Podcast Episode 11: Why Tech Professionals Fall for the Sunk Cost Fallacy (And How It's Destroying Your Wealth)
This is the full transcript of Fireweed Capital Episode 11. Listen on Spotify, Buzzsprout, or use the player above.
The $50,000 Mistake That Started Everything
Here's a question that might hit close to home... Have you ever held onto a losing investment way longer than you should have? Maybe it was that crypto position you bought at the peak. Or those individual tech stocks you picked because you understood the technology. Or even your company stock that's been declining for months, but you keep telling yourself it's gonna turn around.
If you're nodding right now... you're not alone. And more importantly, you're not stupid. You've just fallen victim to one of the most costly cognitive biases in investing. One that's particularly brutal for smart, analytical people like us in tech.
Welcome to Fireweed Capital — wealth planning for tech professionals. I'm Dr. Adam Link, and today we're diving into the sunk cost fallacy. Now, before you think this is just another behavioral finance lecture... stick with me. Because this isn't about theory. This is about money. Real money. The kind of money that determines whether you retire comfortably in your fifties or work until you're seventy.
The sunk cost fallacy doesn't just cost tech professionals a few percentage points of returns. It can derail entire financial plans. And here's the kicker... the same qualities that make you excellent at your job — persistence, problem-solving, deep technical analysis — these exact same traits are what make you vulnerable to this particular investing mistake.
Think about how you approach a difficult bug in your code. You don't just give up after an hour, right? You keep digging. You try different approaches. You research edge cases. You might spend days on a single problem because you know that persistence pays off in engineering. That's exactly the mindset that serves you well professionally but can absolutely destroy your investment returns.
Consider someone who's a senior software engineer at a major tech company. They're making $300,000 a year, they've got RSUs vesting, they understand complex systems... but they're still holding onto that Peloton stock they bought at $120 because they've already lost so much that selling now feels like admitting defeat. Or maybe they're still in GameStop from the meme stock era because they've done the technical analysis and they're convinced the fundamentals will eventually catch up.
This isn't about intelligence. This is about mental models. In software development, most problems have solutions if you just persist long enough. You can refactor bad code. You can optimize slow queries. You can fix bugs through iteration and analysis. But financial markets don't work like that. Sometimes the best move is to cut your losses quickly and move that capital to better opportunities.
So here's what we're gonna cover today. First, we'll explore why engineers and other tech professionals are uniquely susceptible to the sunk cost fallacy. Spoiler alert: it's not because you're bad at math. It's because your brain has been trained to solve problems through persistence and iteration. But investing doesn't work like debugging code.
Then we'll look at the real cost of this bias. Not just the obvious losses from holding onto declining positions, but the hidden opportunity costs. The gains you miss while your capital is tied up in losers. The compound interest you forfeit over decades. We're talking about six-figure mistakes that compound into seven-figure retirement shortfalls.
And finally, we'll build a systematic framework for making rational exit decisions. Think of it as your personal algorithm for overcoming emotional attachment to investments. Because just like in software development, the best solutions are often the most systematic ones. We'll cover position sizing rules, rebalancing triggers, and how to separate your ego from your portfolio performance.
Now, I know what some of you are thinking. 'Adam, isn't this just advocating for panic selling?' Absolutely not. We're not talking about jumping ship at the first sign of volatility. We're talking about having a disciplined approach to position sizing, rebalancing, and loss management. The difference between emotional reactions and systematic decision-making.
And look, this hits me personally too. I spent years at tech companies, including Coinbase. I've seen brilliant engineers make devastating investment mistakes. Not because they lacked intelligence, but because they approached investing with the wrong mental models. I've watched people hold onto declining crypto positions because they understood the technology, even as those positions evaporated 80% of their value.
The truth is, understanding technology doesn't automatically make you a better investor. Sometimes it makes you worse because it gives you false confidence in your ability to pick winners based on technical merit alone.
So whether you're holding onto a losing position right now, or you just want to build better investment discipline for the future... this episode is for you. Subscribe wherever you listen to podcasts, and visit fireweedcapital.com for the show notes and full transcript of today's episode.
Let's start by understanding why your engineering brain might be working against you when it comes to investing...
Why Engineers Are Hardwired for Investment Disasters
So let's talk about why tech professionals fall into this trap more than almost any other group. And it starts with understanding how your brain has been trained to think about problems.
In software engineering, persistence is everything. You don't ship code with known bugs. You don't accept that a system is running at 10% efficiency just because fixing it is hard. You keep iterating until you find a solution. This mentality has made you successful in your career, and it's probably made you a lot of money through equity compensation.
But here's the problem... investing isn't debugging. Markets aren't logical systems that respond predictably to inputs. Sometimes the best solution is to abandon your current approach entirely and start fresh. But your engineering brain rebels against this idea because it feels like giving up.
Let me give you a concrete example. Consider someone who's a machine learning engineer at a major tech company. They buy shares of a promising AI startup because they understand the technology better than most investors. The company has solid fundamentals, innovative algorithms, and a strong technical team. But then the stock drops 30% over six months.
Now, in their day job, if a machine learning model isn't performing as expected, what do they do? They don't just abandon it. They analyze the data. They tune the hyperparameters. They try different architectures. They iterate until they get better results. This approach works in ML because the underlying system is deterministic. Better data and better models lead to better outcomes.
So naturally, they apply the same logic to their investment. They research the company more deeply. They read the technical papers. They analyze the competitive landscape. They convince themselves that their superior technical understanding means they can see value that the market is missing. And they hold onto the position, or even worse... they buy more.
This is where the sunk cost fallacy really kicks in. They're not just invested in the financial outcome anymore. They're invested in being right. Their ego is tied to their analytical ability. Selling would feel like admitting that their technical expertise doesn't translate to investment success. And that's a tough pill to swallow for someone who's used to being the smartest person in the room.
Here's another way this shows up. Tech professionals often have a deep understanding of specific technologies or business models. Maybe you worked on cloud infrastructure, so you think you have special insight into AWS or Azure. Or you've built mobile apps, so you feel confident evaluating social media companies. Or you understand blockchain technology, so you feel qualified to pick which crypto projects will succeed.
This knowledge can actually be dangerous because it creates overconfidence. You might hold onto a position longer than you should because you're convinced your domain expertise gives you an edge. But markets are forward-looking and often irrational. Your technical understanding of how a technology works doesn't predict how investors will value that technology next quarter or next year.
And then there's the analysis paralysis component. Engineers are trained to gather comprehensive data before making decisions. In your job, premature optimization might be the root of all evil, but thorough analysis prevents major mistakes. So when an investment starts declining, you default to doing more research.
You read the quarterly reports. You analyze the technical metrics. You compare the company to competitors. You build spreadsheets with discounted cash flow models. You read analyst reports and technical blogs. All of this takes time, and while you're analyzing, the position keeps declining. But because you're still in research mode, you don't take action.
This is fundamentally different from how successful investors approach position management. Professional fund managers often have strict rules about cutting losses quickly. They might sell any position that drops more than 10% or 15% from their entry point, regardless of their conviction level. They understand that preserving capital is more important than being right about any individual investment.
But engineers hate arbitrary rules like this because they seem... well, unengineered. Where's the rigorous analysis? Where's the systematic approach? The irony is that having systematic rules for cutting losses IS the systematic approach. It's just not the kind of analysis you're used to doing.
There's also the sunk cost mental accounting that happens specifically with tech equity compensation. Let's say you work at a company where your RSUs have declined 40% over the past year. You're sitting on unrealized losses, but you keep telling yourself that selling now would lock in those losses. So you hold onto the shares, hoping they'll recover to your original grant price.
But here's the thing... those paper losses are already real losses. The decline already happened. Holding onto the shares doesn't change that fact. What matters now is whether those shares are likely to outperform other investments going forward. But because you're anchored to your original grant value, you're making decisions based on what you paid, not what the shares are worth today.
This anchoring bias is particularly strong with equity compensation because the shares feel "free." You didn't write a check to buy them, so the losses feel less real than if you had purchased the shares with cash. But every dollar of unrealized loss in your company stock is a dollar that could be working harder for you in a diversified portfolio.
There's also the opportunity cost factor that most tech professionals don't fully appreciate. While you're holding onto a declining position, hoping it will recover, you're missing out on other investments that could be generating positive returns. In engineering terms, you're dealing with a resource allocation problem, but you're not optimizing for the right objective function.
Let's say you bought $10,000 worth of an individual tech stock that's now worth $7,000. You're down $3,000, which feels terrible. So you hold onto it, hoping to break even. But what if that $7,000 could generate 8% annual returns in a diversified portfolio? Over ten years, that's the difference between having $15,122 and having... well, hopefully at least $10,000 if your original pick ever recovers.
That opportunity cost compounds over time. And this is where the sunk cost fallacy gets really expensive for tech professionals. Because you typically have high incomes and substantial equity compensation, these individual position mistakes can represent significant dollar amounts. A $50,000 mistake in your thirties becomes a $200,000 retirement shortfall when you account for lost compound growth.
The behavioral finance research on this is pretty clear. Overconfident investors — and successful tech professionals definitely qualify as overconfident — tend to trade too frequently and hold losers too long. They underperform simple index investing by meaningful amounts, often 2-3% annually. Over a career, that's the difference between comfortable retirement and working into your seventies.
And here's what makes it worse... the same analytical skills that make you great at your job can actually reinforce these mistakes. You're good at building complex mental models and justifying decisions with data. So when you want to hold onto a losing position, you can construct elaborate scenarios where holding makes sense. You can find data points that support your thesis. You can rationalize almost any investment decision if you're smart enough and motivated enough.
But markets don't care about your rationalization. They don't care how clever your analysis is or how well you understand the underlying technology. Sometimes the market is wrong, but it can stay wrong longer than you can stay solvent. And even if you're eventually proven right, the opportunity cost of being right slowly might be higher than the cost of being wrong quickly.
So how do we fix this? How do we reprogram our engineering brains to think about investing differently? That's where we need to build systematic frameworks that override our natural biases...
The Hidden Costs: What Sunk Cost Thinking Really Costs You
Now let's get specific about what this bias actually costs you. Because when we talk about the sunk cost fallacy, most people think about the obvious costs — the direct losses from holding declining positions. But the real damage goes much deeper than that.
The first cost is the opportunity cost we touched on earlier. Every dollar you keep in a losing position is a dollar that's not generating positive returns elsewhere. But let's quantify this with some realistic numbers that'll hit home for tech professionals.
Imagine you're a senior engineer making $250,000 a year with significant RSU grants. You decide to invest $50,000 in individual tech stocks because you understand the companies and technologies. Over the next two years, these positions decline to $35,000. You're down $15,000, which feels terrible, so you hold onto them hoping to break even.
Meanwhile, a broad market index fund generates 8% annual returns over those same two years. If you had cut your losses at $40,000 after one year and invested in the index, here's what would have happened... That $40,000 would have grown to about $46,650 over the second year. So by holding onto your losers, you missed out on $11,650 in gains on top of your $15,000 in losses.
But it gets worse. Let's say this pattern repeats itself. You continue making individual stock picks, and on average, your losers decline by 30% before you finally sell them after holding for an extra year. Meanwhile, you could have been earning market returns of 8% annually.
Over a 20-year career, this pattern could easily cost you $300,000 to $500,000 in cumulative wealth. And that's being conservative. If you're consistently underperforming by 2% annually due to sunk cost thinking, that translates to a 40% reduction in your terminal wealth over two decades. For someone who could have accumulated $2 million, that's $800,000 in lost wealth.
The second hidden cost is what I call "attention debt." Every losing position you hold onto requires mental bandwidth. You check the stock price regularly. You read news about the company. You spend time analyzing whether the thesis is still intact. You stress about whether to sell or hold or buy more.
This mental energy has a real cost. Time spent analyzing your portfolio of individual stocks is time not spent on higher-value activities. Maybe that's building side projects that could increase your income. Maybe it's learning new skills that advance your career. Maybe it's just having more mental space for family and relationships.
Consider someone who holds positions in eight different individual stocks, three crypto investments, and a couple of sector ETFs. They're spending hours each week managing this portfolio. Reading earnings reports. Following crypto Twitter. Analyzing technical charts. Monitoring their company stock price. This time commitment easily adds up to 10-15 hours per month.
Now, what if they spent that same time learning a new programming language or building a side business? Or what if they just invested in index funds and spent that mental bandwidth on advancing their career? For a tech professional earning $300,000 annually, every 1% career advancement from additional focus could be worth $3,000 per year. Over a decade, that's $30,000 in additional income that compounds indefinitely.
The career advancement alone could easily be worth more than any incremental returns from active portfolio management. But because portfolio losses are visible and career opportunity costs are invisible, we tend to focus on the wrong optimization.
The third cost is what economists call "portfolio distortion." When you hold onto losers too long, you end up with a portfolio that doesn't reflect your actual investment thesis. Your asset allocation drifts from your intended targets. Your risk exposure becomes concentrated in positions you never meant to overweight.
Let's say you intended to have a balanced portfolio with 70% stocks and 30% bonds. But because you keep holding onto declining stock positions while your winning positions grow, you end up with 85% stocks. Worse, because you're reluctant to sell your company stock at a loss, you end up with 40% of your portfolio in a single stock instead of the 10% you originally planned.
This portfolio distortion increases your risk without increasing your expected returns. You're taking on more volatility than you intended, but you're not being compensated for it. Professional fund managers would never let their portfolios drift this far from their targets, but individual investors do it all the time because of loss aversion.
And here's the kicker... this concentration risk becomes most dangerous precisely when you can least afford it. Market declines often affect correlated positions simultaneously. So your individual tech stocks might decline at the same time as your company stock, creating a double hit to your net worth just when job security might also be at risk.
The fourth cost is the tax inefficiency. When you hold onto losing positions, you're not harvesting those losses to offset gains elsewhere in your portfolio. Tax-loss harvesting can add 0.5% to 1% of annual value to your after-tax returns, but only if you're actually willing to realize losses.
For someone in a high tax bracket — which includes most tech professionals — this tax alpha can be substantial. If you're in the 32% federal bracket plus state taxes, you might be paying 40% or more on short-term gains. Every $10,000 in tax-loss harvesting could save you $4,000 or more in taxes.
And here's where it gets particularly expensive for tech professionals... You often have substantial gains from RSU vesting or successful company stock that creates tax liabilities. If you were systematically harvesting losses from your other investments, you could offset some of these gains and reduce your tax bill significantly. But if you're holding onto losers because you don't want to admit defeat, you're leaving this tax benefit on the table year after year.
The fifth cost is what I call "learning opportunity loss." When you hold onto losing positions for emotional reasons, you're not learning the right lessons from your mistakes. Instead of updating your investment approach based on what didn't work, you're doubling down on strategies that have already proven unsuccessful.
Successful investors fail fast and update their models quickly. They view each loss as data that helps them make better decisions going forward. But if you're holding onto positions hoping they'll recover, you're not processing the feedback loop that would make you a better investor.
This is actually similar to a concept in software development called "fail fast." In coding, you want your tests to fail quickly so you can identify and fix problems early. The same principle applies to investing. Small, quick losses are much better than large, slow losses that prevent you from learning and adapting.
The sixth and perhaps most insidious cost is the psychological impact. Watching losing positions day after day creates stress and anxiety that affects your decision-making in other areas. You might become overly conservative with new investments because you're already feeling burned. Or you might take excessive risks trying to make up for your losses.
This psychological scarring can last for years. I've seen tech professionals who had bad experiences with individual stocks become so risk-averse that they keep all their money in savings accounts, missing out on decades of compound growth. Others swing in the opposite direction and start gambling on meme stocks or crypto to try to make back their losses quickly.
The sunk cost fallacy doesn't just cost you money on the specific positions you mismanage... it can distort your entire relationship with investing for decades.
So when we add up all these costs — the opportunity costs, the attention debt, the portfolio distortion, the tax inefficiency, the learning opportunity loss, and the psychological impact — we're not talking about small amounts. For a typical tech professional, sunk cost thinking could easily be a million-dollar mistake over the course of a career.
The good news is that once you recognize these patterns, you can build systems to prevent them. And that's exactly what we're gonna cover next...
Building Your Investment Algorithm: A Systematic Framework for Exit Decisions
Alright, so we've diagnosed the problem and quantified the costs. Now let's build a solution. And I want to approach this the way you'd approach any complex problem in your engineering work... with systematic rules and automated decision-making that remove emotion from the equation.
Think about how you write production code. You don't rely on remembering to handle edge cases or hoping you'll make the right decisions under pressure. You build in error handling, monitoring, and automated responses. We need to do the same thing with your investment portfolio.
The first component of your investment algorithm is position sizing rules. Before you even buy anything, you need to decide how much you're willing to lose. This isn't pessimistic thinking... it's risk management. In software terms, it's like setting memory limits for processes so that one runaway task can't bring down your entire system.
Here's a simple rule that works for most tech professionals: never put more than 5% of your investable assets in any single position. And if you're buying individual stocks or crypto, never put more than 2% in any single speculative position. These aren't arbitrary limits... they're based on how much you can afford to lose without it meaningfully impacting your long-term financial plan.
Let's say you have $200,000 in investable assets. That means no more than $10,000 in any single stock, and no more than $4,000 in any crypto position. If that individual stock goes to zero, you've lost 2% of your portfolio, not 10% or 20%. That's a manageable loss that doesn't derail your retirement planning.
The second component is stop-loss rules. This is where you predefined at what point you'll sell a position, regardless of your emotional attachment to it. Professional traders use these religiously, but most individual investors resist them because they seem arbitrary.
Here's a rule that balances systematic discipline with reasonable flexibility: sell any individual position that declines more than 20% from your purchase price, unless you can articulate a specific, measurable catalyst that will reverse the decline within six months.
Notice the specificity here. It's not "the stock is cheap now" or "the technology is still sound." It has to be a specific catalyst with a timeline. Maybe it's an upcoming product launch with quantifiable market potential. Maybe it's a strategic partnership announcement that's already been confirmed. But it can't be vague hope that things will improve.
And here's the critical part... you make this decision when you buy the position, not when you're facing the loss. Write it down. Put it in your investment notes. When you're down 20%, you're not in the right emotional state to make objective decisions about whether to hold or sell.
The third component is rebalancing triggers. This prevents portfolio drift and forces you to sell high and buy low systematically. Set calendar reminders to review your portfolio every quarter. If any asset class or individual position has grown to more than 10% above your target allocation, trim it back to the target.
This rule is particularly important for tech professionals because your company stock can easily become overweight as it appreciates or as you receive new RSU grants. If your target allocation to company stock is 10% but it's grown to 25% of your portfolio, you need to sell some shares regardless of your opinion about the company's prospects.
This feels counterintuitive because you're selling your winners, but that's exactly the point. Systematic rebalancing prevents any single position from dominating your portfolio, even if that position has been successful. It's portfolio diversification through process, not through prediction.
The fourth component is what I call the "opportunity cost audit." Every quarter, look at your individual holdings and ask this question: if I had this amount of cash today, would I buy this specific investment? If the answer is no, sell it immediately and invest the proceeds in something you would buy today.
This mental exercise helps you separate sunk costs from forward-looking investment decisions. The money you have tied up in declining positions isn't "your original investment"... it's capital that could be deployed elsewhere. The question isn't whether you're comfortable with the loss you've already taken. The question is whether this investment is the best use of your capital going forward.
Let me give you a concrete example. Say you bought $10,000 worth of a cloud computing stock that's now worth $7,000. The sunk cost fallacy says "I'm already down $3,000, so I might as well hold and see if it recovers." But the opportunity cost audit says "I have $7,000 to invest today. Would I put it in this specific cloud stock, or would I put it in a diversified index fund?"
If your honest answer is the index fund, then sell the stock today. The $3,000 loss already happened. Holding onto the position doesn't change that fact, but it does prevent you from earning better returns on the remaining $7,000.
The fifth component is tax optimization integration. Build tax-loss harvesting into your systematic rules so that you're actually benefiting from your investment mistakes instead of just holding onto them.
Here's how this works in practice: at the end of each calendar year, review all your taxable account positions. Sell any position with unrealized losses and immediately invest the proceeds in a similar but not identical investment. This locks in the tax loss while maintaining your market exposure.
For example, if you have losses in individual tech stocks, sell them and immediately buy a technology sector ETF. You maintain exposure to the same sector, but you've harvested the loss for tax purposes. Wait 31 days to avoid the wash sale rule, then you can decide whether to switch back to individual positions or stay with the diversified approach.
The sixth and final component is what I call "complexity budgeting." Limit the number of individual positions you hold so that you can actually monitor and manage them effectively. A good rule of thumb is no more than 10-15 individual holdings across your entire portfolio.
If you want more diversification than that, use funds and ETFs instead of adding more individual positions. This constraint forces you to be selective about which individual investments are worth the additional complexity and monitoring overhead.
Now, let's talk implementation. The biggest mistake people make is trying to implement all of these rules immediately across their entire portfolio. That's like trying to refactor an entire codebase at once... it's overwhelming and error-prone.
Instead, implement these rules for new investments starting today. For existing positions, apply the opportunity cost audit quarterly and gradually migrate toward your systematic approach. Set calendar reminders for quarterly reviews. Use spreadsheets or apps to track your position sizes and allocation targets.
And here's the most important part... these rules are only effective if you actually follow them. They need to become automatic responses, not suggestions you consider when it's convenient. Just like you don't skip code reviews because you're confident in your code, you don't skip stop-losses because you're confident in your investment thesis.
The goal isn't to eliminate all investment losses... that's impossible. The goal is to keep losses small and manageable while preserving capital for better opportunities. Think of it as portfolio resiliency rather than portfolio optimization.
With these systematic rules in place, you'll find that investing becomes much less stressful and much more profitable. You'll stop second-guessing every decision and start building wealth consistently over time...
Your Action Plan for Overcoming Sunk Cost Thinking
So let's wrap this up with your action plan. We've covered a lot of ground today, but the key takeaways are actually pretty straightforward.
First, recognize that your engineering mindset, while incredibly valuable in your career, can work against you in investing. The same persistence and analytical depth that make you excellent at solving technical problems can lead you to hold onto losing investments far longer than you should. Investing isn't debugging... sometimes the best solution is to cut your losses quickly and redeploy that capital elsewhere.
Second, understand that the sunk cost fallacy isn't just about the obvious losses from declining positions. The hidden costs — opportunity cost, attention debt, portfolio distortion, tax inefficiency, and psychological damage — often dwarf the direct losses. For tech professionals with high incomes and substantial equity compensation, these hidden costs can easily add up to hundreds of thousands or even millions in lost wealth over a career.
And third, build systematic rules that remove emotion from your investment decisions. Position sizing limits, stop-loss rules, rebalancing triggers, opportunity cost audits, tax-loss harvesting, and complexity budgeting. These aren't suggestions... they're your investment algorithm. Follow them consistently, just like you follow coding standards and deployment procedures.
Now, if you're currently holding onto some losing positions and feeling called out by this episode... you're not alone. Every successful investor has been there. The question isn't whether you've made these mistakes... it's whether you're going to learn from them and implement better systems going forward.
Start with one simple change: implement position sizing rules for any new investments. Never put more than 5% of your portfolio in a single position, and never put more than 2% in any speculative investment. This one rule alone will prevent most catastrophic losses.
Then, over the next quarter, apply the opportunity cost audit to your existing holdings. For each position, ask yourself honestly: if I had this amount of cash today, would I buy this specific investment? If the answer is no, sell it and invest the proceeds in something you would buy today.
And look, I get it. Selling at a loss feels like admitting defeat. But here's the thing... the best investors are not the ones who are right most often. They're the ones who keep their losses small and let their winners run. They fail fast and learn quickly.
If you found this episode helpful, here's what I'd ask you to do. Share it with a colleague who might be struggling with the same issues. We all know someone who's been holding onto that one losing position for way too long, hoping it'll eventually recover. Send them this episode. It might save them thousands of dollars.
And if you want to go deeper on systematic investing approaches, visit fireweedcapital.com for more resources on portfolio construction and behavioral finance. The show notes for this episode include links to studies on overconfidence bias and practical tools for implementing stop-loss rules.
For those of you who are regular listeners and want to explore whether a more systematic approach to wealth planning makes sense for your situation, you can schedule a conversation at fireweedcapital.com/meet. We help tech professionals build disciplined investment processes that align with their long-term financial goals.
But most importantly, remember that investing is a long-term game. The goal isn't to never make mistakes... it's to keep your mistakes small and learn from them quickly. The systematic approach we discussed today will help you do exactly that.
Before we close, I need to share the mandatory disclosure: The information in this podcast is for educational purposes only and does not constitute personalized financial advice. Past performance is not indicative of future results. All investing involves risk, including possible loss of principal. Please consult a qualified financial professional before making investment decisions.
Thanks for listening to The Fireweed Capital Podcast. Until next time — keep building wealth on your terms.