Kalshi’s Defense of Price-Insensitive Users Sounds a Lot Like a Defense of Casinos
The Roosevelt Institute published a report last week claiming that ordinary users have lost $583 million on Kalshi since the platform’s launch.
Kalshi responded Thursday with a detailed rebuttal, calling the report a shoddy hit piece built on a fundamental methodological error, specifically the conflation of maker and taker classifications in Kalshi’s API with casual and sophisticated user demographics. That critique certainly has real merit and makes a significant point. The maker-taker distinction in order book data is not a reliable proxy for user sophistication, and if Roosevelt built its headline figure on that conflation, the methodology is genuinely compromised.
The legitimate critique, however, shares a document with two arguments that undermine Kalshi’s own broader position in ways the company does not appear to have noticed.
Kalshi Misses the Point on Recreational Bettors, in a Major Way
Kalshi’s fifth rebuttal point defends non-price-sensitive users, those who participate in prediction markets without optimizing for expected value, by arguing that demand for event exposure exists regardless of profitability, and that prediction markets serve that demand better than casinos because competition drives prices lower than a monopolistic house would.
The argument is coherent and makes sense, but the problem is that it is also a complete defense of recreational gambling.
Kalshi is describing a user who wants exposure to an event outcome, does not care much about the precise price of that exposure, and finds value in the participation itself. That is not a prediction market user type. That is actually a recreational bettor. The same person who buys a $20 sports bet on their team because they want skin in the game, not because they have calculated the implied probability against the true odds, is exercising exactly the same demand for event exposure that Kalshi is defending here. The casino is not predatory in serving that person. It is filling a demand that Kalshi acknowledges is legitimate and does not require profitable outcomes to justify.
Kalshi cannot simultaneously argue that price-insensitive users on prediction markets are rational actors making a reasonable choice and that price-insensitive users at casinos are victims of a predatory monopolistic system. The users are structurally identical, and the demand is one and the same. The only difference is the venue and the pricing mechanism, which is a legitimate competitive argument but certainly not a moral one.
Kalshi Misses the Mark on the Profitabilty of Casino Players
Kalshi’s seventh point states that approximately 0% of casino users are profitable, because anyone who proves they can win is banned from the platform.
This is simply not accurate; it’s probably something they shouldn’t have even touched on, and it is worth being specific about why.
Advantage players exist at scale in the casino industry. Skilled video poker players achieve positive expected value against certain machines when using an optimal strategy and favorable paytables. Professional poker players beat the rake over time. Sports bettors who line shop and identify soft lines generate long-term profits. Blackjack card counters, before they are detected and banned, operate with a genuine mathematical edge. The claim that 0% of casino users are profitable confuses the casino’s enforcement practices with the underlying mathematics. Casinos ban winning table-game players, which is a real and legitimate grievance about the industry, but the existence of that enforcement practice proves that winning is possible, not that it is impossible.
Kalshi’s own meritocracy argument depends on the premise that skilled prediction market traders deserve to profit from their knowledge and effort. That argument applies with equal force to skilled casino gamblers. A card counter who has invested time in mastering a legitimate strategy deserves to profit from that skill on precisely the same grounds Kalshi uses to defend its top-earning traders. The line shopping sharp deserves to win. Kalshi, describing a Pennsylvania schoolteacher and a Kansas retiree as proof that its platform rewards ordinary, skilled people, while simultaneously claiming casinos produce zero profitable users, is a complete contradiction that it has not reconciled.
The Rebuttal Lands Some Serious Punches, but Needed to be More Precise
It is worth separating these two points from the rest of the document, because Kalshi’s core methodological critique of the Roosevelt report is substantive. They make some really great points and back them up with facts and logic. If API maker-taker classifications do not reliably track user sophistication, and the evidence Kalshi provides suggests they do not, then Roosevelt’s headline claim about ordinary user losses does not follow from the data. A casual trader who places a resting limit order is classified as a maker. A high-frequency institutional trader can appear as a taker. The demographic inferences the study draws from that taxonomy may simply be wrong.
That matters because it is the kind of error that does not get corrected in follow-up reports if it is embedded in the methodology. If Roosevelt’s next three installments rest on the same maker-taker proxy, they will carry the same flaw regardless of how their conclusions differ from the first piece.
Kalshi is also right that the Roosevelt framing of a skill gap as equivalent to a structural market defect is misleading. Every market has participants with varying levels of skill and information; that’s just the reality of market size. The existence of sophisticated traders does not automatically mean the system is rigged against casual ones.
But a rebuttal that lands clean punches on the methodology and then overstates its case on casino comparisons ends up muddying its own best arguments. The 0% profitable casino user claim is the kind of thing that gets clipped and circulated by the people Kalshi is trying to rebut. One would think they would understand how delicate a self-published rebuttal needs to be. The price-insensitive user defense is the kind of thing that ends up in a state legislator’s brief on why prediction markets are gambling. Kalshi would have been better served by stopping after the methodological critique.
The Roosevelt report may well be flawed in the ways Kalshi describes. Whether it is also a product of casino industry influence, as Kalshi asserts in its eighth point, is a separate claim that requires more evidence than pattern-matching on talking points. Both things can have methodological problems simultaneously. That is actually the more interesting analytical problem, and Kalshi’s rebuttal would have been stronger if it had engaged with it rather than resorting to the same kind of motivated-reasoning accusation it spends seven points criticizing.
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