You've built a toolkit over the last several episodes. Logical reasoning. Causal thinking. Mental models. Serious intellectual firepower.

Now the uncomfortable question: When's the last time you actually used it to make a decision?

Not a decision you think you made. One where you evaluated the options yourself. Weighed the evidence. Formed your own conclusion.

Here's what most of us do instead: we Google it, ask ChatGPT, go with whatever has the most stars. We feel like we're deciding, but we're not. We're just choosing which borrowed answer to accept.

That gap between thinking you're deciding and actually deciding is where everything falls apart. And there's a name for it.

Mindjacking.

Not the sci-fi version where hackers seize your brain through neural implants. The real version. Where you voluntarily hand over your thinking because someone else already did the work.

It's not dramatic. It's convenient. The algorithm ranked the results. The expert weighed in. The crowd already decided. Why duplicate the effort?

Mindjacking is different from ordinary influence. You choose it. Every single time. Nobody forces you to stop evaluating. You volunteer, because forming your own conclusion is harder than borrowing someone else's.

What exactly are you losing when this happens?

Mindjacking destroys two distinct capabilities. They're different, and you need both.

Evaluation independence is the ability to assess whether a claim is valid. Not whether the source has credentials. Not whether experts agree. Whether the evidence actually supports the conclusion.

Decision independence is the ability to commit to a path based on your own judgment, without needing someone else to validate it first.

Both skills need each other. Watch what happens when one erodes faster than the other.

A woman researches her medical condition for hours. Journal articles. Treatment comparisons. She understands her options better than most medical students would. She walks into the doctor's office, lays out her analysis. It's thorough. Sophisticated, even.

The doctor reviews it and says, “This is impressive. You've really done your homework.”

She nods. Then looks up and asks: “So what should I do?”

She can evaluate. She can't decide.

Now flip it. Think about someone who decides fast. Trusts their gut. Never waits for permission. How often does that person get burned by bad information they never verified?

They can decide. They can't evaluate.

Lose either ability and you're trapped. Lose both and you're not thinking at all.

How do you know when mindjacking is happening? It has a signature. Four internal signals that reveal the handoff in progress, if you know how to read them.

Signal one: Relief. The moment you find “the answer,” you notice a weight lifting. Pay attention to that. Relief isn't insight. It's the burden of thinking being removed. When you actually work through a problem yourself, the result isn't relief. It's clarity. And clarity usually comes with new questions, not a sense of “done.”

Signal two: Speed. Uncertainty to certainty in seconds? That's not evaluation. You found someone else's answer and adopted it. There's a difference between “I figured it out” and “I found someone who figured it out.” One took effort. The other took a search bar.

Signal three: Echo. Listen to your own conclusions. Do they sound like something you read, heard, or scrolled past recently? If your “own opinion” matches a headline almost word-for-word, it probably isn't yours. You're not thinking. You're repeating.

Signal four: Unearned confidence. You're certain about a conclusion, but ask yourself: could you explain the reasoning behind it? Not where you heard it. The actual reasoning. If you can't, that confidence isn't yours. It came attached to someone else's answer, and you absorbed both their conclusion and their certainty without doing any analysis yourself.

Once you notice these signals firing, you need a way to stop the pattern before it completes.

The interrupt is a single question:

“Did I reach this conclusion, or just find it?”

Six words. That's the whole thing.

It works because it forces a distinction your brain normally blurs. “I decided” and “I adopted someone's decision” are identical from the inside, until you ask the question.

Test it now. Think about the last opinion you formed. The last purchase you made. The last recommendation you accepted.

Did you reach that conclusion, or just find it?

The interrupt doesn't tell you what to think. It tells you whether you're thinking at all.

Finding an answer isn't the same as reaching one.

This matters more than you might realize, because the pattern is bigger than any single decision you make.

Researchers at Penn State looked at 35 million Facebook posts and found something remarkable: seventy-five percent of shared links were never clicked. Three out of four times, people passed along articles they hadn't read.

But that's not the strange part.

A separate study from the University of Texas discovered that the act of sharing content, even content you haven't read, makes you think you understand it. Sharing tricks you into believing you know. You didn't read the article about investing, but you shared it, so now you believe you understand investing.

Worse: people act on that false knowledge. In the study, people who shared an investing article took significantly more financial risk afterward, even though they never read what they shared.

They weren't pretending to know. They genuinely believed they knew, because sharing had become a substitute for learning.

That's mindjacking at scale. Millions of people believing they're informed, acting confident, having never actually thought about any of it.

I want you to try something as soon as this video ends.

Open your social media feed. Find a post where someone you know has liked or shared an article, an opinion, a hot take.

Now ask: Did they actually think about this? Or did they just pass it along?

Look for the signals. Is their comment just echoing the headline? Are they expressing certainty about something they probably spent ten seconds on? Did they add anything that suggests they read past the first paragraph? Or did they just click “like” and move on? Remember: seventy-five percent of shared links are never clicked. That like or share you're looking at? They probably never read what they're endorsing.

You'll be shocked how easy this becomes once you start looking. It's everywhere. People confidently endorsing opinions they never examined. Certainty without evaluation. Expertise without effort.

Why start with what others are putting in your feed? Because it's much easier to spot mindjacking in others than in yourself. Your ego doesn't interfere. Train your eye on what's coming at you first. Then turn it inward.

Awareness precedes choice. You can't reclaim what you can't see.

Now you can see the handoff happening. That's the foundation. But seeing it isn't enough.

Knowing the signals won't help you when you're exhausted and the algorithm is offering relief. Understanding the trap won't save you when everyone in the room disagrees and consensus feels like safety. Awareness alone won't protect you when the deadline is tomorrow and you don't have time to think.

Those are the moments where mindjacking wins. Not because you lack the ability to think, but because thinking starts to look like a luxury you can't afford.

That's the real battle. And that's what comes next.

Next, we tackle the hardest version of this problem: acting before you're ready. What happens when you have to decide, the information isn't complete, and it never will be? Waiting for certainty feels responsible. But sometimes, waiting is the trap.

If you're new here, check out the earlier episodes where we built the evaluation toolkit this series is built on. Watch the series on YouTube. 

Here's a thought: most people will finish this video and scroll to the next one. The algorithm already has a recommendation queued up. Relief is one click away.

But you could do something different. You could stick with the discomfort for a minute. Actually, try the feed challenge before moving on.

If you want to go deeper on mindjacking, the full breakdown lives at philmckinney.com/mindjacking. And if you want to support the team that helps me to produce this content, consider becoming a paid subscriber on Substack. 

What's one opinion you realized might not actually be yours? Share this with someone who needs to hear it.

To learn more about mindjacking, listen to this week's show: Mindjacking: When Your Opinions Aren't Yours

Get the tools to fuel your innovation journey → Innovation.Tools https://innovation.tools

This week, I'm in Las Vegas, Nevada, at the annual Consumer Electronics Show 2026.

If you've been following me for long, you know I do this every year. This is 20-plus years I've been coming to the Consumer Electronics Show. Normally, I don't cover tech and new products here—except for once a year at CES. And it's less about specific companies and what they've announced. You can find that on thousands of channels on YouTube or podcasts. What I like to talk about are the trends—the trends that are emerging—and give you my view and opinion on what they really mean for the innovation space. Are we really innovating, or are we just regurgitating the same thing year after year?

First, let's recognize that the Consumer Electronics Show is now in its 59th year. It's a spin-off from the old Chicago music show back in the late 1960s. Yes, the late '60s. It's gone through some gyrations over the decades and remains one of the few big shows that survived COVID.

As usual, one of the big emphases is TVs, displays, home automation, new refrigerators, new washers and dryers—true consumer electronics, things you would find and put into your home. This year was no different. The big manufacturers were here, along with a number of new smaller manufacturers showcasing new TV technologies. Micro LED is the new buzzword bouncing around the show, and there were plenty of displays to see.

I'm a big TV guy, so I definitely had to check that out and see what could be the next TV I put into my house.

The one thing about this year's show that was just overwhelming was robots and AI. They were everywhere. I couldn't even tell you how many times we saw AI applied to things that make no sense—though some applications were actually pretty smart. But how many AI toilets do you really need at any given show?

On the robotics side, we saw all the familiar ones—like lawn mowers that automatically find your boundaries. One was actually selling the feature that you could program in graphic designs, and it would cut your yard in such a way that the design would appear in your lawn.

We also saw humanoid robots, robots doing backflips, robots dancing with people, dancing hands where the fingers are moving. You could buy just the hands or the arms or the elbows and assemble your own robots. It was pretty crazy.

Then we started seeing the combination of AI and robots—interactive robots where you could stand there, talk with them, point, and they would follow your commands. Pick up this item. Move this item somewhere else. Not programming through some controller, but simply pointing and talking to direct the robot to do what you want.

One thing we've seen in past shows was the big emphasis on electric vehicles. This year, the EV car market—which we've seen slow down generally—also slowed down here at the show.

However, what we saw in its place focused on two areas:

Commercial EVs and Hybrids: There was significant attention on commercial use of EVs, particularly hybrid electric vehicles with combustion engines.

Emergency Response Innovation: One exhibit that really impressed me was a fire truck supplied by Dallas Fort Worth Airport. This massive Oshkosh fire truck is a hybrid that uses electric motors for high torque and high acceleration—literally shaving seconds off response time. Given the limited distance on airport property, if there's a disaster or fire requiring quick reaction, the electric motors can accelerate very quickly.

There are only about 15 of these trucks in the world, and something like six or seven are just at Dallas Fort Worth Airport. I spent a fair amount of time with that team. This is a perfect example of smart innovation—innovation that isn't just because something is shiny and new. They thought carefully about how to use it, when to apply the right design, leveraging the benefits of electric while using the combustion engine to run the water pumps.

Electric Motorcycles: The other area with significant EV presence was motorcycles, particularly dirt bikes. When you're going out for the day to have some fun, the low noise of an electric motor means you're not disturbing rural areas with a combustion engine. Another example of good, smart innovation.

The other big area for the show was autonomous vehicles—not just EVs, but vehicles that can operate themselves, particularly in commercial use like farming.

John Deere has a long history of autonomous farming with very accurate planting using GPS technologies.

Caterpillar had a really interesting exhibit where they were live streaming Caterpillar machines doing autonomous mining from spots all over the world right into the booth. You could see autonomous technology in action.

A lot of people think of autonomous vehicles as something new, with Tesla being the innovator. Just to give you a data point: Caterpillar has offered autonomous vehicles since 1995. That's right—1995. Caterpillar introduced the first version of their machines that could operate autonomously. What we all think is new is really the perfect example of what's old becoming new again as progress is made.

Kubota: I'm a big Kubota fan, so I had to stop in there. They had an interesting vehicle that applies to a variety of different devices—tractors, even things you can do around a small ranch like what I own in northern Colorado, where I'm trying to harvest hay. It's something that fits smaller operations. You don't have to be a big farm to take advantage of these technologies.

Obviously, there were all the other normal things at the Consumer Electronics Show—thousands and thousands of rows of different types of Bluetooth speakers. Battery technology was a big thing, though a lot of it was just more efficiency from lithium-ion.

There was an interesting booth on what they call paper batteries—literally paper where you print the battery and then roll it up into whatever form factor you want.

The show this year was overly dominated by AI—AI everything—and robotics. Those would be the two fundamental themes. That's the walk-away after spending three days and something like 45,000 to 50,000 steps covering all the show floor space.

That's my insight as I wrap up this episode. This is my one time a year that I geek out on all the technologies. If you have any questions or your own thoughts—if you were there and saw something different you'd want to share—go ahead and put a comment down below, or pop over to PhilMcKinney.com and post a comment to the post there.

Next week we'll be back, kicking off Part Two of the Thinking 101 series. We did Part One and wrapped that up right before the holidays. Now we're kicking off Part Two—you don't want to miss it.

To learn more about robots and AI, listen to this week's show: CES 2026: Battle of the AI Robots

Get the tools to fuel your innovation journey → Innovation.Tools https://innovation.tools

Twenty-one years.

That's how long I've been doing this. Producing content. Showing up. Week after week, with only a handful of exceptions—most of them involving hospitals and cardiac surgeons, but that's another story.

After twenty-one years, you learn what lands and what doesn't. You learn not to get too attached because you never know what's going to connect.

But this one surprised me.

Thinking 101—the response has been different. More comments. More questions. More people saying, “This is exactly what I needed.”

It's made me reflect on why I started this series.

Years ago, I was in a room with people from the Department of Education. I asked them a simple question: Why are we graduating people who can't think?

Not “don't know things.” Can't think. Can't reason through a problem. Can't evaluate an argument.

Their answer was… let's just say it wasn't satisfying.

That moment stuck with me. When AI exploded onto the scene—when everyone suddenly had a machine that could generate answers instantly—it became clear: thinking for yourself isn't just valuable anymore. It's survival.

That's what Part One was about. The Foundations. Building your thinking toolkit.

So what's next? For the next few weeks—nothing.

We're taking a breather for the holidays. I'm going to spend time with my wife, my kids, my grandkids.

We'll be back in early January. And if you're heading to CES in Las Vegas that first week—let me know. I'd love to meet up.

But before I go, I have a question for you.

Should there be a Part Two?

I have ideas. If Part One was about building your toolkit, Part Two could be about what happens when you have to use it. Because knowing how to think and making good decisions aren't the same thing. Real decisions happen when you're tired. When you're stressed. When your own brain is working against you.

Part Two could be about that gap—between knowing and doing.

But I want to hear from you first. Should I do it? What topics would you want covered? What questions are you wrestling with?

Post a comment. If you're a paid subscriber on Substack, send me a DM—I read those.

And speaking of paid subscribers—that's the best way to support the team that makes this happen. Twenty-one years of showing up doesn't happen alone.

You can also visit our store at innovation DOT tools for merch, my book, and more.

Part One is done. The holidays are calling.

Thank you for making this series land the way it did.

See you in January.

I'm Phil McKinney. Take care of yourselves—and each other.

Listen to this week's show: Thinking 101: A Pause, A Reflection, And What Might Come Next

Get the tools to fuel your innovation journey → Innovation.Tools https://innovation.tools

Before the Space Shuttle Challenger exploded in 1986, NASA management officially estimated the probability of catastrophic failure at one in one hundred thousand. That's about the same odds as getting struck by lightning while being attacked by a shark. The engineers working on the actual rockets? They estimated the risk at closer to one in one hundred. A thousand times more dangerous than management believed.¹

Both groups had access to the same data. The same flight records. The same engineering reports. So how could their conclusions be off by a factor of a thousand?

The answer isn't about intelligence or access to information. It's about the mental frameworks they used to interpret that information. Management was using models built for public relations and budget justification. Engineers were using models built for physics and failure analysis. Same inputs, radically different outputs. The invisible toolkit they used to think was completely different.

Your brain doesn't process raw reality. It processes reality through models. Simplified representations of how things work. And the quality of your thinking depends entirely on the quality of mental models you possess.

By the end of this episode, you'll have three of the most powerful mental models ever developed. A starter kit. Three tools that work together, each one strengthening the others. The same tools the NASA engineers were using while management flew blind.

Let's build your toolkit.

A mental model is a representation of how something works. It's a framework your brain uses to make sense of reality, predict outcomes, and make decisions. You already have hundreds of them. You just might not realize it.

When you understand that actions have consequences, you're using a mental model. When you recognize that people respond to incentives, that's a model too.

Think of mental models as tools. A hammer drives nails. A screwdriver turns screws. Each tool does a specific job. Mental models work the same way. Each one helps you do a specific kind of thinking. One model might help you spot hidden assumptions. Another might reveal risks you'd otherwise miss. A third might show you what success requires by first mapping what failure looks like.

The collection of models you carry with you? That's your thinking toolkit. And like any toolkit, the more quality tools you have, and the better you know when to use each one, the more problems you can solve.

Here's the problem. Research from Ohio State University found that people often know the optimal strategy for a given situation but only follow it about twenty percent of the time.² The models sit unused while we default to gut reactions and habits.

The goal isn't just to collect mental models. It's to build a system where the right tool shows up at the right moment. And that starts with having a few powerful models you know deeply, not dozens you barely remember.

Let's add three tools to your toolkit.

This might be the most foundational mental model of all. Coined by philosopher Alfred Korzybski in the 1930s, it delivers a simple but profound insight: our models of reality are not reality itself.³

A map of Denver isn't Denver. It's a simplified representation that leaves out countless details. The smell of pine trees, the feel of altitude, the conversation happening at that corner café. The map is useful. But it's not the territory.

Every mental model, every framework, every belief you hold is a map. Useful? Absolutely. Complete? Never.

This explains the NASA disaster. Management's map showed a reliable shuttle program with an impressive safety record. The engineers' map showed O-rings that became brittle in cold weather and a launch schedule that left no room for delay. Both maps contained some truth. But management's map left out critical territory: the physics of rubber at thirty-six degrees Fahrenheit.

When your map doesn't match the territory, the territory wins. Every time.

How to use this tool: Before any major decision, ask yourself: What is my current map leaving out? Who might have a different map of this same situation, and what does their map show that mine doesn't?

The NASA engineers weren't smarter than management. They just had a map that included more of the relevant territory.

Most of us approach problems head-on. We ask: How do I succeed? How do I win? How do I make this work?

Inversion flips the question. Instead of asking how to succeed, ask: How would I guarantee failure? What would make this project collapse? What's the surest path to disaster?

Then avoid those things.

Inversion reveals dangers that forward thinking misses. When you're focused on success, you develop blind spots. You see the path you want to take and ignore the cliffs on either side.

Here's a surprising example. When Nirvana set out to record Nevermind in 1991, they had a budget of just $65,000. Hair metal bands were spending millions on polished productions.⁴ Instead of trying to compete on the same terms and failing, they inverted the formula entirely. Where hair metal was flashy, Nirvana was raw. Where others added complexity, they stripped down. Where the industry zigged, they zagged.

The result? They didn't just succeed. They created an entirely new genre and sold over thirty million copies. They won by inverting the game everyone else was playing.

How to use this tool: Before pursuing any goal, spend ten minutes listing everything that would guarantee failure. Be specific. Be ruthless. Then look at your current plan and ask: Am I accidentally doing any of these things?

Inversion doesn't replace forward planning. It completes it.

Imagine your project has already failed. Not “might fail” or “could fail.” It has failed. Completely. Now your job is to explain why.

Researchers at Wharton, Cornell, and the University of Colorado tested this approach and found something striking: simply imagining that failure has already happened increases your ability to correctly identify reasons for future problems by thirty percent.⁵

Why does this work? When we think about what “might” go wrong, we stay optimistic. We protect our plans. We downplay risks because we're invested in success. But when we imagine failure has already occurred, we shift into explanation mode. We're no longer defending our plan. We're forensic investigators examining a wreck.

Here's proof the premortem works in the real world. Before Enron collapsed in 2001, its company credit union had run through scenarios imagining what would happen if their sponsor company failed.⁶ They asked: If Enron goes under, what happens to us? They made plans. They reduced their dependence. When the scandal broke and Enron imploded, taking billions in shareholder value with it, the credit union survived. They'd already rehearsed the disaster.

Every other institution tied to Enron was blindsided. The credit union had seen the future because they'd imagined it first.

How to use this tool: Before any major decision, fast-forward to failure. It's one year from now and everything has gone wrong. Write down why. What did you miss? What risks did you ignore? Then prevent those things from happening.

You can't prevent what you refuse to imagine.

Each tool is powerful alone. Together, they're transformational.

Imagine you're considering a career change. Leaving your stable job to start a business.

Start with The Map Is Not the Territory. What's your current map of entrepreneurship? Probably shaped by success stories, LinkedIn posts, and survivorship bias. But what's the actual territory? CB Insights analyzed over a hundred failed startups to find out why they died. The number one reason, responsible for forty-two percent of failures, was building something nobody wanted.⁷ Founders had a map that said “customers will love this.” The territory said otherwise. What is your map leaving out?

Apply Inversion. How would you guarantee this business fails? Starting undercapitalized. Launching without testing the market. Ignoring early warning signs because you're emotionally invested. Now look at your current plan. Are you doing any of these things?

Run a Premortem. It's two years from now. The business has failed. Write the story. Maybe you ran out of money at month fourteen. Maybe your key assumption about customer behavior turned out to be wrong. What happened?

One tool gives you a perspective. Three tools working together give you something close to wisdom.

This is exactly what the NASA engineers were doing, and what management wasn't. The engineers were constantly asking: Does our map match the territory? What would cause failure? What are we missing? Management was stuck in a single frame: schedule and budget.

The difference between a one-in-one-hundred-thousand estimate and a one-in-one-hundred estimate? The difference between confidence and catastrophe? It was the thinking toolkit each group brought to the problem.

Here's how to put these tools to work this week.

Twenty minutes. One decision. Run it once, then try it again next week on a different decision.

As you use these tools, you'll notice other mental models worth adding. Your toolkit will grow. Most decisions feel routine until they're not.

That morning at NASA felt routine. Seven astronauts boarded Challenger. They trusted that the people making decisions had the right tools to think clearly. Management had maps. The engineers had territory. The distance between those two things was seventy-three seconds of flight time.

The engineers saw it coming. Management didn't. Same data. Different tools.

When your moment comes, and it will, which group will you be in?

If this episode helped you think differently, hit that Subscribe button and tap the bell on our YouTube channel so you don't miss what's coming next. And if you found value here, a Like helps more people discover this content.

To learn more about mental models, listen to this week's show: Mental Models — Your Thinking Toolkit.

Get the tools to fuel your innovation journey → Innovation.Tools https://innovation.tools

Quick—which is more dangerous: the thing that kills 50,000 Americans every year, or the thing that kills 50?

Your brain says the first one, obviously. The data says you're dead wrong.

Heart disease kills 700,000 people annually, but you're not terrified of cheeseburgers. Shark attacks kill about 10 people worldwide per year, but millions of people are genuinely afraid of the ocean. Your brain can't do the math, so you worry about the wrong things and ignore the actual threats.

And here's the kicker: The people selling you fear, products, and policies? They know your brain works this way. They're counting on it.

You're not bad at math. You're operating with Stone Age hardware in an Information Age world. And that gap between your intuition and reality? It's being weaponized every single day.

Let me show you how to fight back.

Here's what's happening: You can instantly tell the difference between 3 apples and 30 apples. But a million and a billion? They both just feel like “really big.”

Research from the OECD found that numeracy skills are collapsing across developed countries. Over half of American adults can't work with numbers beyond a sixth-grade level. We've become a society that can calculate tips but can't spot when we're being lied to with statistics.

And I'm going to be blunt: if you can't think proportionally in 2025, you're flying blind. Let's fix that right now.

Okay, stop everything. I'm going to change how you see numbers forever.

One million seconds is 11 days. Take a second, feel that. Eleven days ago—that's a million seconds.

One billion seconds is 31 years. A billion seconds ago, it was 1994. Bill Clinton was president. The internet was just getting started. That's how far back you have to go.

Now here's where it gets wild: One trillion seconds is 31,000 years. Thirty-one THOUSAND years. A trillion seconds ago, humans hadn't invented farming yet. We were hunter-gatherers painting on cave walls.

So when you hear someone say “What's the difference between a billion and a trillion?”—the difference is the entire span of human civilization.

This isn't trivia. This is the key to seeing through manipulation. Because when a politician throws around billions and trillions in the same sentence like they're comparable? Now you know—they're lying to your face, banking on you not understanding scale.

Here's the trick they use on you constantly, and once you see it, you can't unsee it.

A supplement company advertises: “Our product reduces your risk by 50%!” Sounds incredible, right? Must buy immediately.

But here's what they're not telling you: If your risk of something was 2 in 10,000, and now it's 1 in 10,000—that's technically a 50% reduction. But your actual risk only dropped by 0.01%. They just made almost nothing sound like everything.

Or flip it around: “This causes a 200% increase in risk!” Terrifying! Except if your risk went from 1 in a million to 3 in a million, you're still almost certainly fine.

This is how they play you. They show you percentages when absolute numbers would expose them. They show you raw numbers when rates would destroy their argument.

Your defense? Three words: “Per what, exactly?”

50% of what baseline? 200% increase from what starting point? That denominator is where the truth hides.

Once you start asking this, you'll see the manipulation everywhere.

Okay, let's do this together right now. I'm going to show you a real manipulation pattern I see constantly.

Headline: “4 out of 5 dentists recommend our toothpaste!” Sounds pretty convincing, right?

Let's apply what we just learned. First—per what? Four out of five of how many dentists? If they surveyed 10 dentists and 8 said yes, that's technically 80%, but it's meaningless.

Second—what was the actual question? Turns out, they asked dentists to name ALL brands they'd recommend, not which ONE was best. So 80% mentioned this brand… along with seven other brands.

Third—scale: There are 200,000 dentists in the US. They surveyed 150. That's 80% of 0.075% of all dentists.

See how fast that falls apart? That's the power of asking “per what?

This is where your intuition doesn't just fail—it catastrophically fails. And it's costing people everything.

Grab a piece of paper. Fold it in half. Twice as thick, no big deal. Fold it again. Four times. Okay. Keep going. Most people think if you could fold it 42 times, maybe it'd be as tall as a building?

No. It would reach the moon. From Earth. To the moon. That's exponential growth, and your brain cannot comprehend it.

Here's why this matters in your actual life: You've got a credit card with $5,000 on it at 18% interest. You think “I'll just pay the minimum, I'll catch up eventually.” Your brain treats this like a linear problem. It's not. It's exponential. That $5,000 becomes $10,000 faster than you can possibly imagine, and then $20,000, and suddenly you're drowning.

Or retirement: Starting to save at 25 versus 35 doesn't feel like a huge difference. Ten years, whatever. But exponential growth means that ten-year head start could be worth 2-3 times more money when you're 65.

When you hear “doubles every,” “grows by X percent,” or “compounds”—stop. Your intuition just became your enemy.

You don't need a calculator to spot lies. You need a sanity check that takes ten seconds.

I'm going to give you the fastest BS detector I know:

Round brutally. 47 million becomes 50 million. 8.7% becomes 10%. Precision is the enemy of speed.

Find the zeros. Is this thousands, millions, billions? Get the ballpark right first.

Do the rough math. What's 7% of 50 million? Well, 10% is 5 million, so 7% is about 3.5 million. Done. Close enough to catch the lie.

Smell test it. Someone claims a new app has a billion users after launching last month? That's one in eight humans on Earth. Really?

I use this every single day now. News article, social media post, advertisement—ten seconds and I know if someone's lying to me. You're not trying to be exact. You're trying to be un-foolable.

Before we go further, let me save you from three traps I see people fall into.

First: Don't become the conspiracy theorist who distrusts ALL numbers. Sometimes 50% really is 50%. The goal is healthy skepticism, not paranoid cynicism.

Second: Don't weaponize this to win petty arguments. “Actually, you didn't do 50% of the dishes”—nobody likes that person.

Third: Don't assume you're now immune to manipulation. These are tools, not shields. Stay humble. Smart people get fooled all the time—they just recover faster.

Let me show you how these four techniques work as a system.

A tech company announces: “We've tripled our user base to 3 million, growing 200% annually, and reduced complaints by 90%!”

Watch this:

Scale check: 3 million users. In social media? That's tiny. Instagram has 2 billion. Context matters.

Per what? Tripled from what starting point? If they went from 50,000 to 3 million, that's actually 60x growth—why understate it? And 90% reduction from how many complaints? Ten to one? Who cares.

Exponential check: 200% annual growth is explosive… and unsustainable. What happens when they hit market saturation next quarter?

Quick estimate: If they have 3 million users and the market is 300 million potential users, they've captured 1%. Still lots of room to grow—or lots of room for competitors.

See how these stack?

Okay, pause this video. Seriously, pause it.

Open your news app or social media feed. Look at the first three posts with numbers in them. Now run them through the test: What's the scale? Per what? Is it exponential? Does it pass the smell test?

I'll give you 60 seconds. Go.

Done? Did you find manipulation? I bet you found at least one. Comment below what you discovered—I genuinely want to know what you're seeing out there.

Let me tell you what just happened.

You learned five techniques. But you actually learned something bigger: You learned that your intuition about numbers is systematically broken, and people in power know it and exploit it.

Remember the opening? The reason you're more afraid of sharks than heart disease isn't random. Media companies know fear drives clicks, and rare dramatic events trigger your brain differently than common statistical threats. So they show you the sharks, not the cheeseburgers.

They're not smarter than you. They're just counting on you not checking the math.

We're entering an era of AI-generated stats, algorithmic manipulation, and deepfake data. Your ability to think proportionally isn't just about making better decisions anymore.

It's about knowing what's real.

The people who can't tell a million from a billion will be led by people who can. And those people? They're fine with you staying confused.

So what are you going to be—the one doing the math, or the one getting played?

If you want to keep sharpening these skills, this is episode 7 in the Thinking 101 series. Each episode gives you another tool for thinking clearly in a world designed to confuse you. Hit subscribe so you don't miss the next one. And if this changed how you see numbers? Share it. Someone in your life needs this.

Choose today.

To learn more about numerical thinking, listen to this week's show: Numerical Thinking: How to Find the Truth When Numbers Lie.

Get the tools to fuel your innovation journey → Innovation.Tools https://innovation.tools

I stepped out of the shower in March and my chest split open.

Not a metaphor. The surgical incision from my cardiac device procedure just… opened. Blood and fluid everywhere. Three bath towels to stop it.

My wife—a nurse, the exact person I needed—was in Chicago dealing with her parents’ estate. Both had just died. So my daughter drove me to the ER instead.

That was surgery number one.

By Thanksgiving this year, I’d had five cardiac surgeries. Six hospitalizations. All in twelve months.

And somewhere between surgery three and four, everything I thought I knew about gratitude… broke.

Five surgeries. Three cardiac devices. My body kept rejecting the thing meant to save my life.

Lying there before surgery number five, waiting for the anesthesia, one question kept circling: What if I don’t make it this time?

And that’s when the comfortable list stopped working.

You know the one. Health. Family. Career. The things we say around the table because they sound right.

But when you’re not sure you’ll wake up from surgery… when your wife is burying both her parents while managing your near-death… when the calendar is filled with hospital dates instead of holidays…

You can’t perform gratitude anymore. You have to find out what it actually means.

The clock isn’t just ticking anymore. It’s screaming.

And that’s when I saw it clearly. Not in a hospital room—at a lunch table with my grandson.

Last month, Liam sat next to me after church. He’s twelve. Runs his own business designing 3D models. And he’d been listening to my podcast episode about breakthrough innovations.

He had an idea. A big one.

“It would need way better batteries than we have now, Papa.”

So we went deep—the kind of conversation where you forget a twelve-year-old is asking questions most engineers won’t touch. He’s already thinking about making the impossible possible.

And sitting there, watching him work through the problem, I realized something: This is what survives when I’m gone.

My grandfather would take me to my Uncle Bishop’s tobacco farm in rural Kentucky. When we’d do something wrong—cut a corner, rush through it—we’d hear it: “A job worth doing is worth doing right.”

Almost like a family mantra.

I heard it on that farm. My kids heard it from me. Liam hears it now.

And that line will keep moving forward long after I’m gone. Not because of the accolades. Because of the people.

But here’s what hit me sitting there with Liam: It’s not just him.

It’s you.

Every week for more than twenty years, I’ve been putting out content. Podcasts. Videos. Articles. Not for the downloads. Not for the metrics.

For this exact moment—where something I share gets passed forward. Where you have a conversation with someone younger who needs to hear it. Where you take what works and make it your own.

That’s what legacy actually is. Not the content I create. Not what’s on a shelf. The people we invest time in. The effort we put into helping them become who the future needs.

My legacy is Liam, yes. But it’s also every person who’s taken something from these conversations and shared it forward. That’s you.

That’s the reason the clock screaming doesn’t make me stop. It makes me keep going.

Because you’re going to pass this forward. And that’s what survives.

I turned sixty-five in September. Both my parents died at sixty-eight. The math isn’t encouraging.

So when people ask me why I keep pushing—why I’m still creating content when I can barely type, when I’ve had five surgeries in twelve months—

It’s because I finally understand what I’m grateful for.

Not my health. That’s been failing spectacularly. Not comfort. That ended in March.

I’m grateful I get to see what happens when you invest in people. I’m grateful Liam asks me about batteries over lunch. I’m grateful you’re watching this and thinking about who you’re investing in.

I’m grateful for what the breaking revealed.

That morning when my chest split open? I was terrified. Thinking about everything that could go wrong.

Now? I’m grateful for what it forced me to see. Who shows up. What survives. Why it matters to keep going even when it would be easier to stop.

This week on Studio Notes, I’m telling the full story. The medical mystery that took five surgeries to solve. The conversation with Liam that changed everything. What my wife actually thinks about me writing a second book while recovering from all this. And what gratitude looks like when the comfortable list stops working.

Read the full story on Studio Notes

But here’s what I really want to know: When was the last time you were grateful for something that hurt you?

Not the easy stuff. Not the list you perform around the table. The thing that broke you open. The thing that forced you to see differently.

Drop it in the comments. Tell me what you found inside the breaking.

Because maybe that’s what Thanksgiving is actually for. Learning what gratitude looks like when everything breaks. And discovering that what survives isn’t what we thought.

Happy Thanksgiving.

To learn more about gratitude through hardship, listen to this week's show: The Clock is Screaming (And My Grandson is Listening).

Get the tools to fuel your innovation journey → Innovation.Tools https://innovation.tools

In August 2025, Polish researchers tested something nobody had thought to check: what happens to doctors' skills after they rely on AI assistance? The AI worked perfectly—catching problems during colonoscopies, flagging abnormalities faster than human eyes could. But when researchers pulled the AI away, the doctors' detection rates had dropped. They'd become less skilled at spotting problems on their own.

We're all making decisions like this right now. A solution fixes the immediate problem—but creates a second-order consequence that's harder to see and often more damaging than what we started with.

Research from Gartner shows that poor operational decisions cost companies upward of 3% of their annual profits. A company with $5 billion in revenue loses $150 million every year because managers solved first-order problems and created second-order disasters.

You see this pattern everywhere. A retail chain closes underperforming stores to cut costs—and ends up losing more money when loyal customers abandon the brand entirely. A daycare introduces a late pickup fee to discourage tardiness—and late pickups skyrocket because parents now feel they've paid for the privilege.

The skill that separates wise decision-makers from everyone else isn't speed. It's the ability to ask one simple question repeatedly: “And then what?”

First-order thinking asks: “What happens if I do this?”

Second-order thinking asks: “And then what? And then what after that?”

Most people stop at the first question. They see the immediate consequence and act. But every action creates a cascade of effects, and the second and third-order consequences are often the opposite of what we intended.

Think about social media platforms. First-order? They connect people across distances. Second-order? They fragment attention spans and fuel polarization.

The difference isn't about being cautious—it's about being thorough. In a world where business decisions come faster and with higher stakes than ever before, the ability to trace consequences forward through multiple levels isn't optional anymore.

Let me show you how.

Before we get into the specific strategies, here's what you need to understand: Second-order thinking isn't about predicting the future with certainty. It's about systematically considering possibilities that most people ignore.

The reason most people fail at this isn't lack of intelligence—it's that our brains evolved to focus on immediate threats and rewards. First-order thinking kept our ancestors alive. But in complex modern systems—businesses, markets, organizations—first-order thinking gets you killed.

The good news? This is a learnable skill. You don't need special training or advanced degrees. You need two things: a framework for mapping consequences, and a method for forcing yourself to actually use it.

Two strategies will stop your solutions from creating bigger problems:

Map How People Will Actually Respond – trace your decision through stakeholders, understand what you're actually incentivizing, and predict how the system adapts.

Run the “And Then What?” Drill – force yourself to see three moves ahead before you act, using a simple three-round questioning method.

Let's break down each one.

Here's the fundamental insight that separates good decision-makers from everyone else: People respond to what you reward, not what you intend.

When you make a decision, you're not just choosing an action—you're sending signals into a complex system of human beings who will interpret those signals, adapt their behavior, and create consequences you never imagined. Your job is to trace those adaptations before they happen.

This strategy has three components that work together:

When considering a decision, list everyone it will affect directly and indirectly. Don't just think about your immediate team—think about:

Most executives stop after listing two or three obvious groups. The consequences you miss come from the stakeholders you forgot to consider.

Here's what research shows: Wharton professor Philip Tetlock spent two decades studying how well experts predict future events. His landmark finding? Even highly credentialed experts' predictions were only slightly better than random chance—barely better than a dart-throwing chimp.

But the real insight came when Tetlock discovered that certain people can forecast with exceptional accuracy. These “superforecasters” share one key trait: they relentlessly ask “And then what?” before making predictions. They don't just see the immediate effect. They trace the decision through the entire system.

The people making million-dollar decisions are operating blind beyond the first consequence. Our job is to see what they're missing.

This is where most decisions go wrong. You think you're incentivizing one behavior, but you're actually rewarding something completely different.

Here's the test: For each stakeholder, ask yourself: “What does this decision make easier, more profitable, or less risky for them?”

Quick example: Remember the daycare that introduced a late pickup fee to discourage tardiness? They thought they were incentivizing on-time pickup. But here's what they actually rewarded: guilt-free lateness. Parents who felt terrible about being late now had a clear price for that guilt. The fee didn't discourage the behavior—it legitimized it. Late pickups skyrocketed.

The daycare asked the wrong question. They asked: “What punishment will discourage lateness?” Instead, they should have asked: “What does a $5 fee actually incentivize?”

Another example: You add a performance metric to improve efficiency. First-order thinking says: “People will work more efficiently.” But what are you actually rewarding? Optimizing for the metric—often at the expense of things you didn't measure but actually matter more.

Sales quotas reward closing deals, not necessarily solving customer problems. Employee of the month awards reward visibility, not necessarily the best work. Quarterly earnings targets reward short-term thinking, not building long-term value.

When you rush a hiring decision to fill a role quickly, you're rewarding speed over quality. The second-order effect? Your team learns that urgency matters more than fit, and future hiring suffers.

The pattern: People don't follow the spirit of your policy—they follow the incentives. And they're incredibly creative at finding ways to game systems when the incentives misalign with the goals.

Now that you know who's affected and what you're incentivizing, trace how they'll respond—and then how the system responds to THEIR response.

This is where the stakeholder analysis and incentives analysis combine into real predictive power.

Example: When ride-sharing apps added surge pricing to solve driver shortages, here's how it played out:

First-order: More drivers show up when prices surge. Problem solved, right?

Second-order stakeholder responses:

Third-order systemic effects:

The original problem (driver shortages during peak times) was real. The first-order solution (higher prices attract more drivers) was economically sound. But nobody mapped how customers and drivers would actually respond to the incentives created by surge pricing.

The key insight: Complex systems don't just accept your decisions—they adapt to them. And those adaptations often work directly against your original intent.

Try it now: Pause this video for 30 seconds. Think of one decision your company made in the last year. Who were the stakeholders? How did they actually respond? Was it what you expected?

If their response surprised you—you just found a second-order effect you missed.

Now you have a framework for thinking about consequences. But frameworks don't change behavior—practice does.

This is your daily practice method. Before any significant decision, literally ask yourself “And then what?” at least three times. Out loud. Make it awkward. Make it unavoidable.

Here's why this works: Your brain will naturally stop at the first answer. The question forces you to keep going. It's a cognitive override—a way to fight your brain's preference for first-order thinking.

The Three Rounds:

Round 1: Immediate Consequence State the obvious first-order effect. This should come easily.

“We'll discount our product by 20%.”

And then what?

“We'll attract more customers and gain market share.”

Round 2: Response and Adaptation Now apply Strategy 1. How will stakeholders respond? What are we actually incentivizing?

And then what?

“Competitors will match our discount to protect their market share. And customers will start expecting permanently lower prices—we've trained them that our regular price was inflated. Early adopters who paid full price feel cheated.”

Round 3: Systemic Effects Trace the second-order responses forward. What happens when multiple stakeholders adapt simultaneously?

And then what?

“We're now in a price war. Our margins erode across the entire product line. We can't fund innovation or customer service improvements. Competitors with deeper pockets can outlast us. We've commoditized our own product and destroyed the brand value that justified our original pricing. We're stuck in a race to the bottom.”

The pattern you're looking for: Are the third-order effects consistent with your goals, or do they undermine them?

Most people never get past Round 1. By forcing yourself to Round 3, you'll see patterns others miss.

Try it now: Think of a decision you're facing right now—any decision. Say out loud what happens first. Now say out loud: “And then what?” Answer it. Now say it again: “And then what?”

[5-second pause built into video]

Did Round 3 surprise you? If yes—you just found your blind spot.

Let me walk you through a decision I faced as CTO at HP. We were under pressure to cut R&D spending by 15% to hit quarterly earnings targets.

Round 1: Immediate consequence. “We hit our quarterly numbers. Wall Street is happy. Stock price stays stable. The board is pleased.”

Round 2: Response and adaptation. And then what? “Our best researchers—the ones working on breakthrough projects with 3-5 year horizons—see the writing on the wall. They start looking at competitors who aren't cutting R&D. Meanwhile, the teams that survive shift focus to incremental improvements with shorter payback periods because that's what won't get cut next quarter.”

Round 3: Systemic effects. And then what? “Eighteen months later, our innovation pipeline is empty. We're selling the same products with minor tweaks while competitors who maintained R&D investment launch breakthrough products. We lose market leadership. Now we need to spend 3X what we saved just to catch up—but our best people are already gone.”

We fought that cut. We protected the long-term R&D. Some of those projects became billion-dollar product lines. But I watched other companies make that first-order decision and destroy their innovation capability.

That conversation took maybe five minutes. But it saved HP from years of playing catch-up.

Take a decision you're facing this week—any decision with financial or operational implications.

Write down the decision at the top of a page. Be specific.

List three immediate consequences. These should come easily.

Take each consequence and ask “And then what?” twice. Write down both second-order and third-order effects.

Find which effect you hadn't considered. That's your blind spot.

Do this for one decision this week, and you'll start seeing consequences others don't. Make it a habit, and it becomes automatic—like a chess player who sees five moves ahead.

Right now, in your company, there are people who seem to always be one step ahead. They don't work longer hours. They're not more talented. But somehow, they avoid the disasters others walk into. They see opportunities others miss. They get promoted while others are fixing problems.

Here's their secret: While everyone else celebrates the first-order win, they're already managing the second-order consequences. While you're implementing the solution, they've already anticipated what breaks next.

That gap—between first-order thinking and second-order thinking—is the difference between running in place and actually advancing.

Your challenge: For the next 30 days, before every significant decision, ask “And then what?” three times out loud. Not in your head. Out loud. Make it awkward. Make it unavoidable.

Because the ones who rise aren't the fastest problem-solvers, they're the ones who solve problems that stay solved..

So …  Start asking the question. Three times. Every decision.

The question isn't whether we have time to think this way. It's whether we can afford to keep making decisions that create bigger problems than they solve.

The Thinking 101 series teaches how to think clearly in a world designed to confuse everyone—here's our journey so far:

In Episode 1, we exposed the thinking crisis—AI dependency is creating cognitive debt, and independent thinking has become the most valuable skill in the modern world.

In Episode 2, we learned to distinguish deductive certainty from inductive probability and stop treating patterns as proven facts.

In Episode 3, we discovered how to distinguish true causation from mere correlation—saving ourselves from solving the wrong problem perfectly.

In Episode 4, we learned how to harness the power of analogies while avoiding their traps—generating useful comparisons systematically and spotting false analogies that manipulate thinking.

In Episode 5, we mastered probabilistic thinking—how to make decisions with incomplete information and act wisely when nothing is guaranteed.

Today, in Episode 6, we learned how to stop our decisions from creating bigger problems—mapping how people actually respond to our decisions, understanding what we are truly incentivizing, and asking “And then what?” until we see patterns others miss.

Up next—Episode 7: “Proportional & Numerical Thinking—Understanding Scale and Magnitude.” We will learn how to think in terms of scale, ratios, and relative magnitude—understanding when numbers matter and when they don't, spotting statistical tricks used to mislead, and developing intuition about large numbers that most people lack.

Hit that subscribe button so you don't miss future episodes. Also—hit the like and notification bell. It helps with the algorithm so others see our content. Why not share this video with a colleague who you think would benefit from it?

Because right now, while you've been watching this, someone just made a decision that solves today's problem perfectly—and just created three bigger problems for next quarter. The only question is: will you be the one who sees them coming?

To learn more about second-order thinking, listen to this week's show: Second-Order Thinking: How to Stop Your Decisions From Creating Bigger Problems.

Get the tools to fuel your innovation journey → Innovation.Tools https://innovation.tools

On Second-Order Thinking and Decision-Making

Marks, H. (2011). The Most Important Thing: Uncommon Sense for the Thoughtful Investor. Columbia University Press.

Dalio, R. (2017). Principles: Life and Work. Simon & Schuster.

Tetlock, P. E., & Gardner, D. (2015). Superforecasting: The Art and Science of Prediction. Crown Publishers.

On Systems Thinking and Consequences

Meadows, D. H. (2008). Thinking in Systems: A Primer. Chelsea Green Publishing.

Senge, P. M. (1990). The Fifth Discipline: The Art & Practice of The Learning Organization. Currency.

On Incentives and Unintended Effects

Levitt, S. D., & Dubner, S. J. (2005). Freakonomics: A Rogue Economist Explores the Hidden Side of Everything. William Morrow.

Munger, C. T. (1995). The Psychology of Human Misjudgment. Speech presented at Harvard Law School.

Note: All sources cited in this episode have been accessed and verified as of November 2025.

You're frozen. The deadline's approaching. You don't have all the data. Everyone wants certainty. You can't give it. Sound familiar?

Maybe it's a hiring decision with three qualified candidates and red flags on each one. Or a product launch where the market research is mixed. Or a career pivot where you can't predict which path leads where. You want more information. More time. More certainty. But you're not going to get it.

Meanwhile, a small group of professionals—poker players, venture capitalists, military strategists—consistently make better decisions than the rest of us in exactly these situations. Not because they have more information, but because they've mastered something fundamentally different: they think in probabilities, not certainties. I learned this the hard way—I once created a biometric security algorithm that the NSA reverse-engineered, where I mastered probabilistic thinking perfectly in the technology, then made every wrong bet with the business around it.

By the end of this episode, you'll possess a powerful mental toolkit that transforms how you approach uncertainty. You'll learn to estimate likelihoods without perfect data, update your beliefs as new information emerges, make confident decisions when multiple uncertain factors collide, and act decisively even when you can't guarantee the outcome. This is the difference between paralysis and power, between gambling recklessly and betting wisely.

But what does probabilistic thinking actually entail? At its core, it's the practice of reasoning in terms of likelihoods rather than absolutes—thinking in percentages instead of yes-or-no answers. Instead of asking “Will this work?” you ask “What are the odds this will work, and what are the consequences if it doesn't?” This approach acknowledges that the future is uncertain and that every decision carries risk. By quantifying that uncertainty and weighing it against potential outcomes, you make smarter choices even when you can't eliminate the unknown.

Today's world punishes those who demand certainty before acting. Research from Oracle's 2023 Decision Dilemma study—which surveyed over 14,000 employees and business leaders across 17 countries—found that 86% feel overwhelmed by the amount of data available to them. Rather than clarity, all that information creates decision paralysis.

And the paralysis has real consequences. When we can't be certain, we freeze. We endlessly research options, seeking that final piece of data that will guarantee success. We postpone critical decisions, waiting for perfect information that never arrives. Meanwhile, opportunities pass us by, problems grow worse, and competitors who are comfortable with uncertainty move forward.

This demand for certainty doesn't just slow us down—it exhausts us. Decision fatigue sets in as we agonize over choices, draining our mental resources until we either make impulsive decisions or avoid deciding altogether. Neither outcome serves us well.

Here's what it looks like in real life: You're the VP of Marketing. Your CMO wants a decision on next quarter's campaign budget by Friday. You have three agencies to choose from, each with strengths and weaknesses. So you ask for more data. Customer focus groups. Competitive analysis. Agency references. By Wednesday you're drowning in spreadsheets and conflicting opinions.

Friday arrives. You still can't be certain which choice is right, so you ask for an extension. Two weeks later, you finally pick one—not because you're confident, but because you're exhausted and the CMO is furious about the delay. The campaign launches late. You've burned political capital. And you still have no idea if you made the right choice.

Meanwhile, your competitor's marketing VP looked at the same decision, spent two hours assessing the probabilities, and launched on time. If it works, great. If it doesn't, they'll pivot. They didn't need certainty. They needed enough information to make a good bet.

That's the tax you pay for demanding certainty: missed timing, exhausted teams, and decisions made from fatigue rather than judgment.

Meanwhile, a small group of professionals thrives in these exact conditions. Professional poker players like Annie Duke understand that good decisions sometimes lead to bad outcomes and bad decisions sometimes get lucky—so they judge their choices by process, not results. Venture capitalists often see that most of their investments will fail, but they bet anyway because one success out of twenty can return the entire fund. Military strategists make life-and-death decisions with incomplete intelligence, not because they're reckless, but because waiting for perfect information means defeat.

The difference isn't access to better information. It's the willingness to act on probabilities rather than certainties.

So how do you actually develop this skill? It's more accessible than you might think. Here are clear strategies to transform how you process uncertainty and make decisions.

The first shift in probabilistic thinking is abandoning single-number estimates for ranges of possibility. When most people predict an outcome, they pick one number: “Sales will be $500,000 next quarter” or “This project will take three months.” But the world doesn't work that way. Every estimate carries uncertainty, and pretending otherwise sets you up for failure.

Professional forecasters think differently. They don't ask “What will happen?” They ask “What's the range of plausible outcomes, and how likely is each?” This approach forces you to acknowledge what you don't know while still making useful predictions.

Watch a professional poker player deciding whether to call a bet. They're not thinking “Do I have the best hand?” They're thinking “Given what I've seen, maybe 35% chance I have the best hand, 20% chance my opponent is bluffing, 45% chance they've got me beat.” They act on probabilities, not certainties.

Steps to implement range thinking:

One of the most powerful aspects of probabilistic thinking is treating your beliefs as hypotheses, not conclusions. When new information emerges, skilled thinkers update their probability estimates rather than clinging to their original position. This practice—called Bayesian updating after the mathematician Thomas Bayes—is how professionals stay accurate in changing environments.

Consider a doctor diagnosing a patient with intermittent chest pain. Initially, based on the patient's age and health history, she estimates a 15% probability of heart disease. Then the EKG comes back with minor abnormalities—not definitive, but concerning. She updates her estimate to 35%. Blood work shows elevated cardiac markers. Now she's at 65%. Each piece of evidence shifts the probability, but none gives absolute certainty. She doesn't wait for 100% certainty to act—she orders more tests and starts precautionary treatment based on her updated 65% estimate. That's Bayesian thinking in action.

Financial firms continuously adjust their models as new data arrives. Weather forecasters update storm predictions hourly. In my own work building biometric security systems, we updated our false acceptance and rejection rates constantly—but I failed to apply that same updating framework to the business model itself.

The enemy of updating is confirmation bias—our tendency to accept information that supports our existing beliefs and dismiss information that contradicts them. When you're emotionally invested in being right, you'll unconsciously filter evidence to protect your original view.

Steps to update your thinking:

Probabilistic thinking isn't just about estimating odds—it's about acting on them. The concept of expected value gives you a framework for making decisions when outcomes are uncertain. Expected value multiplies each possible outcome by its probability, then adds them together. It's how professionals decide whether a bet is worth taking.

Here's why it matters: sometimes a decision with a low probability of success is still the right choice if the potential payoff is enormous. Venture capitalists know that perhaps 18 out of 20 startups in their portfolio will fail or return little money. But that one company that becomes the next Airbnb or Uber can return 100x their investment—more than covering all the losses. That's positive expected value thinking.

Conversely, decisions that seem “safe” can be terrible bets. Playing it safe might give you a 90% chance of mediocre success, but if that 10% downside risk includes catastrophic consequences, the expected value might be negative. This is why you buy insurance: the probability of your house burning down is low, but the cost if it happens is devastating.

Think about a parent choosing between schools for their child. Public school is free but overcrowded. Private school costs $20K/year with smaller classes but adds an hour of family stress daily. Charter school is free with innovative curriculum but it's a first-year program with unknowns. There's no guarantee. The better question is expected value: “Given the probabilities and what matters most to us—academic success, family time, financial stability—which bet has the best expected outcome?”

Steps for expected value decision-making:

A practical way to develop your probabilistic thinking is to keep a Probability Forecast Journal. This exercise builds calibration—your ability to accurately assess how confident you should be in your predictions.

Here's how to implement it:

The goal isn't perfection—it's calibration. After several months of this practice, you'll notice your ability to assess probabilities improves dramatically. You'll know when you're 60% sure versus 90% sure, and you'll make better decisions as a result.

Mastering probabilistic thinking is a journey, not a destination. It requires practice, humility about what you don't know, and the courage to act despite uncertainty. But the rewards are substantial.

When you think probabilistically, you make faster decisions because you're not paralyzed waiting for certainty that will never come. You become more resilient to failure because you understand that good decisions sometimes have bad outcomes—and that's not a reason to change your approach.

You'll find yourself taking calculated risks that others avoid, capturing opportunities that demand action before perfect information arrives. You'll waste less time second-guessing yourself because you've already thought through the probabilities and made your peace with uncertainty. You'll explain your decisions more clearly to others because you can articulate not just what you think will happen, but how confident you are and why.

Most importantly, you'll stop confusing confidence with correctness. In a world obsessed with appearing certain, probabilistic thinkers have the courage to say “I'm 65% sure, and that's enough to act.” That honesty—with yourself and others—is the foundation of better judgment.

Want to see what happens when you master probabilistic thinking in one domain but fail to apply it in another? I wrote about my experience creating a fingerprint recognition algorithm that the NSA reverse-engineered—where I got the technical probabilities right and the business bets completely wrong. 

Read the full story here

The future will always be uncertain. The question is whether you'll be paralyzed by that uncertainty or empowered by it.

If this helped you think differently about decision-making, I'd really appreciate it if you'd hit the like button and subscribe—it genuinely helps others find this content through the algorithm. And click that notification bell so you don't miss the next episode in this series.

If you want to go deeper, I share the behind-the-scenes thinking, mistakes, and extended stories over on Studio Notes on Substack. Paid subscriptions help cover the costs of the team who makes all of this possible—the editing, research, and production work that gets these episodes to you each week. None of it comes to me; it all goes to supporting them. Without this team, there'd be no podcast, no YouTube channel, no articles. So if you find value in this work, that's a meaningful way to keep it going.

The future will always be uncertain. The question is whether you'll be paralyzed by it or empowered by it.

To learn more about probabilistic thinking, listen to this week's show: How To Make Better Decisions When Nothing Is Certain.

Get the tools to fuel your innovation journey → Innovation.Tools https://innovation.tools

Oracle Decision Dilemma Study (2023) – Survey of 14,000+ employees and business leaders across 17 countries on data overwhelm and decision paralysis. https://www.oracle.com/uk/cloud/decision-dilemma/

Thinking in Bets – Duke, A. (2018). Portfolio. On judging decisions by process, not outcomes. https://www.penguinrandomhouse.com/books/552885/thinking-in-bets-by-annie-duke/

How to Improve Bayesian Reasoning Without Instruction: Frequency Formats – Gigerenzer, G. & Hoffrage, U. (1995). Psychological Review, 102(4), 684-704. On updating beliefs with evidence.

Prospect Theory: An Analysis of Decision under Risk – Kahneman, D. & Tversky, A. (1979). Econometrica, 47(2), 263-291. Prospect Theory foundations.

Try to go through a day without using an analogy. I guarantee you'll fail within an hour. Your morning coffee tastes like yesterday's batch. Traffic is moving like molasses. Your boss sounds like a broken record. Every comparison you make—every single one—is your brain's way of understanding the world. You can't turn it off.

When someone told you ChatGPT is “like having a smart assistant,” your brain immediately knew what to expect—and what to worry about. When Netflix called itself “the HBO of streaming,” investors understood the strategy instantly. These comparisons aren't just convenient—they're how billion-dollar companies are built and how your brain actually learns.

The person who controls the analogy controls your thinking. In a world where you're bombarded with new concepts every single day—AI tools, cryptocurrency, remote work culture, creator economies—your brain needs a way to make sense of it all. By the end of this episode, you'll possess a powerful toolkit for understanding the unfamiliar by connecting it to what you already know—and explaining complex ideas so clearly that people wonder why they never saw it before.

Thinking in analogies—or what's called analogical thinking—is how the greatest innovators, communicators, and problem-solvers operate. It's the skill that turns confusion into clarity and complexity into something you can actually work with.

But what does analogical thinking entail? At its core, it's the practice of understanding something new by comparing it to something you already understand. Your brain is constantly asking: “What is this like?” When you learned what a virus does to your computer, you understood it by comparing it to how biological viruses infect living organisms. When someone explains blockchain as “a shared spreadsheet that no one can erase,” they're using analogy to make an abstract concept concrete.

Researchers have found something remarkable: your brain doesn't actually store information as facts—it stores it as patterns and relationships. When you learn something new, your brain is literally asking “What does this remind me of?” and building connections to existing knowledge. Analogies aren't just helpful for communication—they're the fundamental mechanism of human understanding. You can't NOT think in analogies. The question is whether you're doing it consciously and well, or unconsciously and poorly.

The quality of your analogies determines how quickly you learn, how deeply you understand, and how effectively you can explain ideas to others.

Remember this: whoever controls the analogy controls the conversation. Master this skill, and you'll never be at the mercy of someone else's framing again.

Thinking in analogies is a double-edged sword. I learned this the hard way.

A few years ago, I watched a brilliant engineer struggle to explain a revolutionary idea to executives. He had the data, the logic, the technical proof—but he couldn't get buy-in. Then someone in the room said, “So it's basically like Uber, but for industrial equipment?” Instantly, heads nodded. Funding approved. Project greenlit. One analogy did what an hour of explanation couldn't.

Six months later, that same analogy killed the project. Because “Uber for equipment” came with assumptions—about pricing, about scale, about network effects—that didn't actually apply. The team kept forcing their solution to fit the analogy instead of recognizing when the comparison broke down. I watched millions of dollars and two years of work disappear because nobody questioned whether the analogy was still serving them.

The same mental shortcut that helps you understand new things can also trap you in outdated patterns.

Consider Quibi's spectacular failure. In 2020, Jeffrey Katzenberg and Meg Whitman launched a streaming service with $1.75 billion in funding—more than Netflix had when it started. Their analogy? “It's like TV shows, but designed for your phone.” They created high-quality 10-minute episodes optimized for mobile viewing. Six months later, Quibi shut down.

What went wrong? The analogy was flawed. They assumed mobile viewing was like TV viewing, just shorter. But people don't watch phones the way they watch TV—they watch phones while doing other things, in stolen moments, with interruptions. YouTube and TikTok understood this. They built for distraction and fragmentation. Quibi built for focused attention that didn't exist. That misunderstanding burned through nearly $2 billion in 18 months.

We see this constantly where complex issues get reduced to simplistic analogies that feel intuitive but lead to flawed conclusions. Someone compares running a country to running a household budget—”If families have to balance their budgets, why shouldn't governments?” The analogy sounds intuitive, but it ignores that countries can print currency, carry strategic long-term debt, and operate on completely different time horizons than households.

The cost of bad analogical thinking is enormous. You waste time applying solutions that worked in one context to problems where they don't fit. You miss opportunities because you're trying to squeeze new situations into old patterns. And worst of all, you become easy to manipulate—because anyone who controls your analogies controls how you think.

So how do we harness the power of analogy while avoiding its traps? Let me show you five essential strategies that will transform how you use comparison to understand your world.

The first skill is learning to create useful analogies on demand. Most people wait for analogies to pop into their heads randomly, but you can develop a systematic process for generating them whenever you need one.

Map the structure of what you're trying to understand, then search for similar structures in domains you know well. Netflix's recommendation algorithm didn't come from studying other algorithms—it came from asking “How do humans recommend things?” and mapping that social process onto a technical system.

Steps to generate powerful analogies:

Most people fall in love with an analogy and push it beyond its useful range. A powerful analogy becomes a dangerous one the moment you forget it's just a comparison, not reality itself.

The human brain loves patterns, and once we find one that works, we want to apply it everywhere. This is how we end up with terrible advice like “Just be yourself in job interviews” because “authentic relationships require honesty”—taking an analogy from personal relationships and stretching it to professional contexts where it doesn't fit.

How to recognize the breakdown:

A good analogy is a map, not the territory. The moment you forget that, you're lost.

Analogies are your secret weapon for making complicated concepts accessible to anyone. The person who can explain quantum physics using everyday comparisons has a superpower in our information-saturated world.

Match the analogy to your audience's knowledge and choose comparisons that illuminate rather than obscure.

The explanatory analogy playbook:

People will use analogies to manipulate your thinking—sometimes intentionally, sometimes not. Workplace debates are full of analogical arguments: “Remote work is like letting students do homework unsupervised—productivity will plummet.” But is professional work really like homework? The analogy assumes similarities that may not exist.

Recognizing false analogies protects you from being intellectually hijacked. When someone uses comparison to make their argument, your job is to evaluate whether the comparison is valid.

Your defense against false analogies:

The final strategy is long-term: deliberately expand your collection of mental models and experiences so you have more source material for analogies. The person who only knows their own industry can only draw comparisons from that narrow domain. But someone who reads widely, pursues diverse experiences, and studies multiple fields can make unexpected connections.

Steve Jobs famously took a calligraphy class—years later, those insights about typeface and design influenced the Mac's revolutionary interface. The analogy between typographic beauty and digital design wouldn't have been available without that cross-domain experience.

Building your source material:

A practical and effective way to develop this skill is to practice explaining concepts across contexts.

Here's how you can sharpen your ability to think in analogies:

This exercise trains you to think flexibly, draw connections across domains, and understand the mechanics of what makes analogies work or fail. The more you practice, the more naturally these comparisons will come to you when you need them.

Mastering analogical thinking is a journey, not a destination. It requires constant practice, intellectual curiosity, and the humility to recognize when your comparisons break down.

But the rewards are transformative. You'll learn faster by connecting new information to what you already know. You'll explain complex ideas with clarity that makes you invaluable in any professional setting. You'll spot flawed reasoning in arguments before others even notice something's wrong. You'll generate creative solutions by borrowing patterns from unexpected domains.

Most importantly, you'll develop the mental flexibility to navigate an increasingly complex world. When AI reshapes your industry, you'll understand it by comparison to previous technological disruptions. When new social dynamics emerge, you'll make sense of them by recognizing familiar patterns in new contexts.

The best thinkers aren't those who memorize the most facts—they're those who see connections others miss. Steve Jobs didn't invent the smartphone—he saw that a phone could be like a computer in your pocket. Jeff Bezos didn't invent retail—he saw that a bookstore could be like an infinite warehouse. Every breakthrough starts with someone asking “What if this is like that?”

That's the power of thinking in analogies. And now you have the tools to make it yours.

The Thinking 101 series is teaching you how to think clearly in a world designed to confuse you—here's our journey so far:

In Episode 1, we exposed the thinking crisis—AI dependency is creating cognitive debt, and independent thinking has become the most valuable skill in the modern world.

In Episode 2, you learned to distinguish deductive certainty from inductive probability and stop treating patterns as proven facts.

In Episode 3, you discovered how to distinguish true causation from mere correlation—saving yourself from solving the wrong problem perfectly.

Today, you learned how to harness the power of analogies while avoiding their traps—generating useful comparisons systematically, recognizing when analogies break down, and spotting false analogies that manipulate thinking.

Up next—Episode 5: “Probabilistic Thinking—Living with Uncertainty.” You'll learn how to think in probabilities rather than certainties, make decisions with incomplete information, and act wisely when nothing is guaranteed.

Hit that subscribe button so you don't miss future episodes. Also—hit the like and notification bell. It helps with the algorithm so others see our content. Why not share this video with a colleague who you think would benefit from it?

Because right now, while you've been watching this, someone just pitched a billion-dollar idea using a flawed analogy—and investors nodded along because it “sounded like” something that worked before. The only question is: will you be the one who sees through it?

To learn more about thinking in analogies, listen to this week's show: You Think in Analogies Every Day (And You're Doing It Wrong).

Get the tools to fuel your innovation journey → Innovation.Tools https://innovation.tools

On Analogical Reasoning and Cognition

Gentner, D., Holyoak, K. J., & Kokinov, B. N. (Eds.). (2001). The Analogical Mind: Perspectives from Cognitive Science. MIT Press.

On Thinking and Decision-Making

On Innovation and Cross-Domain Learning

Note: All sources cited in this episode have been accessed and verified as of October 2025.

$37 billion. That's how much gets wasted annually on marketing budgets because of poor attribution and misunderstanding of what actually drives results. Companies' credit campaigns that didn't work. They kill initiatives that were actually succeeding. They double down on coincidences while ignoring what's actually driving outcomes.

Three executives lost their jobs this month for making the same mistake. They presented data showing success after their initiatives were launched. Boards approved promotions. Then someone asked the one question nobody thought to ask: “Could something else explain this?” The sales spike coincided with a competitor going bankrupt. The satisfaction increase happened when a toxic manager quit. The correlation was real. The causation was fiction. This mistake derailed their careers.

But here's the good news: once you see how this works, you'll never unsee it. And you'll become the person in the room who spots these errors before they cost millions.

But first, you need to understand what makes this mistake so common—and why even smart people fall for it every single day.

At its core, causal thinking is the practice of identifying genuine cause-and-effect relationships rather than settling for surface-level associations. It's asking not just “do these things happen together?” but “does one actually cause the other?”

This skill means you look beyond patterns and correlations to understand what's actually producing the outcomes you're seeing. When you think causally, you can spot the difference between coincidence, correlation, and true causation—a distinction that separates effective decision-makers from those who waste millions on solutions that were never going to work.

Across every domain of professional life, this confusion costs fortunes and derails careers.

A SaaS company sees customer churn decrease after implementing new onboarding emails—and immediately scales it company-wide. What they missed: they launched the emails the same week their biggest competitor raised prices by 40%. The competitor's pricing reduced churn. But they'll never know, because they never asked the question. Six months later, when they face real churn issues, they keep doubling down on emails that never actually worked.

This happens outside of work too. You start taking a new vitamin, and two weeks later your energy improves. But you started taking it in early March—right when days got longer and you began going outside more. Was it the vitamin or the sunlight and exercise? Most people credit the vitamin without asking the question.

But here's the good news: once you understand how to think causally, these mistakes become obvious. And one of these five strategies can be used in your very next meeting—literally 30 seconds from now. Let me show you how.

Mastering causal thinking isn't about becoming a statistician or learning complex formulas. It's about developing five practical strategies that work together to reveal what's really driving results. These build on each other—starting with basic tests you can apply right now, and progressing to a complete system you can use for any decision.

Think of these as your checklist for evaluating any causal claim.

The Three Tests:

Here's a technique you can implement in the next 30 seconds that will immediately improve your causal thinking: whenever someone presents a causal claim, ask out loud: “Could something else explain this?”

This single question is remarkably powerful. It forces the speaker to consider hidden factors they ignored. It reveals whether they've actually done causal analysis or just noticed a correlation and declared victory. It shifts the conversation from assumption to examination.

Try it in your next meeting when someone says “We did X and Y improved.” Watch how often they haven't considered alternatives. Watch how often their confident causal claim becomes less certain when forced to address this simple question.

Most people present correlations as causations without even realizing it. Your question makes that leap visible. Suddenly they have to justify it with evidence or back down. It's not confrontational—it's curious. And curiosity is the foundation of good causal thinking.

Use it today. Use it every time someone attributes an outcome to a cause without ruling out alternatives.

That question leads us naturally to our next strategy—learning to identify what those “something elses” actually are.

A confounding variable is a third factor that influences both your suspected cause and your observed effect. It creates the illusion of a direct relationship where none exists.

Here's a simple example: ice cream sales and drowning deaths both increase during summer months. Does ice cream cause drowning? Obviously not. The confounding variable is warm weather, which causes both more ice cream purchases and more swimming.

Now here's the business version: A retail company sees both customer satisfaction and sales increase after renovating their stores. Does the renovation cause higher satisfaction? Maybe—but both also increased because they renovated during the holiday shopping season when people are generally happier and spending more anyway. Same logical structure. Same expensive mistake if they conclude renovations always boost satisfaction.

Once you can spot these hidden causes, you're ready to understand why your brain makes these mistakes in the first place. And this next one? It's probably happening in your head right now without you realizing it.

Your brain is wired to see causal connections everywhere, even where none exist. This isn't a design flaw—it's a survival mechanism that kept your ancestors alive. But in the modern business world, this pattern-seeking instinct can mislead you.

Your brain wants simple causal stories. Reality is usually more complex. Once you know what to watch for, you can catch yourself before making these errors.

Here's how this plays out: A manager believes remote work hurts productivity. She notices every time someone's late to a Zoom call. But she doesn't notice the three on-time people. She remembers the one missed deadline but forgets the five delivered early. Her brain is filtering reality to confirm what she already believes.

Now that you understand how your brain works, let's put this all together into a practical system you can use every time you need to make a high-stakes decision.

Mastering causal thinking requires more than understanding principles—it demands a clear approach you can apply when the stakes are high and the pressure is on.

The Five-Question Causation Check:

You now have the complete framework for causal thinking—five strategies that work together to reveal what's really causing what.

But here's what separates people who learn this from people who actually use it—one simple practice you can do this week that makes this framework automatic.

A practical and effective way to internalize these strategies is through practice with real-world scenarios from your actual work.

Here's how to conduct your own causal analysis:

With practice, you'll become skilled at spotting false causation and identifying true cause-and-effect relationships. This skill compounds over time, making you more valuable with every analysis you conduct.

So what does this actually get you? Let me paint the picture of what changes when you master this skill.

The rewards of mastering causal thinking are well worth the effort and will compound throughout your career.

You become immune to the most expensive mistakes in business—the ones where you solve the wrong problem perfectly. When everyone else is celebrating a correlation as success, you'll be asking the questions that reveal what's really driving outcomes. Imagine being in a meeting where leadership is about to allocate $2 million to scale an initiative, and you're the one who asks the question that reveals a competitor's bankruptcy actually caused the results. That's career-defining value.

Your strategic recommendations carry weight because they're based on actual causation rather than hopeful patterns. Leaders who can distinguish between correlation and causation make decisions that actually work. When your predictions prove accurate while others' fail, your credibility compounds—you become the person everyone turns to when stakes are high.

You develop the intellectual humility that marks exceptional leaders. Causal thinking teaches you to question your initial judgments, seek alternative explanations, and change your mind when evidence demands it. These qualities don't just make you a better thinker—they make you someone others trust with important decisions.

So take these strategies and practice them. Apply them in your daily work. Question causal claims, hunt for hidden causes, check your biases, and use the systematic process. This makes you a more effective decision-maker, a more credible advisor, and someone who spots opportunities and avoids disasters that others miss entirely.

And you'll become the person in the room everyone listens to when the stakes are high.

In Episode 1, “Why Thinking Skills Matter Now More Than Ever,” we exposed the crisis: your thinking ability is collapsing, AI dependency is creating cognitive debt, and those who can't think independently will be left behind.

In Episode 2, “How To Improve Your Logical Reasoning Skills,” you learned to distinguish deductive certainty from inductive probability, calibrate your confidence to match your evidence, and stop treating patterns as proven facts.

Today, you learned how to distinguish true causation from mere correlation—saving yourself from expensive mistakes where you solve the wrong problem perfectly.

Up next—Episode 4: “Analogical Thinking—The Power of Comparison.” Your brain doesn't learn through pure logic—it learns by comparison. Every breakthrough idea came from someone who made an unexpected connection. You'll learn how to generate insights through analogy, recognize when comparisons break down, and spot when others use false analogies to manipulate you.

Hit that subscribe button so you don't miss future episodes. Also—hit the like and notification bell. It helps with the algorithm so others see our content. Why not share this video with a colleague who you think would benefit from it?

Because right now, while you've been watching this, someone just approved a million-dollar budget based on a correlation they mistook for causation. The only question is: will you be the one who catches it?

To learn more about mastering causal thinking, listen to this week's show: How To Master Causal Thinking.

Get the tools to fuel your innovation journey → Innovation.Tools https://innovation.tools

On Causation and Decision-Making Pearl, J., & Mackenzie, D. (2018). The Book of Why: The New Science of Cause and Effect. Basic Books.

On Thinking Clearly Kahneman, D. (2011). Thinking, Fast and Slow. Farrar, Straus and Giroux.

On Statistical Reasoning Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press.

Note: All sources cited in this episode have been accessed and verified as of October 2025.