I have observed that some of the smartest or most successful people in the world often have unusual life trajectories. When you read about people who make a lot of money with prediction markets or other unconventional income sources in their 20s and 30s, one thing that stands out is that many of them were extremely unimpressive growing up. They were evidently quite smart, owing to the innate nature of intelligence, but this did not become evident until later in life. They become rich by seeing what the masses miss or by beating less intelligent competition.
For example, on Polymarket or Kalshi, one can identify underpriced or mispriced probabilities by using creative data-gathering methods or by forming a more accurate estimate of the “true” odds than the market consensus—essentially having a more accurate model or system of the world. The idea is that observable phenomena can be broken into what is random versus deterministic. By understanding the knowable parts, it can seem to outsiders almost like having ESP (though it does not violate the laws of physics), and you can make a lot of money this way. This is what Jim Simons did on a large scale with Renaissance Technologies.
In my case, I made substantial profits on Polymarket and by shorting Bitcoin by correctly predicting that Trump would effectively abandon support for Bitcoin. The market had greatly overestimated the likelihood that he would go out of his way to help Bitcoin, and I took the opposite—and correct—side of that consensus.
So why was this evident later in life, for myself and others? Forecasting, trading, or economics prodigies do not really exist, unlike prodigies in chess, sports, or music. Abilities such as systems-based thinking or developing accurate models of the world are not evident early in life, nor are they typically selected for. In school, society tends to reward visible, non-remunerative talents, such as performing arts or sports, rather than skills like forecasting or trend recognition. This is a fair trade-off, I suppose, given that those whose talents emerge later in life can make more money.
This is why the “book smarts vs. street smarts” distinction is incomplete. There’s a third category—what I call the “lock-in” type who fits into neither group but excels at locking-in on problems. This person thrives on difficult, technical tasks that require extreme patience and deep pattern recognition, yet don’t neatly fall under traditional academic intelligence. If you have a hard, complex issue that needs solving, the lock-in type is the one you want working on it.
My research into individual differences of metabolism is possibly another such example, which although did require a lot of study in terms of gathering research–led to new ideas on the overlooked role of metabolism in weight loss–a hypothesis that was dismissed but I think merits closer investigation. The default “book smarts” explanation or path would have missed this. There is now strongly compelling evidence that metabolism plays an outsized role in achieving ‘leanness’ beyond what can be accomplished through diet and exercise.
The rise of prediction markets and other non-mainstream avenues of wealth creation—such as AI—has disproportionately benefited this third category. Where do “vibe coders” or prediction market traders fit within the book-smarts vs. street-smarts framework? They don’t. That dichotomy simply fails to capture this kind of thinking pattern or scope of interest. Understanding macro trends or gathering intel requires study, but it’s typically not of a “book smart” nature. Because it’s digital, it’s not “street smart” either.