Steal from the rich, give to the poor
Trading data reveals a case-study in platform risk and the true distribution weight of Robinhood
On the night of February 12, on an exchange that is usually pretty quiet with sports, three NBA games suddenly lit up the tape. Dallas at the Lakers, Milwaukee at OKC, Portland at Utah. Between the triple-header, they generated over 13 million contracts traded. ForecastEx is a CFTC-regulated prediction market operated by Interactive Brokers. It is a real exchange with a real license, but it had never done any meaningful NBA volume until that night1.
I don’t think ForecastEx achieved some customer acquisition miracle overnight. It didn’t improve its product, launch a marketing campaign, or deepen its order book with more liquidity. What happened is simpler: Robinhood pointed its order flow firehose at a different exchange for a triple-header evening of NBA action.
At the moment, Robinhood is the dominant retail distributor of prediction market contracts. When a user opens the Robinhood app, taps on an NBA game, and places a bet, that trade gets routed to a CFTC-regulated exchange for execution. For most of Robinhood’s prediction markets history, that exchange has been Kalshi. But the user doesn’t know this, nor do they care. The interface is identical regardless of which exchange is on the backend: same app, same button, same odds. The exchange is invisible infrastructure.
The data speaks for itself
Each bar is one day of NBA game volume, stacked by exchange. Blue is Kalshi, red is ForecastEx. Every day is all blue except February 12, where 35% of the volume suddenly appears on ForecastEx2. Then it’s back to all Kalshi again, as if nothing happened.
That red slice on February 12 is three games: Dallas at the Lakers, Milwaukee at OKC, Portland at Utah. Combined, they generated 13.4 million contracts on ForecastEx. The Robinhood user experience was identical regardless of which exchange processed the trade: same app, same button, same odds. The user can’t tell the difference. There is no difference to tell.
This is why the 35% figure matters, because it is a relatively clean read of Robinhood’s market share of NBA moneyline volume across these two exchanges. ForecastEx has essentially no organic sports users, so it’s not unreasonable to assume that every contract on ForecastEx that night was a Robinhood order.
And because the Robinhood interface is identical either way, those users bet at the same rate they would have on Kalshi. It stands to reason that roughly a third of Kalshi’s NBA moneyline volume in Feburary came from Robinhood.
Robinhood controls where the volume goes, and it can flip that switch overnight.
Weather patterns
The NBA routing was brief and dramatic, which makes for a phenomenally clear and striking natural experiment to analyze. But, the rise of weather markets on ForecastEx tells a similar story at a different scale.
ForecastEx and Kalshi both offer daily high temperature contracts: binary strikes on whether a city’s high temperature will exceed a given threshold that day. The markets are the same product, with the same cities and the same dates. The only real difference is the exchange matching the trades.
Before November 18, 2025, ForecastEx had zero weather trading activity. Then volume appeared overnight, with no organic ramp, no gradual adoption curve. The step-function pattern is the same fingerprint as NBA. To measure the overlap, I matched ForecastEx and Kalshi markets on identical city-date pairs, excluding cities that only exist on one exchange. This produced 454 matched city-days.
The first five weeks are Kalshi alone, the baseline. Then ForecastEx appears and immediately captures 60% of the combined daily temperature markets volume. It peaked at 72% in late November and has generally held between 53% and 67% since.
The critical detail: Kalshi’s weather volume didn’t collapse when ForecastEx appeared. The blue bars stayed roughly stable. So, my interpretation is that ForecastEx volume arrived on top of Kalshi’s existing flow. It was most likely Robinhood turning on weather markets for the first time and sending its flow to ForecastEx from the start, and its users didn’t know the difference3.
That distinction matters. With the NBA in January, Robinhood briefly diverted existing Kalshi-bound volume. With weather, Robinhood appears to have added ForecastEx as a parallel destination while leaving Kalshi’s flow intact. Both cases demonstrate the same structural point: Robinhood decides where the volume goes. The exchange receives what Robinhood chooses to send.
Distribution amplifies product-market-fit
NBA and weather show Robinhood can direct flow. Parlays show it can scale demand that’s already growing.
Kalshi launched multi-variable-event contracts (aka ‘combos’ or ‘parlays’) in September 2025, timed to the NFL season opener. The product immediately found traction: weekly volume grew from essentially nothing in September to about 45 million contracts per week by early December. That growth was organic and direct to Kalshi’s platform. Kalshi built the product, filed the CFTC certification, and seeded initial liquidity. The market responded.
Then Robinhood plugged in.
On December 17, Robinhood announced it would surface preset parlays and player props in its app. Within weeks, weekly volume exploded, jumping from the 45-60 million range to nearly 100 million, and then to 300 million per week by late January. The shaded region on the right marks the post-Super Bowl period, when NFL parlays disappeared and NBA carried the product alone. Volume held around 260-290 million per week even without football.
Kalshi did the hard work of creating a new product category. Robinhood’s distribution took it to a completely different scale. Both contributions are real. The question is which one carries more structural leverage.4
It’s not just Kalshi
Kalshi has grown enormously over the past year, from roughly 7 million daily contracts in late 2024 to over 100 million by late 2025. That’s not all Robinhood. Kalshi has built real direct demand: new product categories, a growing base of native users, API traders, and institutional participation. A year ago, Robinhood was widely understood to be the majority of Kalshi’s volume [1]. Today, the NBA data suggests Robinhood is roughly 35% of moneyline volume. That de-risking is genuinely impressive execution.
But Kalshi is not the only exchange whose growth story is really a distribution story
Nadex, the CFTC-regulated exchange operating as Crypto.com Derivatives, tells a strikingly similar tale. Before Underdog integrated with Crypto.com in September 2025, Nadex was doing modest volume. After Underdog plugged in and started routing its users’ sports bets to the exchange, weekly volume exploded by an order of magnitude. Same pattern, different names. Underdog is to Nadex what Robinhood is to Kalshi: the distribution layer that turns a quiet exchange into a busy one.
And here’s the punchline: both distributors have now moved to own their exchanges outright. Robinhood acquired its own CFTC-regulated exchange, and Underdog did the same last week. Two companies, on parallel tracks, arriving at the same conclusion independently.
This is not a coincidence. It’s game theory. If you’re a distributor routing millions of trades to a third-party exchange, you’re splitting revenue on every contract for infrastructure your users can’t distinguish from a white-label API. You’re also handing a potential competitor the data, the volume, and the regulatory track record that makes their exchange valuable. The rational move, once you’re big enough, is to bring that infrastructure in-house. The exchange license becomes a cost center instead of a profit center for someone else.
The weather and NBA data show why this dynamic is so hard to defend against from the exchange side. Even at 35% of volume, Robinhood can add a parallel exchange for weather overnight and immediately send it the majority of new flow. It can route three NBA games to a different exchange on a Tuesday and those games generate the same volume they would have anywhere else. The users don’t notice. They don’t choose the exchange. They chose Robinhood, or Underdog.
I was wrong
(…Again.)
Last year, when rumors surfaced that Robinhood was considering acquiring its own CFTC-regulated exchange, I said publicly that it wouldn’t happen.
I was confidently wrong for two reasons.
First, I knew firsthand from my experience at Kalshi how insanely hard it is to stand up and operate a regulated derivatives exchange: the compliance infrastructure, the surveillance systems, the CFTC reporting, all of it. Robinhood was making enormous revenue from prediction markets while doing ~1% of the work. The exchange did the hard work, and Robinhood collected the distribution fee, and it was the most important partnership in fintech in years! Why mess with a good thing?
Second, I was pattern-matching from fifty years of derivatives market structure. Brokers don’t acquire exchanges. The whole point of the exchange, in the world I was coming from, was that it was the irreplaceable plumbing. The CME is a $90B company with net profit margins second only to Visa and Mastercard, and the reason why is liquidity depth as a moat.
An institutional trader moving a $50 million Brent crude position cares deeply about order book depth, slippage, counterparty concentration. That depth is extraordinarily hard to build and nearly impossible to replicate, especially in derivatives where contracts are not fungible across exchanges. In that world, the exchange earns its structural position. The broker is the commodity.
Prediction markets invert this. The average sports bet on Robinhood is someone tapping a button to put $10 on the Lakers. That user does not care about order book depth. Hell, they don’t even know what an order book is. When trade size is small and the user is unsophisticated, liquidity depth stops being a moat. Robinhood swapped the pipes out on a Tuesday night and the same volume came out the other end.
When trade size is small and the user is unsophisticated, liquidity depth stops being a moat.5
I was wrong because I was navigating off the old map. The structural leverage in prediction markets doesn’t sit where fifty years of derivatives history would tell you it sits. It sits with whoever owns the user in the end.
In fact, I’d already written an admittedly not-so-polite article about how FX was fumbling sports. It may have resonated…
And a tiny sliver of ForecastEx activity on Feb 5, which I can’t explain. It may have been an early test on Robinhood’s part.
It’s also possible that Robinhood is splitting the flow across the exchanges, but there’s no way for an outside analyst to know.
I think this example is debatable because Kalshi’s RFQ system and army of market makers is genuinely difficult to replicate here. There’s a serious technological moat there.
This, combined with the fact that it’s debatable how much liquidity even really matters for prediction markets, makes me wonder if we are game-theoretically headed to a world where every exchange tries to list every market in mimetic competition.
