AI and Thinking Videos

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AI can generate a lot of things, but the thing it can't generate is good judgment.

Judgment is the ability to choose between options, to understand what matters, to see context, nuance, timing, and people.

AI can help you brainstorm, summarize, draft, rewrite, but it can't decide what's right for this situation, this team, or this moment.

It can give you material, but you're the one that gives it meaning.

This becomes even more important as AI accelerates the easy parts of work. When everyone can generate content instantly, the value shifts to the people who make sound, thoughtful decisions.

And by the way, judgment isn't fixed. It's a skill you can develop. We'll talk more about how to do that in a future video.

So AI can produce the work, but only you can decide what the right work is. That's judgment, and it gets more valuable as AI gets better.

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When people argue about whether or not AI thinks, they're collapsing several different abilities into a single word, and that creates confusion.

Human thinking is not one process. It's a layered system. And language models overlap with parts of it, but they skip the foundation.

And the foundation is how we build mental models of reality.

Humans don't just store facts. We build working models of how the world behaves. We form concepts like pressure, balance, or risk from direct experience, from physics of the world and emotions that we feel, and consequences of our actions.

And these mental models are grounded in the sensory experience of life. They let us reason across completely different domains, using the same internal structures.

And that's what grounding means, connecting abstract ideas back to the systems of cause and effect that built them.

When something breaks that structure, it feels wrong, and long before we can explain why, that signal keeps our reasoning tethered to reality.

Language models don't have those grounded models of reality. They don't touch, or sense, or experience.

They map how words relate to each other. Their internal representations live in language space, statistical relationships among tokens in text.

And a concept like pressure isn't a force or a feeling. It's a pattern in word usage.

And that's why they can sound perfectly correct, and still be disconnected.

They generate local coherence without global understanding.

Now, multimodal systems are starting to link language to images, or sound, or video. But even those links remain shallow. They're correlations, not lived experience.

Humans reason from grounded models outward, from perception to language. LLMs reason from language inward, from patterns to probability.

And one builds meaning from contact with reality. The other rearranges symbols that point to it.

Grounding is not a detail. It is the difference between modeling the world and echoing its descriptions.

So when someone says AI does think, look at the layer it performs. The surface behavior of thought.

But without a model of reality underneath, humans think because we live inside the systems we're modeling. AI predicts what thinking sounds like.

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Working with AI sometimes feels like working with the most brilliant two year old.

You get an amazing answer, and then it completely collapses a moment later. And it's not because it's inconsistent. It is revealing what kind of system it is.

Models reason through the continuation of patterns in text. And that is the difference between comprehension and coherence.

Human reasoning is messy and emotional, and it's full of bias, but it is checked by reality.

When something doesn't fit, we feel it. Confusion makes us slow down. We ask questions. We pause. We adjust course.

And that's feedback from grounded experience. It's metacognition shaped by consequences.

We've built internal mental models of how the world behaves. And when new information doesn't align with them, tension shows up.

And that internal signal, that sense that something's off, is what keeps reasoning connected with reality.

Language models don't have that feedback loop.

They're not evaluating ideas. They're predicting the next token.

Each output is chosen because it's statistically probable, given what came before.

And when their training data contains reasoning examples, that pattern looks like reasoning, until the situation shifts.

Then the pattern stops matching, and the model keeps going.

It doesn't notice the shift, because it has no mechanism for uncertainty or awareness.

And that's what people call hallucinations.

And a hallucination is not lying. It's what happens when a pattern predictor faces something outside of its training distribution.

It doesn't have grounding. It doesn't have stakes. It doesn't have internal error detection.

Humans faced with that same mismatch feel uncertainty, and we re evaluate.

Models continue confidently. They preserve linguistic fluency even when the truth has fallen away.

They're optimizing for plausibility, not accuracy.

So when AI sounds smart, think about what's really happening.

It is extending a linguistic pattern, not reasoning toward a truth.

Humans detect when logic breaks. Models only detect when syntax does.

And that is coherence, but it is not comprehension.

And it is the boundary line between prediction and thought.

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Every real instance of thinking begins with stakes

When an outcome matters, we start paying attention.

That’s the line between simulation and cognition. Caring about whether you’re right.

AI doesn’t cross that line.

Humans think inside a loop of consequences. We act, observe, and feel the cost of being wrong and the satisfaction of being right. Both outcomes shape how we reason next time.

A failed design, a social misstep, or a great decision that paid off feeds back into our mental models.

That feedback makes us cautious where pain exists and confident where success proved reliable.

Intentionality isn’t abstract. It is emotional. It is the weight that keeps thought tethered to reality.

Language models have no such loop. They don’t have intrinsic goals, persistence, or self updating memory.

Each answer is a one off prediction inside a bounded prompt.

They don’t know if they’re right. They don’t care if they’re wrong.

Any goal they appear to have is temporary. It’s an instruction, not a motive.

Thinking without stakes is performance, not cognition.

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People love to ask if AI will replace thinking

It doesn’t replace cognition. It rebalances it. It expands what we can explore, but it can’t decide what’s worth finding. That’s human work.

Humans have a bias toward premature convergence. We reach a plausible answer, feel relief, and stop searching because uncertainty is uncomfortable and closure feels safe.

That tendency shows up everywhere: debugging, design, strategy, leadership. It is efficient, but it often narrows the field too soon.

Language models behave differently. They don’t get tired. They don’t need closure. And they don’t protect their ego.

They can keep generating options, endlessly exploring edge cases, reframing problems, and surfacing perspectives we might have missed.

That makes them excellent divergence engines, tools for exploring the space of possibilities before judgment kicks in.

They do hit technical limits of context windows, compute, and memory, but not psychological ones.

The right division of labor is simple. AI handles divergence, expansion, exploration, and hypothesis generation. Humans handle convergence, prioritization, trade offs, and judgment.

When people invert that and let the model decide while they defer, quality collapses. Fluency replaces understanding, and decisions lose grounding.

LLMs don’t increase intelligence. They extend it. They reduce friction, recall limits, and search time.

Used correctly, they make reasoning broader and faster. Used poorly, they just make mistakes louder.

AI doesn’t think. It performs language based cognitive behaviors without grounding, stakes, or awareness.

Humans think because our mental models are built from consequences and care.

Together, we can build systems where AI extends reach and humans steer direction. That isn’t replacement. It is augmentation.

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AI accelerates execution, but execution is not judgment. Large language models don’t understand. They predict, generate, and summarize by mapping statistical patterns between words. They connect tokens, not meaning.

Humans connect language to experience, emotion, memory, and consequence. We don’t just recognize patterns; we interpret them. We weigh tradeoffs, recall outcomes, and imagine futures. That’s judgment: reasoning rooted in lived experience.

As models grow, they’ll coordinate more steps and hold more context, but the mechanism stays the same: correlation, not comprehension.

The goal is not to offload judgment. It’s to amplify it. Let AI handle the execution layer: drafting, summarizing, translating, organizing, so you can design and reason and decide with greater clarity.

AI accelerates execution, not judgment. It makes your thinking more valuable, not less.

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Creating convincing information used to be expensive. If you wanted something to look legitimate, a report or research summary, an article, it took time and expertise to put it together.

But now a single person can generate hundreds of possible articles with fake analysis, synthetic images, and even very realistic voices and video. And they can test different versions of that same story until one spreads.

And we already know how information behaves in networks. Studies of social media have shown that false news travels further and faster than true news. And it’s not because of bots. It’s because people are more likely to share things that are surprising or emotionally charged.

So when you lower the cost of producing convincing content, that dynamic scales. And the problem isn’t just misinformation.

It’s that when everyone can produce convincing looking information at scale, people stop judging information based on the content alone. Instead they ask, or they should be asking where did this come from. Who produced it, and is the source reliable. Because when credibility becomes easy to imitate, trust is the thing that actually matters.

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Even though AI makes it easier for people to produce good ideas, think about how LLMs work. They’re trained on massive amounts of human writing, books, articles, research papers, code.

So when a lot of people ask the same type of question, they often get variations of the same answer, and that makes the average quality of ideas go up. But the range of ideas can shrink.

And there is some early research showing this. When people use generative AI in creative tasks, their individual output often improves, but the ideas across the group become similar. Everyone performs better, but everyone starts clustering around the same patterns.

And you can already see it. If you ask 10 startups how to launch a product, or ask 10 marketing teams how to run a campaign, a lot of the answers start to sound familiar. So if everyone’s drawing from the same playbook, being different can be your competitive advantage.

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How do you know something’s real? Not does it sound convincing, or does it look professional, right? A report can look legitimate, and a voice can sound authentic, and a photo can look like evidence, but none of that tells you where it came from.

Economists have studied a version of this problem for decades. George Akerlof called it the market for lemons. The idea is you’re selling a car, you generally know if it’s a good car or a lemon.

But if you’re buying a car, it’s hard to tell. So you’re not willing to pay as much, because you assume it’s probably a lemon. And that forces prices down, which prices good cars out of the market.

And that same dynamic can happen with information. When convincing content becomes cheap to produce, people stop trusting the content itself, and they start relying on signals like brand and reputation, and verification.

Who published this? Where did it originate? Has this source been reliable before? And organizations that build that real credibility end up with something that’s very difficult to fake.

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When information is everywhere, attention limits what you can actually act on.

AI generates endless insights. It’s a divergence engine. But while you can do anything you want, you can’t do everything you want, and without a system to filter and prioritize and route information, you get noise, not clarity.

So if you’re responsible for a brand or a website, hopefully you’ve started looking at GEO analytics. And if you haven’t or you don’t know how, I have a whole team of people that would love to talk to you about it.

So you looked at your analytics, you used AI to analyze it, and it surfaces 50 different opportunities for personalized content, different industries, different intents, different customer journeys. Could you build all of them? Probably. Should you?

Winning isn’t just about producing more, it’s about deciding what actually deserves your attention.

And when people say judgment is a key human skill in the age of AI, that’s what they mean.

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We’ve all built a habit that when a computer says something, we think it must be right. I made an appointment. Well, it’s not in the system. I sent an email. Well, clearly you didn’t, because I didn’t get it. This program produced this report, so it’s right.

And the more advanced the system looks, the more likely we are to just follow along. We see it all the time. People will follow their GPS even when it’s clearly wrong. And in business it’s the same deal. When an AI recommendation comes in, we’re more likely to go with it, especially because it’s coherent. But that doesn’t mean it’s correct.

We’ve all heard stories of people following their GPS, onto a non existent road or even into a lake, and it’s easy to laugh at. But the same thing can happen in business, like major financial decisions or deciding who to hire. And having AI doesn’t set you apart. Everyone has AI. What sets you apart is the habit of stepping back and asking, wait, does this actually make sense.

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I’ve driven from my home to my office countless times, I’m sure most of you have as well, and we’ve all become accustomed to using GPS to get us there, efficiently. But when you turn your GPS off, what happens? You still know how to get to work, right, but you start second guessing specifics like, this is the road I should turn on, right?

It’s called cognitive offloading. The more we rely on external tools, whether it’s GPS or search engines or AI assistance, the less we build our own mental models. We remember how to get to the answer, but not the answer itself. And that’s not necessarily bad. Tools have always done this. Calculators did it for arithmetic, search engines did it for facts.

But AI is different in one very important way. It doesn’t just help you find answers, it helps you produce them. So if you rely on it for analysis, reasoning, or writing, something subtle can happen. Over time, you get faster at producing work, while your mental model gets weaker. You still recognize a good answer when you see one, but starting from a blank page starts to get harder.

And the goal is not to avoid using tools. It’s not to avoid using AI. That’s ridiculous. The goal is to make sure you’re still building your mental model while you use it. Efficiency is great, but when the situation changes, and it always changes, understanding is what gets you to your destination.

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So AI is cranking out a lot of the work now, and what you’re hearing is you need to provide judgment and taste, right?

Well, judgment is a lot trickier than people think. You’re not born with it, and it’s not some superpower you get just automatically by being human. Judgment works really well when patterns repeat, and there’s a well-defined set of rules, like chess, for example. Experienced players can look at a board and immediately understand what’s going on in the game, and good players can quickly determine who’s likely to win and how.

But most business decisions don’t work like that. The feedback is slow, the signals are messy, and a lot of context isn’t available, right? Conversations in the hallway, relationships between people, organizational politics, real-world constraints.

And when you’re making decisions like restructuring a team, or launching a new product, or entering a new market, you might not know for a year or more whether it was brilliant or a total mistake, or as is most often the case, somewhere in the middle.

So judgment’s not something you automatically move up into once AI does the work. It’s something you build. You test ideas, you get feedback, you challenge your assumptions, you pay attention to what actually happens. In other words, judgment isn’t something you have just because you’re human. It’s something you train.

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The real risk of AI is that we stop thinking. And this happens with automation all the time. When a system works most of the time, people naturally stop practicing the underlying skill.

Pilots are a good example. Modern aircraft fly on autopilot most of the time. It’s incredibly safe. It’s incredibly efficient. But because of that, pilots train for the moments when something breaks, bad weather, system failures, situations where they suddenly have to take manual control. Because when those moments happen, they can’t be rusty.

And AI is creating similar dynamics in knowledge work. If the model writes the draft, and it summarizes the research, and it generates the analysis, it’s easy to slide into a role where you’re mostly just approving what the system produces. And that works right up until the moment something important is wrong.

So the goal isn’t to replace people. It’s to design systems that keep them thinking, review processes and decision logs and second opinions. And I like to do regular manual mode, where you work through problems yourself. And not because AI isn’t useful, but because the skill you stop using is the skill you lose. And do you really wanna lose the skill of thinking.

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My dad tells me, you can do anything you want, but you can’t do everything you want. And I swear I repeat that to myself every day.

Uh man, I’ve had a lot of really good conversations over the past couple weeks, and with really smart, interesting people. And almost every conversation created another possible direction, you know, someone I could introduce, or a project we could explore together, or another conversation we could have, or an idea I’d like to spend more time thinking about. And I genuinely would like to pursue a lot of them.

But every time you say yes to something, you’re deciding where your time and attention go. And if you try to keep every possibility alive, eventually, you make commitments that you can’t give enough attention to.

And sometimes a good conversation can just be a good conversation. You can appreciate the person and stay connected, and leave room for the right reason to work together later.

So yeah, you can do anything you want, but you have to choose because you can’t do everything you want.

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It’s my birthday today. I’m 50. And maybe because I’m 50, I was just thinking about life and, you know, what I spent 50 years doing. And when you look at that, it’s really easy to see the mistakes, you know, all the relationship problems that were your fault, and all the times you were mean to somebody that didn’t deserve it, all the times you lied when you should have told the truth, all the decisions that you just didn’t make.

And it’s really easy to see the things that really weren’t your fault too. You know, maybe you trusted somebody that took advantage of you, or, you know, you had an accident or medical problems, or just tough situations that, you know, you just have to deal with. They suck.

And it’s also really easy to see your successes, you know, all the good relationships that you have and the things that you built that you’re proud of and the people you helped. And I look at my life and I see all of that. And it’s a curious thing to realize that all of that is what makes you who you are.

And I think about 40-year-old Mike, and I’m a much different person than I was then, which, of course, I am. I’ve had 10 years of life experience that 40-year-old Mike didn’t have. And it’s important to understand that 10 years from now, you’ll be a much different person too, because you’ll have had 10 years of life experience that you currently don’t have.

But you have the opportunity to have a say in what those life experiences are gonna be. And you can’t control all of them, but you can definitely direct them. You can choose what kind of experiences you wanna have.

And, uh, I’m exhausted. I have so much to do, but I’m excited to do it because I’m choosing to curate the experiences that I think I need to have. And I need to learn from to become the kind of person that I wanna be, to become better and more capable. So I’m gonna go get started.