Patrick Boyle explains why it's illegal to bet on the price of onions but it's OK to bet on the Dodgers' double header

One of the best overviews I've seen of the strange world of prediction markets, told with Boyle characteristic dry wit. I've included some excerpts to give you a sense of the essay, but the thing is quotable. 





Coincidence

You might expect the federal commodities regulator to step in at this point and clarify that a bet on the New York Knicks is not, in fact, a vital financial derivative. But they have not.

According to the Financial Times, the CFTC has mostly just been avoiding the question. Well, it’s actually a bit worse than that. Under the new administration, the CFTC and the Department of Justice have gone to federal court to block the state of Arizona from enforcing its gambling laws against Kalshi.

So, the federal government now appears to be deploying its legal resources to defend a tech platform’s right to operate what Arizona considers an unlicensed sportsbook, overriding state law in the process.

Whatever your views may be on prediction markets, you have to agree that this is a rather unusual use of the Department of Justice’s time.

Now, if you’re wondering why the new administration might be so accommodating to these prediction platforms, there’s one small detail that’s probably worth mentioning. A fellow named Donald Trump Jr., who seems to be some sort of relative of the sitting president, is currently serving as a strategic adviser to both Kalshi and Polymarket.

I looked up this fellow’s background, and he appears to have no real work experience in either strategy or advice. He seems to be a reality TV star who also worked for his dad’s real estate company.

I can’t think of why they hired him, but I suppose it’s still worth noting that the president’s son advises the companies that the federal government is currently shielding from state prosecutors.

I’m sure that it’s all a coincidence.

The new crypto

So, if prediction markets are not entirely reliable as truth machines, what are they actually for?

To understand the current boom, it helps to look at the broader shift in retail investing over the last few years. Dimitri Kofinas of the Hidden Forces podcast uses the term financial nihilism to describe what’s been going on.

The idea is that traditional paths to building wealth feel increasingly out of reach for a lot of young people. So instead of saving and investing carefully, they try to get rich quickly by putting money into crypto tokens featuring pictures of dogs that were pitched to them by edgy billionaires, or by buying shares in bankrupt companies.

Prediction markets slot in perfectly here.

If you go back five years, crypto was the exciting product that everyone was talking about. But crypto is kind of dull today. Bitcoin is up about 25% over five years, which sounds okay until you realize that a money market fund paying 4% with no risk at all would have gotten you most of the way there.

Your dad has achieved triple the return of Bitcoin over the last five years with his index fund. And he didn’t have to check his phone at 3:00 in the morning or pretend to understand what a layer-two rollup is.

 

 Sharks and Fish

The problem with all of this is that whenever a large pool of enthusiastic retail money shows up somewhere, the professionals are usually not far behind.

According to the Financial Times, large quantitative trading firms like Susquehanna and DRW—firms that normally act as market makers on stock exchanges—are now setting up dedicated prediction market desks. They’re reportedly paying traders base salaries of $200,000 a year to build algorithms that systematically identify mispriced contracts on these platforms.

So, on one side of the trade, you have a person betting on the Super Bowl because it seemed like fun, and on the other side, you have a machine that does this 24 hours a day and never gets excited about anything.

This brings us to what the gambling industry calls the sharks and fish problem.

In the early 2000s, there was a huge boom in online poker. Millions of amateurs—the fish—logged on to play. But it didn’t take long for the professionals, or the sharks, to show up. The professionals didn’t play for fun. They played the odds methodically, and eventually they deployed bots to do it for them around the clock.

The survival time of a new recreational player on these sites was eventually reduced to not very long. The amateurs worked out that they were no longer really playing a game. They were donating their money to a server farm in New Jersey. They stopped logging in. The liquidity dried up, and the whole ecosystem collapsed. The sharks had eaten all of the fish and then starved.

Today, prediction markets are full of retail money and the platforms are growing quickly. But unlike trading a meme stock, where the price is just whatever the next person is willing to pay, an event contract eventually resolves to either true or false. There is an actual answer.

And if you’re a retail trader betting on a geopolitical event based on a feeling, and the person on the other side of your trade is a gamma-neutral algorithm being run by a multi-billion-dollar hedge fund, the odds are not in your favor.

This is not a skill gap that can be closed by doing more research. It’s a structural disadvantage.

  

 

Wonderful except...

So when you look at the mechanics of the whole thing, prediction markets start to look less like a truth machine and more like a wealth transfer mechanism.

The platform takes a transaction fee. The quantitative algorithms extract capital from retail bettors. The insiders extract capital from everyone, and society picks up the tab for the bankruptcies and the unpaid bills.

It’s a wonderful business model for everyone except the people using it.