Original Title: Prediction Markets: They Grow Up So Fast
Original Author: Alex Immerman, a16z
Translation: Peggy, BlockBeats
Editor's Note: For a long time, prediction markets have been viewed as a "marginal product": first as academic experiments, later as opinion tools during election seasons, and then as a kind of extension of sports betting. They seem to always rely on a high-attention scenario but are rarely considered as true financial infrastructure.
However, in the author's view, prediction markets are gradually evolving from a marginal "event trading tool" focused on elections and sports into a financial infrastructure capable of pricing uncertainty.
The author points out that the key changes in the industry are reflected in three aspects: first, the application scenarios are expanding; while sports remain a main entry point, entertainment, macroeconomics, CPI, and other long-tail markets are growing faster and starting to meet institutional demand; second, prediction markets have provided a tradable price benchmark for "the event itself" for the first time, allowing institutions to hedge political or macro risks directly without "secondary betting" through related assets; third, the path of institutional adoption is progressing, from data reference (viewing odds) to system integration, and then to actual participation in trading, which is still in the early stages.
Prediction markets are undergoing a process similar to the early "specialization - institutionalization - infrastructure development" of the options market. Once liquidity, leverage, and regulation are gradually improved in the future, it may become a core market tool connecting retail and institutional investors, used for hedging and pricing real-world uncertainties.
The financial world is highly "vertically layered," and almost every niche field has its own recognized "annual holy land." Leaders from healthcare providers, payers, and biotech companies gather annually in San Francisco to attend the J.P. Morgan Healthcare Conference. Heavyweights in the global macro field and political figures from various countries head to the Swiss Alps to participate in the World Economic Forum Annual Meeting (Davos Forum). TMT, real estate, industrials, financial services, and almost every industry you can think of have their own flagship summits.
At the end of March this year, Kalshi’s academic and institutional research division, Kalshi Research, held its inaugural research conference in New York, gathering academics, Wall Street executives, former politicians, and traders who truly drive the market operations. The composition of the attendees clearly indicates a trend: the industry is "maturing."
The day of the conference opened with a conversation between Kalshi co-founders Tarek Mansour and Luana Lopes Lara with Katherine Doherty. Below are some industry observations extracted from this conversation and subsequent roundtable discussions:
Markets and Life, Beyond Elections and Sports.
During major news cycles, a fixed pattern often emerges: a significant event (such as the 2024 election, the Super Bowl, or the more recent "March Madness" college basketball tournament) occupies the majority of media headlines and subsequently dominates trading volumes in prediction markets. This easily leads to the impression that "the value of prediction markets only lies in these events."
However, while early narratives often viewed prediction markets as tools that were "only meaningful during election cycles," Kalshi's growth in other areas has also been significant.
When the research conference was held, the weekly trading volume in sports-related trades was nearing $3 billion, accounting for about 80% of Kalshi's total trading volume, mainly driven by "March Madness." Tarek and Luana regard this high concentration as a phase phenomenon.
A more explanatory data point is that despite sports-related trades reaching a historical high in absolute terms, their proportion of the total trading volume is at a historical low. This signifies that all other categories are growing faster.
The two founders highlight that categories such as entertainment, crypto, politics, and culture are demonstrating stronger user growth and better trading retention structures than sports. Sports act more like a "catalyst" for the mass market—characterized by familiarity, clear timing, and strong emotional involvement, it is a classic entry product.
Meanwhile, the company has observed substantial growth in more long-tail markets. These markets currently constitute over 20% of Kalshi's trading volume and are expected to play a more significant role in future institutional hedging and information markets.
A subsequent institutional roundtable confirmed this judgment from the demand side.
Cyril Goddeeris, Co-Head of Global Equities at Goldman Sachs, stated that predictions related to macro events and CPI data are currently the most sought-after categories on Wall Street. CNBC's Executive Vice President of Growth, Sally Shin, mentioned that she has been using prediction markets for narratives on "the Federal Reserve Chair's status" and "non-farm payroll data." Troy Dixon, Co-Head of Global Markets at Tradeweb, further depicted a future scenario where large investment banks establish dedicated prediction market trading departments, with financial contracts as core products.
Why Kalshi Can Attract Attention from Wall Street
One important reason for the operation of traditional financial markets is that each type of core asset has a recognized benchmark: the S&P 500 Index represents the overall performance of 500 stocks, and there are benchmark pricing systems like ICE for crude oil.
However, for political and macroeconomic events (such as who wins the election, whether tariffs are passed, and the decisions of the Supreme Court), there has long been a lack of a widely accepted and dynamically updatable "pricing benchmark." Prediction markets have changed this—now, almost any event's future can have a real-time, liquid "price anchor."
Once a specific event (such as "Will the 30% tariff be passed") has a credible price, institutions can trade directly around that price. This allows for trading on the event itself as well as hedging risks from other assets within the portfolio. As Troy Dixon from Tradeweb stated: "Back when Trump was first elected, there were a lot of hedging operations in the stock market; the logic was to short the S&P because if Trump won, the market would surely fall. But that trade failed. The problem was: how do you price these events? Where's the benchmark?"
Tarek also mentioned that this was one of the reasons he founded Kalshi. During his time at Goldman Sachs, his trading desk recommended trades based on the 2024 election and Brexit. Without prediction markets, institutions hedging political or macro events through related assets were essentially betting on two things at once: whether the event itself would occur and the correlation between that event and the traded asset. The second judgment could very well be wrong independently.
When the event itself has a direct pricing benchmark, these two layers of risk are compressed into one. As Tarek said: "Now, this market is starting to price everything."
Three Stages of Institutional Adoption of Prediction Markets
It is evidently still early to claim that large institutions on Wall Street are engaging in large-scale trading on Kalshi. Currently, most institutions are still at the stage of using it as a "data source," rather than a "trading platform."
However, Luana points out that the path for institutions to adopt this market is clear and can be divided into three stages:
The first stage is data integration: bringing prediction prices into the daily workflows of institutions. For example, getting Goldman Sachs portfolio managers to routinely check Kalshi’s odds data like they do with the VIX index. This stage has already occurred to some extent. Jonathan Wright, a professor at Johns Hopkins University and former Federal Reserve official, remarked: "In areas like Federal Reserve decision-making, unemployment rates, GDP, etc., Kalshi is almost the only reference source."
The second stage is system integration: including compliance and legal approvals, technical integration, and internal education—essentially the process of introducing a new financial tool.
The third stage is actual trading: institutions begin to hedge risks directly on the platform, gradually accumulating trading volume and market depth. At this point, more hedging demand attracts speculators, and tighter spreads attract more hedgers, forming a self-reinforcing positive feedback loop for benchmark pricing.
Currently, most institutions are still in the first stage, some have entered the second stage, but very few have truly reached the third stage. An important barrier is that trading on prediction markets currently requires full margin. For instance, a $100 position necessitates a $100 margin deposit. This is acceptable for individual investors, but for hedge funds or banks that rely on leverage and capital efficiency, this mechanism's cost is too high.
As Tarek said: "If you want to hedge $100, you have to put $100 at the clearinghouse. That's too expensive for institutions. Organizations like Citadel or Millennium wouldn’t do that." Currently, Kalshi has obtained a license from the National Futures Association (NFA) and is working with the Commodity Futures Trading Commission (CFTC) to introduce a margin trading mechanism.
What's Next?
Michael McDonough, head of Bloomberg's Market Innovation, summed it up best: "The sign of success is when these things become boring." He compared prediction markets to the options market in the 1970s, which was similarly filled with controversies over manipulation and regulatory uncertainties, but ultimately evolved into a set of infrastructure to the point where hardly anyone thinks much about it today.
AQR partner Toby Moskowitz indicated that he "is willing to bet real money" that prediction markets will become a viable institutional tool within five years, or even sooner.
Garrett Herren from Vote Hub described the end state: "The question will no longer be whether to use prediction markets, but how to use them. Once that question changes, it means they have become indispensable."
In fact, even though the current scale of prediction markets is still limited, the hedging market itself is a substantial field.
Indeed, the "normalization" of prediction markets is already happening.
In the political theme roundtable, former Congressman Mondaire Jones mentioned that senior members of both parties—including President Trump, House Minority Leader Jeffries, and Senate Minority Leader Schumer—have begun to cite Kalshi’s odds data in public settings. Scott Tranter from DDHQ confirmed that prediction market data has now become one of the standard inputs for party committees. Meanwhile, Vote Hub announced that it has directly integrated Kalshi data into its midterm election forecasting model.
All of this did not exist two years ago. Back then, the most successful traders on Kalshi were mostly "amateurs." Today, this label may no longer be accurate.
At Kalshi's "The People Behind the Markets" roundtable, four traders shared their career paths—which sounded indistinguishable from traditional professional traders: some spent 11 years studying the Billboard music charts, others have been honing their skills in prediction markets since 2006 when it was still "a bit geeky and a hobby where you could hardly make money." Notably, none of the four guests came from traditional finance, instead hailing from music, politics, and poker backgrounds. However, they all agree that what the platform truly rewards is deep domain knowledge rather than a shiny résumé.
Prediction markets have come a long way. From initially being seen as academic experiments, to later becoming a "novel tool" during elections, and then being classified as "sports betting products," its positioning continues to change. The clear signal conveyed by this conference is that prediction markets are evolving into an infrastructure—to price uncertainty, serving a wide range of participants and diverse application scenarios, from retail traders to large institutions.
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