エピソード

  • How to Tell a Good Decision From a Lucky One

    How to Tell a Good Decision From a Lucky One
    2026/07/08
    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...
    続きを読む 一部表示
    13 分
  • What Half of 13 Reveals About Your Thinking

    What Half of 13 Reveals About Your Thinking
    2026/07/29

    What is half of thirteen?

    Stop. Answer it. Don't think ahead, just answer.

    You said 6.5. I know you did, because everyone does. Nobody chose to answer that question. Your brain solved it before you decided whether you even wanted to play along. That's the power of a question: whoever hears it cannot stop themselves from answering. Ask a person something and their mind starts working on it right away, whether they wanted to or not.

    If you gave that answer on a math test, it would get ‌marked as correct. Give that same answer on a test of innovation, and you're average, because that's where everyone stops. Push beyond the obvious answer, and that's what puts you top of the class.

    Here's the version of the question that changes everything: How many ways could you answer "what is half of thirteen?"

    Sit with that for a second, because the honest reaction most people have is mild panic. There's the obvious one. Then what?

    Split the number down the middle and you get a 1 and a 3.

    Split the word into syllables and you get "thir" and "teen."

    Every one of those is a real answer. None of them occurred to you the first time, because the first time, your brain wasn't looking for options. It was looking for an answer to the question.

    I've run this exercise for years in my Innovation Boot Camp and the Innovation Essentials Workshop. One professor who uses my book in their class now opens every semester of her course with it. The class brainstorms as many answers to the question as possible. The record so far is thirty two different ways to answer that one question. And remember, asked the first way, that same question only gives you one answer.

    This isn't just a classroom trick. Researchers have measured this exact mental muscle since the 1960s, asking people how many uses they could find for a brick. Some people list four. Some list forty. The gap between those two people has nothing to do with intelligence. It's whether their mind treats the first answer as the end of the search or the beginning of one.

    Practice Exercise: Take one recurring question: a decision at work, a plan for the weekend, even "what should I make for dinner." Before you settle for the obvious answer, ask "how many ways could I answer this?" and write down at least ten. Not ten good ones, just ten. See where the eleventh one takes you.

    This part 1 of a three-part series on how to use questions as the skill behind better thinking, better ideas, and better innovation. Next week, we get into what actually separates an average question from a great one.

    続きを読む 一部表示
    5 分
  • When One Word Steers Your Answer

    When One Word Steers Your Answer
    2026/08/05
    In 1974, a psychologist named Elizabeth Loftus showed a group of people a film of a car accident. Afterward, she asked half the group one question: how fast were the cars going when they hit each other? The other half got the same question with a single word swapped: smashed instead of hit. The smashed group estimated higher speeds. Same film. Same crash. One word. A week later, everyone came back and answered a new question: Did you see broken glass? There was no broken glass in the film. The smashed group remembered it anyway. One word inside one question changed what people reported seeing. Then it reached back and rewrote what they remembered. Somebody did this to you this week. A question steered your answer, and you never noticed. Last week, I showed you that your brain cannot refuse a question. You hear one, you start answering, whether you agreed to or not. This week is the other side of that power. If every question forces an answer, then how the question is built decides which answer you get back. Questions have an anatomy. Almost nobody looks at it. Before we're done, a motorcycle taxi in Bangkok is going to show you what a question built right can find. Let's get into it. ===== Every question you ask carries three things, whether you put them there on purpose or not. It carries assumptions, the things it treats as already settled. Ask "Why did the launch fail?" and you've ruled the launch a failure before anyone speaks. It carries scope, the range of answers it permits. "Coffee or tea?" permits exactly two answers. "What should we drink?" opens the room. And it carries a load, the specific words that steer the answer. That's what "smashed" did. Nobody in that study felt steered. The steering is invisible to the person answering, and most of the time to the person asking too. Once you see the parts, you can take any question apart. Take "why did the launch fail?" one more time: it assumes failure before anyone answers, its scope is only explanations, and "fail" is the loaded word doing the steering. Hold on to that one; we'll rebuild it later. Let's start with the ones built to do damage. The worst question in business "That presentation was fantastic, wasn't it?" That's a tag question: a statement dressed up as a question, built so every answer is closed off except agreement. The person asking isn't really asking, not in any way that risks hearing something different. They're collecting a signature. When lawyers use them in court, it's called leading the witness. When managers use them in conference rooms, it's called alignment. Tag questions did damage for years at HP, in the design reviews, every product went through before customer briefings. One review was for a prototype, one of our first laptops in what's now called the "thin and light" category. The product team wanted to lead that category without straying too far from what already sold. That's the tradeoff tag questions live in. Nobody wants to sound negative about it. So the pushback arrived dressed as agreement: "We'll still have four USB ports, right?" You don't get thin and light with a case full of legacy ports, and the designer knew it. I watched the exasperation cross his face. Before it became a battle, I stepped in and rebuilt the question: "What's the right mix of ports for this segment, and why?" Then: "Have we tested that mix with the target customers?" The answer came back fast. "No need. We know what the customer wants." I nearly smacked my forehead. One rebuilt question had uncovered the real problem, and it wasn't ports. It was an untested assumption sitting in the middle of a flagship product plan. That's what tag questions cost you. The entire point of asking a question is to get information, input, or ideas. A tag question collects compliance instead. Enough of them, and people stop bringing you anything you don't already believe. The two kinds of good questions Real questions are split into two categories: factual and investigative. A factual question retrieves information. How many units did we sell last week? You may not know the answer, but you know exactly how to get it: one phone call. Factual questions keep the world running. They just can't uncover anything because they only retrieve what somebody already knows. An investigative question can't be answered with a yes, a no, or a lookup. It's divergent: more than one correct answer exists, so the person answering has to go investigate. You felt this difference last week. "What is half of thirteen?" is a factual question. "How many ways could you answer: what is half of thirteen?" is an investigative question. One is arithmetic. The other is the one that a classroom answered thirty-two different ways. Socrates built his entire way of teaching on investigative questions, pushing every student past "I've heard it said that..." until they could say what they themselves thought, and why. The first step toward knowledge, he insisted, is ...
    続きを読む 一部表示
    14 分
  • The Customers Who Walked Away

    The Customers Who Walked Away
    2026/08/12
    Think about the last thing you almost bought and did not. You picked it up, you looked at it, you put it back down. You had a reason for choosing one item over its competitors, and you know exactly what it was. Did anybody ever ask you why you didn't choose the loser? Of course not. And somebody did the same thing to you this week. Something you made, or wrote, or suggested. An idea you put into a meeting that got a polite nod and then went nowhere. They considered it, decided against it, and you never found out the real reason. Almost everything that comes back to you comes from the people who said yes. Inside a business, the machinery makes that official. Satisfaction surveys go to people who have bought. Reviews come from people who have bought. The customer list is a list of people who have bought. The one who put it back down is invisible to all of it, and that person is holding the answer. So, where do you point a question to reach somebody who is not there? Last week, we took a question apart and rebuilt it. But a well-made question aimed at the wrong thing comes back empty. Where you point a question is the other half of the skill. Forty-two cards of Killer Questions A killer question is one that has been tested and proven to spark ideas beyond the obvious. The name comes from the old phrase "killer app," where "killer" meant standout, not lethal. I built this collection of questions the slow way. I designed structured tests into my innovation workshops, which I was running: specific questions put to specific groups, and a record of what each produced. The rule for keeping a question was that it had to trigger something in that room. Did somebody walk out seeing their customer, their product, or the way they work differently than when they walked in? If nothing moved, the question was cut. If there was a good idea underneath one and I had simply worded it badly, I rewrote it and ran it again in another workshop. There were hundreds of questions, and most did not survive. Forty-two did. When I sorted the survivors, they fell into three groups. Not by subject. By where they aim your thinking. Three places to aim Every question in the deck points to one of three things. Who, what, and how. WHO is the person or organization who will benefit from what you make. Usually, your customer, though the gap between "usually" and "always" is where a lot of new business hides. WHAT is the product, the service, the solution that creates the value for the WHO. HOW is the way your organization builds, delivers, and supports the WHAT for the WHO. Three places an idea can come from, and the questions exist to send you into each one on purpose rather than by accident. The cards all say "product." Read that as whatever you make for somebody else. A service counts. So does a proposal you hand to your boss. Thirteen of my cards aim at WHO. Thirteen at WHAT. Sixteen at HOW. That split was never a plan. It is where the questions that kept surviving pointed, and eventually, I stopped arguing with it. WHO What are your unshakable beliefs about what your customers want? The phone companies believed their customers wanted reliability above all else, and they were right. For a century, they built toward 99.999 percent uptime. A dial tone that worked in a storm, in a power failure, always. Then somebody turned that belief over and looked at it with no assumptions about what the customer wanted. Where were there people who would give up call quality for something else? That is the question that led to voice over IP, a phone call carried over the internet. In the early days, it sounded terrible. Calls dropped. By the standard the old industry had spent a century perfecting, VoIP was not a serious product. But underneath it sat an idea nobody in that industry had allowed themselves to consider: that many people would trade call quality for price and mobility, and would do so happily. They did. That market opportunity existed before anyone built it, waiting for someone willing to challenge the old standard on its head. WHAT What is surprisingly inconvenient about my product? It does not ask whether the product is good. Your team will defend that all day, and they will be partly right, which will only make the conversation worse. Surprisingly inconvenient means some part of using your product that nobody inside your company has ever seen a real person struggle with. The fifteen minutes with the instructions. The step everyone on your team skips automatically because they built the thing. On the back of that card sits the shortest useful question I own: "Do you use your own product yourself?" Hold on to this one. In a few minutes, it turns up at a Best Buy, and the strange part is that I wasn't aiming at it when I found it. HOW What do people not like about the buying experience for my product? For the whole time I was CTO at HP, I spent nearly every Saturday in a Best Buy. While traveling, I found a local electronics store in ...
    続きを読む 一部表示
    24 分
  • How to Improve Weak Signal Judgment

    How to Improve Weak Signal Judgment
    2026/06/24
    Everyone collects weak signals now. Most of what they collect predicts nothing. A weak signal isn't a thing you spot, it's a prediction you make, and the edge goes to whoever bets on it while being wrong is still cheap. So how do you become the one placing the bet, not the one still collecting reports? Let's get into it. What a Weak Signal Actually Is A weak signal is a faint piece of evidence that points to something a customer will want before they can name it, and before the market has priced it in. Faint, because if it were loud, everyone would already be acting on it. Deniable, because you can always explain it away as noise, and most people do. That deniability is the whole point. The moment it becomes undeniable, the advantage is gone and the price has moved. Why Noticing Stopped Being the Edge Ten years ago, noticing was hard. You needed sources, a network, time to read widely, a feel for the edges of your industry. That was the moat. It isn't anymore. Every team has a trend report and three newsletters and an AI tool surfacing emerging behaviors on a schedule. The noticing got automated. What didn't get automated is the judgment about which signal predicts a structural change and which points to nothing real, and the nerve to act early. Inside Roche's Innovation Board I sat on Roche's diagnostics innovation board, the only outsider in the room, helping decide which ideas got funded. At one point we took on diabetes care. I am not diabetic. So I had Roche ship me every meter and test strip they made, and I pricked my finger up to a dozen times a day to feel what their customers felt. You cannot innovate for a customer whose day you have never lived. Skip that, and everything after is a guess. Roche was a leader in blood glucose testing with its Accu-Chek meters, and the math looked obvious. Someone with type 1 diabetes tests around eight times a day, every day, for life. A big, stable business. Type 2 was the smaller story per patient. Those patients tested once, maybe twice a day, so each one looked worth less, and we filed the category under "less interesting." We could already see type 2 climbing. We weighed it against the per-patient math and explained it away. Then type 2 diagnoses exploded into one of the fastest-growing chronic conditions in the world. And the category stopped being about counting tests per day at all, because monitoring went continuous, the always-on sensors people wear today. We had seen the early edge of both shifts. We even predicted them. We just didn't move fast enough, and the reason is the one that kills most weak signals inside a big company. Project approval and annual budgets are built to fund what's already proven, not to chase something still faint. Roche got there. Accu-Chek SmartGuide, its real-time continuous monitor, is on the market now. I just wish we had moved the moment we saw it, instead of waiting for the next budget cycle to make it safe. How to Read a Weak Signal We didn't miss the type 2 signal for lack of noticing. We noticed. We missed it on the three things that come after, and those you can train. The moves start once you've got a signal you can't quite dismiss, and the skill is what you do with it. Tell the Canary From the Costume A canary in a coal mine matters because the air changed. It signals something structural, a shift in the environment that affects everyone in it, whether they've noticed yet or not. A costume is the opposite. A few people put it on, it's striking, it spreads for a season, then they take it off and the room is exactly as it was. On day one the two look identical. A behavior appears, it's unusual, it's spreading. The only question that matters is whether it predicts a change a customer can't reverse, or a moment that will pass. Back in 2018 I wrote about telling a trend from a fad, and the test still holds: ask what need the behavior reveals. Type 2 was a canary, and we read it as a costume, because we counted testing frequency instead of the need underneath it. That need, millions of people learning to manage a lifestyle disease, only grew. The discipline is refusing to let the size of the spike tell you which one you're looking at. Costumes spike too, sometimes higher. You're reading for the need, not the noise. Read the Window A signal's window is short. Too early, you can't tell it from noise and you waste resources chasing ghosts. Too late, it's obvious, everyone sees it, and the advantage is already priced in. The value lives in the narrow gap between. Waiting for more evidence feels like better judgment, but the evidence that finally convinces you has already reached your competitors. Certainty and advantage move in opposite directions, so by the time you're sure, sure is just another word for too late. The question isn't whether the signal is real yet. It's how much longer you can be the only one taking it seriously. Act While Being Wrong Is Cheap This is the move that separates the people who read signals ...
    続きを読む 一部表示
    13 分
  • How to Improve Your Second-Order Thinking Skills

    How to Improve Your Second-Order Thinking Skills
    2026/06/10
    In 2000, Toys R Us paid Amazon $50 million a year to sell their toys online. It looked like a great deal. The company that defined toy retail for two generations was solving the internet problem in one move. Four years later they were suing each other. Seventeen years later Toys R Us was gone. Every store closed. Every job lost. And every step of what happened was visible from the day the deal was signed. Nobody at Toys R Us saw it. What Is Second-Order Thinking? First-order thinking asks what happens next. Second-order thinking asks what happens to the people who see what happened next. The skill isn't caution. It's the willingness to keep looking after the room has stopped. Inside HP, 2006 In 2005, HP launched Halo, a premium telepresence system co-developed with DreamWorks. For a brief period it reported into my organization. The next year, Cisco launched TelePresence and went straight at us. I called the HP team closest to Cisco and asked what they made of it. The answer was reassuring: Cisco is aiming down-market, we're fine. We were premium; they were chasing volume. That answer satisfied the room. It did not satisfy me. The room was asking "will Cisco hurt Halo?" That was the wrong question. The right one was sitting underneath: why did our partner of twenty years decide to do this without us? Nobody had an answer to that one. The HP team didn't think it was the question. They were focused on the product collision, and I kept coming back to the partnership. A company that had cooperated with us for two decades had just decided they didn't need to anymore. The product was the surface. The relationship had quietly ended, and we were the only ones who hadn't noticed. Three years later, Cisco launched a direct attack on HP's core server business with Unified Computing System. HP responded by acquiring 3Com and going after Cisco's core networking business. A twenty-year alliance ended in under two years. Neither side ran the second-order analysis at any point along the way. By the time the right question got asked, the partnership was already gone. The Three Skills These three skills stand on their own. Each one solves a different problem most decision frameworks miss. The first picks up signals before there's even a decision to analyze. The second uncovers what's actually driving the other party's timing. The third shows you what people will do once they see your decision land. If you've watched the November 2025 episode on the basics of second-order thinking, these skills add to that foundation. If you haven't, you can still apply all three starting today. Sense the Weak Signal, Not the Loud Event Most failures don't announce themselves. The loud event, the launch, the lawsuit, the lost customer, is usually the visible end of something that started much earlier as a quiet shift somebody noticed and explained away. A weak signal is a small piece of information that doesn't fit the story you're already telling. A customer's casual comment that contradicts your data. A team member's evasive answer in a status meeting. A supplier missing a deadline they've never missed before. The reflex is to make it fit the story you already believe. The skill is to refuse. Go looking before you have one. Once a week, scan three places where weak signals live. Customer-facing teams. Data points that surprised you and got brushed off. Topics that smart people you respect are paying attention to, but you aren't. You're not looking for problems. You're looking for things that don't quite fit. Name the thing that doesn't fit. Be specific. "Their CFO made a comment about the budget that didn't match what we were told last quarter." Not "something feels off." The more specific the signal, the more useful it becomes. List the stories that would make the signal make sense. At least three. Force yourself to consider explanations that don't fit your current assumptions. Ask which of those stories you'd act on if it were true. If one of them would change a decision you're about to make, that's the signal you can't afford to ignore. Find one more data point before you decide. A single signal can mislead. Two signals pointing the same direction is usually real. The Cisco TelePresence launch was a weak signal about the partnership. The team read the product. I read the relationship. Neither of us pushed it far enough. Ask "Why Now" Before "What's Next" Most people jump straight to the future: what will the other party do next? That's the wrong starting question. Ask why now first. Why is this happening now, when it could have happened a year ago? The timing tells you what changed in their world, and that change tells you what they're likely to do next, often more reliably than asking the question directly. State the move that just happened. A competitor launched a product. A regulator opened an inquiry. A customer asked for a discount. Name it plainly. Ask what changed. What was true a year ago that isn't true now? What...
    続きを読む 一部表示
    15 分
  • How to Improve Your Inversion Thinking Skills

    How to Improve Your Inversion Thinking Skills
    2026/06/03
    Every playbook, every case study, every innovation workshop is built on the same question: how do you succeed? You map the path forward. You model the upside. Nobody teaches you to ask the harder question. How would you guarantee this fails? That's inversion thinking. Charlie Munger called it one of the most useful tools he had, and he used it for sixty years. Most innovators know the quote. Almost none of them actually use it. By the end of this episode, you'll know why that gap exists, what it costs, and the exact steps to close it. If you want to try this on a real decision right away, I've built a free tool for it. Link below. I'll come back to it later in the episode. What Is Inversion Thinking? Inversion thinking is the practice of reasoning backward from failure. Instead of starting with "what does success look like and how do I get there," you start with "what would guarantee this fails" and design those conditions out of the plan. You'll also hear it called thinking backwards, and when it's aimed at a project before launch, a pre-mortem. Munger's rule was three words: invert, always invert. Or, in his blunter version, "All I want to know is where I'm going to die, so I'll never go there." People hear this and think pessimism. It isn't. A pessimist names the failure and stops there. Inversion names the failure and uses it to redirect the plan, while the fix is still cheap. HP Invented the Category. Then Gave It Away. In 2005, HP built Halo. It was the best telepresence system in the world. You walked into a Halo room and the people on the other end looked like they were sitting across the table from you. Life-sized. Perfect audio. Nobody had built anything close. The team that made it was brilliant, and they believed one thing without question: quality wins. They built rooms that cost $500,000 each. They required customers to run those rooms on HP's proprietary network at a monthly cost that would make your eyes water. Every decision traced back to the same conviction. Make the experience extraordinary, and the market will come to you. Nobody in that room asked the one question that mattered. What if quality isn't what the market is buying? Because it wasn't. The market was buying access. Cisco, and then Zoom, came at the same opportunity from the opposite end. Good-enough quality, on any device, on any network, available to everyone. They understood what the Halo team never tested. In communications, reach beats quality. Every new user makes the service more valuable to everyone already on it, so the product that spreads to the most people wins, even when it looks worse. That network effect beat Halo so completely that Zoom became a verb. HP defined the category and then gave it away. In 2011, under quarterly pressure, HP sold Halo to Polycom for $89 million. In 2022, HP bought the business back, folded into Poly, for $3.3 billion. Thirty-seven times the price, to reacquire a category it had invented. The failure was visible the entire time. It lived inside one assumption nobody questioned: that quality was what the customer cared about most. An inversion exercise would have dragged it into the open. Ask "how do we guarantee Halo fails," and one honest answer was already the plan. Bet everything on quality. Price it for the few. Lock it to our own network. Leave the rest of the market wide open for a cheaper rival. No crystal ball required. Read the plan from the other side and the failure was sitting right there in it. The Three Moves Inversion runs in three moves. The first two are mechanical. The third is where the discipline lives, and where most people quit. Move One: Invert the Question Take the goal and flip it. Write your goal as one sentence. The way you'd say it to the board. "We will win the telepresence market with the best experience available." Turn it into a failure question. Same goal, opposite direction. "How would we guarantee we lose the telepresence market?" List every path to that failure. Don't rank them. Don't defend anything. Write down every way it could happen, including the ones that feel unlikely or embarrassing to say out loud. Price. Distribution. A competitor's move. A wrong read on the customer. Sort each one: recoverable, or not. A slow first year is recoverable. Letting a competitor own the network effect while you keep only the high end is not. The ones you can't undo are what matter here. Set the rest aside. Move Two: Find the Load-Bearing Assumption Behind every failure you can't recover from sits a single assumption holding the whole plan up. Find it. Take your most serious irreversible failure mode. The one from Move One that would actually end the project. Ask what would have to be true for that failure to never happen. For Halo: "Enough customers will pay a large premium for superior quality, and they'll do it fast enough to matter." That sentence is the load-bearing assumption. Ask whether you tested that assumption or inherited it. Did you ...
    続きを読む 一部表示
    16 分
  • How to Overcome Expert Bias

    How to Overcome Expert Bias
    2026/05/13
    Last June, I was on a business trip in Silicon Valley when a second cardiac device failed. Same problem with a second surgical team six months apart. The full story is on philmckinney.com. What changed everything was one doctor who stopped treating what everyone else had diagnosed and asked whether they even had the right problem. That one question uncovered what two surgical teams had missed. That's the expert trap. And it shows up in your business, your career, and your decisions far more than you'd expect. Before you act on the next expert recommendation you receive, there are three checks almost nobody makes. Stay with me, because one of them is going to feel uncomfortable. That's the one that matters most. THE TRAP A friend of mine ran a mid-sized manufacturing company, and a few years ago, he hired a well-regarded industry analyst to help him think through where his business was headed. The analyst had data, slide decks, and a client list that made you feel like you were in good company just being in the room. He pointed to three companies in adjacent categories that had shifted to direct-to-consumer sales and won. He was confident, he was credible, and he was paid well to be both. My friend followed the advice. He put together a team, built the infrastructure, and ran the channel for twenty-two months. He lost around four million dollars, and his best wholesale distributors felt abandoned. Some of them never came back. The analyst wasn't wrong. Direct-to-consumer had worked for those other companies. The data was real, and the success stories were real. But nobody in that room ever asked whether any of those success stories involved his specific customer, his specific product, or his specific buying cycle. The companies the analyst cited were consumer brands. My friend's company was in the industrial supplies industry. Completely different purchase decision. He'd actually noticed this early on, and something felt off, but he never said it out loud because the expert had already spoken. That's the feeling I'm talking about. You notice something doesn't quite fit, but you don't raise it, because who are you to question the expert? That's the expert trap, and it's one of the most reliable ways your thinking gets replaced without you realizing you handed it over. WHAT'S ACTUALLY HAPPENING When you perceive someone as having more relevant knowledge than you do, your brain measurably reduces the cognitive effort it puts into evaluating what they're saying. This has been studied, and it's not a weakness or a character flaw. It's a shortcut your brain developed because trusting domain expertise is usually the right call. The cardiologist probably does know more about your heart than you do, and the structural engineer probably does know more about load-bearing walls. The shortcut works often enough that it sticks. The problem is what it skips. It doesn't feel like you're surrendering your judgment. It feels like being informed. And so you follow advice that was right, just not for your situation, your timing, or your constraints. The advice was calibrated for circumstances that don't match yours, and the moment the credential appeared, the evaluation stopped. The wrong takeaway from everything I just said is to become reflexively skeptical, to walk into every expert conversation looking for the angle, ready to push back. That's just a different way to stop thinking. The goal isn't distrust. The goal is to stay in the evaluation while the expert is talking, instead of handing it over. Three checks help you do exactly that, and any serious expert should be able to answer them without hesitation. CHECK ONE: CONTEXT The first check is one question: where, specifically, has this worked before? Most people ask whether something works and most experts answer that question confidently. But that's the wrong question. What actually matters is where it worked, what kind of organization, what stage of growth, what kind of customer, what competitive environment, what specific circumstances. Expertise is built on pattern recognition developed inside a specific set of situations. The pattern is real, but whether your situation matches it closely enough to actually apply it is a completely different question, and it's the one nobody asks. Even in medicine, good surgeons will tell you that outcomes from major clinical trials don't always replicate cleanly when the patient profile differs from the trial population. The research is real and the expertise is real, but the fit question is what determines whether any of that expertise is actually useful to you right now. Most advisors don't volunteer this, not because they're hiding anything, but simply because nobody asks. So ask. Just simply and directly: where have you seen this work, and where does that situation differ from ours? A good expert has thought about this already. The answer comes quickly and it's specific. If they get vague or keep circling back to the ...
    続きを読む 一部表示
    15 分