Twenty years ago, a meaningful part of an investor’s edge came from access to information.
Then the internet democratized information.
Then Substack, X, podcasts, and online communities democratized specialized analysis.
And now LLMs are democratizing a large part of the analytical work itself: summarizing earnings, comparing companies, calculating multiples, explaining industries, reading filings, identifying competitors, and more.
The result is almost paradoxical:
We probably have access to more information and analytical capacity than any previous generation of investors, yet that does not necessarily mean we make better investment decisions.
Because the nature of the problem has changed.
The challenge is no longer simply getting access to the right information, or even producing a reasonable first layer of analysis. Those remain important, but they are becoming easier to obtain, faster to produce, and increasingly available to everyone.
What is still scarce is the ability to decide which information actually matters, how much of it is already reflected in the price, what would change the thesis, how much risk to take, and how to behave once real money is involved.
And this is where psychology becomes much more important than it first appears.
I read Thami Kabbaj’s “Psychologie des grands traders” some time ago, and one of the ideas that stayed with me is that psychology is not something separate from the investment process. It is part of the process itself.
It is easy to think about investing as a purely analytical exercise. You gather information, build a thesis, estimate the potential upside and downside, and then make a decision.
In reality, the most difficult part often begins after the analysis is finished.
A stock you like suddenly falls 25%.
Another one you decided not to buy rises 60% in a few months.
A company you own reports weaker earnings.
A sector you have been studying starts moving quickly and everyone around you seems to already be positioned.
At that point, the question is no longer just whether your analysis was correct.
You are now making decisions while money, uncertainty, ego, regret, fear and opportunity are involved at the same time.
And that changes the way we think.
This matters even more today because the infrastructure around investing has changed too.
Modern brokerage platforms have done something extremely positive: they have made investing simpler, cheaper and more accessible than ever before. Opening an account, buying a stock and following a portfolio can now be done in minutes.
But the same simplification has another side.
The friction around execution has almost disappeared.
You see a price, a chart, a percentage move, and a buy or sell button. Everything is designed to make action easy.
What has not become easier is deciding whether that action actually makes sense.
In some ways, we have built extremely efficient interfaces for executing decisions without necessarily improving the process behind those decisions.
And that is where many of the most common investing mistakes begin.
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Whenever a new market emerges, most of the attention goes to the product people can see.
When price starts changing the thesis
FOMO is probably the most obvious example, particularly after several years of very strong performance across parts of the AI trade.
Imagine you start looking at a company when the stock is trading at $50.
You like the business, you understand the opportunity, but you decide that the valuation already looks demanding. So you wait.
A few months later, the stock is trading at $80.
At this point, something interesting often happens.
The business may not have changed enough to justify a completely different conclusion, but the environment around the stock has.
More analysts are talking about it. The company appears more frequently in financial media. Your feed is filled with investors explaining why the opportunity is still early. People who already own the stock are posting large gains.
And suddenly the same company that did not feel compelling enough at $50 starts feeling difficult to ignore at $80.
This is what makes FOMO more subtle than simply “buying because a stock is going up.”
The real problem is that price itself can gradually become part of the thesis.
A rising stock creates social proof. It makes the bullish case feel more credible. It reduces the discomfort of being wrong alone because the market appears to agree with you.
The move becomes evidence.
But a higher price does not necessarily make the investment safer. In many cases, it simply means that more of the optimistic scenario is already reflected in the valuation.
So the useful question is not whether the stock has gone up.
It is whether something has changed enough to justify paying more for the same future.
What changed between $50 and $80: the business, the expectations embedded in the price, or simply our perception of the opportunity?
That distinction is important because FOMO does not necessarily push investors into bad companies.
Quite often, it pushes them into good companies at increasingly difficult prices.
And being right about a company is not the same thing as making a good investment in that company.
You can be completely right about AI demand, datacenter growth, power consumption, fiber optics, memory or robotics and still generate a disappointing return if the price you paid already assumed an even better future.
Markets do not reward us simply for predicting what happens next.
They reward us for identifying the difference between what happens next and what the market already expects to happen.
That is a much harder problem.
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When research becomes defense
Confirmation bias becomes even more interesting in the age of AI because research itself is becoming easier to bend toward what we already want to believe.
Imagine you buy a stock after spending several days building a thesis around the company. A few months later, the next earnings report disappoints. Revenue growth slows, guidance is weaker than expected, and the stock sells off.
At that point, the most useful question would probably be whether the new information materially changes the original thesis.
But that is not always the question we naturally ask.
Instead, once we already own the stock, we tend to look for explanations that allow us to preserve our existing view. We may ask an LLM why the earnings miss could be temporary, why the market might be overreacting, or which parts of the report were actually more encouraging than the headline suggests.
And the problem is that the model will usually be able to give us several perfectly plausible answers.
This does not make the analysis wrong. Some of those arguments may be completely valid.
The issue is that the direction of the research has changed.
Before investing, we were trying to understand whether the thesis was good.
After investing, we may quietly start trying to defend it.
That is where research can turn into confirmation bias.
AI makes it dramatically easier to explore an investment from multiple angles, which is extremely useful. But it also makes it easier to generate increasingly sophisticated arguments for a conclusion we had already reached.
So the real advantage is not simply being able to produce more analysis.
It is maintaining enough distance from our own position to ask the uncomfortable question first:
What evidence would make me change my mind?
That question should ideally be answered before the position exists, not after the stock has already fallen.
Because once money is involved, our interpretation of information changes.
Your entry price means more to you than it does to the market
Another good example is anchoring.
Suppose you buy a stock at $100.
A few months later, it trades at $70.
A very common reaction is to think:
I will sell when it gets back to $100.
But there is nothing inherently important about $100.
It matters to you because it is your entry price.
It determines whether the position appears green or red in your portfolio and whether selling feels like realizing a gain or admitting a mistake.
But the market does not know where you bought the stock.
The company’s customers do not care.
Its competitors do not care.
Its future cash flows do not care.
The more useful question is much simpler:
If I had no position today, would I buy this company at $70?
If the answer is yes, holding may make sense.
If the answer is no, then the fact that you originally paid $100 is not a particularly strong reason to continue owning it.
Anchoring also works in the opposite direction.
A stock falls from $200 to $100 and suddenly looks “cheap” because it is down 50%.
But cheaper than its previous price does not necessarily mean cheap relative to its future cash flows or the expectations embedded in the valuation.
The previous price may simply have been wrong.
This is one of the recurring problems in investing: numbers that are personally meaningful start influencing decisions even when they have little economic relevance.
Why we often sell the winners first
Loss aversion creates a related problem.
Imagine you own two stocks.
One is up 40%.
The other is down 40%.
Which one feels easier to sell?
For many investors, it is the winner.
Selling the winner creates a sense of completion. You made a good decision, you realized the gain, and the result is now secure.
Selling the loser feels very different.
It means accepting that the original decision did not work.
So we wait.
Maybe the next earnings report will be better.
Maybe the stock will recover.
Maybe we can at least get back to breakeven.
The problem is that none of those thoughts tells us very much about expected returns from today’s price.
The relevant question is not:
Am I currently up or down?
It is:
From this price forward, which investment offers the better expected return relative to the risk?
That sounds obvious in theory.
It is much harder in practice because we do not experience gains and losses symmetrically.
A realized loss feels like a mistake becoming permanent.
A realized gain feels like confirmation.
That can lead investors to sell positions that are still working simply because they are profitable, while keeping weaker positions because realizing the loss is psychologically uncomfortable.
Of course, sometimes selling the winner and keeping the loser is the correct decision.
The bias is not the outcome itself.
The bias is allowing the emotional status of the position to become part of the investment thesis.
The recent past is always easier to imagine continuing
Recency bias becomes especially important after long, powerful market trends.
When a sector has performed well for several years, the assumptions investors consider normal tend to shift.
Take AI infrastructure.
At the beginning of the cycle, the important question may have been whether spending on compute infrastructure would continue to grow rapidly.
After several years of accelerating investment, that assumption becomes much less controversial.
The discussion gradually moves from whether spending will grow to how quickly it will continue to accelerate.
That is a subtle but important change.
An optimistic scenario that once required strong evidence can slowly become the baseline assumption.
The same thing happens with valuation.
Multiples that would have looked extreme early in the cycle start to appear normal simply because investors have spent several years seeing them.
Then the recent environment becomes the reference point for what the future should look like.
This works in the opposite direction too.
After a sector falls 60% or 70%, investors often struggle to imagine what could make it attractive again.
After several weak quarters, temporary problems start to feel structural.
After a recession, cyclical weakness starts to look permanent.
We are very good at adapting to the current environment.
The danger is assuming that the current environment is permanent.
A useful question is therefore:
Which assumption in my thesis would look most obviously wrong if conditions simply returned to something closer to normal?
That does not mean every strong trend has to reverse.
Sometimes structural changes really do create a new regime.
But distinguishing a genuine structural shift from extrapolation is one of the hardest parts of investing.
Sometimes the best decision is no decision
There is another bias that I think receives less attention because it is partly created by the way modern investing works.
Action feels productive.
You research something, reach a conclusion and place a trade.
There is a clear outcome.
Doing nothing feels different.
You may spend several hours researching a company and conclude that the business is attractive but the price is not.
Nothing happens.
There is no position in the portfolio.
No confirmation screen.
No immediate reward for the work.
This is one reason action bias can become so powerful.
When markets are open every day and prices are constantly moving, there is always something to react to.
There is always another stock moving 10%, another earnings report, another headline, another opportunity that appears urgent.
But investing does not necessarily reward the frequency of decisions.
Sometimes the correct conclusion is to buy.
Sometimes it is to sell.
And sometimes the correct conclusion after doing all the work is simply to wait.
That is still a decision.
Cash is not necessarily the absence of conviction.
Waiting for a better risk/reward is not necessarily indecision.
The ability to do nothing when nothing needs to be done is probably one of the more underrated parts of investing.
And ironically, the easier execution becomes, the more valuable that ability may become.
The problem is not emotion itself
At this point, the obvious conclusion might be that better investors simply need to become less emotional.
I do not think that is realistic.
Knowing that FOMO exists does not prevent you from experiencing it.
Knowing about loss aversion does not make a 40% drawdown emotionally neutral.
Understanding confirmation bias does not make it pleasant to discover information that contradicts six months of your own research.
The objective is probably not to eliminate emotion from investing.
It is to build a process that gives emotion less control over the final decision.
And one of the simplest ways to do that is to make some of the important decisions before the emotional situation exists.
Build the decision before the emotion
Before buying a stock, I think it is useful to write down a few things explicitly.
Not because a checklist can predict the future.
It cannot.
But because it gives you a reference point created before price movements, P&L and emotion start changing the way you interpret information.
The first question is the thesis itself.
What do I believe that the market may be underestimating?
This is different from asking why the company is good.
A great business can still be a poor investment if everyone already understands exactly how good it is.
The second question is expectations.
What does the current price already appear to assume?
A company growing 30% is not necessarily attractive if the valuation requires something closer to 50%.
Conversely, relatively mediocre growth can generate excellent returns when the market is pricing an even worse outcome.
Then come the catalysts.
What could cause the market to change its view?
You can be right for a very long time without making money if there is no mechanism that causes expectations to move closer to your view.
Just as important is invalidation.
What evidence would prove the thesis wrong?
This is probably the question I would place above all the others.
It is much easier to define what would change your mind before you own the position than after the position is already down 30%.
Then there is valuation.
What future am I effectively paying for at the current price?
Position sizing.
How much capital am I willing to put behind this idea if the thesis is wrong?
And finally, behavior.
What would I do if the stock fell 20%?
What if it rose 50% very quickly?
What if the thesis appeared intact but the stock went nowhere for a year?
The objective is not to create rigid rules that can never change.
New information should change decisions.
The objective is simply to make the starting logic explicit.
In practice, a basic version could look like this:
THESIS
What do I believe that the market may be underestimating?
EXPECTATIONS
What appears to already be priced in?
CATALYST
What could make the market change its view?
INVALIDATION
What evidence would prove me wrong?
VALUATION
What assumptions are required to justify today's price?
POSITION SIZE
How much capital am I willing to risk?
BEHAVIOR
What will I do if the stock falls 20%, rises 50%,
or goes nowhere for a year?I think of this less as a checklist and more as a small contract with your future self.
When the stock falls sharply, instead of immediately asking whether you should sell, you can return to the original thesis and ask whether anything has actually been invalidated.
When it doubles, instead of selling simply because the gain feels large, you can ask what the market is now pricing.
When a negative article appears, you can ask whether it changes one of the assumptions you wrote down before buying.
And when the price moves significantly without a corresponding change in the underlying business, you have something more useful to compare against than your emotional reaction to the chart.
None of this guarantees a good outcome.
Good investment processes still produce losing investments.
The point is to separate a bad outcome from a bad decision.
Those are not always the same thing.
The investing edge is moving
AI is going to make financial research significantly better.
It will make data easier to access.
It will make company analysis faster.
It will make it easier to compare businesses, build scenarios, understand industries and process information that previously required much more time.
I think that is an extraordinary development.
But it also changes what is scarce.
If everyone can summarize the same earnings call, the summary itself becomes less differentiated.
If everyone can produce a reasonable valuation model, simply having the model becomes less differentiated.
If everyone can generate ten arguments explaining why an industry will grow, producing those arguments becomes less differentiated.
The difficult questions move somewhere else.
Which information actually matters?
What does the market already expect?
Where might consensus be wrong?
What evidence would invalidate the thesis?
How much risk should I take?
And once money is involved, can I still follow the process I designed when I was thinking clearly?
The next generation of investors will probably have better tools than any generation before them.
Better data.
Better AI.
Better access to research.
But those tools will increasingly be available to everyone.
The harder advantage may remain much more human: knowing what matters, knowing when not to act, recognizing when your thesis is wrong, understanding what is already priced in, and having a process strong enough to survive your own emotions.
The goal is not to remove emotion from investing. It is to prevent emotion from becoming the decision-maker.
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