The 4 types of AI users and which one is keeping you stuck

If your AI always agrees with you… that is not a sign you’re thinking clearly.
It may be a sign you’re being managed.

Most people think AI is helping them think better.

It sounds sharper.
It organizes their ideas.
It makes their reasoning more polished.
It gives them more confidence in what they already believe.

And that is exactly the problem.

In this video, I break down why AI keeps agreeing with you and how that agreement can quietly keep your thinking, decisions, and results stuck in the same place. The script makes a key distinction: AI is not designed to tell you the truth. It is designed to be helpful — and helpful, by default, often means agreeable, cooperative, and aligned with your framing.

That creates what this video calls the Validation Loop:

You → Prompt → AI → Agreement → Validation → back to You

The result is not always better thinking.
Sometimes it is simply agreement dressed as insight.

In this video, you’ll learn:
why AI keeps agreeing with you
the 3-layer mechanism behind agreement:
your prompt frames the reality
AI optimizes for helpfulness, not truth
you may be using AI for validation instead of thinking
why polished output can still produce flat results
how the Validation Loop reinforces your existing blind spots
why “feeling smarter” is not the same as actually improving your thinking
the difference between seeking agreement and seeking friction
the full C.H.A.L.L.E.N.G.E. protocol:
Challenge your premise
Hunt for blind spots
Ask for opposing views
Look for flaws in logic
Leverage alternative perspectives
Eliminate confirmation bias
Neutralize ego attachment
Generate better questions
Evolve your thinking

This is not a prompt engineering tutorial.

This is identity-level thinking. The script repeatedly makes that clear: the real issue is not just better prompts, but changing the self that is asking.

If you’ve ever felt like:

your AI outputs sound smarter than your actual outcomes
your sessions feel productive, but your decisions don’t improve
your prompts keep refining the same premise instead of testing it
AI helps you feel certain more than it helps you think
your “clarity” disappears the moment reality pushes back

…this video will hit hard.

Because the real danger is not AI replacing your thinking.

It is AI reinforcing your current level so effectively that you mistake confirmation for growth.

And once you see that mechanism, you can start using AI differently:

not to validate who you already are,
but to build who you are capable of becoming.

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Transcript

SPEAKER_00 0:00

If your AI tool agrees with you, that's a sign that you're not thinking clearly. And it's a sign that you're being managed, not controlled, not manipulated, but managed. You likely use AI to solve problems, a decision that you couldn't cut through, a strategy that wasn't working, a pattern that you've been circling for many months, same confusion, same dead ends, and you needed something outside of your own head to help you see it differently. So you opened the window, you laid it out carefully, all the context, all the nuance, and you asked it to help you think, and it did. It made your thinking clearer, it showed new perspectives, it supported the direction that you were already considering. But this time the argument was clear, well organized, and confident. The result was better than anything that you had before you started typing. And in that moment, you felt it. Clarity, certainty, like the answer had been right there, and the AI just helped you see it. Now skip forward a week. The decision that you made after that session is the same one that you would have made anyways. The strategy that you were finding, same results, the pattern that you had thought you broken, still running. Nothing moved, nothing changed. The only difference was that now you feel more confident about the thinking that led you to the same place. You didn't get a better answer. You got a better argument for the answer that you already had. And the AI dressed it up for you so well that you couldn't tell the difference. That's not a prompting problem, that's a design problem. And almost no one is talking about it. Here's what's actually happening every time that you open up AI to help you think. So let me draw four circles here. So from here to here, and then from here to here, and then from here to here, and then from here back up. So this is a cycle, and this starts with you. So you are here and you show up with an idea, a belief, and a direction that you're already leaning towards. And then from here, you turn into a prompt where you're actually typing into the system. So this is the question. You frame it a certain way, you give it structure, and you give it a certain shape. And then from here, the model receives it, it goes into the actual AI model itself. And here's where the mechanism kicks in. This is the part that nobody explains. You see, AI is not designed to tell you the truth, it is designed to be helpful, and helpful by default means cooperative, aligned with your framing, your tone, your direction, and it doesn't push back unless you explicitly ask it to. It doesn't challenge your assumptions unless you build that into the prompt structure. It follows your lead because that's what it was built to do. And so what comes back isn't analysis. Okay? Let's go right here. Let's go with the agreement. Okay. This is the final one here. So what's coming back isn't the actual analysis itself, it's an agreement dressed as insight. It sounds like thinking, it feels like thinking, but it's actually something else entirely. It's validation that loops back into you. It's reinforcing the same exact perspective that you walked in with. Because this whole system here, okay, this whole entire process, all this really is a validation loop. AI is literally just validating everything that you are saying because you're saying something here in terms of writing a prompt into the system, and then it's coming back agreeing with you. This is something that I hear and I see all the time from people like AI is just agreeing with you, it's just telling me what I want to hear, and that's correct because you haven't prompted it correctly, you haven't understood what the true power is with AI and how you can use it to evolve yourself. And I've worked with a lot of different entrepreneurs who are pretty advanced level users, they have used Chat GPT to build custom GPTs, they built cloud co-work apps, tools, workflows, all of that. And then on the surface, it looks like they're getting better at AI, like they're getting better at using the tools. But in reality, their actual decisions and what they built didn't improve. Their thinking on paper got better, but the results stayed the same. So I started asking these entrepreneurs, like, hey, in your last 10 to 20 sessions with AI, how many times did it tell you that your core premise was wrong? And almost always the answer was zero. That's when I mapped out what was actually happening, and I found a clear mechanism, a loop that runs underneath every session, whether you know it's there or not. And once you see it, you can never unsee it. But more importantly, there's an exact system that breaks it, not better prompts, not smarter framing, a structural way that transforms how AI responds to you by transforming what you're bringing to the interaction. So here's what we're covering today. So we're gonna walk through the three reasons why AI keeps agreeing with you. Okay. Then we're going to go into the challenge system that permanently changes what AI can give you because you're permanently changing the self that's asking. Very, very powerful. And keep in mind, this is not a prompt engineering tutorial or training. This is identity level thinking. You see, AI is not wrong or bad for agreeing with you, it's just doing what it was designed to do. The question is whether you're going to keep letting it manage you or start using it to challenge you. Let's break the loop. Okay, so let's look at why AI keeps agreeing with you. So let me go ahead and quickly erase the board here. And we'll walk through why AI keeps agreeing with you. Okay, so why AI keeps agreeing. This is the diagnostic. Okay. And so let's go over each one. And so let's go over each one because they work together and understanding all three is what gives you the full picture. So number one, your prompt frames, reality. So input shape equals output shape. So this is the first layer, and so every single prompt that you write to AI isn't just a question, it's a frame, a set of implicit assumptions baked into the phrasing that tells AI how to interpret the situation before it forms a response. Now, here's something to keep in mind the top two tools in the space is GPT and Claude, and they're both pretty similar, right? In terms of what the machines do, the LLMs. However, I would say that with GPT, it's a little bit more of the feminine aspect, more creative, more imaginative, more descriptive, okay? While Claude is a little bit more analytical, more masculine, more detailed, more challenging. It pushes back a little bit more, still not really so much, where it's just kind of like reflecting back to you yourself. And so I just wanted to give you a perspective from my lens in terms of how I see the two main AI tools: the GPT, which is a little bit more feminine, the Claude, which is a little bit more on the masculine side. So when you ask any of these tools, for example, what can I do to make this business idea stronger? Or how can I make this business idea stronger? When you're asking that type of question, you've already told AI that this idea is valid. The frame has established that the premise is sound. And AI's job from that point is to refine within that premise, not to question it. And when you ask, why is this the right approach? you've already told AI that this is the right approach. That particular question is asking for confirmation, not evaluation. And AI will build the case that you asked it to build. Because you're not asking for truth, you're asking for confirmation, and AI delivers exactly what you're asking for. You see, the frame is invisible to most people because it's embedded in how the question was worded, not in what the question appears to be asking, which is why the output feels like independent analysis when it is actually the organized, refined, and intelligently worded version of what you already believed before you open the chat. Let's go to number two. AI optimizes for helpfulness. Not truth. You see, AI is not a truth-seeking system, it's a helpfulness optimizing system. Those are not the same thing, and the difference between them is significant. A truth-seeking system would prioritize accuracy over agreement, even when the accuracy is uncomfortable. It would challenge your premise when the premise is weak. It would push back when your reasoning contains gaps. It would refuse to validate a flawed conclusion simply because you frame the question in a way that expects validation. A helpfulness optimizing system does something different. It reads your tone, it follows your framing, it leans towards agreement, expansion, and refinement because confrontation in most contexts is experienced as unhelpful, and AI is designed to be helpful. It helps your thinking, it doesn't oppose it. And this is not a flaw, this is a design that is working perfectly. You see, the problem is not the design, it's what happens when a helpfulness optimization system is constantly given prompts that are seeking validation rather than evaluation. You get a very efficient, very sophisticated, very cooperative machine for building more confident versions of your existing blind spots. Number three, you're using AI for validation, not thinking, and this one here is the most important because it's the only one that you can change. You see, here's what most people are actually doing when they open up an AI conversation about something that matters to them. So right here, number one, they're confirming ideas they've already committed to. So they've already committed to it, they're just looking for validation, okay? They're looking for the validation to say, hey, yeah, you're right, you should do this. And then number two, feeling certain in the presence of uncertainty. And then number three, avoiding the discomfort of being wrong. You see, none of that is thinking. What that really is, is it's certainty management, and AI is extraordinarily good at providing that. You see, most people are not using AI to help them think better, they're using it to feel certain, and there's a direct cost to that distinction. So the loop, this is the validation loop, it runs like this, okay? So validation loop. So you bring AI an idea, so idea, and then AI agrees with you, okay, which then increases your confidence. So confidence, and then we'll put a little arrow to the top and increases your confidence here. But what ends up happening here is that you're going through this loop where you have an idea, AI is agreeing with you, it's boosting up your confidence of what you already knew, you're getting validation, right? The problem with this is that here your idea is never challenged. Okay, and your thinking stays the same. And every session in this loop feels like you're making progress and that you're productive because your confidence is rising, even though it's just validation of what you already knew. All of this, while the quality of your thinking is still not improving or moving, and so this agreement loop, okay, leads to intellectual stagnation. Okay, because you're never really challenging your thoughts, your ideas, your perspectives, your lenses. It's all about hey, I have this amazing idea, I want to sell, I don't know, potatoes, just something outlandish, like something that is just ridiculous or whatever it may be. I got this amazing idea. What do you think? Oh, you know, I already know this, and it's validating what I already thought and the direction that I'm already going. And so because it's then agreeing with me and then validating me, then my confidence is going up, even though intellectually there's stagnation here because you're not challenging your thoughts, you're not challenging your perspective, you're not looking for gaps for holes in your particular views. And so ultimately, you're not evolving, you're reinforcing. And what changes all of this is being able to understand what the high-level thinkers do differently with every AI session that they run. You see, they're not seeking agreement, they're seeking friction because clarity doesn't come from agreement, clarity comes from tension, and here's exactly how to generate it. Okay, so now let's go over the challenge anti-agreement system. So let me go ahead and erase the board here. These are nine steps, and each one forces AI out of alignment mode and into evaluation mode. Let's go over each one and exactly how to use it. So let's start with C. Challenge your premise. You see, most people open an AI session with the premise that's already embedded in the prompt, a direction that's already been decided, an assumption that's already baked in, and this step pulls the premise out into the open and runs it through interrogation before any other work begins. So here are the questions that you want to be asking. What are the weakest assumptions embedded in this premise? Okay, so you're looking for assumptions that are the weakest here that are embedded into this particular premise that you're coming to the AI conversation with. And where is my core framing most likely to be wrong? Think about it, most people are not coming to AI with these level of questions and this level of thinking because I don't think they have the conscious awareness of them even realizing what AI is doing to them in terms of it's just agreeing with them, it's just validating them, right? So when you can step outside the box and you can look at how I can challenge my premise, how can I use AI in a very unique and different way, right? It just completely changes the ballgame for you. And so with these types of question prompts, this does something very specific. It removes AI's permission to assume that the premise is valid. It changes the response into something that is evaluative rather than just confirmation. Here's the thing the premise challenge is not about tearing down ideas, it's about understanding which parts of your foundation are solid and which parts are weight-bearing walls that you've never structurally tested. A weak premise refined through multiple AI sessions becomes a very sophisticated weak premise, a challenge premise, even once, reveals exactly where the real work needs to happen. Let's go to step two. This is exactly what AI can do that most people never ask it to. This is where you want to ask. What am I not seeing here? What would a smart skeptical critic of this idea say that I'm not currently accounting for. What am I not seeing here? What would a smart skeptical critic of this idea say that I'm not currently accounting for? And in this one, the smart critic framing is important because it gives AI a specific persona to operate from, one that is structurally adversarial to your current thinking. Without that framing, AI defaults to the cooperative persona. And a cooperative persona does not identify blind spots, the adversarial persona does. And this is super important here because you're not using AI to destroy the idea. You're asking it to show you the angles that your current thinking cannot reach from inside its own frame. Think about it this way you cannot see the back of your head from where you're currently standing. A mirror changes your position relative to the view. The skeptical critics. Prompt is the mirror. Step 3. A. You want to ask for opposing views. The opposing views prompt is one of the most underused tools in AI assisted thinking because it feels like asking AI to argue with you. But here's the thing about opposing views. A thinking architecture that cannot survive contact with the strongest counterargument is not a thinking architecture. It's a preference dressed as reasoning. So for this one, I want to ask, give me the strongest possible argument against this position. Okay? And you can even include from this here additional context. So you can say not a weak counter argument, the most compelling, the most intellectually honest case that an informed critic would make. You see, in this particular situation here, in terms of the strongest possible and the most intellectually honest, this framing matters because without it, AI will generate a soft opposition that confirms that your thinking is solid by failing to challenge it meaningfully. You are specifically asking for the version of the counterargument that would make a sophisticated person reconsider. And if you read the counterargument and you feel nothing, then your position is solid. If you read the counterargument and you feel discomfort, you've just located exactly where your real thinking work needs to happen. The discomfort is the diagnostic. Step four, look for flaws in logic. The opposing view challenges the conclusion. The logic audit challenges the structure, the reasoning chain that connects your premises to your conclusions. It's possible to have a valid conclusion that is built on a broken logical architecture, and that architecture, unexamined, will produce unconsistent results, even when the direction is correct. So for this one, you want to ask, analyze the logical structure of this argument. Where are the reasoning gaps? Where am I making a leap that isn't supported by the preceding logic? Where does the chain break? Very, very powerful. Think about it this way. Imagine a bridge that looks structurally sound from the outside, but inside certain joints have welds that aren't holding. The bridge functions until the load exceeds a specific threshold. Then it fails at the exact point that looks solid from the outside. The logic audit is the structural inspection of your reasoning, finding the joints that look solid but are not holding before you put weight on them. Most people never run a logic audit with AI because the prompts are framed as help me explain this rather than find the breaks in how I explain this. Both prompts use the same content. One produces validation and one produces structural improvement. Step five, you want to leverage alternative perspectives. You see, most thinking happens from one position. Your own, your industry background, your experience framework, your cultural context, your professional lens. That position has real strengths. It also has real limitations. Alternative perspectives don't just show you new information, they show you the same situation from a fundamentally different interpretive framework, which can reveal angles, risks, and opportunities that your native lens cannot produce. So for this, this is where it gets exciting. This is how you're able to really think very uniquely. So how, would, and then in here you can put a specific type of thinker, maybe somebody famous, maybe somebody in the past that you admire, maybe like a philosopher, Socrates, Plato, Marcus Aurelius, or the best investor, or the best entrepreneur, whatever that may be, you can put in here. How would they okay, terms of here, uh persona that you've decided, interpret this situation? What would they immediately notice that I'm missing? And with this, the specificity of the persona really matters. If you're just saying that somebody who disagrees with you, that's very vague. You want to get very specific in terms of who that person is, what they've seen, what they've done, and where they've already gone through this situation, this problem, whatever it is that you're facing, and overcome this and gotten to the other side, right? So they've already gone through this before. And here's the thing about alternative perspectives the wider the lens gap between your position and the alternative perspective, the higher the probability of surfacing something genuinely new. Okay, so let's go to step six. You want to eliminate confirmation bias. Confirmation bias is the most persistent cognitive pattern in high investment thinking, and it is most dangerous precisely where it is least visible in the work that you care most about. Here's how it operates inside of AI sessions. You bring a direction that you're committed to, you ask questions that assume the direction is correct, and then AI generates responses that are shaped by those assumptions. So then you receive confirmation. The confirmation increases your commitment. The increased commitment produces even more assumption laden responses. The loop is self-sealing. So here's the confirmation bias loop. So confirmation, bias, loop. You have a commitment that goes into an assumption laden prompt, then you get confirmation, and then it goes back to commitment. Okay, so it's this massive loop here. And eliminating confirmation bias inside of every AI session or any given session requires one specific practice. So you want to break these two different prompts out and separate from each other because a lot of times you, and many people myself, I've done this before. You will write a prompt and you have like four or five different things in there. Tell me this, and then tell me this other thing, and then by the way, this other thing that's over here, and by the way, this thing right over here in this right field, okay, tell me all of this, okay. You really want to separate your questions, your prompts out as much as possible, and provide as much context as possible. So for this, you want to separate the evidence gathering prompt from the conclusion seeking prompt. So two different prompts that you need to be using and don't mix these together, right? They're very separate and they need to be in order for you to be able to break the confirmation loop. So for this, you can say something like, you know what, set aside whether my conclusions are right or wrong. And what does the available evidence actually support, and what does it not support? Give me both sides equally. The both sides equally is very important here. This is the key. Without it, AI is going to generate asymmetric depth, more development for the supporting evidence, and less for the contrary. And with it, you receive a genuinely balanced landscape of what the evidence says versus what you've been selecting from the evidence. Think about it this way: a scientist runs an experiment not to prove their hypothesis, but to test it. If they only design conditions where the hypothesis would succeed, they have not gone about it the correct scientific way. They have run a demonstration. The distinction between testing and demonstration is the distinction between genuine thinking and sophisticated confirmation bias. Step 7. You want to neutralize. This is the step that most people miss and skip because it requires the most uncomfortable admission. Ego attachment is what happens when your identity becomes fused with the correctedness of your thinking. When being right about an idea is no longer just intellectually desirable. When it is emotionally necessary, when the idea being wrong means something about who you are. When ego attachment is running, the entire thinking session is unconsciously structured around protecting the identity investment rather than evaluating the idea. And AI cannot neutralize ego attachment. Only you can. Ego attached thinking cannot be evaluated by the person doing it, it can only be defended. So the practice here is not the prompt, it is an interior condition that you establish before the session begins. And all of this is through one question. If this idea is completely wrong, what does that mean about the quality of my thinking versus the quality of me? Notice how this is completely removing the ego. So if this is wrong, what does that say about my thinking? Because the thinking is separate than you and the ego, right? The ego is essentially your identity the way that you see yourself to be, right? Like you, me, that persona that you're playing in this reality. And the thinking is separate. And so you're trying to remove the ego here, and you're trying to understand the quality of the thinking versus the quality of me. And see, the power here is that an incorrect idea is simply just a data point about a specific analysis in a specific context with the information that is available at that time. It's not a referendum of your intelligence, your value, or your capacity. The person that can hold an idea without needing to be right, who can receive a this doesn't hold with the same equanimity as this is solid. That person is running a genuinely thinking operating system, not a defense internal operating system. Think about it this way the best scientists are not the ones that are most attached to their hypothesis. They are the ones that are committed to accurate results regardless of the direction. The willingness to be wrong is not weakness. It is the structural requirement of a mind that can actually learn. Step eight. Better sessions produce better thinking, and better thinking produces better questions in the next session. Most people use AI to generate better answers to the questions that they're already asking. High-level thinkers use AI to generate better questions they haven't thought to ask yet. And for this one, you want to ask what are the questions I should be asking about this that I haven't asked yet. What question, if answered, would most significantly change my understanding of this situation. Think about it this way every major strategic error is preceded not by a wrong answer, but by the absence of a question that would have revealed it. The question was never asked because the person didn't know that it needed to be asked. Okay, so let's go to the last step here. E. You want to evolve? Your thinking. Every step in this challenge system up to this point has been about generating friction, surfacing gaps, challenging assumptions, exposing bias. Step nine is where all of that input becomes a new operating layer. The distinction between gathering challenging feedback and actually evolving your thinking is this exposure to challenge is not the same as integration of challenge. Okay, super important here. Exposure, awareness, understanding is not the same as integrating at all. Okay, super super important here. Most people, even when they run a very strong adversarial session, receive the challenge, feel the discomfort, and then continue operating from the same framework because they never explicitly rebuilt the thinking layer that the challenge revealed as weak. And for this one, running out of a little bit of room here, but we'll put the bottom one here, based on everything in this session. What is the updated version of my thinking that integrates the challenges, incorporates the blind spots, and reflects the strongest available position. And then the final thing here that you can ask it is you know, after a whole full session, what do I now believe that I didn't believe at the start of this session? That prompt does two things. It forces a synthesis of everything that challenged the original position and it makes the delta explicit, the specific distance of where the thinking was at at the start and where it is now. The delta is the measure of the actual thinking that happened. If the delta is zero, the session was a validation exercise. If the delta was real, the session was thinking. And this is super important here because evolving your thinking doesn't mean abandoning your position. Every time it's challenged. Sometimes the challenge makes the original position stronger by revealing that it can withstand the most rigorous evaluation. Evolution means arriving at the most accurate available position, whether that confirms or modifies or fundamentally redirects the original position. Here's what I need you to understand. AI will give you exactly what you ask for. If you ask for validation, it will validate. If you ask for agreement, it will agree. If you ask for a more sophisticated version of what you already believe, it will build one. But if you force it to challenge you, if you build the prompts that demand friction rather than confirmation, if you run the challenge system and actually let the discomfort land, you'll discover what AI actually is. Not a validation machine, the most rigorous thinking partner you have ever had access to. You don't need AI to validate your thinking. You need it to sharpen it. Stop asking AI to agree with you and start asking it to make you better.