Kalshi Early Employee: Whoever Controls Traffic Controls the Market

Author: Adhi Rajaprabhakaran

Translation: Jiahui, ChainCatcher

On the evening of February 12, in a trading platform usually unresponsive to sports events, three NBA games suddenly ignited the trading frenzy: Dallas Mavericks vs. Los Angeles Lakers, Milwaukee Bucks vs. Oklahoma City Thunder, Portland Trail Blazers vs. Utah Jazz. During these games, over 13 million contracts were traded. ForecastEx is a prediction market operated by Interactive Brokers and regulated by the U.S. Commodity Futures Trading Commission (CFTC). It is a licensed real exchange, but before that night, it had never seen any substantial NBA trading volume.

I don’t think ForecastEx suddenly created a customer acquisition miracle overnight. It didn’t improve its product, launch marketing campaigns, or deepen its order book with more liquidity. What actually happened was simple: Robinhood directed its massive order flow to another exchange, specifically for that night’s three NBA games.

Currently, Robinhood is the retail distributor dominating the prediction market contracts. When users open the Robinhood app, click on an NBA game, and place a bet, that trade is ** routed to** an exchange regulated by the CFTC for execution. For most of Robinhood’s history in prediction markets, this exchange has been Kalshi. But users don’t know this, and they don’t care. Regardless of which exchange handles the backend, the interface is identical: the same app, the same buttons, the same odds. The exchange becomes invisible infrastructure.

A 35% Instant Shift in Trading Volume

Each bar in the chart represents a day’s NBA betting volume, stacked by exchange. Blue indicates Kalshi, red indicates ForecastEx. Except for February 12, every day is entirely blue; on that day, 35% of the volume suddenly appeared on ForecastEx. Then everything reverted back to full blue, as if nothing had happened.

The red segment on February 12 corresponds to those three games: Mavericks vs. Lakers, Bucks vs. Thunder, Trail Blazers vs. Jazz. Together, they generated 13.4 million contracts on ForecastEx. No matter which exchange processed the trades, the user experience for Robinhood users remained the same: the same app, the same buttons, the same odds. Users couldn’t tell the difference. Because, to them, it truly made no difference.

That’s why the 35% figure is so significant—it’s a relatively pure indicator of Robinhood’s market share in NBA win/loss betting volume across these two exchanges. ForecastEx essentially has no ** organically accumulated** sports users, so it’s reasonable to assume that every contract on ForecastEx that night came from Robinhood’s orders.

And since Robinhood’s interface is the same in all cases, these users bet at the same frequency as they would on Kalshi. It’s plausible that about one-third of Kalshi’s NBA win/loss betting volume in February came from Robinhood.

Robinhood controls where the volume flows, and it can flip this switch overnight.

A Similar Story in Weather Markets

The brief and dramatic ** order routing** in NBA markets provides a clear and compelling natural experiment for analysis. But the rise of weather markets on ForecastEx tells a similar story on a different scale.

Both ForecastEx and Kalshi offer daily maximum temperature contracts: binary options on whether the temperature in a city will exceed a certain threshold on that day. These markets are the same product, covering the same cities and dates. The only real difference is the exchange they’re matched on.

Before November 18, 2025, ForecastEx’s weather trading activity was zero. Then, overnight, trading volume exploded—without any ** organic growth** transition or gradual adoption curve. This step function pattern is identical to the NBA case. To measure overlap, I matched markets with the same “city-date” pairs on ForecastEx and Kalshi, excluding cities that only existed on one platform. This yielded 454 matched “city-date” data points.

By the way, this chart offers an interesting case: it shows that platform competition overall benefits the industry’s trading volume. When Robinhood opened the weather markets, it generally increased activity on both exchanges, likely due to cross-exchange arbitrage. Market makers participating in such activities effectively distributed liquidity across the entire ecosystem.

For the first five weeks, only Kalshi was active—serving as the baseline. Then ForecastEx appeared and immediately captured 60% of the daily temperature market volume. It peaked at 72% in late November, then generally hovered between 53% and 67%.

The key detail: when ForecastEx emerged, Kalshi’s weather trading volume did not collapse. The blue bars remained roughly stable. My interpretation is that ForecastEx’s volume was overlaid on Kalshi’s existing flow. It’s very likely that Robinhood’s initial launch of weather markets directed its flow to ForecastEx from the start, without users realizing.

This distinction is important. In the NBA case, Robinhood temporarily diverted volume from Kalshi. But in the weather markets, Robinhood seems to have added ForecastEx as a parallel destination while keeping Kalshi’s original flow intact. Both scenarios demonstrate the same structural point: Robinhood determines where the volume goes. The exchanges can only passively receive the orders Robinhood chooses to send.

The Absolute Amplification of Product Innovation via Distribution Channels

The NBA and weather data show Robinhood can steer flow. ** Parlay betting** (linking multiple independent bets into a single wager, where all outcomes must be correct to win, and a single mistake results in loss—raising odds and potential returns) indicates it can also scale the demand that’s already growing.

In September 2025, Kalshi launched multi-variable event contracts (i.e., “parlays” or “combinations”) coinciding with the NFL season. The product immediately gained attention: weekly trading volume jumped from nearly zero in September to about 45 million contracts by early December. This growth was ** self-driven** and directly on Kalshi’s platform. Kalshi built the product, obtained CFTC approval, and injected initial liquidity. The market responded positively.

Then Robinhood intervened.

On December 17, Robinhood announced it would introduce pre-set parlays and player props in its app. Within weeks, weekly volume exploded—from 45 million to nearly 60 million, then approaching 100 million, reaching 300 million per week by late January. The shaded area on the right marks the period after the Super Bowl, when NFL parlays disappeared, leaving NBA alone to support the product. Even without football, volume remained around 260 to 290 million weekly.

Kalshi did the hard work of creating the new product category. Robinhood’s distribution channel elevated it to a completely different scale. Both contributions are real. The question is: which has greater structural leverage?

Not Just Kalshi

Kalshi has seen tremendous growth over the past year, from about 7 million contracts daily at the end of 2024 to over 100 million by the end of 2025. Not all of this is due to Robinhood. Kalshi has established genuine direct demand: new product categories, a growing native user base, API traders, and institutional participation. A year ago, many believed Robinhood accounted for the vast majority of Kalshi’s volume. Now, the NBA data shows Robinhood accounts for about 35% of win/loss betting volume. This risk-decentralized execution is indeed impressive.

But Kalshi isn’t the only exchange building its growth story on distribution channels.

Nadex, a CFTC-regulated exchange operated by Crypto.com Derivatives, tells a remarkably similar story. Before Underdog integrated with Crypto.com in September 2025, Nadex’s volume was modest. After Underdog’s involvement and its redirecting of user bets to the exchange, weekly volume exploded by orders of magnitude. Same pattern, different names. Underdog for Nadex, just as Robinhood for Kalshi: the distribution layer transforming a quiet exchange into a busy hub.

Most astonishingly: both of these distribution giants have now taken action and fully acquired their own exchanges. Robinhood bought its own CFTC-regulated exchange, and Underdog did the same last week. Both companies, on parallel tracks, have reached the same conclusion.

This is no coincidence. It’s game theory. If you are a distributor channel directing millions of trades to a third-party exchange, and that infrastructure is indistinguishable from a white-label API to your users, then sharing revenue per contract with that exchange means handing over data, volume, and regulatory records—elements that make the exchange valuable to competitors. When you scale enough, the rational move is to internalize these infrastructures. The exchange shifts from being a profit center for others to a cost center for you.

Weather and NBA data explain why it’s so difficult for exchanges to defend against this dynamic. Even at just 35% of volume, Robinhood can flip a switch overnight, creating a parallel exchange for weather markets and immediately channeling most of the new flow there. It can redirect NBA games on a Tuesday to another exchange, generating the same volume as elsewhere. Users are unaware. They don’t choose the exchange. They choose Robinhood or Underdog.

I Was Wrong

Last year, when rumors circulated that Robinhood was considering acquiring its own CFTC-regulated exchange, I publicly said it was impossible.

I was so confident I was wrong, for two reasons.

First, based on my experience at Kalshi, I understand how extremely difficult it is to build and operate a regulated derivatives exchange: compliance infrastructure, monitoring systems, CFTC reporting, and more. Robinhood makes huge revenues from prediction markets but has only done about 1% of the work. The exchange does the hard, tedious work, while Robinhood earns distribution fees—a perfect partnership in fintech for years! Why break this good thing?

Second, I applied the traditional thinking from the past fifty years of derivatives market structure. Brokers don’t acquire exchanges. In that world, the entire purpose of an exchange is that it’s an irreplaceable trading pipeline. The Chicago Mercantile Exchange (CME), valued at $90 billion with profit margins second only to Visa and Mastercard, owes its moat to “liquidity depth.”

An institutional trader needing to move $50 million in Brent crude oil positions cares deeply about order book depth, slippage, and counterparty concentration. Such depth is hard to build and nearly impossible to replicate, especially in derivatives where contracts can’t be cross-listed across exchanges. In that world, exchanges earn their structural position through their own strength. Brokers are commodities that can be replaced at any time.

Prediction markets overturn this. On Robinhood, an average sports bet is just a user clicking a button, betting $10 on the Lakers. That user doesn’t care about order book depth. Heck, they don’t even know what an order book is. When trading size is tiny and users are not professional, liquidity depth is no longer a moat. Robinhood can switch the underlying pipeline on a Tuesday night, and the other end still produces the same volume.

When trading size is tiny and users are not professional, liquidity depth is no longer a moat.

I was wrong because I was still navigating with an old map. The structural leverage of prediction markets isn’t in the fifty-year derivatives history I know. It’s truly in the hands of those who ultimately own the users.

In fact, I’ve written a frankly unflattering article about how ForecastEx botched sports betting. Maybe that resonated… There’s also the tiny activity on ForecastEx on February 5, which I can’t explain. It might be Robinhood’s early testing. Or perhaps Robinhood is distributing flow across multiple exchanges, but external analysts can’t see it. I think this example is still open to debate because Kalshi’s RFQ system and its large market maker team are very hard to replicate. They have a deep technological moat. Also, the question of “how important is liquidity in prediction markets” remains unresolved. It makes me wonder: under game theory, are we heading toward a homogenized competition where all exchanges imitate each other, rushing to list every market available?