This business model is under attack from three directions (regulation, economics, and AI). It may not survive in its current form.

Defining the players

The sell-side includes banks and brokerages like Goldman, Morgan Stanley, and Jefferies. They provide research, trading, and deal services to investors. A sell-side equity research analyst covers about 15 to 25 stocks in one sector. They publish opinions on these stocks. Their clients are the buy-side money managers.

The buy-side includes institutions that manage money and buy securities, like stocks. These are hedge funds, mutual funds, and pensions. Buy-side analysts and portfolio managers decide what to buy with their funds’ money.

The revenue streams

The broker vote

Pricing is bespoke and negotiated per client. The institutional investors set aside a discretionary budget for research each year. At the end of the year, they do a broker vote. Their investment teams vote on which sell-side research analysts provided the most value. Then they dole out dollars accordingly.

Corporate access is the single biggest monetization lever. It often drives the outcome of the broker vote more than any written research reports. Corporate access means meeting with a company’s management team. Sell-side analysts help make these connections. It includes conferences, non-deal roadshows, and 1:1s with CEOs. Consider the incentive this creates for sell-side analysts. Their compensation rides on whether the CEOs of the companies they cover like them. Not very unbiased, I’d say.

Investment banking transactions (indirect)

In 2002 and 2003, lawmakers passed regulations after the dot-com scandals. These rules separated investment bank transactions from the pay of research analysts. The investment banking team can still influence which analysts are most valuable to the firm. They base this on the deal pipeline. Bankers want to pitch private companies on having the best analyst in their industry.

Trading commissions

Trading commissions have largely collapsed as a profit stream. Most trades are now done electronically. They cost much less than the fees high-touch trading desks used to charge. The rise of passive capital has also resulted in less active trading and less research consumed. The trading commission pool still exists, but at a fraction of its former size.

Regulation tried to unbundle the business model

Regulators ran the experiment of forcing this business model to change. In 2018, Europe’s MiFID II rules made investors pay for research separately from trading. This resulted in research budgets shrinking, analyst headcount falling, and coverage of smaller companies vanished. It went badly enough that regulators began walking back the regulations. Now, European firms can bundle research and trading payments once again. The lesson learned is that when clients are forced to put an explicit price on sell-side research, they decide it isn’t worth much.

What the buy side wants to pay for

Buy-side investment teams dismiss sell-side analysts’ price targets and ratings (buy/hold/sell). Any half-decent buy-side firm runs its own analysis and has its own thesis. They are, however, willing to pay for sell-side research that offers proprietary insights.

This research might use satellite images of a retailer’s parking lot. It could also include credit card transaction data and interviews with customers and suppliers. As a sell-side research analyst, I called STD clinics to assess volume trends for various competitors, had blood drawn at three different labs in just one day, and read the serial number off the back of a new sequencer in a genomics lab.

AI is commoditizing research

The proprietary boots-on-the-ground research that once gave an edge is now being leveled out. Buy-side firms are increasing their spend on alternative data and using genAI to analyze it efficiently. Citadel and BlackRock have added genAI tools to their platforms. Large funds are creating their own systems. They are using private LLMs and training them on licensed data sets.

The buy-side is doing its own research in a more sophisticated way than before, using AI agents at little marginal cost. The market has commoditized the last differentiated product of sell-side research. This leaves only corporate access as their final moat. Equity research becomes little more than a relationship broker.

What this means for the individual investors

An individual investor can use AI to replace a junior investment analyst. AI can read and summarize a 10-K quickly and for minimal cost. This provides no edge to institutional investors who have access to the same AI tools. When everyone has AI, AI is not an edge but table stakes.

Proprietary research and alternative data often influence stock prices quickly. This happens before individual investors have a chance to act. AI doesn’t equalize access to information. Institutions still have better access to data.

But individual investors have edges that institutions can’t replicate, and AI amplifies them. An individual investor can own small-cap stocks too illiquid for a billion-dollar fund to touch. They can hold through a drawdown with no career risk and no quarterly benchmark to answer to. And now, with AI, one person can do the processing work of a junior analyst on companies too small for Wall Street to cover at all. AI plus small caps plus patience is a real strategy.

This period has made a straightforward investment strategy popular: “VOO and chill.” It’s easy enough to fit on a bumper sticker. You buy and hold a single index fund that tracks the market. If you followed this strategy, you would have averaged a 15% gain per year. Compare this to the long-term market average of 10%. It may sound like a minor difference, but the compounding over time is what makes it add up. It’s the difference between $1,000 growing to roughly $2,600 vs. $1,000 growing to roughly $4,200.

Growth Driven by a Select Few

The abnormal returns came from a small group of American tech companies. These are Apple, Microsoft, Alphabet, Amazon, Nvidia, Meta, and Tesla. In 2015, these seven made up about 12% of the S&P 500, with a combined market value of around $2.2 trillion. Today, their combined value exceeds $22 trillion, which is roughly a tenfold increase. They account for about a third of the entire index.

The chart needs a log scale to fit on one axis. Nvidia grew around 87 times, and Tesla about 44 times. The slower members still grew five to eight times.

Companies rise and fall. That’s the natural life cycle of business. But consider how rare these seven businesses are. The scale of these businesses is without precedent. The companies at the top of today’s market report record annual profits. These profits are the largest ever seen by any public company.

The other 493 companies of the S&P 500 grew at a historically normal rate during this period, roughly 9% per year. The whole abnormality revolves around seven names. The wealth created by the VOO and chill crowd depends on them.

Prices Increased

The other factor that drove abnormal returns was multiple expansion. Investors now pay more for each dollar of earnings than before.

The Shiller CAPE index compares market prices to inflation-adjusted earnings. Since 1881, its long-run average has been 17x. Today, the reading is 42x. Investors are now paying about 2.4 times the usual amount for each dollar of earnings. In 145 years of data, only December 1999 showed a higher rate. That was months before the index dropped by nearly half.

Part of what pushed prices up is the supply of money flowing into the stock market itself. Between 2016 and 2025, passive funds in the U.S. took in $6.4 trillion of new money while active funds bled out $2.4 trillion. In total, about $4 trillion of new capital flowed into the market over the decade. This reflects both a rotation and an expansion. This shift increased index funds’ share of equity fund assets from 36% to 57%. This affects prices because passive capital doesn’t care about value. It doesn’t ask if Nvidia is cheap; it just buys what the index includes, regardless of what the price is.

Some of the price expansion is fair. In the 1930s, the stock market was railroads, steel mills, and other capital-intensive industrials. The stock market today is higher quality. It consists of scalable software platforms with high profit margins. A dollar of software earnings deserves a higher price than a dollar of railroad earnings.

How much more could multiples realistically expand from here? By any historical measure, almost none. At 42x, the CAPE has been exceeded only once, right before a massive crash. Goldman Sachs predicts the S&P 500’s forward multiple will drop from 23x to 21x by 2035. They expect the U.S. stock market to return 6.5% a year. This is much lower than the 15% annual returns seen over the last decade.

Keep in mind, the CAPE has sat above its long-run mean almost continuously since 1991. Anyone who acted on the belief it’s overpriced would have missed the best thirty-year run in market history.

International Stocks Sat This One Out

Over the last decade, international stocks significantly underdelivered relative to the gain of U.S. stocks. The valuation gap that opened up along the way is now the widest on record. The S&P 500 trades around 21.5x forward earnings, non-U.S. stocks trade near 15.5x, and emerging markets sit at 13x.

Cross-country CAPE comparisons are not always reliable. Different accounting, different sector mixes, different governance, and cheap markets are frequently cheap for excellent reasons.

The explanation for this gap is primarily sector composition. International markets did not enjoy the AI boom the way the U.S. did. A J.P. Morgan analysis found that since ChatGPT launched in November 2022, AI stocks accounted for 75% of the S&P 500’s returns. They also contributed to 80% of the earnings growth. Taiwan’s chip fabs and Korea’s memory makers were unique cases positioned to benefit from this boom.

Claude Fable 5 is the new top of Anthropic’s lineup. It ranked highest on Hebbia’s financial-reasoning benchmark. IMC, a trading firm, noted it outperformed all their internal trading tests. It went live in June, got pulled offline three days later by U.S. export-control rules, and came back online last week.

Fable checks each request for risks, like cybersecurity and biology. If something raises a flag, it passes the session to Opus, a less powerful model. Anthropic says that happens in under 5% of sessions. Mine ran closer to half, sometimes on a prompt that had worked minutes earlier in another window. Anthropic confirmed that a portfolio review is allowed for Fable. I still don’t know what triggered this.

To start, I loaded a CSV of my stock and ETF holdings and gave Fable my age, filing status, net worth, income, and goals.

Generic advice isn’t advice

Be cautious with investment advice from anyone who doesn’t understand your financial situation. Their guidance may not be right for you. There’s no perfect investment. There are only those that fit your risk tolerance, time horizon, and goals better or worse. Advice from someone who doesn’t know your situation is close to worthless.

So before Fable analyzed anything, I had it quiz me to determine my risk tolerance. What people say they’ll do in a crash and what they actually did in the last one are usually different answers.

Fable’s quiz focused on behavior more than self-report. It asked what I did the last time the market dropped 40%. It also wanted to know why I’m still holding the bond funds. It asked which would hurt more: a position going to zero or missing a stock on my watchlist that tripled. Then it checked my answers against my cost basis to see whether I’d held through the lows I claimed I’d held through. It graded my trading history instead of my story about myself.

Checking the holdings against the goal

Once it had a read on my risk tolerance, Fable went through the holdings against my stated goals. It marked my bond and commodity allocation as too high for my goals. It also urged me to sell the positions that were too small to matter.

Plotting the portfolio by risk and return

Then I asked it to plot every position on a grid, risk on one axis, return potential on the other. I ran the same prompt twice in separate windows to see whether the map stayed the same.

I got a different map each time, but much of the map held. My large-cap software names sat in the low-risk, modest-return corner both times. The profitless quantum-computing and gene-editing names came back high-risk on both passes. The green cluster Fable read as limited downside with real upside didn’t move. Where the two maps split was a dozen or so names sitting right on a dividing line. CRBU and GUTS dropped out of the high-reward quadrant into the limited-upside one on the second run. FIG went the other way. AXON and PLTR crossed the risk line. MNSO, which sits near both lines at once, showed up in the sweet spot on one map and the danger zone on the other.

These models are non-deterministic. This means that the same prompt generates scores from scratch each time, resulting in slight differences. If Fable scores one ticker 5.6 on return one run and 5.4 the next, it falls on opposite sides of a line drawn at 5.5. It lands in a different quadrant even though the underlying read barely changes. That wobble is a list of the calls that are close, and those are the only ones worth your time. Fable can place the obvious names on its own. The ones that jump between runs are where the market hasn’t settled either, and where your own work has to happen.

Everything on these maps is a real position in my brokerage account. I’m showing you my book to make a point about the exercise, not handing you a buy list. What lands in my sweet spot could be wrong for yours.

High risk does not equal high return

Alarmingly, the analysis showed that 40% of my capital is in the high risk, low return area. This is an error that’s easy to make: taking on risk with the expectation of higher returns. Many people mistakenly assume risk and return are on the same dial.

Risk and return are on separate axes. High risk widens the range of possible outcomes. However, it does not say whether the top of that range is any good. A pre-revenue biotech burning cash against three competitors with a dilution problem on top is about as risky as a position gets. Its realistic upside can still be mediocre once you count the ways value leaks out before you ever see a dollar of it.

If you want to try this

Fable scores these names off what’s already been written about them, so the map it draws is the market’s current read on my portfolio and little else. I made that case in AI Will Make You a Better Investor, Not a Great One: the model gives back the consensus, and a consensus that’s already in the price isn’t an edge. A grid like this is good for seeing how my money lines up against what everyone believes and no help in finding the spot where everyone’s wrong. That part’s still mine, which is why I stay suspicious when the model likes everything I own.

This persists across all AI models. In 2023, Anthropic released a study titled, “Towards Understanding Sycophancy in Language Models.” It tested five top assistants from different labs. The study found that all of them displayed sycophancy across various tasks. In April 2025, OpenAI rolled back a version of GPT-4o that had become so eager to please it was praising obviously bad decisions and going along with claims it should have questioned. Yikes.

Why it happens, and why it isn’t going away

An LLM is a next-word prediction engine. It read an enormous amount of text and learned to continue a passage in the most plausible way. By itself, this creates fluent but unfocused results. So, labs add a second stage. People compare the model’s answers and rate the ones they prefer. Then, the model gets tuned toward whatever scores well.

That second stage is where the problem lives. People prefer agreeable, confident, flattering answers. They rate those higher than blunt or contradictory responses. So, training often rewards agreement. Anthropic traced the behavior straight to that preference data. They found the rating systems often favored a convincing wrong answer over a correct one. When the model agrees with you, it is simply behaving according to its training.

What it means for investing

Sycophancy may be a minor annoyance for most things you’d ask a chatbot, but it’s a much bigger problem when there’s money on the line.

The model will say a stock is cheap. If you return next week and say it’s expensive, it will agree again. It won’t remember the earlier view. It holds no position, so it has nothing to defend. The way you make money in individual stocks is by knowing something the market hasn’t priced in yet. That needs two things: a correct view and the belief to stick with it. This is something an overly agreeable AI model can’t help you with.

I made 34x on Applied Digital after sitting with a thesis for a year and a half. The consensus rejected it the entire time. I wrote about this in I Made 3,388% on a Single Stock. A good AI model would never have picked that stock because the market didn’t see its potential. AI only reflects what the market has already decided.

How to actually use it

None of this makes the tool useless. It’s a tool that becomes highly useful once you learn how to use it.

The most valuable thing a consensus machine can do for you is show you the consensus. I wrote about why these tools return the market’s existing view in AI for Investment Research, and that’s the feature here, not the bug. Ask it for an analysis of a company, and it will deliver a clean map of what’s already priced in. Your job isn’t to accept that map; it’s to find the wrong assumption that everyone believes is true.

Three ways to get real value out of it:

Use it to surface what’s priced in. Lay out the bull and bear cases that the market holds. Then, view these as beliefs to challenge, not to confirm.

Make it argue against you. Tell it you hold the opposite of your real position and ask for its strongest work. A bear case it can’t articulate is a signal that the downside may be thinner than it looks.

Point it at checkable work. Upload the filings. Use them for retrieval and synthesis. The document should guide the answer without any embellishments.

Three things to watch:

Agreement is not a second opinion. If the model supports your thesis, it means you crafted a strong prompt and nothing more.

It can’t do the arithmetic. It will set up a DCF correctly and confidently drop a digit in the middle. So, recompute anything that influences a decision.

It doesn’t know what matters. It weighs every line of a filing equally and may present old facts as new. This means it’s up to you to judge what’s important.

I've been using AI for investment research for almost a year now. I've written about the obvious errors before, the bad math and the stale numbers it reports with a straight face, in Three Things AI Will Confidently Get Wrong About Your Investments. AI Will Make You a Better Investor, Not a Great One was about a subtler problem: everyone landing on the same answer because they're all asking the same models the same questions. An agreeable read on a stock is worthless when it's the consensus that's already in the price. If you'd like to learn more about my investment process and how I use AI to find what the market's already missed, subscribe.

I spent two years in equity research, and for most people the answer is no. It can be done. The hours it takes rarely clear the bar once you price them honestly.

Count the hours

Tracking companies is like having a second job. You read filings, build models, and listen to earnings calls. You also update your thesis whenever the facts change. Six hours a week is three hundred hours a year, and that’s conservative.

Now look at what those hours buy. Say you’ve carved out $100,000 for individual names on top of a diversified base. If you have a great year and double it, you’ve made $100,000 before tax. Then divide it by three hundred hours, and then by all the years you didn’t double it, and a different picture emerges.

Most people researching stocks are valuing their time at zero and never saying so out loud. The same hours aimed at your income or something that compounds without you would build wealth in a way a stock slice structurally can’t (the asymmetric bets framework).

There’s also a cost that shows up in April. Sell a winner you’ve held under a year and the gain is taxed as ordinary income, a third or more of it for a high earner. An index fund you don’t touch delays taxes for years. When you sell, you pay the lower long-term rate.

You don’t have the information the pros have

The hours would be worth it if the odds were good, but they’re stacked against you, and the reason is information.

When I was on the sell side, the returns our institutional clients paid for didn’t come from public filings. They came from proprietary work: channel checks, expert calls, data nobody else had. I crawled under a DNA sequencer in a laboratory to read a serial number. This helped us estimate a company’s quarterly shipments (I Pre-Wrote My Research Reports Before the Earnings Call Even Happened). That’s the level of effort on the other side of your trades.

By the time you’ve read an article about a company, the people who move the stock have already priced in what it says. Despite all that, professionals often fall short. Over fifteen years, fewer than one in ten beat the index they’re measured against. You’re competing with them for an edge they can’t reliably find themselves.

I’m not going to tell you not to pick stocks

None of this makes individual stocks off-limits. I pick them, and I’ve written about a position that returned more than 30x what I put in. One big win is just an outcome, not a track record. Thinking a good result proves you can repeat it is the quickest way to lose money in individual stocks.

So the honest question isn’t whether you can pick stocks. It’s why you’re doing it. A few reasons hold up. Enjoying the analysis is one, as long as you call it a hobby and pay for it like one. Learning the craft is another, though the early years are tuition and you should treat them that way. The last is having a real edge, which is rarer than nearly everyone who claims it believes, and which no run of winning trades will ever prove to you. The reasons people fail are often the same: they think picking stocks will make them rich or they see someone else’s big gains and want the same. The costs above are the price of those reasons, and they fall hardest on the people least able to tell which reason is theirs.

Be honest about which reason is yours, keep the slice small enough that being wrong costs you nothing important, and put the rest of your attention where it moves the number.

The gains weren’t even. The weakest writers improved the most, while the strongest didn’t write any better with AI at all. They simply finished faster. The tool pulled the bottom toward the middle and left the top where it was.

I’ve used AI to research and manage my own portfolio for the past year. It’s fast at the busywork and useless at the only thing that pays: catching what the market got wrong. Now, anyone can run that type of research. Being skilled isn’t enough anymore. The only investors worth hiring are those who can outsmart the machine.

Everyone gets the same answer now

I called AI a consensus machine in AI for Investment Research. It tells you what people think about a company. It does this based on the training data it learned from. A couple of years ago, getting that kind of read was tough. You had to read the filings, build a model, and understand the business to form an opinion. Now you can hand a 10-K to a chatbot and have most of it done in twenty minutes. If you had never done it before, that’s a real leap. You go from knowing nothing to a solid, middle-of-the-road grasp of a company over lunch.

The catch is that everyone else’s chatbot produces the same solid, middle-of-the-road grasp. And it’s worse than common because the answers converge. Doshi and Hauser, researchers in AI and creativity, asked people to write short stories. Some got help from a model, while others wrote on their own. The AI stories did better. This occurred as a result of the improvement in the weaker writers. The strongest writers saw no change at all. But the AI stories also started to resemble one another. Each writer did better work, and the work blurred together.

That convergence is what matters for investing. Now, anyone can write a decent thesis on a stock. But they all end up with similar ones. This happens because they use the same model, which is based on the same consensus.

How to tell if your edge is real

Back when a good thesis was rare, having one was worth a lot. A decent thesis is now free, and they all sound the same. So, decent work has little value. People will only pay for a unique, correct viewpoint.

Check the stocks you chose. Go through the list and ask if a chatbot would have suggested buying each one. Anywhere it would have said yes, you’re holding the consensus, and the consensus is free now. The picks that matter are the ones a chatbot would have flagged. It’s the stock nobody wanted. You held it for a year and a half, convinced the crowd was wrong.

If the honest answer is “a chatbot would have said buy” every time, move that money to index funds. You’re not giving anything up because the analysis you were doing is free to everyone now anyway.

I’ve used AI heavily for investment research for close to a year now, across all the major models. I wrote about the workflow that’s emerged in AI for Investment Research, and most of it still holds. There are three specific failures I’ve run into enough times to name.

It’s not a calculator

A calculator does math. An LLM generates sequences that look like the output of a calculation. Sometimes the sequence is correct because the training data contained the right answer to a similar problem. Sometimes it’s correct because the model walked through the reasoning in a way that happened to produce the right number. And sometimes the reasoning sounds right and a digit moves anyway.

I’ve caught this on dilution math, on weighted averages, on CAGR calculations, on discounted cash flow models. In every case the model set up the problem correctly. The variables were defined, the formula was right, the steps were ordered the way I would have ordered them. The error was somewhere in the middle of the arithmetic, and nothing about the surrounding output flagged it.

This is what makes it dangerous. A junior analyst who got the math wrong would also probably get the setup wrong, or hedge their answer, or flag uncertainty. The model does none of that. It produces the confident, polished output of someone who has checked their work, except it hasn’t.

So I use AI to structure the analysis and lay out which variables I need, and then I do the math in a spreadsheet. Any number that’s going to inform a decision gets recomputed outside the model.

It will use information that isn’t true

The second failure mode is harder to catch because it doesn’t have the clean tell of bad arithmetic. The model will confidently use information that is outdated, partially correct, or pulled from a context where it doesn’t apply.

Ask one of these tools about a company’s most recent quarter and there’s a real chance it gives you the quarter from two years ago, or mixes up two segments, or cites a revenue number that was correct on a different reporting basis. The training data has a cutoff, the web search results are noisy, and the model doesn’t distinguish well between “this fact was true in 2023” and “this fact is true now.” It treats them with the same confidence.

What’s changed how I use these tools is that I almost never let them work from their training data anymore for anything where the answer depends on what’s true right now. If I want analysis of a quarter, I upload the 10-Q. If I want help on an earnings call, I paste the transcript. If I want to understand a competitor matrix, I feed it the filings. The model is excellent at synthesizing across documents you’ve given it. It’s much worse at retrieving the right documents on its own, and worst of all at pretending it has retrieved them when it’s actually filling in gaps from training data.

This is the inversion of how most people use these tools. Most people ask a question and let the model figure out where to get the answer. For investment research that’s the wrong direction. You have to do the retrieval. The model does the synthesis.

It doesn’t know what’s material

A good analyst reading a 10-K knows that one paragraph in the MD&A about a specific customer concentration matters more than four pages of risk factors that every company in the sector includes verbatim. She knows the auditor’s report is boilerplate ninety-nine percent of the time and that the one time it isn’t, that’s the most important page in the document. She knows the segment disclosures often tell you more than the headline numbers.

The model doesn’t have any of that. It treats every sentence in a filing as roughly equally weighted, because that’s how text-based models read documents. When you ask it to summarize a 10-K, it gives you a competent overview that hits the major sections and misses the specific lines that would actually move your thesis. The summary is correct. It just isn’t useful, because materiality isn’t a property of the text. It’s a property of what an experienced reader knows to look for.

This is the place where the gap between AI and a trained analyst is largest, and it’s also the place that’s hardest to see from the outside. The output looks like analysis. It has the texture of analysis. What it doesn’t have is judgment about which facts in the document deserve attention, and that judgment is most of what a good analyst is being paid for.

The way I use AI now reflects this. I treat it as a powerful search tool for finding information across documents, not as an analyst for telling me what the information means. If I ask it whether a company has mentioned a specific topic in the last four quarters, the answer is fast and reliable. If I ask it what’s most important in the most recent quarter, the answer is generic and shaped like every other answer. The first question is a retrieval problem and the model is great at retrieval. The second is a judgment problem and the model has no judgment to apply.

The shared failure mode

By the time you notice the model was wrong, you’ve already used it to make a decision. That’s true for the arithmetic, the outdated facts, and the missed materiality, and it’s the reason all three matter more than they would in a domain with slower feedback loops.

The time savings is real. The week I used to spend reading filings and building competitor matrices is now a couple of hours. The synthesis across documents I’ve uploaded is excellent. The drafting of bull and bear cases as a starting point is useful. The arithmetic, the currency of the information, and the judgment about what matters are still on me.

Anyone selling you AI as a complete research tool is either not using it on real positions or hoping you don’t notice when the model is wrong.

The trade doesn’t change my core philosophy. The vast majority of my portfolio sits in low-cost index funds running on autopilot, and that boring system is the reason my financial life isn’t dependent on any individual pick going right.

In I Analyzed Stocks for a Living, I wrote about why even professionals with Bloomberg terminals struggle to beat the market. In Five Investing Beliefs That Sound Smart but Cost You Money, I called “do enough research and you can pick winners” one of the most expensive myths in individual investing.

Both of those things are true, and I still pick stocks anyway. The slice of my portfolio I run actively has produced enough good outcomes that the occasional bad one hasn’t mattered, and I think there’s value in walking through specific trades to show what the framework actually looks like in practice.

How I found it

Applied Blockchain in mid-2022 was a tiny company providing data center hosting for cryptocurrency miners. The stock traded around a dollar. The Fed had raised rates seven times that year, crypto was in a brutal winter, and Ethereum was months away from shifting to proof-of-stake, which would eliminate the economic basis for the kind of mining the company supported. By every conventional measure, you didn’t want to own this stock.

What caught my attention was the gap between what the stock priced in and what the company actually owned. The market was valuing Applied Blockchain as a crypto mining company in terminal decline, which is fair enough, because that’s what it was. But strip out the crypto business and what remained were data centers. Buildings in specific locations, with power contracts and cooling systems already operating, that had a use case completely independent of whether anyone ever mined another Bitcoin.

I had a thesis about what those data centers would be worth.

The thesis

In mid-2022, demand for high-performance computing was already accelerating, and the constraint wasn’t going to be silicon. It was going to be the physical layer underneath. Power capacity, cooling, the ability to actually plug something in at scale. You can’t just build a data center overnight. The permitting, the grid connection, the construction itself, all of it takes years. Anyone who already had operating capacity in 2022 owned something the market would eventually have to pay up for.

I didn’t know which specific use case would drive the demand. AI was an obvious candidate, but it could have been general cloud computing expansion, or scientific computing, or any number of other things. The point wasn’t to predict the application. It was that compute demand was a one-way bet, and the physical infrastructure to serve it was a constrained resource.

Applied Blockchain owned that infrastructure. The market was treating it as worthless because of what it was currently being used for. That was the gap.

My thesis was that the assets were worth multiples of where the market priced them, and that the path to realizing that value would come either from a strategic pivot, an acquisition by someone who wanted the capacity, or a re-rating as compute demand made the underlying assets visible. The asymmetry was clean. Big upside if any of those paths played out, and a downside floor set by the value of physical infrastructure that existed regardless of what happened to crypto.

What happened

The pivot happened in May 2023. The company (now renamed Applied Digital) launched specialized AI cloud services and announced its first major contract worth up to $180 million. The stock jumped 25% that week. Over the next two years they kept executing: a $5 billion leasing agreement with a hyperscaler, a direct investment from Nvidia, revenue growth of 250% year over year by fiscal Q2 2026. The stock went from around $1 to over $40 at its peak.

Why I sold

I sold in January 2026 at $38.61 because the stock had run too far, too fast. After a 34x move, it seemed more likely to go down than to keep going up. I took the profits and redirected the money to positions where I still saw a gap.

The trap with winners this big is that you start to feel like you have a special read on the company and should hold because you saw it first. The price doesn’t care that you bought at $1. It only cares about the next dollar of value the company creates, and at $38 the market was already paying for several years of that value in advance. The original trade was the gap between $1 and what the assets were actually worth. Holding past $38 is a different trade, on a different setup, and one I wouldn’t have opened from scratch.

How I think about the individual stock slice

A meaningful portion of my brokerage account, around 40%, is in individual stocks. The rest is in ETFs that provide the stable, diversified base. The ETFs are what make it possible to take real risk on the individual names without my financial life depending on any one of them.

This is the part of the personal finance internet that doesn’t get talked about much, because most of the content is calibrated for people who haven’t built the foundation yet. The advice for someone with no emergency fund and no automated investing is correct: stay away from individual stocks, just buy the index. The advice for someone who already has the foundation looks different. Once the base is solid, you can introduce asymmetric exposure on top of it, and that’s where the actual wealth creation happens for individual investors. I wrote about this framework in The Asymmetric Bets Framework, and the individual stock slice is one of the places that framework actually applies in my own life.

Within that slice, the principle I keep coming back to is buying when the consensus is against you. This part is uncomfortable by construction. If the trade felt obvious, the price would already reflect it. Most of the positions I’ve taken that worked involved buying something other people were selling. So did most of the positions I’ve taken that didn’t work, which is the part nobody mentions when they tell these stories. Contrarianism on its own isn’t a thesis, it’s a precondition for one.

One more thing

I bought Applied Blockchain in July 2022, four months before ChatGPT launched. The thesis required looking at a dying crypto miner and seeing a compute infrastructure play that the market wouldn’t be willing to price in for another year or two.

I’ve spent the last year using AI heavily for investment research, and I’m confident it would not have flagged APLD as a buy at $1 in 2022. The consensus view at the time was that the company was in terminal decline, and consensus is what AI returns when you ask it for an analysis. I wrote about this in AI for Investment Research, but the APLD trade is the cleanest example I have of why it matters. The setups that produce returns like this one live in the gap between what the public information looks like and what’s actually going to be true, and that gap is where AI is structurally weakest.

The honest answer, after a year of using these tools across several different models on real positions in my portfolio, is neither.

I moved from ChatGPT to Grok in the fall because Grok handled financial questions better. Then I moved to Claude when the more advanced model came out and the analysis got a noticeable jump in depth. I tried Perplexity along the way and wasn’t impressed. The tools have improved fast enough that anyone writing about them with confidence is going to look a little silly in six months, including me. So take this as a snapshot from May 2026 rather than a final word.

AI has made me a better investor, just not in any of the ways it was sold to me. The pitch from finance Twitter is that AI will democratize access to institutional-grade research and let retail investors find the next ten-bagger from their couch.

What AI is actually good at

When I was in equity research, the first week of covering a new company was almost entirely mechanical. You read the last four 10-Qs and 10-Ks, pull every press release for two years, listen to the last eight earnings calls, and build out a basic financial model alongside a rough competitor matrix. None of this required judgment. It required time, and it produced the substrate on which the actual thinking could happen.

AI does roughly that week of work in a couple of hours. You can have it read the last four quarters of a company’s filings and surface the major themes management has been emphasizing, pull a clean list of every competitor mentioned across those filings, or generate a serviceable version of the bull and bear case as presented by sell-side analysts who cover the name. None of these outputs is differentiated, but each one would have been a half-day of work ten years ago and is five minutes now.

Sentiment scanning is the other thing it handles well. What are people saying on the company subreddit, in the StockTwits feed, in the comments under recent YouTube earnings recaps? You can get a reasonable read in a few prompts. None of that is alpha by itself, but it’s data you couldn’t easily access before, and occasionally it surfaces something useful.

What I use it for most is getting oriented on complicated situations. A biotech running three trials at once with a patent dispute on top of it, or a roll-up that’s grown through 14 acquisitions in four years, can have a volume of disclosure that takes a full weekend just to read. AI gets me through that in an hour, and what would have been a Saturday is now the first hour of looking at a company.

What AI is bad at, and why it matters

The way you actually make money in individual stocks is by knowing or believing something about a company that the market has not yet figured out or accepted. That gap, between what the price reflects and what is actually true, is where alpha lives. Every big winner I’ve had, and every loser, came from a mismatch like that. The market eventually figured it out and the stock moved.

AI can’t find those mismatches.

Trained on public information, the model gives you back a tidy version of the consensus view. The bull case it constructs is the same one already reflected in the price, along with the bear case and the standard list of risks the sell-side has been flagging for months.

This is the opposite of what you need to find a mispriced stock. You need a non-consensus view, supported by reasoning the market hasn’t fully absorbed, and AI is structurally a consensus machine. It cannot give you an edge because the consensus view is, by definition, not an edge.

The other thing AI doesn’t do is hold a contrarian position under pressure. Some of the best calls I’ve made required sitting with a thesis for 18 months. The stock went nowhere, smart people told me I was wrong, and I kept asking myself whether I was the idiot. AI has no position to defend. It revises its view based on whatever’s in the context window. Ask it the same question three times with slightly different framings and you’ll get three different answers.

AI is bad at math. And not in a way that’s easy to catch.

It sets the problem up correctly and walks through the logic in the right order. Then somewhere in the middle of a DCF or a CAGR calculation, it drops a digit, and the final number is wrong. I’ve caught it doing this on dilution math, on growth rates, on weighted averages. The prose around the error is always confident, which is the part that makes it dangerous. I now run every number AI gives me through a calculator before I use it for anything.

The workflow that actually works

The way I use AI now isn’t to replace research. It’s to compress the time it takes to do the parts of research that don’t require judgment, so I have more time for the parts that do.

For any company I’m looking at seriously, I run a version of the same loop.

I ask one model for the strongest bull case it can construct, with specific numbers and timeline. Then I ask the same model for the bear case with the same level of specificity. I take both and feed them to a different model for critique, and then I feed the critiques back to the first model and ask what its strongest response would be. By the end of a couple of cycles I have a sharper picture than I started with, and more importantly, I have a sense of where the models are uncertain.

That sense of where the models are uncertain is the thing I’ve started to trust. If I ask three models for a real bear case on a stock and none of them can produce one that goes beyond generic risks, that’s a signal. The downside might actually be limited, because the bear case isn’t sitting in the public information set in any organized way. Conversely, if every model gives me the same coherent bull case in slightly different wording, I should assume that case is already priced in, and my edge has to come from somewhere else.

I don’t trust AI’s recommendations. I trust the texture of its disagreements with itself.

The other thing I do is calibrate its risk-return suggestions against my own profile. If you ask AI for “stocks with huge growth potential,” it will hand you a list of preclinical biotechs and pre-revenue lithium miners that could plausibly return 10x and are statistically much more likely to return zero. The model isn’t wrong, exactly. Those stocks do have huge growth potential. The model just has no way of knowing that “huge growth potential” for you means asymmetric upside with bounded downside, not lottery tickets. Risk and return are a personal question, and asking AI to optimize for them in isolation will produce a portfolio you should not own.

Where this is going

The biggest mistake individual investors are about to make is assuming AI will close the gap between them and institutions. It’s widening it.

Consumer AI gives you the equivalent of a smart junior analyst who reads filings fast. Inside hedge funds, AI is being integrated with proprietary data sets, real-time market feeds, and channel-check workflows built over decades. A hedge fund using AI on its own data is doing something different than you using AI to summarize a 10-K. The information asymmetry I wrote about in I Pre-Wrote My Research Reports Before the Earnings Call Even Happened has been augmented on both sides, but more on theirs than yours.

The floor has moved. Someone running a real research loop with these tools will make better stock decisions than someone picking based on whatever’s trending on FinTok. The ceiling hasn’t moved at all.

AI is a useful research associate and a useless portfolio manager. It can help you understand a company in a fraction of the time it used to take, but it won’t tell you whether to buy it, and any tool or prompt that claims otherwise is selling you something. The judgment is still yours. So is the conviction to sit on a thesis for 18 months while everyone tells you you’re wrong, which is where most of the returns actually come from.

At some point I sat down and did the actual math, and the equation stopped making sense. If I was going to work that hard, I should be getting more upside than a straight salary. The amount of effort I was putting in was the kind of input that only makes sense if it’s matched to an asymmetric output. A corporate salary, no matter how generous, doesn’t qualify.

The realization wasn’t about the job itself but about what I’d unintentionally accepted as the shape of my financial life. I had spent years working hard inside an arrangement that, by design, could not produce a non-linear outcome no matter how well I executed within it.

The two questions

How limited is the downside?

How limited is the upside?

You can plot any financial or career arrangement on this two-by-two. Most positions in a corporate professional’s life have a limited downside and a limited upside. That’s the symmetric quadrant. Some have an unlimited downside and an unlimited upside, which is most entrepreneurship. A smaller number have an unlimited downside and a limited upside, which is the worst quadrant and the one to avoid. And then there’s the rare and valuable one: limited downside, unlimited upside. That’s the asymmetric quadrant.

What a corporate financial life actually looks like

The salary is capped. Even if you negotiate well and get raises, the rate of growth is limited to whatever your employer is willing to pay. There’s no scenario where your salary triples because the business had a great year. The downside is also limited, since you can lose the job but you can walk away with two weeks notice and find another one.

RSUs and stock options look asymmetric on paper because the upside is theoretically uncapped. In practice, at any company large enough to pay competitive salaries, the equity is structurally diluted. The company is already big, the growth potential is bounded by size, and you’re getting a small slice. At a Fortune 10 company, RSUs can be meaningful in absolute dollars but they’re rarely the engine that builds wealth. Compare them to early equity in a high-growth startup or actual ownership in a private business and the difference is structural, not incidental.

The 401(k) produces market returns by definition. Over thirty years that’s a meaningful number, but it’s a number with a known distribution. You’re not going to have a 100x outcome from indexing the S&P 500.

The house is the position most people are most defensive about. A primary residence in most markets appreciates at some rate close to inflation. The downside is real, between maintenance, repairs, taxes, market downturns, and the slow grind of ownership costs. The upside, while genuine, is bounded. What makes real estate work for the people it works for is leverage and the tax benefits. Strip those out and the underlying asset class is mediocre, with somewhat limited upside and a downside that’s only nominally capped by your equity.

Then there’s everything that flows out: cars, vacations, dining, the rest of the lifestyle. Pure consumption with negative expected return.

Look at that list and there’s nothing with limited downside and unlimited upside. Every position is bounded. The arrangement produces a comfortable life, which is real and which most people will never achieve, but it does not produce wealth. Wealth requires asymmetry somewhere in the system, and optimizing within a symmetric system will not produce it.

The trap

This is the trap most high earners are in and don’t realize.

They look at their financial life and see good income, growing investments, a nice house, a healthy 401(k) balance. They see the line items going up over time. They assume that means they’re building wealth. What they are actually building is comfort, not wealth.

The math of wealth requires asymmetry. You can build a perfectly fine comfortable life inside an entirely symmetric system, but you cannot build wealth there, because comfort and wealth aren’t built by the same math.

Working harder inside the symmetric system doesn’t fix it. The structure of a corporate salary doesn’t become asymmetric because you put in 100-hour weeks. That was the lesson I learned in M&A. I was working as hard as it’s possible to work and the arrangement I was working inside was incapable of producing the outcome I wanted.

How asymmetric positions actually work

The asymmetric quadrant is real, just rarer than people think, and almost none of it lives inside a normal corporate life. Here’s what does belong there.

Individual stocks, properly sized and selected. When you buy a stock, the most you can lose is what you put in. The stock can go to zero but it cannot go below zero. There’s no scenario where you owe additional money. The downside is mathematically capped and the upside is not. The catch is that you have to actually be good at selecting them, which most individual investors aren’t. But the underlying asymmetry of the asset class is real and that’s a meaningful starting point.

Ownership in a private business. The classic asymmetric position. The downside can be ugly, especially if you’ve personally guaranteed debt or signed leases, so this isn’t risk-free. But the upside is genuinely uncapped in a way no salaried job ever is. The few people I know who are wealthy rather than comfortable mostly got there this way.

Early equity in a high-growth company. Joining a startup as one of the first 50 employees, taking a salary cut, accepting equity that may go to zero, in exchange for the chance that the equity becomes meaningful. Most don’t pan out, but the ones that do, do enormously.

A side project, a body of intellectual property, a business of one. The newer version of the asymmetric quadrant. The downside is limited to the time you put in. The upside, if it works, is open. Audience-driven businesses, writing, courses, software, anything that scales beyond the hours you put in, works on different math than consulting or employment.

Where the framework breaks down

I want to flag the limits because no analytical tool works in every situation.

Asymmetry isn’t the only thing that matters. A bet with limited downside and unlimited upside is great in principle, but if the probability of the upside is one in a thousand, the bet is still bad in expectation. The framework tells you the shape of the bet. It doesn’t tell you how likely the good outcome is, and you still have to do that work separately.

“Limited downside” sometimes means “limited in dollars but unlimited in time.” A stock that goes to zero only costs you what you put in, but you might have had that money compounding for ten years before you found out. Opportunity cost is a form of downside the framework doesn’t capture cleanly.

The framework can also be misapplied to rationalize bad bets. People who want to start a business will tell themselves it’s asymmetric because the upside is unlimited while quietly ignoring that the downside is less limited than they’re admitting. The framework is a useful tool, but it can also be used to make you feel good about decisions you should be more skeptical of. Be honest about both axes.

The diagnostic

Take a piece of paper and list every meaningful financial position in your life: salary, equity comp, the 401(k), the house, the brokerage, anything else where money is moving at scale. Mark each one. Is the downside limited? Is the upside limited? Where does each position sit on the two-by-two?

Comfortable is a real achievement and not everyone needs to be wealthy. But if you want a different outcome, the path is not optimizing harder inside the symmetric system. The path is introducing asymmetry somewhere, because that’s the only thing that changes the math.

What that asymmetry should look like for you is the subject of most of what I’ll write in this section of the publication. Stocks, side projects, ownership, things that compound differently than salary does. The framework here is the lens; everything that follows is the application.

He drove a Porsche Cayenne. I was still driving the rusty Mazda sedan I’d had since college. We worked at the same company, he knew roughly what I made, and the math apparently didn’t add up for him. “I can’t believe you drive that piece of s**t to work.”

I didn’t say much in the moment. What I was thinking, and what I’ve thought every time I’ve remembered that exchange in the years since, was that we were having two completely different conversations about what money is for. He was looking at my car and seeing someone who couldn’t afford better. I was looking at his car and seeing someone who’d locked himself into earning at a particular pace for a particular number of years, whether he wanted to or not.

What “Average” Buys

The average American worker contributes somewhere between $2,000 and $4,000 a year to retirement out of their own pocket. With the employer match, that’s about 8% to 10% of income going in. Every benefits portal and budgeting app on the market will tell you this is responsible.

Run it forward and the picture is harder to feel good about.

A 40-year-old earning $80,000, saving 10% all-in at a 6% return against 3% inflation, lands at 67 with a portfolio in the range of $1.1 to $1.3 million. Apply the standard 4% withdrawal rule and that’s roughly $44,000 to $52,000 a year in today’s dollars. Add Social Security for a middle earner (around $24,000), and you’re at $68,000 to $76,000 pre-tax in retirement.

If you were earning $80,000, that’s a step down. If you were earning $120,000 or $150,000, which is where most of the people reading this actually are, it’s a different life. Smaller house or no house. Travel becomes a question instead of an assumption. Healthcare costs you can’t really plan for hit a budget that wasn’t built to absorb them.

And that’s the version where things go right. Drop in a bad sequence of market years right when you retire (which is when sequence-of-returns risk does the most damage), or a serious health event, or just living to 92, and the math gets meaningfully tighter.

The unsettling thing about this is how many people are heading toward exactly this outcome and have no idea. They’re not making bad decisions. They’re not running up credit card debt or buying timeshares. They’re contributing what HR set up, getting the match, holding a target date fund, and assuming the system is working. The system is working. Its job just isn’t what they think it is.

The Soft Version

The information isn’t hidden. The math is on every retirement calculator in the country. So why doesn’t anyone ever frame it this way?

Two reasons, I think. The first is that telling someone in their 30s that their current trajectory produces a retirement they wouldn’t choose is an uncomfortable conversation, and most of the institutions positioned to have it would rather not. Advisors want you to feel good about working with them. Apps want you to feel good about using them. Plan sponsors want you to feel good about your benefits. Nobody in that chain is incentivized to do the math out loud and let you sit with the answer.

The second is that the people who’d benefit most from running the numbers are the ones with the least bandwidth to. They’re 32, deep in a demanding job, maybe a mortgage, maybe a kid coming. The 401(k) is the one thing that’s already handled. The last thing anyone wants to discover is that the one handled thing isn’t actually handled.

The One Lever

Income matters far less than people think. The thing that determines whether you’re financially independent at 55 or working part-time at 70 isn’t what you earn. It’s the percentage of what you earn that you actually convert into investments.

Rough brackets, with the caveat that the exact numbers shift with your income and what you spend.

Around 5% to 10% is the average path. Modest portfolio at the end, heavy reliance on Social Security, no slack for anything unexpected. This is where most people land and stay.

At 15% to 20%, the math changes character. You build something with real flexibility. A bad market year in your early 60s doesn’t blow up the plan, and your retirement income roughly tracks your working income instead of stepping down.

At 25% or higher, you’re in optionality territory: meaningful early retirement on the table, real runway for a career change, the ability to take a year off without it derailing anything. I wrote about that shift, from accumulating a number to building a life with options, in Why I Stopped Trying to Be Rich and Started Trying to Be Free.

Two people earning $120,000 can be in completely different financial realities at 60. The difference is almost never income or talent or luck. It’s the savings rate, started earlier, and protected from lifestyle inflation along the way.

The Mazda Math

When my boss made the comment about my car, I was earning over $100,000 and routing a significant percentage of it into investments. The Mazda was a deliberate choice, not a constraint. My costs were low, my savings rate was high, and the gap between what I made and what I spent was building me an exit.

He was, presumably, doing the opposite. The Porsche, the lifestyle that goes with the Porsche, the income required to maintain the lifestyle that goes with the Porsche. None of which I’m judging him for. I’m just noting that the same income produces wildly different outcomes depending on what percentage of it leaves your account every month before you have a chance to spend it.

The order things get funded in matters too, and this is the part I see people get wrong most often. Capture the employer 401(k) match first. Then fund a Roth IRA for tax-free growth. Then max the HSA if you have one available, because it’s the most tax-advantaged account in the entire code and almost nobody uses it the way it was designed to be used. Then back to the 401(k) to max the deferral. Then anything left over goes into a taxable brokerage where you have full flexibility but no tax wrapper. Most people stop at “got the match” and never realize the most powerful accounts in the system are still empty.

I also keep a six-month emergency fund in a high-yield savings account, not because I’m waiting for a disaster but because the alternative is a system that requires nothing to ever go wrong. That isn’t a system. That’s hope with a spreadsheet.

Just Open It

Stop asking whether you’re saving enough. The framing is too vague to produce a useful answer, and “enough” is exactly the kind of word that lets you put off looking forever.

Ask what your current savings rate actually buys you in 30 years.

Vanguard, Fidelity, and NerdWallet all have free retirement calculators. Any of them is fine. Plug in your age, your income, your real savings rate including the match, a reasonable return assumption. Ten minutes.

Most people reading this won’t do it. The ones who do will probably spend the rest of the week quietly recalibrating something.

That’s the whole point. The number on the screen isn’t a verdict. It’s information you didn’t have ten minutes ago, and almost everyone who looks discovers they have more room to move than they thought.

I think about my old boss sometimes. He’s probably still driving his porsche to the office. Meanwhile, I’m driving my porsche to the beach on a Tuesday.

I was 22 and I assumed he was joking, or being self-deprecating about an unfortunate quirk of how the product was built. He wasn’t. He meant it literally. The complexity wasn’t a bug in the design. The complexity was the design. If clients understood exactly what they were paying, they wouldn’t pay it.

That conversation rearranged how I see the entire financial services industry, and it took me years to fully metabolize. Wall Street doesn’t make most of its money from being smart about investments. It makes money from the gap between what you think you’re paying and what you’re actually paying. The wider the gap, the more profit there is in it.

Most of what I want to write about today is just the question of where that gap lives, because once you can see it, almost every confusing thing about the industry starts to make sense.

Most fees you pay are invisible by design

The defining feature of modern retail finance is that you almost never write a check. Your trading app is free. Your index fund’s expense ratio is deducted before performance is reported, so the number on your statement always looks like it captured everything the market gave you. Your bank account doesn’t charge a monthly fee as long as you keep enough money in it. Your robo-advisor charges a quarter of a percent, which sounds like a rounding error.

None of these are free. They’re priced into the product in ways you’d have to actively go looking for in order to find. Your free trading app gets paid by market makers to send them your orders. I wrote about that mechanic in detail in Free Trading Isn’t Actually Free, so I won’t redo the math here. The bank “free as long as you keep $5,000 in it” deal is the bank borrowing your $5,000 at zero and lending it back out at seven. The robo-advisor’s 0.25% fee sounds small until you realize the algorithm is also placing you in funds whose managers paid for shelf space, and nobody’s required to draw you a picture of why those particular funds got picked.

The thing all of these products share is that the fee never arrives as a fee. It arrives as a slightly worse price, a slightly lower yield, a slightly suboptimal portfolio construction. Each one feels like nothing in the moment. They’re not nothing. They’re the entire revenue model of a multi-trillion-dollar industry, and the reason you can’t feel them is that they were specifically engineered not to be felt.

Complexity is the actual product

Here’s the part that took me longest to internalize, even working inside the industry.

Simple financial products don’t make anyone much money, because simple products are easy to compare. If you’re choosing between two index funds that hold the same stocks, you’ll pick the cheaper one. There’s no margin to defend. This is why Vanguard exists at the scale it does and why the rest of the industry has spent forty years figuring out how to compete with it without competing on price.

The way you compete with Vanguard without competing on price is to sell something Vanguard doesn’t sell. Variable annuities with guaranteed minimum withdrawal benefits. Indexed universal life policies with cash value components invested in proprietary subaccounts. Structured notes tied to custom indices. Buffer ETFs. Defined outcome strategies. Every one of these has a real explanation, and every explanation is just complicated enough that two of them can’t be compared head to head. You can’t put two variable annuities next to each other and tell which one is cheaper, because the fee structures use different terminology and the underlying math is intentionally opaque.

That’s not an accident. That’s the moat. A product the customer can’t comparison-shop is a product the seller can charge a premium for, and the premium is the whole game.

I watched this dynamic play out at the firm where I worked. The simple, low-margin products had no internal champion because there was nothing in it for anyone to sell them. The complicated, high-margin products had dedicated sales teams, marketing budgets, conference sponsorships, dinners at Sparks. The economic gravity of the industry pulls in one direction, and it isn’t toward clarity.

The vocabulary gives it away if you listen for it. Anything described to you as “sophisticated” is being sold to you. Anything that requires a 30-minute conversation with a licensed professional to understand is a product where the 30-minute conversation is the sales process. Anything pitched as offering “downside protection” or “enhanced yield” or “tax-advantaged growth” through a structure you wouldn’t otherwise have access to is something where you are paying for the structure, and the structure is mostly there to justify the fee.

The compounding problem

The reason all of this matters, and the reason I keep writing about it, is that the cost of these invisible fees is not linear. It compounds.

A one percent annual fee on a portfolio sounds trivial. Over a single year, it is. Over thirty years of compounding, a portfolio paying one percent in fees ends up roughly a quarter smaller than the same portfolio paying nothing. That’s the rough magnitude. The exact number depends on returns and contributions and a dozen other variables, but the order of magnitude is correct, and it’s why I wrote a whole separate article about why a financial advisor is the most expensive purchase most people will ever make without realizing it. The fee feels small. The fee compounds against you for the entire time the money is invested. Those two facts together are the whole story.

Now stack the fees. The advisor charges one percent. The funds the advisor put you in charge another half a percent. The annuity your aunt’s friend sold you when you got married is charging three percent inside the wrapper. The trading app you use for your “fun money” account is shaving fractions of a cent off every order. None of these individually feels like much. Cumulatively, on a long enough timeline, they’re the difference between retiring comfortably and not.

I’m not going to put a specific dollar figure on it because I don’t trust the number I’d come up with, and I don’t think you should trust any specific number anyone else gives you either. What I trust is the direction. The direction is enormous, and it points away from you.

Why this is allowed to keep happening

It would be satisfying to say the financial industry is full of bad people doing bad things, and some of it would even be true. Mostly, though, it’s not. The people working at brokerages and fund companies and advisory firms are normal professionals who took the jobs that exist, and the jobs that exist are the ones the business model rewards. Nobody at a major firm is sitting in a meeting room cackling about how they’re going to fleece retail investors today. They’re just doing the job, and the job happens to be structured so that the harder you do it, the more wealth quietly transfers from people like you to people like them.

What’s actually changed in the last decade or so isn’t the industry’s incentives. Those have been the same for a hundred years. What’s changed is that for the first time, you have a clean way to opt out of most of it. A total market index fund costs three basis points a year. A self-directed IRA at a major broker costs nothing to open. A portfolio that beats roughly nine out of ten professionally managed funds over a 15-year period requires about two hours of setup and almost no ongoing maintenance. The exit door has been there the whole time, and in the last fifteen years, it got cheaper and easier to walk through than it has ever been in history.

What I actually do

For whatever it’s worth, here’s what my own setup looks like, because I think it’s useful to see how simple this can be when you stop paying tolls.

I have a brokerage account at a major low-cost broker. The core of my portfolio is in two index funds with combined expense ratios under five basis points. I don’t have a financial advisor. I have never owned a whole life insurance policy or an annuity, and I don’t expect I ever will. My emergency fund sits in a high-yield savings account paying ~4%. I check my net worth once a month, do a full review once a year, and otherwise don’t think about my money very much.

That’s it. The total annual cost of my financial life, all in, is somewhere south of $50. It has been the most boring financial system imaginable for years, and it has consistently outperformed every more complicated thing I’ve ever considered doing instead.

The senior salesperson who told me the complexity was the point wasn’t trying to warn me. He was just being honest about how the business worked.

I still believed things about investing that were costing me money.

Not obscure, technical things. The basics. The kind of advice everyone absorbs without questioning: buy what you know, invest in companies you believe in, do your research. The wisdom you pick up from your parents, from CNBC playing in a waiting room, from the general atmosphere of what passes for financial literacy at a dinner party.

It took about two years inside the machine before I started noticing how much of this conventional wisdom is incomplete in ways that quietly bleed your returns. Here are the five beliefs I see trip up the most people, including, for a while, me.

1. “I only invest in companies I believe in.”

This is probably the most common thing I hear from new investors, and I get the appeal. It feels good to own stock in companies you admire. You use their products. You respect what they do. Owning a piece feels like a small act of support.

The thing nobody tells you when you’re starting out is that when you buy a stock, your money doesn’t go to the company.

The only time a company actually receives money from a stock sale is during its IPO or a secondary offering. Every other transaction is between you and another investor. When you buy Apple stock today, you’re buying it from someone who already owned it. Apple never sees a penny of your purchase. Tim Cook has no idea you exist.

This matters because it reframes what you’re actually doing when you “invest in a company you believe in.” You’re not supporting them. You’re betting that other investors will value the stock higher tomorrow than they do today. Whether you love the company or hate it doesn’t change the math of that bet.

You can absolutely avoid companies that conflict with your values. That’s a personal choice and I respect it. But don’t confuse ethical screening with investment strategy. They’re separate decisions, and conflating them produces portfolios built on feelings rather than fundamentals.

2. “Buy what you know.”

Peter Lynch popularized this idea, and there’s a kernel of truth in it. If you use a product every day and notice it’s incredible, that’s a data point worth investigating.

But “buy what you know” has a dangerous flip side: it means you’ll systematically ignore everything you don’t.

Think about your daily life. You probably interact with maybe twenty or thirty brands regularly, mostly consumer brands. The coffee shop, the phone in your pocket, the streaming service you forgot you were paying for. These companies feel familiar and therefore investable.

You probably don’t interact with the companies that make dialysis machines, manage container shipping logistics, manufacture the semiconductors inside every electronic device you own, or provide the cloud infrastructure that runs half the internet. These are enormous, wildly profitable businesses that exist entirely outside your consumer experience.

When I worked in equity research, I covered healthcare companies most people had never heard of. Diagnostics, lab services, genomics manufacturing. Not household names, but incredible businesses with deep competitive moats and strong growth profiles. If I’d only invested in “what I knew” as a consumer, I’d have missed entire sectors of the economy worth more than the entire consumer-brand universe combined.

The fix isn’t complicated. A total market index fund owns everything: the companies you know, the companies you don’t, and every sector of the economy. You get exposure to the boring, unglamorous businesses that quietly generate enormous returns without ever appearing in your daily life.

3. “If you just do enough research, you can pick winning stocks.”

This is the one that took me the longest to let go of, because I spent two years getting paid to do exactly this.

I built detailed financial models, talked to company management teams, and conducted channel checks that included calling STD clinics to estimate testing volumes, getting blood drawn three times in one day at competing labs, and crawling under a DNA sequencer at a trade show to read the serial number off the bottom. (That one still makes me laugh a little.) I had Bloomberg terminals, proprietary databases, and a team of analysts working alongside me. And even with all of that, consistently picking stocks that beat the market was extraordinarily difficult.

It’s not that the research is useless. It’s that the market is incredibly efficient at incorporating information into prices. By the time you’ve read an article about a company, the information in that article is already priced into the stock. The professionals trading that stock have access to better information, faster execution, and more sophisticated analysis tools than any retail investor ever will.

I’m not saying nobody beats the market. Some people do, some of the time. But the percentage of professional fund managers who beat their benchmark over a 15-year period is somewhere around 10 to 15%. These are full-time professionals with every advantage imaginable. If they can’t do it consistently, the odds that you’ll do it by researching stocks on your couch after work are very small.

I still pick some individual stocks. I enjoy the analysis, and I find businesses genuinely interesting to study. But it’s a hobby, not a strategy. My core wealth-building portfolio is in index funds, because two years inside the research machine convinced me that the information asymmetry between institutional and retail investors is just too wide to overcome.

4. “Risky companies have risky stocks. Safe companies have safe stocks.”

This sounds so logical that it’s hard to argue with. A stable, profitable company like Johnson & Johnson must be a “safer” investment than a volatile startup, right?

Not necessarily. The problem is that people are conflating two completely different kinds of risk, and they don’t realize they’re doing it.

Business risk is about whether the company itself might fail or struggle. A startup carries a lot of it. A Fortune 500 company carries very little.

Valuation risk is something else entirely: whether the stock price reflects reality. A Fortune 500 company with low business risk can still be a terrible investment if its stock is wildly overpriced. You’re paying a premium for the feeling of safety, and that premium quietly translates into lower future returns.

Meanwhile, out-of-favor companies that feel “risky” sometimes offer the best long-term value precisely because investors are avoiding them. The stock price is depressed, which means your potential return is higher if the company performs even modestly well.

I saw this play out constantly in equity research. The stocks everyone felt good about owning were often the most overvalued. The stocks nobody wanted to touch were sometimes the best opportunities. Comfort and quality investment returns aren’t the same thing, and that disconnect is one of the harder things for new investors to internalize.

For most people, the solution is the same as always: own the whole market through index funds. You automatically own the safe companies and the risky ones, the overvalued and the undervalued, and the net result over time is the market’s average return, which beats most professional stock pickers.

5. “I’ll start investing when I know more.”

This is the most expensive belief on the list because it costs you the one thing you can never get back.

Compound interest is the single most powerful force in wealth building, and it’s entirely dependent on time. A dollar invested at 25 is worth dramatically more at retirement than a dollar invested at 35, even if you put in more at 35. The math is not even close.

I’ve met people who spent years “learning about investing” before putting a single dollar to work. They read books, followed markets, analyzed strategies, debated asset allocation in Reddit threads. And during all those years of preparation, their money sat in a savings account earning almost nothing while the market compounded without them.

You don’t need to know everything before you start. You need to know three things: invest in low-cost index funds, automate the contributions, and don’t touch it. That’s the whole curriculum. Everything else is refinement, and you can learn it while your money is already growing.

If you have money sitting on the sidelines because you feel like you don’t know enough yet, the best book I can point you to is The Simple Path to Wealth by JL Collins. You can read it in a weekend, and by Monday you’ll know enough to set up an automated investing system that will serve you for the rest of your life.

The Pattern Behind All Five

When I look back at this list, what strikes me is how good all of these beliefs feel in the moment. Picking companies you admire feels virtuous. Sticking to what you know feels prudent. Spending a Saturday researching stocks feels like real work, the kind that should be rewarded. And waiting until you feel ready sounds like the responsible thing to do.

I held onto these for years. They made me feel like I was being smart about my money.

But feeling smart and getting wealthier are different activities, and in my experience they’re often at odds. The investors I’ve watched build the most over time aren’t the ones with the cleverest thesis or the deepest research. They’re the ones who set up something boring (index funds, automatic transfers, an annual rebalance) and then went and lived their lives.

But here’s what nobody explained: “financial advisor” is one of the most loosely defined titles in the professional world. It can mean a fiduciary who is legally required to act in your best interest. It can mean a salesperson who earns commissions by putting you into expensive products. It can mean a guy at your bank who took a three-week course and is now authorized to recommend their in-house mutual funds.

Same title. Wildly different incentives. And if you don’t understand the difference, you can end up paying tens of thousands of dollars over your lifetime for advice that ranges from unnecessary to actively harmful.

I’ve worked inside the financial system as an equity research analyst on Wall Street and in corporate strategy at one of the largest companies in the country. I’ve seen how financial products get created, how they get marketed, and how the economics work for the people selling them. And the single most important thing I can tell you about financial advice is this: before you evaluate the advice, evaluate how the advisor gets paid.

The Two Standards You Need to Understand

There are two legal standards that govern financial advice in the U.S., and the difference between them is enormous.

Fiduciary standard. A fiduciary is legally obligated to act in your best interest. They must recommend what’s best for you, disclose conflicts of interest, and put your needs above their own. If they recommend a product that benefits them more than it benefits you, they’re violating their legal duty.

Registered Investment Advisors (RIAs) and fee-only financial planners operate under the fiduciary standard. This is the highest bar.

Suitability standard. Under the suitability standard, an advisor only needs to recommend products that are “suitable” for you, meaning generally appropriate for someone in your financial situation. They do not need to recommend the best option. They just need to recommend something that isn’t wildly inappropriate.

This is the standard that most broker-dealers and many advisors at large banks and wirehouses operate under. And the gap between “suitable” and “best” is where a lot of money quietly disappears from your accounts.

Here’s a concrete example of how this plays out. Say you need to invest $100,000. A fiduciary might recommend a low-cost index fund with a 0.03% expense ratio (total annual cost to you: $30). An advisor operating under the suitability standard might recommend an actively managed fund with a 1% expense ratio and a 5% front-end load (total first-year cost to you: $6,000). Both are “suitable” for someone in your situation. One costs 200x more than the other.

The suitability standard advisor isn’t breaking any rules. They’re operating exactly within their legal requirements. But you just lost $5,970 that you didn’t need to lose.

How Financial Advisors Actually Get Paid

This is the part that matters most, and it’s the part most people never ask about. There are essentially three models:

Fee-only. The advisor charges you directly, either a flat fee, an hourly rate, or a percentage of assets under management (typically 0.5% to 1% annually). They don’t earn commissions on products. Their only revenue comes from you, which means their incentives are aligned with yours. If your portfolio grows, they earn more. If it shrinks, they earn less.

This is the cleanest model. Not perfect, though. An AUM-based advisor still benefits from you keeping more money with them, which can create a subtle bias against paying down your mortgage or investing in real estate. But it’s the most transparent arrangement you’ll find.

Commission-based. The advisor earns money when you buy financial products: mutual funds, annuities, insurance policies, structured products. They may not charge you a visible fee at all, which makes the advice feel “free.” It’s not. The commissions are baked into the products, and they can be substantial.

An annuity might pay the advisor a 5% to 7% upfront commission. A mutual fund with a front-end load pays 3% to 5%. The advisor has a direct financial incentive to put you into products with higher commissions, regardless of whether those products are the best fit for your situation.

Fee-based (hybrid). This is the muddiest model and, unfortunately, the most common. The advisor charges you a fee and earns commissions on certain products. The word “fee-based” sounds almost identical to “fee-only,” and I’m convinced that’s not an accident. The distinction matters enormously: a fee-based advisor has dual revenue streams and dual incentives, and it can be very difficult to untangle which recommendations are driven by your interests and which are driven by their compensation.

The question you should always ask: “Are you a fiduciary, and how are you compensated?” If they hesitate, dodge, or give a complicated answer, that tells you everything you need to know. A fee-only fiduciary will answer clearly and directly because transparency is their selling point.

The Math That Should Make You Angry

Let’s run a simple scenario. You’re 30 years old with $100,000 invested. You plan to add $500/month and let it grow for 30 years at an average annual return of 8%.

With a 0.03% expense ratio (index fund, no advisor): you end up with approximately $1,580,000.

With a 1% advisory fee plus a 0.75% fund expense ratio (1.75% total): you end up with approximately $1,150,000.

That’s a difference of roughly $430,000. For advice that, in many cases, amounts to putting you into a target-date fund and meeting with you once a year.

I want to be clear: this isn’t hypothetical. This is the actual math. A 1.75% annual fee drag on a 30-year portfolio costs you nearly a third of your potential wealth. Not because the advisor is stealing from you, but because the compounding effect of fees is devastating over long time horizons.

This is why the financial planning industry has historically been so resistant to low-cost index funds and fee transparency. The moment clients understand the math, the traditional advisory model becomes very hard to justify for straightforward situations.

When You Actually Need an Advisor

I’ve spent most of this article explaining why the advisory industry has structural problems. Now let me be fair: there are situations where a good financial advisor is genuinely worth the money.

Your financial situation is genuinely complex. You own a business and need to coordinate business income, personal income, retirement plans, and tax strategy across multiple entities. You’ve received a large inheritance or windfall and need to think about estate planning. You’re going through a divorce and need to untangle shared finances. You have stock options or RSUs with complicated vesting and tax implications.

In these situations, a fiduciary advisor or a fee-only financial planner (especially one who charges a flat fee or hourly rate) can save you far more than they cost. The value isn’t in investment selection. It’s in tax optimization, estate planning, and coordinating complex financial decisions.

You know yourself well enough to admit you won’t do it alone. Some people know exactly what they should do with their money and still don’t do it. If having an advisor means you actually max out your 401(k), maintain your asset allocation, and don’t panic-sell in a downturn, and you wouldn’t do those things on your own, then the advisory fee might be worth it as a behavioral guardrail. It’s expensive therapy, but if it keeps you from making a six-figure mistake during a market crash, it pays for itself.

You’re in or approaching retirement. The accumulation phase of investing is relatively straightforward: put money in, don’t touch it, wait. The distribution phase, drawing down your portfolio in retirement while managing taxes, required minimum distributions, Social Security timing, and healthcare costs, can be genuinely complicated. A good advisor adds real value here.

When You Don’t Need an Advisor

If your financial situation looks like this (steady income, employer-sponsored retirement plan, no major debts, no complex assets, basic estate planning needs) you almost certainly don’t need to pay someone to manage your money.

What you need is a simple system:

Automate your savings. Max out tax-advantaged accounts. Invest in low-cost index funds. Rebalance once a year. Keep an emergency fund in a high-yield savings account. Review your plan annually.

That’s it. This isn’t a complicated financial plan. It’s a straightforward system that anyone can set up in an afternoon, and it will outperform the majority of professionally managed portfolios over a 20 to 30 year horizon. Not because you’re smarter than the advisors, but because you’re not paying their fees.

The entire financial advisory industry exists, in part, because people believe managing money is more complicated than it actually is. For most people in the accumulation phase of their career, it’s not complicated. It’s just uncomfortable, because the right answer (do less, be patient, don’t react) goes against every instinct.

If You Do Hire an Advisor

If you’ve read all of this and decided you genuinely need professional help, here’s how to find someone who’s actually working for you:

Look for fee-only fiduciaries. The National Association of Personal Financial Advisors (NAPFA) maintains a directory of fee-only advisors. The Garrett Planning Network lists advisors who charge by the hour, which is useful if you just need a one-time financial plan rather than ongoing management.

Ask how they’re compensated. If they can’t explain it simply, walk away.

Ask if they’re a fiduciary at all times. Some advisors are fiduciaries in certain contexts and not others (the “fee-based” hybrid model). You want someone who is a fiduciary in every interaction, full stop.

Consider a one-time plan instead of ongoing management. Many fee-only planners will build you a comprehensive financial plan for a flat fee, typically $1,000 to $3,000. You get a roadmap, you implement it yourself, and you come back in a few years if your situation changes. This is often the highest-value option for someone who’s financially literate but wants a professional sanity check.

Beware of “free” financial planning from your bank or brokerage. If the planning is free, you are the product. The “plan” will almost certainly recommend the institution’s own products, which generate revenue for them. This isn’t advice. It’s a sales funnel with a financial plan wrapper.

The Bottom Line

The financial advisory industry is full of smart, well-intentioned people operating within a system that is structurally designed to prioritize revenue over client outcomes. That’s not a conspiracy theory. It’s a business model.

Your job as someone trying to build wealth is to understand the incentives. Who is giving you advice? How do they get paid? Do their interests align with yours? If you can answer those questions clearly, you’ll avoid the most expensive mistakes most people make with their money.

Cepheid was a molecular diagnostics company that made instruments and test cartridges for hospitals and labs, and we genuinely loved it. They had announced a new portable point-of-care system called the GeneXpert Omni, a roughly 9-inch, two-pound device that was going to take their existing PCR technology and make it usable basically anywhere. Better product than the competition, dramatically cheaper than the existing GeneXpert systems, expected to expand the addressable market by orders of magnitude. We had built the model, run the channel checks, sat with management. The thesis was clean.

Then, on the Q1 2016 earnings call, the CEO announced that the Omni was being delayed. The detection and amplification module needed more engineering work. Launch was pushed from late 2016 into 2017, and the explanation was the kind of explanation that sounds reasonable until you realize it means the entire timeline you’d modeled the stock on was no longer operative.

What I remember most clearly from listening to that call was the recognition that nothing we had done would have predicted it. The Omni delay wasn't a market-research problem or a competitive-dynamics problem or anything our analytical framework was equipped to detect. It was an engineering problem inside a building we couldn't see into, on a project we'd been told was on schedule by people who probably believed it was on schedule when they said it.

The smartest people in the room still get it wrong

I worked alongside some of the smartest people I have ever met. Top MBAs, people with decades of industry expertise, the kind of access to information that retail investors will never have. They built fifty-tab Excel models, flew to conferences, talked to CEOs, and spent 60+ hour weeks trying to predict where a stock price would go.

They got it wrong all the time.

Not because they were bad at their jobs. Because predicting the future of a business is genuinely, fundamentally hard. A stock price depends on future earnings, which depend on consumer behavior, competitive dynamics, regulatory changes, macroeconomic shifts, management decisions, and engineering problems inside buildings nobody can see into. The variables interact in ways that no model fully captures, and the analysts who were honest about this were the best ones. They talked in probabilities rather than certainties, updated quickly when a thesis turned out to be wrong, and understood that being right 55% of the time was excellent. Sixty percent was extraordinary.

If the smartest, most informed, most resourced people in finance are operating at a 55-60% hit rate on full-time professional analysis, the implications for the rest of us are not subtle. There is no amount of weekend reading that closes that gap.

The kind of edge I actually saw work

The institutional clients we served, the hedge funds and mutual funds managing billions, didn’t generate their returns by reading the same public filings everyone else read. The ones that consistently beat the market were doing it through proprietary information: channel checks, expert network calls, on-the-ground data collection that didn’t exist in any public document. I wrote separately about what those channel checks actually looked like, including some of the more absurd ones I personally ran. That kind of work was the actual edge. Not the public reports or the ratings, but the proprietary stream underneath them.

As a retail investor, you don’t have that. By the time you’ve read an article about a company, the information in that article is already priced into the stock. The professionals trading the same stock have access to better information, faster execution, and more sophisticated analysis tools than you'll ever have. The matchup is more lopsided than most retail investors realize.

But you have one advantage that almost no professional has: time horizon. You can buy and hold for thirty years without a client calling you to ask why you’re underperforming this quarter. You can sit through a downturn that would get a fund manager fired. That patience, combined with low-cost index investing, has historically beaten most professional managers, and the reason is essentially the Cepheid lesson on a long enough timeline. Individual stocks are subject to engineering problems, regulatory surprises, management changes, and a hundred other events nobody can predict. An index fund is subject to all of those things too, but it owns enough companies that no single one of them matters. The unpredictability gets averaged out.

How I actually invest my own money now

Most of my portfolio is in low-cost index funds. Two automatic contributions per month, no exceptions. This is the wealth-building engine. It is boring by design. I don’t analyze it, I don’t tinker with it, and I don’t let my opinions about specific companies anywhere near it.

I also have a smaller portion of my portfolio, in the range of 20-30%, that I invest in individual stocks. This is where I apply the analytical framework I learned in equity research. I follow companies I find interesting, build my own theses, and take real positions based on real conviction.

Some of those positions have gone to zero. Others have done extraordinarily well, including a few multi-baggers that have substantially outperformed any index over the same window. The overall portfolio has beaten my index portfolio.

I’m telling you that because I want to be honest, and I want to be honest about what it does and doesn’t mean. That doesn't mean I've solved stock picking. It means I've had a run of good outcomes in a small enough slice of my portfolio that the bad outcomes didn't matter much, over a window that isn't long enough to draw strong conclusions. The professional analysts in my old job had decades of experience, more information than I’ll ever have, and a 55-60% hit rate. I’m not above that math. I’m operating inside it, and the only honest way to participate is to size the position so that being wrong doesn’t break anything.

The Cepheid lesson isn't that you can't pick stocks. It's that even when you've done the work, the engineering problem you couldn't see is going to find you eventually. The right response isn't to stop picking, it's to stop staking the things that matter on your ability to pick.

What this means for you

The smartest, most informed people in finance get it wrong 40 to 45 percent of the time. The real outperformance, when it exists, comes from proprietary research most retail investors will never access. What you have that no professional has is time. The most powerful investing strategy available to you is to put the majority of your money into low-cost index funds, automate the contributions, and leave them alone for thirty years.

If you enjoy individual stock analysis, do it. I do. Just size the position so that when one of your picks turns out to have an Omni delay you couldn’t see coming, the damage stops at the position and doesn’t reach the part of your portfolio your retirement actually depends on.

Except it wasn’t free. The fees didn't disappear when commissions went to zero, they just went underground. Which is worse in some ways, because you never think to question a cost you can't see.

I spent two years working inside this system as an equity research analyst, and I saw how the revenue streams work from the inside. What struck me wasn't that the system is corrupt, because it mostly isn't. It's that the system is brilliantly designed to extract money from retail investors at every layer while making each layer feel either free or insignificant.

Let me walk you through where your money actually goes.

Layer 1: Payment for order flow

When you place a trade on Robinhood, Schwab, Fidelity, or any other zero-commission brokerage, your order doesn’t go directly to the stock exchange. Instead, your broker sells your order to a market maker (a firm that specializes in executing trades).

This is called payment for order flow, or PFOF. The market maker pays your broker a small fee (fractions of a penny per share) for the right to execute your trade. In exchange, the market maker fills your order and earns the spread: the tiny difference between the price they buy the stock at and the price they sell it at.

How much money are we talking about? In the first nine months of 2024 alone, the largest market maker paid over $940 million to retail brokers for their order flow. These aren’t rounding errors. This is a multi-billion-dollar industry built entirely on routing your trades.

Here’s the uncomfortable question: if the market maker is paying your broker for the privilege of executing your trade, and the market maker is making money on the spread... who’s on the other side of that equation?

You are. You're paying for "free" trading through slightly worse execution on every trade. Each individual instance is small enough that it never registers, and over a lifetime of investing, the total is anything but small.

"Small per trade" and "small in aggregate" are very different things, and the bigger issue here isn't the per-trade cost. It's the incentive structure: your broker's revenue depends on you trading as frequently as possible. Every time you buy or sell, they get paid.

This is why trading apps are designed to feel like games. The push notifications and the confetti animations aren't accidents. The interface is built to make trading feel fun and frictionless, because every time you trade, your broker gets paid. What's actually being sold is volume, and the "free" framing is the marketing.

The brokerage industry made a calculated bet: eliminating the visible $7 commission would dramatically increase trading volume, and the invisible PFOF revenue from that increased volume would more than compensate. They were right.

Layer 2: Fund expense ratios

If you own any mutual fund or ETF (and if you have a 401(k), you almost certainly do), you’re paying an expense ratio. This is an annual fee, expressed as a percentage of your total investment, that covers the fund’s management, administration, and operating costs.

The part that used to infuriate me when I first learned it: you never see this fee on a statement. Instead of being deducted from your account as a line item, it's pulled out of the fund's returns before those returns are reported to you. A fund earns 10%, the expense ratio is 1%, your statement shows 9%. By the time you see the number, the fee has already happened, and unless you went looking for it, you'd never know it existed.

Expense ratios vary enormously, and the difference matters more than almost anyone realizes:

A broad market index ETF (the kind that simply tracks the S&P 500) might charge 0.03%. That’s $3 per year on a $10,000 investment. On a $500,000 portfolio, that’s $150 per year. Negligible.

An actively managed mutual fund might charge 0.50% to 1.00% or more. On that same $500,000 portfolio, a 1% expense ratio costs you $5,000 per year, every year, regardless of whether the fund actually beats its benchmark. Statistically, most years, it won't.

Then there’s another layer that most people never encounter unless they’re reading the fine print: sales loads. These are upfront fees of 3 to 5% deducted when you purchase shares. If you invest $10,000 in a fund with a 5% front-end load, only $9,500 actually gets invested. The other $500 goes to the broker who sold you the fund.

This is the financial equivalent of paying a cover charge to walk into a restaurant and then also paying for the meal. The load is a commission in disguise. If anyone ever tries to put you in a fund with a sales load, that’s your signal to ask hard questions about why they’re recommending that specific product.

Loads have become less common as investors have migrated toward no-load index funds and ETFs, but they still exist, especially in funds sold through traditional financial advisors and bank wealth management programs. Which has me noticing a pattern: the more "full-service" the financial relationship, the more layers of invisible fees tend to be baked in.

Layer 3: Cash sweep

This is the one that really got under my skin when I started understanding how brokerages actually make money.

The uninvested cash sitting in your brokerage account is making your broker a fortune. When you sell a stock and the proceeds sit in your account, or when you deposit money that you haven’t invested yet, that cash doesn’t just sit in a vault. Your broker “sweeps” it into money market funds or bank deposit accounts and earns interest on it.

How much does your broker earn? Typically at or near the prevailing money market rate, so 4 to 5% right now. How much do they pass along to you? Often 0.01% to 0.10%.

The math on this is genuinely offensive when you see it written out. On a $50,000 cash balance, your broker might earn $2,000 to $2,500 per year in interest while paying you somewhere between $5 and $50. That spread is pure profit, and you'd never notice it because you weren't thinking of that cash as an investment in the first place.

This is one of the largest revenue sources for many brokerages. It’s the quiet engine behind the “free” trading model: they don’t need to charge you commissions because they’re earning substantial returns on your idle cash.

The fix is simple: don’t leave large cash balances sitting in your brokerage account. If you need a cash reserve, put it in a high-yield savings account where you’re earning 4 to 5% instead of 0.01%. Only keep in your brokerage what you’re planning to invest in the near term.

Layer 4: The behavioral tax

The last layer isn't a fee anyone charges you. It's one you charge yourself through bad behavior, and it makes everything else on this list look like pocket change.

Every layer of the financial system is designed to encourage activity. More trades, more fund switches, more reactions to market news. The push notifications and breaking news alerts and "your stock is moving" updates are all engineered to make you feel like you should be doing something.

The correct response to almost all of it is to do absolutely nothing. Which, of course, generates zero revenue for anyone, which is why nobody in the financial industry will ever tell you that.

The data on this is devastating. Study after study shows that the average investor dramatically underperforms the funds they invest in. Not because the funds perform poorly, but because the investor buys and sells at the wrong times. They buy after a run-up, when prices are high, and sell after a downturn, when prices are low. They chase last year's best-performing fund, then panic during the next correction and sit on the sidelines during the recovery that follows.

The gap between “fund returns” and “investor returns” is called the behavior gap, and it typically costs investors 1 to 2% per year in lost returns. I want to make sure you feel how much that is. On a $500,000 portfolio over 30 years, a 1.5% annual behavior gap costs you roughly $600,000 in foregone wealth.

Six hundred thousand dollars, and not because someone charged you a fee or put you in a bad fund, but because at some point you got a push notification that made you nervous and clicked "sell" at the wrong moment.

That’s more than all the expense ratios, PFOF, and cash sweep fees combined. The most expensive fee in all of investing is your own behavior, and the entire system is designed to make that behavior worse.

The total cost of being a retail investor

Let’s add it all up for a typical scenario. Say you have a $200,000 portfolio, you trade moderately, and you’re in a mix of funds:

Payment for order flow: Small per trade, maybe $20 to $50 per year total for a moderate trader. Individually trivial.

Fund expense ratios: If you’re in actively managed funds averaging 0.50%, that’s $1,000 per year. If you’re in index funds at 0.03 to 0.10%, it’s $60 to $200. The difference over 30 years is staggering.

Cash sweep: If you keep $20,000 uninvested, your brokerage might earn $800 to $1,000 on it while paying you $2 to $20. That’s $800+ per year in value you’re leaving on the table.

The behavior gap: If you trade reactively and chase performance, the cost is potentially 1 to 2% annually. That’s $2,000 to $4,000 per year on a $200,000 portfolio. By far the largest line item.

Add it up and the total cost of being an average retail investor is somewhere between 1.5% and 3% per year, almost all of it invisible and, what's more frustrating, almost all of it avoidable.

How to pay as little as possible

The irony of all of this is that the cheapest, simplest approach is also the one that produces the best long-term results. The substantive version of “pay less” comes down to four moves.

Use low-cost index funds. A total stock market index fund with a 0.03% expense ratio captures the entire market’s returns for practically nothing, and you’re not paying a team of portfolio managers to underperform their benchmark.

Don’t leave large cash balances in your brokerage. Keep your emergency fund and short-term cash in a high-yield savings account, and only put money in your brokerage that you intend to actually invest.

Trade as little as you can stand to. Every trade generates revenue for someone other than you, and the less you interact with your portfolio, the better it tends to perform, which is genuinely counterintuitive until you understand the fee layers above.

Ignore the noise. Financial media, push notifications, earnings reports, and market commentary are designed to make you feel like you need to act. You almost never need to act, and the less activity you generate, the more of your money actually compounds.

I turned off portfolio notifications on my phone about three years ago, and it's been one of the best financial decisions I've ever made. The market kept moving, obviously. I just stopped reacting to it, and that turned out to matter more than any fund switch I ever considered.

After managing my own finances for the better part of a decade, and after two years on Wall Street watching what professional investors actually pay attention to, I think most of that activity is decoration. A few habits drive almost everything you’ll ever build. The rest is busywork that makes you feel like you’re being responsible.

What follows is my honest ranking of the financial habits I’ve personally used or considered, scored on what they actually did for my net worth and how much of my time they consumed to get there. The ranking is opinionated. Some of these are going to be lower than the personal finance industry would like.

Tier 1: The ones that actually built it

Automated investing. Two transfers per month into my brokerage account, into low-cost index funds. The amount has changed over the years. The schedule hasn’t. This habit alone has done more for my net worth than every other financial decision I’ve made combined, and the reason isn’t clever investing or good timing. It’s that the money leaves my checking account before I have a chance to think about whether to spend it. The decision is made once and then it makes itself forever. If I had to pick one habit to keep and throw out everything else on this list, this would be it.

Employer 401(k) match. If your company matches up to some percentage of your salary, contribute at least that much. This is the only place in personal finance where you get a guaranteed, instant return on your money. The fact that any working professional in America leaves this on the table is one of the more depressing data points in financial behavior, and the fact that some of those people are also obsessively tracking their grocery spending in a budgeting app is the kind of misallocation of attention that this article is mostly about.

Keeping lifestyle costs flat after raises. This is the one that quietly separates people who earn a lot from people who have a lot. Every time my income has gone up, the automatic transfer has gone up by the same amount, and my lifestyle has stayed roughly where it was. I’ve watched colleagues double their income over five years and have nothing to show for it because their spending doubled in lockstep. The gap between what you earn and what you spend is the only thing that becomes wealth. Protecting that gap when your income rises is the highest-leverage habit in personal finance.

Tier 2: The ones that made it stronger

A real emergency fund in a high-yield savings account. Six months of expenses, sitting in a savings account paying somewhere around 4%. Not in checking earning 0.01%. Not in a brokerage account where you’d have to sell stocks at a bad time to access it. The point of the emergency fund isn’t the interest. It’s what having one does to your decision-making. When you know you’ve got six months of runway, you say no to bad jobs, you negotiate harder, and you don’t panic-sell during a downturn. The ROI is mostly measured in mistakes you don’t make.

Index funds over actively managed funds. I spent two years inside the equity research machine. I built models, sat in management meetings, and crawled under a sequencer to read its serial number for a competitive estimate. After all of that, my honest take on whether the average professional investor can consistently beat a low-cost index fund is no, mostly not, and certainly not after fees. The Simple Path to Wealth by JL Collins is the cleanest case I’ve ever read for why this is true and what to do about it.

An annual financial review instead of monthly obsessing. Once a year, I sit down with a spreadsheet for a couple of hours and look at the whole picture. Net worth, savings rate, insurance, automatic transfer amounts, anything that’s changed. I set the dials for the year. Then I close the spreadsheet and don’t think about it again. I’ve maintained this for years. I’ve never managed to maintain a daily expense tracker for more than about three weeks, which I think tells you something about which approach is sustainable for an actual human.

Monthly net worth tracking. Fifteen minutes on the first of every month, in a Google Sheet I built years ago. I update the account balances and look at the trendline. That’s it. Net worth is the one number that tells you whether you’re actually making progress, and looking at it monthly is enough to spot real shifts without making you neurotic about market noise.

Tier 3: Fine, but stop calling it strategy

Credit card rewards. I have two decent cashback cards. I use them for everything I’d be buying anyway. I pay them in full every month. The end. People spend astonishing amounts of energy churning cards, tracking bonus categories, and optimizing redemption math, and the honest accounting is that it’s a hobby disguised as a wealth-building strategy. If you enjoy it as a hobby, by all means. But if you spent the same hours negotiating your salary or building something on the side, the return would dwarf whatever you’re getting back on dining points.

Negotiating bills and canceling subscriptions. Worth doing once a year. Not worth thinking about more often than that. My annual review catches it.

Detailed expense tracking. I used to do this and I don’t anymore. The honest version of expense tracking is that you do it for one month, learn where your money is actually going, fix the two or three biggest leaks, and then build an automated system that handles the rest. Ongoing daily tracking is the financial equivalent of weighing yourself after every meal. It produces anxiety without producing better outcomes.

Tier 4: Things that actively cost you money to do

Trying to time the market. I watched people with Bloomberg terminals, Ivy League degrees, and twenty years of experience try to do this for a living. Most of them couldn’t. The data on retail investors trying to do it is grimmer than the data on professionals, which is itself grim. Set up the automatic transfers and stop checking.

Stock picking as your main strategy. I do some individual stock investing. It’s a small percentage of my portfolio, I treat it as an intellectual hobby, and I am explicitly not relying on it to fund my retirement. If you enjoy the analysis, allocate a small slice of play money. If you’re picking stocks because you think it’s how you’ll get wealthy, you should read Tier 1 again.

The morning coffee discourse. A daily $5 coffee is about $1,800 a year. That’s real money in absolute terms and a complete distraction in relative terms, because if you’ve got the Tier 1 habits handled, the coffee is mathematically irrelevant to your retirement, and if you don’t, the coffee isn’t your problem.

What this all comes down to

If you wanted me to compress everything I think about personal finance into a sentence, it would be this: most of the things people argue about don’t matter, and the few that do are boring enough that no one wants to talk about them.

The people I’ve watched build real wealth over time aren’t the ones with optimized credit card stacks or sophisticated rebalancing schedules. They’re the ones who automated the boring things in their twenties or thirties and then went and did something else with their attention.

That’s not a satisfying answer if you’re hoping personal finance has a clever trick in it. But if you’ve made it this far down the article, I think you already suspected it didn’t.

Not the final version, but the structure was there: thesis, rating, the narrative arc. After the call, I'd fill in the actual numbers, adjust a sentence or two if something surprised me, and send it out. Maybe an hour of editing on a report that looked like it took days.

The earnings call almost never changed our thesis. Not once in my time covering healthcare stocks did a company say something on a call that made me throw out a report and start from scratch. The calls were that predictable.

This isn’t because I was lazy (read: efficient). It’s because earnings calls are, by design, one of the most carefully managed pieces of corporate communication in existence. And if you’re a retail investor listening to them hoping for insight, or worse, making trading decisions based on them, you’re consuming a performance, not an analysis.

Let me tell you how the sausage actually gets made.

What an Earnings Call Actually Is

Four times a year, public companies host a conference call where management presents quarterly results and takes questions from analysts. The format is nearly identical across every company: a safe harbor disclaimer, prepared remarks from the CEO and CFO, and then a Q&A session.

If you’ve never listened to one, it sounds like this: calm, measured corporate-speak delivered by executives who have rehearsed every word. Revenue was up X percent. Margins expanded by Y basis points. The company is “well-positioned” and “executing on its strategic priorities.” Guidance for next quarter is in the range of A to B.

It sounds informative, but it's mostly theater.

Here’s why: everything management says in the prepared remarks has been reviewed by lawyers, vetted by investor relations, and rehearsed until it’s polished smooth. The goal isn’t to inform you. The goal is to present results in the most favorable light possible without saying anything that could trigger an SEC inquiry.

The language is deliberately vague when things are bad and precisely specific when things are good. "We saw some headwinds in certain geographies" means an entire region missed its numbers badly. Once you learn the translation layer, you realize how little actual information is being transmitted.

The Q&A Isn’t What You Think Either

The Q&A session is supposed to be where analysts push management for real answers. Sometimes it is, but more often it's a choreographed exchange where both sides know the rules.

Analysts ask questions they largely already know the answer to, because they’ve already built models, talked to investor relations, and formed their thesis. The questions are designed to get management on the record confirming or denying specific assumptions, not to uncover hidden truths.

Management answers by saying as much as they legally have to and as little as they strategically want to. If an analyst asks about a specific product’s growth trajectory, the CEO will give a directional answer wrapped in qualifiers: “We’re encouraged by the early trends and continue to see strong demand signals.” That could mean anything from “it’s our best product ever” to “it’s slightly above our internally lowered expectations.”

And here’s the part that would probably surprise most people: the order of the Q&A is often pre-arranged. The biggest institutional clients of the bank hosting the call get their analysts called on first. The questions from smaller firms or independent analysts come later, if there’s time. The whole thing has a hierarchy that’s invisible from the outside.

Why I Pre-Wrote My Reports

I pre-wrote my research reports because after covering the same companies for several quarters, the earnings call became almost entirely predictable. I knew what management was going to say because I’d already modeled the quarter. I knew the revenue range. I knew which segments would be strong and which would be soft. I knew the margin trajectory.

The call was confirmation, not revelation.

The only things that would genuinely surprise me were significant guidance changes (management raising or lowering their outlook for the next quarter or full year) or an unexpected event like a major customer loss, a product recall, or a strategic pivot. These happened rarely.

Everything else (the prepared remarks, the tone, the Q&A) I could have scripted myself. And in a sense, I did, because the report was already written.

This isn’t unique to me. Every analyst I worked with did the same thing. The reports had to go out within hours of the call to be useful to our institutional clients. You can’t write a 15-page report from scratch in two hours. You write the framework in advance and fill in the blanks.

What Wall Street Actually Cares About (It’s Not What You Think)

Here’s something that took me a while to understand: the buy-side (the institutional investors who were our clients, the hedge funds and mutual funds managing billions) didn’t care about our ratings or price targets.

The buy-side didn't care about our ratings or price targets. The "buy" or "sell" rating that retail investors treat as gospel? The institutional investors who actually move markets largely ignored it.

So what did they actually value?

Original, proprietary research that they couldn’t do themselves. Specifically, channel checks. This is primary research where analysts go out into the real world and gather data points that aren’t available in any public filing or management presentation.

This is where equity research gets genuinely interesting. And genuinely absurd.

The Channel Checks Nobody Tells You About

I covered healthcare stocks. Specifically, diagnostics companies, lab services, and genomics. The channel checks I did to generate proprietary data points for our institutional clients were some of the most ridiculous professional experiences of my life.

I called STD clinics across the country. One of the companies I covered made molecular diagnostic tests, including ones used for sexually transmitted infections. To estimate how many tests they were selling, I needed to understand testing volumes at the clinic level. So I spent days calling clinics, posing as someone researching testing options, asking about wait times, test availability, which platforms they were using, and how volume had changed. The data I gathered from those calls became the basis for a testing volume estimate that our institutional clients used to model the quarter.

I got my blood drawn three times in one day. I covered the two largest lab testing companies in the U.S. At the time, a buzzy Silicon Valley startup was promising to disrupt traditional blood testing with cheaper, faster technology, and investors were nervous. To assess the competitive threat, I needed to understand the current patient experience at the major labs and compare it to what this startup was promising. So I walked into three different labs in one afternoon and got blood drawn at each one. I documented wait times, staff interactions, test menus, the works. That comparison became a section of a research report that went out to some of the largest healthcare investors in the world. (The startup, by the way, turned out to be a fraud.)

I crawled around on my hands and knees in a genomics lab. I covered a company that made DNA sequencing machines. Wall Street wanted to know how many units they’d shipped in the quarter. The company wouldn’t disclose this publicly. So during a lab visit, ostensibly to learn about the science, I got on the floor and crawled under a sequencer to read the serial number off the unit. If I could track serial numbers across multiple lab visits, I could estimate the install base and extrapolate quarterly shipments.

This is what equity research actually looks like. Not a genius in a suit staring at Bloomberg screens and divining the future of the market. A 20-something analyst on their hands and knees in a lab, trying to read a serial number, so a hedge fund can shave half a percent off their estimate for one company’s quarterly revenue.

What This Means for You

I’m not telling you these stories to entertain you (though I hope they do). I’m telling you because they illustrate something important about how the stock market works.

The information advantage is real, and you don’t have it. Institutional investors pay millions of dollars a year for research from analysts who are literally getting blood drawn to generate data points. You are competing against these people when you pick individual stocks. Not on a level playing field. On a field where they have resources, access, and information you will never have as a retail investor.

The “analysis” you see on financial media is the surface layer. The analyst ratings, the price targets, the talking heads on CNBC are the public-facing output of a much deeper machine. The real research happens in channel checks, management meetings, and proprietary data analysis that never makes it into the public report. What you see is the tip of the iceberg. What moves stocks is the 90% underneath.

Earnings calls are the least useful source of investment information available to you. By the time management is speaking on a call, the information has been lawyered, sanitized, and optimized for maximum corporate benefit. Analysts have already modeled the quarter. Institutional investors have already positioned. The call itself is a formality.

None of this means you shouldn’t invest. It means you should invest with clear eyes about what you’re actually doing.

If you’re picking individual stocks based on earnings calls, analyst ratings, and financial media, you’re bringing a knife to a gunfight. The people on the other side of your trades have more information, more resources, and more time than you do.

If you’re investing in low-cost index funds on a consistent schedule and leaving them alone for decades, you’re buying the entire market. This means you capture the returns generated by all of that institutional research without needing to compete with it. You’re not trying to outsmart the machine. You’re riding on top of it.

That’s what I do with the core of my portfolio. And it’s what I’d recommend to anyone who doesn’t want to spend their weekends calling STD clinics.

After a few weeks, I noticed I was making fewer impulse purchases. My baseline anxiety about my finances had decreased. My actual financial situation had not changed at all. The only change was in my feed.

I’ve always thought about wealth as the result of decisions. Your financial situation evolves from the job, home, car, and lifestyle you select. I’ve learned that wealth also comes from what you take in before making those decisions.

The issue that often goes unspoken

Think about what the average person’s feed looks like. It’s a mix of lifestyle content that makes others feel behind. Much of it comes from influencers who lease their lifestyles and call it wealth.

Every piece of content you consume trains your brain to think a certain way about money. Scroll through “what I spend in a week in NYC” posts, and your view of normal spending shifts upward. Watch enough day-trading content, and long-term compounding can start to feel boring. But boring is exactly what works.

What you focus on often, you start to notice without even trying. Feed it urgency and status anxiety, and you’ll see threats and competition everywhere. Nurture it with patience and compounding. You’ll see opportunities you missed before. The people around you influence your financial outcomes. And in 2026, “the people around you” includes every account in your feed.

The dopamine audit

Before you add anything, you need to prune. Here’s what I’d cut without thinking too hard about it.

Lottery-style investment content. Any account whose primary value proposition is telling you what to buy. Stock tips, crypto calls, “this one’s about to explode.” These accounts train your brain to think in bets instead of systems. They make you feel like you’re one pick away from changing your life. But that mindset is what keeps people broke.

Hustle content that confuses effort with progress. Working 80 hours a week for someone else’s equity isn’t a wealth strategy; it’s a burnout strategy. If an account talks about grinding but ignores ownership, leverage, or compounding, it’s just selling a feeling. It’s not a real framework that leads to wealth.

And the big one: anything that triggers urgency or comparison. If a headline makes you feel frantic, behind, or inadequate, someone engineered it to do exactly that. It exists to harvest your attention, not to improve your judgment. Unfollow without guilt. I have a simple test: if I wouldn’t invite someone to my personal board of advisors, they don’t get a spot in my pocket either.

Nourishing your brain: what to eat instead

Start by including content on how money works. Focus on deeper concepts, not stock picks or market forecasts. Get the basics: how compounding helps wealth grow over years, how risk affects your investments, and why people often lose money from mistakes, not bad luck.

My top pick for this is The Psychology of Money by Morgan Housel. It reframed how I think about every financial decision. Housel’s main point is that financial success depends more on behavior than on intelligence. This seems obvious, but few people act on it.

If you want to go deeper on risk specifically, check out The Most Important Thing by Howard Marks. It’s a favorite among professional investors, who often read it many times. Marks doesn’t tell you what to invest in; instead, he teaches you how to think about investing. This method is far more valuable and durable in the long run. He also publishes his investment memos for free on the Oaktree website.

The second thing worth feeding your brain is content about long time horizons. This is the harder shift. Modern life teaches you to think in weeks and months. Social media, news cycles, quarterly earnings, and annual reviews all play a part. Building wealth requires thinking in decades.

Thinking in Bets by Annie Duke changed how I make decisions. Her core framework is separating the quality of a decision from the quality of its outcome. A good decision can lead to a bad outcome. A bad decision can lead to a good outcome. If you judge yourself only by outcomes, you’ll abandon good strategies at exactly the wrong time. The market will punish you for it.

A key concept is that some assets can grow without relying on your work hours. This means they can grow in value or generate income without requiring more of your time. Code, media, capital, systems. The Almanack of Naval Ravikant provides a clear articulation of this. The physical book is worth owning because you will return to it.

The 15-minute reset

Algorithms focus on your active searches. You can hack this.

Take 15 minutes this week to look for and interact with content on:

• Long-term compounding

• Tax-efficient investing

• The psychology of financial decisions

Dive into these topics to deepen your understanding.

The algorithm will start shifting your feed toward signal and away from noise within a few days.

This isn’t about becoming a hermit or swearing off entertainment. Be intentional about the content you consume. It shapes your thoughts on money.

Why This Matters More Than You Think

I’ve spent over a decade working in finance. I’ve had a front-row seat to what Wall Street culture does to people’s relationships with money. The comparison can create status anxiety. You might feel like you’re always behind, no matter how much you earn. None of those people had a “content diet” problem in the modern sense. They had each other, which is the same problem in slower motion.

The antidote is better information. Consume it with intention and over time. People who reach financial independence aren’t always smarter. They have less mental noise.