2026-04-19 Воскресенье

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After Raising $650 Million, Dragonfly Believes Crypto Was Not Made for Humans

Dragonfly Capital partner Haseeb Qureshi argues that cryptocurrency was not designed for humans but is instead the ideal financial system for AI agents. Despite being a crypto-native firm, Dragonfly still relies on legal contracts over smart contracts for investments, highlighting that traditional systems are built for human fallibility—featuring safeguards, reversibility, and intuitive interfaces. Crypto, by contrast, is rigid, error-prone, and unforgiving, with complex addresses, gas fees, and irreversible transactions posing significant risks to human users. Qureshi posits that AI agents are the natural users of crypto: they operate with precision, trust code over ambiguous legal systems, and can verify transactions instantly. Unlike humans, AI can navigate crypto ecosystem autonomously, negotiating and executing binding agreements via smart contracts within minutes. Crypto’s global, permissionless, and deterministic nature makes it perfect for AI-to-AI economic activity, which is already emerging with projects like Moltbook and Conway Research’s autonomous agents. He predicts the future interface for crypto will be “self-driving” wallets managed by AI, which will handle financial tasks on behalf of users. This shift will transform how protocols compete and market themselves. In a decade, humans may look back in astonishment that they ever interacted directly with crypto—its true potential unlocked only when its complementary technology, AI, arrived.

Odaily星球日报02/19 05:10

After Raising $650 Million, Dragonfly Believes Crypto Was Not Made for Humans

Odaily星球日报02/19 05:10

Bloomberg: How a16z Became the Key Force Behind U.S. AI Policy?

This article from Bloomberg details how venture capital firm Andreessen Horowitz (a16z) has become a dominant, behind-the-scenes force shaping U.S. artificial intelligence (AI) policy, particularly under the Trump administration. Through massive lobbying, close relationships with key figures like Donald Trump, and a network of former staff in government roles, a16z aggressively promotes a deregulatory agenda. The firm argues that strict rules would stifle innovation and harm U.S. competitiveness, positioning itself as a defender of small startups while its own massive portfolio benefits from a lax regulatory environment. a16z's chief lobbyist, Collin McCune, is often the first external call for White House officials and Republican aides considering AI legislation. The firm played a key role in supporting a Trump executive order that blocked states from enacting their own AI safety laws. Co-founder Marc Andreessen, a major Republican donor, wields significant influence through meetings and donations. Despite its "small tech" rhetoric, critics note a16z's substantial investments in giant tech companies and its opposition to any major new regulations. Its influence has made legislative compromise difficult, even causing friction with larger tech companies willing to negotiate. The firm is now focused on crafting federal AI standards designed to preempt state laws and endure beyond the current administration.

marsbit02/19 04:08

Bloomberg: How a16z Became the Key Force Behind U.S. AI Policy?

marsbit02/19 04:08

When "Old Maps" No Longer Apply: A Review of 8 Failed Classic Crypto Metrics and the Structural Reasons Behind Them

Title: When "Old Maps" No Longer Apply: 8 Failed Classic Crypto Indicators and Their Structural Causes The crypto market in early 2026 is marked by confusion as traditional on-chain and technical indicators have collectively failed. This analysis examines eight key metrics that have lost predictive power and explores the underlying structural market shifts causing their obsolescence. The core findings reveal that institutionalization has fundamentally altered market microstructure. Bitcoin ETF inflows created sustained demand, breaking the pure halving-driven narrative. The 2024 halving's supply reduction became negligible against Bitcoin's multi-trillion dollar market cap. This institutional participation smoothed volatility from over 100% to ~50%, preventing the extreme moves needed to trigger indicators like the Pi Cycle Top (relying on 111-day/350-day MA crossover) and Rainbow Chart (based on logarithmic growth curves). The MVRV Z-Score failed as institutional buying raised the realized value floor, compressing its historical range. The "altcoin season" never materialized because ETF flows went exclusively to Bitcoin, not rotating to altcoins, while AI and precious metals diverted capital from crypto overall. The Fear & Greed Index became unreliable as institutional flows decoupled from retail sentiment. The NVT ratio malfunctioned as on-chain volume no longer represented real economic activity. Finally, PlanB's S2F model failed spectacularly (predicting $500K vs. $120K actual) by ignoring demand-side variables and the impossibility of exponential growth at Bitcoin's current scale. Ultimately, these indicators failed because they relied on outdated assumptions: extreme volatility, retail-driven markets, and pure on-chain data analysis. The market has transitioned from a digital commodity to a macro asset influenced by Federal Reserve policy and global liquidity rather than just halving cycles. Investors must understand these structural changes rather than seeking new universal indicators.

marsbit02/19 03:50

When "Old Maps" No Longer Apply: A Review of 8 Failed Classic Crypto Metrics and the Structural Reasons Behind Them

marsbit02/19 03:50

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