There is a particular kind of situation I put off for a long time: conversations where I have to say no. Turning someone down, disappointing someone, setting boundaries — while staying fair, clear, and respectful. Those are exactly the moments when I feel I’m losing my footing internally. I don’t want to hurt anyone, I don’t want to break anything, and at the same time I know I need to say “no.” In situations like that I started using AI as a thinking tool — but not in the intuitive way.

Why “write me a rejection” doesn’t work

The first impulse is often obvious: ask the AI to draft a rejection for me. But that’s the wrong approach. A difficult conversation isn’t a text problem; it’s a thinking problem. If I haven’t understood the situation clearly, no wording will save me. I quickly notice that generated texts are too soft, too harsh, or simply don’t fit. They don’t feel like me because they didn’t grow out of my own clarity.

The Aristotelian angle: acting after deliberation

What really helped me was an idea from Aristotle’s Nicomachean Ethics: good action doesn’t arise purely from spontaneity; you approach the matter “after deliberation.” That means: I take time to think the situation through, consider different perspectives, and prepare a decision consciously. That is where AI becomes interesting for me — it can help structure that deliberation process.

AI as sparring partner, not text generator

I no longer use AI to hand me finished answers but as a sparring partner. I describe the situation in abstract terms, without personal details, and work through questions step by step: What is my goal? What are my real reasons for saying no? What does the other person care about? Where is my conflict? The AI helps me sort these points, spot blind spots, and sharpen my thinking. That doesn’t produce a text first — it produces inner clarity. Only from that clarity do I formulate what I want to say myself.

Privacy: how I handle it carefully

An important point for me is data. When I prepare such conversations, it often involves personal or professional context, and I don’t want to give information away lightly. So I follow a few simple rules: no real names, no specific company names, no identifiable details. I abstract the situation so the structure stays the same but no conclusions about real people are possible. That works surprisingly well, because the thinking process doesn’t depend on concrete names but on the dynamics of the situation.

Realism and limits

Of course I know this isn’t a perfect solution. Even if I enter no sensitive data, I’m still using an account that can be tied to me. Even with European providers and privacy rules, residual risk remains. Long term we’ll need better options where such processes are truly anonymous or local. I’m working on something like that myself, but it isn’t far enough along to use meaningfully here yet. For now my aim is to use existing tools consciously and responsibly.

The real benefit

What has changed for me isn’t only the quality of my conversations but my stance. I don’t walk into these situations unprepared or driven by vague feelings anymore. I take time beforehand to approach the matter after deliberation. The AI helps me order my thoughts, but the decision and responsibility stay mine. That is the crucial point: I’m not delegating communication — I’m improving my thinking.

Conclusion

You can’t outsource a difficult conversation. But you can improve the process that leads up to it. When I use AI as a sparring partner instead of a substitute for my own clarity, something very valuable emerges. I become calmer, more structured, and more confident in what I want to say. In the end I still have the conversation myself — just much better prepared.

I know this from my own experience: When someone asks what I do not want, the answer comes instantly. No thinking required. Crystal clear. I can describe precisely what annoys me, what exhausts me, what does not work for me. But when I am asked what I want instead, it suddenly turns vague. Blurry. Or I notice I do not really have a proper answer at all.

Then this well-meaning advice arrives fast: "Just phrase it positively." Do not say what you do not want—say what you do want. Sounds logical. Almost trivial. As if it were only a linguistic flip.

It is not.

The negative is real—the positive often is not yet

The crucial difference for me is: What I do not want already exists. I have lived it. It is concrete, tangible, charged with emotion. The job that drains me. The situation that feels wrong. The conversation that frustrates me. Those are vivid memories. I have empirical data for them.

What I want instead often does not exist yet. I have not experienced it. It is not simply there to retrieve. I have to create it first. Out of nothing.

That is the first reason it is so hard.

There is rarely "the" positive flip

On top of that comes something I underestimated for a long time: There is almost never one clear flip of what I do not want.

If I say, "I do not want to be dazzled," the answer is not simply "the opposite." Does that mean darkness? Closing my eyes? Soft light? There are countless options that are all "not that"—yet completely different.

So when I am asked what I want, I do not only have to put something into words. I have to choose. I have to pick, from many possible alternatives, one that actually fits me.

That is what makes the question complex.

I lack experience for the new

The third point is decisive for me: For what I do not want, I have experience. I have tried it. I have noticed: That does not work for me.

For what I want instead, I do not have that experience. Even if I have an idea, I do not know whether it really works. I have no empirical data. No experiment that shows me: This feels good, this fits.

So I move in a space of possibilities without clear feedback. And that creates uncertainty.

"What do you need?" is a demanding question

When I put that together, it becomes clear why questions like "What do you need?" or "What would have to be true for this to be okay?" are so hard to answer.

I am supposed to name something that does not exist yet. I am supposed to choose from many options. And I am supposed to commit to something when I do not even know whether it will work.

That is not merely rephrasing. That is real cognitive work.

Why I should stop criticizing myself for it

For me, that has an important consequence: I treat myself differently when I stall in situations like that.

I used to think quickly: "I simply do not know what I want." Or: "I need to understand my needs better." And yes, there is some truth in that. It matters to understand your own needs.

But even when I am good at that, these structural difficulties remain. The problem is not only inside me. The task itself is demanding.

And that applies not only to me, but to others as well.

Conclusion

It is easy to say what I want to move away from. I have lived it, I know it, it is clear. It is much harder to say where I want to go. Not because I am incapable, but because the goal does not exist yet, because there are many possibilities, and because I lack the experience to judge it with confidence.

When I understand that, I can be more generous—with myself and with others. And I can acknowledge that the path from "away from here" to "toward there" is not a simple rephrasing, but a genuine creative process.

It is a strange phenomenon I keep noticing when I program with AI. I sit there calmly, focused, having code generated for me — and suddenly something tips. I get impatient, irritable, sometimes genuinely angry. I notice myself starting to lash out at the AI internally. I think things like “this is complete nonsense” or “this can’t be that hard.” And even though I know it makes no sense — the AI doesn’t feel anything, it doesn’t understand aggression — it still happens. It is especially strong when I’m coding. When I write text or develop ideas, I mostly stay relaxed, but with code it turns emotional fast. That made me curious.

Programming with AI as a psychological experiment

At some point I understood that this isn’t a technical problem but a psychological one. The AI is no longer a classic tool for me; it’s a thinking partner. I give it structure, it gives me something back that almost fits — but not quite. And this “almost right” is exactly where things tip. I have to debug things I didn’t write myself and reconstruct assumptions I never explicitly made. At the same time I expect it to work quickly and cleanly. That mix of loss of control and frustrated expectation is what triggers the emotional reaction.

My bully-and-attack mode

What I observe fits well with a model from schema therapy. I slip into a mode called “bully-and-attack”: attacking, dismissive, impatient. A mode aimed at applying pressure and regaining control — even when that objectively makes no sense. The absurdity is obvious: I’m trying to pressure something that cannot react. Yet in the moment it feels logical, as if attacking the problem would solve it. That is where it gets interesting: it has nothing to do with the AI. It’s me.

The moment of awareness

For me the decisive point isn’t avoiding this mode altogether — that doesn’t work reliably anyway. The decisive point is recognizing it. The moment I notice “I’m in bully-and-attack mode right now,” something important happens. I’m no longer fully caught in it; I have a little distance again. That distance is the lever because it lets me choose consciously how to continue.

Back to the healthy adult

Schema therapy also names the counter-position: the healthy adult. When I manage to return from attack mode to that state, my behaviour changes immediately. I become calmer, more precise, clearer. I stop asking why the AI is “so bad” and start asking what I formulated unclearly. I break the problem into smaller steps and think in structure instead of reacting emotionally. Suddenly collaboration works again. That’s no accident — it’s the state in which I work best as a developer.

Why it escalates so much with code

I’ve also seen why this shows up so strongly in programming. Code is uncompromising. Either it works or it doesn’t; there’s little grey zone. While I can live with ambiguity in prose, bad code blocks me immediately. That raises pressure, and under pressure I fall back on patterns I don’t want. The AI amplifies that because it often produces things that are very close — but not correct. This “almost right” forces me to engage more deeply than if everything were plainly wrong.

What that says about me as a developer

Perhaps the most important insight: this behaviour isn’t new; the AI just makes it visible. Bully-and-attack is a pattern I activate under frustration, and AI is a perfect trigger because it keeps pushing me into those borderline situations. If I take that seriously, it isn’t an annoying side effect but a training ground. Here I learn not only to work better with AI but to steer myself better.

Programming as self-leadership

Programming with AI has become a form of self-leadership for me. I observe myself, notice my states, and practise returning deliberately to a functional mode. That isn’t theory — it affects my work directly. I write better prompts, think more clearly, make fewer mistakes, and reach working solutions faster.

Conclusion

What surprised me most isn’t that AI can be annoying, but how clearly it mirrors my own patterns. Programming with AI is no longer just a technical process for me — it’s a mirror. If I take that mirror seriously, I don’t only become a better developer. I become calmer, clearer, and more effective at what I do.

People have been asking me lately how I deal with the state of the world. To many it looks as if it hardly gets to me. While people around me are genuinely burdened by politics, wars, economic uncertainty — keywords like a Trump administration, conflict in Iran, or rising prices are often enough to trigger stress — I stay comparatively calm. I understand that reaction very well; I’ve known it myself. For a long time I was no different.

It wasn’t always like this

I know the feeling that the world “hits you.” That news isn’t just information but hits you emotionally. That you get stuck in your head, run through scenarios, worry, get angry, and end up exhausted without changing anything. Especially when you care about context and want to understand, things can tip quickly. Then interest turns into rumination, and rumination into strain.

What changed for me

The difference today isn’t that the world got simpler. The opposite. The difference is that I now have a viable context of meaning. I use that word deliberately, echoing Martin Heidegger’s notion of Bewandtniszusammenhang (context of relevance). “Purpose” alone isn’t quite right, because it isn’t only about a goal but about a web of meanings my actions are embedded in.

I have a clear picture of what I’m doing, what I want to build over the coming weeks and years, and long term. I know what matters to me, what I value, and what I need to feel well. I shape my life accordingly. That context of meaning isn’t abstract; it’s concrete and guides action. It gives my everyday life structure and direction.

The gravity of meaning

What I observe is that this context of meaning has its own gravity. It keeps pulling me back to what is relevant to me. When I follow the news or global developments, I do it consciously and with limits. I inform myself, think about it, put it in context — but I don’t stay stuck in it. Eventually this “gravity” pulls me back into my own topics again.

That doesn’t mean I don’t care about the world. On the contrary. I take it seriously, but I no longer lose myself in it. I distinguish clearly between what I can influence and what lies outside my scope. And I choose actively to put my energy where it has an effect.

Why rumination happens less

It used to be that I’d get stuck on problems where I had no real room to act. That creates a sense of powerlessness — and that is psychologically draining. That happens far less often now because my focus is clearer. I have enough projects, goals, and areas of responsibility to hold my attention. That leaves less room for endless loops about things I cannot change anyway.

That’s no accident

I want to stress that this isn’t coincidence or some trait like “that’s just how I am.” It’s the result of deliberate work. Building a viable context of meaning doesn’t happen on the side. It means engaging with your values, making decisions, and taking responsibility for how you shape your life.

Conclusion

The world hasn’t become less complex or less troubled. But my relationship to it has changed. I no longer let myself be pulled permanently into problems I cannot solve. Instead I orient toward what makes sense for me and where I can have influence. That context of meaning gives me stability. And that is why the world stresses me out far less than it used to.

I keep seeing how people defend their ideas, models, and even individual “facts” as if it were about their identity. I know that from myself as well. It quickly feels as though I would be questioning myself if I let go of a belief.

But that is exactly where thinking stops.

Because neither an idea nor a model nor what I take to be a fact is part of me. They are tools I use to try to understand the world.

Why I cling to things so easily

I still remember how, as a child, I found the classic atomic model fascinating. That picture of electrons orbiting a nucleus like planets had something incredibly elegant about it. It was vivid, tangible, almost beautiful.

Later I learned that this model is not accurate in that form. Instead of clear paths there are probabilities, orbitals, abstract mathematical descriptions. Much less intuitive.

And I remember clearly that I did not like that. I wanted the old model to be true. Not because it was better, but because it felt better.

That is the point: I do not hold on to something because it is true, but because it works for me—emotionally.

The moment it tips

The problem arises the moment I identify with my beliefs. When “this is my current model” turns into “that is how it is.” When a working hypothesis becomes a truth I must defend.

Then I start to perceive selectively. I look for confirmation. I screen out contradictions. And I often no longer notice that I have long stopped really checking.

It feels like clarity. But it is the opposite.

What I actually need ideas, models, and facts for

For me, all of these things have only one purpose: they should help me find my way in the world.

An idea is good when it helps me understand connections. A model is good when it explains observations and enables predictions. And what I treat as a fact is only as solid as the foundation it rests on.

When something no longer works, I have to adjust or discard it—not because I crave change, but because otherwise it is no longer usable.

“Facts” are not immune either

What I underestimated for a long time: this dynamic applies not only to abstract models but also to individual facts.

I notice how I often want certain things to be true. That an event happened the way I learned it did. That a connection exists because it fits neatly into my worldview.

But that, too, is ultimately nothing other than wishful thinking—just subtler.

Because what I take to be a fact is always the result of interpretation, sources, and framing. And that can change.

Reality does not care about my preferences

The uncomfortable part is: reality does not negotiate.

It does not behave in the way that would be more comfortable for me. Not in the way that fits my worldview better. And not in the way that feels “right.”

I can wish a model were true. I can hope something is true. I can get used to it.

But none of that changes how the world actually works.

The real mistake

For me, the real mistake is not being wrong. That is unavoidable.

The mistake is when it starts to matter that I am right.

In that moment, my focus shifts. I no longer want to understand; I want to win. I defend instead of checking. And that is exactly where I lose the ability to correct my own assumptions.

Conclusion

So I try to treat my ideas, models, and what I take to be facts deliberately as tools. Things I use while they work—and that I change when they no longer do.

I can wish things were different. I can prefer a simpler or more familiar picture.

But reality will not do me that favor.

Reality does not adapt to me. I have to adapt to reality.

For a long time I believed that naturalism (the idea that there is nothing supernatural) is something that flattens the world. That it misses what matters. That it reduces our experience, our feeling, our consciousness to something that does not do them justice.

And I was convinced of it.

Well into my forties I held exactly that position. I argued with people who reasoned in biological or neuroscientific terms and accused them of overlooking something essential. That they were falling short. That they did not understand the depth of human life and life itself.

Today I see it completely differently.

Where my earlier objection was justified

The interesting thing is: my discomfort was not wrong.

When someone says love is "just a cocktail of neurotransmitters," something is off. It does not just feel wrong, it is wrong. But not because naturalism is wrong.

Because the model is too simple.

That was the point I sensed intuitively back then, but I classified it wrongly. I looked for the mistake on the wrong level. I thought it was a problem with naturalism itself. In reality, it was a problem of bad, over-simplified models.

What reductionism really means

Reductionism is often understood as if it "shrinks" things. As if it breaks down the complex into something simple and loses something in the process.

But that is not the real issue.

The real issue is illegitimate simplification.

When I say the brain is "just" a piece of matter, that sounds like reduction. And it is. What I leave out is the staggering structure of that matter. The density. The complexity. The depth.

The brain is not a potato. It is not a stick of butter. It is an extremely finely structured system whose details we have only begun to understand for a relatively short time.

When I take that structure seriously, the supposed “flatness” disappears immediately.

What is really going on in such phenomena

Take the claim "love is nothing but a cocktail of neurotransmitters":

Yes, neurotransmitters are involved. But that is only a tiny slice. The reward system is involved. The social brain. Attachment mechanisms. Memory. Perception. Expectations. History.

Love is not "just a chemical cocktail." That is materialism, i.e., the most radical reductionism that claims: only matter is real. But love is also not "just a simple electrochemical mechanism," as physicalists might say, who at least acknowledge that matter is only a small part of physical reality. Love is a complex interplay across time. A structured process with unimaginable depth and breadth.

Depth here means: there is a history, a development, a temporal dimension. Breadth means: many different systems are involved simultaneously.

When I take that seriously, I do not get a flat picture, but something like a landscape pulsating with life that stretches beyond the horizon, with mountains, valleys, lakes and rivers, clouds and storms, with vast forests in which tiny springtails live in the endless mountain ranges and valleys of tree bark. And on those, even tinier parasites like bacteria or amoebae live in yet another microcosm of bewildering complexity.

My thinking error back then

My mistake was projecting this justified criticism of simple models onto the wrong alternative.

I thought: if naturalism cannot provide this, then there must be something else. Something "greater." Something supernatural.

And I started defending ideas like that. That consciousness is more. That it does not happen only in the brain. That there must be something immaterial.

The problem: I had no model for that.

What only struck me later

When I look more closely at those "counter-designs," they are remarkably empty.

People speak of "soul." Of "consciousness beyond the material." Of things science supposedly cannot grasp.

But when I ask what that means concretely, there is not much.

They are mostly negative definitions. "Not material." "Not explainable." "More than that." But what exactly this "more" is remains vague.

Exactly what I had wrongly thought was missing in naturalism is missing there: depth and breadth. Structure. Concreteness. That vast, blooming, buzzing landscape.

The real aha moment

The turning point for me was when I started taking naturalism seriously without simplifying it.

When I accept: our consciousness happens in the brain. Without a brain, no consciousness can occur.

And when I simultaneously take the actual complexity of the brain seriously, then I find exactly what I had been searching for before.

Depth. Unfathomability. The uniqueness of each person.

None of this lies outside the natural.

It is a natural process.

Conclusion

The mistake is not describing the world in natural terms.

The mistake is describing it too simply while doing so.

Today I understand my earlier criticism differently: it was a justified reaction to bad, far too simple models. But I directed it at the wrong target.

Real depth does not disappear when I think naturalistically.

It only disappears when I think poorly.

And when I look closely, I find everything I once searched for in the "supernatural" exactly where I long refused to see it:

In the brain. In the unfathomably complex dance of neurons.

Last night, the Artemis II capsule landed back on Earth.

For the first time in decades, humans were near the Moon again. New footage was captured—including images of Earth as a sphere, in part rising behind the Moon. Those images were everywhere in the media almost immediately.

And just as quickly, they were “debunked” again.

On platforms like YouTube or TikTok, countless videos are already circulating from flat-Earth proponents explaining why it is all fake. In parallel, science communicators pick apart exactly those claims.

If I am honest: Of course I tend to trust the scientific explanations. But here I am deliberately doing an exercise. I was not there—and I am asking myself in earnest: How do I tell which account has more substance, and whom I should trust more?

It is not about true or false

You could try to answer that question head-on: Who is right? But that quickly turns unsatisfying. Both sides assert things. Both sides bring “evidence.”

So I take a step back. I treat both positions the same for now: as models.

One model says: Earth is a sphere, spaceflight works, and the Artemis mission images show real events. The other model says: There is no outer space in that sense, Earth is flat, and the images are forgeries.

I am not asking: Which model is true? I am asking: Which model is more useful?

Models must enable predictions

A model is like a map to me. It should help me find my way in the world. And that means above all: It must deliver concrete predictions.

The spherical-Earth model can do that. It can tell me when the sun will set in Berlin tomorrow. It can tell me when the next solar eclipse will occur. It can compute how spaceflight trajectories must run so that a capsule like Artemis can loop around the Moon and return safely.

And that is exactly what happened. The model made a concrete prediction—and then the capsule flew exactly as calculated.

Now I look at the other model. What does the flat-Earth model predict in concrete terms? When exactly will the next solar eclipse occur—and why? How do flight routes between continents run if Earth is flat? How would the sun have to move for day and night to work?

Suddenly it gets very quiet.

Single claims are not a counter-model

The videos I see almost always work the same way: They isolate individual aspects (“This image looks odd,” “This shadow does not fit”) and conclude from that that everything must be fake.

What is missing is a consistent counter-model. If I say the Artemis images are forged, that is not enough. I would need to explain how the world works instead—concretely enough that I can derive predictions from it again.

That does not happen. Instead it stays at individual claims. And I cannot do anything with that.

Conclusion

The question “Who should I believe?” is actually the wrong question in situations like this.

The better question is: Which model gives me verifiable, concrete, useful predictions?

In the case of the Artemis mission, the answer is clear. One model says: “Everything is fake.” The other says: “If we do X, Y will happen”—and then exactly that happens.

For me, the decision is therefore not a matter of faith. It is a matter of function.

For a long time I believed that naturalism (the idea that there is nothing supernatural) is something that flattens the world. That it misses what matters. That it reduces our experience, our feeling, our consciousness to something that does not do them justice.

And I was convinced of it.

Well into my forties I held exactly that position. I argued with people who reasoned in biological or neuroscientific terms and accused them of overlooking something essential. That they were falling short. That they did not understand the depth of human life and life itself.

Today I see it completely differently.

Where my earlier objection was justified

The interesting thing is: my discomfort was not wrong.

When someone says love is "just a cocktail of neurotransmitters," something is off. It does not just feel wrong, it is wrong. But not because naturalism is wrong.

Because the model is too simple.

That was the point I sensed intuitively back then, but I classified it wrongly. I looked for the mistake on the wrong level. I thought it was a problem with naturalism itself. In reality, it was a problem of bad, over-simplified models.

What reductionism really means

Reductionism is often understood as if it "shrinks" things. As if it breaks down the complex into something simple and loses something in the process.

But that is not the real issue.

The real issue is illegitimate simplification.

When I say the brain is "just" a piece of matter, that sounds like reduction. And it is. What I leave out is the staggering structure of that matter. The density. The complexity. The depth.

The brain is not a potato. It is not a stick of butter. It is an extremely finely structured system whose details we have only begun to understand for a relatively short time.

When I take that structure seriously, the supposed “flatness” disappears immediately.

What is really going on in such phenomena

Take the claim "love is nothing but a cocktail of neurotransmitters":

Yes, neurotransmitters are involved. But that is only a tiny slice. The reward system is involved. The social brain. Attachment mechanisms. Memory. Perception. Expectations. History.

Love is not "just a chemical cocktail." That is materialism, i.e., the most radical reductionism that claims: only matter is real. But love is also not "just a simple electrochemical mechanism," as physicalists might say, who at least acknowledge that matter is only a small part of physical reality. Love is a complex interplay across time. A structured process with unimaginable depth and breadth.

Depth here means: there is a history, a development, a temporal dimension. Breadth means: many different systems are involved simultaneously.

When I take that seriously, I do not get a flat picture, but something like a landscape pulsating with life that stretches beyond the horizon, with mountains, valleys, lakes and rivers, clouds and storms, with vast forests in which tiny springtails live in the endless mountain ranges and valleys of tree bark. And on those, even tinier parasites like bacteria or amoebae live in yet another microcosm of bewildering complexity.

My thinking error back then

My mistake was projecting this justified criticism of simple models onto the wrong alternative.

I thought: if naturalism cannot provide this, then there must be something else. Something "greater." Something supernatural.

And I started defending ideas like that. That consciousness is more. That it does not happen only in the brain. That there must be something immaterial.

The problem: I had no model for that.

What only struck me later

When I look more closely at those "counter-designs," they are remarkably empty.

People speak of "soul." Of "consciousness beyond the material." Of things science supposedly cannot grasp.

But when I ask what that means concretely, there is not much.

They are mostly negative definitions. "Not material." "Not explainable." "More than that." But what exactly this "more" is remains vague.

Exactly what I had wrongly thought was missing in naturalism is missing there: depth and breadth. Structure. Concreteness. That vast, blooming, buzzing landscape.

The real aha moment

The turning point for me was when I started taking naturalism seriously without simplifying it.

When I accept: our consciousness happens in the brain. Without a brain, no consciousness can occur.

And when I simultaneously take the actual complexity of the brain seriously, then I find exactly what I had been searching for before.

Depth. Unfathomability. The uniqueness of each person.

None of this lies outside the natural.

It is a natural process.

Conclusion

The mistake is not describing the world in natural terms.

The mistake is describing it too simply while doing so.

Today I understand my earlier criticism differently: it was a justified reaction to bad, far too simple models. But I directed it at the wrong target.

Real depth does not disappear when I think naturalistically.

It only disappears when I think poorly.

And when I look closely, I find everything I once searched for in the "supernatural" exactly where I long refused to see it:

In the brain. In the unfathomably complex dance of neurons.

There are many psychological models. But only a few have reached the point for me where I can say: this truly helps me in everyday life. The Schema Mode Model from schema therapy definitely belongs in that category. Not because it sounds especially elegant or is theoretically persuasive, but because it is very close to what actually happens in the brain and what I can actually observe in myself.

What is behind the model

At its core, the model is surprisingly simple for me when I break it down. It is basically about two things: structures in the brain and their activation.

What schema therapy calls a schema is the structure in the brain.

These are neural pathways that have formed over time. They emerge through experience, repetition, and learning. These structures are relatively stable. They do not simply disappear just because I have understood them once.

The second part is the mode. That is the activation of this wiring in a specific moment.

A schema can exist without currently being active. Only when it is triggered, when certain conditions are met, does it "switch on." And that is exactly when I am in a specific mode.

That can be a functional state in which I am clear, calm, and capable of acting. Or a dysfunctional state in which, for example, I overreact, withdraw, or fall back into old patterns.

Why this is so practical for me

What I find so helpful about this model is that it demystifies things. It is not about me “being this way” or something “being wrong with me.” It is about certain patterns existing and being activated under certain conditions. That is something I can observe. And above all, it is something I can work with.

I can begin to differentiate: What is my current state? Which mode is active? And what are the typical triggers for it? This differentiation alone already creates distance. I am no longer completely identical with my reaction, but can see it as the activation of a pattern.

This is what is really happening

For me, this is the crucial point: this model does not just describe something abstract, it comes very close to what is actually happening. It is not just an explanatory model, but a working model. I can apply it directly in the moment when something is happening inside me.

When I notice that I am overreacting, getting stressed, or falling into old behaviors, I can understand this as the activation of a mode. And if I go deeper, I can recognize which underlying schema has just become active. This turns a diffuse feeling into a structured observation.

Conclusion

The Schema Mode Model is so valuable to me because it builds a bridge between theory and practice. It gives me language for what is happening inside me and, at the same time, concrete starting points for how to deal with it. I no longer see my reactions as random or uncontrollable, but as the result of wiring and activation. And that is exactly what makes change possible in the first place.

At some point, I started to realize that I do not experience reality directly. That sounds abstract at first, but it is actually quite simple. There is a world out there, yes. But what I perceive is always already an interpretation. A model. Something my brain constructs so I can deal with that world.

Reality is not directly accessible

I do not have immediate access to the "true nature of things." What I do have are impressions, perceptions, thoughts - and those are already processed. My brain filters, interprets, simplifies. It builds a picture that is manageable for me. But that picture is not reality itself.

That does not mean there is no reality. It only means I do not experience it unmediated. I am always working with an abstraction.

Models as tools for prediction

When I look at it this way, it becomes clear what this is really about: not truth in an absolute sense, but usefulness. A good model is one that lets me make meaningful predictions. It helps me form expectations and orient myself in the world.

That is exactly what science does. It does not try to capture the "true essence" of things, but to build models that work as reliably as possible. Models that explain why something happens and what is likely to happen next.

And if I am honest, I do exactly the same thing in everyday life. Whenever I think about the world, make decisions, or assess other people, I am working with models.

My brain does nothing else

The interesting point for me is this: my brain does exactly that all the time. It is basically a prediction machine. It is constantly trying to make predictions based on past experience. What happens next? How will someone react? What does this situation mean?

Everything I perceive is interpreted in that context. My brain is constantly checking: does this fit my expectations or not? And if not, the model gets updated.

There is no absolute truth for me

That leads to an uncomfortable but very helpful insight for me: I have no direct access to truth. No one does. There are only models that work better or worse.

At first, this takes away the illusion that I could be "right" in an absolute sense. At the same time, though, it gives me something far more practical: I can evaluate models. I can look at whether they help me understand the world better and act in meaningful ways.

It is about the quality of models

In the end, for me it is not about finding the one "true" model. That is a dead end. It is about improving the quality of my models.

A good model, for me, is one that is clear, consistent, and proves itself in practice. One that helps me make better decisions and be less surprised by what happens.

And that is exactly why models are "the real deal" for me. Not as abstract theory, but as what I actually work with every day - whether I am aware of it or not.

There is a particular kind of situation I put off for a long time: conversations where I have to say no. Turning someone down, disappointing someone, setting boundaries — while staying fair, clear, and respectful. Those are exactly the moments when I feel I’m losing my footing internally. I don’t want to hurt anyone, I don’t want to break anything, and at the same time I know I need to say “no.” In situations like that I started using AI as a thinking tool — but not in the intuitive way.

Why “write me a rejection” doesn’t work

The first impulse is often obvious: ask the AI to draft a rejection for me. But that’s the wrong approach. A difficult conversation isn’t a text problem; it’s a thinking problem. If I haven’t understood the situation clearly, no wording will save me. I quickly notice that generated texts are too soft, too harsh, or simply don’t fit. They don’t feel like me because they didn’t grow out of my own clarity.

The Aristotelian angle: acting after deliberation

What really helped me was an idea from Aristotle’s Nicomachean Ethics: good action doesn’t arise purely from spontaneity; you approach the matter “after deliberation.” That means: I take time to think the situation through, consider different perspectives, and prepare a decision consciously. That is where AI becomes interesting for me — it can help structure that deliberation process.

AI as sparring partner, not text generator

I no longer use AI to hand me finished answers but as a sparring partner. I describe the situation in abstract terms, without personal details, and work through questions step by step: What is my goal? What are my real reasons for saying no? What does the other person care about? Where is my conflict? The AI helps me sort these points, spot blind spots, and sharpen my thinking. That doesn’t produce a text first — it produces inner clarity. Only from that clarity do I formulate what I want to say myself.

Privacy: how I handle it carefully

An important point for me is data. When I prepare such conversations, it often involves personal or professional context, and I don’t want to give information away lightly. So I follow a few simple rules: no real names, no specific company names, no identifiable details. I abstract the situation so the structure stays the same but no conclusions about real people are possible. That works surprisingly well, because the thinking process doesn’t depend on concrete names but on the dynamics of the situation.

Realism and limits

Of course I know this isn’t a perfect solution. Even if I enter no sensitive data, I’m still using an account that can be tied to me. Even with European providers and privacy rules, residual risk remains. Long term we’ll need better options where such processes are truly anonymous or local. I’m working on something like that myself, but it isn’t far enough along to use meaningfully here yet. For now my aim is to use existing tools consciously and responsibly.

The real benefit

What has changed for me isn’t only the quality of my conversations but my stance. I don’t walk into these situations unprepared or driven by vague feelings anymore. I take time beforehand to approach the matter after deliberation. The AI helps me order my thoughts, but the decision and responsibility stay mine. That is the crucial point: I’m not delegating communication — I’m improving my thinking.

Conclusion

You can’t outsource a difficult conversation. But you can improve the process that leads up to it. When I use AI as a sparring partner instead of a substitute for my own clarity, something very valuable emerges. I become calmer, more structured, and more confident in what I want to say. In the end I still have the conversation myself — just much better prepared.

It is a strange phenomenon I keep noticing when I program with AI. I sit there calmly, focused, having code generated for me — and suddenly something tips. I get impatient, irritable, sometimes genuinely angry. I notice myself starting to lash out at the AI internally. I think things like “this is complete nonsense” or “this can’t be that hard.” And even though I know it makes no sense — the AI doesn’t feel anything, it doesn’t understand aggression — it still happens. It is especially strong when I’m coding. When I write text or develop ideas, I mostly stay relaxed, but with code it turns emotional fast. That made me curious.

Programming with AI as a psychological experiment

At some point I understood that this isn’t a technical problem but a psychological one. The AI is no longer a classic tool for me; it’s a thinking partner. I give it structure, it gives me something back that almost fits — but not quite. And this “almost right” is exactly where things tip. I have to debug things I didn’t write myself and reconstruct assumptions I never explicitly made. At the same time I expect it to work quickly and cleanly. That mix of loss of control and frustrated expectation is what triggers the emotional reaction.

My bully-and-attack mode

What I observe fits well with a model from schema therapy. I slip into a mode called “bully-and-attack”: attacking, dismissive, impatient. A mode aimed at applying pressure and regaining control — even when that objectively makes no sense. The absurdity is obvious: I’m trying to pressure something that cannot react. Yet in the moment it feels logical, as if attacking the problem would solve it. That is where it gets interesting: it has nothing to do with the AI. It’s me.

The moment of awareness

For me the decisive point isn’t avoiding this mode altogether — that doesn’t work reliably anyway. The decisive point is recognizing it. The moment I notice “I’m in bully-and-attack mode right now,” something important happens. I’m no longer fully caught in it; I have a little distance again. That distance is the lever because it lets me choose consciously how to continue.

Back to the healthy adult

Schema therapy also names the counter-position: the healthy adult. When I manage to return from attack mode to that state, my behaviour changes immediately. I become calmer, more precise, clearer. I stop asking why the AI is “so bad” and start asking what I formulated unclearly. I break the problem into smaller steps and think in structure instead of reacting emotionally. Suddenly collaboration works again. That’s no accident — it’s the state in which I work best as a developer.

Why it escalates so much with code

I’ve also seen why this shows up so strongly in programming. Code is uncompromising. Either it works or it doesn’t; there’s little grey zone. While I can live with ambiguity in prose, bad code blocks me immediately. That raises pressure, and under pressure I fall back on patterns I don’t want. The AI amplifies that because it often produces things that are very close — but not correct. This “almost right” forces me to engage more deeply than if everything were plainly wrong.

What that says about me as a developer

Perhaps the most important insight: this behaviour isn’t new; the AI just makes it visible. Bully-and-attack is a pattern I activate under frustration, and AI is a perfect trigger because it keeps pushing me into those borderline situations. If I take that seriously, it isn’t an annoying side effect but a training ground. Here I learn not only to work better with AI but to steer myself better.

Programming as self-leadership

Programming with AI has become a form of self-leadership for me. I observe myself, notice my states, and practise returning deliberately to a functional mode. That isn’t theory — it affects my work directly. I write better prompts, think more clearly, make fewer mistakes, and reach working solutions faster.

Conclusion

What surprised me most isn’t that AI can be annoying, but how clearly it mirrors my own patterns. Programming with AI is no longer just a technical process for me — it’s a mirror. If I take that mirror seriously, I don’t only become a better developer. I become calmer, clearer, and more effective at what I do.

At some point while working with AI, I noticed a pattern I now take fairly seriously. I call it my prompt-to-output ratio. Very simply: How much input do I put in—and how much output comes out? That sounds technical at first, but for me it has become a pretty good signal for whether I am producing something useful or just churning out AI slop.

How I recognize good results

I recently created a psychology article for my site with AI. The whole process, from first idea to published version, took maybe 15 to 20 minutes—including German and English versions. That is absurdly fast.

What mattered was not speed but quality. When I read the text through, it was clear: This is usable. It has substance. It is not generic AI mush.

Then something struck me: The prompt I wrote was longer than the finished article.

When the prompt is longer than the output

That was not a one-off. I see it again and again, including when programming. I write long prompts, sometimes several in a row, describing the architecture, the requirements, the background thinking. And in the end maybe ten lines of code come out—but exactly the right ones.

When I look at the total amount of input I provided, it is often larger than the output the AI produced. And in those cases, the result is almost always good.

Because I already did the thinking.

When I think too little, slop comes out

The flip side is just as clear. When I only write: "Write me an article about X" or "Build me a UI for this data," I get output that sort of fits—but is also completely interchangeable.

That is what people call AI slop.

And it is no accident. It happens because I was sloppy myself. I made no real decisions, offered no perspective, set no constraints. I asked the AI to guess what I might mean.

And that is exactly what I get: a statistical average.

What I am actually measuring

It is clear to me now that with this ratio I am not really measuring length, but something else: How much real thinking comes from me?

A long prompt is no guarantee of quality. But in practice it is often a good sign, because many decisions are packed into it. I have thought through what I want and what I do not, who it is for, what the purpose is.

The AI no longer has to "think"—only structure, shorten, and phrase.

A simple red flag

From that, a pretty practical heuristic emerged for me.

When the prompt-to-output ratio is clearly negative—that is, when I give very little input and demand a lot of output—that is a red flag. Not proof the result is bad, but a strong warning signal.

Then I should ask: Have I actually thought this through enough?

A green flag, not proof

The reverse is a green flag for me. When my input is more extensive than the output, the odds are high that I already did the substantive work and the AI is helping me shape it cleanly.

That is no guarantee of quality. I can pack a lot of nonsense into a long prompt. But in practice it correlates surprisingly strongly with good results.

AI as editor, not thinker

At core, for me it comes down to a question of roles.

When I give little input and demand a lot of output, I treat the AI like a thinker. I delegate the actual work.

When I give a lot of input and have the AI compress, structure, and phrase, I use it as a tool. As an editor. As a kind of compiler for my thoughts.

That is when it becomes truly strong.

Conclusion

For me, the prompt-to-output ratio is not an exact measure, but a simple diagnostic tool. A kind of self-check.

When I notice I want a lot of output from little input, I know: I am trying to skip work I should really do myself.

And when I see that my input is substantial and considered and the output becomes compact and precise, that is a good sign.

In the end the rule is simple: If I want good results, I have to think first myself. The AI can speed that up—but it cannot do it for me.