The Innovators Studio with Phil McKinney

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 can they do today that they couldn't do then? Their capability, their pressure, their read of you, their read of the market. Identify the shift.

Use the change to predict their next move. What's the natural follow-on from the thing that made this move possible? That's usually where the real consequence lives.

 
Cisco didn't enter telepresence in 2006 because telepresence was suddenly interesting. They entered because they'd decided the partnership with HP no longer constrained them. "Why now" would have surfaced that. "What's next" wouldn't have caught it in time.
Watch the Response, Not the Result
Your decision produces a result. The result triggers a response from everyone watching, your competitors, your customers, your team, your investors. Most analysis stops at the result. The response is where the actual consequence lives.
 
Toys R Us could have predicted that Amazon would sell more toys. That was the result. What they didn't predict was Amazon's response: opening the platform to third-party sellers, learning the toy business, and using the data to compete directly. By the time Toys R Us understood the response, Amazon had already replaced them.
State the immediate result of your decision in one sentence. What will be visibly different in the world after you act?

List who can see that result. Be specific. Name people if you can, not categories.

For each one, ask: what does the result tell them about you? Your priorities, your weaknesses, your appetite. The result is information about you they didn't have before.

Ask what they're now in a position to do that they weren't before. The result changes what's available to the other actors, not just the market.

Identify the responses you can't undo. A customer who loses trust. A competitor that smells weakness. A regulator who opens a file. Those are the ones to model carefully.

HP launching Halo was the result. Cisco entering TelePresence was the response. By the time anyone at HP said the word "over," the partnership had been over for three years.
Practice Exercise: Run All Three on One Decision
Pick one decision you're currently working through. Run the three skills against it in sequence.
Weak signal. What have you noticed in the last 90 days connected to this decision that doesn't quite fit your current story? Don't explain it away. Name it.

Why now. What changed in the world recently that's making this decision feel urgent now? Was that change visible six months ago?

Watch the response. Who will see the result of this decision, and what does it tell them about you that they didn't know before?

The first time you run this, you'll miss things. That's normal. The skills sharpen with repetition. The fifth time you sit down with a real decision and work through all three, you'll catch signals that other people in the room aren't even seeing yet. That's what improvement looks like.
If any of the three turns up something the room hasn't discussed, you've found the work that needs to happen before the decision is made. Take what you found and run it through the two skills from the November 2025 episode. Map how people will respond. Ask "and then what?" two or three more times. All five skills work as one system. The link to the November episode is in the description below.
Most second-order failures do not arrive as surprises. They arrive as something somebody noticed once, didn't have a way to act on, and explained away.

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 confirm it with evidence, or did it ride along with the idea because it felt obviously true? The Halo team inherited theirs. Quality felt like an objective good, so nobody checked whether the market agreed.

If you can't point to the evidence, you've found your real risk. A plan resting on an untested load-bearing assumption is a bet wearing the costume of a strategy, however solid the rest of it looks.

Move Three: Decide What to Do With It
Once the assumption is exposed, you have three honest choices.
Kill it. If the assumption is false and the failure is irreversible, stop now, while stopping is still cheap.

Change the plan so the failure mode disappears. The Halo team had room to do this. A software tier on any network, at lower quality, to build the user base and the network effect, with the premium rooms kept for the customers who'd pay for them. They'd have owned both ends. The plan allowed it. The conviction didn't.

Proceed, with the bet named out loud. Sometimes you take the risk on purpose, eyes open, because the upside justifies it. That's legitimate. Taking the same risk by accident, because nobody said the word "assumption" in the room, is not.

The one move you cannot make is to see the failure mode and proceed as though you hadn't. That isn't confidence. It's the most expensive form of hope there is.
Why You Can't Do This Alone
You know the three moves now. The hard part is running them on your own work.
You can't fully see your own assumptions. You built the plan. You believe in it. The assumption holding it up feels so obvious that questioning it never occurs to you. The Halo team wasn't careless. They were the best in the world at what they did, and that was the problem. The more expert you are, the more your assumptions feel like facts, and the less it occurs to you to test them.
Then there's the room. Even when someone can see the failure coming, the dynamics of a team work against saying it out loud. You earn standing by backing the plan, not by listing the ways it dies. Raise the failure scenario and you look like you lack conviction, or like you're not on board. So the failure half the room quietly senses stays unspoken until it's expensive. Culture rewards the loudest voice on the upside, not the person who turns out to be right about the risk.
Two walls. You can't see your own assumptions, and the people who might see them are discouraged from speaking.
AI has none of those problems. No ego in the plan, no career to protect, no boss to impress, no reason to soften the bad news to keep the room comfortable. Point it at your work, tell it to find the failure, and it will, without flinching and without politics. It won't make the call for you. It surfaces the failure modes you're too close to see, and then you do the judging.
That's how you practice this skill on your own. You sit down with a real decision and a partner that has no reason to spare your feelings. So I built the AI Prompts for Inversion Thinking for exactly that. One prompt makes the AI write the post-mortem of your project before you've even started. Another has it play a competitor designing your defeat. Then one walks you to the single assumption your whole plan is betting on.  You bring the decision and the judgment. The prompts make sure nothing gets skipped just because it's uncomfortable to look at.
Here's your work this week. Take one real decision you're sitting on, something with actual stakes, and run it through the pack. It's free at innovation.tools, or use the link in the description.
The Long Game
The people who use inversion well aren't more negative than their peers. They're more honest about which risks they can walk back and which ones they can't. That single distinction, made early and acted on, is the difference between a project that fails fast and cheap and one that fails slowly, expensively, in year ten.
The failure that ends your project is usually the one plenty of people saw coming and nobody was willing to name.
Say it now, while it still costs you nothing.

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 those constraints don't exist. It's to be precise about which reality you're dealing with.
Steps to test what remains and build up:
Take each surviving constraint and push on it. Ask: Is this true because it's physically impossible to change, or because changing it would be expensive, unfamiliar, or uncomfortable? Expensive and unfamiliar are not the same as impossible.

Separate the hard limits from the soft ones. Hard limits are what's actually true: things that hold regardless of how the problem is reframed. Soft limits are negotiable. Label them clearly. Most teams never make this distinction and treat every constraint as if it were granite.

State your hard limits in plain language.  Write it down. One sentence per hard limit. These are the actual boundaries your solution has to honor.

Reason forward from what remains. Don't start from where the industry is and work backward to justify it. Now ask: what solution do the hard limits support? 

That last step is where unexpected solutions come from. When you reason backward from convention, you arrive at a modified version of the existing answer. The shape is familiar because you started with it. When you reason forward from hard limits, you land somewhere the category didn't expect, because you weren't anchored to the shape of the existing answer. Solutions built this way often feel strange at first. People will question them. That discomfort is usually a signal you've found something real rather than something inherited. That's what reasoning from what's actually true produces, rather than reasoning from what everyone assumed.
When to Use First Principles
Before running the process, ask these four questions. One yes is enough.
Has the environment this decision was built for changed significantly?

Does every solution on the table feel like a variation of the same thing?

Is the current approach inherited rather than chosen?

Would a bad assumption here cost you more than an afternoon to find and fix?

If all four are no, past experience is the right tool. Use it. 
The 20-minute assumption-strip is cheap. The cost of skipping it isn't.
The Assumption Reversal Exercise
For this exercise, you will need a partner. Have them watch this video first. They need to know what an inherited assumption looks like before they can spot yours. Once you're both ready, grab the free First Principles Thinking Checklist at innovation.tools or find the link in the description. It gives you both a shared reference point before you start. 
Here is how it works:
Each person brings one real problem. Something current, with actual stakes. Not a thought experiment. The problem should be one you've been turning over in your mind without arriving at a satisfying answer.

Work on your partner's problem, not your own. You are trying to find the assumptions baked into how they've framed it. They are doing the same for yours. The reason this works is that you can see their inherited constraints more clearly than they can. You're not inside their problem the way they are.

Each person lists every assumption they can find in the other's problem. Write them down. Don't argue yet. Don't evaluate. Just surface as many as possible. Quantity matters here. The obvious assumptions are easy. Push past them.

Take each assumption and reverse it. If the assumption is "this requires a significant budget," the reversal is "what becomes possible if it requires no budget?" If the assumption is "the customer won't accept a different format," the reversal is "what would we build if they would?" Don't ask whether the reversal is realistic. Ask what it opens up.

Discuss what the reversals revealed. Not every reversed assumption leads somewhere useful. But one of them usually exposes a constraint that was never as fixed as it felt. That's the one worth following.

The point of the reversal is simple. Some assumptions hold when you push on them, and some don't. You can't tell which is which until you try.
The Long Game
Every time you run this process and find something that didn't hold, you get faster at spotting them. The judgment about when to use it gets sharper. That's what improvement looks like in practice: not a dramatic flash of insight, but a practiced ability to find the assumption in the room before it finds you.
The assumption that costs you most isn't the one you haven't thought of yet. It's the one you stopped questioning years ago. 
Find your partner. Run the Assumption Reversal this week. That's where this starts becoming a skill.
Subscribe for the next episode. It builds on this.

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 general principle instead of the specific situation, slow down, because that vagueness is telling you something.
CHECK TWO: INCENTIVE
The second check is the one that's going to feel uncomfortable, but ask it anyway: what does the expert gain from this recommendation?
Every expert operates inside incentive structures, and that's just how it works. A surgeon recommends surgery more often than a physical therapist does, not because surgeons are corrupt, but because surgery is the tool surgeons have. A financial advisor who earns commission on certain products is structurally more likely to recommend those products. A consultant whose business model depends on long engagements has different incentives than one whose model is based on outcomes. None of this makes the recommendation wrong. It just makes it something you need to understand before you weight it.
The way to surface this without it feeling like an accusation is to ask about the logic rather than the incentive. Ask them to walk you through why this approach rather than the alternatives they considered. Think about it this way. If a mechanic quotes you a repair and you ask why that repair instead of the simpler one, you expect a real answer. You get that answer from a mechanic you trust. You should expect exactly the same from every expert in your life, regardless of how much more impressive their office is.
Before we get to the third check, think about the last significant decision you made based on expert input. Could you answer the context question? Could you answer the incentive question? Most people can't. The checks never happened. The third check is the one I almost never see anyone use, and in my experience it's the most revealing of the three.
CHECK THREE: FAILURE RATE
The third check is this: when doesn't this work?
Think about what every expert presentation looks like. Track record, success cases, confidence — the whole architecture is built around what worked. What failed almost never comes up unprompted. But any expert who has used a recommendation enough to believe in it has also seen it fail. They know where it falls apart and what the warning signs look like. That knowledge is exactly what you need, and it's almost never volunteered.
So ask for it directly: when have you seen this approach not work, and what tends to produce a different outcome?
The doctor I mentioned at the top, Dr. West, that's exactly the question he asked. Not how to treat the condition better, but whether they even had the right diagnosis. Every other expert had followed the standard protocol. He asked when the standard fails. He found one paper describing one edge case that had been sitting in the literature for six years. That question uncovered what two surgical teams had missed.
That's what the failure rate check does. It doesn't surface doubt, it surfaces evidence. And an expert who can only tell you what worked hasn't really thought carefully about when it doesn't. That's someone selling a recommendation, not helping you make a decision.
THE SYNTHESIS
Three checks — context, incentive, and failure rate. What they do together is simple. They require the expert to give you something you can actually examine rather than something you're simply being asked to accept. That's the difference between making a decision and receiving one.
CLOSE
You already know which of the three checks you'd struggle to make. That's the one worth starting with.
The friend I mentioned at the top, the one who spent twenty-two months and four million dollars on a channel that was never right for his business, I talked to him afterward. He knew something felt off from the beginning. He noticed the mismatch. But the confidence in the room, the slides, the client list, all of it washed that feeling away.
He said: I knew enough to ask the question. I just didn't know I was allowed to.
You're allowed to.
Drop a comment and tell me which of the three checks is hardest for you to make. I want to know if it splits the way I think it does.
See you next week.

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 a small group of trusted advisors, people I call my kitchen cabinet. These aren't peers. They're almost never inside the organization. They have no stake in the outcome and no incentive to tell me what I want to hear. When I'm about to make a significant decision and I feel the pull of certainty, I take it to one of them.
The conversation has a specific structure. I argue my position, fully and genuinely, the strongest version I can make. Then I stop. And I argue the opposite. Not a token acknowledgment of the other side. A real debate. I take the side I'm most resistant to and make the best case I can for it.
What happens in that second argument is where confirmation bias shows up. The gaps. The assumptions I'd been protecting. The evidence I'd felt the urge to dismiss. When you're forced to argue a case you don't believe, you find the things you didn't want to see when you were arguing the one you do.
An outside advisor is essential. Someone who will push back, ask hard questions, and notice when the flip argument is being faked. You can't do this with someone who needs something from you. The absence of stakes is what makes the honesty possible.
Catch It in a Room: Two Signals to Watch For
I've learned to watch for two signals that tell me confirmation bias has taken over a room. Both are visible before the decision is made. Almost everyone misses them.
The first signal is the unwillingness to debate the other side.
When a room has really decided, before the discussion is officially over, nobody wants to argue the opposing position. Not even hypothetically. Raise the other side and watch what happens. Eyes go flat. The conversation moves on. Someone changes the subject. If a room can't genuinely engage with the strongest case against the preferred direction, confirmation bias is driving.
The second signal is circular justification.
Listen for reasoning that keeps returning to its own starting point. The evidence for the decision is the decision itself. When you can't find an external reason, just a restatement of the conclusion, confirmation bias is driving.
When I hear circular justification in a room, I stop the conversation. Not to embarrass anyone. To name what's happening. "We're not evaluating anymore. We're confirming. Let's go back to the evidence."
That single intervention has changed the outcome of more decisions than any framework I've ever been taught.
Change How You Decide: Full Options, Real Challenge
Here's the most consistent change I've made in my own decision-making, and it comes directly from watching what confirmation bias costs people: I force a full pros and cons analysis on every serious option. Not just the one I'm leaning toward.
This sounds obvious. Almost nobody does it.
The natural pull is to build the case for the option that already feels right and compare it against the weaknesses of the alternatives. That's confirmation bias disguised as analysis. What I do instead is give every option on the table the same treatment. The best case for it. The best case against it. Without knowing in advance which one I'm going to choose.
For decisions that carry real weight, I take it further. I bring in my brain trust: direct reports who will tell me what I don't want to hear, kitchen cabinet advisors, trusted board members. I ask specifically for the challenges. Not validation. Not enthusiasm. The places where the thinking is weak, the assumptions that might not hold, the evidence I might have filtered out.
One question has changed how I approach every major decision: what am I not seeing?
The answers, from people who have no incentive to protect my view, are exactly where the confirmation bias lives.
Confirmation Bias Exercise: Try This Today
This week, before you finalize any decision you've already started leaning toward, do one thing.
Find one person outside your organization, someone with no stake in the outcome, and run the flip debate. Argue your position fully. Then stop and argue the opposite, with the same effort and commitment.
Don't summarize the other side. Argue it. Make the best case you can for the view you're most resistant to.
Notice what comes up in that second argument. The gaps. The assumptions. The evidence you'd been setting aside.
That's where your confirmation bias is living.
Run that exercise this week. Not once. Every time you feel the pull of certainty on a decision that matters.
The Benefits of Overcoming Confirmation Bias
The payoff from these practices compounds over time.
Examined beliefs are more reliable than accumulated ones. Decisions that accounted for opposing evidence hold up better than decisions that filtered it out. Judgment that evaluates rather than confirms earns a different kind of trust from the people around you.
Beyond your own decisions, catching confirmation bias makes you harder to capture. Every algorithm, every platform, and every persuader around you is built to exploit it. Seeing it operate in yourself reduces their leverage over your thinking.
That's what these practices build. Not certainty. Something better.
Examined confidence.