2026-04-17 Пятница

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From 5 Cents per kWh Chinese Electricity to $45 API Export Packages: Token is Becoming the New Currency Unit

The article explores the concept of "Token出海" (Token Outbound), arguing that tokens are evolving from a technical term into a new monetary unit in the machine-driven economy. It begins by drawing a parallel between historical control over information flow (like transatlantic cables) and today's control over AI API calls and value transfer. Tokens now serve a dual role: as a unit of computation in AI and a means of payment in crypto. A key driver is the rise of AI Agents, like OpenClaw, which shift tokens from being a simple "conversation cost" to a "production fuel" for executing complex tasks. This massive consumption creates a competitive advantage for Chinese AI models, which are often priced lower. The article posits that this isn't just about cheap models, but about China leveraging its vast domestic electricity and computing power to export value globally via token-denominated AI services. The convergence of AI and crypto is facilitated by protocols like x402, which enables machines to natively pay for API calls, and ERC-8183, which allows them to enter into complex escrow-based contracts. This creates a machine-native economic layer where tokens act as the fundamental unit of permission, settlement, and value measurement. The conclusion is that while traditional fiat won't disappear, tokens are becoming the foundational monetary unit for the new agentic economy. The future "power to mint currency" may belong to those who can most efficiently compress real-world resources (like electricity and compute) into tradable tokenized services.

Odaily星球日报03/13 04:41

From 5 Cents per kWh Chinese Electricity to $45 API Export Packages: Token is Becoming the New Currency Unit

Odaily星球日报03/13 04:41

a16z: AI Makes Everyone 10x More Efficient, But No Company Becomes 10x More Valuable

a16z investor George Sivulka argues that while AI has dramatically increased individual productivity by 10x, it hasn’t translated into a 10x increase in company value. The core issue is not the technology itself, but the failure to redesign organizations around it—much like factories in the 1890s initially replaced steam engines with electric motors but didn’t see real gains until they fully redesigned assembly lines decades later. Sivulka distinguishes between “Personal AI” (e.g., ChatGPT) and “Organizational AI,” outlining seven key dimensions where they differ: 1. **Coordination:** Personal AI creates chaos; Organizational AI coordinates teams and agents toward unified goals. 2. **Signal:** Personal AI generates noise and low-quality output; Organizational AI filters noise to find valuable signals. 3. **Bias:** Personal AI reinforces user bias; Organizational AI introduces objectivity and challenges assumptions. 4. **Edge Advantage:** Personal AI optimizes for general usage; Organizational AI leverages domain-specific expertise for competitive advantage. 5. **Outcome:** Personal AI saves time; Organizational AI drives revenue growth. 6. **Enablement:** Personal AI gives a tool; Organizational AI embeds processes and enables organizational change. 7. **Promptless:** Personal AI requires human prompts; Organizational AI acts autonomously without human intervention. True value, Sivulka concludes, will come from rebuilding organizations and processes around AI—not just adopting the technology. The future belongs to companies that build “Organizational AI” systems that integrate deeply with institutional workflows.

marsbit03/13 04:40

a16z: AI Makes Everyone 10x More Efficient, But No Company Becomes 10x More Valuable

marsbit03/13 04:40

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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