A dusty database, a refrigerator-sized computer, and the birth of evidence-based investing.
Ask any investor today what stocks return over the long haul, and you'll hear "around 10% annually" without missing a beat. It's the most repeated figure in personal finance, right up there with the 4% retirement withdrawal rule.
But here's the wild part: For most of the 20th century, nobody actually knew this. Not the professionals. Not the academics. Not even the people managing other people's money.
Investing was basically folklore wrapped in pinstripes. People traded war stories about The Roaring Twenties, whispered about the Crash of '29, and made bets based on newspaper clippings and cocktail party tips. The data technically existed somewhere, scattered across dusty ledgers and forgotten filing cabinets, but it might as well have been on Mars.
The paradigm shift nobody saw coming
Then came 1964. Two University of Chicago professors, Lawrence Fisher and James Lorie, decided to do something audacious: compile every single transaction of every publicly traded company they could find and see what actually happened.
For two years, they collected mountains of historical stock data. Then they fed it into one of the earliest computers—imagine punch cards and machines the size of refrigerators—and asked it a simple question: What did the market really return?
The answer changed everything.
Their creation, the CRSP database (Center for Research in Security Prices), revealed that since 1926, U.S. stocks had delivered an average annual return of roughly 10%. For the first time, investors had evidence instead of anecdotes. The market itself, not some genius stock picker or insider tip, was a legitimate wealth-building engine.
This wasn't just interesting trivia. It was a fundamental shift in how people thought about investing. You didn't need mystical stock-picking abilities or a hot tip from your brother-in-law. The broad market had a track record, and it was pretty good.
Breaking free from the inside view
What made CRSP so revolutionary goes deeper than just numbers. It rescued investors from what psychologists Daniel Kahneman and Amos Tversky would later call the "inside view"—that trap where we get sucked into narratives, predictions, and whatever dramatic story is dominating the news cycle.
Before CRSP, investors lived entirely in this world. They obsessed over company rumors, manager reputations, and the latest headline drama. Everything felt unique, special, unprecedented.
Fisher and Lorie introduced something far more powerful: the "outside view." Instead of asking "What makes this situation special?" they asked "What usually happens in situations like this?" They gave investors context. A baseline. A reference point built on decades of actual market behavior rather than selective memory.
The most transformative financial breakthroughs often work this way—they're deceptively simple insights hiding in plain sight, waiting for someone to actually measure them properly.
From mainframes to artificial intelligence
Fast forward sixty years, and we're living through another inflection point. Not from paper ledgers to mainframes, but from human judgment to artificial intelligence.
The way people frame this shift is usually as a battle: Humans versus Algorithms. Emotion versus Computation. Gut instinct versus processing speed.
But that's not where the real advantage lies. The biggest edge comes from combining both, and the best illustration of this principle doesn't come from Wall Street at all—it comes from a 2,500-year-old board game.
What Go teaches us about investing
Go, the ancient strategy game, was considered uncrackable by computers. The game is absurdly complex—more possible board configurations than atoms in the observable universe.
In 2016, an AI system called AlphaGo shocked everyone by beating a human champion, a milestone experts thought was at least a decade away. Major headlines. Lots of hand-wringing about machines replacing humans.
But that's not the end of the story.
A few years later, a top-ranked human player beat the best AI systems. The secret? Playing with a computer as a teammate. Not pure human intuition. Not raw computational power. Human judgment amplified by machine analysis.
Investing follows the same pattern. Pure quantitative approaches stumble when they encounter situations outside their training data. Pure fundamental analysts fall prey to bias, emotion, and narrative seduction. But combine human intuition with machine precision, and you get something more robust—a disciplined process that can adapt to an uncertain world.
Why this history lesson matters now
The CRSP breakthrough reshaped how we understood market history, but it didn't—couldn't—predict the future. Every investment decision still happens in the fog of uncertainty, and that's never changing.
The lesson isn't that we can know what comes next. It's that we can dramatically improve how we respond to not knowing.
That 10% market return wasn't handed down from the finance gods. It was discovered by people asking better questions with better tools. Today, AI might be the next powerful tool in that evolution, but the future will always contain surprises.
The edge belongs to investors who combine insight with discipline, who pair curiosity with evidence, and who augment human judgment with computational precision. Not one or the other—both.
Don't sit around waiting for certainty that will never arrive. Audit your investment process. Tighten your decision-making rules. Build a system that keeps you anchored when everyone around you is losing their minds over the latest market drama.
That's how you turn an unknowable future into a sustainable advantage. Not by predicting what's next, but by building better tools for responding to whatever comes.
Originally published on November 21, 2025, at www.CosmoDeStefano.com. Used with permission.
The content provided is for informational and educational purposes only. It does not constitute legal, tax, investment, financial, or other advice.
