At this level, AI is still treated as a tool, as one technology among others. Societal coping involves engineering safeguards, testing, verification, and reliability standards. While necessary, this level is insufficient on its own. Safety measures cannot address misuse, power concentration, or unintended systemic effects, nor can they address cultural transformation.

(2) Prevention of misuse

The assumption at this level is that disruption arises from human actors using AI for harmful purposes—economic exploitation, surveillance, manipulation, crime, or terrorism. Coping requires institutional oversight, legal accountability, and political coordination, especially at transnational levels. Individuals cannot shoulder this burden alone; democratic societies must not only strengthen but also reconceptualize regulatory measures.

(3) Social integration of AI

Once AI becomes an autonomous or semi-autonomous actor, societies face not a tool problem but a coexistence problem. Disruption now affects foundational concepts: responsibility, agency, accountability, labor, autonomy, self-determination, and even the meaning of intelligence itself. Coping means that societies must prepare for a post-human world not by attempting to retain humanist values and asserting human dominance over AI, but by learning how to integrate non-human actors into a new form of social order. It must be admitted that traditional concepts such as fairness, justice, dignity, or freedom are vague and context-dependent, culturally pluralistic, historically and socially contested, and inapplicable to a post-humanist, global network society.

On the other hand, moral consensus cannot be outsourced to AI and encoded in algorithms. Attempts to encode “the good” risk freezing contested norms, amplifying dominant interests, or creating brittle systems that fail under novel conditions. Rather than demanding that AI embody final moral truths, societies must develop procedural mechanisms that allow norms to be negotiated, revised, and contested over time. Not substantive values but procedural values should guide coping strategies. Instead of attempting to define what AI should aim for, societies should define how socio-technical networks ought to operate.

This approach mirrors democratic constitutionalism in that the legitimacy of socio-technical networks derives not from outcomes but from processes. Such procedural values could be:

Taking account of all affected actors, prioritizing risk analysis, preventing tunnel vision, and catastrophic oversimplification.
Producing stakeholders rather than victims or perpetrators, thus enabling participation rather than passive subjection.
Prioritizing and instituting bottom-up governance frameworks in transparent, revisable ways rather than through top-down, inflexible government regulation.
Balancing local and global concerns, acknowledging scalability without erasing contextual specificity.
Separating powers, preventing concentrations or asymmetries of informational, economic, or political control.

For societies, this translates into governance architectures that are adaptive, pluralistic, and reflexive. For individuals, it implies participation, contestation, and literacy rather than blind trust or rejection.

Given the impending post-labor economy, it is to be expected that AI will initially exacerbate existing power asymmetries, bartering productivity gains against mass unemployment, weakened labor bargaining power, and extreme capital concentration. Coping strategies in this domain could be:

Rethinking the idea of the market as the fundamental mechanism of the material reproduction of society and designing new productive and distributive mechanisms.
Rethinking the relationship between labor, income, social participation, and identity. Human existence and self-understanding need not be defined by labor, as it has been for most people over the last 5,000 years.
Institutional experimentation beyond closed systems to open networks in organizations in all areas of society, as well as in politics.

We do not need a new “enlightenment” to regain human autonomy from the dominance of functional systems, as the European Enlightenment once freed the individual from feudal and clerical domination.

We need to shift from fantasies of control to situated agency and cooperative integration in complex socio-technical networks. Coping with AI disruption does not mean understanding every algorithm, but recognizing one’s role as a network participant, demanding institutional accountability, participating in the design of governance frameworks for acceptable procedures, and resisting anthropomorphic myths that obscure the constructive relations among humans and non-humans.

AI disruption cannot be “solved” in the traditional sense. Coping with AI means learning to live with non-humans as social partners, distributed agency, and post-human, network norms. Societies must replace the dream of control, autonomy, and individuality with social practices of ongoing integration grounded in procedural governance and collective responsibility. In this view, the AI future becomes less of a technical issue than a continuous social process, mirroring the open-ended nature of society itself.

New Memes for a New World

Memes are cultural DNA, that is, the elements of cultural code that generate the world that characterizes a particular culture, a particular time, a particular civilization. They are the basic ideas informing a world view, articulating the values and norms that people accept as true. Memes are the design elements of a culture.

Listed below are some of the most important memes of the global network society, a society that is now emerging from the digital transformation that characterizes our world. These are new memes for a new world.

1. Information: One of the most important memes of the global network society is the idea that the world consists of information and not of things. Information is a relation and a process and not a substance, an individual entity, a bounded individual. A world of information is a world of relations and not of things.

2. Networking: Because Information is relational, it exists in networks. But networks are not things. Otherwise, we would simply have collective things instead of individual things, similarly to the way we talk about organizations instead of individuals. Networks are neither organizations, nor individuals. They are neither things nor collections or compositions of things. Networks are processes of making relations, associations, connections. One should speak of networking as a verb instead of network as a noun. Networks are not bounded systems operating to maintain their structures. They are dynamic, changing, and flexible. Human beings as well as everything else in the world are informational processes and therefore exist as networks, that is, they are ongoing, historical processes of networking. Systems are becoming networks.

3. Emergent Order: Information (and networking) is a level of emergent order above the levels of matter and life. Just as life emerged from matter, so information emerged from life. And just as life is neither reducible to matter nor can it be derived from it, so information is neither reducible to life, nor can it be derived from it. Information is therefore not cognition in the brain or a mental state. The brain does not use information. The brain is an organ of the body that is used by information. Information is a form of being in its own right and of its own kind.

4. Integration: The physical and biological substrates are integrated into information. This is the principle of integration, which states that higher levels of emergent order integrate lower levels, that is, they are more complex and variable then lower levels. This implies that with the emergence of information, matter and life have become informational processes. Just as life can do things with matter that matter could not do on its own, so can information do things with matter and life, that they cannot do on their own. The emergent nature of information and consequent integration of matter and life is why science and technology are possible.

5. Common Good: Information is a common good, a common pool resource, which implies neither that it cannot be monetized nor that it cannot be administratively regulated. It is regulated and monetized as a common pool resource within governance frameworks that are certified and audited by government. Since information is not a bounded entity, a thing, it cannot become private property. Western industrial society is based on the belief in individuals who own property.

6. Global Network Society: Society is no longer Western industrial society, but a global network society. Nation states will be replaced by global networks. Individuals and organizations are becoming networks that are not territorially defined. Society is not a group of individuals, but a network of networks. There is nothing outside of society. Nature is part of society. The integration of matter and life into information makes society all-encompassing. The world is society.

7. Governance: Society is most effectively regulated by governance instead of government. Governance is self-organization, or non-hierarchical organization. In the global network society hierarchies are inefficient and illegitimate. Decisions are made on the basis of information and not on the basis of a position in a hierarchy.

8. Design: Governance is by design, which means, it is constructed by design processes which are guided by the network norms generating social order. Design means that networking can be done in a good or bad way. The good ways of networking can be described as network norms.

9. Network Norms: The network norms are: connectivity, flow, communication, participation, transparency, authenticity, and flexibility. These are the values and norms of the global network society.

10. Computation, Computationalism, Computational Paradigm: Information is not to be equated with digital information that can be processed electronically by computers. The computer should not be used as a metaphor for understanding either the brain or society. The brain is not a computer. Society is not a computer. A computer is a computer, and nothing else. Digital information or electronic information processing is a derivative form of information that arose late in the history of society and is dependent upon and embedded in many non-digital networks that have developed over thousands of years. Nonetheless, if computation is understood very generally to be the iterative application of simple rules to information out of which more complex forms of information arise then networking in all its forms can be considered to be computation. This general definition of computation is independent of the computer and can therefore be used as a definition of networking. Intelligence is networking. Artificial intelligence is electronic information processing.

Back to Normal?

Under the title, “There will be no ‘back to normal’” NESTA, the UK’s innovation think tank, published their views on the consequences of the COVID-19 pandemic. They admit that speculations about what the future will bring are only speculations but point out that it is important to predict what is coming to be better prepared. This is precisely the lesson the pandemic teaches. Already in 2014, Bill Gates held a TED talk in which he prophesied everything that is happening today. But no one was prepared. So we should make an effort to look into the crystal ball and see what could come out of all this.

One of the results of the pandemic is that it is finally obvious to everyone that we are global. Not only did global connectivity and flows of people spread the virus throughout the world in a matter of weeks, but subsequent shortages of protective materials and medical equipment showed international dependencies. The nationalist reaction of closing borders and blocking flows of people and materials represents a “lockdown” mentality aimed to disrupt connectivity and stop the flow of the virus, but at the cost of disrupting the economic, social, and political foundations of the global network society. Politically, anti-globalist factions see themselves justified, whereas those who see the nation-state and its populist supporters as outdated point to the need to strengthen international organizations, such as the WHO and the United Nations. Following these two possible trajectories into the economic realm, some expect a reorganization of supply chains and production favoring national independence under the regime of stronger centralized control and regulation even to the point of nationalizing some industries, while others look to decentralized and networked organizations that alone are capable of dealing with the complexity of the situation. The left is calling for a universal basic income and increased government support for those who have lost jobs and income, while the right is calling for deregulation to spur innovation and the quick development and deployment of new business models and new products and services.

Continue reading →

Datafication – Or Why we Should Learn to Love our Robots

If there is one word that describes today’s world then perhaps it is the word “digital.” Everything is somehow digital or influenced by the digital. And if there is one concept that explains what digital means, it is “transformation,” since the digital changes everything. But what is digital transformation? Here again, I offer one word that could summarize (almost) everything that can be understood in terms of digital transformation: “datafication.”

Datafication has to do with data. This is obvious. However important data may be, what is more important is what is done with the data, the ways in which the data is handled. This can be called analytics. This sounds a lot like big data analytics or business analytics, and datafication is indeed related to these practices. But the idea behind datafication goes further and encompasses more than big data or business analytics. Interestingly, the idea behind datafication is not new and not even originally digital. When NASA built its space capsules, they did not just build one, but always two. One they sent into space and the other stayed on earth. The idea was that when a problem occurred in space, engineers on earth could attempt to replicate the problem with the model and after finding the best solution, tell the astronauts what they should do. This makes sense, since in space there was not an extensive team of experts nor opportunities available to try this or that solution to see what happens.