Source: Generating Alpha Podcast

Translation by: Jiahua, Chaincatcher

Who is Jeff Yass?

This week on "Generating Alpha," we have a unique guest—Jeff Yass, the founder of one of the world's most successful trading firms, Susquehanna International Group (SIG).

Jeff is a legendary figure in finance, applying principles of poker, probability, and decision theory to the markets. Over the past forty years, he has quietly built a global trading giant, operating behind the scenes on Wall Street, trading everything from options to cryptocurrencies, all based on mathematical precision and rational thinking. He is also one of the most influential yet mysterious figures in modern finance, and this episode marks his first-ever podcast interview.

In this short episode, we focused on one topic: prediction markets—why Jeff believes they are the future of humanity's quest for truth, how they can improve business and government decision-making, and the immense power they reveal regarding incentives, information, and human behavior.

I thoroughly enjoyed recording this episode, and I hope you enjoy listening to it as well.

Host: Jeff, thank you so much for joining us, and for taking the time.

Jeff Yass: My pleasure, Amir, let's get started.

The Core Value and Significance of Prediction Markets

Host: To lay the groundwork for this conversation, I’d like to start with a simple question: what is your overall view of prediction markets today? How important are they to you and SIG?

Jeff Yass: Prediction markets are our true passion for many years. They bring tremendous value to the world. Without accurate probabilities of events occurring, you cannot make good decisions. And prediction markets are currently the most accurate way we know to estimate these probabilities. So we believe this is a remarkable tool that will bring great benefits to society.

Host: From a broader perspective, how do you see prediction markets evolving over the next decade, especially in terms of regulation and gambling legislation?

Jeff Yass: In traditional gambling, to be honest, we are not entirely sure. But the world is gradually realizing that exchange models like Betfair in Europe, which allow people to buy and sell from each other, are a fairer system that can significantly reduce costs. The vig in traditional gambling is about 5%, but if people can trade in an exchange, we believe it can be reduced to 1-2%. This is a huge win for those wanting to participate in sports betting.

But our true motivation for promoting prediction markets is to uncover the truth. Our favorite example is the Iraq War. When Bush first went in, he said it would cost only $20 billion, and his economic advisor Lawrence Lindsay said it might reach $50 billion, and he was punished for telling the truth. The actual figure later calculated was between $2 trillion and $6 trillion.

If there had been a prediction market at that time asking, "What is the total cost of this war over/under?" I dare say it wouldn't have reached $2-6 trillion, but it would definitely have been far above $50 billion, possibly around $500 billion. Then the public would see that number and say, "Wait, we don't want this war! Politicians always say wars will be quick and cheap, but they never are!" So we need a credible source, and prediction markets provide an objective, trustworthy source—because if the bettors are wrong, they lose money.

If such terrifying numbers had been visible at the time, the anti-war voices would have been much louder. Prediction markets can indeed be powerful enough to slow down the constant lies politicians tell us, which is why I want to see them thrive.

Host: This is almost the "truth of the people," rather than the polluted, spoon-fed version of the truth.

Jeff Yass: Absolutely! And it's not just for ordinary people; even experts can benefit. You and I might not know how much a war actually costs, but there will always be a small group of people who do, and they will bet, pushing the price to a reasonable level. Ordinary people cannot possibly know the cost of war, but when they see experts battling it out in the market, voting with real money, they can trust that number. Just by looking at the prices in prediction markets, you can be more informed than those politicians who either make up numbers or lie intentionally.

Manipulation and Risk

Host: I guess prediction markets will also be used to price more financial instruments in the future, supporting other decisions. But how do we prevent prediction markets from being manipulated?

Jeff Yass: It's the same principle as preventing any market from being manipulated—if you want to manipulate the price, as long as there are enough participants in the market, you will have to lose a lot of money yourself. For example, if you want to push the Iraq war cost down to "under $50 billion," fine, we can bet you hundreds of millions of dollars that you're wrong. Your manipulation plan will be prohibitively expensive, possibly hundreds of times more than running a few misleading ads (ads only cost a few million, this would be hundreds of millions). So that in itself protects the integrity of the market.

Host: Going back a bit, you were an early professional gambler, playing poker and betting on horse racing. What similarities do you see between gambling and prediction markets? What systemic risks and opportunities might arise from this?

Jeff Yass: I really don't see any systemic risks. What I see is more truth and more rational, objective probabilities entering the market. The real systemic risk is politicians deceiving us with lies, and prediction markets are the antidote. Of course, there may be a tiny amount of manipulation, but compared to the amount of manipulation we face now, it is negligible. Competitive markets will smooth out any issues.

Business Applications and Hedging

Host: Overall, how do you think companies like yours will integrate prediction markets into everyday decision-making in the future?

Jeff Yass: For example, in 15 days, New York City will have an election (the podcast was released on October 23). If you only watch TV and read the news, it's hard to gauge the real probabilities—some say "it's too close," while others say "New York could never elect someone like Maami." But if you look at the prediction market, he has over a 90% chance of winning. If you need to decide whether to move to New York or relocate your company there, you must know that probability; you can't figure it out just by reading newspapers or listening to the news. Having that clear number will greatly assist your decision-making.

For instance, if you are a real estate developer and believe that Maami's election will cause your property to drop by $1 million, you can hedge directly. More importantly, you can instantly get the most reliable probability without having to read a million articles or call polling companies; all the work has been done for you, and you get the best number to guide all your decisions.

For SIG, we have also been looking at the probabilities of presidential elections; the stock market will rise or fall based on who is leading, and we use the probabilities from prediction markets to determine whether a particular stock is overreacting or underreacting to political events.

Host: I can imagine that as the trading volume in prediction markets increases, large institutions will also start participating, using prediction markets instead of traditional financial instruments for hedging. You recently partnered with Kalshi, becoming one of their main market makers. How do you think the participation process of companies like yours will evolve as the market develops?

Jeff Yass: Prediction markets are still a customized product; institutions haven't really entered yet, and the trading volume mainly comes from relatively small players. No giant institution has yet come in and placed big bets on events like "Will the Fed raise interest rates?" But we believe that as regulation becomes clearer and the market becomes more popular, institutions will flock in, and there will be Wall Street-level massive bets. Right now, Goldman Sachs and Morgan Stanley are still a bit cautious, but that will change sooner or later.

What I really look forward to is prediction markets influencing the insurance industry. In many places, insurance is simply unavailable because the government has driven prices too low, causing insurance companies to leave—like in Florida. But if you use prediction markets for insurance, you could set up a contract: "Will the wind speed in your area exceed 80 miles per hour in the next 48 hours?" Assuming a 10% probability, if you're worried about your house being destroyed, you could bet $10,000 to win $90,000, essentially covering your loss. Moreover, you only buy it when there is a threat of a hurricane, saving all the claims, advertising, and operational costs, making it much cheaper and completely tailored to your needs.

Host: And it's much more quantifiable! Traditional insurance companies always want to assess how much you need and how much they will pay you, etc., while prediction markets are straightforward. As these markets eventually become fully regulated exchanges, do you think liquidity will primarily come from Wall Street institutions or retail investors?

Jeff Yass: Both. And it will create enormous opportunities. For example, if you are a weather enthusiast living in Florida and understand hurricane probabilities well, you could set up a market yourself, saying, "I believe the probability of disaster in this area is X." Previously, such expertise could not be monetized, but now you can earn money from your knowledge while helping ordinary people lower their insurance prices.

Impact, Barriers, and Learning

Host: Do you think prediction markets will influence the outcomes of events themselves in the future?

Jeff Yass: No. For example, the French guy who was buying up Trump on Polymarket was just nonsense. We directly bet against him; he pushed the price up, and we pushed it back down, which did not affect the outcome at all. This concern is not zero probability, but it is greatly exaggerated.

Host: What do you think is the biggest barrier to the widespread adoption of prediction markets today? How can it be removed?

Jeff Yass: The biggest barrier is, just like the questions you are asking, you can see where things might go wrong. These thoughts immediately pop into your mind—these things could go wrong. Some things might go wrong, but things are already going wrong now. So, as we get used to it, that barrier will disappear. It may take time, but people have fears, and they will exaggerate the negative impacts. But as the product gets accepted, and people understand its value and how much it can save them, those fears will dissipate. This may take years, but I am very optimistic about our ability to achieve our goals.

Host: Some people worry that certain decisions shouldn't be quantified. What decisions or predictions do you think we should deliberately avoid quantifying?

Jeff Yass: Good question. Theoretically, you could even set up a market: "Should I marry this girl?" Maybe your friends and family would be more objective than you… but that does seem a bit excessive. So my answer is basically no.

Host: What is something that people are not currently discussing that prediction markets could achieve in the future?

Jeff Yass: The most important point: they can prevent wars. Every war, politicians exaggerate, saying it will end quickly, cost little, and have few casualties—all lies. During the American Civil War, the Lincoln administration stopped conscription in 1862, thinking the war would end in a few weeks, resulting in the loss of 650,000 lives. If the public had known the true costs and disastrous consequences in advance, they would have desperately sought alternatives to war.

Another example is self-driving cars. Many people oppose them now because they can imagine robots going out of control and killing people. But in the next 12 months, about 40,000 people will die on American roads; if we fully adopt self-driving, I guess it could drop to 10,000, saving 30,000 lives. If prediction markets show that traffic deaths will significantly decrease by 2030, policymakers will rush to accelerate the rollout of self-driving technology—because we can clearly see how many lives can be saved. Right now, everyone is hesitant, saying, "I don't know if it's good or not." Once there is an objective number, we will move much faster.

A Message Jeff Yass Wants to Share with the World

Host: Before we wrap up, if you could convey one message about prediction markets to the world, what would it be?

Jeff Yass: My mother used to tell me, "If you're really that smart, why haven't you made a fortune yet?"

Prediction markets are objective. If you think the market's probability is wrong, go bet and correct it to where you think it should be. If you are indeed smarter than the market, you will make a lot of money while helping society get the prices right. If you can't make money, maybe you should keep quiet—perhaps the market knows better than you.

This will drive all the university professors crazy because they want to be the experts, but they aren't. A group of speculators battling it out with real money every day will be far stronger than any professor. Making professors mad is a good thing in my view.

For example, when my daughter was 12, Obama was running against Hillary in the Democratic primaries. At that time, the most famous political scientists in the U.S. were on TV saying, "Hillary is leading by 30-40 points, it's a done deal." I had my daughter check TradeSports (the only prediction market at the time), and she said, "Obama has a 22% chance of winning." The market had already recognized Obama's uniqueness and charisma, while Hillary's high name recognition didn't mean much in terms of her lead. My 12-year-old daughter was more accurate than the world's top political experts. This is the power of prediction markets.

Learning and Life Advice

Host: One last question: if you were a high school student today, based on your years of success and hiring experience, what would you advise young people to learn?

Jeff Yass: Of course, you should learn computer science; you must understand programming and AI. But if you really want to be someone who makes decisions under uncertainty—which is the essence of being human—you must learn probability and statistics well.

Too many decisions in the world are made under uncertainty, and if you don't understand the mathematical foundations of probability and statistics, it's easy to make catastrophic decisions. For example, hurricane season comes, and there are many hurricanes—does that matter? Has it always been this many in previous years? How much volatility is there? Is this evidence of global warming, or just random fluctuations? Distinguishing between signal and noise requires knowledge.

When the Soviet Union launched Sputnik in 1958, the U.S. feared being left behind, so calculus was pushed nationwide. As a result, now to get into a good university or medical school, you have to learn calculus—absurd, because 99% of people will never use it in their lives. But probability and statistics are treated as secondary, and no one is required to learn them. So we have a bunch of people who know calculus, but almost no one understands probability and statistics, which is completely backwards.

You must take the initiative to learn probability and statistics; you need to understand Bayesian analysis. Students at Harvard Medical School can get disease probability questions wrong by a factor of 100—these people are incredibly smart, but the school didn't teach them. Even doctors, if you ask, "What do you think the probability is that I have this disease?" they will just say, "Maybe yes, maybe no." You might think: Doctor, can you tighten up the market a bit?

Host: I'm currently learning calculus… looks like I need to catch up on statistics myself.

Jeff Yass: Calculus is beautiful; it's my favorite subject, it's art, it's the key to science. But for the vast majority of people, its practical use is limited.

Host: One last traditional question (I've asked 39 people this, and I'm now 16): if you were to give today's 16-year-olds one piece of life advice (it could be about career, relationships, anything), what would it be?

Jeff Yass: If it's relationship advice—I believe in the market. Don't date someone that all your friends think is crazy. Many people get trapped, and at that point, you need to ask your friends for the truth. You can do it anonymously; have them create a small prediction market: "Am I making a huge mistake by dating this person?" How many people have been ruined by the wrong person in their lives just because no one dared to tell the truth? You need to design a mechanism to bring the truth to light.

We have a flaw: the bigger the decision, the less we think. You might ponder for half a day about buying a stock (which has a negligible impact), but decisions like whom to marry or who to date—decisions that affect your entire life—are often made in a haze. We completely reverse the allocation of our time.

Host: I have less life experience, but I completely agree! I also recommend everyone listen to my episode with Annie Duke about decision-making; it pairs perfectly with this one. Jeff, thank you so much for today!

Jeff Yass: Good luck to you, I’m very happy as well, goodbye!

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