23 Reasons Your Mind Loses You Millions

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In the competitive realms of private equity and venture capital, general partners spend resources building complex risk models and assessments, assuming data alone drives portfolio outperformance. 

However, sustainable alpha is fundamentally capped by human behavior.

How does anchoring bias impact private equity valuations? 

In this masterclass episode of Making Billions, Ryan Miller challenges managers to treat decision calibration as a compounding asset, shifting the focus from financial metrics to rigorous behavioral oversight. 

Discover an engineered approach to investment psychology that will redefine how you run due diligence, manage LP communication, and shield your fund from systemic blind spots.

[THE HOST]: Ryan Miller is a fund manager, capital strategist, and former CFO turned angel investor in technology and energy. He is the founder of Fund Raise Capital and Aequor Capital Partners, and has mentored over 1,000 fund managers across private equity, private credit, venture capital, real estate, and alternative assets globally.

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Transcript

Ryan Miller

About a decade ago, I was one of the controllers for a major energy company's $500 million portfolio of investments in their investment portfolio management division. My job was to support the investment committee, senior leaders making multi-year investment decisions with hundreds of millions of dollars on the line every single week. I built a decision framework. I built the risk models, I built the economic assessments, and I documented every single pattern I watched derail otherwise intelligent professionals. Not because of bad markets, not because of bad data, but because of 23 specific ways the human brain sabotages rational decisions around money. That document has never been fully shared publicly, but today it becomes this episode. Today we're going to learn how your blind spots put you out of business and how to mitigate them so you can build your fund into a top decile investment company. So if you're managing capital right now or you're preparing to, this episode is probably one of the most important things you will listen to this year. Not because of what I'm going to teach you about the market, but because of what I'm going to teach you about yourself. Here we go. 

Ryan Miller

Before we dive in, just a word from our sponsor. When doing deals, we all know that raising capital is the one thing that unlocks everything. That's why I've partnered with Reef Pass Investors that are actively funding deals right now. So if you're a deal syndicator or founder thinking about launching an M&A focused buy and build platform, reach out to Reef Pass Investors at reefpassinvestors.com. They are one of the best investors in the game that are helping you launch a new long-term holding company. So here's what I want you to do click the description in the notes and contact them for a discovery call and potentially get an invite to pitch your next M&A deal. Now, let's get back to the show. 

Ryan Miller

Before we dive in, just a reminder that nothing in this episode is investment, financial tax, or even medical advice. Always speak to a qualified professional before making any decision or diagnosis from any part of this episode or otherwise. Other than that, let's dive in. So here's how we're going to do this. 23 biases. For each one, I'm going to do three things. First, I'm going to show you what it looks like in yourself, what the internal experience is, what the thought pattern feels like from the inside. I call this the mirror. Then second, I'm going to show you what it looks like in other people around you. Those are much easier to see than in yourself. Your investment committee, your team, your LPs, your co-investors, the person on the other side of the table. I call this the room. And then third, I'm going to give you the fix, the specific actionable practice just as a thought exercise to become one of those top fund managers to help you override some tendencies that are making you potentially lose money right now. So the mirror, the room, and the fix. 23 times. And by the end of this episode, you'll have a complete map of the psychological forces that could be destroying investment decisions in your fund or in others. And you'll know exactly what to do about every single one of them. The portfolio I was working on, it grew 10 times over from $50 million to $500 million. That did not happen by accident. Remember, the man at the top of the mountain doesn't fall there. Decision quality compounds. So let's build yours. 

Ryan Miller

So the first bias is anchoring bias. Your mind becomes over-reliant on the first piece of information that it receives. That first number, that first valuation, that first impression becomes invisible gravitational fields that pull every subsequent judgment toward it. You did not notice it happening. That is what makes it dangerous. Now, in the mirror, here is what anchoring looks like in you. You receive a pitch deck. The company is asking for a valuation of $80 million. You spend three weeks doing your own analysis. The work says the business is worth $55 million. But when it's time to make your offer, you cannot bring yourself to go below $70 million. You tell yourself it's a negotiating position. It isn't. That is the first number, $80 million, that has anchored your entire mindset and framework. You're not making a decision from your analysis now. You're making a decision from their opening position. That gap, the $15 million between what the data says and what you feel comfortable offering, that is the pure cost of anchoring bias. 

Ryan Miller

Now, in the room, your investment committee meeting, for example, watch what happens in the first five minutes. If a senior partner opens the discussion with a statement like, This feels like a $200 million business, the entire conversation that follows it is now organized around that number. Every projection, every comparable, every scenario will be unconsciously calibrated against the $200 million anchor, not against the data. Watch the models that come out of that meeting. They will cluster around the opening number in ways that cannot be explained by the analysis alone. The person who speaks first in a deal discussion is not sharing an opinion. They are setting the frame for every decision that follows. This can also work in your favor when you're negotiating. Be the first one to make the offer, and the rest of the discussion goes around that number. So if it's a number you want, you can actually use their anchoring biases, if they have them, to actually work in your favor. 

Ryan Miller

Now, the fix, the override is non-negotiable. Before you see any term sheet, any asking price, any comp, run your own analysis and write your number down. You commit to evaluation before you're exposed to anyone else's. Then you see theirs. The gap between your number and their number. It's just data about the deal, about your potential anchoring exposure. See, if you can't explain why their number differs from yours based on purely analytical differences, the anchor is doing a number on you. Never let someone else's number be the first number you encounter, ever. 

Ryan Miller

That brings us to bias number 2. Bias number 2 is the availability heuristic. Your brain, it overestimates the probability and significance of information that comes to mind easily because it's vivid, it's recent, or maybe it's emotionally charged. What is available to you becomes, in your mind, more representative than it actually is. You do not know what you do not know, but you also dramatically overweight what you do know. So let's take a look in the mirror. Here's what this looks like in yourself. You personally know someone that made an extraordinary return in, say, oil and gas, three times their money in 18 months, right? Pretty good. So when the next oil and gas opportunity comes across your desk, your internal risk register is lower than perhaps the data may suggest. You're not reaching for the base rates. You're reaching for that one vividly emotional resident story. Your brain is treating that one data point as a trend. And you're just pricing risk accordingly, not based on the full distribution of outcomes, but based on the story that is available in your memory. 

Ryan Miller

So let's talk about what happens in the room. You want to watch what happens when someone opens a deal presentation with a vivid success story, a comparable exit, a famous outcome from the same sector. The room shifts. The committees become more favorable. Not because new data has been analyzed, but because a vivid outcome has been made available. So sophisticated deal teams, they know this. They open with stories before they open with data. As I like to say in my community of  Fund Raise Capital, you want to sell the sizzle, not the stick. This is what I'm talking about. Emotionally charged, that helps to work with people's biases. We're not trying to trick people, we're just understanding the psychology of how the brain works. Please don't be deceptive. But when you understand psychology, now you can pitch your message, your deal, your fundraising to them and understand how their psychology and things that they lock in on. So when you understand who you're talking to, now you're in a position to utilize that information, those vivid memories and the things that they care about and say, oh yeah, our deal is just like that, assuming it actually is. So make sure that you're honest and that really helps. See, deal teams understand your brain better than your brain understands itself. The anecdote does more work than the model because it is available, it is vivid, and it is recent. 

Ryan Miller

So let's talk about the fix if you're seeing it in yourself or in the room. So you want to build base rate discipline into your process as a formal mandatory step, not a nice to have. Before every investment decision, pull the cohort data. What percentage of companies at this stage in this sector with these metrics have achieved the outcome being projected? That's why we call them comps. That number is your inoculation against availability bias. And when someone opens a deal meeting with a vivid success story, ask the question out loud: what is the base rate for this cohort or this deal? Not for that one company, for the cohort or the cluster of companies or your competitors. The answer tells you whether the story is data or just entertainment.

Ryan Miller

Then there's bias number 3, it's the bandwagon effect. The power of an idea grows in your mind as more people accept it. Not because the evidence has strengthened, but because the social signal has strengthened. You quietly revise your conviction upward, not because the data has changed, but because the room has changed. This is groupthink in real time. So in the mirror, here's what it looks like in you. You walk into an investment committee meeting, 60% confident on a deal. That is your honest, independent assessment. Three colleagues speak before you. They're enthusiastic. By the time it is your turn, you find yourself saying, This is a strong opportunity with 80% conviction. Nothing in that meeting justified a 20% increase. No new data, no new analysis. The room just moved and you moved with it. What you experienced as building conviction was the bandwagon doing its work right inside your head. Your independent analysis, it didn't change. Just your social environment did. 

Ryan Miller

So in the room, when you're seeing it in anyone else, in any group setting, you want to watch for people who go quiet. When consensus forms quickly in an investment committee meeting, there are always members who had concerns, and then the room moved and they stopped talking. The bandwagon does not just inflate confidence in the majority, it silences the minority. And in investment management, the most important signal in the room is very often the one that just got quieted by the bandwagon. The person who stops talking is telling you something. Pay close attention to who that person is and what they were about. 

Ryan Miller

Now let's talk about the fix. The structural override for the bandwagon effect is pre-commitment and position independence. Before any group discussion on a deal, every investment committee member submits their individual written assessment, specific conviction level, specific concerns, before the conversation begins. Not a show of hands, written position in advance. This preserves independent thinking before the social dynamic can override it. After the discussion, if someone's position changed significantly from their pre-submission, ask them to name a few specific new data points that drove that change. If they can't, it was the bandwagon. 

Ryan Miller

Now, bias number 4 is what I call the blind spot bias. Not recognizing that you have biases is in itself a bias. The more intelligent and experienced you are, the more you know about cognitive psychology. The more frameworks you have studied, the more susceptible you are to this one. Because intelligence gives you better tools for rationalizing whatever your brain was already going to do. Now, looking in the mirror, here's what this looks like in you right now, in this moment. As I've been going through this list, you've likely been thinking about all the other people. You've been thinking about your partner who anchors, your analyst who chases availability, the LP who jumps in on bandwagons. That reflex, applying this lens outward before turning inward, this is the blind spot bias operating in real time. The smarter you are, the faster that calculation happens. And the faster you ran it, the more important it is that you hold back and turn the mirror on yourself first. 

Ryan Miller

Now, let's take a look at the room. The fund manager with blind spot bias is often the most credible person in the organization. They name every bias. They can spot patterns in others with precision. And they're completely unable to see those same patterns operating in their own decisions. You've worked with this person. They may have been your most respected colleague. Credibility and objectivity are not the same thing. The person who is the fastest at diagnosing bias in the room is not immune to it. They're often the most protected from feedback about it, though. Never confuse credibility with accuracy. 

Ryan Miller

Now let's talk about the fix. You cannot solve this one alone. That is structural, not motivational. The blind spots are invisible to you by definition. You need external mechanisms that do not depend on your own self-awareness. A trusted peer who observes your own decision making and tells you what they see without softening it. A structured premortem before every major decision where someone on your team is specifically assigned to find what you are missing. A decision journal where you write your reasoning before you know the outcome. Just so you can compare your memory to the actual record later. The cure for your blind spot is someone else's eyes. 

Ryan Miller

Now, bias number 5 is what I call choice supportive bias. Once a decision is made, your brain retroactively improves its quality. You start remembering the positives more vividly. You ever break up with someone in a bad relationship? I know I have in a in the younger years. And then you look back and you say, was it that bad? Maybe we should get back together. I'm sure that's never happened to anybody, but if it is, that is one example. Now, in investing, the risks you documented before you committed feel smaller after you wire the money. You're not seeing the investment more clearly after you own it. You are seeing it more favorably because you own it. And here's what it looks like in the mirror. After you close a deal, go back and reread that risk section of your investment memo. Compare what you wrote before you committed to what you actually think about those risks today. If the risk feels smaller now than they did when you wrote them, not because new data reducing, but simply because time has passed and now you own that position. That is choice, supportive bias, doing its work. And it's distorting your portfolio management abilities in ways that will be very expensive down the road. 

Ryan Miller

Now, in the room, you want to watch for how fund managers or people pitching you a deal discuss their portfolio companies versus deals that they passed on. Portfolio companies get generous framing. We knew there would be early challenges. We always expected the first two quarters to be the most difficult. The past gets dismissive framing. The thesis was always thin. The team was never quite right. Their assessment quality did not change. Their ownership position did. What you own looks better than what you do not. Not because of the data, but because of the psychology of the commitment. So be careful of that. 

Ryan Miller

Now, let's talk about the fix. You want to keep a decision journal. Write down every risk. I know that the best traders, the best hedge fund managers that I've talked to all do the same thing. So they keep a log of their decisions and try to pull out patterns. You want every risk, every concern, every doubt before you commit. Revisit that journal at six months and at 12 months after you've cut the check. Compare your pre-investment risk assessment to your current assessment. The shrinkage in perceived risk that is not explained by actual portfolio performance is in fact choice supportive bias, likely happening right between your ears. Name it. Account for it in your LP reporting. And never let that fact that you own something make you less rigorous about whether you should continue to own it. 

Ryan Miller

Then there's bias number 6, which I call the clustering illusion. See, human beings are pattern recognition machines. We evolved to find patterns because patterns meant survival. But in financial markets, that same capability generates false signals constantly. You ever look at something, stars or a cloud, and you actually see a face or something like that, there you go. That's the brain working. The clustering illusion is finding meaningful patterns in data that are too small to support them and building investment frameworks on noise. Can you imagine that? So in the mirror, here's what it looks like in you. A fund in your network has produced strong returns for, say, three consecutive years. Your brain registers this as a trend. Your conviction in that manager increases. But three years is not a statistically meaningful sample in private markets. Three strong years can be explained by a single vintage, a single sector tailwind, a single valuation environment, even a single president of the United States or whoever it is. See, pattern matching on a sample size that cannot support the conclusion you're drawing is a problem. The pattern feels real, the sample is too small. 

Ryan Miller

Now, in the room, the most dangerous pitch in fund management is the manager who has identified a system. You gotta be very careful with that. A repeating pattern in deal flow, market timing, or sector rotation that they have observed across a handful of data points now represents an edge. That pattern feels real to them. The deck is compelling, but ask them one question: What's the minimum sample size required for this pattern to be statistically significant? I remember in college when I was a statistics tutor, I remember them saying general rule of thumb is you want at least 30, but that's even too small. So 30 sample sizes to get a statistically significant uh sample size, that is important. Now, I'm not saying that that is a universal law, but the point that I'm trying to make is not necessarily the number, but the point I'm trying to make is to say, have you collected enough samples to get the average or the best or the outliers in this area? If you haven't, if it's three or four people or competitors or whatever it might be, just really be careful that there may be a bias here that is happening. And so you're looking likely, if it's a small sample size, at a clustering illusion dressed up as a strategy. So you really want to be careful, not only in other people pitching you, but also in yourself when you're deciding to invest or not. 

Ryan Miller

So let's talk about the fix. Before you identify any pattern is predictive, ask the statistical question directly. How many observations do I need in this pattern so that I could be sure that it's not random? Then count your actual observations. If you need 30 and you have eight, you don't have a pattern. You have a story. That story may be compelling, but it is not a strategy. Require sample size rigor from yourself and from every manager at your firm who presents you with a system. And let's not forget, people that are pitching you or that you're pitching. The number of observations required is not a detail. It is the entire argument. 

Ryan Miller

Now, the seventh bias is what I call confirmation bias. This one I see a lot. You seek, interpret, and recall information in ways that confirm what you already believe, right? So we see a lot of that. That can be social issues, that can be political issues, that could be economic issues, that can just be you and your spouse talking about how to raise children, and you go off of this. This is one of the most pervasive biases in investment management because the entire deal process rewards conviction. And conviction creates filters. See how that works? See, the problem is that your filters eventually stop working for you and start working against you. See? So in the mirror, here's what this looks like in you. You have a positive initial impression of a deal. So you read the bullish analyst reports, you schedule calls with the company's referenced customers, you model the upside scenario first, you're doing research. But you're doing it in the direction of a confirmation. See, the bare case is in your inbox, unread. The short seller report is in your downloads folder, unopened. You're not building conviction. You are effectively defending a position you already took in your mind before the due diligence even began. Now, looking at others in the room, watch the deal reviews for portfolio companies that are struggling. The fund manager who says the core thesis is intact after 18 months of missed targets, a CFO departure, a declining margin is not reassessing the investment. They're protecting the narrative they built when they fell in love with the deal. Their confirmation bias is not protecting the investment, it's protecting their ego. And the portfolio is paying the price every single quarter that the real assessment is deferred. 

Ryan Miller

So here's the fix: adopt a steel man practice for every investment thesis. Before you finalize any decision, write the strongest possible argument against it. Not a token risk section. The actual best case that a fully informed smart bear would make against your thesis. If you cannot write that argument convincingly, you've not done enough research. And when you find a compelling counterargument in your market, your first obligation is to engage with it seriously before you dismiss it. Evidence that challenges your thesis is more valuable than evidence that confirms it when you are obliterating this thesis. 

Ryan Miller

Then there's bias number 8, which I call conservatism bias. When new evidence arrives that contradicts your existing thesis, you update more slowly than the data warrants. You're not ignoring the evidence, you're processing it, but at a rate that lags behind what the facts now support. This bias is expensive because it keeps you in losing positions and out of changing opportunities far longer than the data justifies. Now, here's what this looks like in you. Let's say your portfolio company misses its first revenue target, then its second, then a key executive departs. At each stage, you have a reason why this data point is an anomaly rather than a signal. You're doing thesis maintenance instead of thesis review. The data is telling you something. You're updating your view, but slowly. So slowly that by the time you fully absorb what the evidence has been saying for 12 months, the damage to the investment is already done, and you might be too. 

Ryan Miller

Now let's look in the room. In investment committee meetings, a conservatism bias shows up as the fund manager who says, let's wait for one more data point. Every single time a difficult decision is required. One more quarter, one more management update, one more month of runway. They're not being rigorous. They're using the language of rigor to delay a decision they do not want to make. The data is almost never complete. At some point, waiting for completeness is itself a decision. And it is usually the wrong one. I still remember my investment banking professor many, many years ago. He came from Wall Street and he would always say, indecision is still a decision. I never forgot that. Props to Jim Engebretsen. 

Ryan Miller

Now, the fix build explicit review and exit triggers into every investment at the time of commitment, before you own it, before you're emotionally attached to the outcome. Define in writing the three specific conditions that would require a formal thesis review. Define the two conditions that would trigger an exit process. When those conditions are met, the review happens automatically. Not because you decided to do it in  the moment, but because your pre-commitment protocol, it's required. Rules made before the emotion beats the rules made during it, hands down, every time. 

Ryan Miller

That brings us to bias number 9, which I call the information bias. More information feels like you can make better decisions. And at certain junctions, it is. Information bias is the tendency to seek additional data beyond the point where it can change the quality of your decision. Because collecting information, it just feels like work. And work feels like progress, and progress feels like you are not yet responsible for the outcome. Be careful of that. Now here's what it looks like in you. You've done the primary research. You have the financials, the reference calls, the market analysis, the competitive landscape. Your three key questions are answered. And you are still pulling more reports, scheduling more calls, requesting more data from the company. Ask yourself honestly, what specific question do I need answered that I cannot answer with what I already have? If you cannot name the question, you're not doing due diligence, you are postponing a decision by calling it thoroughness. Be careful of that. 

Ryan Miller

Now, let's see what that looks like in the room. Information bias in your team looks like the analyst who always needs one more week, one more data set, one more customer reference before the memo is ready. They are thorough. They are diligent. But watch what happens when the deadline is compressed. Suddenly, the memo's ready. The additional research was not driving a quality of analysis, it was managing the discomfort of accountability. See, more information always makes people feel more confident. It does not always make them more accurate. So you want to watch out for that. 

Ryan Miller

So here's the fix. Define your decision criteria before you begin the research process. What are the three specific questions whose answers will drive 80% of this decision? Identify those questions first. Build your research process around answering them. When those questions are answered, make the decision. Set the threshold before you start, not after. Information collected beyond the threshold is not diligence. It is the anxiety dressed up as process and is one of the most expensive forms of avoidance in investment in fund management.

Ryan Miller

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Ryan Miller

Now, bias number 10 is what I call the ostrich effect. When you sense that information will be negative, you avoid looking at it. Not consciously. You would never admit that this is what you're doing. But the portfolio review cadence quietly drops on the struggling companies. The difficult report stays in your inbox an extra three days. The conversation you need to have with management teams keeps getting pushed to next week and the next week and the next week. You see how that works? So here's what it looks like in you. Your best performing portfolio company for three years starts showing some warning signs, missed revenue targets, rising churn, a CFO departure, and somehow making a conscious decision, your review cadence on this company drops from monthly to quarterly. You're not busy. You're avoiding. The more you care about an investment, the more painful the signals are. And the more painful the signals, the more powerful the avoidance. See, the ostrich effect is the most dangerous one with the investments you love most. 

Ryan Miller

So let's explore what it looks like in the room. Look at whose calendar is the hardest to get time on in the portfolio companies that are struggling. The fund managers who are most difficult to reach about a trouble investment, they're not managing it more carefully. They're managing it less. The ostrich effect turns avoidance into a management style. And eventually it turns what could have been a recoverable situation into a total portfolio write-off. The cadence you keep with your worst performing companies tells you more about your psychological health as an investor than the cadence you keep up with the best ones. 

Ryan Miller

So here's the fix. Just create a mandatory heightened review protocol that is built into your fund's operating procedures. Any portfolio company that hits specific negative triggers, missed targets, key person departures, covenant breaches, or competitive displacement automatically moves to an elevated review schedule. More attention, not less. More frequent touch points, not fewer. The worse the signals, the more rigorous the monitoring. And this protocol must be structural, not dependent on the judgment of the manager who is most emotionally affected by the company's performance. So you want to watch out for that. 

Ryan Miller

Now, bias 11 is what I call outcome bias. You judge the quality of a past decision by its outcome rather than the quality of the process that produced it. See, a risky bet that paid off was a good decision. A discipline, well-reasoned decision that resulted in a loss was a mistake. This logic is completely backwards, and it will systematically teach you the wrong lessons from your own experience. You can see how the brain can fool you, yourself, and your experience in the market. So here's what it looks like in you. Let's say you made a decision to invest in a company based on incomplete information against your own process because you like the founder and the momentum, it felt good. The company did a 3X in 24 months. You tell the story at LP meetings, you build your reputation on it, but you made a bad process decision that happened to produce a good outcome. Now, if you do not recognize that distinction, you'll repeat the bad process and eventually the lock runs out. Lucky outcomes from bad processes are the most expensive lessons you will ever not take. 

Ryan Miller

So here's what it looks like in the room. Watch how your investment committee evaluates manager track records. When a manager has a strong recent return, their process suddenly appears rigorous, well constructed, and repeatable. When a manager has struggled, the same process looks flawed. The process did not change, but the outcomes did. The investment committee is working backwards from the outcomes to judge the process, which tells you almost nothing about whether the process is actually sound or whether the returns are even stable. 

Ryan Miller

So here's the fix. You evaluate every decision by its process, not by its outcome. After every major result, win or loss, score your predecision checklist against what you actually did, not against what actually happened. A well executed decision that produced a loss is a learning. A poorly executed decision that produced a win is a warning. Keep a decision log that separates the process quality from the outcome quality. The patterns in that log across 20 or 30 decisions will tell you more about your actual edge than any single result ever could. 

Ryan Miller

Bias number 12 is what I call overconfidence bias. You overestimate the accuracy of your own forecasts. You set narrower probability ranges than the data supports. You underestimate how wrong you could be and how fast it could happen. See, overconfidence is not just common in investment management. It is structurally incentivized. Conviction sells. Uncertainty does not close funds or fundraising. So looking in the mirror, here's what it looks like in you. Take any forecast you made 12 months ago, a revenue projection, a market timing call, a valuation range. Compare it to what actually happened. If you were accurate within your stated confidence interval, more than 70% of the time, you're calibrated. Most investment professionals are not. Most discover when they actually track their forecasts against their outcomes over time that their stated confidence was consistently higher than the actual accuracy. The confidence, it wasn't lying, but the calibration was off. 

Ryan Miller

So in the room, the overconfident person in the room, it's easy to identify. At least it is for me. Maybe you guys haven't. Let me know in the comments, but the overconfident person, easy to identify. They have the narrowest ranges, the strongest conviction, and the least tolerance for the bear case. They're often the most persuasive person in the investment committee. And they're often the most wrong. Confidence is a communication skill. It is not an analytical one. The best forecasters in the world are distinguished not by their certainty, but by their calibration. They know precisely what they do not know and they price it accurately. 

Ryan Miller

Here's the fix. Track every forecast you make. Write down your projection, your confidence level, and the date. And then check the outcome. Do this for 20 decisions. The gap between your stated confidence and your actual accuracy rate is your overconfidence score. And it'll humble you faster than any lecturer. Once you know your calibration gap, you can correct for it. Widen your ranges. Build more conservatism into your base cases. Because the goal is not to be less confident. The goal is to be more accurate. And that is far more valuable than anything else you could do in this area. 

Ryan Miller

The 13 bias is the placebo effect, a tool, a framework, or a process that makes you feel more confident. Not because it's improving the quality of your decisions, but it's because it's reducing your anxiety about making them. See, the placebo effect in investment management is paying for that feeling of rigor rather than the substance of it. So in the mirror, here's what it looks like in you. You implemented a new decision framework 18 months ago. You feel more confident going into the investment committee meetings. Your memos look more thorough, but have your outcomes actually improved? Have you tracked the decisions you made under the new framework against the decisions made before it? If you cannot answer that question with data, you may be experiencing the framework the way a patient experiences a sugar pill. Real comfort, no active ingredient. Comfort and accuracy are not the same thing. 

Ryan Miller

Now, in the room, you want to watch for the fund manager who attributes strong performance entirely to their proprietary process, their system, their framework, their repeatable edge. When you dig into the performance, a significant portion of the returns are explained by market beta, vintage timing, or sector tailwind. Factors that have nothing to do with the framework. But the framework gets the credit and the manager gets the confidence. See, until the beta disappears and the framework is revealed as comfort, you're just experiencing a bias, not an edge. 

Ryan Miller

So here's the fix. Test your frameworks empirically. Run controlled experiments. Use a given process for, say, 20 decisions, track the outcomes, compare them to your baseline. If you cannot measure whether your framework adds value relative to what you would have done without it, if you're not running a process, you're running a ritual. Rituals have their place, but they should never be confused with alpha, and they should never be used as a substitute for accountability. 

Ryan Miller

Then there's bias 14, which is what I call pro-innovation bias. New technology, new business models, and new paradigms get credit, they have not yet earned. You overvalue novelty. You discount the risks of first mover disadvantage, adoption curve friction, and the graveyard of revolutionary ideas that were right about the technology, but wrong about the timing. So here's what it looks like in you. A founder presents a business built on a genuine novel technology platform. The technology works, the market is real, the team is strong, and you're 50% more willing to accept evaluation premium than you would for a comparable business with a proven model. The innovation tax is invisible to you because innovation is exciting. But you are paying a premium for the story, not for a risk-adjusted return, that fundamental support. 

Ryan Miller

So here's what it looks like in the room. Pro-innovation bias is structural in venture and growth investing. Deals with novel technology receive less rigorous scrutiny than deals with conventional models, because novelty signals disruption, and disruption signals upside. But for every transformative technology that achieved mainstream adoption on the timeline investors assumed, there are 20 that were right about the technology and wrong about the pace. Timing is not a detail in innovation investing. Timing is the entire game. And pro-innovation bias consistently makes investors optimistic about timing. Ever heard of a market bubble? There you go. 

Ryan Miller

So here's the fix. For every revolutionary technology thesis, build a realistic adoption timeline using historical analogs. What is a comparable technology and how long did it take to achieve the adoption rate that this business model requires at the projected valuation? If your scenario requires faster adoption than any comp technology has ever achieved in the last 30 years, your thesis contains an assumption that the data does not support. Novelty deserves a place in a well-constructed portfolio, but it does not deserve a pass on the underwriting. 

Ryan Miller

Then there's bias 15, which I call the recency bias. What happened recently feels more representative of the future than the historical record supports. As the saying goes, we learn from history that we do not learn from history. This is what it's talking about. See, after a strong bull market in whatever sector you're in, your your return expectations drift above the long run average. After significant correction, your risk appetite compresses below what a full cycle perspective would justify. You're managing to recent history instead of long-term fundamentals. And here's what it looks like in you. Your current market outlook is essentially a mirror image of the last 12 months. If the last year was strong, you're more bullish than the 20-year average warrants. If the last year was painful, you're probably more bearish than the full cycle data justifies. So your brain is treating recent events as a trend when they are frequently mean reverting. The most expensive decisions in investing are made at the extremes of this cycle. Maximum bullishness at peaks and maximum caution at troughs. 

Ryan Miller

And here's what it looks like in the room. Recency bias is the most visible in LP conversations. See, after a difficult vintage, LPs who were excited about an asset class two years ago are suddenly skeptical. After a strong vintage, capital that sat on the sideline floods in. The capital is moving against the cycle because recent performance is driving allocation decisions rather than a long-run thesis. So you want to watch where your LP base is increasing and decreasing their allocation. Their enthusiasm and skepticism are legged indicators, not leading ones. 

Ryan Miller

So here's the fix. Anchor every market outlook to a 20-year base rate, not the last 12 months. Create a one-page historical reference document covering at minimum three full market cycles that you're required to review before making any market directional decision. When you feel maximum bullishness, check the historical valuation multiples against current levels. When you feel maximum caution, check the long-run base rates for the recovery. The data almost always looks less extreme than your current emotional experience of the market. 

Ryan Miller

That brings us to bias number 16, which is what I call the salience bias. See you overweight information that is vivid, dramatic, or emotionally prominent. A single compelling story does more work on your decision than a full distribution of outcomes. Because the story is salient and the distribution is abstract. Your attention goes where the emotion is, not where the evidence is. And here's what it looks like in you. A founder delivers an extraordinary pitch. The narrative is crisp, the vision is compelling, and the presentation is exceptional. You leave the meeting with a strong positive impression. Two weeks later, during diligence, you discover that the unit economics, they don't work at scale. But the emotional residue of that pitch is still in the room with you. You find yourself looking for a way to make the numbers work because that pitch was so good. That, my friends, is salience of a bias. One vivid experience is doing more work on your judgment than the full analytical pitcher. 

Ryan Miller

Here's what it looks like in the room. Old claims, dramatic market projections, an exceptional anecdote move the investment community more than well-constructed statistical tables because salience is asymmetric. One vivid example activates decision-making systems in ways that a base rate simply cannot keep up. Sophisticated founders and deal sponsors, they understand this. See, the most dangerous pitches are the ones that are most beautifully constructed because the craft creates salience that substitutes for substance, and the room does not always notice the substitution. 

Ryan Miller

Now here's the fix patience “de-salience”. For every compelling story or vivid example you encounter in a deal process, immediately ask, what does the full distribution of outcomes for this cohort even look like? One exceptional case study is not data. It is a single data point. Your job is to understand the distribution, not to be moved by the tail. Build discipline for moving the example to the base rate every single time, especially when the example is compelling, because that is exactly when salience bias is at its most powerful. 

Ryan Miller

Now, bias number 17 is what I call selective perception. You filter information unconsciously based on your existing beliefs, expectation, and prior experience. You don't see everything that is there. You see the parts of the picture that your existing model predicts. And the parts that contradict your model are processed more slowly, if at all. They're weighted less heavily and they are remembered less vividly. So here's what that looks like in you. Before you read any analysis on a deal you're interested in, write down what you expect to find, your thesis, your assumptions, the risks you anticipate, then read the analysis and compare what you expected to find versus what was actually there. The gap between those two lists, what you found but did not predict, and what you predicted but was not there, is your selective perception fingerprint for that deal. And if the gap is large, your filters were likely stronger than the evidence. 

Ryan Miller

Now, in the room, the most experienced people in any investment organization are often most prone to selective perception because their experience was built on the most sophisticated filters. They are pattern matching at a speed and unconscious efficiency that junior colleagues just can't match. But those same filters cause them to miss what does not fit the pattern. The risk of expertise is that it becomes a lens that sees what it expects rather than what is actually there. The most credentialed professional in the room may be the one most blinded by pattern recognition. 

Ryan Miller

So here's the fix. Before reading any deal analysis, write your expectations in advance. What do you expect the financials to show? What risks do you expect to find? What gaps do you expect in the model? Then read the analysis and mark every place where reality deviated from your expectations. Positive deviations and negative ones equally. This practice trains your brain to seek the full picture rather than the confirmation of the picture it already has. Do this consistently and your selective perception will narrow. Skip it, and your filters will quietly run the process. 

Ryan Miller

And there's bias 18, stereotyping. You apply broad generalization to specific situations without sufficient individual analysis. You pattern match on the surface with characteristics, say a founder background, firm pedigree, educational institution, communication style, and those are all done in ways that substitute assumption for evidence. Investment management, stereotyping is replacing rigorous individual assessment with a mental shortcut that may have worked historically, but cannot be trusted as a substitute for the actual analysis. So here's what it looks like in you. You have a mental model of what a strong founder looks like: educational background, communication style, prior exits, network affiliation. See, when a founder matches that model, they receive credit they have not yet earned with you. When a founder deviates from it, they face a credibility gap they should not have to fill. Your mental model of who founders are was built from your prior experience and the success stories you have been most exposed to, which means it carries the survivorship bias of those samples and the selective perception of your own filters. 

Ryan Miller

Now, in the room, you want to watch for how rapidly your investment committee forms view on management teams in their first 10 minutes of a presentation. The quality of the first impression, the confidence, the articulation, the fit with the room is doing an enormous amount of work on the final decision. See, strong presenters who match the pattern expected receive less scrutiny. Founders that deviate from the expected profile receive more recognition. The pattern matching is not analytical, it is stereotyping, and it is costing you deal flow in the parts of the market where the most asymmetric returns often live. 

Ryan Miller

So here's the fix. Build a first principles evaluation scorecard for management teams based entirely on specific, measurable, observable criteria, and apply it uniformly, not gestalt impressions, not pattern match to prior successful founders, five specific criteria scored individually for every management team you evaluate, prior relevant experience, decision quality under pressure, ability to attract talent, capital efficiency and prior roles, response to adversity. The pattern is a shortcut. The scorecard is the work, and we always run the work. 

Ryan Miller

Then there's bias 19, which is what I call survivorship bias. You study the successes, you learn from the winners, you build your frameworks on the outcomes without systematically accounting for the outcomes that have disappeared from the data. See, survivorship bias does not just distort your view of markets and managers, it distorts your view of your own strategy. So here's what it looks like in you. Your strategy is built on a set of case studies. The companies that work, the managers who delivered, the deals that return capital, but the strategy that produced those successes also produce failures that you are not referencing in your set. See, failed companies do not give keynotes at conferences. Failed managers do not appear on podcasts, and failed deals do not make it in the deck. The reference set you're learning from is pre-filtered for success. It's a strategy built entirely on successful examples, and it may be optimizing for the wrong sample. 

Ryan Miller

Now, in the room, when a manager presents their track record, look at the denominator, not just the exits that return capital. All of the capital deployed, including the full write-offs, the restructuring, the portfolio companies that were quietly marked to zero and were never mentioned again. See, the track record you are shown at a first LP meeting has been edited by survivorship bias. The version you need to evaluate is the one that includes. Every company, including the ones that no longer exist. 

Ryan Miller

So here's the fix. For every success story you study, deliberately find and study three failure cases from that same strategy, vintage, and category. And if you cannot find the failures, it may mean that the data has already filtered them out, which is exactly the problem. Build a failure library, require postmortems on every write-off within the same rigor you apply the exit analysis to. The lessons from what did not work are more transferable than the lessons from what did, because they tell you where the actual risks live, not where the visible successes were. 

Ryan Miller

Then there's bias 20, which is a zero risk bias. See, in this bias, you prefer the complete elimination of a small risk over a substantially larger reduction of a bigger one. Because zero risk is emotionally satisfying in a way that significantly reduced risk never is. That feeling of having eliminated a risk entirely produces a disproportionate sense of security relative to its actual impact on your expected outcome. So here's what it looks like in you. You spend 30% of a deal's allocated capital on structures and projections designed to eliminate a 5% probability risk with a manageable loss severity. The expected value math does not support the spend, but eliminating that risk to zero felt rational. What you were actually buying was the emotional relief of certainty, not a meaningful improvement in your risk-adjusted return. See, the capital you deployed on the small risk was capital you did not deploy on the larger risk, and that one actually warranted the attention and the protection. 

Ryan Miller

And here's what it looks like in the room. Zero risk bias shows up in investment committee discussions about deal structure. The conversation consistently spends disproportionate time on low probability, high salience risks, the headline risk, the catastrophic scenario, the tail event at the expense of the moderate probability, moderate severity risks that are actually more likely to determine the outcome. The tail gets all the attention because eliminating it feels meaningful. The body of the distribution goes underexamined because managing a probability feels less satisfying than eliminating one. See how that works? 

Ryan Miller

And here's the fix: run expected value calculations on every risk management decision, probability of the event, loss severity if it occurs, cost of the mitigation, residual risk after mitigation. If the expected value of the projection is less than what it costs, you're paying for certainty rather than return. This is not always wrong. Some risks do warrant elimination regardless of the enterprise value math. But you should make that decision consciously with the numbers in front of you, not because zero feels better than reduced. 

Ryan Miller

Then there's bias number 21. I call it sunflower management. People around a dominant leader unconsciously align their analysis to what they believe that leader wants to hear. The analysis does not disappear, it just gets bent. Conclusions are softened, risks are minimized, upside is emphasized. The decision maker receives a version of the data that has been pre-filtered through the political environment they have created. And they usually have no idea it's happening. So here's what it looks like in the mirror. If you are a senior decision maker, when did someone last tell you that you were wrong with specific evidence in front of the group without softening it? If you cannot remember, it is not because you've been right. It is because you have created an environment where being right is less important than being aligned with you. The sunflower effect means the people around you are optimizing for your approval, not for accuracy. Your own authority is degrading the quality of your information. 

Ryan Miller

And here's what it looks like in the room. In any organization where the senior leader rarely hears no or the data does not support this, sunflower management is the operating model. The analysis has become a performance for the leader rather than an input to the leader. The most dangerous seat in investment management is the one that is never challenged. If everyone agrees with you in every investment committee meeting, something might be wrong. And it is probably not that you are always right. The absence of pushback is a governance signal and take that seriously. 

Ryan Miller

So here's the fix. Just create a written pre-commitment practice. Before any meeting where you will receive analysis or recommendations, write down your own independent position. After the meeting, compare your post-meeting view to your pre-meeting position. If your position consistently converges toward whatever the most senior person in the room said, regardless of the evidence, the sunflower effect may be operating. Build an explicit red team role in your investment process. Someone who is specifically accountable for challenging the prevailing view, not as a formality, as a structural requirement that cannot be quietly eliminated when it becomes uncomfortable. 

Ryan Miller

Then there's bias 22, which is what I call the champion bias. So you evaluate an investment based on the credibility of its sponsor, the person or firm who brought it to you, more than the merits of the investment itself. When a trusted champion is behind a deal, the diligence gets shorter, the concerns get softer, the bar gets lower. You're not co-investing in a company, you are lending your judgment to a relationship. So here's what it looks like in the mirror. A fund manager you respect leads the round. They have a strong track record. You admire their process. So your independent diligence is abbreviated. You do 30% of the work you would have done if this deal came from a stranger. Your memo reflects the champion's thesis more than your own. You tell yourself you are benefiting from their expertise. What you're actually doing is outsourcing your own judgment to a brand name and taking the full economic risk of a decision you did not fully make. 

Ryan Miller

And here's what it looks like in the room. Champion bias is most dangerous in co-investment and syndicated deal structures, where the lead investor's credibility functions as a substitute for individual diligence at the follow-on level. When the most common due diligence process among co-investors is are the right people in this deal? The champion bias has now become structural. The right people being in a deal tells you about demand. It tells you almost nothing about the actual quality of the investment. Demand is not diligence. Familiarity is not analysis. 

Ryan Miller

So here's the fix. Evaluate every investment as if it was brought to you by someone you've never met. What does the deal look like on its standalone merits? What are the unit economics, the competitive position, the management quality, the market, the dynamics evaluated entirely on their own without reference to who brought the deal or who else is in the round? If the deal cannot survive that evaluation, the champion is not adding credibility to the investment. They're adding cover to a decision that you shouldn't be making. 

Ryan Miller

Now finally, bias of number 23. It's what I call pendulation bias. See, after a significant loss, you become excessively risk adverse. After a significant win, excessively risk-seeking. Your strategy pendulates between extremes in response to recent outcomes rather than remaining calibrated to your long-run thesis. This is a bias that turns a temporary setback into a permanent strategy overall. And it turns a lucky outcome into a dangerous level of overconfidence. So here's what it looks like in the mirror. After your most significant investment loss, what happened to the portfolio construction? Did the loss reveal a genuine structural flaw in your process that required a systematic correction? Or did it produce a reaction? An overcorrection in the opposite direction, driven by the emotional experience of the loss rather than the analytical lessons from it. The fund manager who restructures their entire strategy in the wake of a significant loss is frequently showing pendulation bias. The new strategy feels like learning, maybe trauma dressed up as a process. 

Ryan Miller

So here's what it looks like in the room. Watch fund managers in the period immediately following a major write-off. Their investment criteria becomes more restrictive. Their risk appetite compresses. Their conviction levels drop across the board. Some of this is appropriate recalibration. But when the compression is uniform, when the manager becomes more conservative on everything, not just the specific factor that produced the loss, that is pendulation, not calibration. The market does not care about your last result. It rewards accurate analysis of current conditions. 

Ryan Miller

Now here's the fix. Build a base anchor, a written statement of your 12-month portfolio outlook with explicit probability weights and the specific factors driving them. Review it monthly. Every revision must be accompanied by documentation of the specific new data or analysis that drove the change. No new data, no new analysis, no change to the base case. This forces you to update based on the evidence rather than the emotion. The goal is not rigidity. The goal is to know the difference between updating your thesis and reacting to your feelings. That distinction is worth hundreds of millions of dollars over a career. 

Ryan Miller

So 23 biases, every one of them operating in every investment professional in every market in the world right now, including you and including me. The goal is never to eliminate them. The human brain is not a bug with 23 errors to be patched. It is a system that evolved to survive, not to optimize risk-adjusted returns. These biases kept our ancestors alive. In modern capital markets, they're expensive. The goal is three things awareness, structure, and accountability. Awareness is knowing the name of what is actually happening in your head when you're in the room, in real time, under pressure. Structure is building processes that do not depend on you being free of bias in the moment because you're not going to be. And accountability, putting someone in the room whose specific job is to see what you cannot see. I built these frameworks in 2017 because I was watching smart people make expensive decisions that the data just didn't support. I couldn't fix the decisions they had already made, but I could name the patterns. I could build the frameworks and I could give the investment committee tools that reduced the distance between the analysis and the decision. The portfolio grew from $50 million to $500 million. Decision quality is a compounding asset. Build it deliberately. You now have the map. The work is yours, and you're one step closer  to Making Billions. 

Ryan Miller

So if this episode gave you something, if you recognized yourself in even one of these biases, I want you to do two things right now. First, share this episode with the one person in your network who you know has a specific bias that is costing them the most, not as a critique, as a gift. Because the most valuable thing you can give someone in this business is a name for something they feel but have never been able to articulate. And second, I have built a Psychology Self-assessment for you based on all 23 of these biases, behavioral questions, auto-scored, so you can see exactly which biases represent your highest risk of blind spots and get a personalized action plan for each one. The link is in the notes. And if you're serious about building a top decile investment company,  the  Fund Raise Capital community is where this work continues. It is where fund managers come to build the knowledge, the community, and the infrastructure to operate at the highest level. The link to apply is also in the notes. Now I'm selective on who I let in, and I let in the people who are ready. You do these things, and you too will be well on your way in your pursuit  of Making Billions.