Exclusive: Kalshi partners with ‘Blanket’ to help small businesses hedge against risk—with help from the guru of ‘hypergamblification’
Fortune · C · trust 55/100
An independent financial economist has partnered with Kalshi on a new AI tool on top of the prediction-market exchange that aims to help small businesses hedge against everything from bad weather to election shocks—without hiring a Wall Street bank.
The tool, called Blanket, quietly launched in stealth at tryblanket.app and is going live publicly this week, according to materials shared exclusively with Fortune and an interview with its co-creator, Lauris Zminsky , a London-based founder who calls himself a “forward deployed philosopher” on his X account, where he says he wants “markets for all priceable states of the world.”
Blanket is “powered by Kalshi,” meaning it routes users to Kalshi’s CFTC-regulated prediction markets, but it was built and owned by Zminsky, who does not work for the company.
Zminsky, a former consumer fintech founder and trained financial economist, said he began experimenting with Kalshi’s event contracts late last year as a way to prove that prediction markets could have “true, durable economic use” beyond pure speculation. His first project, built with a Kalshi employee, aspires to match S&P 500 companies’ risk factors with existing Kalshi markets. Blanket, he says, is the self-serve evolution of that idea for Main Street rather than the S&P. Zminsky told Fortune he has been working often with Kalshi because of overlapping social circles—”a bunch of my friends … actually work at Kalshi and do a lot of great stuff with Kalshi”—and was coy about whether he may go in-house some day.
After Kalshi launched a promotion with sports bars in May, Zminsky told Fortune , he had an idea: “What if you actually can take this up a notch and build a tool that is not only probably useful for the Kalshi team and their endeavors—to scale their S&P insurance hedging initiative—but a self-service tool where anybody can come in if they own a small business?” Off a prompt, he added, a reasoning engine could be designed to help these small businesses figure out what kind of Kalshi markets could help them run a promotion or hedge against a core business risk.
This may not be transformative for how small businesses run, Zminsky cautioned, “but you can definitely de-risk a lot of your balance sheet exposures” by finding the right markets and sizing the bets correctly. Blanket’s pitch is simple: A bar owner, laundromat operator, or regional restaurant plugs in a concrete worry—hurricane season in Florida, a spike in fuel prices, an unusually warm winter—and the AI “reasoning engine” will suggest specific yes-or-no markets on Kalshi that could offset that risk if the bad outcome hits.
Crucially, Blanket is not a trading app.
“You can’t execute a trade. There’s no money moving through that application or system at all,” Zminsky said. “It’s a reasoning tool. It’s a discovery tool that funnels you towards Kalshi.” Once a user clicks through, execution, compliance, and customer vetting all happen on Kalshi’s side, which operates as a federally regulated event-contract exchange. A Kalshi spokesperson told Fortune that Blanket is a fully external project that simply references public Kalshi contracts and Kalshi’s compliance team was not involved in its creation.
Then Zminsky and I talked about his X essay from 2025 on his theory of “ hypergamblification .”
Kalshi, founded in 2018, has leaned heavily into hedging as its answer to critics who say prediction markets are just legalized gambling. On its own marketing site , the company showcases bars, restaurants and consumer brands that hedge, say weather for ice-cream shops and refunds tied to Knicks games —Utopia Bagels was surely off the hook for free bagels after the Trump-attended loss in game 3 of the NBA Finals.
Nicolas Hull , who runs point on small-business hedging at Kalshi, has already been quietly working with firms using those kinds of strategies; Blanket is designed to give them, and thousands of similar businesses, an on-ramp without a sales call. “SMBs are turning to Kalshi to hedge against the real world,” Hull told Fortune . “Whether it’s the financial fallout from weather anomalies, major sports tournaments, or freight and tariff volatility, business owners are using our platform to protect their bottom lines.” He called it a “massive growth segment” for Kalshi, saying he only expected it to expand as more companies discover this “innovative way to offset costs and manage uncertainty.”
Zminsky argued the technology is less exotic than it sounds—similar in spirit to the opaque derivatives that helped blow up Wall Street in 2008 but fundamentally safer. Calling into London from its leafy Kensington neighborhood, he agreed Kalshi resembles other innovations throughout history, like the nearby Lloyds of London, founded in the late 1600s as a hedging operation of sorts out of a coffee shop , before turning into an insurance giant.
What’s new is casting those protections as event contracts anyone can see and price. “What prediction markets unlock is having the ability to take credit-derivative-like exposures and make the reference asset be an event,” Zminsky said.
In fact, a Kalshi spokesperson likened the way Blanket works to the insurance sector, explaining that Blanket recommendations are tailored to insurance use cases. Large “losses” in such a scenario mean the event you did not want to happen did not happen, while large “wins” mean that it did. “It’s about making sure people lower their overall risk, not about promoting big wins or big losses.”
To Zminsky, it’s another way that the tools that only used to be accessible to Wall Street’s Masters of the Universe are available for all of us now: “You take instruments that were previously only bilaterally traded by extremely large financial institutions and you make them accessible for a broader [audience].”
What is to prevent a 2008-style blowup with all this democratized finance? “Regulation,” Zminsky said proudly. Kalshi’s contracts are standardized, electronically…
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