Sharing an interview that I currently see as the deepest understanding of [prediction markets].

Jeff Yass is the founder of SIG, the largest options market maker in the United States, and is currently also a major market maker for Kalshi.

1️⃣ The biggest role of prediction markets: to prevent wars.

Bush once said the Iraq War would only cost $20 billion, but it ended up costing $2-6 trillion.

If the prediction market gives a number like $500 billion, people might say, 'We don’t want this war.'

In fact, 'the scale of every war is always exaggerated—how quickly the war will end, how much it will cost, how many lives will be lost; our politicians always lie to us.'

In 1862, the Lincoln administration claimed the Civil War would end soon and stopped recruiting new soldiers, resulting in the deaths of 650,000 men.

If predicting the market could prevent wars, how much would this sector be worth?

2️⃣ Quantify decision-making for policymakers

Jeff believes another major role of prediction markets is to drive the implementation of public policies.

He gave an example: approximately 40,000 people die in car accidents in the United States each year. If we put the question, "How many people will die in car accidents in 2030 if we had self-driving cars?" into a prediction market, the result might be 10,000. This would prompt policymakers to accelerate their efforts.

3️⃣ Insurance Innovation

This is another interesting example.

Jeff said that due to the large number of hurricanes and industry price controls, many insurance companies have left Florida, making it impossible to insure your house.

But what if there are insurance-related bets in the market predicting whether the wind speeds in your area will exceed 80 miles per hour in the next two days?

If you believe your home could suffer serious damage if this happens, you can purchase a 10% Yes $10,000 policy. If it does happen, you win $90,000, covering most of your insurance premiums.

Moreover, you only need to purchase insurance when a problem is about to occur, eliminating the claims, operating, and advertising costs of traditional insurance, making this type of "new insurance" cheaper and more flexible.

4️⃣ A message to the world: If you're truly smarter than the market, then place your bets.

Jeff: "My mom often says to me, 'You're so smart, how come you're not rich yet?'"

"If you think the odds are wrong, then place your bet... If you're really smarter than the market, you'll make a lot of money. You'll contribute to society because you'll price things right."

If you're not making money, perhaps you should remain silent; the market may know better than you do.

"This will infuriate every university professor you encounter in the future, because they want to be experts. But they aren't. The group of speculators who fight in the market every day have significantly better judgment."

When his 12-year-old daughter checked the prediction market Tradesports during the Obama-Clinton primaries, she saw that Obama had a 22% chance of winning, while prominent political scientists said Clinton would dominate.

“My 12-year-old daughter was more accurate in predicting who would win the primary than the world’s top political scientists. That’s the power of the predictive market.”

P.S. The most real-world example is the Frenchman who bet on Trump winning the election and made $50 million on Polymarket.

5️⃣ Anti-consensus: The greatest asymmetry in decision-making and life

This part is very interesting; it may be Jeff's most profound observation about human behavior:

“We’re doing something in reverse—the bigger the decision, the less time we spend thinking about it.”

"If you're buying and selling stocks, and the market is basically fair, then you'll spend a lot of time on it."

If you're deciding who to marry, who to be in a relationship with, or anything else, you're basically throwing yourself into it without thinking...

One thing has a huge impact on your life, while the other has a very small impact. Yet, we spend more time worrying about the small things and pay insufficient attention to the big things.

This insight also forms the basis of his dating advice: "Don't date people your friends think are crazy."

An interesting suggestion: create an anonymous prediction market and let friends vote on whether they should be with each other.

"Many lives are destroyed because you get involved with the wrong people and nobody is willing to speak up."

6️⃣ Advice for young people

What should we learn now?

- Computer Science, Understanding AI

- Probability and statistics: Making better decisions, distinguishing between signal and noise. Understanding Bayes' theorem is especially important.

There's a historical context here: in order to gain an edge in the space race with the Soviet Union, the United States popularized calculus in education, even to an unrealistic degree. Jeff believes calculus is great, but its value is limited for most people.

He cited a study in which researchers at Harvard Medical School were “by a factor of 100” on basic probability questions about diseases because medical school did not teach them Bayesian analysis.

"If you really want to be a decision-maker in the face of uncertainty, which is human nature, you have to learn probability and statistics."

7️⃣ Summary

Jeff's insights were truly insightful, allowing me to appreciate the value of market prediction from a higher perspective, rather than just seeing it as a "gambling tool" or an "alternative exchange."

Another interesting point is that SIG is more familiar to Asian users with its $5 million investment in ByteDance in 2012, which has now yielded returns of over a billion US dollars, making it a true grand slam.

Currently, Polymarket and Kalshi are valued at $15 billion and $12 billion respectively in the secondary market, far exceeding the valuations of "gambling" or "alternative exchanges" (DraftKings, the leading online sports betting and iGaming platform in the US, has a market capitalization of $14.7 billion). I think it's more like a story similar to "ByteDance," where VCs initially looked down on the value of "news apps," but it eventually became a story of "recommendation engines," using the powerful tool of "recommendation" to almost completely reinvent all internet businesses.

I plan to write a valuation analysis of the market prediction later, and I welcome comments and discussions from my Twitter followers.