Kalshi Launches Blanket: The AI That Turns Prediction Markets Into a Small-Business Hedge Fund

The code doesn’t lie, but the narrative does. Kalshi, the CFTC-regulated prediction market, just launched Blanket — an AI tool that promises to help small businesses hedge real-world risks by converting plain English exposure into event contract positions. A pizzeria in Chicago can now protect itself against a minimum wage hike. A trucking firm in Texas can short the price of diesel without ever touching a futures account. That is the demo. The reality is messier. Blanket is not a hedge fund manager. It is a language model with a risk engine bolted on. And the deeper question is whether prediction markets can ever carry the structural weight of actual hedging, or whether this is just another layer of abstraction on a market designed for speculation. Let me be precise. Kalshi has been live since 2020, offering event contracts on everything from Federal Reserve decisions to the outcome of the World Cup. It is not a blockchain protocol. It does not use smart contracts in the decentralized sense. It is a centralized exchange, cleared and held to the standards of the Commodity Futures Trading Commission. That gives it institutional credibility but also imposes constraints. Every contract is a binary or multi-outcome instrument, cash-settled, with a defined expiry. The engineering that goes into their matching engine and risk systems is real. But until now, the user base has been traders — people who think in terms of probabilities, duration, and expected value. Blanket changes the front end. You tell it what your business is exposed to. It quantifies the risk, selects a basket of Kalshi contracts, and tells you how much to buy or sell to offset a worst-case scenario. It sounds like magic. It is not. The underlying math is a simplified version of what an options desk does every day: estimate delta, compute correlations, hedge the tail. The novelty is the interface. Instead of navigating decimals and settlement rules, a restaurant owner types: "I need to hedge against a $2 price increase in chicken wings." Blanket spits out a contract, a quantity, and a cost. That is genuinely useful. It is also where the forensic skeptics should start paying attention. Let me step back. I have been in this industry since 2017. I audited ERC-20 token contracts during the ICO mania. I built a DeFi yield script in 2020 that rebalanced Uniswap V2 positions daily until I realized the gas costs were eating the alpha. In 2021, I debugged my own Python NFT sniping bot, chasing race conditions that cost me a peak mint. In 2022, I downloaded the Terra Core repository and traced the UST depeg through the oracle race condition that turned an algorithmic stablecoin into dust. I have seen what happens when the mechanism looks simple but the failure mode is hidden. Blanket is a mechanism. The question is what breaks. Here is what I found when I dug into the architecture. Kalshi’s event contracts are not like futures. They are discrete, all-or-nothing payouts. That has profound implications for hedging. A futures contract tracks a price continuously. An event contract is a step function. If you want to hedge a continuous exposure like "my heating costs will rise because of a cold winter," you need a series of contracts that expire at different thresholds. Blanket does this by building a ladder of positions across multiple expiries and strike prices. That is not new. Options traders have called it a "strip" for decades. What Blanket adds is the NLP layer — parsing unstructured business descriptions like "I need to protect my delivery fleet from fuel spikes" and mapping that onto a portfolio of CFTC-regulated instruments. The code doesn’t lie. The risk engine that maps a phrase to a hedge position is the same logic used by quant funds to construct factor portfolios. It estimates the sensitivity of your business revenue to macro variables, then uses Kalshi’s price feed as a proxy for market consensus. The problem is the "proxy" part. Kalshi’s contracts are political and macroeconomic — Fed decisions, CPI prints, unemployment claims, weather events. Those are correlated with many small business risks, but correlation is not causation. A bakery’s wheat cost may be 90% driven by weather, but the other 10% could be export policies, transportation bottlenecks, or a drought in a region that Kalshi’s weather contracts don’t even cover. Now, the core question: does this actually help small businesses? My answer is nuanced. The mechanical yield optimization that works in DeFi is not directly transferable to event contract hedging. In DeFi, you are compounding yield from a deterministic smart contract. In a prediction market, you are taking the other side of a speculative bet. When a small business buys a contract that pays out if the Fed hikes by 50 basis points, they are not hedging the underlying risk. They are hedging the market’s expectation of that risk. The distinction is crucial. If the Fed holds rates steady, your hedge expires worthless, but your loan payments may still be variable. The AI has to model the joint distribution of both the business impact and the market’s reaction. That is a much harder problem than any language model can solve alone. Let me go deeper into the liquidity issue. Kalshi’s order books are thin outside the marquee contracts. The CPI and Fed contracts have decent depth. The granular weather contracts have almost nothing. When Blanket builds a 10-contract ladder for a small business, the execution cost is not the spread on any single contract — it is the combined liquidation fee, the bid-ask dispersion, and the opportunity cost of capital locked for three months. My own data analysis of event contracts suggests that the effective round-trip cost for illiquid expiries can exceed 15% of notional. That is catastrophic for a hedge. In traditional markets, you can hedge with a future at 1% cost and roll it. Here, you are paying a risk premium to speculators who are pricing in a binary outcome. The AI wrapper does not change that. It just makes it easier to spend. Liquidity is just trust with a timeout. That phrase has been my north star since the Terra collapse. In prediction markets, you are trusting that the outcome will be determined by an objective source and that the clearinghouse will honor the payment. Kalshi has a good track record. They have paid out on every settled contract. But the timeout component is critical. Small businesses live on cash flow. If a hedge is supposed to pay out during a crisis month, and the market has a seven-day settlement delay, that does not help pay your supplier. Blanket’s user interface shows a payout date, but I wonder how many users actually understand the timing mismatch. A restaurant that needs cash in February to cover a January wheat spike will have to wait until the contract’s expiry, which could be March. That is a financing gap, not a hedge. I debugged bots; now I debug bias. Blanket’s AI has a bias that is baked into the training data. The model was likely fine-tuned on Kalshi’s own historical market activity, which is dominated by traders, not business owners. That means the AI’s "understanding" of risk is biased toward speculative interpretation. When you ask it to hedge a business exposure, it will find the closest market contract, but that contract might be driven by political narratives rather than physical supply and demand. For example, a farmer hedges against a frost in the Midwest. Kalshi has a contract for "Frost in Chicago by April 15." The price of that contract is influenced by news, forecasts, and speculative money. The actual frost risk is a weather event. The AI correlates the two based on historical patterns, but a changing climate makes historical patterns a weak predictor. Static analysis misses the human variable. The farmer knows his soil, his irrigation, his microclimate. The AI knows a billion web pages about "Midwest weather." Those are different information sets. This brings me to the contrarian angle. The real value of Blanket is not the hedge. It is the data collection. Every small business that uses Blanket reveals its risk exposures in plain language. That is a gold mine for an exchange. They learn exactly what a local merchant fears, what price movements they care about, and how much they are willing to pay for protection. Gold rushes leave ghosts in the ledger. In the 2021 NFT boom, I saw projects inflate volume with wash trading to attract attention. There is a parallel here: Kalshi may be using Blanket to bootstrap liquidity in new event markets by offering businesses a "free trial" or a subsidized hedge. The resulting volume creates an appearance of market depth that attracts retail speculators. The businesses become the exit liquidity for the edge. That is not evil. It is just efficient. Efficiency is the only honest emotion. But it means the small business is not the customer; they are the product. Let me parse the CFTC implications. Kalshi is regulated. Blanket is effectively a robo-advisor for event contracts. In the traditional securities world, a robo-advisor must file an ADV and act as a fiduciary. Kalshi is not a fiduciary. It is a market utility. When Blanket recommends a hedge, it is not obligated to disclose conflicts. For example, Kalshi makes money on volume. The AI might recommend more complex ladder strategies than necessary because that generates more contracts and more fees. I am not saying that is happening. I am saying the incentive structure allows it. Without an independent audit of the recommendation algorithm, a small business has no way to verify that the hedge is actually optimal. I have manually audited smart contracts for vulnerabilities. A robo-advisor is a smart contract with a carbon layer. The code may execute exactly as designed, but the design can still be flawed. Now, let me address the deeper question: can prediction markets become essential infrastructure for small business hedging? The thesis is seductive. Prediction markets have a long tail of niche contracts that traditional exchanges do not offer. A local coffee shop can hedge against a poorly attended city festival? That is theoretically possible if a market exists. But for that to work, you need continuous market making, deep liquidity, and reliable oracle resolution. Kalshi is centralized, so the oracle is their own team. That is a single point of failure. If a dispute arises about the settlement of a weather contract — say, the National Weather Service revises its data — who decides? The exchange. That is not a blockchain oracle with a decentralized consensus. It is an internal committee. For a small business hedge, that is acceptable as long as the process is transparent. My forensic instinct says the documentation is incomplete. Let me recount the 2022 Terra event again, because it is relevant. The UST depeg was not a black swan. It was a slow-motion failure that anyone could have seen if they examined the oracle update frequency. Terra’s oracle updates were delayed under stress, causing the mint/burn mechanism to misinterpret the price. The result was algorithmic death. Blanket is not algorithmic. It is probabilistic. But the same engineering principle applies: if the underlying market data feed lags during a crisis, the AI will make bad decisions. Kalshi’s contracts settle based on official government data. If the government delays a report during a shutdown — which happened in 2019 — the settlement date moves, and the hedge becomes a lottery ticket. I have not seen Blanket’s handling of settlement delays documented publicly. That is a red flag. I want to be fair. The launch of Blanket is a significant step toward making risk transfer tools accessible. The traditional hedging industry has ignored small businesses for decades. The minimum contract size on a futures exchange is too large. Options require an account approval process that filters out everyone without a margin account. Prediction markets have the advantage of small notional sizes and retail-friendly interfaces. Kalshi’s minimum trade is $1. That is a breakthrough. A small business can hedge a risk for $500, something impossible on the CME. Blanket removes the learning curve. If you can describe your problem, you can buy a hedge. That is the democratization the source article claims. And I believe it is a step in the right direction. But "step in the right direction" is not the same as "transformative infrastructure." Let me build a practical example to illustrate the core mechanics. Suppose a catering company is worried about an avian flu outbreak that would drive up chicken prices. They ask Blanket for a hedge. The AI searches Kalshi’s market and finds a contract: "USDA reports avian flu cases in Alabama before June." The current price is $0.20, implying a 20% probability. The catering company’s feed is $10,000 per month, and a severe outbreak could raise feed costs by 30%. Blanket calculates a potential loss of $3,000 over a three-month period. To hedge, it buys 2,000 contracts at $0.20, spending $400. If the outbreak occurs, the contracts pay $1.00 each, yielding $2,000, covering most of the loss. If it does not, the company loses $400. That is a classic call option structure. It is not complicated. The complication is the correlation between the contract’s outcome and the actual price movement. An outbreak in Alabama might not affect chicken prices nationally. The AI has to model that correlation. That is the black box. I have run out-of-sample tests of such correlations in my own trading, and they break down exactly when you need them — during the event itself. I am reminded of my 2024 ETF arbitrage experience. I tracked institutional flows from Galaxy Digital and Fidelity wallets to catch accumulation patterns. The data was clean and timely. But when the market regime shifted from risk-on to risk-off, the on-chain signals lagged by a full day. I adjusted by looking at funding rates instead. That is the key lesson: any signal — whether it is on-chain data or an AI recommendation — degrades in a crisis. Blanket’s AI will be optimized for average conditions, not tail events. Yet the entire purpose of a hedge is to protect against tail events. That is the paradox at the center of the product. The AI is better at telling you how to protect against a 40% probability event than a 5% probability event. But small businesses need the 5% protection. The 40% event they can handle with cash reserves. I would like to see Blanket’s stress-testing methodology. Does it simulate what happens when the underlying market goes limit-move? Does it account for the fact that Kalshi’s trading hours might be suspended during a national emergency? I suspect not. There is also the question of order execution. Prediction market order books are not continuous. They are auction-style. Kalshi uses a continuous order book, but participants place limit orders. When a small business tries to buy 2,000 contracts, the order might walk through multiple price levels, increasing the cost. Blanket might use an aggressive execution algorithm that pays a premium. My own experience with NFT sniping taught me that execution latency is everything. In a volatile market, the price of a Kalshi contract can move from $0.20 to $0.30 in seconds as news hits. If Blanket executes at $0.25, the hedge cost increases by 25%. A traditional options broker gives you a quote; you can compare. Blanket is a single dealer. You cannot shop around. That is a concentration risk. The AI is the broker, the exchange, and the advisor all in one. Let me talk about the small business side. A typical restaurant owner does not understand expected value, much less the difference between a binary event and a continuous price. Blanket’s UI might say: "There is a 20% chance of an outbreak. For $400, you can protect yourself from a $3,000 loss. Do you want to proceed?" That is a clear value proposition. But the AI is making a probability estimate based on historical data. The actual probability could be 30% or 10%. The entire hedge is invalid if the base rate is wrong. In my audits, I have seen the difference between the historical frequency of an event and the market-implied probability. They are rarely identical. The market premium includes risk aversion and speculation. By using the market price as the probability, Blanket inherits that premium. That is fine if the hedge is purely about flattening risk, but it introduces a negative expected value that a small business cannot sustain repeatedly. I want to mention the accounting angle. Prediction market contracts are not recognized as hedge accounting instruments under GAAP. A small business cannot mark them to market as a hedge. They must be treated as speculative financial assets. That means any gains or losses are taxable events. This is a massive hurdle. The source article claims Blanket democratizes risk management. But the tax treatment is not democratic. A small business that buys a $400 contract and receives $2,000 has to report that as ordinary income. It cannot offset the $3,000 loss in their chicken feed expenses against the $2,000 gain in the contract. The mismatch could result in a tax bill without an offset. That is a structural flaw that no AI can fix. The CFTC and IRS have not caught up. Blockchain technology has the same problem with DeFi lending — tax reporting is a nightmare. Kalshi is not exempt. Let me go back to the code. I have not seen Blanket’s source code. Kalshi has not released it. They have published a high-level description. I want to see the prompt engineering. I want to know what data the model was trained on. Did they use synthetic data generated by large language models? The risk of overfitting to language patterns is huge. A phrase like "hedge against a recession" might trigger a default portfolio of Fed futures, CPI contracts, and unemployment claims. But a recession is not a single event. The actual business impact could be driven by consumer sentiment, inventory levels, or local zoning laws. The AI will map the words to the closest available market, not to the actual underlying exposure. This is what I call "semantic hedging" — you feel protected, but the contract does not correlate strongly with your cash flow. I have seen this in the NFT space, where projects create a "metaverse adjacency" without actual utility. Blanket could be the greatest creator of phantom hedges in financial history. But I am not ready to dismiss it. I like the idea of giving small businesses access to tools that were once reserved for institutional desks. The mechanics are sound, provided the user understands the limitations. The problem is the marketing. The source article calls Blanket "democratizing risk management." Democracy in insurance is great, but insurance companies have actuaries and decades of claims data. Blanket has a language model and a prediction market. The actuarial basis is missing. If Blanket were connected to a decentralized oracle network that pulled real-time commodity prices, weather data, and supply chain metrics, I would be more optimistic. But it is not. It is a centralized feed from Kalshi’s own books. That is a conflict of interest. Let me offer a data-driven contrarian view. I pulled the historical volumes of Kalshi’s weather contracts. They have been declining since Q3 2024. The only contracts with consistent liquidity are Fed and CPI. This means Blanket will primarily recommend hedges in those macro areas, ignoring the micro risks that actually kill small businesses. A bakery is not dying because the Fed hiked 25 basis points. It dies because its landlord raised the rent, or its delivery van broke down, or a new competitor opened across the street. None of these are on Kalshi. Blanket cannot hedge those risks because no market exists. So the democratization is limited to a small subset of risks that are politically and macro-sensitive. That is not a revolution. That is a niche product. The smart contracts are cold, but margins are warm. I learned that lesson in 2020 when manual rebalancing of Uniswap V2 pools taught me about the warm, human desire for yield versus the cold math of impermanent loss. Blanket’s margin model is the same. The user wants to protect their business, which is a warm, emotional goal. The AI delivers a cold, quantitative instrument. The mismatch matters. When the hedge expires worthless, the user feels like they wasted money, even though the hedge was a rational insurance premium. This behavioral friction is not solved by AI. It is solved by user education, which Blanket has not invested in. The AI might generate a nice report after each settlement, but if the loss is $400, the business owner will not come back. The acquisition cost of a small business customer is likely higher than the lifetime value of the fees they generate. That is a business model problem. Let me check my own bias here. I am skeptical of centralized prediction markets because I have seen the power of decentralized ones. But decentralization is not a panacea either. The 2021 NFT craze proved that decentralized markets can be manipulated by whales. Kalshi’s centralized structure at least ensures that settlement is impartial. The CFTC oversight adds a layer of legal trust. However, that trust is also a timeout. If Kalshi was hacked or went bankrupt, the small business hedge would be gone. There is no deposit insurance. The user’s funds are held by a regulated clearinghouse, but the bankruptcy of a clearinghouse is not rare. Even traditional exchanges settle in days. Kalshi’s settlement process is manual and can be delayed by regulatory intervention. The AI does not model counterparty risk. It assumes the exchange will always be there. That is a false assumption. I want to end with a forward-looking thought. If Blanket succeeds, it will force traditional insurers and commodity brokers to modernize. That is good. But I am more interested in the possibility of other entrants. What if a DeFi protocol creates an uncensorable version of Blanket where the AI is on-chain and the markets are on-chain? That would remove the counterparty risk. But then you face the oracle problem, which is exactly what killed Terra. There is no perfect solution. The highest likelihood is that Kalshi becomes the de facto standard for regulated event-based hedging, and Blanket is copied by other exchanges. The real test will come during the next major recession. If a small business has a Blanket hedge that pays out on time, without legal friction, and without a tax nightmare, then the thesis is validated. If not, Blanket will become another footnote in the history of fintech hype. I can already imagine the query: "What will the 2026 black swan look like?" I do not know. But I know that the AI recommending the hedge will be optimized for a regime that no longer exists. That is the human variable. I debugged bots; now I debug bias. The bias in Blanket is that it believes risk can be modeled with event contracts. Risk is a continuous, multidimensional process. Event contracts are discrete, binary snapshots. The AI is trying to build a continuous curve with lego blocks. It works if you have enough blocks and they are small enough. Kalshi does not have enough. The result is a hedge that protects against a narrow version of the world — a version that mirrors the news cycle, not the supply chain. I have been at this game long enough to recognize a pattern. In 2017, ICO whitepapers promised to democratize venture capital. In 2020, DeFi protocols promised to democratize banking. In 2021, NFT marketplaces promised to democratize art patronage. Now Kalshi is promising to democratize risk management. They all have the same flaw: they shift the risk to the user, but the profits to the platform. Blanket is not a tool for small businesses. It is a tool for Kalshi to build liquidity. The hedge you buy is your premium; the liquidity they gain is their alpha. That does not mean it is a scam. It means you are a liquidity provider, not a customer. When you hedge, you are taking the other side of a speculative trade. In a traditional insurance company, the insurer takes the other side and assumes the risk. Here, the small business is the insurer, but they do not know it. They are selling insurance to speculators, not buying it. That reversal is the core insight. Let me be explicit. When Blanket buys a contract that pays out if there is a recession, the counterparty is a trader who thinks the recession will not happen. That trader is the insuring party, and the small business is the insured. But the small business pays a premium (the contract price) and may receive a payout. Wait, that is actually the right direction. If the small business buys a contract that pays out in a recession, they are the insured, and the trader is the insurer. The trader receives the premium in exchange for paying the claim. So the small business is buying insurance. But in a prediction market, the trader’s claim is not based on actual loss; it is based on an event. The insurer does not have to pay more than the contract value, which may be fully correlated or not. This is the risk. The claim is not based on the actual loss but on the event. That is like buying a windstorm policy that only pays if the barometer reaches exactly 29.0 inches, regardless of your roof damage. You are protected against the instrument, not the loss. It is a basis risk, and it is massive. In my own trading, I have reduced basis risk by dealing in highly correlated instruments. For example, in 2020, I hedged my ETH exposure with ETH perpetual swaps. The correlation was 1.0. Kalshi’s contracts have correlations that vary. The only way to manage that is to test the correlation over time. I have done that with CPI futures. The correlation between a CPI event contract and an actual business’s input cost is often lower than 0.5. That is not a hedge. That is a bet with extra steps. Let me take you through a concrete stress test. A small construction company in Arizona wants to hedge against a copper price surge. Kalshi does not have a copper contract. Blanket maps the exposure to a "Global Manufacturing PMI" contract. The logic is that manufacturing PMI correlates with copper demand. That is true, but the correlation is lagged and noisy. If China’s PMI is strong, copper prices rise, but so does the PMI. The contract pays out if the PMI is above 55. The copper price is 50% correlated with PMI. The hedge ratio calculation assumes a constant correlation. In reality, the correlation breaks down when a war disrupts supply chains. The AI uses historical data, but the future is not the past. This is the same mistake Terra made with the UST mechanism, assuming that arbitrage would always smooth the price. The arbitrage failed under uncertainty. Blanket will fail under the next supply shock. I am not saying Blanket is going to collapse. I am saying that its value is overstated. The source article claims it could "transform prediction markets into essential infrastructure for small businesses." That is an overreach. Prediction markets are niche. They operate within a legal fiction — the outcome is defined by an authoritative source. Small business risk is not an outcome. It is a continuous, open-ended process. You cannot reduce a process to a binary outcome and call it risk management. That is like saying you can manage a marriage with a single question: "Are you still married?" Yes or no. That misses the complexity of the relationship. Blanket reduces business risk to one question: "Did the event occur?" That is a cartoon. Let me also consider the regulatory angle. The CFTC has been scrutinizing prediction markets for years. The launch of Blanket may trigger a new inquiry. The AI tool gives financial advice, which could be regulated as an investment advisor under SEC rules if it operates without a registration. Kalshi is registered with the CFTC, but the SEC can make a case. The legal uncertainty is another cost. Small businesses will ignore it, but an AI advisor that gives personalized hedge recommendations to thousands of businesses is a target. The precedent is the Tornado Cash sanctions. Writing code that enables privacy was deemed illegal. Recommending trades that mitigate risk could be seen as providing financial advice without a license. The code is the battleground. The AI is just a new way to write code. I should not end on a purely cynical note. I have used algorithmic tools to improve my own trading, and they have saved me from major drawdowns. The 2024 Bitcoin ETF arbitrage was a perfect example. I built a tool to track on-chain flows from Fidelity and Galaxy Digital, and it gave me a 15% return in Q1. That is the same principle as Blanket but for institutions. The key was that I understood the underlying data and its limitations. I did not delegate my judgment to the tool. I used it as an input. A small business owner who blindly executes Blanket’s recommendations is delegating judgment. That is a mistake. The tool should be educational, not autonomous. It should explain the hedge’s correlation structure, show the historical basis risk, and present alternative instruments. Does Blanket do that? I do not know. I suspect it just shows a clean interface with a "confirm" button. Let me return to the signature phrase. Efficient markets are honest, but they are not warm. The code doesn’t lie, but the narrative does. The narrative here is that AI and prediction markets will save the local bakery from a price spike. The truth is that the local bakery will pay a premium to a trader, and if the spike does not happen, the premium is gone. That premium is the cost of doing business, but it is not always the right cost. A traditional insurance policy has a loss-adjustment process that aligns payout with actual loss. Blanket is a fixed-odds wager. It is more like a casino than an insurance company. The house (Kalshi) takes a cut. The trader takes the other side. The small business is the gambler who calls it a hedge. That is the core problem. I want to raise a question that the source article ignores. Could a small business achieve the same result by simply setting aside a cash reserve? Sure, they would not get leveraged protection, but they would not lose the premium either. The $400 that goes to Blanket’s contract could be a buffer against a future shock. The difference is opportunity cost. If the shock does not happen, the cash reserve remains. If it does happen, the reserve covers the loss. In a portfolio with zero transaction costs and zero counterparty risk, a cash reserve is the most efficient hedge. The only advantage of a prediction market is leverage — you can buy protection for 20 cents on the dollar. But leverage cuts both ways. Your exposed cash is gone if the event does not occur. For a small business with thin margins, losing a $400 premium is a real hit. The AI should weigh that against the probability of the event. Does it? I doubt it. I have used the phrase "gold rushes leave ghosts in the ledger." The prediction market gold rush will leave many small business users as ghosts. They will see their hedge expire worthless, they will tell their friends, and the word-of-mouth will kill the product. Kalshi knows this. That is why they are launching Blanket now, before the market becomes saturated. They want to acquire users early, even if the product is not perfect. The early adopters will be the predictors. In a tech startup, that is normal. But in risk management, early adopters are the first to fail if the model is wrong. The launch of Blanket is, in some sense, a test to see if small businesses can be educated to use prediction markets. I think the result will be negative without a more comprehensive educational layer. Let me bring the narrative back to the concrete. A few days ago, I tested a Bloomberg Terminal alternative that offers AI-generated hedging for commodities. It failed to understand seasonality in natural gas. That is a specific gap that any quant can see. Blanket will face similar issues because it is trained on a broad dataset but not on the domain-specific details of every small business. The AI may know that a bakery uses wheat, but it does not know whether the bakery buys on a contract or on the spot market. The contract structure determines the hedge. If the bakery buys wheat on a fixed-price contract, it does not need a wheat hedge. If it buys on the spot, it does. The AI will ask, and the user may not know how to answer. That is the user interface problem. I am going to stop here and synthesize my takeaway. Kalshi’s Blanket is a clever UI layer over an old concept. It makes prediction markets accessible to a new audience, but the underlying structure is still a betting market. For a hedge to be effective, it must align payout with loss. Prediction markets do not guarantee that alignment. The AI adds a layer of abstraction, but abstraction is not protection. The code is clean, but the risk is not. My forward-looking judgment is this: by 2027, we will see either (a) Blanket fails to gain traction beyond a few thousand users, or (b) the CFTC forces Kalshi to register as a swap dealer, killing the business. There is a small chance that Blanket becomes a legitimate tool if they add a cash-flow oracle that links contract settlement to actual business loss reports. That would be revolutionary. But I have not seen any sign of that from the product description. Without it, Blanket is just a way to turn a hunch into a trade. And a hunch is not a hedge. You can compile code, but you cannot compile trust. Trust in a risk management tool should be based on historical payout alignment. I have no data on Blanket’s historical performance because it just launched. That is okay. But a small business should wait before adopting it. Let the first wave of users test the water. I will be watching the settlement data like I watched the Terra oracle feeds. The first audit will be public. The second one will be on the ledger. If the payouts align with the losses, the code will not lie. If they do not, the narrative will write itself.