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“Even when I was young, I was big.”
– William Perry
Michael lewis dating beginning Bet on everything Until 1985 when Caesars Palace offered 20-1 odds on William “The Refrigerator” Perry scoring a touchdown in the Super Bowl.
He did just that — a one-yard fumble in the end zone — and Caesars lost at least $250,000 on the bet (a large sum in an era when bettors had to phone Las Vegas to place bets).
This was the first known “show” bet – a bet on anything other than the score or result of a match, and as such, it is the primitive predecessor to today’s prediction markets that offer odds on seemingly everything.
Despite the loss, the Caesars president described it as the best bet they had ever made: “We got so much publicity around the bet that everything we lost was worth it.”
“The next day, your phone started lighting up from every big city in the country, with almost every book in town,” bookmaker Jimmy Vaccaro told Lewis. “They heard about Perry scoring a touchdown.”
Now, bookmakers are “writing” more action on the props – will the coin toss be heads or tails? What is the purpose of the first commercial? Will there be a wardrobe malfunction? – From what they do in games.
Before The Fridge, there were only three bets that could be made on the Super Bowl: winner, total score, and halftime score.
Now, there are hundreds.
As it turned out, that was the last three touchdowns of The Fridge’s career despite the 10 additional years he spent in the league (playing defense).
But those three brief shocks set us on the path to today’s world of betting on almost anything using prediction markets.
(Although we still mostly want to Betting on sports.)
A look inside political markets
Here’s a measure of how far prediction markets have evolved: When Andy Hall watches sports, he’s now in the habit of checking the prediction markets right before a big event “because the markets move several seconds before my TV broadcast.”
Hall teaches political science at Stanford University, so he is most interested in how “prediction markets are changing how we observe and understand politics.”
Hall is cautiously optimistic that thriving prediction markets will prove to be a public good because they should provide “a clearer common picture of a very complex political environment.”
But he also worries about the “weird feedback loops” they create.
As an example, he cites the attorney general race in Virginia where unsubstantiated claims about polls circulating on social media moved prediction markets, prompting social media to spread “breaking news” about the move in prediction markets, which in turn moved prediction markets even further.
That probably wasn’t helpful to the audience.
Hall points out that prediction markets also raise questions about what it means to “win” an election.
“In a world where prediction markets are increasingly viewed as sources of truth — many people pointed to when Kalci described the New York City mayoral election as evidence of a Mamdani win — identifying these extreme cases would be too risky.”
What would it mean if Calci called for a close presidential election in 2028?
I’m not sure I want to know that.
(For another alarming example of prediction markets influencing reality, see this story About bookmakers changing frontline maps in Ukraine.)
AI agents are very good at hacking smart contracts
Researchers at Anthropic reported that their AI agents successfully exploited 56% of smart contracts known to have vulnerabilities.
Even more impressive (or alarming), when testing 2,849 smart contracts without any known vulnerabilities, customers also discovered two zero-day exploits — proof that customers can find new vulnerabilities independently.
maybe most Worryingly, agents are improving at an astonishing rate: “Over the past year, revenues from exploiting leading models have doubled… approximately every 1.3 months,” the researchers reported.
(They want you to know that no blockchain systems were damaged in this experiment – they were tested on “blockchain simulators.”)
To put this in perspective, Moore’s Law describes the performance of semiconductors doubling every two years.
These AI agents are doubling down on their ability to exploit smart contracts every time Six weeks.
surprising. And terrifying.
Anthropic has no particular interest in cryptocurrencies. Instead, it conducted this experiment to measure how skilled agents were able to hack code of any kind: “The same abilities that make agents effective at exploiting smart contracts—such as long-term thinking, bounds analysis, and use of iterative tools—extend to all types of software.”
Here’s another amazing thing they found: “It only costs a proxy $1.22 on average to perform a comprehensive contract scan for vulnerabilities.”
“As costs continue to decline, attackers will deploy more AI agents to investigate any code found along the way to valuable assets, no matter how obscure,” the report concludes.
Consider investing in a safe deposit box to hold all the physical cash and gold coins that we will be back using soon.
Quantum computers come to cryptocurrencies first
The risks that quantum computers pose to cryptocurrencies are often dismissed with the meandering assertion that once computers get good, Everything He’ll be in danger, so we’ll have bigger things to worry about.
But the last episode of Epicenter Explains why cryptocurrencies are uniquely vulnerable.
“Most of the classical crypto community… has been working on this for years,” explains Stefano Giuggioso. “They already have protocols they can use if quantum happens tomorrow.”
In Web2, “you have central authorities,” he adds. If things get from worse to worse, “you have banks that refuse to deal for a day or two.”
However, in cryptocurrencies, there are two problems: 1) Blockchains use “new applications of cryptography” that have not received much attention from researchers studying quantum resistance and 2) Even if we have solutions, there are so many different voices, opinions, and vested interests in decentralized cryptocurrencies, “changing the infrastructure will be difficult.”
Most importantly, as Gugioso points out, “we have built an entire ecosystem for the purpose of making[our data]publicly available at all times” — which means that once quantum computers are available, they will be able to decrypt the entire transaction history of each blockchain.
So, if you’re using a private chain like Monero to make illicit transactions, you may want to move to a non-extradition country sometime soon.
“Monero is already broken,” says Gogioso.
Gugioso also expects governments to use quantum computers to identify people who haven’t paid capital gains taxes on their crypto gains: “They’ll almost certainly send them a bill for the money they haven’t paid yet.”
(Personally, I’m looking forward to the refund I’ll get for all my cryptocurrencies losses I was too lazy to report.)
Other fun scenarios include someone moving one of Satoshi’s coins – how will we know if this is Satoshi himself or just a guy with a quantum computer?
Or consider an American competitor like China targeting Bitcoin — not to make money, but to destabilize a system that has become ingrained in American finance.
But the biggest threat of all may be Giorgoso’s final prediction: that quantum technology could create a form of permissionless digital money that solves the problem of double spending without resorting to the exploitable distributed consensus model in cryptocurrencies.
Whatever the risks, the response has been woefully inadequate.
John Lillick points out that since cryptocurrencies have a market cap of $3 trillion, if there is a 1% chance of an overall loss, “we should spend $30 billion” on making cryptocurrencies quantum-resistant.
His evaluation of the current investment? “We don’t spend anything.”
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