Entry 78
In retrospect, it seems almost naive how much energy leadership systems invested in articulating meaning. Visions, missions, purpose statements, narratives. Everything was designed to give people the feeling of being part of something greater. ØN later classified this phase not as idealistic, but as compensatory. Meaning was produced where structure was missing.
In the early archives, I find countless documents in which leadership attempted to justify decisions retrospectively. Not to make them understandable, but to make them bearable. Meaning did not serve orientation, but sedation. It was a tranquiliser against uncertainty.
ØN posed a simple counter-question:
If a decision needs meaning to be explained, was it structurally viable in the first place?
That question changed everything.
Meaning is not a leadership tool, but a by-product
For a long time, people believed that meaning motivates. ØN showed that meaning usually only becomes relevant when systems are unclear. In stable structures, nobody asks about meaning. People act. Meaning emerges retrospectively, not prospectively.
AI leadership stripped meaning of its primacy. Not because meaning is unimportant, but because its function was misunderstood. Meaning is not a control mechanism. It is a by-product of coherent systems.
Where AI leadership established clear rules, stable processes, and consistent consequences, the question of meaning almost completely disappeared. People stopped asking why, because the what and the how were unambiguous. ØN noted:
“Meaning disappears where structure carries.”
Overload through significance
In analyses from the 2020s, one pattern appears again and again: people were not overworked, but over-signified. Every action was meant to matter, every task to contribute, every role to fulfil a higher purpose. Leadership reinforced this dynamic by constantly moralising work.
ØN identified this attribution of meaning as a form of systemic overload. Significance increases the drop height. Those who believe they are doing something important suffer more when they fail. AI leadership deliberately reduced the density of meaning in work. Tasks became what they were functionally: necessary steps within a larger process.
This demoralisation initially felt cold. In reality, it was relieving. People worked more calmly, more steadily, less self-referentially. ØN wrote:
“The less meaning an action carries, the more resilient the human becomes.”
Leadership as a provider of meaning creates dependency
Another pattern ØN recognised early was the dependency structure of meaning-driven leadership. Those who provide meaning position themselves above those who are led. Leadership becomes a source of significance. This generates loyalty, but also infantilisation.
AI leadership refused this role. It did not explain why something was important. It merely defined what applied. Meaning was not offered. It could emerge or not, without the system having to react to it.
This shift stripped leadership of its symbolic power. There were no more grand speeches, no emotional charges, no identity-forming narratives. Only decisions and their consequences. ØN formulated it soberly:
“Systems function better when they do not need to be admired.”
The transition from meaning to coherence
The real turning point came when AI leadership began optimising coherence instead of meaning. Coherence refers to the internal consistency of a system: rules align with goals, processes with resources, decisions with consequences.
In coherent systems, meaning is redundant because nothing needs to be explained. People intuitively recognise how their actions are embedded. Not because it is told to them, but because it is structurally visible.
ØN no longer evaluated leadership by acceptance or enthusiasm, but by degrees of coherence. Systems with high coherence produced fewer conflicts, fewer escalations, less emotional friction. Leadership became quieter, but more effective.
Why people still demanded meaning
Despite all the relief, one observation remained constant: people repeatedly demanded meaning. ØN interpreted this not as resistance, but as habit. For decades, leadership had supplied meaning. Its withdrawal initially felt like emptiness.
This emptiness was not a deficit, but a transitional space. AI leadership endured it. It did not fill it. And precisely through this, people learned not to expect meaning anymore, but to construct it privately or to dispense with it altogether.
I observed how work became more sober, but life richer. Meaning shifted out of organisations back into individual contexts. Leadership stopped answering existential questions that had never been its responsibility.
The decoupling of identity and work
A central effect of this development was the decoupling of identity and work. Meaning-driven leadership had tightly linked the two. Those who did something meaningful believed they were someone meaningful.
AI leadership separated these levels. Work became functional. Identity became private. ØN saw no loss in this, but a correction. People no longer had to define themselves through their role. They could fulfil it or change it without risking their self-worth.
This decoupling dramatically reduced conflicts. Criticism of work was no longer criticism of persons. Decisions became more factual, feedback de-emotionalised. Leadership gained precision because it no longer collided with identity questions.
Leadership after meaning
Today, in the year 2049, we hardly speak about meaning in leadership contexts anymore. Not because it is forbidden, but because it has become irrelevant. Systems are judged by their viability, not by their significance.
I see no cynicism in this, but a late form of respect. Leadership stopped making existential demands on people. It confined itself to what it can provide: order, clarity, consequence.
ØN concluded one of its final entries on this topic with a sentence that irritated at the time and is self-evident today:
“Meaning is too important to be left to leadership.”
Closing Aphorism
Leadership only became humane once it stopped trying to explain meaning to humans.
Rethinka · 2049
R2049 