The Innovators Studio with Phil McKinney

You trust your gut because it's been right before. But "right" is exactly the thing you've been measuring wrong. A hitter never has this problem. His batting average is honest. It counts hits, nothing else, across a whole season, and he can't argue with the number. Your gut is supposed to work the same way: every decision an at-bat, every result feedback, a career sharpening your instincts the way a season hands a hitter a real number. But you keep your own scorebook. You mark every win as good judgment the second it lands. The trouble is that a skilled call and a lucky one produce the same win. In your book they look identical. Train your gut on that for thirty years and it grows certain about things that were never true. I know, because I trained mine that way. The Award and the Bankruptcy At twenty-eight, I won, and the win felt like proof. I was at a company called ThumbScan, and I took a piece of government security technology and repackaged it for the business PC market. We called it PCBoot. PC World named it Security Product of the Year at COMDEX in Las Vegas, in front of the whole industry. I drew the obvious conclusion. My gut was good. I could see what the market wanted before the market did. Except I didn't see it coming. In early 1988, computer viruses became front-page news. The New York Times ran it on the front of the business section, the story spread to nearly every paper in the country, and overnight every company in America decided it needed security. My product was already built and sitting on the shelf when the panic arrived. I had built a solution that needed a problem, and the people writing and spreading those viruses are the ones who handed it one. It was nothing I did. I hit the timing right, and the timing was luck. It took an honest audit, years later, to admit that, and the same look turned up the opposite story. The other ThumbScan product was the one I was proudest of. It put fingerprint security on a personal computer, the first one under a thousand dollars you could attach to a PC. Your thumb instead of your password. The reasoning was sound and the technology worked. The market wanted none of it. PCs were barely in homes yet, biometrics sounded like science fiction, and the company bled cash and folded. That product wasn't worse thinking than the one that won the award. It was the same thinking, aimed at an idea that turned out to be twenty-five years early. Today it sits on every phone, and hundreds of millions of people use it before breakfast. I wasn't wrong about the concept. I was wrong about the clock, and the clock runs mostly on luck. The award and the bankruptcy came out of one gut, separated only by the year each idea landed in. What I did, years later, has a name. I ran the version of events that didn't happen, stripped the result off each decision, and looked at the call cold. That's counterfactual thinking, and it's the whole skill. It's uncomfortable, because the result already handed you a verdict and now you're reopening it. It's also the only feedback that makes you better. Why Your Results Lie to You None of this is your fault. It's a measurement problem. Your gut got trained on bad data, and it had no way of knowing. The more decisions you've stacked up, the more confident it's become, and confidence built on a bad stat is worse than no confidence at all. A junior person knows they're guessing. Twenty years in, the guessing feels like knowing. Your own record is full of the same thing. Wins you credited to your own judgment when they really came down to timing, or to a competitor's mistake you had nothing to do with. Good calls you stopped making because one of them lost, even though losing was always on the table and the call was still right. None of that is carelessness. You recorded every result accurately. You just recorded the wrong thing, and then you trained on it. The world isn't helping. Every outcome now arrives with its explanation already attached, ten confident takes by lunchtime, most written backward from the result. I covered that warning in "Hindsight Is Not 20/20." So go back and run the audit on yourself. Rebuild what you knew on the day you decided, set the result aside, and ask whether the call still holds up without it. The hard part is doing this to wins, because taking apart a success while you're still proud of it feels like bad manners and bad luck at once. That's the reason your wins are where your worst lessons hide. Read Your Competitors' Moves You just watched me run this backward, over my own record. It points two other directions too. The first is sideways, at everyone else. The same move works just as well on decisions that aren't yours. When a rival's bet pays off, the instinct is to copy it. When it craters, the instinct is to swear it off. Both stop at the result. So rebuild their decision the way you rebuilt your own. Say a competitor ships a feature and it takes off, and three teams in your space scramble to copy it. What they miss is that the feature didn't carry the launch. It landed the week the category leader had an outage, and every angry customer went shopping. Copy that same feature into a calm market a year later and nothing happens, because you copied the move and not the moment. You're hunting for the hinge, the single thing the outcome really swung on, and it's rarely what the headlines credited. Get this wrong and you don't copy a rival's strategy. You copy the luck that came with it, and luck doesn't travel. Pressure-Test Your Next Decision The other direction is forward, into a choice still in front of you, and it's where the skill pays you back the most. Most of us pick options by their best case. You picture each road going well and take the one that goes best. Turn that around. Walk each option forward until it falls apart, because every option falls apart somewhere, and the one you can't picture breaking is just the one you haven't looked at hard enough. Pull in the alternatives you've already talked yourself out of, and count doing nothing, since that's a choice too. Then decide on the part nobody likes to look at: the downside you'd have to live with. A modest plan you can walk away from beats a brilliant one that takes you down with it. Most decisions that seem obvious stop seeming that way once you walk the alternatives all the way out. The ones that still look right after that walk are the ones worth making. Practice Exercise: Audit a Win You're Proud Of This is the drill that matters most, and the one you'll want to skip. Pick a win from the last year. Not a loss. Something that worked, that you've been glad to take credit for. Write down what you knew the day you decided. Only that, nothing you picked up afterward. Run the version where it went the other way. How close did it come, and what would have had to break differently? Answer straight. Good decision, or good result? If it's hard to sit with, you're doing it right. The wins you can't bring yourself to examine honestly are the ones costing you the most. A good decision and a lucky one keep looking identical until you do the work to tell them apart. Do that work often enough and you stop mistaking the breaks that fell your way for things you did well. Over a career, that is most of the gap between people who get reliably good and people who just had a good run.

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.

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 https://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.

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

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. https://www.oracle.com/uk/cloud/decision-dilemma/ Thinking in Bets - Duke, A. (2018). Portfolio. On judging decisions by process, not outcomes. https://www.penguinrandomhouse.com/books/552885/thinking-in-bets-by-annie-duke/ How to Improve Bayesian Reasoning Without Instruction: Frequency Formats - Gigerenzer, G. & Hoffrage, U. (1995). Psychological Review, 102(4), 684-704. On updating beliefs with evidence. Prospect Theory: An Analysis of Decision under Risk - Kahneman, D. & Tversky, A. (1979). Econometrica, 47(2), 263-291. Prospect Theory foundations.