A16z New Article: Prediction Markets, Entering the Fast-Forward Stage

Title: Prediction Markets: They Grow Up So Fast
Author: Alex Immerman, a16z
Translation: Peggy, BlockBeats

Author: Rhythm BlockBeats

Source:

Reprint: Mars Finance

Editor’s note: For a long time, prediction markets have been regarded as a “marginal product”: first as academic experiments, then as a public opinion tool during election seasons, and later as an extension of sports betting. They seem to always depend on a high-profile scenario but are rarely understood as a core financial infrastructure.

However, in the author’s view, prediction markets are evolving from a marginal, election- and sports-focused “event trading tool” into a financial infrastructure capable of pricing uncertainty.

The author points out that the industry’s key changes are reflected in three levels: First, application scenarios are expanding—although sports remains the main 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 event itself,” enabling institutions to hedge political or macro risks directly, rather than through related assets; Third, the path to institutional adoption is advancing—from data reference (watching 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 stages of options markets—“professionalization—institutionalization—infrastructure building.” Once liquidity, leverage, and regulation are gradually improved, they could 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 almost every niche having its own recognized “annual holy place.” Leaders in 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 government officials go to the Swiss Alps for the World Economic Forum Annual Meeting (Davos). TMT, real estate, industry, financial services, and nearly every industry you can think of also have their flagship summits.

At the end of March this year, Kalshi’s academic and institutional research division, Kalshi Research, held its first research conference in New York, bringing together academics, Wall Street executives, former politicians, and traders who truly drive market activity. The composition of attendees clearly shows a trend: the industry is “maturing.”

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

Markets and life are more than just elections and sports.

During major news cycles, a fixed pattern often emerges: a large event (such as the 2024 election, Super Bowl, or the recent “March Madness” college basketball tournament) dominates media headlines and also leads prediction market trading volume. This easily creates the impression—almost as if “the value of prediction markets is only in these events.”

However, despite early narratives often viewing prediction markets as tools only meaningful during election cycles, Kalshi’s growth in other areas is equally significant.

At the time of the research conference, weekly trading volume in sports-related markets was approaching $3 billion, accounting for about 80% of Kalshi’s total trading volume, mainly driven by “March Madness.” Tarek and Luana see this high concentration as a temporary phase.

A more explanatory data point is that, although the absolute scale of sports trading hit a record high, its share of total trading volume was at a historic low. This indicates that growth in all other categories is faster.

The two founders pointed out that categories like entertainment, crypto, politics, and culture are showing stronger user growth and better trading retention structures than sports. Sports is more like a “populist trigger”—it features high familiarity, clear timing, and strong emotional participation, making it a typical entry product.

Meanwhile, the company has also observed significant growth in longer-tail markets. These currently constitute over 20% of Kalshi’s trading volume and will play a more critical 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, said that prediction related to macro events and CPI data is currently the most关注 category on Wall Street. CNBC Growth Business Executive Vice President Sally Shin mentioned she has been using prediction markets like “FOMC Chair decisions” and “non-farm payroll data” as content narrative tools. Troy Dixon, Co-Head of Global Markets at Tradeweb, further painted a future picture: large investment banks will establish dedicated prediction market trading departments, focusing on financial contracts as core products.

Why Kalshi attracts Wall Street’s attention

A key reason traditional financial markets can operate is that each core asset class has an accepted benchmark: the S&P 500 index represents the overall performance of 500 stocks, and crude oil has benchmark prices like ICE.

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

Once a credible price exists for an event (e.g., “30% tariff passes”), institutions can trade directly around that price. This can be used for trading the event itself or hedging risks in other assets within a portfolio. As Troy Dixon from Tradeweb said: “Back when Trump was first elected, there was a lot of hedging activity in the stock market. The logic was to short the S&P because if Trump won, the market would definitely fall. But that trade failed. The question is: how do you price these events? What’s the benchmark?”

Tarek also mentioned that this was one of his motivations for founding 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 are essentially betting on two things: whether the event will happen, and the correlation between that event and the traded asset. The second judgment can be completely wrong.

When an event 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’s still early to say that Wall Street’s large institutions are trading extensively on Kalshi. Currently, most institutions use it as a “data source,” not as a “trading platform.”

However, Luana pointed out that the path for institutional adoption is clear and can be divided into three stages:

The first stage is data integration: bringing prediction prices into the institution’s daily workflow. For example, Goldman Sachs portfolio managers might habitually check Kalshi’s odds data just like they check the VIX index. This stage has already begun to some extent. Jonathan Wright, a Johns Hopkins professor and former Federal Reserve official, said: “In areas like Fed decisions, unemployment, GDP, Kalshi is almost the only reference.”

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

The third stage is actual trading: institutions start hedging risks directly on the platform, with trading volume and market depth gradually building up. More hedging demand attracts speculators, tighter spreads attract more hedgers, and the benchmark price reinforces itself in a positive feedback loop.

Currently, most institutions are still in the first stage; some are in the second, and very few have truly entered the third. A major obstacle is that prediction market trading currently requires full margin. For example, a $100 position requires posting $100 in margin. While acceptable for individual investors, this mechanism is too costly for leveraged hedge funds or banks relying on capital efficiency.

As Tarek said: “If you want to hedge $100, you have to put $100 in the clearinghouse. That’s too expensive for institutions. Firms like Citadel or Millennium wouldn’t do that.” Kalshi has obtained a license from the National Futures Association (NFA) and is working with the Commodity Futures Trading Commission (CFTC) to introduce margin trading mechanisms.

What’s next?

Michael McDonough, head of market innovation at Bloomberg, summarized it most directly: “The sign of success is when these things become boring.” He likened prediction markets to the options markets of the 1970s, which were also full of manipulation and regulatory uncertainty but eventually evolved into a foundational infrastructure that today almost no one thinks about anymore.

AQR partner Toby Moskowitz said he “would be 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 ultimate state: “The question is no longer whether to use prediction markets, but how to use them. Once it becomes that way, it means they are indispensable.”

In fact, although the current scale of prediction markets remains limited, the market for hedging itself is enormous.

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

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

All this did not exist two years ago. Back then, the most successful traders on Kalshi were still mostly “amateurs.” Today, that 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: one spent 11 years studying Billboard music charts, another has been refining prediction market strategies since 2006 when it was still a “geeky hobby that hardly paid.” Notably, none of these four guests come from traditional finance; they are from music, politics, and poker. But they all agree that what the platform truly rewards is deep domain knowledge, not a glamorous resume.

Prediction markets have come a long way. From being seen as academic experiments, to “novel tools” during elections, to being categorized as “sports-like betting products,” their positioning has constantly evolved. The clear signal from this conference is that prediction markets are transforming into a form of infrastructure—used for pricing uncertainty and serving a broad range of participants and applications, from retail traders to large institutions.