A popular distinction is that between opportunistic and meaningful action: Opportunism follows the path of least resistance – situational, instinctive, without moral reflection. The meaningful, on the other hand, requires engagement with what is valuable in the long term, even if it’s uncomfortable in the short term.

But there’s also a “both/and”: Reflectively useful action for an extended time horizon. You can choose from various opportunistic options the one that appears meaningful in the long run. You don’t have to choose between “quickly useful” or “long-term significant” – you can have both. Think of a chess player: They don’t calculate all possible moves until the end of the game (that would be impossible), but rather use heuristics – rules of thumb that enable optimal solutions with limited knowledge.

Today, heuristics are often better than detailed strategies. The world changes too quickly for five-year business plans. What we need is the ability to act wisely in the present while keeping tomorrow in view. That’s not opportunism – it’s reflective pragmatism. Useful behavior often must be decided quickly. There’s no time to think through all options. This is where heuristics come into play – mental models that allow you to make good decisions under time pressure. Our experiences teach and internalize heuristics: We sense which quick fix will still hold tomorrow and which is merely a bandage on a wound that continues to fester.

Bruce Lee said: “Be water, my friend.” The situational lifestyle of postmodernity has taught us to be flexible, to adapt, to follow the waves. But water without direction seeps into the sand. The art lies in remaining fluid without becoming arbitrary – acting opportunistically with a compass in your head that points to long-term values.

Imagine you're sitting in a meeting and sense everyone nodding – only you have doubts. What happens? Exactly: You stay silent. Congratulations, you've just experienced the spiral of silence.

People only voice their opinions when they believe they align with the majority. What seems like a trivial observation is actually a powerful social mechanism. The theory of the spiral of silence explains why unpopular opinions disappear – not because they're wrong, but because their holders fall silent.

Elisabeth Noelle-Neumann identified this phenomenon in the 1970s: humans possess a quasi-statistical intuition. Like an internal barometer, they constantly measure which opinions are "safe." Evolutionarily, this makes sense – group belonging was crucial for survival. Even today, social exclusion activates the same brain regions as physical pain.

Think about the restaurant where no one orders first. Or the WhatsApp group where everyone waits for someone else to like a controversial message. The spiral of silence functions like a social autopilot.

John Stuart Mill would have protested. The Enlightenment philosopher believed in the power of free discourse – that truth prevails through diversity of opinion. Reality looks different: it's not the best arguments that win, but the loudest ones. Mill's marketplace of ideas became a monopoly of majority opinion.

Social media has radically changed the game. What was once local group pressure is now globally scaled. Algorithms amplify popular content, likes become safety indicators. A tweet with three retweets can feel more powerful than a thoughtful but silent majority.

Yuval Harari would describe this as the evolution of our collective myths. What we call "public opinion" is often just the story told most loudly. Not the truest one.

The irony: the very technologies meant to give us more voice actually reinforce the spiral of silence. Social media algorithms function like popularity contests for ideas. The winner gets even more reach, the loser disappears into digital noise.

This creates new paradoxes: Vocal minorities can simulate majority opinions through coordinated actions. Echo chambers allow completely different groups to live in parallel realities. What appears as consensus in one bubble is unthinkable elsewhere.

The consequences are real: Politicians orient themselves toward distorted perceptions. Companies develop strategies for non-existent majorities. We make decisions based on what's visible, not what's true.

The good news: The spiral of silence isn't a natural law, but a recognizable pattern. We can consciously design communication structures. Develop tools that promote genuine diversity rather than just popularity. Create spaces where silence becomes safer than false nodding.

Breaking the spiral of silence means: Listening to what isn't being said. Asking who isn't speaking. And sometimes simply being the first to voice an uncomfortable truth. Even when everyone else is still nodding.

Imagine installing solar panels on your roof. Finally independent from the energy provider, you think. But then you realize: The smart grid dictates when you can feed power back in. The inverter’s app runs on servers in China. And the feed-in tariff? Still set by someone else. Welcome to the paradox of technological decentralization.

We’re witnessing a fascinating contradiction: The more distributed our technology becomes, the more centralized its control grows. It’s like building better and better roads, only to see the toll booths become ever more powerful. Blockchain promises freedom from banks, but who controls the major exchanges? The internet was supposed to democratize information, yet Google decides what we find.

Michel Foucault would have recognized his panopticon in this phenomenon—just digitized. The French philosopher described how modern power operates not through direct force but through surveillance and normalization. Today, we don’t need a central watchtower anymore. Every decentralized device becomes an observation post, sending data to invisible centers. Power is everywhere and nowhere at once.

Marshall McLuhan, the prophet of the "Global Village," saw it differently. For him, technology was an extension of humanity. But his vision of a worldwide village overlooked something crucial: In a village, everyone knows everyone, but in the digital village, only a few platforms know everyone. McLuhan dreamed of connection but got networked supervision instead.

Benjamin Bratton talks about the "Stack"—a planetary computing infrastructure organized in layers. Like an orchestra without a conductor, where every musician plays virtuosically, but the score comes from a single composer. Execution is distributed; composition is centralized. Edge computing brings processing power to the network’s edge, but the algorithms running there? They come from headquarters.

The truly crazy part? This centralization isn’t a conspiracy but a logical necessity. As complexity grows, the need for coordination increases exponentially. It’s like cooking for a big family—the more chefs in the kitchen, the more important the recipe becomes. And someone still has to decide what’s being cooked in the first place.

The EU tries to counter this with the Digital Services Act, China builds its Great Firewall, and somewhere in between, Estonia experiments with e-Residency while startups tinker with blockchain currencies. Yet all these approaches only confirm the paradox: Even the attempt to decentralize power creates new centers of control.

Maybe it’s time to shift our thinking. Instead of seeing decentralization as the opposite of centralized power, we should understand them as two sides of the same coin. Like a spiderweb—decentralized in structure, but the spider feels every vibration in every thread.

The question isn’t whether we can resolve this paradox. The question is how we learn to live with it. Because while we’re still debating, the next layer of the Stack is already being built. And it’s not asking for our permission.

Anti-vaxxers and free-market radicals have more in common than one might think. Both believe: The strong survive, the weak fall by the wayside—and that’s a good thing. Whether through viruses or market forces, what matters is that "natural selection" does its work.

This mindset stems from the social Darwinism of the 19th century. Herbert Spencer coined the term "survival of the fittest," applying Darwin’s theory of evolution to society. What works for animals must also be right for humans. Anti-vaxxers see diseases as a test for the immune system. Neoliberals view bankruptcies as just punishment for poor management. If you don’t make it, tough luck.

Kant would be rolling in his grave. His categorical imperative states: Act in such a way that your behavior could become a universal law. Imagine if "Let the weak die" became law. Such a society would destroy itself—faster than an unvaccinated child in a measles outbreak. Kant saw every human as valuable, no matter how "strong" or "weak" they were.

The modern version of this harsh philosophy comes from Ayn Rand. Her doctrine: Selfishness is good, helping others is bad. Ironically, Rand herself relied on social welfare when she was old and sick. But that’s typical: The fiercest advocates of personal responsibility soften up when they themselves need help.

The funny thing is: Both movements depend on the very system they reject. Anti-vaxxers are only healthy because others get vaccinated. Free-market radicals benefit from roads, schools, and courts paid for by the state. They’re like teenagers raging against their parents while still living at home.

The problem: You can’t reason with these people using facts. Studies on vaccine efficacy? Irrelevant. Evidence of market failures? Doesn’t matter. Because it’s not about science—it’s about ideology. They don’t want a society that protects the weak. They want a jungle where only the strongest survive.

Yet the definition of "strong" suddenly becomes flexible when it affects them personally. Then, bank bailouts are unavoidable, and seeing a doctor is no longer a sign of weakness. This reveals the truth: We’re all vulnerable. Anyone can get sick, anyone can fail. That’s exactly why we need solidarity—even if some only realize it when it’s too late.

Everything seems to fall apart. My first, long-term girlfriend leaves me, and suddenly the question arises: Who am I now? The ground beneath my feet feels unstable—a feeling reminiscent of Berlin in the 1990s, when chaos and opportunity stood side by side after the fall of the Wall. These moments of transition can feel like an earthquake. But out of the turmoil, something new often emerges: a new identity, a new apartment, new people. And eventually, stability returns. Yet before that, there is a phase where nothing feels certain.

It’s like the often-mentioned jump from the shallow end of the pool into deep water: only when we let go do we realize that we can swim. These moments of hesitation determine whether we move forward.

No transformation works without instability. Systems only change when old patterns break. This chaos is not a mistake but a necessary state for something new to emerge. Peter Kruse describes this process as self-organization [1]. Systems tip, reorder themselves, and find a new balance. But old ways of thinking can block this restart. Flexibility and openness become critical skills for seeing uncertainty not as a threat but as an opportunity.

The first step is self-observation. How do I feel? What patterns keep repeating? Self-reflection is key to growth. Instead of fighting uncertainty, it helps to embrace and observe it. It signals that we are moving. This moment of wobbling is often the start of something big. Those who use it can become creative, try new paths, and emerge stronger in the end.

Uncertainty is not an enemy but a signal that change is possible. Those who embrace these moments can not only survive—they can grow. Change begins exactly where we feel the most uncomfortable.

Sources and Further Reading:

1. Peter Kruse, next practice

For me, the fall of the Berlin Wall (9 Nov 1989), shortly before my 20th birthday, was a singularity. Suddenly people in my small, west-german hometown were seeing Trabis, I was able to move to East Berlin and the world was no longer divided into good and evil. Actually I got a Trabi myself later, when I was living in Berlin.

In the context of AI, the term "singularity" refers to a hypothetical future point at which technological advances become so rapid and so profound that they fundamentally change human life. This point is often referred to as the "technological singularity". Ray Kurzweil, a well-known futurist and inventor, predicted back in 2005 that we will reach an AI singularity around the year 2045. He claims that in that time we will develop artificial general intelligence (AGI) capable of surpassing all human intellectual abilities.1

Singularities are scary, unpredictable and bring change and uncertainty into our lives. We are not well equipped to understand singularities, let alone deal with them. One of the reasons for this is that singularities usually result from exponential developments, which are generally more difficult for people to digest than linear processes, for example.

The fall of the Berlin Wall brought with it far-reaching changes that still concern us today, both on a small and large scale. The acceleration of developments in areas with a global impact (AI, climate, coronavirus) scares me and makes me think. If someone talks too fast on YouTube, I can reduce the speed. Unfortunately, this is not possible with developments, e.g. in the field of AI. Not even through regulation. How can we preserve proven beneficial systems such as the broad discourse within a democracy and at the same time manage global changes quickly and effectively so that they benefit humanity instead of harming it?

Stone Age graffiti

Think of an organization not as a collection of desks and computers, but as something living, a breathing organism. Culture is the shared values, beliefs and norms that shape the way we interact with each other, how we make decisions and how we achieve our goals.

Sounds pretty strong, doesn't it? And it is. Culture is the invisible duct tape that holds us together. It is the invisible network of rules and norms that structures our day-to-day work. It is the invisible foundation on which our organization stands.

But what happens if we want to change this foundation? What happens if we want to redesign the way we work? What happens when we try to change our culture?

Here's the problem: culture is tough. It is resilient. It is rigid. And for good reason. Culture is there to create stability. It is there to give us a common identity, to give us a sense of belonging. It is there to unite us.

But this very strength can also become a weakness. Because when we try to change our culture, we often encounter resistance. We encounter fear. We encounter uncertainty. We encounter the desire to hold on to the familiar.

This does not mean that change is impossible. It just means that we have to face the challenge. It means that we have to come to terms with our culture. It means that we need to understand what makes our culture what it is, why it is the way it is, and how we can change it.

And that is no easy task. It requires courage. It requires patience. It requires empathy. But above all, it requires understanding. Understanding of the power of culture. Understanding of the role it plays in our organization. And an understanding of how we can use it to enable change rather than block it.

Because at the end of the day, culture is not our enemy. It is our ally. It is the tool with which we can shape and mold our organization. It is the medium through which we can bring about change. And it is the key with which we can unlock the future of our organization.

Going slow

Generative AI is getting better and better. So good that some speak of emergent properties. Of language models building a world model. We are all asking ourselves how we can solve the alignment problem: How do we ensure that AI works for and not against humans and that we can trust that development is going in the right direction? It is not just about technical progress, but rather about the normative framework conditions that must accompany it.

Trust is based on transparency, control and clear ethical standards. These principles are essential in order to establish a technology not only as progressive, but also as trustworthy. In the context of AI, we are faced with the challenge that many systems function as "black boxes" whose functionality and decision-making principles are hidden. This lack of transparency collides head-on with the need for traceability and integrity.

The call for regulation of AI is a call for orientation and security. Regulation can be seen as a signpost that does not limit innovation but guides it along ethically responsible paths. It is important to find a balance that both drives development and sets ethical standards. Bad guys will never take regulation seriously and it would be counterproductive if the good guys were to slow down development while the bad guys uninhibitedly push the boundaries of what is possible. That is why regulation can only set the standards, but not solve the whole problem.

In addition to the normative framework, AI needs transparency and control over data: Even if LLAMA, for example, is not as powerful as GPT4 - it is open source and can therefore at least be viewed transparently. And blockchain technologies open up new perspectives for transparency, control and decentralisation of data and models. They represent a synthesis of technological innovation and ethical responsibility that paves the way for trustworthy AI. We ourselves decide whether to prioritise the good guys or the bad guys.

Sometimes I talk to others to distract myself. When the report I was supposed to write - until today - hasn't been finished, for example. Then I look for straw men.

100 companies are responsible for over 50% of global CO2 emissions. As early as the 1970s and 1980s, researchers from major oil companies recognised the potential impact of CO2 emissions on the climate.(1) Despite the clear scientific consensus, these companies chose to continue to invest in fossil fuels and politicise against climate action. Just like I did with my report.

We tend to ignore obvious facts, creating distractions to avoid uncomfortable change. This strategy of politicising problems instead of tackling them ultimately leads to necessary measures being delayed or prevented altogether.

By politicising, we turn our problems into the problems of others.

Sources:

(1) Carbon Disclosure Project

This substack is meant to investigate change in all its facades. Change is a worthwhile topic because change can be found everywhere and at any time. This means I won't be short of topics for future posts.

However, the breadth of the term also harbours the danger of being thematically too blurred. I will therefore try to use a simple systems theory model for change that we can use as a guide.

Systems are defined by their function and emergent properties and exist primarily through a boundary between the inside of the system and what is not part of the system (the environment). Within the system there are different system elements, each of which has relationships (e.g. forces) with each other. These relationships can be very simple or very complex. Systems interact with their environment through inputs and outputs. Feedback can quickly strengthen or weaken effects. So far, so familiar.

What does this have to do with change? In systems, changes can be brought about by various causes:

Dynamics of internal processes: Changes in the dynamics of internal components alter the system as a whole. This means that the elements or the relationships between the elements of a system change, resulting in an overall change in the system.

Adaptation to external conditions: The external conditions have an influence on the system. If external conditions change, this can have an influence on the structure and/or function of a system.

Self-organisation and emergence: Complex systems develop properties that do not result directly from the elements and relationships, but are rather a consequence of self-organisation.

When analysing changes, you should always ask yourself which system you are analysing and where the change (see above) comes from. In addition, feedback effects and the speed of change (evolution vs. disruption) are decisive factors.

Now, go change!