Friends,
SIG founder Jeff Yass almost never comes out from behind the curtain. But as a staunch advocate of markets’ truth-finding function, he’s a natural ideological advocate of prediction markets. With SIG as a featured market-maker on Kalshi, he’s also throwing his money behind his beliefs (he’s famous or at least infamous for that already, since he’s consistently one of the largest political donors in the US).
Jeff is hyper-competitive and doesn’t mince words. Like I said, he doesn’t speak in public often, so it's definitely worth checking out these 25 minutes. Shout out to the high school student who landed the interview on his YT channel which is gonna multiply the number of subs it had when this dropped this week (~400):
Jeff has very strong views that can be triggering to normies but I’ll say this as someone who worked for him and remember the few talks he gave when I was an employee — his general m.o. is the epitome of Munger’s “Take a simple idea and take it seriously”. Extremely consistent.
Some excerpts below that I used Claude to help organize and at the end you can find the full transcript.
On Truth and Democracy
“Our real motivation for prediction markets is to get the truth out there.”
Yass argues that prediction markets could prevent wars by exposing political lies. His Iraq War example is devastating: George Bush said it would cost $20 billion. His advisor Lawrence Lindsay was “punished” for suggesting $50 billion. The actual cost? Between $2-6 trillion. “Had the people had a prediction market... the people might have said, ‘Look, we don’t want this war. Politicians always tell us that the wars are going to be cheap and quick and fast and they never are.’”
“Every war is exaggerated—how quickly it’ll be ended and how little it will cost and how many lives will be lost is always lied to us by our politicians.”
Even Abraham Lincoln got it wrong. In 1862, the War Department stopped taking recruits because they thought the Civil War would be over in weeks. “He was off by 650,000 deaths.”
On Expertise vs. Markets
“My mother used to say to me, ‘If you’re so smart, how come you’re not rich?’”
This is Yass’s central philosophy:
“If you think the odds are incorrect, then go bet it... If you really are smarter than the markets, you’ll make a lot of money. You’ll do society a favor because you’ll get the price right. And if you can’t make money, you may want to consider being quiet, like maybe the market knows more than you do.”
“This is going to infuriate every college professor you’re ever going to have because they want to be the experts. But they’re not. A bunch of speculators battling it out every day in the marketplace will be vastly greater.”
When his 12-year-old daughter checked prediction markets (Tradesports for those of you that remember that!) during the Obama vs. Clinton primary, she saw Obama had a 22% chance while famous political scientists were saying Clinton would dominate.
“My 12-year-old daughter had a better guess of who’s going to win that primary than the world’s foremost expert in polysci. And that’s the power of prediction markets.”
On Manipulation
On whether prediction markets can be manipulated:
“If you’re manipulating the price, you’re going to lose money... If you wanted to bet that it’s going to be under $50 billion spending, well, we will bet you hundreds of millions of dollars that you’re wrong. So your plan is going to be very, very expensive and it’ll probably be more expensive than just a misleading advertising campaign, which might cost in the millions. This would cost in the hundreds of millions.”
On Saving Lives
“This year, in the next 12 months, about 40,000 Americans will die on the roads. If we had driverless cars, I’m guessing that number would probably be about 10,000—down 75%. We’d save 30,000 lives.”
He argues prediction markets would accelerate adoption: “If we put that up in prediction markets and said, ‘How many people will die in car accidents in 2030?’ and the number’s vastly lower... it would make policymakers hustle and hurry up.”
On Insurance Innovation
Yass envisions prediction markets replacing traditional insurance. Instead of dealing with adjusters and denials, you could bet on objective criteria: “Will the winds get above 80 miles per hour in the next two days in your area?”
“It won’t be as perfect as an insurance claim where ‘My roof blew off, give me my money.’ It’ll be more like, ‘Well, the wind was really bad. I know my house is messed up. How messed up? I don’t exactly know.’ But because it’s so much cheaper, you can much more easily hedge your risk.”
This would “take out all the adjustable claims, all the expense, all the advertising of insurance, make it much, much cheaper.”
On Education
“If you really want to be a decision maker under uncertainty, which is what humanity is, you have to learn probability and statistics.”
Yass attacks America’s educational priorities:
“We have a country that sort of knows a fair amount of calculus but very little probability and statistics, and it’s just not what’s necessary to be a good decision maker.”
He cites a study where Harvard Medical School researchers were “off by a factor of a hundred” on basic probability questions about diseases. “These are very, very smart people, but they didn’t obey the [Bayesian] analysis and they were ridiculous because it was not taught to them in medical school.”
“Calculus is wonderful. It’s my favorite subject and it’s great. It’s beautiful. It’s art. It’s the key to science and everything like that. But it’s of limited value to most people.”
He also explains that the origin of the calculus priority was the national fixation with the space race against the Russians but we still preserve an impractical emphasis on calculus in school.
On Decision-Making and Life’s Biggest Asymmetry
“One of the things that we do in reverse—the bigger the decision, the less time we think about it.”
This might be Yass’s most profound observation about human behavior:
“If you’re buying or selling a stock and it’s basically irrelevant what you’re doing because the markets are fair, you’ll spend a lot of time on it. If you’re deciding who to marry or who to have a relationship with or whatever, you basically just plop into it without much thought…
And one has a gigantic impact on your life and one has a very small impact on your life, yet we spend much more time worrying about the minor things and not enough time worrying about the big things.”
This insight frames his romantic advice—that we should apply the same rigorous, probabilistic thinking to life-altering decisions that traders apply to inconsequential trades.
On Romantic Advice (Yes, Really)
“Don’t go out with somebody that your friends think is a nutcase.”
His solution? Create a prediction market with your friends:
“Say to your friends, ‘Be honest. I won’t punish you. Try and do it anonymously. Give me a marketplace. Am I making a gigantic mistake?’ So many lives are ruined because you get involved with the wrong person and no one wants to speak up.”
On Business Applications
For practical decision-making, Yass uses the NYC mayoral election as an example:
“If you listen to TV, cable news, it’s very hard to figure out what the probability is. Like some people say, ‘Nah, it’s going to be too close. Come on. New York’s not going to elect someone like Mamdani.’ But when you look at the prediction market, you see he’s a little over 90% to win.”
“So if you’re making a decision and you want to move to New York, you want to move the business to New York or or or whatever, you need to know that probability, and it’s very hard to know just by reading the newspapers or listening to the news.”
He extends this further:
“Let’s say you’re a real estate developer and you think that your value of real estate’s going to go down by a million dollars if Maami wins, you can hedge it. So you can buy insurance, but more importantly you can get—you can find out what the best guess is and you can do it in an instant.”
“You don’t have to do all kinds of work. You don’t have to read a million articles and call pollsters and do all the work. All the work is done for you and you get the best possible number you can and that will guide you through all your decisions.”
The Bottom Line
“You basically can’t make a good decision without knowing the probabilities of events happening. Prediction markets are the best way we know how to get the most accurate guess of what those predictions are.”
Kris:
Just a couple comments from me:
- There is a giant contradiction wrapped around all the examples. Consider it a literacy test to find it. I’ll give you a hint: it has to do with trading concept that Soros is most famous for.
- I think the platonic version of what Yass wants is impossible without decentralization of exchanges. I’m not saying that there isn’t a consumer surplus in versions that fall short of this (like centralized exchanges like Kalshi or CME etc). Jeff, despite the dystopian view I think regular people might infer from this, is intellectually idealistic even if it’s off-putting. In other words, he’s being earnest.
- If you think he’s speaking his book, he is. But you are also getting causation backwards. He chose his book. (By the way, this is a common mistake I think people make when they think they’re being clever. “You believe in X because you profit from X” but it’s not that cut-and-dry. You could have chosen to profit from many Xs but to be excellent you must believe enough to choose that X. When I “market” what I do obviously I benefit from persuading you…but I wouldn’t do what I do if I didn’t believe in it. The type of grifter who has no backbone is not hard to identify. They taught house-flipping in 2006 and they teach “sell calls for income in 2025”. Beating the same drum for 40 years when you are well past “richer than God” status is what every episode of The Founders Podcast is about. Conviction not grift.
- My disappointment in prediction markets has to do with the contracts that are NOT listed. And that those contracts are not listed says a lot about the limitations of prediction markets as a social technology versus their theoretical potential. I’ll keep my wishlist of contracts to myself lest I end up on some list.
Stay groovy this Halloween
☮️
Full Transcript
Introduction
HOST: This week on Generating Alpha, in an episode unlike most, I’m joined by Jeff Yass, founder of Susquehanna International Group, one of the most successful trading firms in the world. Jeff is a legendary figure in finance known for applying the principles of poker, probability, and decision theory to markets. Over the past four decades, he’s built a global powerhouse, quietly operating behind the scenes of Wall Street, trading everything from options to crypto, all grounded in mathematical precision and rational thought. He’s also one of the most influential and private figures in modern finance, making this conversation one of his first interviews ever.
In this short episode, we talk about prediction markets, why Jeff believes they’re the future of how we understand truth, how they can improve decision-making in business, in government, and what they reveal about the power of incentives, information, and human behavior. I really enjoyed recording this episode, and I hope you guys enjoy listening.
Thank you, Jeff, for coming on. I really appreciate you making the time.
JEFF YASS: It’s my pleasure, Amir. Let’s go.
What Are Prediction Markets?
HOST: So, to set a foundation for this conversation, I’d love to kind of start simple. What’s your current perspective on prediction markets as a whole and how are they significant to Susquehanna and yourself?
JEFF YASS: Well, prediction markets have been a great passion of ours for years. They add tremendous value to the world. You basically can’t make a good decision without knowing the probabilities of events happening. Prediction markets are the best way we know how to get the most accurate guess of what those predictions are. So we think it’s a fantastic tool that will add tremendous benefits to society.
Evolution of Prediction Markets
HOST: And from a broad perspective, how do you see the kind of evolution of prediction markets playing over the next decade, especially in terms of regulation and gambling legislation?
JEFF YASS: Well in the gambling world, we’re really not sure. I think the world is coming to the conclusion that a system like the one they have in Europe like Betfair, where people can buy and sell amongst themselves, is a much fairer system, will reduce costs tremendously for customers. You know currently the vig is somewhere around 5%. If you can trade amongst yourself on exchange we think that’ll go down substantially, probably to one or two percent. So that will be a big win for people who want to engage in sports.
But our real motivation for prediction markets is to get the truth out there. Our favorite example is during the Iraq war when George Bush first went into Iraq, he said it would cost $20 billion. Lawrence Lindsay, his economic advisor, said I think it might cost as much as 50. He was sort of punished for saying that. The true number has come in somewhere between two and $6 trillion.
So had the people had a prediction market—what’s the over under line on how much this would cost—now I don’t think it would be anywhere near 2 to 6 trillion, but it would have been substantially higher than 50 billion. Let’s say it would have been 500 billion. Then the people might have said, “Look, we don’t want this war. Politicians always tell us that the wars are going to be cheap and quick and fast and they never are.” So we need a trusted source and prediction markets would be an objective trusted source because anyone betting on them is going to lose money if they get their analysis wrong.
So had we seen this gigantic number, I think there would have been much more push back against this war. And something as—predictions markets could be that powerful where they can really slow down the lies that politicians are constantly telling us. And that’s really sort of my number one reason why I want to see them thrive.
HOST: It’s almost the people’s idea of the truth rather than the kind of tainted idea of the truth that’s given to—it’s given to the general population. And I want to kind of—
JEFF YASS: Exactly, but also with experts. I mean you may not know what wars cost—the vast majority of people don’t know—but there’s a small group of people who do and they would be betting it and they would be bidding it up to a price that makes sense. So the public who may not—how the hell you going to be informed about what a war is going to cost if you’re a regular person? But if you see experts battling it out and betting on it, then you can trust that number and you could be more of an expert by looking at a prediction market than a politician can who’s just either making up a number or purposely lying.
Protecting Against Manipulation
HOST: And I also assume in the future that prediction markets can be used and will be used to price more like financial instruments and support other decisions. But how can we protect from prediction market manipulation?
JEFF YASS: Well, in the same way you protect against any other manipulation. If you’re manipulating the price, you’re going to lose money if there’s enough players out there. And you want to get a price up to something for some nefarious reason, you’re going to have to lose a lot of money to do it. So if you wanted us—bet that it’s going to be under $50 billion spending—well, we will bet you hundreds of millions of dollars that you’re wrong. So your plan is going to be very, very expensive and it’ll probably be more expensive than just a misleading advertising campaign, which might cost in the millions. This would cost in the hundreds of millions. So that will protect the integrity of the markets.
Parallels Between Gambling and Prediction Markets
HOST: And I want to take a step back for a moment. Early in your career, you’re a professional gambler, specifically in poker and horse betting. What do you see as the parallels between gambling and prediction markets? And what systemic risks and opportunities do you think are introduced as a result?
JEFF YASS: I don’t really see any systemic risks. I see more truth, more rational objective probabilities getting out into the marketplace. And I see the systemic risk as politicians telling us stuff that they’re trying to trick us, and this is the antidote to that. So I see—obviously there could be some tiny amounts of manipulation, but that’s going to be trivial compared to the amount of manipulation that we have now. Competitive markets will wipe out that—will wipe out any problems that we may see.
Practical Applications for Businesses
HOST: And from kind of like a broad overview, how do you think your firm and firms like yours will incorporate prediction markets into their daily decision-making?
JEFF YASS: Well, for example, there’s an election in New York City in two—in 15 days.
HOST: Okay.
JEFF YASS: Okay. If you listen to TV, cable news, it’s very hard to figure out what the probability is. Like some people say, “Nah, it’s going to be too close. Come on. New York’s not going to elect someone like Maami.” But when you look at the prediction market, you see he’s a little over 90% to win. So if you’re making a decision and you want to move to New York, you want to move the business to New York or whatever, you need to know that probability, and it’s very hard to know just by reading the newspapers or listening to the news.
And to have that actual number helps you dramatically in that decision. Plus, let’s say you’re a real estate developer and you think that your value of real estate’s going to go down by a million dollars if Maami wins, you can hedge it. So you can buy insurance, but more importantly you can get—you can find out what the best guess is and you can do it in an instant. You just look at the price, you have the probability. You don’t have to do all kinds of work. You don’t have to read a million articles and call pollsters and do all the work. All the work is done for you and you get the best possible number you can and that will guide you through all your decisions.
In Susquehanna, we’re constantly looking at what are the odds—let’s say of a presidential election—and stocks are going up and down based on who’s going to win and who’s going to lose, and we use that number to determine if we think a stock has overreacted or underreacted to the political odds.
Market Makers and Institutional Involvement
HOST: And I imagine as kind of prediction markets become bigger and bigger and there’s more volume, that larger firms will start participating and actually hedging on the prediction markets rather than using outside financial instruments to hedge. So my question kind of around that is you recently joined forces with Kalshi to provide liquidity as one of its primary market makers. How do you believe the involvement of firms like yours will evolve with the markets?
JEFF YASS: Yeah, that’s a great question. We right now—it’s still a bespoke product. Institutions aren’t really using it. There’s a lot of action but it’s mainly relatively small players. No giant institution has really showed up and wanted to hedge—will the Fed raise rates or not—yet on these things. But we think as they get regulatory clarity and as they grow in popularity, institutions will show up and there will be Wall Street-sized bets placed on these things. But that has not yet happened.
I mean, if you’re an investment bank, if you’re Goldman Sachs or Morgan Stanley, you’re a little cautious about betting on these things, but you haven’t done it yet. But eventually, that’ll go away.
Prediction Markets for Insurance
JEFF YASS: What I really hope that prediction markets could influence is the insurance business. You know, insurance in some places is impossible to get. The government caps the rate that you can sell it at. So a lot of insurance companies have left Florida, for example, and you can’t insure your home because the price is too low.
But if we had insurance bets on prediction markets, you can live in an area, we can put up a price and say, “Will the winds get above 80 miles per hour in the next two days in your area?” And let’s say there’s a 10% chance of it happening. If you think that if that happens, you may suffer serious damage to your home, you might want to bet $10,000 on it to win $90,000. If it happens, and that’ll cover your—or cover most of your insurance costs.
And you only have to buy it when there’s a problem coming. They will take out all the adjustable claims, all the expense, all the advertising of insurance, make it much, much cheaper and much more sort of bespoke to what you need to have happen. So there’s enormous expense in insurance and this would reduce a lot of it, make it much easier for people to insure what they really have.
It won’t be as perfect as an insurance claim where “My roof blew off, give me my money.” It’ll be more like, “Well, the wind was really bad. I know my house is messed up. How messed up? I don’t exactly know.” But because it’s so much cheaper, you can much more easily hedge your risk than you can with typical insurance products.
Sources of Liquidity
HOST: And it’s so much more quantifiable. The insurers will obviously try to see how much you need and how much they’ll give you and stuff like that. And with prediction markets, it’s so much more quantifiable. And as these prediction markets kind of evolve and mature into someday fully regulated exchanges, I’d imagine, do you think the majority of liquidity will come from large Wall Street firms or do you think they’re going to come from retail flows?
JEFF YASS: I think it’s going to come from both. And I think that it’s going to create tremendous opportunities. Let’s say you’re just a weather person that loves following weather and hurricanes and probabilities and you live in Florida. You can put out your own markets and say, “This area I think is—these are the odds of disruption, and in these areas these are the odds.” And you can have relatively small businesses who have expertise in this stuff, who now have no way to make any money from their expertise, and they can be putting out markets and they could be making a lot of money and reducing the price for regular people.
Can Prediction Markets Influence Outcomes?
HOST: Yeah, I think it’s incredible. And do you think at all in the future prediction markets can influence outcomes?
JEFF YASS: No. You know, that’s like one of the myths that someone’s going to bet—you know, there was that story on Polymarket that the French guy was betting Trump and he was—that was just nonsense. You know, we bet against him. If he bids it up, we’ll bet it down. It’s not going to influence anything. So all that—that’s a fear that comes to mind and it’s not a zero probability that can happen, but it’s really vastly overstated.
Obstacles to Broader Participation
HOST: And what do you think is the most significant obstacle to broader participation in prediction markets and how can one go about removing that obstacle?
JEFF YASS: The biggest obstacles—like as you ask these questions you can see what could go wrong, what can go wrong, what can go wrong. Those things psychologically come right to your mind that these things could go wrong. And yes, something could go wrong, but something is already going wrong. So that obstacle as we get used to it will go away. It’s going to take time, but people have fears and they overstate the downside. But as the product takes place and people learn how valuable it is and how much money it can save them, those fears will dissipate. This may take years, but I’m very optimistic that we’re going to get there.
[Sponsor Break - Rho]
HOST: Before we go back to the episode, I want to take a short break to talk about my sponsor, Rho. The Generating Alpha podcast is presented by Rho, the all-in-one banking platform for startups. Thousands of startups like Perplexity, Product Hunt, and more use Rho. You get everything you need to manage your startup’s cash. Fast banking setup, cards with up to 2% cash back in yield that turns company cash into extra runway. All super important in the early days of launching.
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Rho is a fintech, not a bank. Checking and card services provided by Webster Bank, Member FDIC. See reward terms for details. Thank you. And back to the episode.
What Should We Not Quantify?
HOST: And I know you come from a very kind of probabilistic background, but with the rise of decision markets kind of—there’s under prediction markets. Is there any type of decision or even prediction that we should deliberately avoid quantifying?
JEFF YASS: That’s a good question. You know, you could—I remember you could put up on a prediction market, “Should I marry this girl or not?” And maybe some—your friends and your relatives might be more objective than you are. But I’d say that’s going a bit too far. So my answer sort of would be no.
What’s Possible That No One Is Talking About?
HOST: And what is possible with prediction markets that no one is talking about right now? Or what do you think is possible with prediction markets no one’s talking about?
JEFF YASS: I think the number one thing—it will stop wars. Because every war is exaggerated—is how quickly it’ll be ended and how little it will cost and how many lives will be lost is always lied to us by our politicians. And Abraham Lincoln, in the Civil War in 1862, the War Department stopped taking recruits in the North because they said this war will be over in a couple of weeks. He was off by 650,000 deaths and stuff. So he honestly believed that it was going to be a short quick war. And obviously, it wasn’t. And the—you know, it still reverberates now, the horror of the Civil War.
If the people knew how expensive it’s going to be and how disastrous it’s going to be, they will try and come up with other solutions besides going to war.
Another example I can give is driverless cars. There’s a lot of opposition to driverless cars because people can imagine a robot going crazy and killing somebody. But this year, in the next 12 months, about 40,000 Americans will die on the roads. If we had driverless cars, I’m guessing that number would probably be about 10,000—you know, down 75%. We’d save 30,000 lives.
If we put that up in prediction markets and said, “How many lives—in 2030, how many people will die in car accidents?” and the numbers vastly lower than it is now because people expect driverless cars to happen, it would make policymakers hustle and hurry up and getting driverless cars there because we’re going to have this gain of tens of thousands of people who aren’t going to die right now. You know, you sort of say, “Oh, I don’t know. Maybe driverless cars will be good. Maybe they won’t.” If we had an objective number on it, I think we would see how great it is and we’d move much, much faster.
The One Message About Prediction Markets
HOST: I think it’s an incredible kind of use case of it, especially for quantifying things for policy makers to make decisions based off of. And before I move on to kind of a question or two about advice, what is the one message that Jeff Yass wants to tell the world about prediction markets? If you had to give one message to the world, if you were selling the world on prediction markets, what would it be?
JEFF YASS: It is—my mother used to say to me, “If you’re so smart, how come you’re not rich?”
The prediction markets are objective. If you think the odds are incorrect, then go bet it and go put it—go put it back into where in line where it should be. If you really are smarter than the markets, you’ll make a lot of money. You’ll do society a favor because you’ll get the price—you get the price right. And if you can’t make money, you may want to consider being quiet, like maybe the market knows more than you do.
Now, this is going to infuriate every college professor you’re ever going to have because they want to be the experts. But they’re not. A bunch of speculators battling it out every day in the marketplace will be vastly greater. It will insult the college professors, which as far as I’m concerned is a good thing.
HOST: I agree.
The Obama vs. Clinton Example
JEFF YASS: Let me give you an example. When my daughter was 12 years old, Obama was running against Hillary Clinton in the primary. And one of the most famous political scientists in America was on TV saying, “No, Obama—you know, Hillary Clinton’s going to win. She’s up by 30 or 40 points.” My daughter, I said, “Go check TradeSports,” which is the only place at that time to look. And she said, “Obama has a 22% chance of winning.”
So the marketplace knew that Obama was special, that he was charismatic. He didn’t have any name recognition, and Hillary did. So the fact that he’s down by 35 with months to go doesn’t really mean anything. So I use that in an example that my 12-year-old daughter had a better guess of who’s going to win that primary than the world’s foremost expert in polysci. And that’s the power of prediction markets.
HOST: That’s an incredible, incredible anecdote, incredible example.
What Should Students Study?
HOST: And I want to ask two questions about advice. The first one being—as a high schooler today, I’m a high schooler and given all the success that you’ve had and given all the hiring that you’ve done, what should students today study?
JEFF YASS: I would strongly suggest—I mean obviously computer science. You got to be computer literate and you got to understand where AI is coming from. But if you really want to be a decision maker under uncertainty, which is what humanity is, you have to learn probability and statistics.
So much of what happens in the world is you are making a decision, and if you’re not really informed on the mathematics behind probability and statistics, you can make a terrible decision. So when you see that there’s a hurricane season, there’s a lot of hurricanes—like, well, is this a big deal or are there always a lot of hurricanes and what’s the volatility around hurricanes? Does it vary by a lot? Is this such an outlier? Does this prove there’s global warming or is this just a blip? So it’s sort of the signal versus the noise. And to be able to distinguish which is which takes some knowledge and some learning. But you really can’t interpret events in the world unless you have a firm background in probability and statistics.
The Calculus vs. Statistics Problem
JEFF YASS: And I’ll give you another little anecdote that—like the Russians in 1958 had Sputnik and we were afraid they were going to beat us to the moon. And they did beat us to the moon but not a man on the moon. So the United States put in a science program where everybody has to learn calculus.
HOST: I’ve heard about that.
JEFF YASS: Okay. So we all got to learn calculus—we can’t let the Russians beat us. So now everyone has to—to get into a good college, you’re going to have to learn calculus. To get into med school, you have to learn calculus, which is absurd, but you’re never going to use it. But no one learned how to use probability and statistics because it was not—it was considered secondary to calculus.
So we have a country that sort of knows a fair amount of calculus but very little probability and statistics, and it’s just not the way—it’s just not what’s necessary to be a good decision maker, to be a good citizen. But it’s almost impossible to change these things. So you have to take the effort yourself to make sure that you are literate in probability and statistics and you certainly understand Bayesian analysis.
Because there’s all these studies done that they asked Harvard kids in Harvard Medical School who are going to be researchers some basic questions after they got the data about diseases and they were off by a factor of a hundred. These are very, very smart people, but they didn’t obey the analysis and they were ridiculous because it was not taught to them in medical school.
And if you’ve ever had the frustration of talking to a doctor and saying, “Doc, what’s my chance of having this?” He goes, “Oh, I don’t know. You may or may not have it.” It’s like, “I’d like you to tighten that market up a little bit, doc.” But they’re not trained that way. And that’s a tragedy. You have to make sure that you go out of your way to get that training.
HOST: I think that’s a very valid point. And I’m currently learning calculus. I think I might do a little statistics education on my own. Probably statistics education on my own.
JEFF YASS: Calculus is wonderful. It’s my favorite subject and it’s great. It’s beautiful. It’s art. It’s the key to science and everything like that. But it’s of limited value to most people.
One Piece of Advice for a 16-Year-Old
HOST: And I want to ask one more question that I do at the end of every interview. I think I’ve asked it to 39 people so far. I’m 16 right now. If you were to give one piece of advice to a 16-year-old today, it can be life advice, career advice, even romantic advice. What would it be?
JEFF YASS: If it was romantic advice—I mean, I believe in markets. It’s like don’t go out with somebody that your friends think is a nutcase. Okay? You know, but you can get caught up. And if you say to your friends, “Be honest. I won’t punish you. Try and do it anonymously. Give me a marketplace. Am I making a gigantic mistake?” So many lives are ruined because you get involved with the wrong person and no one wants to speak up.
So you got to come up with a mechanism. “Hey friends, you’re my friend. I trust you and I’ll do this anonymously. Should I—is this person too nutty for me to be going out with?” And you could prevent a lot of horrible relationships from happening there. That would be my number one advice.
Because one of the things that we do in reverse—the bigger the decision, the less time we think about it. You know, if you’re buying or selling a stock and it’s basically irrelevant what you’re doing because the markets are fair, you’ll spend a lot of time on it. If you’re deciding who to marry or who to have a relationship with or whatever, you basically just plop into it without much thought. And one has a gigantic impact on your life and one has a very small impact on your life, yet we spend much more time worrying about the minor things and not enough time worrying about the big things.
Closing
HOST: I mean from my limited life experience I think I agree. And I recommend anyone who’s listening to this to listen to my episode with Annie Duke about decision-making. I think it’s an excellent complement to this episode. But Jeff, it was an absolute pleasure having you. Thank you for coming on. I really appreciate it.
JEFF YASS: Good luck. I really appreciate it, too. It was fun. Okay. Bye.