Conversation Kalshi Co-founder: The maturity of prediction markets is exceeding expectations

Writing by: Alex Immerman and Santiago Rod

Translated by: Plain Blockchain

The financial world consists of many vertical sectors, each with its own recognized “true summit.” Healthcare providers, payers, and biotech leaders gather annually in San Francisco for the JPMorgan Healthcare Conference. Global macro giants and politicians head to the Swiss Alps each year for the Davos Forum. Every other industry under the sun—technology, media, telecom, real estate, industrials, financial services—has its own top-tier summit.

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 energize the market. The composition of attendees reflects that “the industry is maturing.”

The conference opened with a dialogue between Kalshi co-founders Tarek Mansour and Luana Lopes Lara and Katherine Doherty of Bloomberg. Here are some key points from the industry discussion.

1. There’s more in life (and markets) than elections and sports.

During major news cycles, a pattern often emerges: a big event (such as the 2024 election, the Super Bowl, or the recent “March Madness” college basketball tournament) dominates headlines, driving prediction market trading volume and creating the impression—“Prediction markets only excel at this.”

However, despite early narratives suggesting prediction markets are only lively during election cycles, Kalshi has seen significant growth in other areas as well.

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

More revealing data shows that the share of sports trading volume is actually at its lowest point historically, even as absolute trading volume hits new highs. Growth in all other categories is faster.

Tarek and Luana point out that categories like entertainment, crypto, politics, and culture show stronger user growth, with better retention of trading volume than sports. Sports acts as a catalyst for mass-market adoption—it’s familiar, has a fixed schedule, and can evoke emotional engagement as an entry point product.

But the company also sees significant growth in its long-tail markets (which account for over 20% of Kalshi’s remaining trading volume), markets that will become very important for institutional hedging and information markets.

This observation was later confirmed by a demand-side panel discussion.

Goldman Sachs global equities co-head Cyril Goddeeris said that prediction related to macro events and CPI data is among Wall Street’s most watched categories.

CNBC growth executive vice president Sally Shin said she is already using Federal Reserve chair markets and non-farm payroll data predictions as narrative tools.

Meanwhile, Troy Dixon, co-head of global markets at Tradeweb, described a future where large investment banks will establish dedicated prediction market trading departments, with financial contracts as core products.

2. Why is Kalshi gaining attention on Wall Street?

There are many reasons why traditional financial markets operate well, but a key one is: each major asset has an accepted benchmark—like the S&P 500 index, which averages 500 stocks; or crude oil, which has an intercontinental trading platform (ICE) benchmark.

But for political and economic events (such as who will win an election, whether tariffs will pass, or the Supreme Court’s rulings), there was previously no widely accepted (and dynamically changing) benchmark. Prediction markets have changed that. Now, we can almost provide a lively, liquid benchmark for the future of any event.

Once you have a credible price—say, a 30% chance of tariffs passing—institutions can trade at that price. This creates a mechanism for direct trading on the event itself or hedging downside risk in a portfolio. As Troy Dixon from Tradeweb said:

“Think back to Trump’s first election—there were a lot of hedges in the market. The trade was to short the S&P 500 because it was obvious that if Trump won, the market would fall. That was a bad trade. The hard part was: how do you price these things? What’s the benchmark?”

Tarek described the motivation behind founding Kalshi: he previously worked at a Goldman Sachs trading desk that recommended trades linked to the 2024 election and Brexit. Without prediction markets, institutions trying to hedge political or macro events with related assets were essentially making two bets: one on the event itself, and another on its correlation with traded assets. The second bet was prone to error.

With a direct benchmark for the event itself, these two bets are combined. As Tarek said: “This community is now pricing things.”

3. The three stages of achieving true institutional adoption.

If Wall Street giants are trading large volumes on Kalshi, that’s premature: most institutions currently use Kalshi as a data source, not a trading platform.

However, Luana sees a clear path for broader Wall Street adoption, summarized as follows:

The first stage is data: integrating Kalshi’s prices into institutional workflows, so that one day, a Goldman Sachs portfolio manager might habitually glance at Kalshi’s odds, just like they check the VIX (volatility index). This has already begun to happen to some extent. As Johns Hopkins professor and former Fed official Jonathan Wright observed: “For certain things, like Fed decisions, unemployment, and GDP, Kalshi is really the only player.”

The second stage is integration: through compliance and legal approvals, technical integration, and internal education—introducing the new tools.

The third stage is harvesting: actually transferring risk on the trading platform, where trading volume and market depth start to create a positive feedback loop. At this stage, more hedgers attract more speculators, narrower spreads attract more hedgers, and benchmarks become self-reinforcing.

Today, most institutions are still in the first stage, a significant portion are in the second, and only a few are in the third.

One major reason why more institutions haven’t reached the third stage is that currently, trading prediction market contracts requires posting the full notional value as collateral: a $100 position requires depositing $100 at the clearinghouse. This is feasible for retail traders but a major limitation for hedge funds or banks operating with leverage and capital efficiency.

As Tarek said: “If you want a $100 hedge, you have to put $100 in the clearinghouse. That’s too expensive for institutions. Citadel or Millennium wouldn’t do that.” Kalshi has just received approval from the National Futures Association (NFA) and is working with the CFTC to bring margin trading to market.

4. What’s next?

Bloomberg’s market innovation chief Michael McDonough said plainly: “Success means these things become boring.”

He compares it to the options markets of the 1970s: back then, there were similar concerns about market manipulation and regulation, but those issues were eventually resolved, and options became a foundational infrastructure that people no longer think twice about.

Toby Moskowitz of AQR said he “walks the talk” in believing prediction markets will become a viable institutional tool within five years, possibly sooner.

Garrett Herren of Vote Hub described the ultimate state: “The question will no longer be ‘Should we use prediction markets?’ but ‘How should we use them?’ Once you start asking that, you know it’s become indispensable.” In fact, while prediction markets are still relatively small, the size of the hedging market is enormous:

The fact is, normalization of prediction markets is underway.

In a political panel, former Congressman Mondaire Jones pointed out that leaders from both parties—President Trump, House Minority Leader Jeffries, Senate Minority Leader Schumer—have all publicly cited Kalshi odds. Scott Tranter of DDHQ confirmed that prediction market data is now standard input for party committee decisions. Vote Hub announced it will directly incorporate Kalshi data into its midterm election forecast models.

None of this existed two years ago. Two years ago, the most successful traders on Kalshi were hobbyists. Now, that’s different. It’s even unfair to call them “hobbyists” anymore.

In the “People Behind the Markets” panel at Kalshi, four traders described careers built on habits familiar to professionals—such as obsessing over Billboard chart rankings for 11 years or having been active in prediction markets since 2006, when it was just a “geek hobby” with no real money involved. None of these panelists had a finance background; they came from music, politics, and poker. But all agreed that the platform rewards deep domain knowledge, not diplomas.

Prediction markets have come a long way. They were once seen as academic curiosities, then election novelties, then products adjacent to sports betting. This conference clearly shows: prediction markets are maturing, becoming infrastructure for pricing uncertainty for a wide range of participants—from retail traders to the largest institutions.