In the lead-up to the 2008 election, Nate Silver revolutionized the way we talk about politics, bringing cold, hard, numerical facts to a world that had been dominated by the gut feelings of reporters and opinion columnists. Sixteen years later, he remains a go-to authority on the use of quantitative information in the prediction of political outcomes. But despite his popularity, Silver feels misunderstood. In a 2023 interview with New Yorker columnist Jay Caspian Kang, Silver lamented, “It’s very weird to become very well known for the wrong reasons. People say, ‘Oh you have numbers and therefore a lot of certainty’ and they can’t quite process the fact that you can use numbers to quantify uncertainty as well.” Silver’s readers thought his numbers were telling them what would happen and informing what politicians, particularly Democrats, should do. According to Silver, that’s not at all what he intended, and, he told Kang, he was writing a book to clear up the confusion. At stake was something much larger: not just a question of facts and probabilities, but an entire way of seeing the world, and acting upon it. And the key was not in election forecasting, but another field that was Silver’s true passion: gambling.

On the Edge, released last week, is that book, promising a path to riches and glory by treating life like a casino. A revealing look into a hyper-quantified worldview where numbers inform all decisions, it takes us from poker to sports betting to capitalist swashbuckling to battling superintelligent robots. Silver’s thesis is that gambling represents a comprehensive approach to life: a way to decide what to do in literally any situation. If you go by the process. If you make everything into numbers. If you bet the odds. Then you are righteous—even if you lose it all.

Silver sets the stage by introducing us to the two diametrically opposed camps, which he calls the River and the Village. The River stands in for the gamblers, the Village for the anxious liberals who don’t understand them. The inhabitants of the River are the motley heroes of the book: the professional gamblers, the sports bookmakers, the Wall Street traders, the Silicon Valley VCs and founders and the AI engineers. The Village is composed of the risk-averse world of D.C. politicians, academics and the media. Silver writes to explain the River to the Village so that the Village can see just how deeply wrong they are. Those in the River are analytical, meaning that they are capable of bracketing their values and biases when making hard decisions. They know how to assess risk and take shots. From the point of view of the Village, the Riverians often look irresponsible, if not immoral. But Silver argues that the gambler’s mindset is the truly rational one—that the key to winning big in life is betting big. As Silver handily demonstrates, “rich and powerful people are disproportionately likely to be Riverians.”

Silver himself journeyed through these two imagined communities in his rise to fame. In 2008, he created a model of U.S. elections that ran the board, predicting outcomes down to local races far better than anyone else. Most importantly, his analysis consistently predicted that Obama—not only the first black candidate for president but the first candidate to exploit online small-donor enthusiasm, organization and contributions—would handily win the election. Silver told people that yes, despite a history of institutionalized slavery, discrimination and racism, America could indeed vote a black man into the presidency. A salve for the usual anxiety of the Villagers, his numbers were the “scientific” counterpart to Obama’s inspirational slogan “Yes We Can.”

It didn’t hurt that he called the election correctly. Though no more accurate than simple poll aggregating, Silver’s complex methodology gave off an air of numerical expertise in a world filled with qualitative, unaccountable opinion leaders. The result was that Silver went on not just to punditry and fame, but also to become the main public mouthpiece for statistical prediction. This technology, ascendant in the aughts, is now everywhere we look, whether it be in sports, finance or the digital technologies that surveil our everyday lives to predict where we’ll click.

Yet despite the apparent triumph of his numbers-based techniques, things turned sour for Silver in 2016. He first insisted that Donald Trump would not win the Republican nomination, and then, chastened by his folly, ended up giving him a 29 percent chance of winning the general election. When the results were in, Silver adamantly claimed he was more right than everyone else, pointing to the fact that the Huffington Post, for instance, put Trump’s odds of winning at about 2 percent. But why does it matter if other forecasters were more bullish on Hillary Clinton’s chances? For Silver, this speaks to the way he has often been misunderstood. In On the Edge, he asserts that his forecasts were never supposed to make the readers of the New York Times feel less anxious; his job, rather, was “to handicap the race” for those who wanted to bet on it. A good election forecast is one where if you’d bet on it, you’d make money.

To understand how you can make money with Silver’s forecasts, we must start with statistics. Silver is a devout Bayesian statistician. Bayesians believe we can quantify our certainty about any statement we can imagine. That quantification is called probability. What is the probability it will rain tomorrow? What is the probability Kamala Harris will win the election? What is the probability you’ll eat ice cream for breakfast in March 2025? We can come up with a number for all of these. And once you put a number on a proposition, you can bet on it.

Probabilities and bets are connected through something called “expected value.” You should only take bets where you expect to come out ahead. The expected value of a bet is the amount you’d win times the probability you win, minus the amount you’d lose times the probability you’d lose. If someone told you they’d give you four dollars if a coin flip comes up heads but you owe them two dollars if it comes up tails, you’ll take the bet because the expected value you receive is one dollar ($4 x 50% – $2 x 50% = $1). Silver calls people who think in these terms “expected-value maximizers,” and explains that this is the crucial characteristic of Riverians.

Now let’s flash back to November 2016. Say someone offered you a five-to-one wager on Trump winning the election. They say they’d pay you five hundred dollars if Trump were to win, and you’d pay them one hundred dollars if he were to lose. If you put Trump’s chances at 2 percent, then the expected value of this bet would be an 88-dollar loss. You’d never take the bet. But if you listened to Nate Silver and set the probability of a Trump win at 29 percent, the bet would have had an expected value of a 74-dollar profit. That seems like a great bet. Silver’s logic is thus that if your forecasts are better, you’ll make more money betting. Notice, though, that this defense has nothing to do with accuracy in raw terms—he didn’t predict Trump would actually win—and certainly nothing to do with voting or representative democracy itself. It’s about how you might be able to profit off of democratic elections.

Though Bayesian statistics is named for the eighteenth-century philosopher and statistician Thomas Bayes, its modern formulation is better attributed to twentieth-century mathematicians Frank Ramsey and Bruno de Finetti. For Ramsey and de Finetti, the human being was a betting animal; this meant that the best way to quantify uncertainty was to ask people to make bets. These bets didn’t need to be on casino games or sports; you could lay bets on any proposition. Would you take six-to-one odds that a dice roll lands on a 1? Would you take five-to-one odds that Donald Trump would win the election over Hillary Clinton? Though the first question is the only one that feels random, it’s certainly possible you might wager on any of these questions.

In the field of Bayesian statistics, betting has served as a metaphor for the probabilistic calculations that we make naturally—a background, subconscious reasoning that informs every decision we make. But in moving Bayesianism out of textbooks and into the wild, Silver takes the metaphor literally, and in doing so turns a descriptive framework into a prescription for practical success. If everyday reasoning looks like betting, then in order to come out on top, you need to spend your life at the casino—and remake the rest of the world in its image.

This is the mindset behind the world that Silver played a large role in establishing: one of ubiquitous prediction where everything is bettable. Silver insists that viewing all decisions through this lens of gambling is the underappreciated characteristic of Very Successful People. It is true that, as Silver suggests, quantifying everything, and then betting on the outcome, has become a pervasive and powerful technique, at work in fields from finance to culture to sports to politics. But what Silver willfully ignores is that the successful players in this world aren’t the bettors. They are the bookies and casino owners—the house that never loses.

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Silver’s tour of the River begins in the literal casino, and this is where this book is at its best. His view of sports gambling (and the sport of gambling) is wide-angle, deep and informative. He tells the story of how poker became a televised sport, what it takes to actually get money down on a big sports bet (it’s not as straightforward as you’d think) and what’s going on in the weird world of slot machines, the biggest moneymakers with the worst odds on the casino floor. In the first part of the River tour, you can feel Silver’s passion. He breathes the games, he knows the people, and he’s curious about corners of the gambling world he’s never been in before.

This curiosity is what leads him to interview Natasha Schüll. Schüll is an anthropologist who made waves in 2012 with Addiction by Design, a book that described the exploitative underbelly of the world of heroic risk-takers that is Silver’s focus. Slots are bad bets by definition—a machinic embodiment of the adage “the house always wins.” Schüll found over years of fieldwork that slots enthusiasts knew this. They didn’t play to win. They played to play. This ends up being the most revealing part of the book, tracing the precise boundary where Silver’s insight ends.

Silver writes, “Here’s something I learned when writing this book: if you have a gambling problem, then somebody is going to come up with some product that touches your probabilistic funny bones.”

Here’s something we learned while reading this book: no amount of empirical evidence is enough to convince Silver that this might be, well, bad. Silver acknowledges that slots are unfair, but concludes that the problem is with the addicts. He manages to find a few “Riverian” slot players who are gaming the bad odds while hiding their expertise from the casinos. Everyone else is doing it wrong. What he can’t seem to see is that the casino owners—whose operations have in recent years become ever more data-driven—are the ultimate Riverians, and that they are not taking any risks at all. Indeed, it’s precisely the opposite: slots guarantee profit to the casino by impoverishing people who play them.

This lack of insight becomes more egregious when Silver steps outside the casino. He abruptly moves from sports betting to trying to explain how successful people are all excellent gamblers. In the chapter “Thirteen Habits of Highly Successful Risk-Takers,” he interviews some of these successful people. The characters to whom he gains access as a celebrity pundit include Katalin Karikó (whose Nobel Prize-winning research led to the invention of the mRNA vaccine) Dave Anderson (a former NFL wide receiver) and H. R. McMaster (a former national security advisor). What do these risk-takers have in common? Silver tells us thirteen things. Thirteen incredibly banal things. “Successful risk-takers are cool under pressure.” “Successful risk-takers have courage.” You will not be surprised to learn that “Successful risk-takers are good estimators. They are Bayesians.” Of course they are.

You don’t need to interview Nobel Prize winners to come up with this list of habits; you could have just cribbed from a few mass-market business books. But the chapter is necessary to frame the rest of the book.

Again, the central core of Silver’s argument is that successful people just are gamblers. Indeed, gambling is also what he sees behind the success of Silicon Valley in the second half of the book. But this is where Silver inadvertently reveals the limits of applying mathematics to every real-world decision. The book has three successive chapters on Sam Bankman-Fried, the FTX founder who was convicted of fraud and sentenced to 25 years in prison. Bankman-Fried is by all rights a model Riverian: in search of maximum expected value, he applies Bayesian reasoning to everything from personal relationships to Shakespeare to his own jail sentence. Silver quibbles with Bankman-Fried’s use of EV-maximizing, but often only at a technical level—as if, had he done the calculations correctly, he might have avoided committing one of the largest frauds of all time. The lesson, Silver says, is to be wary of all “totalizing utopian ideologies,” rather than consider that Bankman-Fried’s runaway Bayesianism in particular may have blinded him to the obvious ethical and legal problems with his actions; he doesn’t even mention that the entire basis of FTX was simply fraud. We never get a sense of the enormity of Bankman-Fried’s crimes, and the strange directions he was led by those very methods that Silver wants to defend.

Throughout, Silver favors consensus only when it suits him. He claims that economist Emily Oster, who argued for a moderate approach to the COVID-19 pandemic, used numerical, impartial risk-benefit analysis and hence got things more right than others. But he fails to explain the fact that numerous other famous economists used a risk-benefit approach to justify strict lockdowns. He argues we need to take seriously the scientific consensus behind the idea that Large Language Models will become sentient and kill us like in The Terminator. But there is no scientific consensus on this topic. Nonetheless, Silver snidely remarks: “To dismiss these concerns with the eye-rolling treatment that people in the Village sometimes do is ignorant.”

Indeed, Silver sides with Riverian consensus on every page of this book. The River lives on Elon Musk’s Twitter. If you spend any time on there, you’ll recognize most of the arguments Silver puts forward as those advanced by anonymous rationalist accounts with anime avatars. On the Edge is a very Twitter book. The ideology is gambling, but the thinking is all reactionary hive mind. The main characters of the later chapters are all from the San Francisco Bay Area, but they spend a disproportionate part of their lives online. These are people like Bankman-Fried and the crypto bros, pseudonymous accounts like roon and Aella, self-made prophets of computational religion like Eliezer Yudkowsky. On the Edge is a celebration of the community that uses their phones to gamble on everything: to place sports bets, to bet on risky stock options, to bet on cryptocurrencies, to bet on elections.

Is this a world that we want to live in? Silver provides lip service to the counterarguments, but his approach excludes them. The methods that Silver introduces in the book largely lead to personal immiseration and addiction. Several recent studies have found that online gambling activity by teenagers is increasing, and that their calls to helplines are up; about 2.5 million adults in the U.S. have a gambling addiction, a number that’s been on the rise since the legalization of sports betting in 2018. A set of multibillion-dollar industries, from casinos to crypto exchanges to AI, complete the feedback loop, facilitating a gamified Bayesianism and parasitically feeding off society.

Silver ultimately can’t deliver a defense of the River because he fails to explain its real social and economic function. His book explains why quantifying uncertainty is the key to taking risks. But risk-taking itself goes unexplored. In fact, its true role is much less heroic than he makes it out to be. The liberal, affluent readers who made Silver famous read his forecasts for reassurance. They want numbers to tell them the future of the country is safe. And this is the paradox inherent to the quantification of uncertainty—which is, by its very nature, unknowable. Assigning numbers to future outcomes makes it feel like the unknown is rendered known. Putting “impartial,” “unemotional” and “precise” numbers on the unknown makes the future seem less scary. This is not about taking risks but avoiding them. The quantification of uncertainty promises to remove it from sight; but what it sets up in its place is neurosis, the constant refreshing of the odds and the stream of anxious clicks that come with it.

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This exaggerated contrast between risk-taking and certainty—and the compulsion to alchemize one into the other—is embodied in Silver’s overarching symbols of the River and the Village. As a geographical construction, it makes little sense. The River doesn’t seem to flow through the Village; neither does the Village seem to lack water. Perhaps the only striking quality of the fantasy map he provides in the first chapter is that the rest of society is absent. There are the politicians and wonks of D.C. and Boston, the heroic gamblers and entrepreneurs of Vegas and Silicon Valley—but no autoworkers, librarians, schoolteachers, farmers or any other members of our populace.

Why are these two groups in such sharp focus? A clue appears in the endnotes. Silver asserts that he came up with the construct of the Village, casting D.C. insiders, politicians and academics as a blinkered caste of shut-ins who fear the truth revealed by the enlightened quantifier. But the Village is, in fact, an old internet meme. Originally coined by Nation columnist Eric Alterman to castigate the establishment centrists who tut-tutted from the Washington Post opinion page, the Village was a derisive term popularized by Bush-era progressive blogs and NGOs—outlets like the Daily Kos, Media Matters and ThinkProgress. It got particular purchase during the 2008 election—not coincidentally when Silver started blogging about election forecasting on the Daily Kos under the pseudonym “poblano.”

Today, the ideology nurtured in the 2000s blogosphere is mainstream, and Nate Silver is a highly paid establishment pundit. The Village made Nate Silver. Silver became a millionaire by promoting this overquantification to his denigrated Village. He insists on the chasm between what he identifies as the two types of technocrat: woke liberals in D.C. and orthodox Bayesian warriors in the wilds of Vegas, Wall Street and Silicon Valley. But his Riverian interviewees keep telling him that this taxonomy is faulty. In Silver’s discussion with far-right billionaire Peter Thiel, Thiel tells Silver that statistical modeling has become a part of our world, not the best way to grasp it or make decisions. It is a factor you have to negotiate, one you can take advantage of by playing to people’s desire to quantify the unknown. Thiel knows that the most successful Riverians aren’t the gamblers. They are the casino owners.

This, it seems, is what Silver can’t accept: that the River is the casino itself, rather than the gamblers who play there. And at the casino, the house never loses. In the run-up to the publication of On the Edge, the prediction market platform Polymarket announced it had hired Silver (he notes that this might happen in a footnote); now Silver writes articles promoting his digital punditry that move the needle in the betting markets, where anything is fair game for gambling, including election outcomes. These markets are far from neutral parties: they represent the Riverification of our society in a much more pervasive way than we’ve seen yet, feeding the vast complex of addiction and prediction that continues to mushroom across finance, sports and elections. For Silver to take a job at Polymarket while claiming to be the sage for our highly probabilized society is the definition of a conflict of interest. What On the Edge finally shows us is that Silver is no longer just a handicapper: he’s a bookie.

Photo credit: World Poker Tour (Flickr, CC / BY-ND 2.0)