The Innovators Studio with Phil McKinney

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

Welcome to this week's show. I'm recording this episode from my hotel room here in Las Vegas, Nevada, at the annual Consumer Electronics Show 2026.

If you've been around this channel 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 on this channel—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?

I do have some notes here that I'll be glancing at as we go through this today, and we'll be splicing in videos I took on the show floor, along with video supplied to us by CES, to give you a feel for what was here and what's going on.

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.

Make sure you subscribe, hit the like button, and give us a thumbs up. It all helps with the algorithm.

Have a great week, and we'll talk to you next week. Bye-bye.

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.

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

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.

1. Identify a decision you're currently facing. Something real. Something that matters. Write it in one sentence.
2. Check your map. What assumptions are you making? Where did they come from? Who might see this differently?
3. Invert it. Set a timer for five minutes. List every way you could guarantee failure. Be ruthless.
4. Run the premortem. It's one year from now. You chose wrong. Write two paragraphs explaining what happened.
5. Find the overlap. Where do your inversion list and premortem story agree? That's your highest-risk blind spot.
6. Take one action. What's one step you can take this week to address your biggest risk?

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

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1. Rogers Commission Report, Volume 2, Appendix F: "Personal Observations on Reliability of Shuttle" by Richard Feynman (1986). Management estimated 1 in 100,000; engineers and post-Challenger analysis found approximately 1 in 100.
2. Konovalov, A. & Krajbich, I. "Mouse tracking reveals structure knowledge in the absence of model-based choice." Nature Communications (2020). Participants followed optimal strategies only about 20% of the time even when they demonstrably knew them.
3. Korzybski, Alfred. Science and Sanity: An Introduction to Non-Aristotelian Systems and General Semantics (1933).
4. Wikipedia, "Nevermind"; SonicScoop, "Time and Cost of Making an Album Case Study: NIRVANA" (2017). Initial recording budget was $65,000.
5. Mitchell, D.J., Russo, J.E., & Pennington, N. "Back to the future: Temporal perspective in the explanation of events." Journal of Behavioral Decision Making (1989). As cited in Klein, G. "Performing a Project Premortem." Harvard Business Review (2007).
6. Schoemaker, P.J.H. & Day, G.S. "How to Make Sense of Weak Signals." MIT Sloan Management Review (2009). Describes how Enron Federal Credit Union survived the Enron collapse through scenario planning.
7. CB Insights. "The Top 12 Reasons Startups Fail." Analysis of 111 startup post-mortems (2021). 42% cited "no market need" as a reason for failure.

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.

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

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: https://philmckinney.substack.com/p/what-im-actually-thankful-for-after

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.

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.

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:

• Your customers (current and potential)

• Your competitors (how will they respond?)

• Your suppliers and partners

• Your employees at different levels

• Your investors or board

• Regulatory bodies or industry watchdogs

• Adjacent markets or ecosystems

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:

• Customers started waiting out surge periods, meaning fewer overall rides

• Drivers started gaming the system—turning off their apps to create artificial shortages that triggered surges

• Competitors without surge pricing captured price-sensitive customers

• Media coverage made "surge pricing" synonymous with price gouging, damaging brand trust

Third-order systemic effects:

• The solution trained customers to use the service less frequently

• It taught drivers to manipulate the platform rather than respond to genuine demand

• It created a PR vulnerability that regulators could exploit

• The very mechanism designed to solve shortages created new shortages through gaming behavior

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?

[5-second pause built into video]

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?

1. Cost of Poor Operational Decisions Rathindran, R. (2018, December 20). Gartner Says Bad Financial Decisions by Managers Cost Firms More Than 3 Percent of Profits. Gartner Press Release. https://www.gartner.com/en/newsroom/press-releases/2018-12-20-gartner-says-bad-financial-decisions-by-managers-cost-firms-more-than-3-percent-of-profits

2. Expert Forecasting Accuracy and Second-Order Thinking Tetlock, P. E., & Gardner, D. (2015). Superforecasting: The Art and Science of Prediction. Crown Publishers.

3. AI Impact on Medical Diagnostic Skills Romańczyk, M., et al. (2025). Endoscopist deskilling risk after exposure to artificial intelligence in colonoscopy: A multicentre, observational study. Lancet Gastroenterology & Hepatology. As reported by NPR Health News, August 19, 2025. https://www.npr.org/sections/shots-health-news/2025/08/19/nx-s1-5506292/doctors-ai-artificial-intelligence-dependent-colonoscopy

4. Unintended Consequences of Incentive Systems Merton, R. K. (1936). The unanticipated consequences of purposive social action. American Sociological Review, 1(6), 894-904.

5. Second-Order Effects in Economics Henderson, D. R. (2018). Unintended consequences. In The Concise Encyclopedia of Economics. Library of Economics and Liberty. https://www.econlib.org/library/Enc/UnintendedConsequences.html

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.