We all know they’re casinos. It’s time to look at the data behind the froth.
In 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.1
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 inInfo 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.2
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.3
Now let’s see whether the billions wagered on markets each month are supplying these five forms of useful information....
