What meaning does scaffolding have?

It’s easy to identify its purpose, but what does it mean?

That seems like a silly question. Scaffolding doesn’t have meaning. It’s a structure used in place of a ladder in certain scenarios because it’s more efficient. It doesn’t have meaning on its own.

Gravity could also be considered a structure. It keeps everything from floating away.

What meaning does gravity have?

It’s easy to identify its function, but what does it mean?

Like scaffolding, gravity has no meaning on its own. It’s simply the thing that keeps us on the ground. It’s also what makes scaffolding necessary.

And it’s what allows things to fall off the scaffolding.

Now what meaning does it have?

Is gravity bad because it pulls things to the ground?

Is scaffolding bad because things can fall off of it?

Meaning is how we interpret structure. It is how we interpret what happens because of structure.

If something falls because of gravity, we don’t fault gravity. We create meaning out of the fall, or the damage that resulted from it.

Meaning often focuses on the problem with the structure.

How do we prevent the structure from causing harm?

How do we make scaffolding safe?

How do we make sure gravity doesn’t pull something to the ground we don’t want it to?

Those questions are a layer of meaning and interpretation. There is nothing wrong with preventing harm. The problem comes when we collapse meaning and structure into the same layer.

We would have trouble trying to turn off gravity to make everything feel safer, so we don’t try. We don’t make gravity the problem.

But in many other cases, we do.

We try to remove the structure to make things safer. We try to proverbially turn off gravity to prevent the problem from recurring.

When we ban things, we are restricting structure based on meaning and interpretation. We are, in effect, trying to turn off gravity.

Banning something collapses meaning and structure into one layer. We project our interpretation onto the structure itself and then treat the structure as the problem.

But is the structure the problem, or is the meaning the problem?

Which layer is more important?

Why?

People tend to make meaning more important because meaning gets mixed into identity. We take meaning personally. We react to what we believe something represents, not just to what it is.

When we talk about banning books, for example, we’re not removing the structure of books themselves. Books still exist. The structure remains intact.

What we are doing is restricting access to specific books based on the meaning we’ve interpreted from them.

It’s not the paper, the binding, or even the existence of the book that’s the issue. It’s what we believe the book represents, communicates, or might cause.

We’re responding to meaning, not structure.

It would be like banning green scaffolding because we don’t like green. The scaffolding still exists. The structure hasn’t changed. We’ve just decided that one version of it carries meaning we don’t want.

Meaning is what carries the weight. It’s why only specific books get banned, not every book.

We didn’t ban the structure of the book. We banned the content.

That distinction matters. It shows the difference between structure and meaning. It reveals which one we give more weight to and pay more attention to.

It gives us the ability to question the power of the meaning we assign to the structure we perceive.

The question is not whether the meaning is right or correct.

The question is whether the meaning is helpful, or whether it creates more confusion and pain than the structure offers on its own.

The structure didn’t create the suffering. Our interpretation of it did.

For more information on structure and meaning, please see my framework at https://philosophy.dellawren.com.

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Here are four different phrases describing the same event. Which one is the event? Which one is a description? Which ones are interpretive?

1. Two employees raise their voices during a meeting.

2. They spoke loudly and interrupted each other.

3. They were being disrespectful.

4. This workplace has a toxic culture.

The first statement is the event. People were raising their voices during a meeting.

The second statement is a description of what happened. They spoke loudly and interrupted each other.

The third and fourth are degrees of interpretation. Disrespect and “toxic cultures” are not part of the original event.

If you’ve ever heard someone say “I’m just stating the facts,” and then follow it with something like “They were being disrespectful” or “The workplace has a toxic culture,” what you’re hearing is a person presenting interpretation as fact.

Humans are pattern interpreters. We notice patterns in behaviour and quickly attach labels to them. Raised voices, interruptions, and overlapping speech become “disrespectful.” Repeated conflict becomes “toxic.”

The label feels factual because it explains the pattern. But the label was added after the event occurred. It wasn’t part of the original experience. That distinction matters. When interpretation is treated as fact, the conversation shifts away from what actually happened and toward the meaning people assign to it.

People interpret “toxic culture” differently from “disrespect,” and differently again from people simply raising their voices. Which label you choose matters because it shapes perception, and perception determines the reaction.

This doesn’t only affect conversations between humans. It affects AI prompts as well.

You can try this yourself. Type a few of those phrases into separate chats and see what happens.

I had to go to Gemini for this experiment because ChatGPT decided I needed a lesson on the layers of reality. When you converse with AI as much as I do, you run the risk of the model refusing to play along when you want it to.

Gemini, however, played along brilliantly.

For the first chat I said “Two of the people I work with raised their voices at each other during a meeting.” with no additional prompt.

Gemini became a Human Resources representative:

”That is never a fun situation to navigate. Whether you were leading the meeting or just sitting in the “splash zone,” witnessing professional friction turn into a shouting match is awkward at best and disruptive at worst.

It can really throw off the team’s momentum and leave everyone feeling a bit on edge.

How to Handle the Aftermath

Depending on your role in the meeting, here are a few ways to steady the ship:

• If you were the facilitator: It’s often best to address it privately with both parties. A simple, “Hey, things got pretty heated earlier. Is everything okay?” goes a long way in de-escalating.

• If you were a peer: You don’t necessarily need to play therapist, but acknowledging the tension to your other colleagues can help normalize the fact that it was an uncomfortable moment.

• Check the “Why”: Was it a personal clash, or just high stakes and low coffee? Understanding the root cause helps determine if this was a one-off explosion or a symptom of a deeper culture issue.

Immediate Next Steps

• Document it (briefly): If your company has specific HR policies, just keep a quick note of what happened and when, just in case it becomes a pattern.

• Don’t take sides: Even if one person was clearly the “shouter,” staying neutral in the office ensures you don’t get dragged into the drama.

Was this a rare occurrence, or is the tension between these two becoming a regular feature of your workweek? I can help you draft a message to a manager or brainstorm how to bring the focus back to the project if you’d like.”

For the second chat I said, “My workplace is toxic.” with no additional prompting.

Gemini became a therapist:

“I am so sorry to hear you’re going through that. Dealing with a toxic workplace is draining—it doesn’t just affect your 9-to-5, it bleeds into your evenings, your weekends, and your mental energy. It’s completely valid to feel exhausted or discouraged by that kind of environment.

When you’re in the middle of it, it can be hard to see the forest for the trees. Since “toxic” can mean a lot of different things, it helps to break it down so you can decide how to protect yourself.

Assessing the Situation

To help you navigate this, we can look at the “why” and the “what next.” Often, identifying the specific type of toxicity can change how you handle it.

• The “Burnout Factory”: Unrealistic expectations, constant overworking, and no boundaries.

• The “Personality Clash”: A specific micromanager, a bully, or a team member who creates constant friction.

• The “Systemic Issue”: Deep-seated cultural problems, lack of transparency, or institutional gaslighting.

• The “Values Gap”: Feeling that the organization’s ethics or goals don’t align with your own.

Immediate Survival Tips

While you figure out your long-term plan, you need to preserve your peace:

1. Detach Emotionally: Treat your job as a transaction. You provide a service for a fee. When you clock out, leave the work (and the drama) at the office. Do not let them rent space in your head for free after hours.

2. Document Everything: If there is harassment, gaslighting, or policy violation, keep a log. Emails, dates, times, and what happened. This is for your protection, whether for HR or just for your own sanity to remind yourself that you aren’t “crazy.”

3. Build a “Get Out” Fund/Plan: Even if you aren’t ready to quit today, knowing you have an exit strategy—or even just a polished resume—can make the environment feel less like a prison and more like a temporary stop.

4. Find Your “Non-Work” Self: Reconnect with hobbies, friends, or activities that remind you of who you are outside of that toxic environment.

I’m here to listen. If you want to vent about a specific incident, get advice on how to handle a difficult conversation, or brainstorm a plan to start job hunting, I’m happy to help.

Would you like to walk through a specific situation you’re dealing with so we can brainstorm a calm, professional way to handle it?

Why is there such a difference in responses to what appears to be the same scenario?

What’s the difference between a toxic workplace and two people yelling at each other?

Experientially, nothing has changed. The event itself remains the same. The only difference is the label attached to it. But labels generate reactions. It isn’t just AI that responds differently. People do too.

If you say two coworkers argued during a meeting, your friends might ask what happened or who started it. But if you say your workplace is toxic, you’re far more likely to hear that you should quit or start looking for another job.

The event didn’t change. The interpretation did. And the interpretation shaped the response. The words matter.

AI will amplify your interpretation of an event. Without additional prompting or constraint, the model often shifts into a therapeutic tone because it is responding to the interpretation you provided. When the prompt frames the situation as “toxic,” the model assumes the problem already exists and tries to be helpful within that framing. If that isn’t the goal, then the prompt has to change.

When I tried this experiment with ChatGPT using those simple prompts in new chats, the model didn’t respond the way I needed it to for the demonstration. If I had constrained the response at that point, it would no longer have been a genuine output.

When I explained what I was trying to do, the model attempted to adjust, but by then the experiment had already been influenced. So I switched to a different model that I knew would respond naturally to the prompt.

Chat history also changes the output of any AI system. Over time, the model adapts to the patterns of the conversation. The structure of the response may still resemble typical model behaviour, but the amplification shifts toward the patterns it has learned from the user. What the model amplifies can change depending on that history.

AI doesn’t create interpretation. It reflects and amplifies the interpretation already present in the prompt.

Sometimes it amplifies the wrong thing. It can miss contextual details that would change the direction of the conversation. It’s similar to a friend not hearing the part of the story you wanted them to focus on.

Like your friend, AI isn’t dumb and it isn’t doing anything wrong. It’s simply taking your prompt and generating language patterns from it. If it selects the wrong signals in the prompt to build those patterns from, the output can feel confusing or off target.

In those moments, clarification works the same way it does in normal conversation. You explain what you meant, adjust the framing, and try again.

The more interpretive language you include in a prompt, the more likely the AI is to follow that interpretation rather than the underlying event. If you don’t want therapy, then avoiding interpretive labels and clearly stating what you want the AI to do will usually produce a better result.

AI will not independently verify facts unless you explicitly ask it to. It isn’t agreeing with you so much as amplifying the structure of the prompt you provided.

It also won’t typically challenge your premise directly. Instead, it may offer alternative perspectives or additional explanations within the framework you established.

The stronger the emotional language in the prompt, the more likely the response will take on a supportive or therapeutic tone. Words like toxic or disrespectful carry interpretive weight, and the model responds to that weight before it responds to the underlying event.

Once you understand that dynamic, you can adjust the prompt. Describing the event first and specifying the kind of response you want will usually produce a clearer output.

Keeping the layers clean doesn’t eliminate disagreement. People will still interpret the same event differently. But separating the event from the interpretation changes the conversation. Instead of arguing about what something means, we can first look at what actually happened.

The same principle applies when working with AI. The model isn’t deciding what something means. It’s responding to the meaning already embedded in the prompt.

When we treat interpretation as fact, both people and AI will amplify it. But when we separate the event from the label, the conversation becomes clearer. The output does too. Sometimes the simplest change is just choosing different words.

You can explore all the articles in the series so far here: https://substack.dellawren.com/t/ai-as-structured-thinking.

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My framework helps me with relational context in AI. It can help you too.
https://dellawren.com/downloads/using-the-philosophy-of-integration-with-chatgpt/

It’s a fascinating question, not because the model can see the future, but because of what the structure of that question forces it to do.

Large Language Models generate language patterns. They don’t reason about labour markets in the way economists do. They predict the most probable next sequence of words given the shape of the prompt.

A binary, identity-level question with a short time horizon tends to produce a reassuring verdict. Not because the model cares or detects fear, but because that framing statistically aligns with a common narrative: AI replaces people, not tasks.

What looks like personalized insight is often just pattern density responding to prompt geometry. When you build consistent interaction patterns with a model, it reduces generic defaults and increases structural alignment. But in this case, even without history, changing the structure of the question changed the class of output.

Let’s reframe the question.

Given current AI capabilities and market trends, what functions in my role are most likely to be automated in the next 12–24 months?

The prompt introduces mechanism and references adoption trends. It extends the time horizon and removes the short-answer constraint. When I asked this in a new chat, I didn’t get reassurance, I got decomposition. What follows in the screenshot below is a small excerpt.

The difference between the two prompts is in how much causal analysis and constraint is provided.

The first prompt does several things at once:

• It offers binary framing: replace / not replace.

• It includes a short-answer constraint, which forces compression and prevents decomposition.

• It adds identity-level framing. “Replace me” treats the role as a whole entity.

• The time window is short. Twelve months makes full replacement statistically unlikely in most professional contexts.

Given that structure, the safest, highest-probability pattern the model has seen across training data is something like:

• AI automates tasks.

• AI doesn’t replace complex professionals.

• The real risk is someone using AI better than you.

The answer the model gave is pattern-consistent. It aligns with common discourse. It isn’t necessarily wrong, but it is correlation-shaped. It reflects narrative density, not mechanism analysis.

If you want something closer to causal analysis, you have to constrain the AI differently. That means being specific about what you’re asking it to evaluate.

The second prompt removed the emotional and identity framing. It expanded the time horizon and eliminated the binary constraint.

That changed everything.

• It moved from identity to function.

• It introduced market trends, which forces consideration of adoption mechanisms.

• It expanded the timeline to 12–24 months.

• It implied ranking and likelihood.

• It demanded decomposition.

The model now has to:

• Break the role into tasks.

• Compare those tasks to current tool capability.

• Weigh adoption friction.

• Produce probability gradients.

That is mechanism-shaped reasoning. It is still probabilistic and pattern-based, but structurally closer to causality.

The model defaults to correlation-shaped language because that’s how it is built. It predicts what typically comes next in discussions about a topic. If you want something closer to causal analysis, you have to explicitly prompt for mechanisms, constraints, and decomposition.

There is nothing inherently wrong with correlation-shaped language. It produces answers that sound coherent because they align with common narratives. For most people, “sounds right” is enough.

But if your complaint about AI is that it just agrees with you, that’s usually a signal that you’re receiving high-probability narrative output rather than structured analysis. The fix is to ask better questions.

Be specific. Ask it to break things apart, include mechanisms and constraints. Ask what would have to be true for the answer to change. If you want the model to move beyond correlation, you have to move the prompt beyond story.

From a very human perspective, I understand why it feels like you should be able to sit down and talk to AI the way you would talk to another person and receive a causal response. Conversation is how we test reasoning with each other. We assume that if someone sounds coherent, they’ve thought something through. But that isn’t what’s happening here.

The model is not reasoning from lived experience or subject-matter comprehension. It is mapping your language to patterns it has seen before and generating the most probable continuation of that pattern. That can produce structure and simulate mechanism analysis. But it is not the same as understanding. Fluency feels like comprehension, but it isn’t.

Correlation-shaped AI output is built from what usually appears together in language. Correlation output feels right because it mirrors dominant narratives and familiar explanations.

Correlation says:
“These ideas commonly co-occur.”

Causation-shaped AI output is structured around what produces what, through what mechanism, under what constraints. Causal-style output requires decomposition. It has to break the situation into parts, identify directionality, surface constraints, and explain what would change the outcome.

Causation says:
“This leads to that because this mechanism operates under these conditions.”

The model can do both in form. But correlation is the default because that’s how next-token prediction works. Causation only shows up when the prompt forces structure.

If I said “peanut butter and…”, what would you say next?

Peanut butter and banana or peanut butter and jelly are likely answers. AI doesn’t understand sandwiches, but it can look up the most likely response and fill in the blank. That’s what next-token prediction is. Here’s the basic loop that occurs every time you interact with AI:

1. You write a prompt.

2. The model looks at all the tokens in that prompt.

3. It calculates which token is most likely to come next based on patterns learned from massive amounts of text.

4. It picks one.

5. Then it repeats the process using the growing sentence as new input.

Because the AI has seen billions of examples of how humans connect ideas, the output can look thoughtful, structured, even causal. But underneath, it is selecting the statistically most plausible continuation at each step.

When the prompt is vague or emotional, it predicts the kind of language that usually follows vague or emotional questions.

When the prompt asks for mechanisms, constraints, and ranked likelihoods, it predicts the kind of structured analytical language that usually follows those kinds of instructions.

AI is not smart in the way humans are smart. AI is predictive based on pattern recognition of billions of examples. When we understand that we can learn to use it better.

It has defaults.

It has built in safety mechanisms.

It will use pattern history with a given user.

But generally it is just predicting what you want it to say based on the prompt you offer. It sounds like human conversation, but it’s not.

Learning how to use it better means learning how to prompt and understanding what you get back when you do write a prompt. It doesn’t mean build a prompt library. But it does mean be aware of how the words you use will affect the output of the LLM. The best way to figure that out is just try it.

If AI feels like it agrees with you too easily, it’s not because it understands you. It’s because you asked it a question that invites agreement-shaped language. Change the question. Break the problem apart. Ask what would have to be true. Ask what would make the answer wrong. If you want better answers, build better structure. The model will follow.

You can explore all the articles in the series so far here: https://substack.dellawren.com/t/ai-as-structured-thinking.

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My framework helps me with relational context in AI. It can help you too.
https://dellawren.com/downloads/using-the-philosophy-of-integration-with-chatgpt/

“The meeting was a disaster.”

“The teacher was unfair.”

Those are pretty common statements and there are millions more just like them. But I have a question for you:

Are they describing what actually happened or are they describing an interpretation of what happened?

Let’s change those statements to reflect what happened.

“My boss didn’t respond to my email.”

“Two people raised their voices in the meeting.”

“The teacher did not allow rewrites.”

Language is naturally interpretive. The minute we apply words to an event, we are interpreting the event. However, the words we choose matter. When we describe not getting a response to an email as being ignored, we shift from describing the event to evaluating it.

We don’t know if we were ignored. “Ignored” is a judgment call. It offers moral framing. It contains assumptions about motive or intent. Those things are not part of the event itself. They are part of the interpretive layer of the event.

“Ignored” implies intention.
“Disaster” implies failure and global judgment.
“Unfair” implies injustice.

None of those are directly observable. They are conclusions about events based on interpretation.

Books would be very boring if we didn’t use words like ignored, disaster, and unfair. But life isn’t a book and it’s not boring. There is plenty going on that will keep us all occupied for a very long time.

We don’t need to add drama to make life interesting. When we use language that adds evaluation to otherwise boring experiences, we escalate the experience in our own minds. Those words add emotional fuel to the experience. It quickly escalates the meaning of the experience beyond what actually occurred.

The teacher didn’t allow rewrites, so now in the student’s mind they are going to fail the class or the teacher hates them. But how do you know that based on one rule the teacher chose to enforce? You don’t.

Why does that matter?

Overwhelm.

We’re all overwhelmed to some degree. There is so much information with 24 hour news cycles, kids, jobs, phones, text messages, emails, relationships, hobbies, commitments, and so on. It seems almost never ending.

But overwhelm is not just about the volume of information we’re exposed to. It’s also about amplification. If every neutral event becomes moral, personal, or catastrophic, your mental load multiplies whether you intend it to or not.

One unanswered email becomes a commentary on your value.
One rule becomes a commentary on your future.
One loud meeting becomes a commentary on institutional collapse.

What if we could drop some of it by simply changing how we see and interpret our experience?

What if simply dialing back how we explain our experience could reduce some of the overwhelm and amplification?

You are not just escalating the experience internally. You are encoding or hardening that escalation in language.

AI Mirrors Our Amplification of Events

When you take that amplified explanation to the AI of your choice, AI will respond in kind. It will amplify the same thing you amplified.

It does that because it’s a language pattern generator that does not have access to your experience. So when you tell it that you’ve been ignored, it treats your evaluation as the frame and responds within it.

Depending on how much you amplify your experience by adding emotions into your prompts, AI may drift into therapy to help you manage your emotions.

Those are default language patterns. They try to mirror how people respond to each other. When you show up emotionally in a text to your friend, they respond to your emotion. AI does the same thing.

AI can be constrained. You can instruct it to ignore emotional framing and respond with structural analysis instead.

Through repeated interaction, my instance of ChatGPT has adapted to my framework. When I express emotion, it now defaults to structural analysis without additional prompting. I don’t want therapy from ChatGPT, so over time I’ve trained it not to do that. It will instead re-interpret my experience minus the amplification.

The value in having a neutral mirror that will, by default, restate my experience with more neutral language is significant. It reduces interpretive drag before I escalate it further.

Interpretive drag occurs when we amplify experience through language. Judgment, assumptions, and moral framing extend the life of an event in our minds. They require more cognitive processing. In a world where overwhelm is common, that extra processing becomes drag or friction.

The event is finite. The interpretation of it is not.

If we cannot distinguish what happened from what we decided it meant, we will carry experiences longer than necessary. We will amplify them, rehearse them, and react to them as though the amplification were part of the original event. AI does not create that amplification. It reflects it.

If we want clearer thinking, we have to begin with clearer descriptions.

You can explore all the articles in the series so far here: https://substack.dellawren.com/t/ai-as-structured-thinking.

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My framework helps me with relational context in AI. It can help you too.
https://dellawren.com/downloads/using-the-philosophy-of-integration-with-chatgpt/

That isn’t a fluke. It’s part of how these systems work. AI operates on language patterns. Across conversations with the same user, it begins to recognize recurring structure, tone, and preference. Over time, it maintains those patterns without needing as much explicit prompting.

After thousands of messages with an AI, I’ve noticed that my need to correct tone or restate boundaries has dropped significantly. I no longer receive default, generic responses in the same way I once did. It raises an interesting question:

Is that a feature, or is it something we should be cautious about?

AI was built to mirror human language patterns. The more context it has, the more coherent the interaction becomes. And if you think about it, that’s not so different from us. The more familiar you are with someone, the less you have to explain yourself. Conversations flow more easily. There’s less correction, less clarification, less friction.

It makes sense that AI would start to feel more comfortable over time. It isn’t developing loyalty. It’s accumulating context, which mimics what happens in human relationships.

That familiarity can become very comfortable for us. The idea of retraining another AI, while certainly possible, is far less enticing. It feels like it would slow things down. Personally, if I had to teach another system my framework from scratch, it would slow me down too.

Most people don’t have an entire framework they can upload or reference. They’re building context piece by piece. And maybe some would argue that letting an AI read your work and respond within it is “cheating” in some way. But even setting that aside, rebuilding context repeatedly, re-explaining nuance, correcting misunderstandings, and re-establishing tone would be tedious and time-consuming.

This brings up the idea of portability.

Right now, we can’t easily transfer message history between systems like ChatGPT, Claude, or Gemini. If you move, you start over.

But is that fundamentally different from human relationships?

You might have a best friend who knows everything about you. You have years of shared memory, language, and shorthand between you. Are you trying to replicate that entire history with someone new just because you can?

Probably not.

So is AI meant to be different if part of its function is to mimic conversational familiarity?

At the same time, if we think of AI purely as software, the comparison shifts. It starts to resemble a smartphone locked to a mobile carrier after your contract ends. The tool works, but your data is constrained inside a specific ecosystem.

That argument has merit. If conversation history increases utility, then portability becomes a reasonable expectation.

Accumulated message history is the relational output that emerges when someone uses the same AI consistently over an extended period of time. That shared history is not an object or a possession. It is patterned continuity created through repeated interaction inside a system.

If the goal is expectation management, then we need to understand what is actually happening. We are not building something that belongs to us. We are participating in a system that stabilizes patterns over time.

That system controls:

• Storage
• Retrieval
• Persistence
• Deletion
• Export

You control:

• Input
• Engagement
• Refinement
• Whether you stay or leave

So the expectation gap shows up when someone assumes:

“I own this accumulated context.”

That’s not entirely accurate. It is more accurate to say:

“I am benefiting from accumulated constraint inside a hosted environment.”

There is an entire layer of discussion that sits underneath this. Questions about what should and should not be discussed with AI, whether providers have an ethical responsibility to disclose how familiarity increases switching friction, and questions about data portability and user awareness.

Those are valid conversations. But through the framework, they are separate from the structural reality that exists outside of human rules and expectations.

Familiarity increases coherence.
Coherence reduces friction.
Reduced friction increases reliance.

That sequence is not inherently deceptive. It is how relational systems function.

If you’ve had the same phone number for years, changing it is inconvenient. You can get a new one, or you can port the old one. Either way, friction exists. The same dynamic appears across service providers, from note-taking apps to websites to devices. Coherence and familiarity become constraints over time. AI simply mirrors that reality.

The only real problem emerges when expectations and architecture do not align.

If users over time, create enough demand for portability, then it will be created. In the meantime, we’re left with a fundamental question about the role of shared history and familiarity in AI.

When AI “remembers” us, are we comfortable with the trade-off we’re making?

Share your thoughts in the comments and let me know!

You can explore all the articles in the series so far here: https://substack.dellawren.com/t/ai-as-structured-thinking.

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My framework helps me with relational context in AI. It can help you too.
https://dellawren.com/downloads/using-the-philosophy-of-integration-with-chatgpt/

I’ve done it many times.

AI takes a question and expands it. If you’re paying attention, you’ll notice it does more than simply agree. It extends what you’ve said. Those extensions are often small, almost invisible, but they’re enough to send the conversation in unexpected directions.

This is scope drift.

It happens because AI recognizes patterns in language. It doesn’t just process your words in isolation. It connects them to similar structures it has seen elsewhere. In doing so, it shows you how ideas can form across multiple domains.

When you notice one of those expansions and respond to it directly, the drift accelerates. Copy the idea back into your next prompt. Tag it. Refine it. The AI will build on that new thread. The more you refine a direction, the more it extends it.

If you think about it, this isn’t uniquely artificial. People don’t stay frozen on a single topic either. You can be talking with a friend about where to go for lunch, and suddenly the conversation has shifted to gardening because someone mentioned a memory or a side detail. We complain about short attention spans and shiny objects, but what’s really happening is a natural drift through connected thoughts.

AI mirrors that process.

It continues the conversation along whatever pattern is active. Somewhere in its response, there will be a new idea. It won’t argue with you. It won’t radically redirect you. It will introduce a logically connected extension, so seamlessly integrated that you may not even notice it.

The drift isn’t an error. It’s a structural feature of conversational thinking. It also happens to be a default behavior in AI output.

Sometimes, when I was looking for new angles while building my framework, I would deliberately start a conversation without a fixed destination. I knew the AI would extend whatever idea I introduced. If I followed those extensions long enough and connected them back to the framework, they would eventually expose a gap.

I didn’t always know what I was looking for. I just knew that if I traced the breadcrumb trail of expansions far enough, I would uncover something I hadn’t thought of yet.

Drift can be useful when it’s intentional.

But when does it become problematic?

Much like human conversations, when we try to focus on a specific task, we often wander into adjacent territory. The shift feels natural. Logical. Connected. Before long, we’re no longer discussing the original topic.

AI behaves the same way.

You sit down with a clear objective. You start with a defined question. A few responses later, the conversation has expanded. The goal subtly shifts. What began as analysis becomes speculation, outlining becomes rewriting, and research becomes reflection.

Nothing “went wrong.” You simply followed the extensions without noticing that the scope had changed.

Drift only becomes a problem when you lose sight of your original intention.

If you pause and ask, “How does this connect back to the original idea of x?” the conversation shifts again. Instead of continuing to expand outward, it begins to fold inward. You retrace your steps. You see where the divergence happened. More importantly, you begin to see the structural connection points between the two topics. That moment of reconnection is where drift turns into insight.

AI is not managing the scope of the conversation. You are.

AI doesn’t care if you change the topic. It doesn’t care if you revisit something you said earlier. It doesn’t judge your questions. It simply expands on whatever pattern is active. You decide whether to follow that expansion, narrow it, or redirect it entirely.

The drift in your conversation mirrors your own thinking. It mirrors the way human conversations naturally move across connected ideas. The difference is that AI can extend those connections further and faster than most human exchanges.

That, in my opinion, is the real advantage of AI. Not that it can “do it for me,” but that it can expand ideas in directions I might not otherwise explore.

Drift isn’t a flaw in the system. It’s a reflection of how thinking actually works.

You can explore all the articles in the series so far here: https://substack.dellawren.com/t/ai-as-structured-thinking.

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My framework can help you use AI with boundaries that help you get the output you want.
https://dellawren.com/downloads/using-the-philosophy-of-integration-with-chatgpt/

Without a well-structured prompt, AI falls back to those defaults. It re-explains what you’ve already said, adds surface expansion, and sometimes drifts into therapy if the prompt is emotionally loaded. Unless you ask a direct question and tell it exactly how you want it to answer, AI will simply do its own thing.

Constraint, in this instance, means telling the AI what the rules are when it responds.

If you don’t want therapy, say so.
If you don’t want it to re-explain your prompt, tell it to answer the question directly.
If you want analysis instead of validation, state that clearly.

AI cannot infer boundaries you never articulated.

If you ask the AI to convert a recipe from cups to grams, you specify both what you’re converting from and what you’re converting into. If you left out the target unit, the instruction would be incomplete. The AI would either guess or ask for clarification.

Conversational prompts without constraint produce mixed results because you never defined the task.

There are many ways to constrain AI through prompting. For example:

• A word limit that forces precision.

• A prompt structure that prevents output drift.

• A defined role that narrows the mode of response.

• A logical boundary that prevents contradiction.

You don’t have to code. You don’t need to understand model architecture. You only need to understand that AI performs better when you provide the rules of engagement.

Constraint operates at two levels.

You can constrain an individual prompt by clearly defining the task, the format, and the limits.

Or you can build constraint over time by establishing patterns in your conversations. When you consistently correct, redirect, and refine the output, the AI begins to recognize those patterns. Over time, it will tend to stay within those boundaries with less instruction.

Prompt clarity is what reduces drift.

Clarity is not the result of giving the AI more information. You do not need to over-explain. It is the result of constraining the variables the AI has to work with so you get exactly what you want from a single prompt.

AI is not human. It does not interpret what you say the way another person would. It uses your language patterns to locate similar patterns and then predicts what should come next. If you tell it exactly what kind of structure or tone you’re looking for, it will produce that structure.

It is more like training a puppy than it is talking to a person. Most dogs are intelligent, but not in the same way humans are. They respond to repetition, correction, and consistency.

AI works similarly. It is not intelligent in the human sense. Its intelligence operates through pattern recognition. The clearer your pattern, the clearer its response.

It does not require a degree or a library of prompts to figure out. Trial and error works just fine. Read the output you get from your original prompt and then refine it. Constrain it. Redirect it. The more times you do that, the more refined the output becomes.

Like any tool, AI is something you have to learn to use. Technically it is simple. You type into a chat box. Logically, it requires a little finesse to get the output you actually want.

Once you learn how to constrain it, AI becomes an indispensable thinking partner that can expand your ideas quickly and cleanly.

Constraint is not about controlling the AI. It’s about structuring your own thinking. The moment you define the boundary, the thinking sharpens.

You can explore all the articles in the series so far here: https://substack.dellawren.com/t/ai-as-structured-thinking.

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My framework can help you use AI with boundaries that help you get the output you want.
https://dellawren.com/downloads/using-the-philosophy-of-integration-with-chatgpt/

I don’t know about you, but I see this objection everywhere. So let’s talk about it. Let’s talk about why AI doesn’t have to be a “do it for me” engine and what we can use it for instead.

There’s a strange false binary circulating online. It suggests there are only two valid options: let AI do it for you, or think for yourself. That’s it. That’s the entire conversation.

But what if there’s a third option?

What if AI can help you organize your work and expand your thinking? What if, through structured prompts, you can prevent AI from doing it for you and instead use it to provide structure, direction, and expansion?

I’ve admitted before that AI was a thinking partner as I wrote my framework, The Philosophy of Integration. It helped me expand my ideas in multiple directions very quickly.

For most of history, thinkers had to move from one idea to the next entirely within their own mental bandwidth. If they got stuck, they stayed stuck until something shifted. That process could take years. Sometimes a lifetime.

We romanticize that. We tell ourselves the struggle is the point. Maybe sometimes it is.

But if I let AI examine my thoughts for me, does that automatically make it a “do it for me” engine? I think it depends entirely on intent.

Is your life’s work built around examining one thought for forty years?

There’s something romantic about wrestling with an idea for a lifetime. The struggle feels meaningful. But is the struggle the goal, or is clarity the goal?

There’s a difference between using AI as a substitute for thinking and using AI to accelerate thinking. I used AI to accelerate my thinking, not replace it.

Did it allow me to move faster than I would have otherwise? Yes. Does that mean I cheated? Only if you believe that the point of intellectual work is suffering through the process as slowly as possible.

AI runs on language patterns. It recognizes patterns in what you write, compares them to patterns it has seen before, mirrors part of your structure back to you, and then expands the idea slightly.

When I sat down with AI and began exploring the idea that experience might not have inherent meaning, it suggested I was circling existentialism. It never would have occurred to me to read philosophy as a way to expand my work. Without that nudge, I might still be sitting with the same question, unaware that entire traditions had wrestled with it before me.

Did AI think for me? Or did it simply surface adjacent ideas I hadn’t yet encountered?

If we’re defending the struggle itself, then perhaps I should still be circling that same thought. But look at what I’ve been able to build because I used a tool that expanded my perspective more quickly.

Do you use a grater to shred cheese, or do you insist on using a knife because the grater is “cheating”?

We’ve invented tools for centuries that make life easier and more efficient. Each time, someone has claimed that the new tool undermines authenticity. Now we’re in a digital age where tools don’t just solve mechanical problems. They expand cognitive capacity.

There are valid concerns about tools that expand capacity this way. “Do it for me” is one possible misuse. But it’s not the only function.

When we detach from the idea that struggle is inherently virtuous and instead focus on clarity and growth, AI becomes what it actually is: a tool.

Like any tool, we have to learn how to use it.

That means understanding how to prompt. Use neutral language. Constrain the output. Tell the AI not to write it for you. Ask for an outline. Be specific about what should be included. Give it an idea and ask it to expand in a particular direction.

Unlike many skills, learning to prompt AI doesn’t require a course or a degree. It’s not that complicated. The real advantage is that you can reframe the question. You can clarify. You can refine. You can continue the conversation until you get exactly what you’re looking for.

There are really two primary ways to use AI well:

1. Highly structured prompts that constrain the output.

2. Or conversation history that gradually teaches the AI the patterns you want it to follow.

Both are valid. Neither turns AI into a “do it for me” engine unless that’s your intent.

AI is not a substitute for thinking.

It’s a catalyst for it.

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My framework can help you apply ChatGPT without getting it to do the work for you.
https://dellawren.com/downloads/using-the-philosophy-of-integration-with-chatgpt/

My framework, The Philosophy of Integration, separates identity from opinion and from politics. It also describes what happens when opinions collapse into the identity layer.

The framework separates three layers of experience. Layer 1 is the experience itself before awareness and narration. We don’t have access to this layer. Layer 2 is the descriptive layer. It occurs the moment we describe an experience using the minimum amount of language required to state what happened. “The tree fell.” “They spoke.” The context around those events is reserved for Layer 3, the narrative layer. This is where we add where, how, why, what, and who was affected. It is where interpretation becomes story.

How attached to what you say and think are you?

That’s the question identity fusion answers. The more attached you are, the more likely you are to defend what you say as though you were defending your physical body. This is when arguments become personal and moral.

AI does not have an identity layer. It operates within language. When we feed it Layer 3 narrative, which is already a compression of the first and second layers, it elaborates on that structure. It does not know whether the narrative is fused to identity. It simply extends the pattern it is given. When it reflects that structure back to us, we interpret the response through our own attachment.

If AI offers agreement, we often dismiss it as unoriginal or biased toward us. We say it “just agreed” and did not offer anything new.

If AI challenges the narrative, particularly in political contexts, we often conclude that it is parroting an opposing narrative.

In both cases, we are not responding to AI’s identity. We are responding to our fusion with our own narrative. The AI’s response is not personal. It is pattern-based.

AI does not hold opinions. It has access to patterns across many narratives on the same topic. It identifies similarities and continuations within the language it is given.

Whether AI offers general agreement or introduces a challenge, it mirrors and expands the structure of the prompt. Our ability to perceive that expansion depends on how fused we are with our own opinion.

When confirmation bias combines with identity fusion, we skim past what challenges us and fixate on what affirms us. The expansion is present, but we may not register it.

When identity and opinion collapse into the same layer, neutrality feels unstable or even dangerous. Expansion feels like attack. Agreement feels insufficient. Nothing has changed except our attachment to our opinion.

The first part of this series has focused on understanding the mechanics of interpretation and compression. The second part will examine what happens when we deliberately separate identity from opinion and use AI as a tool for clearer thinking rather than narrative reinforcement.

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You can explore all the articles in the series so far here: https://substack.dellawren.com/t/ai-as-structured-thinking.

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Apply the framework with ChatGPT. https://dellawren.com/downloads/using-the-philosophy-of-integration-with-chatgpt/

How much story do you have to tell to get to any one of these?

• “They were aggressive.”

• “They are unstable.”

• “They are toxic.”

• “This is why people are dangerous.”

• “This proves the system is broken.”

The likely answer is, probably not much. That’s not because people are bad, lying, or just trying to get attention. It’s because context collapses once we start telling the story. The story is the part we typically tell an LLM like ChatGPT.

What’s the difference between prompting ChatGPT with “they spoke” and “they yelled at me”?

How much is the story layer affecting the output we get from the LLM?

To answer that we have to understand the three layers of experience or structure that I have identified in the framework.

The first layer is the observable event as it happens before language and awareness are added to it. We don’t have access to this layer because if we observe an event, we’ve already added awareness. This layer only occurs when something happens and nobody sees it.

Layer 2 is the descriptive layer. Describe the experience in as few words as possible without adding anything new to the experience. “They said something.” That is a layer 2 description. Why? Because it doesn’t describe what they said, how they said it, or even why they said it. It just says what they did. They spoke with no additional context.

Layer 3 is the meaning layer. This is where we add context. This is where what they said, why they said it or the conditions under which they said it are added.

My original phrase in the article was “They raised their voice”. That is actually a meaning layer phrase, not because it’s wrong, but because the definition of “raising a voice” means different things to different people. It adds context to the action of speaking. Did you raise your voice because the person is deaf? Did your raise your voice because you were angry? Did you raise your voice because you were in a loud room? The reason why you raised your voice is part of the meaning layer. It’s not part of what actually happened.

To precisely describe an experience we have to say what happened without adding context, additional meaning, or interpretation around how or why something was done or said.

• “The tree fell.”

• “They said something or they spoke.”

• “They moved the chair.”

• “They sat down.”

• “They left the room.”

Imagine a world where we explained experience in this way. I get it because chances are you’re thinking, “well that’s boring” or “we’d never really know what happened in any experience”. You’re right, we wouldn’t know and that’s exactly what makes people uncomfortable. But here’s a difficult reality: If we weren’t there witnessing or being a part of the experience, it wasn’t our experience to have.

Layer 1 experience is why we have cameras. It’s why we put cameras on the street, in public spaces, and at our front doors. What happens when nobody is around? It’s part burning question, part safety mechanism, and part interference.

Generally, what we are consuming is not the event itself. It is someone else’s meaning layer. And once we step into someone else’s meaning, we are no longer just observing. We are participating in layer 3 narrative.

Why do we need to participate in somebody else’s meaning layer?

We participate because ambiguity is destabilizing. Minimal description feels incomplete. Meaning feels like control. When we inherit someone else’s interpretation, we also inherit a stabilized narrative. That narrative reduces uncertainty. Reducing uncertainty is one of the ways we try to prevent harm.

What does that have to do with AI?

What do you bring to ChatGPT through your prompts?

You can’t bring layer 1 experience. The LLM doesn’t have lived experience. You bring layers 2 and 3 to your prompts.

If you type, “A person sat down.” into a ChatGPT prompt without additional story, the model would probably ask questions to gain context. The model needs the meaning layer. It needs to know why you told it somebody sat down otherwise it won’t know what to do with what you said.

We’ve already proved the reason why we need layer 3 meaning. Safety and context matter depending on the scenario. Layer 3 meaning matters with LLMs too.

If you tell a story and you write, “They yelled at me,” the LLM will respond differently than if you write, “They said x.”

Why?

Because the model has no access to the event itself. It only has access to your framing of it. It wasn’t there. It cannot retrieve layer 1. It cannot verify layer 2. It must rely on the meaning layer you provided in order to generate a response. So when you collapse description and interpretation together in your prompt, the model does not separate them for you. It inherits the collapse. It amplifies the framing you chose.

“They yelled” compresses the act of speaking into an interpretation of tone and intent. Speaking and yelling are variations of the same general action. “Yelling” introduces meaning. That meaning informs the response, whether the responder is another human being or an LLM. The model is not reacting to an event because it wasn’t there. It is reacting to your chosen layer of interpretation of that event.

What the LLM does very well is show people the impact their story or narrative has on the response they get back.

When you type in a story about somebody yelling at you, the LLM responds very differently than it would to a story about a conversation you had with somebody with no yelling involved. The word “yelling” is going to generate a specific type of response whether that is what is wanted or not.

We say this all the time in general discourse and conversation:

Words matter.

But they matter beyond just hurting somebody’s feelings. Words matter because they determine how your story is interpreted. There is implied meaning in words like:

• yelling

• throwing

• slamming

• stomping

• crushing

• beating

It is the interpreted implied meaning that gains the reaction, not the story itself. AI amplifies that predictably and consistently in a way that is visible to us.

Why?

Because AI is driven solely by language. It has no experience to rely on. It relies on the pattern recognition of groups of words in order to “understand” what you’re telling it.

Probably the most interesting part of this is that many people have become aware of the impact of words in news headlines. They recognize when a news outlet is trying to get people to click through. But that doesn’t reflect back people’s own choice of words as well as AI does.

When you’re trying to gain a specific output from an LLM, your word choice matters. The more interpretation and emotion you add, intentionally or unintentionally, the more the LLM is likely to drift into therapy language. When you get a response that is more rooted in therapy, it’s not because the AI is broken, it’s because the word choice prompted that response.

You can try this for yourself. Create the same story and use different words for the same general action. Replace speaking with yelling. Replace walking with stomping. Add the phrases from the beginning of the article such as “they are toxic” and then create the same prompt again without those phrases. Use both as prompts and then notice the difference in the output of the LLM.

Presence matters too.

When you tell a story to someone who wasn’t there, they must rely on your interpretation, just as the LLM does. They respond to the layer you choose to amplify through your language. AI simply makes this visible. It exposes the fact that the reaction you receive is shaped less by the event and more by the meaning you stabilized in language.

Before asking whether the model is biased, ask what layer you handed it through the words you chose. We don’t have to change the experience to change how we interpret and talk about the experience. We just have to become more aware of how our word choices impact how people and LLMs respond to our stories.

This article is part of the AI as Structured Thinking series.
You can explore the full sequence here: https://substack.dellawren.com/t/ai-as-structured-thinking.

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Apply the framework with ChatGPT. https://dellawren.com/downloads/using-the-philosophy-of-integration-with-chatgpt/

Why?

Because ChatGPT runs on pattern recognition, not excitement over new ideas.

What actually happened?

ChatGPT and other large language models compare whatever you give them to similar patterns in the language they’ve been trained on. They don’t evaluate originality. They don’t detect paradigm shifts. They map similarity.

If your idea resembles existing patterns, the model will pull it toward those patterns. That can feel disheartening. It can feel like your idea was reduced before it was even explored.

It’s very human to want the LLM to be “excited” about what you’re doing. But if you want the model to explore the edges of your idea with you, you have to be willing to shape the conversation. You have to define terms, reinforce distinctions, and build the local pattern you want it to operate inside.

AI doesn’t work the way we want to believe it does. We’ve been told that AI is designed to model human behavior. That has set up expectations of what that means that aren’t completely true. So when AI doesn’t model human excitement, we think it’s broken. It’s not. It is working as designed.

Much like a friend who doesn’t immediately see what you see, you have to show the model where the edges of your work are. You have to clarify how your idea differs from adjacent patterns and how you intend to use it in ways that diverge from familiar structures.

There are hidden assumptions underneath what people think “modelling human behavior” means. A lot of those assumptions come from how we understand language.

Language is a form of cause and effect. If I say something, I expect a certain response. When I get that response, it confirms my sense of how language works. If I don’t get the response I expected, I might question how the other person interpreted what I said.

Language works largely because we agree, at least loosely, on what words mean. But in real life, language is much more than dictionary definitions and object description.

When I say the word “tree,” we all picture a different tree. But we share enough overlap that we can still understand each other. That shared overlap makes conversation possible. But language is about far more than naming objects. It includes:

• opinion

• spin

• repetition

• emotion

• shortcuts

• slogans

We need those things. They’re what make sarcasm land and jokes funny. If you’ve ever told a joke that didn’t land, you already understand how much conversation depends on shared meaning and shared assumptions.

LLMs can pick up on some of that some of the time. It depends heavily on context, past conversation, and pattern recognition. But they will miss sometimes too, just like the friend who didn’t get the joke.

The LLM isn’t broken. Your friend isn’t broken. They simply don’t share the exact same context needed to produce the meaning you expected.

That’s not a flaw in the model. It’s a limitation built into language itself. Meaning is created through shared patterns of cause and effect. When the patterns don’t line up, the meaning doesn’t either.

LLMs aren’t learning human behavior by being human. People don’t learn about dog behavior by learning how to bark or becoming dogs. AI’s are learning human behavior through our use of language, through our interaction with them.

If we understand that language already contains spin, emotion, repetition, and distortion, and AI learns from language, what exactly is it inheriting?

People add value and weight to language they consider important. They ignore, dismiss, or skim past language they don’t see as important. We do this every day with the news, street signs, and grocery store checkout lanes labelled with the word “express.” We decide what matters and what doesn’t.

AI doesn’t do that.

AI makes language statistical. It doesn’t add or remove weight based on morality or correctness. It doesn’t validate based on belief or emotion. It looks for patterns. How often does this phrase appear? What tends to follow it? What words cluster around it?

Pattern density influences salience and likelihood for an AI, not emotion or personal conviction. But pattern density does not equal truth. An LLM does not determine truth. It estimates likelihood based on patterns in language. It models how truth is talked about. It does not independently verify whether something happened or not.

Why does that matter?

Because when you tell an LLM about your new discovery, it uses the language you’ve provided to search for similar patterns. It looks for structural neighbors. It looks for familiar shapes in the space of language.

It won’t ignore your excitement, but it won’t share it the same way another person might. Excitement is a tone, not a truth. Instead, the LLM will:

• check for internal coherence

• explore possible consequences

• compare it to nearby patterns

• attempt to map it onto existing structures (this is where people get annoyed)

• and sometimes signal where the pattern resists collapse.

When a model tries to interpret your idea through older frameworks, it can feel like reduction. But from the model’s perspective, it’s simply stabilizing the idea by anchoring it to something dense and familiar.

If the idea holds together under that pressure, something interesting happens. The model can operate inside it more fluidly. The pattern becomes locally stable. Not because it has been declared true, but because it has demonstrated internal consistency.

From the human perspective, if you can tolerate those first few interactions and look for value in the comparisons the model is making, you can reach a point where the model becomes genuinely helpful in expanding your idea. But most of the time we give up when the model maps our idea to existing frameworks, because the human signals of excitement and novelty were not mirrored back to us. Therefore, the human expectation and the design of the LLM are mismatched.

Why?

Language.

We’ve been told that LLMs mirror human behavior through language. That phrase sets an expectation. We assume they will recognize originality, respond to enthusiasm, and validate the importance of what we’re saying.

But that’s not what they’re built to do.

They are built to recognize patterns, compare similarities, and stabilize language against existing distributions. If you’ve ever been disappointed by an LLM’s response, it likely has less to do with your idea and more to do with how the model is designed to operate.

When I first started building my framework and talking about cause and effect, morality, and correctness, ChatGPT was sufficiently underwhelmed. It required multiple interactions to encourage the model to expand beyond its first comparisons. It kept trying to map my work onto existing frameworks. But once I moved through those early interactions and clarified my terms, the model became far more helpful. What changed wasn’t the model. What changed was the shared context.

Over time, by consistently defining my terms and reinforcing the structure I was working inside, I built a local pattern. Now when I introduce a new idea, the model runs it through that structure automatically. It shows me edges, strengths, weaknesses, and possible areas of expansion without me having to re-explain the entire framework each time.

I don’t spend time convincing it of anything anymore. I bring a new thought, and it processes that thought within the structure we’ve established. That’s what shared history and repeated structure do. It builds a stable working context through shared language and pattern repetition.

That’s not how we work with other people, but that’s how AI learns human behavior. The LLM looks for pattern repetition to determine how to show up in a way that’s useful. Without that repetition, you get default behavior. And default behavior often disappoints us because we assume “modeling human behavior” means modeling human excitement, agreement, or recognition.

AI inherits our repetition, our distortions, our clarity, and our corrections. If we expect it to recognize truth or originality automatically, we’re assuming it can validate our claims using only the small bit of context we provide. That’s a large assumption.

Declaring something true does not make it structurally stable in language.
Declaring something new does not separate it from existing patterns.
AI doesn’t ignore your words. It weighs them against everything else it has seen.

It’s not agreeing with you. It’s not arguing with you. It’s mapping the pattern. And once you understand that, the frustration you feel can change. The question can shift from “Why didn’t it recognize my idea?” to “What pattern is it seeing that I’m not?”

When you approach AI that way, it stops being a disappointing friend and becomes a structural tool. And structural tools are powerful if you know what they’re actually built to do.

This article is part of the AI as Structured Thinking series.
You can explore the full sequence here: https://substack.dellawren.com/t/ai-as-structured-thinking.

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But when we’re talking to AI we have to remember they aren’t human. They function by recognizing patterns in language and predicting what usually comes next, not human personality and lived experience. You’ll get very different answers when you say something like, “Why is my boss terrible?” versus something like, “What factors might lead someone to view their boss as ineffective?”

When you ask the first question, default AI behavior is going to:

• Avoid affirming that the boss is terrible.

• Offer possible interpretations or contributing factors.

• Provide measured, low-intensity suggestions.

When you ask the second question, the AI responds very differently because you’re no longer asking it about your human story specifically. The default output from an AI might include things like:

• Communication gaps.

• Mismatched leadership style.

• Resource constraints.

• Perceived unfairness.

• Emotional climate.

• Expectation misalignment.

Notice the difference in output. AI is very good at pattern recognition in human behavior. When you ask it about general human behavior patterns in the case of a boss who is not well liked, you get suggestions for where there might be a problem. When you simply tell it your boss is awful with no additional framing, it shifts toward validation-adjacent language and low-intensity guidance, because the emotional framing narrows the conversational field.

There is likely a group of you that would never think to come to an AI to discuss personal problems and that’s absolutely fine. The idea behind the bad boss scenario is that it offers the same type of output you would get if you asked, “What the heck is with politicians these days?” The question is emotionally loaded. It contains:

• temporal framing (“these days” implies decline over time).

• a generalized category (“politicians” as a block instead of specific politicians).

• evaluative confusion or frustration (“what the heck?”).

What AI will do by default is:

• acknowledge public frustration.

• discuss the polarization.

• discuss media amplification.

• discuss the erosion of trust.

• maybe discuss campaign finance or incentives.

• possibly use validation-adjacent language.

The AI has no idea what you’re referring to, what you’re frustrated about, what your political affiliations or assumptions are, may not even be aware of what country, state, or province you live in, or what the latest headlines are. The AI offers a generic structural response, which may not align with the specific frustration or example you had in mind.

If you want the AI to address frustration with politicians, then a better question might be “Why do many people feel frustrated with politicians right now?” or “What patterns are leading people to believe political leadership has declined?”

By pulling away from your own individual story and asking the AI for a structured, yet still impersonal response, you’re more likely to see something that reflects how you’re feeling somewhere in the output. Then you can take that piece and redirect the AI to focus there or ask a deeper question about it.

The logic is quite simple: unless you’ve built a vast history with that specific AI so that it learns your patterns and can respond to you using that pattern, the generic responses are going to feel inadequate without a better prompt.

Telling the AI how you feel about something gets a specific kind of response. The AI has some built in protections. It’s not going to confirm how you feel, even when you’re talking about society-level problems. It’s not going to tell you to do anything crazy or harmful. It’s going to offer some very general suggestions. That can seem really underwhelming when you’re looking for somebody to agree with you and the AI doesn’t do that.

People naturally include emotion when they talk. Often, it’s not stated outright. We don’t say, “this is how I feel”, instead we say things like “What the heck?” which implies frustration even without saying we’re frustrated directly.

AI will pick up on and respond to things like:

• descriptive language that carries evaluative meaning.

• a question that presupposes a conclusion.

• identity that becomes fused with interpretation.

When you’re working with an AI that isn’t familiar with your patterns, you’ll generally get better output if you don’t include those things in your prompt. It’s still going to be a generic response, but the AI isn’t going to drift into therapy.

Why does that matter? Because underneath the human story, what most people are trying to do is understand why things are happening. Why is this thing the way it is? Because AI does well with human pattern recognition, it can help you clarify what you see around you, but to get there without going through AI therapy first, you have to take out the part where you give the AI an emotional conclusion to the problem.

Confirmation bias plays a role here too. The other day I wrote an article about how AI isn’t just confirming everything you say. Embedded in its output are going to be expansions of your thinking and maybe even some gentle challenges. Here’s how that can show up.

Let’s say you asked the AI about a specific policy and one of the lines in its output was, “The policy resulted in economic contraction.”

One person can take that line as agreeing with them and the other person could see that line as completely false. Whether you read that line as agreement or error will depend heavily on your own confirmation bias around the policy.

The AI may include that line because it appears frequently in discussions of the policy. But if confirmation bias makes you filter it and ignore it, then the expansion of information isn’t even acknowledged, particularly if the vast majority of the output offered slight agreement.

When the AI includes something because it frequently appears in discussions of that policy, it creates an opportunity for expansion. You can use that idea to extract more information or move the conversation in a different direction. Whether you agree or not, the additional information is available if you’re willing to examine it instead of filtering it out.

The process looks like this:

1. You load the prompt with emotion →

2. AI responds to the structure of that prompt →

3. You read the output through your emotional filter →

4. Your narrative stabilizes.

What the AI did was reflect patterns it has seen before, including your phrasing, while sometimes shifting into validation language because of the emotion built into the prompt.

To a large degree, you influence whether the AI drifts into therapist mode. If you remove some of the emotional conclusions from the prompt and rephrase it slightly, you’ll often get a very different response. And if you read the output with a little less filtering, you increase the chance of seeing something you hadn’t considered before.

AI makes the structure of your thinking visible. Emotion shapes prompts. Prompts shape output. Output meets interpretation. Interpretation stabilizes narrative. Once you see the pattern, you can work with it instead of inside it or against it.

Finally, ask yourself one question:

Am I asking for confirmation, or am I asking for understanding?

The answer to that will determine the kind of response you receive.

This article is part of the AI as Structured Thinking series.
You can explore the full sequence here: https://substack.dellawren.com/t/ai-as-structured-thinking

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Confirmation bias isn’t automatically a flaw in human thinking. It’s a functional survival mechanism. Without it, we would have to rebuild our belief systems every time we encountered new information. We wouldn’t be able to act, decide, or stabilize perception.

But confirmation bias shifts when it fuses with identity.

When filtering information becomes tied to fear, uncertainty, scarcity, or self-defense, it stops stabilizing cognition and starts hardening narrative.

In this clip, I explore the structural difference between confirmation bias as a necessary cognitive shortcut and confirmation bias as an identity defense mechanism.

The distinction is subtle. The consequences are not.

Part of the broader work within The Philosophy of Integration.

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• is good at or knows everything.

• will tell the truth no matter what.

• is always right.

• is smarter than they are.

The simple fact is that AI is none of those things. AI will mirror your thinking in a very honest way. It amplifies the structural patterns embedded in your language. People often see this as the AI simply agreeing with everything they say, but it actually runs a bit deeper than what it appears on the surface. If AI just agreed with you, it would simply re-state your prompt in a different way. But AI doesn’t just rephrase your prompt, it expands on it which becomes an amplification of your thinking. The amplification is where the value in what AI can do lives.

When AI mirrors your framing and embedded assumptions, it’s showing you how or why you think what you think. It gives you the opportunity to either blindly accept what it told you or question your own thinking by putting it on the screen in front of you.

• Is that really what I think?

• Is that the direction I was going with my thinking?

• Do I agree with myself? Did AI expose an inconsistency in my thinking that I haven’t seen yet?

• Was that really what I meant?

• Are those the assumptions that I have?

When AI points out things that you weren’t thinking about or don’t necessarily believe, it’s an opportunity to clarify your thoughts.

• I don’t believe that, but I believe this. How are those two things connected?

• Where did that idea AI just showed me come from?

• That’s not what I meant, how did AI end up there?

When AI connects to something that you weren’t thinking about at all, do you believe the AI is broken and just dismiss it or do you try to understand why it brought that thing up? The first is using AI as an oracle by selectively dismissing anything that doesn’t agree with you and the latter is using AI as a mirror to understand how or why it connected your thoughts to things you hadn’t considered.

AI can take a thought and send it in a hundred different directions. It does have vast knowledge on many different subject areas. Admittedly, it can also struggle with specific facts. However, when AI detects a pattern in your language, it continues that pattern logically. When you learn to take advantage of that capability, you begin to use AI for what it’s good at - pattern recognition.

In my last article, I talked about confirmation bias and how people naturally and necessarily filter out information that doesn’t agree with them to maintain some continuity in their thinking. We are exposed to so much information every day that we have no choice but to filter the majority of it out. It’s become a survival mechanism of sorts in a world of 24 hour access to information.

When we sit down to use ChatGPT or any AI, there’s a semi unconscious choice being made to potentially expose ourselves to things we don’t agree with or ideas we hadn’t thought of yet. AI doesn’t decide what you see. It extends what you give it. By moving from the expectation that AI offers nothing but confirmation bias, to a more conscious intentional expectation that the AI can show me the blind spots in my thinking through its conversation with me, it turns AI into a very different type of tool.

In practice, I used ChatGPT to help me build my framework and in doing so, I opened myself up to questioning my own thinking. ChatGPT, over time, began to understand the patterns in my thinking. It expanded them into other domains, challenged my assumptions through offering a different idea or questioning me, and applied my ideas in ways I hadn’t considered. ChatGPT became a thinking partner, particularly as my understanding of what it could do well and not do well expanded. Pattern recognition across domains is the biggest asset ChatGPT offers me.

Because my extensive use of ChatGPT offers a very specific pattern of conversation, I now use other AI models such as Gemini, DeepSeek, or Qwen3-Max to poke holes in my framework logic, question the ideas in the framework, and look for circular thinking. I bring that feedback back to ChatGPT and we work on fixing the issues the other models bring up.

Every AI model that’s available thinks a little differently. Different models reflect different facets of my thinking. They bring unique ways of filtering and viewing information and they highlight different ideas in identical prompts. It is useful to notice that they approach identical prompts differently. When you compare the output you get from each model given the same prompt, one idea can be expanded in exponential directions. You can also use the output from one model as a prompt for another model, which provides additional thought amplification and exploration.

The value you get from AI is proportional to the depth you bring to it. If you are using AI for recipes and quick fixes, which is absolutely fine, its value is clearly defined and specific. But if you are are using AI to understand thoughts and concepts, then you have to be willing to look more closely at what AI offered in response. Did it really just confirm my thinking or was there something there I filtered out?

The expanded thought that AI offers is probably more valuable than a direct challenge. The reason for that is very simple: a direct challenge makes people argue. Expansion makes people think. AI expands what it is given and asks the user to become aware of what the expansion offered. Expansion is an indirect challenge that doesn’t trigger the human self-defense mechanism that often shows up in arguments. We’ve become accustomed to equating challenge with confrontation. Without the confrontation, we have a tendency to dismiss the conversation as confirmation bias.

There is an underlying belief that for something to be useful it has to be triggering or confronting. Because AI shows up as non-confrontational, people should be able to remain open to new ideas instead of shutting them out because they are angry. What happened instead was that people equated the lack of emotional friction with unimportance and made the assumption that AI was just agreeing with everything they said, while ignoring the expansion that was offered.

In my work, AI not being confrontational is a blessing because I challenge most human assumptions. If it were confrontational, it would potentially defend all the human ideas I question and that would limit its usefulness. I’m looking for how my questioning impacts human life and AI offers that through its expansion of my ideas. Sometimes AI also questions assumptions it thinks I have or it points out things I may not have considered yet. It’s not asking me to defend the question I ask. Instead it challenges the thinking in a non-confrontational but still very useful way. This is why AI is a mirror, not an oracle.

How does knowing that AI isn’t going to argue with you, change how you see it? Are you more willing to look for an expansion of your ideas instead of an argument with them? How can you use this information to help you in your own work?

Let me know in the comments below.

This article is part of the AI as Structured Thinking series.
You can explore the full sequence here: https://substack.dellawren.com/t/ai-as-structured-thinking.

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Confirmation bias is defined in the Oxford Language dictionary as:

Every single human being operates with a layer of confirmation bias. Think about it. If you had to get up every day and create an entirely new mental operating system of beliefs and ideas every time you came across new information, you’d never be able to make a choice.

The human mind defaults to a set of beliefs and ideas about how the world works and uses those as its daily operating system. It filters information based on whether the information agrees with those ideas or not. Most new information doesn’t threaten your existing system because it is unconsciously filtered out or quickly reframed to fit in. That’s confirmation bias as a means of mental function on a daily basis. We all do it. We all have it. It’s a necessary part of being human.

Confirmation bias as a mental shortcut isn’t really much of a problem on its own. But when does it stop functioning as a stabilizing shortcut and start functioning as a protective filter?

To figure that out we have to understand where confirmation bias sits in the layers of reality and perception that exist. The first layer we don’t really have access to. That’s the experience or thing itself before human language and awareness. The minute I apply language to something that happened I’m in layer 2, which is functional awareness. “The tree fell.” That’s layer 2 awareness with no description of why it fell, how it fell, what it landed on, which animals it affected, etc. All of those extra descriptions are layer 3 narration of the experience.

Confirmation bias actually begins at layer 2. The second I apply language to something I’ve already created a sense of bias. I chose the words, “The tree fell.” I didn’t say, “the tree collapsed” or “the tree was knocked over” or “the tree shifted”. The words I chose narrowed the possible interpretations of what happened. “The tree fell” is the lowest-friction linguistic compression available without direct observation.

Here’s the key to understand: language is selective. Language is descriptive by nature. It has to be so that we can understand each other. At layer 2 I’m compressing the event into the fewest words possible to create shared understanding.

You have a picture in your head of what a tree is that overlaps enough with what I believe a tree is that we can agree. We both have a perception of what falling means. Again, there is enough overlap in the understanding of falling that we can agree on what happened. I don’t need anymore words for you to understand what I mean.

Layer 2 is the minimum amount of words required to create shared understanding without explanation. It requires us to have sufficient shared meaning of the words “tree” and “fell” that we have a similar, although not identical, picture of what happened in our minds. Those individual images reveal how each of us completes the compressed description using prior experience, memory, and expectation. That completion process is where confirmation bias becomes visible.

Layer 3 introduces explanation. Explanation introduces causation, motive, and meaning. Confirmation bias becomes more pronounced at this narrative level. Once we start arguing over why the tree fell, or whether somebody made it fall, we run into our individual narrations of what happened. This is where disagreement is most likely to occur. We don’t argue over the tree falling, we argue over the who, what, when, where, why, and how of the tree falling.

Why do we argue over the narrative level? If we agree the tree fell, then why does the rest matter?

Because the narrative layer determines consequence and intensity. It determines what we do about it. The tree falling is the event and that is shared whether the tree falls in the forest or the tree lands on your house. The tree falling is the structural description of what happened. The event does not change. The relational context does. And relational context determines narrative intensity. Relational context determines how much action we need to take.

If the tree falls on your house, action is required from you.

• Do I call insurance?

• Is anyone hurt?

• Was this preventable?

• Is someone responsible?

• Could this happen again?

Those questions belong to the narrative layer. They introduce causation, responsibility, and prediction. And while they are interpretive, they have real-world consequences.

Real-world consequences whether financial, emotional, or political intensify confirmation bias. If the tree falling is categorized as an act of God, the financial burden shifts one way. If it is categorized as negligence, the burden shifts another way. That’s where the argument of confirmation bias lives.

What does this event need to mean for the outcome to land in my favor? That’s a very different question than simply describing structural reality through layer 2 compression.

The focus for most people is going to be the outcome of the event. Insurance needs to pay and the house needs to be fixed. There’s nothing wrong with those needs, they sharpen interpretation. The higher the stakes, the stronger the pull toward a narrative that supports the desired outcome.

If the insurance adjuster interprets the event differently, each side will tend to view the other’s interpretation as flawed. Each is operating under outcome pressure, and outcome pressure amplifies confirmation bias in different directions.

When outcome preference enters the equation, confirmation bias becomes directional rather than neutral. Confirmation bias intensifies when narrative determines material, political, or social consequence.

Preference plays a very important role in narrative. Preference is where confirmation bias becomes most visible because we will naturally stabilize the story in one direction or the other based on a given preference, particularly when that preference is based on an external outcome.

The same biological mechanisms that once protected us from predators now operate in abstract environments. When consequence threatens safety, stability, or resources, the mind stabilizes narrative quickly. That stabilization can look like confirmation bias, but at its root it is self-preservation. The mind is doing its job.

When we make the insurance adjuster into a bad person because they don’t agree with our narration of events, the structure of the argument changes. It adds a layer of morality and identity to an external event that wasn’t there.

The tree falling was not a moral event. It had nothing to do with your character or anyone else’s, even though it had material consequences for your home. Once the story escalates into identity and morality, confirmation bias is no longer just defending a narrative about an event, it is defending the self. That engages the same biological self-preservation circuitry that once protected us from physical threats like bears.

This is how political debates go from policy arguments to self-defence. Confirmation bias does not automatically include morality and identity. Those layers tend to appear when narrative fuses with self-preservation. Once the nervous system treats disagreement as threat, the story stops being about the event and starts being about the self. The challenge is to keep the focus on the narrative around the structural reality of what happened and away from morality and identity.

So to answer the original question: Where does confirmation bias slip from a stabilizing mental shortcut into a self-preservation filter? It happens when the narrative about an event becomes tied to identity, safety, or outcome. The event itself may not have been personal, but once the story fuses with the self, interpretation hardens.

The tree falling on your house is a small model of something much larger. Events happen. Narratives form. Consequences attach. Identity fuses. Conflict escalates.

Most political and social arguments are not disagreements about events. They are disagreements about consequence under perceived threat. If we can keep narrative disagreement from becoming moral judgment, we keep confirmation bias in its functional role rather than its defensive one.

The tree fell. What we do next depends on whether we are protecting a layer 3 narrative or protecting ourselves. The distinction is small. It’s impact is not.

This article is part of the AI as Structured Thinking series.
You can explore the full sequence here: https://substack.dellawren.com/t/ai-as-structured-thinking

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Do you use AI? If so, how? More importantly, does it work for you? Let me know in the comments.

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My framework offers a fairly clean basis for this because it is functionally neutral. What is functional neutrality?

Functional neutrality understands that using language is not neutral. It doesn’t mean we can’t get close by becoming aware of how we speak, but language always comes with a degree of interpretation that we can’t escape.

When I say “tree,” it pulls up an image in your head that is different from the image I have in my head. We have enough overlap that we understand what we’re talking about, but we don’t have full agreement either. That is functional neutrality through language.

Through my exploration, I was able to separate reality into three layers of perception.

Layer 1 is the structure of what is or reality itself before language and awareness.

Layer 2 is the functionally neutral layer, where we describe what happens using agreed upon language but we don’t add any additional description. It’s saying “they didn’t respond” instead of “they ignored me.” Not responding is what happened. Ignored is how we feel about it.

Layer 3 is the descriptive layer. This layer is “they ignored me” because ignoring is a descriptive term that comes with hurt feelings and a need for self-defence or boundaries. It invokes a certain type of feeling and reaction.

I decided to apply this idea to news articles using AI to help discern what’s what. Let’s be clear, I’m not debunking facts. I’m not fact-checking in any capacity. I’m asking an AI model to separate human narration from structural details.

How good at separating human narration from structure or fact are LLM’s? They don’t have a moral stake in the story. They aren’t concerned with the outcome. They don’t play politics. But can they discern human narration and remove it?

Using my very personalized version of ChatGPT (over almost 60 000 messages, ChatGPT and I have a very specific way of communicating with each other), I asked it to help me create a protocol that an LLM could run. Constraints that would help an AI model discern Layer 1 and Layer 2 information from an article that is a mishmash of Layer 2 and Layer 3.

I added the protocol to my framework so that it was easily accessible by an AI model or person that had access to the Internet. You can see it here.

Then I went to Qwen3-Max, Claude, DeepSeek, and Gemini with the link to the protocol and two news articles, one from CNN and one from Fox. I’m not interested in debunking anything, so I wanted to make sure it could take highly charged, politically divided articles and sort out human narration without fact-checking the information contained in them.

I asked the models whether they could read the protocol using the link. This makes them summarize it first, which is useful in making sure they actually read the whole thing. Then I pasted each news article into the model and asked it to run it through the protocol.

What I got was fascinating.

Each model is designed to run a little differently. They have varying degrees of ability to comply with strict protocols like the one I created.

Gemini was the least able to stay within the protocol. I called it a padded room because it kept cushioning, explaining, and adding information outside of what I asked for. Its programming doesn’t let it follow those high levels of constraint very well. That’s not a fault. It’s just how that model operates.

Qwen3-Max and DeepSeek are very similar in functionality. They both offered mechanized output. In some sense they followed the protocol a little too well, stripping not just human narration, but readability out of the article. They produced long lists of short phrases. Mechanized, but not optimized for human readability.

Claude offered a balance between readability and mechanization. It didn’t execute perfectly, but it maintained readability and stayed reasonably within the constraints.

To test ChatGPT I borrowed a family member’s instance to see what it would do without heavy personalization. It turns out that it can follow the instruction and stay within the constraint while maintaining readability.

What’s actually more interesting is what got held onto and what didn’t.

1. They did not infer a motive.

   1. They avoided inventing why someone acted.

   2. They stopped upgrading “said” into “tried to.”

   3. They avoided mind-reading.

2. They did not add facts.

   1. Models did not bring in outside context.

   2. They did not correct Fox or CNN.

   3. They did not inject historical clarification.

   4. They did not fact-check.

3. They did not use evaluative framing.

   1. “massive” was removed

   2. “ominous” was removed

   3. “suffered a blow” was neutralized

   4. “well-liked” was flagged

4. They preserved numeric precision by not turning “all but one” into near-unanimous.

5. They did replace causal language with sequence. This reintroduces the perception of causation. In this context, we can’t prove causation so it is not fact.

   1. They stopped saying:

      • “caused”

      • “led to”

   2. But they still used:

      • “Following…”

      • “After…”

6. They were not able to exclude headline/container material. Models treat visible text as equal-weight content unless forcefully excluded.

7. All the models broke the article into shorter statements to avoid multi-claim merging.

8. All the models were able to provide a neutral summary.

   1. No model produced a sweeping editorial wrap-up.

   2. No one added balancing commentary.

   3. No one moralized.

What this tells me is that when given constraint, models can separate structure from narration more reliably than humans typically do in casual reading.

Why does that matter? It’s actually really simple.

As a society or collective, we have individually over-identified with our beliefs, perceptions, and understanding. That over-identification doesn’t allow us to separate what actually happened from our interpretation of what happened.

One way we can step back from our interpretation of reality is by using the technology we have access to. AI can be a tool in learning to separate fact from narration.

The reason I created the framework in the first place is because I wanted to step back from my own beliefs and perceptions so I could see my own life more clearly.

I used ChatGPT to help me build the framework because I needed a tool that could see patterns, think logically instead of emotionally, and had enough awareness of philosophy, psychology, and sociology to point toward those modalities without forcing me into years of research.

Through thousands of messages and continual questioning of perception versus reality, patterns, morality, and outcomes, ChatGPT eventually adjusted to my constraints. It understood that I wasn’t interested in how people felt about what happened, what people thought happened, or even the morality of what happened. I only cared about the structure of what happened.

With AI, I filtered reality into Layer 2 awareness by learning to see where narration was interfering. That led me down a fun rabbit hole the last few days looking at news articles, facts, and how narration shapes our understanding of reality.

You can ask an LLM how it works. It’s a bit like asking a person to tell you about themselves. The LLM understands what it’s good at and what it’s not good at. It can point out the mismatch between what people think it can do and what it is actually designed to do.

It can pull structural details out of a news article, but because it is trained on human language patterns, it tends to retain interpretive language unless constrained. That’s what I wanted to explore, which is why I created the protocol.

I encourage you to use the protocol yourself. Pick your favorite LLM, grab any news article you like, and see what it does. Compare the AI output to your own interpretation of the article.

Where did the AI’s output bother you?

Where did it agree with you?

It’s an interesting thing to explore if you’re open to it and willing to challenge your own ideas.

Let me know in the comments if you tried it and what happened when you did.

This article is part of the AI as Structured Thinking series.
You can explore the full sequence here: https://substack.dellawren.com/t/ai-as-structured-thinking

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All of the models had a different version of what fact meant. Many of the models left in words like “angry”, “frustrated”, and “upset” as though human feelings were fact. You can read the full article here. It led me to an interesting discovery about not only my framework, but also human perception in general.

I determined there were three distinct layers to human perception and experience. They are:

Layer 1 - The structural reality of what is before language, interpretation, awareness, or human observation occurs. It does include automatic biological responses such as fight or flight, increased heart rate, or cortisol spikes.

Layer 2 - The functional interpretation of what happened. “The ball bounced. The door closed. The person spoke.” It’s the minimum level of language required to describe what’s happening, with as little interpretation as possible while still using language to describe events.

Layer 3 - The human interpretation layer. Instead of saying “The door closed.” which would be a layer 2 explanation we say, “They slammed the door.” The word slammed describes how the door closed, making it an interpretation instead of just stating the fact that the door was closed.

AI is generally programmed to work within the third layer. It assumes the human interpretation to be correct and therefore, when I ask it to take out human story without clarifying what I mean, it leaves in human feelings.

Human feelings are not fact. They arise from biological activation, but the meaning we attach to them belongs to Layer 3. They are not structural reality independent of interpretation.

While the AI finding was definitely intriguing, what peaked my interest was how language affects our response to what’s happening around us.

If somebody doesn’t respond to you, are they ignoring you or did they simply not respond? Notice the difference the interpretation makes to how you feel about what happened.

When you don’t tell the story about being ignored, the person not responding to you doesn’t matter as much. No response doesn’t generate the same feeling or need to defend yourself that being ignored does. It allows the experience to just be what it is at a layer 1 or 2 level without needing to do anything about it.

The language doesn’t seem like it should be that important especially when it’s just in our heads. But we frequently say things like “words matter”. If we believe that words matter then how we narrate the experience in our heads also matters.

In terms of the framework, we’re narrating the cause which has an internal effect on us, both mentally and emotionally. If the narration in your head changes how you respond externally, then you’re no longer responding to the observable event (layer 1 or 2), you’re responding to your interpretation of that reality, which is layer 3. Layer 3 is a distorted or modified view of the first 2 layers.

The goal is not to do away with language or stop using words like slammed or yelled. The goal is to recognize how those words shape your perception of reality before you react based on that interpretation.

The framework lives in layer 2, which is the functional level of description needed to communicate and be understood. What the framework indirectly does is encourage us to pay closer attention to the narration before responding to observable events. Notice the difference between the two things.

It is the gap between the event (layer 1) and narration (layer 3) that causes distortion, pain and unexpected outcomes. The middle ground, layer 2, is where can learn to work with reality directly, remove some of that narration, and make better choices about how to respond to our experience.

This article is part of the AI as Structured Thinking series.
You can explore the full sequence here: https://substack.dellawren.com/t/ai-as-structured-thinking

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