prediction markets roundup: - the nba signed partnerships with kalshi and polymarket to make them preferred prediction market exchanges while establishing direct integrity frameworks - kalshi and s&p dow jones indices entered a strategic partnership to develop index-based event contracts - bernstein said prediction markets, derivatives and tokenization are overtaking crypto trading in its outlook for coinbase, robinhood, and circle, projecting prediction markets could generate about $1.7 billion for robinhood by 2028 - kalshi ceo tarek mansour argued prediction markets can police insider trading themselves and pressed for one federal rulebook across all 50 states - kalshi and polymarket hit a record ~$2.7 billion in weekly crypto-contract volume which update stands out most?

a market can be perfectly resolvable and still not deserve to exist. polymarket removed a contract concerning potential felony charges in a sexual-assault case after it had already generated more than $450,000 in volume. the outcome may have been objectively verifiable. that does not automatically make it responsible to trade. resolution clarity solves one problem. it does not solve privacy, personal harm, manipulation or the incentives created by financialising an event. this becomes more important as market creation is open to users. permissionless creation should expand the range of knowledge that markets can capture. it should not mean that every tradeable question belongs on a public market. scalable creation therefore needs scalable judgment: systems that can assess not only whether an outcome can be resolved, but whether creating the market introduces unacceptable harm. the difficult question is no longer simply “can we create this market?” it is “what responsibility comes with making anything a market?” all that was baked into kash's llm council, for market creation and resolution.

prediction markets are usually described as the wisdom of crowds. new data suggests the truth may be closer to the wisdom of a very small minority, amplified by the market. researchers analyzed more than 1 billion trades and identified 0.15% of takers who forecast significantly better than the markets they traded against. after one of these traders entered, prices moved an average of 1.7 cents toward their position by the next order and 3.3 cents by market close. the surprising part: forecasting skill was largely separate from profitability. the trader who makes the most money is not necessarily the trader who contributes the best information. profit can come from execution, timing or market structure. calibration measures whether your predictions are consistently right. this changes how we should think about reputation in prediction markets. followers show distribution. p&l shows financial performance. neither necessarily shows who improves the forecast. kash is built for people who are early and right. the bonding curve rewards conviction: identify the correct outcome early and you enter at a better price. the social layer then turns that forecasting record into visible reputation others can discover and follow. the best forecasters should not remain an invisible 0.15

we gave a few members of the kash community the ability to create their own prediction markets here’s what we’ve learned so far about what makes a good market 👇 1 - audience size alone does not create volume. a large following can get a market seen, but it cannot make people care about the outcome. some of the strongest starting points come from creators who understand a specific niche and know which events their community is already following. every community is watching something unfold: whether a football club will complete a transfer, which product a company will release next, who will win an award, or whether a creator will ship something by the date they announced. to outsiders, these events might look small. to the right community, they are outcomes worth predicting. 2 - the next ingredient is genuine disagreement. a market where nearly everyone immediately chooses the same outcome gives people little reason to debate, return or reconsider their position. good markets create tension between outcomes that each feel plausible. polarising does not mean manufacturing controversy. it means finding an event where informed people can look at the same evidence and reach different predictions. 3 - framing matters too. someone should be able to understand what they are predicting, when the result will be known and exactly what determines the outcome within a few seconds. the context can be complex, but the market should feel immediate. then the market begins to drive its own marketing. every trade is a position. every reply adds an argument. every change in probability gives people something new to discuss. the conversation spreads the market, brings in more information and gives participants a reason to return. this is why the creator’s job does not end when the market goes live. good creators add context, answer questions and keep the market connected to the events around it. reach can give a market its first audience. a good market gives that audience a reason to participate i'll be sharing more of the data and analytics behind these lessons over the coming weeks as we roll out market creation to everyone on kash