Episodes

  • How to Improve Weak Signal Judgment

    How to Improve Weak Signal Judgment
    Jun 24 2026
    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 ...
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    13 mins
  • How to Improve Your Second-Order Thinking Skills

    How to Improve Your Second-Order Thinking Skills
    Jun 10 2026
    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...
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    15 mins
  • How to Improve Your Inversion Thinking Skills

    How to Improve Your Inversion Thinking Skills
    Jun 3 2026
    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 ...
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    16 mins
  • How to Overcome Expert Bias

    How to Overcome Expert Bias
    May 13 2026
    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 ...
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    15 mins
  • How to Improve Your First Principles Thinking

    How to Improve Your First Principles Thinking
    May 13 2026
    Most product decisions get made by analogy. Someone says, "This is how we've always done it," or "This is what the market expects," or "This is what the competition is doing." The room nods. The decision gets made. And buried somewhere in the middle of all of it is an assumption nobody checked. First-principles thinking is the discipline of identifying assumptions before the market finds them for you. By the end of this episode, you'll have the tools to strip any problem down to what's actually true and build answers that hold, even when the boardroom is watching, and the clock is running. What Is First Principles Thinking? First principles thinking is the practice of breaking a problem down to its fundamental truths, then building your solution up from what actually holds. Not from industry convention. Not from what worked last time. From what's actually true about the problem in front of you. The alternative is reasoning by analogy: doing what worked before, doing what competitors do, doing what the category expects. Analogy is faster and usually right. It fails badly when the thing that used to be true stops being true and nobody notices. Why Assumptions Go Unchecked In 2005, HP's CEO, Mark Hurd, stopped me in the hallway at Building 20 in Palo Alto and drilled me on HP's R&D funding. The metric he focused on was R&D as a percentage of revenue. He wanted HP's ratio to look more like Acer's. I pushed back. I argued we should be comparing ourselves to Apple, not Acer. Mark didn't hesitate. "We are not Apple, and we never will be." What stopped me in that moment wasn't the disagreement. It was the certainty. Nobody in the room questioned whether R&D as a percentage of revenue actually measured what we thought it measured. That metric had been in use for decades. Every competitor used it. Every analyst tracked it. It felt like bedrock. It wasn't. It was an inherited constraint that had calcified into a rule. R&D as a percentage of revenue tells you about accounting categories. It tells you nothing about what that spending produces, whether the right problems are being attacked, or whether innovation output is growing or shrinking. The assumption underneath the metric had never been tested. Nobody had ever asked whether comparing R&D ratios across companies with entirely different business models actually tells you anything meaningful. The cost of that unchecked assumption didn't show up in the next quarter. It showed up over the following decade. HP's innovation pipeline quietly drained, and the Fast Company "Most Innovative" recognition we'd earned three years running disappeared with it. One inherited metric, accepted as fact by an entire room of experienced people, making a generational decision. That's what derivative thinking actually costs. Not a bad quarter. A decade. The people in that room weren't careless. They were experienced. Experience is exactly what makes inherited assumptions feel like facts. The metric felt like a fact. It was a choice nobody remembered making. That's exactly what a first principles question would have caught. Nobody asked it. The Three Core Skills The three skills run in sequence, and each one depends on the one before it. The first, Strip the Assumptions, finds the inherited assumptions baked into how the problem was framed. From there, Test What Remains and Build Up takes what survived and builds your solution from what's actually true. Finally, When to Use First Principles tells you when the process is worth running in the first place. Skip ahead, and the later skills don't hold. Run them in order, and they compound. Strip the Assumptions Before you can reason from first principles, you have to know what you're actually working with. Most problems arrive already carrying assumptions in how they're framed. Your first job is to find them. Steps to strip assumptions: Write the problem exactly as it was given to you. Don't improve the framing yet. Use their words. Underline every word that implies a constraint. "Must," "can't," "always," "never," "the only way to." Each one is a candidate. Ask, for each constraint: is this physically true, or is it inherited? A physical truth holds regardless of what you decide. An inherited constraint is someone's prior decision that calcified into a rule. Set the inherited constraints aside and restate what remains. This is the real problem. It's usually smaller and easier to solve than what you started with. Treat what survives as your design constraints. These are your real boundaries. Take this list into your brainstorming, and test every idea against what's on it, not against the assumptions you crossed out. This step takes 20 minutes when you do it honestly. Most teams skip it entirely, then spend months optimizing a solution to the wrong problem. Test What Remains and Build Up Not every constraint is an assumption. Some things are actually true: physics, unit economics, human behavior at scale. The goal isn't to pretend...
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    17 mins
  • How to Overcome Confirmation Bias

    How to Overcome Confirmation Bias
    May 6 2026
    Confirmation bias is shaping your decisions right now. Not occasionally. Every day. And the unsettling part is that the smarter you are, the harder it is to see it happening. By the end of this episode you'll know exactly what confirmation bias is. How to recognize when it has taken over a room. And three specific practices that actually work. Not borrowed frameworks, but what forty years of high-stakes decisions has taught me. Let's get into it. What Is Confirmation Bias? Confirmation bias is your brain's tendency to seek out, favor, and remember information that confirms what you already believe, filtering out everything that contradicts it. Most people think that just means seeking out information that agrees with them. That's part of it. But here's what makes it truly dangerous. Once you form a strong belief, three things happen automatically. Unequal Evaluation. Picture two studies landing on your desk. One says your strategy is working. One says it isn't. You read the first and nod. You read the second and start looking for the flaw: the methodology, the sample size, the funding source. Selective Memory. Your brain doesn't store evidence equally. What supports your belief stays accessible. What contradicts it becomes harder to recall the longer you hold the belief. The Backfire Effect. When someone directly challenges a belief you hold, your brain treats it as a threat. The response isn't reconsideration. It's defense. Studies show you actually leave the argument more convinced than when you entered it. Together, the longer you hold a belief and the more it matters to you, the harder it becomes to change, no matter how much evidence says you should. Confirmation Bias in Today's World Confirmation bias has always been part of human thinking. What's changed is the environment around it. Algorithms feed you content that matches what you already believe. Social media shows you opinions from people who think like you. Search engines rank results based on what you've clicked before. Every system you interact with daily is built to confirm your existing views. Not by accident, but because confirmation keeps you engaged. The result compounds. The more confirming information you consume, the stronger your existing beliefs become. The stronger your beliefs become, the more your brain filters out opposing information. The more that information gets filtered, the harder it becomes to update your thinking, even when updating is exactly what the situation demands. This is mindjacking in action. The systematic replacement of your thinking by systems built to do it for you. And confirmation bias is one of its most powerful tools. It's visible everywhere. In public discourse where people can no longer agree on basic facts. In organizations that keep funding failing strategies long after the evidence says stop. In leaders who build teams designed to tell them what they want to hear. You might assume that smarter, more experienced people are less susceptible to this. The research says otherwise. The Smartest Person in the Room Gets It Wrong Here's what surprises most people. Confirmation bias doesn't get weaker as you get smarter. It gets stronger. Dan Kahan at Yale ran a study. He gave people a math problem where the correct answer contradicted their political beliefs. The smarter the person, the more likely they were to get the answer wrong, in the direction that protected their belief. More intelligence, applied more effectively, in service of the conclusion they'd already reached. A smart person who has formed a wrong belief is better at defending it. They find flaws in the opposing data faster. They construct more sophisticated arguments. They're more convincing to others and to themselves. I watched this play out in a board meeting. A CEO had championed a major strategy. Three separate analyses came back contradicting it. Each time, he found a different flaw in the methodology. By the end of the meeting he'd convinced the room the data was unreliable. The strategy continued. The outcome was exactly what the data predicted. He wasn't dishonest. He was skilled. His intelligence was working against him. And everyone in that room let it happen. If you're intelligent, experienced, and confident in your judgment, you are not immune to confirmation bias. You are more vulnerable to it. If you know someone who is always the smartest person in the room, send them this episode. They need it more than most. How to Overcome Confirmation Bias: What Actually Works Knowing about confirmation bias doesn't stop it. I know this from experience, not from research. I've been in rooms where everyone understood exactly what was happening and it happened anyway. What works is different from what you've probably been taught. Catch It in Yourself: The Flip Debate The moment I've most reliably caught confirmation bias operating in myself hasn't come from a checklist or a framework. It's come from a specific kind of conversation. I keep...
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    15 mins
  • Why Most Organizations Aren't Funding Innovation

    Why Most Organizations Aren't Funding Innovation
    Apr 29 2026
    Twelve official definitions for R&D. Zero agreement. The US government publishes at least a dozen distinct official definitions across agencies, accounting standards, tax authorities, and international bodies. Not one agrees with the others on where research ends and development begins. Trillions of dollars flow through R&D budgets every year. Boards approve them. Investors evaluate them. Governments subsidize them. Analysts benchmark them. And the term at the center of all of it has no settled definition. A company can gut its research investment without triggering a single alarm on its income statement. Researchers who gained rare access to confidential federal R&D data found exactly this: when companies face financial pressure, they cut research while leaving development essentially untouched, and the combined number barely moves. Every benchmark, every board conversation, every investment thesis built around the R&D line may be built on sand. Innovation, ideas made real, requires both. Research is how you find the idea. Development is how you make it real. Strip out the research and you're not innovating, you're iterating on what already exists. Strip out the development and you're just experimenting. The problem is that nobody in the room knows which one they're actually funding, because the definition that would tell them doesn't exist. Someone needs to draw the line. This episode is about why nobody has, and the definition I think should replace the chaos. By the end, I'm going to put that definition in front of you and ask you to push back on it. Not to agree. To tell me where it breaks. How We Got Here Four institutions took a run at defining R&D. Each one got it right for their own purposes. None of them got it right for yours. Frascati: Built for Governments In June 1963, OECD economists met at a villa in Frascati, Italy, south of Rome, and produced what became the international standard for measuring R&D across nations. Now in its seventh edition. The Frascati Manual divides R&D into three tiers: basic research (theoretical work with no application in view), applied research (original investigation toward a specific practical objective), and experimental development (using existing knowledge to produce new products or processes). To qualify, an activity must be novel, creative, uncertain in outcome, systematic, and transferable. Used by governments across roughly 75 countries. Solid for what it was designed to do: let nations compare R&D investment on consistent terms. What Frascati cannot tell you: whether a specific company's spending is creating competitive advantage. It counts the type of activity. It doesn't assess what the activity produces for the organization doing the spending. A company can satisfy every Frascati criterion investigating something every competitor already knows. The knowledge is new to them. That is enough. The accountants drew a different line, for a different reason, with a different consequence. FASB: Built for Accountants In October 1974, the Financial Accounting Standards Board issued Statement No. 2, Accounting for Research and Development Costs, now codified as Topic 730. Every public company filing under US GAAP operates under it. The rule: all R&D costs expensed as incurred. Research, development, basic, applied: one line on the income statement. Their definition: research is a planned search aimed at discovery of new knowledge. Development is the translation of research findings into a plan or design for a new product. The rationale is explicit in the original standard. Future benefits from R&D are, in FASB's language, "at best uncertain." Expense everything immediately. The standard solved the problem it was asked to solve, which was accounting treatment: when to recognize the cost, not whether the cost was strategically sound. The consequence: sustaining engineering, feature maintenance, and incremental product updates all land on the same line as genuine exploratory research. Nobody looking at the income statement from outside can see the difference. The number is technically accurate and analytically opaque. Abraham Briloff, the late accounting professor at Baruch College, put it plainly: "Accounting statements are like bikinis. What they show is interesting, but what they conceal is significant." He was talking about financial reporting broadly. He could have been writing specifically about the R&D line. Researchers at Duke and London Business School spent years tracking corporate scientific output and found that it declined steadily across industries even as headline R&D spending kept rising. The combined number was hiding a substitution. Nobody on the outside could see it. Outside the United States, a different standard governs, and it creates a comparison problem most analysts never account for. IFRS: Built for International Investors IAS 38 governs R&D under IFRS, and its treatment differs from FASB in one significant way. Research costs are ...
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    21 mins
  • R&D Spending Is the Most Misleading Number in Business

    R&D Spending Is the Most Misleading Number in Business
    Apr 15 2026
    Every public company's R&D number is a lie hiding in plain sight. Not because anyone falsified it. Because the number was never built to tell the truth. It was built to satisfy an accounting standard written in 1974. And for fifty years, boards, analysts, and CEOs have been making billion-dollar innovation decisions based on a number designed by accountants to solve a different problem entirely. Here's what makes this genuinely strange. The real number exists. The government has been collecting it from every major US company for decades. It would answer the question every innovation leader and investor actually needs answered. And it is locked away by federal law. Confidential. Never published. Never seen by the people who need it most. It's sitting in a federal database right now. And there's a way to estimate it for any public company, without asking anyone's permission. I know it exists because I spent years building it from the inside. Why the R&D Signal Was Blurry When I was running innovation at HP, we discovered this problem firsthand. We had a connection between R&D investment and gross margin that held up across decades of HP history. Better than anything Wall Street was using. But the signal was blurry. None of us could figure out why. The answer came from a question someone on the team asked almost as an aside. What if R&D isn't one thing? Research and Development Are Not the Same Thing Think about what actually lives inside a typical R&D budget. There's a team somewhere investigating whether a new approach could enable a capability that doesn't exist yet. No product defined. No spec written. Asking whether something is even possible. And there's a team building the next version of a product that ships in eighteen months. Spec locked. Timeline set. Engineering executing against a defined target. Both show up on the same line in the budget. Both get called R&D. Both count equally toward the number that gets reviewed every quarter. They are not the same thing. One is Research. The other is Development. Research is the work you do when you don't yet know what you're building. The output is understanding. New knowledge that might enable future products nobody has designed yet. You can't know exactly what you'll find. If you already knew, it wouldn't be research. Development is the work you do when you know exactly what you're building. The spec exists. The product is defined. The question isn't what to make. It's whether it can be made, on time, at cost, at quality. One creates the future. The other delivers the present. And for fifty years, every public company in America has been required to report them as one indistinguishable number. When we split the HP data along that line, Research on one side and Development on the other, the signal sharpened immediately. Research spend, measured against gross margin three to five years later, was a meaningfully stronger predictor than the combined number had ever been. The blur hadn't been in the gross margin data. It had been in the R&D number itself. Two fundamentally different things, averaged together, producing a number that looked precise and predicted almost nothing. But splitting R from D at the company level was only the beginning. The model was still lying to us. Just more quietly. Why Company-Level R&D Splits Still Mislead Even with the split, something was still soft. HP wasn't one business. It was dozens. Printers, PCs, servers, software, each running on different timelines, different technology cycles, different competitive dynamics. What if the R/D split meant something different depending on where it was applied? We pushed it to the product line level. Then further, to the platform level within product lines. Printers were the clearest example. HP's printer business wasn't one story. There were platforms built on established technology. Mature ink systems, proven print head chemistry, products that had been shipping for years. And there were platforms built on genuinely new core technology. New chemistry. New mechanisms. New approaches to fundamental problems that nobody had solved yet. Research investment by platform told a completely different story than Research investment by product category. The Research going into new technology platforms had a completely different relationship to future margin than Research going into mature platforms. Different time horizons. Different risk profiles. Different margin implications years down the road. Laptops told the same story. A traditional consumer laptop line and a high-performance portable workstation weren't the same investment. One was Development-heavy. Defined product, known market, engineering executing against spec. The other had genuine Research behind it. Unsolved thermal problems, new form factor constraints, and materials questions that hadn't been answered yet. When a single R&D assumption is applied across all of that, treating every dollar the same regardless of what it actually...
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