GoKawiilIn 2007, Nobel laureates Kenneth Arrow, Daniel Kahneman, and other notable scholars published a statement arguing that prediction markets could “substantially improve public and private decision-making.” The theoretical foundations were deep. Friedrich Hayek had argued in 1945 that markets aggregate dispersed, local, and tacit knowledge through the price system better than any central planner. In 2000, George Mason University economist Robin Hanson proposed a system he called futarchy, in which markets would be used to evaluate whether policies deliver on promises. Seventeen years later, Philip Tetlock, Barbara Mellers, and Peter Scoblic were championing forecasting tournaments as a way to generate useful policy knowledge for the intelligence community and to depolarize political debates. Institutions including Google, Microsoft, the CIA, the wider U.S. intelligence community, and British government intelligence analysts have all experimented with internal prediction markets. Some of these trials were more successful than others, but all were small. And we know, from both theory and practice, that more bettors make markets more accurate. Hal Varian, Google’s chief economist, likes to call prediction markets “information markets,” and the bettors the “suppliers” of the information. For decades, prediction market optimists — and I count myself among them — have argued that once we build better markets and increase the supply of bettors, accuracy will improve, and we’ll all be able to benefit from a new level of societal foresight. Now, in 2026, public prediction markets like Polymarket and Kalshi transact billions of dollars in volume each month. The vast majority of these bets are not on questions that might produce useful information. Roughly 90% of Kalshi’s trading volume (dollars exchanging hands between bettors) is from sports betting, making Kalshi effectively a sports gambling website with a small prediction market attached. I find that over 80% of the trading volume on Polymarket is concentrated on sports, cryptocurrency prices, or election betting. Much ink has been spilled on the negatives — such as gambling addiction and insider trading — of the growing popularity of these markets. But what of their promise? Are they producing valuable information and making humanity wiser?
Caravaggio, The Cardsharps, 1594.
Demand, demand, demand
To understand how useful this supply of forecasts is, and whether the forecasts really are delivering on the vision of the progenitors of prediction markets, we need to think about another factor: demand. It is entirely conceivable that prediction markets are only being used by bettors themselves. But if individuals, firms, media, and policymakers want (or need) the predictions we see on these markets, this evidence of demand can be used as a proxy for their usefulness. Vitalik Buterin, creator of the cryptocurrency Ethereum, summarized in Info Finance this dual nature of prediction markets: “If you are a bettor, then you can deposit to Polymarket, and for you it's a betting site. If you are not a bettor, then you can read the charts, and for you it's a news site.” I’ve thought hard about how to sell prediction markets to consumers. In 2020, I created Google’s current internal prediction market. Since then, I’ve served as the CTO of Metaculus, a non-market-based crowd-forecasting website, and now run FutureSearch, a startup that provides AI forecasters and researchers. In my work, I’ve found that the benefits of prediction markets fall into five different categories. First, markets can provide risk monitoring. I learned about COVID-19 in February 2020 from Metaculus, causing me to cancel a planned trip that would have left me stranded. Second, they can help with interpreting news, showing whether, and how much, a current event might affect larger outcomes. For example, the closure of the Strait of Hormuz during the 2026 Iran war led to an increase (from ~25% to ~35%) in the forecasted chance of a 2026 US recession due to the spike in oil prices. Third, they can inform planning around policy outcomes, such as whether TikTok will be banned in the US. Fourth, they could create accountability for claims made by political or business leaders. For example, in June 2025, when President Trump said he was contemplating a strike on Iran’s nuclear program, many Middle East experts dismissed the prospect, according to an article from the Council on Foreign Relations. Yet, per CFR, prediction markets gave a 58% chance of strikes that week, and we later learned that seven B-2 stealth bombers were then on-route. Fifth, they could produce novel information, allowing traders to discover or track things others don’t, such as when major AI milestones will be reached. Now let’s see whether the billions wagered on markets each month are supplying these five forms of useful information.
The big spike in November 2024 was due to $400 million bet on Trump's inauguration, and $327 million bet on Romania's election scandal, which involved the first ever annulment of a presidential election by an EU/NATO member.
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Risk-monitoring as a healthy information market
I’ll start in the one area where the supply (bettors betting) and demand (readers reading) for information from prediction markets seem to be in balance: risk monitoring. The most straightforward benefit from prediction markets comes from questions like “Pakistan military strike on India by Friday?” or “Will there be at least 10,000 measles cases in the U.S. in 2026?” or “US bank failure by January 31?” Tracking such risks was the domain of the first experiments with crowd forecasting in the US intelligence community, such as the IARPA tournaments, and of many of Philip Tetlock’s later superforecasting studies. Kalshi and Polymarket have a healthy number of such risk monitoring markets. I count 2,821 in total, with $3.8 billion in volume, of which geopolitical risk is the largest category. The median risk monitoring market has $82,000 of trading volume. Of these, 199 are conflict markets that resolve on a daily and weekly basis, creating a near-real-time escalation tracker. Here, the demand is clear. For the 2026 Iran war, for example, energy traders and shipping companies are the most concrete beneficiaries of the predictions on outcomes and timelines. Importantly, demand comes from mainstream media, which increasingly cites Polymarket, bringing these forecasts directly to professionals in places they already look. Useful as these markets are, they still have important blind spots. While journalists might cite prediction markets to track developments in an ongoing conflict, I haven’t seen media sites reporting stories where prediction markets are the source. This is a function of how public, retail prediction markets work: a story must already be quite large to attract enough traders to produce useful probabilistic information. Therefore, I see evidence of useful monitoring of risks, but not detection of them. Markets that don’t tie into flashy news stories suffer from both less supply and less demand. Health and climate questions, which are arguably as important as conflict surveillance, have not fared well in prediction markets. When Kalshi launched in July 2021, a year into the COVID-19 pandemic, it built exactly the kind of market that experts advocated: consistent, weekly questions about specific vaccine adoption numbers and COVID-19 case numbers. They averaged $8,000 per market, too low to be credible, and had several big misses. For example, “Germany COVID cases above 35K for week ending Dec. 28, 2021?” was trading at 3% a week before Omicron hit, and it was resolved as “Yes.” And it seems no institutional consumer, like a hospital system or government disease tracking body, materialized to adopt the signal. Climate and natural disaster markets, where theoretical support is strong, tell the same story. The markets failed both to attract a supply of traders and the demand of response bodies or the public. A second area where I see preliminary signs that a supply of good predictions could meet strong institutional and public demand is in the last of my five categories: generating novel information. There are some dozens of markets tracking AI, with $25 million in volume on questions that address which labs will have the top models on certain dates. It is not hard to imagine the people or organizations who would demand better information about emerging technologies. However, if one examines these AI markets, it seems that they are too low-quality to be useful to anyone making a decision. I can't imagine that an individual who chooses a model provider, a firm that chooses a partner or supplier, or a policymaker who chooses an AI regulation would have much to learn from them. It’s clear that Polymarket and Kalshi host these markets to serve bettors, not to produce useful information. Take Kalshi’s “Best AIs this week?” markets, which not only cover too short a time period to be useful in any decision-making, but also use Arena to judge the best AIs. Arena, which uses voting, not objective task scores, is not a credible measure according to AI experts. Still, demand for these markets does exist, and it’s plausible that higher-quality markets could emerge in the future to satisfy it.
Where prediction markets are accurate but ignored
In three of the five categories of benefit from prediction markets — interpreting news, policy outcomes, and accountability — I see evidence that high-volume markets are producing accurate predictions, but not evidence that anyone is, or should be, paying attention. First, how useful are markets for interpreting news? These are markets tracking larger outcomes like recessions or inflation that move in response to news, helping readers understand the impact of particular events. Volume appears healthy, with 1,647 markets and $1.25 billion in total trading volume. However, 85% of that volume is in US federal interest rate markets. The median trading volume of markets for interpreting news has actually decreased substantially, from a high of $49,000 in early 2025 to just $13,000 by the end of the year, much lower than the median volume of other markets I categorize as useful. While predicting interest rates is valuable, CME futures, Bloomberg consensus, and professional economists already do it. The same is true for other indicators with high trading volume on Polymarket and Kalshi: inflation, unemployment, commodity prices, mortgage rates. Aaron Brown calls prediction markets “economic oracles,” but the oracle is largely saying what other oracles already say, just updated faster. Still, there is a benefit to speed. On March 11, 2026, the Financial Times reported that, upon news of Iran War escalation, the Polymarket odds of inflation at or above 2.8% rose to above 90%. This illustrated an immediate domestic impact to US foreign policy, which could influence the public in a way that updates months later from professional economists might not. Next, how useful are markets for judging whether claims by governments and CEOs are credible? I found 184 accountability markets with $173 million in total trading volume. The number of such markets is growing, as is the median trading volume, with a median $44,200 in bets. But two-thirds of the total volume is Epstein file speculation, the type of activity that Rohanifar et al. (2026) diagnoses as “prediction laundering.” It's hard to see any decisions changing based on these markets. Most of the rest are about one other person, US President Donald Trump, which feel like a temporary artifact of a particularly entertaining leader with credibility issues in the popular consciousness. Finally, how useful are markets tracking policy outcomes? I found 1,710 markets with $1.42 billion in total trading volume. But the vast majority of volume is on a very small number of highly visible markets: $288 million on the possibility of a U.S. government shutdown, $238 million on whether Judy Shelton would be nominated as Fed chair, $145 million on whether TikTok would be banned in the US. The median volume of markets is increasing, growing in 2025 from $24,000 to $30,000. The section I find most valuable are the 196 markets with $144 million volume on tariff policies. These are actionable in many places around the economy, and I think the wisdom of the crowds is producing novel, useful, accurate information on what tariffs will take effect at what level. Overall, the markets on all three of these categories are dominated by betting on the Trump administration's volatile policies. As Robin Hanson has commented, “A random unpredictable US president has been very good for the prediction market industry.” This doesn't seem to me the vision that academics hoped for: experts wagering on current events, leading to pledges made from serious politicians or influencing the most important bills faced by legislatures worldwide. Markets driven by entertainment value and intrigue to bettors could plausibly deliver this, but I don’t see much of it on Kalshi and Polymarket. The most charitable view is that these are growing pains, where the creation of a healthy information market is bootstrapped by gambling on Trump, and gradually evolves into the more professional betting environments on mature financial securities. Until then, though, I don't expect that people affected by policies will pay much attention. We have another reason to doubt that the money changing hands across all of these markets is providing value. Metaculus, my former employer, has produced thousands of well-calibrated forecasts on global risks, health, and technology for over 10 years, with minimal institutional impact. Metaculus has even explored another item on the economists’ wish list: “conditional markets” which ask “If policy X happens, what will outcome Y be?” Yet these also have not been adopted by information consumers, and there are serious technical barriers to adoption by predictors. Still, the original vision for public benefit from prediction markets depends on them being highly liquid, and billions of dollars in liquidity can significantly change accuracy (or the perception thereof). Polymarket CEO Shane Coplan said Polymarket is “the most accurate thing we have as mankind right now”, while Kalshi CEO Tarek Mansour advertises prediction markets as “quintessential truth machines”. Let’s look at whether trading volume leads to higher accuracy.
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