The following comes from the tweet storm, or threaded essay, pinned to my X account. It was inspired by Naval Ravikant’s “How to Get Rich (without getting lucky)” thread and built around one of my favorite topics and biggest goals: thinking clearly. I consider being called a clear thinker a much better compliment than being called “smart” or “intelligent.” I want to be known as a clear thinker. I hope these 50 maxims and the explanations behind them help you become a clear thinker too. Enjoy.
Clear thinking starts with enough attention to understand a problem and a willingness to test your explanation against reality. AI can suggest options and objections, but you still have to check the important claims and decide what evidence supports them. Before committing to an idea, write down what you expect and try a small test that could change your mind. Use what happens to improve your next decision.
*You can read the essay straight through or return to the maxim that fits a problem you’re facing.
• 01. AI lowers the cost of producing an answer. It does not lower the cost of believing the wrong one.
A polished answer can make you feel as though the difficult thinking has already happened. The sentences connect, the numbers look plausible, and the conclusion sounds reasonable. But none of that tells you whether the assumptions underneath it are true. AI makes it much easier to produce something worth considering. You still have to establish whether it’s worth believing.
Suppose a shop owner asks whether they should open a second location. AI produces a convincing plan based on steady demand, affordable staffing, and customers behaving the same way in a new neighborhood. If any of those assumptions fail, the owner’s actual money is still gone. The cheap part was getting the plan. The expensive part was committing to it.
So use the answer to identify what needs checking. Compare the sales assumptions with your own records, get real quotes, and test demand before signing anything. A quick answer is useful when it accelerates that investigation. It’s dangerous when its confidence lets you skip it.
• 02. Clear thinking means knowing what you believe, why you believe it, and what would change your mind.
You can hold a strong opinion without understanding what holds it up. Sometimes it’s evidence. Sometimes it’s a person you trust, a painful experience, or an idea you’ve repeated so often that it now feels obvious. Clear thinking starts by making that support visible.
Take the belief that remote work makes a team less productive. What does “productive” mean here? Finishing on time? Producing better work? Being available when someone needs help? Once you’ve defined the claim, write down why you believe it and what result would count against it. You could compare similar projects during a trial, including their quality and the effort required to coordinate them.
You don’t need to abandon the belief the moment you encounter one exception. You do need a standard by which it could lose your confidence. If every possible result becomes another reason you were right, you’re protecting a conclusion from examination. Being able to explain what would change your mind is part of having a defensible view.
• 03. Clear thinking depends on a mind that can focus, an explanation that fits reality, and a way to discover when you’re wrong.
You can reason carefully and still reach a poor conclusion because you started with a bad explanation. You can also have a useful explanation and fail to apply it because your attention keeps getting pulled elsewhere. Even when both are working, you need a way to discover whether the decision helped.
Imagine a freelancer trying to earn more. They need enough uninterrupted attention to examine their work. They need an explanation of what limits their income: perhaps pricing, demand, or the hours each project consumes. Then they need feedback that distinguishes those possibilities. Raising prices won’t solve a shortage of qualified inquiries, and attracting more clients won’t help much if every project already loses money.
When thinking goes wrong, inspect the part that failed. Were you too distracted to understand the situation? Did your explanation miss something? Did you act without checking the result? Those problems require different repairs. More concentration cannot make a false assumption true, and reading another book cannot replace feedback from an actual customer.
• 04. Before calling yourself undisciplined, inspect your sleep, your surroundings, and the size of the task. You may be judging your character by conditions you can change.
“I have no discipline” sounds like an explanation, but it’s often just a description of the problem with an insult attached. You didn’t begin the work, so you call yourself someone who doesn’t begin work. That gives you very little to change.
Look at the conditions first. Are you trying to make a difficult decision after a poor night’s sleep? Does every notification interrupt you? Is the task “sort out my life,” which contains about a hundred decisions and no obvious starting point? Any of those can make beginning harder without proving anything permanent about your character.
If you’ve been avoiding a report, try a quiet place and a first step as specific as opening last month’s numbers. Change one condition at a time when you can, so you learn what helps. Responsibility still matters: you’re responsible for improving the conditions you can influence and seeking help with the ones you can’t. But blame is a poor substitute for diagnosis. Give yourself a problem you can actually work on.
• 05. Write complicated problems down. Every detail you must remember competes with the attention you need to examine it.
Try comparing three apartments while keeping every rent, commute, deposit, move-in date, and doubt in your head. Part of your attention will be spent simply preventing those details from disappearing. You’ll revisit the same points because you can’t reliably see what you’ve already considered.
Put them somewhere you can look at. Separate what you know from what you’re assuming, then write the decision you’re actually trying to make. “Which apartment should I choose?” becomes easier to examine when you can see that one exceeds your budget and another isn’t available before your current lease ends. A problem that felt emotionally enormous may be controlled by two practical constraints.
Writing also exposes gaps. “The commute seems fine” becomes “I haven’t tested the commute at the time I’d actually travel.” Now you have a useful next step. The page doesn’t need to be beautiful, and you don’t need a new productivity system. It needs to hold the details still long enough for you to examine how they fit together.
• 06. Expertise is compression. Learn how facts fit together, and you have fewer separate things to hold in mind.
When you understand a subject, you stop treating every detail as a separate instruction. You recognize relationships. Those relationships let you make sense of more information without consciously working through every piece from scratch.
A beginner cook might memorize rules about heat, moisture, and how much food to put in a pan. An experienced cook sees that a crowded pan traps moisture and makes browning harder. That explanation connects several observations at once. It also helps when the recipe changes, because the cook understands what the instructions were trying to accomplish. This is why being able to recite a definition isn’t the same as understanding it. Ask what causes what, when the pattern applies, and what would happen if one condition changed. Then practice with different examples. AI can help generate those examples or explain a relationship, but familiarity with its wording isn’t proof that you’ve learned it. Try using the idea without looking at the explanation. You’ll quickly find the parts you only recognized on the page.
• 07. Find your defaults by asking what happens when you do nothing. Choose that outcome deliberately. It will govern the days when you are tired.
A default is the result your existing setup produces unless you intervene. Your phone opens to something. Your money stays somewhere. Unclaimed time gets filled by whatever is easiest to reach. Doing nothing still leads to an outcome.
Suppose you want to read more, but your phone is beside the couch and your book is upstairs. Reading is possible, but scrolling has the easier starting position. On a tired evening, that difference may decide what you do before you consciously weigh the options. Leaving the book where you sit changes the default without requiring a fresh burst of determination.
Look at what happens on an ordinary bad day, when you’re busy and mildly irritated and haven’t planned everything. Is that setup moving you toward something you want? If it isn’t, change what happens automatically. Defaults deserve occasional review because your circumstances will change. Their value is that you don’t have to rebuild a sensible decision every time you face it.
• 08. Make useful actions easy to begin and distractions inconvenient to reach. A change in friction can outlast a burst of motivation.
Friction is whatever stands between an intention and the first action: a missing file, a login, a cluttered desk, a decision about where to start. None of these needs to be a huge obstacle. A small obstacle repeated at the wrong moment can be enough to send you toward something easier.
Suppose you want to practice guitar after dinner. If the guitar is in its case, in a cupboard, behind three other things, practice begins with a minor household project. Put it on a stand beside the chair, with the next exercise ready, and the first useful action becomes much easier. Put the television remote somewhere less convenient and you’ve changed the competing option too.
You still have to choose to practice. The environment can’t supply a goal you don’t care about. What it can do is stop making the choice unnecessarily difficult. Keep protective friction where mistakes are costly, such as reviewing a message before sending it. Make the useful action easier, not every action indiscriminately faster.
• 09. Do not negotiate with temptation in real time. Move the phone, block the site, or remove the option while you are still thinking clearly.
It’s easy to make a sensible plan when the temptation is somewhere else. The argument changes when the phone lights up or the website is already open. Now the immediate reward is vivid, while the benefit of staying focused is something you’ll receive later.
Make the decision earlier. If you want an hour to write, put the phone outside the room and block the sites you usually visit before you begin. You aren’t relying on winning the same argument thirty times. You’ve changed whether the argument needs to happen at all.
Choose a barrier proportionate to the problem. A temporary website block is different from locking yourself out of something you may urgently need. Leave sensible exceptions and a way to revise a rule that isn’t working. The purpose is to carry a decision you made thoughtfully into a moment when you’re likely to bargain with it. Knowing your future self may be tempted gives you a chance to help them in advance.
• 10. Make your environment work even when your self-control is weak. Removing a distraction reduces how often you need to resist it.
A good working environment should help when you’re having an average day. If it only works when you’re well rested, enthusiastic, and perfectly disciplined, it’s relying on the very conditions it was supposed to support.
Consider someone trying to check a complicated document beside two colleagues having a conversation. Nobody is asking for their attention, and they may never turn around. Yet they keep losing the sentence they were reading and starting again. Telling themselves to concentrate harder doesn’t change the competing speech. Moving to a quieter place might.
That is a different problem from deciding whether to open a distracting app. Some environments keep presenting information you have to process or ignore before you get a choice about pursuing it. The useful intervention may be less noise, fewer visible competing tasks, or a clear agreement about when colleagues can interrupt you.
Try the same kind of work in a quieter setup and compare errors, progress, and how often you need to restart. One good session won’t prove a universal rule about offices, but it can help you identify a condition worth changing. Keep the arrangement simple enough to use on a difficult day. You want your surroundings to absorb some of the effort instead of repeatedly demanding more of it.
• 11. An interruption costs more than the time it lasts. Before switching tasks, write down the next step so you don’t have to reconstruct your thinking.
When you return from an interruption, the document may still be open, but the thought isn’t necessarily where you left it. You have to remember what you were trying to do, which options you ruled out, and why that unfinished sentence mattered. A two-minute interruption can therefore create more than two minutes of lost progress.
Before switching, leave a useful return point. “Continue proposal” tells your future self almost nothing. “Check whether the client’s deadline includes revisions, then finish the delivery paragraph” preserves the decision and the next action. If you stopped because something was unclear, write the question down too.
This won’t make interruptions free, and it doesn’t justify filling your day with them. It makes an unavoidable switch less costly. The note can be a single sentence at the bottom of the document. What matters is that it restores the context you would otherwise have to reconstruct. Write it for the version of you who comes back distracted, not the version who currently remembers everything.
• 12. A mental model simplifies reality by leaving things out. That is what makes it useful, and dangerous: when it disagrees with reality, reality wins.
A map that included every blade of grass would be a terrible map. Its usefulness comes from leaving things out so you can see the roads. Mental models do something similar: they emphasize a few relationships so you can reason about a complicated situation.
The danger appears when an omitted detail controls the result. “Lower prices attract more customers” can be useful, but it leaves out whether people associate the lower price with poor quality, whether you can serve the extra demand, and whether each sale remains worthwhile. A model can be sensible within its limits and misleading outside them. Use it to form an expectation, then compare that expectation with what happens. If the result disagrees, inspect the assumptions and the quality of your observations. You might have measured poorly, or you might need a better model. Either way, the model doesn’t get to overrule the result because it sounds elegant. Its job is to help you navigate what’s actually there.
• 13. A compelling story can leave out as much as any model. Before a narrative decides for you, ask what it left out.
A compelling story usually has a main character, a difficult choice, and a result that makes the choice seem meaningful. Real events contain much more: timing, constraints, other people’s work, failed attempts, and things nobody understood while they were happening. A tidy account selects from that mess.
Imagine reading about a founder who quit their job and built a successful company. The story may accurately describe what happened. It still doesn’t tell you how many people made the same move and failed, what financial support the founder had, or whether customers were already waiting. Those missing details matter if you’re treating the story as advice.
You can enjoy a story and learn from it while asking what it leaves unresolved. Look for the conditions that would have to be similar for the lesson to apply to you. A memorable example shows that something can happen. Establishing how often it happens, why it happens, and whether you should expect it requires more evidence.
• 14. Use AI summaries to decide where to look. Return to the original source before making a decision that depends on what the summary may have omitted.
A summary makes a document shorter by deciding which details deserve to survive. That is useful when you’re trying to find your way around a large amount of material. It becomes risky when the excluded detail is the one your decision depends on.
Suppose AI summarizes a return policy as “returns accepted within thirty days.” The original might limit that promise to unopened products or exclude the item you’re buying. The summary could sound perfectly clear while leaving you with the wrong expectation. Reading the relevant clause is much cheaper than discovering the exception afterward.
Use a summary to locate the passage, identify questions, and decide which sources deserve closer attention. Then inspect the original wherever wording, context, or an exception could change your action. You don’t have to read every page of every source. You do have to check the parts carrying the decision. If the original remains unclear, say so and investigate further. Asking AI to repeat the summary more confidently won’t recover a condition you haven’t checked.
• 15. When reality surprises you, examine the assumption behind your expectation. Investigate the mismatch before explaining it away.
Surprise is useful because it identifies a difference between what you expected and what happened. Something in your picture of the situation was incomplete, or the result included variation you hadn’t allowed for. Either possibility gives you something to investigate.
Suppose you send an offer and receive far fewer replies than expected. “People just don’t get it” protects your original view, but it doesn’t help you improve anything. Check whether the message arrived, whether the recipients had the problem you assumed, and whether the offer was clear enough to evaluate. Also check whether your expectation was reasonable in the first place.
One surprising result doesn’t automatically disprove your explanation. Small samples can mislead, and circumstances can change. The point is to pause before defending yourself. Write down which assumption the result puts under pressure and what information would help resolve it. You want to become quicker at noticing a mismatch, not quicker at producing a reason it doesn’t count.
• 16. Notice what makes you flinch. Sit with it for ten seconds and ask what it’s pointing at.
Sometimes you notice resistance before you can explain it. A question makes you tense. A message stays unopened. A particular line in someone’s feedback makes you want to stop reading. That reaction is worth examining because it may point toward something you’ve been avoiding.
Give it a little space. Ten seconds isn’t a magic threshold; it’s a reminder to pause before distracting yourself. Name what happened as plainly as you can: “I don’t want to check the numbers because they might show this project isn’t working.” Once the concern is visible, you can investigate it rather than letting the discomfort choose for you.
A flinch doesn’t prove the criticism is correct. It can also come from an unfair accusation, a painful association, or a real boundary being crossed. Treat the reaction as a signal to ask a better question. What exactly am I afraid this means, and what evidence supports that interpretation? You can take the feeling seriously without treating it as the final verdict.
• 17. Question every unexamined “should.” Ask who installed it and whether you actually want it before you obey it.
“I should want this” can hide a decision you’ve never actually made. The expectation may have come from your family, your profession, your friends, or a version of yourself whose priorities have changed. Repetition can make it feel like a fact about how life must be lived.
Suppose you believe you should become a manager because that’s what progress looks like in your field. Look at the actual work: hiring, difficult conversations, planning, and being accountable for other people’s results. You may want that. You may prefer becoming excellent at the craft you already practice. The title alone doesn’t settle the question.
Trace the expectation back to its reason. Some “shoulds” reflect real responsibilities, including promises you’ve made and people who depend on you. Others are preferences you’ve inherited without examining the cost. Questioning them doesn’t mean automatically rejecting them. It lets you keep the commitments you can defend and reconsider the ones you’re following mainly because you’ve never stopped to ask.
• 18. Charlie Munger said, “Show me the incentive and I’ll show you the outcome.” Before predicting what people will do, ask what they are being rewarded for.
People respond to more than what a system officially says it values. They also respond to what earns money, approval, promotion, relief, or protection from blame. When those rewards conflict with the stated goal, the rewards deserve your attention.
Imagine a support team paid partly according to how many tickets it closes. The company says it wants customers helped, but the easiest way to improve the rewarded number may be to close difficult cases prematurely. You don’t need to assume the employees are unusually dishonest. You’ve created a situation in which thorough work can make their performance look worse.
Before blaming the people, examine the arrangement. What behavior gets rewarded immediately? What good work goes unnoticed? What happens to someone who reports a problem honestly? Incentives aren’t a complete explanation of human behavior; people also have values, relationships, and professional standards. But if your plan requires people to repeatedly act against what the system rewards, you’ve made the desired result harder to achieve. Change the reward or add a check that makes shortcuts visible.
• 19. A rising metric can hide a worsening result. Measure closed support tickets alongside reopened ones. The number should remain accountable to the thing it represents.
A number is useful because it stands in for something you care about. Response time stands in for service. Views stand in for attention. Completed tasks stand in for progress. The mistake is forgetting that the number and the underlying result can separate.
Suppose a team closes twice as many support tickets this week. That looks good until you discover that many customers are reopening the same unresolved problems. The original metric rewarded an administrative action, and the dashboard mistook it for a successful outcome. Tracking reopened cases makes that difference harder to hide.
Choose a second measure that would worsen if someone improved the first by taking a shortcut. If you’re increasing output, check quality. If you’re cutting delivery time, check errors and rework. You don’t need a dashboard with forty competing targets, each creating more work. You need enough evidence to notice when the number is improving at the expense of the thing it was supposed to represent. Occasionally inspect the actual work too. Two imperfect metrics are still representations.
• 20. Before removing a rule, understand the problem it was built to solve. You can remove the rule and inherit the problem.
An unnecessary rule creates friction. A necessary rule can create friction too. Looking at the inconvenience alone doesn’t tell you which kind you’re dealing with.
Suppose every customer order requires a second person to check the delivery address. It seems wasteful, so you remove the step. Later you discover that the rule followed a series of expensive shipments to old addresses copied from previous orders. The review may still be a poor solution, but the problem it addressed hasn’t disappeared just because the review annoyed you. Ask what happened before the rule existed, which failure it prevents, and whether the relevant conditions have changed. Perhaps the order system now validates addresses reliably. Perhaps a targeted check would replace the blanket review. History gives you something to test, not a permanent veto on change. Once you understand the protection, you can remove it deliberately, replace it, or run a limited trial while watching for the original failure. That’s a stronger improvement than simply making the process shorter.
• 21. Evidence matters when it separates competing explanations. Ask whether you would expect to see the same thing if your belief were false.
Evidence earns its value by helping you choose between explanations. An observation can be consistent with your belief and still tell you very little, because you’d also expect it if your belief were wrong.
Suppose you think a potential customer is eager to buy because they said, “Send me some details.” A serious buyer might say that. So might someone being polite or trying to end the conversation. The reply fits both explanations, so it shouldn’t move your confidence very far. A request to discuss delivery dates with the person who approves the purchase would usually provide more information, though it still wouldn’t guarantee a sale.
Ask what each explanation predicts, then look for the difference. If customers love the product, what should you observe beyond compliments? Repeat use, perhaps, or willingness to pay. Compare that with what you’d expect from people who merely like you. You aren’t demanding perfect proof. You’re looking for information that makes one account of the situation more plausible than its competitors.
• 22. Turn percentages into people. Ten out of a thousand is easier to grasp than one percent.
A percentage can sound dramatic while hiding the scale of what happened. If a website’s sales rise from one to two, sales have increased by 100 percent. If they rise from a thousand to two thousand, the percentage is identical, but the practical result is very different.
Keep the count and the total group together. Ten responses out of a thousand invitations means something different from ten out of twenty. Also check the time window and who was counted. A result drawn from existing customers may tell you little about people who have never heard of the business.
Consider a conversion rate rising from two percent to three percent. In a group of a thousand visitors, that means twenty buyers becoming thirty. The increase is ten buyers. Ten divided by the original twenty is one half, so the relative increase is fifty percent. But the rate itself rose by one percentage point, from two to three.
All of those descriptions are compatible. They answer different questions. The concrete count helps you understand what changed before the most impressive-sounding version decides how you feel about it. Ask for the original count, the new count, and the group they came from whenever a percentage is doing the persuading.
• 23. Begin with what happens in similar cases. Your plan needs evidence for being exceptional, not merely a story about why it is.
Your own plan comes with details that make it feel different. You know your motivation, the care you’ve put into it, and the reasons it could work. Comparable projects usually appear as bare outcomes, without the equally convincing stories their owners told at the beginning.
Suppose you’re planning a paid workshop and expect almost everyone you invite to register. Before building the budget around that expectation, look at comparable workshops: similar audience, price, topic, and relationship with the organizer. What share of invitees actually paid? This starting pattern is often called a base rate. It gives you an expectation grounded in more than the appeal of your own plan.
Choose the comparison carefully. An established teacher inviting past students has a different starting position from a stranger advertising to people who have never heard of them. If you can’t find a close comparison, acknowledge that uncertainty instead of inventing a precise benchmark.
Then adjust for advantages you can defend. Perhaps prospective attendees have already asked for this exact material, or several have made a commitment to attend. You can still believe your workshop will do better. You need to explain why the difference should change the outcome, rather than treating your enthusiasm as the evidence.
• 24. Break a large unknown into smaller estimates. Use ranges. Find the assumption that changes the answer most, and investigate that first.
A large unknown often becomes easier to estimate once you identify what produces it. “How many people might attend this workshop?” is vague. The number you can realistically reach, the share likely to register, and the share of registrants likely to attend give you separate questions you can investigate.
Suppose you can reach about two hundred relevant people. If ten to twenty percent register and around three quarters attend, you might plan for roughly fifteen to thirty attendees. Those figures are hypothetical assumptions, not a forecast you should trust because it contains multiplication. Their purpose is to expose the logic behind the answer.
Now vary the inputs. If a small change in registration rate greatly changes the result, learning about demand may be more useful than refining a minor cost estimate. Watch for assumptions that move together: reaching a larger, less relevant audience might reduce the registration rate. A range is honest only when it reflects plausible conditions. Breaking the problem down makes uncertainty inspectable; it doesn’t make uncertainty disappear.
• 25. When comparing options, consider what you could gain or lose and how likely each outcome is. Write down your estimates so you can see which assumptions your decision depends on.
We often compare options by picturing their most appealing or frightening outcome. A new project offers freedom. A familiar job offers security. Those impressions can hide several possible outcomes with different chances of happening and different costs.
Write down the main possibilities over the same time period. For a small project, that might include strong demand, modest demand, and little demand. Estimate what each would mean after expenses and time, then assign rough probabilities that add up to the whole set of possibilities. You’re making your judgment visible so you can examine it. Decimal places won’t make a guess more trustworthy.
The useful discovery is often which assumption controls the choice. Perhaps the project looks attractive only if customers arrive immediately, while a slow start makes it unaffordable. Now you know what to investigate or protect against. Also include outcomes that don’t fit neatly into money: stress, family time, obligations, and the ability to change course. The calculation should inform the decision you’re actually making, including the parts it cannot precisely measure.
• 26. Weigh both the likelihood of an outcome and its consequences. Then ask whether you can survive being wrong. Favorable odds are little comfort if one loss ends the game.
A favorable average can hide a downside you cannot afford to experience even once. The same uncertain opportunity can be sensible for one person and disastrous for another because they have different obligations and different capacity to recover.
Imagine two small businesses considering the same untested expansion. One can fund a limited trial while continuing normal operations. The other would have to commit the cash it needs to pay staff next month. Even if both owners believe the expansion is likely to work, the second is exposed to a very different failure. A good story about the upside doesn’t make payroll optional. Examine the worst plausible outcome, how it could arise, and what it would leave you able to do next. Reduce the commitment, stage it, or decline it if the downside exceeds what you can responsibly absorb. “Likely to succeed” and “safe enough to attempt” answer different questions. Protecting your ability to continue gives future good decisions somewhere to take effect.
• 27. You do not need certainty to act. You need enough evidence for the size of the bet, a limit on the downside, and a way to learn from the outcome.
Waiting for certainty can feel responsible when it’s really avoiding a decision. At the same time, acting quickly can feel brave when you haven’t understood the downside. The useful standard sits in the relationship between the evidence and the commitment.
You need less evidence to test a new workshop with a small group than to rent a permanent venue for a year. The small trial can reveal whether people attend, whether the material helps, and what takes more work than expected. If it fails, you should still be able to recover and use what you learned.
Define the limits before you begin: the time or money available, what you won’t put at risk, and when you’ll review the result. Make sure the test can actually answer the question controlling the next step. A harmless experiment that teaches you nothing relevant isn’t much help. The aim is to take an action proportionate to what you know, with enough protection that being wrong remains useful rather than overwhelming.
• 28. A good decision can lose. A bad decision can win. Judge the reasoning using what was knowable at the time, then learn from what happened.
After a result arrives, it’s tempting to treat it as a grade on the decision. Success makes the choice look intelligent. Failure makes the warning signs look obvious. But you made the decision before you knew which outcome would occur.
Suppose an outdoor event has a backup venue because the forecast shows a meaningful chance of rain. The day stays dry. That doesn’t automatically mean arranging the backup was foolish. You paid to protect against a possibility that happened not to occur. Conversely, ignoring a serious risk doesn’t become careful planning just because you escaped the consequences once.
Review two things separately. First, did the choice make sense given the information available, the alternatives, and the downside at the time? Second, what does the outcome teach you about those assumptions? A repeated pattern of misses may reveal a broken process even when each individual miss has an excuse. You want to avoid learning the wrong lesson from luck while remaining fully willing to improve the reasoning.
• 29. Write down your prediction and reasoning before the result arrives. Otherwise you can remember being right about something you never predicted.
Once you know what happened, it’s surprisingly easy to remember that you expected it. A vague concern becomes a precise warning in hindsight, and an outcome you barely considered becomes “what I thought would happen.” A written record gives you something less convenient to negotiate with.
Before an important choice, record the outcome you expect, when you expect it, your confidence, and the main reasons. Define the terms. “The trial will go well” is hard to evaluate. A prediction such as “At least eight of the ten participants will complete the exercise without extra help by the end of Friday’s session” gives you a checkable outcome and a deadline.
Then add why you expect that result. You might have tried the instructions with two beginners and found that both finished, while still being uncertain about a larger group. Recording that evidence matters: if the trial fails, you can examine whether the instructions were unclear, the earlier test was unrepresentative, or your confidence simply exceeded what two people could establish.
When the result arrives, compare it with the original words before explaining the difference. Record what you’ll change next time. This doesn’t need to become a diary of every trivial decision. Use it where repeated learning or a meaningful commitment makes an honest comparison worth the effort.
• 30. Estimate from the time similar work actually took. A plan built from perfect days is a promise to be surprised.
A plan often describes the work as though nothing interrupts it: write the draft, review it, make revisions, send it. Real projects contain questions, waiting, errors, missing information, and work that takes longer once you can finally see what it involves.
Look at how long comparable work actually took, from the point you started to the point it was usable. If a previous presentation required research, review, and revisions, those stages belong in the new estimate too. Counting only the hours spent building slides gives you a forecast for an imaginary version of the task.
Use a range when uncertainty is meaningful, and separate your likely finish from a safer commitment. Then adjust for actual differences in scope or experience. You may be faster now, but name what has changed rather than assuming determination will cancel every delay. Historical estimates are useful precisely because they include some of the mess that a fresh plan makes easy to forget.
• 31. Compare your initial time estimates with how long the work actually took. Use the pattern to adjust future estimates for similar tasks.
An estimate becomes more useful when it participates in a feedback loop. Otherwise, you can spend years being “usually a bit optimistic” without learning which tasks you misjudge or by how much.
Keep a simple record of what you expected and what the work required. Be consistent about the measure: active work hours and calendar time answer different questions. A task can need two hours of effort while taking three days to finish because someone else must review it. Both may matter to the next promise you make.
Group similar work and look for a recurring miss. Perhaps you estimate writing reasonably well but forget the time needed to find source material. Perhaps revisions keep doubling a supposedly quick design task. Correct the missing stage before applying a blanket multiplier to everything. Revisit the pattern as your skills, tools, and scope change. The point is to become more accurate, not to turn a small collection of old projects into another rule you’re unwilling to update.
• 32. Every commitment spends hours that cannot be spent elsewhere. Name the best alternative you are giving up before you say yes.
Saying yes spends part of a finite week. The cost includes whatever you can no longer do during that time, even if no money changes hands and nobody sends an invoice for the lost opportunity.
A one-hour meeting may consume the only uninterrupted hour you had to finish a proposal. Add preparation and the effort of returning to the interrupted work, and the commitment is larger than the calendar block suggests. Naming the displaced work makes the trade visible in a way that “I’m busy” doesn’t. Compare the new commitment with your best realistic alternative, not an imaginary hour of perfect productivity. Rest, relationships, and unstructured time can be worthwhile alternatives too. You don’t have to turn every evening into an earnings calculation. You do need to understand what you’re giving up, especially when your calendar fills through small agreeable answers. A commitment can be good in isolation and still be the wrong use of the time you actually have.
• 33. Before delegating, count both the money you will spend and the time needed to explain, check, and fix the work. Ask whether the time you actually free up is worth that cost.
Hiring someone to do a task removes only the work they can complete without you. You may still need to explain the goal, supply materials, answer questions, inspect the result, and request changes. Ignoring that remaining work makes delegation look cheaper than it is.
Suppose a recurring report takes you four hours. Someone else prepares it, but briefing and review take ninety minutes. You’ve freed about two and a half hours, not four, and you still have the fee to consider. If the first handoff also requires building a template, that setup may be worthwhile over many repetitions even if the initial attempt saves little time.
Decide what those freed hours are for and what quality must survive the handoff. Better work, reliable coverage, and less exhaustion can justify delegation as well as additional income. Give the person a clear example of an acceptable result and a way to surface uncertainty early. You want a fair comparison between the full alternatives, not a flattering comparison between their fee and your imagined hourly rate.
• 34. Find the few efforts that produce most of the result. Equal time is a poor default when the returns are unequal.
A full calendar can hide how unevenly your effort contributes to the result. Some conversations lead to most of your useful opportunities. Some product problems cause most of the complaints. Some study habits produce understanding while others mainly produce the feeling of studying.
Look backward at actual outcomes and trace where they came from. If a few types of customer conversation repeatedly reveal important problems, protect time for them before polishing low-impact internal documents. If practice questions expose what you don’t understand, they may deserve more of your study time than rereading familiar pages.
This is a pattern to investigate, not a promise that every situation follows an exact eighty-twenty split. Be careful with work whose benefit is prevention: backups, maintenance, and checking mistakes can look unproductive because success means a problem never appears. Distinguish low visible output from low value. The aim is to allocate effort according to its contribution while preserving the supporting work that allows the most valuable activities to succeed.
Also check whether the apparent winner is repeatable. One large client may account for most of this month’s income, but depending entirely on that client creates a vulnerability. A successful launch may owe part of its result to months of quieter preparation. Concentrate effort where it contributes, while preserving enough support and alternatives that one change cannot remove the whole result.
• 35. When every step must work, one weak step can defeat the whole plan. Improve the bottleneck before polishing what already works.
Some results require a sequence of essential steps. A customer must discover the offer, understand it, decide to buy, complete payment, and receive what was promised. A serious failure at any required stage can prevent the overall result, even when the other stages are excellent.
Suppose a workshop attracts plenty of interested visitors, but the booking form fails on their phones. Improving the headline may attract more people to the same broken form. Fixing the booking problem matters because it lets interest you’ve already earned turn into attendance. This is a weak link: attempts fail as they pass through it.
A queue creates a related but different problem. Every booking might be processed correctly, but if one person must approve each one and cannot keep up, customers wait. This is a capacity bottleneck. The first problem needs a more reliable step; the second may need less work per booking, more capacity, or a simpler approval rule.
Check both failed attempts and waiting time before choosing what to improve. Then measure the final result, not just how busy or efficient one stage looks. Once the limiting point improves, another step may become the constraint. Keep examining the whole path rather than endlessly polishing the part you happen to enjoy working on.
• 36. Getting better at your current method can hide a better method. Test alternatives while the old one still pays for the experiment.
Getting skilled at a method gives you a reason to keep using it. You work faster, make fewer mistakes, and know what to expect. That competence can also make alternatives look worse because you’re comparing your practiced current method with your clumsy first attempt at a new one.
Imagine someone manually assembling the same weekly report. They become extremely fast at copying, sorting, and formatting. A better data export might eliminate much of that work, but learning it would initially slow them down. If they judge only the first attempt, they’ll keep improving a process that may no longer deserve the effort.
Protect the working method while testing an alternative on a limited example. Include setup time, reliability, quality, and the likely number of future repetitions in the comparison. Novelty alone is no reason to change. But neither is your existing competence proof that you’ve found the best route. Give the alternative enough room to demonstrate its value without gambling the output people are already relying on.
• 37. When you have time and results have flatlined, try new approaches. When time is short and results are climbing, double down on what works.
Experimenting spends time in exchange for the possibility of finding something better. Repeating a known approach uses what you’ve already learned to produce a result. Both are useful, but their value changes with your situation.
If you’ve been promoting an event for weeks with little response and still have time, another identical attempt may teach you very little. A different message or audience could reveal something useful. If a particular approach is producing registrations and the event is tomorrow, a major redesign may consume the time you need to do more of what is working.
Check the evidence behind the apparent trend. One unusually good result doesn’t establish a reliable winner, and a short flat period doesn’t prove the method is exhausted. Also consider what failure would cost and how much time remains to recover. You may keep a small experiment running beside the main approach. The useful question is how much learning is still worth buying before you need the result, given what you currently know.
• 38. Treat avoidance as something to investigate. A task may be too vague, too large, or aimed at a goal you no longer want. More pressure will not tell you which.
Avoidance tells you that you’re not doing something. It doesn’t, by itself, explain why. You may not understand the task, may expect it to be unpleasant, may lack a needed skill, or may no longer care about the outcome. More pressure can intensify the discomfort without separating those explanations.
Start with the task itself. “Build my website” contains decisions about the offer, the audience, the words, and the design. “Write one paragraph explaining who this helps” is something you can attempt. If making the next step concrete reduces the resistance, ambiguity was probably part of the problem.
If the resistance remains, investigate further. What do you expect to happen when you start? Is there a missing resource or a conversation you’re avoiding? Do you still endorse the goal? Not every worthwhile task will feel appealing, and discomfort doesn’t automatically mean you should quit. You want to identify whether the useful response is clarification, practice, support, acceptance of temporary discomfort, or a deliberate change of direction.
• 39. Attach each goal to a trigger you cannot miss. “After I put down my coffee, I open the draft” beats “I should write more.”
“I want to write more” leaves the timing undecided. Each day, you have to notice the intention, remember why it matters, and choose a moment to begin. A specific cue removes one of those decisions by connecting the action to something that already happens.
Choose a trigger you can recognize without interpretation, such as putting down your breakfast plate. Attach a small action: open the draft and write the next sentence.
The sequence is specific enough to observe: breakfast finishes, the plate goes down, the document opens, and you write. Keep the document easy to reach so the cue doesn’t lead immediately into a search for the right file.
Then check both parts of the arrangement. Did the trigger happen and did you notice it? If you noticed it but didn’t act, was the action too large or poorly matched to that moment? A breakfast cue won’t help if your mornings are consistently rushed. Adjust the sequence based on what actually happens. The cue gives the goal a place in your day; the small action gives you a practical way to begin when enthusiasm is absent.
• 40. For a small, reversible problem, set a five-minute timer. Finish it, discard it, or define the next physical action. “Think about it later” is not an outcome.
A minor unresolved task can keep returning to your attention without receiving any useful work. You remember it, feel the irritation, and postpone it again. The problem may be small, but the repeated interruption isn’t doing you any favors.
Give it a short, bounded attempt. If you need to find a receipt, spend five minutes looking in the likely places. You may find it, decide it no longer matters, or discover that the next step is requesting a copy from the vendor. Each result changes the state of the problem. “I’ll think about it” leaves you exactly where you began.
The timer limits the investigation; it doesn’t require a reckless decision when it rings. If the issue is larger than expected, define the next physical action and give it an appropriate time. Keep this technique for small, reversible work. Its purpose is to turn a vague open loop into something finished or actionable, not to impose an arbitrary deadline on a choice whose consequences you haven’t understood.
• 41. A cheap experiment can settle what another week of argument cannot. Choose the smallest action whose result could change your mind.
A disagreement can last because both sides keep rearranging the same information. One person thinks customers want a simpler offer. Another thinks they want more features. A longer meeting may produce more arguments without producing anything that distinguishes those beliefs.
Find the smallest test that could change the decision. You might show comparable customers two clearly described offers and measure a meaningful response. Decide beforehand what you’ll count, how long you’ll run the test, and which result would make you continue, change, or stop. Compliments aren’t an adequate substitute for a purchase if willingness to pay is the question.
A cheap test still needs to be informative. A handful of friends may give you useful feedback on clarity while telling you little about demand in a wider market. Keep the conclusion within what the test can establish, and seek stronger evidence as the commitment grows. The point is to spend a little effort making contact with reality when more discussion has stopped changing anyone’s understanding.
• 42. Imagine the plan has failed. Describe how it happened. Look for a plausible cause you can prevent or a warning you can catch early.
Plans are usually described as a sequence in which things go right. A premortem temporarily starts from the opposite end: the plan has failed, and you’re explaining the cause. That change makes it easier to discuss a weakness before anyone has to defend an actual mistake.
Suppose you’re organizing a small event. “Something could go wrong” is too vague to help. “The speaker canceled that morning and nobody had the material needed to replace them” identifies a specific dependency. You can now prepare a backup or decide what you’d tell attendees.
Generate plausible causes, then separate what you can prevent from what you can only detect early. Choose a few safeguards proportionate to the risk. You don’t need to build an elaborate defense against every imaginable disaster, and vivid failure stories aren’t probability estimates. Return to the reasons the plan could work after you’ve checked the risks. A useful premortem improves the attempt; it shouldn’t become a sophisticated way to frighten yourself out of making one.
• 43. Write your own view before asking AI for advice. Otherwise its first answer can become the assumption every later question protects.
The first coherent explanation you read can quietly establish the terms of the discussion. Once AI says the problem is your pricing, your next question may become how to change the pricing. You can spend a long conversation improving an answer to a question you never independently chose.
Before asking, write a short view of your own: what you think is happening, what evidence you have, what remains uncertain, and which decision you need to make. You might discover that you don’t yet have a view. Record that honestly, including the facts you would need to form one.
Now ask AI to examine the situation and compare its explanation with yours. Where do they differ, and what would settle the disagreement? The first note gives you a record of your starting point and makes changes in your reasoning visible. It doesn’t make your initial view superior. You should change it when better evidence warrants it. You’re creating a chance to think before a fluent answer makes one particular framing feel inevitable.
• 44. A question that contains your preferred answer invites agreement. Give AI the problem, the evidence, and the constraints before telling it what you hope is true.
If you ask, “Why is my plan such a good idea?” you’ve already assigned the model its conclusion. Even a softer prompt such as “Help me explain why this is the right move” directs attention toward supporting arguments. The response may be useful advocacy, but it shouldn’t surprise you when it agrees.
For an assessment, give the situation first. Describe the options, relevant evidence, constraints, and what success would mean. Ask what each option gets right, where it could fail, and what information is missing. Research on sycophancy in language models has documented assistants favoring agreement with user beliefs, which is one reason to avoid treating a pleasing response as independent confirmation.
Your preferences still belong in the decision. Wanting more family time or less travel is relevant information, not an error to conceal. The distinction is between giving the model your actual values and feeding it the conclusion you want endorsed. A neutral prompt helps you examine the trade. It doesn’t guarantee an unbiased or correct answer.
• 45. Ask AI for the strongest objection to your plan and the strongest case for the alternative. Give it a disagreement worth investigating.
A useful objection identifies something that could make your plan fail or make another option better. “There are risks” is too vague. “This requires ten hours of work each week, and you currently have three available” gives you a concrete conflict to examine.
Ask AI for the strongest supported objection, not a long list of everything that could conceivably go wrong. Also ask it to make the best fair case for the alternative. That second request can reveal benefits you’ve minimized because you already prefer your own plan, such as reliability, simplicity, or the freedom to change direction later.
Then investigate the claim doing the real work. Are ten hours actually required? Could you reduce the scope? Does the alternative really avoid that constraint, or merely hide it somewhere else? Don’t manufacture equal strength between options when the evidence favors one. The purpose of the exercise is to find an objection that deserves your attention and determine whether it changes the choice. Producing disagreement is only the beginning.
• 46. An AI criticism needs checking as much as an AI compliment. A model can invent reasons you are wrong just as fluently as reasons you are right.
Criticism can sound more sophisticated than praise. A model that finds a flaw appears to be thinking independently, especially when it uses confident language and explains the objection neatly. But it can produce an unsupported criticism with the same fluency as an unsupported compliment.
Suppose AI says your article contradicts itself. Ask it to identify the two passages and explain the conflict. You may discover a real inconsistency. You may also discover that one passage applies to small reversible decisions while the other applies to commitments with serious consequences. The criticism missed a distinction the article already made.
Turn the feedback into claims you can inspect. Check quotations against the text, factual assertions against sources, and predictions against whatever evidence is available. Revise when the criticism survives that check. Reject it when it doesn’t. Asking another model can surface a different view, but agreement between models isn’t automatically independent confirmation. What makes feedback useful is the reasoning and evidence underneath it, not whether the tone is flattering or severe.
• 47. Count the cost of explaining, supervising, checking, and repairing delegated work. An answer generated in seconds can still take hours to trust.
The time shown on an AI generation is only one part of the job. A report may appear in thirty seconds and still require extensive checking before anyone should rely on it. If you count the generation time while ignoring the verification, you can mistake transferred work for eliminated work.
Consider a spreadsheet summary. You need to provide the correct data, explain which rows count, check that the totals match, and investigate suspicious conclusions. If you already have a dependable formula that produces the answer, a long AI conversation may be the slower route. For a different task, AI might remove substantial work even after review.
Compare the complete process with a realistic alternative. Define an acceptable result before delegating, and make errors easy to detect. Start with bounded work whose inputs and outputs you can inspect. As reliability improves, the appropriate scope may grow. The test is whether the whole assignment becomes easier to complete to the required standard, including the cost of fixing mistakes and the consequences of missing them.
• 48. For a consequential decision, state what you chose and why in your own words. An explanation you cannot give without the model is a decision you are not ready to own.
You don’t need to personally know every fact behind a decision. We all depend on other people’s expertise. You do need enough understanding to explain the choice you’re making, the advice you’re relying on, and the risk you’re accepting.
Close the chat and describe the decision to someone who hasn’t read it. What are you choosing? What evidence supports it? What is the strongest reason against it? What would cause you to reconsider? If your explanation reduces to “AI said this was best,” you haven’t yet made the reasoning your own. Notice where you get stuck. Perhaps you understand the recommendation but not the probability attached to it. Perhaps a key term remains vague. Ask for an explanation, inspect the source, or consult someone qualified for the decision. Being able to repeat fluent language isn’t the acceptance check. You should understand enough to identify the assumptions, recognize an important exception, and explain why the choice fits your actual circumstances.
• 49. Let being wrong cost you an opinion before it costs you another year.
An opinion becomes expensive when protecting it determines what you continue to do. You keep pursuing an offer people don’t want, defending a method that no longer works, or waiting for a situation to become what you originally expected. The cost grows while the belief stays comfortably familiar.
Suppose you believe a certain service is what your customers need. Repeated conversations point to a different problem, but you’ve already built the materials and told people your plan. Changing direction now would be embarrassing. Continuing may feel easier today while consuming months you could use to build something more useful. Separate the work you’ve already spent from the value of the next commitment. What would you choose now if you encountered the same evidence without needing to defend your earlier prediction? That question doesn’t require abandoning a worthwhile effort after every setback. It requires giving new evidence a fair chance to change your course. You can acknowledge that an opinion was reasonable when you formed it and still decide it no longer deserves another year.
• 50. Borrow ideas. Take responsibility for what you do with them.
You should learn from other people. Their ideas can spare you mistakes, reveal options you hadn’t considered, and give you a clearer way to understand what you’re experiencing. Taking responsibility doesn’t require pretending you invented everything yourself.
It does require judging where the advice applies. A method that works for an experienced business owner with savings and an established audience may be a poor fit for someone starting from scratch. An AI explanation may be useful while containing one assumption that makes its recommendation wrong for you. Credit the source, inspect the relevant evidence, and adapt the idea to the conditions you actually face.
That applies to this essay too. Pick a maxim that speaks to a real problem, explain why you think it fits, and try an appropriate action. Then pay attention to what happens. If the result challenges the advice, investigate the difference. Borrowing an idea gives you something to work with. What you choose to do next, and how honestly you learn from it, remains your responsibility.
By Payton Bilodeau
P.S. If you are still confused on any of these, take the time to sit with them and think about what they mean, and/or use an LLM to help you understand them.
Same post, just worked hard on it and wanted more reach so shared it to both of my Substacks. To be fair, it is relevant to both of my brands.