Kalshi’s attempt to counter a Roosevelt Institute analysis of retail trading losses has triggered a wider public dispute, drawing criticism from gambling industry figures and prediction market observers. What began as a disagreement over research methods has changed into questions about how the company distinguishes professional trading firms from everyday customers when discussing activity on its platform.
The study takes ‘maker’ and ‘taker’ data from the Kalshi API and conflates these terms with ‘professional’ and ‘casual’ users. This is not accurate, and invalidates the claims of the study. – Kalshi
The Roosevelt Institute estimated that ordinary users of Kalshi and Polymarket together lost about $583.5 million through March 2025. Of that total, roughly $244.5 million was attributed to Kalshi. The report also concluded that about 86% of Kalshi traders lost money, while approximately 1% of users collected nearly 80% of overall profits.
@rooseveltinst prediction market study claims hundreds of millions of dollars of losses for “ordinary users.”
But they count institutional market makers as activity from “ordinary users.” Make it make sense.
A shoddy hit piece masquerading as research.https://t.co/0g1nZ1Wi93
— Elisabeth Diana (@ediyork) July 10, 2026
Kalshi rejected those conclusions and argued the underlying analysis incorrectly grouped institutional market makers with retail participants. The company said professional market makers and other liquidity providers generate about 97% of trading volume, leaving retail customers responsible for only around 3%. Because of that, Kalshi argued the report assigned institutional losses to ordinary users and produced what it described as an inaccurate estimate of retail losses.
Critics argue Kalshi applies inconsistent customer classifications amid Roosevelt Institute report retail loss debate
That explanation has not persuaded several prominent critics.
Author Steven Seril questioned Kalshi’s claim that institutional market makers should be viewed as peers of regular customers.
YOU claim that your market makers like SIG are “peers.” YOU claim they are included with ordinary users. Yet, they get benefits & fee discounts. They have algorithms linked directly to your platform which react to market situations at a rate of speed, accuracy, and efficiency…
— STEVEN SERIL (@StevenSeril) July 11, 2026
“YOU claim that your market makers like SIG are ‘peers.’ YOU claim they are included with ordinary users. Yet, they get benefits & fee discounts. They have algorithms linked directly to your platform which react to market situations at a rate of speed, accuracy, and efficiency,” Seril wrote on X.
Seril argued that firms receiving fee discounts and direct technological integration with the exchange operate under fundamentally different conditions than retail traders.
Former DraftKings executive Jon Aguiar also challenged Kalshi’s position, suggesting the company’s description of market makers shifts depending on the context.
So just to be clear. When a market maker lays $30,000 to win $5 all $30,005 is handle that shows @Kalshi is bigger and growing fast but when the market maker loses that parlay the loss is not actually a loss because the market maker is not a real customer?
— Jon Aguiar (@JonAguiar) July 11, 2026
“So just to be clear. When a market maker lays $30,000 to win $5 all $30,005 is handle that shows @Kalshi is bigger and growing fast but when the market maker loses that parlay the loss is not actually a loss because the market maker is not a real customer?” Aguiar wrote.
His remarks echoed a broader criticism that institutional trading is highlighted when discussing platform growth but treated differently when customer losses become the focus.
The discussion has expanded beyond the Roosevelt Institute’s methodology. Sportico assistant editor Dan Bernstein pointed to Kalshi’s expanding parlay product as another way to examine retail outcomes.
“Just parlays, just through April: $117 million,” Bernstein wrote.
He added that “Takers are an almost perfect proxy for retail users on parlays, considering you can’t be a maker in the app,” suggesting parlay bettors provide a clearer measure because app users cannot serve as liquidity providers there.
Other social media users dismissed Kalshi’s response outright.
“Rich coming from a ‘prediction market’ blaming methodology for retail losses,” one X user wrote while sharing Bernstein’s analysis.
Featured image: Kalshi via Canva
