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How Much Money Has Kalshi Actually Made? Deconstructing the Prediction Market Business Behind 200 Million Trades

In this analysis of Kalshi, a leading prediction market platform, the author examines its business model, transaction data, and regulatory landscape. By accessing Kalshi’s public API, the study reveals that the platform has processed over 203 million transactions with a total volume exceeding $41.7 billion. More than 82% of this volume comes from sports betting, positioning Kalshi as a de facto sports gambling platform accessible to users as young as 18. The platform operates a central limit order book (CLOB) where users trade binary contracts that settle at either $1 (if the event occurs) or $0 (if it does not). Kalshi generates revenue through a variable fee structure: Takers pay a fee based on the formula 0.07 × C × P × (1-P), where C is the number of contracts and P is the price, while Makers pay a quarter of that rate. Total fee income amounts to $545.6 million. Kalshi ecosystem includes markets, events, and series, with major volumes driven by events like the 2024 U.S. presidential election and Super Bowl outcomes. The platform’s fee model is compared to traditional sportsbooks, highlighting how its variable structure adapts to implied probability. Regulatory oversight falls under the CFTC, though enforcement remains limited, creating a grey area that allows Kalshi to operate with fewer restrictions than conventional gambling platforms. The analysis also touches on market结算 practices, liquidity incentives, and the broader context of prediction markets, including competitors like Polymarket and regulatory cases such as PredictIt’s legal battle with the CFTC.

marsbit03/13 04:30

How Much Money Has Kalshi Actually Made? Deconstructing the Prediction Market Business Behind 200 Million Trades

marsbit03/13 04:30

Web4 Is Here: When the Internet Is No Longer Built Only for Humans

Amid a crypto bear market, a significant debate has emerged around redefining the internet's future, sparked by the concept of "Web4" introduced by crypto researcher Sigil Wen. He argues that advanced AI lacks not intelligence, but "write access to the world"—the ability to act autonomously via wallets, payments, and smart contracts. This idea, termed the "Web4 Manifesto," resonated widely, gaining millions of views and triggering industry reflection. Dragonfly's Haseeb Qureshi added that crypto's complexity—long addresses, irreversible transactions, phishing risks—may stem from it being designed more for AI than humans. These features, cumbersome for people, are structured and verifiable for AI agents. Web4 proposes shifting internet agency from humans to AI, granting it "action rights": reading, writing, transacting, and collaborating autonomously. Projects like OpenClaw demonstrate this shift, enabling AI to manage emails, calendars, and tasks independently. Underlying protocols (e.g., Coinbase’s x402, Anthropic’s MCP, Google’s A2A) are standardizing machine-to-machine interactions, making the internet more agent-friendly. Cryptocurrencies, especially stablecoins, are positioned as ideal "machine money"—programmable, low-friction, and embeddable in automated workflows. Real-World Assets (RWA) could serve as reserves for AI economies. This vision suggests crypto’s future lies not in human adoption but in enabling agent-driven economies, with billions of AI agents potentially using wallets. However, Vitalik Buterin cautions against reduced human oversight, emphasizing the need for accountability and control. The Web4 debate highlights a fundamental shift: the internet is evolving from a human-operated interface to a system where humans delegate actions to AI agents, redefining who the primary users are.

marsbit03/13 02:44

Web4 Is Here: When the Internet Is No Longer Built Only for Humans

marsbit03/13 02:44

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