a16z New Article: Predictive Markets Entering Fast-Forward Phase

Original Title: Prediction Markets: They Grow Up So Fast

Original Author: Alex Immerman, a16z

Original Translation: Peggy, BlockBeats

Editor's Note: For a long time, prediction markets have been viewed as a "fringe product": first as academic experiments, then as tools for public opinion during election seasons, and later as a kind of extension of sports betting. They seem to always depend on a high-profile scenario, yet are rarely understood as financial infrastructure.

However, the author believes that prediction markets are evolving from a marginal "event trading tool" focused on elections and sports into a financial infrastructure that can price uncertainty.

The author points out that key changes in the industry are reflected in three aspects: first, the application scenarios are expanding; while sports remain a traffic entry point, entertainment, macroeconomics, CPI, and other long-tail markets are growing faster and beginning to meet institutional demand; second, prediction markets have for the first time provided a tradable price benchmark for "the events themselves," allowing institutions to directly hedge political or macro risks without relying on related assets for "secondary bets"; third, the path for institutional adoption is progressing, from data reference (viewing odds) to system integration, and then to actual trading, which is still in the early stages.

Prediction markets are undergoing a process similar to the early "professionalization - institutionalization - infrastructure development" of the options market. Once liquidity, leverage, and regulation improve, they may become a core market tool connecting retail and institutional investors for hedging and pricing real-world uncertainties.

Finance is a highly "vertically layered" world, with each subfield having its own recognized "annual holy land." Leaders from healthcare providers, payers, and biotech companies gather annually in San Francisco for the J.P. Morgan Healthcare Conference. Heavyweights in the global macro field and political figures from various countries head to the Swiss Alps for the World Economic Forum Annual Meeting (Davos Forum). TMT, real estate, industrials, financial services, and almost every industry you can think of also have their most representative flagship summits.

At the end of March this year, Kalshi's academic and institutional research department, Kalshi Research, held its first research conference in New York, gathering academics, Wall Street executives, former politicians, and traders who truly drive the market. The composition of attendees clearly indicates a trend: the industry is "maturing."

The conference opened with a conversation between Kalshi co-founders Tarek Mansour and Luana Lopes Lara and Katherine Doherty. Below are some industry observations distilled from this dialogue and subsequent roundtable discussions:

Markets and Life: More Than Just Elections and Sports

In major news cycles, a fixed pattern often emerges: a large event (such as the 2024 election, the Super Bowl, or the more recent "March Madness" college basketball tournament) dominates the majority of media headlines and subsequently drives trading volume in prediction markets. This easily creates the impression that "the value of prediction markets only lies in these events."

However, despite early narratives often viewing prediction markets as tools that are "only meaningful during election cycles," Kalshi's growth in other areas is also significant.

At the time of the research conference, the weekly trading volume for sports-related transactions had just approached $3 billion, accounting for about 80% of Kalshi's total trading volume, primarily driven by "March Madness." Tarek and Luana view this high concentration as a phase phenomenon.

A more telling piece of data is that, despite the absolute scale of sports-related transactions reaching an all-time high, their share of total trading volume is at a historical low. This means that the growth rate of all other categories is faster.

The two founders pointed out that categories such as entertainment, crypto, politics, and culture are showing stronger user growth and better trading retention structures than sports. Sports are more like a "detonator" for the mass market—characterized by high familiarity, clear timing, and strong emotional involvement, making it a typical entry product.

At the same time, the company has observed significant growth in longer-tail markets. These markets currently account for over 20% of Kalshi's trading volume and will play a more critical role in future institutional hedging and information markets.

Subsequent institutional roundtables 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 focused categories on Wall Street. Sally Shin, executive vice president of growth at CNBC, mentioned that she has used prediction markets for content narrative tools, such as "the fate of the Federal Reserve Chair" and "non-farm payroll data." Troy Dixon, co-head of global markets at Tradeweb, further painted a future scenario: large investment banks will establish dedicated prediction market trading departments, focusing on financial contracts as core products.

Why Kalshi Can Attract Wall Street's Attention

One important reason traditional financial markets can operate is that each core asset class has a recognized benchmark: the S&P 500 index represents the overall performance of 500 stocks, and crude oil has benchmark pricing systems like ICE.

However, for political and macroeconomic events (such as who wins an election, whether tariffs pass, or the outcomes of Supreme Court cases), there has long been a lack of widely accepted and dynamically updated "pricing benchmarks." Prediction markets have changed this—now, almost any event's future can have a real-time, liquid "price anchor."

Once a certain event (such as "Will a 30% tariff pass?") has a credible price, institutions can trade directly around that price. This allows for trading on the event itself and can also be used to hedge risks in other assets within a portfolio. As Troy Dixon from Tradeweb said, "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 was elected, the market would definitely fall. But that trade failed. The question is: how do you price these events? Where is 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 happen and the correlation between that event and the traded asset. The second judgment could easily be wrong on its own.

When the event itself has a direct price 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 clearly still too early to say that large Wall Street institutions are trading on Kalshi at scale. Currently, most institutions' usage remains at the level of "data source," rather than "trading platform."

However, Luana pointed 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: allowing prediction prices to enter the institution's daily workflow. For example, getting Goldman Sachs' portfolio managers to habitually 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, stated, "In areas like Federal Reserve decisions, unemployment rates, and GDP, Kalshi is almost the only reference source."

The second stage is system integration: including compliance and legal approvals, technical connections, and internal education—essentially a process of introducing a new financial tool.

The third stage is actual trading: institutions begin to directly hedge risks on the platform, gradually accumulating trading volume and market depth. At this point, more hedging demand attracts speculators, tighter spreads attract more hedgers, and benchmark prices form a self-reinforcing positive feedback loop.

Currently, most institutions are still in the first stage, with some entering the second stage, but very few have truly reached the third stage. An important obstacle is that current prediction market trading requires full margin. For example, a $100 position requires a $100 margin. This is acceptable for individual investors, but for hedge funds or banks that rely on leverage and capital efficiency, this mechanism is too costly.

As Tarek said, "If you want to hedge $100, you have to put $100 at the clearinghouse. That's too expensive for institutions. Firms like Citadel or Millennium won't do that." Kalshi has currently 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 Will Happen Next?

Michael McDonough, head of market innovation at Bloomberg, summarized it most directly: "The hallmark of success is when these things become boring." He compared prediction markets to the options market of the 1970s, which was similarly filled with manipulation and regulatory uncertainties but ultimately evolved into an infrastructure that today hardly anyone thinks about.

AQR partner Toby Moskowitz stated that he "would 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 the question becomes this, it indicates that they have become indispensable."

In fact, although the current scale of prediction markets is still limited, the hedging market itself is a massive field.

In fact, the "normalization" of prediction markets is already happening.

In the political-themed roundtable discussion, former Congressman Mondaire Jones mentioned that senior figures from both parties—including President Trump, House Minority Leader Jeffries, and Senate Minority Leader Schumer—have begun to publicly reference Kalshi's odds data. Scott Tranter from DDHQ also confirmed that prediction market data has now become one of the standard inputs within party committees. Meanwhile, Vote Hub announced that it has directly integrated Kalshi data into its midterm election prediction models.

All of this did not exist two years ago. At that time, the most successful traders on Kalshi were still primarily "amateur players." Today, this label is no longer accurate.

In Kalshi's "The People Behind the Markets" roundtable, four traders shared their career paths—these paths sound no different from traditional professional traders: some spent 11 years studying the Billboard music charts, while others have been honing their skills in prediction markets since 2006, when it was still a "somewhat geeky hobby that hardly made any money." Notably, none of these four guests came from traditional finance; they came from music, politics, and poker. But they all agree that what this platform truly rewards is deep domain knowledge, not flashy resumes.

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-like products," its positioning has continuously changed. The clear signal conveyed by this conference is that prediction markets are evolving into an infrastructure—used for pricing uncertainty, serving a wide range of participants and diverse application scenarios, from retail traders to large institutions.