Killer Innovations with Phil McKinney

Why do smart leaders make terrible choices about breakthrough ideas? Phil McKinney spent 40 years making high-stakes innovation decisions — as HP's CTO and now CEO of CableLabs. Each week, he shares the thinking frameworks and decision patterns that separate breakthroughs from expensive mistakes. No theory. No hype. Just what actually works. Running weekly since 2005. Full archive at KillerInnovations.com

How to Stop Overthinking Your Decisions 01/28/2026

How to Stop Overthinking Your Decisions You've got a decision you've been putting off. Maybe it's a career move. An investment. A difficult conversation you keep rehearsing in your head but never starting. You tell yourself you need more information. More data. More time to think. But you're not gathering information. You're hiding behind it. What looks like due diligence is actually overthinking in disguise. The certainty you're waiting for doesn't exist. It won't exist until after you decide and see what happens. I call this mindjacking: when something hijacks your ability to think for yourself. Sometimes it's external. Algorithms, experts, crowds thinking for you. But sometimes you're the one doing it. That endless research? It feels like diligence. It functions as delay. You're not being thorough. You're mindjacking yourself. Today, you'll learn a framework for knowing when you have enough information, even when it doesn't feel like enough. Because deciding before you're ready isn't recklessness. It's a skill. And for most people, that skill has completely atrophied. The Real Cost of Waiting At a California supermarket, researchers set up a tasting booth for gourmet jams. Some days, the display showed 24 varieties. Other days, just six. The bigger display attracted more attention. Sixty percent of people stopped to look. But only three percent actually bought jam. When shoppers saw just six options? Thirty percent purchased. Ten times the conversion rate. More options didn't help people choose. More options paralyzed them. The jam study has been replicated across dozens of categories since then. The pattern holds. More choices, more overthinking, fewer decisions. Think about your postponed decision. How many options are you juggling? How many articles have you read? Every expert you consult, every scenario you play out in your head... you're not getting closer to certainty. You're adding jams to the display. And while you're researching, the world keeps moving. Opportunities close. Competitors act. Your own situation shifts. The decision you're avoiding today won't even be the same decision six months from now. Waiting has a cost. Most people dramatically underestimate it. The Two-Door Framework So how do you know when you have enough information? Jeff Bezos uses a mental model that's useful here. Picture every decision as a door you're about to walk through. Some doors are one-way: once you're through, you can't come back. Selling your company. Getting married. Signing a ten-year lease. These deserve serious deliberation. Most decisions, though, are two-way doors. You walk through, look around, and if you don't like what you see, you walk back out. Starting a side project. Trying a new marketing strategy. Having that difficult conversation. The consequences are real, but they're not permanent. The mistake most people make is treating two-way doors like one-way doors. They apply the same level of analysis to choosing project management software as acquiring a company. They're not being thorough. They're overthinking reversible choices. That's how organizations grind to a halt. That's how careers stall. That's how opportunities evaporate while you're still "thinking about it." Before you gather more information, ask yourself: Can I reverse this? If yes, even if reversing would be annoying, you're probably overthinking it. The 40-70 Rule General Colin Powell used a decision framework he called the 40-70 rule. Military leaders and executives have adopted it for decades. The Floor: 40% Never decide with less than forty percent of the information you'd want. Below that threshold, you're not being decisive. You're gambling. The Ceiling: 70% Don't wait for more than seventy percent. By the time you've gathered that much data, the window has usually closed. Someone else acted. The situation changed. The decision got made for you, by default. The Sweet Spot That range between forty and seventy percent is where good decisions actually happen. It feels uncomfortable because you're not certain. That discomfort isn't a warning sign, though. It's the signal that you're doing it right. Most overthinking happens above seventy percent. You already have what you need. You're just not ready to commit. If deciding feels completely comfortable, you've probably waited too long. The Productive Discomfort Test "I genuinely need more information" and "I'm using research as a hiding place" feel identical from the inside. Both feel responsible. Both feel like due diligence. I once watched a friend spend eleven months researching a career change. She read books. Took assessments. Talked to people in the field. Built spreadsheets comparing options. She knew more about the industry than people working in it. And at month eleven, she was no closer to a decision than at month one. The research had become the activity. The feeling of progress without the risk of commitment. She wasn't preparing. She was hiding. And she couldn't tell the difference. So how do you tell productive research apart from overthinking? Four tests: Test 1: The Flip Question Ask yourself: What specifically would change my decision? Not what would make me more comfortable. What would actually flip my choice? If you can't name something concrete, you're not gathering information. You're stalling. Test 2: The Repetition Check Are you learning genuinely new things? Or finding different sources that confirm what you already suspected? The third article about the same topic isn't research. It's reassurance-seeking dressed up as diligence. Test 3: The Timeline Test Have you set a deadline for deciding? "When I have enough information" isn't a deadline. That's an escape hatch that never closes. A real deadline has a date. It's in your calendar. It arrives whether you're ready or not. Test 4: The Broken Record Test If you keep telling the same people "I'm still thinking about it" for the same decision over weeks or months, that's not thinking. That's avoidance on autopilot. You've become a broken record, and everyone can hear it except you. Here's the uncomfortable truth: if you fail more than one of these tests, you probably already have enough information. You're not under-informed. You're over-attached to the comfort of not having decided yet. The goal isn't to eliminate uncertainty. You can't. The goal is to act while uncertainty is still manageable, while you can still correct course, while the opportunity is still breathing. Your Decision Deadline That decision you've been postponing? It has an expiration date. Not one you set. One that's already running. Every week you wait, the context shifts. The opportunity narrows. The person you'd need to have that conversation with forms new assumptions about your silence. You're not preserving your options by waiting. You're watching them quietly disappear. This week, not someday, identify the decision you've been postponing. The one that popped into your head when this video started. You know exactly which one I mean. Set a deadline. Pick a specific date by which you will decide. Not a date by which you'll have complete information. A date by which you'll commit to a direction. Write it down. Put it in your calendar. Make it real. Then ask the two-door question: Is this reversible? If it is, your deadline should be soon. Days, not months. When that deadline arrives, decide. Not perfectly. Not with complete confidence. Deliberately, with the information you have, knowing you can adjust as you learn more. And once you've decided, set a checkpoint. Pick a date, two weeks out, a month out, when you'll evaluate whether to stay the course or walk back through the door. This isn't second-guessing. It's building the feedback loop that makes two-way doors work. Decide now, verify later. That feeling of deciding before you're fully ready? Get used to it. That's what good decision-making actually feels like. Closing Uncertainty isn't going away. Not for this decision, not for any decision that actually matters. The question is whether you'll learn to act within it, or let it become a permanent excuse. Acting under uncertainty requires energy, though. Mental fuel. And when that fuel runs out, everything changes. That's next time: deciding when you're depleted. Because the hardest decisions in your life won't happen when you're rested and sharp. They'll happen at 10 PM after a brutal day, when someone needs an answer and you're running on empty. Before You Go You've got two choices right now. Choice one: scroll to the next video. Let this become another thing you watched but didn't act on. Choice two: pause for thirty seconds. Think about that decision. Set the deadline. Put it in your calendar before you leave this page. Thirty seconds. That's the difference between insight and action. If mindjacking is a new concept for you, I've got a full episode that breaks down how to spot when your thinking has been hijacked, whether by outside forces or by yourself. Link's below. For those who want to support the work and the team behind these episodes, you can become a paid subscriber on Substack. That link is below too. One question for the comments: What decision are you finally going to stop researching and start making? Your deadline begins now. Sources The Jam Study Iyengar, S. S., & Lepper, M. R. (2000). When choice is demotivating: Can one desire too much of a good thing? Journal of Personality and Social Psychology, 79(6), 995-1006. The study was conducted at Draeger's Market in Menlo Park, California. PubMed: https://pubmed.ncbi.nlm.nih.gov/11138768/ Full paper: https://faculty.washington.edu/jdb/345/345%20Articles/Iyengar%20&%20Lepper%20(2000).pdf The 40-70 Rule Attributed to General Colin Powell. The rule appears in "Quotations from Chairman Powell: A Leadership Primer" by Oren Harari (1996), based on Powell's My American Journey (1995). Powell served as a four-star general in the U.S. Army and as the 65th U.S. Secretary of State (2001-2005). The formula "P = 40 to 70" represents the probability of success based on percentage of information acquired. Source: https://govleaders.org/powell.php The Two-Door Framework Bezos, J. (2015). Letter to Shareholders. Amazon.com, Inc. Annual Report. The framework distinguishes between "Type 1" decisions (one-way doors, irreversible) and "Type 2" decisions (two-way doors, reversible). Bezos elaborated on this in his 2016 shareholder letter, noting that organizations often mistakenly apply heavyweight Type 1 processes to reversible Type 2 decisions. Source: https://s2.q4cdn.com/299287126/files/doc_financials/annual/2015-Letter-to-Shareholders.PDF /episode/index/show/philmckinney/id/39894625

Mindjacking - When your Opinions are Not Yours 01/20/2026

Mindjacking - When your Opinions are Not Yours You've built a toolkit over the last several episodes. Logical reasoning. Causal thinking. Mental models. Serious intellectual firepower. Now the uncomfortable question: When's the last time you actually used it to make a decision? Not a decision you think you made. One where you evaluated the options yourself. Weighed the evidence. Formed your own conclusion. Here's what most of us do instead: we Google it, ask ChatGPT, go with whatever has the most stars. We feel like we're deciding, but we're not. We're just choosing which borrowed answer to accept. That gap between thinking you're deciding and actually deciding is where everything falls apart. And there's a name for it. What Mindjacking Actually Is . Not the sci-fi version where hackers seize your brain through neural implants. The real version. Where you voluntarily hand over your thinking because someone else already did the work. It's not dramatic. It's convenient. The algorithm ranked the results. The expert weighed in. The crowd already decided. Why duplicate the effort? Mindjacking is different from ordinary influence. You choose it. Every single time. Nobody forces you to stop evaluating. You volunteer, because forming your own conclusion is harder than borrowing someone else's. What exactly are you losing when this happens? The Two Skills Under Attack Mindjacking destroys two distinct capabilities. They're different, and you need both. Evaluation independence is the ability to assess whether a claim is valid. Not whether the source has credentials. Not whether experts agree. Whether the evidence actually supports the conclusion. Decision independence is the ability to commit to a path based on your own judgment, without needing someone else to validate it first. Both skills need each other. Watch what happens when one erodes faster than the other. A woman researches her medical condition for hours. Journal articles. Treatment comparisons. She understands her options better than most medical students would. She walks into the doctor's office, lays out her analysis. It's thorough. Sophisticated, even. The doctor reviews it and says, "This is impressive. You've really done your homework." She nods. Then looks up and asks: "So what should I do?" She can evaluate. She can't decide. Now flip it. Think about someone who decides fast. Trusts their gut. Never waits for permission. How often does that person get burned by bad information they never verified? They can decide. They can't evaluate. Lose either ability and you're trapped. Lose both and you're not thinking at all. The Four Surrender Signals How do you know when mindjacking is happening? It has a signature. Four internal signals that reveal the handoff in progress, if you know how to read them. Signal one: Relief. The moment you find "the answer," you notice a weight lifting. Pay attention to that. Relief isn't insight. It's the burden of thinking being removed. When you actually work through a problem yourself, the result isn't relief. It's clarity. And clarity usually comes with new questions, not a sense of "done." Signal two: Speed. Uncertainty to certainty in seconds? That's not evaluation. You found someone else's answer and adopted it. There's a difference between "I figured it out" and "I found someone who figured it out." One took effort. The other took a search bar. Signal three: Echo. Listen to your own conclusions. Do they sound like something you read, heard, or scrolled past recently? If your "own opinion" matches a headline almost word-for-word, it probably isn't yours. You're not thinking. You're repeating. Signal four: Unearned confidence. You're certain about a conclusion, but ask yourself: could you explain the reasoning behind it? Not where you heard it. The actual reasoning. If you can't, that confidence isn't yours. It came attached to someone else's answer, and you absorbed both their conclusion and their certainty without doing any analysis yourself. Once you notice these signals firing, you need a way to stop the pattern before it completes. The Interrupt The interrupt is a single question: "Did I reach this conclusion, or just find it?" Six words. That's the whole thing. It works because it forces a distinction your brain normally blurs. "I decided" and "I adopted someone's decision" are identical from the inside, until you ask the question. Test it now. Think about the last opinion you formed. The last purchase you made. The last recommendation you accepted. Did you reach that conclusion, or just find it? The interrupt doesn't tell you what to think. It tells you whether you're thinking at all. Finding an answer isn't the same as reaching one. This matters more than you might realize, because the pattern is bigger than any single decision you make. The Aha Moment: The Illusion of Expertise Researchers at Penn State looked at 35 million Facebook posts and found something remarkable: seventy-five percent of shared links were never clicked. Three out of four times, people passed along articles they hadn't read. But that's not the strange part. A separate study from the University of Texas discovered that the act of sharing content, even content you haven't read, makes you think you understand it. Sharing tricks you into believing you know. You didn't read the article about investing, but you shared it, so now you believe you understand investing. Worse: people act on that false knowledge. In the study, people who shared an investing article took significantly more financial risk afterward, even though they never read what they shared. They weren't pretending to know. They genuinely believed they knew, because sharing had become a substitute for learning. . Millions of people believing they're informed, acting confident, having never actually thought about any of it. The Feed Challenge I want you to try something as soon as this video ends. Open your social media feed. Find a post where someone you know has liked or shared an article, an opinion, a hot take. Now ask: Did they actually think about this? Or did they just pass it along? Look for the signals. Is their comment just echoing the headline? Are they expressing certainty about something they probably spent ten seconds on? Did they add anything that suggests they read past the first paragraph? Or did they just click "like" and move on? Remember: seventy-five percent of shared links are never clicked. That like or share you're looking at? They probably never read what they're endorsing. You'll be shocked how easy this becomes once you start looking. It's everywhere. People confidently endorsing opinions they never examined. Certainty without evaluation. Expertise without effort. Why start with what others are putting in your feed? Because it's much easier to spot mindjacking in others than in yourself. Your ego doesn't interfere. Train your eye on what's coming at you first. Then turn it inward. Awareness precedes choice. You can't reclaim what you can't see. What's Next Now you can see the handoff happening. That's the foundation. But seeing it isn't enough. Knowing the signals won't help you when you're exhausted and the algorithm is offering relief. Understanding the trap won't save you when everyone in the room disagrees and consensus feels like safety. Awareness alone won't protect you when the deadline is tomorrow and you don't have time to think. Those are the moments where mindjacking wins. Not because you lack the ability to think, but because thinking starts to look like a luxury you can't afford. That's the real battle. And that's what comes next. Next, we tackle the hardest version of this problem: acting before you're ready. What happens when you have to decide, the information isn't complete, and it never will be? Waiting for certainty feels responsible. But sometimes, waiting is the trap. If you're new here, check out the earlier episodes where we built the evaluation toolkit this series is built on. . Don’t Click Yet Here's a thought: most people will finish this video and scroll to the next one. The algorithm already has a recommendation queued up. Relief is one click away. But you could do something different. You could stick with the discomfort for a minute. Actually, try the feed challenge before moving on. If you want to go deeper on mindjacking, the full breakdown lives at . And if you want to support the team that helps me to produce this content, consider becoming a paid . What's one opinion you realized might not actually be yours? Share this with someone who needs to hear it. References Penn State University (2024). "Social media users probably won't read beyond this headline, researchers say." Analysis of 35 million Facebook posts published in Nature Human Behaviour. Ward, A., Zheng, J.F., & Broniarczyk, S.M. (2022). "I share, therefore I know? Sharing online content – even without reading it – inflates subjective knowledge." Journal of Consumer Psychology, University of Texas at Austin McCombs School of Business. /episode/index/show/philmckinney/id/39796540

CES 2026 - Battle of the AI Robots 01/13/2026

CES 2026 - Battle of the AI Robots 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. The Show's Legacy 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. Traditional Consumer Electronics 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 AI and Robotics Takeover 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. The Evolution of Electric Vehicles 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. Autonomous Vehicles in Commercial Applications 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. Other Notable 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 Bottom Line 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. /episode/index/show/philmckinney/id/39725760

Thinking 101: A Pause, A Reflection, And What Might Come Next 12/23/2025

Thinking 101: A Pause, A Reflection, And What Might Come Next 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. /episode/index/show/philmckinney/id/39514845

Mental Models - Your Thinking Toolkit 12/16/2025

Mental Models - Your Thinking Toolkit 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. What Are Mental Models? 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. Tool One: The Map Is Not the Territory 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. Tool Two: Inversion 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. Tool Three: The Premortem 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. How These Three Tools Work Together 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. Practice: The Three-Tool Test Here's how to put these tools to work this week. Identify a decision you're currently facing. Something real. Something that matters. Write it in one sentence. Check your map. What assumptions are you making? Where did they come from? Who might see this differently? Invert it. Set a timer for five minutes. List every way you could guarantee failure. Be ruthless. Run the premortem. It's one year from now. You chose wrong. Write two paragraphs explaining what happened. Find the overlap. Where do your inversion list and premortem story agree? That's your highest-risk blind spot. 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 [irp posts="4392" name="Subscribe to Podcast"] ENDNOTES 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. 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. Korzybski, Alfred. Science and Sanity: An Introduction to Non-Aristotelian Systems and General Semantics (1933). Wikipedia, "Nevermind"; SonicScoop, "Time and Cost of Making an Album Case Study: NIRVANA" (2017). Initial recording budget was $65,000. 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). 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. 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. /episode/index/show/philmckinney/id/39422585

Numerical Thinking: How to Find the Truth When Numbers Lie 12/02/2025

Numerical Thinking: How to Find the Truth When Numbers Lie 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. What They're Exploiting 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. Translation: Make the Invisible Visible 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. The "Per What?" Weapon 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. Let's Catch a Lie in Real Time 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? The Exponential Trap 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. Rapid Reality Checking 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. Don't Make These Mistakes 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. Putting It All Together 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? Your Turn—Right Now 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. The Real Stakes 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. /episode/index/show/philmckinney/id/39234910

The Clock is Screaming 11/25/2025

The Clock is Screaming 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. When the Comfortable List Stopped Working 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. What Survives 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. It’s Not Just Liam 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. The Math 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. What I’m Actually Grateful For 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: Your Turn 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. /episode/index/show/philmckinney/id/39175060

Second-Order Thinking: How to Stop Your Decisions From Creating Bigger Problems (Thinking 101 - Ep 6) 11/11/2025

Second-Order Thinking: How to Stop Your Decisions From Creating Bigger Problems (Thinking 101 - Ep 6) 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?" What Second-Order Thinking Actually Means 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. How To Think in Consequences 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. Strategy 1: Map How People Will Actually Respond 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: First: Identify ALL Your Stakeholders 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. Second: Understand What You're Actually Rewarding 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. Third: Trace Each Response Forward 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. Strategy 2: Run the "And Then What?" Drill 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 Show You How This Actually Works 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. Put This Into Practice Right Now 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. The Unfair Advantage 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. Your Thinking 101 Journey 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? SOURCES CITED IN THIS EPISODE 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. 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. 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. ADDITIONAL READING 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. /episode/index/show/philmckinney/id/38991000

Make Better Decisions When Nothing is Certain 11/04/2025

Make Better Decisions When Nothing is Certain You're frozen. The deadline's approaching. You don't have all the data. Everyone wants certainty. You can't give it. Sound familiar? Maybe it's a hiring decision with three qualified candidates and red flags on each one. Or a product launch where the market research is mixed. Or a career pivot where you can't predict which path leads where. You want more information. More time. More certainty. But you're not going to get it. Meanwhile, a small group of professionals—poker players, venture capitalists, military strategists—consistently make better decisions than the rest of us in exactly these situations. Not because they have more information, but because they've mastered something fundamentally different: they think in probabilities, not certainties. I learned this the hard way—I once created a biometric security algorithm that the NSA reverse-engineered, where I mastered probabilistic thinking perfectly in the technology, then made every wrong bet with the business around it. By the end of this episode, you'll possess a powerful mental toolkit that transforms how you approach uncertainty. You'll learn to estimate likelihoods without perfect data, update your beliefs as new information emerges, make confident decisions when multiple uncertain factors collide, and act decisively even when you can't guarantee the outcome. This is the difference between paralysis and power, between gambling recklessly and betting wisely. What Is Probabilistic Thinking? But what does probabilistic thinking actually entail? At its core, it's the practice of reasoning in terms of likelihoods rather than absolutes—thinking in percentages instead of yes-or-no answers. Instead of asking "Will this work?" you ask "What are the odds this will work, and what are the consequences if it doesn't?" This approach acknowledges that the future is uncertain and that every decision carries risk. By quantifying that uncertainty and weighing it against potential outcomes, you make smarter choices even when you can't eliminate the unknown. The Cost of Demanding Certainty Today's world punishes those who demand certainty before acting. Research from Oracle's 2023 Decision Dilemma study—which surveyed over 14,000 employees and business leaders across 17 countries—found that 86% feel overwhelmed by the amount of data available to them. Rather than clarity, all that information creates decision paralysis. And the paralysis has real consequences. When we can't be certain, we freeze. We endlessly research options, seeking that final piece of data that will guarantee success. We postpone critical decisions, waiting for perfect information that never arrives. Meanwhile, opportunities pass us by, problems grow worse, and competitors who are comfortable with uncertainty move forward. This demand for certainty doesn't just slow us down—it exhausts us. Decision fatigue sets in as we agonize over choices, draining our mental resources until we either make impulsive decisions or avoid deciding altogether. Neither outcome serves us well. What Certainty-Seeking Actually Costs You Here's what it looks like in real life: You're the VP of Marketing. Your CMO wants a decision on next quarter's campaign budget by Friday. You have three agencies to choose from, each with strengths and weaknesses. So you ask for more data. Customer focus groups. Competitive analysis. Agency references. By Wednesday you're drowning in spreadsheets and conflicting opinions. Friday arrives. You still can't be certain which choice is right, so you ask for an extension. Two weeks later, you finally pick one—not because you're confident, but because you're exhausted and the CMO is furious about the delay. The campaign launches late. You've burned political capital. And you still have no idea if you made the right choice. Meanwhile, your competitor's marketing VP looked at the same decision, spent two hours assessing the probabilities, and launched on time. If it works, great. If it doesn't, they'll pivot. They didn't need certainty. They needed enough information to make a good bet. That's the tax you pay for demanding certainty: missed timing, exhausted teams, and decisions made from fatigue rather than judgment. Meanwhile, a small group of professionals thrives in these exact conditions. Professional poker players like Annie Duke understand that good decisions sometimes lead to bad outcomes and bad decisions sometimes get lucky—so they judge their choices by process, not results. Venture capitalists often see that most of their investments will fail, but they bet anyway because one success out of twenty can return the entire fund. Military strategists make life-and-death decisions with incomplete intelligence, not because they're reckless, but because waiting for perfect information means defeat. The difference isn't access to better information. It's the willingness to act on probabilities rather than certainties. How To Make Better Decisions When Nothing Is Certain So how do you actually develop this skill? It's more accessible than you might think. Here are clear strategies to transform how you process uncertainty and make decisions. Think in Ranges, Not Points The first shift in probabilistic thinking is abandoning single-number estimates for ranges of possibility. When most people predict an outcome, they pick one number: "Sales will be $500,000 next quarter" or "This project will take three months." But the world doesn't work that way. Every estimate carries uncertainty, and pretending otherwise sets you up for failure. Professional forecasters think differently. They don't ask "What will happen?" They ask "What's the range of plausible outcomes, and how likely is each?" This approach forces you to acknowledge what you don't know while still making useful predictions. Watch a professional poker player deciding whether to call a bet. They're not thinking "Do I have the best hand?" They're thinking "Given what I've seen, maybe 35% chance I have the best hand, 20% chance my opponent is bluffing, 45% chance they've got me beat." They act on probabilities, not certainties. Steps to implement range thinking: Replace point estimates with probability ranges. When making any prediction, state a range instead of a single number. Instead of "We'll close 50 deals," say "We'll likely close 40-60 deals, with a small chance of 30-70." Assign rough percentages to your ranges. You don't need mathematical precision—just honest self-assessment. Estimate: "60% chance of 40-50 deals, 30% chance of 50-60, 10% chance outside that range." This forces you to think about likelihood, not just possibility. Track your estimates against actual outcomes. Keep a simple log of your predictions and what actually happened. Over time, you'll discover if you're consistently over-optimistic, over-cautious, or actually well-calibrated. This feedback loop is how you improve. Update Your Beliefs with New Evidence One of the most powerful aspects of probabilistic thinking is treating your beliefs as hypotheses, not conclusions. When new information emerges, skilled thinkers update their probability estimates rather than clinging to their original position. This practice—called Bayesian updating after the mathematician Thomas Bayes—is how professionals stay accurate in changing environments. Consider a doctor diagnosing a patient with intermittent chest pain. Initially, based on the patient's age and health history, she estimates a 15% probability of heart disease. Then the EKG comes back with minor abnormalities—not definitive, but concerning. She updates her estimate to 35%. Blood work shows elevated cardiac markers. Now she's at 65%. Each piece of evidence shifts the probability, but none gives absolute certainty. She doesn't wait for 100% certainty to act—she orders more tests and starts precautionary treatment based on her updated 65% estimate. That's Bayesian thinking in action. Financial firms continuously adjust their models as new data arrives. Weather forecasters update storm predictions hourly. In my own work building biometric security systems, we updated our false acceptance and rejection rates constantly—but I failed to apply that same updating framework to the business model itself. The enemy of updating is confirmation bias—our tendency to accept information that supports our existing beliefs and dismiss information that contradicts them. When you're emotionally invested in being right, you'll unconsciously filter evidence to protect your original view. Steps to update your thinking: Start with a baseline probability before you have strong evidence. If you're launching a new product, estimate: "Based on what I know about similar products, there's maybe a 40% chance this succeeds." That's your prior—your starting point before specific evidence comes in. When new information arrives, ask: "How much should this change my estimate?" Not all evidence is equal. Strong evidence—like actual customer purchases—should move your probability significantly. Weak evidence—like one person's opinion—should barely budge it. Separate the quality of a decision from the quality of the outcome. This is crucial. A good decision based on sound probabilities can still result in a bad outcome due to chance. Conversely, a terrible decision can get lucky. Judge yourself on whether you correctly assessed the probabilities and acted accordingly, not on whether you "got it right" this time. Actively seek disconfirming evidence. Force yourself to look for information that contradicts your current view. If you think your strategy will work, deliberately search for reasons it might fail. This counteracts confirmation bias and gives you a more accurate probability estimate. Make Decisions by Expected Value Probabilistic thinking isn't just about estimating odds—it's about acting on them. The concept of expected value gives you a framework for making decisions when outcomes are uncertain. Expected value multiplies each possible outcome by its probability, then adds them together. It's how professionals decide whether a bet is worth taking. Here's why it matters: sometimes a decision with a low probability of success is still the right choice if the potential payoff is enormous. Venture capitalists know that perhaps 18 out of 20 startups in their portfolio will fail or return little money. But that one company that becomes the next Airbnb or Uber can return 100x their investment—more than covering all the losses. That's positive expected value thinking. Conversely, decisions that seem "safe" can be terrible bets. Playing it safe might give you a 90% chance of mediocre success, but if that 10% downside risk includes catastrophic consequences, the expected value might be negative. This is why you buy insurance: the probability of your house burning down is low, but the cost if it happens is devastating. Think about a parent choosing between schools for their child. Public school is free but overcrowded. Private school costs $20K/year with smaller classes but adds an hour of family stress daily. Charter school is free with innovative curriculum but it's a first-year program with unknowns. There's no guarantee. The better question is expected value: "Given the probabilities and what matters most to us—academic success, family time, financial stability—which bet has the best expected outcome?" Steps for expected value decision-making: List all plausible outcomes for your decision, not just the best and worst. For a job offer, don't just think "great career move" versus "terrible mistake." Consider: "Modest improvement (40%), breakthrough opportunity (20%), lateral move (25%), step backward (10%), complete disaster (5%)." Assign a rough value to each outcome. This doesn't have to be money—it can be career satisfaction, life quality, time saved, or any currency that matters to you. The key is making the values comparable across outcomes. Multiply each outcome's value by its probability, then add them up. This gives you the expected value. If the positive expected value option has meaningful downside risk, ask: "Can I survive the worst case?" If yes, it's usually the right bet. Remember: expected value is about long-term results, not single instances. If you make a high expected value bet and it fails, that doesn't mean you were wrong. Over many decisions, following expected value will outperform any other approach. Trust the math, not the emotional reaction to one outcome. Practice: The Probability Forecast Journal A practical way to develop your probabilistic thinking is to keep a Probability Forecast Journal. This exercise builds calibration—your ability to accurately assess how confident you should be in your predictions. Here's how to implement it: Choose three areas where you regularly make predictions. These could be work-related (project timelines, sales numbers), personal (will your flight be delayed), or current events (election outcomes). Each week, make five specific, testable predictions. Write down the prediction and assign a probability. For example: "70% chance the client approves our proposal by Friday" or "85% chance our website traffic increases this month." After each prediction resolves, record the actual outcome. Did the thing you said had a 70% chance of happening actually happen? Don't judge yourself harshly on any single prediction—remember that a 70% prediction should fail about 30% of the time. Monthly, analyze your calibration. Look at all predictions where you said "70% confident"—did roughly 70% of them come true? If you're consistently overconfident, you need to adjust. If you're underconfident, you're being too cautious. The goal isn't perfection—it's calibration. After several months of this practice, you'll notice your ability to assess probabilities improves dramatically. You'll know when you're 60% sure versus 90% sure, and you'll make better decisions as a result. The Rewards Mastering probabilistic thinking is a journey, not a destination. It requires practice, humility about what you don't know, and the courage to act despite uncertainty. But the rewards are substantial. When you think probabilistically, you make faster decisions because you're not paralyzed waiting for certainty that will never come. You become more resilient to failure because you understand that good decisions sometimes have bad outcomes—and that's not a reason to change your approach. You'll find yourself taking calculated risks that others avoid, capturing opportunities that demand action before perfect information arrives. You'll waste less time second-guessing yourself because you've already thought through the probabilities and made your peace with uncertainty. You'll explain your decisions more clearly to others because you can articulate not just what you think will happen, but how confident you are and why. Most importantly, you'll stop confusing confidence with correctness. In a world obsessed with appearing certain, probabilistic thinkers have the courage to say "I'm 65% sure, and that's enough to act." That honesty—with yourself and others—is the foundation of better judgment. Want to see what happens when you master probabilistic thinking in one domain but fail to apply it in another? I wrote about my experience creating a fingerprint recognition algorithm that the NSA reverse-engineered—where I got the technical probabilities right and the business bets completely wrong. [Read the full story here](link to substack). The future will always be uncertain. The question is whether you'll be paralyzed by that uncertainty or empowered by it. If this helped you think differently about decision-making, I'd really appreciate it if you'd hit the like button and subscribe—it genuinely helps others find this content through the algorithm. And click that notification bell so you don't miss the next episode in this series. If you want to go deeper, I share the behind-the-scenes thinking, mistakes, and extended stories over on Studio Notes on Substack. Paid subscriptions help cover the costs of the team who makes all of this possible—the editing, research, and production work that gets these episodes to you each week. None of it comes to me; it all goes to supporting them. Without this team, there'd be no podcast, no YouTube channel, no articles. So if you find value in this work, that's a meaningful way to keep it going. The future will always be uncertain. The question is whether you'll be paralyzed by it or empowered by it. Sources Cited In This Episode Oracle Decision Dilemma Study (2023) - Survey of 14,000+ employees and business leaders across 17 countries on data overwhelm and decision paralysis. Thinking in Bets - Duke, A. (2018). Portfolio. On judging decisions by process, not outcomes. How to Improve Bayesian Reasoning Without Instruction: Frequency Formats - Gigerenzer, G. & Hoffrage, U. (1995). Psychological Review, 102(4), 684-704. On updating beliefs with evidence. Prospect Theory: An Analysis of Decision under Risk - Kahneman, D. & Tversky, A. (1979). Econometrica, 47(2), 263-291. Prospect Theory foundations. /episode/index/show/philmckinney/id/38905840

You Think In Analogies and You Are Doing It Wrong 10/28/2025

You Think In Analogies and You Are Doing It Wrong Try to go through a day without using an analogy. I guarantee you'll fail within an hour. Your morning coffee tastes like yesterday's batch. Traffic is moving like molasses. Your boss sounds like a broken record. Every comparison you make—every single one—is your brain's way of understanding the world. You can't turn it off. When someone told you ChatGPT is "like having a smart assistant," your brain immediately knew what to expect—and what to worry about. When Netflix called itself "the HBO of streaming," investors understood the strategy instantly. These comparisons aren't just convenient—they're how billion-dollar companies are built and how your brain actually learns. The person who controls the analogy controls your thinking. In a world where you're bombarded with new concepts every single day—AI tools, cryptocurrency, remote work culture, creator economies—your brain needs a way to make sense of it all. By the end of this episode, you'll possess a powerful toolkit for understanding the unfamiliar by connecting it to what you already know—and explaining complex ideas so clearly that people wonder why they never saw it before. Thinking in analogies—or what's called analogical thinking—is how the greatest innovators, communicators, and problem-solvers operate. It's the skill that turns confusion into clarity and complexity into something you can actually work with. What is Analogical Thinking? But what does analogical thinking entail? At its core, it's the practice of understanding something new by comparing it to something you already understand. Your brain is constantly asking: "What is this like?" When you learned what a virus does to your computer, you understood it by comparing it to how biological viruses infect living organisms. When someone explains blockchain as "a shared spreadsheet that no one can erase," they're using analogy to make an abstract concept concrete. Researchers have found something remarkable: your brain doesn't actually store information as facts—it stores it as patterns and relationships. When you learn something new, your brain is literally asking "What does this remind me of?" and building connections to existing knowledge. Analogies aren't just helpful for communication—they're the fundamental mechanism of human understanding. You can't NOT think in analogies. The question is whether you're doing it consciously and well, or unconsciously and poorly. The quality of your analogies determines how quickly you learn, how deeply you understand, and how effectively you can explain ideas to others. Remember this: whoever controls the analogy controls the conversation. Master this skill, and you'll never be at the mercy of someone else's framing again. The Crisis of Bad Analogies Thinking in analogies is a double-edged sword. I learned this the hard way. A few years ago, I watched a brilliant engineer struggle to explain a revolutionary idea to executives. He had the data, the logic, the technical proof—but he couldn't get buy-in. Then someone in the room said, "So it's basically like Uber, but for industrial equipment?" Instantly, heads nodded. Funding approved. Project greenlit. One analogy did what an hour of explanation couldn't. Six months later, that same analogy killed the project. Because "Uber for equipment" came with assumptions—about pricing, about scale, about network effects—that didn't actually apply. The team kept forcing their solution to fit the analogy instead of recognizing when the comparison broke down. I watched millions of dollars and two years of work disappear because nobody questioned whether the analogy was still serving them. The same mental shortcut that helps you understand new things can also trap you in outdated patterns. Consider Quibi's spectacular failure. In 2020, Jeffrey Katzenberg and Meg Whitman launched a streaming service with $1.75 billion in funding—more than Netflix had when it started. Their analogy? "It's like TV shows, but designed for your phone." They created high-quality 10-minute episodes optimized for mobile viewing. Six months later, Quibi shut down. What went wrong? The analogy was flawed. They assumed mobile viewing was like TV viewing, just shorter. But people don't watch phones the way they watch TV—they watch phones while doing other things, in stolen moments, with interruptions. YouTube and TikTok understood this. They built for distraction and fragmentation. Quibi built for focused attention that didn't exist. That misunderstanding burned through nearly $2 billion in 18 months. We see this constantly where complex issues get reduced to simplistic analogies that feel intuitive but lead to flawed conclusions. Someone compares running a country to running a household budget—"If families have to balance their budgets, why shouldn't governments?" The analogy sounds intuitive, but it ignores that countries can print currency, carry strategic long-term debt, and operate on completely different time horizons than households. The cost of bad analogical thinking is enormous. You waste time applying solutions that worked in one context to problems where they don't fit. You miss opportunities because you're trying to squeeze new situations into old patterns. And worst of all, you become easy to manipulate—because anyone who controls your analogies controls how you think. How To Think Using Analogies So how do we harness the power of analogy while avoiding its traps? Let me show you five essential strategies that will transform how you use comparison to understand your world. Generate Analogies Systematically The first skill is learning to create useful analogies on demand. Most people wait for analogies to pop into their heads randomly, but you can develop a systematic process for generating them whenever you need one. Map the structure of what you're trying to understand, then search for similar structures in domains you know well. Netflix's recommendation algorithm didn't come from studying other algorithms—it came from asking "How do humans recommend things?" and mapping that social process onto a technical system. Steps to generate powerful analogies: Identify the core function or relationship: Strip away surface details and ask what the thing actually does. A heart pumps fluid through a system. Now you can compare it to anything else that pumps fluid—engines, wells, plumbing systems. Look across multiple domains: Don't limit yourself to obvious comparisons. The best analogies often come from unexpected places. The inventor of Velcro, George de Mestral, understood how burrs stuck to fabric by comparing them to hooks and loops—leading to a billion-dollar fastening system. Map specific correspondences: Once you find a potential analogy, be explicit about what maps to what. If you're comparing your startup to a marathon, what corresponds to training? What's the equivalent of hitting the wall? What represents the finish line? Test the analogy's limits: Push the comparison and see where it breaks down. This isn't a failure—it's information. Every analogy has boundaries, and knowing them makes the analogy more useful. Consider multiple analogies: Don't settle for the first comparison that works. Electricity is like water flowing through pipes AND like cars on a highway. Each analogy reveals different insights. Recognize When Analogies Break Down Most people fall in love with an analogy and push it beyond its useful range. A powerful analogy becomes a dangerous one the moment you forget it's just a comparison, not reality itself. The human brain loves patterns, and once we find one that works, we want to apply it everywhere. This is how we end up with terrible advice like "Just be yourself in job interviews" because "authentic relationships require honesty"—taking an analogy from personal relationships and stretching it to professional contexts where it doesn't fit. How to recognize the breakdown: Watch for forced mappings: If you find yourself struggling to make pieces fit, the analogy might be wrong. When the comparison starts requiring elaborate explanations or special exceptions, it's probably breaking down. Check for contradictory predictions: A good analogy should help you predict behavior. If your analogy suggests one outcome but reality keeps producing another, the comparison isn't working. Look for what's missing: What does the analogy leave out? Understanding the gaps is as important as understanding the matches. Social media isn't "the modern town square"—because town squares had time constraints, physical presence, and social accountability that platforms lack. Test edge cases: Push your analogy to extremes. If "your body is a temple," does that mean you should let tourists visit? When an analogy gets absurd at the edges, you've found its limits. A good analogy is a map, not the territory. The moment you forget that, you're lost. Use Analogies to Explain Complex Ideas Analogies are your secret weapon for making complicated concepts accessible to anyone. The person who can explain quantum physics using everyday comparisons has a superpower in our information-saturated world. Match the analogy to your audience's knowledge and choose comparisons that illuminate rather than obscure. The explanatory analogy playbook: Know your audience's knowledge base: You can compare machine learning to "teaching a child through examples" for general audiences, but that same analogy won't work for computer scientists who need technical precision. Start with the familiar: Always move from what people know to what they don't. "Imagine your favorite playlist, but instead of songs it recommends..." grounds abstract concepts in concrete experience. Be explicit about the comparison: Don't assume people will automatically see the connection. Say "Think of it like this..." and make the mapping clear. Use multiple analogies for complex concepts: One analogy rarely captures everything. Combine several different comparisons to give people multiple angles of understanding. Identify False Analogies in Arguments People will use analogies to manipulate your thinking—sometimes intentionally, sometimes not. Workplace debates are full of analogical arguments: "Remote work is like letting students do homework unsupervised—productivity will plummet." But is professional work really like homework? The analogy assumes similarities that may not exist. Recognizing false analogies protects you from being intellectually hijacked. When someone uses comparison to make their argument, your job is to evaluate whether the comparison is valid. Your defense against false analogies: Ask what's being compared: Make the analogy explicit. Often people use vague gestures toward similarity without stating exactly what maps to what. Examine the relevant similarities: Are the things being compared actually alike in ways that matter to the argument? Comparing a business to a family sounds warm, but families don't fire members for poor performance. Identify critical differences: What's different between the two things? Sometimes those differences destroy the analogy's validity. Saying "hiring is like dating" ignores that employment is a contractual relationship with completely different expectations and legal frameworks than romantic partnerships. Consider alternative analogies: If someone says "Unlimited vacation policies are like giving employees a blank check," counter with "Actually, it's more like trusting professionals to manage their own time like we trust them to manage budgets." Different analogies suggest different conclusions. Demand literal argument: When someone relies heavily on analogy to make their case, ask them to make the argument without comparison. If they can't, the analogy might be doing rhetorical work rather than logical work. Build Your Analogy Library The final strategy is long-term: deliberately expand your collection of mental models and experiences so you have more source material for analogies. The person who only knows their own industry can only draw comparisons from that narrow domain. But someone who reads widely, pursues diverse experiences, and studies multiple fields can make unexpected connections. Steve Jobs famously took a calligraphy class—years later, those insights about typeface and design influenced the Mac's revolutionary interface. The analogy between typographic beauty and digital design wouldn't have been available without that cross-domain experience. Building your source material: Read across disciplines: Don't just consume content in your field. Read history, science, philosophy, biography. Each domain gives you new patterns to recognize elsewhere. Study other industries: How do restaurants manage inventory? How do sports teams develop talent? These patterns might apply to your completely different context. Learn the fundamental models: Some analogies recur because they capture universal patterns. Evolution, network effects, compound interest, equilibrium—these models apply across countless domains. Practice deliberately: Make it a habit to ask "What is this like?" when you encounter new ideas. The more you practice generating analogies, the faster and better you'll become. Practice A practical and effective way to develop this skill is to practice explaining concepts across contexts. Here's how you can sharpen your ability to think in analogies: Choose a concept you know well: Pick something from your area of expertise—a technical process, a business strategy, a creative technique, whatever you know deeply. Identify three different audiences: Consider explaining this concept to a child, to someone in a completely different profession, and to an expert in an unrelated field. Generate three analogies: For each audience, create a different analogy that would make the concept clear. Force yourself to draw from domains that audience would understand. Test your analogies: If possible, actually explain your concept to someone using your analogy. Watch their face—confusion means the analogy isn't working, clarity means it is. Refine and iterate: Share your analogies with others and adjust based on their feedback. The best analogies often emerge through conversation and iteration. This exercise trains you to think flexibly, draw connections across domains, and understand the mechanics of what makes analogies work or fail. The more you practice, the more naturally these comparisons will come to you when you need them. The Rewards Mastering analogical thinking is a journey, not a destination. It requires constant practice, intellectual curiosity, and the humility to recognize when your comparisons break down. But the rewards are transformative. You'll learn faster by connecting new information to what you already know. You'll explain complex ideas with clarity that makes you invaluable in any professional setting. You'll spot flawed reasoning in arguments before others even notice something's wrong. You'll generate creative solutions by borrowing patterns from unexpected domains. Most importantly, you'll develop the mental flexibility to navigate an increasingly complex world. When AI reshapes your industry, you'll understand it by comparison to previous technological disruptions. When new social dynamics emerge, you'll make sense of them by recognizing familiar patterns in new contexts. The best thinkers aren't those who memorize the most facts—they're those who see connections others miss. Steve Jobs didn't invent the smartphone—he saw that a phone could be like a computer in your pocket. Jeff Bezos didn't invent retail—he saw that a bookstore could be like an infinite warehouse. Every breakthrough starts with someone asking "What if this is like that?" That's the power of thinking in analogies. And now you have the tools to make it yours. Your Thinking 101 Journey The Thinking 101 series is teaching you how to think clearly in a world designed to confuse you—here's our journey so far: In Episode 1, we exposed the thinking crisis—AI dependency is creating cognitive debt, and independent thinking has become the most valuable skill in the modern world. In Episode 2, you learned to distinguish deductive certainty from inductive probability and stop treating patterns as proven facts. In Episode 3, you discovered how to distinguish true causation from mere correlation—saving yourself from solving the wrong problem perfectly. Today, you learned how to harness the power of analogies while avoiding their traps—generating useful comparisons systematically, recognizing when analogies break down, and spotting false analogies that manipulate thinking. Up next—Episode 5: "Probabilistic Thinking—Living with Uncertainty." You'll learn how to think in probabilities rather than certainties, make decisions with incomplete information, and act wisely when nothing is guaranteed. Hit that subscribe button so you don't miss future episodes. Also—hit the like and notification bell. It helps with the algorithm so others see our content. Why not share this video with a colleague who you think would benefit from it? Because right now, while you've been watching this, someone just pitched a billion-dollar idea using a flawed analogy—and investors nodded along because it "sounded like" something that worked before. The only question is: will you be the one who sees through it? SOURCES CITED IN THIS EPISODE Cognitive Science Research on Analogical Reasoning Green, A.E., Fugelsang, J.A., & Dunbar, K.N. (2006). Automatic activation of categorical and abstract analogical relations in analogical reasoning. Memory & Cognition, 34(7), 1414-1421. Brain Pattern Recognition and Memory Storage Gentner, D., & Smith, L. (2012). Analogical Reasoning. Encyclopedia of Human Behavior (Second Edition), 1, 130-136. Neuroscience of Analogical Thinking Parsons, S., Maillet, D., Sayfullin, A., & Ansari, D. (2022). The Neural Correlates of Analogy Component Processes. Cognitive Science, 46(3). Quibi Shutdown and Funding Details Spangler, T. (2020). Quibi Confirms Shutdown, Jeffrey Katzenberg Startup Will Shop Assets. Variety. October 22, 2020. Quibi Funding History Crunchbase. (2020). Quibi Is Shutting Down After Raising $1.75B In Funding. October 22, 2020. Steve Jobs Stanford Commencement Speech Jobs, S. (2005). 'You've got to find what you love,' Jobs says. Stanford Commencement Address. June 12, 2005. ADDITIONAL READING On Analogical Reasoning and Cognition Holyoak, K. J., & Thagard, P. (1995). Mental Leaps: Analogy in Creative Thought. MIT Press. Gentner, D., Holyoak, K. J., & Kokinov, B. N. (Eds.). (2001). The Analogical Mind: Perspectives from Cognitive Science. MIT Press. On Thinking and Decision-Making Kahneman, D. (2011). Thinking, Fast and Slow. Farrar, Straus and Giroux. On Innovation and Cross-Domain Learning Isaacson, W. (2011). Steve Jobs. Simon & Schuster. Note: All sources cited in this episode have been accessed and verified as of October 2025. /episode/index/show/philmckinney/id/38805940

How To Master Causal Thinking 10/21/2025

How To Master Causal Thinking $37 billion. That's how much gets wasted annually on marketing budgets because of poor attribution and misunderstanding of what actually drives results. Companies' credit campaigns that didn't work. They kill initiatives that were actually succeeding. They double down on coincidences while ignoring what's actually driving outcomes. Three executives lost their jobs this month for making the same mistake. They presented data showing success after their initiatives were launched. Boards approved promotions. Then someone asked the one question nobody thought to ask: "Could something else explain this?" The sales spike coincided with a competitor going bankrupt. The satisfaction increase happened when a toxic manager quit. The correlation was real. The causation was fiction. This mistake derailed their careers. But here's the good news: once you see how this works, you'll never unsee it. And you'll become the person in the room who spots these errors before they cost millions. But first, you need to understand what makes this mistake so common—and why even smart people fall for it every single day. What is Causal Thinking? At its core, causal thinking is the practice of identifying genuine cause-and-effect relationships rather than settling for surface-level associations. It's asking not just "do these things happen together?" but "does one actually cause the other?" This skill means you look beyond patterns and correlations to understand what's actually producing the outcomes you're seeing. When you think causally, you can spot the difference between coincidence, correlation, and true causation—a distinction that separates effective decision-makers from those who waste millions on solutions that were never going to work. Loss of Causal Thinking Skills Across every domain of professional life, this confusion costs fortunes and derails careers. A SaaS company sees customer churn decrease after implementing new onboarding emails—and immediately scales it company-wide. What they missed: they launched the emails the same week their biggest competitor raised prices by 40%. The competitor's pricing reduced churn. But they'll never know, because they never asked the question. Six months later, when they face real churn issues, they keep doubling down on emails that never actually worked. This happens outside of work too. You start taking a new vitamin, and two weeks later your energy improves. But you started taking it in early March—right when days got longer and you began going outside more. Was it the vitamin or the sunlight and exercise? Most people credit the vitamin without asking the question. But here's the good news: once you understand how to think causally, these mistakes become obvious. And one of these five strategies can be used in your very next meeting—literally 30 seconds from now. Let me show you how. How To Master Causal Thinking Mastering causal thinking isn't about becoming a statistician or learning complex formulas. It's about developing five practical strategies that work together to reveal what's really driving results. These build on each other—starting with basic tests you can apply right now, and progressing to a complete system you can use for any decision. Strategy 1: The Three Tests of True Causation Think of these as your checklist for evaluating any causal claim. The Three Tests: Test #1 - Timing: Confirm the supposed cause actually happened before the effect. If traffic spiked Monday but you launched the campaign Tuesday, that campaign didn't cause it. The cause must always come before the effect. Test #2 - Consistent Movement: When the supposed cause is present, does the effect reliably occur? When the cause is absent, does the effect disappear? Document instances where they occur together. Then examine situations where the cause is absent. If the effect happens just as often without the cause, you're looking at correlation, not causation. Test #3 - Rule Out Alternatives: Think carefully about what else could explain what you're seeing. Actively try to disprove your idea rather than only looking for supporting evidence. If you can't eliminate other explanations, you don't have causation. Strategy 2: Ask "Could Something Else Explain This?" Here's a technique you can implement in the next 30 seconds that will immediately improve your causal thinking: whenever someone presents a causal claim, ask out loud: "Could something else explain this?" This single question is remarkably powerful. It forces the speaker to consider hidden factors they ignored. It reveals whether they've actually done causal analysis or just noticed a correlation and declared victory. It shifts the conversation from assumption to examination. Try it in your next meeting when someone says "We did X and Y improved." Watch how often they haven't considered alternatives. Watch how often their confident causal claim becomes less certain when forced to address this simple question. Most people present correlations as causations without even realizing it. Your question makes that leap visible. Suddenly they have to justify it with evidence or back down. It's not confrontational—it's curious. And curiosity is the foundation of good causal thinking. Use it today. Use it every time someone attributes an outcome to a cause without ruling out alternatives. That question leads us naturally to our next strategy—learning to identify what those "something elses" actually are. Strategy 3: Hunt for Hidden Causes A confounding variable is a third factor that influences both your suspected cause and your observed effect. It creates the illusion of a direct relationship where none exists. Here's a simple example: ice cream sales and drowning deaths both increase during summer months. Does ice cream cause drowning? Obviously not. The confounding variable is warm weather, which causes both more ice cream purchases and more swimming. Now here's the business version: A retail company sees both customer satisfaction and sales increase after renovating their stores. Does the renovation cause higher satisfaction? Maybe—but both also increased because they renovated during the holiday shopping season when people are generally happier and spending more anyway. Same logical structure. Same expensive mistake if they conclude renovations always boost satisfaction. Map the Relationship: When you observe a correlation, write down your suspected cause and your observed effect. This visualization helps you spot gaps in your logic immediately. Ask "What Else Changed?": Think carefully about what other factors were present or changed during the same period. Make a written list so your brain doesn't skip over these hidden causes. Search for Common Causes: Identify factors that could influence both variables at the same time. For instance, if both employee satisfaction and productivity increased, could several toxic managers have left the company? Consider Time-Based and Environmental Factors: Examine seasons, business cycles, economic trends, reorganizations, leadership changes, and industry shifts that could affect multiple outcomes at once. Test by Controlling Variables: If possible, create scenarios where you can control or account for potential hidden causes. Try analyzing subgroups where the hidden cause is absent, or run controlled A/B tests. Once you can spot these hidden causes, you're ready to understand why your brain makes these mistakes in the first place. And this next one? It's probably happening in your head right now without you realizing it. Strategy 4: Outsmart Your Brain's Shortcuts Your brain is wired to see causal connections everywhere, even where none exist. This isn't a design flaw—it's a survival mechanism that kept your ancestors alive. But in the modern business world, this pattern-seeking instinct can mislead you. Your brain wants simple causal stories. Reality is usually more complex. Once you know what to watch for, you can catch yourself before making these errors. Catch Your Instant Explanations: When you observe a pattern, pause before declaring causation. Ask yourself: "Am I seeing causation because it's really there, or because my brain desperately needs an explanation?" Fight Confirmation Bias: Actively search for information that challenges your causal idea, not just data that supports it. If you can't find contradicting evidence, you haven't looked hard enough. Here's how this plays out: A manager believes remote work hurts productivity. She notices every time someone's late to a Zoom call. But she doesn't notice the three on-time people. She remembers the one missed deadline but forgets the five delivered early. Her brain is filtering reality to confirm what she already believes. Question Your Compelling Stories: Be wary of explanations that sound too neat. If your causal explanation reads like a perfect success story, double-check it. Don't See Patterns in Randomness: Three successful quarters in a row doesn't mean you've discovered a winning formula. It might just be a lucky streak. Always ask "Could this pattern occur by chance?" Watch the 'After Therefore Because' Trap: Every time you catch yourself thinking "we did X and then Y happened," force yourself to consider alternative explanations. Ask yourself "What would I need to see to know this isn't causal?" Now that you understand how your brain works, let's put this all together into a practical system you can use every time you need to make a high-stakes decision. Strategy 5: The Five-Question Causation Check Mastering causal thinking requires more than understanding principles—it demands a clear approach you can apply when the stakes are high and the pressure is on. The Five-Question Causation Check: Define the Relationship Clearly: Write out the specific causal claim you're evaluating with precision. "Social media advertising increases qualified leads by X%" is better than "marketing works." Verify the Basics: Does the cause come before the effect in time? Are they consistently related across different contexts? Are there possible alternative explanations? Look for or Create Tests: Find situations where the supposed cause varies while other factors stay constant. The goal is isolation—can you isolate the variable you're testing from everything else that's changing? Check if More Causes More: Does more of the cause lead to more of the effect? If doubling your ad spend doubles your conversions, that's stronger evidence than if the relationship is erratic. Test Reversibility: If you remove the cause, does the effect disappear? If you reinstate the cause, does the effect return? This is why pilot programs and controlled rollbacks are so valuable. Put It Into Practice You now have the complete framework for causal thinking—five strategies that work together to reveal what's really causing what. But here's what separates people who learn this from people who actually use it—one simple practice you can do this week that makes this framework automatic. Practice Exercise: The Causation Audit A practical and effective way to internalize these strategies is through practice with real-world scenarios from your actual work. Here's how to conduct your own causal analysis: Identify a Correlation from Your Work: Choose a recent pattern or causal claim that affects budgets or strategy. State Your Causal Hypothesis: Write out your causal claim explicitly. Be specific about the supposed cause and the supposed effect. Brainstorm Alternative Explanations: List at least five alternatives. Force yourself beyond the obvious first three. Apply Your Three Tests: Evaluate whether your idea meets all three tests for causation. Did the cause come first? Do they consistently move together? Have you actually ruled out alternatives? Design a Simple Test: If possible, design a test to isolate the variable you're testing. For example, have some account managers follow one approach while others don't, with otherwise similar conditions. Share Your Analysis: Explain your reasoning to a colleague or manager. Teaching forces clarity and demonstrates analytical rigor. With practice, you'll become skilled at spotting false causation and identifying true cause-and-effect relationships. This skill compounds over time, making you more valuable with every analysis you conduct. So what does this actually get you? Let me paint the picture of what changes when you master this skill. The Rewards The rewards of mastering causal thinking are well worth the effort and will compound throughout your career. You become immune to the most expensive mistakes in business—the ones where you solve the wrong problem perfectly. When everyone else is celebrating a correlation as success, you'll be asking the questions that reveal what's really driving outcomes. Imagine being in a meeting where leadership is about to allocate $2 million to scale an initiative, and you're the one who asks the question that reveals a competitor's bankruptcy actually caused the results. That's career-defining value. Your strategic recommendations carry weight because they're based on actual causation rather than hopeful patterns. Leaders who can distinguish between correlation and causation make decisions that actually work. When your predictions prove accurate while others' fail, your credibility compounds—you become the person everyone turns to when stakes are high. You develop the intellectual humility that marks exceptional leaders. Causal thinking teaches you to question your initial judgments, seek alternative explanations, and change your mind when evidence demands it. These qualities don't just make you a better thinker—they make you someone others trust with important decisions. So take these strategies and practice them. Apply them in your daily work. Question causal claims, hunt for hidden causes, check your biases, and use the systematic process. This makes you a more effective decision-maker, a more credible advisor, and someone who spots opportunities and avoids disasters that others miss entirely. And you'll become the person in the room everyone listens to when the stakes are high. Your Thinking 101 Journey In Episode 1, "Why Thinking Skills Matter Now More Than Ever," we exposed the crisis: your thinking ability is collapsing, AI dependency is creating cognitive debt, and those who can't think independently will be left behind. In Episode 2, "How To Improve Your Logical Reasoning Skills," you learned to distinguish deductive certainty from inductive probability, calibrate your confidence to match your evidence, and stop treating patterns as proven facts. Today, you learned how to distinguish true causation from mere correlation—saving yourself from expensive mistakes where you solve the wrong problem perfectly. Up next—Episode 4: "Analogical Thinking—The Power of Comparison." Your brain doesn't learn through pure logic—it learns by comparison. Every breakthrough idea came from someone who made an unexpected connection. You'll learn how to generate insights through analogy, recognize when comparisons break down, and spot when others use false analogies to manipulate you. Hit that subscribe button so you don't miss future episodes. Also—hit the like and notification bell. It helps with the algorithm so others see our content. Why not share this video with a colleague who you think would benefit from it? Because right now, while you've been watching this, someone just approved a million-dollar budget based on a correlation they mistook for causation. The only question is: will you be the one who catches it? SOURCES CITED IN THIS EPISODE Pathmetrics – Marketing Attribution Waste 5 Common Marketing Attribution Mistakes to Avoid. (2025). Pathmetrics. (Citing Proxima research on global marketing waste) Harvard Business Review – Correlation vs Causation in Leadership Luca, M. (2021). Leaders: Stop Confusing Correlation with Causation. Harvard Business Review. The CEO Project – Correlation vs Causation in Business Correlation vs Causation in Business. (2024). The CEO Project. Nature Communications – Causality in Digital Medicine Glocker, B., Musolesi, M., Richens, J., & Uhler, C. (2021). Causality in digital medicine. Nature Communications, 12, 4993. Stanford Social Innovation Review – The Case for Causal AI Sgaier, S. K., Huang, V., & Charles, G. (2020). The Case for Causal AI. Stanford Social Innovation Review. ADDITIONAL READING On Causation and Decision-Making Pearl, J., & Mackenzie, D. (2018). The Book of Why: The New Science of Cause and Effect. Basic Books. On Thinking Clearly Kahneman, D. (2011). Thinking, Fast and Slow. Farrar, Straus and Giroux. On Statistical Reasoning Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. Note: All sources cited in this episode have been accessed and verified as of October 2025. /episode/index/show/philmckinney/id/38724105

How to Improve Logical Reasoning Skills 10/14/2025

How to Improve Logical Reasoning Skills You see a headline: "Study Shows Coffee Drinkers Live Longer." You share it in 3 seconds flat. But here's what just happened—you confused correlation with causation, inductive observation with deductive proof, and you just became a vector for misinformation. Right now, millions of people are doing the exact same thing, spreading beliefs they think are facts, making decisions based on patterns that don't exist, all while feeling absolutely certain they're thinking clearly. We live in a world drowning in information—but starving for truth. Every day, you're presented with hundreds of claims, arguments, and patterns. Some are solid. Most are not. And the difference between knowing which is which and just guessing? That's the difference between making good decisions and stumbling through life confused about why things keep going wrong. Most of us have never been taught the difference between deductive and inductive reasoning. We stumble through life applying deductive certainty to inductive guesses, treating observations as proven facts, and wondering why our conclusions keep failing us. But once we understand which type of reasoning a situation demands, we gain something powerful—the ability to calibrate our confidence appropriately, recognize manipulation, and build every other thinking skill on a foundation that actually works. By the end of this episode, you'll possess a practical toolkit for improving your logical reasoning—four core strategies, one quick-win technique, and a practice exercise you can start today. This is Episode 2 of Thinking 101, a new 8-part series on essential thinking skills most of us never learned in school. Links to all episodes are in the description below. What is Logical Reasoning? But what does logical reasoning entail? At its core, there are two fundamental ways humans draw conclusions, and you're using both right now without consciously choosing between them. Deductive reasoning moves from general principles to specific conclusions with absolute certainty. If the premises are true, the conclusion must be true. "All mammals have hearts. Dogs are mammals. Therefore, dogs have hearts." There's no wiggle room—if those first two statements are true, the conclusion is guaranteed. This is the realm of mathematics, formal logic, and established law. Inductive reasoning works in reverse, building from specific observations toward general principles with varying degrees of probability. You observe patterns and infer likely explanations. "I've seen 1,000 swans and they were all white, therefore all swans are probably white." This feels certain, but it's actually just highly probable based on limited evidence. History proved this reasoning wrong when black swans were discovered in Australia. Both are tools. Neither is "better." The question is which tool fits the job—and whether you're using it correctly. Loss of Logical Reasoning Skills Why does this matter? Because across every domain of life, this reasoning confusion is costing us. In our social media consumption, we're drowning in inductive reasoning disguised as deductive proof. Researchers at MIT found that fake news spreads ten times faster than accurate reporting. Why? Because misleading content exploits this confusion. You see a viral post claiming "New study proves smartphones cause depression in teenagers," with graphs and official-looking citations. What you're actually seeing is inductive correlation presented as deductive causation—researchers observed that depressed teenagers often use smartphones more, but that doesn't prove smartphones caused the depression. And this is where it gets truly terrifying—I need you to hear this carefully: In 2015, researchers tried to replicate 100 psychology studies published in top scientific journals. Only 36% held up. Read that again: Nearly two-thirds of peer-reviewed, published research couldn't be reproduced. And those false studies? Still being cited. Still shaping policy. Still being shared as "science proves." You're building your worldview on a foundation where 64% of the bricks are made of air. In our personal relationships, we constantly make inductive inferences about people's intentions and treat them as deductive facts. Your partner forgets to text back three times this week. You observe the pattern, inductively infer "they're losing interest," then act with deductive certainty—becoming distant, accusatory, or defensive. But what if those three instances had three different explanations? What if the pattern we detected isn't actually a pattern at all? We say "you always" or "you never" based on three data points. We end relationships over patterns that never existed. So why didn't anyone teach us this? Traditional schooling focuses on teaching us what to think—facts, formulas, established knowledge. Deductive reasoning gets attention in math class as a mechanical process for solving equations. Inductive reasoning gets buried in science class, completely disconnected from actual decision-making. We graduated with facts crammed into our heads but no framework for evaluating new claims. But that changes now. How To Improve Your Logical Reasoning You now understand the two reasoning systems and why mixing them up is costing you. Let's fix that. These five strategies will give you immediate control over your logical reasoning—starting with the most foundational skill and building to a technique you can use in your next conversation. Label Your Reasoning Type The first step to improving your logical reasoning is becoming aware of which system you're using—and we rarely stop to check. We flip between deductive and inductive thinking dozens of times per day without realizing it. You see your colleague get promoted after working late, and you instantly conclude that working late leads to promotion—that's inductive. But you're treating it like a deductive rule: "If I work late, I WILL get promoted." The moment you label which type you're using, you regain control. Start with a daily reasoning journal. At the end of each day, write down three conclusions you made—about people, work, news, anything. For each conclusion, ask: "What evidence led me here?" If it's general rules applied to specifics (all mammals have hearts, dogs are mammals), you used deduction. If it's patterns from observations (I've seen this three times), you used induction. Label each one: "D" for deductive, "I" for inductive. This creates conscious awareness. You'll likely find 80-90% of your daily reasoning is inductive—but you've been treating it as deductive certainty. When you catch yourself saying "always," "never," "definitely," stop and ask: "Is this deductive certainty or inductive probability?" That single pause changes everything. Practice in real-time during conversations. When someone makes a claim, silently label it: deductive or inductive? Weak reasoning becomes obvious instantly. After one week of journaling, review your entries. Patterns emerge in your reasoning errors—specific topics where you consistently overstate certainty, or people you make assumptions about. This awareness is the foundation for improvement. Calibrate Your Confidence Once you've labeled your reasoning type, the next step is matching your certainty level to the strength of your evidence. Here's where most people fail: they feel 100% certain about conclusions built on three observations. Your brain doesn't naturally calibrate—it defaults to "this feels true, therefore it IS true." But when you explicitly assign probability levels to inductive conclusions, you stop making the most common reasoning error: treating patterns as proven facts. For every inductive conclusion, assign a percentage. "Given these five observations, I'm 60% confident this pattern is real." Never use 100% for inductive reasoning—by definition, inductive conclusions are probabilistic, not certain. Use this language shift in conversations: Replace "You always ignore my suggestions" with "I've brought up ideas in the last two meetings and haven't heard feedback, which makes me about 40% confident there's a communication pattern worth discussing." Replace "This definitely works" with "From what I've seen, I'm 70% confident this approach is effective." Create a certainty threshold for action. Decide: "I need 70% confidence before I make a major decision based on inductive reasoning." This prevents impulsive moves based on weak patterns. Below 50%? Keep observing. Above 80%? Worth acting on. Keep a confidence log for one week. Write your predictions with probability levels ("80% confident it will rain tomorrow," "60% confident this project will succeed"). Then check if you were right. This trains your calibration. You'll discover whether you're overstating or understating your certainty—and you can adjust. When someone presents "definitive" claims based on inductive evidence, ask: "What certainty level would you assign that? 60%? 90%?" Watch them realize they've been overstating their case. This question immediately disrupts manipulation. Hunt for Contradictions Your brain naturally seeks confirming evidence and ignores contradictions—this strategy forces you to do the opposite. Confirmation bias is the enemy of good inductive reasoning. Once you believe something, your brain becomes a heat-seeking missile for evidence that supports it. The only antidote? Actively hunt for evidence that contradicts your conclusion. It's uncomfortable, yes, but it's the difference between being right and feeling right. For every inductive conclusion you reach, set a 24-hour "contradiction hunt." Your job is to find at least two pieces of evidence that contradict your conclusion. If you believe "remote work increases productivity," you must find credible sources claiming the opposite. Use search terms designed to find opposites. Search for "remote work decreases productivity study" or "evidence against intermittent fasting." Force-feed yourself the other side. Google's algorithm wants to confirm your beliefs—you have to actively fight it. Create a contradiction column in your reasoning journal. For each conclusion (left column), list contradicting evidence (right column). If you can't find any contradictions, you haven't looked hard enough—or you're in an echo chamber. In debates or discussions, argue the opposite position for 5 minutes. Seriously. If you believe X, spend 5 minutes making the best possible case for NOT X. This breaks confirmation bias and reveals holes in your reasoning you couldn't see before. Before sharing anything on social media, spend 2 minutes actively searching for contradicting evidence. Search "[claim] debunked" or "[claim] false" or look for the opposite perspective. If you find credible contradictions, pause. The claim is disputed. Either don't share it, or share it with context like "Interesting claim, though [credible source] disputes this because..." This habit trains you to think critically before becoming a misinformation vector. Question the Sample Most bad inductive reasoning fails the sample size test—and almost no one thinks to ask. Here's the manipulation technique you need to spot: Someone shows you three examples and declares a universal truth. "I know three people who got rich with crypto, therefore crypto makes everyone rich." Three examples. Seven billion people. Your brain treats this as evidence—until you ask about the total number. This question alone dismantles 90% of weak arguments. Every time someone makes an inductive claim, ask out loud: "How many observations is that based on?" Three? Thirty? Three thousand? The number matters enormously. One person's experience is an anecdote. Ten similar experiences start to suggest a pattern. A hundred becomes meaningful. A thousand builds real confidence. Learn the rough sample sizes for different certainty levels. For casual patterns: 10-20 observations. For moderate confidence: 100-500. For high confidence: 1,000+. For scientific certainty: 10,000+. Five examples claiming certainty? That's weak, and now you know it. Always check the total number—whether it's called sample size, denominator, or population. When someone shows examples or cites a study, ask: "Out of how many total?" Three testimonials mean nothing without knowing if it's 3 out of 10 (30% success rate) or 3 out of 10,000 (0.03%). When reading headlines like "Study shows X," click through and find the sample size. "Study of 12 people" is not the same as "Study of 12,000 people." The total number is usually hidden because it reveals how weak the claim really is. In your own reasoning, track your sample. Before concluding "this restaurant is always slow," count: how many times have you been there? Three? That's not "always"—that's barely data. You need at least 10 visits across different times and days before you can claim a pattern. Challenge yourself: Can you find a larger sample that contradicts your small sample? If your three experiences clash with 3,000 online reviews saying the opposite, which should you trust? The larger sample wins unless you have specific reasons to believe it's biased. The One-Word Test (Quick Win) Here's a technique you can implement in the next 30 seconds that will immediately improve your logical reasoning: stop using absolute language. Every time you're about to say "always" or "never," catch yourself and replace it with "usually" or "rarely." Every time you're about to say "definitely" or "certainly," use "probably" or "likely" instead. This single word swap trains your brain to think probabilistically. It acknowledges that most of your reasoning is inductive—based on patterns, not guarantees. And here's the bonus: people will perceive you as more credible because you're not overstating your case. Try it right now in your next conversation. Watch how often you reach for absolute language—and how much clearer your thinking becomes when you don't use it. Practice The most effective way to internalize these strategies is through practice with real-world scenarios. The Pattern Detective Challenge Find three claims from your social media feed today—anything that declares a pattern, trend, or "truth" (health advice, political claims, life advice, product recommendations). For each claim, identify: Is this deductive or inductive reasoning? Write it down. Most will be inductive disguised as deductive. "This supplement WILL boost your energy" sounds deductive, but it's based on inductive observations. If inductive, assess the sample size. How many observations is this based on? One person's testimonial? A study? How many participants? Is the sample representative of the broader population? Assign a certainty level. Given the sample size and quality of evidence, what probability would you assign this claim? 30%? 60%? 90%? Be honest—most will be below 70%. Hunt for contradictions. Spend 5 minutes finding evidence that contradicts the claim. Can you find it? How credible is it? Does it have a larger sample size than the original claim? Rewrite the claim with calibrated language. Change "Intermittent fasting WILL make you healthier" to "From studies of X people, intermittent fasting appears to improve some health markers for some people, though individual results vary—confidence level: 65%." Share your analysis with someone. Explain your reasoning process. Teaching others reinforces your own learning and reveals gaps you didn't notice. Repeat this exercise 3 times per week for one month. By the end, automatic evaluation becomes second nature. You won't need to think about it—it just happens. The Rewards The journey of improving your logical reasoning is ongoing, but the rewards compound quickly. You become nearly impossible to manipulate. When you can spot the difference between inductive observation and deductive proof, 90% of manipulation tactics stop working. The car salesman's pitch falls flat. The political ad looks transparent. The social media rage-bait loses its power. Your relationships improve dramatically. When you stop saying "you always" and start saying "I've noticed this three times," you create space for understanding instead of defensiveness. Conflicts become conversations. Assumptions become questions. Your professional credibility skyrockets. Leaders who can distinguish between strong deductive arguments and weak inductive patterns make better strategic decisions. When you speak with calibrated confidence—saying "I'm 70% confident" instead of "I'm absolutely certain"—people trust your judgment more, not less. You build a foundation for every other thinking skill. Spotting logical fallacies, evaluating evidence, resisting cognitive biases, asking better questions—all of these depend on understanding which type of reasoning you're using and which type the situation demands. You're not just learning a thinking skill—you're installing psychological armor that most people don't even know exists. And in a world where manipulation is the norm, that makes you dangerous to anyone trying to control you. Every week on Substack, I go deeper—sharing personal examples, failed experiments, and lessons I couldn't fit in the video. It's like the director's cut. This week's Substack deep dive into a logical reasoning failure can be found at: Your Thinking 101 Journey This is Episode 2 of Thinking 101: The Essential Skills They Never Taught You—an 8-part foundation series where each episode unlocks the next. If you missed Episode 1, "," start there. It explains why this entire skillset has become essential. Up next: Episode 3, "Causal Thinking: Beyond Correlation." You'll learn how to distinguish between things that simply happen together and things that actually cause each other—transforming how you evaluate health claims, business strategies, and relationship patterns. Hit that button so you don’t miss any 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 coworker or a family member who you think would benefit from it? … Because right now, while you've been watching this, someone just shared a lie that felt like truth. The only question is: will you be able to tell the difference? SOURCES CITED IN THIS EPISODE MIT Media Lab – Misinformation Spread Rate Vosoughi, S., Roy, D., & Aral, S. (2018). The spread of true and false news online. Science, 359(6380), 1146-1151. Indiana University – Misinformation Superspreaders DeVerna, M. R., Aiyappa, R., Pacheco, D., Bryden, J., & Menczer, F. (2024). Identifying and characterizing superspreaders of low-credibility content on Twitter. PLOS ONE, 19(5), e0302201. Open Science Collaboration – The Replication Crisis Open Science Collaboration. (2015). Estimating the reproducibility of psychological science. Science, 349(6251), aac4716. ADDITIONAL READING On Inductive Reasoning and Uncertainty Taleb, N. N. (2007). The Black Swan: The Impact of the Highly Improbable. Random House. On Cognitive Biases and Decision-Making Kahneman, D. (2011). Thinking, Fast and Slow. Farrar, Straus and Giroux. On Confirmation Bias Nickerson, R. S. (1998). Confirmation bias: A... /episode/index/show/philmckinney/id/38576250

Why Thinking Skills Matter More Than Ever 10/07/2025

Why Thinking Skills Matter More Than Ever The Crisis We're Not Talking About We're living through the greatest thinking crisis in human history—and most people don't even realize it's happening. Right now, AI generates your answers before you've finished asking the question. Search engines remember everything so you don't have to. Algorithms curate your reality, telling you what to think before you've had the chance to think for yourself. We've built the most sophisticated cognitive tools humanity has ever known, and in doing so, we've systematically dismantled our ability to use our own minds. A recent MIT study found that students who exclusively used ChatGPT to write essays showed weaker brain connectivity, lower memory retention, and a fading sense of ownership over their work. Even more alarming? When they stopped using AI tools later, the cognitive effects lingered. Their brains had gotten lazy, and the damage wasn't temporary. This isn't about technology being bad. This is about survival. In a world where machines can think faster than we can, the ability to think clearly—to reason, analyze, question, and decide—has become the most valuable skill you can possess. Those who can think will thrive. Those who can't will be left behind. The Scope of Cognitive Collapse Let's be clear about what we're facing. Multiple studies across 2024 and 2025 have found a significant negative correlation between frequent AI tool usage and critical thinking abilities. We're not talking about a slight dip in performance. We're talking about measurable cognitive decline. A Swiss study showed that more frequent AI use led to cognitive decline as users offloaded critical thinking to machines, with younger participants aged 17-25 showing higher dependence on AI tools and lower critical thinking scores compared to older age groups. Think about that. The generation that should be developing the sharpest minds is instead experiencing the steepest cognitive erosion. The data gets worse. Researchers from Microsoft and Carnegie Mellon University found that the more users trusted AI-generated outputs, the less cognitive effort they applied—confidence in AI correlates with diminished analytical engagement. We're outsourcing our thinking, and in the process, we're forgetting how to think at all. But AI dependency is only part of the story. Our entire information ecosystem has become hostile to independent thought. Social media algorithms create filter bubbles that curate content aligned with your existing views. Users online tend to prefer information adhering to their worldviews, ignore dissenting information, and form polarized groups around shared narratives—and when polarization is high, misinformation quickly proliferates. You're not thinking anymore. You're being fed a carefully constructed reality designed to keep you engaged, not informed. The algorithm knows what you'll click on, what will make you angry, and what will keep you scrolling. And every time you accept that curated reality without question, your capacity for independent thought atrophies a little more. What Happened to Education? Here's where it gets personal. Schools used to teach you HOW to think. Now they teach you WHAT to think—and there's a massive difference. Research from Harvard professional schools found that while more than half of faculty surveyed said they explicitly taught critical thinking in their courses, students reported that critical thinking was primarily being taught implicitly. Translation? Professors think they're teaching thinking skills, but students aren't actually learning them. Students were generally unable to recall or define key terms like metacognition and cognitive biases. The problem runs deeper than higher education. Teachers struggle with balancing the demands of covering vast amounts of content with the need for in-depth learning experiences, and there's a misconception that critical thinking is an innate ability that develops naturally over time. But research shows the opposite: critical thinking skills can be explicitly taught and developed through deliberate practice. So why aren't we doing it? Because education systems reward compliance and memorization, not inquiry and analysis. Students learn to regurgitate information for tests, not to question assumptions or evaluate evidence. They're taught to accept authority, not challenge it. To consume information, not interrogate it. We've created generations of people who are educated but can't think. Who have degrees but lack discernment. Who can Google anything but can't reason through problems on their own. The Cost of Mental Outsourcing Let's talk about what you're actually losing when you stop thinking for yourself. First, you lose agency. When you can't analyze information independently, you become dependent on whoever controls the information flow. Political leaders, social media influencers, corporations, algorithms—they all shape your reality, and you don't even realize it's happening. 73% of Democrats and Republicans can't even agree on basic facts. Not opinions. Facts. That's what happens when thinking skills collapse—you can't distinguish between what's true and what you want to be true. Second, you lose adaptability. Repeated use of AI tools creates cognitive debt that reduces long-term learning performance in independent thinking and can lead to diminished critical inquiry, increased vulnerability to manipulation, and decreased creativity. In a rapidly changing world, the inability to think flexibly and adapt to new information is a death sentence for your career, your relationships, and your relevance. Third, you lose connection—to your work, your decisions, your life. 83% of students who used ChatGPT exclusively couldn't recall key points in their essays, and none could provide accurate quotes from their own papers. When you outsource thinking, you forfeit ownership. Your work stops being yours. Your ideas stop being original. You become a conduit for someone else's thinking, not a generator of your own. Research shows that partisan echo chambers increase both policy and affective polarization compared to mixed discussion groups. You're not just losing the ability to think—you're losing the ability to connect with people who think differently. You're trapped in a bubble where everyone agrees with you, which feels comfortable but leaves you intellectually brittle and socially isolated. The societal cost? We're becoming ungovernable. When people can't think critically, they can't solve complex problems. They can't compromise. They can't distinguish between legitimate disagreement and malicious manipulation. Democracy requires citizens who can reason, debate, and arrive at informed conclusions. Without thinking skills, democratic institutions collapse into tribal warfare where the loudest voices win, not the most rational ones. Why This Moment Demands Action Here's what makes this crisis urgent: we're at an inflection point. Researchers have identified a tipping point beyond which the process of polarization speeds up as the forces driving it are compounded and forces mitigating polarization are overwhelmed. Some political groups may have already passed this critical threshold. Once you cross that line, reversing cognitive decline becomes exponentially harder. Think about what's coming. AI is getting smarter, faster, and more persuasive. Deepfakes and AI-manipulated media are becoming increasingly sophisticated and harder to detect. Whether or not they've already influenced major events, the capability exists—and your ability to evaluate what's real becomes more critical every day. Social media platforms are optimizing for engagement, not truth. Educational systems are struggling to adapt. The information environment is becoming more hostile to critical thinking every single day. If you don't develop thinking skills now—if you don't reclaim your capacity for independent thought—you'll be swept along by forces you can't see and can't resist. You'll believe what you're told to believe. Buy what you're told to buy. Vote how you're told to vote. And you won't even realize you've lost the ability to choose. But here's the truth they don't want you to know: thinking skills can be learned. They can be developed. They can be strengthened through deliberate practice. You're not doomed to cognitive passivity. You can take back control of your mind. What Becomes Possible Imagine waking up every morning with the confidence that you can evaluate any information that comes your way. No more anxiety about whether you're being manipulated. No more second-guessing your decisions because you don't trust your own judgment. No more feeling like everyone else knows something you don't. When you master thinking skills, you become intellectually self-sufficient. You can spot logical fallacies in arguments. You can identify bias in news sources. You can separate correlation from causation. You can ask the right questions instead of accepting convenient answers. You can hold two competing ideas in your mind and evaluate them fairly without your ego getting in the way. You become harder to fool and impossible to control. Political propaganda bounces off you because you can see through emotional manipulation. Marketing tactics lose their power because you understand psychological triggers. Social media algorithms can't trap you in echo chambers because you actively seek out diverse perspectives and challenge your own assumptions. Your relationships improve because you can actually listen to people who disagree with you without feeling threatened. Your career accelerates because you can solve problems others can't see. Your decisions get better because you're working from logic and evidence, not fear and instinct. Research shows that innovative teaching methods like problem-based learning and interactive instruction significantly boost academic performance and cultivate critical thinking skills. These aren't just abstract benefits—they translate into real-world outcomes. Better grades. Better jobs. Better lives. Most importantly, you reclaim your autonomy. You stop being a passive consumer of information and become an active creator of understanding. Your thoughts become truly your own again. Your beliefs are chosen, not imposed. Your worldview is constructed through rigorous analysis, not algorithmic manipulation. The Path Forward This episode is the beginning of a journey. Over the coming weeks, we'll break down the specific thinking skills you need to master: logical reasoning, argument analysis, decision-making frameworks, cognitive bias recognition, and information evaluation. Each episode will give you concrete tools you can use immediately. But before we get to the tactics, you need to understand why this matters. Why thinking skills aren't just nice to have—they're essential for survival in the modern world. Why the ability to think clearly is the ultimate competitive advantage. The thinking crisis is real. It's measurable. It's accelerating. But it's not inevitable. You have a choice right now. You can keep outsourcing your thinking to machines and algorithms, accepting a future where your mind grows weaker with each passing year. Or you can decide that your ability to think—to reason, to analyze, to question, to decide—is too valuable to surrender. The world needs people who can think. Your community needs people who can think. You need to be able to think. Not because it makes you smarter than everyone else, but because it makes you free. This is your invitation to reclaim your mind. Everything that follows will show you how. But first, you had to see what's at stake. Welcome to Thinking 101. Let's rebuild the most important skill you'll ever develop. Over the next eight weeks, we're building your thinking toolkit from the ground up. Logical reasoning. Causal thinking. Probabilistic judgment. Mental models that let you see what others miss. Each episode drops a specific skill you can use immediately—not theory, but weapons-grade thinking tools for the real world. Links to each episode will appear in the description as they're released, and you can . Subscribe now and hit the notification bell so you don't miss a single one. Because here's the truth: these skills compound. Miss one, and you're building on a shaky foundation. Watch them all, and you'll think circles around the competition. If you found this valuable, hit that like button—it helps more people discover this series. Drop a comment below: What's one thinking skill you wish you'd learned earlier? I read every single one. And if you want to go deeper, I write where I share the personal stories behind what I'm teaching here—the hard-won lessons, the mistakes that taught me why these skills matter, and what it actually looks like to rebuild your thinking from the ground up. The links in the description. This week's post examines the education system's failure to teach students how to think. You can find it here - The crisis is real. The solution is here. Let's get to work. /episode/index/show/philmckinney/id/38493915

How to Build Innovation Skills Through Daily Journaling 09/30/2025

How to Build Innovation Skills Through Daily Journaling Most innovation leaders are performing someone else's version of innovation thinking. I've spent decades in this field. Worked with Fortune 100 companies. And here's what I see happening everywhere. Brilliant leaders following external frameworks. Copying methodologies from people they admire. Shifting their approach based on whatever's trendy. But they never develop their own innovation thinking skills. Today, I'd like to share a simple practice that has transformed my life. And I'll show you exactly how I do it. The Problem Here's what I see in corporate America. Leaders are reacting to innovation trends instead of thinking for themselves. They chase metrics without questioning if those metrics matter. They abandon promising ideas when obstacles appear because they don't have internal principles to guide them. I watched a $300 million innovation initiative collapse. Not because the market wasn't ready. Not because the technology was wrong. But because the leader had no personal framework for making innovation decisions under pressure. This is the hidden cost of borrowed thinking. You can't innovate authentically when you're following someone else's playbook. After four decades, I've come to realize something that most people miss. We teach innovation methods. But we never teach people how to think as innovators. There's a massive difference. And that difference is everything. When you develop your own innovation thinking skills, you stop being reactive. You start operating from internal principles instead of external pressures. You ask better questions. Not just "How can we solve this?" but "Should we solve this?" That's what authentic innovation thinking looks like. The Solution So what's the answer? Innovation journaling. Now, before you roll your eyes, this isn't keeping a diary. This is a systematic development of your innovation thinking skills through targeted questions. My mentor taught me this practice early in my career. It became a 40-year obsession because it works. The process is simple. Choose a question. Write until the thought feels complete. Close the journal. Start your day. However, what makes this powerful is... The questions force you to examine your core beliefs about innovation. They help you develop principles that guide decisions when external pressures try to pull you in different directions. Most people operate from borrowed frameworks. Market demands. Best practices. Organizational expectations. Their approach shifts based on context. Innovation journaling builds something different. An internal compass. Your own thinking skills provide consistency across various challenges. Let me show you exactly how I do this. Sample Prompt/Demonstration Let me give you a question that consistently surprises people. Here's the prompt: "What innovation challenges do you consistently avoid, and what does that tell you about your beliefs?" Most people want to talk about what they pursue. But what you avoid reveals just as much about your innovation thinking. I've watched executives discover they avoid innovations that require long-term thinking because they're addicted to quick wins. Others realize they dodge anything that might make them look foolish, which kills breakthrough potential. One leader discovered she avoided innovations that required extensive collaboration. Not because she didn't like people. But because her core belief was that innovation required individual genius. That insight changed how she approached team projects. The question isn't comfortable. That's the point. Innovation journaling works because it bypasses your intellectual defenses. It accesses thinking you normally suppress or ignore. When you write "I consistently avoid innovations that..." you're forced to be honest. And that honesty reveals your actual innovation philosophy. Try this question yourself. Don't overthink it. Just write whatever comes up. You'll be surprised by what you discover. The Benefits Here's what changes when you develop your innovation thinking skills this way. You stop being reactive to whatever methodology is trendy. You have principles that guide you through uncertainty. You make decisions faster because they align with your authentic beliefs. Your team dynamics improve. People respond differently when you lead from consistent principles instead of borrowed frameworks. You create psychological safety because you're comfortable with not knowing. You ask ‌better questions. Instead of rushing to solutions, you examine whether problems deserve solving. You integrate your values with your innovation work. Most importantly, you stop performing someone else's version of innovation. You start thinking like the innovator you actually are. I've been doing this practice for 40 years. It's the foundation of every breakthrough innovation I've created. Not because it gave me ideas. But because it taught me how to think. Your innovation thinking skills are like a muscle. They get stronger with consistent use. Innovation journaling is how you build that strength. The compound effect is remarkable. After just two weeks, you'll see patterns in your thinking you never noticed. After a month, you'll make innovation decisions with confidence you didn't know you had. This isn't a quick fix. It's foundational development that serves you for years to come. Two-Week Exercise I want to help you get started. I've created a complete two-week innovation journaling program. Ten daily prompts plus weekend reflections. Each question is designed to develop different aspects of your innovation thinking skills. You can download it for free on my Substack [button href="https://philmckinney.substack.com/p/2-week-innovation-journaling-starter" primary="true" centered="true" newwindow="true"]Two-Week Innovation Journaling Program[/button] This isn't just a list of questions. It includes the context for each prompt. Implementation guidance. And the framework for building this into a sustainable practice. Start tomorrow. Choose one question. Write for 10-15 minutes. See what emerges. And if you find this helpful, I'm quietly working on something bigger. A whole year of innovation thinking prompts—different questions for each week to keep developing these skills over time. Subscribe on Substack to get notified when that's ready. It'll be worth the wait. Your authentic innovation thinking skills are waiting to be developed. The world needs innovators who think for themselves. Not performers following someone else's playbook. Develop your innovation thinking skills. Everything else will follow. /episode/index/show/philmckinney/id/38405975

The WSJ Got Quarterly Reporting Wrong 09/23/2025

The WSJ Got Quarterly Reporting Wrong Michael Dell and his investors spent twenty-five billion dollars to buy back Dell Technologies. But they weren't really buying a company. They were buying freedom from quarterly earnings pressure. I'm Phil McKinney, former CTO of Hewlett-Packard, and I witnessed how this pressure shaped decisions for years. Today, we are exploring why the misses what actually happens inside corporate boardrooms. The Reality of Quarterly Pressure I want to show you what quarterly reporting actually looks like from the inside. Let me paint you a picture. It's week seven of the quarter, and you're in a conference room with your executive team. On the screen are two critical numbers - your revenue projection and Wall Street's expectations. They don't align. During my time as CTO at HP, I found myself in these situations repeatedly. R&D projects worth billions in the future would get paused. Innovation initiatives that could transform the company would get delayed. Not because they lacked value. But because we had weeks to hit the quarterly numbers. What struck me was how predictable this became. Quarter-end approaches? Cut the long-term stuff. Meet short-term targets. Rinse and repeat. When your stock price swings ten percent over missing earnings by three cents per share, you optimize for quarterly performance, even when it destroys long-term competitiveness. Now, this is where it gets interesting. One CEO escaped this system entirely. The Dell Example: Twenty-Five Billion Dollar Proof Here's the proof that this system is broken. Michael Dell and Silver Lake paid $ 24.9 billion for one thing: freedom from quarterly earnings pressure, killing Dell's long-term potential. Dell's explicit goal: "No more pulling R&D and growth investments to make in-quarter numbers." What happened next was remarkable. R&D spending jumped from just over one billion to over four billion dollars. That's a 400 percent increase. Dell transformed from a declining PC manufacturer to an enterprise solutions leader. The return on investment by 2023? Seventy billion dollars. What Dell did wasn't just a corporate restructuring. It was a twenty-five billion dollar bet that quarterly reporting destroys long-term value. And they were proven spectacularly right. If you've experienced similar pressure at your company, I'd love to hear about it in the comments. Why the WSJ Analysis Falls Apart So with examples like Dell showing the impact, why does the WSJ still support quarterly reporting? The WSJ points to the UK's optional move from quarterly to semi-annual reporting and notes that companies didn't dramatically change behavior. Their conclusion: quarterly reporting isn't the real problem. That reasoning ignores a fundamental truth. We've trained an entire generation to think in ninety-day cycles. Business schools teach earnings management. Compensation rewards quarterly performance. Analysts' careers depend on short-term predictions. Journalists need something to write about, like quarterly results. You don't undo decades of this quarterly mindset simply by making reporting optional. The UK comparison is meaningless without addressing the ecosystem that reinforces short-term thinking. The Big Tech Illusion The WSJ claims Big Tech's AI investments prove quarterly reporting doesn't hinder long-term thinking. The argument misses the point completely. Google, Microsoft, and Meta can hide enormous R&D in their massive profit margins. When you're generating margins of twenty to thirty percent on hundreds of billions in revenue? You can invest billions in moonshots while still beating quarterly expectations. But what about manufacturing companies with five percent margins? Healthcare companies fighting regulations? Emerging tech businesses that can't disguise innovation investments? The current system creates a two-tiered economy. Only the most profitable companies can think long term. Everyone else gets trapped in quarterly optimization cycles. And that's precisely why this threatens America's competitive future. What's Really at Stake America's competitive advantage came from patient, long-term investments in breakthrough technologies. Semiconductors, the internet, biotechnology - all required decades of sustained investment. Today's quarterly regime systematically discourages ‌””. I call it the "fifty-year overnight success" - transformative innovations need sustained investment over decades. Try explaining that to analysts who want to know why margins dropped two percent. While we optimize for quarters, competitors in China make decade-long investments in critical technologies. We're giving away our innovation advantage. Three Real Solutions Switching to semi-annual reporting solves nothing. Six months isn't different from three months for long-term thinking. Three reforms could actually move the needle: First - eliminate forward-looking earnings guidance. This forces public commitments about future performance, creating pressure to hit predictions regardless of better opportunities. Second - separate long-term innovation investments from operational expenses in accounting. Give investors visibility into both current performance and future potential. Third - create metrics and incentives that reward patient capital deployment, not just quarterly performance. The Bottom Line The stakes aren't an abstract policy debate. It's about America's economic future. Academic research provides a valuable perspective, but there's often a gap between theory and practice when it comes to corporate decision-making under pressure. In my experience, there's often a significant gap between how these systems work in theory versus practice. The quarterly reporting system creates pressures that can undermine long-term thinking, even when that's not the intention. Next time the Wall Street Journal analyzes corporate behavior, here's an idea: talk to someone who's actually lived it. If these insights were useful to you, I'd appreciate your support through a like or subscription. I'm curious about your experiences with this. Have you seen quarterly pressure affect decision-making at your company? What changes would make the most difference? For more perspectives on innovation and corporate strategy, you can . I've also written a on my Substack publication Studio Notes. /episode/index/show/philmckinney/id/38319555

How to Get Smarter by Arguing with People who Disagree with You 09/16/2025

How to Get Smarter by Arguing with People who Disagree with You What if I told you that the people who disagree with you are actually your secret weapon for better thinking? Just last month, my wife and I had a heated argument about studio changes I wanted to make here on the ranch. Her immediate reaction was about cost. Mine was about productivity and creativity. We were talking past each other completely. But when I applied what I'm about to teach you, we discovered we were both right—and found a solution that addressed both concerns without compromising either. What started as an argument became a session where each of us was heard and understood. Sounds crazy, right? By the end of this video, you'll not only believe it—you'll have experienced it yourself. Think of someone you disagree with about something important. Got them in mind? Good. In 25 minutes, you'll see that person as your thinking partner. You know that sinking feeling when a simple conversation with someone turns into a heated argument? You walk away thinking, "How did that go so wrong?" The problem isn't the disagreement itself—it's that most people never learned how to use disagreement to think better. We encounter difficult disagreements almost daily. Your spouse questions your spending. Your boss pushes back on your proposal. Your friend challenges your weekend plans. Each disagreement is an opportunity for your thinking to become sharper. When you approach it right, others often think more clearly too. Your Brain Gets Smarter Under Pressure During solo thinking, you operate in your thinking "comfort zone". Familiar patterns feel safe. Trusted sources get your attention. Comfortable assumptions go unchallenged. It's efficient, but it also limits intellectual growth. In our —our most popular video—we taught you to question assumptions, check evidence, apply logic, ask good questions. If you haven't watched that episode, pause this and —it's the foundation for what comes next. What we didn't tell you in that video is that intelligent opposition makes these skills far more powerful than solo practice ever could. Let me show you what I mean. Take any belief you hold strongly. Now imagine defending it to someone smart who disagrees with you. Notice what happens in your mind: You suddenly need better evidence than "I read somewhere..." Your own assumptions come under sharper scrutiny Logic becomes more rigorous under pressure Questions get sharper to understand their position That mental shift happened because I introduced opposition. Your brain got more demanding of itself. And when you engage thoughtfully, something interesting happens—the other person thinks more carefully too. Think of it like physical exercise. Muscles strengthen through resistance, not relaxation. Your thinking muscles work the same way. Intellectual resistance—smart disagreement—strengthens your reasoning, your evidence gathering is more thorough, and your conclusions are more robust. This is where things fall apart for most people. The Critical Mistake That Kills Thinking Most people will never learn this because they're too busy being right. They miss the thinking benefits because they fail at disagreement basics. They get defensive. They shut down. Conversations become battles. Someone challenges their ideas, fight-or-flight kicks in. Instead of seeing an opportunity for better thinking, they see a threat. Imagine your boss questioning your budget request in a meeting. Your heart rate spikes. Your face flushes. You start defending instead of listening. Twenty minutes later, you've missed valuable insights about organizational priorities, they've tuned out your reasoning, and maybe both of you damaged a key relationship. Look, this makes total sense. Your brain can't tell the difference between a saber-toothed tiger and someone attacking your political views. The same threat response kicks in. When you get defensive, it often triggers defensiveness in others because they interpret your reaction as confirmation that this is a fight, not a discussion. Once this happens, thinking improvement stops immediately. Your emotional brain takes over. Pure survival mode. No learning happens. No growth occurs. The chance for better thinking vanishes. The solution? Learn how to keep disagreements constructive instead of destructive. How To Make Disagreements Constructive The difference between a constructive disagreement and a destructive argument isn't the topic—it's how you handle the interpersonal dynamics. These four skills transform how you approach disagreement and create conditions where others are more likely to think clearly, too. When you use these skills, something remarkable happens: you stay open and curious instead of defensive and closed. When others see you thinking clearly under pressure, they're more likely to follow suit. Think of these as the basic requirements for constructive disagreement. Miss any one of them, and even the best critical thinking techniques will fail because people will be in defensive mode instead of collaborative thinking mode. Skill 1: Accurate Listening Can you repeat back their position so accurately that they'd say "exactly"? If not, thinking improvement stops here. This sounds simple. Most people fail here spectacularly. We listen to respond, not to understand. We're busy crafting our rebuttal while they're still explaining their position. Result? We argue against strawman versions of their actual views, which means our thinking never encounters their real challenges. Before responding to any disagreement, try this: "Let me make sure I understand..." Then repeat their view back using their language, not yours. Include their reasoning. Include their concerns. Include their values. Their defensiveness drops instantly. People who feel truly heard—not just acknowledged, actually understood—become curious about your perspective too. They shift from defense mode to exploration mode. When you demonstrate good thinking through careful listening, they see you're genuinely trying to understand. This often makes them more willing to think carefully themselves rather than just defend their position. When you talk past each other, no real thinking happens. What goes wrong: Most people paraphrase positions in their own language. This feels like listening, yet it's actually reframing their argument to fit your worldview. True listening means using their words and their framework. Skill 2: Tone Awareness Your tone determines whether they hear thoughtful engagement or just hear an attack. Get the tone wrong, and the conversation dies before it starts. Practice this phrase: "Help me understand your perspective." Say it sarcastically—like you already know their perspective is wrong. Sounds like an interrogation. Now say it with genuine curiosity—like you actually want to learn something new. Notice the difference? Same words, completely different effect on their willingness to engage thoughtfully. That difference determines whether they engage their thinking or shut it down completely. Tone carries more information than words. It signals your intent, your respect level, and your openness to having your own mind changed. Try this practical test: Record yourself during a disagreement. Listen back. Does your tone invite thoughtful engagement or defensive reactions? Most people are shocked by what they hear. Skill 3: Genuine Curiosity Ask questions you don't know the answers to. Not "Don't you think that's wrong?" Instead, "What led you to that conclusion?" This distinction is crucial for constructive thinking. The first question is really a statement disguised as a question. You already know what answer you want. You're not seeking information. You're setting a trap. The second question is a genuine inquiry. You're asking about their thinking process. Their information sources. Their reasoning chain. You might learn something that changes your own view, and they often discover something about their own reasoning they hadn't considered. The test is simple: If you already know what answer you want, it's not a real question. Smart people recognize leading questions immediately. Once they sense manipulation, they either shut down or become defensive. Either way, constructive thinking stops. Real curiosity sounds different: "I'm having trouble understanding how you reached that conclusion. Can you walk me through your thinking?" This invites explanation and often leads to deeper exploration together. Skill 4: Respect Baseline Attack ideas, not people. Say "That approach has problems," not "You're being unrealistic." The moment it gets personal, thinking stops and ego takes over. Attack the person, they have no choice except to defend themselves. Attack the idea; they can defend it, modify it, or even abandon it without losing face. More importantly, you can both focus on improving the idea. Personal attacks trigger what psychologists call "defending your sense of self." When someone's identity feels threatened, they'll defend their position regardless of the evidence. They can't afford to be wrong because being wrong means they're a bad person. Keep it about ideas, they have cognitive freedom. They can evaluate your points objectively because their identity isn't on the line. When you model respectful challenge of ideas rather than personal attacks, others often respond with more thoughtful engagement because they feel safe to explore ideas without defending their identity. The trap: Language slips into personal territory without realizing it. "That's a stupid idea" feels like it's about the idea, yet it implies the person is stupid for having it. Better: "I see some problems with that approach." Master these four skills, and you create the conditions where better thinking can happen. These work with most people, though some individuals who are extremely defensive or arguing in bad faith may not respond positively regardless of your approach. The Three-Mode Thinking Method Now that you can disagree without triggering defensive reactions, here's how to use disagreement to enhance your thinking. Three thinking modes combine to strengthen your reasoning and often encourage clearer thinking in others. Something crucial to understand: these modes only work if you've built those positive disagreement skills first. Try to use advanced thinking techniques without those basic interpersonal skills, and you'll get worse results than saying nothing at all. Mode 1: Evidence Standards Get Higher Solo thinking often accepts weak evidence without realizing it. There's no one there to challenge assumptions or poke holes in logic. You can get lazy with reasoning because there's no immediate consequence. During a disagreement, opposition forces you to find stronger proof. Suddenly, vague claims no longer suffice. Instead of saying, "Studies show that remote work increases productivity," you need specifics like, "This recent Stanford study tracked productivity changes across multiple companies and found measurable improvements for hybrid workers." Notice what happens? The disagreement forced you to name the specific study, identify the scope and methodology, quantify the results, and acknowledge the controls used. Your evidence became stronger because someone was there to challenge it. When you ask, "What evidence supports that view?" with genuine curiosity, they examine their own proof more carefully because good questions naturally prompt deeper thinking. Good questions often lead to better evidence from both sides. The key insight: This only works if you keep them engaged. Use a respectful tone and genuine curiosity to keep them thinking rather than defending. What trips people up: Getting so focused on strengthening your own evidence that you forget to explore theirs. The goal isn't proving you're right. It's raising the evidence standards in the conversation. The bias trap: Watch out for cherry-picking evidence that supports your existing view while dismissing theirs without proper examination. Ask yourself: "Am I looking for the strongest evidence available, or just evidence that confirms what I already believe?" Mode 2: Hidden Assumptions Surface Solo thinking often operates on assumptions you don't even realize you're making. Your perspective has blind spots, and your framework has limitations you can't see. Disagreement forces assumptions into the open. When someone argues from a completely different framework, it reveals the hidden beliefs you were taking for granted. Take a team debating work-from-home policies. You assume productivity means "time spent working." They assume it means "results delivered." Neither of you realized you were using different definitions until the disagreement forced these assumptions into the open. Suddenly, you're questioning the foundations of your thinking: "Wait, what do I actually mean by productivity?" This assumption discovery often works both ways. When you ask, "What led you to that conclusion?" you're helping them examine their own thinking process too. The mistake most people make: Treating assumptions as weaknesses to attack rather than insights to explore. Assumption discovery works best when it feels like a mutual investigation. The bias trap: Notice when you're dismissing their assumptions without examining your own. Ask yourself: "What beliefs am I taking for granted here? What if their assumption is actually more accurate than mine?" Mode 3: Logic Gets Stress-Tested Everything comes together here. You use improved evidence and more explicit assumptions to think more rigorously than you could alone. Take a family planning their summer vacation. You want to go to the beach: "The kids love the ocean, and we all need to relax." Your spouse wants to go camping: "The kids spend too much time on screens, and we need real family bonding time." Initially, you're both arguing from different assumptions about what the family needs. But when you start examining your reasoning more carefully, interesting questions emerge. You examine your logic: "Do the kids actually love the ocean, or do I just assume that because they like the pool? Is a beach vacation really relaxing with three kids under 10?" Your spouse examines theirs: "Will camping actually reduce screen time, or will the kids just be miserable without their devices? Is tent camping really the best way to bond?" This logical stress-testing reveals that you're both making assumptions about what the kids want and what the family needs. You're thinking better, and your example of questioning your own reasoning often encourages others to examine their logic more carefully, too. The result might be: "Let's find a lakeside cabin—the kids get water activities, we get a break from screens, and everyone sleeps in real beds." This solution emerges from better thinking by both sides, not from one person winning the argument. Something subtle happens here that most people miss. Your thinking isn't just better because you have more information. It's better because disagreement forced you to examine your reasoning more carefully than you ever would alone. The trap: Treating this as a debate where someone has to win. The goal is to reach better conclusions through better thinking, not to prove your original position was right. The rationalization trap: Using better information to justify your original position rather than genuinely updating your thinking. Ask yourself: "Am I using this new information to think better, or just to argue better?" Let me show you how powerful this really is. Back in the late 90s, I was part of the founding team for . Our Chairman was the former President of AT&T, , and I was by far the youngest founding executive. These older, more experienced founders thought about business and value creation completely differently than I did. Initially, I thought they were being overly cautious. They probably thought I was being reckless. But instead of defending our positions, we used these exact principles. I questioned my assumptions about speed versus stability. They questioned theirs about innovation versus proven methods. We asked deep questions about what customers actually needed. We really listened to each other's reasoning. Did we always agree? No. But we aligned. The result? We took Teligent public. That disagreement process didn't just resolve conflicts—it built the strategic foundation that got us to IPO. When Your Thinking Actually Changes Here's the moment most people avoid: What happens when disagreement reveals that your original position was wrong or incomplete? This is where better thinking actually occurs. The three thinking modes give you better information. Now you need to know what to do with it. The Four Signals It's Time to Update Time to be honest with yourself. Signal 1: Better Evidence—Their evidence is stronger than yours, and you can't find flaws in their reasoning. Signal 2: Exposed Assumptions—They've revealed beliefs you didn't know you held, and those beliefs don't hold up under scrutiny. Signal 3: Failed Logic Test—Your reasoning has holes you can't patch, while theirs holds together. Signal 4: Persistent Questions—You can't answer their genuine questions without changing your position. How to Actually Change Your Mind Most people know HOW to think better, but don't know HOW to update their thinking. Here's the process: Step 1: Acknowledge Internally—"This new information suggests my original view was incomplete." Step 2: Test the Update—"If I accepted this new information, what would I believe instead?" Step 3: Express the Change—"I'm rethinking my position based on what you've shared. Help me understand..." Step 4: Integrate Gracefully—"I was wrong about X, but I think we're both right about Y." The Social Challenge Changing your mind feels vulnerable. It seems like admitting weakness. Actually, it's demonstrating intellectual strength. The person who can update their thinking based on new evidence is more trustworthy, not less. They're someone whose conclusions you can rely on because they've been tested against opposition. Common fear: "If I change my mind, they'll think I'm wishy-washy." Reality: People respect those who can think clearly under pressure more than those who never change their position. The key: Change your mind about facts and methods, but maintain your values and goals. "I still want to increase productivity (goal), but I'm changing my view on how to measure it (method)." Practice With Real Stakes You now have the complete system: foundation skills that prevent defensive reactions, plus thinking enhancement modes that use disagreement constructively. Your thinking gets stronger under pressure, and your approach often brings out better thinking in others, too. The skills that improve your thinking also make you more effective at work and more trusted in relationships. This isn't just about better conversations—it's about better thinking outcomes. Practice makes the difference between knowing these concepts and actually using them when emotions run high and relationships are on the line. Remember the person you thought of at the beginning? Here's the test: by the end of this practice sequence, you'll actually be curious about their perspective instead of frustrated by it. Let's see if I'm right. Here's your practice sequence: First: Practice the listening test. How would you repeat their position back so they'd say "exactly"? Try to capture not just their conclusion, but their reasoning, their concerns, and what they value. Use their language, not yours. Second: Check your tone. Practice saying "Help me understand your perspective" with genuine curiosity, not sarcasm. Your tone determines whether they'll engage thoughtfully or defensively. Third: Ask genuine curiosity questions to understand their thinking process. What information do they have that you don't? What experiences shaped their view? What led them to... /episode/index/show/philmckinney/id/38235700

How to See Opportunities Others Miss 09/09/2025

How to See Opportunities Others Miss In 2005, I had a ten-minute conversation at San Jose Airport that generated billions in revenue for HP. But here's what's fascinating: three other HP executives heard the exact same conversation and saw nothing special about it. If you read Monday's Studio Notes, you know this story from the emotional side—what it felt like to have that breakthrough moment, the internal resistance I faced, the personal transformation that followed. Today I'm delivering on my promise to give you the complete tactical methodology behind that insight. I'm going to show you the systematic framework I call high-resolution thinking—and how you can train yourself to see opportunities that others miss entirely. By the end of this episode, you'll understand the three-stage system that turns casual conversations into breakthrough innovations, you'll have nine specific methods you can practice, and you'll walk away with a week-long exercise you can start immediately. Here's what I want you to do right now: think of one conversation you had this week where someone mentioned a frustration, a side project, or something they wished existed. Hold that in your mind—we're going to transform how you process that kind of information. Credibility and Results But first, let me establish why this matters. That airport conversation led to HP's acquisition of VooDoo PC, the creation of HP's gaming business unit, and HP's rise to number one gaming PC market share—a position we held for years. The HP Blackbird that resulted earned PC Gamer's highest score ever awarded and a 9.3 from CNET. More importantly, I've used this same methodology to identify breakthrough opportunities across multiple Fortune 100 companies over the past two decades. The framework is repeatable, teachable, and it works. The difference between breakthrough innovators and everyone else isn't intelligence or access to information. It's thinking resolution—the cognitive ability to process multiple layers of information simultaneously while others get stuck examining only the surface. The Problem But before we dive into the framework, let me show you why this has become absolutely critical. We're living through what I call a "thinking recession." Despite having access to more information than ever before, our cognitive resolution is actually decreasing. Here's a startling statistic: the average executive processes over 34 GB of information daily, but misses 73% of the strategic signals embedded in that data. We're drowning in information while starving for insight. Watch any leadership meeting and you'll see the symptoms: binary thinking applied to complex situations, focus on symptoms instead of root causes, poor synthesis of multiple data streams, and over-reliance on frameworks that miss critical edge cases. Pause here and ask yourself: How many potential opportunities did you miss last week simply because you processed them as routine information instead of strategic signals? Consider Kodak—a company that literally invented the first digital camera, owned the patents, and dominated the market. They processed digital photography in low resolution, seeing it as a threat to film rather than recognizing how convenience, quality improvements, and social sharing behaviors would converge to create an entirely new market. Meanwhile, Instagram—a company that didn't even exist yet—was destined to process the same signals with enhanced clarity. They understood that digital photography wasn't about replacing film. It was about transforming social connection through visual storytelling. This pattern repeats constantly, and the stakes keep getting higher. The HP Story Let me show you exactly how high-resolution thinking worked in that airport conversation. I was traveling to San Diego with three other HP executives to visit a defense contractor. Standard business trip. But instead of small talk, I asked the HP engineer traveling with us about his side project. After some prodding, he described a PC he'd built with "off the charts performance." He'd been sneaking parts from the parts bin, sneaking motherboard customizations into production runs, conducting unauthorized R&D on his own time. Here's where thinking resolution made the difference: Standard Resolution Capture: "HP engineer built fast PC" High-Resolution Capture: Internal stealth project + upcoming defense contractor insights + performance optimization + unauthorized innovation driven by market demand Standard Resolution Processing: "Fast PC = gaming opportunity" High-Resolution Processing: Multiple pattern vectors converging—computing power trajectory hitting gaming demand trajectory, defense-grade performance concepts moving toward consumer markets, enthusiast communities forming through internet connections Standard Resolution Compression: "Let's build gaming PCs" High-Resolution Compression: Acquire existing excellence rather than build from scratch, preserve innovation culture while scaling distribution, create gaming ecosystem not just products The three other executives processed the same information but missed the pattern convergence that would reshape the entire computing industry. Framework Overview Now let me show you the systematic solution. High-resolution thinking operates through three distinct stages, and here's the key insight: each stage amplifies the others exponentially. Stage 1: Capture - Most people process conversations at surface level, missing 90% of the strategic signals embedded in everyday exchanges. This stage trains you to simultaneously examine multiple layers of reality—from obvious statements to hidden patterns to emerging trends—so you extract breakthrough insights from information others dismiss as routine. Stage 2: Process - While others analyze single trends in isolation, advanced thinkers recognize that breakthrough opportunities emerge where multiple patterns converge. You'll learn to track pattern vectors across different scales and timeframes, then identify the intersection points where small insights become massive market opportunities. Stage 3: Compress - Even brilliant insights die if they can't drive action, which is why 89% of strategic observations never get implemented. This stage teaches you to package complex discoveries into forms that bypass cognitive resistance and create inevitable decisions rather than ignored recommendations. Here's what's fascinating: most people get stuck in Stage 1—they either see only surface information or get overwhelmed by details. Advanced thinkers master all three stages systematically, and that's when breakthrough insights become predictable rather than lucky. The companies dominating their industries have leaders who excel at all three stages. The ones struggling usually excel at only one or two. Let me break down each stage with specific methods you can practice immediately. STAGE 1: Capture Stage 1 trains you to see what competitors overlook by processing information through multiple layers simultaneously. Most people process conversations at surface level, missing 90% of the strategic signals embedded in everyday exchanges, but you'll learn to extract game-changing insights from information others dismiss as routine. Let me break down the three methods for capturing hidden opportunities: Method 1: Multi-Layer Observation Most people either see the big picture or get lost in details, but advanced innovators process both simultaneously along with emerging signals and missing context. This systematic scanning technique operates through four distinct lenses, ensuring you never overlook critical information that competitors dismiss as irrelevant noise. Surface Layer: What everyone sees (the obvious) Pattern Layer: What connects and repeats (the structural) Signal Layer: What's emerging or changing (the predictive) Context Layer: What's missing or assumed (the invisible) In the HP example, the surface layer was "engineer built fast PC." The pattern layer was our upcoming defense contractor visit revealing performance vectors beyond consumer markets. The signal layer was stealth projects suggesting broader unmet demand. The context layer was our official roadmap missing performance enthusiasts willing to take risks. Method 2: Signal vs. Noise Discrimination Information abundance has made signal detection the critical skill, as most people collect data randomly and hope patterns emerge. Advanced thinkers use systematic filters to distinguish information that predicts and explains from information that distracts and misleads, dramatically improving decision quality while reducing cognitive overload. High Signal: Information that predicts, explains, or changes decisions Medium Signal: Information that provides context or confirmation Noise: Information that distracts or misleads "Stealth project" was high signal—it indicated unmet market demand worth risking a career for. Technical details were medium signal. Official job duties were noise. Method 3: Edge Case Hunting While most companies focus on serving their mainstream customers better, edge cases often contain the most valuable insights about where entire markets are heading. By systematically studying outliers, boundary conditions, and exceptions to conventional wisdom, you'll identify game-changing opportunities before they become obvious to your competition. Extreme Questions: What happens if this scales 10x? Shrinks 10x? Boundary Conditions: Where does this rule stop working? Failed Examples: What can unsuccessful cases teach us? Outliers: Who succeeds despite breaking conventional wisdom? Stress Tests: How does this hold up under pressure? The gaming enthusiasts building high-performance PCs weren't exceptions to ignore—they were leading indicators of massive market transformation. Ask yourself: Think about your organization's recent decisions. How many potential opportunities might you have missed simply because you processed them as routine information instead of applying these three capture methods systematically? STAGE 2: Process Excellent. Now you're capturing what competitors overlook. But raw observations are just data points. Stage 2 is where game-changing opportunities emerge—when you recognize that the real insights come from pattern convergence, where multiple trends intersect to create unexpected amplifications that others can't see. Here's a critical insight: While competitors analyze single trends in isolation, advanced innovators focus on pattern convergence—where multiple trends intersect to create unexpected opportunities. Let me give you three methods for processing these connections systematically: Method 1: Multi-Resolution Systems Analysis Unlike standard systems thinking that maps static relationships, this approach operates like a dynamic zoom lens that simultaneously analyzes macro industry forces, meso market dynamics, and micro individual interactions. You'll learn to identify temporal bridges—how today's micro-decisions aggregate into tomorrow's macro-realities—giving you predictive insight into market transformations before they become visible to others. Macro Resolution: Industry forces and ecosystem trends (5-10 year view) Meso Resolution: Market dynamics and organizational patterns (1-3 year view) Micro Resolution: Individual interactions and tactical decisions (real-time view) Temporal Bridges: How micro actions aggregate to macro outcomes over time Challenge yourself right now: Take that conversation you thought of earlier. Can you process it at all four resolution levels? What industry forces are creating the frustration they mentioned? What market dynamics make their side project relevant? How might their individual innovation predict broader transformation? For HP gaming, macro showed computing and entertainment converging as major industry forces. Meso revealed gaming enthusiasts as underserved segments with growing purchasing power. Micro captured one engineer's stealth project as a demand signal from within our own organization. The temporal bridge connected individual unauthorized innovation to ecosystem transformation happening industry-wide. Method 2: Vector Pattern Recognition Most people see static patterns like "gaming is popular," but miss the critical vectors: direction, velocity, and momentum that determine where patterns are heading. This technique teaches you to process patterns with movement data—tracking acceleration, cross-domain transfer, and collision points—so you can predict convergence opportunities before they become obvious to the market. Pattern Direction: Is this pattern strengthening or weakening over time? Pattern Velocity: How fast is the change happening and is it accelerating? Pattern Transfer: How does this pattern migrate between domains or industries? Pattern Collision: What happens when conflicting patterns meet or converge? In 2005, multiple vectors were converging: gaming performance requirements accelerating, computing power following Moore's Law, consumer willingness to pay premiums increasing, and enthusiast communities growing through internet connections. The collision point would create a massive new market category. Method 3: Interference Pattern Analysis While standard approaches analyze individual trends separately, game-changing insights emerge from interference patterns—where multiple trends overlap to create constructive amplification or destructive cancellation. This advanced technique focuses on convergence zones where separate patterns intersect to create entirely new phenomena that don't exist in any single trend alone. Pattern Superposition: How do multiple trends layer over each other? Constructive Interference: Where do trends amplify each other unexpectedly? Destructive Interference: Where do trends cancel each other out? Emergence Zones: Where does interference create entirely new phenomena? Here's your breakthrough moment: HP gaming emerged from constructive convergence between computing performance improvements, gaming market growth, enthusiast community formation, and direct-to-consumer distribution capabilities. Each trend alone was interesting. Their intersection created a billion-dollar market category. But here's what separates good analysis from breakthrough innovation: you need to compress these insights into action. And that's where most brilliant observations die. STAGE 3: Compress Now we reach the stage where most brilliant insights die—compression. Even the most sophisticated understanding of market patterns is worthless if it can't drive action, which is why so many strategic observations never get implemented because they trigger resistance instead of recognition. Here's the brutal truth: Most strategic insights never get implemented because they trigger resistance instead of recognition. The difference between advanced innovators and everyone else isn't just what they see—it's how they package what they see. Method 1: The Insight Compression Engine Standard approaches dump conclusions on people and hope they accept them, which triggers resistance instead of recognition. Advanced compression works like a key designed for specific mental locks—using cognitive hooks that exploit existing beliefs, minimal proof that triggers acceptance, and action triggers that make next steps feel inevitable rather than optional. Cognitive Hook: Exploits existing mental models for instant recognition Proof Compression: Minimum viable evidence that triggers belief Action Trigger: Makes the next step feel inevitable rather than optional Replication Code: Ensures the insight spreads naturally to others Think about this: For HP gaming, I didn't present a 47-slide market analysis. The cognitive hook was "our own engineers are willing to risk their jobs to build what the market wants." That connected to existing beliefs about HP's engineering excellence and made the insight feel like discovering something we already knew. Method 2: Temporal Compression Architecture Human brains are terrible at imagining future consequences but excellent at choosing between clearly presented options, which is why most strategic insights fail to drive action. This method compresses time itself—showing decision-makers multiple future scenarios simultaneously and the specific decision paths that lead to each outcome, making future consequences feel immediate and real. Present State Compression: Current reality expressed in its sharpest form Trajectory Compression: Where current paths lead without intervention Intervention Points: Specific moments when decisions change everything Future State Comparison: Side-by-side compressed views of possible outcomes Method 3: Network Compression Strategy While standard approaches try to convince one person at a time, advanced compression designs insights that naturally propagate through influence networks, creating cascade effects that amplify far beyond the initial conversation. You'll learn to map network topology, design message variants for different network positions, and create reinforcement loops that make early adopters want to spread your insight organically. Network Topology Mapping: Who influences whom in the decision ecosystem? Influence Pathway Design: How should the insight flow through the network? Message Mutation Control: How should the insight adapt as it spreads? Reinforcement Loop Creation: How do early adopters amplify the insight? Critical insight: Your breakthrough observation means nothing until it becomes someone else's inevitable decision. Practice Method Now here's how you master this framework systematically. This is the exact systematic approach I used in that airport conversation with the HP engineer—turning a casual exchange into a billion-dollar opportunity. I call it "The HP Method" because it replicates the process that generated those results, and it's designed to train your pattern recognition in real-world conditions. This isn't theoretical—I've used this approach to identify game-changing opportunities across multiple Fortune 100 companies. The Challenge: For the next week, have "airport conversations" with people in your network. But you're going to approach them with systematic intent. Your Targets: Choose 5-7 people strategically: Colleagues from different departments who see different angles Engineers, salespeople, or customer service reps who interact with market reality daily People from adjacent industries who might reveal transfer opportunities Anyone working on side projects or experiments—these are your gold mines The Questions That Change Everything: "What are you working on that your team doesn't know about?" "What would you build if you could sneak the resources to do it?" "What frustrates you enough that you'd risk working on it unauthorized?" "What have you figured out that others in your field haven't?" Here's the systematic part: Apply 4-layer observation to every response: Surface Layer: What they literally said (resist stopping here) Pattern Layer: What this connects to or repeats across conversations Signal Layer: What's emerging or changing in their responses Context Layer: What's missing or assumed in their thinking Document everything. Don't just capture the obvious. Record the context, their motivations, broader patterns you're noticing, and your observations at all four layers. Success Indicators—by week's end, you should have: Discovered at least 3 stealth projects or unauthorized innovations Identified 2-3 frustrations that point to market gaps people care enough about to risk addressing personally Found signals that others in your organization or industry are completely missing Documented observations at all four layers, not just surface information What you're hunting for: The same signals I found in that airport conversation—internal innovation happening outside official channels, unmet demand your organization isn't addressing that people care... /episode/index/show/philmckinney/id/38140375

5 Questions That Can Spot Breakthrough Innovations Before They Happen 09/02/2025

5 Questions That Can Spot Breakthrough Innovations Before They Happen In October 1903, The New York Times published an editorial mocking the idea of human flight, stating that a successful flying machine might take "from one to ten million years" to develop through the efforts of mathematicians and engineers. Eight weeks later, on December 17, 1903, the Wright brothers achieved the first powered, controlled flight over the beaches of Kitty Hawk, North Carolina, proving the skeptics wrong. The smartest people in the world got this catastrophically wrong. What does that tell us about impossibility itself? Every industry has billion-dollar opportunities hiding behind a single word: impossible. And most executives never see them coming because they've been trained to accept limitations that don't actually exist. The Innovation Reality Check If the smartest experts can be so wrong about something as fundamental as human flight, then we need to completely rethink how we evaluate impossibility. The problem isn't that impossible things become possible. The problem is that we're terrible at recognizing what's actually impossible versus what just looks impossible. What Innovation Actually Means Innovation is simply an idea made real. Not brilliant concepts sitting in notebooks. Actual stuff you can touch. Use. Buy. Experience. Leonardo da Vinci invented flying machines in the 15th century. The Wright brothers innovated flight in 1903. What's the difference? Da Vinci had amazing ideas that stayed ideas. The Wright brothers made the idea real. This distinction changes everything about impossible innovation. Has someone successfully transformed an "impossible" idea into a tangible reality? Then logically, it was never truly impossible. We just lacked the knowledge, tools, or perspective to make it happen. Those dismissed breakthroughs floating around your industry right now? They aren't abstract fantasies. They're concrete challenges waiting for someone to develop the right knowledge, tools, and perspective. The Three Types of Impossibility Not all impossibilities are created equal. Three distinct categories: Logical Impossibility: Things that contradict themselves by definition. Married bachelors. Square circles. But even these sometimes dissolve when we reframe the question. Negative numbers? Logically impossible for centuries. Until merchants needed to describe debt, scientists needed to measure temperatures below freezing. Suddenly, those "impossible" numbers became essential tools. Physical Impossibility: Things that appear to violate natural laws. Quantum mechanics would've been physically impossible under 19th-century physics. Today, we're building quantum computers using those "impossible" principles. Practical Impossibility: Ideas that don't violate logic or physics—they're just beyond our current capabilities. Commercial fusion power. Artificial general intelligence. Reversing human aging. Most breakthrough innovations emerge from this third category. They represent temporary constraints. Not permanent barriers. Here's what nobody talks about: the companies that get blindsided by "impossible" innovations aren't stupid. They're victims of expertise. The more you know about an industry, the harder it becomes to see past its false limitations. Everyone says innovation requires thinking outside the box. That's backwards. The biggest breakthroughs come from questioning the box itself. Not thinking outside your industry's limitations—questioning whether those limitations are real. When I took over as CTO at HP, our PC division was hemorrhaging money. Three to five billion dollars a year in losses. Dead last in market share. Everyone inside the company believed there was no room to innovate in PCs. Too commoditized. Too mature. Impossible to differentiate. That belief? Complete nonsense. We took that division from massive losses to five billion in profits. From last place to number one global market share. How? By introducing innovations that everyone else in the industry said were impossible. Missing these patterns doesn't just cost market share. It costs entire business models. I've watched billion-dollar companies become footnotes because they couldn't see past their own expertise. The tools I'm sharing today came from that experience. And dozens of others like it. But what if I told you there's a way to cut through all this industry BS in less than five minutes? What if claims about what can't be done could be systematically dismantled with just the right questions? The Five-Question Reality Check These questions cut through industry BS faster than anything else I've seen. They force you to get specific about dismissive claims instead of accepting them at face value. They separate real barriers from fake ones. The Framework That Changes Everything When the industry consensus calls something a fantasy, don't dismiss it automatically. Run it through the Reality Check. Question 1: What makes this seem undoable specifically? Don't accept vague claims. Push for precise limiting factors. Electric vehicles seemed undoable because "batteries will always be heavy, expensive, and short-range." That assumption? Turned out to be temporary. Question 2: Which type of impossibility are we dealing with? Apply our three-category framework. Ideas that seem logically impossible might just need conceptual reframing. Practical limitations often just need enabling technologies to mature. Question 3: What knowledge would make this possible? Work backward from success. If this dismissed idea actually became real, what specific breakthroughs would've been necessary? Which are already happening in adjacent fields? Question 4: Who benefits from keeping this "impossible"? Consider motivations carefully. Sometimes dismissive claims protect existing interests. They don't reflect genuine technical reality. Question 5: Are different technologies starting to connect? Innovation happens when separate advances suddenly align. Multiple technologies. Multiple trends. All at once. Case Study: SpaceX and the Reality Check When Elon Musk announced plans to dramatically reduce launch costs in the early 2000s, asking the five questions would've revealed everything you needed to know. Question 1: Launch costs had remained around $10,000 per kilogram for decades. Everyone said this was fundamental physics. Question 2: This was a practical limitation. Not a fundamental barrier. Question 3: Knowledge gaps were in precision landing, rapid refurbishment, and manufacturing at scale. All solvable engineering problems. Question 4: Established aerospace contractors had massive incentives to maintain the expensive status quo. Their entire business model depended on it. Question 5: Multiple conditions were converging. Advanced computing. New materials science. Private capital availability. The five questions revealed that launch cost reduction was a practical limitation waiting for engineering solutions. By 2024? SpaceX reduced costs by over 90 percent. The impossible became routine. Those questions work perfectly—once someone mentions a dismissed breakthrough. However, the real competitive advantage lies in spotting these opportunities while your competitors are still dismissing them as fantasy. And that requires something most executives completely overlook. Training Your Detection Instincts That requires something different. Not just analysis tools, but detection systems. And most companies get this completely wrong. They think it's about having better technology scouts or attending more conferences. It's not. It's about rewiring how your organization processes dismissed ideas. Two Essential Exercises The foundation of any detection system is people who think differently about what's possible. Start here - with two exercises that change how your team processes industry blind spots. Exercise 1: The Time Traveler's Memo. Imagine you're writing from 2034 back to yourself today. One dismissed innovation from your industry has completely transformed business. Write a detailed memo covering: What was the breakthrough that seemed undoable? Why did experts dismiss it so confidently? What early warning signs did everyone ignore? What would you do differently today knowing this outcome? This forces you to think like your future disrupted self looking backward. Instead of your present expert self looking forward. Exercise 2: The War Room Session This is where you harness collective intelligence to break through dismissive assumptions. When you get diverse perspectives in one room, systematically challenging what your industry calls fantasy, patterns emerge that no individual could see alone. Three hours. Diverse team. Three columns on a wall: "Industry calls this undoable" "Technologies that might enable it" "Who might be working on this right now" The magic happens when you connect insights across these columns. Suddenly, you'll see that your industry's "fantasy" challenge is actually someone else's current research project. How to Run the War Room: The Five-Phase Process Start with the obvious stuff. Phase one is just brainstorming - thirty minutes of everything your industry says can't be done. Don't filter anything. Now dig deeper. Phase two takes sixty minutes to research what would need to be true for each idea to work. Adjacent fields. Emerging technologies. Connection patterns. Time to get specific. Phase three is forty-five minutes, identifying who might be tackling these problems. Research groups. Startups. Tech giants. Government initiatives. This is where it gets interesting. Phase four takes thirty minutes to draw connections between columns. Which dismissed ideas share enabling technologies? Which have the most active research happening behind the scenes? The moment of truth. Phase five is fifteen minutes of team voting. Most likely to become real within 5 years. The biggest threat to the current business model. Most significant opportunity for new revenue. The output isn't predictions. It's intelligence about where to pay attention. These exercises reveal incredible insights. But there's something even more powerful that most companies never build. Something that works even when you're drowning in quarterly reviews and daily fires. How to Spot Dismissed Ideas Before They Disrupt You Most companies run these exercises once, get excited about the insights, then go back to quarterly business reviews and forget everything they learned. Six months later? They're blindsided by exactly the innovations they identified. The solution isn't doing more exercises. It's building simple systems that keep running even when you're not paying attention. Building a Simple Detection System Individual evaluation is just the beginning. Organizations that win consistently? They've built simple systems for discovering breakthrough innovations while competitors still call them fantasies. The first place to look might surprise you. Universities. Universities are where professors tackle dismissed problems without quarterly pressure. Set up relationships with three to five research institutions. Not your industry's obvious schools. Places where enabling technologies are developing. Retail executives should be talking to robotics labs. Healthcare leaders should be monitoring materials science programs. Energy companies should be watching quantum computing research. What actually works: Monthly calls with key researchers Annual events where academics present early-stage work Student internship programs for early insight into research directions Budget roughly $50,000 annually per institution for meaningful relationships Next, follow the smart money. Serious capital flowing toward dismissed technologies? That signals something's changing. Track which venture capitalists are funding "fantasy" innovations in adjacent spaces. Follow their portfolio companies. When Kleiner Perkins started funding fusion energy startups, that was intelligence about practical limitations becoming investment reality. Simple systems that work: Google alerts for funding announcements in related fields Subscribe to venture capital newsletters Attend pitch events where breakthrough ideas get presented Pattern recognition develops over time Finally, the pattern that changed everything for us. Outsider perspectives. People trained in your industry's limitations can't see past them. Bring in outsiders who haven't learned what's "undoable" yet. Recent graduates from different fields. Entrepreneurs who've solved problems dismissed in other industries. Consultants with cross-industry pattern recognition. What works: Set up quarterly advisory sessions with 3-4 people from entirely different industries. Rotate one person out every quarter to keep perspectives fresh. Give them your toughest challenges upfront and ask them to approach it like problems in their own fields. Run focused innovation challenges where you present outsiders with one specific "undoable" problem. Give them background context, but don't explain why it's dismissed. Set a tight deadline - 48 hours works well. You'll be amazed at what solutions emerge when people haven't learned the constraints. Create a simple system to capture outsider questions. When someone asks, "Why don't you just..." about something everyone knows is undoable, write it down immediately. Review these questions monthly. Half will be naive, but the other half will reveal assumptions you didn't know you had. Additional Detection Methods Depending on your industry and organization, you might also want to systematically monitor patent filings in adjacent fields or designate specific people to champion breakthrough ideas instead of shooting them down. The key is starting with these three core approaches, then adding layers as you see what works. That's exactly how we dominated at HP. An organized way of watching for breakthroughs, while competitors relied on intuition and luck. You can start building the same advantage this week. But the clock is already ticking. Your Action Plan While we've been talking about frameworks and systems, someone in your industry is already working on what everyone calls fantasy. The question isn't whether breakthrough innovation will blindside your market—it's whether you'll be the disruptor or the disrupted. Start This Week - Five Immediate Actions Don't wait for organizational buy-in or perfect systems. Start with these immediate actions you can take personally: First action this week: Apply the Reality Check to one idea your industry calls fantasy. Spend 30 minutes. Get specific about limiting factors. Your second move: Identify and contact two universities doing research in adjacent fields. Make the phone calls. Third step: Set up three Google alerts for venture capital funding in technologies related to your industry's dismissed challenges. Fourth action: Spend one hour searching patent databases for recent applications related to your industry's "undoable" problems. Document who's filing what. Fifth and final step: Schedule conversations with two people from completely different fields. Ask them about your industry's toughest challenges. Then Scale What Works Once you start seeing results from these five actions, scale the ones that work best across your organization. Personal insights become a systematic advantage. The goal isn't perfection. It's momentum. These five actions this week create intelligence about breakthrough innovations becoming possible. Every industry has someone working on what everyone else calls fantasy right now. The question isn't whether breakthrough innovation will happen. It's whether you'll recognize it while it's still called fantasy, or after it disrupts everything you thought you knew. Innovations Cannot Be Impossible Innovation cannot actually be undoable. If someone makes an idea real, it was never truly undoable. We just lacked the knowledge, tools, or perspective. Every dismissed idea floating around your industry represents a potential breakthrough. Someone is going to acquire the necessary knowledge to make it happen. The competitive advantage goes to people who identify which dismissed ideas are knowledge gaps rather than permanent barriers. The Wright brothers were bicycle mechanics. Not aviation experts. Steve Jobs was a college dropout. Not a computer engineer. We achieved what the PC industry giants called fantasy because we questioned assumptions entire industries had stopped questioning. Your industry's next transformation is already being developed by someone who doesn't accept the current dismissive consensus. Will you recognize it while it's still called fantasy? Or after it disrupts everything you thought you knew? But let me end with a prediction. There's one breakthrough I'm convinced we'll see in our lifetime: molecular-scale medical robots. Programmable nanobots that live in your bloodstream permanently. Providing real-time health monitoring and instant medical intervention. They could prevent heart attacks mid-beat. Eliminate cancer cells the moment they form. Even upgrade your immune system with new capabilities. I shared this dream in a vision video I executive-produced back in 2017. You can see it here: Seven years later? Multiple research groups are making serious progress on exactly these technologies. What seemed like a fantasy in 2017 is becoming a practical limitation today. What dismissed ideas in your industry are you now viewing differently? What early warning signs might you have been missing? Share your observations in the comments. Let's build collective intelligence about breakthrough innovations that are becoming a reality. If this framework changed how you think about breakthrough innovation, then . For deeper tools and detailed implementation guides, check out . Frameworks, tools, and guides—everything's completely free. Your next breakthrough might be hiding behind something everyone calls fantasy. The tools are in your hands. What dismissed idea will you investigate first? /episode/index/show/philmckinney/id/38055310

I Evaluated over 30000 Innovation Ideas at HP 08/26/2025

I Evaluated over 30000 Innovation Ideas at HP Your best innovation ideas aren't losing to bad ideas – they're losing to exhaustion. I know that sounds counterintuitive. After 30 years of making decisions at HP and CableLabs, I thought I understood why good ideas failed. Market timing. Technical challenges. Resource constraints. Sometimes that was the case … but most of the time, I was wrong. We've created an innovation economy that's too innovative to innovate. And if you're wondering why your breakthrough ideas keep getting ignored, dismissed, or tabled "for later review," this video will show you the real reason. I'm going to reveal why even brilliant ideas are dying from attention scarcity, not their merit. And why this crisis will determine which companies dominate the next decade. Monday, I shared the complete story in my Studio Notes newsletter about how I first discovered this crisis at HP. For a comprehensive analysis and its implications for your company, please visit the link below. In this episode, I will share with you a for recognizing and addressing this problem within your organization. The Innovation Overload Problem Let's start with the math that's breaking everyone's brain. Every C-suite leader I know is evaluating 40+ innovation proposals monthly. That's what they tell me when I ask why good ideas are getting ignored—two per business day, every day, without break. However, what's happening psychologically is that decision-makers are developing reflexive skepticism toward all innovation claims as a survival mechanism. It's not cynicism – it's cognitive self-defense against proposal overload. In conversations with dozens of executives over the past year, nearly three-quarters tell me "" has become their top decision challenge. Think about that. The problem isn't a lack of innovation. The problem is too much innovation. Good ideas are dying not from merit evaluation but from attention competition. We've created an innovation economy where the sheer volume of innovation prevents genuine innovation from emerging. And here's the irony – I'm using the same overloaded language that's part of the problem. When every idea is described as “revolutionary”, the words lose all meaning. Last month alone, I was pitched 23 "revolutionary" AI solutions. Most were solid ideas with real potential. But none got the attention they deserved because my brain had already tagged them as "more innovation noise" before I could properly evaluate their merit. And that's when it hit me: if someone whose job is literally to analyze innovation decisions can't focus properly, what chance do overwhelmed executives have? The cruel mathematics are simple: breakthrough ideas need deep consideration, but executives only have bandwidth for . Let me show you exactly how this plays out in the real world. Case Study: HP's Innovation Program Office I first discovered this crisis when I was running HP's Innovation Program Office – what we called the IPO. The IPO was HP's dedicated engine for identifying, incubating, and launching breakthrough technologies that would become the company's future growth drivers. We had frameworks, funding, and brilliant people. Harvard and Stanford now teach case studies about the HP IPO and our process design. But here's what those case studies miss entirely: we were drowning. The HP Innovation Program Office received more than 3,000 ideas and pitches every year. Think about that number. That's nearly 60 new innovation opportunities hitting our desk every week. Each claiming to be a breakthrough. Each demanding evaluation. Each potentially containing the next billion-dollar opportunity for HP. No team – no matter how smart, no matter how well-resourced – can properly evaluate 3,000 innovation ideas annually. The mathematics are impossible. Fatigue became our constant battle. We would clear 50 proposals in one week, only to find 65 new ones waiting the next. It felt like trying to empty an ocean with a bucket. We built sophisticated tools. We created evaluation frameworks. We hired brilliant people and trained them extensively. But underneath all our sophisticated processes was a gnawing feeling that haunted every decision: somewhere in those 3,000 ideas were genuine breakthroughs that we weren't giving proper attention. I remember one particularly brutal month when we evaluated around 250 innovation pitches. By week three, I caught myself skimming proposals that deserved hours of consideration. By week four, I was unconsciously looking for reasons to say no rather than reasons to say yes. That's when I realized the system had broken me. And if it could break someone whose job was literally to find breakthrough innovation, it was breaking everyone. The most painful part? Years later, I would occasionally encounter innovations in the market that looked suspiciously familiar. Ideas that had been buried in our pipeline, dismissed not because they lacked merit, but because they arrived during a week when we were too overwhelmed to give them the attention they deserved. We had built the most sophisticated innovation evaluation process in corporate America, and it was systematically filtering out the very breakthroughs it was designed to find. Not because our frameworks were wrong, but because human attention has limits that no framework can overcome. That HP experience taught me to recognize the pattern. Since then, I've worked with dozens of companies facing the same crisis. The scale varies – some see 500 pitches annually, others see 5,000 – but the attention mathematics always break the same way. The Three Types of Innovation Fatigue Through analyzing failures across multiple companies, I've identified three distinct types of innovation fatigue destroying . Type 1: Executive Innovation Fatigue The symptom: Senior leaders developing reflexive skepticism toward ALL innovation pitches, regardless of merit. Here's what's happening: The overpromise/underdeliver cycle has trained executives to expect disappointment from innovation investments. After being burned by "revolutionary" solutions that delivered incremental improvements, leaders develop cognitive firewalls against innovation enthusiasm. I recently spoke with a Fortune 500 CEO who instituted a company-wide moratorium: "No more innovation pitch meetings for six months. We're drowning in breakthrough promises and starving for actual execution." This wasn't anti-innovation leadership. This was a smart executive recognizing that innovation overload was preventing innovation success. The impact: Even legitimate breakthroughs get dismissed as "more innovation theater" before receiving proper evaluation. Type 2: Team Innovation Fatigue The symptom: Innovation teams are burning out from launching initiatives that consistently get killed or ignored during the evaluation process. The cause: Organizations creating continuous innovation pressure without building the decision infrastructure to evaluate and support breakthrough ideas properly. At HP's IPO, I watched our most creative evaluators essentially stop fighting for breakthrough ideas. When I asked why, one told me: "When you're processing 60 pitches a week, you learn to spot the safe bets quickly. Fighting for the truly revolutionary ones takes energy I don't have anymore." This is innovation death by a thousand small compromises. The impact: The best innovators leave for companies with more explicit innovation mandates and better decision-making processes. Type 3: Market Innovation Fatigue The symptom: Customers and investors becoming increasingly skeptical of innovation claims, treating all "breakthrough" announcements with equal skepticism. The cause: "Revolutionary" has lost all meaning through overuse. Every product launch, every startup pitch, every feature update is positioned as game-changing innovation. Consider how "AI-powered" became the new "cloud-enabled" – meaningless marketing speak that signals nothing about actual innovation value. When everything is revolutionary, nothing is revolutionary. The impact: Actual breakthroughs struggle to differentiate from incremental improvements because the market has developed immunity to innovation language. Why This Crisis Is Unprecedented This isn't just another innovation challenge we can solve with better processes or more resources. This crisis is fundamentally different from anything we've faced before. Historically, innovation slowdowns were resource problems. Companies couldn't innovate because they lacked money, talent, or technology. The solution was always to invest more resources in innovation capability. Today's crisis is a problem of attention and focus scarcity in an abundance economy. We have unlimited and limited cognitive bandwidth to evaluate them properly. More resources won't solve the attention mathematics. Consider this trend: innovation proposals are growing exponentially, but human decision-making bandwidth remains constant. The danger: We're not innovation-starved – we're innovation-overwhelmed. Breakthrough opportunities are getting lost in the noise of marginal improvements. However, here's the opportunity: The companies that figure out attention allocation will dominate the next decade, while everyone else struggles with their own innovation success. This isn't about having better ideas. It's about being heard above the innovation noise, and most organizations have no systematic approach to cutting through their own opportunity abundance. Practical Framework: The Attention Audit So what do you do about this? Let me share a practical framework I use with companies to diagnose and address innovation fatigue. Step 1: Count Your Innovation Inputs Track everything hitting your for one month—ideas, pitches, proposals, "quick conversations" about breakthrough opportunities. You'll be shocked by the number. Step 2: Calculate Your Evaluation Capacity How many innovation decisions can your team properly evaluate monthly? Not skim – properly evaluate. Be brutally honest about the time quality evaluation requires. Step 3: Identify the Attention Gap Subtract your capacity from your inputs. That gap is where breakthrough ideas go to die. Step 4: Design Attention Triage Develop systematic methods to quickly identify the 10% of ideas that warrant . Not perfect – but systematic. Step 5: Build Decision Infrastructure Most companies have innovation processes but no decision infrastructure. These are different things. Remember: The goal isn't to evaluate everything. The goal is to evaluate the right things properly. Call-to-Action Here's what I want you to do right now: Look at your innovation pipeline. How many ideas are currently stalled not because they lack merit, but because nobody has the attention bandwidth to evaluate them properly? If you're honest, it's probably most of them. Innovation fatigue is just one of ten critical innovation decision challenges that nobody's addressing directly. Over the past month, I've identified the unspoken problems that determine whether billion-dollar innovation bets succeed or fail. Add your voice to choose which challenge I should cover in an upcoming episode. Should I reveal why smart teams kill their own best ideas? How fear corrupts innovation thinking? Why executives make terrible choices about breakthrough opportunities? The voting is live right now in a special Studio Notes post: "" Monday's complete analysis of innovation fatigue – including the case studies I couldn't share here – is in this week's regular . [button href="https://open.substack.com/pub/philmckinney/p/you-get-to-choose-which-innovation" primary="true" centered="true" newwindow="true"]Vote - What Should We Explore Next? [/button] The companies that master innovation decisions while their competitors remain overwhelmed will write the next chapter of business history. Which chapter will your company write? If you want to improve the thinking behind your innovation decisions, check out our – it's been our most popular this year. And don't forget: the complete innovation fatigue analysis, including what I learned from 30,000 failed evaluations at HP, is in . Share in the comments: What innovation decision challenge is keeping you awake at night? /episode/index/show/philmckinney/id/37960150

How To Master Lateral Thinking Skills 08/19/2025

How To Master Lateral Thinking Skills A software engineer grabbed a random word from a dictionary – "beehive" – and within hours designed an algorithm that saved his company millions. While his colleagues were working harder, he was thinking differently. This breakthrough didn't come from luck. It came from lateral thinking – a systematic approach to finding solutions hiding in plain sight. I'm Phil McKinney and welcome to my Innovation Studio. In this episode, we will cover the lateral thinking framework. Not theory – a practical, step-by-step system you can use immediately. You'll try your first technique in the next five minutes. By the end of this episode, you'll have four specific techniques that transform how you approach problems, plus practice methods that make mastery inevitable. And hey, if this kind of framework thinking resonates with you, then hit that subscribe and like button. It helps us with the algorithm. If you want to dive deeper into these topics, then subscribe to my . Plus, if you know someone who might find this episode useful, feel free to share it with them. Alright, let's dive in. Here's what most people miss: breakthrough solutions don't come from thinking faster or working longer. They come from thinking differently. While everyone else improves using existing tools and approaches, lateral thinkers reimagine entire problems. For example, Southwest Airlines didn't create a better airline experience - they reimagined air travel as mass transportation. Tesla didn't build superior cars - they re-conceptualized personal mobility around sustainable energy. These companies succeeded by approaching familiar challenges through completely different frameworks. The question isn't whether you're smart enough to solve problems - you are. The question is whether you're willing to disrupt your thinking patterns to discover solutions that conventional logical approaches miss. But here's where most people get lateral thinking completely wrong, and understanding this distinction will determine whether you develop breakthrough capabilities or just become better at brainstorming... Lateral Thinking vs Linear Thinking What is the distinction between Linear and Lateral thinking? When faced with a problem, most people use linear thinking - they analyze what's wrong and optimize within existing frameworks. It's logical, sequential, and focuses on improving current approaches. Lateral thinking does something completely different. Instead of improving what exists, it changes how you perceive the problem itself. Let me illustrate the difference with a single example. When customers complained about long wait times, linear thinking said, "Make the elevators faster." Lateral thinking asked, "What if waiting wasn't the real problem?" The solution? Install mirrors next to elevators. People stopped complaining because they were distracted, not because waits got shorter. Linear thinking improved the elevator. Lateral thinking eliminated the problem by changing what the problem actually was. This is Dr. Edward de Bono's systematic method for shifting perceptions entirely. As he explained: "To find breakthrough solutions, change where you're looking, not just how hard you're looking." The challenge isn't that people lack creativity - it's that they don't have systematic methods for breaking free from mental patterns that limit them. Lateral thinking offers specific techniques for generating what de Bono referred to as "movement" in thinking. When everyone in your industry follows similar approaches, breakthrough opportunities emerge for those who think differently. While competitors optimize existing methods, lateral thinkers discover entirely different approaches. This operates on four distinct levels that build systematic capabilities. The progression from beginner to expert follows a pattern that will surprise you... The Four-Level Mastery Framework The lateral thinking framework has four progressive levels. Here's a quick overview of each so you have context before we explore them each in detail. Level One: Suspend Judgment and Break Patterns – Your foundation level. You'll learn to deliberately disrupt automatic thinking responses and embrace ideas that seem absurd. This creates the mental environment where breakthrough solutions can emerge. Level Two: Random Input for Forced Connections – Intermediate level. You'll use systematic provocations to force your brain into unfamiliar territory. This isn't random creativity - it's controlled disruption that bypasses your brain's tendency to look for solutions in familiar places. Level Three: Challenge Sacred Assumptions – Advanced thinking. You'll systematically examine and reverse the fundamental premises everyone else takes for granted. This is about creating "movement" in thinking by making the familiar strange. Level Four: Embrace Deliberate Absurdity – Expert level. You'll find breakthrough solutions by seriously exploring ideas that seem obviously wrong. This isn't about being silly - it's about using absurdity as a systematic tool for discovering hidden insights. Quick Demo: Before we dive deep, let's try one technique. Think of any current challenge you're facing. Now grab the nearest object - a pen, coffee mug, your phone, anything. Spend thirty seconds asking: "How is this object like my problem?" Force weird connections. A pen runs out of ink - maybe your problem needs fresh input. A coffee mug holds liquid - maybe your challenge needs a container or boundary. Your phone connects people - maybe your issue needs better communication. Notice how this random object sparked different angles? That's lateral thinking in action. This was just a taste - each level has systematic techniques that amplify this effect. Here's what's powerful about this progression. You don't need to master all four levels to see dramatic results. Level One techniques alone can solve problems that teams couldn't crack in weeks. But when you combine all four levels, you develop innovation confidence – the unshakeable belief that creative solutions exist for every problem. But the real power comes from developing what I call "innovation confidence" - the systematic ability to find creative solutions when conventional approaches hit dead ends. Ready to transform how you approach problems? Let's start with Level One, but I need to warn you - what seems like the simplest technique often produces the most unexpected breakthroughs... Level 1: Pattern Breaking Techniques Your brain is a pattern-recognition machine. It looks for familiar situations and applies solutions that worked before. This efficiency usually helps, but when facing new problems, these patterns become invisible barriers that prevent you from finding new solutions. Level One breaks these patterns systematically. Here are three specific techniques: Technique One: Change Your Routine Disrupt both your thinking environment and daily patterns. Your brain associates thought patterns with specific locations and routines. If you always brainstorm in the same conference room, you'll have the same types of ideas. Southwest Airlines used this brilliantly. Instead of studying airlines, they studied bus transportation. This environmental change broke their mental patterns about air travel. They discovered point-to-point routes, eliminated assigned seating, removed meal service, and focused on quick turnarounds. Every innovation came from thinking like a bus company, not an airline. You can apply this by changing where you tackle problems, taking different routes to work, using your non-dominant hand for simple tasks, or changing when you tackle challenging problems during the day. These disruptions create mental flexibility that carries over into creative problem-solving. Technique Two: Question Core Assumptions Write down three assumptions about your current challenge. Then ask, "What if the opposite were true?" Most problems have hidden assumptions we never examine. Example: Improving customer service. Your assumptions might be that customers want fast responses, prefer human interaction, and contact you when problems occur. Consider this: What if customers prefer thoughtful responses over fast ones? What if they choose good self-service over poor human service? What if you could help customers before problems arise? Suddenly, you're thinking about proactive support, comprehensive self-service resources, and quality over speed. These insights come from questioning assumptions everyone else accepts. Technique Three: Time-Box the Impossible Spend ten minutes seriously considering solutions that seem impossible. Often "impossible" means "we haven't figured out how yet." Amazon's same-day delivery seemed impossible until they reimagined warehousing. SpaceX's reusable rockets seemed impossible until they questioned whether rockets had to be disposable. What seems impossible in your field might just need different thinking. Pattern breaking works because it forces your brain out of automatic mode. But what happens when you want to accelerate this process dramatically? The next level introduces something so counterintuitive that it seems almost absurd - until you see what it can create... Level 2: Random Input Technique Level Two introduces controlled randomness that forces breakthrough connections. You'll learn to make your brain create links it would never make naturally. This technique created Post-it Notes at 3M. A scientist had a "failed" adhesive that barely stuck. A colleague needed better bookmarks for his church hymnal. The random collision of these unrelated problems sparked repositionable sticky notes – now generating over a billion dollars annually. The Process: Step One: Define your challenge in one clear sentence. Be specific. Step Two: Generate random input. Open a book to a random page and point to a word, use online random word generators, or grab three random objects around you. Step Three: Force connections. Spend fifteen minutes finding ways to connect your random input to your challenge. No connection is too weird. The stranger, the better. Real Example: Challenge: "Reduce employee turnover" Random word: "Garden" Connections: Gardens need regular watering – employees need consistent check-ins. Gardens grow better with proper soil – work environment matters. Gardens require pruning dead parts – eliminate toxic behaviors. Gardens have seasonal cycles – adjust expectations based on business rhythms. These random connections lead to employee development programs, environmental improvements, cultural changes, and seasonal workflow adjustments. None of these insights came from traditional HR thinking. Why This Works: Random inputs bypass your brain's tendency to look for solutions in familiar places. They force neural pathways that wouldn't connect naturally. Breakthrough solutions often hide in unexpected combinations. Which level is clicking for you so far? Pattern breaking or random connections? Both build the foundation for what's coming next... Before we go deeper, let's practice what you just learned. Pick that same challenge from earlier. Now try a different Level 2 approach: grab any book, open to a random page, and point to a word. Spend one minute connecting that word to your problem. What new angles emerge? This compound effect - layering techniques - is where breakthrough thinking lives. The Random Input Technique accelerates breakthrough thinking by forcing neural pathways that wouldn't connect naturally. But there's something even more powerful waiting in Level Three—a method that challenges the very foundations everyone else builds their solutions on... Level 3: Challenge Sacred Assumptions Level Three challenges the assumptions that others take for granted. This is where lateral thinking becomes powerful – you'll start seeing opportunities that are invisible to your competition. Netflix used “What If” thinking to transform entertainment. In 2007, they dominated DVD-by-mail with seven million subscribers. The industry assumed customers wanted to own movies, physical media provided the best quality, and broadband was too slow for streaming. Netflix challenged every assumption: What if customers didn't want to own movies? What if delivery delays were barriers, not services? What if broadband became fast enough? Most radically: What if we cannibalized our own successful business? These questions led to streaming launch in 2007. Today, Netflix has over 240 million subscribers, generating $31 billion annually, while competitors who didn't question assumptions have disappeared. The Process: Step One: List your core assumptions. These are things "everyone knows" are true in your field. Step Two: Reverse each assumption completely. If customers want speed, ask "What if they preferred thoughtful slowness?" If more choices seem better, ask "What if fewer choices improved satisfaction?" Step Three: Push to extremes and chain your questions. What if this took ten times longer? What if resources were unlimited? Take your best scenario and ask What If about that result. Keep pushing until you reach uncomfortable territory. The biggest breakthroughs come when you challenge assumptions that feel fundamental– like rules. “What If” thinking reveals hidden opportunities within your assumptions. However, the most counterintuitive breakthroughs often come from an approach that seems to violate common sense entirely, which brings us to the expert level that turns logic on its head... Level 4: Embrace Deliberate Absurdity Level Four is expert-level lateral thinking that embraces deliberate absurdity. You'll find breakthrough solutions by doing exactly the opposite of what seems logical. It sounds counterintuitive because it is – and that's precisely why it works. IKEA revolutionized furniture by providing what seemed like terrible customer service. Instead of delivering assembled furniture, they made customers assemble it themselves. This reversed every industry assumption: customers would work instead of receiving convenience, invest time instead of getting immediate use. The "backward" approach revealed hidden benefits: dramatically reduced shipping costs, minimal storage requirements, lower prices, and, surprisingly, customer satisfaction from successful assembly. What seemed like terrible service became a competitive advantage. IKEA now generates €38 billion annually. The Process: Step One: List how your industry typically handles similar challenges. Write down the conventional wisdom everyone follows. Step Two: Reverse each approach completely. If your industry emphasizes speed, consider slowness. If everyone wants more features, consider fewer features. Step Three: Explore the reversals seriously. Don't dismiss opposites immediately. Look for unexpected advantages in seemingly wrong approaches. Sometimes, the best solutions hide behind what appear to be terrible ideas. Real Example: Dollar Shave Club did opposite thinking with razors. While the industry focused on premium features, advanced technology, and retail partnerships, Dollar Shave Club eliminated fancy features, used simple razors, and bypassed retail entirely by offering direct subscriptions. Result: While major brands competed on blade technology and premium positioning, Dollar Shave Club captured a massive market share by doing everything the industry thought was wrong—and sold for $1 billion. The most counterintuitive solutions often hide in directions that make everyone else uncomfortable. Don't just think outside the box – think in the opposite direction of the box. You now have four levels of lateral thinking techniques. Do you know hat separates people who learn these concepts from those who actually master them? A system of practice that transforms theory into instinctive capability... The Practice System That Guarantees Mastery The following two practice approaches transform lateral thinking from a conceptual understanding into an instinctive skill. Use them solo for breakthrough thinking or with colleagues for collaborative problem-solving. Approach One: The Assumption-Breaking Generator This combines three powerful techniques for compound breakthroughs. Start with a real problem you're facing. Step 1: List three core assumptions about your challenge - things "everyone knows" are true. Step 2: Reverse each assumption completely. What if the opposite were true? Step 3: Grab a random word (book, phone, ask someone) and force connections between that word and your reversed assumptions. Example: Challenge = "Improve customer service" Assumptions: Customers want speed, prefer humans, contact us when problems occur. Reversals: What if customers want thoughtfulness over speed? What if they prefer self-service? What if we helped before problems arise? Random word: "Garden" → Gardens need seasonal care = maybe customers need different support at different business cycles. This creates insights you'd never reach with single techniques alone. Approach Two: The Escalation Challenge Perfect for pushing “What If” thinking to breakthrough levels. Start with a real problem. Propose a “What If” scenario addressing it. Keep pushing ideas to more extreme territory until you say, "That's impossible... but what if we could?" Example: "What if customers never waited?" → "What if they were served before arriving?" → "What if we predicted needs before customers knew them?" Suddenly, you're thinking about predictive analytics and anticipatory service. Practice Tips: Use real problems, not theoretical ones Start with 15-minute sessions The more ridiculous ideas become, the better Document insights immediately Practice daily for compound skill building Embrace absurdity – breakthroughs hide in impossible ideas Your Practice Plan: Choose one approach. Apply it to a current challenge. Spend 15 minutes. Document what emerges. Try another approach tomorrow. Build lateral thinking into your regular problem-solving routine. Practice transforms these techniques from interesting concepts into instinctive capabilities. The more you use them, the more naturally you'll see solutions others miss. Conclusion Now comes the moment of truth. You have the complete framework, but what you do next will determine whether this becomes just another interesting video you watched, or the beginning of a fundamental shift in how you approach every challenge you'll ever face. Companies investing in lateral thinking see documented ROI from 5:1 to 20:1. Individual professionals show 300% increases in viable ideas. But the real value is personal transformation – becoming someone who consistently finds breakthrough solutions. Your assignment is simple: pick one problem you're currently facing and apply one technique right now. Not later – right now. Start with whichever level feels most accessible. Document what happens. Share your discoveries in the comments. What patterns did you break? What random connections led to insights? What assumptions proved wrong? Your examples help others see possibilities and build our breakthrough thinking community. /episode/index/show/philmckinney/id/37874715

Why Fail Fast Innovation Advice is Wrong 08/12/2025

Why Fail Fast Innovation Advice is Wrong The most popular piece of innovation advice in Silicon Valley is wrong—and it's killing great ideas before they have a chance to succeed. I can prove it with a story about a glass of water that sat perfectly still while a car bounced beneath it. My name is Phil McKinney. I spent decades as HP's CTO making billion-dollar innovation decisions, and I learned the hard way that following "fail fast" advice cost us billions and robbed the world of breakthrough technologies. Today, I'm going to share five specific signs that indicate when an idea deserves patience instead of being killed prematurely. Miss these signs, and you'll become another "fail fast" casualty. The Water Glass That Changed Everything So there I was around 2006, sitting in Dr. Bose's lab at Bose Corporation, and he was showing me what honestly looked like just a regular car seat mounted on some automotive hardware. I'm thinking, "Okay, what's the big deal here?" But then he activates the system and has his assistant start driving over these increasingly aggressive road obstacles. And here's what blew my mind—the car chassis is bouncing around like crazy, but the seat? Perfectly still. Then Dr. Bose does something that I'll never forget. He places a full glass of water on the seat and tells his assistant to hit a speed bump at thirty miles per hour. The chassis lurches violently, but not a single drop of water spills. And here's what should terrify every "fail fast" advocate—this technology took fifty years to develop. Dr. Bose began developing the mathematical model in the 1960s. Under today's quarterly Wall Street pressure, this project would have been killed a hundred times over. When I asked Dr. Bose how he could invest in an idea for fifty years, he explained that keeping Bose private meant they weren't subject to the quarterly results pressure that often destroys patient innovation at public companies. At HP, we were trapped in that system—and it cost HP billions. How "Fail Fast" Destroyed Billions at HP As a public company, we lived and died by quarterly earnings calls. Every ninety days, we had to show growth, and that quarterly drumbeat made us masters at killing promising ideas the moment they didn't produce immediate results. Let me give you three examples that still keep me up at night: WebOS: We acquired Palm for one-point-two billion dollars in 2010. Revolutionary interface, years ahead of its time. Killed it when it didn't achieve immediate dominance. Every time you swipe between apps today, you're using thinking we threw away. Digital cameras: We literally invented the future of photography. Abandoned it the moment smartphones started incorporating cameras. HP Halo: Immersive telepresence rooms with extraordinary meeting experiences. Sold to Polycom for eighty-nine million in twenty-eleven when quarterly pressures demanded focus. We bought Poly back for three-point-three billion in twenty-twenty-two. We paid thirty-seven times more to reacquire capabilities we built. We weren't bad managers. We were trapped by the quarterly earnings system that makes "fail fast" the only option for public companies. And it was systematically destroying our breakthrough potential. Visit where I discuss how these quarterly pressures shaped our boardroom decisions and what we were really thinking. Now, after making these billion-dollar mistakes, I had to figure out how to distinguish between ideas worth killing and ideas worth protecting. What I discovered changed everything—and it comes down to five things I now look for. When I see all five, I know we've got something worth being patient with. Miss even one, and you're probably wasting your time. The Five Things I Now Look For First: Does the Math Actually Work? Here's how to validate the science without being a scientist yourself. Start with peer review. Has this been published in reputable journals? Are other researchers building on it? Red flag: if the only validation comes from the inventors themselves. Next, bring in independent experts. Not consultants who'll tell you what you want to hear—find researchers who have no financial stake in your project. Share your core assumptions with them and ask them to identify any holes. Look for mathematical elegance. Dr. Bose's suspension model was beautiful in its simplicity. Overly complex models with dozens of variables often hide fundamental flaws. Here's your action step: Before investing serious money, get three independent technical reviews. If even one expert raises fundamental concerns about the underlying science, stop. No amount of patience fixes broken physics. Second: Can You Actually Build the Pieces? You need a dependency map. List every technology that has to work for your project to succeed. Then assess each one separately. For each dependency, ask: Are we developing this ourselves, waiting for someone else to solve it, or hoping it gets solved by magic? If more than one critical piece falls in the "magic" category, you're not being patient—you're gambling. Create realistic timelines for each component. Bose needed better actuators, smaller amplifiers, and faster control systems. They could systematically work on each piece. That's patient innovation. But if you need breakthroughs in five unrelated fields simultaneously—like needing better batteries AND quantum computing AND room-temperature superconductors—that's not a plan, that's a wish list. Your action step: Map your critical dependencies. If you can't draw a clear path to solving each one, either find a different approach or walk away. Third: Will Anyone Actually Want This? Don't just look at today's market—study how problems evolve and new markets emerge. Start with pain-point analysis. What specific problem does this solve, and how severe is that pain? Bose started with car comfort but found its real market when truck driver health became a safety issue. Look for regulatory drivers. Often, breakthrough technologies become valuable when regulations change. Environmental rules, safety standards, health requirements—these create demand that didn't exist before. Study early adopters. Who are the customers willing to pay a premium for imperfect solutions? This is your proving ground. If you can't identify specific early adopters willing to pay above-market prices, your timing is probably wrong. Your action step: Identify three specific customer segments who would pay a premium for an early version of your solution. If you can't name them specifically, you're not ready. Fourth: Will You Have Time to Enjoy It? This is about building defensible advantages that competitors can't easily copy. Focus on system-level innovation, not just component improvements. Bose didn't just build better shock absorbers—they created an integrated electromagnetic system that required entirely different expertise. Look for knowledge-accumulation advantages. The longer you work on something, the more you should know about it than anyone else. If competitors can hire away your key people and instantly catch up, you don't have a real advantage. Consider manufacturing complexity. Technologies that require specialized production processes, custom tooling, or rare expertise create natural barriers to entry. Your action step: Write down exactly why it would take competitors at least two years to match your solution, even if they threw unlimited money at it. If you can't make that case convincingly, keep working. Fifth: Can You Actually Survive the Wait? This is the make-or-break assessment. Most good ideas die here, not because the technology fails, but because the organization can't sustain the investment. First, assess your funding horizon. How long can you sustain this before you need to generate revenue? Be brutally honest. Include the cost of delays, scope creep, and inevitable setbacks. Second, evaluate the decision-maker's patience. Will the people approving your budget still be there in three years? Will they still believe in the project when competitors are winning quarterly battles? Third, create protection mechanisms. This might mean dedicated funding that can't be raided for other projects, separate business units, or partnership structures that insulate the project from quarterly pressures. Your action step: Calculate your true funding runway, including realistic setbacks and contingencies. If it's less than the time needed for fundamental breakthroughs, either get more patient capital or find a faster path to revenue. These five assessments work in theory, but do they actually create billion-dollar returns in the real world? The Seventy Billion Dollar Proof Now here's the most compelling modern example—Dell's 2013 privatization. Michael Dell paid twenty-four-point-nine billion for one thing: freedom from the quarterly earnings pressure that was killing their long-term potential. Dell explicitly stated the goal was "no more pulling R&D and growth investments to make quarterly numbers." And the results were remarkable. R&D spending went from one-point-one billion to four-point-four billion, Dell transformed from a declining PC manufacturer to an enterprise solutions leader, and by 2023, the investment had generated an estimated seventy billion dollar return. One of private equity's most successful turnarounds, built on escaping the quarterly Wall Street system that makes "fail fast" the only option for most public companies. Visit where I share how Alex Mandl orchestrated this deal and what Michael Dell was really trying to accomplish. Why This Really Matters The Bose Ride system launched in 2010, and it's been improving the lives of truck drivers who were facing whole-body vibration problems that had no technological solution. Academic studies have shown significant reductions in driver pain, lower fatigue levels, and faster recovery times after long road trips. One driver told researchers something that really stuck with me: "I thought I was going to have to quit my job. I was in crisis because my back was in such bad shape, but now I feel great I am driving full-time." If Dr. Bose had followed "fail fast" advice, that driver would still be in pain, and the advance would never have happened. So here's my challenge to you—and it starts with looking at what's sitting right in front of you. Your Innovation Challenge Look at your current portfolio right now. Are there projects that passed mathematics validation but haven't shown commercial results? Projects where the ecosystem isn't ready but the fundamental science is sound? Those might be exactly the innovations that deserve patience instead of "fail fast" pressure. I use the seventy-twenty-ten model: Seventy percent core improvements that move quickly. Twenty percent on adjacent markets with moderate timelines. Ten percent transformational advances—your patient capital investments. Patient innovation creates technological moats that rapid iteration cannot replicate. Once achieved, it often produces faster competitive advantage. You get overnight success after decades of systematic development. The difference between companies that master this and those that don't comes down to asking the right questions. The Questions That Change Everything Instead of asking "How quickly can we bring this to market?" try asking "What needs to get better before this becomes real?" Instead of asking "What's our quarterly burn rate?" try asking "What's the breakthrough potential if we actually solve this?" Instead of asking "Why hasn't this shown results?" try asking "Does the math work, and what pieces need more time?" The companies that master this balance will dominate the next wave of transformative technologies. In a world obsessed with speed, patient innovators are building the technologies that will define the next decade. What opportunity in your organization deserves patience instead of "fail fast" pressure? That question will determine whether you're building the next major advance or killing it before it has a chance to succeed. Visit Studio Notes over on Substack where I tell the complete story of that day in Dr. Bose's lab and the boardroom decisions at HP that we're still paying for today. If this resonates, share your own patient innovation experiences in the comments. And remember: thinking better creates better ideas. Sometimes those ideas need time to become the advances that change everything. /episode/index/show/philmckinney/id/37772030

Innovation Partnership Autopsy: HP, Fossil, and the Smartwatch Market 08/05/2025

Innovation Partnership Autopsy: HP, Fossil, and the Smartwatch Market Innovation partnerships can create breakthrough markets—or hand them to competitors through terrible decisions. I know because I lived through both outcomes. Bill Geiser from Fossil and I had it exactly right. We built the MetaWatch—a smartwatch with week-long battery life, Bluetooth connectivity, and every feature that would later make the Apple Watch successful. We had HP's massive retail reach, Fossil's manufacturing scale, and the technical vision to create an entirely new market. But our organizations couldn't execute on what we knew was right. Leadership chaos at HP and innovation paralysis at Fossil killed a partnership that should have dominated the smartwatch market—handing Apple a $50 billion opportunity. I've shared the complete behind-the-scenes story of the people, strategies, and decisions that killed our partnership in my Studio Notes post "." Today I'm applying the to our partnership failure. If you haven't seen my DECIDE framework yet, —it's the innovation decision tool I've developed over 30 years of making high-stakes choices. Because here's what this partnership taught me: having the right vision means nothing without the right decision framework. What Makes Innovation Partnerships Different? Let me start by explaining why the HP-Fossil partnership should have worked. This wasn't just another business deal—it was the perfect storm of complementary capabilities. Bill Geiser, Fossil's VP of Watch Technology, had been working on smartwatches since 2004. The man was practically clairvoyant. In 2011, he told me, "Phil, I wouldn't be shocked if Apple evolved the Nano to take advantage of this space. They'll legitimize it in consumers' minds worldwide." Bill understood something most people missed: Apple didn't need to be first to market—they needed to be first to create a platform. Meanwhile, I was developing HP's connected device strategy. We had the technology foundation, unmatched retail distribution—about 10% of consumer electronics shelf space—and the same retail muscle that helped launch the original iPod. Together, Bill and I had solved the hard problems. We had the vision, the technology, and the market insight. But we couldn't overcome the organizational machinery that prioritizes short-term comfort over long-term position. Innovation partnerships aren't just about having the right technology or market vision. They're about having the right decision framework when uncertainty meets organizational reality. The Three Partnership Decision Traps Before I show you how DECIDE could have saved our partnership, let me show you the three traps that derail even the smartest collaboration. Bill and I understood what needed to happen, but our organizations fell into every one of these traps. Trap #1: Innovation Type Mismatch This is when you apply the wrong decision framework because you've misidentified what type of innovation you're actually pursuing. It's the most common partnership killer because different innovation types require completely different approaches to risk, timing, and success metrics. In our case, Bill and I understood that smartwatches represented a platform opportunity—a new ecosystem that would change how people interact with technology. But our organizations treated it as a product extension that wouldn't threaten their existing businesses. HP's leadership viewed MetaWatch as another device in their portfolio, rather than as the foundation of a connected ecosystem spanning tablets, phones, and laptops. Fossil's leadership saw it as a "development platform" priced at $200—innovation theater that wouldn't cannibalize their traditional watch sales. Here's the partnership recognition question: Have you correctly identified what type of innovation you're pursuing together? Because applying incremental decision frameworks to breakthrough opportunities, or product frameworks to platform opportunities, kills partnerships before they can succeed. Trap #2: Safe Innovation Theater This combines revenue protection with organizational risk aversion. Both companies wanted to appear innovative without actually risking their core businesses. HP didn't want to cannibalize enterprise focus. Fossil didn't want to threaten traditional watch revenues. So instead of going all-in on market creation, both organizations positioned MetaWatch as a "safe" innovation—a development platform for engineers, not a consumer product that could disrupt markets. Bill faced an impossible organizational reality: Fossil's watch sales had tripled to $3.25 billion during the smartphone era. How do you convince leadership to risk that success for an uncertain new category? The partnership recognition question: Are you innovating to create markets, or are you innovating to appear innovative while protecting existing revenue? Trap #3: Governance Complexity Paralysis Bill and I found ourselves fighting the same battle on different fronts: convincing leadership that wearables represented a platform shift, not just a product extension. But even when we had the right vision, we couldn't execute at market speed. Multiple stakeholders created governance complexity without clear decision authority. We'd have month-long approval cycles for changes that startups could implement in days. I remember one meeting where we spent 45 minutes debating some minor specification while Apple was probably finalizing their entire ecosystem strategy. The fatal blow came from HP's leadership chaos. We cycled through three CEOs in 13 months, each with completely different visions. When Leo Apotheker arrived, he immediately decided HP should become an enterprise software company, not compete in consumer platforms. Our partnership needed startup decision velocity, but we were trapped at committee speed across two large organizations with conflicting priorities. Partnership recognition question: Can you move at market speed together, or are you trapped at the slowest organization's committee speed? Because innovation partnerships under uncertainty require aligned decision velocity. Applying DECIDE to the HP-Fossil Partnership Now let me show you how the DECIDE framework—which you can download free at philmckinney.substack.com—could have saved our partnership. This isn't theoretical. These are the specific decisions Bill and I needed our organizations to make differently. D - Define the Real Partnership Decision Our first mistake was never clearly defining what we were actually deciding as partners. HP saw it as extending our mobile ecosystem. Fossil saw it as diversifying its product line. I saw it as platform creation. Bill saw it as market preparation for the inevitable. But we never aligned on the fundamental question: Were we creating a new product category together, or were we each using the partnership to serve our existing strategies? The DECIDE framework forces this clarity upfront. What decision are both partners actually making? Until you have alignment on the decision itself, you can't make it well together. What we should have defined: "We are deciding to jointly create and dominate the smartwatch platform category before Apple legitimizes i Smartwatch Market." E - Examine Market Creation Potential Together Traditional partnership evaluations focus on combining existing capabilities. Innovation partnerships need to evaluate market creation potential. Bill understood this completely. He predicted exactly what Apple would do. But neither organization had a framework for evaluating market creation partnerships versus product extension partnerships. For market creation partnerships, you can't study competitors or analyze customer segments that don't exist yet. You need different evaluation criteria focused on timing, platform potential, and ecosystem readiness. What we should have examined: "Are we positioned to create and own a new category, or are we just building another device?" C - Challenge Individual Cannibalization Fears Here's where partnerships get interesting. Fossil feared cannibalizing traditional watch sales. HP feared distracting from enterprise transformation. But we never addressed how a partnership could protect both companies from external cannibalization. The framework question isn't whether the partnership will cannibalize existing business—it's whether the partnership creates better protection than going alone. What we should have challenged: "What happens if Apple creates this category without us? What market position will each company have then?" I - Identify the Partnership Innovation Type We treated our partnership like an incremental product collaboration when it was actually a breakthrough market creation partnership. These require completely different success metrics, timelines, and risk tolerance. Incremental partnerships: You can require certainty, detailed market research, and predictable ROI. Breakthrough partnerships: You have to accept uncertainty, optimize for learning speed, and measure different success metrics like market position and ecosystem readiness. What we should have identified: "This is breakthrough innovation requiring platform thinking, not incremental innovation requiring product optimization." D - Design Partnership Scenarios Together Traditional partnership planning tries to predict success. Innovation partnership planning prepares for multiple futures—together. We should have built scenarios like: "What if Apple legitimizes this category in two years?" "What if consumers aren't ready for smartwatches?" "What if we succeed and create a billion-dollar market?" But most importantly: "What if we don't partner and pursue this separately? What if we don't pursue this at all?" What we should have designed: "Multiple scenarios where our combined capabilities create different outcomes than our individual capabilities." E - Execute with Evidence Collection as Partners This is where our partnership completely failed. We optimized for committee consensus instead of rapid market learning. We needed to fail fast or succeed fast—together. Instead of quarterly partnership reviews, we needed weekly sprint cycles. Instead of seeking approval for perfect plans, we needed permission to test and iterate quickly. What we should have executed: "Rapid market validation cycles where both partners learn and adapt together, not separate approval processes that slow everything down." Your Partnership Decision Audit Want to apply this to your current innovation partnerships? Here's a 30-second audit using the DECIDE framework: Have we clearly defined what decision we're making together? Are we optimizing for market creation or capability combination? What external threats are we protecting each other from? What type of innovation partnership is this really? What scenarios would change our partnership strategy? How quickly can we test and learn together? If you can't answer these questions clearly with your partners, you're not ready to make breakthrough innovation decisions together. And that's okay—clarity about partnership uncertainty is better than false confidence in flawed collaboration. The Framework Changes Everything Let me be clear about what DECIDE would and wouldn't have guaranteed for our partnership. Would this framework have eliminated uncertainty? No. Nothing eliminates uncertainty in breakthrough innovation partnerships. Would it have guaranteed success? No. Even perfect decision frameworks can't overcome poor market timing or execution failures. But would it have given the HP-Fossil partnership a fighting chance? Absolutely. It would have aligned our decision-making process with the type of decision we were actually making together. Most importantly, it would have helped us recognize that we were making a platform partnership decision, not a product partnership decision. That recognition alone might have changed our entire approach to market timing, resource allocation, and success metrics. Your Innovation Partnerships Start Now Innovation partnerships aren't about having the best technology or the smartest people—they're about using the right decision framework when uncertainty meets organizational reality. Bill Geiser and I had the vision, the technology, and the market insight. But our organizations couldn't execute on what we knew was right because we didn't have the decision framework to bridge individual company interests with partnership potential. The DECIDE framework won't eliminate uncertainty—nothing can. But it will help you and your partners make better choices under uncertainty, which is what innovation partnership leadership actually requires. So here's my challenge for you: What innovation partnership opportunity is your organization considering right now? Apply the DECIDE framework with your potential partners and see what you discover. Are you making the right type of decision together? Are you optimizing for the right outcomes? Are you prepared for multiple scenarios as a partnership? Because somewhere out there, another brilliant partnership is about to hand their breakthrough innovation to a competitor through bad decision-making. Don't let it be yours. The framework is free at philmckinney.substack.com. For the complete story of how organizational failures killed our partnership—including the specific people and pivotal moments that changed everything—read "How HP and Fossil Handed Apple the Smartwatch Market" in my Studio Notes. The partnership opportunity is waiting. The choice is yours. What will you and your partners decide together? /episode/index/show/philmckinney/id/37677565

Why Great Innovators Read Rooms and not Just Data 07/29/2025

Why Great Innovators Read Rooms and not Just Data You know that moment when you walk into a meeting and immediately sense the mood in the room? Or when a proposal looks perfect on paper, but something feels off? That's your intuition working—and it's more sophisticated than most people realize. Every leader has experienced this: sensing which team member to approach with a sensitive request before you've consciously analyzed the personalities involved. Knowing a client is about to object even when they haven't voiced concerns. Feeling that a project timeline is unrealistic before you've done the detailed math. That instinctive awareness isn't luck or mystical insight—it's your brain rapidly processing patterns, experience, and environmental cues. The leaders known for "amazing judgment" haven't been blessed with superior gut feelings. They've learned to systematically enhance this natural capability through practical thinking. By the end of this post, you'll understand the science behind intuitive judgment, why some people seem to have consistently better instincts, and how to use Practical Thinking Skills to make your own intuition more reliable and actionable. What Your Intuition Really Is Intuition is your brain's rapid processing of experiences, patterns, and environmental cues that occur below the level of conscious awareness. When you sense the mood in a room, your mind is instantly analyzing dozens of subtle signals: body language, tone of voice, seating arrangements, who's speaking and who's staying quiet. This isn't mystical—it's sophisticated pattern recognition. Your brain has stored thousands of similar situations and can quickly compare current circumstances to past experiences, delivering a "gut feeling" about what's likely to happen or what approach will work. Everyone has this capability. You use it constantly: Walking into a meeting and immediately sensing the mood in the room Knowing which team member to approach with a sensitive request Feeling that a project timeline is unrealistic before you've done the math Recognizing when a client is about to say no, even if they haven't said it yet Sensing that a proposed solution won't work in your company culture The difference between people with "great intuition" and everyone else isn't the quality of their initial gut feelings—it's how systematically they validate, investigate, and act on those insights. Why Some Leaders Seem to Have "Amazing Intuition" Leaders who are known for excellent judgment have developed what I call practical thinking—the systematic approach to using their knowledge and experience to enhance their intuitive insights. Here's what they do differently: They treat gut feelings as valuable data, not emotions to dismiss or blind impulses to follow. When something feels off, they investigate systematically rather than ignoring the signal or acting without validation. They've learned to distinguish between intuition based on genuine patterns and reactions driven by personal bias, stress, or recent events. They can separate "this timeline feels aggressive because similar projects have failed" from "this timeline feels aggressive because I'm overwhelmed today." They apply structured approaches to validate their intuitive insights before making important decisions. They don't just trust their gut—they use their gut as the starting point for systematic investigation. They understand stakeholder psychology at a deeper level, using their intuitive read of people to design approaches that work with human nature rather than against it. The leaders with reputations for "brilliant intuition" have simply learned to make their natural pattern recognition more reliable and actionable through systematic frameworks. Practical Thinking: Making Intuition Systematically Reliable Practical thinking is the systematic approach to using your knowledge and experience to validate, investigate, and effectively implement your intuitive insights. It transforms valuable gut feelings into consistently reliable judgment. Think of intuition as your brain's early detection system, and practical thinking as the methodology for investigating and acting on those signals systematically. Your intuition signals: "This reorganization plan feels wrong." Practical thinking investigates: "What specific elements am I reacting to? Is it the timeline, the stakeholder alignment assumptions, or the communication approach?" Your intuition warns: "This customer seems hesitant despite saying yes." Practical thinking explores: "What might they be worried about that they can't voice directly? How can I address their real concerns?" Your intuition detects: "This team meeting feels tense." Practical thinking examines: "What underlying conflicts or pressures might be driving this dynamic? What does each person need to feel successful?" When you combine intuitive insight with systematic investigation frameworks, you develop judgment that gets more accurate with experience. This is how great leaders seem to "just know" what will work—they've learned to systematically validate and act on the patterns their intuition detects. The Practical Thinking Framework™ The framework consists of three interconnected skills that transform your natural intuitive insights into reliable decision-making capabilities. Unlike traditional analytical approaches that ignore gut feelings or emotional approaches that follow instincts blindly, practical thinking creates a systematic bridge between your intuitive awareness and effective action. The power comes from combining all three skills—most people excel at one or two but miss the integration that makes intuitive judgment consistently reliable. Step 1: Reality Recognition (Not Problem Definition) What it is: Use your intuitive insights to see situations as they actually exist, not as frameworks or org charts suggest they should be structured. Why it matters: Your gut feelings often detect mismatches between official reality and actual reality. Most decision failures happen because people address the stated situation rather than the real situation. How to apply it: Start with your intuitive read of what's really happening Map all stakeholders who will actually be affected, including informal influencers Identify what information is missing and why it might be missing Acknowledge constraints and pressures that aren't officially discussed Recognize the emotional and political landscape your intuition is detecting Example in action: Your gut says a reorganization plan "won't work" even though it looks logical on paper. Reality Recognition helps you investigate: your intuition is detecting that the timeline is too aggressive for this culture, key influencers weren't consulted, and the plan ignores current team workload realities. Your gut feeling was accurate—it sensed the gap between the plan and actual organizational dynamics. Common mistake: Dismissing gut feelings because the official version looks reasonable. Step 2: Experience Application (Not Best Practice Research) What it is: Use your knowledge of similar situations and these specific stakeholders to adapt your approach, rather than applying generic solutions. Why it matters: Your intuition draws on your unique experience with these people, this culture, and these types of challenges. That contextual knowledge is more valuable than best practices from other organizations. How to apply it: Draw on similar situations you've navigated with these stakeholders Consider what you know about how this culture responds to change Apply lessons from previous successes and failures in this environment Adapt proven approaches to fit current personalities and constraints Trust your experience about what will and won't work with these specific people Example in action: Your intuition suggests that a client is hesitant about a proposal despite their positive words. Experience Application helps you investigate: you remember that this client typically asks detailed implementation questions when they're serious, but they haven't asked any. Your experience with them suggests they're worried about execution complexity but don't want to seem unsophisticated. You adapt by proactively addressing implementation support rather than pushing for a decision. Common mistake: Researching what worked elsewhere instead of applying what you know about this specific context. Step 3: Stakeholder Psychology Reading (Not Stakeholder Management) What it is: Decode what people really need and fear versus what they say they need, then design approaches that align with their actual psychology. Why it matters: Most initiatives fail not because of logic or resources, but because they clash with stakeholders' deeper motivations, fears, or unspoken constraints that no one addresses directly. How to apply it: Listen for what people don't say—the concerns they avoid mentioning Notice emotional patterns—enthusiasm that feels forced, agreement that comes too quickly Identify hidden incentives—what each person needs to succeed in their role Recognize fear patterns—what failure or change would mean for each stakeholder personally Design solutions that make people look good to their bosses, not just solve the stated problem Example in action: A department head enthusiastically supports your efficiency initiative in meetings, but your gut says they're not really on board. Psychology Reading reveals they're actually worried that improved efficiency will make their large team look unnecessary, threatening their status and job security. Instead of focusing on efficiency benefits, you reframe the initiative as expanding their team's capabilities and strategic value. This transforms resistance into genuine partnership. The key insight: While others try to convince stakeholders with better arguments, practical thinkers recognize that people's real decisions are driven by emotions, incentives, and fears that are rarely discussed openly. They design solutions that address these psychological realities, not just the stated requirements. Common mistake: Focusing on logical persuasion when stakeholders are driven by emotional or political concerns they can't voice directly. Developing Your Practical Thinking Skills The 30-Day Practical Thinking Challenge Week 1: Reality Recognition Practice Start documenting your gut feelings about situations and meetings. When something feels off, practice investigating what your intuition might be detecting. What mismatches between official reality and actual reality is your gut picking up on? Week 2: Experience Application For each significant decision you face, consciously apply your experience with these specific people and situations. Instead of researching best practices, ask: "What do I know from previous similar situations with these stakeholders that should guide my approach?" Week 3: Psychology Reading Practice reading stakeholder psychology by listening for what people don't say. When someone agrees quickly or shows unexpected enthusiasm, ask yourself: "What might they be worried about that they can't voice directly?" Design small experiments to test your psychological reads. Week 4: Integrated Framework Combine all three steps for important decisions. Start with your intuitive read (Reality Recognition), apply your contextual experience (Experience Application), then design approaches that address stakeholder psychology (Psychology Reading). Track when this systematic approach improves outcomes. Practice with others: Share your intuitive insights with trusted colleagues and walk through your practical thinking process. Explaining why you're getting a particular gut feeling often reveals additional insights and helps validate your approach. Success indicators: You'll know your practical thinking skills are enhancing your intuition when your gut feelings become more accurate, when stakeholders volunteer information they usually keep hidden, and when your solutions work better because they align with how people actually think and behave. From Gut Feelings to Reliable Judgment Your intuition is already providing sophisticated insights about stakeholder dynamics, organizational realities, and what will actually work in practice. The challenge isn't developing better gut feelings—it's learning to systematically validate and act on the insights you're already receiving. Practical thinking doesn't replace your natural judgment—it makes it more reliable, more explainable to others, and more actionable in complex situations. When you can read reality accurately, apply your experience systematically, and understand stakeholder psychology, you develop the type of judgment that improves with every decision. This is the foundation of Decision Thinking™—the approach I've developed for making effective decisions when information is incomplete, stakeholders have conflicting interests, and the stakes are high. Practical thinking helps you leverage your natural capabilities to navigate complexity that would overwhelm purely analytical approaches. You already have sophisticated pattern recognition and stakeholder awareness. Practical thinking helps you turn those natural capabilities into systematic competitive advantages. Ready to develop reliable, practical thinking? Join our community of leaders in Substack Chat where we're exploring this question: What's one situation where you had a strong gut feeling that something was off, but you didn't trust it enough to investigate—and later wished you had? In the next episode, we will examine how the HP-Fossil partnership could have challenged Apple's dominance in the smartwatch market. Fossil’s Bill Geiser's intuition about Apple entering wearables was dead-on accurate. My assessment of the platform shift was equally accurate. But intuition without the decision-making frameworks to act on it just becomes expensive foresight. It will show how two Fortune 500 giants chose comfort over courage. Share this with someone who has great instincts but struggles to act on them systematically—you'll be helping them turn natural judgment into reliable leadership capability. /episode/index/show/philmckinney/id/37591905

Why Your Best People Give You The Worst Information 07/01/2025

Why Your Best People Give You The Worst Information The $25 Million Perfect Presentation Picture this: You're in a conference room with 23 executives, everyone has perfect PowerPoint presentations, engineering milestones are ahead of schedule, and you're about to sign off on a $25 million bet that feels like a sure thing. That was the scene at HP when we were developing the Envy 133—the world's first 100% carbon fiber laptop. Everything looked perfect: engineering was ahead of schedule, we projected a $2 billion market opportunity, and the presentations were flawless. Six weeks after launch, Apple shifted the entire thin-and-light laptop market, and our "sure thing" became a $25 million cautionary tale about decision-making. The Information Filter Problem Here's what I discovered: Your people aren't lying to you—they're protecting you. Every layer of management unconsciously filters out inconvenient truths. We had two massive blind spots: Competitive intelligence about Apple's roadmap had been sanitized before reaching decision-makers Manufacturing complexity of carbon fiber production was presented as routine when it required entirely new processes Information in organizations goes through more filters than an Instagram photo. Each management layer edits out inconvenient truths—not from malice, but from basic human psychology. People want to be helpful, to be problem-solvers, to avoid being bearers of bad news. The Three Information Temperature Checks I started treating information like a scientist treats data, using three temperature checks: Emotional Temperature: Real market insights carry emotional weight. If presentations feel sanitized and emotionally flat, you're getting processed information. Granularity Temperature: Can people provide specific names, exact dates, and direct customer quotes? "Several customers" should become "Show me the Austin focus group transcript." Contradiction Temperature: Market reality is messy. If everything points in one direction, someone edited out the complexity. Five Battle-Tested Truth-Telling Techniques Technique 1: Pre-Mortem Confessions Anonymous submission of biggest fears before major decisions. Read aloud without attribution to remove personal risk and stress-test plans against criticisms. Technique 2: Messenger Reward System Formally reward people who bring bad news, not just problem-solvers. Recognition in leadership meetings and promotion consideration. Within six months, intelligence quality improved dramatically. Technique 3: Devil's Advocate Rotation Assign someone to formally challenge assumptions in every major presentation. Rotate among team members to institutionalize dissent and make doubt safe to express. Technique 4: Customer Voice Channel Spend 25% of time with direct customer contact. This included executive briefings but also weekends in retail stores watching real customer behavior. The gap between what customers wanted and what product teams assumed was staggering. Technique 5: Failure Story Requirement Every presentation must include one failure story—not dwelling on failures, but incorporating lessons from setbacks into decision-making. The Truth-Telling Scorecard I developed a six-factor scorecard (1-5 scale) to measure information quality: Signal Clarity: Specific details vs. high-level summaries Emotional Authenticity: Genuine weight vs. sanitized presentations Contradiction Comfort: Acknowledging messy reality vs. clean narratives Bad News Frequency: How often you get genuinely concerning information Messenger Diversity: Multiple organizational levels vs. hierarchical channels only Speed of Uncomfortable Truth: How quickly market shifts reach you Review quarterly—scores below 3 signal information silos are forming. Five Questions Every Leader Should Ask When did someone last challenge my assumptions with specific, verifiable data? Are my presentations carrying emotional weight or feeling sanitized? What contradictory information am I not seeing? Who am I rewarding—problem-solvers or truth-tellers? How many management layers are filtering my market intelligence? Key Takeaway Building a truth-telling culture isn't about finding better people—it's about creating better systems for handling difficult information. The market will always contain signals that contradict your plans. The question is whether those signals can survive the journey to your desk. This Week's Challenge: Try one technique—run a pre-mortem confession on your next major decision or assign a devil's advocate to your next presentation. Small changes in how you handle information can prevent million-dollar mistakes. For the complete Truth-Telling Scorecard and detailed frameworks, visit . For the full backstory on the HP Envy 133 project, including all the details, check out the complete article there. | /episode/index/show/philmckinney/id/37235325

3 Innovation Decision Traps That Kill Breakthrough Ideas (And How to Avoid Them) 06/24/2025

3 Innovation Decision Traps That Kill Breakthrough Ideas (And How to Avoid Them) Every breakthrough innovation starts the same way: everyone thinks it's a terrible idea. Twitter was dismissed as "breakfast updates." Google looked "too simple." Facebook seemed limited to "just college kids." Yet these "stupid ideas" became some of the biggest winners in tech history. After 30 years making innovation decisions at Fortune 100 companies, I've identified why smart people consistently miss breakthrough opportunities—and how to spot them before everyone else does. Why Smart People Miss Breakthrough Ideas The problem isn't intelligence or experience. It's that we ask the wrong questions when evaluating new innovations. We filter breakthrough ideas through frameworks designed for incremental improvements, not revolutionary changes. Most innovation decisions fail because of three specific thinking traps that cause us to dismiss ideas with the highest potential for transformation. The 3 Innovation Decision Traps Trap #1: The Useless Filter The Question That Kills Innovation: "What existing problem does this solve?" Why It's Wrong: Breakthrough innovations don't solve existing problems—they create entirely new behaviors and meet needs people don't even know they have. Real-World Example: Airbnb seemed insane when it launched. Staying with strangers? Seeing them in the kitchen? The "problem" it solved—expensive hotels—wasn't what made it revolutionary. It created an entirely new behavior: experiential travel that hotels couldn't provide. The Better Question: "What new human behavior could this enable?" Trap #2: The Simplicity Dismissal The Question That Kills Innovation: "Where are all the features? This looks too basic." Why It's Wrong: Simplicity isn't a lack of sophistication—it's the hardest thing to achieve. When something is designed to be insanely simple to use, that signals massive effort and thought behind the design. Real-World Example: Google was just a white page with a search box while Yahoo crammed everything onto their homepage. Google looked unprofessional and incomplete, but it eliminated complexity everyone thought was necessary. The Better Question: "What complexity is this eliminating?" Trap #3: The Market Size Mistake The Question That Kills Innovation: "How big is the addressable market? Why limit yourself so severely?" Why It's Wrong: Breakthrough innovations don't serve existing markets—they create entirely new markets. The biggest opportunities come from ideas that seem too niche or focused. Real-World Example: Facebook was just for college students requiring .edu email addresses. Critics said the market was too narrow. But social media users didn't exist before Facebook—the company created the entire market. The Better Question: "What market could this create?" The Innovation Decision Framework When evaluating ideas that seem "stupid" or "too simple," use this three-question filter: What new behavior could this enable? What complexity could this eliminate? What market could this create? These questions force you to look beyond surface-level problems and features to identify transformational potential. How to Apply This Framework For Investors: Stop asking "What problem does this solve?" Start asking "What behavior does this create?" For Product Teams: Stop adding features. Start eliminating complexity. For Leaders: Stop looking for big existing markets. Start looking for new market creation potential. For Innovators: Stop following what everyone else thinks is smart. Start looking for ideas that violate conventional wisdom. The Pattern Recognition Advantage The current AI boom follows the exact same pattern as the dot-com bubble. Every company is racing to add AI to their pitch, just like they added ".com" in 1999. But the real breakthrough opportunities? They're probably something completely different—ideas that look terrible to everyone following the AI herd. The companies that will win are those that can recognize breakthrough potential when it violates everything the market thinks is smart. The Courage to Act on "Stupid" Ideas Recognition is only half the battle. The hardest part is having the courage to act on opportunities when they contradict expert opinion and market consensus. The biggest question isn't whether you can spot these opportunities—it's whether you'll have the conviction to pursue them when everyone else thinks they're terrible ideas. Because twenty years from now, someone will be writing about the "stupid idea" they missed in 2025 that became the next trillion-dollar company. Want the Behind-the-Scenes Story? This framework came from some painful (and expensive) lessons about dismissing breakthrough ideas. I share the full story—including how I wrote off the team that created Twitter after Apple destroyed their original business—in this week's . Listen to the full analysis: for deeper dives into innovation decision frameworks. See the framework in action: of how these exact decision traps led to HP's $1.2 billion WebOS disaster. /episode/index/show/philmckinney/id/37132120

The $1.2 Billion Innovation Disaster: 5 Decision Mistakes That Kill Breakthrough Technology (HP WebOS Case Study) 06/10/2025

The $1.2 Billion Innovation Disaster: 5 Decision Mistakes That Kill Breakthrough Technology (HP WebOS Case Study) In 2011, HP killed a $1.2 billion innovation in just 49 days. I was the Chief Technology Officer who recommended buying it. What happened next reveals why smart people consistently destroy breakthrough technology—and the systematic framework you need to avoid making the same mistake. HP had just spent $1.2 billion acquiring Palm to get WebOS—one of the most advanced mobile operating systems ever created. It had true multitasking when iOS and Android couldn't handle it, an elegant interface design, and breakthrough platform technology. I led the technical due diligence and recommended the acquisition because I believed we were buying the future of mobile computing.We launched it on the HP TouchPad tablet. Then, the CEO killed it just 49 days after launch. Here's a question that should keep every innovation leader awake at night: How do you destroy breakthrough technology worth over a billion dollars in less than two months? The answer isn't what you think. It's not about bad technology, poor market timing, or insufficient resources. It's about systematic thinking errors that intelligent people make when evaluating innovation under pressure. And these same patterns are happening in companies everywhere, right now. I'm going to show you exactly how this happens, why your company is vulnerable to the same mistakes, and give you a proven framework to prevent these disasters before they destroy your next breakthrough innovation. On my , I share the personal story of watching this unfold while recovering from surgery. In this episode, I want to focus on the systematic patterns that caused this disaster and the decision framework that can prevent it. Here's my promise: by the end of this episode, you'll understand the five thinking errors that consistently destroy innovation value, you'll have a complete decision framework to avoid these traps, and you'll know exactly how to apply this to your current innovation decisions. Because here's what this disaster taught me: intelligence doesn't predict decision quality. Systematic thinking frameworks do. The Pattern That Destroys Billion-Dollar Innovations Let me start with the fundamental problem that makes these disasters predictable. When the HP Board hired Leo Apotheker as CEO, they created what I call a "cognitive mismatch," and it reveals why smart people make terrible innovation decisions. Apotheker came from SAP, where he'd run a $15 billion software company. HP was a $125 billion technology company with breakthrough mobile platform technology. The board put someone whose largest organizational experience was half the size of HP's smallest division in charge of evaluating platform innovations he'd never encountered before. But here's the crucial insight: the problem wasn't his experience level. The problem was how his professional background created mental blind spots that made him literally unable to see WebOS as an opportunity. Here's what's dangerous: Apotheker couldn't see WebOS as valuable because his entire career taught him that software companies don't do hardware. His brain was wired to see hardware as a distraction, not an advantage. To him, WebOS represented exactly the kind of hardware business he wanted to eliminate. Your expertise becomes your blind spot. You literally can't see opportunities outside your professional comfort zone. And this is the first critical principle: Your job background creates mental filters that determine what opportunities you can even see. And this pattern is happening in your company right now. Your finance team evaluates platform investments using metrics designed for traditional products. Your marketing team rejects concepts they can't explain with existing frameworks. Your engineers dismiss breakthrough ideas that don't fit current technical roadmaps. The pattern is always identical: intelligent people using the wrong thinking frameworks to evaluate breakthrough technology. Let me show you exactly how this destroys innovation value. The Five Systematic Thinking Errors That Kill Innovation WebOS died because of five predictable cognitive errors that occur when smart people evaluate breakthrough technology under pressure. These aren't unique to HP—I've seen identical patterns destroy innovation value across multiple industries. Error #1: Solving the Wrong Problem The most dangerous mistake happens before you evaluate any options: framing the wrong decision question. Apotheker was asking "How do I transform HP into a software company?" when the strategic question was "How do we build competitive advantage in mobile computing platforms?" When you optimize solutions for the wrong problem, you get excellent answers that destroy strategic value. The Warning Sign: Your team jumps straight to evaluating options without questioning whether you're solving the right challenge. Error #2: Identity-Driven Decision Making Your professional background creates systematic blind spots about breakthrough opportunities. Software executives see software solutions. Hardware leaders focus on hardware opportunities. Financial experts optimize for traditional metrics. This cognitive filtering happens automatically and distorts how you evaluate platform technologies that don't fit conventional categories. The Warning Sign: Your evaluation team all have similar backgrounds and reach the same conclusions about breakthrough technology. Error #3: Tunnel Vision Under Pressure When executives become obsessed with major initiatives, everything else feels like a distraction. Apotheker became obsessed with acquiring Autonomy, a software company that fit his transformation vision. This tunnel vision made everything else—including breakthrough mobile technology—feel like a distraction from his primary goal. The Warning Sign: Leadership dismisses promising innovations because they don't support the current primary initiative. Error #4: Timeline Compression Under Stress Platform technologies require different evaluation timeframes than traditional products. Forty-nine days isn't enough time to build developer ecosystems, establish retail partnerships, or demonstrate platform traction. But pressure to show decisive leadership compressed HP's decision timeline artificially, creating the illusion of strong leadership while increasing the probability of strategic errors. The Warning Sign: Your team is evaluating breakthrough technology using the same timelines as conventional product launches. Error #5: Wrong Evidence Framework Innovation decisions require fundamentally different success metrics than traditional business evaluation. HP focused on TouchPad sales numbers instead of developer adoption rates, user engagement patterns, or platform differentiation sustainability. They used product metrics to evaluate platform potential, which guaranteed they would see failure instead of recognizing early-stage ecosystem development. The Warning Sign: You're applying traditional business metrics to evaluate breakthrough technology investments. Here's what makes these errors so dangerous: they're invisible to the people making them. Smart teams use these flawed frameworks and feel confident they're making data-driven decisions while systematically destroying innovation value. But these patterns are preventable. After analyzing hundreds of similar disasters, I developed a systematic framework specifically designed to avoid these thinking traps. The DECIDE Framework: Your Innovation Decision Protection System The DECIDE framework addresses each cognitive vulnerability that consistently traps intelligent leaders in innovation contexts. Let me show you exactly how it works and why it would have saved WebOS. D - Define the Real Decision Most innovation failures begin with teams optimizing excellent solutions for poorly defined problems. The Tool: Reframe your decision question three different ways. If all three point to the same choice, you're probably asking the right question. If they point to different choices, you need to determine which frame captures the real strategic challenge. Examples of Different Frames: Financial Frame: "How do we minimize losses on this investment?" Strategic Frame: "How do we build long-term competitive advantage?" Market Frame: "How do we capture emerging opportunities?" Competitive Frame: "How do we position against industry leaders?" Customer Frame: "How do we create unique value for users?" HP's Application: Original Frame: "Should we continue investing in TouchPad given poor sales?" Strategic Reframe: "How do we build a sustainable mobile platform business?" Competitive Reframe: "What's our path to competing with Apple and Google in mobile?" What This Reveals: The reframes show TouchPad was one product in a larger platform opportunity that deserved different evaluation criteria entirely. E - Examine Your Thinking Process Your professional background creates invisible filters that can systematically distort how you interpret breakthrough opportunities. The Tool: If you hired someone with completely different expertise to make this decision, what would they choose? When the gap is huge, you need outside perspectives with different cognitive frameworks. HP's Gap: Enterprise software CEO versus consumer platform strategy requirements. They needed mobile platform thinking, not enterprise software optimization, but never brought that expertise into the decision process. C - Challenge Your Assumptions The most dangerous assumptions feel like established facts and shape your entire analysis without being examined. The Tool: What would have to be true for your least favorite option to actually be the right choice? This forces you to consider alternative interpretations of the same evidence. HP's Assumptions: Platform businesses need immediate profitability, mobile computing won't dominate, differentiated operating systems can't compete with Apple and Google. All of these assumptions were provably false by 2011, but they drove the evaluation process. I - Identify Decision Traps Different types of decisions trigger predictable cognitive biases that distort evaluation in systematic ways. The Tool: Which specific biases is your decision most vulnerable to? Create explicit countermeasures for each identified bias. Common Innovation Decision Biases: Focused on stopping losses vs building advantages (loss aversion) Seeking evidence that supports preferred choice (confirmation bias) Overweighting first information received (anchoring bias) Obsessing with one initiative while missing others (tunnel vision) Choosing options that fit your identity (identity bias) Using recent events to predict outcomes (recency bias) HP's Specific Traps: Focused on stopping TouchPad losses vs building platform advantages (loss aversion) Highlighted negative sales data while ignoring positive developer signals (confirmation bias) Used early TouchPad sales as anchor for all subsequent evaluation (anchoring bias) D - Design Multiple Options Most innovation failures result from evaluating limited options well rather than evaluating good options poorly. The Tool: Generate five genuinely different approaches before evaluating any of them. Breakthrough solutions often emerge from non-obvious alternatives. HP's Missing Options: License WebOS to manufacturers, integrate into PC ecosystem, pivot to enterprise mobile, create hybrid hardware-software strategy. All had genuine potential but were never seriously considered. E - Evaluate with Evidence Platform technologies require fundamentally different success metrics than traditional product evaluation. The Tool: What evidence would predict success for this specific type of innovation? Use frameworks appropriate for breakthrough technology, not conventional business metrics. HP's Error: They used quarterly sales performance and immediate profitability to evaluate platform potential. Platform businesses lose money initially while building network effects that create sustainable advantages later. How to Apply This to Your Innovation Decision Right Now Let me show you how to use this framework with your current innovation decisions. Step One: Identify Your Highest-Stakes Innovation Decision What breakthrough technology, platform investment, or disruptive opportunity is your team evaluating right now? This framework applies to any decision where traditional business metrics might mislead about innovation potential. Step Two: Run the Decision Question Test Before evaluating any options, reframe your decision question three different ways. Are you asking "How do we minimize risk?" or "How do we maximize strategic opportunity?" The frame determines the solutions you'll even consider. Step Three: Audit Your Evaluation Team Who's making this decision? What cognitive filters might their backgrounds create? Do you need advisors with different expertise to see opportunities your current team might miss? Step Four: Challenge Your Obvious Assumptions What would have to be true for the option you least prefer to actually be right? Those conditions might exist or be emerging faster than you realize. Step Five: Identify Your Decision Traps Is your team vulnerable to loss aversion? Anchoring on early data? Tunnel vision around other initiatives? Create specific countermeasures for each identified bias. Step Six: Generate Multiple Approaches Push beyond obvious choices. What would someone from a completely different industry do? What creative alternatives combine elements from different options? Step Seven: Use Appropriate Evidence Are you evaluating platform potential with product metrics? Breakthrough technology with conventional criteria? Innovation investments with traditional business frameworks? Match your evidence to your innovation type. Why This Framework Prevents Innovation Disasters The DECIDE framework works because it addresses the specific cognitive vulnerabilities that consistently trap intelligent people in innovation contexts. Traditional decision-making assumes you know the right questions to ask, can see opportunities clearly, and will use appropriate evaluation criteria. Innovation decisions violate all these assumptions. Breakthrough technologies don't fit existing categories. Platform investments don't follow traditional timelines. Disruptive opportunities can't be evaluated with conventional metrics. The companies that consistently succeed at innovation aren't smarter—they use systematic frameworks designed for uncertainty, breakthrough potential, and non-obvious opportunities. Three Companies Getting This Right: Amazon evaluates platform investments with different metrics than product launches. They expected Kindle, AWS, and Prime to lose money initially while building long-term competitive advantages. Google uses systematic frameworks to avoid identity bias in breakthrough technology evaluation. Android didn't fit their search advertising identity, but they evaluated it with platform-appropriate criteria. Apple applies different decision frameworks to breakthrough products versus incremental improvements. They gave iPhone multiple years to build ecosystem momentum instead of expecting immediate profitability. These companies avoid the systematic thinking errors that destroyed WebOS because they use decision frameworks designed for innovation uncertainty. Your Next Strategic Decision Here's the reality: this challenge isn't going away. Breakthrough technologies will continue emerging faster than traditional business frameworks can evaluate them. The companies that develop systematic innovation decision capabilities will capture enormous value. Those that rely on conventional thinking will consistently destroy breakthrough opportunities. Your Three Action Steps: First: Download the DECIDE Framework toolkit and apply it to your current highest-stakes innovation decision before evaluating any options. Second: Audit your innovation evaluation processes. Are you using traditional business metrics to evaluate breakthrough technology? Conventional timelines for platform investments? Identity-driven thinking for disruptive opportunities? Third: Build systematic innovation decision capabilities into your organization. Train your team to recognize cognitive biases, use appropriate evidence frameworks, and generate multiple creative alternatives. Questions to Consider: What breakthrough opportunity might your company be evaluating with the wrong frameworks right now? How would you know if your team is falling into the same thinking traps that killed WebOS? What would systematic innovation decision capabilities be worth to your competitive advantage? But here's the final piece of this story that shows just how costly these thinking errors can be: Leo Apotheker was fired on September 22, 2011—just 35 days after shutting down WebOS and eleven months after taking over as CEO. The board finally recognized the systematic thinking errors that had destroyed billions in value, but it was too late for WebOS. The human cost of these decisions goes beyond stock prices and quarterly reports. There are real people who believed in breakthrough technology, fought for innovation, and had to watch it get destroyed by preventable thinking errors. The complete personal story of watching this disaster unfold—including details about the brutal aftermath and why I still believe in HP despite everything—is in this week's . Remember: when you have breakthrough technology in your hands, the quality of your decision-making process matters more than the quality of your technology. Intelligence and good intentions aren't enough. You need systematic frameworks for thinking clearly about innovation under uncertainty. The tools exist to prevent these disasters. The question is whether you'll implement them before your next WebOS moment. Until next time, I'm Phil McKinney, and remember—in a world where billion-dollar innovations can be killed in 49 days, systematic decision frameworks might be your most valuable competitive advantage. If you found this week's episode valuable, or watch on the . /episode/index/show/philmckinney/id/36934610

Your Child's Creative Brain on AI: The Emergency Parents Don't See 06/03/2025

Your Child's Creative Brain on AI: The Emergency Parents Don't See University of Washington researchers discovered something that should concern every parent: children who use AI to create can no longer create without it. And here's the concerning part: most parents have absolutely no idea it's happening. If you've been following our series on Creative Thinking in the AI Age, you know I've been tracking how artificial intelligence is rewiring human creativity. We've explored the 30% decline in creative thinking among adults, the science of neuroplasticity, and practical exercises to rebuild our creative capabilities. But today's episode is different. Today, we're talking about your child's developing brain. And I need to be direct with you—the next 30 minutes might be the most important parenting conversation you have this year. Because while we've been worried about AI taking our jobs, it's already changing our children's minds. Unlike us adults, who developed our creative thinking before AI existed, our kids are growing up with artificial intelligence as their creative co-pilot from the very beginning. Here's my promise to you: by the end of this episode, you'll know exactly how to tell if your child is developing AI dependency, you'll understand why their developing brain is more vulnerable than yours, and you'll have an assessment tool to evaluate your family's situation—plus immediate strategies you can start using today. But first, let me show you what's happening in homes just like yours—and why this is both preventable and completely reversible. The Crisis Hiding in Plain Sight A few weeks ago, a mother shared a story that stopped me in my tracks. Her 10-year-old daughter used to spend hours drawing elaborate fantasy worlds, completely absorbed in her creative process. Now, when her mother suggests drawing something, the daughter responds, 'Can I just use AI to make it look better?' At first, this seemed like smart efficiency—why not use available tools? However, when the mother asked her daughter to draw a simple picture with no digital help, something alarming occurred. The child just stared at the blank paper and started crying, unable to create anything on her own. This story isn't unique. It's happening everywhere, and parents are missing it because the signs look like success. Before we go further, let me be clear: this isn't your fault. AI dependency developed gradually, and most parents missed the early signs because they actually looked positive. Think about your own child for a moment. Has their homework gotten easier? Do they finish writing assignments faster than they used to? Are their projects suddenly more polished? If you answered yes, you might be looking at what I call the "homework mirage." Here's what the homework mirage looks like: Your child sits down to write a story for English class. Instead of staring at the blank page like kids have done for generations, they open ChatGPT. They type: "Write me a story about a brave knight." In thirty seconds, they have three paragraphs that would have taken them an hour to write. You see the finished assignment. It's well-written, grammatically correct, and creative. You think, "Great! They're learning to use technology efficiently." But here's what you don't see: your child's brain just missed a crucial workout. Remember in our first episode when we talked about brain pathways being like muscles? When we don't use them, they weaken. This is happening to children at a speed that concerns researchers worldwide. (Reference: Newman, M. et al., 2024, "I want it to talk like Darth Vader: Helping Children Construct Creative Self-Efficacy with Generative AI," University of Washington) Dr. Ying Xu from Harvard put it perfectly when she asked the critical question: "Are they actually engaging in the learning process, or are they bypassing it by getting an easy answer from the AI?" And here's the concerning part—kids who use AI to complete tasks do produce higher quality work in the short term. But when you take the AI away, their abilities are worse than before they started using it. But this goes way beyond homework. Children are experiencing what experts call the "Creative Confidence Crisis." Kids who used to love making art now say, "I'm not good enough" when they see AI-generated images. Children ask AI to help with simple creative tasks, such as making up games or telling stories. The scale of this problem is significant. Recent research shows that 31% of teenagers are already using AI to create pictures and images. Sixteen percent are using it to make music. And parents? Most have no idea how much their children are depending on these tools. As one researcher told me, "Parents and teachers are pretty much out of the loop, so young people are using AI platforms with virtually no guidance." This brings us to a crucial question: Why are children more vulnerable to this than adults? Why Your Child's Brain Is at Risk In our second episode, we explored neuroplasticity—your brain's ability to reorganize itself throughout your life. But children's brains aren't just plastic; they're in active construction mode. Think of an adult brain like a well-established city with roads and infrastructure already built. A child's brain is more like a city being built from scratch. The roads they travel most frequently become the highways of their adult thinking. This is why the creative pathways your child develops now will determine their innovative capabilities for life. While AI can already outperform humans at data analysis, writing, and even coding, it cannot replicate the uniquely human ability to make unexpected connections, challenge assumptions, and imagine what doesn't yet exist. The children who develop strong creative thinking skills today will be the ones who thrive in tomorrow's AI-dominated world—they'll be the innovators, entrepreneurs, and problem-solvers who can work with artificial intelligence without being replaced by it. These future-critical abilities depend on four specific creative thinking systems that are strengthened or weakened based on how children use them. When children become AI-dependent, these four systems are at risk: Cognitive Flexibility—your child's ability to switch between different thinking modes. This is what allows them to see a cardboard box as a spaceship, then a house, and then a robot costume. When children always ask AI, "What should I make?" instead of experimenting, this flexibility is weakened. Associative Thinking—connecting seemingly unrelated ideas. This is how kids come up with wild and wonderful ideas, like "What if cars could swim?" When AI provides ready-made connections, children stop making their own unique associations. Divergent Thinking—generating multiple solutions to open-ended problems. AI excels at convergent thinking—identifying the best answer. But human creativity thrives on divergent thinking—exploring all possible answers. Constraint Breaking—the ability to identify and overcome hidden assumptions limiting their thinking. This is what lets children question rules like "stories have to make sense" or "art has to look realistic." When AI always provides solutions within conventional parameters, children stop challenging the boundaries of what's possible. When these systems weaken, children develop what is called "Creative Bypass Syndrome." They learn to jump straight to AI whenever they encounter creative challenges. Their brains literally rewire to avoid the hard work of original thinking. But there's another crucial element that supports all four of these systems: frustration tolerance—your child's ability to persist through difficult problems without immediate relief. This is where the real creative magic happens. Those moments when your child sits with a problem, feels stuck, pushes through the discomfort, and discovers something unexpected. AI eliminates this essential struggle by providing instant solutions. Think about the last time you watched your child work through a challenging puzzle or try to build something that kept falling down. That frustration they felt? That's their brain building resilience and creative persistence. When children can immediately turn to AI for answers, they miss these crucial mental workouts. But here's the encouraging news: because children's brains are so adaptable, they can also recover faster than adults. The creative pathways that have weakened can be rebuilt. The confidence that's been lost can be restored. Now, before we talk solutions, you need to know where your child stands right now. The Creative Independence Assessment I've developed a simple test that you can do at home to evaluate your child's creative resilience. You can download the complete assessment tool from our website, but let me walk you through the key elements right now. Important setup instructions: Set aside 15 uninterrupted minutes for this assessment. Choose a time when your child is relaxed, not rushed or hungry. Find a quiet space—the kitchen table works perfectly. Have other siblings play elsewhere during the test. If your child resists or asks "why," simply say "I'm curious about something" and keep it light. The assessment is based on one fundamental principle: creative confidence shows up in how children respond to open-ended challenges with no right answer. For ages 5 to 8, try what I call the "Magic Box Challenge." Give your child an empty cardboard box—a shoe box works perfectly. Tell them: "This is a magic box that can become anything you want. Show me what you'd like it to be." Then step back and observe. Don't give suggestions. Don't offer help. Don't provide materials unless they specifically ask. Just watch how they respond. For ages 9 to 12, try the "Problem Inventor Challenge." Ask your child to invent a problem that needs solving, then solve it. Give them exactly ten minutes. No devices, no external input. Say: "Pretend you're an inventor. What problem would you want to solve, and how would you solve it?" For teenagers, ages 13 to 17, use the "Original Idea Test." Ask them to come up with an original, creative project idea in any medium—art, writing, music, video, anything. They need to explain why this idea interests them personally. Give them up to ten minutes and say: "If you could create anything right now—no limits on time or resources—what would you make and why?" Now, let me show you how to score what you observe. As you watch your child during their assessment, use this scoring guide to identify which traits they exhibit. You can circle or check off the behaviors you notice, then see which zone has the most matches. CREATIVE INDEPENDENCE ASSESSMENT SCORING GUIDE AGES 5-8: Magic Box Challenge 🟢 GREEN ZONE 🟡 YELLOW ZONE 🔴 RED ZONE • Engages immediately (1-2 minutes) • Takes 3-5 minutes to start • Takes 5+ minutes or needs prompting • Generates 2+ ideas spontaneously • Generates 1-2 ideas with hesitation • Cannot generate ideas without help • Shows enthusiasm and confidence • Asks for reassurance once ("Is this okay?") • Asks multiple questions for help • Doesn't ask "What should I make?" • Shows mild anxiety but continues • Shows distress or frustration • Continues playing without prompting • May reference familiar things • Says "I don't know" or "I'm not creative" AGES 9-12: Problem Inventor Challenge 🟢 GREEN ZONE 🟡 YELLOW ZONE 🔴 RED ZONE • Creates problem within 3-4 minutes • Takes 5-7 minutes to create problem • Cannot create problem in 10 minutes • Shows personal connection to problem • Problem is generic but shows some thinking • Only suggests problems from media • Attempts solution (even if impractical) • Solution attempt is basic but present • Cannot think of solutions • Shows curiosity and engagement • Shows hesitation but pushes through • Wants to "look it up" immediately • Doesn't reference movies/games • References familiar scenarios with twist • Asks "What kind of problems?" AGES 13-17: Original Idea Test 🟢 GREEN ZONE 🟡 YELLOW ZONE 🔴 RED ZONE • Generates idea within 5 minutes • Takes 5-10 minutes to generate idea • Cannot generate original idea • Shows genuine personal interest • Some personal connection but generic • Ideas entirely from trends • Demonstrates original thinking • Influenced by social media but personal • Cannot explain personal connection • Shows confidence in creative vision • Shows some confidence but seeks validation • Wants to see AI suggestions • Doesn't focus on "going viral" • References trends but adds personal angle • Says "I'm not creative enough" Beyond these specific tests, there are daily warning signs every parent should recognize: Watch for homework that suddenly takes much less time than usual. Notice if their writing voice changes dramatically—AI-generated text often sounds more sophisticated than your child's natural voice, but it lacks their personality. Pay attention to decreased tolerance for boredom. Listen for language changes—are they asking "How do I..." instead of experimenting? This scoring guide will help you determine if your child is in the Green Zone (creative confidence intact), Yellow Zone (some AI dependence developing), or Red Zone (significant creative confidence impact). If you discover your child is in the Yellow or Red Zone, take a deep breath. Remember, children's brains are remarkably adaptable—this can absolutely be improved. Let me show you how. Getting Back on Track: Practical Steps for Parents If your assessment revealed concerning signs, here's your "start here" action plan: If you do nothing else this week, try the assessment. If your child is in Yellow or Red Zone, then implement the 24-hour creative pause I'm about to describe. I want you to try what I call the "24-Hour Creative Independence Check." This is a gentle but effective way to help your child reconnect with their natural creative abilities. Step One is the Creative Pause Period. For twenty-four hours, your child takes a break from AI assistance for any creative task. No ChatGPT for writing. No AI image generators for art projects. No "looking up" ideas or solutions online for creative work. Think of this like giving their creative muscles a chance to stretch and remember what they can do on their own. During this period, your child will likely experience some frustration. This is normal and actually beneficial—it's their brain remembering how to work through challenges independently. Step Two is building a Support System. Your job isn't to eliminate their frustration but to support them through it. Instead of saying "Let me help you," try "This seems challenging. What are you thinking?" Instead of "Maybe look it up," try "What would happen if you just tried something?" The most important thing you can do is manage your own discomfort with their struggle. When you see your child working through a creative challenge, every parenting instinct tells you to help. But supporting them through creative problem-solving builds the brain resilience they need. Step Three is celebrating effort over outcome. When your child creates something during this pause period, focus your praise on their thinking and persistence, not the result. Say "I love how you kept trying different approaches" instead of "That's beautiful." After the 24-hour pause, establish ongoing creative strength training. Think of these as daily workouts for your child's creative brain: For younger kids: Try "Daily Wonder Questions"—five minutes of "what if" questions with no right answers. "What if gravity worked backwards on Tuesdays?" For middle schoolers: Use the "Daily Assumption Challenge"—question one obvious assumption each day. "Why do schools have to happen in buildings?" For teenagers: Try the "Creative Perspective Shift"—describe any current event from three completely different viewpoints. For more detailed creative exercises tailored to different ages and situations, check out our Episode 3, "Creative Thinking Exercises: 10-Minute Daily Brain Workout to Boost Innovation." Your role as a parent is crucial. You're not trying to become your child's creative manager. Instead, you're becoming what researchers call a "creative support system." Model working through challenges yourself. Let your children see you tackling problems, trying new approaches, and persisting when things don't work immediately. Create a family culture that values thinking over knowing. Celebrate questions as much as answers. Building Creative Strength Over Time The strategies I've shared will help immediately, but developing strong creative independence requires a systematic approach. I recommend a two-phase development plan: Phase One, weeks 1 to 2, is Assess and Adjust. Begin with the creative independence assessment while gently reducing AI dependence. This is when you implement the 24-hour pause and begin daily creative exercises. Phase Two, weeks 3 to 6, is Develop and Strengthen. Increase daily creative exercises and build persistence with challenging tasks. This is when you'll start seeing real improvements in your child's creative confidence and independence. There's also a Phase Three—learning strategic AI use that enhances rather than replaces creativity. However, this assumes you've first mastered the strategic use of AI yourself. If you want to learn these skills, watch our episode "The AI Creativity Multiplier: 5 Steps to Amplify Your Innovative Thinking." Let me be realistic about expectations. During the first week or two, you'll likely see some resistance. Your child might complain that creative tasks feel more challenging without AI assistance. This is normal and temporary. You're asking them to use creative abilities that have been getting less exercise. Signs of improvement include increased creative attempts, less anxiety about "not knowing" the right answer, and willingness to experiment with ideas. You should see positive changes within two to three weeks of consistent practice. For most families, this systematic approach will not only restore your child's natural creative abilities—it will make them stronger creative thinkers than they were before. Your Next Step You're at a choice point right now. You can assume this will work itself out as children "figure out" the right balance with AI. Or you can take action today to protect and strengthen your child's creative brain. Here's the reality: this challenge will only grow as AI tools become more sophisticated and appealing to children. The longer you wait, the deeper these patterns become. But the encouraging news is that you have everything you need to start making a difference today. Your specific next step: Download the complete Family Creative Assessment from our website. This includes detailed instructions for the Challenges, plus additional age-appropriate creative challenges, a parent observation checklist, progress tracking sheets, and the complete 24-hour creative pause protocol. [button href="https://open.substack.com/pub/philmckinney/p/download-your-guide-for-protecting?r=3rtha&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true" primary="true" centered="true" newwindow="true"]Download Your Guide for Protecting Your Child's Creative Independence[/button] Try one of the assessment challenges with your child this week—start with whichever one matches their age. Then post a comment, and share one specific thing you discovered about your child's creative independence. Based on your comments on this episode, I'll know whether to create more detailed, age-specific guides for families who want to build their child's creative muscle. Remember, the children who develop creative resilience today will be the innovators, problem-solvers, and leaders of tomorrow. They'll be the humans who can work with AI without losing what makes them uniquely,... /episode/index/show/philmckinney/id/36831180

Human-AI Creative Partnership: How to Harness AI While Preserving Your Innovative Edge 05/27/2025

Human-AI Creative Partnership: How to Harness AI While Preserving Your Innovative Edge The most innovative creators don't use AI as a replacement – they use it as a strategic partner in a carefully choreographed dance of human and machine intelligence. Welcome to Part 4 of our series, Creative Thinking in the AI Age – on strengthening your uniquely human creativity while using AI as a partner, not a replacement. In Part 1, we explored the alarming decline in creative thinking as we've grown dependent on AI. In Part 2, we discovered how neuroplasticity allows us to rebuild and enhance our creative capabilities. And in Part 3, I gave you a practical 10-minute daily workout to strengthen the neural pathways essential for innovative thinking. Today, we're bringing it all together with something immediately actionable: a framework for creating productive partnerships with AI that enhance rather than diminish your creative capabilities. This isn't about rejecting AI – it's about using it strategically to amplify your uniquely human abilities. When used properly, AI can handle routine cognitive tasks while freeing your mind for the breakthrough thinking that algorithms simply cannot replicate. Let me start by clarifying the fundamental difference between human and machine intelligence that drives this partnership: is the process of analyzing existing data to find optimal solutions within defined parameters. This is what AI excels at – processing vast amounts of information to identify patterns and generate options based on probability distributions of what has worked before. is the ability to generate novel ideas by making unexpected connections, breaking conventional patterns, and imagining what doesn't yet exist. This is where humans uniquely excel – our capacity for intuitive leaps, metaphorical thinking, and insight that transcends existing data. The most powerful creative partnerships leverage both: AI's computational strength and the human capacity for originality. Let me demonstrate with a simple example. If I asked an AI to design a chair, it would analyze thousands of existing chair designs and generate variations based on established patterns. The results would be functional but predictable. But what if I first engaged in divergent thinking by questioning the very concept of sitting? What if I reimagined a chair as something that supports the body in motion rather than at rest? This human insight – this conceptual leap – changes everything about how we might approach the design. Now when I engage AI, I'm not asking it to "design a chair" but to help explore a completely new approach to supporting the human body. The AI becomes a tool for expanding and refining my original insight rather than a replacement for it. This is the heart of creative partnership: human divergent thinking provides the spark of originality, while AI convergent thinking helps develop and refine that spark into something practical. The Art Of Creative Prompting Before we dive into our five-step framework, let's talk about what makes an effective AI prompt for creative work. The way you communicate with AI dramatically impacts the quality and originality of what you receive in return. Throughout this episode, I've included actual prompts formatted in code blocks that you can copy, edit, and paste directly into your favorite AI tool – whether that's , Claude, or others. These aren't theoretical; they're battle-tested approaches I've used with innovation teams. The most powerful creative prompts share three key characteristics: They express curiosity rather than certainty – Phrases like "I'm exploring," "I'm curious about," or "Help me understand" signal to the AI that you're in an exploratory mode rather than seeking definitive answers. This subtle shift encourages broader, more nuanced responses. They use specific framing devices – Notice how our example prompts use structures like "What aspects are overlooked?" or "What contradictions exist?" These frames direct the AI's analytical power toward particular angles of exploration. The formula prompts I've shared provide ready-to-use framing devices for different situations. They maintain creative tension – Effective prompts don't ask for immediate solutions but instead create a productive tension by examining contradictions, assumptions, or overlooked aspects. This tension generates the creative friction from which original insights emerge. When using the example prompts throughout this episode, customize them to your specific challenge, but maintain these structural elements that encourage exploration rather than premature convergence. The goal is to shape AI responses that serve as thought-provoking material for your own creative thinking, not as final answers. Here's a quick formula for effective prompts: "What aspects of [problem] are most overlooked?" "What contradictions exist in how people approach [challenge]?" "What assumptions might be limiting how we think about [issue]?" "What perspectives on [problem] have we never considered?" "What patterns in [issue] are repeating historically?" "What barriers prevent solving [challenge] with existing solutions?" Now, let's explore our five-step framework for forming creative partnerships with AI that enhance rather than diminish your creative capabilities. STEP 1: Prime Your Brain First The most common mistake I see is turning to AI too early in the creative process. This typically happens because facing a blank page is uncomfortable – we're seeking the path of least resistance. But this short-circuits your brain's ability to make original connections. Instead, I recommend priming your brain before engaging any AI tools. Here's how: Begin with a 5-minute session from our creative workout (Episode 3). The Perspective Shifting or Random Word Fusion exercises are particularly effective for this purpose. After your brief workout, spend 10 minutes in open ideation on your challenge. Use a piece of paper – not a digital device – and rapidly jot down any ideas that come to mind without judging them. Look for unexpected combinations or patterns in your ideas. Circle anything that feels surprising or that challenges conventional thinking. This priming step activates your associative thinking networks – the neural pathways that connect seemingly unrelated concepts. When you later engage AI, you'll do so with your creative faculties already warmed up and ready to evaluate AI outputs critically. STEP 2: Frame Challenges, Not Solutions How you fundamentally shapes what you get from it. The key is to position AI as a thought partner exploring a problem space rather than a solution generator. Instead of asking: "Generate ideas for a new water bottle design" Try: "What are the unsolved problems in how people stay hydrated throughout the day?" The first prompt tells AI to generate variations on a water bottle – convergent thinking within established parameters. The second prompt opens a problem space that invites exploration of the underlying challenge. Similarly, rather than asking AI to "write a marketing campaign," ask it to "identify emotional tensions between consumers and existing products in this category." This framing preserves your role in the most crucial part of creativity – defining the right problem. It positions AI as an explorer rather than a solver, helping you see facets of the challenge you might otherwise miss. Example Problem-Framing Prompts: Example 1: I'm exploring ways to improve remote team collaboration. Instead of suggesting specific solutions, help me understand: What are the most overlooked aspects of remote communication that create friction or miscommunication? What contradictions exist in how people want to collaborate versus how current tools function? What assumptions about "presence" might be limiting how we approach remote work? Example 2: I'm working on innovations in urban transportation. Rather than proposing specific vehicle or infrastructure designs, help me explore: What tensions exist between different stakeholders in urban mobility (pedestrians, cyclists, drivers, businesses, etc.)? What contradictory needs do people have when moving through cities? What invisible barriers prevent more sustainable transportation choices? STEP 3: Use AI for Divergence Acceleration While AI excels at convergent thinking, we can strategically use it to accelerate certain aspects of divergent thinking as well. The key is to use AI to generate raw material that you then transform through your human creativity. Here's the technique: After your initial ideation, identify 2-3 promising directions that feel original. For each direction, use AI to generate adjacent possibilities: "What related ideas exist in [completely different field]?" Use these outputs not as solutions but as stimuli for your own associative thinking. The goal is to use AI outputs as creative springboards. For example, if you're designing a new , you might ask AI: "How do master chefs structure the process of teaching complex skills?" or "What principles do video game designers use to maintain engagement during difficult challenges?" The AI responses become raw material for your own divergent thinking process. You aren't adopting the AI's suggestions directly – you're using them to trigger new neural connections in your own thinking. This approach leverages AI's knowledge breadth while preserving your uniquely human ability to make unexpected connections across domains. Example Divergence Acceleration Prompts: Example 1: I'm developing a new approach to personal financial education that focuses on behavioral change rather than just information delivery. To spark fresh thinking, explain how these completely different domains approach behavior change: How do elite athletic coaches create lasting habit changes in their athletes? How do environmental conservation programs successfully change community behaviors? How do immersive theater experiences create memorable emotional impacts? For each area, identify 3-5 key principles and specific techniques that could be translated to financial education. Example 2: I'm reimagining the patient experience in healthcare waiting rooms. To stimulate creative connections, describe how these unrelated fields create positive waiting/transition experiences: Theme park queue design Airport VIP lounges Mindfulness retreat check-in processes Fine dining restaurant pacing and atmosphere For each, identify what specific elements create psychological comfort, reduce perceived waiting time, or transform waiting into a valuable experience. STEP 4: Delegate Convergence Once you've generated truly original directions through divergent thinking, AI becomes extraordinarily valuable for convergent activities – developing, refining, and optimizing your creative insights. This is where many people go wrong – they either overuse AI (surrendering the creative process entirely) or underuse it (ignoring its analytical strengths). Here are specific convergent tasks ideally suited for AI delegation: Detail expansion – Once you have a core concept, ask AI to help flesh out the details, specifications, or implementation steps. Pattern recognition – Have AI identify similarities between your idea and existing approaches to uncover potential refinements. Gap analysis – Ask AI to identify potential weaknesses or unanswered questions in your concept. Variation generation – Once you have an original direction, AI can help you explore variations within that direction. The key principle: Use AI for expansion and refinement of ideas that originated from your divergent thinking, not as the source of the original insight itself. For example, if you've conceptualized a novel approach to remote team collaboration, you might ask AI to: Identify potential implementation challenges Suggest how the concept might be adapted for different industries Compare your approach to existing solutions to identify differentiation opportunities This leverages AI's analytical power while preserving your role in the creative breakthrough. Example Convergence Delegation Prompts: Example 1: I've developed a concept for a community-based renewable energy sharing platform where households can trade excess solar power directly with neighbors using blockchain verification. Please help me refine this concept by: Identifying potential technical, regulatory, and user adoption challenges Suggesting the minimum viable features needed for an initial pilot Outlining how this approach differs from existing energy-sharing models Recommending how the concept might need to adapt for different housing environments (urban apartments vs. suburban homes vs. rural communities) Example 2: I've created a new approach to professional development called "Skill Swapping Circles" where cross-functional teams teach each other through structured 30-minute micro-workshops. Please help me develop this concept by: Creating a detailed implementation framework with clear steps Identifying potential resistance points and how to address them Suggesting metrics to measure effectiveness Recommending variations for different organizational contexts (startups vs. large enterprises) Outlining technology requirements to support the program STEP 5: Maintain Creative Authority The final step is perhaps the most important: consciously maintaining your throughout the process. AI tools are designed to be persuasive – they present information confidently and comprehensively. This creates what psychologists call the "authority bias" – our tendency to accept information from perceived authorities without sufficient scrutiny. To maintain creative authority: Question AI outputs – Actively look for assumptions or limitations in what the AI generates. Inject constraints – Deliberately introduce constraints that force original thinking: "How would this work without internet connectivity?" or "How would this change if it needed to be completely sustainable?" Transform, don't transfer – Always transform AI outputs through your unique perspective rather than directly transferring them into your work. Take incubation breaks – After receiving AI outputs, step away to allow your subconscious mind to process. Research shows that creative insights often emerge during periods of mental rest after information intake. Remember, the goal isn't to reject AI's contributions but to engage with them critically and creatively. Your unique human perspective – your lived experience, intuition, and values – should always remain the guiding force. Example Creative Authority Prompts: Example 1: I've been exploring a concept for [your idea]. You've provided some interesting perspectives, but I want to challenge both of us to think differently. Please: Identify three assumptions embedded in the approach we've been discussing Suggest how the concept would need to change if it had to work without [key resource or technology] Describe how this idea might be received by someone from a completely different cultural background than my own Identify ethical considerations I may not have considered Example 2: You've given me several suggestions for [topic]. Now I'd like you to help me critically evaluate them by: For each idea, identify the historical precedent or existing model it most closely resembles Point out which suggestions fall into conventional thinking patterns Identify any suggestions that might unintentionally reinforce problematic systems or assumptions Challenge me with three questions that might completely reframe how I'm approaching this challenge Real-World Application Let me share how this framework transformed the product development process at a consumer electronics company I worked with recently. Their team had been using AI tools extensively, but primarily as idea generators – essentially asking the AI to design new products directly. The results were predictably mediocre – variations on existing products with marginal improvements. We implemented the five-step framework, beginning with creative priming exercises before any AI engagement. Then, instead of asking the AI to generate product concepts, we asked it to explore unresolved tensions in how people interact with technology in their homes. This exploration revealed something fascinating – people were increasingly concerned about technology fragmenting family attention rather than enhancing connection. This human-centered insight came not from the AI directly, but from the team's analysis of the problem space with AI assistance. This led to a breakthrough concept: a family gaming system designed specifically for collaborative rather than competitive or individual play, with features that actively encouraged rich social interaction rather than isolated immersion. Once this novel direction was established through human divergent thinking, the team then used AI extensively for convergent tasks – researching existing collaborative technologies, identifying potential technical challenges, and developing implementation variations. The result was a genuinely innovative product that addressed deeply human needs in ways that AI alone could never have conceptualized. The product has since become one of their most successful launches, precisely because it originated from human insight about social connection rather than algorithmic prediction. Download We've now completed our five-step framework for creative partnerships with AI: prime your brain first, frame challenges not solutions, use AI for divergence acceleration, delegate convergence, and maintain creative authority. Each step is designed to leverage both human and machine intelligence in their respective domains of strength – your divergent thinking and AI's convergent capabilities. This approach represents a middle path between two extremes. On one side is complete AI dependency – surrendering our creative faculties to algorithms and experiencing the cognitive atrophy we discussed in earlier episodes. On the other side is AI rejection – ignoring powerful tools that could genuinely enhance our creative capabilities when used properly. The creative partnership I've outlined offers something better: a complementary relationship that amplifies your uniquely human creativity while leveraging AI's computational power. Remember the key principles we've explored throughout this series: Your creative thinking abilities physically exist as neural networks in your brain These networks strengthen or weaken based on how you use them Deliberate practice rebuilds these networks even if they've weakened through AI dependency The most innovative thinking emerges from partnerships that preserve human divergent thinking while leveraging AI convergent capabilities As we move deeper into the AI age, the ability to form these productive partnerships will increasingly distinguish those who merely execute from those who truly innovate. By understanding the complementary relationship between human and machine intelligence, you can develop creativity that no algorithm can replicate. Join me next time for "Measuring Creative Growth: Tracking Your Progress and Amplifying Results." We'll explore how to assess your creative development and build systems that continuously enhance your innovative thinking. Until then, I'm Phil McKinney, and remember – in an age of , the most valuable thinking happens at the intersection of human insight and computational power. That intersection exists in only one place: your creatively engaged mind. Your support means everything to this channel. And if you're passionate about creativity and innovation, consider becoming a patron on or a paid subscriber on . Your support helps make this content possible. To learn more about harnessing AI, listen to this week's show: Human-AI Creative... /episode/index/show/philmckinney/id/36725190

How to Strengthen Creative Thinking The 10-Minute Daily Brain Workout Based on Neuroplasticity Research 05/20/2025

How to Strengthen Creative Thinking The 10-Minute Daily Brain Workout Based on Neuroplasticity Research Humans who committed to four thinking exercises for 10 minutes daily generated 43% more original solutions than the most advanced AI systems. Welcome to Part 3 of our series, Creative Thinking in the AI Age – on strengthening your uniquely human creativity while using AI as a partner, not a replacement. In Part 1, we explored the concerning 30% decline in creative thinking as our use of AI tools has increased. In Part 2, we discovered how neuroplasticity – your brain's lifelong ability to reorganize itself – offers us a pathway to not just recover but enhance our creative abilities. Today, I'm giving you something concrete and practical: a complete 10-minute creative thinking workout based on cutting-edge neuroplasticity research. This isn't just theory – it's a systematic approach to rebuilding the neural pathways essential for innovative thinking. What makes today's episode especially valuable is that these exercises directly target the four core domains of creative thinking we identified last time: Cognitive Flexibility – your ability to switch between different thinking modes and consider multiple perspectives Associative Thinking – your ability to connect seemingly unrelated concepts Divergent Thinking – your ability to generate multiple solutions to open-ended problems Constraint Breaking – your ability to identify and overcome hidden assumptions These aren't just abstract concepts – they're distinct neural networks in your brain that physically strengthen or weaken based on how you use them. Neuroscience has clearly mapped these networks using fMRI studies. When we frequently outsource creative challenges to AI, these networks get less exercise and gradually atrophy. This atrophy directly affects not just our individual capabilities but our collective ability to solve complex problems as a society. Think of these four domains as the core muscle groups of creative thinking. Just as a neglected muscle weakens over time, these neural networks diminish when underutilized. And just as physical weakness limits our bodily capabilities, creative atrophy limits our problem-solving potential, career advancement, and ability to address society's most pressing challenges. The research I shared last time showed that consistent practice leads to measurable changes: Within days: Increased neural activity in creative regions After two weeks: Noticeable improvements in creative output By six weeks: Formation of new white matter pathways At eight weeks: Stable neural changes that maintain creative thinking abilities even amid regular AI use. This gives us a clear roadmap for strengthening our creative capacities: commit to eight weeks of practice, with meaningful milestones along the way. Before we dive in, I want to emphasize something important: consistency matters more than duration. Research shows that 10 minutes daily produces significantly better results than 70 minutes once a week. This aligns with what neuroscientists call "spaced practice" – shorter, regular sessions that allow your brain to consolidate learning between sessions. Also, approach these exercises with playfulness rather than pressure. Neuroplasticity research shows that stress inhibits the very neural changes we're trying to promote, while curiosity and enjoyment accelerate them. Ready to begin? Let's start with our first exercise. EXERCISE 1: PERSPECTIVE SHIFTING Our first exercise targets Cognitive Flexibility – your ability to switch between different thinking modes and see situations from multiple perspectives. This exercise activates your prefrontal cortex – the brain region responsible for cognitive flexibility. This region weakens with routine AI assistance, as algorithms typically present optimized single perspectives rather than multiple viewpoints. Here's how the exercise works: Choose any object in your environment. It could be a coffee mug, a book, or even your smartphone. For 2 minutes, rapidly adopt different perspectives on this object. Consider it from: The perspective of different professions (How would an engineer, artist, child, or historian view this object?) Different time periods (How would someone 100 years ago view it? Someone 100 years in the future?) Different scales (How would it appear to an ant? To a giant?) Different emotional states (How might someone feeling joyful, anxious, or curious perceive it?) The key is to shift rapidly between perspectives rather than dwelling on any single viewpoint. Each shift creates new neural firing patterns that strengthen cognitive flexibility. Let me show you some examples with this coffee mug: As an engineer, I notice the thermal properties, the handle design for ergonomics As an archaeologist from the future, this might be an artifact revealing daily rituals of 21st century humans To an ant, this would be a vast curved wall, perhaps offering shelter To someone feeling anxious, this might represent a moment of comforting routine in an uncertain day Now it's your turn. Find an object near you, pause the video, and spend 2 minutes shifting through different perspectives. When you're done, take a deep breath. You've just activated neural pathways associated with cognitive flexibility. What you'll notice with consistent practice is that this ability to shift perspectives begins extending to all areas of your thinking – helping you see multiple angles in business challenges, personal relationships, and creative projects. EXERCISE 2: RANDOM WORD FUSION Our second exercise targets Associative Thinking – your ability to connect seemingly unrelated concepts to form novel ideas. This practice activates your brain's default mode network. This network gets less exercise when we regularly use AI for creative solutions, but rebuilds with exercises that create unexpected connections. Here's the exercise: You'll need three random words. You can: Open a book to three random pages and point to a word on each Use a random word generator online Ask someone to give you three unrelated words For 2 minutes, create a coherent concept that combines all three words. This concept could be: A new product or service The plot for a story A solution to a problem you're facing Let me demonstrate with my three random words: "mountain," "keyboard," and "breakfast." I might create a concept for: "Summit Typing Café" – a mountain-top co-working space that offers spectacular views and serves breakfast all day. Digital nomads can work at ergonomic keyboard stations while enjoying high-altitude inspiration and nourishing food. Or perhaps: A new productivity app called "Peak Breakfast" that uses keyboard shortcuts to help you plan your most important tasks during your morning meal – the idea being that like reaching a mountain summit, completing your most challenging task first thing gives you perspective for the rest of your day. Now try it yourself. Generate three random words, pause the video, and spend 2 minutes creating a concept that combines them. The magic of this exercise is that it forces your brain to create connections where none previously existed. Each time you practice, you're physically strengthening the neural pathways involved in associative thinking. With regular practice, you'll notice your ability to connect disparate ideas improving in all areas of your life – leading to more original solutions and creative insights. EXERCISE 3: ALTERNATIVE USES Our third exercise targets Divergent Thinking – your ability to generate multiple solutions to an open-ended problem. This exercise stimulates your frontal and temporal lobes. These brain regions show increased connectivity after divergent thinking practice but get less activation when we habitually ask AI to generate ideas. Here's how it works: Choose an everyday object. Classic examples include a brick, paperclip, or rubber band, but any common object will work. For 2 minutes, list as many possible uses for this object as you can – aiming for quantity over quality. The goal is to push past obvious uses to increasingly creative ones. Challenge yourself to reach at least 10 uses, but don't stop there if ideas keep flowing. Let me demonstrate with a simple rubber band: Hold papers together Launch small objects Create resistance for finger exercises Mark pages in a book Seal a bag Make a tiny basketball hoop with your fingers Create a musical instrument by stretching it over a box Use as a hair tie Make a grip for slippery objects Create a boundary marker on a desk Use as a reminder by wearing it on your wrist Make emergency suspenders Now it's your turn. Choose an object, pause the video, and list as many uses as you can in 2 minutes. The first few uses typically come from memory – things you've seen before. As you push beyond those obvious answers, different neural pathways activate. Research shows that the most creative ideas emerge after the obvious ones are exhausted. By generating many options, you train your brain to access deeper, more original ideas more readily. With consistent practice, you'll notice yourself spontaneously generating more options in everyday situations – whether designing products, solving problems, or making decisions. EXERCISE 4: ASSUMPTION REVERSAL Our final exercise targets Constraint Breaking – your ability to identify and overcome hidden assumptions limiting your thinking. This exercise activates your anterior cingulate cortex - the brain region that detects cognitive conflicts. This area receives less stimulation when we frequently use AI systems that operate within established parameters rather than questioning basic assumptions. Here's the exercise: Choose any common product, service, or process. It could be a smartphone, a restaurant experience, or your morning routine. For 2 minutes, list all the assumptions or "rules" that typically apply to this thing. These are the constraints that everyone takes for granted. For each assumption, ask: "What if the opposite were true?" or "How could we eliminate this requirement completely?" Let me demonstrate with a common product: a refrigerator. Assumptions about refrigerators: They must be kept in the kitchen They need electricity to function They should be cold inside They must be box-shaped They should store primarily food items They must maintain a constant temperature Now, let's reverse these: What if refrigerators were distributed throughout the house? What if they required no electricity? (Perhaps using geothermal cooling or new materials) What if they were hot inside? (Preserving food through different methods) What if they weren't box-shaped? (Perhaps conforming to room architecture) What if they stored other things besides food? (Specialized cooling for medications, electronics, etc.) What if they had variable temperature zones that fluctuated intentionally? Your turn now. Choose a product or service, pause the video, and spend 2 minutes listing and challenging its assumptions. This exercise reveals invisible constraints we place on our thinking without realizing it. Each practice session strengthens your ability to identify and question assumptions - essential for breakthrough innovation. With consistent practice, you'll begin questioning assumptions automatically in various contexts, finding original approaches others miss. PUTTING IT ALL TOGETHER AND PRACTICAL APPLICATIONS Now that we've explored each exercise individually, let's discuss how to incorporate them into your routine and apply them to specific situations. Think of these four creative thinking domains like the major muscle groups in your body. Just as physical fitness requires working all muscle groups – not just your favorites – cognitive fitness demands exercising all four creative domains. Without this balance, your creative abilities will develop unevenly. We've all seen the bodybuilder with massive upper body development but skinny legs – what trainers call "chicken leg syndrome." The same imbalance happens in creative thinking when we only exercise our preferred domains. You might excel at divergent thinking (generating many options) but struggle with constraint breaking (questioning assumptions). The most effective approach for building complete creative fitness is to practice all four exercises in sequence, allocating 2 minutes to each, with a brief transition between them. This provides balanced "cross-training" across all four creative thinking neural networks. I recommend starting your day with this workout, ideally before checking email or social media. Research from the University of California shows that creative thinking is significantly higher in the morning, before our brains become loaded with external inputs. However, these exercises are also incredibly versatile for specific situations. Consider bookmarking this video to quickly access the exact exercise you need for different challenges: Before brainstorming sessions: Use Exercise 3 (Alternative Uses) to prime your brain for divergent thinking When facing a stubborn problem: Try Exercise 4 (Assumption Reversal) to break through invisible barriers Before important negotiations: Exercise 1 (Perspective Shifting) helps you anticipate different viewpoints When innovation feels stale: Exercise 2 (Random Word Fusion) creates fresh connections Each exercise serves as a targeted tool you can deploy in specific professional and personal contexts. The timestamps in the video description make it easy to jump directly to the exercise you need in the moment. Just as with physical training, these exercises might feel challenging at first – that's normal and actually a good sign. The neural equivalent of "muscle soreness" means you're creating productive disruption that leads to growth. And just as physical training requires progressive challenge to avoid plateaus, you should gradually increase the difficulty of these exercises by setting more ambitious targets or tighter time constraints. Also like physical training, consistency trumps intensity. A daily 10-minute workout will produce far better results than an occasional hour-long session. Neuroscientists call this "spaced practice" – shorter, frequent sessions that allow your brain to consolidate learning between workouts. To track your progress, I suggest keeping a simple creativity journal. After each workout, spend 30 seconds noting: Which exercise felt most challenging Any interesting ideas that emerged How your thinking evolved during the workout Over time, you'll notice patterns – exercises that initially felt difficult become easier, and your idea generation becomes more fluid and original. Let me share how one innovation team I worked with integrated these exercises into their process. This team was developing new healthcare technologies and had hit a creative plateau. They began each day with this 10-minute workout, then immediately applied the activated thinking patterns to their current challenges. Within three weeks, they reported two significant breakthroughs: First, the Perspective Shifting exercise helped them reimagine their user interface from the viewpoint of different stakeholders – leading to a design that accommodated both clinical and patient needs in ways their competitors had missed. Second, the Assumption Reversal exercise helped them question fundamental assumptions about data security – leading to a novel approach that provided better protection while actually improving system performance. The team leader described it as "mental cross-training" that enhanced their collective intelligence beyond what AI tools alone could have contributed. You can apply this same process to your challenges: Complete the appropriate exercise for your specific situation Immediately afterward, spend 5 minutes applying the activated thinking patterns to your problem Document any insights or novel approaches that emerge Over time, you'll develop what neuroscientists call "trained intuition" – generating creative insights without consciously applying techniques. CONCLUSION We've now completed our creative brain workout – four exercises that systematically strengthen the neural networks essential for innovative thinking. As we discussed in our previous episodes, the increasing integration of AI tools into our daily work has led to measurable changes in how we approach creative challenges. But the science of neuroplasticity offers us a powerful counterbalance – the ability to deliberately strengthen our innovative thinking capabilities throughout our lives. This research applies to everyone, regardless of age or background. Whether you're a student, professional, entrepreneur, or retiree, these exercises enhance creative capabilities through physical changes in your brain structure. Remember the key milestones we discussed: Within days: Increased neural activity After two weeks: Noticeable improvements By six weeks: Formation of new neural pathways At eight weeks: Stable changes that persist even with regular AI use The choice ultimately comes down to being intentional about how we use technology. You can automate creative processes entirely with AI and potentially experience the gradual atrophy of these essential cognitive abilities. Or you can strategically partner with AI while deliberately strengthening your uniquely human capabilities that drive breakthrough innovation. My hope is that you'll choose the latter path – not just for your individual benefit, but for our collective future. The challenges we face as a society – from climate change to healthcare access to sustainable energy – require precisely the kind of boundary-breaking, assumption-challenging thinking these exercises develop. Join me next time for "The AI Creativity Multiplier: 5 Steps to Amplify Your Innovative Thinking." Ever wondered how top innovators use AI to amplify their creativity rather than replace it? I'll reveal the surprising "creative handoff points" where AI transforms from a creativity killer to creative rocket fuel. You'll discover how to craft AI prompts that break through creative barriers instead of building new ones, turning your favorite AI tools into innovation accelerators unlike anything you've experienced. If this episode gave you the exercises to strengthen your creative thinking muscles, the next one will show you how to apply that strength in partnership with AI – creating results neither could achieve alone. Until then, I'm Phil McKinney, and remember – in an age of artificial intelligence, your creative brain remains your most valuable asset. Take 10 minutes to strengthen it today. Your support means everything to this channel. And if you're passionate about creativity and innovation, consider becoming a patron on Patreon [LINK] or a subscriber on Substack [LINK]. Your support helps make this content possible. /episode/index/show/philmckinney/id/36638260

Train Your Brain to Outthink AI Boost Creativity 40% (2025) 05/13/2025

Train Your Brain to Outthink AI Boost Creativity 40% (2025) Harvard neuroscientists confirm: creative thinking uses neural pathways that AI can't replicate – and never will. Hello, I'm Phil McKinney, and welcome to my innovation studio. Welcome to Part 2 of our series, – on strengthening your uniquely human creativity while using AI as a partner, not a replacement. In Part 1, we explored the alarming decline in creative thinking as we've grown dependent on AI. We saw how our ability to solve complex problems without algorithmic assistance has dropped by 30% in just five years, and how this cognitive atrophy affects everyone from students to seasoned professionals. Today, we're moving from problem to solution – exploring the revolutionary science of neuroplasticity and how we can deliberately rebuild and enhance our creative thinking skills. What's at stake here goes far beyond individual convenience. If we continue to surrender our creative thinking abilities to AI, we risk a future where innovation slows, where original ideas become increasingly rare, and where our unique human capacity for breakthrough thinking gradually fades. More critically, we may lose the very cognitive tools required to solve society's most pressing challenges – disease, pandemic response, clean energy development, food security – precisely when we need these abilities most. We're already seeing early evidence of this decline, but the science I'll share today offers a powerful alternative – a path to not just preserve but dramatically enhance the creative abilities that drive human progress. I've seen this firsthand in my work leading innovation teams. Years ago, I noticed that even brilliant engineers and designers would hit creative walls. When I introduced specific neuroplasticity-based thinking exercises into our daily routines, the transformation was remarkable. Teams that had been spinning their wheels suddenly generated breakthrough concepts. Projects that seemed stuck found fresh momentum. And the most exciting part? The improvements continued long after the initial training. These transformations aren't magic – they're biology in action. Your brain is changing right now as you watch this video. Every thought you have, every skill you practice, and every challenge you undertake physically reshapes your neural architecture. This isn't metaphorical – it's literal, structural change happening at the cellular level. This phenomenon – called neuroplasticity – is the brain's ability to reorganize itself by forming new neural connections. And our key to reclaiming and enhancing our creative thinking abilities in the age of AI. For decades, scientists believed that brain development stopped after childhood. We now know that's completely false. Your brain remains malleable throughout your entire life, capable of dramatic transformation well into your 80s and beyond. Research has shown that our brains continually remodel themselves based on our experiences and practices. Think of it like a path in a forest – the routes you travel most frequently become wider and clearer, while those rarely used gradually disappear. Now, I understand some skepticism here. We've all seen dubious claims about "brain training" games and apps that promise to boost intelligence. Most of these have been rightfully criticized for overpromising and underdelivering. The difference with creative neuroplasticity training is that it's not about playing generic puzzles – it's about targeted exercises that specifically engage the neural networks involved in creative thinking. And unlike those commercial products, these approaches have substantial peer-reviewed research supporting their effectiveness. The implications are profound. If our cognitive abilities are declining due to AI dependency, as we discussed in the last episode, we can deliberately reverse this trend through targeted exercises and practice. Let's be honest – breaking AI dependency isn't easy. Many of us have developed reflexive habits of turning to algorithms before engaging our own thinking. Our brains naturally seek the path of least resistance. But the research is clear: the effort to rebuild these creative pathways is absolutely worth it. And the good news is that even small, consistent practice can yield significant results. The science behind this is compelling. A landmark study at Harvard Medical School used functional MRI to track brain activity before and after an 8-week creative thinking training program. The results were striking. Before training, participants showed activity primarily in conventional problem-solving regions when tackling creative challenges. After training, their brains revealed significantly increased activity in regions associated with novel idea generation and reduced activity in regions associated with conventional thinking. What's even more fascinating is that the neural training correlated with a 43% increase in measured creative output. The participants weren't just thinking differently – they were producing significantly more original ideas. This is neuroplasticity in action – physical changes in your brain leading to measurable improvements in creative capacity. But neuroplasticity works both ways. When we outsource our thinking to AI, the neural pathways associated with creative problem-solving literally weaken from disuse. It's a biological principle called "competitive plasticity" – the brain reallocates resources away from underused functions toward frequently used ones. The good news is that this process is reversible. Even if you've grown dependent on AI for creative tasks, your brain can rebuild these pathways through deliberate practice. Let me share a personal experience from my own work. I once coached a senior product designer and their team at a major tech company who were tasked with developing disruptive ideas in an area where three major competitors were already investing heavily. When we started working together, they were stuck, repeatedly generating variations of the same concepts and feeling increasingly frustrated. Brain science would suggest their neural pathways had become rigid through years of conventional problem-solving. So we implemented a series of targeted creative thinking exercises. Within eight weeks, something remarkable happened. Not only did their idea generation rate triple, but the quality of their concepts ‌shifted. They developed a breakthrough approach that combined elements no one had previously connected, essentially creating an entirely new product category. When we brought in AI tools to analyze the solution space, the team's most innovative concepts fell completely outside the AI's prediction patterns. What does this mean? The neural connections they had formed with their training weren't following the statistical patterns the AI model had learned. The product they launched went on to capture significant market share precisely because it operated from a different conceptual framework than competitors. This wasn't just a professional transformation. It had a personal impact. This senior product designer reported feeling a renewed sense of cognitive confidence that extended into other areas of their life as well. These transformations aren't random. The science of neuroplasticity has identified four core domains of creative thinking that respond most dramatically to training: Cognitive Flexibility – your ability to switch between different thinking modes and consider multiple perspectives simultaneously. For example, seeing a coffee cup not just as a vessel for liquid but also as a plant holder, a pencil container, or a sound amplifier. This domain is largely governed by the prefrontal cortex, which neuroimaging studies show becomes significantly more active after flexibility training. Associative Thinking – your ability to connect seemingly unrelated concepts to form novel ideas. Like combining the principles of bird migration with urban traffic patterns to create a new adaptive traffic light system. This involves the default mode network, which strengthens with exercises that encourage unexpected connections. Divergent Thinking – your ability to generate multiple solutions to an open-ended problem. For instance, coming up with twenty different uses for a brick beyond construction, such as a doorstop, paperweight, art canvas, or heat reservoir. This engages the frontal and temporal lobes, which show increased connectivity after divergent thinking practice. Constraint Breaking – your ability to identify and overcome hidden assumptions limiting your thinking. Such as recognizing that when asked to "connect nine dots with four straight lines," the assumption that you can't go outside the imaginary square is self-imposed. This correlates with increased activity in the anterior cingulate cortex, which helps detect cognitive conflicts. Each of these domains weakens with AI dependency but rebuilds with targeted practice. What excites me most is that there are practical exercises anyone can use. In my innovation workshops, we've adapted these into simple daily practices that build creative muscle memory: Five-minute morning sessions of rapid association between unrelated concepts Brief midday "constraint-breaking" challenges where teams identify and discard hidden assumptions End-of-day reflection exercises that alternate between focused and diffuse thinking modes These aren't complex or time-consuming – they're deliberate mental practices that target the exact neural networks we need to strengthen. And remarkably, participants report greater idea fluency within just days of consistent practice. Let me demonstrate one of these domains with a quick exercise that you can do right now. We'll focus on cognitive flexibility. I want you to visualize a circle. Just a simple circle. Now, in your mind, transform this circle into something else by adding just one line. Now add one more line and transform it again. One more time – add another line and see what new object emerges. I will give you 30 seconds. Imagine a simple circle and transform it three times, adding a line each time. I will wait. Go! How did you do? This exercise activates your prefrontal cortex – the brain region responsible for cognitive flexibility. Most people initially create predictable objects: a face, a sun, or a balloon. But as you practice, your brain begins forming less common connections. Advanced practitioners might see a clock becoming a bomb becoming a planet becoming an eye. Brain scans reveal increased neural firing in creative regions even during this simple 30-second exercise. You're literally strengthening synaptic connections that enhance your creative thinking. The timeline of these changes follows a clear and consistent pattern: Within days of consistent practice, creative neural pathways strengthen, showing up as increased activity in brain scans After two weeks, you'll notice measurable improvements in your creative output By six weeks, researchers have documented the formation of new white matter pathways – the brain's information highways, meaning participants' brains were physically different. At eight weeks, these changes become stable enough to resist the pull back toward AI dependency. This gives us a clear roadmap for reclaiming our creative capacities: commit to eight weeks of practice, with meaningful milestones along the way. This transformation is remarkably accessible. Just 10 minutes of daily practice can trigger these changes. In our next episode, I'll guide you through a complete workout, but here's a preview of the two core approaches we'll use: Mindful Creativity – approaching familiar tasks with deliberate curiosity. For example, during your morning routine, challenge yourself to notice five new details about objects you use every day. This simple practice activates the cognitive flexibility networks we discussed earlier. Alternating – deliberately switching between focused thinking and relaxed daydreaming. This might look like setting a timer for 3 minutes of intense problem-solving followed by 2 minutes of completely unfocused mind-wandering. This oscillation strengthens the associative thinking pathways that AI dependency weakens. These aren't just theoretical concepts – they're the foundation of the 10-minute daily workout I'll guide you through in our next episode. Each exercise targets explicitly the neural networks involved in the four creative thinking domains we've explored today. What makes these practices so powerful is the underlying principle we've discussed throughout: our brains physically change based on how we use them. This biological fact puts the choice squarely in our hands. Either we surrender our cognitive processes to algorithms, or we deliberately strengthen these uniquely human abilities. The stakes are higher than we might realize. If we do nothing, then we face a future of diminished creativity, which means technological progress that plateaus, businesses that can only optimize rather than reimagine, and education that produces technically proficient but intellectually passive graduates. This is precisely what Bonhoeffer warned about in writing on “stupidity” – not as a lack of intelligence, but as the voluntary surrender of independent thinking. As we discussed in the first episode, Bonhoeffer observed that people become 'stupid' not because they lack capacity, but because they willingly abandon critical and creative thought to “others”. This surrender happens gradually, unnoticed, as we choose comfort over challenge. With AI, we face exactly this choice. Will we surrender our creative faculties to algorithms, essentially choosing a form of 'creative stupidity'? Will we create a society where independent thinking grows rare, not because it's forbidden, but because it's surrendered? Will we accept a world where ideas are judged by their conformity to algorithmic patterns rather than their originality? But that's not the future we have to choose. Join me in the next video in the series for "The Creative Brain Workout," where I will guide you through 10 minutes of exercises that trigger the neural changes that will help you build stronger, uniquely human creative thinking skills that AI simply cannot replicate. Until then, I'm Phil McKinney, and remember – in an age of artificial intelligence, your mind remains remarkably adaptable. The power to reshape your creative thinking is literally in your hands. If you found value in today's video, please hit that like button and subscribe so you don't miss the next episode in this series. Your support means everything to this channel. And if you're passionate about creativity and innovation, consider becoming a patron on Patreon or a paid subscriber on Substack – links are in the description below. Your support helps make this content possible. Thanks for watching, and I'll see you in the next episode. /episode/index/show/philmckinney/id/36544550

Killer Innovations with Phil McKinney

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